From 1bb4cf411ad0b9ea6d9c6e5cf7ddbb3032726a40 Mon Sep 17 00:00:00 2001 From: xiaodongdong <695492835@qq.com> Date: Tue, 21 Jun 2022 10:39:53 +0800 Subject: [PATCH] =?UTF-8?q?Revert=20"=E6=9B=B4=E6=96=B0=E6=95=B0=E6=8D=AE"?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Deep Learning/README.md | 3 +- Machine Learning/3.2 GBDT/3.2 GBDT.md | 6 +- Machine Learning/9. KNN/README.md | 2 +- Machine Learning/README.md | 3 +- NLP/16.6 Attention/README.md | 106 +-- NLP/16.7 Transformer/README.md | 245 ------ NLP/16.8 BERT/README.md | 241 ------ .../bert-Chinese-classification-task.md | 46 -- NLP/16.8 BERT/data/dev.tsv | 30 - NLP/16.8 BERT/data/test | 1 - NLP/16.8 BERT/data/train.tsv | 300 -------- NLP/16.8 BERT/download_glue_data.py | 137 ---- NLP/16.8 BERT/modeling.py | 474 ------------ NLP/16.8 BERT/optimization.py | 180 ----- NLP/16.8 BERT/run.sh | 50 -- NLP/16.8 BERT/run_classifier_word.py | 697 ------------------ NLP/16.8 BERT/tokenization_word.py | 342 --------- NLP/16.9 XLNet/README.md | 176 ----- NLP/README.md | 6 +- Project/README.md | 2 +- README.md | 10 +- images/2019-9-28_21-34-11.png | Bin 279792 -> 0 bytes images/README.md | 1 - 23 files changed, 23 insertions(+), 3035 deletions(-) delete mode 100644 NLP/16.7 Transformer/README.md delete mode 100644 NLP/16.8 BERT/README.md delete mode 100644 NLP/16.8 BERT/bert-Chinese-classification-task.md delete mode 100644 NLP/16.8 BERT/data/dev.tsv delete mode 100644 NLP/16.8 BERT/data/test delete mode 100644 NLP/16.8 BERT/data/train.tsv delete mode 100644 NLP/16.8 BERT/download_glue_data.py delete mode 100644 NLP/16.8 BERT/modeling.py delete mode 100644 NLP/16.8 BERT/optimization.py delete mode 100644 NLP/16.8 BERT/run.sh delete mode 100644 NLP/16.8 BERT/run_classifier_word.py delete mode 100644 NLP/16.8 BERT/tokenization_word.py delete mode 100644 NLP/16.9 XLNet/README.md delete mode 100644 images/2019-9-28_21-34-11.png delete mode 100644 images/README.md diff --git a/Deep Learning/README.md b/Deep Learning/README.md index 2a9d4b4..758b8e9 100644 --- a/Deep Learning/README.md +++ b/Deep Learning/README.md @@ -16,6 +16,5 @@ | 深度学习 | [15. 深度学习的优化方法](https://github.com/NLP-LOVE/ML-NLP/tree/master/Deep%20Learning/15.%20DL%20Optimizer) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | -> 欢迎大家加入！共同完善此项目！NLP学习QQ2群【207576902】NLP学习群② - +> 欢迎大家加入！共同完善此项目！NLP面试学习群 diff --git a/Machine Learning/3.2 GBDT/3.2 GBDT.md b/Machine Learning/3.2 GBDT/3.2 GBDT.md index 41d75f5..ffccaaa 100644 --- a/Machine Learning/3.2 GBDT/3.2 GBDT.md +++ b/Machine Learning/3.2 GBDT/3.2 GBDT.md @@ -81,7 +81,7 @@ GBDT的原理很简单，就是所有弱分类器的结果相加等于预测值 1. 预测阶段的计算速度快，树与树之间可并行化计算。 2. 在分布稠密的数据集上，泛化能力和表达能力都很好，这使得GBDT在Kaggle的众多竞赛中，经常名列榜首。 -3. 采用决策树作为弱分类器使得GBDT模型具有较好的解释性和鲁棒性，能够自动发现特征间的高阶关系。 +3. 采用决策树作为弱分类器使得GBDT模型具有较好的解释性和鲁棒性，能够自动发现特征间的高阶关系，并且也不需要对数据进行特殊的预处理如归一化等。 ### 3.2 局限性 @@ -93,11 +93,11 @@ GBDT的原理很简单，就是所有弱分类器的结果相加等于预测值 **相同点**： -- 都是由多棵树组成，最终的结果都是由多棵树一起决定。 -- RF和GBDT在使用CART树时，可以是分类树或者回归树。 +都是由多棵树组成，最终的结果都是由多棵树一起决定。 **不同点**： +- 组成随机森林的树可以分类树也可以是回归树，而GBDT只由回归树组成 - 组成随机森林的树可以并行生成，而GBDT是串行生成 - 随机森林的结果是多数表决表决的，而GBDT则是多棵树累加之和 - 随机森林对异常值不敏感，而GBDT对异常值比较敏感 diff --git a/Machine Learning/9. KNN/README.md b/Machine Learning/9. KNN/README.md index 9ae0dc8..13d65e0 100644 --- a/Machine Learning/9. KNN/README.md +++ b/Machine Learning/9. KNN/README.md @@ -77,7 +77,7 @@ ![](https://latex.codecogs.com/gif.latex?D_{Chebyshev}(p,q)=max_i(|p_i-q_i|)) - 这也等于以下Lp度量的极值： ![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-24_22-19-41.png)，因此切比雪夫距离也称为L∞度量。 + 这也等于以下Lp度量的极值： ![](https://latex.codecogs.com/gif.latex?\lim_{x \to \infty}(\sum_{i=1}^{n}|p_i-q_i|^k)^{1/k})，因此切比雪夫距离也称为L∞度量。 以数学的观点来看，切比雪夫距离是由一致范数（uniform norm）（或称为上确界范数）所衍生的度量，也是超凸度量（injective metric space）的一种。 diff --git a/Machine Learning/README.md b/Machine Learning/README.md index 24cd0c9..c8a01f3 100644 --- a/Machine Learning/README.md +++ b/Machine Learning/README.md @@ -25,5 +25,4 @@ -> 欢迎大家加入！共同完善此项目！NLP学习QQ2群【207576902】NLP学习群② - +> 欢迎大家加入！共同完善此项目！NLP面试学习群 diff --git a/NLP/16.6 Attention/README.md b/NLP/16.6 Attention/README.md index 80ae363..d7d0d37 100644 --- a/NLP/16.6 Attention/README.md +++ b/NLP/16.6 Attention/README.md @@ -1,16 +1,10 @@ ## 目录 - [1. 什么是Attention机制](#1-什么是attention机制) -- [2. 编解码器中的Attention](#2-编解码器中的attention) - - [2.1 计算背景变量](#21-计算背景变量) - - [2.2 更新隐藏状态](#22-更新隐藏状态) -- [3. Attention本质](#3-attention本质) - - [3.1 机器翻译说明Attention](#31-机器翻译说明attention) - - [3.2 注意力分配概率计算](#32-注意力分配概率计算) - - [3.3 Attention的物理含义](#33-attention的物理含义) -- [4. Self-Attention模型](#4-self-attention模型) -- [5. 发展](#5-发展) -- [6. 代码实现](#6-代码实现) -- [7. 参考文献](#7-参考文献) +- [2. 计算背景变量](#2-计算背景变量) +- [3. 更新隐藏状态](#3-更新隐藏状态) +- [4. 发展](#4-发展) +- [5. 代码实现](#5-代码实现) +- [6. 参考文献](#6-参考文献) ## 1. 什么是Attention机制 @@ -28,9 +22,7 @@ -## 2. 编解码器中的Attention - -### 2.1 计算背景变量 +## 2. 计算背景变量 我们先描述第⼀个关键点，即计算背景变量。下图描绘了注意⼒机制如何为解码器在时间步 2 计算背景变量。 @@ -57,7 +49,7 @@ -### 2.2 更新隐藏状态 +## 3. 更新隐藏状态 现在我们描述第⼆个关键点，即更新隐藏状态。以⻔控循环单元为例，在解码器中我们可以对⻔控循环单元（GRU）中⻔控循环单元的设计稍作修改，从而变换上⼀时间步 t′−1 的输出 yt′−1、隐藏状态 st′−1 和当前时间步t′ 的含注意⼒机制的背景变量 ct′。解码器在时间步: math:t’ 的隐藏状态为： @@ -71,89 +63,13 @@ -## 3. Attention本质 - -### 3.1 机器翻译说明Attention - -本节先以机器翻译作为例子讲解最常见的Soft Attention模型的基本原理，之后抛离Encoder-Decoder框架抽象出了注意力机制的本质思想。 - -如果拿机器翻译来解释这个Encoder-Decoder框架更好理解，比如输入的是英文句子：Tom chase Jerry，Encoder-Decoder框架逐步生成中文单词：“汤姆”，“追逐”，“杰瑞”。 - -在翻译“杰瑞”这个中文单词的时候，模型里面的每个英文单词对于翻译目标单词“杰瑞”贡献是相同的，很明显这里不太合理，**显然“Jerry”对于翻译成“杰瑞”更重要，但是模型是无法体现这一点的，这就是为何说它没有引入注意力的原因。** - -没有引入注意力的模型在输入句子比较短的时候问题不大，但是如果输入句子比较长，此时所有语义完全通过一个中间语义向量来表示，单词自身的信息已经消失，可想而知会丢失很多细节信息，这也是为何要引入注意力模型的重要原因。 - -上面的例子中，如果引入Attention模型的话，应该在翻译“杰瑞”的时候，体现出英文单词对于翻译当前中文单词不同的影响程度，比如给出类似下面一个概率分布值： - -（Tom,0.3）(Chase,0.2) (Jerry,0.5) - -**每个英文单词的概率代表了翻译当前单词“杰瑞”时，注意力分配模型分配给不同英文单词的注意力大小。**这对于正确翻译目标语单词肯定是有帮助的，因为引入了新的信息。 - -同理，目标句子中的每个单词都应该学会其对应的源语句子中单词的注意力分配概率信息。这意味着在生成每个单词yi的时候，原先都是相同的中间语义表示C会被替换成根据当前生成单词而不断变化的Ci。理解Attention模型的关键就是这里，即由固定的中间语义表示C换成了根据当前输出单词来调整成加入注意力模型的变化的Ci。增加了注意力模型的Encoder-Decoder框架理解起来如下图所示。 - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-25_20-18-36.png) - -每个Ci可能对应着不同的源语句子单词的注意力分配概率分布，比如对于上面的英汉翻译来说，其对应的信息可能如下： - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-25_20-49-11.png) - -其中，f2函数代表Encoder对输入英文单词的某种变换函数，比如如果Encoder是用的RNN模型的话，这个f2函数的结果往往是某个时刻输入xi后隐层节点的状态值；g代表Encoder根据单词的中间表示合成整个句子中间语义表示的变换函数，一般的做法中，g函数就是对构成元素加权求和，即下列公式： - -![](https://latex.codecogs.com/gif.latex?C_i=\sum_{j=1}^{L_x}a_{ij}h_j) - -其中，Lx代表输入句子Source的长度，aij代表在Target输出第i个单词时Source输入句子中第j个单词的注意力分配系数，而hj则是Source输入句子中第j个单词的语义编码。假设下标i就是上面例子所说的“ 汤姆” ，那么Lx就是3，h1=f(“Tom”)，h2=f(“Chase”),h3=f(“Jerry”)分别是输入句子每个单词的语义编码，对应的注意力模型权值则分别是0.6,0.2,0.2，所以g函数本质上就是个加权求和函数。 - - - -### 3.2 注意力分配概率计算 - -这里还有一个问题：生成目标句子某个单词，比如“汤姆”的时候，如何知道Attention模型所需要的输入句子单词注意力分配概率分布值呢？就是说“汤姆”对应的输入句子Source中各个单词的概率分布：(Tom,0.6)(Chase,0.2) (Jerry,0.2) 是如何得到的呢？ - -对于采用RNN的Decoder来说，在时刻i，如果要生成yi单词，我们是可以知道Target在生成Yi之前的时刻i-1时，隐层节点i-1时刻的输出值Hi-1的，而我们的目的是要计算生成Yi时输入句子中的单词“Tom”、“Chase”、“Jerry”对Yi来说的注意力分配概率分布，那么可以用Target输出句子i-1时刻的隐层节点状态Hi-1去一一和输入句子Source中每个单词对应的RNN隐层节点状态hj进行对比，即通过函数F(hj,Hi-1)来获得目标单词yi和每个输入单词对应的对齐可能性，这个F函数在不同论文里可能会采取不同的方法，然后函数F的输出经过Softmax进行归一化就得到了符合概率分布取值区间的注意力分配概率分布数值。 - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-25_20-28-58.png) - - - -### 3.3 Attention的物理含义 - -一般在自然语言处理应用里会把Attention模型看作是输出Target句子中某个单词和输入Source句子每个单词的对齐模型，这是非常有道理的。 - -**目标句子生成的每个单词对应输入句子单词的概率分布可以理解为输入句子单词和这个目标生成单词的对齐概率，**这在机器翻译语境下是非常直观的：传统的统计机器翻译一般在做的过程中会专门有一个短语对齐的步骤，而注意力模型其实起的是相同的作用。 - -如果把Attention机制从上文讲述例子中的Encoder-Decoder框架中剥离，并进一步做抽象，可以更容易看懂Attention机制的本质思想。 - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-25_20-33-33.png) - -我们可以这样来看待Attention机制（参考图9）：将Source中的构成元素想象成是由一系列的数据对构成，此时给定Target中的某个元素Query，通过计算Query和各个Key的相似性或者相关性，得到每个Key对应Value的权重系数，然后对Value进行加权求和，即得到了最终的Attention数值。所以本质上Attention机制是对Source中元素的Value值进行加权求和，而Query和Key用来计算对应Value的权重系数。即可以将其本质思想改写为如下公式： - -![](https://latex.codecogs.com/gif.latex?Attention(Query,Source)=\sum_{i=1}^{L_x}Similarity(Query,key_i)*Value_i) - -其中，Lx=||Source||代表Source的长度，公式含义即如上所述。上文所举的机器翻译的例子里，因为在计算Attention的过程中，Source中的Key和Value合二为一，指向的是同一个东西，也即输入句子中每个单词对应的语义编码，所以可能不容易看出这种能够体现本质思想的结构。 - -至于Attention机制的具体计算过程，如果对目前大多数方法进行抽象的话，可以将其归纳为两个过程：第一个过程是根据Query和Key计算权重系数，第二个过程根据权重系数对Value进行加权求和。而第一个过程又可以细分为两个阶段：第一个阶段根据Query和Key计算两者的相似性或者相关性；第二个阶段对第一阶段的原始分值进行归一化处理； - - - -## 4. Self-Attention模型 - -Self Attention也经常被称为intra Attention（内部Attention），最近一年也获得了比较广泛的使用，比如Google最新的机器翻译模型内部大量采用了Self Attention模型。 - -在一般任务的Encoder-Decoder框架中，输入Source和输出Target内容是不一样的，比如对于英-中机器翻译来说，Source是英文句子，Target是对应的翻译出的中文句子，Attention机制发生在Target的元素Query和Source中的所有元素之间。**而Self Attention顾名思义，指的不是Target和Source之间的Attention机制，而是Source内部元素之间或者Target内部元素之间发生的Attention机制，也可以理解为Target=Source这种特殊情况下的注意力计算机制。**其具体计算过程是一样的，只是计算对象发生了变化而已，所以此处不再赘述其计算过程细节。 - -很明显，引入Self Attention后会更容易捕获句子中长距离的相互依赖的特征，因为如果是RNN或者LSTM，需要依次序序列计算，对于远距离的相互依赖的特征，要经过若干时间步步骤的信息累积才能将两者联系起来，而距离越远，有效捕获的可能性越小。 - -但是Self Attention在计算过程中会直接将句子中任意两个单词的联系通过一个计算步骤直接联系起来，所以远距离依赖特征之间的距离被极大缩短，有利于有效地利用这些特征。除此外，Self Attention对于增加计算的并行性也有直接帮助作用。这是为何Self Attention逐渐被广泛使用的主要原因。 - - - -## 5. 发展 +## 4. 发展 本质上，注意⼒机制能够为表征中较有价值的部分分配较多的计算资源。这个有趣的想法⾃提出后得到了快速发展，特别是启发了依靠注意⼒机制来编码输⼊序列并解码出输出序列的**变换器（Transformer）模型**的设计。变换器抛弃了卷积神经⽹络和循环神经⽹络的架构。它在计算效率上⽐基于循环神经⽹络的编码器—解码器模型通常更具明显优势。含注意⼒机制的变换器的编码结构在后来的**BERT预训练模型**中得以应⽤并令后者⼤放异彩：微调后的模型在多达11项⾃然语⾔处理任务中取得了当时最先进的结果。不久后，同样是基于变换器设计的**GPT-2模型**于新收集的语料数据集预训练后，在7个未参与训练的语⾔模型数据集上均取得了当时最先进的结果。除了⾃然语⾔处理领域，注意⼒机制还被⼴泛⽤于图像分类、⾃动图像描述、唇语解读以及语⾳识别。 -## 6. 代码实现 +## 5. 代码实现 **注意力模型实现中英文机器翻译** @@ -167,12 +83,10 @@ Self Attention也经常被称为intra Attention（内部Attention），最近一 -## 7. 参考文献 +## 6. 参考文献 [动手学深度学习](https://www.lanzous.com/i5lqo4f) -[注意力机制的基本思想和实现原理](https://blog.csdn.net/hpulfc/article/details/80448570) - ------ diff --git a/NLP/16.7 Transformer/README.md b/NLP/16.7 Transformer/README.md deleted file mode 100644 index bcd15c9..0000000 --- a/NLP/16.7 Transformer/README.md +++ /dev/null @@ -1,245 +0,0 @@ -## 目录 -- [1. 什么是Transformer](#1-什么是transformer) -- [2. Transformer结构](#2-transformer结构) - - [2.1 总体结构](#21-总体结构) - - [2.2 Encoder层结构](#22-encoder层结构) - - [2.3 Decoder层结构](#23-decoder层结构) - - [2.4 动态流程图](#24-动态流程图) -- [3. Transformer为什么需要进行Multi-head Attention](#3-transformer为什么需要进行multi-head-attention) -- [4. Transformer相比于RNN/LSTM，有什么优势？为什么？](#4-transformer相比于rnnlstm有什么优势为什么) -- [5. 为什么说Transformer可以代替seq2seq？](#5-为什么说transformer可以代替seq2seq) -- [6. 代码实现](#6-代码实现) -- [7. 参考文献](#7-参考文献) - -## 1. 什么是Transformer - -**[《Attention Is All You Need》](https://arxiv.org/pdf/1706.03762.pdf)是一篇Google提出的将Attention思想发挥到极致的论文。这篇论文中提出一个全新的模型，叫 Transformer，抛弃了以往深度学习任务里面使用到的 CNN 和 RNN**。目前大热的Bert就是基于Transformer构建的，这个模型广泛应用于NLP领域，例如机器翻译，问答系统，文本摘要和语音识别等等方向。 - - - -## 2. Transformer结构 - -### 2.1 总体结构 - -Transformer的结构和Attention模型一样，Transformer模型中也采用了 encoer-decoder 架构。但其结构相比于Attention更加复杂，论文中encoder层由6个encoder堆叠在一起，decoder层也一样。 - -不了解Attention模型的，可以回顾之前的文章：[Attention](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.6%20Attention) - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-25_23-4-24.png) - -每一个encoder和decoder的内部结构如下图： - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-25_23-25-14.png) - -- encoder，包含两层，一个self-attention层和一个前馈神经网络，self-attention能帮助当前节点不仅仅只关注当前的词，从而能获取到上下文的语义。 -- decoder也包含encoder提到的两层网络，但是在这两层中间还有一层attention层，帮助当前节点获取到当前需要关注的重点内容。 - - - -### 2.2 Encoder层结构 - -首先，模型需要对输入的数据进行一个embedding操作，也可以理解为类似w2c的操作，enmbedding结束之后，输入到encoder层，self-attention处理完数据后把数据送给前馈神经网络，前馈神经网络的计算可以并行，得到的输出会输入到下一个encoder。 - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-25_23-8-46.png) - - - -#### 2.2.1 Positional Encoding - -transformer模型中缺少一种解释输入序列中单词顺序的方法，它跟序列模型还不不一样。为了处理这个问题，transformer给encoder层和decoder层的输入添加了一个额外的向量Positional Encoding，维度和embedding的维度一样，这个向量采用了一种很独特的方法来让模型学习到这个值，这个向量能决定当前词的位置，或者说在一个句子中不同的词之间的距离。这个位置向量的具体计算方法有很多种，论文中的计算方法如下： - -![](https://latex.codecogs.com/gif.latex?PE(pos,2i)=sin(\frac{pos}{10000^{\frac{2i}{d_{model}}}})) - -![](https://latex.codecogs.com/gif.latex?PE(pos,2i+1)=cos(\frac{pos}{10000^{\frac{2i}{d_{model}}}})) - -其中pos是指当前词在句子中的位置，i是指向量中每个值的index，可以看出，在**偶数位置，使用正弦编码，在奇数位置，使用余弦编码**。 - -最后把这个Positional Encoding与embedding的值相加，作为输入送到下一层。 - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-26_14-45-31.png) - - - -#### 2.2.2 Self-Attention - -接下来我们详细看一下self-attention，其思想和attention类似，但是self-attention是Transformer用来将其他相关单词的“理解”转换成我们正在处理的单词的一种思路，我们看个例子： - -The animal didn't cross the street because it was too tired - -这里的 it 到底代表的是 animal 还是 street 呢，对于我们来说能很简单的判断出来，但是对于机器来说，是很难判断的，self-attention就能够让机器把 it 和 animal 联系起来，接下来我们看下详细的处理过程。 - -1. 首先，self-attention会计算出三个新的向量，在论文中，向量的维度是512维，我们把这三个向量分别称为Query、Key、Value，这三个向量是用embedding向量与一个矩阵相乘得到的结果，这个矩阵是随机初始化的，维度为（64，512）注意第二个维度需要和embedding的维度一样，其值在BP的过程中会一直进行更新，得到的这三个向量的维度是64。 - - ![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-26_8-59-3.png) - -2. 计算self-attention的分数值，该分数值决定了当我们在某个位置encode一个词时，对输入句子的其他部分的关注程度。这个分数值的计算方法是Query与Key做点成，以下图为例，首先我们需要针对Thinking这个词，计算出其他词对于该词的一个分数值，首先是针对于自己本身即q1·k1，然后是针对于第二个词即q1·k2。 - - ![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-26_9-1-36.png) - -3. 接下来，把点成的结果除以一个常数，这里我们除以8，这个值一般是采用上文提到的矩阵的第一个维度的开方即64的开方8，当然也可以选择其他的值，然后把得到的结果做一个softmax的计算。得到的结果即是每个词对于当前位置的词的相关性大小，当然，当前位置的词相关性肯定会会很大。 - - ![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-26_9-3-3.png) - -4. 下一步就是把Value和softmax得到的值进行相乘，并相加，得到的结果即是self-attetion在当前节点的值。 - - ![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-26_14-47-17.png) - -在实际的应用场景，为了提高计算速度，我们采用的是矩阵的方式，直接计算出Query, Key, Value的矩阵，然后把embedding的值与三个矩阵直接相乘，把得到的新矩阵 Q 与 K 相乘，乘以一个常数，做softmax操作，最后乘上 V 矩阵。 - -**这种通过 query 和 key 的相似性程度来确定 value 的权重分布的方法被称为scaled dot-product attention。** - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-26_9-6-6.png) - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-26_9-6-41.png) - - - -#### 2.2.3 Multi-Headed Attention - -这篇论文更牛逼的地方是给self-attention加入了另外一个机制，被称为“multi-headed” attention，该机制理解起来很简单，**就是说不仅仅只初始化一组Q、K、V的矩阵，而是初始化多组，tranformer是使用了8组**，所以最后得到的结果是8个矩阵。 - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-26_14-49-14.png) - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-26_9-13-50.png) - - - -#### 2.2.4 Layer normalization - -在transformer中，每一个子层（self-attetion，Feed Forward Neural Network）之后都会接一个残缺模块，并且有一个Layer normalization。 - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-26_9-30-31.png) - -Normalization有很多种，但是它们都有一个共同的目的，那就是把输入转化成均值为0方差为1的数据。我们在把数据送入激活函数之前进行normalization（归一化），因为我们不希望输入数据落在激活函数的饱和区。 - -**Batch Normalization** - -BN的主要思想就是：在每一层的每一批数据上进行归一化。我们可能会对输入数据进行归一化，但是经过该网络层的作用后，我们的数据已经不再是归一化的了。随着这种情况的发展，数据的偏差越来越大，我的反向传播需要考虑到这些大的偏差，这就迫使我们只能使用较小的学习率来防止梯度消失或者梯度爆炸。**BN的具体做法就是对每一小批数据，在批这个方向上做归一化。** - -**Layer normalization** - -它也是归一化数据的一种方式，不过**LN 是在每一个样本上计算均值和方差**，而不是BN那种在批方向计算均值和方差！公式如下： - -![](https://latex.codecogs.com/gif.latex?LN(x_i)=\alpha*\frac{x_i-\mu_L}{\sqrt{\sigma_L^2+\varepsilon}}+\beta) - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-26_9-35-22.png) - - - -#### 2.2.5 Feed Forward Neural Network - -这给我们留下了一个小的挑战，前馈神经网络没法输入 8 个矩阵呀，这该怎么办呢？所以我们需要一种方式，把 8 个矩阵降为 1 个，首先，我们把 8 个矩阵连在一起，这样会得到一个大的矩阵，再随机初始化一个矩阵和这个组合好的矩阵相乘，最后得到一个最终的矩阵。 - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-26_9-17-14.png) - - - -### 2.3 Decoder层结构 - -根据上面的总体结构图可以看出，decoder部分其实和encoder部分大同小异，刚开始也是先添加一个位置向量Positional Encoding，方法和 2.2.1 节一样，接下来接的是masked mutil-head attetion，这里的mask也是transformer一个很关键的技术，下面我们会进行一一介绍。 - -其余的层结构与Encoder一样，请参考Encoder层结构。 - - - -#### 2.3.1 masked mutil-head attetion - -**mask 表示掩码，它对某些值进行掩盖，使其在参数更新时不产生效果**。Transformer 模型里面涉及两种 mask，分别是 padding mask 和 sequence mask。其中，padding mask 在所有的 scaled dot-product attention 里面都需要用到，而 sequence mask 只有在 decoder 的 self-attention 里面用到。 - -1. **padding mask** - - 什么是 padding mask 呢？因为每个批次输入序列长度是不一样的也就是说，我们要对输入序列进行对齐。具体来说，就是给在较短的序列后面填充 0。但是如果输入的序列太长，则是截取左边的内容，把多余的直接舍弃。因为这些填充的位置，其实是没什么意义的，所以我们的attention机制不应该把注意力放在这些位置上，所以我们需要进行一些处理。 - - 具体的做法是，把这些位置的值加上一个非常大的负数(负无穷)，这样的话，经过 softmax，这些位置的概率就会接近0！ - - 而我们的 padding mask 实际上是一个张量，每个值都是一个Boolean，值为 false 的地方就是我们要进行处理的地方。 - -2. **Sequence mask** - - 文章前面也提到，sequence mask 是为了使得 decoder 不能看见未来的信息。也就是对于一个序列，在 time_step 为 t 的时刻，我们的解码输出应该只能依赖于 t 时刻之前的输出，而不能依赖 t 之后的输出。因此我们需要想一个办法，把 t 之后的信息给隐藏起来。 - - 那么具体怎么做呢？也很简单：**产生一个上三角矩阵，上三角的值全为0。把这个矩阵作用在每一个序列上，就可以达到我们的目的**。 - -- 对于 decoder 的 self-attention，里面使用到的 scaled dot-product attention，同时需要padding mask 和 sequence mask 作为 attn_mask，具体实现就是两个mask相加作为attn_mask。 -- 其他情况，attn_mask 一律等于 padding mask。 - - - -#### 2.3.2 Output层 - -当decoder层全部执行完毕后，怎么把得到的向量映射为我们需要的词呢，很简单，只需要在结尾再添加一个全连接层和softmax层，假如我们的词典是1w个词，那最终softmax会输入1w个词的概率，概率值最大的对应的词就是我们最终的结果。 - - - -### 2.4 动态流程图 - -编码器通过处理输入序列开启工作。顶端编码器的输出之后会变转化为一个包含向量K（键向量）和V（值向量）的注意力向量集 ，**这是并行化操作**。这些向量将被每个解码器用于自身的“编码-解码注意力层”，而这些层可以帮助解码器关注输入序列哪些位置合适： - -![](https://julyedu-img.oss-cn-beijing.aliyuncs.com/quesbase64156846894583861613.gif) - -在完成编码阶段后，则开始解码阶段。解码阶段的每个步骤都会输出一个输出序列（在这个例子里，是英语翻译的句子）的元素。 - -接下来的步骤重复了这个过程，直到到达一个特殊的终止符号，它表示transformer的解码器已经完成了它的输出。每个步骤的输出在下一个时间步被提供给底端解码器，并且就像编码器之前做的那样，这些解码器会输出它们的解码结果 。 - -[解码器动态图请点击](https://julyedu-img.oss-cn-beijing.aliyuncs.com/quesbase64156846899939997439.gif) - - - -## 3. Transformer为什么需要进行Multi-head Attention - -原论文中说到进行Multi-head Attention的原因是将模型分为多个头，形成多个子空间，可以让模型去关注不同方面的信息，最后再将各个方面的信息综合起来。其实直观上也可以想到，如果自己设计这样的一个模型，必然也不会只做一次attention，多次attention综合的结果至少能够起到增强模型的作用，也可以类比CNN中同时使用**多个卷积核**的作用，直观上讲，多头的注意力**有助于网络捕捉到更丰富的特征/信息**。 - - - -## 4. Transformer相比于RNN/LSTM，有什么优势？为什么？ - -1. RNN系列的模型，并行计算能力很差。RNN并行计算的问题就出在这里，因为 T 时刻的计算依赖 T-1 时刻的隐层计算结果，而 T-1 时刻的计算依赖 T-2 时刻的隐层计算结果，如此下去就形成了所谓的序列依赖关系。 - -2. Transformer的特征抽取能力比RNN系列的模型要好。 - - 具体实验对比可以参考：[放弃幻想，全面拥抱Transformer：自然语言处理三大特征抽取器（CNN/RNN/TF）比较](https://zhuanlan.zhihu.com/p/54743941) - - 但是值得注意的是，并不是说Transformer就能够完全替代RNN系列的模型了，任何模型都有其适用范围，同样的，RNN系列模型在很多任务上还是首选，熟悉各种模型的内部原理，知其然且知其所以然，才能遇到新任务时，快速分析这时候该用什么样的模型，该怎么做好。 - - - -## 5. 为什么说Transformer可以代替seq2seq？ - -**seq2seq缺点**：这里用代替这个词略显不妥当，seq2seq虽已老，但始终还是有其用武之地，seq2seq最大的问题在于**将Encoder端的所有信息压缩到一个固定长度的向量中**，并将其作为Decoder端首个隐藏状态的输入，来预测Decoder端第一个单词(token)的隐藏状态。在输入序列比较长的时候，这样做显然会损失Encoder端的很多信息，而且这样一股脑的把该固定向量送入Decoder端，Decoder端不能够关注到其想要关注的信息。 - -**Transformer优点**：transformer不但对seq2seq模型这两点缺点有了实质性的改进(多头交互式attention模块)，而且还引入了self-attention模块，让源序列和目标序列首先“自关联”起来，这样的话，源序列和目标序列自身的embedding表示所蕴含的信息更加丰富，而且后续的FFN层也增强了模型的表达能力，并且Transformer并行计算的能力是远远超过seq2seq系列的模型，因此我认为这是transformer优于seq2seq模型的地方。 - - - -## 6. 代码实现 - -地址：[https://github.com/Kyubyong/transformer](https://github.com/Kyubyong/transformer) - -代码解读：[Transformer解析与tensorflow代码解读](https://www.cnblogs.com/zhouxiaosong/p/11032431.html) - - - -## 7. 参考文献 - -- [Transformer模型详解](https://blog.csdn.net/u012526436/article/details/86295971) -- [图解Transformer（完整版）](https://blog.csdn.net/longxinchen_ml/article/details/86533005) -- [关于Transformer的若干问题整理记录](https://www.nowcoder.com/discuss/258321) - - - ------- - -> 作者：[@mantchs](https://github.com/NLP-LOVE/ML-NLP) -> -> GitHub：[https://github.com/NLP-LOVE/ML-NLP](https://github.com/NLP-LOVE/ML-NLP) -> -> 欢迎大家加入讨论！共同完善此项目！群号：【541954936】NLP面试学习群 - - - - - - - - - diff --git a/NLP/16.8 BERT/README.md b/NLP/16.8 BERT/README.md deleted file mode 100644 index 77ab482..0000000 --- a/NLP/16.8 BERT/README.md +++ /dev/null @@ -1,241 +0,0 @@ -## 目录 -- [1. 什么是BERT](#1-什么是bert) -- [2. 从Word Embedding到Bert模型的发展](#2-从word-embedding到bert模型的发展) - - [2.1 图像的预训练](#21-图像的预训练) - - [2.2 Word Embedding](#22-word-embedding) - - [2.3 ELMO](#23-elmo) - - [2.4 GPT](#24-gpt) - - [2.5 BERT](#25-bert) -- [3. BERT的评价](#3-bert的评价) -- [4. 代码实现](#4-代码实现) -- [5. 参考文献](#5-参考文献) - -## 1. 什么是BERT - -**BERT的全称是Bidirectional Encoder Representation from Transformers**，是Google2018年提出的预训练模型，即双向Transformer的Encoder，因为decoder是不能获要预测的信息的。模型的主要创新点都在pre-train方法上，即用了Masked LM和Next Sentence Prediction两种方法分别捕捉词语和句子级别的representation。 - -Bert最近很火，应该是最近最火爆的AI进展，网上的评价很高，那么Bert值得这么高的评价吗？我个人判断是值得。那为什么会有这么高的评价呢？是因为它有重大的理论或者模型创新吗？其实并没有，从模型创新角度看一般，创新不算大。但是架不住效果太好了，基本刷新了很多NLP的任务的最好性能，有些任务还被刷爆了，这个才是关键。另外一点是Bert具备广泛的通用性，就是说绝大部分NLP任务都可以采用类似的两阶段模式直接去提升效果，这个第二关键。客观的说，把Bert当做最近两年NLP重大进展的集大成者更符合事实。 - - - -## 2. 从Word Embedding到Bert模型的发展 - -### 2.1 图像的预训练 - -自从深度学习火起来后，预训练过程就是做图像或者视频领域的一种比较常规的做法，有比较长的历史了，而且这种做法很有效，能明显促进应用的效果。 - -![](https://pic3.zhimg.com/80/v2-4c27ee0ff1fb87f27d55b007cb4ceb06_hd.jpg) - -那么图像领域怎么做预训练呢，上图展示了这个过程， - -1. 我们设计好网络结构以后，对于图像来说一般是CNN的多层叠加网络结构，可以先用某个训练集合比如训练集合A或者训练集合B对这个网络进行预先训练，在A任务上或者B任务上学会网络参数，然后存起来以备后用。 - -2. 假设我们面临第三个任务C，网络结构采取相同的网络结构，在比较浅的几层CNN结构，网络参数初始化的时候可以加载A任务或者B任务学习好的参数，其它CNN高层参数仍然随机初始化。 - -3. 之后我们用C任务的训练数据来训练网络，此时有两种做法： - - **一种**是浅层加载的参数在训练C任务过程中不动，这种方法被称为“Frozen”; - - **另一种**是底层网络参数尽管被初始化了，在C任务训练过程中仍然随着训练的进程不断改变，这种一般叫“Fine-Tuning”，顾名思义，就是更好地把参数进行调整使得更适应当前的C任务。 - -一般图像或者视频领域要做预训练一般都这么做。这样做的优点是：如果手头任务C的训练集合数据量较少的话，利用预训练出来的参数来训练任务C，加个预训练过程也能极大加快任务训练的收敛速度，所以这种预训练方式是老少皆宜的解决方案，另外疗效又好，所以在做图像处理领域很快就流行开来。 - -**为什么预训练可行** - -对于层级的CNN结构来说，不同层级的神经元学习到了不同类型的图像特征，由底向上特征形成层级结构，所以预训练好的网络参数，尤其是底层的网络参数抽取出特征跟具体任务越无关，越具备任务的通用性，所以这是为何一般用底层预训练好的参数初始化新任务网络参数的原因。而高层特征跟任务关联较大，实际可以不用使用，或者采用Fine-tuning用新数据集合清洗掉高层无关的特征抽取器。 - - - -### 2.2 Word Embedding - -![](https://pic2.zhimg.com/80/v2-e2842dd9bc442893bd53dd9fa32d6c9d_hd.jpg) - -神经网络语言模型(NNLM)的思路。先说训练过程。学习任务是输入某个句中单词 ![[公式]](https://www.zhihu.com/equation?tex=W_t=（Bert）) 前面句子的t-1个单词，要求网络正确预测单词Bert，即最大化： - -![[公式]](https://www.zhihu.com/equation?tex=++P%28W_t%3D%E2%80%9CBert%E2%80%9D%7CW_1%2CW_2%2C%E2%80%A6W_%28t-1%29%3B%CE%B8%29) - -前面任意单词 ![[公式]](https://www.zhihu.com/equation?tex=W_i) 用Onehot编码（比如：0001000）作为原始单词输入，之后乘以矩阵Q后获得向量 ![[公式]](https://www.zhihu.com/equation?tex=C%28W_i+%29) ，每个单词的 ![[公式]](https://www.zhihu.com/equation?tex=C%28W_i+%29) 拼接，上接隐层，然后接softmax去预测后面应该后续接哪个单词。这个 ![[公式]](https://www.zhihu.com/equation?tex=C%28W_i+%29) 是什么？这其实就是单词对应的Word Embedding值，那个矩阵Q包含V行，V代表词典大小，每一行内容代表对应单词的Word embedding值。只不过Q的内容也是网络参数，需要学习获得，训练刚开始用随机值初始化矩阵Q，当这个网络训练好之后，矩阵Q的内容被正确赋值，每一行代表一个单词对应的Word embedding值。所以你看，通过这个网络学习语言模型任务，这个网络不仅自己能够根据上文预测后接单词是什么，同时获得一个副产品，就是那个矩阵Q，这就是单词的Word Embedding。 - -2013年最火的用语言模型做Word Embedding的工具是Word2Vec，后来又出了Glove，Word2Vec。对于这两个模型不熟悉的可以参考我之前的文章，这里不再赘述： - -- [Word2Vec](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.1%20Word%20Embedding) -- [GloVe](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.3%20GloVe) - -上面这种模型做法就是18年之前NLP领域里面采用预训练的典型做法，之前说过，Word Embedding其实对于很多下游NLP任务是有帮助的，只是帮助没有大到闪瞎忘记戴墨镜的围观群众的双眼而已。那么新问题来了，为什么这样训练及使用Word Embedding的效果没有期待中那么好呢？答案很简单，因为Word Embedding有问题呗。这貌似是个比较弱智的答案，关键是Word Embedding存在什么问题？这其实是个好问题。 - -**这片在Word Embedding头上笼罩了好几年的乌云是什么？是多义词问题。**我们知道，多义词是自然语言中经常出现的现象，也是语言灵活性和高效性的一种体现。多义词对Word Embedding来说有什么负面影响？如上图所示，比如多义词Bank，有两个常用含义，但是Word Embedding在对bank这个单词进行编码的时候，是区分不开这两个含义的，因为它们尽管上下文环境中出现的单词不同，但是在用语言模型训练的时候，不论什么上下文的句子经过word2vec，都是预测相同的单词bank，而同一个单词占的是同一行的参数空间，这导致两种不同的上下文信息都会编码到相同的word embedding空间里去。所以word embedding无法区分多义词的不同语义，这就是它的一个比较严重的问题。 - -有没有简单优美的解决方案呢？ELMO提供了一种简洁优雅的解决方案。 - - - -### 2.3 ELMO - -ELMO是“Embedding from Language Models”的简称，其实这个名字并没有反应它的本质思想，提出ELMO的论文题目：“Deep contextualized word representation”更能体现其精髓，而精髓在哪里？在deep contextualized这个短语，一个是deep，一个是context，其中context更关键。 - -在此之前的Word Embedding本质上是个静态的方式，所谓静态指的是训练好之后每个单词的表达就固定住了，以后使用的时候，不论新句子上下文单词是什么，这个单词的Word Embedding不会跟着上下文场景的变化而改变，所以对于比如Bank这个词，它事先学好的Word Embedding中混合了几种语义 ，在应用中来了个新句子，即使从上下文中（比如句子包含money等词）明显可以看出它代表的是“银行”的含义，但是对应的Word Embedding内容也不会变，它还是混合了多种语义。这是为何说它是静态的，这也是问题所在。 - -**ELMO的本质思想是**：我事先用语言模型学好一个单词的Word Embedding，此时多义词无法区分，不过这没关系。在我实际使用Word Embedding的时候，单词已经具备了特定的上下文了，这个时候我可以根据上下文单词的语义去调整单词的Word Embedding表示，这样经过调整后的Word Embedding更能表达在这个上下文中的具体含义，自然也就解决了多义词的问题了。所以ELMO本身是个根据当前上下文对Word Embedding动态调整的思路。 - -![](https://pic4.zhimg.com/80/v2-fe335ea9fdcd6e0e5ec4a9ac0e2290db_hd.jpg) - -ELMO采用了典型的两阶段过程，第一个阶段是利用语言模型进行预训练；第二个阶段是在做下游任务时，从预训练网络中提取对应单词的网络各层的Word Embedding作为新特征补充到下游任务中。 - -上图展示的是其预训练过程，它的网络结构采用了双层双向LSTM，目前语言模型训练的任务目标是根据单词 ![[公式]](https://www.zhihu.com/equation?tex=W_i) 的上下文去正确预测单词 ![[公式]](https://www.zhihu.com/equation?tex=W_i) ， ![[公式]](https://www.zhihu.com/equation?tex=W_i) 之前的单词序列Context-before称为上文，之后的单词序列Context-after称为下文。 - -图中左端的前向双层LSTM代表正方向编码器，输入的是从左到右顺序的除了预测单词外 ![[公式]](https://www.zhihu.com/equation?tex=W_i) 的上文Context-before；右端的逆向双层LSTM代表反方向编码器，输入的是从右到左的逆序的句子下文Context-after；每个编码器的深度都是两层LSTM叠加。 - -这个网络结构其实在NLP中是很常用的。使用这个网络结构利用大量语料做语言模型任务就能预先训练好这个网络，如果训练好这个网络后，输入一个新句子 ![[公式]](https://www.zhihu.com/equation?tex=Snew) ，句子中每个单词都能得到对应的三个Embedding: - -- 最底层是单词的Word Embedding； -- 往上走是第一层双向LSTM中对应单词位置的Embedding，这层编码单词的句法信息更多一些； -- 再往上走是第二层LSTM中对应单词位置的Embedding，这层编码单词的语义信息更多一些。 - -也就是说，ELMO的预训练过程不仅仅学会单词的Word Embedding，还学会了一个双层双向的LSTM网络结构，而这两者后面都有用。 - -![](https://pic2.zhimg.com/80/v2-ef6513ff29e3234011221e4be2e97615_hd.jpg) - -上面介绍的是ELMO的第一阶段：预训练阶段。那么预训练好网络结构后，**如何给下游任务使用呢**？上图展示了下游任务的使用过程，比如我们的下游任务仍然是QA问题: - -1. 此时对于问句X，我们可以先将句子X作为预训练好的ELMO网络的输入，这样句子X中每个单词在ELMO网络中都能获得对应的三个Embedding； -2. 之后给予这三个Embedding中的每一个Embedding一个权重a，这个权重可以学习得来，根据各自权重累加求和，将三个Embedding整合成一个； -3. 然后将整合后的这个Embedding作为X句在自己任务的那个网络结构中对应单词的输入，以此作为补充的新特征给下游任务使用。对于上图所示下游任务QA中的回答句子Y来说也是如此处理。 - -因为ELMO给下游提供的是每个单词的特征形式，所以这一类预训练的方法被称为“Feature-based Pre-Training”。 - -**前面我们提到静态Word Embedding无法解决多义词的问题，那么ELMO引入上下文动态调整单词的embedding后多义词问题解决了吗？解决了，而且比我们期待的解决得还要好**。对于Glove训练出的Word Embedding来说，多义词比如play，根据它的embedding找出的最接近的其它单词大多数集中在体育领域，这很明显是因为训练数据中包含play的句子中体育领域的数量明显占优导致；而使用ELMO，根据上下文动态调整后的embedding不仅能够找出对应的“演出”的相同语义的句子，而且还可以保证找出的句子中的play对应的词性也是相同的，这是超出期待之处。之所以会这样，是因为我们上面提到过，第一层LSTM编码了很多句法信息，这在这里起到了重要作用。 - -**ELMO有什么值得改进的缺点呢**？ - -- 首先，一个非常明显的缺点在特征抽取器选择方面，ELMO使用了LSTM而不是新贵Transformer，Transformer是谷歌在17年做机器翻译任务的“Attention is all you need”的论文中提出的，引起了相当大的反响，很多研究已经证明了Transformer提取特征的能力是要远强于LSTM的。如果ELMO采取Transformer作为特征提取器，那么估计Bert的反响远不如现在的这种火爆场面。 -- 另外一点，ELMO采取双向拼接这种融合特征的能力可能比Bert一体化的融合特征方式弱，但是，这只是一种从道理推断产生的怀疑，目前并没有具体实验说明这一点。 - - - -### 2.4 GPT - -![](https://pic1.zhimg.com/80/v2-5028b1de8fb50e6630cc9839f0b16568_hd.jpg) - -GPT是“Generative Pre-Training”的简称，从名字看其含义是指的生成式的预训练。GPT也采用两阶段过程，第一个阶段是利用语言模型进行预训练，第二阶段通过Fine-tuning的模式解决下游任务。 - -上图展示了GPT的预训练过程，其实和ELMO是类似的，主要不同在于两点： - -- 首先，特征抽取器不是用的RNN，而是用的Transformer，上面提到过它的特征抽取能力要强于RNN，这个选择很明显是很明智的； -- 其次，GPT的预训练虽然仍然是以语言模型作为目标任务，但是采用的是单向的语言模型，所谓“单向”的含义是指：语言模型训练的任务目标是根据 ![[公式]](https://www.zhihu.com/equation?tex=W_i) 单词的上下文去正确预测单词 ![[公式]](https://www.zhihu.com/equation?tex=W_i) ， ![[公式]](https://www.zhihu.com/equation?tex=W_i) 之前的单词序列Context-before称为上文，之后的单词序列Context-after称为下文。 - -如果对Transformer模型不太了解的，可以参考我写的文章：[Transformer](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.7%20Transformer) - -ELMO在做语言模型预训练的时候，预测单词 ![[公式]](https://www.zhihu.com/equation?tex=W_i) 同时使用了上文和下文，而GPT则只采用Context-before这个单词的上文来进行预测，而抛开了下文。这个选择现在看不是个太好的选择，原因很简单，它没有把单词的下文融合进来，这限制了其在更多应用场景的效果，比如阅读理解这种任务，在做任务的时候是可以允许同时看到上文和下文一起做决策的。如果预训练时候不把单词的下文嵌入到Word Embedding中，是很吃亏的，白白丢掉了很多信息。 - - - -### 2.5 BERT - -Bert采用和GPT完全相同的两阶段模型，首先是语言模型预训练；其次是使用Fine-Tuning模式解决下游任务。和GPT的最主要不同在于在预训练阶段采用了类似ELMO的双向语言模型，即双向的Transformer，当然另外一点是语言模型的数据规模要比GPT大。所以这里Bert的预训练过程不必多讲了。模型结构如下： - -![](https://github.com/NLP-LOVE/ML-NLP/raw/master/images/2019-9-28_21-34-11.png) - -对比OpenAI GPT(Generative pre-trained transformer)，BERT是双向的Transformer block连接；就像单向rnn和双向rnn的区别，直觉上来讲效果会好一些。 - -对比ELMo，虽然都是“双向”，但目标函数其实是不同的。ELMo是分别以![[公式]](https://www.zhihu.com/equation?tex=P%28w_i%7C+w_1%2C+...w_%7Bi-1%7D%29) 和 ![[公式]](https://www.zhihu.com/equation?tex=P%28w_i%7Cw_%7Bi%2B1%7D%2C+...w_n%29) 作为目标函数，独立训练处两个representation然后拼接，而BERT则是以 ![[公式]](https://www.zhihu.com/equation?tex=P%28w_i%7Cw_1%2C++...%2Cw_%7Bi-1%7D%2C+w_%7Bi%2B1%7D%2C...%2Cw_n%29) 作为目标函数训练LM。 - -BERT预训练模型分为以下三个步骤：**Embedding、Masked LM、Next Sentence Prediction** - -#### 2.5.1 Embedding - -这里的Embedding由三种Embedding求和而成： - -![](https://gitee.com/kkweishe/images/raw/master/ML/2019-9-28_20-8-22.png) - -- Token Embeddings是词向量，第一个单词是CLS标志，可以用于之后的分类任务 -- Segment Embeddings用来区别两种句子，因为预训练不光做LM还要做以两个句子为输入的分类任务 -- Position Embeddings和之前文章中的Transformer不一样，不是三角函数而是学习出来的 - - - -#### 2.5.2 Masked LM - -MLM可以理解为完形填空，作者会随机mask每一个句子中15%的词，用其上下文来做预测，例如：my dog is hairy → my dog is [MASK] - -此处将hairy进行了mask处理，然后采用非监督学习的方法预测mask位置的词是什么，但是该方法有一个问题，因为是mask15%的词，其数量已经很高了，这样就会导致某些词在fine-tuning阶段从未见过，为了解决这个问题，作者做了如下的处理： - -80%是采用[mask]，my dog is hairy → my dog is [MASK] - -10%是随机取一个词来代替mask的词，my dog is hairy -> my dog is apple - -10%保持不变，my dog is hairy -> my dog is hairy - -**注意：这里的10%是15%需要mask中的10%** - -那么为啥要以一定的概率使用随机词呢？这是因为transformer要保持对每个输入token分布式的表征，否则Transformer很可能会记住这个[MASK]就是"hairy"。至于使用随机词带来的负面影响，文章中解释说,所有其他的token(即非"hairy"的token)共享15%*10% = 1.5%的概率，其影响是可以忽略不计的。Transformer全局的可视，又增加了信息的获取，但是不让模型获取全量信息。 - - - -#### 2.5.3 Next Sentence Prediction - -选择一些句子对A与B，其中50%的数据B是A的下一条句子，剩余50%的数据B是语料库中随机选择的，学习其中的相关性，添加这样的预训练的目的是目前很多NLP的任务比如QA和NLI都需要理解两个句子之间的关系，从而能让预训练的模型更好的适应这样的任务。 -个人理解： - -- Bert先是用Mask来提高视野范围的信息获取量，增加duplicate再随机Mask，这样跟RNN类方法依次训练预测没什么区别了除了mask不同位置外； -- 全局视野极大地降低了学习的难度，然后再用A+B/C来作为样本，这样每条样本都有50%的概率看到一半左右的噪声； -- 但直接学习Mask A+B/C是没法学习的，因为不知道哪些是噪声，所以又加上next_sentence预测任务，与MLM同时进行训练，这样用next来辅助模型对噪声/非噪声的辨识，用MLM来完成语义的大部分的学习。 - - - -## 3. BERT的评价 - -总结下BERT的主要贡献： - -- 引入了Masked LM，使用双向LM做模型预训练。 -- 为预训练引入了新目标NSP，它可以学习句子与句子间的关系。 -- 进一步验证了更大的模型效果更好： 12 --> 24 层。 -- 为下游任务引入了很通用的求解框架，不再为任务做模型定制。 -- 刷新了多项NLP任务的记录，引爆了NLP无监督预训练技术。 - -**BERT优点** - -- Transformer Encoder因为有Self-attention机制，因此BERT自带双向功能。 -- 因为双向功能以及多层Self-attention机制的影响，使得BERT必须使用Cloze版的语言模型Masked-LM来完成token级别的预训练。 -- 为了获取比词更高级别的句子级别的语义表征，BERT加入了Next Sentence Prediction来和Masked-LM一起做联合训练。 -- 为了适配多任务下的迁移学习，BERT设计了更通用的输入层和输出层。 -- 微调成本小。 - -**BERT缺点** - -- task1的随机遮挡策略略显粗犷，推荐阅读《Data Nosing As Smoothing In Neural Network Language Models》。 -- [MASK]标记在实际预测中不会出现，训练时用过多[MASK]影响模型表现。每个batch只有15%的token被预测，所以BERT收敛得比left-to-right模型要慢（它们会预测每个token）。 -- BERT对硬件资源的消耗巨大（大模型需要16个tpu，历时四天；更大的模型需要64个tpu，历时四天。 - -**评价** - -Bert是NLP里里程碑式的工作，对于后面NLP的研究和工业应用会产生长久的影响，这点毫无疑问。但是从上文介绍也可以看出，从模型或者方法角度看，Bert借鉴了ELMO，GPT及CBOW，主要提出了Masked 语言模型及Next Sentence Prediction，但是这里Next Sentence Prediction基本不影响大局，而Masked LM明显借鉴了CBOW的思想。所以说Bert的模型没什么大的创新，更像最近几年NLP重要进展的集大成者，这点如果你看懂了上文估计也没有太大异议，如果你有大的异议，杠精这个大帽子我随时准备戴给你。如果归纳一下这些进展就是： - -- 首先是两阶段模型，第一阶段双向语言模型预训练，这里注意要用双向而不是单向，第二阶段采用具体任务Fine-tuning或者做特征集成； -- 第二是特征抽取要用Transformer作为特征提取器而不是RNN或者CNN； -- 第三，双向语言模型可以采取CBOW的方法去做（当然我觉得这个是个细节问题，不算太关键，前两个因素比较关键）。 - -Bert最大的亮点在于效果好及普适性强，几乎所有NLP任务都可以套用Bert这种两阶段解决思路，而且效果应该会有明显提升。可以预见的是，未来一段时间在NLP应用领域，Transformer将占据主导地位，而且这种两阶段预训练方法也会主导各种应用。 - - - -## 4. 代码实现 - -[bert中文分类实践](https://github.com/NLP-LOVE/ML-NLP/blob/master/NLP/16.8%20BERT/bert-Chinese-classification-task.md) - - - -## 5. 参考文献 - -- [【NLP】Google BERT详解](https://zhuanlan.zhihu.com/p/46652512) -- [从Word Embedding到Bert模型—自然语言处理中的预训练技术发展史](https://zhuanlan.zhihu.com/p/49271699) -- [一文读懂BERT(原理篇)](https://blog.csdn.net/jiaowoshouzi/article/details/89073944) - - - ------- - -> 作者:[@mantchs](https://github.com/NLP-LOVE/ML-NLP) -> -> GitHub:[https://github.com/NLP-LOVE/ML-NLP](https://github.com/NLP-LOVE/ML-NLP) -> -> 欢迎大家加入讨论！共同完善此项目！群号:【541954936】NLP面试学习群 diff --git a/NLP/16.8 BERT/bert-Chinese-classification-task.md b/NLP/16.8 BERT/bert-Chinese-classification-task.md deleted file mode 100644 index 5e15002..0000000 --- a/NLP/16.8 BERT/bert-Chinese-classification-task.md +++ /dev/null @@ -1,46 +0,0 @@ -# bert-Chinese-classification-task -bert中文分类实践 - -在run_classifier_word.py中添加NewsProcessor，即新闻的预处理读入部分 \ -在main方法中添加news类型数据处理label \ - processors = { \ - "cola": ColaProcessor,\ - "mnli": MnliProcessor,\ - "mrpc": MrpcProcessor,\ - "news": NewsProcessor,\ - } - -download_glue_data.py 提供glue_data下面其他的bert论文公测glue数据下载 - -data目录下是news数据的样例 - -export GLUE_DIR=/search/odin/bert/extract_code/glue_data \ -export BERT_BASE_DIR=/search/odin/bert/chinese_L-12_H-768_A-12/ \ -export BERT_PYTORCH_DIR=/search/odin/bert/chinese_L-12_H-768_A-12/ - -python run_classifier_word.py \ - --task_name NEWS \ - --do_train \ - --do_eval \ - --data_dir $GLUE_DIR/NewsAll/ \ - --vocab_file $BERT_BASE_DIR/vocab.txt \ - --bert_config_file $BERT_BASE_DIR/bert_config.json \ - --init_checkpoint $BERT_PYTORCH_DIR/pytorch_model.bin \ - --max_seq_length 256 \ - --train_batch_size 32 \ - --learning_rate 2e-5 \ - --num_train_epochs 3.0 \ - --output_dir ./newsAll_output/ \ - --local_rank 3 - - 中文分类任务实践 - -实验中对中文34个topic进行实践（包括：时政，娱乐，体育等），在对run_classifier.py代码中的预处理环节需要加入NewsProcessor模块，及类似于MrpcProcessor，但是需要对中文的编码进行适当修改，训练数据与测试数据按照4:1进行切割，数据量约80万，单卡GPU资源，训练时间18小时，acc为92.8% - -eval_accuracy = 0.9281581998809113 - -eval_loss = 0.2222444740207354 - -global_step = 59826 - -loss = 0.14488934577978746 diff --git a/NLP/16.8 BERT/data/dev.tsv b/NLP/16.8 BERT/data/dev.tsv deleted file mode 100644 index 2ff6836..0000000 --- a/NLP/16.8 BERT/data/dev.tsv +++ /dev/null @@ -1,30 +0,0 @@ -game EDGϣʦʾ̭ ϷԭֹվõȡΥ߱ؾFnaticʦɹʾһƱȫ򡱵ĽǿŶACD0-3ս˵ǴǵʿΪ0-3Ŀ֣Ϊֻ۳ߵĿܣ֮ǰʵߵķʽк֣ܶ١ֻҪһĶս֡Fnaticʦ조ʱ˺͡Ĵ˵׷ҲǡС6-02-4·ߡFnatic״̬ãһ4-0ʵת˫ɱIMTGAM°ǿƱŴLZһôôõһʦʾEDGĽDzǸأһɣEDGС֣SKTC9AHQEDGҪԼ뱣ֺ״̬õijɼȻսȫʤѡҲֻSKTӱȫʤӮC9C9IMTDZսӣСڶֺܲˡBuffӳɣC9S5ʱһ־ȫʤǵڶֱӺͽIMTһ̭C9ⲻҪBuffһ֡EDGӮC9ѣӲʵ˵C9ԭͲEDGĶ֡һֵIJҰ¬ǰڷƣC9ϵõӡֻҪܵһֵЩȶأƴ汾ͶEDGȫɻC9ġӮAHQAHQҲѣEDGAHQҪкڵDzָӳʧӲʵAHQеȷʵûEDG㣬Ż油ֻҪScoutߣEDGԻAHQҪ˵EDGսAHQSKTΪʵǿжΪһսָӾϳֵʧԭƵEDG̡һڵʱEDGѵܽ᲻ԼʧֱEDGǷӮSKTSKTDZȽѵһ֣ϾSKTķǾԲٸEDGһ1ƵĶԾֵġEDGӦÿЧµһֵսɱijһ·ʼFakerҰʵSKTϵлͦǰץġֶսSKTʱFakerץˣŵڶıӦҲEDGϣSKTҲнϴϣ3ʤǴ3-3ôEDGߵĸʾͻdzߡ4-4һSKTҲûϵFnaticõӣҲLZȻ2-4Ļִ˼ҪC9AHQĶԾ֣ҲC9ӮIMTһ2-4ףAHQӮC9SKTEDGSKT֮⣬ȫ2-4ĻִˡEDGʵ־кܴˣضEDGڶֻC9AHQҲƣͻˡʱܵ˵EDG߲ѣҪEDGѧϰFnaticԼ״̬áͳߵĸ·߻ҪԼ2ʤսġFnaticսԽEDGƾʲôأԲҪ̫EDGǵʵѡϣEDGܹúѧϰFnaticLPLϮɣȻҪһ㣬EDGսȫʤ3-3SKTõ4ʤˮõ3ʤC9EDGǻᱻ̭...ԣԼŬҲҪսӵıˡϷԭδֹתظлλ֧~ĹעdzµĶ -game CSGOҡţһ± бCTץ شϲѶCSGOҡţѾɹץò˵Чʻġ õĽ϶һҳ˵ټĻ϶ҲһȥϷ֣Ҳȥˣڼ򣬾ôľˡ ˵СڹʷҲЩ޷ʹŰIJɾϷˡЩڿƼʹ֧ ֶˣͱ̱ͷͱǽͷȵȿŵĻɱʽ˺ҶϷϲҸе˾ һȾڹһЩеķҴʩпϣ룬ѵһ鵽δĹƽеСȻе̡ ԴӹʼеʱػѾδķˣ2WҵʺˡһνĹ ˻ϲľʼᵽCSGO̵ҡţ߱ץˣڶࡰţӡ޼ҿɹ飬νԴϲ ˡCSGOţijбץ ޼ҿɹġţӡѾҵIJ֪ˡ ijţQQȺѾ ֱQQȺCSGOؼʵЩȺŲٵȺ˵ġţȺɡ ﲻò˵һ£ĶȫúܶȰCSGOҳһ裬ЩӵǾųһ亹 ֱAWPһֱЩߺʹߡ͵¶ͷȻһǹǵĹͷ ҹDzǸòˣǵġʳĸѾˣͻֵˡ žһֵĴĻúܶ࣬ɴܵijҲ˺ֲܶ࣬Ҫˡ ˵ʱһRUSH Bɡ -game LOLŮҲʤʶ˻в ںܶϲADC˵ǰ汾ȵŮձӶȷʵ˿ЩۣΪÿڴ֮󼸺û˵dzĻᣬºͳƣŮʤѾǵһˣ Ҫ˵IJңİȺ۵ĵһ̶1ʱ˵һںܶ˻ڴȭͷܹλADCмǿʱ򣬽IJԷ˵ Ϊˣ εٴŮļӽиĶܵʱ䣬һ˼ӵijʱ䣬ǰ˵кڻ΢һĻڵĻdz׷ˣ Ҿ̳ŭȭͷýƽС Ŀǰ7.19汾IJԷڱܼĶصôŮӢۻGGˣ -game ֿΡڴּӸУҪIJǾԱ ϷƼƤСǡĿ汦ʦ˵Щڴ֡ؼʸλɻɣͯĿʱ⣿ڡϷУҪƼƷǡΣPokemonΪֿΣWorkemonֻϷϷԱɿֿΡһ淨Ч¡ڴ֡ƷƷʱկƷϷ֪ҪߵȥϷУ㽫Ϊһ̳ϵĹ˾ĿDzԱΪѹկͶĴʦڴʽĿWokremonPokemonֿΡһȤģ⾭ӪϷϷҿ鸻CEOģ˹ԼǮϵǼ̳еһҹ˾ϷռĿǾӪҹ˾ҪļԱͬʱͨ˿нˮѹեԱͶӹ˾档̳ϵ־ ΪԵܲáֿΡ淨һ̶Ͻ˷ȫġڴ֡սģʽͼռϵͳ˼ӸСʽԱȡ˾飬ҪӦͼͨķʽƸǣģ¿ڴֵŶ˸еһЦΪ˻ԭҳνӴڴֵĸжϷĿ·ֱڴһëһսͼֿڴҪѱЩҰԱIJǾҪѹǵнڴѹǵ,ֱԸţΪֹɹܴ˾ϰ࣬Ϊҵ档Գ˿ڴֵҪ⣬ʵ󲿷淨ǾӪɡȻϷѹեͶҪҪһϰ壬ǵðʱԱʣҪȻȫ·Ļ˾ҲƲˡϰ廹ѹեԱͶֿΡľӪҪ쳣ḻﻹǽܲ꣬ϵ̾͸ΪԱѵԱӪգ˾ڿĹλµ򣬶˹ߵȣӦĿֻ಻٣Ϸǰڳʽľ㲻봽ֻͨۿ׬ȡĽҡҪǮĿܶ࣬ȱǮιɡֿΡ˻ڴ֮⣬ϷȤ¼ҰⲶԱʱû׼ԼСϵܺܵõһǮ׻ᷢļҪн¼ΪһԵܲã˭Ҹ㿪̸ǹʾͿץˡܽ˼ؽˡڴ֡ȴֲֹģ¡ԼḻϷ淨͸˻һЦĹ˵ֿΡϷĿȤζȷʵڷƵĿ޾ӪϷƼλ档Դԣ״Ϸʱѡ񡣸Ϸڰг -game սDZ̵ս˵ļ Ҷ֪ϿеÿӢ۶иԵص㣬ЩǰǿƣЩЩӢǶ˵Ĵڣս˱СſЩӢ۰ɣһļϰӢۣÿܶܶģ2ܵĶ˵ըпǸɴ򲻵Ѫʱмټܣ׷ϡڲǷϲķʦˣϿǷ˵ңŻ÷һضǣг˺˸ĵǴ氮Ƥ褣ֳһƬõ廨԰ߵӢۣӢ²¡սе˷еˣŴͻȻѪˣҪȥкţһܾͶ㲻֪ȥˡСұġսеһʺ۶ôֻͣҪ϶㶥ķѣûͻȻֺתܣҲDzҲǡ -game LOLԭ¯ļǿ ϷԭֹվõȡΥ߱ؾLOLPBEռ7.19汾£ԭߣӢ۸ĶƽȻΧŮȵչװ¯ٴνֵ΢һĽɣԭٻʦͷƤĶԼعƤֱӴлƶЧƽףعǹ䡣عЧӢ۸ĶWǰĶüܻɱĿЧԷһֵ˼ǿĶԭĻɱ6/8/10/12/14ӵ20/25/30/35/40ԻһβСļǿڻǰڶʱվ֪ʦ˸ĶУDzطеCarryλأŮWܳʱ30/22/16/12/10Ϊ30/24/19/15/12Ĵȫȼ30½20ʱ30/40/50/60/70Ϊ30/35/40/45/50롣һƽԵĸĶʱӳĽͣʱ̡ŮļȻãǺڵʱ20룬ϳܵӰ죬ڵļСŮ[Ч]Ŀ귨Ҳɶħ˺Wȴʱ12/11/10/9/8Ϊеȼ8˺60/115/170/225/280Ϊ60/105/150/195/240ٳʱ3뽵͵2ƽԵĸĶȴʱ٣˺ʱ̡װĶ¯ǰһѾӹ̶ֵĻϰ汾ЧĹٶȼӳɴ[20-35%]Ϊ[20-40%]˺[20-35]Ϊ[20-40] [Ч]Ψһٶ/лѪЩӳɽҲʩԼûPBEԵ¯Ǹĵ¯Լ趨¯ڸͷŻܼӳɵʱͬЧҵʩԼӢۻdzΪA˾ͲδӹٺѪЧADCӦûסȻĿǰֻڲԷĸĶǷʽдδIJԽҫ۸2500ǵ2650ϳɷô950ߵ1100˵ľۺܳɱ2200ߵ2250ϳɷѴ200ߵ250Ϸԭδֹתظлλ֧~ĹעdzµĶ -game DNFǹͺǹְҵ, ʷʫһ DNFħǹʿְҵǹͺǹְҵħǹʿְҵôְҵʷʫѾ£ֻǼ򵥵ĺһ£ҲοôȽܵǹʷʫǹ60-90ֹûкֻԺ60ʷʫǹBUFF+215+5,30+3,35+3,45+2ٶ+10%˵BUFF͵70ʷʫǹ˼+3+3%ǹ˻ָٶ+40%ǹȴʱ+50%˵רΪǹ˼׼ġ75ʷʫͷ+2%ǿ+18Թħ+2%ʱ8%Ļ˺30ܹ+20%40ܹ+20%80ʷʫͷ+2%ǿ+32Թħ+2%ʱ17%˺ʱ5%500PXΧ5硣85ʷʫͷ+2%Թﱬ+5%ħ+7%ħ+13%ʱ30%˺̡״̬·ڡ90ʷʫͷ+2%ﱬ+6%ħ+8%ʱ17%˺ʱ100ڣʱ˺5%(40%CD0.1)90ʷʫͷ+2%ħ+2%й+18%װBUFF150Sٶ+10%ͷ+15%ʱ˺35%ĸӡǹʷʫôǹʷʫЩ?60ʷʫǹ60ʷʫһһBUFF͵70ʷʫͷ+4%ħ+2%-1%ٶ+30%ʱ5%ļ30Sڷ壬30%ٶȡ75ʷʫͶǹͨ+4ʱ˺+20%80ʷʫ35ȴʱ-20%45ܹ+25%ըΧ+30%ʱ18%˺85ʷʫһ+1ʱ25%˺й+17%500PXΧжԱĹ+10%+10%ͷ+15%90ʷʫͶǹͨ+1һ+3ʱ˺42%ӣܹ+10%90ʷʫͷ+4%Կ+35ħ+2%-1%ʱBUFFʱ35%˺ħ+18%ǹͺǹܣħǹʿְҵǷñȽ죬һ?ְҵǻ? -game Ӣ7.18ԷS7ƤߣӢ۸Ķ 7.18ԷΪܵĸĶѾˣڱԤܸ£S7ȫܾı汾ҪΧƽиĶԷڰŮΥƤ羺ƤϣϣƤƤ۰ٷֱȻ뵽ءƤϹս (1350RPħڡ׿Ƥͬһϵ)ھ֮ʸ ϣ(1350RPS7ȫܾ羺Ƥ۰ٷֱȼS7)ٻʦͼ2ھϣͷ3ģʽͻͷ2S7ȫܾͷ55S7ȫܾضͷ񣬵ֻнS724֧ͷƤ1S7ȫܾƤ1S7ȫܾĽɫƤ(ֻùϵͳ)ŲƤ (ɫ)ھ֮ʸ ϣ(ɫֻͨϵͳ)±5Ӣ۵45S7ȫܾض¾ģʽͻԽϷвS7ȫܾ¼ų¶ļͼ΢¶ٻʦͼ΢S7ȫܾϿѾӵͻ˿ͻезĹܴƤнӢ۸Ķὣħ п˹Qڰ֮ԾڷпʼʱΪ赲̬ڷ·пɱɳĮʵ ȶR֮ǽ˺150/225/300ӵ150/250/450ħ֮ӵ E˫жĿɵĶ˺15/40/65/90/115͵10/30/50/70/90APӳ0.4ӵ0.5֩Ů ˿WԱ֩(̬)˺60/110/160/210/260͵55/95/135/175/215APӳɴ0.8ӵ0.95E˿(̬֩)ȴʱ26/23/20/17/14ӵ26/24/22/20/18˫ ưȱ֮ٴ15/25/35/45%ӵ20/30/40/50%R˫սڷưĿ긽ʱþ֮Ķټӳʧ֮ ɶһֵ540͵510Ͱ ˹һ״29.05͵26.05Eⵯĩ˵ײ Eֶùϻ˺50/70/90/110/130ӵ60/80/100/120/140˺APӳɴ0.7ӵ0.8ǻ Wƽʥֵܻ65/90/115/140/165͵60/85/110/135/160ÿһзӢṩĻֵ2/2.5/3/3.5/4%͵2/2.25/2.5/2.75/3%äɮ Wֵֻ40/100/160/220/28055/110/165/220/275ʥǹ ¬E׷ȴʱ18/17/16/15/14ӵ22/20/18/16/14̩̹ ŵ˹QֵͨĴ60/70/80/90/100͵ȫȼ60˵Σһ뷨ֵĿҰŮ εEҰԱ(̬)APӳɴ0.275ӵ0.325ɽ֮ ¶ʦڰ¶ﵽ13װWϢ˺Ŀ굱ǰֵٷֱȵĿֵٷֱעòʦʾĶȡ˰֮׷ ׶Ӷһ״22ӵ25ķʦ WĽAPӳɴ0.2ӵ0.6 ˿Eը𻨶ADӳɴ0.5/0.65/0.8/0.95/1.1͵0.5/0.6/0.7/0.8/0.9װĶֵ325͵300֮ȴ10%ӵ20%ܼ۴2200ӵ2300˪Ůʵָȴ10%ӵ20%ܼ۴2200ӵ2300˪֮ȴ0%ӵ5%ʿܼ֮۴2300͵2200ֵ350͵250עӡĶǻعָصʦ(¶棬Ťֵͼ)ܼ4550ϳɼ۸1050+55APע7.17汾ݸ֮(¶)ֵ38+22*ȼ͵35+22*ȼѾȥ5֮ܵϻĻЧûЧ0עӡʦʾBUG(-¶)ϳ·ߣɳ©+1000ܼۣ3900+100AP(+30AP)+60(+15) -game LOL˵ʮһӵұԪպǵãѧѧ˼ңٿ㣡 LOL˵ǵʵӵLPL˵̨ͱܺܶ˵ĹעȻ짵ŵǷзŸ׵ʧܣCPҲֻͬǵȻûзŹƯӣͱڽĿбҪȫ2017ӵ΢ʲôġ ڽıDZԪúõijһ ģ˵ʮһǴҹϵӣҲɹһźʹԼ﷭ĺ Ԫת󲢸@˼ǵãɹһʮһһƬǻǡ ǵõȻ˷ʾԼҲӵұֻզôԾأ ǵʦϽҸŮѰɣ -game ACGһң˶ԪﻹϷҵġ" ͬɡͬˡȻѾ㲻һϷˡ2014 6 25 յ½ƻƽ̨ Playstation mobile20157תսiOSƽ̨iOSսʼ㣬Ҳнӽ3ʱ䡣Ѿӽ3꣬Խ۹ͬɡȻҲһƷʼߣҾ߱ߵıʶȵΡͬɡѡϵɫ֮һ¼˴ԪĿBվܺVϵСеġͨDiscoǿνBվ2016ںӵĿݳݳͨDisco˼ߵȳBվfans׷ױʾǡ˴Ԫڡ ʷʱ̡֮⣬粻ͬƵ棬ҲáͬɡӾ˾öĿһ¡ǵԻøУǶԪʱĶ⣬ֱкϵʮСڹĿУöҡַdzŷȡгmiss20157¡ͬɡiOSϼʱϷֻ21׸20172£ͬɡ¼ĿѾﵽ100סһ˵㲻̫ߡǰѡͬɡбȽҪĸһҲVҶԪ֮⣬һЩĶͬɡµһθУ˺galgameNEKOPARAլѾõϷܡ˾ֻҪĿλŮװӣ¶˻ĵЦݡԡͬɡ˵Ͳǵһˡ20165£߿֮ǰͬɡƳˡ߿100졷ϷνѡǸһͬ߿ϷϵһİڡͬɡУ㻹ҵڶƽ̨϶¼ѼġICEYԸΪҵΡŮǹŮǰߡЩϷٵĴŶԪԡ˵ǶԪȵ㣬˵ЩϷ׷ȵ㡣ͬģС롶OPUSƻµŵĶϷЩϷƷȶȲرijãӵмõĿڱ֮⣬ͬɡˡ桱ֽλȶ⡣֮ѡϣǷ·ܿͬɡһλϷ˫޵ҡǡд˽ԼϲϷƷкһͬϷӸԼϲϷԪأȻصȴͬϲЩԪصҵʵ֮̾ijϷû֮ŲɱʲôԱͬѧڿμԼϲ̳ϰԼˮϷڴʱϷ¸ֹ㿴ЩµĹһЦ˲䷢Լҵָ֯оáͬɡܸĸܲһදĿô֮ͬͬͨһţһȷϱ˴ݵĬȻָоҪһ仰ǴžǺܶǰѾġACGһҡչĶ -fashion IJѧ¶ɱ,㻹˼˵Լʱgirl? Ƿʮ,紩һּ㡣¶˴ȺС,ǵСǧӡ,һּ,µǺͼ粿źԸ,͵ȴҲ,øոպ!һĨջȻĶʱһ¶װӴ,Ըͷŵǡô!ر¶װ,һŮ! ԴʷȥƷƵװһ,ȥ,ɳ һȻdz,ǾԲһ˵ʱԪ,෴ʷdzƾáڷǰ,ʱʱװʼ,÷ŮԵķװչ¶Ůߵ¶Ԫ,һӾͳΪʱʱֵij2050,ܵ˺ܶŮӰǵϲ,ϷϷⶼϲһڹСӰǰձ(Audrey Hepburn),ڡʹ˼䡷,¶粿,ر߹š,ɰձݵİݹʹһּ,һ,ѹǡ˳ԡElizabeth TaylorĺƯ,رɫ¶ȹ,ֱһžµͻˡ¶(Marilyn Monroe),д漯һȹ,Ըкչֵ쾡¡ ǵʱֿ,ԼͬİѡսתƵزϡ¶硱Ϊʵʱд,Ƕ¶lookڹ,ҲԸһ,šԡȵȷͨ¶ĵƷӰӰѡ¶ȹɫԵôʫʫСȹһִͷ߸ЬִŮ,ʫʫ̫ÿݨALL BLACK LOOK¶ͶԵʱֶȱ!װٺΨҶ¶chokerθзḻ,װţп㹻˼˼,¶һȿLOOKǴʱѡ ˭ʺ¶װ źܶMM볢¶װ,Լʺ,ߴ̫¶С̴̽ʲôʺ¶װ,¶װƯ1СŮĽСŮ˵,¶¶һͼӰ,ͬʱһּõ͹СŮԸ,ֹǸ2񻢱ͷ,ҵƴ,Լȴһõͷʵ,ѡһص¶װ,¶ǰشļ,ѡһ粿ڿյĿʽ3ӿشʱʱֵɱ,һСľͻƷװԭеļ,濴׳¶װҲܽ,ѡܹ͵һǰ͸,ӾչְSߡ4ƽȻ˵ƽزʱ,ҪúҪһ,ʱѡҶ¶װܴӾСȱ,ͬʱ,͵ֱۿϸ5С¶Ҫ,ҵĿ,עתơ,ڿʽѡϿѡɵġ¶,ܾˮ֮,ɼöʧбĿʽرһֲƵĸо㿴ʮ ¶+װȿ,쳬޵дȡ̿¶װ,ϸ,Ůıַӳ,档¶ȹװ¶ȹװ,,,ΪԪصص㶼չȹϡĽͺ˵,ȹװʮӦ֮ʡҪɫΪ,¶ȹװǻһЩá PS:Щʹһּ ַ˦·˼ 糤¶ڵ,ڹʲôַ,ĵŮζչ¶š̫?ǾͰͷͷ,ˬ,յıرֱӰͻ,ˬͬʱ,һ~̷?ǾͶһ黨,ַӻΪŮ㲻ϲͷ,Ǿ͸ɴˡLOBWOBͷξѡ,Ȼ,ʱֶȱʲô컻һּϺʵķȥԼ~ -fashion ر֯7ˣ ˵رƷǰʱд£ʵ֯Ҳһٹܾ֯ڽУܶ಩ңʱڣһ֯ܽ㡰ഩһȣٴһ䡱Ρ֯ڻǰ£һЩС˼Сƣʱְ~~п֯һضһص£Ϊߣı쵽죬һֱܻӭ콫ҲպÿԴ֯ǹɫ֯ڸսȥļڣǹɫ³Ȥζʵɫ֯ŸнϣɫҲ쳣ʱ֡˶֯˶֯˶֯ĻѪĿʽȥͿʼУȻʱ֡λÿԿԵڣڱС죬ҲԱV죬һ֯ʱȦиŷ𣬸Ľ֯һĸŸУͬҲһdzٴĵƷ2017ԪأҲӦõﶬ֯УҲ˺ܺõֱáڼǵģѾ㵽֯ˣҪ̫Ļֻڲ䲻ȵļˣˣһͿס...֯ġ֯ģӣʵΪinʱеƷ֮һˡ֯ôɫ֯ԴɫΪҽǹɫҲȽУһɫ֮Ҫôźáɫǹɫ֯ɫװԵ÷dzţҽ׿ҲʮСɫţȹҲʮֵഺлɫɫһɫϵʱ򣬵͵ĺɫDzˣеĸɫһɫĵʱ֡ͬɫϵ߼Ҫʵڲ֪ôźãǾѡͬɫϵɣɫϵôҲײͬɫϵɫһ𣬻ܴθСײɫɫңײɫ䣬ɫҪײˣָ߼ڽ˲Сˡ磬ڽײɫУɫͻɫĴϾʮָ߼֯Nִ֯+ȹڳʱڣ֯ԴİȹʱֵӦ²ҪǾ촩ȹеȹӿԴһ㣬˫èЬˡ֯ڴǰ˵֯ҲǽеƷ˻촩ȣ촩ʵѽ촩ʱڴһѽOliverʱ֡һЩȽϸԵ֯ģڴһһ֡⴩ʱָС֯+ŷֿ򵥿ʽ֯򵥵ľŷȿҲܺҲ֮֯һ֯ľŷţп㣬Եøʱ֡֯TԽT֯ܺÿ֯رƷ -fashion ɵɵĻǮȥȾˣʡǮСڸ ÿһʱ䣬ҪͣȾʱзͣʱгԷʵ˺أѹ̫ʱ䷳ȾڣҪΪڰ׷Ͼÿ˶ǰġܶʮʮ꣬ͳ˰׷˻˵̫棬ֵԼϾ֪׷IJÿϡʹ˶̫ⱻ˵׷࣬οأԼҲ֪Ⱦã̲סҪڸǣöǮ꣬ŷ֣ԼڼȾҲܼ򵥣ֹˣʡ²СһǮһֲںȾʡѡȾʱҪע⿴ɷ֣ݱֲﺬߵĸӽȾһԵģӴɳڷʹãȫģɫѡȱʺϰ׷ȽٵģڸЧáضֲȻ޴̼ɳرȾȻֲɷ֣ӣȾͬʱ޸ɿݡë귢ʣӵ˳㷢жɫѡʺʹáά˿ȾװʱԼȾãǾ٣ȾΣϺõȾᣬȾӪҺŽͲڣ򿪰СڻԶĭȾֱͷܾȾȻÿиôȻ޴̼ȾࡿиʹõȾκȫرߣֲɷ֣ϴˮһʹãϴϴ͸㶨ַ㡣̬ȾһԵȾȿںʽĸЩԴߣӦ÷Χ޽ǡͷֻҪа׷ڸǣˢĸӾϸ壬ʹü򵥣Я̬屴ֲȾҩȾҲDZϲ屴ӡڡỨȲҩɷ֣ڶ⣬»ָɫ -fashion 꿺γʱiconս,˭ʤ ı֮ʱicon,ѡʱ,Ϊǵą̂Ѿһλ޷Խһ л!Ƚ˱ְ۶ȸ,˵ʱдҲǶάžӸ߲µȶ,ͷһѸ¶~ijЩԲŮæĴͨ塢,Դʱ,ⲻ֪賴ųž!Ƚһֱҵнɱ,ʱ!,ڡȽûбʱ֡ϹŸܸ⿪ɡą̂һλ,˾øȤǶλ!ą̂Ƚ,عߵľ꿺γˡȻ,λҲͺԸ糴CP,ֻȽγĽλӦԷ,˸ϲ꿵ĶλڶʼӴʱȦ,Ħȭ,֮н˫ĽɹΪװ־Ƭ,ͨ΢Ѷ뻮һı硰ͻƳ߶ȵװȷʵʵ,Ƭķ꿽ȻͬһγԸ·ߵֵù,ĵҲ,ʵѡ־ƬѶȱͲ,ּԸл,Ԧ̶ȿ֪ԴӿˡöĴ,һԸ,뵽ݵ!ôڴӪǵ꿽,ˮһ,һֱ²,Ҷ꿽ż!Ȼ,꿽㲻ʼϷ,ʱȦ³,һλâС γ,Ǻ׵ȥ˰Chanel߶㳡,ʵñһ±ơ΢poˮKaty Perry,Tilda SwintonܺӰ״ʱװ,ͱݿս̸߶㳡,װChanel¿ˮƿְ,Ϸ绯λ,Ʋ,ȷʵб⻹DZ˵һ,̻¶㹵,ƽӹ!˵Pͼ,뾭Ǽº,½ûٸγ˰,ܲ!εƬ޳̶Ȳ,ë׵һ˿һĸɸɾ,ѹϲ˭ˡ...ο,Ʒй,Լͨ㵽볡ȯλһ:̰߶λü伫,Сܸ㵽Ʊ,ҲǼ׵¶һֿ,õýƱ(Խϴ,Ϊλڰÿ΢˹ٷ);ڶֿ,ؽƷVVIP,Ʒֱܲ(ȻԸֿԱֿ,ɹôҲҪϸһʱŹҰ)Ҫ˵,꿽϶Сǰ!2016ﶬŮװ꿽ͱ,ʱͺܶӦûӡ,Moncler Gamme Rouge㳡,ʱȦδõ꿽һСײ˲ʦ֮ˡµĴ,ֱʦһ,꿽20롣2017սİʱװܵ꿽,ȾһͷHigh fashiondz,ϯAlexis Mabille㡣ص꿽¸߶,dzɫͷŷ,һ·ˡȡװ,ȷʵСǰͷ,⻭治ֹһˡӰ¥д,롶װӰʦӦз˰ɡ,꿽־Ƭ֪˦Сü֡程ϵ程Ի÷程ŷ程ֱҲ࿴һۨ꿽,ˡСͳһ˶Լʶ,ǰдƬfreestyleĴƬ,켷ۡ·ҲϲĴƬơԴ־Ƭ,ƴһƴ˽ĵƷ¶СԼһ¶紩ȥʱװ,Ҳ˳Ա׬׬쨋෴,꿽ĴƬʲô,˸ϲ꿽˽ըĴ,ԡһʱװڼ,ͻʱв,,Ƭ˲Ծػÿ˽ҲǴѴѵĨ˭ʺϿʱicon֮·?׫:jean / ͼƬ: -fashion ȹ,е! Ŀ·ʱ,ѧ,ʱд!ɫϲɫ֮һŮмֱǷַӵŮҲϲڳϯʱСȹ˭ĸÿ?ʫʫʫʫһ¶˿ȹɫһִͷЬƨDior̿СɴȹŮʮ~chokerʱֵϢ~ݨһҶȹͭɫЬŮ~ȹԵøӾ䶼иʮӫɫһִЬ۶Ⱥܸ!ܶꨋܶҲСȹжһɴȹɫͬ߹͢ŷһ¶СȹԼchokerŮд˧ĸоҲԻСЬCHICи!һװ͵СȹԵñȽʺϳϯʽ~̨ǾͺϲڳϯҪϵʱѡɫʮֲһ˿¶һ׸Ůζ~ӰӰLOOK˾޴˺ܶԪҲԵҸԲԲԲԲȹƸкǿСӡĵ׺òô~һLOOKҲŮԼֻѡȹƤµĽһ˫СЬԹ!ӱӱһȱһЩʵԼһЩ۵~程һȹԵʷdz~ͷܹȫ͸!ĨѡdzİɫӡTȹСñСѥ˹ķ~һ׺ײ~˵ƴŮĸоľǾķ~ǶǶСȹҪȽϸ~ĺõӿԳԿֿʽ~󲿷ӵһСȹ󲿷ֶdz~ʺʱֶȼ߶ҲʴСЬǵһѡഺҪϰഩŴһ˫߸ЬOK֪ԵͬʱҲԵֱͲĿʽȽʺϸ߸~СҲ볢ԵĻ߸Ьϥʮ׵ĿʽӴСӹѡȹʱҪǵߡϥʮ׺͸߸ЬԸ߿Խ׵ȵȵƷҪʽϴѡһЩñȽӲͦĿʽʺ˿ҲСȹȽϳϲͬƻܴһķССѡֱĻߵƵĶ̿~ɰƤŮζǿҴ߸ЬЬһַʽAεȹͬʺСӵĹЬƽЬdz곬ͬСȹı־Ԫ֮һĨȹʺϸԵӽȽԵĵƷŲԵùԸ~һСȹ㴺³ذ -fashion Ь | ȻLVͬϰЬķ糱⼸Լ۱ȵϰЬһҪ֪ ԵģûLVҲԼҵϰЬܲܣϰЬȷǻˣͷһֱ˵ڵyeezy 700ֻbalenciagaTriple S׬ͳݻLVⳡսУIJò˵ʱȦô~ LV˫ϰЬӾ˵ķdzϰЬصϣе׵Ե÷dzǰϰDzܽܵġ˵ǰоѶȻͦߵģЬ໹Ūôߣûпŵȳû취Ԧˡ ȻϰЬ糱ѾϮˣǰ˵ϰЬҪüµܻҵǾòֵ̫ˣôûĿʽҼ롰ϰЬأ Converse ʷҲҶ˵ˣNike֮ǰNBA˶ԱЬӦģһ֮ǵҲϽŹЬӡ û˵ľConverse Fastbreak ǵŵGU汾Fastbreak඼ǵױǩǵ̤NBAǰһİ壬һֱǩԼͿ֮ǰ㶼ܿǵԿǶôϲ ʷϰЬ糱ٶϤnikeְֶˣЬ¸̲ƳۣΪһ¼nikeְΪгϵĹǷѾ˼ ˣEDCҲϽһµFastbreakҲǽڼvapormaxһһ֪Ƿأ Nike converseѾˣNikeôʷͲһ۵ϰЬ Emm.....Air max 97Щ˭֪ˣйϴ󣬼۸ҲǾӸ߲£ҪԼ۱ȾͲǽ۵ķΧˡ 97кܶûģƩ˫Nike Air Spiridon˫ͬ90Ьӣһ֪ϰЬеٮٮ 90nikeȲzoom air˵ҲֿȵЬŶԡεAir max̫ˣҲڽ OGɫõǻɫƺɫswoosh logoԶȷ97Ӱ档 ʵzoom airǰҲ¸ƳһΣʱNike Mercurial FlyknitڵڶصͬĻȣܵҵ׷ɼDzСġ 꿴Air max 97ΪŵnikeϰЬ Asics ˵ЬĻAsicsôܲأAsicsΪЬңܶ˵ӡҲͣרЬϣֻGEL-LYTE档 ҷһ˫ʮͿĸЬɫҲʮ¡Asics Tiger Gel-Diablo OG ˵AsicsŸжôôغãǵĸЬҸоƫ̵ģźһһеܵĸо Ƽ˫ЬϾ˳ö࣬ʽҲӵ˳µɫһʱҲǴ򶯵ҡ볣ĵĴϵĸɫɫҲӰٴϰһкǿ֪ҲǷϲأ Reebok ֪ǷǵǰʱyeezyadidasƳЬأɫϲƫŵװɫǴϻɫ϶ŨŨĸϢ˵ͨһ˫ŰЬyeezyļӳҲ Yeezy x adidas Originals Powerphase Ը߰ļ۸񣬺ܶ˽Ŀת൱Reebok Club C㻹˵֮ƶǸߣǴͳСЬͣԶǷֲ塣 ReebokҲǿнϰЬ糱½̳ûдǿ˵ʷ̷ḻعʻԲ㡣Ʋȳ̶˵˫ЬϽЧһ㶼yeezyҪһ˫ҪҪ˳ۡ ˳İЬ֮⣬ReebokдЬͬҲǽŽķ糱¸ƳںܶRBK˿˵һĻعǽĸжЬ״㲿λǶĻͨЬ㣬һĵڵ˵Ǿdzǰġ ε¸̣ΪڵϰЬгסŸ˵reebokΪdzָķɡ -fashion 죺˭˵ʱСſò֣ⲻص... 117գCHANELڳɶٰ2018紺ȼϵзᣬѸʫʫŷȡΰ˼Ƕۼڴˡʫʫΰ硢˼ΰ̲áҲɶCHANELɶ2018紺㣬ɫëƶţп㣬λĺΰâĺÿأûжԱȾû˺ֳ˿׽Ӱȫɱ~Ҫ˵̲áӮң2009ƾһ̲Ƭߺ죬޵ɾ24ɹԾ2017²Ƹ500˰񡷣Ϲǿһӵ388ңͬÿ챧Թǰ;ãʱ·𿪹һĻ˴еһСȻƽġ̲áǰ˵: ҵűǵ졣ǽ꣬ѷ֣ԴЧӦġ̲áʼߵĻعҰ;ʱжԽƵ2 £ʽʱйƵᡢʩƷ̵dz4 £ЬЭΪԱAAFAָͰٻԾŮ˵ݣŦԼٰһ˽ʱ硣쵱Ĵicon95Iris ApfelڵŦԼʱȦʿ˽ĵϣ̹ǿǡݡܿ򸾵ϵܡ5 £μʿݳƷChopardɵӰڼٰSPACEɶԡͬʱ Chiara FerragniMcQ Ʒƺ JD X ϵзΡ6£ھվ·˸еݳƷִƬˢ΢Ȧʱȴͬһʱγ 3.97 ԪͶӢ֪ʱеFarfetchΪʱƷչʵ죬ڴչµʱҵͬʱҲŬͦʱȦ7¸ߵȥ߶ʱװܿ㣬dzDior 70߶ϡȻDior 70̺ΪͽˣnononoDiorգڹ߶㳡ͷšͣChanelĸ߶ϡ㳡IJѷɫŶռ7µ΢ȵʱѶͷ8 £Ϧ˽ǰϦChopardھӪ콢꣬ƷƷƬChopard9µŦԼʱװܣϯ DVF3.1 Phillip LimOscar de la Renta ʱװ㡣in DVF in 3.1 Phillip Lim in Oscar de la Renta׶ʱװܣ Mary KatratzouHuishan Zhang ʦƷƣӢʱװЭɺ 2018 1 ˫ͬƽ 2018 BFC/Vogue ʦʱлĿ̡10 £ ELLE йϯִйټѩƵƵͬࡣ1010գ˳ƳݳƷƽ̨TOPLOFEToplife x SuperELLEϳƷƷгڣǵӢ־ Hong Kong Tatler ʮ¿档ΪTatlerʱдƬͷֱ DiorNina RicciChristopher Kane鱦ΣChopardHong Kong Tatler áDIGITAL DARLINGֳڵķ⣬罻ý˵ݳƷʱеỤ̊ǿĹת䣬صעڴչľʱҵڲɷҲᵽ֪ʱе Net-a-Porter Ĵʼ Natalie Massenetǰ;һҲɿҪй Net-a-Porter ġԴǰƵ߶ʱװܣһȥƹͲʵҲּΪ̯ݳƷȨľĹעȺʹڸСϾǿǿ֮£̲õĶžĶ£ʱѵı仯йʱҵѾȫ汬80 90 Ϊ߸Ըұã˸ԣصڳΪߵҪԭʦʱֵɳʱѾʱҵµҵģʽƽ̨ϣʵƷơʦ֮õӣⲻغƽ̨ʦߣҵӦŻѿϢϢء Toplife ھվӪ뾩ģʽͻ۷񡢿ͻ͹ӦûĹģʽƷΪƷƹٷ콢ģʽӸʹݳƷƣΪʱʿݻʽļ߿ݳ֮á;ȥһʱж֮㿴Ұ1017գ˫11ȫڵķϣѶͬƳںϵ޽۽ۿ˫ʮһüޣ£ѶǸԼһǿ롣ѶǰͰٶȡͷס滢Ƚˡȥھġ־սۡУèΨһĶ֣ҲǾ㽻ѵĿڡʵϣèĶԹ˾֪ڹȥ6181212ȹϣǵĦϼӾ硣9³ǿºԣ5ڳԽèΪйB2Cƽ̨ɴ˿ǿԾ֪ǣèɴڲࡣ£˫ʮһڵĵⳡսĻҩζԽŨҡȻèijɳҲĿõģǵھŸ˫ʮһ2009꣬è˫ʮһɽ0.5ڣ2016꣬è˫ʮһɽѾﵽ˵1207ڡ˵èijɳйĵ̷չǺϵģҲץס˳ڵè˽ĸ߶ȡǿþèأ -fashion װ:＾OLͨװLook,ͨװʱʸ! װ:Ҵͨװʾ!ʲôͨװ?򵥵˵ǾOLְҵװ,DZְҵװһЩ,TţȹӡȹԳ֮Ϊͨװwifi¹ۿo(**)o~~~Ԥ:´䲻֪ǰС(⹫)ʾûĶ?ǰȿµľݻع˳ȿļճ似!＾´ļlook!ںƷƼ~ͨװ+ţпԵø첲޳ͬʱһЩŮζ,ɫͷЬ©ͽҲȲ޳!+ȿٴİ׳Ըߵİɶȹһ!ȿԴ!ƽЬһɫʸ!+װһ׼OLͨװ,Ƹеİ׳װһ˫ͷЬʷǷ!ȻƽЬҲdz!Polo+ȹװżõ,ڴ̫Ͳ̫,Եúˬһ׻ܰ!֯´ʺϴ֯ʱ,֯ůڴɼԦ!ֱͲ㡢װ㻹ţп㶼Դ,һ״!ڴŮ֪!ðȹҲԸȳĻƷ,򵥵Ĵ㼯֪һŶװװ׵ѡ,͵ĸƾкŨĸϢ,＾ͨװȹӴ´ȹǸ߸ӻǰӶ˲!＾ȹر,,ɫ׿ҲӰ!ëµִܼɫ俴ȥȴܸ߼,ʵһɫؾ!ƼƷŮװ콢Ỷϲ³MyLittleMushroom ĢĵϹϹNAGUAGUͨװο24װŮ´ͼƬ,ͨװʺ18-38Ů＾ͨװ´װͼƬ!27ʺϳͨװװͼƬLOOK!ë+ȹ=2017ﶬʱֵĴ򿪷ʽ!ٵĴ!Ը10cm!ȿ+ƽЬ=2017ﶬʱֵĴ´!˽Żظؼ,鿴ݡ΢֡ȴ֡СӡΨ䡢ġƻġشƽءӡ֡߸ӡȼ١Ьӡװӡļ＾ -fashion Ůж̷ͼ СΪҴеŮж̷ƣʱУҲdzļ򵥣ϲӲҪˣһСһ°ɡڿ۵ж̷ƣ΢΢бֵ䣬ɴﵽյЧ͹ԳɫȾҲԵüʱС΢Ķ̷ƣƫֵݣԵúʸУɫͷҲԵʱд󷽡ھж̷ͣɫͷ͹Գʱ£ԵúƫֵҲﵽݵЧһɫ̷ƣʱУʸʮ㣬ƫֵҲﵽݵЧʮֵĺÿ΢Ķ̷ƣɫȾʱԵúлУʮֵĺÿʵƫҲﵽݵЧɵ̷ͣƫֵ䣬гµʣɫͷҲԵȻʱС -fashion 2017й | ǵô̵İѧ 127գ Marie ClaireЯְARTISTRYƷƣΪֵ2017й糬ʢҫĿǡʢԡYOUNG޽硱Ϊ⣬ȫʱи㲻һYOUNG˲ױʢҫع飬ΪģǴStyle China߷ĸʱױݣڴɹʸ߶Ůʱװ־Marie ClaireġйStyle Chinaʶʱƴͣ2008𣬾ȫ򶥼ʱװƷΧƼȶƼǡǡйStyle Chinaʮ꣬ͬʱҲǡMarie Claireʮգ˴ȦʵԼʱд󣬾ڴ2017йStyle Chinaʢ䣡ݣƵǰ֪~ǵȥйʢֳϺ̺ǹڵôװͶȺǣע⵽İôٵ˰?2016йֳɫװεȹúɫִպͶɫӦЩú鶯ĸо2016йֳOscar de la renta 2017ϵкɫҶ߿Ĩְְݻг׺ɫĸ߼ʸС2016йֳɫɴƴ͸í֯ȹýɫǶְǽɫְLookɫȴңָν͵صӷֵƷ2016йֳʯΣźɫɴ鳤ȹɫLady Dior廨͵ȤɫչDior߼ʱװ빤ս2016йֳŷChanelɫڴװףɫChockerƲԻۣбChanelɫ2016йֳɫɴ鳤ȹɫƬְְɫʹ͸вθУƬʵֺɴγɺӦ2016йֳװٺChanel 2017紺ϵкɫ廨ƴ۵ȹChanel 2017紺ϵֱͼǿְdz谮İһTAرԭLOUIS VUITTONAsnieresֹֻñ䣬йΪ֮ĵһɫĵϷ䡣ĵйе󻨻ܣнǧʷɫޡŨйֵ֮һһеĿʼԴһ䣺·ǵľ·롶Marie Claire Style AwardAsniresʾӹֻñ䣬һɫĵԴйɫĵĵӵΪ֮صĻͻ滭ƷܷḻйеزܣɫޡŨǧȻǧ˹ʷ·Asnires ʾӹʱգӲ䡢رƲƷϡƤִÿʱװִϵȻAsniresʾӹAsniresÿ450رƷб䵽䡢ɰڷ30ijмܣζأǶжҪ⴫ͳֽ͸رDiorDioramaִԴڵϰĻ԰ϵѼ۷Եϰ¸߼ƹľտֹաϰƤֹĹʦDioramaִںִ黳Dioramaִ޷ֵķǷǰԪһϰ¾ٸƣCannageͼΪϰµıʶȫµDioramaִϣΪһ䡰 IT BAGGIORGIO ARMANI˿ر2016йStyle ChinaİԴڴͳռõӡһСвþϣȾЧֹ޶ɡʽͼֹϸ˿ƤϵļFENDIΪһԴݳƷƣֹһֱƳŵļաMTOֹPeekabooΪֹһһIJֻǸ߼ƵרУһ黳һֶֹյĴСǵ׺Ĵ飬ִڹ·һĿʣϵĻ俴Ϊ壬Ϊ鶯Silvia Venturini FendiSilvia Venturini Fendi˵ԼոµǰС׷ͳһӱʱʱƷλ߹ŮԸе⡣ݼԼԴԺݻִʵΪȴ̲عѡϣȤĶԱȡֻѡPeekabooŮʿſԷִ۲дڴϡJIMMY CHOOƷCelesteðͼһƬԡMarie ClaireֵѧԺƷ뷨ڴֳɫʵҲ˻µĸоȤСMICHAEL KORSǽ봿ֹϵƷǵĶһضĻٴ졣Щ䶼ɴֹɣðƤíƤƳɵĻ̶ִ֮ϡMichael KorsɫһֱMichael Korsɫ󵨣ԸУ붯Сرķʽɫ׺ִϣΪɫйͳĻҲ塣ִMichael Kors CollectionMirandaִΪԭƵġϣܹΪŮṩѡԣֱִһɵڵб⣬ִıһԽִʹߵֻ˵֮⣬ͬʱܷʹȡáROGER VIVIERViv Micro' Crystal ΪסζԢ֮УݻϣֲͬĻġȻĶŮ󡣴ֹIJ޻ϣϻ׵ЧʹʮViv50־Miss VivĦǽ;󷽣ױȵľ¹աϸIJͨ͸Өҫdzҡ飬鶯ţⲻTORY BURCHԴֹйĴͳĻɫȦƵӡʯԼƬԪأйӵIJƸ˲䴫ͳֹշͬʱе顢ͭӵȻϵ˴ȻáStyle ChinaֳɰLOUIS VUITTONASPINAL OF LONDONTORY BURCHMICHAEL KORSJIMMY CHOODIORFENDIͰһðΪԼӷ֣ѡʺİ֮ǰЩĹ¡㶼˽2017йʮʢϣϲǣֻ;ϲأ127գڴ˵2016Style ChinaѾǾʷ׳ʣô2017йStyle Chinaʮ곬ʢһʢ磡ϣڽʢϿ˭أԸ߼ν~λģױȫARTISTRY˶ͼƬԴ /IC/Ӿйöˣȫ -houseliving 140OִԼ ǽƲ һִԼװް,ں˶Ļ,ڹŵִ֮һԵڹ,˷ḻװʹ󵨵װɫ,ÿ䶼չֳصϣװްܸ׼װ޵ĴҴһЩС:90O::ִԼغ,ŵľDzˡĵ뾫µĻ;,òͿռҲ˸ߴϡ,һĵ,ʹռвθСǽװƷõƽпͲ,Ϊʹռ䲻ô,ǽûɫҪԺڰ׻ɫΪҪɫ,ʱдֲʧ¡һ,һ,һ,Լ鷿ǰˡ˾Ԫ,˶صӾСԵƳֳδװص,ɫɫԵÿƻøʮ㡣ɫ¹ճ󡣡ǽΪռ˶صĹ۸,ֳһֱԴйŵĸ,Ӫŵ˯߿ռ䡣ش,䴰ɴʵ,ɫĴͷǽɫƷ,ʹռвθСİɫשʹռӼͨ͸,ϾʱǶʽԡݸҲǼõ,˾ѡĻ,Сòз,ʪЧķֹˮɢ -houseliving ž,̽Ѱε֮ ͸ˮཨ,ϸ,dzૡӲǰ֮,Ǽҵܰ塣,ȥķ,Ȼﻨ,,ɹ·1Լ֮,ɫ˾ij,ƻпɼҵů,ɾ⡣2,,ӲʵDZżҵ,Ϊصıܷۡ3ͳʽԺ,ԺΧ֮γѪؼԲѧ,鸳轨,ͼ߻סլ4ɾIJ䳾,ŵİӪӯʸ,·һֻ鶯صȻ衣5Ĺʽ,֮,ĵ,ϸͦε,̾,̾!6ͥԺ,ʵķ,ѡõ͵ɫ,ֻ¶,Ľѧ7Ӹ߿ո,۵,Ȼг,˲,¶8Χ,һ޶ʿռ,סլƷ,9ҵ¶Ȼ,ůҷ,ڴ˾,ذꡣ -houseliving 10Wӻʹ,120OŷЧο! ʾ:ĩβȡ4+ÿ5120ƽ׵װʲô?ʹһιһ׼ŷװЧͼдŸƷĵ,ܰʸҲDzֺġ: :120O :10Wװ޷:ŷ,Ҳȫչֳŷʽ,ÿһϸڶž֮ӱǽ˴ͳװ,ηܺ,ʱд󷽡컨ҶװαڻÿԵó,ζƺŵ,ĵθƥ䡣벻СеĿҲҫ۶Ŀ,Ҳչʾÿϸڵľ°ѿ,ڻۼ,שӡ̡ǽڻ͵ǽڻγ˺ܺõĺӦ,ҲͳһСŵװòɫ,ǴשװͲ,ռָǡôȻû̫֮,ϵӾЧǷdz۵,ǽɫ,ں϶ĸߡҿһ߷һɫŷʽ¹,ɫǽֽ,ˬȻ,׺,װЧҲʴ35Żղװαǽ,120Oӳ˽չ90Сתλ԰,110OŮ·С,90OװԴ92Oһװ򵥴,ֻˢ˴ǽ!ENDװʿں̨ԻϢСἰʱظһ!װװü,Ҹ￪ʼסάע!ԭĶȡơ -houseliving 107O±ŷ18,̨ɷ ʾ:ĩβȡ4+ ÿ5̨ 107ƽ׵ıŷ,ʹԭľɫ,˿ռȻʵϢ,ÿλصҶһķɡСļҴžͳⷬģ,¼Լ,Ȼƣ,ҪҲҸ!Ϣ:ŷ ::110ƽ :18wռҾ߶ԭľ,ɫɳǽ,,ɫɳ,׺ɫϵ,˲佫ռСɵļҾʵ־,һɫЬɳ,˿ռϡװĿ̨,ʮֳ,µװ,벻ϲ~ʱ뱳ǽΪһ,ƸгǿġֲԾռ,ҲڱزٵװƷǶȿ,ڶ档̨ĻǽҲǽӦ,ӾзŴЧ˵ĽIJ̨,ŶܾǼӷϲЩСԵIJοŶ~dz,˸,ʹǰɫǽIJҲõ̻ɢɫұɫǵɫ,ƮIJֱ,ɺϢ~ҵIJϲ,dzdzɫ,ƺһ㡣һܲӴͯȻٲ,ռɹ÷dz,dzþõ,ʱ䳤,,ռDZزٵ!ɾıŷ,³ҲΪһ!ש˷Ҷ,ýҲԳһ羰ԡԡƽͼ:һױŷСӵĽ,ǷҲɵ?ϲķ,ӭ·²СŶ~ϲ132Oִʽ,ҵ20,Ǿֵ?88OСװ޻6,걬100ƽ,Ȼһñ!11.8㶨130Oʽ32,ȫЧõ޲ڡ80O6װ,һμǽ+ŵIJ!12136OԼʽķ,Ų -houseliving ֻ75O,ȴ120Oʽռ Ϻƴܶ˼,Ϻ仧,ⲻϸСҵķװ޺,˷ܵ˵ʦԲܾúܾõʽ,Ȼװ޹гһЩ,սǺ,ĺܸлʦһ𿴿¼Ұ~ʽĿǰdz˻ӭװ޷,ӵȻɵзΧ,׷ʽ,ܵһ˷˿׷Rachalʽҷ,ϴѧһοʽװ޷¶ԺԼ˼һҪװʽ,Ҳͦʽ,ǼķôشõȻɫ,ˬɾǽؾİɫ¹,һžʹŨŨȻ,ԵúȻгʽװκ͵׺,ͼ,ѡرעȻʸСdzľɫƵļҾߺ͵ذÿռʮֺгһֹɫķΧֲʧִʱеϢԲεľʲ輸,չֳŨʽ񵭡Բεİɫ,ʽŨʸеļӹʽĴ侹ȻΥ͸Сɳǽԭľ,Լһ,ʹԵÿտ,ܺõʽЧڻ÷̬ӯСֵ,˿¡ҽֲΪҴ޵,ǿȻнֲ̨ܽԻ,ֲ赲Ĺ,Ĺ޼ɵÿһ,÷ﱾͰɫǽ忴ռиӵ㹻˾÷ʮֵĿ,˴ĸҲ,Ըһʮ־׺µĻʿǽγ˼׿,ԭľɫIJδɵòͻ뿪ʽijΪõĹߡ԰ɫԭľɫΪɫ,ࡣ㹻СչʾʽǿɹܡƮó˼ײ,Χʵصıʹʽʷ񡣳ÿһռ,ҵĹսǴ鷿,鴦ͷ˺ܶࡣʯʵذ;ԡӾЧ,ĻƴשͰϴ̨Ի֡ʽװ޵ij˵ĸоǸɾࡣòƲմȾһͻҳƵġʽ̶̻ܴϼ̶Գ,Ƕʽ΢¯봿ľľȻһ,ֽԼռ,Ҳһ̶ֳȻСľƵ˾׽ȫݼҾo2o߶˶СլO2OƷζ,Ϊ!|ihomeo2o.comĶԭļɻø߼ʦ -houseliving ůƬô۶ӣֺãյ͵ůòˣ ҶůƬֻϷͬ־ǼҲõ١仯Ӿ磬ϷʱҲʼѰso~СдƪůƬ»бҪůƬ˼壬Ǽȿ£ʶȡˮůůůDzԭһֳȡůʽůůƬŵ1. ȿ죬õůƬԼΪ10ӿʼɢСʱﵽԤ¶(202)2. ԵûҪ󣬲pbܻܸϹܶµܣӰĵ档3. ãȵԼʱ䡣ֻůƬҪģĻdzռռϳůƬͨܳ󡭡տ˱ʾܽ΢ù۵Ҳ֡ʰ쵵ŮġôУС־ҵּȺÿֺõĴůƬڵкůƬDZزٵIJǵůƬdzIJһȽϸߴе㡰¼+ůƬĸоƽԵʹýµ·õëеطԡҵ˵ﾡ˨ΪʶΪ豸ߴҴлĴʵҲضйɫĸоǹ˷ľУҪøߴһǾһ˶ëҲװʵǽͬɫdzгҾȻ˵УִԼҲܼԦƸмǿɹӵͿܺгʡһҾ߱ܿԷЩҪõСװƷԡСɵ̶ܴܺϽԼ˿ռ仹ʺСӵӴ~֡СӡСӻϲϰָ׼ֽһڼ㺢ϷЩůƬDzǴĴţûдdzdz򣿹ȻСಢûӡҪҡ ̨ظйؼ ҾƷζ | | ¹ | ױ̨ | ñ | | 鷿 | | | 칫 | | Ʈ | ̨ | | ̨ | | | | ͱ߹ | | ƹ | | ǽ || ӹ | 輸 | ɳ | ͯ | ´ | ͯ鷿 | | ִ | Լ |ʽ |ŷ -houseliving һɲ䣬Ʈ һգģռٿռ䣬ȴʵáװУȶȾӸ߲¡˵ܲµʲôûǡաƮСΪҷͼҵװ޵ƮһһƮ޵ҴСܶһءƮӹɣмΪĶǣһױ̨dzճLƮҲǼװġһƥܳеɹܣҲикȥҴСҪôɿռ䣬Ŵ¿ռ䶨һŵعƽʱЩСдҲDzġ鷿иƮɴ˳һף棬ԼҪдﶼDzѡ񣬺ÿʵá鷿͵ĴǸ泯IJ·͵װ˷ѵϧͨƼҾ߽Ʈ״С鷿˲๦ܷƮ״ϵӵĵͯϢ״β¹һƣ˿ռ䡣أϿ캰ʦƣԼҵĴɣûԹעʫҾ100ں̨С󣬻ȡƮưޡ -houseliving Ȼ¼,ԭǼҲô˧! һ԰ԼĶֳĿռ,Բȥ,߲ձ˵ȥݵӰһ,;һ,ȫԼϲ,ûжԴҽ쿪,ϲľǶԵ,޹ڱˡ ȻFrom Ȼר ELLE DECO Ҿ 2017.07뾮ļ,Ǿͱ侲ɫ,ܶ˾Ļϻ߹ҰеľȻů,˵,˽,ʵǽѽӽһ,Щ䡱ļӲֳĬķ,Ҳ⡣ҲԳֳһЩļʡݡ䡱,ϲʵġת¥,һеķ:ˮǽ桢̶ͬȵѼɫ,һ侲ļҡTom DixonĻͭ¼ԼءʻɫdzɫƴƤ¡ɫƤHermsȻļҸ˵ĸоܹǡ䡱,ΪսĿչʾ¼,һֱ˵ǡ䵭硱,Ҳܻ,ֻǻԼںһ:ƽɫѡĢɫͼȲͬѼ,Ļԡϲڰ׻,ϲַḻIJ˵ظС һԭеĸ,,ֻһԡһŻоЩ,ֵҲǾȻ뱣ֵİFlexformɳ˴ĵط,ִ,ȻΪӲˡ,ѡˡӲˡźȥѡ䡣رϲɳȵ,Ƥ,Ҳ,dzʵáǰ輸DieselMorosoϵ,Ӧ̺֯ʴ̺,ڰ׻ҵëҰ,ǽϵĻҳ׿ƷбƴӵĵذӦǽɫ,ĴҲǾȻرѡ,ͬʵغɫԸCeccottiĻͭصԼ,ɫֹҰɡ³ɫţпLouis VuittonҾҵһ,ҷdz̡ڲʦ,ϲһҪһ㡣¼Ҹĵһ,Ǹıռ,ÿռԵøֱ,һ¥ȫһſռ,վλһࡣΪװ޵ʱǵ,ӹԢĸо,ϣһ,ƴͳĸ֡ λԭdz,֮ͨʽǽĹžȻġӻ̡,żʳҰɸ˺ܶҾƷĽ,ҲæѡྫʵIJƷVitraFritz Hansen,Jonathan Adlerĺڰ׻ƿһ㲻ͬķ,GubiɫơʦPiet Hein EekƵƴӲǾȻձʱϵ,ʱһų,λûԲθʺϡϰڷڸռմ,ǷAstier de Villatte,дһƤķʽĬϲġҳappˢƵͼƬ,Ƭɵʱ򶼻ῴ,رϲľͷʦڹʱҲȥּҾӵ,õܶѾ㹻Ӻ,һܶʱ޷Ӷݵ״̬,,ĵط,ȻϣȫԼɡͨ͸ġϲһҸϲһеô,Ҫ,;ÿɸˡ,һ뵽ҵҾͻܷ,ܿ,λؼҡ˵ؼҸȥ?Ҳ,ҾǾҵĴ,ҵĿ,ҿκ顣 ڵijԭһ,ͨΪʽ֮ռ䶼ͨ͸,ΪġӹԢѾ㹻ǽϹŷ˿͸Ļ,̡λԭƻһչʾղصı,ûҵʵ֮ǰżȻÿ궼ȥ,ÿȥһֻAstier de Villatteı,Ҳʹòͬıӡչʾڼ,ġ׳Burberry ϲЩ,Ƿdz,ֺܵƤȻʵֲǺܱߺ,ʱ,ַʽСе㾪ֻ28ľȻ,Լôָ߼׷˵Լʱ,Сүү߳,һ˯,ֱвԼĿռ,һ10ƽ׵С,һŴһд̨ˡ,һֱڴһԼľסռ䡣 ˽ ¥ݺþȻdz,һ侲񱻲ɫɫ,˽˿ռ͡׳ͰɫȿBottega Veneta,dzɫHerms,ѺϽ۾Bolon,Ի,İɫдLouis Vuitton ˳¥ʰ,ԿݶԲβ,ͶıһַΧ¥Ծ˵鷿,ƻĶһ⻨,ֲΧлдĵķɡFritz HansenӰڷCassinaǰ,ѡľɫ;Gubiɫ͵,úӦ̺֯ĵ̺ˢĥۼĻɫǽڡˮ¥ݡӲеֱǡʸеװ,Ȼļһָ߶ͳһ,ҵζʮ,ȴʱִġһЩϸڴѡܵĸӦ,ñĵ,߹ȥԶ;ٱǶǽĴ,һ,ҲԼð,Ĭ ñ ñĽɫȡҵĢɫγɲͬ,ǽϹŻľΩƷA԰ϵ֮һ,մԼءñЬñڷŵþ,Eero AarnioOriginalsPonyһЩͯȤ ñ,,̨ϰڷDossofioritoֲ۲߻ԵɫȻѡĢɫ,һѼ,,رʺ˯ߡɫװBurberry,ɫĸLouis VuittonմϺһҼҾߵѡ,Ȼһ¾ͿʸСɫΡľƴͷë֯Loewe,ɫͷGubiƷǾȻرӵļҾƷ,ʱ̫ҲЯϴ Եϴּʱеˮĥʯ,һҵʮ㡣ڿӰǵԡҳʱʦϴּ,ڵĽҲȿһЩС,Ҳӵס,˽ܡ˾Ȼļҿ,дסռ | õ˾ӻ·ά벢ʶ,ӹעע:תԼҾ,ID:elledeco2004,ʮָлСϤ,ͼԴַȨ,뼰ʱϵ,ǽһʱ -houseliving 3ɫʴ似ɣҲЧͼ! IJߣҾװɫҲǵİɫǺɫʿװ޵ĴɡСһЩҾװɫ似ɣһѧϰ°~ݿռIJδɫǽ桢桢컨ϴĵطʺdzɫΪ׵ر컨壬ɫ̫һѹ֡ظСɷһЩ컨塢ǽɫԱСڼüԵóݷ书ܴɫɫΪذ塢컨ǽɫȽϸߵdzɫɫIJ輸ůɫɫΪ޵ǿ˵ʳԸůɫɫΪɵĵطмɫΪ ɫɰ˵ϲãȽϸߵɫʿȽʽྻݷ䳯ɫķѡdzůɫϵķѡɫķѡɫķѡůɫɫҪdz󾭵ɫʴ1++=㾭 +׿ӪǿҵӾЧɫ򻺺˺֮ӾͻھסַΧԡרҵС2+= ɫܴɫʣǹʹðɫҲһֵУӾƣ͡ɫԵúܿͻ˰ɫɣֵкʽþӼҵķΧȻ3+=ϲ ɫһ۵ɫʣϲãʺϼСļͥɫ˸оƽ꣬ɫʺķʹá Ҿװ޵ɫװ޵ķкܴӰ죬ûѧרҵɫ䣬Լ뷨ϣʦļҾɫüɫӺгͼƬԴ磬Ȩɾ -houseliving 127ƽ¼ס оDzһ ҿ װȫǸԼ뷨,ʱʦ,װ޵סҲ˿һʱ,ͦ尾ġ¼ҵģ,һжԺˡס·оDzһ,ɹɹ:һ鷿:127O:еʽζ,ɫͦḻ,Ǵͦõ,̫ͻأ,ֱ̨Ӱװ˲,оÿ,յͦġͦ򵥵,ˢ齺,ǽװ,컨ͦ,ͦвθеġڵѡıȽһЩͦ,ɹҲ,͵ɫͦÿ,еҰ׺׽ϵӡˮѡIJǺ,оôȽϺһЩЬƵͦر,ʽߵ͹,ЬȫԷ,ͦʵõġЬ̨,ƽʱԷһЩԿʲôġˢ齺,̵ľذ,Ҳµ,Ʈ˲ɹЧ,忴ŲԵǽĵɫıֽ,˸,ͦġͯװ޵еС,ɫϱȽϻԾ,ԺӱȽϺʡڷһС,Ӷѧϰʹá鷿ͦС,Ҫ칫õ,ڷ̨,Ͳ,,öͦİɡ鷿иСƮ,˻Ϣ,оͦõġȽһЩ,ò˸ʪ,Чо̨ʽ,ϴһǶʽ,ͦʵõġ̨װ˸,ڷһЩֲͦġװϲ?װʵܼ,ǰ㶨!ĶԭơȡƷ diff --git a/NLP/16.8 BERT/data/test b/NLP/16.8 BERT/data/test deleted file mode 100644 index 8b13789..0000000 --- a/NLP/16.8 BERT/data/test +++ /dev/null @@ -1 +0,0 @@ - diff --git a/NLP/16.8 BERT/data/train.tsv b/NLP/16.8 BERT/data/train.tsv deleted file mode 100644 index 6170fba..0000000 --- a/NLP/16.8 BERT/data/train.tsv +++ /dev/null @@ -1,300 +0,0 @@ -game 5000,ͶϷж!Ϸǿÿα Ϣ,һлĶ Ϸǿÿα!/ԣС5µһܿν߳𡱡رǿտպ˺ǻԵ¼(ͬѧտպؼ)µĽչ,˵Ůҵ߸迪5Wн,LV,չͶ5000,Ųù˾ʽҽƱաµĽչ,С57ٴη,ǻԵָ,Ͷ5000ǷϤˡݡɹԭί9Ӧڴ¼,С಻,ǡ5000W,ϷȦ,ٸ黳뷨ĴҵŶӰ!,ܶͶ,ͶϷͥ!һӭ!ͥ׳,ݲͳ,ϷѾ200ֻ,֤Ϸ๫˾IPOָڲ塣53,֤ᡶϷ๫˾IPOָڲ,ļϷ๫˾IPOļЧָꡢϢ¶ȸָ˽ϴĶڴֲ²֮ʱ,54,֤ȯ֤زʿ֤Ϥ,õЩžʵйزűʾ,IPO˹,ҵż͸ڵҪޱ仯,Ϊй˾,Ϸӿ롱,¹ɷг̬ͬʱ,ˡϰѹءҲڳΪ̬ڴ,Сһ򡰹桱,֯͸ͨý巽ʽṩϢʱ,۸ġ޽ӡ鹹ϢһϢ칫52շµġϢ涨,ȷ˻ϢɡСල顢ε,ý롣ù涨61ʩСҺ,ڻû61աŰ췢¹涨,Ļ̨ġĻڹ淶ϷӪǿºܹ֪ͨѾʲôˡ201751֮,ϷҪжûʵ,ǿδϷ໤,ҹϷп鿨ǿĸʡ ΪӦҺ,ѾNϷ˵,N+1Ϸ𡣡ӢˡҲһκ˹սƷıʡʾ,սƷ,ƤĽΪ29.255%,ٻʦͼƤĸʶΪ2%Ϊûκμֵ,ûиܵôߵĽ,Ҿ͸ʵļ㷽۸һʵ,СһܹԹ!!!,λKhemist49eBayԶʱһΪѩӻƷ,۸ͦ˾ԺطоȻһšǼԡհԴ̡Դ1998,ԴڻܼϢ,ͨҼֵϡƷھδŹ̵,ѾҪλСƷˡѩٷ֪һ֮Ҳϵ,ٷŶŹ,Ϊа֪ʶȨҵܡԴҲѰ˷ɽ,Ź̽ѩ˾Khemist49᲻,ȴ൱ϲ:ѩһݡȷ桷Ϸ,ֵ250Ԫս,֮ⱩѩרµKhemist49,ȥμӽıѩ껪λСΪòϡƷ,ջǾ黹,ٷΪ˽λСҲһݴȥμӽıѩ껪֪ԵϡƷô?ô,ɴ˿ɼѩٷͦ۷˿ǵġѩ֮ԱΪְ֡,ΪȫҵĹ,رڵӾϷǿǰܱйýѧýרҵ(ֺ͵Ӿ),ⲻ,ϺѧԺҲͲסˡ,ڡӾѡ2018żӴ˻Ϣ,Ϣ͸¶,ϺѧԺרҵ조Ӿ˵רҵ򡣶ڴ˻,ʽ챾иУеһӾרҵҳ,ӾͬڴϷ,ҵ,רҵĿƱһ顣ֵע,羺гȷ,ݱ,2021ʱйϷгĹģܻ2017260Ԫ350Ԫ(Լ2413),ɼģ֮Ϸй˿ռ44%,һ2021ʱ,54%,ʱϷйĻûռıӽ90%ǿ̺Ϸ,Ժܺ˽ЩȺ,һ޴ĻϷ,ϰʵҲһšʦҫΪ,ֻϷٱСС,PCηͥ񡣽,Щɿ̻,רɿר,˿ͶϷר,ԿģPC桶ҫ,֮ҲֱֻͨϷڲɵר,ûPC,Դֻ,ֻṩWi-Fiͳ缴ɡɴǰĴԴ,ȶģʽ,ٵڵġר,ɼҲʱ׷ʱеij! ˵ʱ𱬵ֻϷ,DZȻǡҫĪҶڡҫԽԽԶ.......Ҫ˵λСνˡý屨,ݵϴѧһҽԺһλΪҫշԹԺС!17С()ҫ˿,Ϊ˳嵽ҫ߶λ,ᴲ,ս40Сʱ,м3СʱֻСӦһٷ!Ŀ,ϷͻȻԼͷΡͷʹ,վҲվȡʶܲ,ǰخijҽԺн,֢״תϴѧһҽԺרҾ˴Ź׵̺,ȷСľԹȻСҲΪλϵͳ,ò׻һ,һ,ǸͰˡڴ˻ϣеϷ,ֵͬʱҲҪѿغϢʱ,Զ롰ԡ ҫڹڿνǴ,˶мܲסջ,С,ҲϷ,һͦ6Ȼٷʾʰ,DzΪ桶ҫɷѿġ˷ǽ,ΪܺúõϷ,Ҳʹȥѧϰ,ĿijܸиõĹͨ,Ҫһ!!!ܹϷһȺͷIJ˼ѡĵ,СͻȻֱܡҫƵеߡû취,˭ȫһء,˵NԺ,ҫܹ롰ϷáҲ˵һ,Ϸá,Strongֲ2015,ýֻPCƻֻȫƽ̨ϷӰȷijɾ͡201754,ŦԼ˹صStrongݹһڻ,ֱ:ա⻷:һս鱦:/̡͡ͷ2СصطһϷõƷ,СսʿGTA3ﴫ˵ո֮áԶˡPongģˡ̫ߡֵܡ˹顷͡ħ硷ϱ񽱵ĿƷ,С෢ʷƾõġա1981,Ҳǹï֮֮:黳,!Ȼ,СΪ,ϣгһܹйIJƷϴĵ!ϵϷǿⶩ:ֱӲҡϷǿ⡱GameTHKע:ӭѯϷǿ-˼˼ QQ:2819435219Ͷ:뷢 ed@gamethk.comϷǿAPP:1Ϸǿ΢Źںҳ2ǿɫ˵3iOSء򡰰׿ءϷǿ201410,һڹϷг,ͬʱۺϷҵý塣ϷǿϷߡ̡ý塢Ͷ˼ҵʿ,Ϊṩ߼ֵѶݡͨȽIJƷ̬,˫֮ʵ -game LOLS7Χ̹˿ȰWEÿ鶼SKT ʱ915գӢ˹ٷ2017ȫܾΧʽ̡ΧLPLWEսӲμӡһWEķ˿Ҳ׷ܵWEĹٲ¡׻׻̵WEҪÿһWEı23պ24աϢ2526ƱWE˿еץˣ΢϶뻻Ʊ󲿷ֵķ˿΢ΪWEͣWEÿһҪBO5һÿһֶҪSKT򣡻Ѿġ̡WEʵ˵WEҪΧͻΧʤ㻹DZȽϴģȻҪСġϾB01ʲô鶼пܷ̭FNC C9ȶҲǺܺôġϣWE˿ǽһÿһֶSKTԴɣ -game LOLܾ: վPP, WEȫϰ񿵵۷Ұλһ 2017ӢܾΧ̸սãLPLWEսҲ˳뵽ܾССWEС鵽һС飬DϣWEܹLPLӦеķɡܾ105ʽʼLPLͣΧ֮󣬹֪վBEST.GGΧ׶θλѡֵijPPַ֣ڰУWEսӵԱȫϰ񣬵֮سΪΧǿսӡϵλУWEսӵȸ957ƾԼȶϵռϵĵһWEսУ957ǶΪȶһλãȻʱرʣȴ˷ġΧеĶʵԽ957֮϶Ҫ綥ϵʱʱڴҰλϣСԾǣ۵ı־ȻûһֻԳ165ppֵڶһC9ĴҰˡWEΧ7ֱУCONDIеֱmvpһGMBضƣ´ľͰcondiΪ۵Ļ̭ڶУezҰһɷ̡СൣĵǣȻĹâezҰ·ЧòƲWEսӻҪһ£رʱѡҪء˫Cλϣҹеѡڶppֵߴ186WEսӵADCѡmysticppֵΪ193ھҵADѡеWEսӵ˫CȷʵͣɱΧѡڶ͵mysticʵȶԣСûֵͦģ·λ˫飬еѡҹ֮ķȷСൣĵġϾܾУܶսӵʵǿѡֺcarryĵ㶼еλϣҹܷסѹԼΪԷͻƿWEȡʤĹؼڸλϣWEսӸԱϰZEROһBENġڸλϣWEսӿѡZEROѡִḻ״̬BENѡΪ׷Ͷı֮࣬ĥĬáϣWEսеıܹù۷ٴǾ仰 -game ҫ׸Ӣ εè ڴһϵӢ֮󣬡ҫڴƳ͵ԭӢˡڸոոµУҫһèӢۡ档ӼϿӦһϵŲԱķϵӢۡ Ȼأļȴйоأһ+ܵϡ ͼʾıĿǰΪʳΣڼƶٶȵͬʱչѪֵܷΧȤǣСЧʹüǰʹú棬Ļˮ⣬ЧЧҲչ϶Ҫ棬Ҫ似չνӡ һΪξƣ˵ʵһʦܣеĵıܱ֮ģܱʱɷ˺⻹˴ȵļ١ȻĿǰٵʱֻ1.5룬ԴƤķʦ˵ѪҲһ˲¡ ξͬǸȤļܣӳȥجλ˺ͼ٣ĻܽաҪجˢ˵ҪӢۣҪú÷ÿجεջʱˡ ӽϿξһԴλƵļܣͷŴ֮󣬳ɷ˺ͼ֮⣬ͻᱻλƽеĵشĴĵ˴ɺ˺ЧҪעǣмĵجȻԻգԣҪȥôجεİѿؽǹؼ Ŀǰҫٷδ͸¶½ʽȷʱ䣬ҲֻΪһӢ۷̽Ӣ۵ĵ½͸¶һźţҫڼӴԸԭӢ۵Ĵȡ ôDz½᲻èһӢ۵dzأ -game ջһֻ赮 ȸлСǵ֧֣ǰĶ⼼ܹĽܣ˺öҶĽԼϳ裬һӾĺϳ裬һ4ֻһֻǶһĺó裬Ǹ߳ɱϳ裬ĽѾˣ1&1ϳ裬ûظŶһֻͳɾˣɳ1.3ҪըˣڶֻҲըˣȻֻ2ظ7+3=10ξͶˣ˸10ܵijɾͣһӾ͵õ2ѵijɾͣϲңһֻֻϵĺÿ5+5ȴ2صڶֻѾõ10ܵijɾˣصʣɳ1.268ܸߣŶòֳ裬ǵĻⷨҪˢµˣ󿴹ʣλ34ǵ10341534أ𰸽1534ŵķһˣŵһľǼûأǰĸû󣬿ܣⲨĵĦУһֱӵˣϲDzǿѪڣҲһ¡ྫݣעλε԰һţͷĹעťעǣǻÿΪṩʱȤλݡ -game LOL⿨ѡUZI ˿²ֳۣ һλɵĴϣ⿨DWսӵĴҰѡLPLְҵѡUZIûӡ̶Сʱ֮䣬ٹڵ²ۡϾŵʱһλɵĴϣ⿨DWսӵĴҰѡLPLְҵѡUZIûӡ̶Сʱ֮䣬ٹڵ²ۡڽıǶĿãC9DWΧУC9ҰŮDWҰ֮IJģ˴СһҰϸ׽಻ϡһC9Ұǰʮ飬ٶԱһDWҰǰʮӣڱʾһΧ򲻺õˣˣUZIǹˣȻUZIıбֲ̫ãѲǸƴ꣬ϸĵ˶֣UZIڵĴ򷨸ƫŶӣŶԼԾС֮ԴãСUZIҪͬʱUZIܻὫԼDzԴøСǴҶܿ顣˵UZIûӣֻ˵жûпˣ -game ̵㼸ҫϷҫ׻ܻꣿ ԭֻϵ̰ߣɫħֻҫֶҲֻܾ̾ԽԽˡǵDZҫ֧Ŀ־ÿᣬķϣֻܿҫˣڵֻҫNOЩϷܺܶ˶ûһǾƷһɡ1ַųԼܶ࿪̶صбַ򡷣ʮֱ׵ijһֻġɱҲһ㣬˺ܶҡ2ԪͨѶһϷҪһֱѡشйͨԿп罻Сͻ3ҵϷϷ˵ϷˣҪҪͨŬȥҵȤСȥһԡ47һ߶ɵĽðϷ7ǰʧȥ˼䡭 ΨһûǵģǸԼ 7㡭 Ǻֶ벻ij˵ԼΪȡʧȥļƬ Ϊһ춨µԼ һصС5һ߶ɵ3DɳϷûеȼ͹ƣûض淨ֻƻʹȤ;ԴһУҲһȺСսĺڰֿɽˮı硣6̰ߴսǵͯṴֻ̋ǿĻ䰡ܻҲ̰ˣ淨࣬ҪͨλñײϡЩϷǷdzȤģҲõĶѵͻȻһϷMAXϷȻ棬ǹѧϰҲҪˡҷֻϷôҷһPPTģɡ·۾ͣҷģС͵ġɰ칫ѸPDFת칫ļת -game B:Ҫɵɧ!حBBƷ Сƹح滳һͬ¡ ƽʱҴNS˾,ʲôҰ֮Ϣ,ôŵϷ,ûҡ B粻֪ĸһָ,˾ըˡ cһ20,תһ,ͷۻ ,˵õϷں?Ϸ,Ȼȫֶ?벻,ݵ?תһϷ? Ϊ,ר,ԭⶫô! 94걻05,С桢桢80ϴүҲ֡ youtube400WƵ˹רдƪ,2017رİ칫ߡ(û,ͳƸаǸ) ˵ָ滺ѹ,ƽ ҿdz,ⲻϴү,ı ǸʱС,˻ ⲻ,cһɶҲûд,ϴ硣 Ȼ,ЩȫBĹ,,ҪռҵƷר,ݶ: ʲôָݡ ͷ!B̬,еʵû¶ɵֵ,΢,һ(Bһ,۵ͷ) Ҳ,Ҳû......ٺ! Ƶ! һ,Ϲء ϲBĿȥBվռ,,ġɶ,ɾڵĻ(B,ÿῴ)Bվռ,Ķԭľˡ ɶBС,Ҳԡ ʲôȰ㰡,B߲ܡ Ʒ˺ҽܷɻ.......˺þá˵B, ,ʱBB,¶ϷլҪ~~~ ظBB,ÿһϷ -game ĩһΣ7.20ǿLOLϷӢƼ λСҺðѾųϢȭͷʮһ·ݽĩ㣬ȥǹӦûʮһѮ㣬Ҳ˵㻹вһµʱ䣬ƻвһûԼǵĶλɣģǾƼǿϷӢۣǶ7.20汾Ϸֺ֣ÿһdzáʿ 7.20汾ϵӢۣѾü汾ͳϵˣԴڼǰıһֱdz˼ЧֱԱøǿֱڣкܶҲ˽ӢۣһеͷֶΪԣܶ˲˽𣬸֪ôӦԣһЩսʿϵʱкܴƵģرһЩϵEܺپܳķݵˣںܻӣ𶼷dzô򣬶ЭҰ׷˶dzǿ̹ܷݣһdzϵӢ֮ۡŭ ɪׯݼȻǿϷӢۣǴҰλһã汾ûбøȶĴҰˣڱ·˾֣ıֶͳμģÿ̹˴ҰҰ죬ǿ̹ȸߣǰھ;вgank̹ȸߣܿܶ˺ǰݻϺݶdzͿͽ֮þͿԳʱ䲻ؼңһЩСײ󣬵˿ҪؼҲпͽȫõģһӾͻˣþdzͽҲdzܿڱĴڣüڿʱɱ̹ȼߣһҰôСԪ еھһ֮ջǻصϣȻ汾еǿӢۣDZ·˾֣ͳȻǿơϵѹǿ߰ԼĶ·裬ֳ˺ߣǿ£;еҴҾ˵кcarryѡֵװ˼·󣬹߷ñӷЬֺƣ+ñ˺ߵıըWܵʵ˺˵ˣ汾е֮ϷʹӢۡ ˹·Ѿ7.20汾¯Ȼ¯ijִ˺ADijߣADУȶӢĪСڣڰǿڶıУBPѾݱɫһֱСˣ֡˭С˭ӮĸоȷʵӢСȻûĴбը˺ҲûдWܸ߶İٷֱСеľͿ඼Ӣ߱ģСڿԴ࣬ٽԼǿڣӵ׺СڵսͷdzˣС汾ȶADӢֵۣһáԴMISS -game Ѱɡ60װ65װԱ Ϸװѡȡ ںܶѰ˵Ѱ³ĺɫװİװ˼װֺɫװȽְҵװװԸһЩһһЩսӡ⣬ԵһǣװԶһʯװĿףɫװܱڡ׿һλҸչʾһװְҵװԶԱͼͬ70ñӣװñӵľӵ˳ƷԡӵĻļͬ2.2%ְҵӵŮ̷ӻ˺ĵֿ2.5%ͬ70·ս500֮⣬ְҵ׵ԸӺһЩھϺװռơӸоôԣǮӲıʤ֮⣬װ졣ЬӵĻԻDzȽϴġξͲ IJ໹ģ˲ࡣλҸĽۺǣʵ󣬾Ƕףƽֱӻ70׾Ϳˡ -game øSuper Mario Run½ɫģʽ ժҪý929ΪiOSAndroidϷSuper Mario Runµĸ£нһµ磬ģʽͿѡĽɫø½һֳΪRemix 10ģʽԿشϷؿƴ10֣Ҳʺ罱¡10ֽȹգϷѡɫҲܹ⿪һΪ֮ǡ磬аŸ¼𣬵˺һЩϷơθ»һЩСС£ҿԻµƷĢҿϷʱԼ֡Ҳ9291012ڼ佫Ϸ۸ʱһ롣 -game ҫ16ϷBUG ȫ˵ӦȥƱ! ҫϷп϶BUG,̵ҫз16BUGȫ.... ȥƱ˴ֵ!һû... 㻹ǸȥƱֵ!Ȼ˼BUG,ǵԷŶ~о!ҵijӢô?û˸߹ ҿ?װᰤ?С......о治ĴôǮ??ɹ....?ȥTMDĻ!ͼûëôӻ??4ôɱ?½ĺŰ!!û?û ˭ǵһ??ôļҿ?Ϸwww.putaogame.comQQȺ:١Ⱥ Ⱥ:428433456 ֱϷƽ̨ ǧ׿ֱϷ -game ںSLGMMO԰λƻƼ+4.5ֺ ϿעҡJҵûXҫûXʦ2014꣬桷дʱһδ档źܶүԡ桷Ҳӡ󣬽봴½ϵİ뼴ʱƿ淨һʱͷȽġũҩΪȨ¼ܣղòС桷桷Ŀ˿ûоʹֹǣųƣʱ䣬зһ԰־ĺԣţ 921ڹʽߡ԰ϼ֮ܵ˸ƽ̨ĹעAPP StoreBannerƼλ׿ƽ̨ҲͶ൱ƹԴôõĺԣ԰ָҵհ汾ǸϷأ淨ϽSLGMMOд˵԰ĺ淨ȽϽӽSLGҪԼijDZأ̽ĵͼϷĴµҪڰ뼴ʱսͽMMOĽģʽ档SLGɲӢ۶սİ뼴ʱս塢趨⡶ħսһЩȦɵ΢¡ͬSLGೣĻغϲԻֱսħսеӢ۲ڡ桷İ뼴ʱͷżܣСΪսʽ֡⣬֮ͬҲ趨˹ϵǹƲеλȵȡԣڼͷʱѡͲֲϣбȽϸߵҪ󡣱Ƚֵһĵطǣں֮󣬽ڵĸ¼˸ͱ֣ԡħ硷塢Ŀ趨Ϊй+ձȵȡͬиԵ幥ƽ⡢óҩȿƼ/ʿȵȣ佨ҲͬҿԸϲѡ֮תMMOǿPVPҡ˸ǿֵӰ鲻ѳSLGΪĴ֮⣬԰ڽҲЩ¡ϷǿҺ˵ĸóͼͬһҵϷҲĹҺˣCOKH1Z1˱Ŵ龰ϷҲ̽ͼҵңǵжԹңԷս˵ܶүҲѾϷı׶ڣָ봡ǽԪضԴ󣬵ƾϴ󲿶ӣҪΡ̽ͼȡԴӢͨ䡢λãֱӾ˿IJǿƵӢۼܸǿսң˶ԡ봽𡱵Ĵ󡣶PVPΪĽģʽһ˸εۡƽҵIJ࣬յ˲ٲ֮IP淨ͬʻгϣԴƵ˿԰һĴ²ƷͳЩһġ޼Һձڵָ봵⣬ħܵ˵ֵү飬СС봳ʼɡϷƣ԰Art of ConquestƼ̶ȣƷпɣ淨һ£ζȡ봶ȸߣԣƽ̨׿iOSiOSطʽApp Store԰ɰ׿طʽ׿԰ءȡϷҵ˾ڲġӭ·˽ࡱĹעǣϷѶϷȦİĻŶ -game FGOӽ̳ ͺĸЧǿװ FGOװǿԣӽ̡̳Ӿþ̳СݵȼQPȻɵĵͺǿ»λӦöмʮˣôһǿװĺûᡣ(ߴӵԺǵװһǿ)Ӳ裺1.ǿѡ1װ20װǿA*20ǻὫװ19Ϊֹ˷ѣ20Ӧٺһͻơ2.ǿѡ1װ20Ӱ˳15װ+A+4ͬͻƣA**ʵʲУͬͻֻ1-2ڱⳬޣ17+1+2ɡ3.ظ2ֱû3װ20ӵIJ֣öװB4.ǿѡ1װ20Ӱ˳15װ+B+4ͬͻơB*5.ظ4ֱûжװ20ӵIJ֣һװC6.ǿѡ1һװ20Ӱ˳151װ+C+4ͬͻƣC*7.ظ6ֱûһװעÿζ20ÿζڸ1װιЧʵͬʱʡǮʵʲA*B*C*ںУһԽͬͻĵɡҪԻ֣ΪΪ󣬿ԲؾǶһǵ˳ֻҪ֤20Լ·Լʱ䡣ʵʲʾ -game ۲ | ۾ȲVR ŵ91 ʵгĿǰȴ˵ԣӲ۸񰺹ǵԵԭ֮һΪ˸ıгԣVR̡ᡢHTCOculusѾʼۡ VR̵Ľ۱ΪȡůͼҵٷŻıͨ۴̼¼VRгӿƼʼûкõVR֧ţ۶VRҵķչûʲô塣ǻΪһõϷõIPȥϷƻVRһЩСϷĻͺѴʹûVR豸VRҵһֱͨչгռʡ ȲVR 829գụ֣Ϻ޹˾91й½PlayStation VRPS VRװµ300Ԫ2999Ԫҡ821գHTCҲVive۵Ϣԭ6888Ԫ5488Ԫҡڸ֮ǰOculus Rift399ԪԼ2630ԪVRͷPS VRHTC ViveOculus RiftƷ Ʒۼڻǿ󲻵һVRͷԽôݡ917գ߷λڻǿ񹤳¥ļVRר꣬ɢ˼VRר꣬оèƽ̨ǰUGP Ϊ߹UGP۸˸Żݵļ۸èƽ̨ۼΪ899ԪVRͷֻҪ750Ԫ1980ԪIJƷ1000ԪͿ򵽡ڼ۸Ĵò۸˳ƣΪǹļ۸Ҫ UGP۸˸߼ߣVRͷ1.7̨ңȥձ89̨»Ƚԡ ΪVRȱ󼶵Ʒ̼ߵĹ顣 ȻкõVRҲҪõVRӲΪ֧ţĿǰVRӲͼ޷ûʹʱѣ⡣ϷΪΪ˱֤VRϷ飬VRϷʱһ㶼ڰСʱڡVRϷﻭڶʵûжʱ䳤ûһѣСЩʱϷdzã豸ﲻҪĿǰVRϷʱҲʮӡ;Ϸҵܾڽܼ߲ɷʱʾ ұصVR豸ֻ˵ûҪ׼ͷԡ/Ƶ£ҪһΧϰܷˣ Ӳݾ׬Ǯ û顢ݵȱټϸ߰ļ۸ʹóϷҷ⣬ͨ߶ڹVRƷʼȱ飬VRƷCгĽܶʼղߡ⵼ĿǰϳֵϷƵҪСΪߡ ܣVRC͸ʲߣϷƷϣܶϷ̸Ըȥ֮ϷĿǰVRϷǰȽϺõģҲǺзͶƽ׬Ǯĺ٣Ǯ һ㣬Ӹ֮ǰFacebookɢOculus Story StudioͿɼһ㡣 Oculus Story StudioƾƬDear Angelica͡HenryʵƬŻ󽱣УʵƬDear Angelicaʥ˹Ӱϴʣͬ9¡HenryֻðԭĿʹOculus Story StudioΪVRҵĶҡ VRǸ޵׶Oculus Story StudioǰһƷLostʱʮȴ1000ԪFacebookԸó5000ԪרϷVRݵĴǮֱͶҡFacebookɢҾ˴ǰڲVRݲԵʧܡ ߴҵʿ˽⵽VR̬ϵҵӯ״롣ĿǰйVRӲҵࡣVRʿڽܼ߲ɷʱʾֻVRͷĻԾгϡӪƷзҲļҡ ӢضϯִйٿΪVRʽڽ׶Σ˽׶У󲻶ϼ£·ѭսγѭGartnerҲڡ2017¼߱桷нVRλڡȲĹڡVRʧĵͳѾȹ ʾVRһڵķչͿԾʤġֻ˴Ӵ󡢹ܻٵܻ׶Σм˴Ŷʮʱ䡣ʵVRҵΪVRʮ굽ʮʱ䣬ڵһ׶ζû꣬Ҿ׶Ҫеġ һ棬ȻھͷBATûнVRսԲ棬Ѿʼ͵VRǰġbuyǰٶߵVRٶVR+յVRӰӣԿЩͷδҵͼVRռһλá : IJ ЭȨ תעԴ http://www.nadianshi.com/2017/09/182694 -game ҫߣάֻΪ գҫӭ£İ褼ĿصǵСڴǰСһ°褣Ȼ󶼶ûλƼܣߵ˲䱬ʹΧAOEܻǰһзŵĺãսе˺Ҫڸ߽!褣ڼǰҫӭһζݵʱάάݺȷ褡Ϊιٷ˻𼱻ǵ?ǾǰԶԱȡܣ˺135(+9*Ӣ۵ȼ)(+0.18AP)125(+9*Ӣ۵ȼ)(+0.17AP)1ܣͨɢĻ˺135(+25/LV)(+0.18AP)125(+25/LV)(+0.17AP)2ܣֱ˺270(+50/LV)(+0.36AP)250(+50/LV)(+0.34AP)2ܣ˺135(+25/LV)(+0.18AP)125(+25/LV)(+0.17AP)εĵпԿ褵˺Լܺͱ⵽˴ȵ˵DZһ֪λСΪôǷѡϷ? -game ۲ | ͵򼽸У羺ѡԲĻ 201791617գһƶص羺ΡĻȲҵٷչĵ羺ھؽŵؽţȫ׽͵򼽸У羺ѡ׶ΣԾʮУ֧սӼͬʱչʤ߽͵򼽸У羺ܾ922ںӱ׹͵չս ͵򼽸У羺ѡ׶ηΪվվӱվվΪ⣬վΪĽŽձվսӡվӱҵѧսӡӱվھְ̨ҵѧԺGDսӺǾְJWKսӻսʸ񣬽922ասںӱ׹һͬϲ֧ʤ飬ܹƾԼʵŬԼĵ羺θһڴܹܾӵгɫı֣Ϊ羺Ǵ˶ĸУ羺Ծ վھս վھӱҵѧ()ս ӱվھְ̨ҵѧԺGDս ӱվǾְJWKս ǿ֧֣ȫУ羺 201792123գ׽챣βҵչὫڸ߱(᳡)ˡ׹³ͬʱСʱΪ÷ҪԪصй׹Ҳͬʱٰ죬෽龫ʷ׳ʵĻĿɫĪں͵羺Ļڰ顣 Ϊ͵羺ĻڵҪɲ֣ɺ͵ҹƼ޹˾Ͼٰĺ͵򼽸У羺Ŀǰǿĵ羺Դ˾򼽵ĵ羺Ϸ˳Уѡ׶θǽĿǰ֪У˸󼯺ϣ廪ѧһѧݷݷѧһãĿǰй羺ȺĽ ڱվ±УվսƾļĬŶϡѵӦ䡢Լ϶ӳӢָӣճΪվĹھΪһ֧ܾı顣ͬʱվӱҵѧ()սӡӱվGDսӺJWKսҲնճΪܾǿ顣 ܾУ֧սӽ뼯ӱ׹͵ģһѧĶ羺£ھ˭ңעУ羺µIJӱ׹ֳһͬעⳡעдʷĸУ羺 ɫƶй羺ҵٷչ Ϊ״ιٰͬĶ羺£͵򼽸У羺ǰ׶ʼܵй羺ҵĸ߶ȹעƾǿԴͶɫ͵򼽸У羺ȻΪĿǰйУ羺еĴĴɫdzΪй羺ҵһ羰ߡ ͵򼽸У羺վ± 1һ廯ڵ羺״ʵ ׽챣βҵչ;һ廯ߵĶ磬˴κ͵򼽸У羺˾صԴɺ͵ǣͷ״νһ廯սĿǰ𱬵ĵ羺ҵںϣھ򼽵˿ȵĵ羺˳ 2УԴ󼯺 ͵򼽸У羺ڶɫ˾ʮУIJ룬иDz廪ѧѧУ˾صĸУ羺ȣҲΪУ羺εĴѧṩ˶ĵ羺̨ܹ֮·ԽԽ! 3羺ǵǿ ڼĺ͵򼽸У羺У췽˶ֲ˵ֳսӺ͹ڻӴݣн˵αΪCocoܣŹעLOL羺һеİ ֪羺Ů˵Coco 4רҵĵ羺ӪŶ ǿݣ͵򼽸У羺רҵĵ羺ӪŶӣܵıڱƽΪ羺ǴרҵıֱһʮĸУ羺 5羺Ԫͬʱ ͵򼽸У羺(͵羺Ļ)ͬʱеĻк͵ʶչֳڿԽܶԪϷCoserֳܻCosplayӢ㽫Ϊ͵羺Ļڵһ㡣 д ͵򼽸У羺ɺ͵ʺͰҹƼ޹˾Ͼٰ죬922ںӱ׹ٰܾʱ͵ʽڵ羺ҵͬһΪй羺һɫĶУ羺¡κ͵򼽸У羺ٽ羺ҵٷչƶ׹羺ĻС򡱵ɣӶص羺ҵٷչƽβҵŬʵԴʡβҵǿʡ¿Խ׹ΪϵҪĻġ : IJ ЭȨ תעԴ http://www.nadianshi.com/2017/09/182514 -game Switch4.0ϵͳӼϵͳãҿû˵ 1018գSwitch4.0ϵͳϵͳ˼¼תƵȡڴѾá¹ܣһЩϸĵҷ֣ºϵͳгˡ/English͡/English ȻֵעǣⲢ˵ýϵͳСԺԷ֣ѡʵΪӢĵIJϵͳνġġ롰ġʵָǸϷе趨ϵͳҲΪʲô˸/English ͼƬԲ NintendoSwitch ʵڴǰþѾSwitchϷˡ£˫ȺϷֻͨӢĵIJϵͳ鵽ݣҲΪҴһЩ鷳˴ڱʶϵĸҲֻΪѡȷѡֻڷϵͳ£ӢϵͳſᵽݡϵͳĺĿǰٷûиصϢ־ҲûᵽѾΪСҲԿһЩͷϵͳҲ̫Զˡ ǰNintendoSwitchҲйغ ȻС⣬Switch 4.0ϵͳĸݻǷdzҾϲģУ¼ܺתƹܵļDZθµ㡣 ڹٷĸ־пԿҿµ¼ܶϷݽ¼ƣֵעǣֹգùֻڡﴫ˵Ұ֮Ϣ8ARMS͡սʿ2ȫʹá ⣬תƿǺܶSwitchҵĸҿͨãϢ浵бݡתһϵͳУʹһɱ󽵵ͣͬʱҲԸõϢİȫԡ -game ʦҪũҩ棿Ǵһҫ ʦΪ16ıΣһ߾Ȧ˵ҪʦMOBAˣѵҪҫսĽ׻˵ǣ봲Ҫ˵ֵҫֻӣʦ˵İְ֡Կ˸ΪϷƷġʦ˽ֵĻ硣ԴϵĹ磬ֱŮͨԣͼDZֽҲɽüֱdzмƷȵֵըʦеĸ߸˧Դţөݰְ֣СȤϷĻֻһ佨飺ǧֵӲεĺ㶮~εıй¶˵زͼǡʦMOBAɡеһЩӢ۾Ϥʽ񣬿磬ףȻôġû񣬿;֪Ǵ̿͡εĸӿ϶ǡϿȡ֮ĵͼˣMOBAϷijսͼӰǴ칷ͻҹ 䣬϶ǸͷʦˡʦȻǻغƵս񣬵Ƿʦ̹˸ȱһҫϷҿ϶ҲˡҾ˵ϷǡġƽսŶ꣡ԼţôСǣڴʦMOBAôྫʣע΢Źںţwzry365 -game ʦ죺ȥʱʦߣʱʦڷ 죬ƻ̵ϸʦappʱλԵȫΪлҶȹһЩʱ⡣ᡣȥʦԺһʱʢǰ˵ʦӪòƵĽƣϵʦۣ΢תһȦʦңʦĸܱ߲ƷߣcosplayְסһΨ磬÷ϷôھԽԽأȥ10ʦĿӡҶôӵġҼǵõʱϷʱûô档ʱݡһõµĻ磬˲һսģʽҿʼʦĿӡ죬賿4˯Դ˲԰ΡصԺ󣬸ӷ񡣼ÿ춼ֻ棬ʱڹ֮ȥҲʦһ¶ϻؼңȻҲdzʽս浱СһĩǾ˫ʱֻһƻһСףһһͨˢʦĵĪڳʽˡעüţΪ˳ʽ񡣵һеSSRС¹УʱֿļDzС¹˺þãżٵϲ쳣ġֳ˻Ĵ֮еƵSSRڹǶʱ䣬Լķ˰ʦڴʱҵ׸ˡʱ172·ݡҰҵĺŸҸ桭Ҹ˵ҵС¹Ūûˡӽ2·ݿʼұҲʦˡڣûȤȥʱ䰡ҪȥҪһЩ㵱ʱΪõĶҴǰϲħ磬һ档ʦħIJͬնʦϷģʽǽɫģʽħ񣬵кܴIJͬûʤڣʽҲȱԷḻĹ£ĸоûжݵġÿ½Ϸ˳ʽܴһЩȤ֮⣬ĺܻеΨһȤijʽijȡҲǵ͵ÿ£죬ʦ죬½šƺʽȡSSRʵһЩȻһûκһȥʱʦߣʱʦ죻ʱʦڷ񣿵ҪлҶȹһЩʱ⣬˵һЩ׵ʱ⡭д201792723㡣׷ڶĺšսƽɡȨУתϵߡ -game LOLȫܾ񳲾SKTSSGս̨ƳʵٻʦϿ ꣬LOLȫܾľ114վСǰʱ䣬ѾѱϹڸLOLȫܾijغͶս̨LOLȫܾľѾӽɣҲѱ˸ɵLOLȫܾ񳲾ijغ֧SKTSSGĶս̨ѱϵѾɵľؿSKTSSGĶս̨ϯ̨棬λڳ롣ֵעǣSKTSSGĶս̨ƳLOLٻʦϿȵģSKTSSG֧Ķս˫ˮأ·ߣмкӵ˫ԵҰҲ˳˵˫Ķս̨ƳٻʦϿȣҲˡǣܶLPLҿﶼЩᣬΪܾûLPLӰLPLȴ֧LCKսLPLȻͷɬ -game ҵϷ˼άӪ Ϸͷ΢ź:gametoutiao()й׼ҹʽý嶨Լýݵ:ϷΪˮޡ,ӳϷDz˼ȡ,˳ϷDzҵ,ԼϷ,Ҳؿ,̲סӡ˳Ե״̬,ʵӪռĿ,߶Ʒơ޷԰ΡʹϷӪĸлӪĸͨϷ̬ƺȥ͹,Ե񾭻Ϸȥ,ȥ֪,ȥĿ,ӶʹתΪDZ,һƷƻл ϷӪĺôϷӪҪܹͬʱﵽΡ䡢ϵûרעԼû֪ĿꡣÿһĿ궼ּΪṩϷĹ,ʹϷ,ʹû۵桢,Ӷ߲ƷҵЧ档һ,ϷĹɳΪ˲Ʒûֱ֮ӻֻϷͲֻһ򵥵ĻApp,һעеý塣ûרעͶ뵽Ϸ,ƷͨϷȻĹ潫ѳ嶯ݸ,ҾͻDZʶγɸʵĸϷĹ治ȴͳӾ,ܸû߳еΧڴϷ,沢Ҵ,ڹʽϵĴɸͶŵĻرʡûΪһ桰桱ϲ,DzŻעҪݱ ϷӪµ˼·1Ϸֲ(In-Game Advertising,IGA),ƷƺͲƷŵƵϷ,չʾ,ֱӰ쵽Ϸ淨,еӴû,ƷϢ2ƷƶƻϷ,ΪǶƳAPPϷSNSϷ,Ʒȫλ͸Ϸ,߻Ϸ۵Ʒ,ͬʱ԰ƷƲƷΪ,̼ҵIJȡ3ƷϷIPCo-Marketing,ƷϷIP,Խ򵥵Ʒֲƶ,ƷIPп,ȿ,ھIPӰ˿á ϷӪԱʵӪϷһ,ҪѰһȤĿǶû;ҪڹһЩջʽԪû;ҪûѰйӶڵúõӪ,úõϷһ,Ŀû,ӶΪṩο㡣ϷӪĺڶԱʵ,ؼǴٽû,ĿԶûȤ,ǵڶ,ͨⲿֶδ̼,Ӫֵ,ӶӪЧʡϷͨмԪ:ȼսʵ影ƷȰ񵥡еĻضӦ:͡λɾ͡˱֡1ϷԪ:ս++ҵ˷ܸʶ,ͨԼIJŬ,ԾԼڻ,ҵIJĻԾ͸ˡ,ܿʵʱ,òִ˲ƣȥ񡣵ȡýϺóɼ֮ʱ,ܹȦзչʾ,ȡóɾ͸С2ϷԪ:+ϲ+΢šܡɹ,ҲdzϷ˼άЩ϶Ľ,ǸϲϲĽ,оϻ˴һ,ڵĶͰ,˿С߻ΪġȷľϲС,ϷС˹ϵ,Ǯ,̫๦,Ƕ˺ܶȤԪء3ϷԪ:ȼ+ʱ+Ȩֱڻϱ,Ҳ˲Ĺ,Ҫһƾǡ񡱡ֱĿˡͻ䡢ͧ,˸ʱ,Ļϻͻʾ,һʵʱ,лߡϷPBLʱϵͳ(PointsBadges¡Leaderboardsа)ۿֱܵʱPBLĴ̼,ʱ,ֱֱ,ʶ,ĿС,ﵽԽԽߡЧFrom:ϷϷͷ΢:Alex_Xu_JwxdͶ:alex_xu_jxwd@163.comעϷͷ ԭ*ֵ*۵ ˽ҵѶϷ򿴸, Ķԭ -game ϷһȰ | ƪ̫ܻӭ,ϰ ýƽ̨ϷһȰ811817յİ񵥳¯!,ϷǶڿʲô͵ºͶƵ?ѶϷƵڴ,չʾһ/Ƶ,ҵĽ,LOLȷԼͷݵİλ,TOP10ռ7ƪ,TOP1Ķ˦TOP2Ĵ30W+Ʒ,LOLѲһϷ,ĵ羺ֱֲҵ,͸ϷȦĸ,˺֪ܶѡ֡,γһȦ,ý˵,ھĽǶȡɰԵҲġ,LOL¹Ȼ,дLOLݵýҲ൱֮,ν,ҪԼɫ,ŸӱĶԭġɲ鿴ĶNO.1:LOLص֮һ¶,ôС¶жԶ?,ȻܽˡLOL/ҫа,ȥ2ϷķѶ֮,ûϲʲôĵ?ܵġLOL/ҫа,Լȶƺ,TOP5ûзӰ,ǡŮǹ񡱵ġCS:GOռ˰,ŮFPSϷһֱҶעĵ,Ů+FPSϷƺȡ1+1>2ЧɡDNFһֱȶȵĻ,ǡLOLáƪĵ¾ܵǰ񡭡ʮֺ,һԸƷݼġ֡ƪ㷭ա½,STEAM϶һۡȻûзԼ,Ϊβܸһλᡱ,žûϲԭɡϷƵһ˫ϰ,NO.2LOLǴҹעص,¡ϷȤµȶǺܺõزĵ㡣LPLļչ,LCK,ټLOL6ٽ,ݵȶҲ֮,S7йսʱ,עȻһ¥Ϸ֮Ծò˥,ڻ۵˱Ƚ϶,Ϸڴ,պ,ýӪкܴڡһȰ,ýƽֽ̨!ÿ񵥵TOP5н!: ͼܰ: Ķǰ5: NO.1 300ñ NO.2 200ñ NO.3~NO.5 100ñ LOL: Ķǰ5: NO.1 300ñ NO.2 200ñ NO.3~NO.5 100ñ Ƶ: ܲǰ3: NO.1 300ñ NO.2 200ñ NO.3~NO.5 100ñ ý,ʼ beckywei@tencent.com ȡϵ,ͨĽˡ ýƽ̨,ӭλפ!½ om.qq.com ע! -game Rookieл˿ңҪжˣ ȻS7ȫLPLͶƱֻ˰ʱ䣬ȫͶƱ˵һҲؼʱ̣Ϊ󲿷ҶͶƱڽɣµļֻһЩɢɢͶƱԴĿǰƱȷLPLȫǵݡеλõľΪҵģĿǰrookieҹԼСƱֱǰrookieĿǰ38.4%ʱ߾еһrookieȻû뵽ԼܻôͶƱڸոգѱrookie˿ȺԷ˿˸лò˵һĺ~ȻĿǰյĽǻ֪ǿórookieη˿İҲǷdzĸжrookieS4ĩLPLһֱIGһˣ˵LPLûиƭ˵ģЩļҲڲϵĽS7ȫȥĻҲȽͬϾIGѵе֧LPLS7ܾ飬ⲻǴѣ -game ̵ȭŮԽɫΧ һλŮ Ҫ˵ʲôйʱϷǼ̵Ļ䣬ôFCϷͽֻԿԽǰףFCԲ˵ʹɽկСһЩϷҲֺһ١ֻͬDzеİҹɰ루ΪǸʱڣҵĿڴﶼûжǮġǸʱĽֻϷжԸϷΪڸϷУߵĵȻȭˡ ȭеŸָĽɫȭʽ綼֪IJSͰһ޵УŮԽɫĻһЩûнӴȭʵҿֻ֪֪衣ʵȭʵ統лŮķȫȲ֪鹬ŵ ߣ163CM أ49KG ΧB83W57H82 鹬ŵȳdzSNKϷսʿһӵгձŮӸż֣趨Ⱦͻ˲ҵע⣬䳬ŵȹתáԸ趨Ͽʻã񡣲֪Ƿϲλ崿Ůأֶǧ ߣ169CM أ52KG ΧB85W57H84 λ縺ŴٶȷӡһԼSԹΣǰǧףΪԸ͸¶֡ǧ׽dzʱҲͦˣвʹùս ߣ154CM أ45KG ΧB75W54H78 ڡ֮ȭ⴫ȭʡϵϷеdzɫһγ֮ȭ⴫УKOF96KING ֪ŮӸ񶷶ӲμӱλŸβſֵװŮȻʱ䲢࣬ȴҲʵʵǵӡĶ ߣ173CM أ68KG ΧB92W63H87 KOFŮƵĵط.ĶȴܱΪKOFͷ˱ʡȻûȫ¶ò.ȴ˲һ"һ"ĸоΪʱװģòȷʵKOFκŮԸ񶷼ңǴߵ֮һ.ҲĶ𣿲֪ ߣ165CM أ48KG ѪͣB ΧB89W55H91 Ϊ֪ȺܸߵIJ֪裬ܻвҲ˽ɫԭ֪ͣ鷿ԭϸ佭βԭԷʱŮ˶ż񡣴ű¶ԸеIJ֪ʵȴһ͸ӵԸķҲǰһ³ ߣ168CM أ49KG ΧB88W54H85 λСǸ顷ĵdz18дģȭ֮еdz֮⣬ҲڶǴ˵г̨֡ҲǷdzһ㣬ڵʼʱ򻹻е˼Ϊ̨һһhĸо23333 ߣ168CM أ53KG ΧB82W56H80 ȻǼˣȭзΪŮԸ񶷼ҡԱĴȽ϶ࡣ94KingͲ֪һΪӢ״εdzҪ˵ĻССӡžǣKOFŮƽȵĵطһ֤ŮҲѷ˵ĴһdzҪսʤԼִŮӡ׿ ߣ177CM أ58KG ΧB88W57H87 ¬飬ĸĽ㣬һüһ۶˷顣˳׿96չֳ׵ʵжıɱѹԵĴ򷨣ٶȣϺ֣˼ߵIJпòһơ ߣ168CM أ300 ΧB92W60H89 Ź־İúõһɫUC֮ضkof14Ĺٷ趨ʹһдģ֯NESTSĸˡ񶷷ǡȴʹķʽʱ˵ܵСЦкܶ࣬׽ĸԡԿǷdzܹٷϲģΪǸˣ԰ظߴ300ⲻܹ˵ŶȻᱻɵ~ ߣ160CM أ45KG ΧB77W54H79 λʹйȭŮ糵ijdzҲڶǴ˵Уںkof֮󲻽ûбƻ˲٣ʽƶԴ׷¼ʱҪѭȷȤ鷳顣ڡЩձձóԵĶڵأɵ ߣ169CM أ48KG ΧB81W57H83 ȭ2000СBOSSҲѡеѡ˽ɫͷɫģֻսʱɫɫijֱ䣬ʱҲʹã˶Ľ·趨ǡKĵˣĪؾͿʼˡ˫Kɯ 174cm 58kg Χ: B90 W58 H86 һλ֮ǰĶͬǷ岼ʲĺᣬܹʹùսңԶ֮ΪжԹϵԸƈЦͨҲDzٴɫ Ҹ㣬ʵӦͲSֲ¡ Ȼ˽ܵЩɫ⣬ȭʵлǣϲһأϽԺʹ۰ɣ -game ҫ¹ϴΪüѧ ҫѾԲ!ǵҫʹ¹ս֮ҹҲعԼӢ!ǾǴС㡪!ʵС֮ǰһ֪ΪӢ۾Ϊ˹ͮ!¹Ϲͮع֮!һ˰dz¹Ϻ͹ͮС!ʱսϾͿԹ۲쵽¹Ѿ͹ͮڽ֮УҲֻ!Ͼÿֻ͹ͮڣͮʹòļӢۣר¹ϵ·!̸ķʽͨҲడ!ˣ֮ǰڳĺһֱ˵Ǽٵġ¹ս֮ҹ͸¶ԼĶλDzֶνӽʯ!ǰdzСžǾ㣬ֶDzֶΣ͹ͮڵ֤ݾ͸ȷѽ!û뵽¹õķʽҲǴ!Ǻúÿúѧ! -game DOTA2ո7.07b汾ˮţ ҹʡܵ553ڣϵC5GAMEԼӭͼ / ҹ / ҹʽ賿DOTA2Ѫս֮º׸MinorԨʽĻLiquid2׷3VG¹ھNewbeeVPֱλе֮󣬱DOTA2ְҵսӻ£Ԩ󣬽DOTA27.07b£ͬڡѪսֻ֮һƽԵС£Ҫϸ汾imbaĵзʦ֮ԴаӰ顢Ӣ۽ͬʱǿ˱徫顢ʯ۽ʿССӢۣºı徫Ҳʽ뵽ӳģʽС뿴ϸ־DOTA2 7.07b־ ϷԸ- ͵˺ʹ25%40% Ʒ֮- ͼֽ۸17501675- ׽ʹ67ս- ӳɴ50㽵45Ǵ- ÿ˺60115㽵50/90㣨Խǽ Ӣ۸µзʦ- ɳ1.51.3- 1512- ܴ26/34/42/50%20/30/40/50%- 15츳˸ɿػΪ+15 - 20츳+10 ȫԸΪ˸ɿػ- 25츳+30% ܽ+25%- 츳˺250%300%- ޸츳Աѡе֮Դ- 10츳ȡΪ+100 ʩ- 15츳+150 ʩΪȡ- Ұ֮ħĴ60/65/70/7580- 20츳+120 Ұ֮˺+100- 20/30/40/50㽵16/24/32/40аӰ- Թڳ˺ÿ0.5һΣ- ȴʱ20Ϊ40/30/20- ˺75/150/225Ϊ60/140/220- ڵʩ800600ʿ- 15츳+40% ȡΪ+35 ׿- 20츳+1.5 ʱ+2- ׼⻷Ĺӳɴ20/26/32/38%16/22/28/34%³- ָ2/3/4/5㣯4/5/6/7㣯ɯ- ɳ1.951.5- 10츳10% 12%- 15츳+10% ȡħ+15%- 20츳+2 ʯӳʱ+2.5- Ѽ˺35/50/65/80%30/45/60/75%- ݴ20 + 3.618 + 3.2徫- ¼ӳģʽ- 幥ݣ˺ϵ0.250.5- 幥ݣ˺ϵ0.5/1.0/1.5/2.01.0/1.5/2.0/2.5- 幥ѣδ0.250.5- Ա任Ķݺ3/4/5/64/5/6/7- 10츳+20 ƶٶȸΪ+300 ̬- 25츳+800 ̬Ϊ2 ̬- 25츳4 鴬3ʯ۽ʿ- ڻʹӢ۹Ϊ24/42/60/78㣬˺ζſԴѪȣ- ׶ܳ˺ʹ8/10/12/149/12/15/18%- ׶ܳڻʹӢǰƶ225루ԴԽΣ- ׹Ҫ1.2ʩʱ䣬1.2ʩ- ׹תٶȴ105/110/115120- ׹ƶٶȴ550/575/600600- ׹ƶٶΪ̶ֵ- ׹˺200/250/300200/275/350- ޸ƵĹԼߵλЧ- 25츳-12 ׹ȴ-16- 15츳-4Ļȴ-5- ݴ3430ѻ- ڤһȴʱ80/70/60Ϊ100/80/60˹- ֮ӳɴ12/24/36㽵10/20/30- 25츳-8 籩֮ȭȴ-9ʥô̿- 10츳+200 +250- Ķ˺100/125/150175/250/325СС- 4- ץȴʱ40/32/24/1615- ץĽ˺70%100%- ץԵзλĶ˺25%30%- ץĹ2/3/4/52/3/4/6- ĹٶȽʹ-30Ϊ-20/25/30- Ͷλܵ˺20%30%- 10츳+25 +30- 10츳+15% ħ+20%֮- ѾĻʱ7뽵6- 3ά- Ӷ޵Ĺ25/40/5530/45/60 -game ȭͷٷS720ǿѡûϰ?:̩ȭ! ڸո,ȭͷʽȫͬ˴˴ιٷS720ǿѡ!Ƿֱ:LPL6ϰ,ֱ:EDG SCOUT(19)EDG MEIKO(9)WE CONDI(18)WE MYSTIC(11)RNG XIAOHU(5)RNG UZI(14)СǷLCKѡߵLPLѡ,ǰܹעEDGҰȻûϰ,ʵе,Ȼвѱʾ,ȭͷȻȭͷ,ѵ̩ȭ?Ҳע⵽,ǰ10,LCKռ7ϯλ,ıȶɭǿ˵ʮ,ʦŵ˵20,,LCKռ10ϯ,һ밡!ٷǿ֮,֪˴αڵһfakerܲܶסⲨǿ?񵥵ַ:http://lpl.qq.com/es/act/a20170919top20/ -game ҫΣͽʦҪǧ֮⧲ɾ ѴҺãӭտҵҫǵѡͯСҫһʦһ飬û¾ʹԼһ֣˿ܻ͵ƤɶģǽȴһСЦõʦͽ¼һͽܺһʵʦ¼ǺģǼӼСŴϷ֮ʦͽ죬ҲԿʦDzģϷϢСҲôģȻȴе⧲ǽſͽܿʦڴ򣬾ţҲ֪dzʲôԭ̽Եʦһ䣺ʦҸǧ֮ɣλҲһЦȻʦȴˣʦûϢͲסˣ̶ַһ䣺ʦȴһĵϵͳʾʧܣӶԷΪѣԲۣʦҲ̫ʵ˰ɣҪƤɾСò˵һ666ҲģΪʦһЦһֱڷϢֱ˵ԼҪƤˣϷֱȽҪȻʦû˼ӦҲǡ߳ŭ˿¼СҲеˣ֪ôûأƪµ˾͸һˣɽģˮͬһʱ䣬ͯ㣡 -game LOLСɣ7нǰѹƣ ֪LOLĶǺҪģеûбץ¾·̵߱ԭҪڶԷƾ;ãѩԽԽ ֪һЩӢ۵ļ⣬ҲǺҪһõģȡѪϵƣöԷ߲򲻵òسDzһ̶ƶֵķôڶ߹УӦôأ1.IJģ˼ǿֲļ϶ȥĶ֣dzõķڶͼʱֱչȥĶ֣ö©ǵѪѾء2.ϷУ㾭ԿһĻijӢȥĶԷһףȫУȴԷѪĵĸءкܴСǵ˺Ҫ֪ǰڣСĹDzһӢ۵ġ԰ڶߵĹУվԶ̱ĸ㱻ʱСͻɹԷޣȻһĶ֡Ƕ˵Ʋر£ҪŴ󲨱ӲնԷСĵòʧ3.سǰĵ׼سʱԳûĶ֣ǧҪעԼ״̬Ҫ˱ְװ״ܻͳسǰijǣѪѾûãʱѪͬһѪֻҪ֤ԷҲ൱ڰװױĺܶʱؼңҲͻ˷ͬľ顢ãؼҵĻٶȻصϾͻȡѪϵƣ׬⡣4.ȼƴ˻˺֮⣬Ӣ۵ȼڹؼ(3.6)һܣԿȼƴǷdzҪһͨ·7(ڶһ)15(ǰ)ǾͿڼ˲ȥֶƴ(ҲҪעֵȼ)öֲԴĴʵ˲ֱӽ̶ˣӶСɱԷ5.CDƴCDָͨԷؼܵȴijЩӢ۵ĿơҪսܡһЩؼԼܱƭͿԴ󵨵ȥһס6.˻ѪľĿơܼظȥͬԷѪĵ˵ͬʱԼܵ˺QWAEEAWQEȣͨܵĻͬ˻Ѫ7.նɱ߻Ѫնɱ߻ѪһԻɱΪĿļɣָͬԷѪѪѹ͵ԼһױߵķΧַȻԼѪҲ⵽ģ׼ὫһߡѪնɱҪǶ˫ܵ˺CDһ˽⣬Ȼпܳ˺߷ȱֻɱ¶߼ϣҲֻеһС֡ϾʵϷлֵ̫̫࣬ÿǴҰgankӢۿƵ԰üɣǶȡʤ֮ -game жǿ?޸MAUǧ (ϷGameLook,΢)ϷߡѶӦһױӵ,๦APPΪܼ,ʵʻרŶӦкܴ,԰Ϸ޸ĵȲʵĽɫȻȨʶ,ҵձΪûϰѾиĹ,Ͽƽûȫʧǰ,ڻлQuestMobile2017＾,Խ˽9·ϷӦMAUǰ50,һͬϷӦòݹ۲,вٸӦ»Ծûƽǧ,ϷĸӦ,ӢˡҫMAUdz2500Ϸ޸MAUƽǧQuestMobile＾,»Ծûǰ50ϷӦ,Ӣˡҫ֡СϷ4399ϷС«TapTapҵӺϴԽһ8ܻӭѶϷĸӦ,һ3,ֱ8941ӢˡҫָΨMAUֱ𳬹ǧǧصIJƷΪѶϷĸӦ,ΨһϰǶҵӡ桶ҵ硷,ϵ˾ֹͣȨ,˵Դ,ƺδԶҵûɴΧʧһϰĹƷСϷTapTap4399Ϸеȴ,֮Щ,СϷMAUﵽ2300,˵TapTapٴС,MAU7004399Ϸо,MAUΪ1200򡣶˾ȵ,ԵϷ޸Ϊĺ«ϰ񡣸״ͼ,«9·MAU24,ԼΪ900ɰAPPָ8·ݵݿԵ֪,«¶豸Ϊ770¶豸MAUϸ,,«׶Ρ9·ݰAPPָʾ«¶豸½,5һ»,QuestMobileͻ,Ҹò顣ڻ˿ںʧ,Ӧ,廥Իȡͬ½ġ«һ޸ΪӦ,вٵ޸ķĸ衣,«ͬһϵвƷʴ«3¥,Ӧú«ҵ硣Ȩ׼Ȼ޸,谭ֱ۵·ڰȨ⡣«޸ҪȡֻRootȨ,Ӳܴ̿ڳƤĿܡ޸ϷȻֻڵϷ,ֻҪ޸ıлַƷȨ˵ĺϷȨķա«ܷյķǽΪҵ,ûЭȡ,Ҳнӹ,ݾΪûϴܷܵԭ򱣻ܷԭָڷȨ,ڽӵȨ֪ͨ,ṩ߸ݡϢ紫ȨϿȨƷ,еΡܷԭƵվӦ,첥Bվ˷,Ҳûɷӳ,«ܵͶߺɾԴ޸Ϸ,«ͬṩѶƵӦغݺ«зĸ˾,ĸ˾Ͷعɷ2017ȲƱ,2017ϰ,«ϵAPPۼưװͻ1.1ڡŵ̳ۼƷ13.3ڡȻ޴,ûϴ,ѶȺܸ,Ǻ«ĺܺ,Ȩס20144,һҲΨһһȨ20159¡йĹʾ,MOBAΡ֮սз̶,ߺ«зַȨ,֮ߴɺͽ⡣ͬ·ݶ类½ĸ櫻鿴,׼5Ԫļ۸չ51%Ĺɷ,Ǻ,²̸«ΨһһȨԽԴϲĽճ,ͶҪչ,ϣٷɲľײ޹ϵ,ͬҲͺ«ǰ겢ûûйءݺӵ,«ʱܴŸӰ,ҲһûоƷĵ·Ͽ񱼵оֵ9MAUаǰ50ϷӦ,޸ֻܵк«һ侺ƷձϷ޸ȵȲټ,34ǰֹͣ˰汾¡ȨʱٺƷҲ伣״,ûк«,޸Ӧҵͣ״̬«ڻ޸ҵĴ,ζ˾Գô˻һҪ̺«ĺ,ʵ򵥡«ܰȻô,ĸ˾ĸ˾һáǰᵽ,«з,Ƕȫӹ˾,Ͷعɵȫӹ˾ͶعʵʿΪʿع޹˾,ʵʿΪйĻʲල칫ҡ20161212,Ͷعͬӹ˾Ϻܼ 4.1 Ԫù100%ĹȨ2017321,Ͷعյʰ,׼չĿʲ,ͶعɿʼȨչɺ,95ͶعͶ4Ԫ,ṩ2Ԫȡʵ˵,«ǹʲ«ͬʱҲ,ܹϷMAUǰ30,롶ʦͻ̨ͬ«Դ޸еչܴﵽޡʵ,޸Ǻ«ҪӪҵ,һͷֶΡ«ȻȲ桢Ҳûȡá«ӯ,Ȼͨ޸,Ӧ÷ַݻTapTapһֳ,«ԿΪĸ˾ṩҳΡΡH5Ϸ,зһ,㲻,Ҳܴɹۻرԭ,޸Ӧðҵ߶ȳѸлĻ,ҳͶϷĻԱ̭ˡ«ij֤,ʱڱ仯,Զûбôסƫ޸ҿ᳤ܻڴ,,ʱûвƷ« -game DNFûʷʫ? Щɢʷʫ׾! ֪ŵʱųһۿ壬ȽϸߣôͻͬȽϵͣųܻܾôûʷʫװֻʷʫɢβְܴҵ?ʵҪȽϸߵ壬ЩʷʫװǧҪֽ!˵ӳɱȽϴǼӳ466徫ӳ470˵ְҵȽϴ󣬶ڸ̵ҲDZȽϴԣһܵԻû磬90ʷʫͷԱȽ죬40-50+2һļӳɣԶ˵Իȱ١˼Ԧؼףؼװ130112+10%˵װڽɫӳɻDZȽϴġսȣս15-40+15%Ķ˺174156˵ҲDzɶõʷʫװͳ˧սЬЬӵļӳɱȽϿֲ˵525˵516һЬӾͼӳ500࣬Ƚϴ֯ͺ׷߻ȣ90ʷʫɢļӳɱȽϸߣԶڰٷְֱҵ˵õ90ʷʫɢ90ʷʫɢУӳɵҲDZȽ϶࣬ҲDzɶװ90ʷʫɢװڽɫȽϾ޴󣬾û90ʷʫװЩʷʫɢװһԴﵽij̶ȡʵЩɢװﵽǧ﹥ﵽŻûҪ?壬ƺܶųҪ!ԣû90ʷʫһԴţЩʷʫɢװǧҪֽ! -game iOS112죬ˢϷ· ߣGameLook920賿һ㣬iOS 11ûʽ£App Storeʷһθİ棬ӦƼƺعⷽʽԱ仯°appstoreƻ༭׫дTodayԼϷǩֱȡѰ͸Ѱڲ˵ֻʾTOP3һϵش仯ֱӴ˹ڰĿٴϴˢɵIJƷѸʧgamelookϸ˶iOS 11ǰ죨919ա921գʵʲƷ츲صı仯һ㶼Ϊһ仰ܽᣬǾѰ˲ع׺ŷϷƷʧˢʼٵľǾʵʵϷƷʼعǰСApp StoreİѰ񸶷Ѱֻʣʾǰ3Ҫ򿪲鿴ĶؽתʣʹмֵĻƽλֻʣ3ҫ̾1ࡶ֡ҵ硷ֶֶƲ磬ˢҪĴ̫ߣúͬˢƷˢƷ˶𲽷ˢ919գѶµġֶԪµġֱ֡ˢƷѰ912App StoreİĽ죬921աֱֶ֡͡Ѱ24ζмʮѰλóԭTOP10ڵġԽߡǰ10ͬʱĿǰڵˮﵽ8000ġ«ޡѰ7ĴǡɱسԼΣǡPCϷɱȵøոպãȻĴ󡣲鿴919հ񵥣Ѱǰ50ڴƤˢƷҷ˻dzˢʹõƴʺšGamelook۲죬919Ѱǰ50У˻Ϊƴ׵IJƷ35һѸ½10Уǰ50IJƷ619»½˻вƷֻ4ЩڷѰˢ񡣽壬iOSѰTOP50Ȼ˵ĿǰiOSְ˵ֹ̾ͣˢĶѰ񡰰ơʧijԳƷڡӲѰأ919ոѰǰ50Ϊƴ˺ŵIJƷ24920գ˺ŷIJƷȻ17iOS 11ǰ7Ѱٵ25вٲƷл7½ǰ50˻Ʒֵעǣ21ûһ˻Ʒ˸Ѱǰ3ζŴ󲿷ԸѰλˢҲ𲽷Ѱ䡢ѰơȣѾiOS11ʧijûг̫仯ԳƷĵ̡ȻiOS11ȡ˳񣬵ʵֻҪûiphoneֻiOS11ϵͳϰiOSϵͳappstoreȻںܳʱ䣬ͬʱҲܱappannieͳƹ˾ץȡ飬iOS 10ܺû͸ʲﵽ48%1ºʻӽ70%iOS 11²2죬˻гûͶȻܿ񵥡ʹδѿ϶޷ΪϷȻʧȥгӪļֵiOS汾ڣСְҵ񡱡ԳϷҲðϷȫװֱ򲻿ȡģҲҪģȻ̣ԱʱϷꡣδԷ˵ǷԳֵ˾⣬񲻴ǻᱻרҵʿ鿴Ѫ⵽DzҲ֮󽫼ڴƫԲƷApp Store2а£ƻƼƵĴ¡ȡ񵥡ƻû̵Ȩһеıƻķַشı䣬iOS 11ӻ㷨Ƽơöɸ˹Ƶı༭Ƽƣұ༭Ƽкǿļֵ۵򡣰ȻûиϽɱüأѼѿͨappstoreȻĻ볹״ơĿǰTodayΪƻƼģʽʵdz΢ŹںšϢʽÿ̶34ϢϷռλʵҲƻеƼȻ֮ԡɷӡİޣҲҪ󿪷˽ʲôIJƷܲƻģõĿƼλԴTodayѾµͣƻͷáһ֧ƻ¼IJƷƷܱƻԼΪרƼлƻȫ档ǰùiPhone ƻ＾＾ϣƻARKitߴARϷThe Machinesͻƻ顣ڶǶϷƻΪϷͷƫСľƷѲ£TodayУ༭ƫΪϷ׫д¡һЩӵȤ߹¡ΪƷ塢ʾеõӦøܵ͸רҵĴԿ棬纽ŮսʿȲƷȻԵƼܹͨרϼķʽ뾺Ʒһͬõع⡣ʵƻصĶԣǡǡǹΪѷIJ֣Ҳǽ̶ѺעϷڳԸƵġѾ൱翨ơMMOMOBASLGǶԪŴͬʻƻȱƼ塣ͬҲһ⣬źӱĸƻĿǰгĵϷҪƻƼ֮ѡiOSİ֮󣬹ҵϷҪƻٷƼѶȲСƻԼASM۹ٳٲйҲ೧̶ʱֱЧֱӵappstoreϹ̱ʵûѡѡ񡣸İApp Store򿪷߹һϷԵôֲڡԵó¾²󣬶һûɫ -game ſ£ѡҵʮ9ֱ ҶϷҵΪά硱ϷҲκϷӦеԪأ˵Ů˵ϷĻ涼ǻصϸһһȥоģһƬ˴ҶϷҵΪά硱ϷҲκϷӦеԪأ˵Ů˵ϷĻ涼ǻصϸһһȥоģһƬˣҲһƴնɵģ̵һ⼸ʮǧҪſŶ 1.ϷĴź೤IJ࣬ȫǣŨŨһüڴıӣɫ۾ҪľȻǴͺͷǵŵģ״һȭĶemmmmm...Ҷġ2.ҵΪ֣ܶʬ¿ӽӡжֵɫԼöֻƣΨһܽܵľûлƣɫ㣬аװߣѩ״ߵݬ 3.ʬʬв̬ͬ»Ϊͨʬʬװʬ꽩ʬеŶʿȵȡʿһ꽩ʬһֻϣʵ뻹ͦɰģǿƶеЦá 4.ŮϷŮ׺ʹʵеƣ񶼳Ů׻õȥŮ׺ʹͬľɫijۣɫħñɫijѥıһ룬һ΢Ť 5.ʵʵе㣬ó෴ĴֻһЩɰϷİϷȫɫаֻֻ֣۾оûɣܵ˺ʱī֭ 6ϷһС͵ƳǵƤɫģ۾ǺɫģҪҪȺϵĹﵰ֮УΧںһ𡣶ҵܵ˺ֵ֮ᷢ 7.𻵻ϷĵĿ˺ܶô춼ҪˣΪɫ̫ˣǵƤɫһצӣеʱ񶯳Ļ档 8.֩뻵Ϸ֩ҲһȽϳǵɫģںڰ֮Уô۾ᷢɫĹ⡣һ֩ǶѨ֩룬ǵɫģ۾Ǻɫ 9.ʬͷϷýʬϷԳƵϲֱˡԽʬֵ̬ģȫƤΪӽ𣿶žŽо䣬ֲײ࡭10.½һֽˣƤǻɫģΧּźܶɫĵ㣬ȫȼӣ۾Ǻɫģϸܲô׷֡ ʮ־ǻϷȽϳһЩˣĿе죬ûУ߻ۡϲ¾һҪQQȺ()(δ)δδδδδδ߿Թעdz΢Ŷ벻ľϲӴ~ -game DNF:һλϷһųĸʯԺһ DNF˧˧ԭϽϮתأΥ߱ؾͶ߾ٱţµļ~ллףϷ ˧˧ ѰҴװรҪĿ˽ ϲң͵Ұ~ҵԸϷңȫҵ~dnfԨǺܶϷңɹ֮·ıؾ֮·̫ңԨſڣ90ʷʫAĵһȱһҲǣǣеңһλܶΣĻҧгݡǽ죬СңĸͬʷʫǾʯȸҿһҵĽɫһܵĴŽ󣬽ϸ꣬ħսˢԨҪˢĽ꣬ˢԲۣʱdzȻϽͼټԣʯԣҲ֪ʱ20%ļ18%ļܹȻ60scdȫ̡ټάǿֲ90һʷʫҲۣ顣ȻΪ£Ҿͳˢˣ˯ģΪսߺħƱȽ٣ˢ꣬õۡٸˢƱ̣սҲʯʱҾ;ˣǸϽȥ12࿨Լ15.800wŪ֮ˢ˼ѣһֱҾͷˣףս߽ҪͷˣܼˣǻħɡûƭˣʱʯԺҾͲѵԶˣԲۣô6ô֮ǰһλϷ˵ҪһһװرԵͻһֱȻ뵽СɫÿδţĵĹž޽꣬ڣޣ̰֣裬룬ڸˢһ£϶ٳ˰ɣֵǣţ66.ʱˣҵDZģĪı˰ɣ£Ȼǻҽͼԭˣֵܿĺţȱʯ㲻ᣬûʯɣȻССĴ̼һ±DNF˧˧ԭϽϮתأΥ߱ؾͶ߾ٱţµļ~ллСֻ˵ร˵ĵ˵ģһ죬㷢ԼļţųһʷʫôϽĸɫȱϽˢȻֻȥˢֻһ絳ؼģһҪԸรʵǺܶ˿˵þûԸˣش˷һףԲαDNF˧˧ԭϽϮתעԴΥ߱ؾͶ߾ٱţµļ~ллףϷ ˧˧ ѰҴװรҪĿ˽ ϲң͵Ұ~ҵԸϷңȫҵ~ -game ɱ±رʵüɻ ɱϷҪΪʤߣļDZزٵģСҴǾɱʵüɻܣֲ֪ɱôһ°ɡ1.Ϸǿƻģȷź˵ĻҪɵĿţֱǹɨרθֲħ2.ͼʱ⵽˹һʱƤλһߣŵзԤСȻȷǹλãѸзв⵽ſ£ġ3.ͼʱסaltʱ۲ΧҪҾִҾղͼ͵ϮĻС4.㷢һijһҪӱСģ˺пڸѹΡ4.һطѹˣеӦöǿŵģ㷢йŵš˷dzпܾͲ档5.ϷеĽŲǹdzķֱǵ˵ĴλáѶʱעŲ6.ɡʱܿ?ÿɡĸλãлҸԼһطʡԿɡλúҪΪÿϷɻǴλýֱ߷УеҶܷɻϣԷɻĵصٻȥǿһ㣬һЩɻ޷ɵĵص㣬ͽܹȥַġ7.ںʮҪܼͼ񣬺ڰȫΧСڷӸšʱҪװѵ˱ƳԼλʮֲ¿ͻΧ8.׼ʱ԰סshiftʹ׼ζԶĵʱеãսûá9.θӵĵʱС򣬼װȵطûӻ߳棬ðǹлģʽӦڽĵˡ10.Ҹ˱Ƚϲ䣬 Զǹ+ѻ>Զǹ+>Զǹ2>Զǹ+ӡϲãá11.ϷУȭͷ˺ҲDzСģɡûǹȥһ׾ȭЧȭͷԴ70-80ҵѪ()12.ڻɱҺ󣬲Ҫȥ˺ܱܿǹȷû˺ȥһҪſѾɱ˲֪ٸվ.12.װԽԽҪҪ33ͷΪԼ޵ȥ˸գϷװǹװƵʱױƣܻ14.밲ȫرԶҪøе˵עϣϷɻľDZ¶λ.15.4뱬ըڲҪӿһ㣬ڰվͻᱬը.16.·Ǽģ￪ĻϷеϼģԲҪô³Ͳ񡣿һЩ.17.ϷУһˣڵ״̬˵жѣֱ˵ǶѾ(ͷɱֱûж)18.ʹø߱˴dzԿʱǿ԰סҼʹõ˳׼.19.ϷЩڵǹе߽˸ʱǰtabΧһΧжʾڱ. -game YYһڽǷĪ ƽ̨֮IJ 껪Ļ,YYͼ1500ڽǶ֪ŮĪϢ贫ⲻǵһҪڽǷĪ,1·,ϢYYԻʵҪ1ڷĪ,ᡣҵ»ӰYYֱʱʾ,Լ׼ѷͳӱ1500򽫷ĪǩԼYYⲻǵһ˵ҪڽǷĪ,1·,YYԻʵҪ1ڷĪ,ᡣȻ,ЩϢ,޷֪ڽǷÿԿ,ļֵȻӸ߲,۸ֱҵ侲Ѿˮ,ԶӰ첻໥ڽֱӵ۱ڽڸж,2015-2016,ڽǼΪֱҵ̬2015,㻨6000ӻ6;ӶPisܱϷ;8·,ʮ;ɭ붷˺,ɭս;,աۡȴӶȫ2016,ս̰˯֮,¾ն٢;2017,۶㡣лè顢ս졢㡢ȫ񼸼ƽ̨໥ڽ,βȡֶ,ȻⲢֹƽ̨ڽǴսĹؼڽ޷ž,ΪֱҵĻ,еɫPDD忪Сǵ,˿ȺӴ,ȲС,ȫֱʱСֱ,С,ȫֱ˵,ֹעǡǡƽ̨Ҫġ忪Լ㡢1ǩԼMISS,ļ,ҪΪһԽԽ,Ŀǰӵмʮ˿ĴȻΪġ2017ع,ҫĴҲһƽ̨ע,ǰûա߮ΥԼ۶,⵽⡣ڽǵżҪָĴС,˵㿴һΥԼ۵һƽ̨,ԭƽ̨Ҫ⳥!ǵ,ƽ̨Ƕ,һҪķѾ޶,ûΪƽ̨ⷢȾƽ̨,Ȼ١,⼸ΥԼ۱ϷԺİټ,ûһλľ޶ӷԺʵҲ˵,ƽ̨ЩΥԼҪΥԼ,ɷԺоʱΪǰûа,ֻܰʵʹоһһʿ3,ôԺоʱͻᰴʵʵڵ3Ԫȥо,Ȧӵаļֵȥоƽ̨ûиķסķչ,ֱƽ̨ݹѾdzḻ,ֱ+ֱ̡+աֱ+Ԫȵ,ƽ̨ݷ治ϳԡڵ,Ϸֱ㳡ֱ,ڶȻռλ,ƽ̨ȻҪסڡҲʾ,ҵļ۸ֻһ,ûƽ̨;1,һ,Ϊ׬ǮġΪֱƽ̨صԴ,Ǵ,ķѪڽǿͨöƽ̨һ겻ƽ̨γǹؼȻڽ¼˷ʤ,ôƽ̨ʲôֶܽʧԤ?,ĺͬԼȫȻĿǰûгֱ̨ΥԼķɷ,Ȼƽ̨ڽǵĵһսġLyingmanǵͰ,JYѩ,ս˽ĿפߵľءҲսһѵ,תΪƽ̨ܸƽ̨,ƽֻ̨Ȩֻǵһ,ҪٽתΪ˿,漰ƽ̨̬˿ͨȤСƽ̨,תΪƽ̨;ٱĿ,ǰͨĿ,ĿIPһĿ,ôΪƽ̨IPԴԴϵ,ҲļֵõַӡڽǶ˫ƽ̨ʧ,ֻƽ̨Ǵ,ʲôʱƽ̨ڽʱ,ֱҵӭΪʱϷƵ΢:sinagame2014Ͷ弰:zhangmiao1@staff.sina.com.cnһϷĵγάע -game LOL-S7:WḘLCK＾񳲾кܺ ʱ1029գӢS7ȫܾWE 1-3ǣԵ󣬹Դչۡ- ̫ûˣûʲôۡ- ʵʵ˵ij̫ѿˡWEȫһֹס- S3 - SKT S4 - S5 - SKT S6 - SKT S7 - /SKT վıߣ- ǣΪ˹ߡSKTķ֮һбֳǻǿܱܵġ- ǻӮȻS8S9S10SKTĹھȻS11 ȦӽһֱֱLOL군- Ψʸ𽱱սӡ- Ӣ˵羺90%ͽŶйأͬĶһֱӮǺ¡EDGܴMouseClearloveٴӮйSKTSSGõĽŶǽӮȥǵѡʵ̫ûզ- һFaker- ȷSKTܴһֻΪFaker- FakerSKTRNGıй¶carry- ҵֻ˵ǣҵ궼ڽкSKT֡SKT3-2ʤ- LCK＾񳲾̫ʺǵĺˡ- ڴⳡšSKTSKTʵSKTš- ţˡû˻ǵƱˡ -game ҫ|ӢҲʷ?!ԭ˭?! ҫӢۡʷʵ,ǾǹʫάԪǰ 70 ,ά(ȫ:ղ˹ά˹),С衷ũʫ͡˹ǡ,ƽλѯٶȻάٿơڵ,άһ·˵,ΪʫƪеҪɫ 2001 ,ձϷ˾ CAPCOM ƳϷ˹ĸͽά,˶ȡԡ,ݽǰ˰ħ ˺,άΪɫ,ڶϷжеdzڡĹٷ趨,ҲСϵħ塱ħֻѪꡱ硱άҪɫۡͷһë:άڶ,о:1. ῳһһ:2. ѵӳȥתת:3. ɴ׵ħ:,ϵлһɫ»,ֲƺɫ޶ż,ġƤ,ҲֲΪص㡣˵,ҫġзdzȷԭ -game ɽկԼϮǹ̫Ӳ Steam ̫ ֻϷµķչٶȷdzѸͣҫΪһϵֻϷʱۻ˴ûӯҲdz¡MOBA Ϸڵ羺ҵһdzҪɲ֣ԱȽǿֻ˵ļ򻯲Ҳ͡ȻϷͺƹ֮⣬IP ҲһϷܷǰڻûעص㣬֮ǰȵġ桷ǻ Dota Ϸ IP ˽⣬桷ϷûиܣDota ͼ IcefrogκȨĺǰ Dota 1 ǰְҵѡ 09 иƽ̨ҪĵЩ桷ƣԱᵽġԼϷɱΪ IP IJȵϷֹ׫ʱ䣬ǿ App Store Ѻ͸ϷϣԴɱΪϷҪҫţҶ졣Ϊʲô˵أϷϷģʽ֮⣬Ϸɱ޹ϵϷ£ϷڳŹڹ顣һЩû۽ͼ˵ֱۣ˵áͱԹ˸֮һЩεġˢϷҶؽ棬IJ࣬ѵһȥ֮ҪŶӰСʱ߿ 10 30s ĹϷÿζҪ VIPϷ֮оûʲôѶȣܻνҶ NPCƥٶҲܿ졣ʵϷΪʲôɵϹ App Store аˡɱĴȹ⻷֮ûԭ򡣸 Steam ĵ˺ŸݣɱҪȺйռ 42% ңա 1400 㣬йҿ 580 Ӵûϸ߻Ծȣڡغ˵൱ʡһѡһϷDzöҲ֪ǻϲܲóά£ΪӪֶΣ඼׬һͰͳˡ״͸ǰҵһҵһвٶЩһ·ɱϷѾһʱ䣬ǹϢٳûг֣ϷڵĻҲãҺ͸ӳٵһֱڡSteam ЩзĹǸ֮ⲢûʲôĽ취ڹҸӳٵα궼ûκáĴڲһ̶ӰϷӪ״Ҳпͬʱεܱ߲ƷĽ̡ȵ IP ûãȻ׬ǮáһƼվ TapTap ϣĿǰǶаϷԳԼʹɱΪԪԿ׿ƽ̨İװѾǧ򼶱Ϸʾڽ App StoreѾϼܵϷΪаǰʮ˺ܾãҲֵòˣغڹЩֱ룬ҲϷڵĴ롣App Store аĴĶDzģǿ༭Ƽ鱨ָ϶û˵ֵܼаҪ߳١jdong_news -game Steam 92%ϷƼȭʵľ񶷴 ϿעҡJ˵ϷΪڴ֪Ŀ϶ǡȭʡȭЩϵСһЩϷ˵ɱֱܣKiller InstinctϵҲDzѷȭʵƷʽ񶷵ĴڴǰҪxboxƽ̨ۣҲ̫ϤSteamƳPC汾92%ĸߺʣһ̽ɡʽ񶷵ĻȻѾ94ƷϵˣPCֲ14΢xbox汾˻澫ϸ̶ȻǿġŨʽԡ⡱СС㶼ͨͨʲôɫˣʬãҲҶָСɫĶƺʹУ񶷲Ӳ˵ˬʮһϷҪʲôȻսϵͳˣΪһƸϷɱֱֵⷽʮֳɫָƳɫȭȭ⣬뼼νӺЩлԼͼ⼼ӾЧҲʮ𺳡ѶȭƫӲĸϷͬɱֱܸǿսˬжѶȡͷż򵥣ǿ˳ֲе൱֡淨ͳ˶սḻֿ֧ƽ̨;Ϸһɱֱܵĵ淨ҪɹģʽϷ飩ģʽѡһ̶ɫսɫVSɡ淨ȻͳҲ㹻ܾˡڶ˶ս棬߶ս߶ս֧֡˴ͳ1V1ģʽ⣬ϷṩһŶսģʽұΪ飬ÿս˫ɳһλжսԱһΧۡģʽ£ҿѧϰ˵IJɣٽҼ佻Ϸֿ֧ƽ̨Steamxboxǿ̨ͬս˵ΪһϷɱֱڻ桢еȷıֶdzɫȻ޹ٷģϷӰ첻󣬸ϷDzҪϷƣɱֱܣKiller InstinctƼ̶ȣսˬ죬ֶ֧˶ս޹УѶƫͣƽ̨Steamۼۣ112ԪȡϷҵ˾ڲġӭ·˽ࡱĹעǣϷѶϷȦİĻŶ -game Ҷ봽֮ ǮʱǮ Ϸڹںܶ೧̶ʹõһֶΣǶ봽ΪҲǺ֮ǡڸݷSuperdataıʾȻЩݷdz˺ޣһǻԼǮ֧Щѷ2012ȣ2017Ϸڷݻõֱӷһ ƪУSuperdataƾܷŭǶԡսǰ2ijӣںʷEA˵ֳӡѾǼҳ㷹ˣSuperdataսǰ2ΪϷҵɳʹȻŲʡ SuperdataָĿǰСض΢׽ʵ飬ͼڲŭҵǰӸչʵڼгɹҲʧܡȻڷǹиݱԹǻѡǵǮֻ֧ڷĸݡ ͼΪSuperdataܽġPC/ƽ̨Ϸ񼰲Ʒ򡱡ͼɫdzPCϷ棬PC/Ϸ棬PC/չݵ档SuperdataPCϷPC/ƽ̨չΪһࡰڷݻõ롱service-based monetizationPC/ƽ̨ϵϷǡڲƷ롱 ͼпԿݵ۶Ѵ󳬹˴ͳһϻܴ棬һû˿ļ2012PC/ƽ̨ڷ130Ԫ2017ѷһ࣬ﵽ270ԪɫΪFIFA 17չݵ棬ɫΪϷ SuperdataָPCϷķʶÿгֻ޵ڣǾ뷽跨һߴе׬ǮͬʱͨżոĹ˿͡ƽ̨ϷԣֲԶǷdzɹġEAһϵСFIFAڹʱյĵƾٵö࣬FIFAϵӺչݴÿ궼2λٷֱȵıĿǰѾ󳬳60Ԫۼ۴롣սǰ2ҲģһѲԣҲһЩؼԵIJͬ⼸ͬǡǡھϷɰܵĹؼϡ Superdataʾ3AԽϷתΪһַ񣬺ܿδ60ԪϷIJԣתѡһֲƷ̬ϵͳproduct ecosystemsͨһʡŷápay for subscriptions servicesϷݣȻǿѡϷΪԼϲڹݸѣҲܴӯ ڱSuperdataʾEAӦûԲͬϷ΢ݵķӳѧһЩӵġʹٻϵлڽɫԼ΢ϵͳֵľһֱEAġսءϵҪáͬʱҲʾEAΨһһΪǮԱŭȵķ̣̿߼۵΢׾ǵŭ΢׵ҲֻﵽϷչݵ7% -game TapTapϷ | һһװ Ҫڵйȥ 壬ͺһɱʬĩƾŸĸݣٴζˣϷƺţС̫Ϊ֤ԼƼլʵȻػҪһ·ڵйȥɱѾ½ϵսΪߵشɱܷ̼أη棬Ϸ2IJԵˣƾԶ󿪵ƣɹҳС TapTap ϲϷʲôԸǰɣ˵Ĺ£ذTOP1˼Сʱ˵һ˵оɣǺԴ˶̫ѺãһֱûΪûк֮źûӣͨ׶ҵһϷӦö׻˼ϷһֱܻϷܰ԰ֻ˵֮Уģͣ棬淨֮඼dzǻʷdzֵóޣҲ̫ΣûͼʱϢһ£ÿԴͼҲҪ̫ʱ䣬ԺܸߡһͬȷʵԽTOP2ֵϾ˰Сʱʵ20ʱѾڵйȥˣֻȫˡϷԶ󿪣ϷûνĻɶģֻǿ淨淨⣬ȱ֮ͨû˼ˣǾ˵ܺ档TOP3ǴȽؼ۱:Ϸ̣ڵġϷ淨ҪͨϷֻӦʹ߼̲ãϡͼһϷ棬ȫԸhap lnc.Ϸ˵浽48(ʯ)(ȽѾûͨأ˼ۣ)ǹغᣬȥԣ˵ָסʯͷȻϽʯͷߣ֮һָ㡣չϵƲʯͷ˵ҵֻ()оTOP4_{ؼϷʵpcľشɱ淨࣬ľͲ̫ˡƶ⣺ΪʲôֻûжףûſأҺٶȷdzĽڻ⣺ԽӾͺˡװ⣺Щƾѻ̵ʲôһص⣺һ𿪺ڣһƥ䣬ֶʾ˾ɱǵѶֲ塣ܶһЩϷģʽԲpcIJDzڴֻϷΪһεģѵôֻǣĽTOP5٣һдϷϷ֮ǰȷ̬֮ǰ̬ƽô̬֮·类ĸѿһԭرըûϷѶȷdzߡ(ţ:ڡʶˡ)ֻҪĻϷĵͻȣзԾϷкܶģʽɽӰӣԭתȦȡҳԸȤҲܹĽµĵͬĵDzһġ˵ÿһѵƷͬȤϷTOP6WatchcatXDҺãһ̱ԴϷ֮ұһλḻˣǼбָѶȵıҶ֮еΡ(ϷӦýС̱μǡ)Ϸʼ3ֵÿСʱһֵȫǵƴ˶ڶȣǶµСڽϷͷʱTOP7.Ϸܼ򵥵ģǰøӿҬҬӺľģҬܼӼˮˣȻȥʯͷʯͷԴìȥɱë⣬ë޺⣬ʨƤë޺⣬¹ûɶúɱֻƤ⣬ҲƤëޣƤ㣬Ӻʨ㿿㣬Ѫߵ¿վߣһѪԿͿܻѪָߵļˮ֣ѪȻ˵˵ɱĶ󣬴󹥻ܸߣվߣ3ɣҪŷݣһ£עʯͷס˾Ƚ϶Ƥ⣬ûбҪðȥɱɱˬˬˣұߣΣûҪ죬̫ˣù򲻵ˣϲҹĻ˵ͷ˯(><TOP8dz棺ô˵أϷ˿Ÿоɾֽ࣬ŵıµ()Ϸ淨ûʸֻսСһ*(`)*ȤĵطҾϷԼϷؿȻҲ˴Ĺؿ(ţ )ϷкָܶĻʣͨϷؿ浽һʣȻϷͨеõĽԼϲĻʡΨһȱ: ÿͨضй֣Ȼ治ôеĹֻܰϵǸسصеܡҸ˰ɣǻе㷳ب߷ذTOP1СڣϷħָֻ֣ܰˡϷǽ׷˵ܵܶʮأʵУ׷ҽʮꡣţһֻһĽ㡣׶԰ߣĵģСѧ͵ʱ䣬͵ɣѧҴС棬˰衣һ޵滨꣬˻ţΪʲôΪʲôҪΪʲôСѧѧҪΪʲôȥ߿ӲãȻڸҪһԤưࡣһ׷DzͶͬһѧͬһ졣ԭӣTOP2˯ΪһԼˣÿԣͨˣûҽ󣬾Ϳ˶ʮ߹أ ȻȻ󡭡Ҿȥкܶȴ֮ͬ޻һԲϾͰߣܻܵ͵һһбܵʥǧҵܵǼظжȻż˵ģҲͦɰģȥʱ򡭡ʮһҵbugҵ죬ˣҶٱˡˣһϲһأȫ͵γлĻеĻԱǰ̨һ浽һأϷʽУǸмشĸǿһĸ֣мܣDZʶΪȫĵطҲû˭ˣϷô߲ѾʼȵˣTOP3bUˣ֮һһ͵Ϸĵж֮㴩Խڵ֮УΪֽ꣬İǧĹ¡ʵһĻĿϷ(_`) νϰˣԼԲԲɧˣİĻȥһġϷdz㣬ԣϷֵĴУʹϷ˿˳ȻϷŰֲеģңжַ3ٵҲ˾ѶȲ̫ضֲУȻǩ(_`) νеϷҿǷϵСѶӦòӰλĿٵϷ顣 -game ƮϷӲ 粻ġصǸճƮŸĵطеijΪʹ㳡û˾dzϷйҶΪ̵㳡ɱ׳ơԼSteamϵĻԾѾԽCSGOϷСɱձӰɱҲעֵ϶͸һɺݾӮñ򵥴ֱɱÿ100Ҳ룬˷ɻսصؼѰʣȻɱˣõʡڴڼͼϵİȫ᲻СȫҽܵԶ˺ɹ̭/ӣĻϾͻ֡󼪴ϳԼ8֣ҲΪʲôϷֽСԼԭ򡣡󼪴ϳԼԡwiner weinerchicken dinner˹ά˹ijһӮǮ˻˵ôһ䡣Ӱս21㡷ҲйϷʷûһϷܱȡɱĸΪ׼ɱڹ淨ҲһͬϷͬĹڱǹսϷ˶Ϊ100ֻܴ1ˣ1ӣϷCSIJܽǡװ򡱣ɱ쾭壬Ϊɱ¾Ψһ淨򡣶ϴַǷңǷҪйˣǵһҪ塣Ӳָ߷յܲɾͲɣǹɧġ˶뵱ǸȸڡͲÿֶáաŷѡǷϲɵ£û֮һ˶ǿӱǹҪǷššȺϰ߶ȱˡͿݡû١ɱԽгɾ͸е¡ԳΪΪסġǷеLYBBռҪȺ壺1.ħȺſž;ţðʱķվڲݵר˺·ҴԽپԽִ򷨣ΪͼϵİȫСʣ¼壨ȫǸȦϷһʼСȦһ᲻ϵѪ2.Ӱ̹˻Ӱ̹ˣ쾯˾̹֮һȻ޷ΣȴȽĹƫԭαװľԼеĻӰ̹֮ͬӰ̹ŭħͼʾеĻӰ̹˾ǶڹľαװԼҡȺΪǰţſڲݴķħҰơִ򷨶ҼֻҪαװĺãѱ֡Ϊ뾾Ӱ̹ˣ붢ŹľԿþãʱѱɸӡĿͶʣ÷ħͻӰ̹˸ӽЧЩȺ˸ɹɧĪڡ̥Ϊ˷ǿٽȫлСܶϷнͨߵ趨ʹƵߵĽͨɾǸֻؾߣһˢڹ·Ի߳ⲻЩһҵһ;л̥ãҼ治ɱҲ治桱һֶ׷ŶȦܣ׳ơʱڰȫ֮һֳɣһǴҩǹʼ־ɣһǡܲȥҲҪ汳ƹˤɡһŶȥܶңȺ˱֮ΪӡǷҶƻ˵ϷΪΡݴȺ˵ķIJǵСܶǷÿֶŵĽӳȥԶ಻õ˴ǷǷͼϽʾ51Ҵͬʱų20ǷŰӰ͵͵DZΪǵجΣȺ˷·ϵһ㣬ǵ˶ˡ ҲʱҷǰһƬ¾ãֻҪһͻ˺ǹҴɷúԶŷ쳯ԽԽ࣬Ҳ׸ɵӡLYBҲڳʱըʽӸϡеߣƽ׹һԡ㡱·ѧǷһķʽĻ͹ƽ׹ֱϷ˺ߣҪǹƨϻԵӵ˸ԣ50˹ͬƽ׹ƽ׹ûĽĿ7-8ŵأ񼶻ס¡ϷչϣESLݱĽУڸոսsolo˫УǷֽҲƽ׹......ѡʻýƽƽ׹˵һĻƽĵڶ͵Ҳֱͭ......ϣйҲμαġè4űΪ쳯һƽ׹......꣩newfhmͻͷƨ / ν / ɫ / 칦й / Ůº / ë / սλ / ƨ / / Զ -game DNFҴ70ʷʫˢͼԲŷǵͶ˴ DNFһݴ񼶱ǿװǴеıرȻijħˢͼһ70ʷʫʥǹ֪꣬70ʷʫԶܲְ汾˵ʵ̫ͣԻԶأںĵʹ¿һ壬û뵽һͶ˴УԭDNFô档էһװ㣬ע⿴99.2%൱100%ˣڿװЬħʯʷʫ֮⣬ȫԶԼ˹оף¸ǿۣҲǵͶ˴ˡ70ʷʫΪĺƽְ汾ûԸˢͶԨԺټˣ22%ӳɼϻ2˺+25%һˢͼ100%˺ȥͻȻ뵽ʿҲƵԣСűҲԲολҵװһ100%ĹʿϾͶȽ٣ܿɹۡ -game ̵DNFһNPC, Ƿ񻹼ǵ DNFкܶNPCŴתʧˣΪȥ翴ˡ֮ðռҲˣ´½ǵ¼СһعЩNPCǡ1ҸоDNFʵŮˣΪɯĴǰЩͼֽΪǵǰиǿҪðռҸͻͲˬˣΪ취ðռǡΪҲǾҪðռȥϲץˡڴתƵʱ֪ȥˡֻΪ˸ģ򰲡ðռھз򰲳ĺǺҶ²bossҲΪɫħŮ򰲻ûǾͲö֪ˣֻǿϧDNFôƯŮˡ2ΪDNFˣݵȥDzģȻĶûһǺûкܶҾݹӵBUG¸ģһڽǿ޿Ȼٷ߻ˡDNFʷţĹ, һڽҿ޿ݿ˵DNFһ𻹷NPC˰ɣȻΪΪݹ¶ˣԺֻʹ壬ҵﻹǺʧġ3Ӿһûпյġ仰Ҷ˰ɣڸ󸱱ſڹֵܶ᲻ͣĽܶҵˡϧԴӴתƺӾžʣˣֵܶûˡҲŬĽˡΨһļʣ׹ͷװˣò˵ڿĻǾװܰǺɡ -game ѶسԼ118ż, Լ? ڲ Ѷһҳ棬˺ܶ˵Ĺע飬ܶѶ˵һCF˳Լģʽǵɨ˹΢άԺ ᷢûô򵥡Ѷ΢ͼôʶ½βҲдdzԼΣǸϸĵˣ㾭TAPTAPעThe Last One֣ᷢѶͼǵͼһģһӦòר[Chao Xi]İɡThe Last OneִɱǸСŶӶзһԼϷ֮ǰһЧͼȾƵûнйκβԣһֱأҲܵ͵Ҳһֱθڡ֮ǰCF̳ɾ˴νڲͼʱĻֱֱӣģͶûãֱһƬ棬ٷҲûжԲκλظ˵ҾתͶ̵ijԼΣ׷ױʾѶijԼϷˡԽߣǹսߡҲһ棬ĵѵԼģʽ1117ڰ׿׷ߣҲCF״ʽԼģʽԵϢ˿ѶҲ׼ԼΣһCF1117ŲԣһThe Last One118ŹCFõӪɵģԼӸ淨ʲô⣬ĿǰкϷӪɵijԼֻCF,׵ս2СǹսңԼϷĿǰǾܾģԼϷҳ̶Ҫȥ󣬲ſʽӪѶ΢źQQÿ쳬10ڻԾӴûȺ˫ͬʱʹгңɼģ˶ʼ棬ԼҲû˱ѡ԰ѡ棬ѣѵѶȥ棬ҫѶεijɹõ֤ -game ϷҪеʵ?حʱ ʱǵʴ𻥶Ŀ,ÿƳÿһش𻥶,ʲôϣҿĺ,Ҳں̨ǡλүǺ!һʱֻ֡Ϸ,?,õ:쵰ʿǰ򵽼Ҿ൱ڿ,Ϸ־൱λ쵰ʿүֹںŷϢʱ콱콱(ϰǾͻϵ)~ҲллλүӻԾԡӭλүڱʱ·,ǻΪõͳ50ԪµiOSSteamϷѡһн(79)ʱĿйڵҪ˽һ,ôһ:桰ҫϷְ,㽭13кëë¥,ȫദۡ627,(www.thepaper.cn)㽭ѧҽѧԺͯҽԺ˽⵽,ëëûΣ,̬̬Ӱ졣ëëǵ׼ͥ,Ͱְǹ,㽭ꡣ,ëëԼֻ,桰ҫ1,3ѧ,ҹæ,°Ҫ6,һڼҵʱӦöϷ,һͬѧҲÿ춼桱62219ʱ,°ؼҵëëֻͷϷ,Щڶп,һֱ档Ҽ˵,û뵽һ¾ϴ̨,¥ȥ˵,ëëȻ͵ҽԺ,ΪҹɹжιۡɹԶ˷ԹۡƹǷԹۡҸǹ,пˡ24ʱ,ëëתԺԺϷ̫,͵һҽԺʱ,Ҫֻ,˵Ҫ½Ϸ˺š⼸סԺ,ﻹ˵Ϸ̨ʡ˵,ԼȻԶϷ,æǵ׼ͥ,Ҳ̲ܽˡԺﴴƸԹǿ,ëëĿǰʹת,ɹԶ˷ԹۡƹǷԹۿܻϥؽڵӰ졣Աڵ:ϷҪеʵ?γ캡æǵ׼ͥ,Ҳ̲ܽˡڡûСҫ,ҲһﴦͬλϡȻô򵥵½,ǰѡҫڴ¼ûηϷҪҪеʵΡôĻʱ,ҲͲôˡν⡱˵ϷеĹ,,,ϷΪһʵڵĸʱɲһ䡰⡱,Ϸȷʵʵеˡ¡Ӱ,һЩ,ҲӦеʵΡʵ,ġҩ,ijЩض**,ֵط˵,Ϸʲô˵,ʲô¥һֱáԽ,Խ仰ͦõ,ȻǿˡҲбȽʵһ,ǿ߳еԽ,ʵҲǿij档ϷΪһ,ԽԽ,ʼΪóеʵ,ҲԼ档嵽ijϷ,гʵ׷,ôеʵξɡŪΪϷȻҪΪʵ,һЩϷɱʵ,ܸϷһ¡ϾʲôըɱɱֲֻϷ,Ϊ˭Ϸɱ˾˵ģϷɡ˵,ҫû¥,ǡܳûпܽ¥Ҵè!Ҫ,ôεʲôԤȴʩ,Ҫֵ̽⡣ϷꡱѾ̸,ȻҪ̸,Ϊûгĵġǿ˵֯ʲôӦĴʩʩȻһе,dz̨ҵ淶;ҲҪ,Ϊҵ㹻û,˵ľESRBǶʷ,ںܲһҵ,γ,ڷ,øĸѧУ?ܷܲһ?ܲиҵϷָ?˸оһı,Ϸħķ˼¥ѵΪ?ϷֻѹյһݡëëסԺʱȻҪֻ,ҿһС״־ǿĿߡϷҪ?Ǹĩõ⡣Ԫ϶Ҫ,˵,ϷŴʶ̬,ҶٵܵӰ졣,ϷȻҪϷݽһ̶ȵİѿ,ģ»ԭҪʵֶݵЩӢĵӰһ,ʱĺܰ,εġν5,ǰġѻӡⲻϷǵӰŷּƶ,ܵıӵͬʱ֤ҵȤʧԱȹڼ򵥴ֱһе,ּȻǸǵѡ,Ϊʲôּ,һˡ÷ַҲ´˵ĿˡҳԲѵ˵:Ϸ֮Դ,!Ϸҵߡûӵ,Բѵ˵:ⶼǺû!Ϸħ!ûôԡỷּƶȡǴ󱳾,ؾ,˵ҳźӾͲϷ?˵ϷҵԼŲ߻ʱ,ûдӡԵĽǶȳôƭǮ?һϷڸյΪ,˶ⲻϵ,úڴ,ΪԼ?ҪԿ,Ҫ̶,˦ϷҪеʵ?ӭλ·,ǻΪõͳ50ԪµiOSSteamϷѡһн(79)ʱĿйP.S.˶,Ǹϣλʵ뷨!ϻشΪ༭˹۵,ֹٷحڻعحֻϷ,?,E3˭Ӯ,˭?߿ǰʲôϷ?С,Ϸô?ʲôʱϷеAIAI?ح͹ؼʲ鿴ѡحح¼حäحֻححʵححСѧححĻححϷحححһ -game ְҵѡֽᣬKTSKTˣDeft¶ΥЦ 921ձʱ1㣬羺ЭᣨKeSPAٿ2017羺ѡᡣѡְLCKKeGLOLսӹ80ѡ֣FIFA3Ǽ2¯ʯ˵ȡDeftScoreMataҲ˻᳡Deft¶˾ΥЦݣ黹LZPrayҲˣûгԷﻹһɿɡSKTĶԱҲμλBangԺֵķdz߰ѵҪǣȥѡ֣ǩԼʱҪע⡣ҪƭһЩȥйӡȻǹ׬˴Ǯ֮ôôǮǮ򷿡ء״̬ƺѡƺûСְҵѡһЩְϵѵ֮ǰLPLҲʼѡִѧһЩǰְҵѡְϵѵǣְҵѡֲô򵥰ɡ -game 1---ԼԼԶǹһѣ Һ ΰ֮ǰDzôϷֻϲӰͼƵȴˡԼĿһСĵϷϷ趨ͦϲŷǹսƬĸо100ŵĵϴԼǹеװҩƷȵҿԻ๥˵װƾ»ֻһʤĻϻһ֣󼪴ԼPSϷҲӣ׳˫ţĸӣ׳ţΰڴϷͬʱҲдһھС˵߾籾ϾʱҲڲ϶񲹹Ϸ֪ʶͼҲ˳ڹںһұϾֲ֣ǡϵеĵһƪԶǹͼȫϷԶǹ5һ1AKMʣAKMϷԶ׷ӭѡҿѡȫԶʽﵽʱɸ˺Ŀġҩͣ7.62mmϻ30չϻ40ϡжȣݵ (AR, S12K)ٵ (AR, S12K)ݵ (AR, S12K)ǹAR,ǹڲAR, S12KAR, S12K׼ȫϢ׼24815ϸݣ2SCAR-LԿ5.56mmھAKMͬM416һս󣬸ӵƿơҩͣ5.56mmϻ30չϻ40ϡжȣٵ(ARS12K)ݵ(ARS12K)ݵ(ARS12K)ֱǰհ(M416,SCAR-L,UMP9,Vector)ֱհ(M416,SCAR-L,UMP9)ǹ(ARS12K)(ARS12K)ǹڲ(ARS12K)׼ȫϢ׼24815ϸݣ3M416Ϸ׻õ£M416ӦսеĶ󣺾ѻ֡ӵеͺоսɨߣǵɱܵܶҵϲҩͣ5.56mmϻ30չϻ40ϡжȣM416רսǹУֱǰհѣֱհѣݵ (AR, S12K)ٵ (AR, S12K)ݵ (AR, S12K)ǹAR,ǹڲAR, S12KAR, S12K׼ȫϢ׼24815ϸݣ4M16A4M16A4Ϸõڳ뻹оսжзdzõܡЩһڽս滻ǹǹM16A4һɫɿʽҼȿѡԶʽҲѡȷ3ҩͣ5.56mmϻ30չϻ40ϡжȣݵ (AR, S12K)ٵ (AR, S12K)ݵ (AR, S12K)ǹAR,ǹڲAR, S12KAR, S12K׼ȫϢ׼24815ϸݣ5Groza¸µԶǹ˺ͬAKMһģAKM֮ͬ䲻ʹ4ϵľѻԼȻҲˡҩͣ7.62mmϻ30չϻ40ϡжȣڲҵݵ (AR, S12K)ٵ (AR, S12K)ݵ (AR, S12K)ǹAR,ǹڲAR, S12KAR, S12K׼ȫϢ׼24ϸݣС飬ϲһԶǹΰһıעĵͼֽʱҵѽ㡣ԭͼ1080P壬ںţΰӰң̨ظԼɻȡأ -game LOL³װյBUGһתû LOL񣬸汾ƳһٶӢۣȫмϷڶУLOLӦһЩӰϷˡϷڶжô𱬣ʱĸ棬ЩϷܻᱻһϷ棬ԭBUGӦǻٵϷ϶һءѱʾLOLʱΪһװװûˡвҷԼ¼˳ŬŬʱҰװϳص̵ҳװûˡ˱ʾαϵͳԵɣعƵԼ޹أBUGϷձAggroΪһȫΪBUGܴΪҾ߶ȼУװ˻ûӦLOLҲв֪BUGվȪˮҲɱˡǹ޵˫ȣȭͷ޸BUGAggroʾ˵λѷشBUGȭͷӳܻýءСƵ -game DNFһħ, վ8W﹥, 4692 ΪװںܶװǿƣϾװװְҵ˵ܱȽǿƣǶְҵﲢѡλħȴĵĴװħڵ³ϡְҵŶڼְҵħϷСλ񡱵ħܱΪһħΪħվֵﵽ4692﹥Ǵﵽ8W1ij̶ȡ¹ħְҵﵽĻδ֣⡰ħ˫ԿװӦ13ϡңħվֵսﵽ41򣬿ƿֲܴﵽ̶ֳȵħмʵֻһħϷмħְҵĻDZȽ϶ģλһġ˹ħŮװⲢһⷨϷҲDZȽǿǿԿⷨĴ޾޾СϷжȽϵټⷨҲȽϵĸߣ¶ܴ˺ħȻϷпܱȽϵϡУְҵ˺DZȽϸߵģװȽϺõħһͷءħΪͷϷжǿ -game 湫DLCԤվս ؿĦСݣȫDLCݡϷ117շذ棬127շʵ棬۸Ϊ5800ԪԼ341ԪУذصΪ5ֳʵصΪԭCD趨Լ⣬ٻˡDLCԪ䡱ԤƵĴսĻDLCԤƵԪ䡱̨ڴ֮սʱĴսµĵˣߣܣػ飬ȵ⣬ģʽ޼ֹ޼Уǿĵ˰Ƿı꣡ոߵǾնսԼļޡDLC926ϼܣڴƵ棺 -game ȡʽع,Ҫҵȯ,ռ߼Ѷһ ҫмߵ70λӢ,ǾǶλսʿ,Ӣ۽715յ½Ϸ,Ϸ¶ȵĸ,ʧ˼ԻľΪӳijСӢǷdzǿƵ,Բοĺϵ½Ϸʱǿȡһӷɵ,ڵеĴ,ֻһĿɼ2һСλƼ,˺,ڶѣλЧٻħ,һ״̬,ȫ״̬֮ǰıм۸13888,588ȯû뵽һź,񼴿ռӡ,15ŵ½ϷҲԻӡ,ӡǿѶһӢⲻ,ϲ?ʱ֪,ԸλǧҪӢŶ,Ƽһ׳װ:+Ь++籩+֮+ꡣıҪڿ֮,ļ֮󿪴Ⱥ,ҫϷ,עҫ! -game DNFװ˲Ѫ, 㾭? ۵ܻĻָһHPֻѪȽٵʩšȻ90汾ô󣬵ǣڵνǺ۵񼼣жΪܾԼһ?ȷʵѪDZȽҪģְҵȱѪں˵ȽϵҪں˵ѪȽٵʱ򣬶ѡװˣȻʹܼܣӶԼѪ˲Ļڵʱ֪ģرͼܶ˶ѡװˣȻͼܻѪΪװ˵ʱֻôѪװѪûмӣ㴩װ˲ܻѪBUGȻֻк۵ܻѪڵʱѪҩƺҲЧԺܶԼѪʱ򣬰ԼװѵȻϻѪҩȻװ˲䣬Ѫͻǣ̳ҩЧҪáЩѪڵȽϵĻ𣬺ܶ˶ЩѪƴӶʡҩҪǣԼ֪ʿǣûоװѪ? -game RNGSKT±Уѵ٩ţζİɣ йͳѽˣٽS7УԺܶйѡ־ûͼžˣȻЩźɣܺһЩСһҲġ֪𣿺ҲǹŶǽڽϦڣΪ˴ζǣҲڽǰԼף SKTٲϢǸոյRNGAHQսӵͼƬӦ±У Ӧζ Ҳѵ٩ⲻţζİɣ ιιιok ˵ӦEDGһɣϾҶA飬ҪɼܵŶ -game DNFĺűһ, ˭ʶ? ĺͻȻѾ⣬òҵ룬Ѿ֪߰꣬ⱻĺŻ?ΪңӶʱ䣬ʱѾţͻȻţѾȥԶ˵ⱻĺѾȥôãñʲô?ģҵ¼Υ˺šҵһţװĴڽϣϡļǵãΪһװ60汾˵DZȽϰġֿл൱Ƚ֪⽣ȡڸ񶷼ɫУҵͨװװԣװװװԿ˵ټǸװװԵIJࡣʵһսɫսɫΪһҲǰש˺ܾáսŵ˭ʶ?ο¾֮Уܻ˵¾֮Уǿȷĸ㣬¾֮УֻҲ֪ȻЩŽɫڵǰ90汾˵Ⲣֵһᣬ˵Ļ䡣ȷʵ˵ʷʫװ룬ԽԽ񵥻ϷͷˢʬһĻĿ -game LOLŮѺΪʲôû˹עҵʵ LOLȦ˲£͸S7ܾWERNGǿ55͵ɫֳ飬ֱŮѻɳɫ55˺ѾռͷȻⲨݣֱռѵһLPLĹûˣΪͷеࡣ 276㣬º׶ȷijơΪʲôû˹עһֱʵֻϲ嶯飿Դѱʾ㵹Ǻúô򰡣Ƕ֤Լһûгɼôܵõעֱԣ£˶ûѧᣬôɴ£ ļʱΪNB油ADѡ̨֣Ǽֺܲܲɷʱ˵Ҳʱ˸̾ڳˣҪתְҵˣȻûٵdzһ滹Ǹ棬ɽ׸ıưɡ ϷձAggroãɫ55˺ԼֱŮ£ǿ˹һɵġţ֪᲻ᷢЩ¡ -game ҫ°汾Ķҷ²ۣdzǮͲǮ Ӧ1019տµİ汾ҫٷȴ°汾bugԭ1019ո°汾ƳٷS9Ҳ°汾һһѿը˹ˣ޷һʱ鵽µİ汾ĽҲҪƳˡϿȵµͼģͷųҲ⵽ѵ²ۡٷΪµͼƳһҲ硱ĻȴҸ²ۡŵͼɫһְɫ˲٣ҷ׷²ۣdzǮûǮµĸĶȴҵ⣬ȥҩ裿λô -game RNGҰMlxgֱʱũ Եȫǣ RNGսӡӢˡֲҰѡMlxgֱʱһٶȻ󲨡ֱʱڿһʧֱԶԷǡũ񡱣ʵũ˼ʱʾũɵX˼ֱ֮䵯Ļ˲䱬ըѷʾ˼DZIJˣ豸ûרҵû׬Ķ࣬ô߹ũֱϷͺ?ҲΪ⣬ǵصũ񡱾һ٩˼ǶĨũ˼ڴѵУ˼޼ڸɺ̳л磺ڲǰҲ΢϶Դ½˵ǸıⲢũ񣬵ûע⵽Ǹֱʱ˵ȷܲõӰ졣ڵͷչ㲻ᱻ̬ըµijҲʶһ⣬ǾǴ󲿷ְҵѡ䶼ȽСDzʱõԽнʺĿ˵֪λСοһ? -game Ϸ¼Switchж?ǺͿ ΢VGTIME2015 עϷʱVGtimeSwitch͹Ϸ76,ڶ Wish Fang ƽ̨ϷI and Me Switch ƽ̨ۡ629,Ŷ Hunter Studio ĺ涯ϷʧDZ2017ļȵ½ SwitchI and MeЩ⡣,PCϷڹܵԽԽĹע,ԽԽʷ/ԸЩƽ̨ƳϷSteam йûѴ1800,PS4/Xbox One Ƴ˹а,ڳ,Ѷ,Ҳع/ԼϷƽ̨ڹڿ/˵,Щƽֿ̨ɼ,ʩչ̨ Switch ⡣ǫ̃ϷԷ,ǿϷݡصļû & Я豸Ի˴Ĺע,гҲ൱á Switch ûй½,öԴ½гһֱȡ̬,,ںܶϷ˵,õƽ̨ǡһ㡱ġ½ƽ̨,ûô򵥡ʧDZôһϷ,βܵ½ Switch?ʹDzɷˡI and MeĿ Wish FangʧDZ Another Indie ڹҵĸ EricѾж Switch ϷĹڷнϷ(CIRCLE Ent.)ϴʼ/CEO ChrisڶϷƽ̨ indienova ķо ROY,ϣܶԹϷ½ Switch Ĺи˽,Ǻ Switch ֮ľ롣ΪʲôҪ¼Switchڡε½ Switch֮ǰ,ҪȻش:ڿ/˵,һϷ,ΪʲôҪ½ Switch?I and Me2016 Steam Ϸ,һֻɫ״Сèͬж,ͬʱյΪĿꡱĽϷ, Wish Fang һ˰ơ̡֡Ϸ Steam ϻáر(96% ), Wish Fang ˵:(Ϸ)Ժٿ,Ķë,õĵ,ֶһëϷȫƽֲ̨,ʱдһԼϷI and Me淨һĿȻI and MeǴֲȫƽ̨,Ѿ½ Switch,Xbox One Ѿڽֲ,PlayStation ƽֲ̨δȷ,iOS Ͱ׿Ժ϶ʧDZҲ2016 Steam ,кֽ, Switch,Ѿʼ PS4 ֲ,Xbox One 滹δȷϡ Switch ,з Eric ʾıģʽdz Switch ԡȥ,һ Joy Con Ϳһ档Ƴ4˾ģʽΪ,϶ϣϷ½ܶƽ̨,øҽӴ Eric ڲзʱᵽ,(½µƽ̨, Switch,)ɱҪ뿼,治ܵӸƽ̨,½ƽ̨Ϸع,ƽ̨дٽЩDzġAnother Indie ǰҵҪǺϷ,硶:֮š,ڵҪҵǹϷĺⷢ,硶ʧDZԭʼó̡ڸƽ̨ѡ,indienova ķо ROY ʾ:Ƕ,ƽ̨Ƹ,Ϊƽ̨Էе֧ȶȽϴ,Դ,̳չʾԴԼýԴҲҪǵ,Steam ķбȽϷ,Լܲ;ƽ̨Ϸϼ̱Ƚϳ, Switch Ϊ޷ÿ,һЩ鷳ڲɷù,ҷ̶Ϸ½ Switch ƽ̨ʾ,Ҫ¼:һʻȵƽ̨Switch Ƴƽ̨,ܹעȺܸ,һֱ,ںȻȻ Switch ȫװֻм(ùٷ:ֹ20173,Switch ȫ274),Ƕġʺ϶Ϸﲢ˵ Switch ֻʺ϶Ϸ, Switch ʺ3AϷнϷ CEO Chris ָ:Switch ĿǰƷһ·,Ҫгɱϵй,õûԶϷҲõӭ϶ȡEric ҲᵽʧDZк:Ϸdzʺ SwitchSwitch ϷҵǶ,DZȻעROY ʹ̸:Ŀǰʱdz, Switch Ϸȱ,ڵ½,DZȻкڵġ EricҲʾ:ҵǶ,(ѡ½ Switch)Ϊ Switch ƽ̨ϵϷࡣõ黳νܲɷõķԼǽӴĿ̶ʾ:ϲ,ϲ Switchҵ̫ɹ,ҲԸô,Ҳ˵黳ɡ ̸ꡰҵǶȡ,Eric ˵(Ц)нϷ, NDS ʱھͿʼкϵĿ/, Switch δʽʱ(ǻ NX),Ѿ׼ Switch Ϸ, Switch ׷ϷաǸġChris ˵:Switch ǰDzûй࿼г᲻,ֻǵƽ̨ƳЩƷ ԼȦεIJɷö,нϷרעƽ̨ķ̡Chris ʾ:ڷ̶,ӦȽóгŻ,гϸԼȦ,гһչ,ȦԼ,עԼý,Щ˾ǿӲֶġʼնڹ᳹һµ˼롣нϷѾ Switch Ϸ7Ϸ,ж Switch Ϸǰé,ڷ۵ġ۱(Implosion) eShop շŷҵѴﵽڶ,ӢλӰסнϷкܶϷƻ½ Switch,һֱŵ,йԭ׷ Switch ƷȻ,ҷ̶ԼĹ滮,ѡһƽ̨,Ǹȫƽ̨,Ƿ̸ԴгжϡϷƵ Switch ϡ,ЩһжϡнϷһרעƽ̨ķ̹Ϸ¼Switch,ѹڿ Switch ĿȨǼѵġChris :ȷʾ,ֹй(̨)Ȼ,⿪ Switch ȨҲûô,Ŀǰδȫʽ,ܶŷ̵δͨнϷȽ,ձ˾ PSPNDS Ϸ,ֱ֮ȫl NDS3DSWiiU Ϸ,11ꡣ Chris ж,öȫӳ̶ȴԼ:ձ > > ŷ > > йг,һֱŽ̬, NDS ʱ,òų̨ij,ֻǶԴ½ȽϽ(ʱ½),3DSʱ,޲ȡսIJԡdzõ:ءϷչ BitSummit 4th(2016,),óչ ȫò,ձءEric ڵķŶ Another Indie Bitsummit ձöνӴ,Eric ôǺձ̸ʧDZʱ:,ǵϷ,ܿ!ѽѽ,ۡĺÿ!̸̸ܲƽ̨°!Ŷûˡ Eric ,öйƷӳ̶ԶԶġΪʲôöйг̬˽?ùٷûиӦ(ҲӦ),޷Ӳ²,dzгж,Ҳûȥ˵֪ͺ,˵֪֮Ƕòȡij̬,֪,ȷԼڱȱʲô,ҪôǰһChris ںõĽӴ(ǰñij߹)˽⵽,öйϷ̬ǡáԷδ˵ԭ,Chris ݾ²,ΪйûȫҶ֪Ϸ,Ϊйƽ()ԼֳڹѡIJ˵,ٴ,Ϸƹպ͹ȷԵIJࡣнϷƻƳĶϷ,ȻйƷ,󲿷ֶǺ⿪̵ϷⲻҸ˶йϷ̬,й(Ϸ)кܴDZӹɵġ Switch ֮ǰ,ϷҲ½ WiiUô,I and MeʧDZЩϷô½ Switch ?˵Ҳ:ίӵ Switch ϷȨĺⷢ̽зСI and Me Ratalaika Gamesձ Rainy Frog зСʧDZе Another Indie 5,ֱİϡӢ Iainڿ Vladйڵ Eric AllenձDZں,תϵŷ,DZߵ̬Կ,ɴ˻ Switch ϵķȨʧDZǵĵһ Switch Ϸ Switch ķȨȫͳһ,ŷû÷Ȩ,ȫϷROY ڲɷбʾ:ķ̶йϷdzȤ,dz֧йϷ,½,ҲкܶຣⷢԼϵϷΪ޷ÿ,ڿҪϷԴ뽻,÷ֲ̽ػԴ ROY ᵽ,һЩ߶ԡԴ롱ִҪܿ,ֲйڿ˵鷳,ȻֲҪ֧ġWish Fang ʾֻҪֲػ(롢ݵطּ淶޸Ϸزĵ)еһЩϸȷϡI and Meձʱ,ⷭΪܥȃWǡзæһ PV,Wish Fang ʾԼĺ,ʢ޷̵רҵ档,пôô?,ǰڵĹͨɱ,ֲʱɱ( Switch ֲϷһʱ3,ƽ̨һҲ3~6,ʱϷ), Another Indie ķ̻ṩػ,ЩػʱԼеáڷеҪʱ,ּз,ձ,ȫ eShop ְϷ,ƽ̨(ձ CERO,20Ԫ,ԱһЩ)⻹еص˰ԴȪ˰(Ԥ˰,withholding tax)ƽ̨ķֳ(30%)ȵȡзҪΪϷųչýˡⶼϷڵ½һƽ̨ʱ,/ҪġЩ,һ()Ϸ½ Switch ʱҪһ:ϷΡż:ǿϷô? Eric :Another Indie ôõ Switch ķȨ? Eric ˵:Ҫ鹦Ϸ,Ϸ˵ġ˵ Switch ϵϷһ,ûвƽӹϷ,˵,/ѡһϷ½ Switch(ƽ̨)ʱ϶Ҫ:ƽ̨١жһϲϷɱ,3000ݺ30000ȻвġкܶϷ½ Switch, Chris ,ֻмһϷúܺáôϷ Switch Ƚܻӭ?Chris ΪǰһЩȫ/ġ΢羺/ˡϷ Switch ϺDZ,һ!(Snipper Clips)õӡһ! Switch շ2017ϰϷھнϷеĶϷ, Switch Ƶԭ(ԭ)ƷֲƷҪǿܶࡣֲƷ,߱ʶ:ΪʲôҷҪ Switch ϷֲƷڵ½ Switch ʱԼһЩݡ֧ HD 𶯵ȵ,ʱ,Ӧ˽ƽ̨ûƫúͷеϷгĻ,ԴΪƵij㡣ѡϷǰ,̻,Chris Ǵ·Ϸ:ȡϷԡгλǷƷЧӦ깤ڡ޸ĸӶȡ̵϶ȺԵȵȡChris ΪϷӦڴﵽһ߾ȡȥǵ½ƽ̨,ԽһЩ߿ȰƷŵ Steam ԼϴĥƷƽ̨,ֱӵ½ѹʵġ,˵,Steam Ϸ㡣ʧDZ Steam ʽǰѾṩ,˴µ,ۺҲڲ׷,Ҷȡ½ Switch İ汾Ƚơһҷ˵,һггͶܵгϿɵIJƷչΨһѡ񡣵Ȼ,󲿷Ҳôɵġȷһҷ̽к,͵ĬҷδвƷõƽ̨Ϸ,ϷΥһЩƶȻ⵽Ͷ,ûὨϷ¼ܡ 3DS / WiiU ʱ,һЩϷ̴,ڽ Switch һʱͲ̫˳,þЩ̿/Ȩʱ,Щ̵ȼdz͡νгϿɡ,͡Ͽɡһ¡ЩõϷ,ûЭƹ,չṩչλ,Ϸ,ûƽضԴϿɵϷһ,ЩûõϷҲдӡг,Ϊ,ȻϷòͬϷƷ,Ϸ,϶úõ,ϿɡڶйгйϷ̬ȱ,Ҳȱ㹻Ĺע,òֹѡʲôϷβɷõķ̶ڹעйϷ,кܶຣⷢڹעйϷ,óϷ, Switch ƽ̨,Ҳ߳һ·߳һ·νġ·:йϷ,ȫϷҵעйгҵ񲢲Ϸ,ҪùϷͨƽ̨⡣Switch Ȼûй½,Ϸ½ Switch,ͬչʾԼƷ,úע;ȫ̶йϷгĹעڲߡ˵öйг̬ȱء, 3DS Switch õһϷƳ/ƻƳİ,İ,IJעйгô?ҲȻڹڿ˵,Ϸ½ Switch ˡҪһ֡԰ڡ̬, Chris ڲɷ̸,ΪΪ,١԰ڡ,㡣ں̫ϡƽҾӦץס,ѧϰһºгڹϷǺͺϷͬһƽ̨ȫû,ҪϷ⡢ƹաĻںԼľ,ڱԼɫͬʱ,ҲҪȥ˽ڻ㺣˵,Switch ʲôϷ?Ϊʲô?˵ܶΪʲôʿ,ʼΪȥ̽ԭ˷ʱ,҂ӦաʿɹƷľ,ӭûϰԵơ Chris ʾǴδֹͣ۲ѧϰʿ½ PCMac,Լļƽ̨,ֹ 20161214,ۼ150ס 3DSWiiU ˲йķ,ؿʵϷͨ·,ȰѹϷƵ,ҲȡϷйг,,İ档Indienova Ϊ񿪷ߵĶϷƽ̨,Ϊṩ̳,ƹ,ͨߺϵһǺຣϷнӴ,Эڿߺͺ̹ⷢͨAnother Indie ǰҪϷĹ,ҪϷȫСڻ Switch ϷȨ,ǼƻϷƳ Switch ,һϷԭʼó̡ͻ½ SwitchнϷк͹ڿߺϷ,ƻ½ SwitchΪϷʱ,Chris ʾǽܶϷ,ԷṩĻ()İϷûô򵥡һİжֵ?ɱʱǶ?϶ô?ⶼǿжǷİʱῼǵ⡣нϷֻܽ鿪̼,Chris ϣи˵ϢÿԸ( Switch )Ƴİ,ڹٷûйݡ򵥵,йж Switch û,û֪(ÿ֪)Dzһȥƿ,,΢ת5000εͼ߿,Ҳڴ1/3˻ΪǵҡΪ,Chris ˵ЩΡ,ֻϣλѸ⡣ͬʱҲϢϢ,йܸĺ,ĻܻϷ֮ȷβɷ,ǽӴķҲ,ǵϷһõ,ԸѾ,⹫˾,ȡ Switch ˵ƽ̨ϷϷ Switch µĻ,ϷｫͺϷֱӾ,罫ͨƽ̨ǡǵҵһֱȫ,ͨƶ̼,ʹҵδעйг˵,ڳһ·,ϧʱ䡣Chris ڿʼܲɷʱ,ǵԸͼƬϷʱApp,ȡྫݡĶԭġ -game ҫ: S9֮ADƪ, Ϊʲôô ûӮΪûעڰɣҺãǿھӭĵ羺ѶϹأۺģϷ׼ҫS9Ѿ򣬸λADиˣƪߴĹԣУadڸ߶˾ʵҪλãŵ5Ϊ߶˾ֻûкadӾллˡԹadʶΪadһǧãҰļҰ˫buffץ˻ǾͲøad͸·һضߣΪˣһˣһʱֱӴӲݴǹȥѶϵ߸ˣҾͲ˵ˣӦöסپad͸һ˶ɱʱһмҪֱȥ·֧Ԯеǿ·Ҳ˵adһ·ad͸ѹƶϵʱһҪԹԵߣʱֱӺ͸ȥֶҰƶҰáadмDz˶Էʱ·äĿ뷢ٸӡ ·Լ߷ʱһ࿴ͼϵʹҰģ㿴ͼҰץϵʱѡߣм̽ݴԲܱϵˣסһ£̿ͺսʿʹ㣬㣡ʲô²ٴߣܹӵȥⲨʱСͼֶҰͼУϵҲޣʱһ̫룬ܿץˡadҪʡָýͽҪȵٽ˽͵ûسǣһץͽˣѪܽöسǵڼ俪ţadһסŵʱҺԼλãٺòվλãо·ϵɱԺ㸴ԺһҪעⲻҪäĿȥú죡пܶں㣨߶˾ְٷְٶ㣬ȥúһСʶadһѲóɵģʽܶʱˮadȥ˲¶ˮʣһѪûԼȫ˶淭̵ĻᡣΪھԭδֹǷתأ -game ҫ460أͭ99ǶСװ ҫУΪ˵ʱʲôأӶѹһͷСֻܸ˵Щʲôëë갡ʷʫ460Ͳ˵ˡСʹʶһҫеЩ460ӳٰɣ ԭǴ˵е¶ˣΪʲô¶ȻijƺšЧС಻˵ֻϼӵƤܴŶ~ ǹӽ۶𣿲ûȫǿˣѪڵָû¯ˡ ʲôҵ죬ҼţĶλǿͭIII99ǵġǵٴ100Ҳͭ˰ɣֵܣΪ ۻ𣿻һߣҫʲôʱ򣬳һװ氡ĵĵһɫԭ̡װѵǹٷһǰϣˣ¾ΪҷˣϣϲСĬĬһ䣬ҫУЩȽ460أӭ۽ -game 糤˵07ЩϷıද˴ƴĨȥĺʷ "Ϸıද"ЩƷ ܴ뵽ģǡFate/Stay Nightʯ֮šֽЩƷСʱ򿴹ġ鱦Ρ棬ֻǵʱСС顷汦ڴ֮֡ġ 񡶳¡ıĶܴҾֻжţIJˡһΪö1980ʱΪԶûмʱڣһڶԭϵвδΪ䡱ٷ򡢽ڵҰ ʵˡ¡֮⣬ʱһЩϷ˫ˡʲôģҲǸı˶ƷһЩڹڵ̨ҲŹ糤ЩƷʱ򻹷֣DZָͬһ˾ҹ˾ǸϷͶΡõȵҵͷɺһʱȴת˲֮ȵףΪ֪ ڡ糤˵ҹ˾ǸϷıදƵԤעǵĹںšϷо硱yysaag󣬷¹ؼʣɿྫݣ񿼾 | | ֻ | | 糤˵ | ־ | FC | ׻ | Ǿ | | ħ | | ʦ | VR | ȷ | 鱦 | ʦ | ҵ | ̵ | 糡 | ǿ | -game ⡿2017»ٿ »AGAIN2017 AutumnҺãǶ飬ֵ˿زʱ䡣ݹٷͨ棬1110ά1124άǰ»ٻȻдAutumnȻѾüˡҲϰˣֻҪлͺã԰ɡ֮ǰλԵIJ»ˢͼ:ڳˢ300ͭƶһһݳװȻ˳Ƶ߼(߼)߼ƻ߽װˢ300ƺ(Ľ࣬ҲࡣҿÿһѵĽƵȻձԼװĶ)Ȼȥ߼ˢ˳Ƶ߼֮߼ˢڳˢͭӶһ̵IJϣȻ߼ˢƶһ̵IJϣȥ߼ˢ»һ׶ι֮ǰıͻرգԤѡ;ƻͲɫƻˢ꣬߼Ƚٺˣص»ʷһˡǵţ̳ĸףȻԵǿãյ12ڿŻ³ܣʥ˵öʮּϣʥ޵ͷͳ137£dzԵˡ»Ȱһʫˡߡݳߣߡٰʢľᣬ³ڹԪ60ٰ˵һ»ڣ֮ÿٰһΡ»ϲμӱdzΪʤߣҪΪԪԺͰڶˡѹǵųô(Τأ)ͳƣ»һõĽһǧ»ֻ죬Ϊûȵ͹=_=һεĿԤӢ鶼ף»ǵڶµľӢ飬˽޶ɫFGOлźܸߵѾٰټ310»ˣȻǵӾװһC׺쿨ڣǿȲͣڴҲΪοصӢ鶼ףͼ򵥲һ£ҪǸ˽ںӵһЩοһ. »˵ۣDZȽֵֵӢ飬ÿͲˣǿҲԸԽӣ˵һȽȫӢ顣շڿ˱߱Ҿǻ봸Լ۱Ҳߡʵ˵޺ܶ˻᲻Ծ˾ضԱȣ϶Ǹӡְ汾»޵ı⣬ȽԴħŵ˾һ࣬Dzò˵ǿûޱģϾԴ45%npȡ˾ظעر˾DZӴּ߱ؾ÷ϵߣó˾ûл޺˾á40%&Ƿ50%غϣ3000Ѫ&20%غϣ˵ǹؼ汸ң޵ܶǿѡģֽ׶кܶҰѻ޵գҲһֲĴ򷨡ҾǸɡ. »dz黨޸ĸƷܴ˳鵽޵Ļһ鵽»ҪѲ̫ģȻֽ׶ιԽԽ˭֪w()w»۵ļܴžܡʹӭϦغ3սCD10غϣǹڣB廹Dzһ㣬Ϳˣ˵ϡ ȻοصӢ鶼upһ»ս. װò˵滹൱ϲģ»ղĿһ봡װʹڻҲкܸߵķԣװаҲǺܿǰġҾͲ˵ˣҸкö񡭡ȻЧϿһŻûʲôõװij̶ֳϽ͵ʯͺ챦ʯߵĹ233ʵҲǻôװкħţȥľGˢͷʱõ󻬶鿴һܵ˵λĹؼ޳غ͸ѱûл޵Ľ΢һ£лԤ޵ĵþͲҪˣϾ֮ǰһƭ봿񳱣ϣΪףɶɶ FGOյ -game ֧Ϸʱ, ͸? ڲ͸Ϊڹвټ˵ȥֵûл㱵ġ2017ϷIJڹҲܶûģˣSTEAMƽ̨ϸֲˢȻôһЩҾǺ˵ҶΪҲˬôãϷйŪĸСøǺϷģ㿴ǰûʲôڲİɣȻڸƽվӪݱʲôƽ鷭ĺDzΪûе˲ȥ棬ǡǡΪ֧㣬ŻƷϻǮǣǮұлǮܵӦеĴϷѵͬʱ׼ȷأϷǮˣ˿ûг⣬Ǯ鲻ûǮģộΣһ⣬ϷȻйˣ͵ÿйҵĿζûʲôɣҲϷʽĻҼϣйҿԿö˵ʲô𣿽ȫûѶȵİĻԸӣϷԵúˣȥֱ߳Сԣϰ㲻ҪˣϰǸ콷ҾŲţƼƼҵꣿҲѾʴ˺ðɣ㲻ͨϵͳ˵㻹ʲô˵ҷǸϷѾ˼㷴ʲôֻͨϵͳʾϷ˾ȷʶԼĴ󣬱ϾϷ˾Dzģӣ裬Ƶ鲻ͨʵʵʧȥͱõĴ𸴣ϵͳ⣬Ϊ˿ȡİɣϷ۵һϷ۵һϷҲ۵һʲôΪ۵ı׼ΪɶƫƫһǷġأ㵱˶ȫ𣿾ǹפʹҲ˵Զȫοҵͨѧ磬ûзû飬ûʲôȤԣˢôˣϷҵĸоǡ⡱ǰСǮϷôʸۡ˵ҵȻǮϷˣôʹʸʱ᣿˵ʲôûǷľǵĹOKͶԺѣϷûģܲ˰ɣ -game ҫ5Ӣ۲ǿ,׺ӲԶ ңԶľ벻,Ҳվǰ,ȴ֪Ұ,ҪӢϷ,ȴbanˡӢ۱ban˵Ӣ̫ǿ,Ҳڶ,ô5Ӣban?NO.5߽,ban:46.61%,ʤ:53.24%(ͯԧһ)ױȵ˿־,,Ҳѽһ˵ߵ˱ض,˱ض,Ǹ߽൱Ǽ,ֱսըȱûʱ,ҵرǿҲûбҪʡ,κ֪զʱȿ˵,̹ȼ١NO.4,ban:55.67%,ʤ:50.13%,Ѿǰսûͷ,˺ʵޡͷյĻ,ոǺǿ,õĻպü,޽⡣һŵˢҰܿ,ԽʱҪΪԼ޵,ϾѾܶ,һᷢȽ֮NO.3ļ,ban:65%,ʤ:74%ܶӢҪ,ļȴ,Ǿ㲻Ҳܺǿʱ,1ܺʹ˫,ͺĶѡһ2ܳǿѣ,׷׷,ӲIJļֱǵ˵ج,ɱɱĶѡϲļҪ,Ѫӷǿл,ͱƵòļܳװNO.2̫,ban:75.86%,ʤ:55.09%ƺ,ؼǴпԸ,൱75,սǿ,ƽʱӢ۶ôô,û뵽,Ӣۻϰռһbanλ̫ҲҪ,лѪҲǴӷǿл,Ƶ̫Ҳܳװ̫ҵ˺ǿ,ҪСըŶNO.1¶,ban:79.92%,ʤ:52.87%¶ҲDZ˺ܶ,ֱȲϴǰĻ,Ի¶ȻǺ,ֲȷû¶ȵĸ,banΪʵҲ̫ϲbanرԲӢ,ѡ¶úܺðҴӮ,ҲˡϲbanԲҲܶ˵Ӣ,ڶbanѾ,㿴һbanѾúܲ˵Ļ,벻Ҫ,˵ء*ԹٷƵĿݷ5Ӣ۾ģʽ,ban5Ӣ,ǿעִ򷨸· -game ųԼ֮ѡսƪ 羺ձ ׻˵ĺã£ڳԼ;УֵdzɹԼҪڵŵʱÿֻѡЯôҪѡԵ÷dzҪˣ ڵųԼѡУһһԶһ˵ǻһִ䣬һԶһнõѡڸ¶ԴݲȵӦԵˣƼS686 ŵ˵S686νǵųԼڵǿԱ˫˲ոر֮ѹװʱһ˫ڳΪոS686ȱǻdzijȱлᱻ޵ķŴȻڵȴûôǣ󲿷ֶ1V1ijǹ˫һŵ㱻ŴˣԵ㿴S686ڵϵʱ򣬲Ҫԥѡɣ S1897 S1897ڶŵʱS686ģϾӵ5ӵӦĵˣŵʱ΢ѷɫS686Ȼĵ˺һģһڵ֮УS1897S686ġ ķѷǹ °汾Ժ֥ӸֻǹǿȫͼˢµģΪijǹķѷĿǰνǹĽս֮AK֮ķѷѾȡAKнĵλڴϾѻǹ֮ǿĸоĻĿǰߵһ UZI 뽻սУǹԵֲǹģûҵǹʱUZIҲһѡUZIǹŵǶʮֵѺã㲻ϰڽӵкʹ׼ĻUZIõѡ(PS:༭СξܾUZIԭǹ̫) UMP9 UMP9׳Ƴǹо൱ɫӵڶ֮һоdzڽı־Ͳôˡ Vector ڶ̽ǹոߵʱνɱɱ޽ǿĺDzڵĶ̽нֻ13ŵݵӵǶ̽Ӳˣѡʱʵ벻ǿѡ̽ʲôΪ˸m416һŵλðɣ սƪȵˣȻҸ˵ļҲʮֵijɫǷҲ2000ǻϣдаԼļҲôѧһЩɣɹԼ·ϸһһڵлʹҽһͻǹԼѻǹѡϣܹϲ -game LOLӢܼЩװҽһβԣ ǰLOL˵139λӢС޺ģʵײУһЩڲԷʵսЧQڷǿ֮˺ߣѪĴƤһ¾ˣˣWԷָΪLOLһȫµĻƣWܾԸЩܺװЧЩұȽԺһöԴ˽һβԣDzϾλҵȫԽԿЩװܸƻDZȽϱ̬ģ꣬ưܵȵȣݲԣǶСӦнһ˽˰ɣ -game CSGOɳĮ2ͼһǷų ˼Һ԰ CSGOɳĮ2ͼ ͼܳΪһÿһCSУӦöôźϲһ治ĵͼtrainaztecinfernodust2ȣеľͼرdust2侭̶ӦCSı־ԵͼˣΪܹϲͼdust2ܱ֮ΪǹͼһԭŵͼڹڷdzܻӭһԭΪ2005ESWCܾϣսwNvԾ޴¶ھŵͼwNvսΪTԲ1ӵʱ¶֣֮14֣ƾžԵıȷսʤ֣һٳΪǿCSսӣɴdust2Ҳdzͼdust2ֻCSϵϷбܻӭFPSϷȻȵͼ֮һCFսϷУdust2ɳĮҡС򣬾ѱ޸Ļƣ䱾ʻdust2졣Ҳ˵dust2ŵͼľ̶ȣ㻻ϷȻܵǵϲǰʱһֱдdust2ӭϢûʵ֡1010賿CSGOƷijdust2¸Իع飬ǻķųһdust2ϷͼѱʾnukeinfernotrainѾˣʱֵdust2ˡŽͼĽǶʾҲ²ӦǷ˼ҺԺǶǽҲΪA⡣ϷձAggroCSGOҵƵ㣬ֻǷ˼ҺԺһЩȻλɫȶ޸ģAggroŹٷһᱣdust2ĺԪأȫµdust2ҲҲһϷ顣ʵ֮ǰdust2ѾCSGOٷ˼С޸ĵֻľ˷ʤȻƽ⡣Ϊ˾ĹԣCSGOǾȡͼλãĻinfernoͼò˵ҪĪϾdust2ŵͼڴУ˲ɻȱλáAggroţÿһλͨ˵dust2治ͼrush bp90Ҳľ䡣СǺβ޸ǰڹ漸֣ϾҲǵഺǹͬĻ䡣 -game CFλĵѵAWMô_CFλĵѵAWMҵ CFλĵѵAWMôCFλĵѵAWMҵѻǹӵгԶĴңߵľ׼ȣҲþѻǹڻĵѵУӵв׵ĵλСǣСϸĽCFλĵѵAWMô֪ľһɡ CFλĵѵAWMô ѻǹӵгԶĴңߵľ׼ȣҲþѻǹڻĵѵУӵв׵ĵλ ѻǹ˺ޣͬҲ˰ҽһѴҳľѻǹDRAGUNOV͡AWM ȻDRAGUNOVAWMͬǾѻǹǣǶӵиԲͬص㡣DRAGUNOVľݡ 71 ׼40 ͸83 Я16 ȶ42 17 ٿһAWMľݡ 73 ׼40 ͸84 Я15 ȶ39 15 ǿпDRAGUNOVAWMľ׼൱AWMʹ͸DRAGUNOVǿܿAWAĹΪ㡣DRAGUNOVıЯԺȶAWMǿ ۺѾѻǹĶԱȣܸȷĿDRAGUNOVAWMԵȱ㡣 DRAGUNOVʹ͸DZԺȶԸߡAWMȫ෴ϸߵĹʹ͸ɥʧһֵıЯԺȶԡ е»ʣʲôDZ?ָǴĸأӰ쵽ǵƶٶȣCFĵѵҪ;ģʽƶٶǷdzؼ! һѺõľѻǹĹҪĸܣҪڻĵѵ¶ͷǣҪҪôúþѻǹ ѻǹĿDRAGUNOVAWMٶһģѾѻǹһ⣬ǿƫҪ׼Ŀ꣬ϰPCϵäѲƶȻѶȺܴ󣬵ǿԲֻĽĵ㣬һλãܴǿã С飺ѻǹĹʽǹҲвͬıֶ֣ԤУֹǿС飬ϰһɱֲʹϴAWM ѾѻǹõͬѧʹDRAGUNOVΪDRAGUNOVȻǾѻǹȴȻҪһʱĻ壬DRAGUNOVٶȽAMWĴһǹһε˲һ㣬ңҲͦϲDRAGUNOV֪Сϲϲ? ǹϵ ǸտʼĵʲôûУСǿʯͷãǹԼûбǹеСҲȼǹģȻķݣСһһʱ䶪ʡڱռطȻһûȫ̶ǹҰɡ ǹϵ Ҵһʱ䣬Ƕ䣬ͻµѡ֪ڻĵǹν׼ȸߣԶоDzǹȽϺãСһ񵽲ǹͲټǹеˣһѲǹĵ ǹϵ СľǹˣǹС಻ôãһֻ͵Ϯʱã߶׵ȵʱźãҪˢͼʽܣ鲻ҪǹԶǹIJռʲôƣͼСʱã־׵ʹá ǹϵ СȻڻĵķѷǹdzѽС˵ǹDZǹҪһģϾٿһ㣬ûǹӵôƮԶҲܴߵˣҳǹЯܷ㣬СÿDzǹ֮ĵڶѡǾ? ѻǹϵ ʵС಻ʹþѻǹϾҶǾ񣬶һĵģʽûԶ׼ѻܻһѶȣοѻӵǺܶ࣬㿪2ǹʱû׼ԶĵѾòǹˡûռߴλ鲻Ҫʹþѻ ǹϵ ϷﻹMG3ĻǹӵҲҪԼʰȡģûǹӵ࣬ǾʹشˣΪĵǹеûӵģһǹر·ҲرҲҪѡǹʶĴСҲûǹ˵ңһ666 Ͷ ͶҲҪѡģʱһܾŶȻϷﻹⵯЩ˾ͲҪһҪڱؼʱڻԼˣҪСͶȻըԼͲˡ ĵѵмDzҪһ·һ·ԼҪʲôǹеͼʲôǹеûõǹһҪȻռű㻹ãȵҪʱҪֶͺܵʱˣȻûǹеܼʲôţܱȳֿȭҪõĶࡣ ϾСΪҴġCFλĵѵAWMôϣԸλѶעϷ -game LOL̸£ްŵԺ;ëתᣡ Сǿ˲ݮ΢ЦǸǹӦöθдɣWEȷһȥءһԣһλС˵յʱ˵̲סҲˡ 1020WE϶ӳա ˲ݮȥһգǵʱ˺ܶ࣬˰˹ȥ˵롭Ƕ˵תᵽEDGʱ΢Цûк޹ŵԺ;ëʱݮЦЦܿ϶˵ûУ˵ߵﶼֵܣȴ˵ޡк޹ǡݮDzWE״̬»Ƕʱǣ˵ǡ˵۵Ƕʱ䣬˺ܶսӸ߲㣬ѣְҵѡ֣Լ֧ǵķ˿ʵʱк޹뿪ǺӦá˵ĺ޺ֺ޲һһףWE΢ɡ ݮһ԰æ˵ҲǺģΪʱҲ˶ɼУЩ⡣ ջDZʾ˰޽ӲҰںޣô˵ְ޽ӵĸоûгëIP5õھԲᣡܶɼòġںޣΪȸȻʧȥˣʧȥĹ̶˵ʹࡣǽĺܶණףͰΪԼеĶʼңΪԼҪĶȥƴŬ ݮ˵˵ʵʱҲģ˵ʵʱȴǺˣҲܴЩܲˣûкǡ ʾ֮Ǵ˺ܶ5V5ëȥ۱лdzϵ ʵҲܺеĺްɣĺ޲dz޵ĺޣDZ޵ĺޡžΪõһΪۣΪھתᡭľѹȥٻ겻ѣ͵͵ᡭ񣬼ֵҲֻʣһˡĻۺְ֧ɡ ףһ䣬տ֡Сԭš̷ԭδɣֹתء -game S7кս2Ӣ۾ԷŲ ϷԭֹվõȡΥ߱ؾȻܾS7һƫ¯ADCİ汾UziMysticĴùǺܿģò˵İ汾S5һڽΪεİ汾S5ܾӡӦö֪ɫֱBanӢ۴+СͬBanӢۼ+˿ӢۼDz̫ܱųˣֻʣк֧սӣӢ۵̫ǿԴﵽΪΪǿȡô˿ͼΪʲôǿأǰ100%Banʣһɣ˿Ӣ۲ܷţΪĿǰ¯̬ADCԭ˿ͱȽƽA쫷¯ߡڸɼܾ޵У͸֮ڻౣ+˿ȷǿһϴţţƣAOE˺һADC¯İĿǰǿĴڡܾS7׸ɱǿ˿µģDeft·˾ֿ˿ΪS7СԺû˸ҷſ˿˺ܶҲ֪Ӣڶְҵѡ¯жֲͼDeft·˾ڿ¯µ쫷+ưܾô̬޽⡣Ѫ˿ֻҪ¯ıκʱ1඼зɱĿܡDeftˣUziBangMysticһǿ˿أӢڱӦû100%˭ų˭...ͲöˣFakerСҹйµijǿ֡˼ͿܾΪΪFaker¼µǿֻҪӲˣŶԮԼװȻӵв׵˺Ӣ۴һֱܻܾİ汾ȻսϵбȽֵһΪDZǴкںģ˼£ǰֻҪԶᵣӮˡWE϶ֱѹͺ¾Ŷ¯ADCǰһ޷ԽǽкӢ۵ʹҪŷߡºͿ˿ӦûǾԲųӢۡ˾ҲһS5ʱͬ⣬˿ͼͬʱų˭أٽĿǰûκսӸ...Ϸԭδֹתظлλ֧~ĹעdzµĶ -game ̵񾭲ϷDzǴͿ Դӷ񾭲СϷѾһˡرܡһʽԶĽϷǰдġϷ衷еơ淨϶ǴijЩأʹijֵʵֻرܳɹʵ淨ij򵥣ЩϷòʦԶʹ⡣Ϸÿһضһر⣬еͨѼﵽرܳɹ磺Скϲȴûֽô죡һֻӣ϶ôӿ԰ȱֽôʹˣֻС.......ٴε־ֽȷĴ򿪷ʽԶ˷ˡеǽһЩԵСϷнգһؾеӵĸоʵҶϷȤĻЩ񾭲ϷƣÿһضľXX棿ζ㿪ϵĺڰСҪ񴦵ץˣֻõǹͬѧܳɹܿڰ֮⣬ϷܻῴͷĹ顣ҲٻͯͬѧֻΪ˱ܿһ»Ϸµʽ²۵ˮʶ磬Ϸһ30Сÿһضжľ͹¡ΪһнϷûշĿǹֲ벻١Ϸͨɡ˭ô棡ҾˣϷһػһĴϼǿ֢......㣡 -game ߷ĸҲ治Flappy Bird Flappy BirdڻǺ˵ټˡ ýϢFlappy BirdDong NguyenѾΪiOS 11ϷˣԭiOS 11Ѿ֧32λӦˡʵԽƷת64λȻָ£ǾFacebookΪƻϷʽ Ȼƻ豸޷iOS 11汾ӵСFlappy BirdôȻԼƷƻ豸ѾiOS 11ôǽ޷ٴϷ Flappy Bird2013524״ϼApp StoreϤֹ20142·ݣiOSϷѾ5000򣬲д˵NguyenÿԿ׬5롣29գNguyenԡܳΧѹ Ϊ¼ˡFlappy Bird ڴ˺3䣬ȻƷѾ¼ܣһЩiPhoneû᳢Ըַȥ浽ϷȻiOS֧32λϷЩûײ浽Flappy Birdˡ Flappy Birdϼ ҶAppĸְ2014ֹˣ֮ôNguyenFacebookлظĿǰ˵ġ -game ЩΪ˵ǸԼеIJ˼, ֻụĵ! ɺLYBСµľҷңԶ֪֪ŵǹ׼ҲսõǹʱһһһէĺȻͻͻˣȻһ䡰˼ˡһξķսУһӻȻԼǰƽȡԣһӵȫߣһﻹȻ֪ɻΪʲôһǹıƽ׹ˣΪ˱ⱻӣȻôһ׷˼ţֻ˼е㲻ôˣЩҲԵ˼ˣ㷽ʽ˵ʱı㷽ʽһ̣9085֮䣬ҿˣ˼ı㷽ʽǡұˣұߣԲۺҲһǵǸϽǹͷûˣҸ˾ûˣǵ˰ɣ淽ʽ˼һ㶼DZŰģһֻ˼ĿԺܵͣǵһǹ̰ʲô˼أǼһ㱧ŰѾѻŸͷսʱҲԸ̽ͷһǹ֣̰ǰѰȫнװϣһһѰװͲ׷䣬ǴǵģռҵģôԣֶһҲԷŵģΪǸӳǴڴܶԣȻûɶãϷ̬Ϸ̬˼DzӮDZʾϷȤԲСĿǹ򵽶ѿһԲˣռҩƷһԼҪò䣬õǹҲԶԼŲˣʱΪһ㲹ɱѣŶѶ¹ȥɱֶл˰ɣʵڲԼͻͻҲãĸʱײ˻ֻõһ䡰ûмաѪѵ~~ǹʽ˼ǹҾÿݣǾDZ⿴ģʱһǹͷʱߣDz˼ûжʶģֻҪǹǹӵ˾ͻȥͻͻȫҲܲͻУʱû˲˵һˣ棬QԼ˵ -game LOLѸԭ̫ɫ飿ĸ¶ĸˣ ϷԭֹվõȡΥ߱ؾLOLѸ֮ǰԤƬǶ欣ݡý罻վٴع˹ѸܽԭԭѸ·þ͹ˣ󣬹ѸȻĸˡô°汾ĹѸʮأһɣLOLESĹѸȷʵ֮ǰ˵ѸԺ󱳺dzƳһ̵ģ̻͹Ѹ¼йءȻڰӰеɱ֣ҸģҲƻԭһȺ˷˹Ѹɱ...һԵIJͬ...·ˣûʲô·...SһЦݣDzѾúܶ˸е˷أ....ͺͷɫҲһ㣬֮¹ѸڼиĶȻҲһ㡣٣ħŮĶ̫󣬲ųὫӢǰ׺һˡѪ֣ڱ֮䣬Լ˽оĹǶ֡ܽ°汾DzǹѸأоӦûиţƫ°涨λǰ׺°汾ܽԤƽ7.20汾ߣҲܣĿǰPBEûоϢ°汾ĹѸôأڹѸԽԽôϷԭδֹתظлλ֧~ĹעdzµĶ -game LOLһADCը10ֽ Ů΢ӦҲǾ ΪӢ˵ңС֤˵羺ȦԲӼ˵£ЩΪ˸ĸȻԶԲģӢը⧲һҳʮִ󷽣ʮֽǵŮʵǺըӢ֪ADC쫷·ϲҲȦ˲ٷ˿Ϣһ˲ըǵۣϾһЩŮ˿̤ʧıԵڲݵը׷ʽҲըԷʾķʽǵĶDzǸôˡŵ׳ɹͷʮԪĺѡ֮˿һȴӦըֱе㱡ãŮŵŵӦʾ԰666Ǹ̸ˣȥɡίĴӦ˰ɡǵʮֽҲˣӢȦĺѶ׷׷ص磬ͬǺըŵŵһͬŵĹܳŹҲףһڴըŵŵܸҴ޵ֱͬ·ϡȻҭС໹ףըŵŵܹԶҸȥ -game ˸ԶܴϷ·ֹһn ԭ 2017-09-20 Զ wuliʶһػܵ黳hiҺãҪϷرܡǸûֻСϷĿǰر2ÿصⶼҪرij£粻ҪܵѽҪͬѧϵĺڰҵѽܲҪһѽȵȣǺճij趨ĵڣ˼·ҪԶ󿪣ҳɹʧܶһֻһŶٸӣУȫԱὲǵķϲǽ˯ţͻᱻ̵ץסѵһ٣ʧˣи취ǣΧͬѧм䣬ʵһťǸťУͱɻȥǵȻҲû˯ţǻᱻŭĽ̵ץסѵɹİ취һֱУͷļٷ̫ͺķϹۣDzˣɹȻϷȫģһҲӰ⣬ͼ٣еֻԿͼ˵סǰؾͻ˹ЦղܡһһռϷͼ淨ʹرܳɹҲòȫͼŶԾҪԻҲǸͷdzЦصǣ浽һؾȻܸܵ黳гٴεdzʱԴһֵ˵ллˣúܿġǸһ棬Ǿ͸˼ᷢÿ˼άʽDzͬġһץҾֱȥĿɹأҼСһýԱߵijӴܣԼɽۻȻջֻӡиȵǣ㲻ǣҲԼԵġһѧȥУDzʱðóԵѽŮͬѧѽϷѽԴԱߵһ֣ʦͻùӺݺһѧͷصķأȻ·һֻСèʱʦȻʦ֣һЦʦôʦȻǸèūҲ̫ɰ˰ɣһأҪˮȥֱ̱ˮᱻˮĸݣԾҪȥֺô󱴿㣬һDZˮϺˮڶֳɹķDzϵˮĸǣ࣬һ㣬һ̵ˮĸǾͿֱȥȥˡȻ㼸عؿֻôһ£һúÿɰѽر12غе᲻ãȥͬ˾ġ˦رܡǸûʲô˼ûкԻ̫࣬ʵgetȵ㡣ϴƼSteppy Pantsܺ棬ĸͼѾ40kmռһѷװȺƺȥˡƼϷѽMuaʶԶ󿪹 -game ҫͭΪɶϲߣСѧע ====ԭֹתأΥ߱ؾ====ҫϷλһֱŷdz̵塣˵ʵڴ£ͨ鿴ҵϷλǿԿҵϷˮƽġȻҲųһЩҿΥϷƻϷʵλҪƥ̼ܶ࣬ǿ߶λҲȷʵǾҵ׷󡣵ҫϷ棬ǿλϾϵģȻҲ˵ϾϵġʵȱһɡôǾоһ£ǵΪʲôϲǿأʵɣǾӦˡ1.ϷھʧȥDzѪȷʵջ󣬵׷ѪʱĻҪ࿼һ£ϾѪ׷Ƿɱȴʵ֡˵׷ѪӢ۵ʱһҪܵһ㡣2.ƢƢõңϷǾԼƾһƽ̨磬˵ʵһϷÿľͺãûҪ̫֡Ͼڵ˶ԣҾͣҾ͹һô졣˵ʵۼֻĸ˼֣ȷʵ㡣3.Զ֪øʲôЩ̿͵ʱ֪òдƤ̹˵ʱȻں棬ִȥ˵ʵԶ֪ԼڸʲôңддϷ֣ȻҪ˵ǿˣϻƽ𶼿⡣4.úͶҫһ˵Ϸʮ弶һпܣһһǾԲܵġ˵ҪͶһģȻcarryDzܵġ5.ѡӢӢݵĺλҲǺҪģ巨ģʽֻʺżƥ䣬ʺλ˵ѡӢ۵ʱÿһǷ֪⼸ԭǶ˼أ -game ȭʽɫƴ ɫ汾² 1994굮ȭʡȺƳˡȭ94ȭ95ȭ97ȭ98ȶಿϵϷƷϷа֡S֪֪񶷼ҴĿ֣ôһɫأûһϷǰСȭʡɫأȭ9736ɫȭ971997꣬ȭϵǰ汾ڸнϴĽȭϵеij֮йdzܻӭʱգȻŴġȭ97ҡڡȭ97Уһ36ɫ/١άD桤͡ʡա¬̩Sۡǡ˹ȡK'ȴûеdzȭ9851ɫΪһûеڡսȤϷȭ98ⳡΪս֮սƷǰɫۼһã50λɫѡáȻȭ98ɫڶ࣬ȻȱΡġӰߡK'˹ꡢWhiȫ졢˹ڶɫȭ1454ɫȭ14SNK2016귢еġȭʡϵеʮIJͳϷйʮ֧飬ÿ֧鶼Աټ2ؽɫȭ14һ50ɫȭmugen114ɫȭmugenȭϵеķʽ棬ԼģĿǰϴڶ汾ȭmugen3.0汾У114ɫȭ硷ʷȫ񶷼ݵƶϷʱSNK2017ƳKOFϵȫƷȭ硷Ρһ3DMMORPGΣϷKOFϵнRPGй½׷Ϊȭҵ飬ȭ硷и񶷼뵽Ϸߡ֧߾УϷƽҽǽͬʱϷ񶷼һȡżͣ󲿷ָ񶷼ҾѻãϷҲԺڶ񶷼ҲսԱΪڶ񶷼ǶصļܴСȭ硷ονʷȫ񶷼ݵȭϷϲ֡Sǧ׵ڶȭʾɫһҪȥŶ -game EDG̸SKTرӮ šӢˡS7ķ֮һĻLPL֧WERNG˫˫8ǿĶٶȻܾЯͦϺѾǼS4LPLٴλعǿҲڼſھS7ȫܾ˻һ°ɡWERNGĽڱʤϲ֮࣬Ҳ人Сͱ̭EDGսӡΪ˴LPLһӣȴڻδǿؼңϧ֮Ҳ˺ܶ˼գEDGսӵԭʦξֱоEDGսS7ϵı˷ƣEDGһܣڶȻѹC9AHQմƱEDGٴη̣ȷʵԼˡͬʱҲʾSKTÿEDGʱѡȵݡ·+ADC+·̹ˣĶ鶼ѡûӢֻۣܴܣ·ݣоǺرӮǡڰеĶرӮһЩ飬Ǻһ£SKTرӮEDGġSKTǰEDGʱǵĸWOLFƵйEDGоûнEDGֻDzSKTDzEDG׼EDGջԽɼ˱ӾʼԽӢ۵ģEDGֹʮǿ;̸Ǿؼңʲôԭ򣬶ϲǺ͹עǵԽܡϣEDGԺúܽᣬݺʹ򷨣Ŭ! -game WiiUģﴫ˵Ұ֮Ϣ WiiUģCemu°棬θ֮汾Ŵﵽ1.10.0ϷԸһ֧֡ǴҶڡﴫ˵Ұ֮ϢģЧҲõڸԤƵչʾˡǴҶģЧ֮ǰ1.9.1Уģһθ1.10.0ЧFCϷϼ2Ҳһģ⣬ԭа汾û°ģ⡣1.10.0ڡﴫ˵Ұ֮ϢģЧ֮ǰĵģCemu1.10.0ѶԾǰأûǮûĵȴһʱ䡣 -game ʲôϷСǮ;?̵׬Ǯĵ羺Ŀ! ӭע羺ҵϷɫ,ע!羺ҵչݱ,ҵģٶҲ쳣Ѹ͡ 2017 ϰ,йӾϷгʵʹﵽ 359.9 Ԫ, 2016 ȫΪ 504 ڡ羺ĸٷչҲʹõ羺Źģְҵķչ,Ͷǧ򼶱ĵ羺ԽԽ,ְҵѡֵҲԽԽߡϷ,ѾDzҵĴ,Ѿв˿Ϸ¸ǾЩϷְҵ͡NO.5 ־ӢCSȻ CS 2011 ˳˵ʱģĹʵ羺¡Ӿ(WCG),֮ CF ȡ,֮Ĺ CS ҲûʲôˡѾûôϷ,ЩṩܽȻŵλ, 1ǧԪ,֪ CS ڵʱĵ羺Ŀж𱬡ǿ CS ѡ֡f0rest(Patrik Lindberg)Ҳ׬ȡ˶ 19 ½,۷ʱ 2009 ѾһС֡ϧ CS ûáf0restCS ǿߵҲԶͣ˹ȥ,Ѿת桶CS:GOˡNO.4 Ǽ2Ǽԡ˵ RTS(ʱսϷ)еij ˡǼԡ롶ħ3:геļҾ,ȻûзƳġǼ 2dzư RTSǼ2¶µ 4367 ֮,ĿǰϷ,ܽҲﵽ 2300 𡣺MC(Jang Min-chul)׬ȡе 50 ,ΪǼ2ѡ׬һλΪͳ˾ܺȱѵ,ȡ÷dzijɼ,ò˻ԼôŬΪʲôNO.3 CS:GOȻ CS û, V 粢ûз CS Ƶ˼,2012 귢۵ġCS:GOѾص CS λ,Ϊһ FPS 羺Ϸܳ黳,Ҳǹе FPS ûеֿ,ĿǰCS:GOѡ 7788 ,ȻϱġӢˡһ,ϵ羺ѡĵ羺ĿƬ;úİCS:GOѡ֡FalleN(Gabriel Toledo)ȷʵǺʵǸ 90 ,ѾͨCS:GOó 57 ԪĽ,һ׵İ,Ҷ˼˵ԼҲǸϷ 90 ˡNO.2 ӢΪĿǰϷ֮һ,ӢˡҲȻǰбƸݹͳվ Superdata ,Ӣ 2017 굽ĿǰΪֹߵĵϷܺٿΧǰ漸ϷĹ,һΧۡLOL µij档Ŀǰ LOL ͳĽѾ 4100 𡣶Ǯ LOL ѡ־ǶϤġħFaker(Lee, Sang Hyeok)ˡֻμӹ 36 µȴѾ˳ 1 Ԫı롣ֻ 21 ,λ廹 5 ,ʵ˸̾׳Ŭ,ŬҲƨNO.1 DOTA2ĿϷƴ,ϷְҵѡҲƴֵĻ, DOTA2 վ,Ϸƨҷšȥġ DOTA2 (The International 2016,TI6),ؾʹﵽ 20770640 ,ҲΪֹߵĵ羺¡ TI5 TI4 ֱǽڶ͵ߵĵ羺,TI4 Ľȵĸߵġ2016 LOL ȫܾ(LoL 2016 World Championship)߳һ˷,ѡȻҲǸĿǰ羺ѡлñǰһλ羺ѡ,DOTA2 ѡ־Ͷ 71 λ!ýһѡ֡UNiVeRsE(Saahil Arora)Ĺý 2826896.47 Ԫ,ֻе 28 Faker ӽ 3 !㻹ûгΪһְҵ羺ѡ,ûԼһΪ羺ѡ,,ûһ뻻Ϸչչij嶯?ԺϷŮ˵ʱ,Ҳ԰,,Ϸǿ׬Ǯ!......END羺ҵ̬ȵĵ羺ںɨ,ע -game ٷ·׻ NBϷҺãNBΪ𱬵ϷܶҶְֺޣΪӢϷ¢ϣµϷҵõ飬޵Ƿάҹٷ˳˵Ǹÿ첻ǸоӦҪǷˣɣͱ̭ܶ񶼱ʾٲңҲȥˣٷʶҪԣڲϸ·׻ƣNBϷθµݰѡҴʼŴ׿¼ϷȦӰȽϴ󣬶ҺܶҲʹùңٷʾƻϷƽΪ̵Ȼûб仯εĸǴʩ쳣û֤»ƣһ⵽쳣ʱƸ˻ķȨޣԽһΥΪǷʵNBϷٷ˳Ͽҵ£ǶûʾǸϰһֱ֣չеϣ㱻Ѷ԰ԴĿǰزŶԴ˲Ӵ֪᲻أNBϷϷûгԹΪֻɱˣɱˣķħڳżƨɣһȦ棬ǸֵܲҪˣҿˣҾȻˣվbiubiuӴ˼ҵ϶ -game ǿӢ۰:ְ,ôʤʶǰ! 褵ʤʳ,ʤȶ˵ڶ56%ʤ,ֱ߸!Ĺʤʴӵ2˵5!| λʤǰ10սʿ,߸ĵλdzȹˡλı仯,ׯܵǰ10.뿪Banλ| λϳǰ10߱䶯,İ褳ʴӰٷ֮0.5,˵İٷ֮14.7. ǿӢ(ܰ)λʤ:56.7%ƥʤ:54.2%ܶԾʤ:56.0%߸Ȼǿ,dzʴ1%ӵ3%ûˡߴ½һֵ,ֵ߳ʶϲȥ! ̿Ͱ|| ߾ȻDZĴ,Ŀǰ׺͸սӴ׵ʤʲϴ!λʤ:52.7%ƥʤ:51.7%ܶԾʤ:52.2% ְ|| ʽȻǵʽΪ,Ŀǰɲޡʤʿǡɼ˼ļ,ʲߡλʤ:53.4%ƥʤ:53.8%ܶԾʤ:53.5% ʦ|| εķʦdz˼,褡⡣зʦʤʶ50,汾ԷʦĶ!λʤ:56.1%ƥʤ:54.5%ܶԾʤ:55.3%| ȵӢ1. ҹĵĹǴӵڶ˴мٺ!2. 褸İȷʵǿ,ûλڸ߶˾ֽʹá3. ʦʤ͵Ϊ(ҫα)ԭ,ش! -game ҫŮܹٻڰ󴸸ʵ㣡 Ů洸ոճкܶѾˣŮ洱Զ̵Ĺ룬ӵв׵ŮԭƤЧҲŮ洵Ƥ˵еcpӢ۵ijܶҲʼ棬С෢ֻһ󴸸Ҳ棿󴸸ŮĻ Կ󴸸ҲһװˮɱˣԤϻdzһСûõͷ˴󴸸ǰһֱе䣬50һһдо䡣Ȼûйз˳Ů죡ֻ΢΢һЦ͡ȥˣ󴸸绹ǺܸˣֱпԿ󴸸ҪƤӢˣûӢۼƤ󴸸ʾֻҪע΢۾лԻƤӢۡڴ󴸸ijװСܽһ˺ǵĸߣŮ洸ոճܼ˺ûŶҲԿǵô󴸸ֱпŮٻȻԼȥԣֱȻ㶮-.-ںӵ֪ճ߹ԣ˽ճעŶǧݡټ -game µV3Steamڼ Ƴİ գSpikeChunsoft˾926PCƽ̨ƳµV3ҵɱѧڣDanganronpa V3: Killing Harmonyļİ棬øй鵽Ȥ µV3SpikeѧԺðսϷ˺ڰԼѧϵͳνȫµ̨Ĺ̨ϣ֮ѧ԰Ƶ˱޴դIJѧ԰ͬʱϵͳĶʹ˸ӽġչصĺɫĬ¡ ǰġơϵһֱзңڽSpikeChunsoft㹫Ϸķİ棬ڽ۶չϣ ʽİķΪ926ա񣬼İҪʹҼ棬923տԤԼڴ Ƶ棺 -game 汾˷ǸŭΪIJ ¼֮ʵѾõطijͷֱֲƽ̨Լȭͷ˵ְҵĻˣֱҲˡ鷢˴ʱҲеһʱӦӦɶԼɣʵȷʵУNBѾǷݽԼȷʾΪʤеƮƮȻ޹ٻμӱѡ־fakerһʵΣȭͷ֪ͨ󣬻ʶµԣڽ磬֣صķһƪǸ£ǶݵǸʵкֵܶ˵ĵطһı̫ڶֻδŮѶԴ˲Ҳʾһ̻Ϊ -game LOLϵƤһɽկҳεļӸ ǰվˣLOLһϵƤ⣬йϵУϢˣӢ۷ֱǽʥȽϷϣҲܷ⣩Ů㲻ΪʲôŮҲʺϣվվǷdzģȭͷôǵƤվмҪ˵ƤЧĺܲҲȫT3ǵ׷ʵҪ˵ϵ ۼ1350rpȼT3԰ʿУҲDZϵе֮һӽ·ģͿҲйĴڣЧҲйɣһϼӵζǿƵͻ֣ᷢչȻĽͺˡȭͷġϵ ۼ1350rpȼT3ʥһƤĻǺģسǴɫķɽеҲйͳС˵ijƤվõľǽʥһϵ ۼ1350rpȼT3LOL100ӢۿԳϵУȭͷȴƫƫѡȣ˼õҲǽǸij˳˻سǰγȫѽһѹӣЧû˵Ӳð͹˼ȻϵеƤõ˵۹DzһɣchinaƤվԤ϶һ޶ġϲվԹעһ£ôô -game LOLS7սӲμȫܾȷ ʱ913գȭͷٷڹϸ˽S7սӵĶԱݹÿսֻЯһ油ѡ֡ҲһЩ˿顣սӵĶԱ¡LCKSKTSKTļһֱdzUntaraû֮ϣHuniСȻ׷֮СǣȻȥȥǾݡ飺ļ׷ھ⣬һ油ϵLPLWEWEνBenŵ油λãDzӰʲô֡EDGѡ޼˿ڸϵµΪ油Ҳкܶ˿ѡʾRNGѡҰźΪ油ѡ֣ܸߵKorolѡԲСLMSǻ֮ǰ׷ԱAHQŻ油ϯϣڴټС~HKAΪ½뵽Ķ飬6ѡִҶǺϤŷLCSFNCG2Ϊŷ޵һε׷MSIǾݡMisfitsLCSC9IMTTSM -game ҫһϼܣŵܺ󣬰ٷְٱ ҫΪһֻϷֻĻĴСΪָ赲ֳġֻ⵼ʧôǽĻЩһļܡһϵļӵ㣺̫һܣڳŵϵĻΪʲôżʱ֪жˣҲżܣǷŲΪҪһͷγɵķܽͷšڶǴǵĶܣΪһԼܣǰͬûʲôôرһŻʱ򣬽ϳʱĻسӳٺѷãԶһܵĻɼٺܵijĬЧݼܣΪͼкͷʱõʱֱӵֵһ׼ܣıǰڻûʲôãʱ̣࿹ԷһܣݼΪԶӢۣһŵʱԶͷһģٻܵзǴСĶܣȻǿӵֵ˺DzǰڻкڣһֱȽСԶһܵķλƼáСûһӴܵľأDzǶʱ;ޱȣҸһ𣬸ϽȥΡijԭתڱԲעߡ -game ֱ ɱּ ڼ䣬УȻǰЩˣʱ˿Ӱҵ顣˵ĵطңȻԼٷһֱڴңʹá һҷǰһ͵ļңýɫƶٶȱȿ졣·ͼʾĽŲʮָЦ ˿ЦõĻֹ㣬ұʾҵ׷ԼȻΪҪҪңСֻܱʾ⾴ҵ̬Ҳû˭ˡ ֮ǰһֱΣֱŴ¼űع⣬ŴױҲ⵽˷ŵĴҼֱǰ׳ղŻʹãҿԺڽ˿Լĺһ֮ͲС໹ǷȰЩҵҺΪ֮ɡ -game Ե󡱼ĿƷƵ 1029գѶϷɽЯٿĽԵƷԲĻϣٷʽ˫δĺƷֹ滮ݹ滮ͬʱҲع˼ߵĿɫĺƷɴڷҲҵֳܹĿƷôǿɣ½ԵΡġ¡䡱ϣԵΡ˼߻ĬԵΡʽΪ½ԵΡΪϵеһΣҲɽӺѶĵһƷԵΡԼȫĿڡ½ԵΡУϷ˾淨ͬʱΧ۵չ¾顣°汾νƽνŵĹؼʱ㣬ʱǽսңͬʱҲž鷢չǨ3Dٰ浱ǿԿǣ½ԵΡǴｨģ·dzϸ˵Ϸ϶øΪ¡ڲԵУٿصĽɫ°汾еdz׸˫Աɡɽɡпŵļʩ⣬ҲǸܸܵģԵ2Сġͻơ롰ڡԵ2СǾΡԵ2ֲ棬ɽӡԵ2ԭŶӴͬIPMMORPGΡϷùʶ4˶εľɫΪγꡣͬʱϷڲ˫趨ҿһ˫ɽл1V14V4սȶֳϵ淨£϶ص趨ŻΪɡСʼûãDZѾпıǰεĻʺ̫ˣεУҿʹṦУṦ״̬µﶯϸڴʮֵλҿɵṦɷСڵ㿪¹װۿѡʵ̫ˣװ֮ĶȻڶֻ밲һӵ۵ӡڼʹ÷棬ɫĶҿлһɵIJͬʹ顣Ƿڶΰ汾ոµġԵ2СεУȫµӾͲС3ָ⽭ġȫɫȽΪ3ΨһٷΣ3ָ⽭һֱǵڴڶεȫƣԹдļֽ磬ȫ3D硣Ϸ˶ϤġšȫɫȺɫ飬ڻԭԭľ䳡ͬʱȫӽΪҳֳϽѩϵͳȫֶ̬յĻȾӪ侳Ϸ顣ͬʱϵͳþͼ״࣬ʿʱԿ̾˲䡣ʱѡˡ򻨵ӡΪȻϤһ壬ɽ¡dzʹ򻨵ṦǶṦЧʮֻԭεṦЧװﶯİڶȻˮṦɳʱҲˮϽˮȻˣϷһлɫɫڶǿԴӲͬɫӽȥ¡ĹŷϲĪƷ¡ķһڡ3ԹŷۺչĴ3Dŷ绻װΡ˵һװϷĺģ¡Ϊṩ˷ḻװ淨ϷԶ͡߼Ʒװ֮ɡѡȵȡϣȽIJȾʵֹӰЧϡ¡˵һݶ飬ͬеͬҲʾԡ¡żȤÿлһϷжвһijлЧ³дĹŷװҿл˵ܻϷﳯ˼ĺװعһֱӾͽɫʢգɫӦĽɫ֡⣬ʹϵͳҿԼϲĽɫģŮһͿʼһ߹ŷɫĻװϷܹΪǴһӾʢ硣ĿǰϷڵԽ׶Σ¼ƵƵҲֻϷзеĻ棬ϷʽԸոµòԹϲĻǡԵIP߹20꣬ѶϷɽִ조Ե󡱼ƻһڴȫµδɣáappgamecomעǵ΢Źںţÿ춼кϷϼܣħ֮Ӣ޵УսԪ;ΡӢս衷궷ޣٻʦϷۻлӮȡϷܱߣ -game DNFְ汾ְҵIJְҵв DNFо仰һ汾һ񣬴汾Ȼǵĵ٩Ҳ˵ڹ۵ų̶ˣһЩ˵һְҵǷdzֵ¿๦ȥģǾ˵˵ǰ汾񡣽ˢͼֿسԴԹʸߣھpkһΧԶ벻˺cdˢͼָкܺãпбо۹ؼDz򵥡һٷְֱҵҪܸߣûǿĻ˺ȽϵͣȺܱȽ٣ˢͼװҪȽϸߣеѼܣҪ淨ûдʱͽǮͶеڼڼҲƵıְҵ֮һһ޵ֵĴڣȻ5.25İмǿȲߣֲ˲䱬ҲDzСģȱûпƵ·dz׿շŴСŮҩ̵CDѭʱ䣬ˢͼٶȱȽϿְҵҼܴ󲿷ͷҲṩߵıռ䣬Ҫһ٣ֲоͳˡŮеŮеڼǸĶȽϴְҵ֮һлܵĸĶҲ˻еıGϵеҲеһĿ -game TapTapԤԼһ,ҹͬĥˡϷ ˵¶ĥ,һкֽϷ,Ҫʮĥһĺͼ̹֡һζȫϷҪʡһʲôϷ,áɡռغϰá¾䡢ܸԸ⽫Ϸں˸زĵ̹һ顢֡ӵ,ϷĹҲк͡Ρл,Ϸں淨ҪԼҪݵĽɫռĹɫ,ͨƽؿɽɫÿһɫвͬĶλص,TDPS̵ְҵλԼ츳֧,ҪͨʵսȥͰšδսǰ,ߺ츳֧,Ƕʲô¸ĸɫ,ʱͷŴе,ڲԵ˼롣Ϸϵͳ൱ḻ,ں˵淨˵͸ơСϷռ,Ϸĺص㡣Ϸдᵽ,ŶӶԽʵ̫(Pokemon뱩һˡ),ԡһԽΪĵϷſڴֺ뱩ķ˿,ڽİ,Ϸ,ϷĿܸܵN̬ˡ֮ͬĹֺܶ,ռԪȻҲһ㡣غϰ,ǺսģʽڴͳغơҷdzĻ,ʱϵŻĽ,ս˫ͬʱ,ЧЧ,սҲҪͷŴ,ս൱ڰһ״̬,ͬʱغҡսɫ,սԵüȸ졣¾,˵Ϸ,ԴЩľʱϷӡǡ,̥ڡPatapon,ҴеļӰ,˵һӡĺøжȱȽϸߡܲҶšPataponӸе,Ϸݺ,ʵҲں淨ȫͬPSPʱΡPatapon,ڱϲķ˿òͬķʽڶഫšʵ,ϷĻFlashʱϷĴͳ,Ȥ,֪С,˵ϷԼʶС޹Ĵ½,۵趨ȴеdz˼,Ϸʱ򲻻áҪ趨ܶϷиõĴ롱,ǡм䷢ϷȤ¡,ôϷİǼӷʵϸڵ͸,߻Ҳ˹ġǡҪ,սıʵģ͵ײ,ֵͨĵﵽ,һԲϾͿЧ,,ǰġijˡӻһɫĿߺҵȥ8, TapTap ϼԤԼ汾,ܿ༭õϷ԰,ҲΪһûϷԡ˴ԼһСʱ,˵ʱָġʾ顱,ֹ˴ԼһСʱ,ҳΪϷսǰʮ(û,Ϊ)ҷµ,Һܿõ˹ٷĻظ˺ҵ,ˡÿһθµIJ롣,һ,εİ汾,ҲڶеһԱ,֤ˡ񡰶ӻһһδβ,һδռ,һδϸ΢߸۰̨ Google Play App Store ,ջ˲׵ı,TapTapҲͻ67000(ֹɸʱ)ײϷһ,ҲһЩ鲻õĵط,һҳڽŶ,Ҳܿ촦ñ¶⡣ϷĿŶ,Ҳ TapTap ϵеһԱзŶNTFusion2010,ҲDZȽһĶϷŶ,Ͷз,Ҳͬƽ̨Ʒ, TapTap ԤԼͲ,Ϊһҵġ2·,5·ɶ20%,кսİ汾,ٵһ,̱һʻĽ,ʼѹûһôªdemo,³дʾȺͼǰ̳,̫ж˰쵰!(s㧥)sһ·,ҵĸҶ,мкܶ˵Ļ,ͽлڡ︶,ǵ֧ԽԽ,һڽ,ƴȫ,Ϸ!һԿߵĸл,ʵϷҵһ΢Сʱ, TapTap ԤԼԵϷÿ춼кܶ,Ψһûб仯ҶϷпϵĽ,͵İϿı޲ߡij˵,ϷһƷ,Ҿ,ܹͨһֱ˫ͨ,һϵȷ֮,ܼз쳵ĸäĿ,ҲһϷĥĹ̲Ǽһ仰,ϷڴĥĹҲȡҵİݺ֧֡Եѭ,ҲƱػΪϷҵġ֮, TapTap һ,Ҳ񡣶Ҹ˶Ϸ,ҲһЩСǡ TapTap ΪTap༭,һͬɳǵ,Ϊ,û˭Ǹ߸ԽеĴ,ϷļʹĥϷһҪʱƷϷϢ:Ʒ:̽黥::ۼ:ƽ̨:׿ / iOSչĶĶԭġϷ~ -game ҫϿȸTop10 ˭ҫеĽɫ һһȵϿȸֹ,İ񵥱ȥ˽ϴı仯,һЩͳҵĴз׷׵ǰʮ,ҽҩҵвٴмǰʮ,żҲ200ˮ,һ˭ϿǮĽɫɡTOP10:Ѿ Ƹܶ:202ˮ,ȫߴ½ȫŮߵӰӸǡTOP9:̫ Ƹܶ:215ˮ漰ҽҩͲ,¯Ӳҩ⿡̫Ҽż¢ߴ½ҽҩͲҵΨһܹҽҩijҽȵҲDZѹTOP8:Ϸ Ƹܶ:246ˮһִѧԺ,Ÿߴ½ѧǰѵĸҵ񡣽ҵ⼸,ɹǰ10ݴеɫ,ѧԺѧſڿܳӰ,ܳϷиϡTOP7: Ƹܶ 301ˮ,˵ϵ5ˮͶ,ʱƸֵ60Ǻܶߴ½ʾšǶ˽ڶŮ,:Сǡݼȡо,Ą̊́Ӳ,û¡TOP6:֪ Ƹܶ 311ˮ䷢չ֮·ƴ,һʼΪסഺ,һЩСͶƬ,û뵽ܵߴ½ڶլз˿ķϲ޺,¢½ĵӰҵѾԳݽɫ,Ͼ۲һˡȻųߵһլŮ񡱡Ȼ˫աѾìܡTOP5: Ƹܶ 460ˮߴ½ĴBOSS,ɵʽܴijǹܴά,ҹ¡ʼƯ,Ҳǰ10ܶשҷƯʽ֤TOP4: Ƹܶ:479ˮߴ½ƿ׶,ΪӡƸҪΪƤ,˵һ1.5ˮҡȫ½һTOP3: Ƹܶ:520ˮߴ½Ů,ʲȫŮӵСǹܴӳʽʵӵߡTOP2:ɪ Ƹܶ:533ˮũҩ,͹ˡTOP1 ҧ Ƹܶ:800ˮ+ߴ½Ľڴ,û֪ж฻С˵ȡˮ λϿǮļһgzh ·־˵,ں׷Ƥޡ -game Щ׻ϷɵӰıģģ һӰijɹIPֵõٴηŴķʽк֣ܶиıϷһֺܺõ֡ǵġս2DzҲϷأʵӺ׻ʱдϷıãСͨĻ䣬ܽһЩɵӰıϷûǸϽȥ°ɡξ1944꣬ӳһοĵӰӳijӰɹ͵Ӱ˾RepublicɣʮеϵƬּƬ Ϊ壬ڹڵǰγdzûлԤڵķ죬ܶ˲ӰƬڹڵ޸ģ˵ӳݴһսӢ۱˵ط٣ǽƣְͻûгֵȵȡⲿӰļֵ¾ӳĴɡƬ1948й½ӳʱڵΪ޵д̽ξϷıԵӰӳӳսѻӺ˱˻Ƿʾʲô޴¼Ҫ Ƕӳӥ۳ӪȻˡⲿϷFCкƳģԻкã̽ϳͨû1СʱͨصģѶȽϴرǶԳٹؿԹڲBOSS൱ʹҪǵľУʹþͳɹһ롣ԣϷFCϷвɶõļBatman ֵܵӰ˾ƷĿƻƬɵķٵݣķķס˹ϱ磬˶١ܿˡƶɭ𡤱ݡƬһԳơͶƵĹ¡1989623ӳFCϵͳܼӣûжҪأ׷Ϸԭ㡰Ծ롰ʱȤȻ㻪ԡϷƽ⡢桢ֵȶ෽ɶȺܸߣûԵȱ㣬ijЩ쳯˵ѶȸһЩⲿƷϷеĽFC䶯ϷҲҿdzɫƷFCҪҲ򵹶JokerҲֻһӰԹܱؿ֮(Cutscene)üؿﵽԵӰ̨Ϊοԭ͵ij̶ȣȻϷ󲿷ԭȫ޹ϵɫ軭GothamнֵƮȣʹƷȫΧغ񣬲û۱ħսߡսߡƻõӰϵУӰ־Ӱܿѡ20ֵղصһӰʱƬƱλӵһⲿӰȻһ30ǰϵĿƻƬڵЧѾ൱Ƶ2017νһšⲿӰĹھλ˵ֳǿҵʽӢͳɫĵӰƽԺЧǷֲġħսߡFCϷͬӰΪĸıģϷ淨˼ƣ㲢û˼ô¾͹ˣһѪ·ϻѪԽӣϷеҲǻж··ѡģҪʺϵľٿǹɱϢϷӵ޵ģԿԾĻӻˡ -game һӺڵٵسIJʼǣЩƬ һӢңһ֪һмֲ֧ϵңڵٵءһʼǣϷгһ飬ôϲ㣬㽫ʼǣͲˡʵڵٵسIJʼDZЩƬÿڵٵذ˿Ǵųֵʱ򣬺ڵͻųһƯСӵƬɣҪĻħղܡҲΪˣڵٵصķ˿ȫз˿Ůۺ١ÿڵٵعСڵŴʱڵֱ䶼֡ʱ绹ƯһֱƤѸǵϷڵ꣬ʾָijɽк˿ٵأ˿ͽк˿񷢸ĺڵٵѾĵãճڵԼֱĺڵƢѾ˺ܶ࣬ЩƬҲڳˡںڵȻϲϵӢۣ20600Qͷ2Ѫ𲽵ĴӶƴ򷨡ϵҴ䣬ٻʦdzS8ϾҪ٣֪ڵٵֻǴʲôϲ -game ҫӢƵжʧܣʤʳʾ֪ˣ ũҩԼߡӢ۸ճڵٴѹҲҪʷУԭӢIJף СŹ۲һ¸ֶεʤʣѵóһۣǾԭĻƾٴ¡ijBUGʵܵԡ ԭʧڣȱܱߺIPװﵥʷһǹŷɻƲܲ٣ֻҪĹ־д棬Ҳܸߡ رǹʵʷҴ黳ڡ ûʵԦԭֹϢôࡢ£ʲôãһ˫èȣŮңԶңͬʱϸߣݴʵͣԶŮңȫì壡 񣬴ϴʵԭÿʳʺ λСǾòǵ÷ԼĻϾöɲܶŶ Ҫ˽ҫѶǵùעȤŶ´ټ -fashion 2017ﶬеĴë£ 3ִ䷽͹ ã㵽ϣʱѶעдﶬȻoversizeë£С໹صȥһ2017ﶬʱװ㣬ִƷƶëϵУͷڰOͣҲԽԽߣoversizeëȻǽʱȦĺôźÿأ¶½ůĴë±Ϊͷǰͱزٵİϸ֣ᷢëô䣬ʼձķؼɫԸ߼ܺܺõʼ͡䡱Ϊһ塣1ë+ϥѥСΪִÿ½ģŲһ˫ϥѥסȲůȳŷʱдǵϲһֻ⡣߼һֱʱǰɫɫٴֵ͵һ˫ѥвθС֮ɫ̫ң+ǾĴϡ2ë+װŵŮϲë£ëµ°ںд׹УȿҲѡ񴹸нϺõIJʣ߽һܸܳһţֻдܸ֮ܡȿëʱܽë°ܸõ͹ߣѡͬɫϵ䣬˸߼ӾܣϵĺɫְΪ׺߼ʱֶϸڴ֡ë´ţпҲDzѡ񣬿ɵĴë¸ůŵĸоԲţп㣬һһǶõĽϡϲѥС飬ţпʱѡŷֿ㣬Աŷȵ״ССͺϲ͵ۡ3ë+ȹӴë»ԴȹӣԽɵëԽҪŮȹӣdzɫëҲӦôdzɫƵɴȹšɫë´ȹİȹ׵Ľ㣬+ͻȴӣ֡ʳģžoversizeë´ȹӣ:Ĵʱװĸ߼硣ȻĻԴȹȹﴩһ˫࣬ͬʱ硣 -fashion պŮʫʫ֣㾧ײײͣվ˭ εʱװе㾫ʣŮŮ˧˧ۻĿϾӣȫһĸ߼ֵʢ硣ȰŮȰлŮҲֲֻϾֵ·ץҲholdסŮɡʱװֻܲŮСӢʱװȷв˧ŮȴƫƫҪ硰ӢĸλС㡣ǵġӢСǵе̳ΪTODSƷƴʹʫʫTODS 2017ﶬɫпˣɫָЬTODS 2018 ĴֳˬĶ̷Ƥ¿˵Ӣˣlookʫʫѡ˳ɫ£ڴɫTͽϵ TOD'S СЬãȵ Wave ԶʧšԵϹ¡Ҳһ͵˾ſ¶ͬshopping¡޿ħ費㣬ʫʫμTOD'SϯDiego Della Valle͸ϯAndrea Della Valleֵ˽硣ʫʫTOD'S 2018˫ɫƴƤ¶ȹTOD'S 2017ﶬDouble TɫTOD'S 2017ﶬ߸ѥټDouble TɫƤ׺Լʷеסʫʫְͷһ侭ʫʫķ˿зĶԻʫʫͷԽԽ̣ѾLOBBOBĶ̷ʫʫҲ磬һּɫ˿ȹڹȻŮǰELLEʱȻ˵㣬LOBͷгԳɾˬֶŮԵڡ硷ķһͷƤ̷ϯʫʫRed Valentinoȹȥֱӡ10ꡣµʱװܣǵʫʫСٴ޼ͷֱӱӢһ㳡պŮǣǶǶ̷Ҫ˵TOD'S㳡һȤ¶ǣ˴ʹʫʫձijͺ֣㾧Ҳϯ㡣ʫʫײƴɫëǡʫʫƷƴƬҲͬͬɫиζΪġijҲձŮĴƮƮDZȻġҲBOBʱڣΪĴڣ廹˵Ůż񡣡ڵ̷ˬȥŮɬ¶˳ŮԵıbattleλҲdzߵС֣㾧ɵǵֺͳײϿƤȹʮŮ̷㾧״ߣƷƬҲ˧ʮĺɫƤɫƤ㣬ϳ̷Ӳ˧Ȼĵ㾧СȻܿᣬdzڴк֣㾧ȫȵӡ⻹ʶǸ㾧𣿿ɰ㣬Ӳ˰ȥǰһͷijһëҲײˡ װʽװɢŨŨŮζ̷㾧׳ڿ׼ְҵװɫëëñȴƤַȫŴIJͬ֡ͬһ㳡ʵպŮDzͬʱײȫ̷ĻҲDzࡣҿϧ -fashion ȿ,һڽ! Ϸ֡Ůѧ´䡿עȿг̶DzҶǿ,Ϻ,ܺܺõ,൱ܵڵϲ׷,ֵøƷƵڶִ˲˵Ƴ¿Ҳ׼ͱǽһȿ㡪ȿ,Ȼ,е㲻ͬ,ƮݵѩIJ,ǸӲͦ,оװеĿ䲼,ʮֵȴ,˧һڽСȻȿէ֮ºѵĹװ㡪䡢̻ɫĿʽ,ĿϿƤ,ʵ״ʡ䲢,򵥵ϰT,ͺ¸С߸Ь,˶Ьֿɰ,ѧԺ硣ͰTĴȻÿ,＾,˹㶫ϵı,Ӧöʵ˰,ͱǽȿļմɡȿ+ԷĴһ,ȫ,Ǵ¹,ԳŮɰСҪ̫,ѡˬİ׳еƳ,ü򵥵ķʽͻƴĿ̰幫ʽ,ܳΪȺе㡣ȿ+֯ǰȽˬķ,֯һЩŮ,Ϊ֯͹Ů,߸Ь,ĴŮζֲ塣ĺõı,Դ֯,,޳ϰ,°,Ҳ֡һСӵı,ԴԿһЩĿʽ(oversizedŶ~),΢΢һ½,Ҳȿ+װһʱֶȱ͸,Ŀ,װ,ʺְ,ʮԸѡ̰װʽ,ɫעdzвθ,ɫʴһסѧԺе,ѧ԰ЬӻɵЬ,ϰ߸ͺԸˡ- Կ -Щ,㶼֪Լôÿ~ᴩ | շĴ,ԭҲô!ᴩ | ʱ־ĴСĻ,ԭôchic!ŷֿ+ѥ,2017ﶬ!ȹװһͿѵƷ...άע -fashion ʺÿĴ,׷Ǹֻ󴩵 ҽܵĴǺһ鴩ִҲҪԸ񲻼ʺ˵Ǻ,ԽȻԼ,ȽϳʺϹǶڲϲȹӵһʵĿӾͿճ look칫¶ȱȽϵҲѡ͸ԱȽǿijͬʱɹһû߿ʱټһھʻع:ҵ߹ʱ!ܶ꾰¶Žʱ!ʲô;ʲôЬ!Ьǧ!ɫ,ɴʱз!Tȫ䷨,ҪĶ!˶糱,ܲ? -fashion | Zara£5 Һ~ϸ³ZaraԭģZaraˣֵˣ֪Ƿ񻹼ǵãǿƼһɫ¡Ȼڱͬѧڴ·Ӧе㲻ˣǶڼյʱǿ˿յûУ򵥸ŵ㣺ɫdzޣDZȽڵĻҷɫͿԣȻ۸񣬵Ǹ߼леȱ㣺ڴĿװ̫׿ǰҲдɫΪһֲ͵ɫҷɫɫӽһЩڰ׻һǺ״ġһɫϵdzķɫɫȽʱֵĻҲԳԺɫûԺɫ,ɫLook 1ԼȽϲһڣЬҲǺڵģĴԸʺϸáЬӻСЬdzɫЬӣͼң΢ġЧdz򵥣ͻµĸ߼УLet the coat do the talkingһ˵ĴʺϴijԪصףԱֺ۵ĸʱֵĸоŽȫҴ䣬ҲøòſԵLook 2Ҫõͻ²ͬɫĸϷѺɫ»˷dzڴĺڰƣͼ󣩣ȫڴӲˡǣէһպϣµĺڰ׶Ա̫ǿ˺ķɫholdסһơּһװңһбȽϺõƽ~ ǵöǰдɫҪӷ~ ٿһŽҲƵģûǸɫChanelİæһҪۿLook 3һԣһɫţп㣬Ϊоձ黹ɫţпȽ϶ࡣʵϷ۴+ӣ״ˣǺIJɫţп㣬ƫɫţп㣩ұĺºͺЬͼ󣩣ֱӻɫţп㣬оԼĶÿԡȻҾͰºЬҲȫɰɫϵң̾˳˺ܶɣȫdzɫرаɫʱ򣩣ڷɫ˵ȽкгŶ±DzӵģϼдðѴ³ȽŶ˵˵Գ˵ƫͨһĴ~Look 4ȣ洩ȹӱȽѴµĴϵݵġ֪󲿷ֵطܴƽЬlolҿԻѥһ洩һ޿(J.crew)һıëȹ(Club Monaco)ΪȽϱȽ԰ϵЧʺͨ~ Look 5һ䣬˺ͷɫıȽϽɫĻëºë°ȹëDZȽɫȹ̿ɫɣͼȹӸӽʵɫԱɿˣû뵽һͦ˳~ͬһɫϵǻЧԱȽŮζЬҲ⻻һ˫ļͷЬ(Bally)עʱ뵽עҪȹڵɡȹɡȹƻºֱ͵߸УһЧDzզݣһ˿lowlowģͼУֱͲȹͲ⣨ͼңⲩĽĵȷвٴȹɡȹ䳤׵ĴǶǰѴڼϴţϣͱȽʵֿ˵봩̵ܶȹӣҲǿԵģֻҪȹӱԱȽϽҲִܴµĸ߼ˣ磺˵ǹϥѥȹ̫ҲУôͲοͼóë¸סȹӵһ룬¶ȹڵһߣͿ̶ȽȹӶڴµIJЭ̶ȣͬʱ汸˴ϥѥϸŵpsһ£¶8ѮģӦZaraһ＾¿һԱ~µĻǣ4968/221ӡбdz3ɫɣɫɫɫд֮ǰһ£èɫѾɫĶۡҲҴ~֮ǰظұ֤һĵģ˵Ŷͽܼ -fashion ô£һϹ һһȵĶֽڹȥ˲ʲôľͺã˫ʮһǰvs˫ʮһ㨋ûˣ ջ~Ȼ졪ÿջһõʱһȤĴϷijߣܲʪЬ޵ë£Żͷһܸߣʱ֮䣬ֻһëľ룡ֱŮ·ÿ...ϰִҲÿġԭ봩ɵ·һչ̬֮ʵȴġɫں죬ܰبӱң ԼôԼûX𣿨ԸӾˡֱԣ·30첻ԷҲ°ɣŮݴС磬Ǹ顣GDͬůͬѧ㴩ȥϿʦһöҪĨԲԲͬëشŨҵر߹طӮ ø߹õţ˲ŮҲĽС磬ġȡ˸߲ҵظ·ֵϵģǸ󻹲Цˣ͵ް -fashion Ըе¶װ,㲻һûа?? ֪ûй,ȥ̳Ħ˺ܶȦʾ,Ȼ¶װ¡¶װ˵ʱֵļԪءҶߡϸơ,ʽͷ,ѡҲĿ¶װ,¶ˬ,¶ʱ,¶,ѡʹϻҪһС˼ġСһѧϰһ°~ ѡ¶װ ¶װΪܺõ͹ŮԼ粿,ѡ¶װʱ,һҪǷܹԼļ粿ֳ,˵,ܹԼ,ѡ¶װ,һҪȿԼ,Լ羱̬ĽСŮʺ¶װ,粿ļܹСɵαøͽΡ,СֻŮѡ¶װʱɶ,ܶѧϰ,粿ڿյһСᡢV졢һֵ,dz鶯͡粿ڿ+ϸ,Vĸ߶ȸոպ,ڿյƽ粿,ŮСԸСһСȹѰ,ͷСƤ鶯,ɫchokerһһ㶼 ɵV,ԵûôСֻ,Щÿɰᱡ˿ʵĵ,ʺϳĵˬ,ڵ粿Ļ,ɰֲʧԸССŮѡǰװκҶߵĿ,Ȼ߽ϸߵݵСŮѡ¶װʱ,ѡڽϽɵһ,Ϻڿ͹Լ粿,ɵִһ㺢Ŀɰ,ҲԺһȹ,ɵļ粿Եø,һӪϸСһοպ,ýӰЩСŮ˵ġȽϷŮѡ¶װʱҪһЩȿһʾ,һ׽¶װ䱳,ȻǼ,ԿϾиӸ,¶װԵðԲ һ,߼ƫĻ,ȷĴ򿪷ʽȾҪ¶̫,ڲҪ̫,΢һ,ӾϾͲ͹Գʵļ򡣽ѡĴ,ϸߵڲ¶Ĵ,ɵӻڸǽʵı۰,ˬķ,˲١ŮҲѡV¶,͹֡ V첻ڸǴ۵,չְġʺԼ¶װ,ǿô! Ҷ+ţп ҶѾdz鶯,ϼԼţп,ˬˬ һ+ȿ һ+ȿĴνݷ,һü粿س˳,ȿȲ,,ֻͤͤݡ ¶װ+Сȹ+ͷЬ ¶װϺСȹ,鶯,ϼͷЬ,ֶһ ¶װ+СЬ ¶һԵĿ,һ˫СЬ,˻,ԸС -fashion 1׬6ڵı˺ķ˹˧һӣ ˺ķ򳡵Ĵ۷ʱڲ֪²˶ŮġȻ˺ķѾ۶꣬ǵлԾţֻǻΪ˼ͥ˺ķӢ˻ϣǧŮԼУԶ޷Ŷͼ42ϸɲƾִԣ۲ǣ׬6ڡ3˧Ӻ1ŮĽǡǰŹ42յŮСһСӣŮū˺ķֵٽĺ~ôôиɻͼƬ ICʱ2017910գŦԼ˺ķ뿪Ƶꡣȫ˧İ˹....ӣͷҲɲȴ˧ʮ㣡˺ķôԽԽζɱζʮ˧壡ҲףҲ࣬ʵϳʣȻõԵá -fashion ɵɵëţп㣿MMڴ˫ޣը еְٴĵƷ˵˫ëش£װij˷ݸʡ㵱˶ڳʱе׷ɵİȫĴÿءµëˬЧdz˫ëɴŮζµĹգdzϸÿŵʣŷdzԴѡϺ;Ը߼Сġͦʣľŵĸоžİ˿ȥʱ࣬360ȻƽָãͿ䡣¿ëرʸеһƤݣëIJʣʸʮ㣬dzʣdzԹֱͲݲģdzݣOLϰ߲μĩۻȳϣ¶ȵѡɫͿɵİǡôʽжһЩᣬͿʹгëë׼Լİƣŵһ棬ůƷʣ㴩ŵͬʱֺ͸оѡϣָϸ壬ӷף෴ӵвߣ³ϥڽͷзԳŮԵƤãܺõβߡ -fashion һٴСڿ,ܸ㶨ﶬʱִ! ,˵СŮѾë¡׵ȻƷں,ȴΪ촩ʲôաʵһٴݵСڿ,ܹ䷳աСڿ㿰ļܴıgetƷ촩ɫǦʿ,ﶬֵôֵ,DZů͵ĺɫƤĪˡܳаﶬд,¶󡣰ܴ?!ΪƤ㲻ô,ʵֻǴ򿪷ʽ˴!ᵽɫƤ,ܺܶ౦鲻Խ뵽Ƥ˫ɷ塣˾Ͷ䵥ƷȴĻ,ǿʹش!ʵʶָʮƤԶеĸٴô,Ҵ˻ͨĻӳʵöࡣͬΪƤ氮۵Ĵ,Ƥȷ򿪷ʽ˽ҲһֱڴƷһֱߵμ,׷׻Ƥ˧¸߶ȡϪСɫƤп+Ƥ,ֱӽɱŷ˽,ɫƤʱ־,ǿְ©ʱȦ16ꡱģKaia GerberͿᰮƤ㴩,Ҫ!ɱΪƤֻdz˵ר ,160ͷĴ°Olivia PalermoͺϲһɫƤͲͬƷ,תͷ硢·硣дܰС˴°ܶ,ɫƤҲΥ͸С΢Ů,ʵֻҪѡõ,ҲȫԼԦƤ㴩ġҶԱһ²ѷ,ɫƤ㻹кܲЧľ~HoldסɫƤ㴩,ʽѡǹؼ!ӳϿ,ɫƤһԾŷֿ㳤Ϊ,¶һؽ,ͼ,˿ȥҲΪƤʵصԭ,ʹСΪƤƤҲʱȦȻߺ졣ǰڳϯYSL2018Ĵʱ,úɫSmoking״ɫƤ㿪girlģʽԱһ²ѷ,ʵصƤ,ȴʺҵȷճšοûмһ,ﭹƤӾϲ͸,ûһѡ񳣹~ӿϿ,ҪĺɫƤֵֻ֡Ҫõ,Чһ,ҲʵĴܡȱݷdz,ôֱȽϿɵƤȿܻʺ㴩š֮ǰһpo,ԱǸͬӡԸ,λһ氮ƷƤ㡣͵͵,ι,ҿȫǿһСڿ!ճھѡijƤʱ,ŵһ㿴ĶƤֵƤѡõǽڷǸϻPU,͡,ؼdzʹڴ˵Ĵײ,ô¶ôϴֱƤ,ԷĴĵϡİðĵֱȥ,ѹûһ,൱Ͳ˲ƤıǶзɫƤ,˫ӾϿȥ˳Զڴ˶ԶǷdzѺõġůԶںﶬڴһĿꡣƤõ˫A+߿ڲ,˻ϵͨƤ𵽸õıůá,һõƤ㻹Ǵ+dz͸ġھ⴦άӳ,ƤкܳɫźʪЧ,ʱ̱Ƥˬ͸㿴,90ȵˮ,ҲȻ𵽿ɢȵЧͨƤԱ,Ƿdzԡѹִ⼸߶,ö᲻!Ϊ?Ƥ֮ʵԶ!Զ!!ֹ!!!߿ƼƤ㡱߿Ƽɷ,кܲȼ֬ЧƤ,м˽顢ѵȶȻֲɷȡɵȼ֬׽ҡ㴩·˶ʱ,ϽĦ,Ӷʹ׽,ͷȼ֬ɷּȼ֬,𽥷ЧᾫŴҶİ,ǻƷвɻȱıʪɷ֡ڷ֯׼뽺ԭ׾һӵ,ǡ,ǵȲ𵽱ʪۻáƤͱȽϸ,һﶬܻ׸,촩Ƥ֮,зȲñ֮ǰۻءԽ,ůݡȼ֬ȶŵ,Ӳdz͸ʵƤ,Ǵ㻻սƷ⾪ϲһ֮ǰʵ˽΢źŰһСϷ,ͼ,ЩҪϣҲöİǴŻ,ٶϼ顢͹,һ~By The Way,㻹зƤʵĿʽѡһ޷֯֯,иʵĴŸСͬҲзdzɫΡ޸ЧȿԵ׿ճ,ҲֱĽ˶,˵ǷdzˡȻwuliǵij*ʶά, *ʶ,һٴɫƤ,аﶬʱִ!ƤȴһСľͻƫ?ܿΪ㴥Ƥһ,Ƥ㱾͹,ԴӦμǡLess is MoreԸ߼СһЩָ,ֻ˵һѡ²˻,ЬҲܾͬɱء߷ˮ̨̫ŵЬ,ԶպԶĺɫƤ㡣˵ô,ͺʹһ𿴿,Զǰ,һܺɫƤаﶬʱְɡƤ+=׳Ҳһļܹľ䵥Ʒð׳Եְ̫̰,ʱһɫƤܺܺõĽ!˰׳,߽ͷƳţгҲɫƤܰ䡣зֲ,ӰμѾͶôƤ+=˧еǵΪڴ,ſƤcp,ܻͷʱ͡Ƥ+oversized֯=޵ĵĹ,ƤԵÿȴ,ѡOversized֯䡣½ľϼܺܺ͹ԳƤIJƤ+=ʵͬƤͬ＾رľ䵥Ʒ,ɫƤ㿴ȥ˧ˡAll blackԴһ֡ĸ,ѡɫƤ,ȺӱɫƤҲܻײһʱֻ𻨡Ƥļӳֿ׵ʽ,ȴֲʧ޷ְճ,·ֲʧʱַŮ㻹ԺͿ϶һţɫƤCP,˧󡣿ϵ»ϵһλicon GigiԱп+ɫƤĴ·ص,ͬѭ½,޵еġԭ,̿ѡOversizedиõĴЧƤ+=Ƥ볤¸֮,ȥů,˧޹ҵƤʺɫϵĴcp,ɫײ,ĿǡôƤ+ͷ߸Ь=߹Ƥͷ߸ЬŮ䡣ͷϸ߸ЬƤѡһصľŷֿ,¶ϸСȸͽ,㿴ȥӸˡƤ+Ь=м仹ڶСŮҲ赣Ƥƫ첻üԦһ˫Ь㿴ഺ޵,ʱֲƤ+ѥ=ȳǿƼǺɫƤ+ѥͬɫϵ,Ӿ˫ӳӲӲάʹ,Ҳ䰮,׷״״ȵļӸС˽ķ͵,һٴɫƤ,ﶬֵӵ!Ҳ˴ͬɫƤ,յӭpoһ,ʹ㽻ĵáʲôֵﶬƷ,ҲԸߴ,֮ҲѡҡӴ~Ƥٴݡаﶬʱִ*ʶά, Tips:14:00֮ǰ췢,14:00֮󸶿췢 Ķԭ,ҲֱӹŶ -fashion ح˵˵Ϥij ͷڽͷڵ̡̾ǰǵľֲڳͽ֮ҲͨĻƷˣγйģȦӡ ϳ˵GGMMԽԽ࣬ʵ峱ҲһȴģʧŴҶҪԹҲ濴أʵ峱ܹӴԵƷ߱ģֽصҵҰС ʵ峱꣬һר sTOREĿǰѾʮҳ̡ VOL.11 VOL.10 VOL.9 VOL.8 VOL.7 VOL.6 VOL.5 VOL.4 VOL.3 VOL.2 VOL.1 VOL.0 Ļ ϲҲﶼһij꣬ҲɷùģҲǻδֵġ˵ְɣʣҲͻδijһ sTORE ֮УøѸڲͬ UNDERCOVER x sacai ɽ޶ֳЯһʮǰ -fashion ݵĿӲǺÿ У㡱һֽСƿ㡱Ϊ̫СʱУֽСУ㡱УĴִΣеIJƿϮɹ ȡȴɫȿ˼׺Ҳȿжεŵ㣬ҲǽͷĿʽΪ˷ֹǧƪһɣܶʽڲ£ʱر𡣱糣ɫƣ桢ʽíֱӰƷLOGOʶڲϣò˵Ҳhin󵨵ȨСŻšſСſ˶з磬ЧҲõġ ôѡƿ㣿ϣѡ˶ʣһֻܴ˶磬Ѵճ߼У ׹ǿϣ͸ͣͣ˶ܼԦƫź͸ ƿʽϣԽϸԽʽԽֶҶ࣬С ܶƮݵIJƿ㣬е˶磬ԴһҪŮԻԪش䡣 ЬӴǿ߼СʱдõľǸ߸Ь˶磬Ըкʱֶȣɴsporty chic硣 ϰ߸߸ЬԻաͷѥȣЩ䡣 װ˶硪¿ѡЩģкͿװԸУtee֯׵ȡ΢¡µȸЩĵƷʱֶ׵ģʮ㡣ҪƤЩģԴɳ£ɰë»е¡ɫӦƿȴɫɫʸãҪʸк͸߼ȣԾɫĻӦͬɫϵʡ µɫͲһ£Ӧɫһ£̶ȱƿУ㡱ôÿʱеħԽԽԽԽԵǶõĹ -fashion ͷ | ôҪ϶ܻհ! ǵùϣơC?ѳΪ޳Ĵʡʱ⵹˰,Ǹʱ䳤,Ųһ˫֮,ٴ CLOT C,ڵʱijˡųݽ,CҲڹȥǶλԻʷ,ʹ ϣҲѾٱȳˡյƱָô,Ŀǰӭʲô?ǰʱҳо˸󴩴վ,Ϊһ 4 ˱ѧ,ĸ˧һЩͿƪˡҪʽķڵĴվ϶ԷΪҪ,,ֵĸߵ;ͳ˷DZҪء(ʵΪԼֵ̫ҽ)ȻǷƽ̨,ֻܴ˧,¶¶ν,ֻǸ,ûá̫,ӰЧĻ,ֻҪסɡ1-2-3 !,һ,!!!ȻҲֱֻס,һġڱʽķԶ׼,ڱ·DzҪĵһ졣ҪЩԼֵôһŵȺڱĵʱ,⼸ҪҪסҪȻ,һ㾭ĵطͻȻƮһɳƨ,ʱDzҪסһ?PS:סʱǧҪ,ȻΪŵ˲õζǹʽķ,ⲻϲǸǹֻһǹӰˡֱǹס,Լֵų̶ȥ¶,Ҳ΢΢ָ,ָ¶(ɫ)(ɫ)ְͨ¶ص,ӶԷĺ,ʺϷȦ,ȡҮ/֡õ֪ȥ,ҪDZƸ񹻸!ͷʽķ˭˵ȫͷSupremeVisvimGivenchyBalenciagaYEEZYĴŽг,һ̬,һֶԳļ֡ǹ,Ȼ܌ŵġȻ,ɷ,ֽԲķʽչ,̬Ʊ,Ҳ·ĺļּװϵЬĶܿһ˵,ΪΪһƬ,Ҫϼʮ,˵ġӰɱʽķΪֹĿǰ򵥵һշʽ,ֻҪĴ㹻,û˻ں()ʱѡһ㹻˵logo,˲Żעȫе·ϡ㳤ίһ,ֻҪӰĵĹ,޵ġ!ϱҳΪij˱ر,øλѧû?ҲӭǺĵķԼ,!ǻ˺!΢ŵһýԼ,ͷ΢ID:swagdogάע -fashion пԱ˵, 鱦Ǻڰͷ""! Ҫ˵һʱװܡdzǰͬͽʥһ㣬ɶ˻𣬷ܲ·Ůʹ涷ޣһСͣͳˡֳ ʱװܴͻƣɫ¿װˡgetlook ˵ȷ򿪷ʽ~ ׳Ĵ䷨Ҳʱнʱ⡣߷ּϣӰת׾ܳΪľ䡣Ǻڰ׻׺ӦǼĵɫ伸㾦鱦Ůִ˲ƣľ䡣 01 һɫȹ 2018ϵС ֳɫļԼǰСЩʱиУɫİ͸߸ЬڲͬĦIJõıȫSerpentiϵа׽ָߵϡָ䣬㾦֮ʵĸӳ 02 Ůݣ˽Ʒζܵ׷ϯʽϵҲٳ ڡBAZAARǴҹϣѡһЩɫĨƴӺɫȹPiagetLimelight߼鱦ϵָװͣתԣׯصijϣ ˽Ȼͬĸ߹ŵʣΪȫĿĽ㡣 03 ɺ Ů˿ȹȻûɫײԼڴƣѾʱиʮ㣬ټFoevermark̺ϵе鱦׺гŮͨ͸ԵáȻԶ 04 ി׵´ɫȹȹϵİɫͼʵlookһ˿Ƥζx鱦װУһľʻɡ̤֣һ黨ּ侫һ㡣 05 ٳ ٳһɫ˿ɫ˿ޱĦдСƤִƾʵãƤСѥʮۣENZO鱦ΪڰlookСϲķԱ۾Ҳǿᵽûѣ 06 ݵɫ˯ȹϼοպѵ ˯⴩ΣʱиУгͬʱθƵChopard Ice Cube Pureϵнµʱָںϣһɵ顣 ǴġСסೢԺףһ鱦㡰ʧholdסгϡ -fashion ƭñȷС | յĽŲԽԽñֿԴˣñӣҲ󲿷ΪʱΪ䲻ã󣬻ƻ͡ѵûñʺУǾDZññΪŮʿñеľ߽ʱȦܵڶྫŮʿĻӭʺ͵ˣʹǿɰԲñҲһ񡣱ñУ 70 ﵽ˶壬⻹Ҫ鹦DzӰ۴ӰƬУ Faye Dunaway ıñͣʱˣʱ˧ˣñз񶼱쾡¡ڻعˡ۴ľգŵڿҲʱֵûѣŸ֯䱴ñͶԡFaye DunawayñĴ䷽ʽӵӰʵǧ򻯣ַ񣬱ñΡڣǾһññ 5 ־һһʺ㣡籴ñ£˵һԣﶬִʽ൱ʱֺÿġñµศɣ߽ϣһ̨סƤʵıñĦǸʮ㣬ʮĴ£Ҳȫû⡣ñϵΧɫͳһɫӦӺгñ²ʱֺÿѡëءëߵȲʣůҲǼѡ쵱ñŮʮĴʱͻøƤɰͿϲŷʮֹɵıñ֯һչʾŮľϡ찮ññŮ紩ʱѡŮɫ Slogan װαñҲǽdzеĿʽͷ߻ɫµıñʹɫףҲ㿴Ůʮ㡣ȫТʢñηͣ㿴籴ñһԣԴʣϸĵҲᷢвңDZñʵӵñʿ㿴Ҳdzţ㵱ʱĴññţ Look Լ߼dzϢතıñ Look Ҳʮ㣬ʮְݶˡƽıñһͻҵȻʡ̷뱴ñҲdzʣ仨ףжյůСԴԾװıñԴ˧ԣ˧ٺʲĵȫ Look һƤʱñʸʮ㣬ʮ˧ⶥñܱñԣӲļпˣᾢʮ㡣Ըеװ뱴ñϣ Look ʸиһ¥˧Ĵ䣬ȻٲƤ Look ññ׺ʮֺгѸʵǴȹװһñҲ͹ʡ 70 ĸŵƷñ Vintage Look ЧˣȵĸŸƵƷ൱ʱ֡ͨɫԱȣ͹ԱñʡñɴͬڸŷĽʸеƷ˿ȹ T ţп䣬ԴڷʽĸŮ Look ԼˡڣññDzǺأ -fashion ֵ¶ȵļ,,! ,Ůѧ~ĺÿ?ŵӾӦô󵨵©Ĵ!ţж̿,򵥵ͨԲʷҲ˲һζˬĴˡţмֱҪ̫˺ҲΪ,һ϶ţ,ַ·ˡ¶Ȼʹţװһ!ʱпǶ!յԽĿʽڴŮ˵һʶ,ǻԽԵȸdz׳켸ÿŮᴩȹ,ʵͬȹӳȻֱӰӾߺΡ˵,ҪﵽݡԸߵЧ,ȹͳȹѡϥ15׵ȹܴഺƤ,͹ԴŶ~¶ű϶ЬdzڵЬ,͸Ьǡŵijȳ,ű¶ö,ܲȳôȱȽϸ,Ǿѡ̿,׵İѴչʾ(Ҫ,ŵ!dz!ܰһ!),˵,Ҫ·Ͽ˳ϸ,ͳ۾Ĵ͵ôˬ,һɫ,һɫë֯,´,´ɫ̿¶γɸ,ŴƽװЬҲʱкȳʱ࿪!ůů ÿ侫ѡѴڲŻȯŶ~עС˽˺ -fashion ˼ | ӱȰ͵ľ飬ΪDzЩУ Ƕ֪ͣҲܹŮζû˹۲òÿЧġϾһdzϵķͣҲ⡣ȰΪһάֽͷ࣬ƫַ˵ǾÿĹؼͷƤͣƯҲԵûϴͷһɣʱʦжοʼͻȽˣ°ؾˣϰؾͻԵøֱ͵һëܷŻ½￴ͷô٣͡ÿ˼СҪ̯ͨˣЧҲͦģźĶԱȾܿɶھҪˡӱҲԾʾ˾Ӷ࣬ͷˣҲܹͺۼӱԾϲˡ˭룬ӱҲйľʱ¸ǣ̫ϡԼȽխͷ͹ϢġƫȻģԴƬʹΪͷǻԵʱлΪɶǶɸоͻֱͶȣӶҲɸУͨԾʹСĴ÷˿ҲɸУԵࡣϸͨͳһڷ˿෴ȼÿһƬ˿ĻȺС԰еķ˿ῪҲ԰򵥶ͣշͲܹholdסش󳡺СҲƽʱԼиĿʱиеڳֱϸŷܺʱ֣תҪײһҪױݵġʱֱʱиУΪھпиķϸڱ仯ŷŷ硢ϵϵȵȣ任ͺ;ȴСܵõΪͷɶȺͲθУͬķþͷֱõͬЧͨŵall backβ̷Ǹʺ͵ġ ô1 ƽоþʱҪƬչ״ֱϾȻ˳һÿƬ2 þӷ˿жοʼƵȦʱȻȡps΢еŶ3 ͨھþжοʼ˳һȻῪעȲҪ̫󣬵Ҫƺþķʵһ򵥵ķǷֺ÷ƬÿӷβʼϾµλáȻ綨͡ʱCOSMOԭδȨֹתתأǻȡȨ˰㰮ġʱCOSMOöŶһʱ俪ʱ鿴ྫݣ -fashion ȶ˹ǡٷᡭϢҪ ʯԶһ ʯ̼Ԫɵľ壬Ǧо̫ڴͳʯ̵Ĺ£ǮʯĶ֮ļꡣ ijƽֽ̨мһԪʯ ʯȻԴ˾ƣȶ˹ǡٷĹʾͷ׬һʵҴϢܻЩͳʯҵͷγش ÿվţע⵽¼LLAƼ˾һɹǿֻһʵֲȻʯѷɫʯЩʯĴ֮ߣȻ綼ʮֺԼֻռʯܴ1%2%ʯ ͼƬԴBusiness Insider Ҫǣʯļ۸񣬱ͬȳɫȻʯ40%ҡʯƷʲ ʵʯĪ⣬ʵϾǽ̼ʵȻȻ缸ǧ귢ı仯̵¶ѡ ÿվţ˽⵽ԱģȻʯڵؿγʱĿɵġʯӡŽ顰ϡС̼ԭӻ𽥸ڡʯӡϣɳΪʯ塣̴ԼֻҪ̶̵1012ܡ ͼƬԴ˹ ù˾гܼ࿭򡤹ŵ±ʾ ǺȻʯijɷһģһǻʵʯֻǰѴȻеȻӦʵ ֮ŵ»ʾڼ۸棬ʯͬȼɫȡи̶ȵȣȻʯҪ˴Լ40% ŦԼʱʯѧоִδ˹̹Ҳ˵˹ʯͿʯΨһԴ,ֻڸ΢²֡ ĿǰΪ˷ڽʯʯֳ еʯҪüӡ Ӱʯ ʵʯļɱʡ˴Ҿɫɳչơ䣬ʯƷʺͳɱϴﵽ˿ҵˮ׼ͼƬԴYouTubeôͳȻʯ̸еǣġǧһ1980-2000ˣԼ۸ɫʯʮӰ򡤹ŵ±ʾǵǧһ˻ѡʯ Ҳϲءʯ迪ƻر̼ŷŸ١ ʯ˾Pure Grown DiamondCEOɯ˹ҲΪ ʯδƣǸԵIJΪǷϿɳչ ɯ˹չʾʯ ĦʿһݱʾĿǰʯгԼռȫʯг1%ȻĿǰгСԤƵ2020꣬ʯгݶܻ15% ʯܡԶ Ȼʯڻͼ۸ʤһںܶ˸ٹ̵ĹֻдȻʯ⡣һŴȶ˹(De Beers)ߵܽ(Tiffany)ʯԺܶ˵ȻǸ߹ݵ ʵϣƵʯ̼ҶߵźǿʶΪ߶鱦ʯҪȻʯΪҲԸΪ˸ѡ Դͳ鱦ͷȶ˹ĹŶҲǿ˵ ʯһȻʮǰڵϾγˡ ϳɲϺȻʯᲢۡ ʯ֮һֱץסǵΪھϺ϶Ŷһ޶ļֵ ԹʯģIDCİĪҲΪ ʯʵǽдʯаѼ۸ڵһλⲢǰҪġ ¼ʯϯִйٵϴԼ 1ʱЩ仯275̣ʮŴĵϢ3725ڣΪһҫTAϰ4ȻҲã315ڣ¿ĸ˾ɶ -fashion սʤֵľȻֵ?ȿҲ! ǰǶԵò,ø,Դӿܶ¶ȵľͷ,ԽԽñֵߵʵġֵ!ûдʲôȳװ˵ĸ,ҲûдСȵĸ߸Ь,Justһdz͵˶ᴩLook,˲ǽա˶µõΪʲô:ƽ·,ƽĴȡһŮǵ,Ҫ߸ЬƬֵܰƬװ˶Ьʱָе,ûһ˫,붼~жٱŮ,ϲгgirlȿ,ĿгġͣȺƵʱ,ȵصǵ·ϵķ羰~ ƽЬȻô,ϸ,ȺСȵıкܴϵŶ!(һ)СʱƬͿԿ,˫ȱֱֱ,ϥǸ߶ȱһСѵϥǶ߳һءɼССȾͱһ˳!СȳɱȴȳȳŶ~ ȻСȺʹȵıӳһѾ,ǿԡ߾ȹ!ôŻͰ~СȾҪСȱϸStep1:貿ͬʱСȿ,ͨ뵽С,ͨ֬ͰĦ,֬͵Чġ֬Сһ㶼ػܴ,Ȳ֬϶,ĵŮȽϳǡ֬͡С,ҲǼȽ϶ࡣСȼγɺͿ貿й,Ϊ貿λ·Ʋȷ,ʹС,γɡĥ𡱵ļ~ ϰԽ貿,ͬʱβ,ȥȻҲԳŶ~1. 貿϶,,״,˫ֿԾƽ,βµʱעⲻ,ظ3Ρ2. ,סͷ,һ֧һ,ʼȵĶ,˶һֱ״̬,ظ5,ԼСȵķ,ӴƵ,ֲϰȺβ3. ֧ͷ,ͬһֱڿлȦȦ,3ٻһࡣһ׶3顣Step2:ϥǺܶŮСȲôϸ,ϥ̫,ϥǻȲȫ,ԵȺӷסôϥ?˵,ֻҪ˶ܰϥܲӾ,Զϥ,ϥۻ֬ϥΧ׸øʵTips:Ů˵,ϥDZȽϴ,ÿϥǵĸ̫,ϥؽˡػȽϴŮ,ǵŶ~:http://www.lamer.com.cnӯʵ,滺弡,ΪṩЧʪLA MER֮˪ĹͶصԷ,޻˪һޡ廤,Ϊˮ,ƼˮĶѭ:http://www.whoo.com.cnطѩɢ,͢׳ɷ˰ɢԼɣƤȡʵ熨״,ܹº޴̼ϷϽʺƤ֬,ʹɫ,˳:http://www.origins.com.cn̺ʡȵ12ֲ;,ȡڴٽѪҺѭ,ٰĦաرʺϱ¶Ȳ,ͼ粿,ʱʹáպӮ,ĽıͲҪǶ˶~ ˶Ļٶɳٶ޲ˮ,ȻǻӴ~ ˶ǵֵ춯,Get!ཡϴ軹ܼ?ϴϴ!˺,άʹɱﵻ!עʱаɯѡƵСBazaarVɨ·άעʱаɯBazaarV΢ŹںŹעʱаɯѡѶСʱаɯin -fashion һη8,⼸ֱʡ ǵϻĹֱʵõĹʲô?ѽ!ӦҪһƪ,ʵݵijҸΪ,ʱҪ72Сʱ,㳤һ8䳤,λеи,ξѡ񡣨TISSOT-T-CLASSIC ϵT097.407.26.033.00 еб :4,389.00 ȡ׶Ϊ˹µƷ,ȻõETAоľˡETAΪƷзһ80Сʱо,ӦõϡоΪPowermatic 80о,ûӷ,ͨӳ͸ıݲʵ80СʱЧȻݲִһ,󻬵ص㻹ֵÿ϶ġ80оͬõĴ󱬿,ʵ̫ˡصѡϢŨĸϵ,Զ׼,dzõĽ,ʸзḻıȦ,Ȼ˵͵ĵ¹Ǯ,Ƿıǿֱʽ,ʺصʿMIDO- MULTIFORTϵM025.407.36.061.10 еб :5,382.00 2016ȫƳȶ˶Ϣ,4λͻijʱֱʱϢ,ͬʱ˶ļװ,ȫغɫţƤ̵ʱɫһ,ɫɺٻصо,ȰоCALIBER 80,ƹƺڴı,ԿȾƴĥı־Զ,Ĵĥ̶Ȳ¡100׵ķˮȺͰٴöܹӦִճ׶Hamilton-ϵH77715553 еб :5,662.00 ׶,оij˴󺺼ҵH10оȵCaliber 80Powermatic 80,Ҹ˾ú׶ٵĴĥΪȫ,ﵽ˹ϵtopͨ͸Կ,оĴ󲿷ֿհ׵ƵĴĥ,οƵԶӳH,Ȼ̶ֳȵĸѶȲܸ,ʾλij⡣޳ͱ,ŵָ,ŵ,ֳ׶ǿҵĺӵϢ,43ı̳ߴʺֵĸߴ,ṩ200׵ķˮ,ԾȥӾˡʱZenith-Eliteָӹϵ03.2270.6150/01.C493 еб :38,400.00 100СʱʱElite 6150о,һֱΪг׼ɿĻо֮һʱŶΪ˴һöԵijо,ԭȵEliteоһ,ͬʱоֱ30,СΪ,ֱľ׼ȡоʹ195,Ƚ3.92,ȳķнϵĸо,dzɾ,̶Ըɾʾ,Ҿĵ̳ƸİЧ,ϸڴԾáϸŵҶ,ɫĻϻ,˸о̤ʵġԶӵһ,ʱ̤ʵ,׷еյij֮ġɺPaneraiLUMINOR MARINAϵPAM00564 еб :42,543.00 8ʵҲɺһɫɺרְΪṩʱ,ѾΪʱҵı书ˡȫԲP5000оԴķʽ,ﵽӳȵЧ,ӶΪֱṩ192СʱġЧɺԪö϶ܿ,޴ıڻšʽ,ҹЧΪ˱֤Եͬʱ,ɺѽ,ĥɰ,̳ߴԵųжȡBlancpain-Villeretϵ6606A-1127-55B еб :59,860.00 ڳŽִ׷,ʹֻǻſڶʱҲʧVilleretϵװ,ʹñȫз11C5о,öоֻ3.33mm,72СʱĶЧ˵Խֱ,Խ׼Ȼή,ľȴʼֵֶоı͸,ηĻоапȻ,аԵֹǴĥʾĴҷ緶СƵ氲ƽеĶʾ,ɴСĶʾֱ塣пҪһöƷƵĻ,һdzѡ񡣨ٿEberhard-8 JOURS ϵ21027.1 CP еб :30,700.00 ٿ˵8Դ1997״Ƴ,ͳΪƷƵһɫеıǶ,ֱ۸񲻻̫,ٿȴܺõؿ˳ɱ,͹˶е8ɫ,ʽġ͸׸ǵʯ,Կĵ8Ƽа,ȷֱΪ0.31.25׵˫ɼ,ṩогһڵĶ͸¶ٿŨĸ黳,ȰĻʽʱ,ɫҶԼʽСȦ֤8նָʾŵ910мλ,֮Եǵֱ8 JoursʫVacheron ConstantinTRADITIONNELLEϵ89000/000R-9655 еб :1,324,400.00 þûռ,˽ʫ١VCչʾױȵijӷּ,ӵ14ijµĶʱ2260о4,ÿ䱸һܳԼΪ2.2׵ķ,Լ13Ȧ;ĸܹͬʱ,ʱȽϻƽ,ת൱ڵеķ֮һ14Ķһ280ȵʾ޴ʮֿܵӷֱ,14Ķ·ҲװΡֹĥװԴӷװɼ,ӵĻеڽ,ֹܿиĺۼӷֵ˱Ե,ҪƱʦ11СʱɼʫٶϸڴĥҪ֮,IJи׷ҲΪʲô2012ƳΪ֤߱׼,ʫȻӵ֤߱Ʒơд,ֱг,Ŀֱеõij?Ըǰɡ б۸񡢻ŻΪ׼б䶯,ˡ֪ͨ -fashion ˫ЬȦڴbaby͵ȰͶͽţ һȹټһ˫߸ЬԸвʧţɫϵܲȰͿǷdzϲ ɫһִЬӣ϶ϯ Ը ķһִЬΪЬСȹ ʮ ΪȦᴩµĵ䷶ СμҲǾһִЬӳϯֻ һִЬҲСμѸӵʱгAngelababyҲǾϯɫĺɫĻкɫɫģbaby˫Ьӵϲ -fashion ˫ƴ,·Ҳ! ,µĴǻ,dz·ºAȹĵ߸ֽ⹹ʽ,,ⲻϵƿ𵽴һʱ,ֲˡƴӿ,һȤڡ̡ݹԹŮ,˽ȴȦɵĹعرϲ봩,봩,Ǿһһˡٳ½Ƴƴӿ,ҲǺܴJacquemus2016ĸоԺ󶼲Լʲô?Ҵ,,,Dz?O(_)O~ʱװƴӳĻ治,ֲͬײܳϲ?ΪϤMonse2017㳡,½⹹,ƺͳµķdz淨ǰһϯƷƻʱ򴩹,үDZȽϱصĿʽ,߿ʽͬ,ɫͬ,¶Ĵdzио,Ųʧ⡣Ψ򴩵dzȹĿʽ,һdz,һȹװ,¶ͲԳ°ƺݳĺܴԱZoe Saldanaϯ򴩵ƴӵȹװװȹװƴӿ,˫ƴײɫ龫ֵĸԱ¶Selena DomezҲ˫ƴȹ,ɫۻɫ,¿ɰɵƺһӰķƴӰ׳,ԪصԭijʱҲһƴӳ,һǾʿ,һǰɫ,˧Ų,ϳģ,ʱвMargaret ZhangʩƬʱʹһ˫ƴ,ƺͺƽ,θзdzḻƵƴ,ϸͬƴ,ƲŷѡĿǰƲŷѾ,˫ƴǾ?ԭij õĶԭġǵתͷǶĹһ̬ȵʱй -fashion | ˶Polo,㲻һʱ ҪΪPOLOֻ˵ר,ŮҲܴ˵LOOKPOLOīسɹ,رŮװļ˲ԪسΪȫµĵƷ,ʱ־ϲ㳡ҲǵӰ㻹ϿҲһ01ԲPOLOT02ѧԺ纣Tȹ03poloƶT04POLOĸT05ɴƴ¨06аٴ POLOT07ѧĸPOLO08ſ֯poloȹ09ŷɫ˿֯䨋10ѧԺ緭T11ٴ̿poloѧT12ѧԺPOLO13˯·ƴɫѧT14˿֯ȹ15ײɫƴӶPOLO16ѧԺɴƶ䨋17֯POLO18Polo Լȹ19˯ƴɫȹ20ѧԺͷPOLOƴɫȹԭ ֧֨΢ | -ʰ΢ų˽ | wangbole123΢Źں | wangbole02 -fashion ģBen Hillʮͷףֵһ Ben Hill1978꣬39ˡڵIJ᲻ͬBen HillӲȥıƺͰ׷òҪԶᣬֻҪԼеžͿˣ ʵһʼLana Del Reyĸ泪Ծʱйµ˵һеֵͦġȻһ쿴Ben Hill̫ÿˣ׼Daddyˣ֤ʵ䲻DZԭBen Hillͷ׷ɢɶɵ͡ ͷһɽInstagramܻӭʮƬ ʿͦġÿοijͷ СXXhuskyҾ뷭ۣл԰Ȯ -fashion 185ǰûͺ˵ֻĩ 185Ǽ֤ȫ¼ķʱʽǼ¼ٱȫܲûŵ (Walter von Kaenel)2017꣬ʿӱٱӭ185ӽ11𣬵㿪ٱĹٷվܿרΪ185ƳרÿһƷƹ£ٱƾʷͳԺաž׼ӱѡķʽʱ䡣¸ĩЩʿѣǵٱݴ˺196021ͳڵʮöϡҪ̫йӱߴ185ʿƱͳ̬ȡƪ΢ţһ뺯ܿλߣһμٱ185ջᡣ ȲҪЩϡٱ185Ĺ½ʡϾҪٱţʱ⵹صաڹϵʿСǣʱͣ½ŲʿӱٱԴء1832꣬ٱɰ¹˹ءAugusteAgassizʿǡڣٱڴͳġtablissageƶ´ӱ൱ΪӱڼҹٽƷṩó׹˾ءƱǵļͥʱ19ҶǵƱҵѾչʮֺIJƷԶޣӡȺԶСģҼһԼæӱֱ1960ٱļ̳ŷ˹ءErnest FrancillonΪʵָÿ˶Ʒĺ룬ʼĿڻеʱӭڶιҵķӦڴδԵʱʷٱѡӭսȸǵʱƱҵＫΪʼΪ185ʱУٱƱͳʱ̽ȷսʱ١1927520գ33Сʱ30ӵij;У˹μʻʥ·˹֮񡱺ŷɻڰ貼Ȼɹ½ʷ״β϶ԷԽ׳١ΪʺϻĹٷʱٱΪ˴ηȫ̼ʱм¼֤һʷĻԻͳɾ͡Щ̽ռDzֱսľҲΪٱ׼׿Խļʱдõעš185ٱʼռƱͳֲʱֻΪʱÿһʱÿһˣֱսͬС Щ꣬ٱÿһͻƣܿڼ֤ ꡷ܿ2017ӱؿΪעĿӱչý壬ܿÿӱؿٲٱĴԼˡԣֵصӣһ̫ɣϾ˺ûᣬĩ2죬Ҳ֮չʮϡ㻹лĿٱȫ·Ŀϵϵڻом뵥˿ɾٱ׸бʿ̨֤COSCϵУͬʱԶеоںŵԼ׼ƷʣٱΪ185رס ڱԲμ ܿ ٱ  Ķԭ תдʵϢμӡԿʱתٱ185ʷʱչǰ30ͨıĶߣԽͼܿ΢ź̨ʱΪ׼1118յĻֳȡٱ׼ذһݡ1118 - 1119գ̫㡣 / ༭ / ·ͼһµ¿ -fashion #ֱ# ŷ޹ĵ ʿˮ ʾʱġͷϣָʾʱһ־˵Ҳ˵ı䣻ֱһõֱһֱɹֱ𣿿⣬DZˮһʱĺͬѧˡϲֱϲƺǴ󲿷ȤôǽĸӰʿźܶѶʿһ֪Ͼ һ ֲҽеġôҽͽ㲻һģ˵˵ҵŷ޹ճʹáʿٷĽ һ,ʿƾ׿Խ,Ժշ񼰴¼ΪչֵʼԺշȵľ.˽йʿȫϵ,ʱ,ͺ˫ʱ һСգˮ˼DZˮֱĿǰһƷһ1953귢ʱҲ׿ˮﵽ100׵רΪDZˮƵIJֱر̵ķˮʽǡڿǴӵڶʼݱ䣬ݣĴĸıǸΪתȦ˵ʱDZˮԱˮ´⣬һĸı䡣ȵҲڿһ 2005ıCerachromȦȦʿʹüӲƳɣ׿ԽĿʴֹɫȦʹ䣬뺬ȵˮˮӴ Ȼ˿𡣴ӴҲῴƵʿˮʿˮȤƼĶCollezionare Rolex SubmarinerΪϸڹŷҲʮӰʿʹõı־һָֻ쿪ƣԢƷȫֹƣݱΪ֪ĻʹڣչʿƱҵĵ֮ڹгϣһֻͨʿֱļλ3300Ԫ15000ԪȡȻ۸񲻷ƣǻΪֵⲻʿƷʾΪжصͶʼֵֵǿ˶¡װԣȥɼװĵôΪʲôѡʿ? ҪΪˮٴ90ŻеӰӰ죬ϾһҲһǩͨһҷԼľǶԵġΪֱһ˵dzҪرݡһҷֱû𣬸оڳֲûпܶ࣬ҾóֵҲһԼǵֱҵĽܡô˵ΪΪʲôϲʿ? 衶ɯؐж@һ䌦ףobƣʿDS㣬S]ЄʿDŽe˿㣬ǸҊ㣩ŻеУº·ҲBĽ͡ɽúϣٻͷҸϰ壬DzǺидСۡձŷޣ㣿е˻ʣۡձ̨ңЩҸ˸ءȣʿƼġҲ˻ʣʿֱӦȥʿô ȥʿֱܶ˶ôΪʿֱһҲˣһԭǻ⡣Ҳͬٻ⣬۵Ļȥû̫ˡȻձպֱеʱȷԣֱŵ⣬պֱŷޱôһѡԭ򣬵2016ҶһŷԪĻʴ6.8 ˵DZȽϻ㡣һ仰ܽᣬǡȥijһطȥȥƱȥŷޡȫŷ޻㡣ŷŵ깺ͲŪܴ񼶱ѾдϸƷƹ¡Ʒ۵ȡ˵ԼļܡϾ˭ǮǴģڹʱҸר깺򣬶໨Ǯġ2016222һرӹ򡣵ص ǡܶѶһ⣬ھƷԲͨô졣ڳҲȽϵ⣬ҽʿרҶǵġΪ󲿷ֶйĵ( СرƯ) ȫ̹ͨϰ⣬йܼҼʿΪҽرܰ ⣬⣬ŷ޺ңٵҵĵ׼һLOGOĴӣƷƴӲ棬ġƯڴɺĵ ˰˰17%Ҳ˵ҿ17%˰ѣ˵Ҳһʲ֣ԼһҰɡΪһƱ---ʿԱҪд˰˰Ҳ˵˰Ҵȫ׶ظ֪ﲻܰ˰Ҫſԡҵһ뷨ǣDzDz˰?? ڵεЭͬ½Ҫֱ˲ſ˰˾һ⣬һӶô? ;˴ô? Σһ˳ɡɹ˰˰飬ҴΪҹݳƷҪ뿪˰ҵг ---ҾҪں˰ｨǰд˰׼ÿһѡ񺽰ʱתʱ価һûʱ˰ˡ д˰ÿһҪдм! ŷ˺Ժпû˺дǮ͵ŷˡʿʹõı־һָֻ쿪ƣԢƷȫֹƣݱΪ֪ĻʹڣչʿƱҵĵ֮ΪԼϲɫټϺɫǰٴûôۡĺĻʹ֤ô дֵıҵĸܴĺˮҵؽѣô ҵĴ𰸿϶Dzڣֱⶫÿ궼Ǽۣڵļ۸ԼжЦʿһħƷƣһĵƷơȻ㲻϶ƷƣڴڹϵһƷƣһҾѡ߿ѡ΢һļżɹʱ򻹿ɹװôңϲô ҵĴϲֲϲˡͷǽһƺı䣬Ͼӽ1000ҵĶٶи飬ͻϲµĿʽԼһ򵥵İһʱ˻ͷӰը侲£һǾõͨûҲǸѡ񡣺ˣе㳶Զˣ#ֱ#͵ɡ -fashion ȺƤ̶,ȴʱȦ һλ,Ϊɱ·ģʱг,¡Ȧ5¿ʱа,ȫ򶥼ʱ־Elleް6µķ档DzǾϤ,ܶʱдﶼõĽ,24,ȴڰʱнžصĵλ,ýΪӰŮԡinsӵķ˿,ýһ,ϹǴ쳯ӡŶ,Сǰ,߲160,IJϴ,е㡰󡱡һ,ƾʲôڿȵʱȦ÷ˮ?Ůա,ӢMargaret Zhangϼڶ,׹ԭ,һؾ˰ޡһĿ,ܻܵеС˧ġʵMargaret Zhang֮ǰijɳ,ѧ,ûʲôܸʱйϹȻ16ʱ,һʹ˸ıԼһľٶԼIJ,СSHINE BY THREEڲ,Margaret ZhangСǷ͵Ĵ,²۸ִʱװ޷԰Ρ˶ΪҲͣ,Ȼû,עʱȦ̬,ʹMargaret Zhang֮ģءӰʦʦʮ,䡣Ϳ,˵С֮,ͰԼʱиҵѧУĴҵ,ȻɹõǮȥŦԼʱװ...ҵ,ѧ̵ġûҪǶȥģ,Ҫһѵͷ,֪ԼҪʲô,Ҳ֪ԼҪǧټƵĴﵽĿꡣһ·ҵ Margaret ZhangBusiness Of Fashion ѡΪʱҵ500ǿ,BOF,ŵҵͷԡҲ,Ҳջ˺ܶݳ޴ƵMargaret ZhangѾ,ÿñ˵ͼ˿Ƿ,ԼϾǿʼĥӰ,ոһնԹߡ豸Ƕȵ,ӰҲûû˵ʩ2015ϵйƬ,ģMiranda KerrԡϵйƬMargaret Zhangִ,Miranda Kerrͬͷǰʱ,ҲѷɫMargaret ZhangЯPolo RalphLaurenһĹƬSENSOƬҲMargaret ZhangִʱԼļ,Margaret ZhangҲǾϲ,Ȱ,ÿһöҪ¡,಻ò̾һ,˳òÿ֮,ϵƺMargaret Zhangе츳ܶˡMargaret Zhangּܵը,Dzǻᷢ,ʵԼ̬Ⱥ,˭ں߶׶,ȻMargaret ZhangҲԼһ״,ʱиԵҲԡĴȵȿMargaret ZhangȻŹ,Ǵɫǻƿ,ɼԦϴĻǹŰij,ʮchicԪرز١ϵϵͽеʮֺӦ,һͨĴɫȹҲΪ·ϵʱָСchocker+ǰV+ŵĿ,ڽ벻˽Ķ!ƶţпһ˱ر档ij°¶IJ,ĿŹȷʵʮlookԵƤ,ʺ߿ɰֽƶţб+ɫ,ٴһ˫Ь,ûë,ּ䡣Margaret Zhangʱв,ı紩,У䵭,ŵżͷ߸,ʱ־㡣+Aȹ,Ů˵ַ֪ըѡɽ,ϸҲúܵλײɫ,»,ȹ,ϵԴԸ,͸ôMargaret Zhangѧ,·ĺܺ,ޡͬɫϵƷһʵҲǺзġ+Aȹ,вʸ,С㲻׼!ʵMargaret ZhangѡĿŷ,Ҳսźݻװ,ܽ֡ҲԶԻ,ײͬ,ԼһסŰ˵Ҫ硱,Margaret ZhangѾˡûͣ½Ų,ƴ,ִ˲ƣؿ㳡,ɡڻǰ,ͷԡڷܺǿ,ʵسΪýŮҵִšͺѧ,ΪһģӰʦȷʵһ,Margaret ZhangҲòɷ Դ:йID:chenliegongheEND³girlֲ,ÿ촫ڵıʱеСtips,ӶShare the beauty of womenlady8844com -fashion ¶׺ʹ̿ѵСŮ¾ûбľˣ ֮ǰһСδµ£޷ǶǴ̿£߻¶ףЩ˵СѾģӾƣ͡ʵ˵ʵڵģ̿¶ںĶ˵һͺսһ춼̿ڰ˵ʵе㲻ϳ...˵£Сӵǿ϶ֻæsay no..ΪǵĸСע볤·ԵʹԴҲ޷ǿͷ˵ߵȼ..ȫûǰһԽܵɡƳ棬˵ЩĵСɣӴµĿʽɫԼ似֣һǿķڷ˶̿㻹ýЩԸߵġƽ𡱳˵һСӴµĽɳȡһDzҪպõϥǵĴ£ΪԭͲߵĴмһΪȶ̣ʡξdzңϵسĴעݸ߸ijǾͲҪˣϾڽϱҲǺε...ԶСٵСȶǵijԸߵĻƽ𳤶ȡԭ̿һ̿ǰϰ̣Ȼ°ͳСȶǵĴӾϽСӾȻԸˡǵвijȣҲʺСŮҲ͵ġЧ䡣Ժɫsay noɫĹ˵ԸߣܶŮһǾѡɫʵɫȷݣɫûãΪ̫ˣȥۣҲԡ̡ı׶ˡɫǸܺõѡɫɫỹǻɫϵᷢΡ֮⣬߸ԼԦɫϵҲСǵרԵ˷dzͻӶȥԸߡͬɫϵӪӾͳһΪûɫϵķֲ㣬ԿȥȽԸߣֳĸ߼Сѡߣܾݵڴ֪СΪԸӪһ߷dzҪ¶ȵĴѡʱҲһҪ౸ֱͲЧûĿʽãoversizeһҪֹģҪס˧֮ڸ߸ŮӵŮֻݶ֣dzקߡξ£Ӫһߣ֣fashionʮ㡣Ӫһ統³ͿΪԸߵСߣʵ⣬ص൱ʱ֣˴µΣڴѡҲҪСмѡɢڴֳȹrealʺϣȻƸУǶĽС㣬ϻԵøݣһὫСӶеƤһ򾡡ԻǹԹѡϸװɣȲһڴȹӸŶҲҪһ㣬ŵ̬Ӱиõ̬ʲô·ԼԦ˵ģԭͼƬ磬Ȩϵɾллϲƪ֣ӭתȦ -fashion ŮǵСһС䡱һΪ һʽСôԽݣԽʶŵȦʱ־ϣҪɡôԽ䣬Խ ԽԽһжΪ ֪ҷûȦŮһļĴſȥû̫ı仯رʱҲǴô⡰ʧ١ǡʧ١ģŲʣIJ𣿿ʱֵĶŮݵ˽ָڹعһƬɫ粿οë´ɫƶ㣬ڵƻԻ͵ĽֵˡС¶Ըжʱ֣ϾݵIJǶ֪ġͱ紺Ѿ¶˸СϽֻˣο¶ëĵʱвʱڲ䲻ȵãҪĵضҪⲻס~ͷɫ֯ñɫο£¼ֳһĺºȹrockerɫñӺͻֺСŮζһкͣᡰΪ⡣Ƕǹȣһ˾ɪɪҪ˵οյƿɲֹ£Զ޴ʱȦĸλϡ档򡤱µɫοյопΪۣʱ´ȵĵо޼οԪؼӳ֣򵥵ĴһɫҲܳ¶ǶǸ¶Carvenɫб´ɫȹϲͬഺŮ磬С¶ȣ·Ҳͦģġţ䲻䣬Ȱ˵~PܾеELLEдϣҲһϮС¶£ЩĸоˣIJμԼ¾翪ᡣɫб粻ԳƳ£һ¶ʱָǽһСס¶뿨ɫȹĴǸţ֮ǵһlookİ貢Υ͡ʱֵƴɫбëԵп㣬ܴͷз硣һ׶ʱװϣСܶBurberryб;ͺܳ¶꣬ʱְðݵСҲ¶£ɫ¶֯˶ȿĴик㣬С¶ֳԸУӻ~ɫñCrop Top´ҪȹĦŮɵûתСԸСعⲻõİһĦǴ翴ȥΪһЦôҶܵáTallia StormǣŮҲDzģ¶°͵ͬʱŴʱ׼ůʵԵҲʱַȻҲŮǶģϮʱʵȽϿס˶װϵС;μeasy chicĴ·һд翴žͺů͡Сšͷɫ֯ñɫ˶װůӾҲůĸоӱlookзʮ㣬򵥣ϸϻпģɫƴƮ³úܵ͵+ϵСߵ¼ݸϷɻҲIJɫ֯Ѷɫ£ůзgetʵ·ߵĴδɫڴ·糬ʱ֡Դķرǵݽ̻ǣ첻֣ܵˣڻɫĴΪԼһɫ˶йоصlook -fashion ＾,Ҫʲô?!! Ƿˮƽһ֮,׷,ڹ,ҪӾá,ͬҪвͬװ,ô＾ЩҪҪ?# Overrall Dress ȹ #ȹ,25Ů˵,ܸоȵѼԦһЩ˳ĵ,ٴĺɫϵҲǺѡ,ʵֻҪռɾͲԵúѡ70ŮǻᴩĺǦȹʽ,ŵ,չ΢ԸŮζǿ͵ȹ,̫Ь,Ե̫΢,Լ¶˵ļ,µЬѡԲοij˴ϸЬﶬҲܻϥdzѥ,򵥼ܴĵȹ# Backpack 󱳰 #ӞѧŻõĺ󱳰,ͻȻŵİ֮һ,fķ,ŮҪϰĴѡ񾫿@,͵baby blueʵĸ̶ʮ֪Եʸɫ,ʹװҲȻص塣ճİѡͼ,Ǵ󵨳޵ɫϵ,one piece װʮ͡# Polo Shirt #Poloܸһĸо,ƺԳŻᴩ·,ȷʵŮ˵üԦ,һСľͻٞӡ˾ѧԺ,һοijNPoloƷ,˿ѡ֯,۲ϵĴ,ƮݵĿ㷴һʡֻǾ߸ŵ׵·,ȹӺЬҲĺʱ,īǻ㾦Ҫ# Ÿ,· #ܿܵԪ,Ǵ㻳ɷĸŸ,ֱӴиţп,ط90׷װ,,ǽǰĽ⹹ѧ,ֳʱĴ,ĿϾ# ߹˿,Ҳִܺ #Ů˶޲ʶǴְֺ޵״̬,һչֳͷŴߡײɫϵıװ,׺۵,ӵй,Ь,ֳܳʱִϢ# Maxi Dress Style #ϷʱҲԴϸֻݲݵͼװ,ȻҲѡ繫ijȹ,ҲЩ˻ᵣ̫λ᲻᲻ô?ʵһ㶼, 򵥵ķʽDzοĴ˵Ĵ,ѡԼɫTȹƽ۵Ļɫ,Ͻͷʱ·˶̲סͷüΡ# Maxi Dress װ #Ѿijװ,ɱٴϸ߸Ь,ִ䷨Եʽ,ʹͽСҲҪdzŲѡЬ,Чɢ˵ĦʡڰĴ,@ѡ,ϾװҪɢЩʱ,ϴʱֲ# fanny pack ȹװ #fanny packȹװ,һᷢ,񲻴?ʿʱвKristina BazanƮݵijװ˴,Ҫļ춿ѡϸ,Ͳ̫кĸоɫҲѡ͵İɫdzŮζ㿧ҪЩ,ȻҲѡֿfanny pack,ǽij¶гȹ,Mʮ͡# fanny pack װ #ʵfanny pack˴󷽵Ʒ,Դװͷdzÿ,ԲοChiara FerragniԶ̿Ĵ,Ͻ˫Ըгѥ,ȫɢʮʱȻfţпǿ,ѡϞʸеƤ,ŲԵ̫f,һСľٞ顸Dad Style# Ȱfanny packģ, #Kendall JennerȻ,ȴȰŵĵƷ,ȥﶬﶬ,Kendall׽üMȰLouis VuittonChanelfanny packϽ,ţпf˶,Էdz͡ -fashion | ѧ5ɣʱ µʱѶ,飬ױɣǰ,ȨʱϢʱʦѼȫʱѶȫ׼Ҷλ߶ʱѶƶý壬רעΪϴĶ顣ʱװܳʱеѶ֣ᷢװʱѾһǰǿװӡͼڣַ߽𣬾ƷʱѾͷϣʹз硣ַ߽ΧһһǿɵʵơʱУʱеʸСأѾΪ׷ʱȺרⲨFashion carJerryУ 1. Ь뷫󱳰ͷ һַStyleγҪƷϣܳĶԱȡԼʸеķ󱳰͸ЬʡĴ˶Ь󱳰װdz׵ĿװϲʼijͻײӱӾЧ߽ͷзʯͷţƤƴʵϸ̥ţƤʱׯصײĩβġĶԭġ΢ϵJerry˽飩 2. ɫСɫĬ ɫװΪףټƻɫ鵥Ʒӷװӡ: ЬñӡΧǿɷӵĵƷѡɫǿҲĬСΪ͵ĵ׺㣬ΪЩɫĬͯȤƣʱװܽͷͶȤζ 3. Щϵķ羰 ϵϸڲɺӣڳϯһЩ罻ʱ֡ðԼһЩ뺯ƬֻȣⲿҪʱչֳԼϸʸеҪ󡣲Ҫ̫ӣһð;ͻֲ⣬ôҪƷζʱ̣ͳʱˡ Ȼͷİ뼫򸴹ŷĵ 4. ưԽͷ ڽϻῴһЩʱд˲ϻϵһЩ˿ΧĴҲԴŮСôһװΣдӵʿģкܶʱʿЩ򵥡Ե͡ 5. ñκ ͷѾҡ˵ζװ˶ãϼİñɡʱиУûжlogo ӡĽΣԼΪýͷķΧǡôʿšEND -fashion û,׳ʱֵĴƷ! ĩ,ֵ˰׳Ȼ׳Tһǻ򵥵ܼ򵥵ĵƷ,Ҫ,Ҫоо2017＾ָνһͨͨİ׳ʱַ?СΪһ＾׳ĵĴָ,СʹΧ۰~ 2017＾׳㳡 ɫij¹ﲻȱٵĵƷ֮һ,ԼٴĿʽԴκһȹװװÿİ׳ӦҲൽɡOLĴģ,׳ֵӵ!1993ɵӰϵ׳+ȹlook,Ȼ˾̾ģҲΪ~׳ô,ȦСǵȻҲһ䴩μѵİȹɫʺϾǴ䴿ɫ,ҪʱеĵȻǴ׳ܴŮζ󵤽ֽ㡹׳,ƵļӳֳȼӸû⡣찮ӰĽ,¿޵ȳһ԰ٷְģ,ûж߸Ҳܴȵĸоϡ˼̷ϵĶװ䳤͸ɴһҲ,ʱָһ¥ϷСŮ,ڻû,һʱְɡֻҪѡ,ҲܳΪchicŮѡ֮ǰ,ɻҪѧϰһµ ׳βܴʱָ?# ʱֵ,СϸڲܷŹ,ʹ͸ˬ,Ӫһ湤СһСʱָһ# ⿪ӳǻ,Ȼܰٴ,״̰ġнģ,ҪһЩ,ǵؿڴĿٿ2# ϵ½Ҫڲͬ,Ǿϵ׳½ǰɡ°·ſӽ⿪һ,Ӫĺñװ,ͨʱָ,껹ǺôŶ! 俼Ʒζ Get! # ţп׳ţпǾ,÷Ѷ˼ܴõĵƷҪΡñ,ЬôҪIJҪô°ͷҪô½һ,ЩСϸڷַʱַ# ǦȹϲŮζĹȹһҪ,ȥְ֡ûѶȡְճҪһ,:ϰ°ɫ,ԵøһЩ;ճɫһġӡ˿,ȽһЩ# ׳ȿȿԸߡݵ,͸,һֱϲ´,ķŴս# ׳СǦʿͬǼСſCP,Ůְһľ䡣# ׳²ļ,+,ʼΪɫֿɱů# ׵һ,ҹ²,вθֲ֮ڵһŷֿ¶Ըߵܾ# ׳+ʽµһЩʽ׾ͿԵdz,dzҪŶ,ΪڴǺʱе,۴װȹװԦ,ɴ סŶ! ·ЬҲҪ!ոŷЬ,ļںŶ!ﶬѥ,dz˵ı䡣СЬ+ȴʱָС߸Ь,Ůζʮ!,ѧô׳Ĵ,췭¹İ׳԰!΢:@TOPWOMENŮװƹϵ΢:topfashion02,QQ:2213715575 -fashion ͷ|""֣ˬΪʼձʱȦ֮? ȫȦԼԴʵ֣ˬַˡǰμӵӰմӳ,,ͷҲصCenterλվλ,ΪһŮ׼,wuliСˬڴƬ,Ů,˿,ڷ۰,֣ˬֻ,ȡⳡCλ֮Ѽۡǽͷ֣㰡//֣ˬġʵԼ,Ҳֲ,DZϵͷˡȫ´ӳµ,ۼۼ20,һߴơСˬ,ᵽʱеϢ?ɫȹƳ FENDI 2017ȹװۼԼ1w6,ۼԼ1w4Сˬ VS ģ֣ˬľ,ô,˼ӼҷorΧȹ...ϵЬҲFENDI,ۼ۴7000ࡣռijʱǰصɫЬ...˵,Сˬ컹ۼԼ14wҵ,CHANEL 2017ϵ,ͬ:Сˬ VS ģ""չʾԼCHANEL:,"ͬ"һ Roland Mouret,FENDIŮ֣ˬǽȻͬġߴϼ޹ݳƷƵƷ֣ˬͳͳԵ൱ӵ,͸һǧԪŮƷƵϢ,ʦſڲˡ Louis Vuitton 2017ϵţмп˺Ͱȹ,ϯӾ硶δᡣ⵹ǷҪ˵Low첻Low,ֻ֣ˬ崿ֵڼ,ijŲ...,ͬʱŮ˱,ʱз,Ͻġ//С,ĸŬʱзҰIJ,Ƿ켫...(֣ˬ)92Ĺ,+˽Ʒζ,ʱԴҲԴԴTee & & Ь|Dolce&Gabbana x LVR|Angel Chen|BalenciagaЬ|Saint Laurentֻ|Louis Vuitton,ڳΪ"й躺"·βȹ&ī|Saint Laurent|ChanelTee|Y/Project|ChanelЬ|Balenciagaī|Gucciӱ,һ·,Ҳ㰾ͷܵDiorʶ......Ψ֣ˬ,ֻ֣ˬ,ʱԴΪ0...20w FENDI+CHANEL ˬΪΰֻҲʱȦ?ԭ: ޷ŷ,ڶҲǰ,ˬԴ,Ҷǧٹֵĸšˬ㿪Сŭڷ,սԻͶЩ200ĻСϷһԲϾ,á㡱Ʒһϵˬˬ,˲ٱϸջҲкڻкźĸ,ȷѪ...ʱȦϲû,ˬĴ򿪷ʽ,̫ȷһ°װٺ̩Ļ,ý߱ƳĸϢ,ְDzǷ׷ӾͲˬ?ˬǰô,ĸƷƸ? С̫һûɶֹۡCλͿˡȻ˵桢¡Լ, but 10֣ˬ,Ҳ׵,ô,Եú...,̫üŪ,ڼС¸ͷ仨С?ҲûɶԭԴ,֣ˬ˾ÿɰɡ,00´Ȧ,91֣ˬʵڲʺ߿ɰ·... ûɶʱи֣ˬ̬,ۿɼ,dzһ㡣,޷ʵݡ,ԴڵӾκεط,dz·˽ϵBoyishķ,ó,Ҳʱи,ʵһijǴաˬӴǰ˵,ֻ׷úϷ,ԲӴԡոʱ...˼Ҹû׼ʱ,so,,low,Ҳܲš -fashion ŮԽµĸ߸Ьʱ棬׺ʸIJ ȻЩ˲֪ƽָô£ٸ߸Ь޿ɷϵġЬԽϸСԽ˿ܼԦĻܸ벻Ч˿ļÿŮıرƷͣɢԸеʣȵļڣݵ˿׼𣿲߼仹ͣЬͷ۵ķ羰ߣ˿¸Ტãתĵʱִ䡣۵ĺɫЬ𵽻㾦ĵ׺á˵ɫĸ߸Ь䣬ɫĸ߸ЬʱУǾƺɫĸ߸Ьͽһܴȹװǿӣȥϰ໹ȥ棬˴ңҲӰ찮ӴϾƺɫ߸Ьȥʱߡλţ߸ЬŮ⻬ЬԵóȼ𪣬߸ЬʵŮ˵һף顢ƢԸһŵд档ɫ纣ĸ߸ЬһƽŮתΪһ߹Ů񣡲ߵĸ䣬ӾЧ߸ЬŮԴIJţһ͵ʶŮζÿŮЬﶼĸ߸Ьȴѡ˫Ϊÿ˫߸Ьóϲͬÿ˫߸ЬԼºɫ̫ƣ㲻»Уɫ߸ЬֻжׯķҲ˦һŵʸСЬͷ˿ÿòУʱζʮ㣬Ĵȵ͵ַdzз 㲻Ŷ΢Źںţ˿ ѧͼںÿȿɾ -fashion õ΢ µʱѶ,飬ױɣǰ,ȨʱϢʱʦѼȫʱѶȫ׼Ҷλ߶ʱѶƶý壬רעΪϴĶ顣ȣµݶģΪܸı̫ࡣһϯ΢ȴɿƵģЩǶһͷŮ񼶳ĽʵԼҲԶŶֽϽѧɣʱҪͣȴֲ͵ǮȻŪһ̶̫ĹʱͣҲֻҪһѣʱõ;ͺˣԼͷʦر𣬶ҲԳƳ֡һЩСϣѧϰôȻľɡȻ΢ķβʸвӪ죬ֱΪŮϰ֮һ֮󊅾ͱòŻֵʹ˰ԼʹNG˺ܶ飬Ҳϰ볢԰ɣTIPS 1 һһҪͷɺͰʪɵͷˮһϾ˷ҲûоȡTIPS 2 ˮԸ־ÿþˮǶ־öͣصǣĨͷҲһҪȵȫɲſʹõҲ튅ᵣĵ˷ʣʵһܶлΣˣңľǣʵȥ̷ҪมTIPS 3 򵥷ֲȵȸô·һ㣬ϷٸüӼ𡣵ȵϾʱȴͷʼٴϷģõĿԲǰͲˣTIPS 4ʹǰԤĵԵ£һ㷢ǿ125桫150棬ϸ;ٽ25 ɣϸϽ25mm պáTIPS 5 ԼϲϾԷھڱȽϿɰ£ͱȽϻԸІץһСϾŲϲ˾ȡǵþ겻ᣬָˣȻë޹⣬ԼļţͿʹþᡣTIPS 6綨͸վȵͷᴵ԰ͣҪȥһ㶨ͿάһˣTIPS 7ǰȻķβúˣһȥԷ䯺ĺ׷ѣҪͷɵĿоҪԼϷֶ͵ķʽͬӳõҲüаг΢ñüае˾УԼҪٵϾһӦɣϣһֲеǿƪԻþ!һ𿴿Щ΢ֽɰȨԭУȨϵɾ -fashion ޱϰɫë£DzŮ ˳ԺޱԽԽŮζû붬ޱѾǰ˰ɫƴë£ðֵԸһٲȻһλƤġѩôأСһɡɫƴëƣޱһ̧֣ëɫ°ڶɵĸоһѷ磬аȫСϸëµɫҲǴɫеװɫƴӵĴ׵°γͬɫϵĶԱȣ·ǰԵĶľڵĶǵ˺ʱ򣬴ҲŻᴩϸΧΧ֪DzǿԽԽǿҪںҲأʵȫİ취ޱƴëµӷdz󣬲ܽƤȫвߴ̴̵ĸо⣬ɵƻ԰ĵССģDzǺܰǿƤޱͰ¶ڸں棬һ˫Ư۾עӵľͷ̫ɰë´Ŀһ㶼ţпߴ׿㣬ޱĵĴ˸ӿ㡣˵˲뵽Լүү̡ʵʱľԪأޱƴë´ӿ㣬ﶬҪһӵĸŷ糱ˡɫ͸Ԫص·ʵܰٴdz͹Ůζ£βޱġװôޱϰɫë£DzֹɵŮ -fashion 걬ĸԪ,Źʱ! йʱгһĿ·ֹע ƷʱԴ:MogĢ(cydb555 )ǰͬ´һǧĿϰ,Ҿû,ǿ㴩ǧĿͦÿġ,ͬǧʲôҾдƪɨä,֪ʶҪѡﶬԪϲһ,һǸ!Ϊʲô??Ϊǧ,ײѰո:ɫ仯ɧ,ɵɫ+(ֱ)֯,2ɫ,6ɫǧ:ϸʵɳǧͬһֻС͵ͼظ仯γɡͲ:ͬСķ̸ģѧ֪ʶ,ֵʱԸװϿ,֤һһġ˻,,ÿָʺôCOME ON~ոTARTANոСӢ۹ʷijƺ,ʱתķɿĽ,ʱнҲǴո on RUN WAYո,зȾϲMashamaŮϵ,ѷţƴӴɫո,ֱᵽ춯ءMashama 2017fall/winterBally 2017fall/winterBurberry 2017fallոƴҾոÿĵƷdz,ǿͳȴֽȻͬĸо,ľûдijݡʵڲϲŵ,찮ӸرԬϵҲDZһζ~ҶʱνУϵϵĻ䰡dzѡ񳤿ոƳ,ֺ̿ܲĸо,һ˼Ԧ~~̿ոƳͰȹǾѵCP,ְܹҵܴһֳϢԽ½ȹҲϵһ,ֻҪ½¶̫СϲһȹСҲԳСAȹ,IJοܶĵĴ,˿꿵ϵ롣ϲиеСͿ̿,½ȫ̿ͺˬˡΪʲô½ȥ?ΪֳijȾͻӪһûӵĸо,ȻֻҪ㲻Ҳô!ոԪرͱȽϻ,ΪȹֵʱעЬӴɫ,ɫͰɫ֤ΪƼ:ǧGlen Plaid˶˵ɯɾǧ,ұʾ,ǧֵ,ǽܻᷢġɯ,ÿ궼̲סáǧ on RUN WAYOFF-WHITEǧԭɫȻʸ,˵߸˶һĹˡOFF-WHITE 2017fallERMANNO SCERVINO 2017FALLWANDA NYLON 2017FALLǧǧ͵൱һ,Ժڰɫڴ,ܰѾ䴩ʸСǧ,뵽һǴְҵȹǽенͷ,¶ƶë߿,,ַӴиССǧ״СȹԳһɡСǧİȹϽINijñѷ̽س,ôΪƼ:ͲGINGHAMͲ,ҰͲΪһλԱ,ûʱһ˵ڽ,ͲĵƷ췶Χ˵dzĹ㷺ˡɫǿʽ,ﵽһ߷塣Ͳ on RUN WAYʦôתͲ,һǴDzͲľֲͽϼ򵥵IJͲ,֭VIKA GAZINSKAYA 2017FALLDICE KAYEK 2017FALLBAUM UND PFERDGARTEN 2017FALLTIBI 2017FALLAQUILANO.RIMONDI 2017FALLͲͲĿӴɫƷи߼,һ˵С¡һҪ֪,ɫIJͲ񴩲ûе,Ծѡڰס׵ĸ,Ų׳񡣲ͲӴʵĻ,Ļˡʽ:ͲȹѡûAȹ,Ȼ˰㵱и,ͼˡ߼һ,Ͳ¥ôһ˫۵ЬӺͰ׬ͷ,DzͲINĴ򿪷ʽͲijϷŷ,V컨ij,ֱͲİȹţ׺ɫë,ﲻһиСΪƼ:˵ô,DzѾǵĸ?³ĸƵƷóŴһ~ -fashion СŮ´ʾ,ôʱ! ֵ˴ůļ,СǸϽᱡסoversizeëºƶţпسȥ˰,ʱдʾŮ¼,һлȡ´ Style 1:δ+ڴͬɫƷ СӴҪԸ,õļɾ,ڴѡͬɫƷ,ȴͬɫͬʱ,׳¶ڴɫһ,Ҳխ,ӾϷdzݡͬʱҲѡ伫е,Χ,ӾҲʮԸߡ Style 2:̿¸Ծ СŮıرƷN̿¡̿ţײʱֻٴ,ڴһT,ḻθеͬʱסβ׸ޡ Style 3:Ʒ СŮѧߵĸּɡѡ񳬶̿,߸ȹ,ٽȥ,֮, Style 4:Ʒʮ пӳ֯ҲȽʺСŮ,,вijȸԸߡǸMMҲԳԳСȵij,Ƥʮ㡣 Style 5:̿+߸ЬԳ ̿߸ЬܹɴȵļӸ,һƸеƶëҲܾ۽Ŀ,ֿᾢʮ㡣Ҳӽʦ΢:sshh3014,һһݵ90!!! Style 6:ɫ߸Ь+ʱָ Ȼǰ,,ʱܿԼѡ񡣲࿪֯ţסƤͰͱرĿñ,LOOKǷʮ,ϵɫ߸ЬʮԸ,Լʧ˧ Style 7: ϸҲǽʱгֵİ,ͬɫ߸Ь,ˬˡ Style 8:ͷ+ Խͷ,ԸȻɫoversizeë´ɫƶţп,,ּ߽ͷʱָС -fashion ѹŮ˶ˣдɰ£ݱů °ڣʱиУɢ֪ԵϢѡ ϣʡͨͼС̬ܺ䡣ײɫñƣÿСϸڣֱͲ𣬲ӷףǡôϸʵůȽϺô䣬Чܺãƴӵĸִĸоƴӣֶر֮¾С£ñƣζ·ĿɸУˡ뿪ѣƿڴʸк磬ӡմȾΪ·ů鶯ƴӣؿοգ¶ζײɫ죬θиԣʣ򵥴󷽡ĸĺʺﶬʹŶ һţпп㶼ԣ ٴֱͲİ͸ĺ͡ĺɫȷʵͦÿģ·պÿԵסȱȽϴֵIJ֣Ҳʵһ͹ˡ£󷽣 аͣס⣬ڣ죬ͷţʣͨӡͿ -fashion ȫһԸŮͳձ˭Ĵ Ϸɫעת±ϵȨԼһӣϴνƽı̰︴ϰըǵİĹˡ˵ô˹ϴ˵ģˮ̫ٷļֻܺǺǡֻ̫Ϸⷢգɵ˹ͷϷô㣬ԼԼʵѹ˹ؼļƳ˽ڡ󲿷¹ļվܲ鵽棬2800˭óʱÿæ˦ͳԼѷڹļ֤3KԱŰʹʹһ΢Ц˦Ϊɶ˺ÿģĸ޹ء˸ŻˣǸǹְ˹߶²ǵ˹ǧ𷢺˵FBIֳ˦Ǹǹֱɱǣ鱨ֻܹ־ûá˦˳˦ݰԣ޲ءϣϵܵܣ¶лŶFBIӰһṩ鱨Ҷ֪ṩİԴҲ֪¶ʮʷΨһŮԣǰΪԸУΪͳСҲŸֵйءðɣ̰໹ڷƮٽһ¶֮¶Marilyn Monroe20ǰԭŵꡤˣ192661ճǰĦгߣҲûĸǵӰʦܲ˴ؽԺ¶8ͱ̷ǿˡСʱûû˰Ů뿿ɫҸɽˡʱÿɱĹάɧˣ˵Ƭһźɫɳרcasting couchŮԱ޲ſǶDZֵز¶ǸʱЩʲôʮϰ߱ΧתӾװģءաѵáѵáòѵá֮վڵοϣķȹӴù춨ԸŮλģ¡һֱ͡׳ˡ˿һ˵ı׼Ҳһɽ¶ػ顣һֻ16꣬Ϊиң޸ھӶӣɷξ飬û뵽źɡڶɷǰ򽡽ǣ˵з׾̫󣬶¶ôܷأֻά274θҰɪգ¶ޱȳ˵о⣬IJܳɹ¶ָ˶Թǡݡ֪ʶӣЩ˶Сôǿˣֻͳˡ˵get˧㣬ÿʱ۲ͬģ˧ģʤڵСؼػݽ׹ԹȺϲݽˣƻͳԴ⻷ϵԱʱʶ¶ҲԼ¶ܵСλ磬1961÷˵á͸Լұϡλ÷ҲԱͳţһ˼ִﻶġȻϲƯ÷ϴᣬͳ˽ܿڶȼ٣¶÷ҹ˽̽ƣ¼ֳ֮˵¶ٷīԼᣬг̱鱨աCosԼ᳡ʵأ¶˵ϻ𱬶װģͶˣҿǸٶHe is a quickyЩǹһܰ¶Ȼ˽ɶҲԶˡ˵ûɶȤ¶һİϿϡϾʮԱˣܵΪ㡣ɵ£עһƹݿ÷˵¶ѵ绰˰׹ҽ̯ܿƣ˵ϱ֤顣Сʾһƿڴܿ˵üˣ׹еְ𣻶أһȥһ㡣 ¶ýܿû취ÿҲû취̫æˣǸϴͳ硰½¡ŰΣǽ·¡Ȩ˶޵¼ƻԸԽսͳĥԿͣ˯Ůˣ¶Ȼȡͳˡſ죬¶˹Ѹ£ˮɫһʿдţܿˣ¶Զ㡣Ϊ¶׳÷͸ϳˣƤΡԸеȫ飬Happy Birthday, Mr President֮¶ֵȥ鷿Ц绨ֻܾͷͳ㾵ͷܵһ߿ŴһֱдŴ绰вҪұ綶šϵܵ޲صʱ˾ȥȰһȰһ˯һû޲Ҳˡ3˶𣬶Ī޲¡¶˽dzſֻ3£196285գ¶ֻ36꣬ҲաʬҽΪܴ֮ɱҲϲδθв°ҩôìܵʬ氡˵¶λֵ³عڣҲվסšѾɣ¶͸װ׳Ϊһ׮ڣ̫һ١ɫΪŵ˵ǻصFBIֳϡļƣ¶֮һռйءӵ鱨¶죬ĵûڵĸֻܣͳشˡ¶һȻдˣ䵽ô죿ֵܶϾ绰ѣ˵֪һЩΣյܡڶ賿DZռҲʧˡһˣҡһĴֻ޹ҲաţһձĹ¡СǵĿ¼ȫѿҳظĿ¼ͿԿںÿ£ϲֻҪһ¾УҲҪظ -fashion һʺԼĴ£ʱΪ ܰʾϷ"ɫ"עǻӭͶ塢˻ĺʱڣŮǿ϶³úһЩ·ֵôЩ·ĺˡﶬ³УұӵеĵƷܾһˡƷȷˡͶʸߣżȴһװߡԣܶŮѡʱ򶼱ȽϽ³2~3㹻ˡѡµʱһҪʺԼһҪٴһʺԼ·ʱΪ֡ôѡһʺԼĴ£ЩѡµĹؼأؼһɫﶬڣ󲿷ŮɫΪؼضΡĵΣﳤܶʡõξͻ֡⻹һϸڣܶ͡˺ԡµǷֱͣӾǷԵȡѡεʱҪ˽ѡµεʱ򣬾Ҫ֪ԼΡҪ㡿ηΪ棬һ͡һ·ͣȴ͵ijУʧȥЭȡԲҪ͵أΪװ·͵ѡȡ͡AISۿҪΪֱࣺͺΡְֱΣΣΣʯΡΰԲΣԲΣΣ͡ÿ͵صͲܣѡװ͵ʱֻҪ֪ԼֱͻͣĿǰ͹ˡסһ㣬ѡ͵ʱֱҪֱӣҪӣ˳Ӧķ˿ЭҪ㡿ֱ|ĶΣʱеŮǿҲ֪һЩΣXͣYͣO͵ȵȡʵֱϿԷΪ࣬ӦͣҲֱͺΡֱΣΣΡƻΣɳ©Σ8ΡЩӰŷװεѡͬסǸ˳Ӧֱ˴ֱΣ˴Ρֱ+ֱ | +ֱ | ֱ+ | +ĵǸΪҲǸӵһڣͺϵһʱϣǾѡκϸҪءͺ͵ɣγΡֱ+ֱġ+ֱġֱ+ġ+ġDzôӣ򵥷Ϊֱֱͣͣϣ͡ոᵽֱֱôôأΪҴӴµϸ˵ʲôֱʲôѡµʱ򣬱뿼ǼַװһǷװҲ·ļûⲿ·һеǵֱߣе͵ߣе£֮䡣ͶӦϸոᵽεֱֱֱͣͣͣ͡ | ֱ | ֱ͡ʹص㡿ԵǣֱġԵļߣǰߺ°ߣȽǷĸоǼ졢ƽ졢ڳĵֱ߱Ե졢졢ǹͷ͡ڴԵԣǷΣߴDz͵Ĵ¾Ͷ֮ǰᵽֱΣΣΣʯΣ+ֱģΣΣΣֱ+ֱڹƷУ͵ķҲdzġChanelGucciAdolfoAndre LaugȵȹһƷƣڷװ϶ֱߵƣҲǷdzʺֱĵ˴ŵġ2017ChanelGucciƷȻ񡣡ֱʹص㡿͵Ĵ£ⲿȽƽֻͣһߣϽṹȽϲϸܻΪֱߡװ͵ģԷϴ˿͵͡ΪԷװûм˵ֱԲֱˣԴȻΪǻͣߣıȽֱߣʺߵĻϡֱ+෴Ҫֱܱ߳Ƚ϶࣬˳Ӧŵַ͵Ĵ¾Ͷ֮ǰᵽ+ֱġֱ+ĵ˶ǺʵġεĹƷƣIssey MiyakeլһGiorgio ArmaniᣩCalvin KleinġGianni Versaceᡤ˼ܣЩлķװǷdzʺϻε˴ţֻҪעԼµǷЭɡʹص㡿ִµⲿģϾСЩͨ·ĽṹߺʵֵġԲģģԲαԵ졣ҲԲΣΣбءڿлߵģҶߵװΡڴǸǴԲλڴģܹʾŮߣߵġ͵Ĵ¾ͶӦ֮ǰᵽԲΣԲΣΣͣ+ģƻΣɳ©Σ8Σ˴ţǷdzЭġεĹƷƣValentino׵ŵChristianDiorϰ£Halston˹٣Emanuel ungaroŦ¼ޣƷơµõߵģŮԻеġͼDiorɫ£ͣҲǾ߸еġѴһ㣬ѡµʱ򣬲ü˵װʽ磬ԲĴż͵ԲȦĴ£ֱʹżֱߵĴ¡DZȽֱ͵һ࣬ñԵԡֱ߻еѡԼƵķװģܴﵽȡ̡õЧгŮѧIDShoesgod Ҳһ עǣ ͿԹע磬Ȩԭ -fashion ŮμѣдʱеؾһñӾ͹! ŮDzŮμѡڿʼɻǰһաɫ֯ëµİμѣͷë߱֯ñкڿ㣬μѵд˵ǼۣμѣڹݣŮԱСʦáǡǰιԡĹۡСġǡǵʮ꡷׷εġСաôûһ ԭĻȻأʵеİμѣ˽ƷǾֵѧϰμɫװźɫͷݱñɫȫȻһִŮ˵ʶȼװ硣ԼTİμѣͬɫҶȹͬǴݱñΰμѿǽŮ鶯Ƥչ֣TȹĴҲǾԺʱŶ~ԲʱɴdzɫȿİμѣĴȻΥ͸С˰μѵҲ֪ԭȿ㲻ֻţеİɫСİμѣͷ䲱˫chokerʱз԰ɫװɫɴȹͣҲðμѿ¡ͷôڴȫװɫƤ´ɫ߷ֿİμѣһڿװǹʱ֡ȫΨһǷİɫñμ˼ֻ˶!λɫѩ´ɫ㣬͸о˰ɣһɫë¿ǽμѵչֳ˾ðμѵЦҲǺϵأǴϷޣӦ׵ĴεĽоƯIJȻԴ!ҿμѴˣôҲϲԲŮμԭͼƬ磬Ȩϵɾлл -fashion ˳ͬݷ12500飬ƷƣٳЦˣ /Max˳СĪʱǰأưңBalenciagaƳһۼ12500ij£˳Ʒ̰档׷崩һ·̱й˧Աҽйȡеĵʱдơҡйʱд⼸꣬ҵIJƣܶй˿˸еʶҸС֣bianᣨzhi㳡һ򣬷dz׳ۡûһڴ˻վĸоò˵йƷơٱ̰˯°ҵģش㳡һߣۼ۷ҡֱñҡйë̺ޱרðһ̥ǡĵûɽӵƣڴϣϾ͸ƽӹ·˻˾롣ֶغͽdzɹʿҪĸоҲףЩƿ벻˹˿͵ۡǴ۶ӣҵҵƳЬڹֱȺ塣аٷְүүǰĵоһŵšԿΣۼ630ӢԼ5500飬һ㶼󡣡fu*k˵ֱDzʱʱʱС˰ҵƣ㻹Цֱе伣18꣬ϮҪƿŲһٶǰ˼ҿŨŨĹ巶Balenciagaɰʵں͸һӰĹʵƷƵ־ǴʼCristbal BalenciagaүӵϡBalenciaga 1895һСɥСŵ÷ĸסʮʱżϵĹˣСС󵨵ظṩϵо˴ΪʶȥļѵBalenciagaԴϮΪʱʷϼļȻơֶ÷Ƶƴʦ󡢱ʱɯ˵ȶָƵʱװҲʱװ־ѡΪŵˡĦɸGrace KellyBalenciagaƵʱFabiolaʱĻɴBalenciaga1968꣬һ򵽼µ󣬾͹ر˷޵İҶˡ֮77İүš18꣬ʱнδŰҵĴ˵Թסdz1986꣬˯İҲſʼգŶΪŬߡƷʦDemna GvasaliaְҺ󣬰ƷƴһƯķ̡Demna GvasaliaֻһСûиֻ󡣱ơֽİۼ876Ӣ7600+ñҰ...һ⣬Ѳ²ɶһĦгӾӸеְıҲ˧ըʸҰĦкŮDZţҲֻаҸˡˣBalenciagaľ򣬷ͬϵб2030ԪԱۼ12ԪһǧƷdzǿ˻жᵫֻһ죬Ͼļ۸ϵ˯ҲЩݳޡǿܻBalenciagaǮʵˡΪʵذǮӡ·ΪֻǸߴԼǮ ̨ظ йؼ ȡɻս봴ʼͬУ -fashion ֯:֯ôз?5֯乻ʱ! ֯:һһ,ҹڵijоһҹ,糿վڴԸ½Ҫʲô·͸ܵһ˿,Խ׼һƪ֯ôз?עıůķɹŶ~֯˵һ,DZƽʱһҪô,һ֯ԴЩƷ,Ϊʱִ!֯+֮ţп͸ϳ,ҿԿ϶˵ܶһ춼ûţп,ΪǰO(_)O~ţп仹Ǻܾġ١˹¶ (Behati Prinsloo)´ƶţп㲻ʮ㻹ɹŸ!Ľ~֯ëҲDzѡ,ΪȽϼ򵥷,ֻܶԸ⴩ҲԸ··,֯ë¾˰͵Ʒ!йĺܶϲʱ֯Ժ,ܻģһЩ,СµӿԳִŶ~֯Դ䱳,ʱʱ̲̿Ů,ɼDZ!֯+ȹĩǺʺϴȹ,ȹӴƮݵ,Ҳʺ＾Ĵ,·硣ӡȹӯʵڴ,Ұٴ,֯´ӡȹ,ƽЬҲܴ!ɫ֯װ,ʳڸʮ,¸Ը!֯+̿˵ܶСŮǷŲ¶̿Ĵ,ΪʱǺȵ,Զ̿֯,,ֶΪ,ɰ!´̿ȳŶ!һг֯,Ժġ֯+ȹﴩʲô?ȹһѡ!Ϊ֯ȹcp,ֺÿ,ֻҪɫ׼û!ɫӡɫȹ֯holdסĵƷ!֯,ŷʮ!֯֯䲻޹涨,ֻҪÿ԰Լķȥ!ԺܸԵװҲԺԵļ򵥴䵱Ȼ˶װװҲԺܺÿݺɫ֯ɫ+ȹ,һ˫ƽЬŮʮ㰡~֯Ӧÿ¹رĵƷ~֯ʽѡŷպл!Ķ?＾Ʒ,Ͻ~~~ھƼﴩʲô?ȹһѡ!ȿ˫!ȿʱָ߼!´ʲô?31ʱд´ʱһ!˽ŻظĹؼϲ鿴!΢֡ȴ֡СӡΨ䡢ġʿ֡Tȿ㡢ȹЬȹװñӡشƽءӡƽЬī㡢Է硢˿ȹں졢Ӿ¡ɴȹ߸ӡţסꡢȿ㡢߸Ь＾('')СŮ~˵ʮŶ! -fashion ﵥƷ--൫ô!ҪʵõĵƷ... ſѧĵ,ҲĽ,ﵥƷҲ׼׼,൫ô,ϾһǮһѲʵĵƷ!һЩĽijֵļƷ,ûҪĸϽԼ嵥,ѧҪĸıԼ!# #Ǵ＾·,ÿڶٴڴԡܶŮҲ˳,õƷˡֲ,߷ֿ,ԼȻģСЬʺ㡣ȿʱ,,ĸ߷,Դѷ硣ɳ⴩,ӭľһԸӦĸй顣ν,Űɫͻɫп,ˬ,Լ,ͷŮʮ㡣ٴƷӵͬȿ,ǿԴǿ߱,ӳºţг,СñϺɫī,dzƯ,ƽ׵Ьokѡӡ黨߲ij,츴ŷ񡣰ѳ׵Ĵ,ӳģǶϲ˽͡ѡ񳤿ոպ,·绹ܷɹ ʱ򵱱״,һЩԴס# Ь #ȷĴ򿪷ʽȹװ+˶Ь,ÿMCƽʱ̫߸Ь,ӦƽĹϵ,ϲ̤̤ʵʵƽЬЬ,ʱ,ֿɶڷЬ,н֢,ǿŷĵ,ķЬôfeel...׼һҵİЬȥĨҡһ,ʱ,ɡVans˶Ƶľ䡣İЬԦ𸴹Ƹȹװʽװȹ˸ġաȹװ,ҪڲͬĻ,ѡһ˫˶ЬͺܺÿˡҲMix and Matchڰ!Ըʱֵʱȹװ,ϳưЬ֮,Ը,ͷԷ硣С׵ռǺǿ,֯Ըнȹ黨ѩȹ,һгȫ㶨Converse߰Ь,һֱdzߵһ겻ľͼ򵥵LogoһʱеĻˡЬ⼸ȶDZȽϸߵһ,ƪ·Ƕ˫Ьٴ˵Ьٴһ㶼Ϊ,黨Ů͵ĻƤ¿˧͵,ȫHoldס!ô+ʱ֡ӡЬļµĦǸ,ü򵥵Ҳܺһʱ졣ԡͷԡһЬ,,ϸŮԵƷʱ,Ļôо:Ҷʱе漰Ļ̫Ƿȱ!ԭһ֡&ʱ֡Ĵ# #Ҳܶ˻ѡĵƷ!ò˵ĺ,ѧʱڿʼͽǵ¹,Ҳܼ,ʵ־,á¦ץ,ɫ´ţж̿,ʮ㡣ޱɫµġʧ١,ϸֱ,׵!ݻĺڰ׺ɫ,ɫ´ɫñԼСЬ,ȳݽڴµĴDzٵ,·ʱɫ´䱪ƶȹ,ɫ֮ǰFenty x Pumaɫñ,GivenchyСЬ,¼ơ֮ӡձ,ͷ´ţпҲһʱ!һ㶼ţж̿ȹ,Եøഺɴࡰʧ١,¶ȡѡ¿԰Ѵ!ʵţпһʺÿ˾ܰ,ϸ߸Ьܺʱ֮֡󻹿䡣 -fashion | ﳣĺÿȻӵģһ ˺ܶĽͼֳЩѿڶĴư֮⣬һ˵ǽıˡڲǵ˽и洦ɼ캣һ˶İƷơStaudStaudɼСƷƣ2015ReformationǰܼSarah StaudingerReformationǰδܼSarah Staudingerһ2015StaudΪǰ˾ͬɼƷStaudIJƷУнΪķγͷɫ鲻ϳϵУɡ״͵ΪStaudεбɫϸڣƤƳɵðΪStauddzƷ BissettˮͰDzһˡİƴӲͦΣʸгáһӰɺ (Alexa Chung) 򰲵÷ (Leandra Medine) Ҳ֡Ƽ򵥵Ƹʮ㡣StaudƳ˽е޵ˮͰ˽һɽƣСɵİҲڽгΪһ㡣˿ɰıStaudŮװҲǺ˵ġStaudҪԸС·ߣ¡ϷԵĿӡԸеȹµȣֲãзɫɫµɫ崺ĸоΪƼɫŮװ1ȾStaudϷ䣬¡ȹӡ֯DZɴټԼĵ׺úܾʡɫŴ󷽣ɫƴӸԸԿɰ2ɫȹҵȹStaudҵȹţԼķ񡣼ǴɫģɫҲǵ͵ģˮɫɫīɫȡżһЩ壬䣬ҶߵΪ׺͹Իʡ ɫʷˮƣһ˿СԸСStaudһЩɰȤСӡͼá3Ըз磬StaudĸŷҲǺͻģŵСͺϲÿһɫʶвͬЧ֡4Լŵּ򵥵ĸСΪ׶Ǻܺõѡ񡣶ڷװ˵ɫʺͿʽһҪStaudһϵеСɫ۵ķɫɫַӹŮġ -fashion ²ǽеİٴƷÿ棡 ýܼ֮ڿʼתҪ˵ôchicĻƷԷĪŽ˶ԷʱȦȻɹϮΪԴʱֱǩİʵִܣ۵orڴʱ͡2017ﶬT̨Ͽ洦ɼȻǽ񼾵ijص㡣ǵĻĻ˽ѷµijʿ൱ĸߡйģ˽Ȼױ³аĽࡣھỻδܴʱиУһ˧ٴʵ¾Ϳ㶨ʱִ䡣е¿ʽΪر³ҲкֿܶơеҲֵøλֵģ⼸oversizedµһҪǿƼıoversizedˡʱȦд󡱲СڱԷʵ¶ԣһҪoversizedźÿܴʱͷҪעǣoversizedµİͲָ³ӣֺοɸСͼְͿɵĶ̿£ʵҲdzзֿijҲʱİֿᾢʮ㣬򵥴ͿԺʡ룩¡޸졢ʱ֡dzŵĸԪأҲ뵽µƵС쵽Χ»ͨ¸ʱ˧һָŸ߼СѡְĿʽҲֺܶиǻĸ¡ԲµȻԲһֱܴҰҾ˶ܼԦĻƷԲƵ±ȽϻѰ˵ʱѡӡͼĿʽʱָܸһ¥ԲҲʺĿʽ¶һس죬ȷḻ͵ʱֲθУҲշIJӱȽ϶̣ôֿ»ѡV͵ƿӾľͻкܲáVµƣڱ˧ĻϣֽܴεСŮζñ¶ñ¸ˣʱ𱬡׷ͬȵƷϹԼһŮͳͳڴñ²Чʣͬ׻Ҳײͬʱֻ𻨣ֵӵСѧµNʱִʱһoversizedȵȸoversized¿ɵİ볬Ʋݲ⣬ֱӵDZȵİ֡°ʧ١ĵ·ӾֱȲԸЧ޵УǴƽЬor߸ЬǴд˧ޡҲʺճҪֱ˶Ьcpˣഺʱ֡ŮݵĽı䣬oversized+СЬͰñףƼȴdzƷʺճ㻹KendallͲһֱoversized»߸Ь˧ԸŮζںϡϥѥoversizedҲǺѣСҲԷijԣľ·160+ӾЧ+̿̿һĩѴcpڵʺϴһѡţж̿㼴ɣ˧ֺл¶ܺÿȻƼĵȻoversized̿ϳ¶̵Ĵ·ʱ֡+ҲǸλgetĻ·ůӱͬʱܷḻ͵IJθУԳʱֺƷζԲʱ˿¶֮⣬Ҳֻ¶һس°ڡִһԷ+ǰᵽġϳ¶̡½Ĵ䷨ͬ·ÿɵoversized´ȿ㣬Ӿϵķл˫ȿȥ˳ǵѡεľŷֿŶԸ߼maxһ˵ɫСſoversizedµЧ޵Сݡʱֹ۶ôѡϥǴƶƵľŷֿ㣬˧Ȼȱ͹ϸҲýţпɫСſ㣬˧chic+ȿȿļӳ£зҲܴ·ĿĽСܶƾ+ȿlook˲һĹȿ㱾ǿɵİͣԴ¾ͲҪٿǿɸˡһѡ̿ãԴõıȫȵļӸСҲ԰µ°ߡ+ֱͲиʵֱͲ㣬뾭µĴҲǴдۺÿ˧ɴͷ·ģ˽ԿɼԦʺճĻ·֮һ+˶˶糱ߺ죬ͬʵϮĵƷ˶㡣ֵƷһ֮ϡʵĴȴַdzᾢʮ㡣+ȹҪ˧еͬʱܼŮζôûб+ȹľˣɴʱinman˽ܴ׷ʫʫԲԲdzӰԻϡڶȹ¶ȲʹܿoversizedҲȫԵӷףԣֿʽĶȹ·dz䡣Ҫ޲ŮstyleƤȹ+µϲɴһɫorɫϵѡͻᾢʮĸԡȫǰɫϵ̫ƵĻѡͼӡorĸӡĿʽʱֶȡ+̿ȹǻƤഺ޵УһطŮʱİȹµĻЧҲdzޣӯǿƼҳ䡣㻹ýȵıɴȹֿĸСŮ+»ﶬʵٴڴе׶һĽࡣƤпڴ¾˧ӱţҲǴͷľcpģKendallƤʵ֡½ЧϥѥԸζͷʱΪ׵ڴҲǽ곱ǷdzԵĻ·µļӳֿ׹еְͨʣø˧ͷʮ㡣׷ԴıKendallܶһսɫΪorʱȽʽڴһñ£Ϊ＾ͷҫ۵Ĵڡٵ͵Ļ+Աп˵ϾʱůͳͳӵСѹָ·dz˶cp꣬Ϊһµı䴩ܴףҲdzġͺʺϰ˶ΪʷµڴкͷµijʣlookƤ·ǵĽĴͬųߵijʡ˽ķ͵촩²ʱ֣ÿְٴʵǻŶӴ -fashion Miuo˿Ͷ,˽!104 ΪͶ,,֮ġ˽Ŀ,ҵҪҲ,پճԸ,ĻзĽ,Ҳ桱Щ------濪ʼĵ-----һλͶ,ϴͶܴ,֮ǰһֱûԼ,ֻоﲻ,ֲ֪,ʦһ:̫ȷʵ,̫,Եÿ̰,΢һЩĽ,Ǿûõͻ,ϣҪܵõʦָ,ڳ·ϲϽ,!:{ͬѧӡͦ,Ͷ,Ҿǰ,εĽdzͬѧ38,168}{ ЩһͶƬ,ҵʱָǡһ,ȻҲûʲô,·Ҳʸ,Ҫġŷ,ȱѵĸо}{ ڶͶ,Ȼע⵽һЩϸӪ조СĴ¼,Ьӷɫʴ䡢ε׺ȷ,һЩС⡣}{ εͶ,ǰһ,ýdz,ɫϡЬĴϡɾɫ,·ͺʸ,Ϊ30+lady,ѡ/ȹ,Ƕȹ,߸Ьļͷ,Եš }{ Щ,Ҿõһ׺ɫT+ɫAͳȹ,ر,T+ɫ+ɫ,γɫϵĺӦ;ɫЬлɫȹɫʺӦ,ЬĿʽҲܺ,,ƽ׿Ҳ׺}{ ,ͬѧķ,ȻǺڳֱ,DzͷƤһĺڳֱ,β΢,дüƪ,ǿ͵ҪԡͶֵʵӰ,dzǶ̷,ֵر,Ȼһķͺ,㻹۾,ǻ,λͬѧÿķ,ҲΪʼӷֲ!}{ ,ͬѧ38,ıֵĺܺáȻдܶܶԸݵĴ似,˵ʵڵ,ܱõ,ѡΧ㡣,һļ,лʸ,Ҳ,ġ}-ڶλΪһλϷ,뷢==Ⱥܸлmiuo!⼸ĸÿؿ,˾,miuoԿҵķ¼֪ҵı仯ˡϣõmiuoָ,һôı.ܸʱƷζзһЩ.ϲ֮ǰʦУŮʯԭ͡лmiuo֮·~ȶϸڵԸз:{ ,ڴ´,ѡΧ㡣ҿԿĴCF,ǶȹĴ,ȹӶdz,ûиǹȶ֮һΪͬѧ,Ҳϸ,ĺϸ,ֳȹ,źÿŮ! }{ Ҳ˵,IJݡе֡Ǵ׸,ô,ͲҪȹ!ȹֻᱩ¶ȵķ⡢δŪûɫĸо!ֻݡ֬͵,ʺ϶ȹ!IJݵͬѧ,ϥҵȹ,ЬխЬͷԼ/խ¶űЬ,!ȹЬӿʽ,ȱһ!ȱһ!}{ ΪݡֵҲ,ֻҪҴעЩ似,ûи֪,ڷҲݹ6,˴A,ǺڰɫĴ,Ҫ ʱƷζзһЩ. Ҫи˷ʶ,սɫɫÿ,ʸС}-λ28,160cm,53kg,˶,ȳơҵλϰ,һ,ұʵСһ㡣ǰűȽ,ʦѧϰһʱ,Ҫ㷢չлʦڳͿ,ɫ,Ծ¡ƷҪԡʦ,ҵijǷȷлʦ!:{ͬѧĴ¿,,ܾЩ˵ıŤ˰,ͻȻʶ,dz,λͬѧIJ,е!ҲDzǰ̬,濴,Եòܶ,ԵüҲȽϿ }{ ݹ6,ҸоõǴD,˿ʽࡢ++ʸɫ,ǼɫTǴV,VӾԲ޳!ټϸȶ̿ЬխİЬ,ȲҲȽϽ,ԴGﳤܶˡ}{ ļ,ABCE,ٶ̫Сβ⡣}{ôBʾ,״,T+ɫAȹ,ЬɫʺӦ,ƺûʲô,ȴúÿ,Ϊʲô?Ϊ׳ˡЬЬͿ֡Ьֺܴ֡ȫסű,ҲԶ̡ }{ ͨPS,ҰT,СԲĴV,ȻͷŵӺȥ,ȫ¶Ӻǵ,ӾӾԳ;Ȼ,ҰDZصĸ߸Ь,Ьխ¶űİЬ,¶ĽűΪСȵ,ҲԳԵӯˡǿ,ǡЩ߸Ь,ƽЬȳõӰ!BԭͼеĿЬЬô֡űȫס,Ь,ʹٸ,Ҳȶ̡}{ ,ҰBеɫ,P˺ɫ,һЬɫʺӦ,иǿ,,+Ǿɫ,ûоøÿ䡢лchic?}{ ⼸,AC,Ҳ̫,ûӶ̵⡣,֮ó,Ϊ,״,+Aָȹ+߸Ь,Ůľ,Ϊ̫Ů̫ľ,Դ۾,ԵùŰ塢Ȥġصİ,,Ҫô,Գ԰ͷڿ̾,Ȼ,Ҫô,ͱ۾ˡ}{ FʵŴ仹,鳢ԴƽСñ,ñ,Ĵ,ͻϸڸиchic}{ EEһζȹ,۴ӿʽɫ,ŮԻ!ּҲĶȹ, ¶֫һ߸Ь,Ǻݵƽġҳ䵭,ȻסԸС!}{ ͨPS,Ұĸ߸Ь,ɺɫƽװЬڲȶ̵ͬʱ,ƽЬˡԸС,ĸо,ͬʱɫҲ¼粿ĺɫγɫʺӦ;ΪIJӱȽ϶,µڱȽϸ,Ǽ粿һɫƴ,ټϳ,ӾϴСںͺƴɫԾС(ʽֻʾ,ʽϲ) һ,ûиоö?}-λͬѧ,ûи㹲? -fashion ûAngelababyǿӵbaby ԸԸϣAngelababyһֵĽʺ޵ĶòΪܶηҵϣҪǣӵһϹһв˸пȻ˶ҵõˣӵAngelababyһǿӵˮ覵baby֮ǰڲժĤϷڣٻҪΪAngelababy͸׼ޡȻüëߡëױڣʪĤһϲٺĵױҲ÷ˡȻͷǰAngelababyȻ覣Ӥķ۹ʣۡʵAngelababyڽĿҲǵһˣеһؼʱӱֱûϴƤȻ͸覡ôõļȻ벻ճľĺǻСAngelababyңҪʲôݻƲƷʹǮѧҲաһɹнĿУAngelababyҪʱڻ¼ƽĿΪ˱֤ߵ˺ɹͳ֮ء¼ƽĿĿʱAngelababyֳɡڵ̫ڽ⣬ʱʱͿϷɹ˪ȷɹЧΪʱڻɹһAngelababyʹ޻ЧĻƷΪƤ£ɹ޸áҶˮϴAngelababyտԿļƣʮ㡣뱣ּĵԣѡˮϴԽ沿ƤԣϴһЩ֬ĻƷĦӣǿƤѪҺѭӼĵԡȥΪ˱ּ׹⻬ΪûƷӪܸõرգȥǺбҪġȥܹྻüظҪעǣȥʱ䲻˹ӲԼ3-5ӡȥƵҲ˹ߣƤƤ1-2/1ܣƤ1/1ܡǽﶬȥʵĴ԰ʵ١ģĤʱĻ⹤󣬼ȱˮAngelababyץʱĤΪˮ֡ĿǿŷһĤ֡ĤʣľҲҪ˷ѣĦٽա˯ĤҲAngelababyõݷַ㡣壺άCάCЧҶ֪AngelababyÿάCϰߣΪⲻԹǿȺܴ˵dzʺϡ˷⣬Angelababy˪άCܱʪܼƣһá -fashion ҵǰʵһ͸ĵӾ?! ǰʱ䡶塷֮,һ¾һİ칫һȳ:ҵǰ㲻ϺܹѪ·,ôҵ鶼ŮϹ,Ůһ,ԼϲȴϲŮⲿһЩɫװ,ŮĴ˺ܶ˵ĹעžŮǿְ,dzΪȰϻᴩŮԡPԬȪƹݼŮԱ,ǿǿݶϷ,ЩѪżÿˡPݵӾ,ǰʮһִš˼Ϲܵȫְ̫̫ܶ˶˼ΪʲôӾʵǷҲᱻϹ!һᶨػش,ȫҵǮϹ׬,ֻӻ޶Ȼ,ô˭ܲˡӾôӻ޶ȷɡʼľͷӾ̳ Azzaro Ь,ԿóЬӵҪܸ,һ˫ʹԼ 2 ԪҡCeline ë¼ Delpozo ȹ,˵ڴʱѡһЩɫϵ򻥲ġӾɫͻɫɫĴһʱ,˸оӾϵIJ(ͼƬP),ٴδ Celine ĸë,һɫİȹ,˺ܶࡣٵȥǰڵӾ,ֹǻ̻ƴ,ϲ̡ڴɫ, Stella mccrtney ,һߵ(ͼƬP)Ӿײɫʱ,ʺԪءNeedle&thread ȹ,о˲˺üꡣ˵ȹ,ͿʼŮۡԵӷ֮,ѡͬɫ Peter Pilotto İȹ,ҲһŮĸоûС Victoria Beckham ȹ,һӾǰڴÿһˡӵӾ,ĸһ?ܶŮǼе,޵з֮ʲô,ֻᱧԹͻǮһûеĻ,θͻúܴص,к,ӾʼԼµͷ,ɫҲ˳ࡣ(ͼƬP)ٿ,ɫĸë¡ɫ IMMI סɫ Burberry ְвֻܴڰ׻,ԵǸ(ͼƬP)ʵһP̫ʺɫϵ·,Եûʲô, Delvaux İĺܺÿ(ͼƬP)෴,ɫͺ͹,ЦҲܱ¡ص:Ҫμ֮,ɫһܺõѡ,ķɫ(ͼƬP)ĺϲɫϵĶ,ӾһѡƴɫĿʽ MSGM ׾ԵûôȤͳ,ڴӦ LV Ϸ,ϲһ񡶻̡ÿһһ·,ҲûôҵǰŮ·ظij,ֻǻһִ䷽ʽ,ֵýĵطܶŮܻ˵û·,ʵ㻹ûѧ,ѧһ·ִ,Ͳ˵Լû·ˡ-THE END-˽ʲôİ,ڡһ,!㻹뿴ЩǮ | հ | ѧ | TOTE Сڰ | ˫ | | ͨڰ | ƽ۰ ͷ,һͺ < 뷢ʼ coolbuy@ifanr.com > -fashion ݾͿһͺܸ߼ڴûholdסģ еˣɴϹǵָҲµżƯ¶ȣһ˼Ҳ䲻¡ﶬװѡȴǺ٣ҪôҪôȹӣȻ죬ûмŮֻӣϾȹӲŮչʾ˵촩ȹӵŮˣ˵ҩȹﶬõѡ񣡴򵥻׳㰼͵ʵƷս֣͹Ůζʱилdzǿ˵ȹӣһϵ㳡&ķ֣ⷢȫʱŮ˶ڴȹӣԣdzIJ̫ԳƵȹӣҪôdzȲԳơҪôɫԳơҪôƲԳƴCelineJil sanderСƷƻdzȲȡɫͬǹղͬ...֮ǸеƵIJԳƸУ㳡ܶŮҲվڵ˼ϣμӻѡľгȹβμӻҲɫƴɫIJȹһ·dzᣡPμӻѡҲֲԳƵͰڵİȹʽdzȲľǸԣڲòôһȹˣȹӣѡڼϥѣԿ˲ǵĽģϴĶϥµijȣԶȹӴҲЩͲȵѥӣůֺÿϥȵȹӱȳһ˿顢ȶȹһ˿ׯDzμӻճϰij϶ɴ䣡ʵӴľͿʼгijȣ⼾ֻǼѡˣʺﶬıůءͦƤʵȵȣAnnaȹӵȹҲǷdzIJܿ°dz޳ֲ̫ƣMarniCelineﶬ⼾ƵĿʽϸֿ͢бòԳƼ䣺гȹ+ѥ/ѥ׿óȹ+ЬΧȹӣһббĿ÷dzȳƤϵѡ˼ֿᾢֿõĿƴǰȽϳʾͷdzˣ⼾ͬdzձǼϥгȹɫĿʽһ+׾ͷdzʣĴɫȹǺôôִЩףЬȻѵĴ䵥Ʒͳѥdz󵨣ܶƷƻ־posterô䣬Ҳ׷high-fashion ѡһѥԼѥǺܲѡûжǡô¶ϸIJ֣2018ĵfashion trends ɳĿȹ糱һֱ2018괺ģƴɫԳƼ䣺ƴɫȹ+Գѥƴɫȹ+жѥ/СЬȹƶdzǽʵĵƷеĻǷdz򵥵AȹȹһϣΧޡáϵƴɫȫǺÿ޷ܣѡȽдеIJʣһoversizeë£ѥӻСЬdzÿ Loewe ĿҲDzƴɫǽеɫ+ɫůʸУжѥͺܺÿȻѡ񸴹ƽ˶ЬҲdzÿ۵޲ȹƼ䣺ȹ+СЬͷЬ̿£ȹʱװܵͷᷢİȹIJˣΧʽʽİȹȻƸӵȴʵĴ䣬һ򵥵»ҲDzסƸгǿǰһDZжֲʡսϺͱ仯͸бøǿ·ȫChristopher Kane΢е㴹еIJʺͱɴĽҲdzMM6 Maison Margiela 2018ҲƵĿʽƸзdzǿбȽǿʺϴ̿¶ȻܻӷŶЬҲںأ¶ϸĽ׸ѡܺҶ߲ĿȹƼ䣺ԳƺҶȺ+ϵЬЬë£ԳƺҶȹë´ƮݲʵĺҶȹܻdz⡢ĸоЬѥdzÿҶԪڷdzеԪأdzñϴϺҶߵСϸǸֵĺÿǺΧȹ߷顣ŮӦöܾ˵ĺҶߣJane BirkinһһֻӣŨķʽߣиԣɵĺҶȹڣֱ̫ÿһһ͵ij+ԳƵĿʽҲǽdzеȹͣƸзdzǿĵĻԷdzϾֻճһ+Ҳʱֱˣ㳡ҲԿǿIJԳз糱ʵͿȹеƣһ¶ȲdzȲһ˫ѥdzÿȻԳأص㲻ԳȹװĴصǣ1ëµѡһҪԲͬĿʽȹװʽԼ򵥣ǻǦȹȹ֮ġİȹƸжȽǿԲʱȽϴĿʽĶȽ͸Уʺ϶̿µĴ䣬ǵðѳȹ¶ͲԵӷˡ2׵Ĵ䣬Ҫô̵¶ڡҪôȹڣ볤̵ʽײλңӾ֣䰫졢֡3ЬӵѡѡȽеѥ״սԵӷףٴСЬЬҲǺܲѡ񣡸о⼾ȹӣĻʧˣ -fashion ѧŷŮĻ ȹ! һЩŷŮdz˹Ǽƫ,˫Ҳϸ,̰˹ٹȡ𡤿ɺȵȶǴ˵Ĵȴ,Ҳ˿¶,Ϊȴ!ѧ!ϣ(Hilary Erhard Duff)BAD:Ǵȴ־ͲҪѡ̿+,ôԵĽȱݱ¶ſɻСBEST:ȹ޿ĵĺð,ɿڴȼǵķ,ҵԼ˷ĵƷ,°빦³Ħ(Drew Barrymore)BAD:±˿ȴ?۵װpassBEST:ɫijȹԽȵⰲȫڸ,ţгȹķ,͹Ըеߡȡ˹(Selena Gomez)BAD:Ķ̿㲻ȴ׳,ճӾȦBEST:ѡһŵĶ̿,Ǹ,°,ȳƲ١ݡ˹Ƥ˹(Britney Spears)BAD:ϱ¶±߻¶,ȷֵı԰ȶ̡BEST:Сȵķ֮һǼǿϰӾЧ,ת,¶֮١̴DZرƷ̰˿(Beyonce)BAD:?!ϥͲСȵѨ!!BEST:Ҫôһdzɵ,ҪôͲҪ!ͲɲǴ,taʲôʱл,ҿ°ʱװϵĶٹ(Rihanna)BAD:ϱ¶±߻¶,ȷֵı԰ȶ̡BEST:ȿȹ,ܹڽϴ׳ĴС,Ŀʽֿ,˫߸Ь͸,ֽı,ѩIJʵȿˬһġ!ղݸ(Jennifer Lopez)BAD:ȴһҪ,ȱǿ϶,λȲΡBEST:ƷǿӾϵ,Ȳĸ,ܹתƴߵĻпŵΡʲô뷨΢ݴʦнŶ~Ҫ˽?Ҳӭ΢ѯ!רҵݹΪ!רҵݵʦΪƱ,עʱд!,˽⹺ݴʦƷ!> -fashion ䷢,ɸͷ ع,˧졣 Ȧ˸˵˵:һõķĶֵкܴ˵˵õ˺ܶ˵ͬ,ΪЧġڴʱ,ϽʵķͰɡʵDZ̨,˺þõķ,ȥ̸ͷ,!!!,ȫµԼQuiff һʿľ䷢,1950 Ϳʼ,ͷĨƷ,ȫ,ŻгɷɻͷDZ˺ͷ֮,ʵQuiff ͷ¡ķ˿Ե,㸴ʿ Rockabilly StyleĴ,֮ǰܹPompadour ӱ϶ͷе,ʵȴеСͬPompadour ǽͷȫͷ,Ὣһ,,Quiff Ȼһǽǰͷȫɾ,Ƿ,,Pompadour ִҲ,ͷֱ̡ ͶQuiff !Quiff ͷ븴Ʀ⽫ͷ޶,Quiff ͷͷ,ʱ⽫ͷȥͷһȥֱ,ͬͷȫ,߸,ķǼĸƦ緶!ķڴϺͶʿͺ,ƽķOffice Look ˧ڰ칫,ҪϻParty ʱ,,츴еDandy 񡣳Quiff˽ͷQuiff 벨˱ȾQuiff ӵ,ΪĹϵ,ٸ߸,ʱΪͷ̫,ץȥķ˿ͷ΢΢ɢ,Ȼߵ,ȴԸСQuiff ,촩ʳ»ָë,ɢһʸеĩʱ̬,Դůеĺ,,,΢ҵQuiff ,Ҳ׵չԼԸ!QuiffԴ,ͷ,ԵĶԱ,Խŵij,ԱȾԽ,н޵ƽͷij̶,ٽͷ˿ץ΢,𾭵Quiff ƦʿͳQuiff ,Quiff ͱȽϸ!ϵQuiffĿŲ,ϿԶʹӲͦʵĵƷ,Rockabilly ҡĸо,Ƥ͵Mix and MatchƤסϸӳ,°ֱͲţпбȽصѥƤЬ,չèʱ뵱ϵĴζŲʱ̬! -fashion ɺ,յķ | ̬ȵʱ һĵĵ422״̬,ǴС,Dzշ~ĵ˶һĨյζշװ,ر޵,Ҳֵһ۾Ϲ·۾װ練Ⱥе㲻,͵ƺûдڸ,Ŀӹ֮,ȴ̲ס࿴һ,ƽƽ,ȴ˵ͿǿDz?~վӦûᰮһڰɡ 󵨵ɫײʮ,ɫӴοչ˿,Ůζ,շİ˽ñ,ǻŶ~ ɫ֯ºɫȹĴҲ̫ÿ˰,ĵŮ,һͶһյζ ţбȹֽһҲ̫ഺ޵˰,ʹǴɡ,ҲڲסŮС ѡһԻɫĿҲʮֶص뷨,ɫĸt,вոС īɫĴţȿ,ֽĶ̷,һԵշС:22 :160m :45kg һ ֮,ΪȻͬķ,holdס,ȿ,Ь,Ů˵ͷ:30 :155cm :43.5kg һ ɫȹ,v,Ը͸һ˿,һ˫ɫͷƤЬ,ʹɫҲϢ 񳤶ȵһķͱͺո,Ů,ҲСɡ:18 :155cm :40kg һ ΪշΧ,ֽҲ,JC:22 :179cm :60kg һ յ,ϳѥ,INĴ,װ,:22 :163cm :45kg һ ţȹɫڴ,ɫΪӦЬ,ഺŮ Ůstyle,һ۾ĶĴ,߼չ¶,Ķǽ㰡վ,,˹ -fashion ˻˧úϲ£ȣȫ ƽʱܳ˧ŮȦգDZɱˣ˧ˣĵDZ˻ôһȺ˵Ĵڣ ˧úϲ£ȣ ȴɱ ĥƽǣ @YvetteLovesMarkǺǣⲻ˵ǿҵϹͶ ǰ35ǰƬȻ351ţ@Himi-CHMȷ˵ҵܣ@1þ׻˵úãǰɱ@ЦRegina˵ֱͼ@⿴_ˮڿǷ⣬ԽԽԽԽ󡣨@ACoCoҪһҼ~@JcjdnidmdǰӦݱ @ǸҶDz...@bumhaeҪƱ˧Ĺ˹@С֪˵ʲôˡ@־2009~ڣôҲ֪Ϊʲô@ĿиѧܣֻҪݣ֣Ĭ~@౻ֵܣ@Сھ˸ֵӳУʹ˿Ҳö̾Ϣɣ@Уѧڣֻ˵˵˶...@vozeenµҲòóԼѹ׵ƬˣУݣŮǰĶ񷢸·˼סʱָߣŮŮӣеĽҾҪʱһһ꣬ɡŮħλСෲ¹ϼֵȻǿƲסԼ죡Ҫ˵˧˭˧ңȻϧһ㶼ԲǼ䷽ô죬Ҳܾλ߸˧˧ڡ𣿨һԣԼѱĺʹ@ҹ±׶...@Ϧекһ̾Ϣӵһһã̫ȣ͵Щֻ֮˵ٸҼ10 -fashion ƷζѪǧԪ5Ʒ ôݴֱŮأһǶġֻܵʱֱѲDZƷЬںĹܶ࣬ŮûΪԼֱĹơƷעʱ䣬Ƕһְѳ֡ò˵ķDZƷǶһּ֡οֱʱ䣬ҲʱСҲһϲŮôСھƷƷһϳڵ㡣ĩŮVery Mojo 2015ڷVery MojoֵǹϵòVery Mojoǣǵʲô̶ȣ϶ֽţ÷ۺɫϵŮʮ㡣12λãеĿʽСǡСǣֻһemojiЦŮǵĻС˼ϵıﶼһСForever youngWork hard and dream bigƼ򵥣ʵҲʦƵ塣ụ̋·ñԼ顣Very MojoһȤĶŮܶ࣬ǿɰġкܶ춼ԪġǣVery MojoҲĵطIJֱ̣50mˮܣҲ˵ճˮȫû⣬ֱϴҲ¡Ҳѡ񡣽ǿŮ֮ҲϽרҵ̬ȡŬȻԼƤѤöʣϼԸݽ黻ء۸Ҳ1000RMBңѧϰѧֻչıҵҲ£Լδ޿ܣҪһһӡ¸Olivia Burton ˵ꡰŮһλӢɭŮOlivia Burton2012׶شƷƣ¶˷OBϲڱпգ־Ļ̣͵ɫ¶˵ͼϵֱŮһǾµġ̣ѻ3DģһСҪܻĿתһء㿴λɭŮȻӦÿܶҪһѻڼŮܻɭŮĻ滭ϸѾжáСϲҵ۷̡꣬͵С۷ѾΪʱ³裬Gucciǰ۷졣ʱ޷ӵд۷䣬׺һֻС۷󣬶Լʱ䡣OBзοʽ·񣬵һĸоʺӵжԵıһŮ۸1k~2k֮䡣СҪΪԼһЩСʱ򣬿ЩɰʲҲԼŬֵõġThe Horse The Horse֣԰޵ƷƣС໹ΪһmanֱƷơTHȫ棬òСTHϵиᣬȦӣ۱õõ𡣿ΪTHһԷ޴ԳTHҲƫԷ񡣱ClassicϵУϵк˼ϻСС̡stoneϵйҲ˵ideaɫ̣ȴǴʯƳɣ˵Ǿ侭ʱ֮ Less is more.TH˫Уһӣۣһ̣dzǵɫƤѥƷƣԱõƤ϶DZȽϳɫġTHȷʵܻɫ+ڱ̣+رȲ˵ײĺѣҪǣɫҪҪô߼ǵģTHı1000ñҲͿ£ЩֻһĿʱУһЩֱԽԽãҲԽԽϧŷPrisma ֪Prisma˻治ࡣԺСƷơΪʲôСĸԸΪcallֻΪͼȦ˷ۡڱ̽쪵ĸŷ硣Pģ1948ʹƱҵˡPһбߣӲʷŮҲпӰ죬ƫýѴҲһ㲻Υ͡PrismaƺvintageֱھУܶʽŨĸŷ顣˫ۻȻǰְֵľֱžʱģ¿ClassicƺٴʮҲʱϲPrismaŮˣ϶ǸְǿˡɫP£Ҳǿҹ̴۽ûдģPһܹӣ۸Ҳֻ1k~2k֮䡣һϲŷĿŮҲҵwatchмԼAuteur AuteurһԷֱƷơAҵλʦרעӽлȡУֱ䣬Լ̬֡AиקϵСmoonlight¹⡣óĶӡһ̶ɵİ͹ͼԢΪ档¹Ϊֱϸ׹⹤գ֮£һ¹֮СƼ򵥣ʵȴ˺ܶЩԼmoonlightAuteurֱǽΪ3.9mm⹤Ҳֵó۵ġʵʵڵģԼ򵥡Aһֱϸڴ⡣ RevolutionϵУȦúľƳɣÿһֻжһ޶أAuteurرÿĻϵСMementoMementoԴʽǿԿʱҲƷɫľƤǿȺԶܺáĵAһṩַֻãֱָͨصĸ˷񡣰װҲǼģһ顣ϲֱյʽСִķĵֱ۸Dz1k~2k֮䡣ƾ󷽣ֹȤļԼи̫ʺеġˡ2030꣬ҲۻרĶʱӡǣúʱָߣ5Ըʱǰݵ ʱŻݣ2100Ԫ- - - ԱԿ - - - ƣBeautifulWatchֻԱ ѯ΢IDTwippoGirl END -fashion ΧǧϹ3ɫٴеģ ԽԽ䣬ŶʵʵġȻĶ࣬ΪŮһҪƯΧҪʱֲʡ궬еɫٴʱУһҪһɫΧһۿΧӦһ˰ɣҶ֪ɫĿɫΧȻһɣΧŲתһȦܺϵΧܿɰƤΧɴһţԵرСɫΧɫĺΧɫ֯ĺڻɫ䣬ȫΧȦϵڲϣĻһ룬ůءֺʵΧһȦȦѵķʽȴֿΧϡůԲ˵ῴȥζ˫ſۿɴ´íЬdzɫĺΧȫΧdzůʱСɫΧɫΧɫٴģɫdzƵĺɫܴ߼ʸСɫΧɫ䣬ȫڲdzͶȲͬΧʹ£׾ͷֳΣܾơ -fashion 촩Ųϯ侫࣡ ӦöİСеʱһ̲ƬϴˣΪijȽ崿ҶʼΪ̲ãǿϲһкܴķҲ˺ܶ˵ףȻһֱ˳˳ˮոգһɫμӻ侫ࡣһʵʹijе˼ұϾѾΪ˼ӣˣ˵Ҳûʲôʵġ⣬⻹ɫȹӣϻױ˶׹Ǻܵ;µģ쾭һЩ񳡺ϣȻСĺ̬ܶԼķʽһ˳졣ƽʱʱҲͬСźܴIJ͵ǵУܶ˶ֻ֦չ·򾭳ȽĴ硣һëصףʵ͵ͬѧǺٴģΪǵӡУֻ30ϵŮŻᴩɣΪҪȥμһЩȽϸ߶˵Ļ飬ôҲôֳ?ܹʹѧʱǷֲģѧʱھѾԼˣϴѧʱͣźɫװɫоҲģġ󣬻ҪףͬѧܹҸһֵ֪ҪвŻŮԣִڶⶼŮϣܹȥǾDzǺܺÿ? -fashion ·ؼҾͲԾ?ԭܳڵ￴ijмģ ͼƬԴ: Chris Taylor PicturesӢִѧյһָ,ùIJָ߽ʱװƷʹõļģص,еݵùˡʱװģǶݵò,ⲻģ,ڵ̳еģģ͡,Ӣִѧһʳʧ֢ѧ顰Journal of Eating Disordersʾ,󲿷Ӣ߽ʱװƷƵչʾļģ,ͬһǰ,Աȳ˵ı׼нϴ⡣õ鷢,ɼ32ŮԼģصĹ,ԼģȻ10,ֻһ϶ΪݡҪԼģصĶڡ׳ˡԱġʵģ,пܸߴDZڵIJӰ졣ķߡִѧоϵEric RobinsonڶBBC,ݵŮԼģػ߶Լʶ,շȺձڵʶʳʧ֢ڹ׳Լģ,߶ԼĵIJ,ӶӦôڵûƷƺԸ뵽,Eric RobinsonںŶԱֻܿӾĿͼ򵥵IJʽóϽۡװۻʹõļģصѾǵһܵˡEric Robinson˵,1992,һƵ19301960ļģεĵ,óĽۺͽ졪õ黹ָ,һʵŮԵ,Ҫͬļģһ,ô¾ʧͣĿܡ2015,ΪLaura Berry ŮԹ˿ԼFacebook˺Ϸ,TOPSHOPеļģض,ʵյ߲ԼԶ,ͬ·ЧԶﲻڵº,TOPSHOPķ˱ʾ,ƷʹõļģصȷǻӢŮװߴ8Ļ,޸Ķ(Laura BerryĿ,Щų8ļģصʵֻ6С),ԭģɲάƳ,Ϊ˷㹤ԱзװѶĵ,TOPSHOPʾδٲɹͬͼģ,˴ĸӰ˿͵Ǹȥ,Ӣٻ˾DebenhamsӢ16ļģ,ĿľǸڹ˿ʵӾ򡣸רҵ,·ĶԭġءšAPPھ:ͼƬ,鿴һλйŮʱװ̬CoachΪһݳƷ?ɶǵѾZara˳Ԥ 㲻Դ·?ǿ ʲôǿʼ¹עThe North Face? -fashion ˵ܶĺÿ ҹIJ! ܶС㻹,ϷݳԼС椷,ҲԼζΡеıĿ,һ׷ġʧֶζ,,ܶϷ趼ϲ,ֹڴ,ÿο,νĿⶼŵ鱦ŲһͿ鱦μνǰЩ컹,֪ܶϡ,ҡĹô?˵ܶ?ô,ֲûвμӹĿ!˵ܶ³?ô,֮ǰͳŮ!ֱĿ,ܶ꽫һ׷ġʧµζ,νŶ!Ȼ֪ĩġǾû,ܶһɤùǼѵݲôġʧԭͨ͸,ܶ·չһζ,ú!ɫȹ,΢΢Ѭױ,ÿһϸڶμνְݵ鱦!ϴζֳŮʱ,ҲһϮɫȹ,ҲDzԳƵĶ,Ҳͬ˾һ!һ:ܶ鱦ôԴôÿֲ?ʵڡŵʱ,ܶѾö˼νĿ⡣͵ĽڶϱճеߵĴҲӷ,С龫ֵĶȤ,ֱҪ100ֵϲ!鱦 Ȼܶ鱦ǷŮǵƵ,ܰ鱦ԼζŮ˲ŸȤ,ܶС൱,Ǻ̺Ҫ,鱦ȻԼstyleϯйӰЭ2016Ƚ佱ʱܶѡײɫ,鱦ѡѡ貵Ķͽָɴ䡣CHAUMET Josphineϵ鱦,ŸбŶ!24챱ѧӰڱĻʽ,ܶͬѡ˼ײɫ,鱦ҲͬˡּͬԼƵChopard Ice CubeϵнָۼChopardHappy Dreamsϵ,ϵ׺Chopard߼鱦ϵж׹,㻹˵,͸Щŵܶ귴ʵӰ,Oscar de la RentaChopard鱦Ĵá塱һ,Լһ!VɴȹͶ,ܶСŮɹ!ʱԪһҲݹܶ鱦,ʱԪؿνһҲûԳƵ鱦,˺öchoker似Ҳһֱ,ͯȤûŹ,νһ˵Գ鱦˴ܶ꾿жమԳƵ鱦,˵Ҫ˵ҹ!ⲻ,һ뻹鱦ϴˡ׼̡֮һһϷMFPĶܶҪԳƵĴеˡͬMFPĶС硱ܶꡣHoopһֱԴŸ,ܶIJԳƴ¸˵ԵСȻ,ҲһһС˫ԲͷֲҲǵŮ鱦زٵ鱦Ԫ֮һ,wuliܶȻҲ,˼Ȼѡ˲ԳƵĴ򡣺,Miu MiuĶ൱ɹ,ɫʵһ⡣chokerȻ⼸chokerһֱ,Ҹ·ʱֿǷ׷Լchoker moment,choker滨,϶gigi֮һҲܶСӡChlo׳ܶת,Ϻ;ϵ鱦Ȼʱֵû˭!Alexander WangĶChinchinchoker,˫ʵĻ,ֻһ׳װ,ҲȻʧȤνѾᵽ鱦,ܶ겢ڶϼӿ,ʱָҲһŽ򡣲˵صchoker,ܶchokerʽҲʮҪ,һעͻᱻ»ϤܶСһӡ,ٹҲҪ鹦ӳֵchoker˰!ԵchokerɫϽ˽ɫ,˰ƺͱʯϸ,ٱ鶼塣鱦Ӱϲ㡷ܶ,ȹ黨ɹ(ȻҪ)ϸܶϵͶŷ,QeelinС鱦ȸʮ,Ǽ˲ԳƵĴһ๷ͷ,һǿ»ȫ,ʵڸ˼ʱֵʽҲܶϵķ羰֮һ,Ҷϸȹɫ,ɵܶҲholdסTuleste¶APM Monacochokerһ,ܶһ˿Ůʽ,,ÿ㿴,ܶ굽ǿʲô,ôÿץӵ˰ٿļ,ѡ鱦ԼӵԼķҪҲ,ʲôܶ,²ǿ鱦 -fashion ﴩʲô?3ٲ! Ŀ·ʱ,ѧ,ʱд!һijô,翪＾ʱִģʽ,Щ＾Ʒʹ´似ɡſѧĵ,ҲĽ,ﵥƷҲ׼׼,൫ô,ϾһǮһѲʵĵƷ!һһЩĽijֵļƷ,ûҪĸϽԼ嵥,ѧҪĸıԼ! Ǵ＾·,ÿڶٴڴԡܶŮҲ˳,õƷˡֲ,߷ֿ,ԼȻģСЬʺ㡣ȿʱ,,ĸ߷,Դѷ硣ɳ⴩,ӭľһԸӦĸй顣ν,Űɫͻɫп,ˬ,Լ,ͷŮʮ㡣ٴƷӵͬȿ,ǿԴǿ߱,ӳºţг,СñϺɫī,dzƯ,ƽ׵Ьokѡӡ黨߲ij,츴ŷ񡣰ѳ׵Ĵ,ӳģǶϲ˽͡ѡ񳤿ոպ,·绹ܷɹʱ򵱱״,һЩԴסЬӳȷĴ򿪷ʽȹװ+˶Ь,ÿƽʱ̫߸Ь,ӦƽĹϵ,ϲ̤̤ʵʵƽЬЬ,ʱ,ֿɶ֮ǰƹһ:ڷЬ,н֢,ǿŷĵ,ķЬôfeel...׼һҵİЬȥĨҡһ,ʱ,ɡVans˶Ƶľ䡣İЬԦ𸴹Ƹȹװʽװȹ˸ġաȹװ,ҪڲͬĻ,ѡһ˫˶ЬͺܺÿˡҲMix and Matchڰ!Ըʱֵʱȹװ,ϳưЬ֮,Ը,ͷԷ硣С׵ռǺǿ,֯Ըнȹ黨ѩȹ,һгȫ㶨Converse߰Ь,һֱdzߵһ겻ľͼ򵥵LogoһʱеĻˡЬ⼸ȶDZȽϸߵһ,ƪ·Ƕ˫Ьٴ˵Ьٴһ㶼Ϊ,黨Ů͵ĻƤ¿˧͵,ȫHoldס!ô+ʱ֡ӡЬļµĦǸ,ü򵥵Ҳܺһʱ졣ԡͷԡһЬ,,ϸŮԵƷʱ,Ļôо:Ҷʱе漰Ļ̫Ƿȱ!ԭһ֡&ʱ֡ĴҲܶ˻ѡĵƷ!ò˵ĺ,ѧʱڿʼͽǵ¹,Ҳܼ,ʵ־,á¦ץ,ɫ´ţж̿,ʮ㡣ޱɫµġʧ١,ϸֱ,׵!ݻĺڰ׺ɫ,ɫ´ɫñԼСЬ,ȳݽڴµĴDzٵ,·ʱɫ´䱪ƶȹ,ɫ֮ǰFenty x Pumaɫñ,GivenchyСЬ,¼ơ֮ӡձ,ͷ´ţпҲһʱ!һ㶼ţж̿ȹ,Եøഺɴࡰʧ١,¶ȡѡ¿԰Ѵ!ʵţпһʺÿ˾ܰ,ϸ߸Ьܺʱ֮֡󻹿䡣 -fashion ЩŮǴƷζôأ찮˳Ҳ۾ ԽԽˣػص½ǸŮѾʼڻеŮѾ֮ˣ¶ôıˣɵС贵һô¶֮⣬ûʲô޵ˡСѾ˺üıˣô~ǵСתʱȷʵһأijҲϹ۾ˣеǴܰ·أĺ붼εЦлĺν࣬һĴеˣɫ¾ȻһƺɫСƤȹĴеأŵһ˫ɫƤЬԵóͻأģһţеװ翴ҲǺ˳أǴѡ˵Ʒܷӡ찮һװе˴۾ɫ˵ĸоеһֻ˳棡Ƥ㴩ҲǺĦˣĸое̫ǰ˵Сеܲˣô϶ů͵IJҪҪģϾë̺ˣ -fashion ˴ʲô·ÿ ˴ʲôɫ· Anne Hathaway NY¼ABC̨ 'The View'ĿĽ,ɫɴ´ɫ黨ȹ,ϵİɫͷ߸ЬչһȻĸЬƷ걦̬֮İԽз,NYõ⡣΢зδ䡱ע!ƼĶ>>>9,8ټ!!!>>>ŮԶҪԭ!>>>æ,Ҳں>>>ȷ㻹...ٳ>>>ϲ·ĸϽ,!! -fashion ν30Ϊ... ʱװܿĻڶʱлѾٰ죬ûDZúæµֽȻϣԻLookΪϺ˶װʮܳ裬˭Ĵʱ֣֮Ըѡɣ2017910ձĻϱμBAZAARǴҹ뾩СڶñȽ⣬͵·ڻѱǵˡװʱ֡ĵڶսϸɲʼղڴУ͵Сڼже㲻ϰߣʵҲеӡ賾ֳϣǵòˡӡ賾201799ձļӳñ£Vetements Champion֥&ƽ20179³ڻִŵ (Goyard) ʮĶǴǣС֣֥ƽϷģķ201797ձϱܵ͵ĬںгIJ봩Сν201795ϺĺνԽԽˣʳɹıøοǺƴڽŶȥ30ҲΪԬȪ201798ձյԬȪñۣʳ+ţпLookµ~ԬȪһ˽Ҳֲ̫۵Lookɫ͸ŵշԬȪ201797ϺŻij³ԥ201797Ϻ³ԥ˷ƷҲʼң+˶㴩쳣ĺÿ⣬ҪDzѡŶ~³ԥ201798Ϻ201798ձĺɫȹY'sƽЬMartiniano̫ų (Gucci) ݴڵLookʵĴ޸л¶ϸ󡢽ԾװϿ¿ҲȻס2017910ձģǰ2018ŦԼʱװȫƷ˸ (Michael Kors) ǰŦԼʱװܿĴݻߵĹƣȫMichael KorsƷ˶Look𷶶ԼĴ͹ϸ˫ˮۼҾ߻ͮ201799ڻӡTʥ (Saint Laurent) ˶Ьadidas Originals Pharrell Williamsζ (Chanel) ѼñSMFKĹͮĺ¡ڿгƣTǵĺ˶ЬȾɫǵ㾦֮ʣóƵĺڵLookþʶ߲θСɺ201798ϺܱȽæµɺ蹱Look䶼ǿߵļ磬ױ£̬ʡɺ201796ձСS-201795ձСSֱͲ͵ñȹɫ˶ЬִȤ̫ (Mickey Mouse) ζϸڣˬҲʧСS빫ʱצγԱȣٽ״̬ԺáElla-¼201794ձĴǰڡϱSȤ˵ManEllaִܳԷװ᳤ģͺŮˡ20179³ڻģͬƷƤЬЬ+ų (Gucci) ̫ʥ (Saint Laurent) һö䡱Elvaڰ֣¿ϼԼˬЬ̫̫Over20179³ڻĺ201795ձĸElvaһѡ˰׵װĻк룬Ĵ˶磬촩׺׵òԹѵ201794ձӡT˼ (Stussy) ˶ЬͿ (Nike) Cortezñ99%isT̨սѡѡ˰T+ţп겻ʱϣӡƶԪ볱УԲʧ˧Сű̳201794ձڻͬҪȥϷɻű̳˾ȻײЬˣôɵȻҪͬһ»ű̳201794ձģӡT (Chiara Ferragni) ˶ЬͿ (Nike) Cortez201796ձĵOff-WhiteʿJolinԲúôµķʽϣȿ㣬ס2017910ձĸӡʿ׳ɽ (Alexander McQueen) ϥѥάС (Gianvito Rossi) ˫ζ (Chanel) GabrielleϵаϷɻʱJolinúÒױһ¾ͳˡרץͣҲԸеʧLookϣֻǿijѥСӵ10˵̫Jolin¼λҲͬСУغ20179³ɶļпˣ·סLV (Louis Vuitton) غҹɶĻȶ̡201798ջģǰȹڸưȹ (Balenciaga) ƽЬ˹ͼءΤ (Stuart Weitzman) ͨɣķ (Thom Browne) ɫñSeiCarinaYϰһӵиϸڵĵƷϣ͹ڷԵòײࡣ20179³ţף˿ (Stella McCartney) ѥ (Hermes) ִϰ (Dior) Lady DiorñPeaceminusoneĸһʧٴ20179³ܶ201799ձģɰķ˹صع˶ףꡰĻʽ (Opening Ceremony) ˶ЬͿ (Nike) Flyknit Racer Multicolor˫ (Burberry)ñChampionܶͦĵģɸ˶LookһһЧͬϲűҲˣܶ°Ͷһ̣˵㡶вʱΪϤ˵ġܶ2017910ձģ׼ǰϺƴɫ˶ףJulia Seemann 2017ﶬϵаT+LeggingͿ (Nike) ָЬе˹ (Tod's) ӡࣺAlmond RocksĪ (Marc Jacobs) ñLifeAfter Life x Mao Mao˵СӴǵ˽ЩŮǵĽģ20179³ǽģվñ£ά (Vetements) ˶Ь˼ (Celine) ࣺAder ErrorOff-WhiteñBalenciaga ColetteҦ2017910ձ98μ201799ձģ̩ȻعӡT+˶㣺Palm Angels˶Ь·סLV (Louis Vuitton) 2017910ձģ׼ǰ̩ԲӡȹParoshѥ˹ͼءΤ (Stuart Weitzman) ִ+̫ʥ (Saint Laurent) ѩܽ201795Ϻij¶201794ձŷ201794ձӡTYohanixѥVetements Dr. Martensɫ˫ζ (Chanel) ѼñVetementsŷ201795ձŷ2017910ձOversizeT+ִϣ (Givenchy) ˶ѥ (Balenciaga) Ÿ201799ձİζ (Chanel) GabrielleϵŸ2017910ձԬ201798ձĸӡTϰ (Dior) ţȿ㣺Marques' Almeida˶Ь (Reebok) ų (Gucci) ۾JINNNNԬ201799ջģϺǰӡT˿ (Stella McCartney) ˶Ь (Reebok) ִ˹ (Valextra) ۾JINNNN20179³Ĵƽ֯+Ь+ (Burberry)̷201796ձ201795ձTų (Gucci) ִϰ (Dior) Lady Diorϵƶ20179³¶£Aniracţп㣺RatָЬų (Gucci) è (Loewe) Ѽñ (Balenciaga) ͼƬԶIC201794ջģɱǰϺͼƬĶǽ Nike Nicholas Kirkwood ӡT (Balenciaga) BVLGARI Nicholas Kirkwoodϵ˶ЬͿ (Nike) Air Vapormax201794ջģɱǰϺ201795Ϻ201795ձ201796ǽģϺվ£DueplayϦ˽޶ӡ˶ЬRevenge Storm201797ձ201798ϺķɫTų (Gucci) 201796ջģϺǰݻɫ֯DazzleTͿ (Nike) ţж̿㣺Free Peopleɫ˶ЬFenty Pumaޢ (Chloe) DrewϵѼñBalenciaga Colette201796Ϻģ׼ǰݳTͿ (Nike) ţж̿㣺Free Peopleɫ˶ЬFenty Pumaޢ (Chloe) DrewϵѼñBalenciaga Colette20179³֯֯ų (Gucci) ɫTMajeЬ (Converse) ų (Gucci) Ѽñ (Balenciaga) γ201795Ϻĺɫ£ (Balenciaga) 2017ﶬϵСЬʥ (Saint Laurent) ζ (Chanel) γ2017910Ϻӱ201794ǽģϺŸվܱʳ201797ձĪ201798Ϻëͮ2017910ձijŮǣǵijLookҲ£ѧ201798ϺŻģ׼ǰӡñ£iceberg201797Ϻģ׼ǰʻñףά (Vetements) ӡTĪ (Marc Jacobs) ˶ЬHogan2017910ձ201799ձź2017910ջģϺǰݳLouis Vuitton Supremeţп㣺ʥ (Saint Laurent) ˶Ь·סLV (Louis Vuitton) 201798ջģɱǰϺĸӡTAnti Social Social Club˶ЬͿ (Nike) Air Vapor MaxִLouis Vuitton Supreme2017910ϺִLouis Vuitton Supremeű2017910ձ2017910ձ201794ձİTXander Zhou˶ЬPUMAְ (Balenciaga) ˸201798ϺŻĺܻǵĽĴžЩϲԬȪɺ衢СSǼΪļ͡أĶԭġ 鿴wapվʱдdzһʱգ -fashion Ա򵽵ģ42ձŮװƷ嵥 | ղ ߣ׳ Ȩת΢Źںţ׳IDyanzaitashuδɣתء ֵϲĴֲݻ 42è콢ձŮװƷƣ˵ĿǰȫˣдоҪڳ԰׽𣩡ƷƷµͷȺܴ󣬼۸ҲӼʮԪǧԪȣûϲġ еĵֻΪ˷ҽ̣ûдû~ûӵģҿֱԱƷҵ~ ĩһҵĶ־飬~ Mark Styler ձĴŮװ֮һĿǰ Ungrid Evris Emoda 15Ʒơ Mark Styler èһƷƼϵRUNWAY channel Ŀǰפе9Ʒơ 128-1800rmb Ϣ rnCr0Vcun2t ֻԱ ȫʣձֱʡԼҪö࣬ҲȴҪб֤ 4ƷԼè콢꣬Ʒƾҵ Ungrid ʽ | | ţ 199-1699rmb Ʒ Ungrid ˼ǡûб׼һԵУԼÿľͺãأ ȻձƷƣߵʽз磬Ҳ۵IJǷ ˮϴţеƷ Ungrid ~ MURUA Ħ | 129-1499rmb Murua ϵݶԭӴ06꣬ٷƷ˼ڼǿŮҲԿɰķΣ۸˵ ʦƫɫϵ۴ĻﶬῴɫϵƷҲΪ Murua Ķ Juetie | ͷ 369-1299rmb ƷһԽûʦ Ami & Aya ģּ Mix Girl ǻ ԽͷΪںҡԼŮ硣йɵĸƳҲʮ˿ȹӿ˷ļпˣԭһݰɵĸо Evris ͷ | | ʱ 399-1199rmb Evris ϲͷԪ븴ŷںһ𣬴ȴķ ȫʽС£СӦǸŮᰮϵƷơ MASH MASH Ŀǰ9Ʒƣè򵽵5Ʒƣ Snidel | | Ů 228-3280 rmb ᵽϵ磬 Snidel ӦõһƷƣڰռձ־ҳ档 ҲӰӾͷϲ̸﹧Ӵľ ʯԭڡУŮSnidel ҲʵܶΡ ﶬ¿Ǹ˿黨ԪأԼλɴƲʡ Lily Brown | | 222-2700rmb Snidel Lily Brown ʱɵɵֲܵ˵ Lily Brown ţлһ˿ԣûôߡ ײɫͦ书ġ Fray I.D Լ | ͨ | 258 -3560rmb Fray I.D ƫ죬ʽʺͨڡ ö䣬dzʵһЩصСƣͨװô Mila Owen | ͨ | 169-2669rmb Mila Owen MASH ϺȤźƷƣ2014꣬һסŦԼĽõΪС ƷȻûйװΣں˽ͷСŮζ˶ȶԪء۱ Fray I.D ԵһЩָʸлĺѡ Gelato Pique | Ů | Ҿ 54-700rmb MASH רҾӷƷƣǡǵ㡹ɰ磬ȵַڻ Gelato Pique DZ˼ҵ˯¾񿴵һ޷ܡ Gelato Pique ɫܱ裬ܰϣָҲܺá Collectpoint Collectpoint ADASTRIA ƷƼϵ꣬2009йĿǰ3ŮװƷƣ۸ӼʮԪǧԪңԼ۱Ⱥܸߡ LOWRYS FARM | Լ | 44-1199rmb Heatherich ǰ | | Ů 59-1299rmb JEANASiS ֪ | ߶ | ʱ 74-3299rmb Onward Onward ձĹʱй˾20ŮװƷƣèϿһ콢 Onward Crosset Ŀǰפ14Ʒơ 嶨λƫŮи߼λƷƶΪνϵơ ʽ󷽣Ϊŵɫ͸ŵ͵ 80-8000rmb Ϣ8PU40VVTs4H Rosebullet ICB 23Ʒƣ ONWARD CROSSE è콢򵽣иԶ콢ֱ꣬Ʒƾҵ Rosebullet | Ʒ | 39-1639 rmb Rosebullet һӲ̫ȶƷƣ콢󲿷ֵĿʽէ֮·ƿɳ£ϸҲԳһġ 23 | ׯ | 96-7560rmb 23Զӵ23ġš ICB ְҵ | η | 60-3000rmb ICB 򡸴Ӱ칫߳ʱָСܵձְҵŮԵϲ 23ԵøһЩ Ѹ ˵ձװƷƣ϶ܲ¿⡣ٶ¿ƷGu Ҳֵһ ޿˼ ޿16ƷƣϤ Moussy SLY ģйƿ120ҵꡣè򵽵Ҳӡ Moussy DENIM | STANDARD | VINTAGE 199-4998rmb MOUSSY һʼţеƷҵģпIJŮϣƾصƷ͹Ůķܵ˹ע ƫŷϵе̶࣬ǺںڿġֵעǣӳеƫdzԽСӲ̫Ѻá SLY Ը | Ʒζ | 224-3009rmb SLY ƷԸˡʱкͻɫʼƣʺеСɧ cool girl ϲõģľӣµŮҲԴŵĶиС CROSS COMPANY CROSS COMPANY ձһŮװţ10װƷơ earth music & ecology ɭŮ | | 68-1350 rmb ϲС·ĹӦö֪ EME ÿһƬĵձӰһա ˵ģҪѿ򵥵ɭϵÿʵһ׵¡͹ŮߵķװģǸ߸ݵСŮÿţ̯̾~ E hyphen world gallery Ƥ | | 98-1098rmb E hyphen 1998ĶӲ顣 EME E hyphen ˺ܶྫŹֵĸо Samansa Mos2 ԰ | Ȼ | 78-1598rmb Samansa Mos2 ĵԴ1980 һ¼ʱս״̬СŮ Samansa Ϊƽʹ߳Ī˹ƣڼů΢ЦΪϾʱϵ͵һ⣬űֽ㷺 ¸Ⱦ Samansa Mos2 ʱĴʼˣҪһ˰ġůķװƷƵ뷨Ů Samansa Ī˹Ƶij Moscow ɵ Samansa Mos2 Ʒɴ˵ Ʒ񵭡ȻԼɫ԰סɫΪ黨˿Ԫأ϶òͬĴȻϣޡ顢˿ëȡ Crisp | | ɭϵ 99-468rmb 2015йƷƶλȺ18-24ŮȻµɭϵɫǴ˿Ԫء Nice Claup ഺ | | 56-998rmb Nice Claup ƫŮϢУȽճҲʡ ϲʹøɫɢഺ޵еš Cocodeal ͨ | | 57-2016rmb Cocodeal ǷdzʺͨڴƷƣ˵ҵ֮һٺ٣ǡô󷽡 ȿ C ֵƼĵƷ֮һúãŷƷƸʺŮΡ Jill Stuart | Ը | ɰ 144-1992rmb дһˣè Jill Stuart 콢ꡣײⶫֱʡȻ󣬾Ϳͼͺˡ ־飺 󲿷ƷձĶй6-7ңۼԹ34۵ļ۸򵽡 Snidel Lily Brown ڹڶǧƷƣҪԺʵļ۸֣618˫11֮Ա٣һ⻻ֵ̻ۿȻ3-7ۡ Runway ձֱʵĵ̣ۻϺձһ£ۿҲձһ£Ϻ˰ʻ͸ֵּ֣۸ձۺ޼ ɡ׳ԭ΢Źںţyanzaitashu ȨŮۡת Ҷ -fashion ԴС˽صĸ߸Ь | ̽ ڿŵĵһ,Ҿ׫дСĻ,ǻ,˵СȦ,ܶ˵wedding,ܵСӰ졣ȥϺ,СԼĸ߸Ь档߽ȥʱ,ҲΪԼҵЬ,¥,ÿһ㶼,оߵ,ҲҪhigh hills.ȥҪгҲҪԷҪҲҪ緹ʱ,С㴩Ÿ߸ЬһֱڸҰijóԵ,,û,緹,緹,Լ´δ緹ͿʼһԳԳ!ǰԸѽ,ڸ߸ЬԷȿ,Ȼ̲סȥԴ˸߸Ь,û뵽,Դܰ,ܶ࿴ܸߵЬ,Ͻźط,ҪԼ۱ȸߵĻЬƼ,ƪ̽͸!Ҵ4·(ɴ+),anyway,wedding shoes ̽꿪ʼ!:߸73Сʱ:߸73Сʱ:ȫ10ŵ(Ϻ,,Ͼ,..п)λ:1280-2599Ԫ:ȫƤ˵:е,Ƹʮĸ߸ЬƷơƽЬ߸Ь,ٵЬϵ,ÿһcollectionĺ,رСϸ,չ˵ûнźŮǡƼǵʵԴ,һ6cm,10cmĸ߶顣Ǹ߸Ьˡ,Դĵ,ҺС㻹ıһֱ,ֱʼ֮ǰ,Ҵҵĸ,һ˫8.6cmĸ߸Ьվ˽Сʱ,Ȼȫû.Ұɫķ,Դ˲ͬϵС73СʱǺܸߵ,ϸµ̽,ٻͷ,ܶʽѾsold outԾĿʽ,,ҪԼʵ,ԱѰҡanyway,Ь,һǽԼԴ,Ըܡɫϵdzο:1280Ԫ:8.6cmʸе˿˺ɫ,װɫ˻ο:1280Ԫ:8.6cmҳ˿޲,Ϻɫ,оԼĽŶ˲!ǾޱŮο۸:1480Ԫ:6.5cmЬӵĸ,οյļ,Ӳ濴Ը~~dzɫϵɫ,ɫ,ɫ Frejaο۸:1880:8.6cmһ˫ĻЬ,кڰ׺ɫ,һЩblingblingΨ Cupidο۸:1680:9.8cm˫ЬرankleIJ,һë͵װλкܶdzɫϵЬӿԴɴblingblingذˮЬˮЬ-Сϵвο۸:2199Ԫ:9.8cmһμ˫Ь,ڵӾ硶,жγֵ͸ŮˮЬ˫!ˮЬ˵Ը⡰,ͯһ˫Ь˿԰ڽ,ִ~~ˮЬϵвο۸:2599Ԫ:9.8cmӿݳϷ˿ӱ˫blingblingˮЬ~еĻЬϵ,Ь׿,֮ǰڶӵĿ,˿ӱ˫blingblingˮЬ,Ь׾Ϳ:ӱ(ҵ,34Ь),̽͵,СⷢһŻȯ,1000100,ֱӸƵԿ,ȻԱAPPͿȡ!ϢO34RZuhQoIRֻԱǿֱɨάעThanks to:ɫ˿ɫɫɴfrom:@INESLʱֹƻױ:@FindMe_SharonӰ:@BNicoGuoǸ߸73СʱṩġKiss The Bride˿~һѡ5λͳ!Ʒͳһĩ,뾡Ȧ -fashion +ȹ,ʱŮζ! ,Ůѧ~,Ůѧ~쵽,֪һµķ!¼ΪÿذܵĵƷ֮һ,ȹŮζıɱ,ΪʲôԷ+ȹ?ͬķ´䲻ͬʽȹ,ײһʱл𻨡˧,ɴ,,Nַлܰ,׸Ĵ,ôٵǵļӳ?,˧Ůζ?Ů,Сȶ,ǿһ˫ѽ~,ţȹļŮ񲻶˵Ҳ֪,ǵŮţжȹŮ?ԲԲ,ŮԵĺչֵ쾡¡ͷļӳ,ŮС?Ҫѷ´áƽ,Щ+ȹӵĴ,Ŷ~+ȹֽᱧԹ:ϲ,԰,ô?ʱڴҪע,ڴһϮȹ,ıҪ,Ҫһ,ڴһţȹ,׵Ůʮѽ~˧Ŷ~һminiȹ,ĴȡȹĻͺʺϰֽ~+ϥȹȹ,ϥȹ˿ȡԼ̫Ͳÿֽ,һ¡Ψİȹ,ǺܶŮرĴĵƷ˧ķ´Ʈݵİȹ,ڸĵȱ,ӯ鶯ľ+ȹ+ȹ,ɹСMMҲ,Ҫȹӵĸߡ߸Ьӡһյʱ,·ں,Ͳ÷̫,Holdס+ȹôֱӴһȹOK,һÿȹҲܴп!ʡʱʡʡġ߸ӱƽЬ,Сӵı߸ЬԸơ˧ķ¿Ի˿ȹŮζ,Ь,˧ѥ,Ů˾͸߸ЬӺ濴,΢΢¶һص˿ȹ,ҲǺܺÿʵ̫!ĵ,ŮҪȥһ!Ů!Ůܼҹ ÿ˹ŻȯŶ~ע -fashion 10򵥴󷽰ӵķͼ ֺÿǵŮᷢôŮȽŮı෢ͣ˽̳̾ճÿһ㣬ʮŶ~Լÿִ󷽵ıӣ򵥴󷽰ӵķͼƬ⣬µı෢Ψഺ~Ůӱ̷ô෢͸ۣŮķУ෢Dzȱٵһ֣ô෢ʣӱȻҪԼġŮӱ̷ͣӻڷϡŮ෢Ϊ״򼣬Ծ֡Ůܱ෢ʲôģΪ¡෢ͣԺֱӸͷɸУͳᡣŮβ跢ŷŮ෢ͣϵͷֱӻƹȥڲĵطɳβ跢ͣͷβ˻һС鷢֣ɰţʺ϶㡣Ů跢ͳͷŮָ跢ͣͷıֱ룬ͷԳţDZرĿʽ˴θǿҡŮβ෢ͷεı෢ͣϱߵͷİߵ跢ͣʣͷൽβ裬β̶ʱ򣬽ϱߵͷƵͲ෢һܣڲ̶ŮӷƷΪŲɵŮ෢˸߸ߵβ֮ڱϳһͷǰɵıӣͷͬв̷Ůӷͨɵӷ˹ͷͣͷֱֳ෢Ȼǿʼд̷ͣչֳķͿȥʱмˡŮӷƴӲͬķͼƬܹܺõıÿŮͷϷɵЧ˼ֵŮƯУϸڱķơŮӷֻɵŮͣ˵ĸоƯӺ濴̵ͷɵЧŮijʱидڣ෢ơŮֱӷƲͬŮӣÿһȥǺֳܺͣŮĸ볱ƣŷŸеķָͣͷ -fashion ֻһ·,һ! йʱгһĿ·ֹע ƷʱԴ:ȫʱ(vixixi)֯ȹʵ,ů,,dz,ȲٵĵƷ֮һһ֯ȹ,,Ҳdzʡ,һMadam!һƴɫ֯ȹ,,dzʺϸϷɻʱ,һ󰮵ñ,һֻţа,,ҿҲ÷dz϶ѡһ֯ȹ,Ŀʽ,ģ¶,ȡʵƿʽ֯ȹһֱǺܶĴ˵ͷáϥϵȹ,ЬָЬЬ,кܲЧСӵŮҲԸEmmaѧ,ʵ¶Ըߡ֯ȹѡһĿʽ,ߴкõ֯ῴ߼ȵ,֯ȹڴ,ֻҪһʱʵ,dzOKɫ֯ȹһ,ѡһʵ,õ,ܸչߡ֯ȹŮζʮ,Ҳůֿ,Ϊ֯ȹֻʺŮʹ,ѥЬҲܷdzСܶ˶֯ȹԶ֮Ϊ̫,Ͼÿ˶гģ,һСı¶ȱͲˡʵ,ѡʽʱѡԵͱ,ȴֶϰ,ѡ°ڿijȹdzɫϵ֯ȹ,ڵĹﶼ¸,Ϊʵ̫!вͿܰԼһͷ,Ȼرõ⡣ҳdzɫ֯ȹӿѡʵ¶,oversizeǸѡ񡣨ϸһ,Ȳ,֯ȹܺܺð,ǧҪǵĽסϻŰ,Ҫץʱ,¶¶ʲô,֯ȹһ㡣СǹڿѽȥɽˮʱҪ˰Լ~ -fashion FendiŮحƱ̲סǮİ ÿһ,İFendiҵBy the way㲨ʿٰտ˸By the way״Fendi2014ﶬT̨,,ŷʽ,Ҳܰٴ,һٳΪFendi˳ĴС,BTWmini sizeİ,ʹŲͬ,mini,Сֻο۸:$1,900,13,000ϸڰСţƤ,׺޻õIJɫСëfollowʱеѴŶע⵽,ɫëԪ,оʱȥȼ١FendiҵëŲһ޼,˳ˮƳ,׵ë˫ֱ,׺Զۺ͵,ɲжɵڵļ,ðʵԸǿ!Ч,С㻹ԷſƬЧһ,By the wayıҲԡɱɿ,ͬл,ͬһֻFendiԱַŵϹIJɫë߼ô?ûе¶!,˾õСʾֻЧƱǮרѧбŮһ㶼޵ĵ米Сܽ±ֻľ֡ ͼƬԴ,΢Źںšݡ(BLACKCARDLUXURY)ԭתע³,δת -fashion ﶬʱֵЬ˫ֲòܶ궼ڴ 2017ĺɫر죡ˢBella HadidġҲƵɫûһĴ˻𡣲˲ֺܶͣСĺЬ׺ÿ׽ܡŽͼʾߵĺѥհӦɢɫŵijƣʲ⣬ұߵĹһȹͬÿԱ֮¼ԦѶȶЩСĴ˺easyСЬ˳ȻΪĺɫƷʲôʱְٴоDzο˧ѥѥһܴѺɨʱװ㳡⣬ɫΪۡͼķͷͼļͷʽʱ֣ԲͷIJҪǣ겻Сƽ׵ĺѥ˴࣬Ϊѥ󲿷ǰס׵Ŀʽƽ׻᲻̫Ըߡ󲿷˻ѡСѥͼСϸͼСָࡣܴѥȥ굽ڣȳһֱûнѥСȺͽ׵ԵϸԬúѥ£ȫ鹦ѥӵʱ˰úڰСѥη·~ѥӴ߼ʸУͼô׺õĴºӦظš߱ͶײɫСѥҲɼԦɫɫײۡƤʵѥӽҲܻ𣬸ҿʱƷƶгɫײСѥӡװԵ֪ŮЩѡɫһСѥ临ŸƸһָЬһĵָЬʹţװɫָЬַҲԼƤʵƷ˧ᴩSienna MillerúָЬ׺ƴë£ʵ㡣ɫֽԸЩµĺӦҲܼӷŶ~ɫָЬӴҲ񣬳Leandra MedineʾףÿжصǻָЬдָʽģȥЩЬǺɫЬְĴˣͬһЬͣʱۣҲ֤СЬİٴôơλɫЬĴǵAlexa ChungСˣͰһƷԴ񣬽µ˫ʽȥZaraġȽϲָЬκη䶼дŮϢèЬDior׵ðèЬˣĸϵ׺㣬һϼܾͳ˱3-5׵ϸ̺ϽţèŽż·ڴӦһ˫èЬзȣʵЬٴʵЬɫȻҲܷŹϾһ˫СЬȣʱֻͦԣɫɫСЬٴĿˣͼӢÿɵţװСЬʱֲװʱҲóñӺͱ̰ƤԡɫЬ븴װЧҲÿ -fashion ӵСµ,Ҳů һĵĵ345켸εվ,ҿʼ,ֿɬ,ִſɬ,Զ,¸з·ֻŻпɬΪʵǻԳɲ,ǽֽǸҵĸо,ijһǻᷢСŮλõСɡ,·λõӡ,ֱҪðһdzɫ,һ˫ɫѥ,¾õķ羰黨oversizetĴŮǿ!һľĸо~ɫȹ,Ӷ,ܷŮʵȿˬ,˸оǺˮƮݵɫ¶ȹ,ıԵЩλЬ,ʵ,ɫķԵֽǰƵȹ,ǺӵĺӦ,һħЩǶһЩ,ƽΪ˺ÿ,ıٻ:˵ѧ˶ĺÿ!˵Ц˶úÿ!˵дֳ˶úÿ!˵ë˶úÿ!˵,ǾͲ˵?һ˵עһġĶŮ!!Ҳþ˵仰վ,ĵת,ǶõĹ -fashion ɯ90롿֮쵽,Ը嵥׼? λǴҺ~ӭܵİɯʮ롣8,¼Ϯ,ǷҲĦȭƿʼ׼װ?ڰɯ90͸߲οֵIJο³һĨҫɫ~ǴSaint LaurentCommes des Garcons,ǴChristopher KaneMichael KorsʦǺչֶɫϲ㲻ԼDisco Ball,Դһ˫ѥһִֻʼChanelProenza SchoulerǺѡޡSaint LaurentCommes des GarconsChristopher KaneMichael Kors CollectionProenza SchoulerChanelҲһűŵĺɫŮ~PradaFendiGivenchyVictoria BeckhamǺɫԶʱġPradaFendiGivenchyҸװȻ㹤װѡCalvin KleinJason WuAlexander Wangƾʱ,ʵ,BalenciagaķºStella McCartneyװȹ,ǰ㽫쵽칫⡣Calvin KleinJason WuAlexander WangBalenciagaStella McCartney㻹òԳڿ͵۹ĺɫƤȹBottega VenetaɫƤȹװ,FendiĺɫƤ,ͲbiubiubiuǹսˡBottega VenetaFendiŮ·ߵĻǵÿëԪ޾ӡƷһƷPradaSonia RykielDolce&Gabbana,ͻᷢ,Լɡ޼DZıˡPradaSonia RykielDolce&Gabbanadz˶зLeisure suitˡֻнĹ;ۻ,һ˶ûʱ,ԶEffortless chicLacoste߻:ʱаɯʱװ:Ƭ:Yve Xuڱ༭:Yve Xuʵϰ༭:April Xiao¼:Yve XuӾͳ:::毸90Sɯʮ롿ʱ뻷ּ,ɳҲ!ҪŻ!Щֵʦ,ֻ뾲ؿ!עʱаɯѡƵСBazaarVɨ·άעʱаɯBazaarV΢ŹںŹעʱаɯѡѶСʱаɯin -fashion һ,ʮ!ԽԽ!̫! ͰȹĴû˲صഺʱ~ɾˬij鶯ഺİȹ,ȴ˾áҲഺƤܶ!̿İȹܴഺĸо,ȳ!~һصУ԰ʱ!ԴСЬЬ,ҪеŮζ߸ЬҲ˿ûΥ͸!˳ùùҲж~Ħȵչ¶쾡!AngelababyڷҲѡ˰ȹ,ʮС,۵ÿˮ!һֱԳұʾ˵Blake Lively,ϳȹҲصgossip girlѧʱö̿ļԦЩѹ~ϲķ,ֻҪȹӵijЩıͿ!ѡгİȹ,ϥijȸʺϰ߳ϯ!Ůļس~СӵĹǵҪһ˫߸Ь!°ҲҪȹ~СΪѡ2017΢,ͬ,ͬ,ϣӱķ! -fashion Ψһһʱ׸ģأʳˤŹ 2017άǵܡʱдѾ1120Ϻ÷˹ĻĻάܳ˾̽T̨ϵʹǣһdzĻ⣬йģ⾪һˤάʧϼҲѣ϶ˤ¼᲻һΪȫģΨһһϺſڡսŴˡάҲܸӵطָһ׳ɵoutfitԼһƯij򡣵ƫƫɵɴȹռʱˤ˸ʺ¹ʷ²ۣһˤʳˣӵ׸ⲻˤŹ֣ȥάʱӵġע׸⣡ҪΪάʹԼҪܺãıҪҲܸߣҲҪꡣˤһ㶼⡣ϵ׸֪ˡעλģһ׸ⶼûСٲȻѾһרҵģӦе̬ʲôӵģڹԼģȻ̫ãˤҲƬΨһһʱ׸ģأҲάˤģزⰡ -fashion δŮ?⼸С~ ˵Ů,С뵽ľʱŵļλСʻÿοŮǵijִ,˸̾Ľԭ֮,̾С̫̫̫ᴩ,˳㡰͵ʦһǵŮд!贲ݵġŮӳ,Сྭܿ贵·ݻĽԭ֮,¸ʮŮҲСˡAȹǴŮĵijƷ!ȹװ̫ȶ,һһʧȥ~¶ƵĵƷ˵ڽˡ¶ƳȹҲǽdzʱֵƷ,ƮݵŮ绹ҪɴȹŶ~ȹϴڵʵһ˺ѼԦ,ԼҪȽϸ,Ȳ̫ʿܻϱ150cmѩµɫᱡʵŮϢ,ӡ֮±СӡһЩŮζʡʶʹĶѡڴлƵȹʶľƼǶʮ,֮´ĵİɫҶ߳ȹӾдŮζTʽȹҲdz,̷Ϻһһ״˲ͬ񡣰ɫԴѧ,ɫͻӿһЩ~BUY IT!BURBERRY ɫȹNEEDLE & THREAD ɫӡȹFIGUE ȹҲæԼĻŷ~ȱȼֱǰTе׵Ľడ,ÿ״T,Ʒõÿζܳ!ԼĻTȻdz״,ҪʵѡȹDZѧɰиβҲŮر֮һ!ŮһҪмT,ӡ˳ƤСŮҲϡɫװִ˿girlӸСģT!򵥰ٴİTȹȻ꾭䴩,ţϵĴƻɫðȹΪʱָ!ݺ˼ҲdzƫT,ƻߺTǷdz״ĵƷţаȹܴʲôdzٴ,Ҷ̿ȳ!ڰײŮ?ǾͮһѳŮĵķɫ!СЬţж̿ʮ㡣BUY IT! MARC JACOBS ɫTGUCCI LOGO TEETHE ELDER STATESMAN ɫTΪٶܹעСƷҲ۵ˮ!,ѳҲСŮ崿Ƥ~ǻٻDzμӿ챾,Ǽ򵥵İ׳Ƥഺͷ⿪ſӲû̫Ըзʮ㡣żԿһЩ,򵥵ţж̿ҲȻƷ~ɫ̫,㾦ǡô!׳ţȻ׵ijʵ!ѳ,ַӱɴ!ȵɫôԴ!СֽϿѧܶĴСTIPS!Ʋܱ⽿СĸоŶ~BUY IT!THEORY JOSEPH ɫPROENZA SCHOULER ɫ¶ûŮıﰡ?ᴩС˭ѽ?ԺС໥~ʶά ѧԺעޱ,ྫ -fashion PoloŲר,ݾʹúܳʰ ɫ֡´㡱ע,Ӵ!תرԭ,΢ź:Dressing-show,ȡȨ,תʱעԴID(´,dressshow)֮ǰдƷʱ,дݴŮʮPoloȹ,Ҳд𴩵ѧʮPolo,˶()רƷʱָ:Ͼ,Ҫ֪Polo׼İ:˵˵Poloȷư~Polo+װ¶ѭĻǸţп,ֻһҪ,Poloƽֱĺ°,ȻúһÿĶԱ:°΢еƸ,ͨƽֱ,ҲʲӰĻ,Dzԡһԭ,Ĵ////ٵɫ,۹ԹԵıܿ߰ɫ,ڰ׻ҲīЩɫüԦ~Ϊ˽ͼԦѶ,ڵĿӽ⿪,²Ҷ˵˰~Ҫʱһ԰ѿӻɱȽеȿȿ֮,ǿϸڵĥëճ̿ŵȿʽ~ţп,ип,̿,ֻҪע°ںɫ,µɫʵı,һDz׵:еIJ˶,PoloƥҲܸ,˶+˶Ч:Polo+ȹûʲôڵĿװ,ǴȹиоҲӶ䡣ϥϵĶ̿ȹAֶȹ,dz״ѧԺ,ʺѧŶ~ȽϳְֱͲȹǿȹ,ô˼,忴ĶȹҪŮһЩ,,ʲͬҲиķ仯:ȹڱȽϴһгȹ,ȹгȹ֮,ƫջ򸴹һ:ƷߵĻ,ײɫҲǷdz,ʱָкǿ,ɫҲ,ĺܿɫ~ܿdzͼ,籪ơ,ԦѶȲһĴ󡭡PoloȹݴľPoloȹ,ĿʽȻPoloһŮСҶ߲ǰ׷ŵ,ּ򵥵ĶװPoloȹ,СЬ,˶Ů,ݴļ,ҲüԦһЩ:,Polo,һر͸,ǿholdס,ҲǺʺϴ˵Ŷ~һʲô뷨,ӭŶ~ע,һ~ -fashion ƹ˫ʮһ ЩҲҪ ˫ʮһҪˣƽ̨ĴҲ׷סǩԼ˲ͬƷƣҲҲ㣬һ˫ʮһأЩ۸ߣЩ۸ĺʣݹԣ׼ܹǵƪʿǻе˳Ϊĸ߶ʿܳ׸̼ҴʱŻݵdzֵѡ TISSOT ȱͬ˹ţͬҲʿһҰӱƷơͬʱҲע˶ĿװҲ塣ƷƷ񲻾ͬͬҪȥ飬֪ĸIJˣ TISSOTϵԶбTISSOTϵԶбԭۣ3910Ԫ˫ʮһۿۼۣ 3559Ԫ TISSOTʯӢŮԭۣ2300Ԫ˫ʮһۿۼۣ 1955 ԪʿУdzƷ֮һйӵƵۡۺһ廯һ۸6ǧԪ¡оȫʿETAо˹רΪ׼ƷŸбȽǿװʺϰʱ MIDOȶϵԶеб ԭۣ6399Ԫ˫ʮһۿۼۣ5999Ԫ (MIDO)ֱ ϵԶб ԭۣ6000Ԫ˫ʮһۿۼۣ3788Ԫ FIYTAǴǴһֱǹеıȽƷƣǴＯҲԴƷʱ׼ʱУǺܶ˹еѡ֮һ FIYTAǴҶϵԶŮFIYTAǴҶϵԶŮԭۣ3680Ԫ˫ʮһۿۼۣ 2699Ԫ FIYTAǴӰʦϵԶԭۣ4980Ԫ˫ʮһۿۼۣ2909Ԫ DANIEL WELLINGTON DW DWУ˴ˣԴפƽ̨DWҲȲһȫܹ230Ԫۺ82ˣԻǺܺʵġͬ40׿ʽԼ28׵Ůʽѡ28׵ĸŹŵ䡣DW32ʱŮԭۣ1290Ԫ˫ʮһۿۼۣ 1060Ԫ -fashion h뻭СüữüëŮǻӱ... ȦŮǼÿ춼ӲױⲢÿ˵Ļױţÿ춼䱸ױʦ£Ů˸ûʲôԼױĻᡣҪhữױŲµﻯױʦ~¼عݵഺ磬㷢һ¶ǣԭǵijģ벻ôữױ~ Ȼ⶯ˢӵ¿ģһhǰ¶ˡ ˼ҿʱвģױ¶Ȼڻ¡ ̺뻭üë൱зġ ȻСü ԭdzhüëӡ¡ ζɵ~ СüСüָüëñȽϴ֣ɫdzһµüë ü͵Ůֻ֪Ҫ㲻ûüëֵĻ粻~ ˶ȥdzأܻǻҲ⡣ӱдԵıӱÿһᵽСü뵽ӱڡɼɼˡı֣ Ҳӱɾһ~ǻĻױʦôء ӱԲüëộԵԲ ְüëôֺڣױʦӱüëϰëֶ ϴκʹ˵ŮƣҲDzửüëһ ܰԼüëôֺڴֺڡ DZӱһľǣٰûԡ ˣ㷢֣Ŵ׷Ҷϸüеģ ܾСüüüͺ͵ıϣǧҪüë̡̫ ӱĺСһޡ üβλнġüʱһ£ڱ۽ǵϾüβλá ϸ㻭һüβüüëĴϸһҪֳ һüͷüбȽϴ֣üβȽϸ һϸḶ́ǰ涼ܴ֣һüβļͻȻϸ Աh̺ȻüβֲֺеĽ鷴ȴüͷ Ļʶ֪üͷüβĴϸ̶ȡȻüβ׵üͷúܴ֡ ɫdzǺܶ˻üëʱ򶼻᷸һǰüëɫͿ÷dzȡ üëҪúÿɫdzǹؼ ɫĵطüУʵüβüͷ üͷֻҪһأСµĸоϳ֡ Ȼ㲻ҪΪ֪dzλԺľdzľͺdzȻhữױ˵ĻױһҪȥгЧ⵹δء׻˵õױݾǿȥûڴҶ׷ױˡ СtipsüˢǵüʻһͷDZʣһͷüˢüҲһҪӵ üˢüëɫкܺõĵáijλеˣüˢˢ ʵüˢҲеģǾǩɡ üߺֲܶеdzϲһЩüߣ׾ٵð㻭üë ЩüһõĵDzÿüëɫdz 㿴hõüëӡ¾֪ˡ һֹü֮ǰҲҽܹӡ׿ɫϵdz ΪֻṩһüͣɫԼȥġͱȽϺÿüɫdzˡ üͿںܶϵĽ̶̳ȻһüͣҪһüͿ򡣺ܶ˾ͷdzȥ򡣶һú ᷢüȻˣüëЧûеȣΪüͿ̫ˡ ˵üͿûǿҵı߿СڻʱһҪķʽȥ~ĸֵholdסСüǧҪŶ~ -fashion 36ݼŮ,ȴ20,Ȧᴩ·! ĿؽġʱQQ1835988ᵽμ,ܶ뵽λݼʵŮԱ,иӡ̵Ľɫضﰮ޷̷ʶ˧ǿ̵ڷ¡ϻѪĹ1980μǹֵĴͳŮԱ,Ȼ36ȴȻ20,ҡΪȦᴩµŮ֮һ,ʲôʱȦ?ԼΪ,²׷ֵʱд,򵥵ʹʱи~< >ĺڰ,ԵĴ仹˻õŮ,36?°׳ʵİȹ,ֲᵥİ׳ϵ׺ɰ,Ե̫ơ< ţп >˶еţпҲԴ»,ϸּ򵥵Ļ,ţпҲ~ɫţп,һ~< +ȹ >ɫɫСȹͷ,ഺ,۵İЬͻԳơ򵥰Tţȹ,ӾЧ,Եĸ޳װ׺ͳСȹ,ʧٴ,Ȳѽ~׷ͻ,ǰؽ־䵥ƷʱϢܶ˶,ܲսͻƲʱеǰزġᴩ< ˯ϵ >·ʱȦƷ˯ʽܿ鴩ߵʱй,Ϊ͵·״Ҿӷ̫ĸо˯¿,дһζ~˯¿,ʱеƤ~˯¿,ţ,л͸¶һСԸ< ȿϵ >ȿҲ˽괺ġ߷ȿ,䴿С,벻䶼Сʽȿ,βŵİβ,·,ŶһӰ~ȿӪ쳤Ч,ĨƸĵ÷ò~< ϵ >㱾Ǻķ,ҲΪʱеƷ,˴ϱЧѸͰT,Ĵ,У԰ѧϢ~;ɫС,һñ,׵Ů~36μӲԼĺƷԦס˸ֲͬķС¶,֪,СŮ~һװ,ԵŮܲ,˧ʮ~ȹƮ,תǸ߹޵Ů~˭˵40Ů˲Ůһ?μһ,ϲ·,ĺÿƬ,šƯԱĹ⻷,ǸաСͨŮˡȥҴʱ,ʻĹõµÿһ˲䡣Ķά3 ʶͼжά롱ɹעŮǿˡŮǿ΢ǿŮѵ(΢ź:ssvv999) -fashion 쵰Ů Ů ˵Ůķ֮һͬ͵ŮҲиԵķDZСΪҽܼ쵰ŮijͣôҼ֮ԸŮ񷶡쵰Ǽ׼ͣӵж쵰ŮκηͶÿ֪쵰ŮĻôŻŮ𣿲֪ûϵ֪쵰ŮͶ쵰˺ÿ󷽵¶ûʲôԣͲõƫƣһȻɢٵһ󣬳Ӫ߲ԳƸ֮⣬ŮIJչֳڼǰеɵɢ¶һСзֵƷdzʺ϶쵰Ů쵰ͺÿݵз͸ŮˣȵͷͷȻ£ڶʿʼͷо̴ʹ°벿ͷþɣͻŮСɵ°ͣϴ·ϷۣϢ硣˵쵰ܺÿڵҲδá޼ƽڶǰסͷ͹Ů٣һͷС״ijಢסԵŮϸСůɫķɫ˿˱УǸƽ׽˵Ůһͷٲij󣬷βЩУԵúǶأְڶͷеñĶ쵰ľˣǾŻ΢΢һЦ֪ŮӸСڳֱķȻ£󷽵зٺʲˣзʹŮƯĶ쵰ֳʹʵּŮͦıϣ¶ͷҲˬ͸ֱֱijɢ£Ȼһ崿Ůһ԰ijͣijɢڱҫ·˿Ļȳ鶯Уᱡüʮ㣬ֵĶͷͻŮ٣෢˿ڶһĶ쵰֣ͷһñ·Ұȼ٣ȻϢ֪Щ쵰ŮijʹǷأȻֻǶ쵰ŮķͣϲʺԼĻǾȥһ~ -fashion Dzǰֱһã ʮй˽رĴоֱĵλʱܴһֱǶô飬͵ͬǮгзںܶҲῼֱ֤˴˵İ顣ӵһֱɲ飬򵥵Ľ޷ǮʵڣǮ򰺹󡣹֮õģΪһʱװãоҪԸֻصӰ죬ࡰĻɣβðߵøԶ̶ܴͦϣȡճһϰԼպı״̬֪ͨϰӳֱ֮ǰһҪȸĻоǻеĻʯӢΪʲôô˵ֱɶǾܵģ׼ȷʱΪ׼ߵĽṹԭȴǾ޴ģʯӢоˮʽӱˮƬԡԷһŦ۵ΪƵʿɴ32768ȣеһ򱶣ƵԽԽСΪʲôʯӢʱۼ룬еֻԭ򡣻еож񶯴ϵͳΪ˿ṩ봢÷ĵΧڷͨԭôϵֵﵽݵٺָλתĿģжǷΪеоİ취ӣڲǷɻеɣԷΪֱ̹˵ֱʵһҲʡģҪһҪһ£ظûоԹϵģܶೡʤԶ齫ѳΪֱܴŵɱ֮һһ޴ڵĴųһȤ¼б۹ųֱܴŵİijĿһλÿ10ӣά޶ζδת֮Ʒά޵ĽΪ˴ųIJ죬ӰֱʱȻϵΪųӰʱ˵ǴǶԼҽѧǶȳȱݣԵʱҸһ򵥵IJԷһжеIJǸֻģ˿֡ݣܴųӰ֢״ڣֱߵĺúõģͻȻͲʹʼ(磬֮ǰΪ10ڣͻȻÿ3ӵ)ȻȻĴųֱӰ΢΢ģƼĸıġųСӣβܾܱ⣿źûķеĴųǿѡԵǹһЩDZڵķأ磺ֻŵ̨̫ʽʼDZ̫ӻȵһеIJƷпܳΪֱܴŵԪס1Ⱥױųԣݽ豸ԱһȻпıѱԹԼֱܴţܶʼ˼úóĽǣñȽϸߣڵﵽ13ϲֱڳڣʵ˽ϵܴ֢״2жֱǷܴţ򵥵İ취Īֱȥӱά޵⡣ȻϵҲһȽϼ򵥵ķɹҲοһ״̬õָƽţȻֱָԡС̬ΧתȦÿһȦмǸıֱķλָ붯ˣô˵оIJܴˡ3ֱܴӦô죿˴żɡһıѿȥƼƼһã̽ͼ飬Ǿӱά޵꣬һ㶼ŵ豸4ȻֱܴţôӱƷΪЩʲô취ڳڣֱֹܴŵİ취һԡ֡Ϊࡣʹְ취Ʒڻоһڿǣֱܵ˴ųӰ죬óžͿɣǶԻоһֱʽʽҲڲٱ׶ˣһڿƱؽӱȣͬʱû취оɱ͸ʽҪǣֽҲûоܵд¾ʿȻMilgaussڷϵһشͻƣ֮ǰǵһﯺϽɫParachrom˿ʵܴŵIJΪ˿Milgaussո֮ļ۸񱻳ĺܸߣٵ񣬷Ѳǡ߿ƼҲ͹ƽǰĵ齫ʹֱܴţԭԶ齫ƹǿɵģٵȵѾֱܴŹҲɵIJˡŷȴһԴ齫Խ齫򻵶ܴŵĻоΪﵽ߷ˮ׼ڻоҪʹ˷ǴԲϣҲʹõײʡֱķˮΪڷˮ̸ڶвٱѶΪֱˮһܰʵȻˮûбȻϵģˮĵȼ̶ܴȡڱĶλҪŴӷȥӾʲôģȻDz̫Ҫ100׷ˮȼֱζſԴDZˮ100ף֮ĸвġ1ȷֱķˮȼһ˵ֱķˮȼעڱ̻ϣXXMףXXAtm׼ѹʾ1Atm10ףԹʱ׼Ĺ涨ԣעзˮʶõ2׼ѹ20׷ˮȼֲԱ׼¶ȱ20-25϶ȣֱˮʾֹ״̬½еġ2ֱעķˮôɿ𣿲ֱ̫ϵıעֻ˵ԵġһijƷϴʱҵ½ˮͨǿԿбʶΪ300׵һˮȼԿɼˮڲڲࡣһʼǻΪʹ޵⵼·ˮȦϻӶˮ˽⵽ʹðĺ...ֻܶΪϴֵˮͷˮѹԣ羭ҪˮıǣȿܷǵDZˮǵԷˮʶӢۡʵֱĽˮѵϰܲɷֵĹϵбŷˮȼճϴˮ裬ʱһֱķˮȦ¶ȵӰӶˮյĽ룬տʼֻˮ֣¶ȶʧһˮҲˣôDz˵ֱˮ֢״Ѿʧȼȥֱıŷ֣ΪˮյIJ͸оҲѱءٴᵽǣоԸֲΪˮԸֵɱǼģˮ⣬ջۣʴij̶ȻԽء3ճαˮˮҪʹӵвˮܵҲ˳ʱˮӴˮ衢ɣЩԹ԰ֱȡΪ׵DZˮ;ҪȥѡӾӾмǼǷѾ4ֱˮδǡһ΢ˮԿǺɡ磺򿪱ڣһȷ׼ͷڣͷֹͣȴԺظֱ̣ˮʧȻ취Ҳб׶ˣǾǼӿ˷ˮȦϻǼֵȽϸߵֱһСϵͽӱվмͲԣˮ-ˮ-¼ˮҪʱ˶ҲֱоܣֱƷƶ˶ıζʺȥ˶˼ֻһ"˶"ˡǵǰһλѿᰮڹdz̶ȵ˺޲˯ҲţҲκĹֱϴ⣬ÿܵͷȥһлعʱÿ3룬ڷƫͦģδֹϵڡ֮࣬һ䣺°Ժϲ Ի򡱡ԭڴˡ1˶ӰֱģеֶԶ豸еߵ׼׼̶ܴ϶ڰ֣Balance Wheel˿һ԰ֵķȶ280315䡣ھ˶ʱ׵°ڷԼ330ȣͻײڡҲֱάʦ˵Ļ󣬴ֵ̰ڣֱʱͻƫ죬ʱԣھ˶ʱһǵýֱժ¡2ɱ˶һҪô죿ʯӢûȻǷǴɣʯӢѡܻеҲʱϲƵ36000оֱͨƵάֽϺõڣҪ֪оȶɲǽɰ˿˶бܵ˺ҲDzģοиָӵĴϵϵ˿ȵȡǣʯӢоƾֳˣоṹԼҳƵھ˶ܵСģбϷ˵ȥļӶˣŷܽῪƽʱֱĹУ͵豸ӴһȾȥֱķˮܣ˶ʱժժ޷ǾǾȥЩ˺ֱľٶϾʱķҵĻԸ---Щ޹ֱļֵȻҲɲصһĹ -fashion GUCCI̺УֻͿЬ ΪʲôôϲGUCCIΪÿΪΪ⣿룬׼ȷ˶֪RolexLVһݵλҲǷ˰ͣNBAǴһƶϴǧ֮󣬻ѡЩһߴƵԭɣGucci֮˽ǮϿɺʹڸлǸġò˵GucciƷƵıʶȳߣ̵ײɫܾĴڡGucciԴƷƺ˶֮ԨԴڽ̶ϵķ֯˹ɵȫݳƷг֯е50ѸٳΪҵͷ˵׷Gucciνľ䣬ôǿЩGucciΪˡӭϡгץסЩ͸ı䣿 Gucci ͳһ黬껳ȸĹƬʱҾ;е㡰ӭˡ2011 ꣬Gucci Alexander McQueen ĸ˾ PPR չװƷ Volcom ϢһûIJƷ Analog & Gravis Ĵ Mark Oblow ˵ƷƵļıҵϷǰһþߵֻԼҪģڴƷС˾Ϊ߻ϲġһ㶼ᡣҪİ滬壬֪ûضƷͨTɵĿӺһ˫ĥĻЬҪȫװ֮ԿΪЩиԵĻֺĻʱװƷƻ˴ѻԪشƣȴԾɲлִԭΪȥӲسһʽӭгƷƣܵͷҵİɣȻGucciIJϺҵ⣬ȰԪصDZչˡ㡱롰¾ȻҲвʧܵİ棺ҲõĴ⣺ȥĶñֱһ󡣺ܶڸϲãҲر Custom ơGucciɫЬҲҵĴǡ˽˵Ķ㣬ɫײȻᱻƵĹ˾NikeΪǴܶ龭ġGucciɫЬNike Air Foamposite Pro GucciнΪɫģž˫Nike Air Foamposite Pro GucciˣЬԡGucciԪΪУĥɰʸеĺɫݲʴЬ壬ɫɫ׺ϸڣɫ׳֣͵ͬʱ͸һɹAir Jordan 1 Retro Highʱ䣬2012 ʥΪƳAir Jordan 1 Retro HighҲܶ Sneaker ϷΪGucciɫBlack Sheep x Nike SB Dunk High Premium˵Сݳ޸СġGucciɫЬô˫ Black Sheep x Nike SB Dunk High PremiumһܲˡɫƤӡСGucciԵ Monogram ͼϽɫSwoosh/ɫڳġɫЬԼнɫƬװεĺɫףͺˡNike Air Force IINike Air Force II Ȼһֱ²𣬵ҲNikeЬ˫High Premium GucciȫЬ˾µ Laser ͼ۵ĪЬǺ Gucci ɫװμЬNike SB P-ROD 2.5Nike SB P-ROD 2.5 ҲġGucciɫ˺ɫʹЬͼ׺Ь⸽һɫͺɫɫЬUNDEFEATED Nike Air Max 97ϾҪеģըGucciNikeЬ¾ҪˣΪAir Max97Ĵ꣬һɫľЬһ̣Ÿŷ糱ij£Air Max97ҲԽԽôعģɼͷ UNDEFEATED Nike Air Max 97 มUNDEFEATED ھ Air Max 97 ϵƽ뵺ᣬоĺ졢ɫɣЬIJ״Ƥײɫ֣ɫӳֵǩʽ UNDEFEATED Logo ϸڵ׺ͬʱҲ˸ЬijǿݡϵĶƴЬںɫӪʸ͹ԡƵϣҲ˵һӦ뵽GucciϵκεƷһͬкʱܴͷɫͿ Air Max 97 Dri-Fit ˶࣬ԼӡСProperty of UNDEFEATED͡WORLDWIDE Logo ñ T-Shirt---·ͼƬĶ-ûУ㿴ַϲϲЬ..ĶǸʹģ90%˶̫֪ô£GDһͿѻҾҲʺ㡣-ɹע- -fashion Ϊʲô㴩߸Ьʹ?ΪЩС㻹֪! ӭϷѧ㴩´䡱Ѷ!δ߸ЬʹС,ÿŮӦ֪!ÿŮĽźѪʷѽ!ЬС:صЬ,ĥ,ʹǼǧʱҲ,Ļʹడ!̴ҼСŰЬĥšнŵĿաЬĥŵط,һǽź,һǴֺࡣ1. ʪëĥŵЬӲλ漸,Ȼÿɵëסŵĵط,ôü,Ьĥŵĵطûƽ,ЬƤʱƽͲĥˡ2.űֽ,һ,մˮ,Ҫ̫ʪ,ҪŶմˮ,ȻŸɵıֽסʪıֽ,ڼŵIJλ,ȻЬܷһϴ,ҹ,Ϳˡ3.Ь̫Сн,ʪëʪ,ЬШŴ,˳ˡ4.Ьڴ֮ǰ,ÿ(ƽʱõҲ)ŸӴIJĨһ㱡,Ь˱ù⻬ĥš1͵4㱾Թö,ЧŶ!Ҳ!ٸܺõСŶ!ղЬСһѽ̵ġЬ,֮ЬеIJ,һ,ͿĿЬӵඡܷġС߻ЬҲԲȵŶ!ЩMMЬ,ЬⲻС߻,µЬһӡ,ѿġ֮ʲôð취?̴һСС,ָ!ǺɫЬӻ,úɫָ,СĵͿɫЬӱ߻Ь,Ժ,ɫ,ҶָҲй,ЬӿȥƯ!ֻҪͬЬɫָСͿһ±߻Ь,ЬӾͻÿܶӴ!ɫķЬϴС:ɫķЬʱ䳤˾ͻ,,ķϿ,ϣá1:ʲôϴ,ϴԺЬϾͿһ;2:ֽЬ,ͬʱְһֽ,Ьֽһ;3:ɹ,һԼ2~3Сʱ;4:ֽһ˺,ͻֽᷢ,ЬӱðװġСΪѡ2017΢,ͬ,ͬ,ϣӱķ! -fashion ľ˵ԶҪϴţп㣬仰Ķ ʱڵϿ Best Jeans ľ˵ԶҪϴţп㡣ȻҲΪţ֮·νţ취ţвϵȾ䣬ϹؽڻͼDz˽⡰ţҲǰ͡ϴţп㡱ϵһ ϴҪţһһû è롢ѡˮơ𳵹Ȳͬɫɫ䡣Щ̣򼸸£һϣֻΪ˴һһ޶Ŀӡôţп㵽Ҫôأ ţĹУܿҳӲɳ˶ΪѲŪԼţпѳЦԷǵĹʵΪţ塣ţпרΪصĿƵĥ ŷһֱǸܻĻȦӣԴŷձõ˷󣬺պĻڶȦڵķУ󴫵˹ڲŷһĸ˲ͬϸ࣬дڽ̶ܳߵţзΡ ԶţпĹȱȻDzҪġдռһijɽ·ˤµӡҹ滭Ĩϵϣҹҹµġè롱͡ѡ ˵ˣèʲôӣĸλã è룺ڲбµ塣 ѣϥǺµۺۣסϥǺѡ ˮƣխŶѻЬγɵ壬ѻŻˮƵɫ䡣 ţžϴĥ· DzѾĶʼ䵹ţп㿪ʼţˣʵţ£ҲĹⲻϴеģҪһԭţԭţٰ¼Ҫ Ҫһ ȥԭɫţп϶ unwash ͡ onewash ʣĽ;δϴˮһϴˮ Unwash ԭɫţпǰᵽģûоϴˮĥȴţп㡣 Onewash ڳǰԤˮ˽ĹգЩϲ봩Լ·еţп㡣 Cityshop ԭţп 1,465 CNY ԭɫţпм Ounce дΪ oz ˾Դһᾭ oz oz ţп㡣Ҫǵأ϶ԣһ 14.5oz Ƚʺżʿţ˵ԪΪ׼ RALEIGH JONES 12.5 OUNCE 1918 CNY Ҫ ԭţõţвϣʽ֯֯խţвֲ selvedge ţв self edge ˼ֲͻصڱԵλһɫ߲ɫIJߣͨġ Redline ĸɫIJ̶ֱͬڶȡ Ҫ ȾƲϵȾһȻֲȾϣȾϽ Indigofera Tinctoria ľ塣ûκλƵġ ţп㵽׸òϴ Levi's ִг Chip Bergh ҪϴţпۣȻе㼫ˣһõţп㻹ͦнʲôʱϴʲôϴ Ϊʲôϴ ϴӼţвϵɫʹﲻȻɫЧţϺأϳڲϴһζʱ䲻ϴĿҲΪû¹ˮʧȥԣԱֿԭеĿDzġ ʵϴҲΪţпѽţпΪʲôϽأڱ֯ţвʱɴ߾ͱϽˣǼӹɴǿԣ֯ʱͲжѵڼʱҲӷԼɴﵽţпĶЧѽȥţпϵĽˮⲿʹԭɫţпΪʡ ʲôʱϴ Ƕ˵ԿÿϴһΣƽʱҲҪϺþֲԵſɡһԭɫţڴ 3 º״͹̶ˣʱͿϴһˡûʯҲϴͷˮ档 ϴ ע⣬ţԲϴ»ϴʹôˮ裬ԡףע㹻ˮžͽ׼õţпŽˮȻѽܶ˻˵ҪŰ״׻Σںõţпеһ٣õƷƶиߴʮεȾɫգΪţпɫġա ˮʱ⣨ʱȫԭţûţпݸ 2-3 Сʱһˮˮʱóţп㲻Ҫκβݸ 2-3 Сʱȫѽ꽬ͿԿϵάij֣ ɹ ԭɫţпˮóκšɵȹֱӷڹ߸е͵ĵطȻɹüдɹ֮ģҪЩʽӣȻɺѿӡӡɺԭɫţпͿˡ ʵţ ҪĻڣͿԿţпˣԷŴ˶......֮ţпȻɫͻ϶Ϊһͱרţˡ еţ˻ڿڴһЩƤСոһյĦγеɫ䡣 ֲ߰ڿڴֺͿӵĦγɫ䡣 -fashion ׶ רעֱ鱦DZĺŵ59ƪԭ£ҪרҵעǡֻһʣҸʲôʣ˭ҪʲôҸȥ»һֱڸʣӺ۽ǶԷϣܽƼ£ʲôСNomosյԭꡭܺѽŷѰɣ˼Ҷʶ ŷϵ̨С2762,600Ą̊́ԼʴʺϣŵСDzͣʣҪʲôʲôֵ򣿹⣬ȦиܽᣬڻֵýһҪƷơܽORCPOȻŷѣSwatchŵĵһƷ֪ȹߣԼ۱ҲߣRʿ˵ˣܶһ˵ֱֱӾ뵽ˣCǣ弯ŵֵPٴ ʿˮ 65,900ƷƵIJƷáۺλЩͲ˵ˣҪƷ֪ȹߡһȥ˭ϵóһ䡰ϵóѾ˴󲿷˵һɡʱԴһDZ̨ʣҲбһˣSupposeҲеǮˡ󲿷ǴʶƷƿʼġʱҪرƷƵſ۸βͷġһ׶ΣŷʲôʲôˣYou are what you wearΪһijȦ㣬ͰԼ޴ڻ˽ޡ Heure Hϵ򵥵бƷűԤ˽⣺1ǧ~5ǧԪŷŸǡǴ׶5ǧ~1ԪȡĦ1~2Ԫ١״ʿNomos̩š2~5Ԫŷѡǡ۶桢顢5~10Ԫʿɺҡʱŵ10~15Ԫꡢյ١ſ˵ޡǿᡢű޽ܶű15Ԫϣᡢʸ񡢰ˡʫ١ٴ˱оٵֻһŵԤ㽨飬ƷƵʵʼ۸䡣ʿ3ԪҲвѡ񣬵۸5ԪԹ鵽5Ԫһʸſ۸108ǧԪű15Ԫһ 42,500ԼƷѡ񣬱ŷѻ߿ǵģǾǵ۸ҡԤȷõ2սͿԴӼ۸ӦƷƺͱ ˹˵һ׶εߣڵIJأEsteemҪ˶ϣԼȶλҪ˵ͳɾ͵õijϡ ɺLuminorϵ 33,300ʱԼҪѡֱҪСϵóֲܹҲְϵֱѧֱһܱϰǺڵϰı١ŷѡǡҲͲΪˡ Bulgari Bulgari һ׶ĽײԻ˵ԷdzǿһҪԣȻٿԤ㣬ԲûôҪ Ʒ⣬ҪƷƵĴԱнǿгʶȣ˵ͬһȦһ۾ϳ൱һʶҲΪʲôʿˮ˻ʼ罻ýϻȵԭ򡣡 Happy Diamonds 129,000DZʶԽǿֱԽܻӭгرõıһdzԵġDzָ߱ʶȱBulgari BulgariHeure H̩Ħɸ硢ŷϵСHappy DiamondɺLuminorϵСʿˮ˻ʼϵ밶͡ٴݡȻ׶ȫ߱ǰμһݳƷƷᣬ˺ܶ۱Ҿñľңˡ֮ǰǶϲɺ˶𣿡Ϳһֱš VilleretϵСڡڡ˫ʾ 275,500ڵһ׶εĹߣζԱرϡҲСıǩ꿼ѧ־ԸҪѧ汾£ѡȫ԰רҵıҵѧȴ̰ԸԵרҵԺ󣬱ѾΪҵмᣬȴŭ𣺡˭ᱱ󣡡ֵ򡢺ˮɺײ֣ҲʶԼʱˡһ׶źͬڶ׶ΣԱԼĸˡѡһЩƷơꡢᡢǿᣬЩƷҵڵλܸߣÿijޣںͷ죬ڹȴҪвŸöʸ񡢸յ١NomosЩպͬ߳ȴ͵ûѵĵ¹Ʒơ ʸ1815 307,000رм֣Ʒƶæ׷ǣȴĵĻѡΪйгرлʾйоҲǾرƵġᣬںܳһʱʵıӷּҷģҲ൱áǿᣬеĶƱʦݻֱƷеġһ׶Σ㻹ǿƷƣѡһЩرıͱ磬˶ҵ2.55ȴϲLe BoyϵСҪǻΪʿֻкˮʹشˣÿƷƶвͬϵУԲͬεЩе˲ŻʿʿϵСһϵһֱDZѺӵģ ۸ʿ־ʽȫΡܶ˶Ϊʿûб͸ֱȴΨһ͸ģĻоѵõؾ˾ϸĴĥչȫµʿƳ֮ҲµĹע ʿ 208,000ŷѵŷѡϵСһϵеԭͱѡŷѲݣҶ50ϵľҽʦٱȣ۸ŷͨҪ߳߼㻹ѡɵļ׷ʱ ŷѲرϵ 133,100ΪDz˹ʹ˱ᣬںͺձ棬Ҳ̳ MARINEϵ5827 283,300ʼֻǴ˰ǰҰһ棬ȴȫJules Audemarsϵ㿴ͬİˡҪ͵Ļòѡˡһ͵ıÿƷƶLogoһ˺ѷֱʲôƷơٴRef.5196ʫٴϵСJules AudemarsϵСAltiplanoϵеſѡ AltiplanoϵвʵĻѡ׽𡢲ͲҪѡƽõӾϽӽڲ֣вƲ¶ġһ׶Σ㿪ʼ˽ԼԼʹֵۡۡʵϣǶѡ̣Ҳ֪Ĺ̡գᷢչԼʽԼֱʶɸѡҼҪȥDZˮ˻ʼ밶͵IJɫ̫ÿˣÿɫDZˮû⣿ רҵDZˮDZˮ㹻ˣƽʱֳҲٴǾˡһ׶ΣǶı׼ ƸɵʱᷢԼڼ۸ôˡƷҲˡ۸ı׼ı˵ĶǿģصDz㹻ϲҪǰһ׶׷Сڣڿʼ豹ˡƸܾһУ濴˿һԴŮݳ޵ıҲһܿϡģ˼۸أҲԼƷƵĸֵѡԱúܴˡٴҲAPMӰٴҲAPM˱Ƹ񡣼ǰþƺò̵IJ졣ʿɺй˵ĴϣһֱȨ˵ø·ȫСڵʿɣѾƫִĵزʲôټʲôԼDzϲΪԵñȱ˸Щձʿ֮Т˵ûϲһʿɣ˵ÿһʿɶDzͬζԼĸԡһֱ̼ձľ֣һ¾ˣҲʼҵĸо ѡʿҲһĵֱǸʽѡ㿴ѡƷƵ۹ⲻǼ۸񣬶֪ԿʼǺĽʱH.Moser&CieRomain JeromeĶƱʦƷơ ĽʱHeritage 520,000ϲУڻ10ԪҿԿмգزٵҲҪһֻԱ鲻˵ˣרԱһӰԼǰϺμӻ֪ҲǷи֣зܵģľǰ顣ͬģIWCͱķԱ 鳬Ļϵ 34,700ҲкܶϲDZˮȥDZˮʥ֯ȦӻҪDZˮʿĺʹ˻ʼ밶͵ȻѡƽһģʱһֱרҵDZˮܷdzɿҲǷdzõѡ񡣡 ʱͷDZˮִ Լ17,800 Լ16,500ϲ˶ɵĻרҵһһѡSuuntoأƷרҵ˶ԵġһߣԿŵķű߽ʫٺݪ棨Maki-eֱİߵĻʫLes Cabinotiers¥Celestia Astronomical峬׿3600ܸӵ֮һ߱23ҪĸӹܡJacob & Coӷ֣ǰСϡ ʫLes Cabinotiers¥ǽʦԿռṹȤArt DecoֱҵReversoǺܺõѡ񡣳λһĻ˹ٵOPUSϵУ֥صĶӷ֣ƱʦƷMB&FԺܶθڡ˻ʼοӷ֣гӹܵļ൱ǰ ˻ʼϵгοӷ220,500 ʿɣ˰һһ°³ƷƬԴԲ˿Made in Le BrassusŻٵɼ˿Patrick ClairִרҵſģΪʲôôܻӭ֪ ƬԴԲ˿𺳣ԿһЩرƿƷҲԽԽѺñйгյԭ͸SKPϵƳֱһֱǻӷֺʱʱ»Ϊ36ʱͨʱĸƣʹ2Ӵʾͨ8λİťͿԷУ յԭԱʱйSKP 201,000ڶƻĵ·ϣûĸƷƱԾˡܣѩƷưͼޡأArturo FuenteϵƳرı̺ͱ׸رѩ̲ͼƷƵıʶ3ֲʸ100ֻ˵ѾԤһˡ ںϸ 230,000ȻиߵĽ׶Ρ˽˶Ƶżûôˣ;Ҳdz͸ȥ׶άֶһסعʽӢףȥھƣֻ벻롣ǰᵽIJ3ǰʼոͰװʿɣԼľƱ꣬͸Ƿȷ󣬿ҶƱ˰㶨ƶһ޶ıڻʱӱǹרʱ򣬶öƷʽġҪ˽ĻԺ̨ѯ༭MarselС | ӾAllison ͼƬƷƣ磩ϵmarsel.ma@flxh.meȨ˵ĺݾΪԭתں̨ԡڶⳭϮǽ׷鵽ףɵȨ»4λ̳󿧺ʿģ8090˵ӱ鱦Ļָϡ -fashion | ݡĴɫΧжʱ֣ALL IN ܺãζӭladyﳩΡ| ζ߶߶ |ﶬƷĪӭȽ̸~ʱŮһ̶ֱ߶䣬ֻŲӲţʱƷΧ~̾ţﶬůʱеƷΧˡΧůmaxﶬʺϰ͵İٴŶû˲θؼdzʱʺ𡣸ΪҪǣΧΪŮеҪӴӾġ¡Burberry΢ӰThomas BurberryëΧһⶼůΪӰжճߣЩΧΪļڴĻ䡣 ĸ˲䱻ֵIJ~ΧѡһǾĴɫΧܺܶŮǵϲߵ¼ܴݶڴɫΧֱǰ֣˵ϰȫԱǵ͵ﻹ~ҪΧز֪ôѡԲԣһʱƷζ汸İٴcashmere ΧɡʸСʱֺôȫˣ޿Ʒνġƽ𡱣ɽ϶ʱΪëһϸܵëٵġ޾ᡢůص㣬ϸάرʺϵ䡣ʶ֣ʹзҲʹáѡϣʣȥָгãһˣǺ֡ΧȥʾտգͣȫӰճҲһжûУĺܣΧõı֯ʵ͸źůԺǿijãϲΧıиرůͣëΧƼţֲʧƯںԪأͬʱ˸ʣʱضܴƵĸоΧ߼յĹգŵͬʱ˼ԡմֱÿһúܾϸߣȫء޾ҲŮǿԷĵġҲܲһֺܱܺõĴֱУ㾭Ҳ̵ġ˵ΧظߵһǺɫˡÿһ׵пʽڷ仯ɫΧȴʼһѽεܰٴƷĺɫΧѾΪѡ~ߴ200*70ijһ׵ıůɫ13ɫΧǿѡɫϵɫôholdס~һΧ㻻һ飬ʤ10£ɫëΧô·ˣЩôÿΧһҪһһźšͼƬɹʱй˵ͼƬɨ -fashion KITH x adidas Soccer 2017 ȫϵеƷ ֮ǰ,KITH Ronnie Fieg ѾԼ Ins Ԥеһϵ:KITH x adidas Soccer,еĽȻ˫ɫ ACE 16+ PureControl UltraBOOST ˡ˫Ь³ͱعⲢұڴ,˴ڽӭʽķۡ˵ ACE 16+ PureControl UltraBOOST һЬЬĽ,緢ȥ7,伫Ͳ,ʹÿһɫܵЬԿȵ׷,˷ KITH 汾жܻӭѾԤˡȻ,ڴ֮ǰ,Ҫһ KITH x adidas SoccerһϵеƵҹŦԼ,ĸ¥,Լĺֲ̲֧ŦԼ Cobras ܵ Flamingos,Խʱպ͵,˸о·ͬһӰƬܹһϵе,Һ˴ӷεЬĵճźʵսĸֵƷ,൱ḻ,Ҳ֪ KITH x adidas Soccer һϵаˡCobras͡Flamingos,ôǾһCobrasⶼЩƷCobrasϵ KITH ŦԼֲ Cobras(Ϊ۾ߡ˼) ,úɫɫɫͻɫΪɫ򡢼пˡ㡢ñȵȵƷ,־Ե adidas Three Stripes Logo ӡ·ȵط, KITH Logo ศ,ݡ KITH x adidas Soccer COPA Mundial 17 UltraBOOSTCobrasһϵеĽЬȫµĴɫ COPA 17 UltraBOOST,˴ƤƤʺϸ,ȫ BOOST ,ɫġCOPAadidas Logo ԼЬͷе׺ϵеܹͳ,ֱǺڰ˫ɫ,߲˲ͬɫϿǰ KITH غڴġCobrasӶӻյϸڡȻ,Щûʲôر֮,㲻 KITH ҷ۵Ļ KITH x adidas Soccer COPA Mundial Tango TurfԱ²˳,ɫϺɫ,ͬɫ Mundial Tango TR,ɫϼ߱ʶȡ KITH x adidas Soccer Copa 17ȥʽ۵ Copa 17 Ь ACE XϵеƷ,ҲԿǾ Copa Mundial ˴ϵ˫ڽɫ Copa 17 ֮ǰЬɫɫƷ,ôƤЬԼԲЬĴͳסӽ뿴,֮ǰ˵ûʲôر,ƺ?鲼ķͼƺʾ²IJʽ,ֲʿԷˮ?ȻЩǷǰIJ²,ֻеȵõʵ˲ĶۡһһϵСFlamingos(ǡ񡱵˼),һϵͬ˼пˡȵƷ,˿˵ KITH ɫĵɫ,˰ɫڽϵ˫ ACE 16+ PureControl UltraBOOST,˵ʽΪ,ˡȥɫļп˺ͳ֮,ճƷлһɫTһɫTпԼһ, ACE 16+ PureControl UltraBOOST ڡFlamingosϵеճϵеƷ,ȻҪһϵеװ,ɫºһ˫ɫͲɫƴӶɵ ACE 17+ PureControl ЬACE 17+ PureControl adidas ACE 17 ϵͬʱҲִһЬ,ͬʱҲΨһһ˫ BOOST ЬЬ,ɼر֮Flamingosϵеһ˫Ь ACE 17 TR1,Ԥ⽫һϵег˳˫֮׵һ˫ЬɫɫͲԼɫߵĸѵһ൱ͿĵƷ,ƽʱĴŴҲdzۡע,ɫѵ(ڳ?)Ϊһ KITH x adidas Soccer ϵеܻӭķεƷ,ܹ򵽻뾡֡ͬΪͳĵɫͻɫ汾,ؿӡ KITH Box Logo Flamingos Logo(һֻ),㷽,ɫ¿Դһɫ׵ɫҲԴһɫɫ;ɫֱӴɫ㼴ɡɫԱҲ̫ɧ,ϻ᲻веļӸ?,ҲҪ,ʱ:,һϵнڱʱ6223, KITH зۡ˵һС,Ҫ̫ʱ˫ ACE 16+ PureControl UltraBOOST ϡǿԿʱеӰԽԽ,ŷ޵ĻϵϮĽͷʱ,Դŷ޵ adidas еĵʹ˴ KITH ĺ,Ϊ adidas Soccer 緶ΧڵӰһȫµĴšڻع(ͼƬв鿴)δ֪| New Balance ε,εƷеᡣδ֪|KD10,֪ĺ㲻֪ġδ֪|Converse ִȫ, UNDEFEATED -fashion ŮһҪѧ5ͣƯ~ µʱѶ,飬ױɣǰ,ȨʱϢʱʦѼȫʱѶȫ׼Ҷλ߶ʱѶƶý壬רעΪϴĶ顣ÿҪͷЩ˵鷳βʹҴҳˡȻŮ˻ҪԿּ򵥵ͷһҪϿѧͷϱһ仨ʵֻҪ裬β仯ҲֻҪ໨ӣüӼһ¡ҪijԭȰβõһξʡµġЩͼСؾŮһҪԿһڶ -fashion ܶƷնBurberryʹ,㿴Ĵ֮·? ,BurberryٷܶΪƷʹ,ͬʱܶҲΪΪƷεΨһŮ!ͬʱ,ƷƻһܶBurberry ϵŮװ,׶ɵһʱдƬ,Сʹһ𿴿ܶBurberryԨԴ~ʱװ & Ʒƻ˵ܶBurberryԨԴҪӽBurberry ϵзῪʼ,90ܶ겻Ǵ˴δΪƷƹٷΨһйŮ,ǸƷСˡBurberryӢ߶չ,ܶBurberry ϵ,Ƥɰڽ,ܶBurberry 2017ϵ,BurberryױʱƷ꿪ĻɶԡڸոսBurberryϵд,ͬͷſ,Burberryȫ¾ϵ֯ɫTľѥ,ȥʱƤƬͬʱ,ܶ껹Burberry¼װ־,ʱԴʱָMarie Clairę10¿NYLON 10¿üSELF 9¿Madame Figaro4¿Marie Claire6¿OK!ʡ 125ڡVogueMe4ºšGRAZIA288˽´ܶճҲѷ,Burberryʵ׷,ߵ,BurberryĵƷDZر,ܴԼķBurberry+HouseƴƤThe BannerִBurberry Burberryٰʹ+ʻ˶+The DK88ִBurberryб˶Burberryʾ+AƴڴţаȹBurberry˿Ƕװ+Ƥжѥ+TheDK88б֮,ǰôȵܶҲΪƷʵʹܶĴ֮ʽ,ڴ~ ƼģLEEȳ6LOOK ްʱװ¹Ϲ֤ͮȫ⡭ʱװȰ׵T̨ά ʱصϷս˵10ŶƵʹĶԭġ -fashion СӱգƷֻǿԴͷȴ 㻹ڹzaraͯװ𣬵ȻǶС˵ʧΪһܺõѡ񣬵һСӱĨǸ...ûϵרΪСӷƷƣСĴȥð~ Brandy MelvilleBrandy MelvilleһеŮƷƣΪȫе·ʽֻS룬ţҪôݣҪôȥ~һ˼ģشЧǽʹұը~һĬĬȥɡغܶӶһߺšΪƽΪԼBrandy MelvilleƴʡһֻSŵ·Ʒƣļ۸ҲʮСŶһٶٶˣ·Ҳ60ӡԺ޲ͯװСӡСϽ~ҪΪ򲻵ʵ·գѡܶŶССţףTС~С֯С廹Ĵءǿɰ˶ףҲССһС£㹻ѡ죬㴩϶~~ɭŶ~ Petite StudiopetiteʾǽС˼ƷϾܿ˼ҾΪСǷ𣬳СxxsʱֵIJСڽȵĸװСDzǺܿԼŲȻoversizeҲСŲаPetite Studio㡣СĿʽ㰼͹²ӷסгĻƷĺƷȥС룬Petite StudioֻС롣ٷմְҵװɿˡϸĵӦ÷ҵģҲһɫСӣ߳160ĶǿɰССŵȹǼҲȴһֱʣСӵܴܲææoversize׳֯ȹȻǺIJźÿһֱûѡ֯ȹ㣬ʹƷưɡ˸~~һֱı~ԭͼƬ磬Ȩϵɾллϲƪ֣ӭתȦ -fashion Ǯ˵ҲֱеǮµʿ ϲӦûв֪ٴģϵ֮ı۸߰ȻٴǺܶ˵ġĿꡱҲֻԶۡٴȺǽĸ¹ֱ׵˵ǮˡⲻһֵİٴоҪ9飡ɶӷdzϡкһԭװİٴľСԴһرĹŶ1976ĵһNautilusֱRef.3700ֵһǣӻУзdzϡУûм⣬ı״dzãΨһҲ$13,000ԪԼ90000Ԫңļ۸ˡľʱϿԿٴLOGONautilusdzϸԷֱеĺϷ촦IJֱ߿ĥ𣬵ǵ䣬״ѾdzˡȻͬҲаٴLOGOɫģǰٴ1976ĵһNautilusֱRef.3700ߴ39ףװ28-255 CԶһоʱʾȴֻ3.15סټNautihisõ׵ıǣ׸ȫܷ⣬ʡԵһˮṹֱֻĺȻ8סȻDZˮȴʺװʱǿԴͼĹпֱ֮һDzġ󡣱DzľʺͲֲʽϣ֪DzΪһʱĸֱǿԿڲľм˵dzáһRef.3700УDzãebayϴۣۼ$12,995ԪԼ89634.29ԪȻ9ǮһʿˮʣǮһ鱦ʮxֻŷѺ...޷˵9ĺӵֵDzֵѰܾúֵ֮ܲΪʲô˻ôǮֺӡݹٷͳʾ˿ＸʮġʿʮСٷҲڡܶʿ׷׽⡰΢šںšԭλʮ˾ڽ̴ΰԿף -fashion ȫƷɹ嵥ղƪ ﱸӦ˼ĸƵһʱ䡣ΪҪͣ׼Ʒʹп޵СҲñԷһԣ׼Ʒڼ䣬ĸ˿һϽΣΪȥǸû......¸ĸingԣ۾ƪ˼ĸǽѵƷ嵥ȫԣÿҪʹõƷһһгﱸڼ䡿β֡ɴ+ƷɴƣǰµʽƣҪǰꡣЬԱ˫һ˫ɴĸ߸Ьһ˫ʴ侴Ʒĵ͸ƽЬԸ˯+ۣ͹ձر۾з++ڴ++ᣨ+ƤЬ+Ӱɰʻĸװװ+++߸Ьְʽ+ƤЬ ·֡Ϸߣָѹ塢٤桢ĩĤ......Ʒϲֶϲǡ̡ӣֽƱƷҵƷƷƷӱϴ»ʳƷ֡ӻϲǡӹԲϡࣨơҶ/ԲϣһǣԲӡ桢Բ˻ιĻãӣʱԣԢӣ β֡鱦 + + +ɫΣϼͷΣ ֳװβֳ֡ӭơϯλָССоƱϲǺУʻػȣ̺ϲǩ߼ʡڻֽͷϵijϵϲ֡ ر񼱾ȰߡСָ׵Сӡͷ뱸ó˿ࡢ۾ҺΣˮˮֽߡױޡЬ桢ҩ̱Աɢɢǣ 㶨³ѣֹͷʱ ϲǡˮ̡񡢻ꡢǩ ͳʽرƷ+֧ +軨Ǵͳʽбزٵĵߣ൱ڵĻ鳵֧½֮ǰɳģһ·ϿմȾаġڷڴſڵĻ裬ȥԢӺ𡣸ͷ+ϼ+ϲӴͳУҪľԭ֭ԭζķϼʽĻɴͷɴһϲȥͷĵߣԢ⡰⡱++ϲ+ɡ̻йеĵλԶǴͳӪյһ֡ϲǻҪװƷԢáҸãӺʽ+«ʽԢδıԸһ˳«һȡг»Ԣ⼪飻ɽʳưȽƣʽĴͳǣų֮Ϊ(jn)Ƶư˸һưƣ˺϶Ϊһ- END - -fashion ѩӭ¾¿ˣµһã ͮעȱȽϸߣͬѩӭԵе͵ˣʵѩӭһֱæ¾ءɹѩӭµĻƬҲ¾ͣdz¿ˣС໹µһءѩӭܽûڹ¶棬˿ʾdzءɹµĻգҿԿʱĴС£һ򵥵İɫ䴩һɫţжȹϴһ˫ɫЬdzԼǿźڴϸһѩӭ磬ʵİɫţжȹĴ仹ǺܳģϵȴǺܶصģ΢ر崿ŮϢС໹ѩӭϲɫ·СһعһѩӭĴµһþϲɫ·ڿͣһɫijҲͬɫϵİȹ·ȹоµĻƣеرҲܺÿѩӭĺϲɫǰɫijǰȹȹϲڿͣһϮɫȹͺţϾµױݺֲʧŮζѩӭϲɫijϲɫȹôİɫȹϲģٴͷdzµķͣĻлءѩӭµһϲϲɫ·Ǿúÿ -fashion Գ16δͣƿ 죬Գ־ḴϵϲѶˢСͻȻ뵽һ⣬ҵӡУԳʮһһֱһͣôҲ벻͵ӣû취С̫ǿͿʼԳЩķͣķ֣ûʲô仯Ŀ԰Գ֮Ϊ겻ˡλСǣСһعһԳķͣǷഺ䣿2002꡶ǻ԰ǻ԰ ԳݵĵɬŶ80һ˵ľ䡣ҲһҼ̵Dzͷlalalala~СDzѾǸǵʱһƣеԳ˷ͷͣǺӰгġܶ׷ʱеҲ׷ЧF4泤2006꡶ɫԳΪֱ鵥ɡ2008꡶Գݶ裬Ϳڣ°ԣ߰˧Ըջһ˿Ǹıһ·͵ģΪϾԸ񣬷ԱȴǰһЩͷ΢һ㣬ûʲô仯Ǹ2010꡶㡷Գƽʦһֻص˵¡2011꡶ҵIJԳƣһɵĸ˸ɫΪҾϲǵ2015꡶ǰѡǰѡƣƻͼܾ˼ǰѡû̫ı䡣2015ҵŮʱԳ̫ӡʮ꣬һֱһַ͵ҲDzһֱһͻô˧ľ͸ˡ֮ǰö˿Ц˵F4ֻʣԳǵҲͣ ܾջ氮ˡʵ֤ʮ겻͵Գʮһյİһأһջ氮ףԳ־ͯеӹһֱҸȥ̫ ²̫࿴ſľõǾԳķ -fashion ȹÿȴ,Bugô? ÿȹ¶ĸô޶Ӿ?ż,СĻ㴩С!Ļһ:ѡԼ򵥵!򵥵!򵥵!Ҫ˵!ȻֽƬ,ôȹӵѡϾ;ʽ򵥺ͿʽΪҪ׼ָӷ׵ȹ,Щ80ŹǾǰ,ͨ˾ͷŹԼҲ̣ȹˡ̫ɵİʽҲǴ,Ϊ34DѪҲȫʾĺ,ҸӾʮԲ!,Ҫ˫,Լѡ!Ļ:VVVV²֪ı,һ᲻ðɱ!ֻʶҪμҵİҲͬ,ϸĴV,׼·Ĭ㡰Ļ满!Ļ:ƺܹؼ˵ͬǵȹ,ӵôƲҲ൱ġֽƬ˵,ȻDzϸϾҪϵĴ,Ǻ޲ȫ֪Լ!ı˵,΢һļ,ϾDZһһԭ!СĻ㶼ѧ?Դ:ױ- END -ʶά,עǡ·Ķԭġ鿴 -fashion ʲôԵ᣿ TOP涺 ׼Ц Ц㴩 THE END -fashion жģأýŦԼʱװܱеů ʱװܵT̨ʲô0ųȾǵר̨ȻµŦԼʱװϣһöڲͬģءⲻģ˾߼ƷEckhaus LattaVogue RunwayƵNicole Phelpsˣҿ21ʱװ㣬˽죬ֻһλеģءʱúˡ岢ޣPhelps˵ģԼ2015ʱװܵDolce & Gabbana㳡ʱģBianca BaltiҲǴһнǵĹǸӵʱҲеཱུġEckhaus LattaƷأMike EckhausZoe Lattaɵ˫ʦƷơΪ͵ǹġ˹־30under3030 30λܰ񵥣ϣƵƷһѰ·ٰ˼˼ҵӲͼһгУܿҼ·δģأҲְҵģءMaia Ruth LeeᲴĺңŦԼȦҲǷˮԼҵͨģELʦ˵Lee߽ȥԴһëȻ󡰺okǣ㳡չʾξͽɣҪ֪󸹱Lee¸¿ɾҪ¡жˣʦ֮һMike Eckhaus˵һûʲôĻֻΪIJͬ׶Σһ¡Zoe LattaҲ˵۶ͬͬ͡ߡͬױݵˣִŸо·ȥһĵ𣿡87.75%Ȥζˣڿ̸ʡ΢̸ʣtanziappظؼʡաȡVIP¿ -fashion dzϯߵȭӳ 塷ﶬ̨ͬ 926գӰߵȭӳŮǴϺջؾϯڡסȺѻۣطաﶬʱٶ̨ͬ һڰװ̺Լ󷽾ߺġӱصһּɫȿ㣬гʣֻ״Ϊ˼Ƥͱ¡ ߵȭУ뿪黨ǰԵʹǫʾܲѾdzҡֳǰӰĹ͸¶ǵϷݲ࣬ȴر͵߸ľͷһ֣ӰԺһ󾪺ӱЦƣߵȭ롶طապӦӶҿˡһ÷¡ĺ -fashion ӰҵXXOOҸ÷ ˺l -fujislz-һ˽Ȧ飬ԭҪһλС˵С֪ @²۾ ԼķգӰҵXXOO......Ҹòüȥզأ֪ģΪϵij˭֪λСﻹPoԼϢеҳsirִ򽻵ȻֳУȻһͷˮҾü򵥵һ仰ܽ˼XXOO½ˣҸòüWHAT ȻΥֻΰһXǵǽȴСˣλСй⣬󲿷ʵDz򵥵ģʶ ǼǷؼȼһ鲻䡣ǷҲǺǿ ڼ콡һҹߴɣôܣغͪڣʱ᳤ܶడDzģԼһֱֽԺܸθ㣻ΪΪ㲻ϢϢ죬̩ϣҪ״̬õʱ۵ĵȻӰˣҵĽͺаֵܣһһСʱ䣬ҽ㿴пƣֻ֪һߣСʱôģӦþͼӾͽôܱһСʱʱơƶǴӣмǿµСʱ90%й ϰ㲻ȥXXOOʱ ɢע Ҫ̫ͻ ȴҪýǾͲֵܡ Ϊһ˵ʵĺúڽһϱ˵žҶûΪij۰ֻкҲλСǰѽȷʵҿһģܶ˻Ӵ󵰰ʵ룬ĵʻӴĸǷйأʱ̣ѿᰮ̸˿ꡣʹӽʼʱ̵ij棬ǰȻҲôиʮӰСʱӶûСڣ꣬¼һΣһһΣһӣ¡ԣҺ˺ô԰·Ҳвߣ˵ԭԹϲͻ㽡̫Ƶ̫ݣ϶˳ԭԹ½Ҳ⡣ģСߣͦܳԿడһĴμɣҪΡⱾҲҪʱʵûҪʾDzֿĶ˾ʱԣԿԡ׿ٶЩģ̸ʱӲָ꣬ѧĽѵ50%ʱӳɡѧĽ˶ȫĸ̶֪ȺעߡʱĹؼڡעĺ䡱ֻעȫڡҪ֮΢̼ǿȾͿܽе෴ǡؽע䵽ļȺϣܾս˥󣬽һЩ̼ӣغͪڵѵǰ󹭲๭Ǽ׼ߵӲеСǸϽŰ-THANKS FOR READING-ŮIJŽмƷͽ¶ڼ׶ߣˣAnllela SagraĽճǶѪ˽߶ע˸˺lɨע -houseliving ͷһεżǿЦã鷿û˭ ǴϷﳤģ޵ΪܱȽԶҾǰϹסĵżңΪ׼Ļ鷿ҼֱDzţеЦðرdzźܶϷ˶ûżס˲Ÿоʵûʲôֵģ˵Ӻӱʡһֱũÿÿû𿻡ܱϷûůΧǸƻ߶µǿҾءżҵķС¥װ޺ͨŰʰÿ춼ǺܸɾDZĿҪڶڴرůͣһ㲻̫ӲҲڷһɳijķװ൱IJͲϴ»ҲǽʱżȻûϷĺãϹŶҶܺãҲϲҡͼƬԴ磺Ȩϵɾ -houseliving 10뽡йصļװϸ,һܺ ŸҾװ(΢ź:jiufujiaju)ÿΪ΢ѷװ޼Ҿͼ,װ޾֪ʶ,΢ڼװϸ,Ϊ΢ʡǮʡ,װ!ÿ˶ע,עʳ˶,ע,ⲻ֪ʵϸھ,10뽡йصļװϸ,ƽʱӦע⡣1ֽѡȺɫо,ɫɫǿɫ˽,ӰѪܽ鷿ǽֽѡɫ,dzɫ,þԵø,ܷ顣2Ӿɳ1~3ײܵж,λþĻ1~3ס̫ƣͺͷʹķա3ŲҪ̫о,һ򵥵ĶܻӰ,Ǹոշ顣4ҲҪ·35ֱϵ(൱һʱ)ͻѪѹߡ񾭽šסԶɵ1ϵಡ,ٽס27%泯·,Զ·ķӱ,ֲ5ƾ̫߱ڲɫԴۻ,Ŵ,ʹͷĿѣʧߵȡ,ڵƾѡӦתơ˸ԼɫʺʽڸӵĴչ,ѡ͵Ľܵơ6Ҫ̫Ҵּ̫ʽϸߡѧһʵ鷢,˯ʱ¶ڹµСȺڰеС50%,ʹĿ·ͬоԱ,Ϊƹ,Ӱ³´л7װʽʽſ,Ӣȴѧоָ,ֲпǷϵͳ²Դһֱ׼ʳ·;ʱŪ,շס׵ȼ8Ҫͨܶʱûд⡣ʪͻ,컨׷ù,дܺܺõͨûд,Ҫװһʴܺõȡװʱ,Ҫˮܰʵʵ,⳱ɢȥ;²аҶ,ͨԡ9ƼҪװЩζѧо,Ķ,ij,ôҪij嶯ͻһ,پõķǰѼеľƷŵε,׼װƼ,龡ƵһЩ10·ϴ²ƷеζһΪӷлкѧ,ɴ̼Ⱦ,շԻ,齫¼̨,ɹʱͨ硣ʹ·ġĶԭġװ! -houseliving ֻ飬װޣӱǽоϷأ ·̵ȥ·ԿףΪҳʡװ޵Էܸ˵ҪһеűƽʦҲDZȽεģ˵ŴװϲʲôװʲôŻصУƿżֱôȢôһϱ̨̨Ĵשϸ񹫹ĵשԡʵ벻ͨôһ²ۣ95ģںܻDz65ģӱǽ~ʵڲ˵㲻оǽе}ûôDzǸɶϲˮģһҪ˵ʵ﷢ëϴּɫǸϰɣֶ΢Цûdzһӱǽҿŵӻ̲סȥµҲʹ󵨵˵ˣ̫Ϸصĸоˣ벹ҲˣϱҲֻϣ¶ҼҷӵĿҪ֪ˣиҪ󣺲ϱ -houseliving ũǷ޸ô죿16X83¥40 ʵũ彨У޸ҪĵΪչ˵ڵ⣬ͬʱҲΪ˴ϻһ߶ȵƣǷ׼Լ˿϶࣬Ϊ¥õĽչʾʵȴľסռ䣬״ⶰ16ף8.24ףռ142ƽף301ףܸ߶10׽4 6 2 1 3¶̨ 2̨ 1 1ñ 1ƽ沼ͼţײռ佫ȫںһһֽϴĿռչʾǰӾЧӳɫסи׷䣬ռ󣻶һΪ¶̨ƣɹ¥ǿͷ򣬿ռãҵȣֶơüָ΢Źںţסլ԰500ױͼֽͥԺʩ -houseliving 90O,ϲı߰~ Լļװʱ,ʵǵһԭ,ͬʱҲҪۡҷһ90Oıŷԭľװް,һ±ŷȫݶǶôľʰ!һƽͼ,,һ̨,dz,Dz,ȫĶdzľذŶ,忴һԳƵĻ͡ռܴ,dzɾ,һ׿ռԭľذ,ԵȻº͡ǺIJ,شʹռͨ͸,˼ѵIJɹ,׵ɴƮҲۼӴ~ɳǽ͵Ĺһ,صҲʮص,ټϵ̺ĵ׺,ʹռ߲θкˡŷɳ˼ҷԭľ輸,رǰɫкɫɫԱ,һʵȻ,ľ?û˵ľʵذǿռ¶ȵ?(IJ!)ľذϾԱڴשҪů~˵ľɫƴӲ輸͵ӱǽװι,ҲDZ,ڲĶǽϻһľ,ӾЧҲܰŶ!ʹ˴ľ,ǿռ͸ӵθ,ǾÿͲù,ʵѡװι,ܺܺõķָռ,𵽳ǿ!ܼ,ǿȴܰ,IJδһյصĵ,ӪһܰľӼҷΧ,DzǶʱθڸ?Ƕȿ,ɫIJ׻ɫ,ں,Ա߱dz,һΪ,ܹ֤Ӿͨ͸ԡٹ鷿,鷿ɹ÷dzֵ,˹ͬʹáһʽ¹,ռDZزٵ,ϲþǿӾ,Ʋʹ鷿̫ѹ֡Ҳľذķ,ڴƷʹѡʹɫ,һӾЧøʡ֮Ĵܰɫ,ԼɫĴƷҵϢ,IJɹҲܱ֤ҵԡͷdzĵ,߶Գƺɫ,ʹһƿҲŵһ,һͷôСһ㻭װ,Ҳ˭˵ԾͲа칫?Ҳһ׵Ĺ,ճ,߱һɹŶԵ΢С˵,Ĺ,ƽȺȲ,ͻһdzʺϷɵĵطĿǰûзôƷ,Ծһ򵥵ɳεIJ,һʽİɫ¹,!˵¼ԼŶɫϵɳ̺ʵ,ٴһֻСľСɵװƷ,ǾµòϽз,ܹɽʪ,ϲ侲͵ɫ,һҪѡڰ׻ҵŶ~ -houseliving װ74ƽСӻ13 Ѷ˵ֵƯҪ ԭĶȡ3װ뱨:74O:һһ:ŷ:13ǼװʵӲװûô,ҪľװĴ,еļҾ߼ҵװƷԼ,Լ뷨ʦ֮,װܹ13,ҵǼҶ˵װεرƯҼǮҲʵ,ҪЩװεӡԼҲǺܽءƽͼƽ沼ͼ,Ҽҷ64.6ƽ,˴9.54ƽ,ܹ74ƽ׶һ㡣صĹѡ,ɫ·е㸴ŵĸоɡؿͲǽǵӹܵ,ϲĸСֲ,,رС̨֮ǿʽ,Ե̫,ɳԱ߷˸ľʵĶ౦,鱾ԶܺáͲҲǿʽ,ֵŶ,ɳС輸,ܰɳǽϵװλ,Ļ,ö඼ƯɳϸװΡɹ,ʵƬǽʽ,ԸʱСɹΧϸװΡǽرѡ˲ɫĴש,ԵûһЩ,ʱáұȽܰһ,Ҳ¹,ͷһ,һ˸С,鿴綼ܷ㡣Ҿͺ¿ɰ,Ժ󱦱ĶͯҲǸ鷿,׵ʽ,ɺԺ,ҲͷϢҰϴ̨,ʹñȽϷһ,ֱש,ҲƯʡǮƼĶ:С޻8װ78ƽŷ ǽŴռ̫90Сװ76ƽСͻ5 ǽɹʱʵóעȡ500װްԤ -houseliving 170Oִʽ,Ҽ,ʵ! 170ƽ׵ķ,ִŵʽװ,ͬʱĻ,ڿռַҲDZȽ϶,ӵ,ȻϳȴƵ൱ž,һؼҾ͸ܵ˶صܰϢظֱȽϳ,źֱ,ÿλؼҺһʱϴ;ߵǽҲǹһܰƬǽ,һžܸܵҵζ; ұߴֱЬ뻻Ь,Ьмλյ̨,һСֲ,źܸܵµϢǽԸשͨ,ͬʱԲɫשΪ;Ь뻻ЬʵײյĿռ,طɾЬ;ЬԸӵͼ,󷽵IJɫҹ,ССĿռ䶼Եøľĵǽװ϶Գŵװ,ȴֳŵĵθСɳǽͬĶԳ,2µװλ,ɫIJɳ԰ĸɳ,ԵŶִ󷽡שĵ,ռ䶼ֳݻߵķΧйо,ʵӲװ,ݻļҾװ,Եָߵǽװ˸ͱ߹,Ϸ2,԰ڷʳĻװƷ,ʵáʽʵľIJ,һµ,ֲĵ׺װ,òʵϢԱߵǽи,һһԲι,ԵֱӴ󷽻ۡɫľذ,ɫĴ¹,dzɫ,жִŵִʸСͬʱ˸СƮ,԰Ĵ,Ʈϻװɴ,㲻ͬĹ߻Ҫ󡣴԰Ѵڷ,Ϊڷ¹ȡ˿ռ,ɫĴ,ɫĴͷ,ͬʱ׻ɫĴ,ռδԵú¡ͯľɫ+ɫ,ͬʱиһС,СӳΪӵͯȤء鷿ԲŸ,òɹڹͬʱӵпĴҰ;2࿿ǽڷ,СռʡƮλýƮӿװС,ڱҪʱ򻹿Աɿͷʵáɫij,ɫǽש,޻õɫ,˻ĸоװɫשĻ,ɫȵĴשƴ,һֶصСĵש+ǽשĴ,ЧԵøĴ⾫¡ 񰴸֪ϲҵķ -houseliving װޱƷ1ùװޡʮݽڣʵݴؼң ҵװ޲϶ Ͱ¹񡢵ذ塢שɳָһꡭ ùװ12ۻ900װҵڱùװڳɽݡҵرڹƳװޡʮݽڴƼȫ 5ɱƷ1һݴ 1 ѡװ޴ùװѡݱװ޴ƷƣΪѡʽļҾߣװʡʱʡĸʡƷƳ˱ؼۡʱɱƷ֮⣬ɹƷȨʹ10Ԫ100Ԫ50Ԫ300Ԫ100Ԫ500Ԫȯװ޸ʡǮײĶԭ-᳡ƷȨ 2 ɱ925930գװޡʮݽĻ˿ͼɱƷʱ׬ֽѧҶɣܶ˼װ޻ǻѡҶһߡˮ⣬ܸİҶ˵ѡˣؼɱŻݼ۸ð50Ʒҳɯԡ©3ֻװ ɣֵ©װò˿洦ţʹðȫˮˮٶȿ ׶ˮĸģƷҳӱǽɣװ޻϶Ҫӣϲ࣬ǿѡȩԸߴ85.7%ĹƷƣѡϣԷֹϷ÷꼾ǽ巢ùŶ10Ʒҳ¹¾ ɣ˼ôȱ¾ǻСô죿¾԰ﵽ㣬ת¾׹ģ۴󷽽ʡռ䡣30Ʒҳ3Mǰùɣ3Mˮг֪ȻDZȽϸߵģǿȵ˿ǣѹԺã40΢ײо˾ȸߣʹʽϴоƷҳ 3 101105գ¡صdzÿѡڲƷʱƣۣ59Ԫ/Oгۣ178Ԫ/O޴שۣ64Ԫ/Ƭгۣ198Ԫ/Ƭñذ ۣ208Ԫ/Oгۣ448Ԫ/Oɿأ20ֻײۣͣ488Ԫ/гۣ1584Ԫ/׷ɯˮ ۣ599Ԫ/гۣ1280Ԫ/ŷԡԡҹ ۣ1999Ԫ/гۣ2880Ԫ/׷̫̻ ۣ4680Ԫ/гۣ7026Ԫ/ɿۣ6099Ԫ/гۣ18965Ԫ/ϽоٲֱƷҳ档ƷδǰԤԼ 4 װޡʮݽԹЩѿ˻㿴ҪôأСϸ룡1.ιĩײĶԭĽװޡʮݽ᳡Ʒʱ䣬Ȼϲĸĸ925-930գǰ鿴925-927ա928-930ʱɱľƷǰù׼Żݾû925-105գʱڣѡݱװ޽ıƼֵ⼸ģIJƷ׼˾Ϳ2.ɺβ鿴ùװApp߹ٷ΢ŹںŵĸIJ鿴ĶҲùװװ޹ܼҵ΢ţΪʵʱʣ⣬Appûȡ188ԪŶ켴ãʱֱӵֽÿû1Σ 5 װޡʮݽȺڼ䣬װޡʮݽȺ:ɱƷʱѣƷʵʱƼڲƷϸڡʽеⶼΪùװƽ̨ϣʡĸģ ɨ·άװ޹ܼ΢űעɱȺͼʾһڶ*λȨùװиƷŻݴĶԭĽ᳡ΪϢŹ۵Υ桢Ȩϵǽʱ -houseliving 90ƽִݻ,˳ȵĸ߹ ϽҽһִݻװްͻĦǡʱ,뷨ƷưԪ,ɫԳɫΪ,ͻռšʡļҾߡµĵ,Ӫ߹ݻĸо䰮˷ĹһƷɫ,ůɫΪ,˳ȡɫʹܰš ִԼ߹ݻ,ѡůɫΪɫ ڲ,øߵʵľͽ¶ʡ ˳ȱµһʹܰš Բεĺɫ輸,鼮,ʸС ŵƥװλҵķΧŨƷλ ݻˮ,뾫µIJ;ߡ ƤӲIJ,õʯ ӲӵĻ ˿ķ,˿õְ,˳ȵ Եʵľذ,ǽø߼齺,ıǽ߼ʮ㡣 ͱµĴͷ񡣡 Ҳͬķ,ͷ˰˳,鶯á һ߷һ,Ϊ칫ط ͷһװ޾ûӭעװ3000ЧͼŶ~㡸Ķԭġ,ȡ! -houseliving Ǯһмװ޻8򣬼Ҿ߽Ϳס һװƺ·6ѮͿʼװˣǮˮװûʲô⣬ҪȻѰˡװԭҲһ㣬κǮϵʱǴСӣϸģȻЩſҲû취Ӳװ깤8㣬ܶˣװůƬƬҿͦġڽŴЬ棬ڱȽϸɾһͱ߹мտԷһЩõ800*800שŴһЩմɨɾ׼ѽһ顣ЩɧѡɫĹţŴչյĵط˵ôװôװˣǽһЩװޱ߻ûüȥеβûеţɳǽˢҰɫ齺ᣬǽõıֽװΣװŵģʦ°װˣ˵ǸЧã֪DzǺҡԭαشģʦһӣƮЧɣ۶ҲҲͬˣ깤ЧȷʵǽֽӡģƷζһ㣬Ҿͱ²ˡ¹񵽶ƣڲҲֵĺܺãͦõģΨһȱҪʱϴ¹ͦİɣԸģ˺üأ700飬ǰЩˮͷûװأԽԽϲߴֱġ̨ɹҰdzܱ߻ɡԵеӵܻǺȫģⰲװ˲ʪ룬ȻͰŲãʵûб÷õ -houseliving ƽլ,ɵ԰ µСƽ,,ڹϾܴȻá,紩,ҶƬ,ĿĴ䡣ѡȡȻɫ,ϸɫصغݶ,ɫɫһ,彨Χ,˹,ҲȻ!װ۵ĻɫƤɫһĿռ,ɫ滺˻õķΧ,аȻϵij,ˮƽ ȻľԪ,洦ɼ,͸Ȼ;װԼΪ,ɫ嵭,ڱ仯,ǰӰ ⴩ش,ʵĹ,ĵӰҲҡҷ,һӵ衣һʽ,ݶҲ촰,ʳͬʱ,ҲܾȻ֮,֮ů鶯,ľɫ,ÿռȤζ,һ޾ͯȤ,ҲܵõɡƽСլ,쾫ԵľӼ,ȻȤζ -houseliving ,ʵ APP Store һļҾ־ɵװ,ijŴ,ʧµŵΧžȻķ,ôһIJõŷʽ,һ˵롣Ŀռ,ӵ񷹵Χ,ܱڡشľϵعЬλתǿռ,ľƤǽ桢ʯƴ컨,˼,ӭʸС,þĺڰ׻Ϊɫ,ԴɫϵʽȻʯĵذ,Χء,͸¶ȵں˫Ƭش,ǿӵеľѲ,ϰɴͽڲϴ,ݷΪĹ⾰һɴ,Լء컨塢ڰ塢ǽ,Կɿ˵߰,͹Կķ,ں,ǽı,򻮷ֳһռ䡪ѡԲʽʵľ,Բž,Ϸ컨,Բ߰,Բˮ,ºӦ;ֹƵй,,չƷζ,ԲͲӳһŨʽζĻڿĹռ,ִ߱Ԫ,赵ں,Ļ϶Ϊһ,ĸ̵Ŀռ⺭ƿ,ǰɫϵľij߹,Լʵõе̨,óɫϵʮ㡢ռҲʮֿ͸ÿһתÿһůķ羰ͬڹռĻԻ,˽µϡܹļ׷һҵĹ滮,ʵľƤ,˽Ϊʵľľذ,ǽɹΪȻʵľƤ,гġáұڹҵ,źɫɫܺƻ,任ĽĿݡԸٿͽڱֽ,߰塢ʵľذ,ʹɫϵĴ顢,ÿһ䶼ѹĸ,Ŷ߹ΪdzľƵIJα仯,ǽɹ,ʵչʾղƷ,ʮ,¯ͼϽƷ,ƮɢŨŨŷʽζ,ɾһӾ硣ʦ: 1. ϣռӵзʽĺΰ,Ϳִ,ӪķΧ 2. ϣ˽IJֿԴѹĻ,ʵ,˳׷,Ӫȼٷ S o l ut i o n 4 01 ӳĶ˽ǶȨԵǽοոդչʾ,ȡʵǽΪռ綨,صѹȸ,ռеͨ͸,Ұ촩͸,ÿռиӿŴ02 ŷʽ¯ڶůĵĽ,ԸդʽΪƬ,ռĴ͸,ӳ,رƵı¯,ŷʽ񷢻ӵ쾡,Ϊռ䴴һĨ˵ů⡣03 DZһļҨȵռ,ķʽ,ŷʽζľԪ,ʯذϸۼ͵ƾ,ݷŷʽDZλ04 ɫŷһèԺͿҲŷʽľԪ,ŵİɫ䵭ŵɫ,,ʵľذǽ,Լڱڲɴͷ,ŷʽŵΧHome Data ƹ˾:ʦ:ҶѧԢֱƷ:緿״:ëݿռƺ:600Oռ:52Ҫ:ѡʯʵľƤרhttp://www.searchome.net/PChouseҾ־Ȩ ؾӭת ע>> չĶ <<>>ŷ,һ<<>>36ƽСݾñʹԡ<1.45m0.25~0.35m*̨֮ľ>0.55m : ϴ̨:Ϊ0.55~0.65m߶Ϊ0.85mϴ̨ԡ֮ӦԼ0.76mͨ ԡ:һΪ0.9X0.9m߶2.0~2.0m ˮͰ:߶0.68m0.38~0.48m 0.68~0.72m ڳóߴ:1ǽߴ(1)߽Ű;80200mm(2)ǽȹ:8001500mm(3)Ҿ߸:16001800(ľ߶)mm2.(1) :750790mm(2) θ;450500mm(3) Բֱ:500mm.800mm,900mm,1100mm,1100-1250mm,1300mm,ʮl500mm,ʮ1800mm(4) ߴ:700850(mm),1350850(mm),2250850(mm),(5) תֱ;700800mm:(ռ500mm)Ӧ500mm(6) ͨ:12001300mmڲ:600900mm(7) ų̛:900l050mm,500mm(8) ưɵʸ;600һ750mm3.̳Ӫҵ(1)˫ߵ:1600mm(2)˫˫ߵ:2000mm(3)˫ߵ:2300mm(4)˫ߵ;3000mm(5)ӪҵԱ̨ߵ:800mm(6)ӪҵԱ̨:600mm,:800l 000mm(7)*:300500mm,:18002300mm(8)˫*;;600800mm,:18002300mm(9)СƷ::500800mm,:4001200mm(10)е̨:400800mm(11)ʽ:400600mm(12)ʽۻ:ֱ2000mm(13)տ̨::1600mm,:600mm4.ͷ(1)׼::25ƽ,:1618ƽ,С:16ƽס(2)::400450mm,*:850950mm(3)ͷ:500700mm;:500800mm(4)д̨:;11001500mm;450600mml700750mm(5)̨,9l01070mm500mm400mm(6) ¹::8001200mm16002000mm500mm(7)ɳ::600һ800mm:350400mm*1000mm(8)¼ܸ:17001900mm5.(1);35ƽס(2)ԡ׳;һ122015201680mm;;720mm,450mm(3);750350(mm)(4)ϴ:690350(mm)(5)ϴ:550410(mm)(6) ԡ:2100mm(7)ױ̨;:1350mm;450 mm6.(1)Ļҿ:߳600(mm)(2)ʽ߼ҿ;߳700l 000mm(3)ʽҷͨ:600800mm7.ͨռ(1)¥ݼϢƽ̨:ڻ2100mm(2)¥ܵ:ڻ2300mm(3)ͷȸ;ڻ2400mm(4)ۺʽȿȵڻ2500mm(5)¥ݷָ;8501100mm(6) ŵijóߴ::8501000mm(7)ijóߴ;;4001800mm,(ʽ)(8)̨;8001200mm8.ƾ(1)С߶:2400mm(2)ڵƸ:15001800mm(3)ƲСֱ:ڻڵƹֱ(4)ʽͷƸ:12001400mm(5)ظ:1000mm9.칫Ҿ(1)칫::12001600mm::500650mm 5;700800mm(2)칫:;400450mm:450450(mm)(3)ɳ::600800mm;:350400mm;*:1000mm(4)輸;ǰ:900400400()( mm);:900x 900400(mm)700700400(mm);:600400400(mm)(5)::1800mm,:12001500mm;:450500mm::1800mm 6:10001300mm ;:350450mmƼ:2017ܻӭ100ױǽ!ղػŶ!,ͻ!װ?װ?װι˾װ? ŷĴΪʲôװ޹˾ǩԼǰܾҵԤ?һװ޹,ֱ,ѡĸ?Ƽ:8000GԴ!(Ҫȫ)---- TOP20 Ƽ ---װ޵21,dzʵ,99%ղ!100Ưǽ,dzް!Ѫ:װ,Ҳͼ!ʵ¼ӹ,ǽDZ˷! ǰһǽǸ!װһ,ʧ51681Ԫ!һװ޹,ֱ,ѡĸ?ÿװ޹˾Ϊʲô? ˶!ש¥,ȻƯ!װ޹300Ԫһ춼,꿪Ǯһ!ļҾ߳ߴȫ,Ϊ˵Ľ!10Ҫסļ,ʵ̫ǿ!ΪʲôҪ?ƺ?ƪúܺ,ҲdzƯ!װ޺,ÿס?Ҫװ޺,̨֪!׵,ֻ1%֪!¹񾿾ô,ȫٵ?װ޵·,ֻ뾲 ...14ֻʹס,!װ޲ʦôһ!Ϊʲôװ޹˾ǩԼǰܾҵԤ? !¹ؼ,ɻȡʵ:ǽ | ǽ | | С | | | | | ͯ | | | ̨ | Ʈ | ʽ | ʽ | ŷʽ | ʽ | ִ | ŵ | Loft | к | 칫 | ˮ | | Ƶ | | | ռ | | | | ̳ ...·Ķԭġ,롰ؼʡѳҪ!(ǵõ ɸѡ ضȡ ,׼Ŷ!) -houseliving 138O·װ,ؼҶվŻЬ! شõصΪŻЬṩ˷,ֽԼ˵طשм˻ɫʽĵשԡ:138O::ɫǽľʵذ,ټľʵذɫĵ׺,ʹ䲻еСȴ󴰻˺ܺõ͸Ч,ͨ͸,ƵΪһĨ,ɫɴڱ֤õЧʱ,־ԡɫĵ̺,ľʵIJ輸,ǰһĸоɫ,ڷԼϲ,͹Ʒ,ɵͬʱ־ԡľʵijβ,ڼͥаڷ,ռ̫ϰڷŵֲԺÿIJ;߶ʹڳԷʱиõ顣õŵ,Ųռÿռ䡣ʱŹ,ʹ̡ܵСɫ,ʹúܺô,,ʹжź,Ϊȡṩ˷㡣ֻһǽ֮,ռµĴ,ɫǽ,Ӫʵ˯߻ͲƵ,Ҿװ|༭δ΢ID:vipjjzxάע -houseliving װ޾Բܷ8󣬹ǮĽѵ װ׳ĵطˮù©ˮȵȣһСëἫӰҺdz鷳ԴװĹУֻרעУҪ⡣Ʋֻٱ鹫ƽ̨鿴žµװƣղΪ֮Ƶаɣ1. ¶ȹˮպ¶ǺҪģΪ˸õˮĸһˮڶҪ¶͵ĵطֹˮ2. šˮܵ޿Ϊ˲ˮܵˮһڰװˮ֮ܵҪš⣬ҷܵļ޿ڣΪպά޵ķ㣬һ㶼Ὠұ3.ͰΪ˸׵ĴɨͰѡҲһҪ飬һõͰ᲻ɫƣȽϴ4.©׼ȷװҲҪ©λˮ繤๤ҪϺãòۣװ׼©ȷĽоװ֮ܺܺõķֹˮζ5.שˮΪ˴һʵ䣬שѡҲһص㡣שһҪѡˮʵͣˮЧõIJʵġשʱҪעӷ׼ȷͶ룬⣬ҪעҪҪһйˮ¶ȡ6.ͰͰװʵҪϸҪȥʩ ȣˮƽȻӰԲεˮΧשӴĨƽ÷棬ֹζ©ھIJɿͼͰΧǷյģΧִԽͰǸϺͰ᷵ģǣͰǿյģͰ潻Ӵûô׷Σ׵ζܳġԣװͰʱҪչ淶ɡ7.ڵǰͨҲãԴIJþѡPVCġ8.PPRӷ©ˮܱȺڰװĽӿںáڽǷκõϴĵطÿεİװжҪϲòҼ©Ϊװ޷΢Źںšֻٱ顿Сܰװ޷ɣ԰㾫׼ۣҪǮײ -houseliving װ׷ܽ15ѵװź װһḶ́ССѧʲ٣ҵ·رҷעЩСϸڣװһЩ£װ޵ѣһҪסЩѪѵٷˣ1ﰲװʱûпǷèλãס֮ŷ֣èѹŲ¡2װƾߵʱûпǵİڷλãסŷֲѹڲ룬ÿγԷоܱŤ3װ޵ʱǸװܷסŷÿιŶСѡ4Ҽ̨ûשһ죬̨ϵˮ©ӡѩ׵ǽϣʮѿ5ƾߵʱֻۣûпʵԣĵƣڶҪ鷳ֱװʵã6Ҫѵˮڵ棬Ϊһڳ⣬ά鷳7ĵذɫһҪѡdzһЩģ׿ҡǹ߲õĻͣСͣdzɫĵذһ̶ȡ8ڸװôƾߣ˷ǮѸʵõľôĶ˰衣9ʱѡصģÿüҵʱذβͷֻҪһɸ㶨Ҽû򣬳ڣ10װ޵ʱкܶϸҪע⣬DzİװҪǰԤλãԺʹõʱܷ㡣11һҪˮ¶ȣСԡһҪʩСˤӡ12Ьʱûпǵ߸Ьĸ߶ȣס֮ŷЬ̫߸ЬͲѥѹͷŲ¡13װǰ˵һҪװغͲҼҾװһ壬һ绰壬סŷ֣ʵǿԺ϶Ϊһ˲Ѫ14ͼˣðˮдש죬˲һ£׷ڷ죬ѿ찡3440ԪһƽҲ󣬿úܶ࣡๤ķ죬ûпˣã15طãĸоΣգͱȽϳʪһ©ôҪһˮֲУ -houseliving ҪڿԺ鷿֮,һ! ƽʱôס˵ĿԱ+鷿+ؿռ+;ͼƬ60,չʾ˸ʺϵ,ֱӿͼɺ,˵,ϣܰﵽ -houseliving װ޵Ļ鷿,ʱƯ,һ鷿! һ115ƽִԼ鷿,ҵǶ,װ޵Ļ鷿dzƯ,׷Լʱ,,Ҫķ,ֻҪʵþͺ,һͬһǵĻ鷿ɡ,Ҫӱǽ,ֻҪһܰǽ,ͲǴͨһ,ǽ塣,ԳҲ˿ʽ,߻˸̨Ҽܰסž鷿dzƯ,ͬʱ̨鷿 -houseliving ̨ͨͿݶô200ƽ׵ķӣ ԭľ ܶ˾ͻ뵽MUJI뵽ʽʱҲɻԭľּ򵥵ЩIJôռԼһҲë˵Ŀ֣ǿϲ಻ǿʹϸˮ̺޲һԭľҲę̈СݣҲǼԭľ֮ڳɶôһ˰٣Ů귽25ij˹ͬ110OĿռ乲ÿһ峿ͰԭʼĻͽṹڿ뻧ţ3̨ѹҺͲͳĿռɹͨҲܺá ԭʼͼһڵĴ⿪뻧ţԵӴ˳ռ仹ѳĴҸij鷿 ͼ̨ͨԺ󲻽ҺͲͳɹͨҲ֮Ե̨Ů˵ñԭľļ۴ӾЧǾס϶۵ϲ̨ͨغͿͨӾϸӿɹ⡢ͨ綼øƵĴڸDZ˿IJÿռά˲ĵԵǽΪؽṹܸĶҲ˸ʵõĹסǽĹӡҾߴѡ˳ҶƲͳһǰ¿Ҳˬǵ÷ͺ˵¼𣿿ǴƬİҶǽӡ̴ɫůƬ䴿ɫҶҲǿҿռչԲȵDzһ㲻䲻ȵʱȵ΢ÿռͨеļҲҪ̫ռIJʡͳһҵڿռҵϤӪƽͺķΧǶӶͳƽ̨߶85cmǽ1.25m߶DZڲij߶ľʸеijҲÿռһµķպ΢¯ĿλͨοǰԤʦϸ΢֮ΪǿǵĸԿʼʱѾ͸ԭԵ̨ñĿռûйҲӴӵذ壬ǽٵƷаͱͶȵ͵ɫʷdzʺϰͺƷζνԶ˰Ůɫǽȹպø߹ƮůƬʵƮһȦɫŮԸռŮе鷿ԭԸмߵһԱij鷿ȻԭľŰҶͶµİ쵹ӰÿռʱɳĴҲÿռĹܸñ̨ĵñŶӱԿһŰòɹʹռõ˹ԭһ׿Ĺͨǽòҵ·һЩԴźhaiһ乫ѡ˶ױȽʡɱҲǰΪ˿˵ʵɫĵשɫǽש࣬ռɾԭľ绹˸ʪʵãʸзϢͣ 3ʹ110ƽ׷λãĴ ɶװ޻ѣ30ԪƵλ֮ʦ潡 -houseliving 40³ⷿƷҶסȥˣ hizhukeﶬůůĺڶæµţҲݳƷⷿһլڼͿɹ̫ijǵ˶죬иֱ̫ⷿҸˣôⷿôƺÿأһΪⷿư01ܴ͵ⷿһ¶̨ͥԺһⷿ촰شʹ㣬ȫѼһȲ졢塢齫ɹ̫Ҫ仯ⷿʵ˾ҺܽӴʹںĶգҲΪӪһܰľһ02ܷⷿƶ⺮ⷿԹܴ磬Ӱ죬ϾһС﷢һ˵ʱ⡣03ò͵ⷿⷿΪевĩǿⷿڳԸһŴķ羰ԻƽʱڲԷĸоһ04ⷿⷿĿǰѾΪִ׷Ȼ׽ȻʱСⷿһĹ߹󡣰INҾֻΪ㳤볱Ҿ -houseliving ͨð·ˣǶȻǰ˰Ծô Ǵũд򹤵ģܶ˺ö꣬ڴһ׷ȻûôǮһ·Ҳ׶ֵģҲ·һȫԼ뷨װһ飬ͨˣ׼סˣסû죬дγʱ˵ֶǰ˰ԾⶫDzҹҵ𣿸հҲ찡ҵʱ¼ҵʱŰݷùѽסһϷޣҲܺΪʲôͻȻϰԾ̫ȱ˰ɣۣDzŰֲִãҵ׸ô찡ȸҽҵļҰɣǰʱ±ȽеļԼģŵĴЬŵ뷨ҪǺܶЬŶö˵ʱԴŪDzʵũ˶ܺÿ͵ģʲôò˶ʾҪҪƷ£Ǵе˴뵽ģֱòŷָõǽϹĻɴպһЩջһգϷĺʵð˶ʱóãǴǶһһĿҲŪ˼ԼıǽԱ߷С칫DzǺޣֱӿҵҰҺŵķ䣬DzǺаѽϲľƮˣƮҲ˺ܴĹ򣬸оDzǺܺãҹ죬̸ԣǸŮõģŮѾ9ˣһ˯ˣܺһӣҲһҪװƮڶ̥߳ˣҺҲҪСӣȿſһžͿõⳡоϾͲˣ~DzҪȥҸľջȥϵ϶ֵģûСҵͶ߶ŰҰæ취 -houseliving СسҲʽ,Ľܻ16.8װľ ʾ:ĩβȡ4+ ÿ5Դ ʽԽԽ,˵סسǵıܼҲװʽ,ĩ,п,ȥһҪ180ƽķ,װ޾Ȼ16.8,ĽҲس׷~Ϣ:ʽ :ľ:180ƽ :16.8װ޴,ʽеζ˵,˺þҪҪ̨,վŸ,˿,Ǹǵѡ񡣡ľذһdzɫĻƵ̺,סҾúе鷳׻ɫıǽ,ԳƵĹ,Ͱ˵Ρ֮Եĵط,ûϸĸϡҵص~ιҵij,һҲ뵽ʱζ~Dz,ԲβԲĵӦ,ԢԲԲ뷨ɡɹ,ĶϢ̨ľϴ»İڷ,պǶڵ·ұʽ,˸̨̨Ƶ,С,ҶҲŶܵŮ,ϾҪѧ,סĻ,ӦÿԻⲻѹڷ,ҲǰС鷿,ᵢѧϰҲܲ,ԡͰ䡣һ,°󻹿ĵݸˮ~鷿ռ䲻󡣡һ͵ɹͬʱʹ,һԻ,˻Ϣ,ֶΪ?ϲŮҵ7װ115ƽʽ,Ч֤ë!90ƽԼС,,OR?ĵӹǵһμ ʵû׬·װϹ߹˵,ھӶģ -houseliving ΪʽװޣԤ㳬һ࣬ر ʽʽļҾߴҵɫʵİʽҾߣӳִ׷ľסҪ󣬸ӭʽҾ׷ӵƷʹʽʵáִС126ƽ ã11.9 ͣظɻȡذЧͼէһǷdz͵ִذûвľʵذ壬Dz˴ʯĴש棬Ȼûôǿȴʡ˲Ԥ㡣ش˳ĹߣÿdzɫϵɫֱȹɴۣһֳȵĸойͨƿͲ廨չֳȫ۶йװޣӷ϶͵ʡظɻȡذЧͼԲ㹻һãɫִһЩĸоظɻȡذЧͼĽɫij˵ǽǽȵɫˣҲóȥʸСһظҡɻȡذЧͼ˿ķװϲԭľɫãñŷ洦顣ͯظͯɻȡذЧͼͯ»ãСӵĸԷչԭľɫÿռ俴Ӹɾࡣ鷿ظ鷿ɻȡذЧͼ鷿һٹװΣʡ˲Ԥ㡣ڱѩ׵ĿռƣڵҾѹҲûô150ƽʽȻôһ¶...ֿסլ200OķסŶ೨ -houseliving 鷿װ̫ƯˣƸгǿʽ磬һضˣ ڼҵװΣܶ˽ԼʵãҲЩϲѼװεʰ쵣һϣķḻʣװ޷ʵúܶɫģΪ׳ִ䲻ãҵĸоԻҪʰ쵻ҪĺгͳһǷdzѵģ͸ҷһƫʽĻʵ98ƽӲװȫ15أһŸ˵ĸо·ͯ磬ɫЬ񡢻ɫʰ쵵ĵשٴֲһͷ׵ɭֹ԰ԭľɫIJ輸Բӻʽɳƴɫǽڷ·һ磬ɫʵĴûһҵĸоһпôĺгź˶ɫʵĴ䣬ƣ˿ռʣU͵ijƣҲ̶ϵ˿ռʣң˵ûͣȥӿտӵĸоԭľҾߵĴ͹ʽعȻص㣡ƮľʵƮ̨棬ʯı䣬濴ٺʲˣ̨ɫʴҲֻʰˣ׻ɫʴ䣬ʵڿռҲķdzõģƯʵõĻϲ -houseliving | ׷,Լ! һ һ ΢ ʱ ֯ ģ Ϊ ʫ ¸߸ЬɷشЦרǵĿռзնͳͳ׵輸۵͵⿧ȵζڱƷζҵҪеʽÿһСг̻Ψʳ밮ɹñССŮСȷÿһС,ǰӡDzŹ,ֿʱԭľ,,ʻÿһ˲еÿ䶼ܴ˷۷۵鷿ŮıǿҵӾ伸鶯ƽ沼ͼĿַ:Ŀ:94OƷ:ʽĿ:һһĿ:ʫĿװ:ʫլ䡶 ˹ С ѩֱ ʱ һ , ɫײͷ׵Ŀռγµʱٴ¶ôԾ,ȵĺʱײ延ޱڵȷ,ҲڵɫĿ,ɫǽڽɫĵ,ɫռ,ǵ鶯ļͼҪѿռ۵ħû鶯ս鷿ԭľʱľʵ,Ƥʸᾲ׵Ŀռοʼĵط ƽ沼ͼĿַ:Ƴл԰:150OƷ:ִʽĿ:ķһĿ:ʫĿװ:ʫլ䡶 Yes,I do !˰㻶˵ʱⰮõٺϰòΨһ˰ϰǵ ˵Ұ",شYes,I do!黰......ͳʽĶԳƲ,漰ʵĴʹ÷,ӾҲõɫֻ,˿ռIJθִʽ,˿ʶʱеʽdzɫ,ůҺõ,ȴÿռ˲һǵ,ȴֳǷΪ I doĻ,ǰԽյ˫,ǵĿռ,ԭľƤݵĽͬIJʸǷḻʵĸȡԼķʽȡԼһ,ǼҸİ,ɫ,״һص,ͨһ,ҰȻ,Դ˰쵶Ŀ⴩ɴϴɫ黳,ɫȴ˰ĵǼҵůµĿռһƬʱ ƽ沼ͼĿַ:Ƹ:112OƷ:ִʽĿ:Ŀ:ʫĿʩ:ʫʩʫҨƷ ʫ δȨ,ֹת С΢:tuozhe111 顶ߵ3 ƷԤ! | ȻĦ,޴!ԽȻ,ԼԽǶ! -houseliving 35ƽַװ޻23ԭ̫⣬Ǯֵ Ϣ35O񣺼Լ񻨷ѣ23Ưҵӭĵһҵλ۸о40¥ĴڣʵʹԼΪ35ƽף¥ϸߣɹáǰԿ˶ַĵӲǣǵҵ򸾵ľ1Ȱ⿣ϣ㹻IJռ;Ĵռ䣻2Ȱ裬һҪԡ׺ϴԡã׻˵ȸСȫ3м̽ףͣϣ2˵޿ռ䣻4ҵ򸾹죬ϣϣŵĹռ䡣Ʒԭͳ2.3ƽףŵ䣨2.6ƽ׺1.9ƽףռһ嶯ߣзdzǽγҰӿһռ䡣пʱһռñռ䡣ɳ򿪣ɿԣ۵ŹرգԺԣռ˽ܣʹΪҵĸϡʪij÷Ͻǽλòԭ2.3ƽСγɳȡԭеL̨棬һͣ󻯳IJռӾͨ͸С϶Ϊһԡסԡҹ񡢴ԼɹܡռȫĴǽƳխĸ塣ڿǽ洦Ĺ̨ɹʹáһűɿĸԼش󼴷ҷֱ۵1.5׺ִ١߿ɿ򿪵۵š̬ӯϸղ輸üε̺ΪɳذȻɡҡ˵Ķߡɫɫع񣬼ṩ˵ǿռ䡣ɫСƳʹռøͬϵаɫ;߹ҼС͵ͨҡȫˡǶȥIJһࡣ̨ôɹǫ̃ڴ档Ͽ̨ƹҲԲȫ¹ӿռӾУ¹ұߵļзһ㴢񣬲ŹκοõĿռɣϾСҪǵľⰡ۵Źرʱ·ۣγ˽ܿռ䡣ʹøŰɫ˺ͺɫשÿռ࣬ӦΪ¥Դ߻ţʱΪر״̬һͨ߻ƬܽĸɹʹĿǽɵҸˣɹʱɷ£ռռ䡣ͬʱװůȣرʪʱҲ¡ů۶ʱҲõţΪĶ䣨ͬѧҪЦﴰĸСʦ˵һвʵʵöֽϻΪôĸ˽С͵޿ܣսȰģջۡ -houseliving һش,һʫ! ˵Ƿ۾ôشһ۾Ҳռͨ͸뿪֮ļؿǵ߷Χԡǿյĺ,ᶼеķ,˵ľǶôݻĺլԺֻҪӵһشļҳʵϢʵϺܶཨʦҲʹش׷ڵĿҰشͲҲһ˾ĿԲչǵҰһϵܱ߻شĸоı仯Ȼһ͵ķʽרݽķشͲŵƿԽһҵĿﵽDzߵ͹ԳڵĿ֮гʽרɫҶƮεŭ塢ĭֻҪĴ𾴡мDZյЩӲ鿴Ķⷿ+ش,ҪĶشװ,ôװļ԰ ʳ,ɾԼº,һݵĸ䷶Ʈ,ַҶһ䷿ʷȫ!ŷҾƷ,ŷŲֻ˼ס±,Žǽ,ϵȻɫ,ԴȻʦѧ,֮! | QQ : 7111980ת | ̨: "ת"˽ҪͶ | ̨: "Ͷ"˽ʷ | ·Ķԭѧƻг,ѯʽ 13760671980 -houseliving 95OԼʽС,¡ȻԵõС! Ŀúɫװ,ɫʷ,ϵװλռһĨȤζ,һЩµռζ ɳ,ʽĻľԪ֮һ,ɫֱ, ŷ̨,һǡôĸоĩ,ɳ,ϲȻ,ϲϸڴϵΨо,ŵĽϡӲ,гĿռ,ϸڵ,ȫֵ,ɫʴͳһ,Ҳǿ˿ռ!!غǼij,ǵʽ,ûвÿʽ,͸ŵļʹռͨ͸õĹ״̬Ҫһõ˯ά,Լ˿ɫ,侲ŵķ,˸ӵĺߡ۵Ů,˫㴲ҲǷɫ,ƷҲǰɯ,һλõͯС硣鷿һ,ɫӹͭɫֺ̨,ɫĵذ,ɫɳ˿ռ,岻ɫЭԺ,ҾͿȫݼҾo2o߶˶СլO2OƷζ,Ϊ!|ihomeo2o.comĶԭļɻø߼ʦ -houseliving 115O»鷿,18װҸ! : Ԥ:18.0:115.0ƽ: -houseliving 80O+Ͳ,ѿռõ˼! üұʶܰ?80OıŷСҾܸ,ѼװԼϲ,ҪԼҪ!ͼ,һ,űdz,ش,ֱ̨,һ,ӾЧǺܲŶڽŵĹùһװ,ȻǼ򵥵ĺڰ׹һ,ɫǽ濴Ƿḻ˲ٰ,˵װε׺ǡôںܶ˶ڼﰲװͶӰ,һĩ,Ժͼһ𿴿Ӱԭľɫĵӱǽ͹һװεɳǽ,ʹʱʡǶȿ,ͿĹʮȻ,ԭľIJͻһƵӴ,ɫɳͻɫIJ輸ҲӳȤ,ܵ˵,ĿռɫʴַdzС輸ϰڷų,֮Ҳܴ,֯,ʵ,ŵСʲôĺܷŶ~ֱdz,ڳϻǺעʱие,ڰ׻ҵɫʱ,Ҫǿʮָɾ,ſŶ,Ҳ´򿪱װ~˵֮и,ڲ˼,Ĵڸ,һкǿװ,ĺŶ~ɫĴ˵ҵӾ,Dz¸?дͷϵ֦һ,ǼԼ,ȴҲոպ,ɫڴλáҵ̨ڷ,һ,Ϳȹõʱ⿩ͬʱ,ɫĴ,֦ͬһ,ŵŵĴ¹,ֵǺܸߵ,ҵСҲȤŶ漰ǽˮשǽ,ÿռ俴,û?Ͱ,ɿհ,ĵơȫݼҾo2o߶˶СլO2OƷζ,Ϊ!|ihomeo2o.comĶԭļɻø߼ʦ -houseliving 90ƽʽԼС,90ֱװȫ̡ װԤ,,ޡҪ˵!ʱ̵װ޵ʱ,ʡǮĵطʡǮ!13ҵĵ,ҵԺ,ͱϸȥ,,Ӵ˾˻IJ·ϡǵõ̴6.55%,һ6000н귿ͳˡһֻװ޷ݹֱֳ:Ͱװ޹̸׼װ޵ͬǽһ¡һ.ǰ׼Ԥѡ,ȷװʦͨͼͳ˵֮װͼֽһҪ,ƾ롣װ޵ÿһҪмѰ,ʩͼ,װ޹̵IJ,֮һ仰,Ҫװоݿѭ,ֹͼʡ¶װɵ1.ȷװ޷ҪȷԼϲװ޷(ϲԭľϵ,ȫʵľ,ÿ,ǽù,ֳᡣ)2.һ·ݵĻƽͼʵû̫,װƵʱֻ˼򵥵ı䶯(ʵԺԲƵı䶯)1)ؿ˳2)СĿռ3)Ʈ˹ջö漸Ҫ:1.С,ʽ(賬ϲʽ)2.Ҫǽ3.ijҪ4.Ԥ(Ҫƿ·ϵͳյ͵ů),ѡ+һůʵġʩʩʱһҪװ޹˾Ҫһװʩȱ,ӹ,ͲҷƤˡܸЧӴ!1.ˮ(һЩʲôҾ˵,˾Щʲôɻ,ܽĸʵܹƵдɻ,ʶľղ˰!ٶȶҲ)1)ˮ·Ҫֿǵ©ȡˮ㡢ȵǧҪŹ˵߲õ©,ϴϰòˮ֮Ĺ,Ƕ͵ϵĽڰˡҼĸ,ʱ˸˵ûҪװ©,ŻؼֱҪ̫2)·ĵ߿ٸҼǵõʱװ޵ʱһĸҿ̵ĵ߲,аѵȫ,һȥؿ,̵ֿǽϵȼ,ҲǴ,˰ԶȼʵһȼĶ,ĵ߹ʰʰҲ֪ô3)IJӶ,Կײȫһײʲôλ,ʵȫ̶ı׼,еϲ,ڿ5.1,ôһʼҪɳԱߵIJͲ;еϲڿõ԰칫,ô輸·һزܻʺ;е˿һӦȫ,ôԤ㹻ǽԵúбҪIJβͷҲһܲ,иƻ״̬ļҵֱӹر,Ӷӽܡ4)Ԥ߹ܿ߱ø˺ڿܻ漰ļҵ,ӹܺܺõز,ô߱ķdz,Ԥһֵ߹,ø߶ǽͨ,Ŀҿȥࡣ5)ֿԤϴλõˮLZź,ʱܾԼڼĻ᲻̫,ϳС,ϴռ̫ռ,ڷֳ극ԲÿϴɳϿӼֱҪ̫ҸϧҪװĻɱʵ̫,ϣװ޵ǰھܿǵ6)ǰþˮװˮǰþˮȻ,װڳĹܵռôĿռ,ˮռ㹻,ˮǰھֱӸ㶨ɡ7)λԤ,˿̵䡭Ľ·źֱӱ,·òڵӹ,û,µӹܵסȷʵǿ֢2.ˮש1)ˮʩˮõǵ¸ߵķˮײ,۸ȻԹ,ˢͿ㻹Ǻ˷ĵġ˽ȡˮĵطˢȡˮ,ˢ1.8m,Ҽʪһ8O,ײ㹻,˫ʮһ,רͻ,µڶ͵,ʱЧܲ~2)שڴשѡ,ҵĽ:һƷƸ˾ü۸е,һƬ600*600ȫɷ¹ש۸λ,ʵȴ(Ϊ)ѡ˶Ʒ,ȷʵûʲô,ϣָܹһ600*600ȫɷ¹ש,30һƬ,שһжǽ,Ч~۹,˸ƫ¹שº͵ɫͷ⡣ܰʾ:ʵשǿõש,ЩשΪʽʱԭ򱻴,ʵȴ,ڿĵطԴװ޳ɱҵʱõ60Ƭ300*300ש,ʱ߹ʦ,绰ȷЩשҪʲôط,ΪɫһµȫשģͻDz,ԴשʱĹ~ȫשΪѼӹԭ,ûɳˮשǽǷdzι̵,ԵôשڶԵ¸콢겻ӡ,˫ʮһҵĴש,ʵ֤ճ϶ȻǺܲġע,שǧҪˮ!ֱˮʹüɡ3)orڽԼԤĿ(仰Ӧÿüˡ),ѡ,Ȼ˫ʮһڻ¸ߵ,ס,Ƽ,ˡھӼҵȷʵĽʵ,ýԼԤĵطҪûǮ3.ľש,ǽ桢ƽǷdzؼľڡڿҪͨĻ,ΪͬĹӽʹϰҪɲͬĸ߶,¹ֱӹϵŲ㡣õñE0ӣ,ʵ깺,255һšľ˹5000,ĺ͸һ10000,һ1w5㶨ȫ,ʹXX,۸ֻܸ㶨һ,ǿٻ塣4.ǽˢԿ,ѡǰȵĹ,صıҲȷʵҴһ,λıһַԱѵһҹר,Сúܾϲ,˫ʮһ˫ʮµĺôͻԶ׼,ôȷʵˢûŵζ,ȩҲȷʵ;Ǻӷһ,ָһһӡ,άҪʮСġ׵Ȼ˵¸,֪ص7һ,¸10!Ը·ң֪!(ɲƹ氡,ĵĺܺ,άȽ鷳)5.šš񡢼ɵЩƵļҾ,ֱϵ,Ҳ벻ľϲ!6.ˮ缰ƾ߰װһ,װ޾͵β׶~һҪעǸѡļҵӦĹӵijߴ,̨ϴ¹ڵƾߺͿ,Աֱһһ,ҪʲôĶСѡȫϵʵľ޼ĵƾ,һ2500ȫ㶨Ұװмַ´ʦĵ,۸ҲޱáͲƺ͵ƴȫŷ콢,˫ʮҲǺüֵۡһŴ辭Ƶ510ʵ̨,ߣ,һëҲûС7.ľذװѡľذʱɰҾһʱ,ʱϲذ,ϾΪļذϰȻȰѡ񸴺ľذ,ذڸ׿,Ȼذʸ,ҲաűŹ,һؼ۸ϵذ塣סֻǸϵذ巽,ʪϰҲûɶ¶,Ҿ߰ȥҲûʲô,˵,ǸϵذۺôʺҰɡ8.Ҿߺͼҵҵʱ,Ǻʵݵ!ĿԵ!(ûĹ˲Ƽ,Ϊ滻Ǻ鷳)Чͼ,һǾһеĸֵáӴ!װЧͼ,˵ô,װDz֪,װ޵ĹĺܷǮ,СëһݶҪ㿼ǽȥװ޺õķijɾ͸!ܻ(ע̨ظؼ)ظۡ| 鿴װԤͱۻظơ| 鿴ŴƷظ̡| 鿴ϸװ˽װϢ?֪װ޷ҪǮ?½ǡĶԭġ10뼴յϸװޱ嵥Ŷ!ѻȡ4װƷ -houseliving ˬ,յļֻҪɫ APP Store һļҾ־ɫ2017ɫ֮һ,ůů,һζûɫ,ֲɫŮװĵͥԺ,ʤաٱĻκܶ,ɫȴ,˸ʮ,һ̳ΪͥԺĽ㡣ΪͥԺ,شҲҪɫȾһЩٲݱ֯ƷԱ:!һֻо޼ֲ,бҰ״̬һ,ʱ,ΨзĻҾ߿֮䡣ⰻȻ,Ҳ,ֲ,ɫ΢еijġɫɫм֮,ϸ֮ӰΥ͸СïܵҶһ˽ܵĻԡ,ǻعȻij嶯,һµԡȻǵ㾦֮ʡ༭:mademoiselle.sӰ:Alexander van Berge:Cleo ScheuldermanPChouseҾ־Ȩ ؾӭת ע>> չĶ <<>>챱ŷ,ôٵЩװƷ?<<>>ŷ,һ<> չĶ <<>>ߵĴ,ɫļ<<>>ò,ļ<> չĶ <<>>ľṹLoft,Ķ֮<<>>ŷϤС<>>>ĶȫIJ鿴:̫!סļԼ繫Ԣ,û֮һڰ+ľɫ,䱱ŷʽԢ,Ƹк,һ/װķ48ƽа̨ԡ,ʽͥԺ,йδݳƷ -houseliving 90Oִʽ,ȴ̰! ʽ񾭳Ϊ߶¥̵Ʒ,Ʒ֮ԽԽ̵ܵ,ȴ̰,׷ɺԵʽ,ǡǡԵĹͲĸڶȺӻҪ󡣱ǵ͵ִʽȻͲ,װЧͦ,־µСݻ,ֳִĸװԴ̨С鷿ʵʡȨԭ,Ȩ뼰ʱϵ! -houseliving ˯ǰ,µ·? ˯ǰ,µ·?ķڴ߻һ?ǰֻĴŮܹ,޴ڴŶӵβڵ̺ϡȻһͷ,ˢֻ硣ʱһһ,пûüϴ·ͶѳСɽ,Ҳ̶áûһطרŷŻ·?һЩҲõС㲻ò֪ ɿʰµ·ٺò,Ǵӵĸǿһ,ϸӿԳ䵱ױʡҵ,򿪾Ǵ洢ռ,ռطҲҡСӷڴβдβʵЧ!һӾ¨,ûиӵƤ¨Ǻܷ㡣:õļҾӷһ,ʺϱŷϵ԰ϵ,ҪԼҷѡʽ¼пռĻԿǷһʽ¼,дϴ·,ţп,Ž¹Ҳ̫,ͿԹ¼,˯ǰµ·,ֻҪڸϾͿ!ɫ¼ܿȥ,ܰٴȻ,һĺɫ¼Ҳǿᾢʮ㡣¼ԱȻٲȫ,ʱ,ڼҲҪա¸˻ǹҹǽ,˵ռ,СͿѡ¸ơ:ʽ¼ܶҵĿռҪռСʽ¼ִѿѡѡꤿƶĿʽ 糿վ˳õϵ·,߶Ⱥ,Ҳ˳֡Ҫֵߵװdz޵ġϲ·,˯ǰĶҲ־ܴȥ,Լֻ̨,ҹҲ¹߰ʵǾƵ,Ҫ浱ɴܡһְĿԵ,Щֲ,ǽǻռض:﹦ҲҪ̫ණ,һӵij,ڶǷ۶ȡµ˯¡ðŶ β һڴβij,ܷ,ױ,ϢʱҲܷŹҹ ֹؾصij㴲β,ϻƿָ˯㹻ˡInsϺܻĦҲԳ䵱β,ڴβʵá:ͬԡͶ,Ͷ׼׵,ʱβҲû!㻹ϲ˹Ҿߵɻг,װ500O AirbnbСƳܰʹֲ!,Ӹһ,˲70OļҸָ/ Leahͼ/ 程㰮ҳʶͼжά,עڼҡάӶTRENDSHOME -houseliving լˮʵеһЩã߶լˮȣʵղ μҵıΪҪʲôҪעףΪһҪȻſЩܷЧк֢μҽɣ̲ɱţͷͬСˡ ̲ɱɢҲ񲻼áɢ峣вʡҪĻľ壬ڹȺõĴезӦУܲһӰ죬СʮУһִ˯áĴ˯˯ã㱡ڲɳšԲɣӰ졣ͷӰ졣嵽Ͳá3ƻˡʹˣҽš̫Ҳɣ¹ңҹΡҲɣߡ黢ûп϶˯ܴϷҶ˯ȡݴ²ɻײʹһľ壬ľ߹ϥǣΪ˯˫ҽԺܣײ ޴̲ɶվӣӷ㱡ߴϲɿ촰װвƲ촰򲻿ڿ˯쾮촰оӣ׳ࣩ֮಻ɳӰ졣Уùӵһ¡ҲɿšָͨȥšͥͣǷǶ࣬ࡣ11ͷҪпա12Ҫڹ۲ȫլҪġλչһˣܻһλ岻ãʺڵ ϰӰϾ֣֮λһ֮գʱһӲݣ Űż٣ԸΪʺϵû˯һ𡣰żٰլɽģǹ̶ġ˶žšǬɽ޿ɽǬҶȻ֮ܳġլǬݣšſڴҪעסԪŰԪţżΪ̶ģģһһ䣬߶󡣷ÿ仯ڹ̶ġͲ̶ܿİΪΪݣΪΪ??䶯ΪͣųİΪͣΪţ빬Ϊţ룬áΪţڿţáԪǬ˳άԪ𿲹˳Ԫ޹˳ά2001ΪԪޣҳ㣬îǬ㡣šҪݷɽϸֵ24ɽ֮ɽ˳Ϊά֮ɽΪǬɽǬɽΪǬǬΪάǬΪΪΪ˳šţ˳ţ˸˳˶žǰްܳáһҪףҪɷȻþǰ˽ⴲ򡣴ͷΪͷɽУɾǣйǵȽϡΪ5ôΪΪޡǰһҪҪ䡣紲룬ΪΪȻڷ̣ںηΪţǣΪޡ䡣Żţʾ޶˸鲻Ǣٽ̥䣬ΪΪޣˮ˻һԵࡣٿλ䵽λľִ֮Ȳ࣬飬ӣӲԸҴšҴΪΪޣλǬΪΪ䡣б߻أãdz͡Ϊ޻ãΪʱֱášΪΪ߳ҽΪΪޣз䣬̫̫˳̫̫ġλҽ𣬴ν򣬺áҪλɵǵˣ򲻿һͿ֪޹ϵͨ˯3º̫̫֡дСһλΪ̫񡣱꣬Ϊ̫ڴ̣һͷ÷̣ܵɵȸţҵ̫λзʱͷͷڴλϺɻԭλ˯̫λ пھ꣬һλ̫λ꣬磬ϹΪ̫λ̣ҵ̫λ簲ڴ̣ҵ̫У̫λ̫һʹôЧѡҪעסŮ̫λ̫̫꣬ɥţ̫ٷƣ£׻£ͣ꣬3λ̫λ̫3λ̫񡣶լԢ¥һ໧ûһ졣ֵѡ͸췿ͬҪɷĻ־ӦĻϡ¥ļ׿¥򡣿լڵܣԺòòλ??¥Ƿȶ仯̫ԻȶҲȶбȱݡլ㷨ѡլ¥IJá¥ǣֻDz伪ѡլֻ¥ݿȥһ췿Դ¥µķֱӵ¥ϡǣ̰ľǣҾǣǣλľǡǣƾǣǣ»ǣɷˮǡҴμٴΣλٴΡ1¥ڽ¥Ŵ¥ţΪ׼¥ΧͬһΧǽڣ͸ˮΧǽ㣩Ϊͬһػڣ¥һ2λ3ԣ֮ǣ¥򣩣ԸΪһǬӾ壬ӣӻ죬ӾӾ¥룬λꡣӡ??Ϊ¥Ϊǣ˵һ¥Ϊǡ4ܲ5ʱɷԡֻһһԭǼףײ5¥£ÿֻȡһ÷λľ¥¥·λáһ¥ΪҾǣ¥Ϊǣֻһף¥Ϊɷˮǣ¥̰ľǣλã¥ǡIJΪáλӾŵ¥Ϊľ4¥Ϊᥥ·λáϣ6¥ͬҪ˫һΣ¥˫㣬ý8¥˫㣨8ȫãŲϰȫáھȵ ǻǡ߲㣺š壬Ϊ»ǡһΪǣΪǣΪǣIJΪǣɷˮǣľǣ߲ǡ˳ţ֮ǰǡ˲㣬šλҪá8¥˫㡣ľ̰ľľṵ̂ľһΪΪңͬľһΪǣΪǣΪǣIJΪǣɷˮǣλľǣ߲ľǣ˲ǡ9¥ϰ9¥ȫ8¥ķʮ㣺šԣӣҴɷˮǡһΪɷˮǡˮľľھĸȵȵǣȵǡ˳ŵľΪλľǣľǣIJǣǣǣ߲ǣ˲ǣŲɷˮǣʮλľǣʮһľǣʮǣʮǡ˫еķǺףţӻ죬λӦŵʲôΪһ㣿լ120ֽΪἰѰʮҪһɽ5ֽ5пмûдûдIJáΪ켺ɹķֽ𶼲ãͨϲʹߵΪɽ97ͬ׻֡97ͬгɷ֮࣬ͬ׻̲֡ģˮ۲·ˮˮںõķλˮҲΪáˮʽ٣϶µٲˮ̫õˮϴҲΪ٣ԽԽáʣ׸ɵûˡǣǣɾɾһǣҪ峺Ϊǡ̲̫dzˮ̫٣ʯͷõļ̫졣lˮˮܿ峺ģгζС񣿣еˮɶҪ֧֧ˮѷֻƽȶѡȻˮΪã˹岻ˮҪͨլͬˮˮʽˮ֣ǰˮϣˮֱլ˺Ҫˮ֣ǰˮֱ䣬ҪڱõġſػաˮķҪЩȥˮķҪխЩȥˮˮȥˮйأʯͷϰ˳ˮ֣ǰȥDZȥȥбˮᣬ˺Сֱȥ˺󡣱ˮ֣ˮǰǰһ־ǰ棬լ󼪡ˮ䡣ˮ֣ˮԶʮǧ֮ǰγһڵij򲻼˫أ˫ΪޡЩ˿⣬вмˮ֣ˮǰǰȥûоǰǡ֣ˮľ֡ˮʽˮཻ֯ԽˮԽϰ᣺ۼΪլլˮһլ棬լһˮ֮ϣֻҪһΪַָ䡢ȸ׻֡ǰֱ䣺ǰֱȥΪסʮˮլΪլΪլɽΪ˫ɽСˮáֽк鷶Уɱң𡣼꣬ˮδ϶Ǭîȣľ ˫ɽУԡڴٹ١˥Ĺ̥ɽ鷶ˮͬӳˮ֡Ϊԡڴٹ??Ǭɽ鷶ϳϽ֣ȳԡ??ˮҪɣ˫ɽתɡֻɻǿɵ֧Ǽף򿴡ˮֱӲ飺??ˮϣ뼪λȥˮôӲλߡʮˮ̫áԪˮܳȻ·ΪˮмطǣλλλˮҲǰˮֻ۶ѡλе߸ˡȣ԰λˮǰͿ˵Ѿܵƻһûס˳ֿġҲλڿ̣ɷλŵˮм䡣λвͬĽɽ١ϲɽ϶ࡣλɱݡţָǴţ嶼ȡƻұ߲Ҫ̫ƽط̫С԰ѻ߿£ебûл߻ʹ߼ҡ壨λȥ·塢ڵȣգλλϲϲλеλױȽϸߵҲϲλ治ǷԷλķ¡λҪȰμҪףλáǷ㣬ûз£λġǷѾסլԱΪ δ ʱȿտκòãΪȿûгɱУظ裿壬壬ûСɱӳɱδϳɱڳɱڳ󣬺îδɱ硣ɱδ֧Ϊɱɱɱɱй˿ɽ⣬һɱһ˽⣬ɱ˽⣬ɱ޽⡣ɱʹн⣬ûDzҪáδǸɱȻ֧ɣòạ́ị̣̂δ̣̣̣Ϻ̡ù˿ɽ⡣ҹ˵ķУͷɷϳҪעͷɷӳɱδδ˲áϳɱڳ˲ãɱڳ󣬳˲ãîδɱ磬˲áտμϷλļסУֳƼУîϣδͬʱһģټӦس֣򲻿áîϣм׸á׸˼îϣáɣ??ϾֵĻͷɱǿһɥеһ˶ܷլգңҪ廢ɷƵȡû⣬ƵҲɰᡣҪװޣլҪװޣδ廢ɷδֻҪȵδͿԣӼķλյҪǴӼķλˮˮۣ֮п񣬸ų֮֮ˮų˵֮֮Ѩų˵֮νصѨΪ֮ˮΪ֮ˮֻӦ٣ѨӦٻһԶ͡ˮߣȻˮַãˮΪ֮ѪˮУˮ֮طᡣȻˮֽԿ֮ˮмףˮڼλ֮סˮţȥˮƵػ˿ػ˱գҪϺˮ·ͬˮΪҪбˮΣУˮʽˮߣ١ʡǡ̲lˮʽʽߣˮ֡ˮ֡˳ˮ֡ˮ֡ˮ֡ˮ֡֡ˮʽʽߣ֯ᡢ䡣ֱ֮Э뼪ʼˮ֮ˮʽˮߣ١ʡǡ̲lˮʽʽߣˮ֡ˮ֡˳ˮ֡ˮ֡ˮ֡ˮ֡֡ˮʽʽߣ֯ᡢ䡣ֱ֮Э뼪ʼˮ֮Ű˴֣ǬȻˮΪˮֺ٣˸Ǿûáȥ򲻼ܣ򲻼伪ȥˮף¥ǽԲȥ򲻻׵á·ɵˮԼڵˮ㣬ȻΪáˮҪլǰ㣬Ͳ㡣ʮˮɷɱλȣǰˮλŰ˴֣ˮķλˮȥˮλǵˮɽΪҪˮɽˮɽˮΪɽˮΪӡָԪϸֵ24ɽ֮ɽ˳Ϊά֮ɽΪǬɽǬɽΪǬǬΪάǬΪΪΪΪɽˮԣмîɽîˮΪˮDzģҪʹɽɱΪɽˮжˮ:յ:˶,¡. :˶,ɢ.ˮ:յ:ò.:Ʋ,ƽ.ˮ:ɳ.ٷе߸,ݽ,·,,,ˮ,ˮ.լ,Ѫ,.ˮ:ɳ಻.̷е߸,ݽ,·,,,ˮ,լ,Ѫ,.ٷ:е߸,ݽ,·,,,ˮ,լ,Ѫ,.λλˮ:֮,뷿ͬס,򷿿.ɷ,ɷ,ɷ:е߸,ݽ,·,,,ˮ,լ,Ѫ,.ˮ:յ:,Ůǿ.:.ɷˮһˮˮ֣ˮۡîδˮӳˮîˮϳˮˮ۶ȥһӦˡˮδ۶ȥһӦ֮ˡλȥˮˮеŮ潣Ѳصˡˮ֡ࡢԵơ²˳ȣԸԵ΢źţ17065117058 -houseliving ģķˮھʵã ڣҼ׼ǣڿഫ񡣷ˮҲвٿھ԰Ǻ׼ȷжϷˮûôһɡѧɷ⽨ţΪ峯ĩ֮й󱻰˹ڵдͳĻ⵽ɣѧ֮ѧִͳĻΪɡ1ǰƶ󸻣ҳ¡ǹĵطǰİ͵֮⣬һЩƷסլǰҪٶǮҲľ֮󷽵סլͺƷԻḻ2Ŷ̨ưܲ۲ơΪɷǴԺš󴰡̨һֱߣڱ˴ԣȥȥԲ۲ơ3סլ͹˶в֢סլλӦͥͬԱլβȱ𣬶ӦԱͻн⡣4ȱĸףȱϸȱСڡԺ췽λΪĸףΪǬףΪޣУҲӡ5粥ףסȥԴλɢ£λ˿6ŶԴţײʣҺãҳơԣһˡ³ྭҲԣһˣŲ壬һסҲͨ˵ġɷҲСȸɷԾǹ̬ȣٴ͸7ʻУ˲Բ˶̼࣬ز8ӶԴ󴲣ҽæ辵̡ͷɰӣױ̨ɶԴͷ9ǰ·ϣաΪɷָסլǰзεĵ·סլҶ档סլǰзɷ˥˲Ե10סլǰխɡƣǰխɽ˵լԺǽܵĽΣխ߿լԺγɵгʢøӦ11סլڶ·Ľ洦סΪ·塱ҲС·׶򡣿ֲǽ̩ɽʯҵ⡣12ݱ޿ܲլΪ䡱λпûкʵߴ֮˲д¡13ƳЩɰ˶ˮơˮƾۣˮɢɢָլǰ࣬׻λլǰˮֱйСƳ֮ƣ򲻾۲ơ14ݶΪڡ۲ơ15ΧǽȸڡסլΧΧǽӦø߹ӣӦÿȸڣ൱16ͬбһסΪˮ༤׳ɡˮδáֶ֣֮17żǮڣ18Χǽװ˿ֲû19ĹӦѰ·20ӵǣаס21ŶԿݣŮޡ22иװޡǽ϶и23λӷ룬ʹ¡24еҲ辴λլ25ѻ򸾣סլλŮƬҲɰڷһϷλƬҲɰڷһ򣬱һ26סլڷŸ޵ĵ񣬷ɷ27ڵĵӡԲҪ봲ͷ 3֮ڣʹ˶Σͷ͡28ǰ̴ס29ڲҪ̵ͱȣѪ֮֡30Ĵǰл31סլǰ԰԰ס32컨̫ͣסѹ33ſԲɶѷžʯرǴк˿ľʯᵼ¼34ϽDzɷž޴ʯͷ򽨼ɽ35סլǰխխС֮һΪլ˶Ů36סլĻʯ¶ƶʱ˲á37סլǰɵظߣмߣǰˮȥ޺38ݣ麣ܣ39ͥԺӲݣ֮40ǽߣɥ֮ҡСŴƲƿڽǡ41ǰùͷ̸ףػˡ42ԣǽߴ󴰣׻ڡ43ɳһţм𲢷ţಢ࣬޲44¯ɳ꣬겻¯ѼƣͲɰţ¯򲻷45Ժڲֲѣ࣬ݹ½л˲46̫С׬Ǯ಻ˡ47ŶԳ壬У޳Ʒλͬ48լŲߣ֪պ49סլдף·١50סլǰһҪϽǿ׼ӣϽǿŮк -houseliving 124OԼȻִԭľ,޾ռ~ װ ,ÿѼǰصļװơʩĵ,Ϊװĺð߹ܶ·,ܶ羰,ȴķ羰ԼߡǸ˺ܶ,õ˺ܶ,ȴƷʵ,ҪǮĶ,ҪݻͶ롣Ǿ˺ܶ,ȴõǴ롣ΪÿһΪŬ˶ֵӵиõΪÿһڼҵ붼ӦñԴ˰Դ:ҵϲɾˬʽ,ŰǽԭľҾ;ϲ޾ռ,ռ򵥶Ȼһ,ֻϣҵϣҲƵ,ÿһڼҵ붼ӦñԴΨϵɫ,رȾ,ȻľҾ,ͣ͵뷨,˲䱻Ⱦʵɳ,伸֯,򵥶ӡɳԵ,һֲ,,ĽҲ~شʹľԪ,һžܸܵȻ,,ʵԺֵǰġIJúܼ,ӵƾߵζر,Լ򵥳ʹùȲ,Ȼ,Ҳܽʡռשǽֿ͹ĴȻľɫ,ֻȻɵ·顣ɫ,ȥdz,Ȼñ,ʵ!ױ̨Сϲ,غܶŮҪһ!鷿ͷͯС,ǺӿֳɳСظʪĹ,שѡÿַԴ: Ӱ:Ʒ֧:ɽҾ,ս,ľ,ŵ˴ש,籲,ʦ,¬ɭذ,ɭ װĺð װ΢ź:decorative-designάʶӹע -houseliving Ҿߵijߴ,װҪзִ! ӭעƽ:ȫװޱ,ÿһƪװ޼!ֳļҾ߳ߴ,Ҫܳ(ͥе)Ҿ߳ߴ,һЩȽϺļҾ߾Ͳ˹!,ÿҾ߳ʵеijߴ粢о׼,ܵijߴο!λѡʱ,ʦȸݻͳߴ,岼ַ!һΪ뻧źĵһռ,ҪְܾǻЬ,ЬЬرȽҪļҾ;1Ь:40-60cm,߶40-55cm,һЩС,ҲһЩ25-30cmֱСԲ,ΪʱЬõĻ,Ҳͦ;2Ь:ЬijߴҪǽ,ǶװЬļͥ,߶һ㶼ǰԼһʹС,ֻҪҪЬĽһ35cm,ȻЬλҪͳѥλ,ϳЬŲµ;ߴѡ,һҪݿɳ輸ӹ岼,λҾ֮ǰ,Ҫ滮Ҿߵijߴ,ŷֲþ!1ɳ:ٵɳ:80-95cm,85-90cm;˫ɳ:126-150cm,80-90cm;ɳ:175-196cm,80-90cm;2ӹ:ȸݿǽ,40-45cm,߶40-60cm;3輸:60-180cm,45-80cm,40cm;4Ǽ:70cm,60cm,ݷ;һ˾Ͳ͵Ŀռ,Էʱ̫,̫Եÿ,в˲,˲IJҪݼͥ˿!1ĸ߶70-76cm,Ļݲݿ,һ㳤70-240cm,85cm;ԲĻ:50cm80-90cm110-120cm;2:76*76cm,߶45cm;3ƹ:ƹĸ߶һݰڷžƹλõijߴ,һ30-35cm;4ͱ߹:ȸݾ,40-60cm,90cm;5:Ҫǽ45-50cm,߶45cm,ȵĻһʵ;ġΪĿռ,ǸƵռ,ÿһϸƶܻδľס,ijߴýǡ1̨:60cm,̨70-80cm,ϴ80-90cm,ĿԸʹߵ߶;2:ĽһСڲ̨,ȸ߶̨IJ;3:30-35cm,߶ȵĻ񶥽,ײ̨75cmΪ;Ȼÿųߴ粻70cm,ž;塢Ϣ˯߿ռ,ļҾҪǴ¹,Ϊ˱ҿռʶ,ijߴҲҪպù!1:200cm,120150180200cmȹ,45cm;2ͷ:40-60cm,35-45cm,50-70cm;3¹:60cm,߶ݾ廧ͳߴ;4ױ̨:40cm,70cm,80-130cm;鷿ִС,ܶ඼׶֮,ֳߴͲ,ҪܹƷҾߵ鷿ļҾ߳ߴ硣1:75-80,55-60cm,һ㵥110-120cm,峤ȻԸ鷿ɶõ;2:45-48cm,55cm;3:30cm,߶30-40cm,񳤶35-80cm;ߡϴ衢Ͱԡ֮Ҳ֪òҾߵķ,ЩƽҲ˳!1ϴ:80-100cm,48-60cm,68cm;2Ͱ:75cm,35cm;3ԡ:122152168cm,72cm,45cm;ˡ̨̨һϴ»ϴ֮ijߴ,ϴ»صϴ»Ļ,ô߶ȻҪһµ,˷Ųȥ!1ϴ:60cm,߶75-80cm,ȸݾ̨ȶ;2ϴ»:ϴ»ijߴ,ע߶ȶҪһijߴ,ϴ»ȥ,ϴ»ijߴԲοﳣн!3ɹ:Ľ30-35cm,׾ϴ̨75cm;̨߶ɹ,ôԸ;(¹60cm,Ǵ30-35cm);֮ǰ,ƽҲһƪڼﳣ,ȤѿԿһ¹~ںװ޸ɻ,ݴ!ӭ·Ķ!1ľשΪʲôԽԽ?2ҾҪܰ,̺?3̨,ҪЩ?ÿһƪװ֪ʶ,ӭƽ΢ź:gujianvrenƽԭ,ںתػظתء -houseliving լ,ȫʽʱ! йĻǿƻعʱҲÿһλ˵ʱDZ,ڷزںܶඥĿ׷תʽDT.͸ҷʽ԰־1 򾰹:˹ƼŻǻõƳľլĿʾΪʽʽԺ,Ԫض,שʯġַͭḻIJα仯̩ɽʯʵؼӲ,,ӡԾ,ֱӻԭ˴ȻۡĿѡ˶̩ɽʯֲ뾰ʯķô,ֳɽˮ⾳̺ͭصĶ,ŶȡǶԲͬͭνϸͼӹ,شĿռĶϡ2Ͼ³ кɽ:Ϻ徰³кɽĿ,ȫ´¥(ɽż),˵ڲȡ2500֮,νѧȡϡΪ³ϵϾصĵڶƷ,³кɽõؿʼܵĹע,ϡɽۻ,кɽһȫµĶ--йԺӡԺ뻧,һһ;Է,˺һ;,԰ͨġ3Ͼ³ ɽ:ĦλϾ·ʯʨ·㴦,ǧϽɽ´,·ǻʼ,ֻдٹˡ󳼲ſɵ˴,ѰղڡĿѡַĿ,ĴɽˮȻ,ڴﵽ֮⡣ʦššȡ,йʽ,йʽ,ӪݽǸ,űйͳž,ṹ,ˮƽ,ʵڡ4Ͼ ö:ɽˮȵԡȫΪƸ, ö̾ڳǶϽɽ,԰ļȻ,ֲ˴ϾصԪ,ͩӣ÷ֵ,ýζӷïľ֮ал԰԰ƺ,ϸ΢֮,Ӫһʡܡ̬ռ,ԹŶšԼе,ڸž۵Ƶӳ,,ҹ,ů,İ5п ɽZ:ʵʱĻʼ԰Ǹ԰Ľ,ڹܺͲϳֳǿҵׯءʽظСȡ˴ͳ԰԰ַ,ռȡ:Ժ䡱ؾַۡ,ΡϵIJϽийٵĵӡ[ͷ][ձ][ǽ] һĿռϸ,֤ĹӺݡŴ˼ɽˮ,[⾳][Զ],ϲɽˮ֮,֮ǽȻõձڡǽ桢ͤ¥ǰӪ΢ء仯ƽ档6̡̩йԺӾ:̡̩йԺλڳֶ,侩˺Ӻ,81000O˺ԭӵ,Ȼij̷,ʹԺӳΪĵһдԺйԺӳּ̳ж԰־,԰ַϴﵽȻ滭ѧͳһԺǧ,̨ͤӳˮɪ㡢ʫ黭⡣뾲ϡʵֱ,й԰޲͸Сǽͥ,ͳԺ佨Ļڡ̡̩йԺӡ,ԺΧϸԴ֡ȫ԰ȫľֵ,ƾװɽˮ԰,չйԺߡĴ⾳Ӳ鿴Ķ ľդʽװ,ܶѶ,ܴʷʬꡱʽͥԺ,ŵԺһʽ,鷿ƽⶾ ˵ʽʽװλ,ԽŵԽʱһֻ,һʦ,һ߹50ȸ߼,ˬǸ߼ڡͬʽϨҾװչ+ŷ֮ѧ ˽ -houseliving 120Oʽ| 18Ӳװ,װ˵ʮɶ! ʾ:ĩβȡ4+ÿ5ɰļҵĵڶ׷,ڵһװ޵һͷˮ,ζ˼ִݡźӵij,ԭ70ƽ׵ķӸס,رǶĶ֪ȥ,׿ķӾüˡ120ƽ,ʽ,úʮֺŶ~Ϣ:ʽ ::120ƽ :18žܿǽϾѡĸŵװλ,ϴɫĴש,ʽζŨ,ʧҵܰСӲ?ˢ˻ɫ齺,мĸоǵɵ,ûнɳڷŵƮĵط,ճռڷŽǹ,⡣Ȼûд̨,ƮҲСʰ~ɳ,ǽҲܼ,ܸҵĹСŴһ9,ϱԺҲõЬӺ·֪ȥˡӻǽ˸лȵ,ռһȤ,Ҳ԰ϼСڼ,Ŀһôһ,֪ô?ʽ,ȫðɫ,һ⿿ռԵøɾˡǽСשװ,Ǹ뷨Ŷ,ǽDzŶϱ˵óԷζһЩ,Ͳַͬ͵,ɫҲһ,ЧǾû?Ȼһ,ڵ滹Dzͬġŵ,ϸŵ,дһݳȡΪҸ͵ĵƾ,ҿǹԱ򵽵ġҡܰ,ԵµĴɫϸŵĴǽֽ,ռиǿҡ¹ɫʹɫһ,ڸŷ,ǽֽѡñȽܰķɫ,ʹռӵܰTIPS:ѡֽͬʱ,ҲҪ˽һ»Ĥ(ǽ)ۡʹù,ǻֱӰֽʩͺЧԷķԾͲһ,СһЩΪ,ҵתǵǽ,Ʈ,֮ءõԡ,ϴֱ̨,滹רһС,ŷЬƷһ˵ȫ㹻,ԭСƮŷϴԡƷ,ǽúͳһСש,ɫº,Ҳܳȡʴ112ƽ鷿6wװ깤 ȻԼĽ๤120OԼŷ鷿,㿴Ҳ˵ÿ130OԼʽ32,ھ˵̨,װ˸ͬ124OԼ25,ЧôþȻ˵ֵ,Ȧ!!Ҽʹľذ ²ϲENDװʿں̨ԻϢСἰʱظһ!װװü,Ҹ￪ʼסάע!ԭĶȡơ -houseliving 316ʽ㾡ԡҿռ䣬õװ֪ʶ ԡҼҾߵѡ񣬿ں̶ܴӰԡռЧʡƺڷŵõļҾߣԡƷҲ޶ȵؽʡռ䣬ҽԵҵĹߵʵãյõӾЧ1ʽñ߽ǿռļҾߺܶԡһijһǽûװ߻ƷռݣʱǽĿѡһʽɹǽܹòԡƷ2ʽѡһõļҾΪӭϣܳÿռҪںܶԡҼҾ߲һܣͼʾΣпԴ򿪵ĸӣһǾӣڻױ滹ԷһЩƷһЩĴĹҲˡǽ·ռõ3ʽǽ·ռԡﰲװǹǽʽ̨ʽ裬ô·ռ˿õأ氲ÿշƷԡҹdz÷ʽܹڱ·ĹߵӰռе壬ԵúܹڽռʹòͬҾߵ4ʽڽռʹòͬҾ -houseliving 70ƽִСʺСڵļװܰ ˵Ļ˵һ䡰СϷӣûеݣÿ컹¥ݣΣϷӵĸֱȽϲװв˲ٵǽ壬ǿɹȽϻ谵ôʦΰСķӸһ黳ƷλˬǿСأ棬һ~Ϊͽṹ⣬˶صĴЬ񣬵ȴҲһġɼǿİЬ񣨲ߣֻһסķ˵Ҳ㹻ˣ뻧Ŵ򿪿龰ӳDzԼ˿ײɣ˴﹦ܣͬʱҲʡ˿ռ䣬ԭСIJòôӵpsĵƺƯľ?dzȥӽǣòΪϣ˰̨ϣַʽĸϣСĿռ䡢ӾϿÿռ俴Ҫǿù߸ͨ͸Чؽ˿ɹⲻõ⣻L͵IJɳߵǽˢɫ齺ᣬһڰ׹һԵúС£ҲرԵƷζиԭľɫ¹Ȼ࣬ΪһƷȿװΣҲɣһãƵĵӱǽԡʱУΪÿĹ߸ԴǽҲһزʱɱø˽ûʱѴӹߣ԰̨ϲ߷ױ̨ұ˲ʽñ䣻ǽˢůɫϵƹ£ӪһܰʵҷΧԴԣƳףƮ¹+ȻʹǺܷ㣬ɻ·żһιҲԵòˣUͳ̨̨һԵúʵãֱڰ̨Ͳͣ˶ʱɰѲ˷ڰ̨ȥ䣬ԡϴ̨Ͱһϴ»صǻӵƽ沼ͼϲǵĹעС࣬װҾڴ -houseliving һס¼ ܰ ԷƮ ÿʵ ܲס¼ңһ׼ҪʼҸ¼~~ߣȥؽŲԶDzλˡεѡɫԭľʽʵáûɶװμ򵥵ģͦÿġƣװζͦġʵɳʵõIJ輸ӹ䡣Ѵߺܺã ˲ռ˲ءڴλÿͲСǿɳŽȥͺЩӵˣѵǽǶȵ⡣ɫĶƳڴ󴰻Ĺ°ԵʮֵĿƯǽשѡDZȽůɫģƵijɲٵĶԣĸķ䣬ʵûʲôɹһãһ󡣴ƳƮλãÿĹǽǽֽÿ临ʽ󴲣һζԣǵķ䣬䶼ͦģһѴƳƮλãʵûۣܲƣҴĴ̼ʵûͯѡ̵Ĵ̣һͷ񣬼ʵáǷɫϵģ򵥺ÿҸرĴС鷿λãѡĶʵûͦÿģص㻹ˣмͦտģλôŷҲҲ泩䣬ϴֳ߿λͦģöɡŽȥԡλã򵥵˸ʪ룬ͦõġָߡװϲװУ10000׾װްͼ·ע΢ŹںŻȡѯʽռװޣrskjzx -houseliving ͬѧװ·18Ч͵֣Ҿ߻ûͺƯ ·Ӳװװϣ񱣽ɣ͵ȼҾ߼ҵ볡вּҵѾ˵ˡͬѧװ޵ģӲװ+װ+Ҿ߼ҵȫзü18ι۶˵Ưͬѧû׬ǮװޡҲ͵֣̫ˡͲȫͼж˱һˣ+´ʯʵĴשʱлҷdzĸ߶˴ŽһСϣȻոDzˡͬѧǽţȿʽáұȽϲһʽĮ̇ƽʱ³Ƚ٣İʽijһ󴰻ɹdzIJIJɹܴ󲿷ڳ󴰻ӱǽ԰ɣѡʮıֽʱֽԼɱӵˣǵӹûȷڵϿЧ濴ֽ3DӡЧܲԵģĺܹƵͬ˺ö࣬ʡǮֺÿֶΪ̨ɹ·ĵطͬʱҲ˳ɿռ䣬ܶƽʱҪõĿͲмȣͨԡҺ͸䡣ƾֵɣ϶ԱȺþòѡģ۸Ҳ󡣺ܶΪʲô¹?ʱûһҪƶģ̳ûкʵijߴ磬ľġ׿ûëǷdzġϾʱ࿴װЧͼװʱб޻ע΢Źںţȫװ ÿʰװ޹ԣ -houseliving 160Oʽʽ,ȴ͸ŲһĹ :ʽ:160O:ʽװκܾ,ܾ~ӱǽװκܼ,ȴͿҾߡӶѡʵľ,۸԰ǻ߶˳˻ɫǽש,ɫϲɫһ¥ĸĸ,Ӥ,ͲıСˤ,ɫıֽܰһС鷿⻹һͯ,ѡõĶǻIJϴ鷿߶˴ϵ,ԼĹҾװ|༭δ΢ID:vipjjzxάע -houseliving ȫ-,װЧͼ ,ռ䡢ɡ!ҽһҾ,һԽ,ǿƽ!,鷿һȽС,,Ͳ÷Ŵ,żСס,õѡ񡣽,һĴ,Ϊ޵Ŀͷ,ΪҪĹ֮һŶȻӣҵӡ׵Ĵ໥,ӪȻܰԷΧնĿռ䲼,дһ,ÿҲоҵܰ˵ijŶ~Ʈ,̫ƮһС͵,Ͼʱڴ̨ˮ,ƾ档鷿,ûϷһת,Эͳһ,,س,ʹճ칫Ķӷ㡢ġ졢顢ȺȲ,Ƿdz¡ϷΪ,ǽ񡢸߰ν,¡Իƺײɫ,¶ŷҾӷ,鷿Եշʮ㡣ɴ,ǿСƳ,·ΪɻƷĴ,Ϸװι,ͷŸ,ճ鼮װƷͳͳɽȥͳһ,ʱд׵ĺô,ռĶ,˿ռĴɹܡϽҾؼ! -houseliving 4ײ3Loft? ䷿Ƭһ! 4ײߵ12Oô,ôܷļֵ?ʦ@¬籬12m,߽˺Ƭ,קſŵը!ʶϢ::Ϻ:12m:Ӳװ4w װ1.5wͼǰһ,ֻ2.35,5,4,䱻ָ2,С,,ÿ߲2,ʹڵIJɹdz,Ψһ쾮ɹĴȴռ,ϷҪ24Сʱơڶ,С,ķ,ǵ,ڵIJֺչ,ϼסլֻ¥ݼǻ,ಿֶľṹ,ҷ˿,涼Ǹǽ,ûг,Ψһ֧õ컨ľ,ڽṹҲһȫµսһ ڲɹ,ǵIJǰŲĶͷӶѲɹų,οĿռǴֲ,øĹ߽,ĿռҲʹڿռܲԵôѹ֡ԶסΪIJ֮,2.3ȴ4ײߵĿռ,ռϵ̽ΪƵĹע㡣ĿռͲκܿ˳˼,ϣ㡱Ӱ췿IJɹ,ͬʱ߿ռĿռܡṹ֧õֻе컨ľ,Ϊ˲ƻԭеĽṹ,ǾһȫµĽṹϵ,ṹԼΪһϵ,ֻҪԭľṹͨ½ṹս,Ȼһ֧ϵ,ǷҪļܹ,ϣ,ϣһֽṹʽͬʱ,,ռ,װ,,Ҫ;ֲļҾߡҲǵһΰѽṹΪһƵ,ȥۺϽ⡣һֽṹϵ,ͨ͸н,ֳеԵĹ,ʵʩϸݲͬλѡ2cm,4cmķͨΪҪIJϡʩǷԭĸнṹ,ǾҪһֽṹ,֮µĽṹӡԭĸĸּܿǵͰȫ,ǰҪĽṹֳ4,4ڳҶƶ,ֳװɡṹGIFͼֽṹľ̶еIJֶƴɺ,ϰ3顣еIJֶƴɺ,ƬձߡװLEDƴװëʽ޺۱֯ڵ㡣вв¡ǶȿԿһͶҡΧɫɫϵƴռ俴ӿ,¥ﻹҪ,֮ǰΪ˾ܱ֤ͨ͸жû¥ݿ,֮󻹻ijһЩȥ,ȥͲʹˡһ¥򿴶¥ЧȻ3,ռвͨ͸Сһ¥Ͽ¥Ч,ûкƬӸ!Ӷ¥һ㡣һϢռ丩ӡƽʱ俴ȺȲ,Ǻġһ㹦,߲&书ܡҿռȻ,ںú,һ˯ĵط,ʵԡռ,˯ڵϡӵ¿Чһ϶㡣·......ڽŸл̫áӶ㡣¿,СĻҪŶ,ԼҵҪʦ@¬ƽ̨תػ,ϵ΢ź:xiongxiaomei01ͼƬδͬ,ʹ,Ȩؾ ˱˵ĸĶ?ô,Դֲ֪?;,ʱ򽲾һ˼ҵҪ48Сʱݸ˵װټ򵥰һļ참¬Ϊɨά ...ϰ,ԼһլҵɴһȻĶļ- END -ĶԭIJ鿴Ʒ嵥 -houseliving 20̨,ȴ·?! ʮǰµ,ͻʫ񿴻»,黳Ѵ׬Ǯdz׵ʱ,Ҿڷdzױ׬ǮĴáҺ,С͵̨,÷ⴰܰСռ䡣ֻ70ƽ׵С,̨1.3,3׶೤ɡôúһСռ䲼õ?ֲֻ̨ݡȲ졢ɹ·?ⶼõ÷ʽԼ̨,ͼʵκ⡣Ҫһݵؽɹ·,ǻɻ,ǧǮʡ1/8ƽˡ̨;,ֹ·DzõϾͿʼ,Ҫ̨ϺȲɹ̫DzԼļʱ,Ҫһʶ:ҪʲôĹ,Ҫôʵ֡еĻ̨,ôкͼȫֿ,Ҳڴõ¿ˮ̸ЦˡеĻȻ̨,̵,̨ڵȫΪ͵,̨,ֻܷһŴƲ̨,Լһ,ĿռҪʲô,ЩطʲôҾߡ10ҪڼʵֵĹ,ɹ·򡣰ȫҶ,ʲô©,Ǿ˵ʵڰڲ,ֻ̨ܽˡҪDz̨,Щ󶼵õ,Ǿ̫!һʲôԵ̨ˡ̨ͬ,ſɲһרҵվߴ,̨ʲôʽʽ,ÿͶӰʵô,Ǿǿ̨ĸ䡣󲿷ֵĹڻ,޷Ǿ̨ڿһ,ٲֵĻ䡢鷿ϡЩ̨λòһ,;Ҳһų̨,ʲΪõѡ񡣿ˮ,ɹҲհ,ʹܷ㡣ҪdzСò,ҲԿǰ̨ϲʹáⲿֵ̨ͳһ÷ˮ,שˮ齺,ࡣſ̨,ΪɹҲ,ѿӰɹ,·ô,Ҫڵߵ,ҲΪɹЭ֮,ΪͲ,,ϵ̨һò,һСӾͺą́òͲ̫,Ϊӳ߹ȥЩԶ,ǿԲκֲǡһñˣĵ̺,ȫIJɹ⡣˵˵鷿̨,ɹô,ΪҪӲȥ̨ϡҪ鷿ǼͥԱ,ҲΪ鷿,ø򡢵̨,߷һ̨ܲ鷿ֻһ,Ǿ͸ԼⲼˡҵ̨,ӦǼ˽ܵĽ,ҵ˶ռһҪȥ̨,߲,ұӦе˽Ҳ˵,̨ôȫԼ,ȱʲô,̨ܿʲôܡֻDz̨֮ͬһЩҪע,ʹôʡС̨ܵһ,̨ͬʱеֹ,·ֻ,߲ͻԿƪ,ÿռĵӴҲ̨ϡⲻ̨,Ǹֱ̨һبŲ:˷,Ҳÿϧر𷴶ԵһСͿõò,㶫ʪĻ,ݺݵظϷá˵һơⲻ̨,ǵÿԼⴰֻ̨Ĵ,൱ڰ̨,ֻǿ̨ܻӺֳռĻַ֡,̫㹻ļͥ,߾̨ûʲô̫;֮,ܹ֤,ȥɹܡɨķ,Ҳשˢˮ,װ޷ʽһ,(Ҳ)ʡһǮʱ,ڷ̨ʱ⿪,Ѵȫʱ,ͨЧų̂ʱһáⴰַŷ̨Ĵ,̨֮Ҳһšķ̨ʽ,ڰʮǰϷö,Ҳĺôĺô,,ͱЧõȼ˳̨鷳,̨ܹ֤Ķ,Ļ,ͨܵһӰ졣ڴĶֱò,Ӱɹ,Ϸ̨֮仹һǽ,ǽϿ̨ȫ޷ʹ,Ҫ÷ˮ,ð̨ڽӴIJܷ,ֹˮࡣ̨ϵƷ,غݿ̨,һ軨̨϶ľΨһĺôȴ޷ܾ:һ̶ȽӽȻĽ,þþӳеҸһ˿˿ӵԼͥԺĿ֡˵˾:ԸԸΪ?ȵ,һϵ۵Ҫ:̨Ҵʵⲻ̨ȡڰѲ̨⼸ƽ׻,̨Ļ̨ȷʵ,ǺڻûаëǮϵ,˵ļûһ˵,̨쿪Ŵ,ǺûҲûκײڰ̵ש̨,dzࡣЩ͹ԭ̨Ȼк̨ܶҡ̨Ĺ̨ij,Ҳ˵̨ļ̨ԡҵĿ,ôһЩʵÿǿǡ©ˮ:ûˮϵͳ̨,û취ϴ»ϰѳء:Ϸ̨ʹõĻ,Ҫպ֮,ڱ²,ܹһ,Ҫdz䡣סլIJ˾͸,ҪijҪ,ҲԼһǽ֤˽ͱ¡:ӽ濴,Ҫ̨ǵ͹,ͽСʽ̨,̨ȫ¥,û֧ŵ,ȼططŴ衢ס:̨ij,Ҫľȼ̡ȼõ¯,̺ܶͥѡֱ̨ϴŷšôʵܲ,Ⱦ˴¥,ֻӰ¥¥µھӡ̻̹ǣ̫,ӰЧעѴ,ͬ㡱Ҵ,രڽĵ~ʱ,ҵ~һļװ΢ź:happysun328Ӻע˾ְСʵ:ظӡ,ø㽲̫ζ!Ŵһƽ̨΢@Ѵϵ010-67898001Ͷ:so@isouchuang.comվ:www.isouchuang.comÿջ:һζļҵĶȫġô˵~ -houseliving Ըһ104ƽСѣ18WĴ磬Ч £18Wװ޺õķӣdz⣬һУԼļңŻȶоĴ˵ĸоҽܽǿʽװ޷DzڿԱߣڼ˽ԣɾ򵥾ͺǶͯкܿɰȻҾ̫ˣǼȻѡʽ񣬲ŪһʽİDZ߰ѡǼĻͼʦ滮ĻǷdz -houseliving ̨ôܶ࣬Ƕ˷10.12 ̨зʽҲаʽҲ¶̨̨ʲô˶죬ϴ·鷿ңȵȣص㿴һ̨Ĺܲáľذ裬һҲУƷ˽֮ҲСտпռ䣬ʱȲ裻Բʱڤһ¥̨ĺҡκϡ̨רΪҹɶҲżʱûǻɡϰĵطһأˮٺòСӾ͵̨ϴ»ټһϴֳأҲȻҲ˱˻ѽľذ壬DZһ¥̨Ѿˣϧ̫С̨̫֪ʲôãҲգɰɣǸڿˣɳ̨ܰˡӻ취ʡռ䣬ֱɵֱ̨ӿԴ˯ˡиõķ̨ҲľǽϲֲﻨݣʵӣӸ󣿡̨鷿ˮ̨Էϴ»ϴͿҲһ͵طһֵ÷ϴ»ҲǿԵġ¶̨ϲݵˣ԰ֲ̨С;͵ð̨õ¡ɿռ䣬̨ҲͲˣ̫󣬹þСпռ䣬ֱҲǿԡĻⷿٺòҲ㣬ֻҪ¡ϲľƷĿ󲿷ּͥϴϴ̨¼йԣּǫ̃˵ô࣬ô࣬֡˼̨ĸоʵֻҪ滮οȫ˼ҵЧȻҲз࿴Ժ²ۣȵһ׷˳ӻϣкΪ -houseliving ۽ʽ,֮׷ ǻ۹ͶʽΪÿ˶ʽĻܶ,Ʒζֵ۽׷ȵ˸,׷Ӿʽ̵̺ı¶ԳɳⲻĵƲܾ۽ǵ۹ִ֪,Ტõữƴⶼʽ׷ռ䲻ͬάȵˮīʸڿռȾҲжµľ͵ƾɢŹâʽĿռŴķΧִеɳ˿ɫԾ鶯ڹ˿ȴƷװѡ̺ʽζԪڴռ빹ִȥȥһĸвζеĿռ̬ľĵʸԭŨɫɫԵʽдʯذ巴ͨ͸ɫƺһжƵĸոպ,ֲʧ߼ʸпռɫƲ,Ҳˮƽ߷ΧɫȫȻչ˿ռĵд󵨵ֺʽص֮,ҲӵȨʽռḻԪ辰ֲ,ŵıIJóǽİ뿪ƶȫȻصdzɫ֮˷ڴߵ˻ĵƽѰʵƼʽ,ҲʽĶһռůʸԭĸжûйװҲִܵҾƸߵʽֹ׷׷ǾзȸевںаĿԸ۽ʽƵԭǵﲻŶҼֵ,йĻͬͬҪ,Ҫڵ diff --git a/NLP/16.8 BERT/download_glue_data.py b/NLP/16.8 BERT/download_glue_data.py deleted file mode 100644 index 56b8860..0000000 --- a/NLP/16.8 BERT/download_glue_data.py +++ /dev/null @@ -1,137 +0,0 @@ -''' Script for downloading all GLUE data. - -Note: for legal reasons, we are unable to host MRPC. -You can either use the version hosted by the SentEval team, which is already tokenized, -or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually. -For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example). -You should then rename and place specific files in a folder (see below for an example). - -mkdir MRPC -cabextract MSRParaphraseCorpus.msi -d MRPC -cat MRPC/_2DEC3DBE877E4DB192D17C0256E90F1D | tr -d $'\r' > MRPC/msr_paraphrase_train.txt -cat MRPC/_D7B391F9EAFF4B1B8BCE8F21B20B1B61 | tr -d $'\r' > MRPC/msr_paraphrase_test.txt -rm MRPC/_* -rm MSRParaphraseCorpus.msi -''' - -import os -import sys -import shutil -import argparse -import tempfile -import urllib.request -import zipfile - -TASKS = ["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"] -TASK2PATH = {"CoLA":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4', - "SST":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8', - "MRPC":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc', - "QQP":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5', - "STS":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5', - "MNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce', - "SNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df', - "QNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLI.zip?alt=media&token=c24cad61-f2df-4f04-9ab6-aa576fa829d0', - "RTE":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb', - "WNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf', - "diagnostic":'https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D'} - -MRPC_TRAIN = 'https://s3.amazonaws.com/senteval/senteval_data/msr_paraphrase_train.txt' -MRPC_TEST = 'https://s3.amazonaws.com/senteval/senteval_data/msr_paraphrase_test.txt' - -def download_and_extract(task, data_dir): - print("Downloading and extracting %s..." % task) - data_file = "%s.zip" % task - urllib.request.urlretrieve(TASK2PATH[task], data_file) - with zipfile.ZipFile(data_file) as zip_ref: - zip_ref.extractall(data_dir) - os.remove(data_file) - print("\tCompleted!") - -def format_mrpc(data_dir, path_to_data): - print("Processing MRPC...") - mrpc_dir = os.path.join(data_dir, "MRPC") - if not os.path.isdir(mrpc_dir): - os.mkdir(mrpc_dir) - if path_to_data: - mrpc_train_file = os.path.join(path_to_data, "msr_paraphrase_train.txt") - mrpc_test_file = os.path.join(path_to_data, "msr_paraphrase_test.txt") - else: - mrpc_train_file = os.path.join(mrpc_dir, "msr_paraphrase_train.txt") - mrpc_test_file = os.path.join(mrpc_dir, "msr_paraphrase_test.txt") - urllib.request.urlretrieve(MRPC_TRAIN, mrpc_train_file) - urllib.request.urlretrieve(MRPC_TEST, mrpc_test_file) - assert os.path.isfile(mrpc_train_file), "Train data not found at %s" % mrpc_train_file - assert os.path.isfile(mrpc_test_file), "Test data not found at %s" % mrpc_test_file - urllib.request.urlretrieve(TASK2PATH["MRPC"], os.path.join(mrpc_dir, "dev_ids.tsv")) - - dev_ids = [] - with open(os.path.join(mrpc_dir, "dev_ids.tsv")) as ids_fh: - for row in ids_fh: - dev_ids.append(row.strip().split('\t')) - - with open(mrpc_train_file) as data_fh, \ - open(os.path.join(mrpc_dir, "train.tsv"), 'w') as train_fh, \ - open(os.path.join(mrpc_dir, "dev.tsv"), 'w') as dev_fh: - header = data_fh.readline() - train_fh.write(header) - dev_fh.write(header) - for row in data_fh: - label, id1, id2, s1, s2 = row.strip().split('\t') - if [id1, id2] in dev_ids: - dev_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2)) - else: - train_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2)) - - with open(mrpc_test_file) as data_fh, \ - open(os.path.join(mrpc_dir, "test.tsv"), 'w') as test_fh: - header = data_fh.readline() - test_fh.write("index\t#1 ID\t#2 ID\t#1 String\t#2 String\n") - for idx, row in enumerate(data_fh): - label, id1, id2, s1, s2 = row.strip().split('\t') - test_fh.write("%d\t%s\t%s\t%s\t%s\n" % (idx, id1, id2, s1, s2)) - print("\tCompleted!") - -def download_diagnostic(data_dir): - print("Downloading and extracting diagnostic...") - if not os.path.isdir(os.path.join(data_dir, "diagnostic")): - os.mkdir(os.path.join(data_dir, "diagnostic")) - data_file = os.path.join(data_dir, "diagnostic", "diagnostic.tsv") - urllib.request.urlretrieve(TASK2PATH["diagnostic"], data_file) - print("\tCompleted!") - return - -def get_tasks(task_names): - task_names = task_names.split(',') - if "all" in task_names: - tasks = TASKS - else: - tasks = [] - for task_name in task_names: - assert task_name in TASKS, "Task %s not found!" % task_name - tasks.append(task_name) - return tasks - -def main(arguments): - parser = argparse.ArgumentParser() - parser.add_argument('--data_dir', help='directory to save data to', type=str, default='glue_data') - parser.add_argument('--tasks', help='tasks to download data for as a comma separated string', - type=str, default='all') - parser.add_argument('--path_to_mrpc', help='path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt', - type=str, default='') - args = parser.parse_args(arguments) - - if not os.path.isdir(args.data_dir): - os.mkdir(args.data_dir) - tasks = get_tasks(args.tasks) - - for task in tasks: - if task == 'MRPC': - format_mrpc(args.data_dir, args.path_to_mrpc) - elif task == 'diagnostic': - download_diagnostic(args.data_dir) - else: - download_and_extract(task, args.data_dir) - - -if __name__ == '__main__': - sys.exit(main(sys.argv[1:])) \ No newline at end of file diff --git a/NLP/16.8 BERT/modeling.py b/NLP/16.8 BERT/modeling.py deleted file mode 100644 index 66b0de6..0000000 --- a/NLP/16.8 BERT/modeling.py +++ /dev/null @@ -1,474 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""PyTorch BERT model.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import copy -import json -import math -import six -import torch -import torch.nn as nn -from torch.nn import CrossEntropyLoss - -def gelu(x): - """Implementation of the gelu activation function. - For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): - 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) - """ - return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) - - -class BertConfig(object): - """Configuration class to store the configuration of a `BertModel`. - """ - def __init__(self, - vocab_size, - hidden_size=768, - num_hidden_layers=12, - num_attention_heads=12, - intermediate_size=3072, - hidden_act="gelu", - hidden_dropout_prob=0.1, - attention_probs_dropout_prob=0.1, - max_position_embeddings=512, - type_vocab_size=16, - initializer_range=0.02): - """Constructs BertConfig. - - Args: - vocab_size: Vocabulary size of `inputs_ids` in `BertModel`. - hidden_size: Size of the encoder layers and the pooler layer. - num_hidden_layers: Number of hidden layers in the Transformer encoder. - num_attention_heads: Number of attention heads for each attention layer in - the Transformer encoder. - intermediate_size: The size of the "intermediate" (i.e., feed-forward) - layer in the Transformer encoder. - hidden_act: The non-linear activation function (function or string) in the - encoder and pooler. - hidden_dropout_prob: The dropout probabilitiy for all fully connected - layers in the embeddings, encoder, and pooler. - attention_probs_dropout_prob: The dropout ratio for the attention - probabilities. - max_position_embeddings: The maximum sequence length that this model might - ever be used with. Typically set this to something large just in case - (e.g., 512 or 1024 or 2048). - type_vocab_size: The vocabulary size of the `token_type_ids` passed into - `BertModel`. - initializer_range: The sttdev of the truncated_normal_initializer for - initializing all weight matrices. - """ - self.vocab_size = vocab_size - self.hidden_size = hidden_size - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads - self.hidden_act = hidden_act - self.intermediate_size = intermediate_size - self.hidden_dropout_prob = hidden_dropout_prob - self.attention_probs_dropout_prob = attention_probs_dropout_prob - self.max_position_embeddings = max_position_embeddings - self.type_vocab_size = type_vocab_size - self.initializer_range = initializer_range - - @classmethod - def from_dict(cls, json_object): - """Constructs a `BertConfig` from a Python dictionary of parameters.""" - config = BertConfig(vocab_size=None) - for (key, value) in six.iteritems(json_object): - config.__dict__[key] = value - return config - - @classmethod - def from_json_file(cls, json_file): - """Constructs a `BertConfig` from a json file of parameters.""" - with open(json_file, "r") as reader: - text = reader.read() - return cls.from_dict(json.loads(text)) - - def to_dict(self): - """Serializes this instance to a Python dictionary.""" - output = copy.deepcopy(self.__dict__) - return output - - def to_json_string(self): - """Serializes this instance to a JSON string.""" - return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" - - -class BERTLayerNorm(nn.Module): - def __init__(self, config, variance_epsilon=1e-12): - """Construct a layernorm module in the TF style (epsilon inside the square root). - """ - super(BERTLayerNorm, self).__init__() - self.gamma = nn.Parameter(torch.ones(config.hidden_size)) - self.beta = nn.Parameter(torch.zeros(config.hidden_size)) - self.variance_epsilon = variance_epsilon - - def forward(self, x): - u = x.mean(-1, keepdim=True) - s = (x - u).pow(2).mean(-1, keepdim=True) - x = (x - u) / torch.sqrt(s + self.variance_epsilon) - return self.gamma * x + self.beta - -class BERTEmbeddings(nn.Module): - def __init__(self, config): - super(BERTEmbeddings, self).__init__() - """Construct the embedding module from word, position and token_type embeddings. - """ - self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size) - self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) - self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) - - # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load - # any TensorFlow checkpoint file - self.LayerNorm = BERTLayerNorm(config) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, input_ids, token_type_ids=None): - seq_length = input_ids.size(1) - position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) - position_ids = position_ids.unsqueeze(0).expand_as(input_ids) - if token_type_ids is None: - token_type_ids = torch.zeros_like(input_ids) - - words_embeddings = self.word_embeddings(input_ids) - position_embeddings = self.position_embeddings(position_ids) - token_type_embeddings = self.token_type_embeddings(token_type_ids) - - embeddings = words_embeddings + position_embeddings + token_type_embeddings - embeddings = self.LayerNorm(embeddings) - embeddings = self.dropout(embeddings) - return embeddings - - -class BERTSelfAttention(nn.Module): - def __init__(self, config): - super(BERTSelfAttention, self).__init__() - if config.hidden_size % config.num_attention_heads != 0: - raise ValueError( - "The hidden size (%d) is not a multiple of the number of attention " - "heads (%d)" % (config.hidden_size, config.num_attention_heads)) - self.num_attention_heads = config.num_attention_heads - self.attention_head_size = int(config.hidden_size / config.num_attention_heads) - self.all_head_size = self.num_attention_heads * self.attention_head_size - - self.query = nn.Linear(config.hidden_size, self.all_head_size) - self.key = nn.Linear(config.hidden_size, self.all_head_size) - self.value = nn.Linear(config.hidden_size, self.all_head_size) - - self.dropout = nn.Dropout(config.attention_probs_dropout_prob) - - def transpose_for_scores(self, x): - new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) - x = x.view(*new_x_shape) - return x.permute(0, 2, 1, 3) - - def forward(self, hidden_states, attention_mask): - mixed_query_layer = self.query(hidden_states) - mixed_key_layer = self.key(hidden_states) - mixed_value_layer = self.value(hidden_states) - - query_layer = self.transpose_for_scores(mixed_query_layer) - key_layer = self.transpose_for_scores(mixed_key_layer) - value_layer = self.transpose_for_scores(mixed_value_layer) - - # Take the dot product between "query" and "key" to get the raw attention scores. - attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) - attention_scores = attention_scores / math.sqrt(self.attention_head_size) - # Apply the attention mask is (precomputed for all layers in BertModel forward() function) - attention_scores = attention_scores + attention_mask - - # Normalize the attention scores to probabilities. - attention_probs = nn.Softmax(dim=-1)(attention_scores) - - # This is actually dropping out entire tokens to attend to, which might - # seem a bit unusual, but is taken from the original Transformer paper. - attention_probs = self.dropout(attention_probs) - - context_layer = torch.matmul(attention_probs, value_layer) - context_layer = context_layer.permute(0, 2, 1, 3).contiguous() - new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) - context_layer = context_layer.view(*new_context_layer_shape) - return context_layer - - -class BERTSelfOutput(nn.Module): - def __init__(self, config): - super(BERTSelfOutput, self).__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.LayerNorm = BERTLayerNorm(config) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, hidden_states, input_tensor): - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -class BERTAttention(nn.Module): - def __init__(self, config): - super(BERTAttention, self).__init__() - self.self = BERTSelfAttention(config) - self.output = BERTSelfOutput(config) - - def forward(self, input_tensor, attention_mask): - self_output = self.self(input_tensor, attention_mask) - attention_output = self.output(self_output, input_tensor) - return attention_output - - -class BERTIntermediate(nn.Module): - def __init__(self, config): - super(BERTIntermediate, self).__init__() - self.dense = nn.Linear(config.hidden_size, config.intermediate_size) - self.intermediate_act_fn = gelu - - def forward(self, hidden_states): - hidden_states = self.dense(hidden_states) - hidden_states = self.intermediate_act_fn(hidden_states) - return hidden_states - - -class BERTOutput(nn.Module): - def __init__(self, config): - super(BERTOutput, self).__init__() - self.dense = nn.Linear(config.intermediate_size, config.hidden_size) - self.LayerNorm = BERTLayerNorm(config) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, hidden_states, input_tensor): - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -class BERTLayer(nn.Module): - def __init__(self, config): - super(BERTLayer, self).__init__() - self.attention = BERTAttention(config) - self.intermediate = BERTIntermediate(config) - self.output = BERTOutput(config) - - def forward(self, hidden_states, attention_mask): - attention_output = self.attention(hidden_states, attention_mask) - intermediate_output = self.intermediate(attention_output) - layer_output = self.output(intermediate_output, attention_output) - return layer_output - - -class BERTEncoder(nn.Module): - def __init__(self, config): - super(BERTEncoder, self).__init__() - layer = BERTLayer(config) - self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]) - - def forward(self, hidden_states, attention_mask): - all_encoder_layers = [] - for layer_module in self.layer: - hidden_states = layer_module(hidden_states, attention_mask) - all_encoder_layers.append(hidden_states) - return all_encoder_layers - - -class BERTPooler(nn.Module): - def __init__(self, config): - super(BERTPooler, self).__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.activation = nn.Tanh() - - def forward(self, hidden_states): - # We "pool" the model by simply taking the hidden state corresponding - # to the first token. - first_token_tensor = hidden_states[:, 0] - pooled_output = self.dense(first_token_tensor) - pooled_output = self.activation(pooled_output) - return pooled_output - - -class BertModel(nn.Module): - """BERT model ("Bidirectional Embedding Representations from a Transformer"). - - Example usage: - ```python - # Already been converted into WordPiece token ids - input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) - input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) - token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) - - config = modeling.BertConfig(vocab_size=32000, hidden_size=512, - num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) - - model = modeling.BertModel(config=config) - all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) - ``` - """ - def __init__(self, config: BertConfig): - """Constructor for BertModel. - - Args: - config: `BertConfig` instance. - """ - super(BertModel, self).__init__() - self.embeddings = BERTEmbeddings(config) - self.encoder = BERTEncoder(config) - self.pooler = BERTPooler(config) - - def forward(self, input_ids, token_type_ids=None, attention_mask=None): - if attention_mask is None: - attention_mask = torch.ones_like(input_ids) - if token_type_ids is None: - token_type_ids = torch.zeros_like(input_ids) - - # We create a 3D attention mask from a 2D tensor mask. - # Sizes are [batch_size, 1, 1, to_seq_length] - # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] - # this attention mask is more simple than the triangular masking of causal attention - # used in OpenAI GPT, we just need to prepare the broadcast dimension here. - extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) - - # Since attention_mask is 1.0 for positions we want to attend and 0.0 for - # masked positions, this operation will create a tensor which is 0.0 for - # positions we want to attend and -10000.0 for masked positions. - # Since we are adding it to the raw scores before the softmax, this is - # effectively the same as removing these entirely. - extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility - extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 - - embedding_output = self.embeddings(input_ids, token_type_ids) - all_encoder_layers = self.encoder(embedding_output, extended_attention_mask) - sequence_output = all_encoder_layers[-1] - pooled_output = self.pooler(sequence_output) - return all_encoder_layers, pooled_output - -class BertForSequenceClassification(nn.Module): - """BERT model for classification. - This module is composed of the BERT model with a linear layer on top of - the pooled output. - - Example usage: - ```python - # Already been converted into WordPiece token ids - input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) - input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) - token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) - - config = BertConfig(vocab_size=32000, hidden_size=512, - num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) - - num_labels = 2 - - model = BertForSequenceClassification(config, num_labels) - logits = model(input_ids, token_type_ids, input_mask) - ``` - """ - def __init__(self, config, num_labels): - super(BertForSequenceClassification, self).__init__() - self.bert = BertModel(config) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - self.classifier = nn.Linear(config.hidden_size, num_labels) - - def init_weights(module): - if isinstance(module, (nn.Linear, nn.Embedding)): - # Slightly different from the TF version which uses truncated_normal for initialization - # cf https://github.com/pytorch/pytorch/pull/5617 - module.weight.data.normal_(mean=0.0, std=config.initializer_range) - elif isinstance(module, BERTLayerNorm): - module.beta.data.normal_(mean=0.0, std=config.initializer_range) - module.gamma.data.normal_(mean=0.0, std=config.initializer_range) - if isinstance(module, nn.Linear): - module.bias.data.zero_() - self.apply(init_weights) - - def forward(self, input_ids, token_type_ids, attention_mask, labels=None): - _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask) - pooled_output = self.dropout(pooled_output) - logits = self.classifier(pooled_output) - - if labels is not None: - loss_fct = CrossEntropyLoss() - loss = loss_fct(logits, labels) - return loss, logits - else: - return logits - -class BertForQuestionAnswering(nn.Module): - """BERT model for Question Answering (span extraction). - This module is composed of the BERT model with a linear layer on top of - the sequence output that computes start_logits and end_logits - - Example usage: - ```python - # Already been converted into WordPiece token ids - input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) - input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) - token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) - - config = BertConfig(vocab_size=32000, hidden_size=512, - num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) - - model = BertForQuestionAnswering(config) - start_logits, end_logits = model(input_ids, token_type_ids, input_mask) - ``` - """ - def __init__(self, config): - super(BertForQuestionAnswering, self).__init__() - self.bert = BertModel(config) - # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version - # self.dropout = nn.Dropout(config.hidden_dropout_prob) - self.qa_outputs = nn.Linear(config.hidden_size, 2) - - def init_weights(module): - if isinstance(module, (nn.Linear, nn.Embedding)): - # Slightly different from the TF version which uses truncated_normal for initialization - # cf https://github.com/pytorch/pytorch/pull/5617 - module.weight.data.normal_(mean=0.0, std=config.initializer_range) - elif isinstance(module, BERTLayerNorm): - module.beta.data.normal_(mean=0.0, std=config.initializer_range) - module.gamma.data.normal_(mean=0.0, std=config.initializer_range) - if isinstance(module, nn.Linear): - module.bias.data.zero_() - self.apply(init_weights) - - def forward(self, input_ids, token_type_ids, attention_mask, start_positions=None, end_positions=None): - all_encoder_layers, _ = self.bert(input_ids, token_type_ids, attention_mask) - sequence_output = all_encoder_layers[-1] - logits = self.qa_outputs(sequence_output) - start_logits, end_logits = logits.split(1, dim=-1) - start_logits = start_logits.squeeze(-1) - end_logits = end_logits.squeeze(-1) - - if start_positions is not None and end_positions is not None: - # If we are on multi-GPU, split add a dimension - if len(start_positions.size()) > 1: - start_positions = start_positions.squeeze(-1) - if len(end_positions.size()) > 1: - end_positions = end_positions.squeeze(-1) - # sometimes the start/end positions are outside our model inputs, we ignore these terms - ignored_index = start_logits.size(1) - start_positions.clamp_(0, ignored_index) - end_positions.clamp_(0, ignored_index) - - loss_fct = CrossEntropyLoss(ignore_index=ignored_index) - start_loss = loss_fct(start_logits, start_positions) - end_loss = loss_fct(end_logits, end_positions) - total_loss = (start_loss + end_loss) / 2 - return total_loss - else: - return start_logits, end_logits diff --git a/NLP/16.8 BERT/optimization.py b/NLP/16.8 BERT/optimization.py deleted file mode 100644 index cd08d55..0000000 --- a/NLP/16.8 BERT/optimization.py +++ /dev/null @@ -1,180 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""PyTorch optimization for BERT model.""" - -import math -import torch -from torch.optim import Optimizer -from torch.nn.utils import clip_grad_norm_ - -def warmup_cosine(x, warmup=0.002): - if x < warmup: - return x/warmup - return 0.5 * (1.0 + torch.cos(math.pi * x)) - -def warmup_constant(x, warmup=0.002): - if x < warmup: - return x/warmup - return 1.0 - -def warmup_linear(x, warmup=0.002): - if x < warmup: - return x/warmup - return 1.0 - x - -SCHEDULES = { - 'warmup_cosine':warmup_cosine, - 'warmup_constant':warmup_constant, - 'warmup_linear':warmup_linear, -} - - -class BERTAdam(Optimizer): - """Implements BERT version of Adam algorithm with weight decay fix (and no ). - Params: - lr: learning rate - warmup: portion of t_total for the warmup, -1 means no warmup. Default: -1 - t_total: total number of training steps for the learning - rate schedule, -1 means constant learning rate. Default: -1 - schedule: schedule to use for the warmup (see above). Default: 'warmup_linear' - b1: Adams b1. Default: 0.9 - b2: Adams b2. Default: 0.999 - e: Adams epsilon. Default: 1e-6 - weight_decay_rate: Weight decay. Default: 0.01 - max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0 - """ - def __init__(self, params, lr, warmup=-1, t_total=-1, schedule='warmup_linear', - b1=0.9, b2=0.999, e=1e-6, weight_decay_rate=0.01, - max_grad_norm=1.0): - if not lr >= 0.0: - raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr)) - if schedule not in SCHEDULES: - raise ValueError("Invalid schedule parameter: {}".format(schedule)) - if not 0.0 <= warmup < 1.0 and not warmup == -1: - raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup)) - if not 0.0 <= b1 < 1.0: - raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1)) - if not 0.0 <= b2 < 1.0: - raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2)) - if not e >= 0.0: - raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e)) - defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total, - b1=b1, b2=b2, e=e, weight_decay_rate=weight_decay_rate, - max_grad_norm=max_grad_norm) - super(BERTAdam, self).__init__(params, defaults) - - def get_lr(self): - lr = [] - for group in self.param_groups: - for p in group['params']: - state = self.state[p] - if len(state) == 0: - return [0] - if group['t_total'] != -1: - schedule_fct = SCHEDULES[group['schedule']] - lr_scheduled = group['lr'] * schedule_fct(state['step']/group['t_total'], group['warmup']) - else: - lr_scheduled = group['lr'] - lr.append(lr_scheduled) - return lr - - def to(self, device): - """ Move the optimizer state to a specified device""" - for state in self.state.values(): - state['exp_avg'].to(device) - state['exp_avg_sq'].to(device) - - def initialize_step(self, initial_step): - """Initialize state with a defined step (but we don't have stored averaged). - Arguments: - initial_step (int): Initial step number. - """ - for group in self.param_groups: - for p in group['params']: - state = self.state[p] - # State initialization - state['step'] = initial_step - # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(p.data) - # Exponential moving average of squared gradient values - state['exp_avg_sq'] = torch.zeros_like(p.data) - - def step(self, closure=None): - """Performs a single optimization step. - Arguments: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group['params']: - if p.grad is None: - continue - grad = p.grad.data - if grad.is_sparse: - raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead') - - state = self.state[p] - - # State initialization - if len(state) == 0: - state['step'] = 0 - # Exponential moving average of gradient values - state['next_m'] = torch.zeros_like(p.data) - # Exponential moving average of squared gradient values - state['next_v'] = torch.zeros_like(p.data) - - next_m, next_v = state['next_m'], state['next_v'] - beta1, beta2 = group['b1'], group['b2'] - - # Add grad clipping - if group['max_grad_norm'] > 0: - clip_grad_norm_(p, group['max_grad_norm']) - - # Decay the first and second moment running average coefficient - # In-place operations to update the averages at the same time - next_m.mul_(beta1).add_(1 - beta1, grad) - next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad) - update = next_m / (next_v.sqrt() + group['e']) - - # Just adding the square of the weights to the loss function is *not* - # the correct way of using L2 regularization/weight decay with Adam, - # since that will interact with the m and v parameters in strange ways. - # - # Instead we want ot decay the weights in a manner that doesn't interact - # with the m/v parameters. This is equivalent to adding the square - # of the weights to the loss with plain (non-momentum) SGD. - if group['weight_decay_rate'] > 0.0: - update += group['weight_decay_rate'] * p.data - - if group['t_total'] != -1: - schedule_fct = SCHEDULES[group['schedule']] - lr_scheduled = group['lr'] * schedule_fct(state['step']/group['t_total'], group['warmup']) - else: - lr_scheduled = group['lr'] - - update_with_lr = lr_scheduled * update - p.data.add_(-update_with_lr) - - state['step'] += 1 - - # step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1 - # bias_correction1 = 1 - beta1 ** state['step'] - # bias_correction2 = 1 - beta2 ** state['step'] - - return loss diff --git a/NLP/16.8 BERT/run.sh b/NLP/16.8 BERT/run.sh deleted file mode 100644 index c9733c9..0000000 --- a/NLP/16.8 BERT/run.sh +++ /dev/null @@ -1,50 +0,0 @@ - - -export GLUE_DIR=/search/odin/wuyonggang/bert/extract_code/glue_data -export BERT_BASE_DIR=/search/odin/wuyonggang/bert/chinese_L-12_H-768_A-12/ -export BERT_PYTORCH_DIR=/search/odin/wuyonggang/bert/chinese_L-12_H-768_A-12/ - -python run_classifier_word.py \ - --task_name NEWS \ - --do_train \ - --do_eval \ - --data_dir $GLUE_DIR/NewsAll/ \ - --vocab_file $BERT_BASE_DIR/vocab.txt \ - --bert_config_file $BERT_BASE_DIR/bert_config.json \ - --init_checkpoint $BERT_PYTORCH_DIR/pytorch_model.bin \ - --max_seq_length 256 \ - --train_batch_size 32 \ - --learning_rate 2e-5 \ - --num_train_epochs 3.0 \ - --output_dir ./newsAll_output/ \ - --local_rank 3 - -#python run_classifier_word.py \ -# --task_name NEWS \ -# --do_train \ -# --do_eval \ -# --data_dir $GLUE_DIR/News/ \ -# --vocab_file $BERT_BASE_DIR/vocab.txt \ -# --bert_config_file $BERT_BASE_DIR/bert_config.json \ -# --init_checkpoint $BERT_PYTORCH_DIR/pytorch_model.bin \ -# --max_seq_length 128 \ -# --train_batch_size 32 \ -# --learning_rate 2e-5 \ -# --num_train_epochs 3.0 \ -# --output_dir ./news_output/ \ -# --local_rank 2 - -#python run_classifier.py \ -# --task_name MRPC \ -# --do_train \ -# --do_eval \ -# --do_lower_case \ -# --data_dir $GLUE_DIR/MRPC/ \ -# --vocab_file $BERT_BASE_DIR/vocab.txt \ -# --bert_config_file $BERT_BASE_DIR/bert_config.json \ -# --init_checkpoint $BERT_PYTORCH_DIR/pytorch_model.bin \ -# --max_seq_length 128 \ -# --train_batch_size 32 \ -# --learning_rate 2e-5 \ -# --num_train_epochs 3.0 \ -# --output_dir ./mrpc_output/ diff --git a/NLP/16.8 BERT/run_classifier_word.py b/NLP/16.8 BERT/run_classifier_word.py deleted file mode 100644 index c3f6ed6..0000000 --- a/NLP/16.8 BERT/run_classifier_word.py +++ /dev/null @@ -1,697 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""BERT finetuning runner.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import csv -import os -import logging -import argparse -import random -from tqdm import tqdm, trange - -import numpy as np -import torch -from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler -from torch.utils.data.distributed import DistributedSampler - -import tokenization_word as tokenization -from modeling import BertConfig, BertForSequenceClassification -from optimization import BERTAdam - - -logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', - datefmt = '%m/%d/%Y %H:%M:%S', - level = logging.INFO) -logger = logging.getLogger(__name__) - - -class InputExample(object): - """A single training/test example for simple sequence classification.""" - - def __init__(self, guid, text_a, text_b=None, label=None): - """Constructs a InputExample. - - Args: - guid: Unique id for the example. - text_a: string. The untokenized text of the first sequence. For single - sequence tasks, only this sequence must be specified. - text_b: (Optional) string. The untokenized text of the second sequence. - Only must be specified for sequence pair tasks. - label: (Optional) string. The label of the example. This should be - specified for train and dev examples, but not for test examples. - """ - self.guid = guid - self.text_a = text_a - self.text_b = text_b - self.label = label - - -class InputFeatures(object): - """A single set of features of data.""" - - def __init__(self, input_ids, input_mask, segment_ids, label_id): - self.input_ids = input_ids - self.input_mask = input_mask - self.segment_ids = segment_ids - self.label_id = label_id - - -class DataProcessor(object): - """Base class for data converters for sequence classification data sets.""" - - def get_train_examples(self, data_dir): - """Gets a collection of `InputExample`s for the train set.""" - raise NotImplementedError() - - def get_dev_examples(self, data_dir): - """Gets a collection of `InputExample`s for the dev set.""" - raise NotImplementedError() - - def get_labels(self): - """Gets the list of labels for this data set.""" - raise NotImplementedError() - - @classmethod - def _read_tsv(cls, input_file, quotechar=None): - """Reads a tab separated value file.""" - file_in = open(input_file, "rb") - lines = [] - for line in file_in: - lines.append(line.decode("gbk").split("\t")) - return lines - -class NewsProcessor(DataProcessor): - """Processor for the MRPC data set (GLUE version).""" - - def __init__(self): - self.labels = set() - - def get_train_examples(self, data_dir): - """See base class.""" - logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv"))) - return self._create_examples( - self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") - - def get_dev_examples(self, data_dir): - """See base class.""" - return self._create_examples( - self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") - - def get_labels(self): - """See base class.""" - return list(self.labels) - - def _create_examples(self, lines, set_type): - """Creates examples for the training and dev sets.""" - examples = [] - for (i, line) in enumerate(lines): - guid = "%s-%s" % (set_type, i) - text_a = tokenization.convert_to_unicode(line[1]) - label = tokenization.convert_to_unicode(line[0]) - self.labels.add(label) - examples.append( - InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) - - - return examples - -class MrpcProcessor(DataProcessor): - """Processor for the MRPC data set (GLUE version).""" - - def get_train_examples(self, data_dir): - """See base class.""" - logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv"))) - return self._create_examples( - self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") - - def get_dev_examples(self, data_dir): - """See base class.""" - return self._create_examples( - self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") - - def get_labels(self): - """See base class.""" - return ["0", "1"] - - def _create_examples(self, lines, set_type): - """Creates examples for the training and dev sets.""" - examples = [] - for (i, line) in enumerate(lines): - if i == 0: - continue - guid = "%s-%s" % (set_type, i) - text_a = tokenization.convert_to_unicode(line[3]) - text_b = tokenization.convert_to_unicode(line[4]) - label = tokenization.convert_to_unicode(line[0]) - examples.append( - InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) - return examples - -class MnliProcessor(DataProcessor): - """Processor for the MultiNLI data set (GLUE version).""" - - def get_train_examples(self, data_dir): - """See base class.""" - return self._create_examples( - self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") - - def get_dev_examples(self, data_dir): - """See base class.""" - return self._create_examples( - self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), - "dev_matched") - - def get_labels(self): - """See base class.""" - return ["contradiction", "entailment", "neutral"] - - def _create_examples(self, lines, set_type): - """Creates examples for the training and dev sets.""" - examples = [] - for (i, line) in enumerate(lines): - if i == 0: - continue - guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0])) - text_a = tokenization.convert_to_unicode(line[8]) - text_b = tokenization.convert_to_unicode(line[9]) - label = tokenization.convert_to_unicode(line[-1]) - examples.append( - InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) - return examples - - -class ColaProcessor(DataProcessor): - """Processor for the CoLA data set (GLUE version).""" - - def get_train_examples(self, data_dir): - """See base class.""" - return self._create_examples( - self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") - - def get_dev_examples(self, data_dir): - """See base class.""" - return self._create_examples( - self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") - - def get_labels(self): - """See base class.""" - return ["0", "1"] - - def _create_examples(self, lines, set_type): - """Creates examples for the training and dev sets.""" - examples = [] - for (i, line) in enumerate(lines): - guid = "%s-%s" % (set_type, i) - text_a = tokenization.convert_to_unicode(line[3]) - label = tokenization.convert_to_unicode(line[1]) - examples.append( - InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) - return examples - - -def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer): - """Loads a data file into a list of `InputBatch`s.""" - - label_map = {} - for (i, label) in enumerate(label_list): - label_map[label] = i - features = [] - for (ex_index, example) in enumerate(examples): - tokens_a = tokenizer.tokenize(example.text_a) - - tokens_b = None - if example.text_b: - tokens_b = tokenizer.tokenize(example.text_b) - - if tokens_b: - # Modifies `tokens_a` and `tokens_b` in place so that the total - # length is less than the specified length. - # Account for [CLS], [SEP], [SEP] with "- 3" - _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) - else: - # Account for [CLS] and [SEP] with "- 2" - if len(tokens_a) > max_seq_length - 2: - tokens_a = tokens_a[0:(max_seq_length - 2)] - - # The convention in BERT is: - # (a) For sequence pairs: - # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] - # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 - # (b) For single sequences: - # tokens: [CLS] the dog is hairy . [SEP] - # type_ids: 0 0 0 0 0 0 0 - # - # Where "type_ids" are used to indicate whether this is the first - # sequence or the second sequence. The embedding vectors for `type=0` and - # `type=1` were learned during pre-training and are added to the wordpiece - # embedding vector (and position vector). This is not *strictly* necessary - # since the [SEP] token unambigiously separates the sequences, but it makes - # it easier for the model to learn the concept of sequences. - # - # For classification tasks, the first vector (corresponding to [CLS]) is - # used as as the "sentence vector". Note that this only makes sense because - # the entire model is fine-tuned. - tokens = [] - segment_ids = [] - tokens.append("[CLS]") - segment_ids.append(0) - for token in tokens_a: - tokens.append(token) - segment_ids.append(0) - tokens.append("[SEP]") - segment_ids.append(0) - - if tokens_b: - for token in tokens_b: - tokens.append(token) - segment_ids.append(1) - tokens.append("[SEP]") - segment_ids.append(1) - - input_ids = tokenizer.convert_tokens_to_ids(tokens) - - # The mask has 1 for real tokens and 0 for padding tokens. Only real - # tokens are attended to. - input_mask = [1] * len(input_ids) - - # Zero-pad up to the sequence length. - while len(input_ids) < max_seq_length: - input_ids.append(0) - input_mask.append(0) - segment_ids.append(0) - - assert len(input_ids) == max_seq_length - assert len(input_mask) == max_seq_length - assert len(segment_ids) == max_seq_length - - label_id = label_map[example.label] - if ex_index < 5: - logger.info("*** Example ***") - logger.info("guid: %s" % (example.guid)) - logger.info("tokens: %s" % " ".join( - [tokenization.printable_text(x) for x in tokens])) - logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) - logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) - logger.info( - "segment_ids: %s" % " ".join([str(x) for x in segment_ids])) - logger.info("label: %s (id = %d)" % (example.label, label_id)) - - features.append( - InputFeatures( - input_ids=input_ids, - input_mask=input_mask, - segment_ids=segment_ids, - label_id=label_id)) - return features - - -def _truncate_seq_pair(tokens_a, tokens_b, max_length): - """Truncates a sequence pair in place to the maximum length.""" - - # This is a simple heuristic which will always truncate the longer sequence - # one token at a time. This makes more sense than truncating an equal percent - # of tokens from each, since if one sequence is very short then each token - # that's truncated likely contains more information than a longer sequence. - while True: - total_length = len(tokens_a) + len(tokens_b) - if total_length <= max_length: - break - if len(tokens_a) > len(tokens_b): - tokens_a.pop() - else: - tokens_b.pop() - -def accuracy(out, labels): - outputs = np.argmax(out, axis=1) - return np.sum(outputs==labels) - -def copy_optimizer_params_to_model(named_params_model, named_params_optimizer): - """ Utility function for optimize_on_cpu and 16-bits training. - Copy the parameters optimized on CPU/RAM back to the model on GPU - """ - for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): - if name_opti != name_model: - logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) - raise ValueError - param_model.data.copy_(param_opti.data) - -def set_optimizer_params_grad(named_params_optimizer, named_params_model, test_nan=False): - """ Utility function for optimize_on_cpu and 16-bits training. - Copy the gradient of the GPU parameters to the CPU/RAMM copy of the model - """ - is_nan = False - for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): - if name_opti != name_model: - logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) - raise ValueError - if test_nan and torch.isnan(param_model.grad).sum() > 0: - is_nan = True - if param_opti.grad is None: - param_opti.grad = torch.nn.Parameter(param_opti.data.new().resize_(*param_opti.data.size())) - param_opti.grad.data.copy_(param_model.grad.data) - return is_nan - -def main(): - parser = argparse.ArgumentParser() - - ## Required parameters - parser.add_argument("--data_dir", - default=None, - type=str, - required=True, - help="The input data dir. Should contain the .tsv files (or other data files) for the task.") - parser.add_argument("--bert_config_file", - default=None, - type=str, - required=True, - help="The config json file corresponding to the pre-trained BERT model. \n" - "This specifies the model architecture.") - parser.add_argument("--task_name", - default=None, - type=str, - required=True, - help="The name of the task to train.") - parser.add_argument("--vocab_file", - default=None, - type=str, - required=True, - help="The vocabulary file that the BERT model was trained on.") - parser.add_argument("--output_dir", - default=None, - type=str, - required=True, - help="The output directory where the model checkpoints will be written.") - - ## Other parameters - parser.add_argument("--init_checkpoint", - default=None, - type=str, - help="Initial checkpoint (usually from a pre-trained BERT model).") - parser.add_argument("--do_lower_case", - default=False, - action='store_true', - help="Whether to lower case the input text. True for uncased models, False for cased models.") - parser.add_argument("--max_seq_length", - default=128, - type=int, - help="The maximum total input sequence length after WordPiece tokenization. \n" - "Sequences longer than this will be truncated, and sequences shorter \n" - "than this will be padded.") - parser.add_argument("--do_train", - default=False, - action='store_true', - help="Whether to run training.") - parser.add_argument("--do_eval", - default=False, - action='store_true', - help="Whether to run eval on the dev set.") - parser.add_argument("--train_batch_size", - default=32, - type=int, - help="Total batch size for training.") - parser.add_argument("--eval_batch_size", - default=8, - type=int, - help="Total batch size for eval.") - parser.add_argument("--learning_rate", - default=5e-5, - type=float, - help="The initial learning rate for Adam.") - parser.add_argument("--num_train_epochs", - default=3.0, - type=float, - help="Total number of training epochs to perform.") - parser.add_argument("--warmup_proportion", - default=0.1, - type=float, - help="Proportion of training to perform linear learning rate warmup for. " - "E.g., 0.1 = 10%% of training.") - parser.add_argument("--save_checkpoints_steps", - default=1000, - type=int, - help="How often to save the model checkpoint.") - parser.add_argument("--no_cuda", - default=False, - action='store_true', - help="Whether not to use CUDA when available") - parser.add_argument("--local_rank", - type=int, - default=-1, - help="local_rank for distributed training on gpus") - parser.add_argument('--seed', - type=int, - default=42, - help="random seed for initialization") - parser.add_argument('--gradient_accumulation_steps', - type=int, - default=1, - help="Number of updates steps to accumualte before performing a backward/update pass.") - parser.add_argument('--optimize_on_cpu', - default=False, - action='store_true', - help="Whether to perform optimization and keep the optimizer averages on CPU") - parser.add_argument('--fp16', - default=False, - action='store_true', - help="Whether to use 16-bit float precision instead of 32-bit") - parser.add_argument('--loss_scale', - type=float, default=128, - help='Loss scaling, positive power of 2 values can improve fp16 convergence.') - - args = parser.parse_args() - - processors = { - "cola": ColaProcessor, - "mnli": MnliProcessor, - "mrpc": MrpcProcessor, - "news": NewsProcessor, - } - - if args.local_rank == -1 or args.no_cuda: - device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") - n_gpu = torch.cuda.device_count() - else: - device = torch.device("cuda", args.local_rank) - n_gpu = 1 - # Initializes the distributed backend which will take care of sychronizing nodes/GPUs - # torch.distributed.init_process_group(backend='nccl') - if args.fp16: - logger.info("16-bits training currently not supported in distributed training") - args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496) - logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) - - if args.gradient_accumulation_steps < 1: - raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( - args.gradient_accumulation_steps)) - - args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps) - - random.seed(args.seed) - np.random.seed(args.seed) - torch.manual_seed(args.seed) - if n_gpu > 0: - torch.cuda.manual_seed_all(args.seed) - - if not args.do_train and not args.do_eval: - raise ValueError("At least one of `do_train` or `do_eval` must be True.") - - bert_config = BertConfig.from_json_file(args.bert_config_file) - - if args.max_seq_length > bert_config.max_position_embeddings: - raise ValueError( - "Cannot use sequence length {} because the BERT model was only trained up to sequence length {}".format( - args.max_seq_length, bert_config.max_position_embeddings)) - - if os.path.exists(args.output_dir) and os.listdir(args.output_dir): - raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) - os.makedirs(args.output_dir, exist_ok=True) - - task_name = args.task_name.lower() - - if task_name not in processors: - raise ValueError("Task not found: %s" % (task_name)) - - - processor = processors[task_name]() - - tokenizer = tokenization.FullTokenizer( - vocab_file=args.vocab_file, do_lower_case=args.do_lower_case) - - train_examples = None - num_train_steps = None - if args.do_train: - train_examples = processor.get_train_examples(args.data_dir) - num_train_steps = int( - len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) - - label_list = processor.get_labels() - - print("label_list.size:%d\n" %(len(label_list))) - - # Prepare model - model = BertForSequenceClassification(bert_config, len(label_list)) - if args.init_checkpoint is not None: - model.bert.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu')) - if args.fp16: - model.half() - model.to(device) - #if args.local_rank != -1: - #model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], - # output_device=args.local_rank) - #elif n_gpu > 1: - # model = torch.nn.DataParallel(model) - - # Prepare optimizer - if args.fp16: - param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_()) \ - for n, param in model.named_parameters()] - elif args.optimize_on_cpu: - param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_()) \ - for n, param in model.named_parameters()] - else: - param_optimizer = list(model.named_parameters()) - no_decay = ['bias', 'gamma', 'beta'] - optimizer_grouped_parameters = [ - {'params': [p for n, p in param_optimizer if n not in no_decay], 'weight_decay_rate': 0.01}, - {'params': [p for n, p in param_optimizer if n in no_decay], 'weight_decay_rate': 0.0} - ] - optimizer = BERTAdam(optimizer_grouped_parameters, - lr=args.learning_rate, - warmup=args.warmup_proportion, - t_total=num_train_steps) - - global_step = 0 - if args.do_train: - train_features = convert_examples_to_features( - train_examples, label_list, args.max_seq_length, tokenizer) - logger.info("***** Running training *****") - logger.info(" Num examples = %d", len(train_examples)) - logger.info(" Batch size = %d", args.train_batch_size) - logger.info(" Num steps = %d", num_train_steps) - all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) - all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) - all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) - all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long) - train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) - if args.local_rank == -1: - train_sampler = RandomSampler(train_data) - else: - - train_sampler = RandomSampler(train_data) - #train_sampler = DistributedSampler(train_data) - train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) - - model.train() - for _ in trange(int(args.num_train_epochs), desc="Epoch"): - tr_loss = 0 - nb_tr_examples, nb_tr_steps = 0, 0 - for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): - batch = tuple(t.to(device) for t in batch) - input_ids, input_mask, segment_ids, label_ids = batch - loss, _ = model(input_ids, segment_ids, input_mask, label_ids) - if n_gpu > 1: - loss = loss.mean() # mean() to average on multi-gpu. - if args.fp16 and args.loss_scale != 1.0: - # rescale loss for fp16 training - # see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html - loss = loss * args.loss_scale - if args.gradient_accumulation_steps > 1: - loss = loss / args.gradient_accumulation_steps - loss.backward() - tr_loss += loss.item() - nb_tr_examples += input_ids.size(0) - nb_tr_steps += 1 - if (step + 1) % args.gradient_accumulation_steps == 0: - if args.fp16 or args.optimize_on_cpu: - if args.fp16 and args.loss_scale != 1.0: - # scale down gradients for fp16 training - for param in model.parameters(): - param.grad.data = param.grad.data / args.loss_scale - is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True) - if is_nan: - logger.info("FP16 TRAINING: Nan in gradients, reducing loss scaling") - args.loss_scale = args.loss_scale / 2 - model.zero_grad() - continue - optimizer.step() - copy_optimizer_params_to_model(model.named_parameters(), param_optimizer) - else: - optimizer.step() - model.zero_grad() - global_step += 1 - - if args.do_eval: - eval_examples = processor.get_dev_examples(args.data_dir) - eval_features = convert_examples_to_features( - eval_examples, label_list, args.max_seq_length, tokenizer) - logger.info("***** Running evaluation *****") - logger.info(" Num examples = %d", len(eval_examples)) - logger.info(" Batch size = %d", args.eval_batch_size) - all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) - all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) - all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) - all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) - eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) - if args.local_rank == -1: - eval_sampler = SequentialSampler(eval_data) - else: - - eval_sampler = SequentialSampler(eval_data) - #eval_sampler = DistributedSampler(eval_data) - eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) - - model.eval() - eval_loss, eval_accuracy = 0, 0 - nb_eval_steps, nb_eval_examples = 0, 0 - for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: - input_ids = input_ids.to(device) - input_mask = input_mask.to(device) - segment_ids = segment_ids.to(device) - label_ids = label_ids.to(device) - - with torch.no_grad(): - tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, label_ids) - - logits = logits.detach().cpu().numpy() - label_ids = label_ids.to('cpu').numpy() - tmp_eval_accuracy = accuracy(logits, label_ids) - - eval_loss += tmp_eval_loss.mean().item() - eval_accuracy += tmp_eval_accuracy - - nb_eval_examples += input_ids.size(0) - nb_eval_steps += 1 - - eval_loss = eval_loss / nb_eval_steps - eval_accuracy = eval_accuracy / nb_eval_examples - - result = {'eval_loss': eval_loss, - 'eval_accuracy': eval_accuracy, - 'global_step': global_step, - 'loss': tr_loss/nb_tr_steps} - - output_eval_file = os.path.join(args.output_dir, "eval_results.txt") - with open(output_eval_file, "w") as writer: - logger.info("***** Eval results *****") - for key in sorted(result.keys()): - logger.info(" %s = %s", key, str(result[key])) - writer.write("%s = %s\n" % (key, str(result[key]))) - -if __name__ == "__main__": - main() diff --git a/NLP/16.8 BERT/tokenization_word.py b/NLP/16.8 BERT/tokenization_word.py deleted file mode 100644 index a34d64a..0000000 --- a/NLP/16.8 BERT/tokenization_word.py +++ /dev/null @@ -1,342 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Tokenization classes.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import unicodedata -import six - -def convert_to_unicode(text): - """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" - if six.PY3: - if isinstance(text, str): - return text - elif isinstance(text, bytes): - return text.decode("utf-8", "ignore") - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - elif six.PY2: - if isinstance(text, str): - return text.decode("utf-8", "ignore") - elif isinstance(text, unicode): - return text - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - else: - raise ValueError("Not running on Python2 or Python 3?") - - -def printable_text(text): - """Returns text encoded in a way suitable for print or `tf.logging`.""" - - # These functions want `str` for both Python2 and Python3, but in one case - # it's a Unicode string and in the other it's a byte string. - if six.PY3: - if isinstance(text, str): - return text - elif isinstance(text, bytes): - return text.decode("utf-8", "ignore") - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - elif six.PY2: - if isinstance(text, str): - return text - elif isinstance(text, unicode): - return text.encode("utf-8") - else: - raise ValueError("Unsupported string type: %s" % (type(text))) - else: - raise ValueError("Not running on Python2 or Python 3?") - - -def load_vocab(vocab_file): - """Loads a vocabulary file into a dictionary.""" - vocab = collections.OrderedDict() - - index_vocab = collections.OrderedDict() - index = 0 - with open(vocab_file, "rb") as reader: - while True: - tmp = reader.readline() - token = convert_to_unicode(tmp) - - - if not token: - break - - #file_out.write("%d\t%s\n" %(index,token)) - token = token.strip() - vocab[token] = index - index_vocab[index]=token - index += 1 - - - return vocab,index_vocab - - -def convert_tokens_to_ids(vocab, tokens): - """Converts a sequence of tokens into ids using the vocab.""" - ids = [] - for token in tokens: - ids.append(vocab[token]) - return ids - - -def whitespace_tokenize(text): - """Runs basic whitespace cleaning and splitting on a peice of text.""" - text = text.strip() - if not text: - return [] - tokens = text.split() - return tokens - - -class FullTokenizer(object): - """Runs end-to-end tokenziation.""" - - def __init__(self, vocab_file, do_lower_case=True): - self.vocab,self.index_vocab = load_vocab(vocab_file) - self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) - self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) - - def tokenize(self, text): - split_tokens = [] - for token in self.basic_tokenizer.tokenize(text): - for sub_token in self.wordpiece_tokenizer.tokenize(token): - split_tokens.append(sub_token) - - return split_tokens - - def convert_tokens_to_ids(self, tokens): - return convert_tokens_to_ids(self.vocab, tokens) - - -class BasicTokenizer(object): - """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" - - def __init__(self, do_lower_case=True): - """Constructs a BasicTokenizer. - - Args: - do_lower_case: Whether to lower case the input. - """ - self.do_lower_case = do_lower_case - - def tokenize(self, text): - """Tokenizes a piece of text.""" - text = convert_to_unicode(text) - text = self._clean_text(text) - # This was added on November 1st, 2018 for the multilingual and Chinese - # models. This is also applied to the English models now, but it doesn't - # matter since the English models were not trained on any Chinese data - # and generally don't have any Chinese data in them (there are Chinese - # characters in the vocabulary because Wikipedia does have some Chinese - # words in the English Wikipedia.). - text = self._tokenize_chinese_chars(text) - orig_tokens = whitespace_tokenize(text) - split_tokens = [] - for token in orig_tokens: - if self.do_lower_case: - token = token.lower() - token = self._run_strip_accents(token) - split_tokens.extend(self._run_split_on_punc(token)) - - output_tokens = whitespace_tokenize(" ".join(split_tokens)) - return output_tokens - - def _run_strip_accents(self, text): - """Strips accents from a piece of text.""" - text = unicodedata.normalize("NFD", text) - output = [] - for char in text: - cat = unicodedata.category(char) - if cat == "Mn": - continue - output.append(char) - return "".join(output) - - def _run_split_on_punc(self, text): - """Splits punctuation on a piece of text.""" - chars = list(text) - i = 0 - start_new_word = True - output = [] - while i < len(chars): - char = chars[i] - if _is_punctuation(char): - output.append([char]) - start_new_word = True - else: - if start_new_word: - output.append([]) - start_new_word = False - output[-1].append(char) - i += 1 - - return ["".join(x) for x in output] - - def _tokenize_chinese_chars(self, text): - """Adds whitespace around any CJK character.""" - output = [] - for char in text: - cp = ord(char) - if self._is_chinese_char(cp): - output.append(" ") - output.append(char) - output.append(" ") - else: - output.append(char) - return "".join(output) - - def _is_chinese_char(self, cp): - """Checks whether CP is the codepoint of a CJK character.""" - # This defines a "chinese character" as anything in the CJK Unicode block: - # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) - # - # Note that the CJK Unicode block is NOT all Japanese and Korean characters, - # despite its name. The modern Korean Hangul alphabet is a different block, - # as is Japanese Hiragana and Katakana. Those alphabets are used to write - # space-separated words, so they are not treated specially and handled - # like the all of the other languages. - if ((cp >= 0x4E00 and cp <= 0x9FFF) or # - (cp >= 0x3400 and cp <= 0x4DBF) or # - (cp >= 0x20000 and cp <= 0x2A6DF) or # - (cp >= 0x2A700 and cp <= 0x2B73F) or # - (cp >= 0x2B740 and cp <= 0x2B81F) or # - (cp >= 0x2B820 and cp <= 0x2CEAF) or - (cp >= 0xF900 and cp <= 0xFAFF) or # - (cp >= 0x2F800 and cp <= 0x2FA1F)): # - return True - - return False - - def _clean_text(self, text): - """Performs invalid character removal and whitespace cleanup on text.""" - output = [] - for char in text: - cp = ord(char) - if cp == 0 or cp == 0xfffd or _is_control(char): - continue - if _is_whitespace(char): - output.append(" ") - else: - output.append(char) - return "".join(output) - - -class WordpieceTokenizer(object): - """Runs WordPiece tokenization.""" - - def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100): - self.vocab = vocab - self.unk_token = unk_token - self.max_input_chars_per_word = max_input_chars_per_word - - def tokenize(self, text): - """Tokenizes a piece of text into its word pieces. - - This uses a greedy longest-match-first algorithm to perform tokenization - using the given vocabulary. - - For example: - input = "unaffable" - output = ["un", "##aff", "##able"] - - Args: - text: A single token or whitespace separated tokens. This should have - already been passed through `BasicTokenizer. - - Returns: - A list of wordpiece tokens. - """ - - text = convert_to_unicode(text) - - output_tokens = [] - for token in whitespace_tokenize(text): - chars = list(token) - if len(chars) > self.max_input_chars_per_word: - output_tokens.append(self.unk_token) - continue - - is_bad = False - start = 0 - sub_tokens = [] - while start < len(chars): - end = len(chars) - cur_substr = None - while start < end: - substr = "".join(chars[start:end]) - if start > 0: - substr = "##" + substr - if substr in self.vocab: - cur_substr = substr - break - end -= 1 - if cur_substr is None: - is_bad = True - break - sub_tokens.append(cur_substr) - start = end - - if is_bad: - output_tokens.append(self.unk_token) - else: - output_tokens.extend(sub_tokens) - return output_tokens - - -def _is_whitespace(char): - """Checks whether `chars` is a whitespace character.""" - # \t, \n, and \r are technically contorl characters but we treat them - # as whitespace since they are generally considered as such. - if char == " " or char == "\t" or char == "\n" or char == "\r": - return True - cat = unicodedata.category(char) - if cat == "Zs": - return True - return False - - -def _is_control(char): - """Checks whether `chars` is a control character.""" - # These are technically control characters but we count them as whitespace - # characters. - if char == "\t" or char == "\n" or char == "\r": - return False - cat = unicodedata.category(char) - if cat.startswith("C"): - return True - return False - - -def _is_punctuation(char): - """Checks whether `chars` is a punctuation character.""" - cp = ord(char) - # We treat all non-letter/number ASCII as punctuation. - # Characters such as "^", "$", and "`" are not in the Unicode - # Punctuation class but we treat them as punctuation anyways, for - # consistency. - if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or - (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): - return True - cat = unicodedata.category(char) - if cat.startswith("P"): - return True - return False diff --git a/NLP/16.9 XLNet/README.md b/NLP/16.9 XLNet/README.md deleted file mode 100644 index 2f0fffe..0000000 --- a/NLP/16.9 XLNet/README.md +++ /dev/null @@ -1,176 +0,0 @@ -## 目录 -- [1. 什么是XLNet](#1-什么是xlnet) -- [2. 自回归语言模型（Autoregressive LM）](#2-自回归语言模型autoregressive-lm) -- [3. 自编码语言模型（Autoencoder LM）](#3-自编码语言模型autoencoder-lm) -- [4. XLNet模型](#4-xlnet模型) - - [4.1 排列语言建模（Permutation Language Modeling）](#41-排列语言建模permutation-language-modeling) - - [4.2 Transformer XL](#42-transformer-xl) -- [5. XLNet与BERT比较](#5-xlnet与bert比较) -- [6. 代码实现](#6-代码实现) -- [7. 参考文献](#7-参考文献) - -## 1. 什么是XLNet - -XLNet 是一个类似 BERT 的模型，而不是完全不同的模型。总之，**XLNet是一种通用的自回归预训练方法**。它是CMU和Google Brain团队在2019年6月份发布的模型，最终，XLNet 在 20 个任务上超过了 BERT 的表现，并在 18 个任务上取得了当前最佳效果（state-of-the-art），包括机器问答、自然语言推断、情感分析和文档排序。 - -作者表示，BERT 这样基于去噪自编码器的预训练模型可以很好地建模双向语境信息，性能优于基于自回归语言模型的预训练方法。然而，由于需要 mask 一部分输入，BERT 忽略了被 mask 位置之间的依赖关系，因此出现预训练和微调效果的差异（pretrain-finetune discrepancy）。 - -基于这些优缺点，该研究提出了一种泛化的自回归预训练模型 XLNet。XLNet 可以： - -1. 通过最大化所有可能的因式分解顺序的对数似然，学习双向语境信息； -2. 用自回归本身的特点克服 BERT 的缺点； -3. 此外，XLNet 还融合了当前最优自回归模型 Transformer-XL 的思路。 - - - -## 2. 自回归语言模型（Autoregressive LM） - -在ELMO／BERT出来之前，大家通常讲的语言模型其实是根据上文内容预测下一个可能跟随的单词，就是常说的自左向右的语言模型任务，或者反过来也行，就是根据下文预测前面的单词，这种类型的LM被称为自回归语言模型。GPT 就是典型的自回归语言模型。ELMO尽管看上去利用了上文，也利用了下文，但是本质上仍然是自回归LM，这个跟模型具体怎么实现有关系。ELMO是做了两个方向（从左到右以及从右到左两个方向的语言模型），但是是分别有两个方向的自回归LM，然后把LSTM的两个方向的隐节点状态拼接到一起，来体现双向语言模型这个事情的。所以其实是两个自回归语言模型的拼接，本质上仍然是自回归语言模型。 - -自回归语言模型有优点有缺点： - -**缺点**是只能利用上文或者下文的信息，不能同时利用上文和下文的信息，当然，貌似ELMO这种双向都做，然后拼接看上去能够解决这个问题，因为融合模式过于简单，所以效果其实并不是太好。 - -**优点**其实跟下游NLP任务有关，比如生成类NLP任务，比如文本摘要，机器翻译等，在实际生成内容的时候，就是从左向右的，自回归语言模型天然匹配这个过程。而Bert这种DAE模式，在生成类NLP任务中，就面临训练过程和应用过程不一致的问题，导致生成类的NLP任务到目前为止都做不太好。 - - - -## 3. 自编码语言模型（Autoencoder LM） - -自回归语言模型只能根据上文预测下一个单词，或者反过来，只能根据下文预测前面一个单词。相比而言，Bert通过在输入X中随机Mask掉一部分单词，然后预训练过程的主要任务之一是根据上下文单词来预测这些被Mask掉的单词，如果你对Denoising Autoencoder比较熟悉的话，会看出，这确实是典型的DAE的思路。那些被Mask掉的单词就是在输入侧加入的所谓噪音。类似Bert这种预训练模式，被称为DAE LM。 - -这种DAE LM的优缺点正好和自回归LM反过来，它能比较自然地融入双向语言模型，同时看到被预测单词的上文和下文，这是好处。缺点是啥呢？主要在输入侧引入[Mask]标记，导致预训练阶段和Fine-tuning阶段不一致的问题，因为Fine-tuning阶段是看不到[Mask]标记的。DAE吗，就要引入噪音，[Mask] 标记就是引入噪音的手段，这个正常。 - -XLNet的出发点就是：能否融合自回归LM和DAE LM两者的优点。就是说如果站在自回归LM的角度，如何引入和双向语言模型等价的效果；如果站在DAE LM的角度看，它本身是融入双向语言模型的，如何抛掉表面的那个[Mask]标记，让预训练和Fine-tuning保持一致。当然，XLNet还讲到了一个Bert被Mask单词之间相互独立的问题。 - - - -## 4. XLNet模型 - -### 4.1 排列语言建模（Permutation Language Modeling） - -Bert的自编码语言模型也有对应的缺点，就是XLNet在文中指出的： - -1. 第一个预训练阶段因为采取引入[Mask]标记来Mask掉部分单词的训练模式，而Fine-tuning阶段是看不到这种被强行加入的Mask标记的，所以两个阶段存在使用模式不一致的情形，这可能会带来一定的性能损失； -2. 另外一个是，Bert在第一个预训练阶段，假设句子中多个单词被Mask掉，这些被Mask掉的单词之间没有任何关系，是条件独立的，而有时候这些单词之间是有关系的。 - -上面两点是XLNet在第一个预训练阶段，相对Bert来说要解决的两个问题。 - -其实思路也比较简洁，可以这么思考：XLNet仍然遵循两阶段的过程，第一个阶段是语言模型预训练阶段；第二阶段是任务数据Fine-tuning阶段。它主要希望改动第一个阶段，就是说不像Bert那种带Mask符号的Denoising-autoencoder的模式，而是采用自回归LM的模式。就是说，看上去输入句子X仍然是自左向右的输入，看到Ti单词的上文Context_before，来预测Ti这个单词。但是又希望在Context_before里，不仅仅看到上文单词，也能看到Ti单词后面的下文Context_after里的下文单词，这样的话，Bert里面预训练阶段引入的Mask符号就不需要了，于是在预训练阶段，看上去是个标准的从左向右过程，Fine-tuning当然也是这个过程，于是两个环节就统一起来。当然，这是目标。剩下是怎么做到这一点的问题。 - -![](https://pic4.zhimg.com/80/v2-948e085be7a9a2eb7eac2d12069b1a93_hd.jpg) - -首先，需要强调一点，尽管上面讲的是把句子X的单词排列组合后，再随机抽取例子作为输入，但是，实际上你是不能这么做的，因为Fine-tuning阶段你不可能也去排列组合原始输入。所以，就必须让预训练阶段的输入部分，看上去仍然是x1,x2,x3,x4这个输入顺序，但是可以在Transformer部分做些工作，来达成我们希望的目标。 - -具体而言，XLNet采取了Attention掩码的机制，你可以理解为，当前的输入句子是X，要预测的单词Ti是第i个单词，前面1到i-1个单词，在输入部分观察，并没发生变化，该是谁还是谁。但是在Transformer内部，通过Attention掩码，从X的输入单词里面，也就是Ti的上文和下文单词中，随机选择i-1个，放到Ti的上文位置中，把其它单词的输入通过Attention掩码隐藏掉，于是就能够达成我们期望的目标（当然这个所谓放到Ti的上文位置，只是一种形象的说法，其实在内部，就是通过Attention Mask，把其它没有被选到的单词Mask掉，不让它们在预测单词Ti的时候发生作用，如此而已。看着就类似于把这些被选中的单词放到了上文Context_before的位置了）。 - -具体实现的时候，XLNet是用“双流自注意力模型”实现的，细节可以参考论文，但是基本思想就如上所述，双流自注意力机制只是实现这个思想的具体方式，理论上，你可以想出其它具体实现方式来实现这个基本思想，也能达成让Ti看到下文单词的目标。 - -这里简单说下“**双流自注意力机制**”，一个是内容流自注意力，其实就是标准的Transformer的计算过程；主要是引入了Query流自注意力，这个是干嘛的呢？其实就是用来代替Bert的那个[Mask]标记的，因为XLNet希望抛掉[Mask]标记符号，但是比如知道上文单词x1,x2，要预测单词x3，此时在x3对应位置的Transformer最高层去预测这个单词，但是输入侧不能看到要预测的单词x3，Bert其实是直接引入[Mask]标记来覆盖掉单词x3的内容的，等于说[Mask]是个通用的占位符号。而XLNet因为要抛掉[Mask]标记，但是又不能看到x3的输入，于是Query流，就直接忽略掉x3输入了，只保留这个位置信息，用参数w来代表位置的embedding编码。其实XLNet只是扔了表面的[Mask]占位符号，内部还是引入Query流来忽略掉被Mask的这个单词。和Bert比，只是实现方式不同而已。 - -![](https://pic1.zhimg.com/80/v2-2bb1a60af4fe2fa751647fdce48e337c_hd.jpg) - -上面讲的Permutation Language Model是XLNet的主要理论创新，所以介绍的比较多，从模型角度讲，这个创新还是挺有意思的，因为它开启了自回归语言模型如何引入下文的一个思路，相信对于后续工作会有启发。当然，XLNet不仅仅做了这些，它还引入了其它的因素，也算是一个当前有效技术的集成体。感觉**XLNet就是Bert、GPT 2.0和Transformer XL的综合体变身**： - -1. 首先，它通过PLM(Permutation Language Model)预训练目标，吸收了Bert的双向语言模型； -2. 然后，GPT2.0的核心其实是更多更高质量的预训练数据，这个明显也被XLNet吸收进来了； -3. 再然后，Transformer XL的主要思想也被吸收进来，它的主要目标是解决Transformer对于长文档NLP应用不够友好的问题。 - - - -### 4.2 Transformer XL - -目前在NLP领域中，处理语言建模问题有两种最先进的架构：RNN和Transformer。RNN按照序列顺序逐个学习输入的单词或字符之间的关系，而Transformer则接收一整段序列，然后使用self-attention机制来学习它们之间的依赖关系。这两种架构目前来看都取得了令人瞩目的成就，但它们都局限在捕捉长期依赖性上。 - -为了解决这一问题，CMU联合Google Brain在2019年1月推出的一篇新论文《Transformer-XL：Attentive Language Models beyond a Fixed-Length Context》同时结合了RNN序列建模和Transformer自注意力机制的优点，在输入数据的每个段上使用Transformer的注意力模块，并使用循环机制来学习连续段之间的依赖关系。 - - - -#### 4.2.1 vanilla Transformer - -为何要提这个模型？因为Transformer-XL是基于这个模型进行的改进。 - -Al-Rfou等人基于Transformer提出了一种训练语言模型的方法，来根据之前的字符预测片段中的下一个字符。例如，它使用 ![[公式]](https://www.zhihu.com/equation?tex=x_1,x_2,...,x_{n-1})预测字符 ![[公式]](https://www.zhihu.com/equation?tex=x_n)，而在 ![[公式]](https://www.zhihu.com/equation?tex=x_n) 之后的序列则被mask掉。论文中使用64层模型，并仅限于处理 512个字符这种相对较短的输入，因此它将输入分成段，并分别从每个段中进行学习，如下图所示。 在测试阶段如需处理较长的输入，该模型会在每一步中将输入向右移动一个字符，以此实现对单个字符的预测。 - -![](https://img-blog.csdnimg.cn/20190407095512873.png) - -该模型在常用的数据集如enwik8和text8上的表现比RNN模型要好，但它仍有以下缺点： - -- **上下文长度受限**：字符之间的最大依赖距离受输入长度的限制，模型看不到出现在几个句子之前的单词。 -- **上下文碎片**：对于长度超过512个字符的文本，都是从头开始单独训练的。段与段之间没有上下文依赖性，会让训练效率低下，也会影响模型的性能。 -- **推理速度慢**：在测试阶段，每次预测下一个单词，都需要重新构建一遍上下文，并从头开始计算，这样的计算速度非常慢。 - - - -#### 4.2.2 Transformer XL - -Transformer-XL架构在vanilla Transformer的基础上引入了两点创新：循环机制（Recurrence Mechanism）和相对位置编码（Relative Positional Encoding），以克服vanilla Transformer的缺点。与vanilla Transformer相比，Transformer-XL的另一个优势是它可以被用于单词级和字符级的语言建模。 - -1. **引入循环机制** - - 与vanilla Transformer的基本思路一样，Transformer-XL仍然是使用分段的方式进行建模，但其与vanilla Transformer的本质不同是在于引入了段与段之间的循环机制，使得当前段在建模的时候能够利用之前段的信息来实现长期依赖性。如下图所示： - - ![](https://img-blog.csdnimg.cn/20190407095601191.png) - - 在训练阶段，处理后面的段时，每个隐藏层都会接收两个输入： - - - 该段的前面隐藏层的输出，与vanilla Transformer相同（上图的灰色线）。 - - 前面段的隐藏层的输出（上图的绿色线），可以使模型创建长期依赖关系。 - - 这两个输入会被拼接，然后用于计算当前段的Key和Value矩阵。 - - 该方法可以利用前面更多段的信息，测试阶段也可以获得更长的依赖。在测试阶段，与vanilla Transformer相比，其速度也会更快。在vanilla Transformer中，一次只能前进一个step，并且需要重新构建段，并全部从头开始计算；而在Transformer-XL中，每次可以前进一整个段，并利用之前段的数据来预测当前段的输出。 - - - -2. **相对位置编码** - - 在Transformer中，一个重要的地方在于其考虑了序列的位置信息。在分段的情况下，如果仅仅对于每个段仍直接使用Transformer中的位置编码，即每个不同段在同一个位置上的表示使用相同的位置编码，就会出现问题。比如，第i−2i-2i−2段和第i−1i-1i−1段的第一个位置将具有相同的位置编码，但它们对于第iii段的建模重要性显然并不相同（例如第i−2i-2i−2段中的第一个位置重要性可能要低一些）。因此，需要对这种位置进行区分。 - - 论文对于这个问题，提出了一种新的位置编码的方式，即会根据词之间的相对距离而非像Transformer中的绝对位置进行编码。从另一个角度来解读公式的话，可以将attention的计算分为如下四个部分： - - - 基于内容的“寻址”，即没有添加原始位置编码的原始分数。 - - 基于内容的位置偏置，即相对于当前内容的位置偏差。 - - 全局的内容偏置，用于衡量key的重要性。 - - 全局的位置偏置，根据query和key之间的距离调整重要性。 - - 详细公式见：[Transformer-XL解读（论文 + PyTorch源码）](https://blog.csdn.net/magical_bubble/article/details/89060213) - - - -## 5. XLNet与BERT比较 - -尽管看上去，XLNet在预训练机制引入的Permutation Language Model这种新的预训练目标，和Bert采用Mask标记这种方式，有很大不同。其实你深入思考一下，会发现，两者本质是类似的。 - -**区别主要在于**： - -- Bert是直接在输入端显示地通过引入Mask标记，在输入侧隐藏掉一部分单词，让这些单词在预测的时候不发挥作用，要求利用上下文中其它单词去预测某个被Mask掉的单词； -- 而XLNet则抛弃掉输入侧的Mask标记，通过Attention Mask机制，在Transformer内部随机Mask掉一部分单词（这个被Mask掉的单词比例跟当前单词在句子中的位置有关系，位置越靠前，被Mask掉的比例越高，位置越靠后，被Mask掉的比例越低），让这些被Mask掉的单词在预测某个单词的时候不发生作用。 - -所以，本质上两者并没什么太大的不同，只是Mask的位置，Bert更表面化一些，XLNet则把这个过程隐藏在了Transformer内部而已。这样，就可以抛掉表面的[Mask]标记，解决它所说的预训练里带有[Mask]标记导致的和Fine-tuning过程不一致的问题。至于说XLNet说的，Bert里面被Mask掉单词的相互独立问题，也就是说，在预测某个被Mask单词的时候，其它被Mask单词不起作用，这个问题，你深入思考一下，其实是不重要的，因为XLNet在内部Attention Mask的时候，也会Mask掉一定比例的上下文单词，只要有一部分被Mask掉的单词，其实就面临这个问题。而如果训练数据足够大，其实不靠当前这个例子，靠其它例子，也能弥补被Mask单词直接的相互关系问题，因为总有其它例子能够学会这些单词的相互依赖关系。 - -当然，XLNet这种改造，维持了表面看上去的自回归语言模型的从左向右的模式，这个Bert做不到，这个有明显的好处，就是对于生成类的任务，能够在维持表面从左向右的生成过程前提下，模型里隐含了上下文的信息。所以看上去，XLNet貌似应该对于生成类型的NLP任务，会比Bert有明显优势。另外，因为XLNet还引入了Transformer XL的机制，所以对于长文档输入类型的NLP任务，也会比Bert有明显优势。 - - - -## 6. 代码实现 - -[中文XLNet预训练模型](https://github.com/ymcui/Chinese-PreTrained-XLNet) - - - -## 7. 参考文献 - -- [XLNet原理解读](https://blog.csdn.net/weixin_37947156/article/details/93035607) -- [XLNet:运行机制及和Bert的异同比较](https://zhuanlan.zhihu.com/p/70257427) -- [Transformer-XL解读（论文 + PyTorch源码）](https://blog.csdn.net/magical_bubble/article/details/89060213) - - - ------- - -> 作者:[@mantchs](https://github.com/NLP-LOVE/ML-NLP) -> -> GitHub:[https://github.com/NLP-LOVE/ML-NLP](https://github.com/NLP-LOVE/ML-NLP) -> -> 欢迎大家加入讨论！共同完善此项目！群号:【541954936】NLP面试学习群 diff --git a/NLP/README.md b/NLP/README.md index caf2e9d..db0c41b 100644 --- a/NLP/README.md +++ b/NLP/README.md @@ -13,11 +13,9 @@ | NLP | [16.4 textRNN & textCNN](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.4%20textRNN%20%26%20textCNN) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | | NLP | [16.5 序列到序列模型(seq2seq)](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.5%20seq2seq) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | | NLP | [16.6 注意力机制(Attention Mechanism)](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.6%20Attention) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | -| NLP | [16.7 Transformer模型](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.7%20Transformer) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | -| NLP | [16.8 BERT模型](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.8%20BERT) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | -| NLP | [16.9 XLNet模型](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.9%20XLNet) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | +| NLP | 16.7 BERT模型 | | | -> 欢迎大家加入！共同完善此项目！NLP学习QQ2群【207576902】NLP学习群② +> 欢迎大家加入！共同完善此项目！NLP面试学习群 diff --git a/Project/README.md b/Project/README.md index c93323f..6f98592 100644 --- a/Project/README.md +++ b/Project/README.md @@ -11,4 +11,4 @@ | 项目 | 19. 评论分析 | | | -> 欢迎大家加入！共同完善此项目！NLP学习QQ2群【207576902】NLP学习群② +> 欢迎大家加入！共同完善此项目！NLP面试学习群 diff --git a/README.md b/README.md index a142043..2cf3d1f 100644 --- a/README.md +++ b/README.md @@ -5,8 +5,7 @@ - 此项目以各个模块为切入点，让大家有一个清晰的知识体系。 - 此项目亦可拿来常读、常记以及面试时复习之用。 - 每一章里的问题都是面试时有可能问到的知识点，如有遗漏可联系我进行补充，结尾处都有算法的**实战代码案例**。 -- 有意向一起完成此项目或者有问题、有补充的可以加入~~NLP学习QQ群【541954936】~~ -- **1群已加满，请加2群，NLP学习QQ2群【207576902】**NLP学习群② +- **有意向一起完成此项目或者有问题、有补充的可以加入NLP学习QQ群【541954936】NLP面试学习群** ------ @@ -49,9 +48,8 @@ | NLP | [16.4 textRNN & textCNN](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.4%20textRNN%20%26%20textCNN) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | | NLP | [16.5 序列到序列模型(seq2seq)](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.5%20seq2seq) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | | NLP | [16.6 注意力机制(Attention Mechanism)](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.6%20Attention) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | -| NLP | [16.7 Transformer模型](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.7%20Transformer) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | -| NLP | [16.8 BERT模型](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.8%20BERT) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | -| NLP | [16.9 XLNet模型](https://github.com/NLP-LOVE/ML-NLP/tree/master/NLP/16.9%20XLNet) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | +| NLP | 16.7 BERT模型 | | | +| NLP | 16.8 XLNet模型 | | | | 项目 | [17. 推荐系统(Recommendation System)](https://github.com/NLP-LOVE/ML-NLP/tree/master/Project/17.%20Recommendation%20System) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | | 项目 | [18. 智能客服(Intelligent Customer Service)](https://github.com/NLP-LOVE/ML-NLP/tree/master/Project/18.%20Intelligent%20Customer%20Service) | [@mantchs](https://github.com/NLP-LOVE) | 448966528 | | 项目 | 19. 知识图谱(Knowledge Graph) | | | @@ -59,4 +57,4 @@ -> 欢迎大家加入！共同完善此项目！NLP学习QQ2群【207576902】NLP学习群② +> 欢迎大家加入！共同完善此项目！NLP面试学习群 diff --git a/images/2019-9-28_21-34-11.png b/images/2019-9-28_21-34-11.png deleted file mode 100644 index 76ceff8373970b691acbe05be32caa5e95755799..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 279792 zcmZU41z20#*6lgBlv1HMG`PD!^22~H_)1&UKFw@7g*?(RW~2G5~Lai=&b?xbk2 zkhkf5_r34?|HT&|z+P*vx#k>mjJY?FS{jPD&&Z$s_19mx%1UxNfBl8k_Sav3GvQzZ zKiNo#*8b}++P{?Lr1gCNKJ0$_Pjj>c&F%pTo`<~EAT+`;;*2ePs?w-{8|lsaDtU-? zPkVTzF?B*ex==64aA`5^Wm8jmlS-3HIk%49EbCEzY7}$*SKFkm?6c1CGt9WZ)Ho{0 zZcy2MK$MTAwU)zUtp9(FPL+lE|2bOb*eL$jK#AE{{m-%SuI$7A8p{XCvHxrM=hWBz z&*4_9`}%*4W3M5n?h(V$nAwpBvsze2V+`zUP18YSw*6*80xnLr4-0lM+xCZLocId~ zaF4`f{CDCL44J^jvEH$T07E305Xxx#XSWhekxQ8%A6r|~T^A_haa6|F-Z0?x@H;2^ z$k`F4!uHXacJ|2GwT}sMgaZHF<3>|O`^wJ|Lnb#LcGNean*ma;vQ;2_H~^pU2V;-*!S8Z_ZyaqI|KCoZSO9M_ zLCVWSLr#NhAHVIpK>xVGO*0cDu#sYv+UHF5@(Gmj&xL^H|MBJ(8n~qDWq1e}+{%&o z;11jV{f{F5J%3+k6-yz3M8NrfOgl5?7f4J8DL**;PI}J;ycGx71t$yd>8|JezZTX; z_N=`(A|-1x0UV*cOtMm4ITFM8-&4J(1>PLn+cLWO3}k!YWrCoL+@3H-VA4OkgE0cL z0B)d*6kkN5yxSi(X*Ri1IRxzV;me;0xaw4~^b(=u99~CR{ofxe0apHZiFXtaCIUyR z3@kF3eP(=`z4PYBT)^m@Z@)mU#ebsZH6x#ZKDeYZVB|+Y3@QkTYF1fCIoiHK?`fiD z&C#>Dx63<~?0=4aLkRd&g$+RoL$Op;}#FN36`Ta0tPaJ+GeuVOeX7C9l z{(rYE9(hZyGe~~F?HIqEKl1*wtFOH)bRJ_)gXY&9kiNN~qdDM+9wHKHLcG|k-9Kqa zvZy_zZOm+|knHwk_C>ob) zRi?WTy>R2#4@Wmb)S@a?-#9c?fj)m{b`-Cu2-b3P{?o|;omW>MqLSVDA zBP|H@@y^0{Q1g>Fzy8)2uuADQyJ-0#F@|^l3vb^AT+&_*xV?6IomE%ANE%WI>(Zk;FL#-A=@;kq%avM%1WA-?Rp5u4Z|0BqzTb?(;5{O5b9w+|0T zfJ9NTdD>B1vZ778!_x`-he^-{q5y+Sp@szp5p0!`XkTYQ~n2k{taL!I2 zwCK_kL=0SNCtnCKlqWw|E*_fv$h%lg80<#p!cm|@AJ;z_A??&yb`6=n@&=2v*hW?o?hx8C7Cj50hN_4<%*7T-`){om zo7p!eDI|H@EmltIj5Qgiy9sJ+|N8#E5mx|6n3MXn&XKcR)SDIO2ScH@{nNRZg1|10 zn*mpJbiIo8_TMO;)Sry&*Y=<7cW%`SLUxe zl`U=W7HiR&i~w2#u~l6;wUT?5z0-2No%MiHHb(!!jN>irjpY`X4#y+?jWOa7H}!Vj zS3CVV#STLKRv}S(DuHX-5##E`g}&d@Km|TS+(=tN_VXm zg z$y|)>YxyUovL(kPHP*2gY4LMTi)C}gr8GvX1*wd0#=ct}$b>3m+(pAe4nTwm!F#cjQ@U7N;N&_$}IkR4j(;yZ+ zn4BcD*0+^_Sf&__Q1Q(yESYB&JBU>i81{TpYrE9TPG01Tl1aS4CTfg_0D zW|j@QS^2^$oIfJfP0+1It!5}WDUmJNjr#})=cGh83IH$p6UyHAt{EJ;|Lts{6%g}mht*AIjjPf zppEONW*VAo=@cOcPIg^JoUPVa{DG*}J^|QAEAInWF+U+OU$YC^w)2FoxYz-_1E5K6 zKJ%Zrc7d8zsh4ux%oz>#(OOc$*KIBVWX*(lkt ziIIP-Pb8sFo1X5ATcQGp&!WyTO((8YNsdo8!O(^cMif&dZxYKNL0>~rU72plivxD{ z(js)ERL^$Gcoa+*osn<)$|hwY$z7*kIXebiluf+oWl^PK(j-H?(Pm#%=b`gLm6GbE zT`H3;sj?nPNMn+>PJi{fmkDr`?JHvs{IatCf#PL6&>|s$v9U2vSS3Kod~;G23VIzi z$-~@rUM@O5?blB3ragJKUR;OO6a0QsU%MvSV~ZrTg@|qpH@G`N>zO)b%JVShqv9Om zjdB^26ltK~Iafuw@hcT*AG7Lj$rP=#E)M?PJ$BSP_#TIuGMiIZKhlXjR;CqPq^&@p z2x4KI3)3zfTvaZ31tX)VHGQCZRSz`rx$?3;KyyEe?7GydfdL_L^@!hvrv?N|^CpH7 zZL!AjYv{Oe)PQ=us*_JuVvCBa`Ugb-Cg8i(-wMRl#sg7&#{y0PNH{&f*nyM>c$KXJ z*a)E7$J|@guLS_Qc%0fhqGY)tG&No01fn+#=uvh;+N;y^16%8OE0PeUv7E?44;s8~!T3&q>;>L|*Th-r9yDlU6vR9c@ zfbJ{rSf94=`WSA%5Yrg06_`85B*j}tuwQx69@-L%k2U=LRg$;hD>c5Y?;=mvz0CiZ z2asq+Zbmmn@D}PJj^)4=4=izx#R|*{6t3k+cRTD2{jhpRnQtP(eZHfyCM8t3b?h5s z$tJN&PrHNN?ToW2V#HYm7sFbZBZ}OCLP=KxQrwU!iZm$75nSY$wucG3ki_u6vPq7K zMPva)##X^63n0+?fI8?ONe2O|Rk~Xkk*+wW^a5{zJV3dJxRJ#=k#BK>6i+H~u>mEs zaj^&}7@$IsB^rWLfI1Y$c&s4~$Qf?VVSq$G&=k&vf{3E|X<6<%-u^L-?<2d(V7jek zUfE!NDs1VxAHg}iBZqZb(XtIQAWPHf-d6UMoLNSC`k`v|Z-nQS-RxbriSgYY<0#13LdU-KZE={c11v$}0eL@>;+J#i=1Urk$M)K`ofvq++ zA|QO0*#YnX(rj4-6zWDe_u^@YbasqF!K-33fc>&sBp*z@64ZS$B-HnQQQK5_Q905h zL_~}=ti^V@^*uS43L8v7sVJLrLXAMdNdD=NkegrxSey+Sk^}xWeUvSot6E zqGXu@Xt(AE#r1$se*|D>cXClnh=K!4^pa!p>;v@1>JNDVsqa2@%sSLPNC@P_E9mP8 zFrNzvU^tLbzErOtG6I0NKT-`~6^zF&zzG7FFaij;%KHBR8q(w?4{Ch}K7j1_y>%(! zJ={H#9yv6Ii|ULQ{xr~I+UBmzLN1NJGxl!%F&r(`@vFqHt;Ue5jcL+?VA{FDdv~h! z5Ero<+j$a#_^0L@BytCH`TQx(t-Wh1AgoG>k$$egbK_LB;YLnwT-x_ zLijZ!B$5wZc*6x!zS0>r=D4Wn^;3=+*0dnAIx2(%iYN|~my`v@QfT`vcLm1%e!7Z7 zIjHY#UDxY*ozo-&-&MoXK0zlsYqGe6#a?4Ul?c`;_s}Rwsuaj9u;R;lt(>G6B1YC zP60a1NHn#Kk~Y=t537$#nzwqjqY^B=EG=L&Yk{=3+Yp*q0W>#w>!S!z00BswU8z(7 zpcD|m1?}LJ2eR{!#Mmb)jXD5=A8FvOXVwq-J_hP)V^TePqc|C5Ht~8UF#Q8+hBE;Q zv;`=74`LXQ&JWZYz|4o-)_;)0K$67Hjm+-cwDH`T=H1RBj%~&3%RbU-aGSN}N!^Sg zkBOwNrvt8Hkw`-J89`yGVHU`t;_WdFdi-T;FJ-yK7Ypf3V3K+Tl zbc1x?HkFAtOJ7T%B2pH30ySFwhXqs3b1ABMBZrDDQiJloOccq~b|^)cl}XNqzqw}! z+$;--8^1qxWljuD4LgWYRferq7_SMRrX0zYFeIx#I& zE;wHlzB~(do9Q$F7Xr?Bv5`4pIHB%nCsi%uKuAz(-CxshZ&ge>Id#b4ExJ)Mei0gf z?~x1XDGETsugX@>apwzSBc}Wo6D^;~cf3XYYUU~ zy`0Y5`vJKtlQ?UIZ)~G|ce6TEd_Ij>N$ejXbT&?tXC-@;<+3SQsQ6h!LIGd`+B$y@ zoi-#n>GjKVC6a~)a>hd{PEtU(_LQ&i+K`+drg%szYU+s!k7Pi{G0x+GtE!#nQ?i|X zCKA24eN3at4)wg9X<{4u-2y=yp!IrHS2PX^K4N1UcsLfe0yTXCAtC8QNg|;d-EHR#0iGv>pc(oJyeack+aF@hGK8B$E!%rnX9y zeahHwPkLy9c9P?(Hj~*gZC;I1%D%}KUV7VpH7NTquL7Y}GH)C+K~Y_C2E7{WX~mS` zgZ>^<0Wg(4QheV9meRZ8V7Kwk*~MM<^0EaJf*2`6oH(nQH(BqVwDb5y+DV=*1l^bA z?QcNVcF;ot_i^JlG4Wn~t)P9|cFEnvPZL>PO+EpeG*fmw2DAKIQ=C9W=UmRFSZ?%> zPy{s8e(AIDwE2Z44!lpj3=;cX2(?i#AgJ~FM}^_bwV0dQPh-=NQQ?3E#D#~{t-$>t zUO<|#-!CRqEAmmtlGW;{N!R%=#~P6r2K?`SS0T8{p?&wNxG<2@@tWN!qT1%>G!_)IPQw>DIg4*FX zYi}zX) zOSo&h*&z2eZ=!UJHrtCRxiHZBR<;?CpXlhWgFLi}uz8RN;zk0oa}w`w&^xBo$1c>e z4-49ulemQ3zvc0FxLwSpJ`Zze2(M>l-VwCPX zK_NAN8}!eUYYNEO^Nt<{nss-W9eNvbHt*xF$>TdY>LMM@PdHYpWj`6`jp9RNOKwGK zp7&|q3?~A@@*y=IMlG#dqg0Gdq)R2GS3|N1fbB8@Axa6mJ@2@phsKn}*9*y^hInw3&-hR|T#x=vK3W>Y4{ zlEc{gTbZ{~MHVd*!l2(z-^bq$XkKn<-e95HHY>VC4!_l(2TQAcnL zU(a;fRzXLtAicnR*QDzjc^zr)*H(LXi#WD$^>0bnibzXCd!3^3KyFhcV?(Zob9) z2lHUs^N@6|zJoT!;gkV=&ml`>L1SgNm9-dd^ZQ;F|A`R9Oj+Ra6BAPJbU;w^st+9% z4;{VO9;wNc)wQX3EfA*@9O6Nzv|bF{AkYRoUaHkCBQU3b(;T`%_Wc9r3?vqw1Psa$!Cd}O}e7yF4GyYl%f%$P4#&g9DIS_F5G(WVpCVk0@<*=QvOSn!u#7a=a zB#%eGHt}Sb5WLGz)yQd68LLg&ZAr4?-);tqO=R!aCbYs2r*4-a+S%^QJ)_V(o5{P0 z-^3nU^a)sk-6s6#gv#p;+|#NmXSNQGad}&&alHFEX>Y0%4C-%+Xo6^ryvO>rU;6X-tu~;4)?$P)=Sy9a@GBVn*X{it z${&u|v*1P`gI16DkZWN)z7>HfoLgP!f5ES_$*#7_Iww+|bVd63{!nZ+-H>c|pB}Z_ z9tuH!RqP0R5AgO3y03p4=)Uq2jGU@fIw89b;4n*|H6c^Lx;>%O7c@I;=@eWt+G0nu+u}du7%Dv{*1svNM#~_(wY%Pf z|7$3<6;zibxA|r)jXF* zo*BBHg(C*gJIsSNPquhXjZJk$c!k~OMGtFU9cE{RY{s;zto)d4%;0aDw-SD$N*+xx zJ*j@Q+e*=Sav(+d(7BN@eq)=+mqO4JqAN1MoJxQZJ_^^Vo3?^7r_AvE>qe$ ztsrSMU#js)!uyXcgLpti^WA>_?iek3HGYfIM6+Sme&nr7VTL?9HkKrpZA@3go`z8B zEuQDbwYm5G9TG~d)5|{cxBv3y;p6c$0-6i5yYq33kL%^n_)9S2cYMHvzr-Rp=h!IM zc}AMlPKM+fk>nWaen9gk8Gdg>jb^z|_6h2>*?xOj`Tg(?NptbEYcCS=RV1^ZzcuS_ zUlW~xhuPIU$4H*R0>+4>=GyQ7nftUM3`vIE@#KD|-P-;A0&$*#INXrjfWBG(8h<$+ zv>E>9D#%p&i%F)91<3D9C?`PI0m5F%Im?a(%L!yh}$an9IqBYp|w zU1dFB$-5U`ubH%I}^O|I+ZXUHWF zkOLSy7f0)k!wmTGwA76(`eOV_P3nGz26YTsH(N$Y;{A?*L3j|CD^kp@gKO9DW7WKK zw$%%&)kRzb(smAE-!sLI>iGM4__iJ7==z;ylR!gD@-XGg)oY;8i+4_&+;r32SV*DF z-?7VPG6K2@%f@O0RU6s>J&*3#1i{xEBvHe6V+-p=nEkTreb*aHJpYL?64PgG^XAQO zcIklMc1^HIu03kG?(5orE;R@7-;VAaX?0BPl${Ja6kV>O6p0Zqm6u@{(#H1Rw%ukA zd>;6*sg-=}<40&^KPa{*7eBU{_&c#d5A`h@Q=xj^*V5nZ(ZO{y|9uvK=M;^&Mc2IZ zvx00hV0dVLzl--CevZjPUL7OP+#A7s>FaORO$ei#;eMrB8ZYh7*J2i|L`=7S>maTm zkXRb@LfNn^&6Y{mZJsXz71TIv0b*$lRXx7_9e&+hi_t-zzjj?O1#XkV0)~CWRldP$ ze@TkrO~@t`@Pa7c?5`mD$bv4*S_FB{o=aUL?@l3*T!hykowz_4-EP_U?1`t-PPxfGBU_m)~AQc5xUh-GEJuA zbN2oEx4wd?gP;SMt)a{X<_C4a52JT!PkOqJk;v?fg@y0OQ~tWi?DEnCIIpp^rK9MZ z<%sxFiHj_iXABGbN#$ef`f=v*<*~jND(N~rQwc9&;Ivtg6xg}zLiyWbYn_pw1yCnR z-M!gqt1K-gOxCgX)T3gXsp6kzAb*tN9d*c7V(-=1R%vPcir;!50TbAlAmvDq$=VpW zqA^*RSNs_#ez)O#^~v6AYIM2t2#z&0vbaK}v2?adg_v#DSY6ecG>{5RCjZJF%KWU7 zkZok>1#yNlb4h;7#&L=9NS2fVOx96d)j^+5ciJ=5$XPWrr71I&Qs1Kul+!5H~&%WC#A~_3nOQEtK)BU zH6)Jwsdo!%JZ}>Z0#GJpVwfLk&|Q$yqe$gBQ?FJ5cwlBUcc(EIwy{$hm)9km)M;OB z&{Fifd-gPQw8 z&-`rr>IL1f!MS?tV%wSd`LnsG5X1h@3m(3$5T;etK|(;K=5M*;#e(WHYcGX;?8%P^aAn|`9Ik+ z=?QRyU$^NLU{z}I$wOkz-}y!6Ax{4t>peehMblX#`ic)Gi9y%)JTS23)gx|KZlidu}URhs)8s`efW_N zO3iD|XEJ539euo%>YN}9ZDGgNKW&q8@-Cb22msRTO6zge-_lbo{M_kEgj z!Y+Mp8k{>PDbIMnXxW@cNKUdmvnjaFLLQetqOr;`JjfOpulM6xoIP$^(?Q%G!+^_B zZ;pNy1?-IEWj6TAJ|`RX83w2MFCCa~l+<2{qb|M2ma%zM#`V?Sa$~8bs8&Bm1zk_U)BvO#M)7Q%j0?tsyqKeksY|$ z_pj>Y&r>>ko^AY3sf1+Z`KsBSNoL=EZDM#_;w{g`9o+A7m1sGDSdelTw)Cmi1w+hvxoUEZG;;$DYiPE)GT%n&o45oTd^^uvrKeC?p-dP#RQZMgePS#Cg9~krY*Mo;wE}+zBP-c#WG!Xm0KoPVu&L)}k^u?b`CnwoIryr!x zbxk%ext8=zqY%?d?LT>HJ4PA<@kExJ#j81R&sm(?(4?yuKjCf@P)Q5R(c;01+>^Q* zP>HO~?GX&teRu5wKf#7AyS0B>a&^G^92Go5{2fT1a_eh;;pLW4TUl`_3)javf?75Z z6;F!BO8lswtla{+9ZB&)m;NqoCkn%<7a!`nLtE9EL^o!ThK;_@e?TsX!{NU`^{^=e zNeo-9)a8l>=4Mc`33=9mA-Rpv*{eP^sFmmslj`~&*M7Wam3Nhh&h04DsPVe6k@6Zj zSltZHd$(U}4yhv2xGR=tkk6`~JX8%hd2W8XOx86YE_qI1;?kmI=G_2?-1?)>$}~K_ zN1A(#9Ndp+EV<;z*Ej^CcshIb5SY*ZOe)fF@pg5$<$C@89CUxJ8n89|!++1hpB(hG zT3?Z{pxL@KBA8x+(L|d((U>hP2D`71%)Bb?L-Xv<<^|g>hrTLSy$ZJ2N^qg%xgFM}?OkKqmD z_Z}IoNFA*bhrR?l*)K1yxiZ-32cl?=g%O@1fvAP0KveClfu3~*DP7uDViHXc-uNI>*D6;3Pdn~ruXEyhG(Xy(_)kELUb z?4Uo4`6gi&p5W?4GlqrfJ;X*~QoP3`Op=}*_gUqHu#DZy(f;OnC!aitW2?(0m#-R3 zszMA==LtIJ`^j&u2~v2WIKgTvS_#r%5;^a3X=n7^zWJ2HhvuHR!l_6Zy?Vn=zmn!Y zN`-!f5q4HRVl4G$xm{&-UVdZG7c=@0OD-mLP=uRPodGlhMYsT2VmVw%8i=cK^S%6(^yh1{E-WnCsfow2u9DJp&VNW`WLjCCK$J4wir9gHK&B8nS zelEGtPK+x1shXjW#>F7l-mjOOpWjNkkwvRb?1>#-E8eV=b#3%}z>mue;43xlHFgCG zEFmAfz|Wi~^Z@YR^)uv40QWL_HD$SWEi|eN&o2k_5jvj>Rw{`#X?oi$j1I_5k>1$z zu_(wLIX>Hev~65zTYzs&z}9%&vGv;g`mXG5S}|T&w7w$H{8Y!pc}j>TR#N$AVX};P zMOL67v+5kyc45A@miqJA7@d&;`WK;pk^`dYt90Qc?`tS4ACs?W=J?JZK3UQK#v|si z33fpiLvc{epIJRJ!)AuAP-Ym72r9Op;*^M-Jy}SBtnj# z(AFmWg~!s9SOaQ7t?ipd6p!aQ#r^X7m;Sf7(+WjQ2GgrYie=QU7nRARL$+N_9@|iC z`V#H$uH`zcGRzkGvBWa_k(@HJQwru!Wqv%Q@123zmpeCAXY5hR_`vT;ftM|iDIsMO z;sU9MPS^+(Vm;QspohL$&%A_PmI(pAc z0$m~r_388>g)y_aF9$v(p>kj`Gqn9-<6PeS&WZSbX0kJ)34RwA&3#v4IYor`X69h^ zxA!f2@MZrS#yb|0#}!x34zza~1jKl3((;g@sdi++o^i)2wpia=A%^LWPtUDs{TJ^; z9RR$+;g95bh?Z zo(e|cV2uYH6TFr@KI3jb)2|KOBUlxLYbW8biRZ3tJ+wpsjw_ zNq&~BVPJkzp5uMlv2$1(&5Ms4)nooK6a_gnacSM=pIh;T#(FPLiAr_+v;Q==8A){1 zwPN~NFd^l|8BJ%ASzb##$9T)+^-j*tCx0p%#$aZ~%M~AUkN%OUx0!JJf>_~&2`?XW z*V#&Nsn-iR8_Vx=@O!VD1K+WRJe&ItY?2~x#F?`9iZhv=nR^bArbu^9`+d{zZ-pwM zwm{hJ7g8~7u2teFbS3Cd-_ zQ!(oneKCR@BhQK-asYISsE~`mDVSuHJpURm`VmPr+7}TeL-<)PG3=?eMkI~^JqcJo zUH=XJopFA3)I@Vk+$5*rT%!5?clEiGc`ADjb-_jnF+w9(BmauI?YFZ8%FnRhR$-?b7JZI7Dd%U6DHv|KSdp=M>MF= zqjuxzC@sNf>aVD1z8ZG9m%No`Y&F2Gpt`!_P8b~IGUaD6ph{ZNn;~a3W{sUmm(P_(y#DVCz$Go=UtD`At$1r*V z?0=PL7vWb7>MN6Dlru?+9gSK4pgf4b`l?BV_64!t)Z6^Y-So;L6SU)R> zPE1)8W`Gj16)82alTWj2YirrdGzKz+ojg7}`t;A=UFZt|^sh#w$j9)Vec}cc#b*U> zJt=do_n9^?CD#|a?$CLeQOQhd&+(KSiR32mnqtaJ>~XPh#ZojJ82J-IjhMv#1UhQ( z)v02w;I6$F6MNxQGB1=+_ua{QPvGISx!2B$&&}X?P2(Er?G%!uMAmcmYinTwHveP# zC`D^>u7X0^FxD{p-I3Yo)|*}XV*C={euD68wI#p%VZ{105)$qre77NP4vmk{BDBcQ z&m;gBgZ6|Ozspv#3mU0K=c@?RXTBB_Q>k7w2J6WW5>Hy>$WwQ2ydD!pNy9rfW#U8! zk_(+^rx>0s>N_y+X^I_mSZwT4!U z`zcP=U6T1b*+ z*T&XNF)g4!I~jvtMmd0$no4!V90?z5&%D)_2jk;}em>75)_Xu^{!O29TQFQ-eOS%t zQP>B)27Xyvl9F;IIpu}YQZ_l&(2-)|CIp!>)*js&Y{odHSPn)}la{9U~ zU?Bu@W_s2hqo0{rpF?bn5ZU^m+b#X%80ln}>!jZ7sH3jx=ft{}smgOUPIH|u5ae4^ zf+uMo5U^b1|7H;S4uu^qiOMLH=Bh8omm!ocjQ~n)0v%ivz-$xqrOS~B1?I;k=_%#P z76HfUF1q;o)yI1v*2g3kmCaLC5%^zAZJ5~!8o|;~doO!`+s100s3=Z4CWT#2?_X<* z{%3hKjuvLyEs*b5LVd1|cs{~22%M|SvO~qu2L+G?6TJHXKFs7q$51n)y z|I|GxcJcQPWQU@r!@Hs>ogW`K-omMjLbJ7Q6PMpd0uW2*7_ndZk4AB=$&cbDvb6l2 zgu>+XA4TqeCKn_Ms0N*{+gM!ziED^(oXGPVGNKaTr8#V))p*Lbx>EUo+^}^t;F%P_O8xh zwT~GCfs^W7W`BWQ{a_TS)s3DD?0CzVf7=Wn!vYo_9mA25c+2fGW~k*&3XEH}f;nz7 zF>47cPUsi%FKU9t$`P|(21X&CHT=Di;I%;l^poW*N(7>|Hd;+t9R~5@) zSJTB@jwU45HQ+k>=Xu5ie(YOYjwX7j0||&)CzM^UI`s?7@dcLIa=-y0Y+2NTUl|sXr1CwU1Hbdk7lY0U1(EXB1=QjH8M@K*LLj6BBe)NgLRm^NN#Y zvNB!fl&Uj}`JnRbI#1-;!qf`*zzp4J)q|tc03s^{3wIS9#vR(Mb|{e&raEJx|rSbLYl|QU%MVyj=fgGMbZs zT6C6=$!q1>S2&MBwsQZ`07zm$nU<7>l%|MQP+0rP+2+0VH2s~F*XlZ3%+HS)Tax-& zZj_1q%cDoD z*QvtodzUHt{z`sIC;njw9W_8b>!_d-)kQ=K8x8-A7gQ8 zdA5lBxtl!_gQAeaYFjx$TfqYJr^Tf5H94nqJdg_9ozas8opbz;(fFF9}QxwiL{nunUQlWC` z8>+&>XaC_8r{W}V6A~tQy$V;xImZdw?+j7IuP7E5)Fqay2Li~1t`fX5SlI<3c>aq-fR3)VI z!ZK(bG8N=}4dv*9^YhL_ByX^9w1f)Q8@)mMRsJ;2x5|V_sgV8 zcvWm?hKSN_fS{(`p~8Vng-&IBDl5%*P`lQ19X6J7_8L{{14q23h&OtlFW`7q4XbZy z_~{J4i#+3Q>rHkDywUCYUBT0ORZqdnAXlWD@ED9$Ptq5IZ*34WQa8CyaKuQ1xpH%9 zFLNDg{1Vi1Uzugrx+zIilr126ux+o_HMh>gjvm3Nq1VZ$`7wy%1_nG?{dWn&+KmV$Bv1+ zhvQ_S(1ZqG)5w$i``EZ^;2E9cPg2wS1I?Pak7FljUs&sCRYqv>CYlvtGc#x3H6>Mj zP;|ze_ej`A$9myQlMgV4=p15LH0yVMKKImLJ?|xj5HGqeP2T+_Q(&M?XImAhnX>P&6%^8BYUL$g3c39ryVm1 zuoIOsWw1!t)T5{Ps++3`%fZ%kQ8U^hKpXAtRg>JGHXa=&MaH`vH!57dQO+D;jFh|- zdMS0K6)t)3c3gD#uO~F;oKI*xfje}b36F9wOmC^~_+2p*@T_uM8%O$ziX+c+)d86s z40J$eZ=zPhxxl9_eE6aNqzh8vj8d?cBszW53n4V*Zp{n*EvDB z*l*D%OO7i5a{i)(x15tv!{(AqKQ7eDL3H#VS@2UhOCN#0Mrnmv=7(C{pMtw?MqOJ^ zchcrjPus<>{*88SUTwK`ZSAip%h}7F^`JbzlWIaxFTIlH(|XqAwP2Ih>dJrEgi88Q zKlz4%nvf1FO6{Su|Luj?SxnX3#Ts765*_fYmxre4plaOV_2v($4?moR@o@=-Rswg- zR?pk4vQJi24Zk(OI#Hd?i=J`)#`R@O#AXu1$c{eo)k}TzyJbbr^bqpnt{YNU$hhKp z>Gy_r=Y$kIufXm@0(l-0TvB&@Qk`3Vu0cntffrZfi}sW0{cqX2PQ!Sv|51!@_+YK; zPxCQfZrW3be41aE{;PC(>8y&PHj96oVbE~(nB=YL_J=)?Vn?q%-DuQX>}O)zF5kL( z@D_hG<-J+hE6ZDJZbvwF#y%_AMcr1$A(*sB*|7^{1;(SzuN`-?QAFla=m3cK%-ZVj zc$(in5bu`OjaZk0W{Hw-m)%dOFd3Tum~g4RuBN{1i#g#{Dxtzcm6T?7ozcqV!t@kn zEJC3aLY*j_;wrG5;&eKRt&Oi?rv4~&ds%v#8N*$W?Gqs$+1gtp3)9g{^P<~y9-MNWp?Z?eYiV{(}LEa@! zHO3Z6W~8(&$RIlil5^>(5q{*es^HO6U0;Y_sjbU=3}5<0c4r!0^zQ!IN*stTnO_Am zln38!mAt6U^BkpuwckZ$N%&!^!}RWqO&kCs4BTem@p^GxL>hhJ9>;9lnWDN<#~-SP zE9aQmFLqF6mDWjf0D3}oNK$*lK~W=Yz~b+u( z2*H_w3u^<<3s2Hr;i64rhE|3ANgV4byv(TxF^#g5FT(Iy(2m(*Fvs!v8XQ9rE_H%s z;?fnSfsf6vt+uO@e7M(a`#AEgz0zX798M^$wpgRbhlVrm+_t6EF31{5P{3YF?&XMt z*Q6YSfIQZU*OC9gSMHThWWYbD+-3(%?Qy?3Fd37AYCkU79WmNnP?Q>9TD^QNj{KHV zF4*_B+p(knOYAq;VY&Dcd8bpt;zFQ9G6WkXU`pbI)Pr6{|rT9Rz~4 z<->pa!oi-Q3JN6JTtSCp8duve^p;bc*U5N|`}$6;JZ&te;g8AMPbEH`$WBrrV(Y^9 z^%o%m5GBEv0jn0<|A(=+fQ#ze+JI^47&;^eB$bd5P#R|t=|*Zu>9Gju9ztMHLL^l{ zLO@bUaR8C-p#_u{L|S6LjsN%l@4fGP@ArK_hGFImbI#stul1~FJ!hYN9<1gb-Zoa} zpQWmTs+)ulKx%& z6gKRd1Sqg7Q(kg$!PI!CkFL_N)j{FI+L@j+-5LEgvIM;%5@GTV{fm#j@!Z&tOqqE! zUe+A=t8U|Lt#FM`i%)9cxd|0fhDwAwH>J8-kGM)YTVXZ9|iY`1UPyib+%Y`C=Xqh2?tt#7K-%_wnYrA9EuM9Aq?G!cqa zozyrzRWP5S<@G?Cddw@bwrb`4{M`KXWYY8c`WNo)&traqyWA*oG2VFAgQ)(jukZ$? zK9?<(mlLL1gZsbpgntj#wHTpCPi*};`lo`D1USCm?(^$XrH5=%djyjn1Qzq5S0MbWY9 zb*GXiJC(d3tAoK1wqtz~v3~o`{r0E)vZ8jlGDfPqn!UwBe@@|@+&DjV*1MC)b^0p! zWTjB(;-o4=*z>2VaW^ckk_g{Um#)R43Ud}QENaoJWk8pC&R8sLl&6t-m1L=O%Mw)j zyqd-j^rl9x+F5X?(5jEGL}gyX4Rt#@-=QI%S6cTyV#j^#p8KgopLOK#l2pK$A=IN~ zbd?kRjP&`nqO%3Zn{o$X-2sc4h1ji($66y$UMO!zjO&g^-Co9(puR$-v7MA0>-i1^ z&+){!33byQo4VNH#-oeGFM)vb%)rQA%V=uz%-7k*H`M0rL|&<7$*H&T;q{{~-FNLI z>z^%dYjfIJbSewUOb7Xl-|#LU?e%AS3JnYTvHDb%wQiXHz~@Z&^1WeW*$=;-XDU`W z4Situa_JA;m+;EE2H`6(W)-Vhn?aUyy?6}ztkid(MoC#EeAf-+e;n&GWu|;J!|Qtk z%O4z;vzm6e<`yFib#}s~T9z+3Lj=>Mf-BV$gOX)iPG=tZ_mHruv%y?lMZUz$0GU7h zV4@ev6;!_Xq(SQ{k9^$4qame(w={vDNp|iEYw4yd+FLz{qt(oEy?C1PSAATTmWpImsu4-R)nH-Xr7k@YNt>bVaKlb-8uruq>f7B-2C*B&NDp8;us?93d z!Jo?_3!%$5hKm}eextd~O8>@4P^cTqkZUgP`jG_{6Q(td2amTzr4FL?a>F1FgXSwc zvn{%_%T4{OHwp_IoL56l@VJe!vJ@|}9`d2>t( zL|$$_;_zqUDYP8-EpeZP^J7%57%Eb9L9?ydtvid-zPCM|(xSLZRK2CB_@e zbEgDng-Tx)a6P!C&EOMa&L*kmd0m{N^>$Hz@bI!9!}udIi@FbwPkwB9A9#LCKG?z9 z`-~7m_S;R$gIYSV!EKgvzaKr+^?{_=Jws7|w)hHMVzwTsy*V@KBiW;8;UrkBvk<|n zCc59=m}5y{$jDkZ#;dljsqvyBk}G zNV+MZW2WH(hp7&t_5jlPPmhDE@IY|PM7^oTh20`>(xox8`9O>it^)+ zqYA&RUpP^EIpwCr9WkzBA+Oy^FJHb-Yc)MxZTDV!iXzi4&+YaG&(fR?Or89mJ6aFG z_IsD4`W9ElO)xNfc;M%-HG$z1TF*J0=uj>o6` z=x5>?OU{0W?!g*OKqTTmHY{?)-N!6(b>FpR(dX=P%eL&9#oW=w;L+7x@|?5#!J&zV zpL6coF`7R}R+#K{Zd^?}?)Liqta;h%>>`-{eM*R1yQsAKNoj$vXX@P(kSBGvBH z_xIAvPitEi-0pvuU);XOi1>t3$gDeQ-)hCy^-k0UblUJ`c1pdaqYKv{Q4>UqSnnk_ zA6+>WOKgywujV``PQ-C5{j^uo^6TudZE)%+`znlSb8&G+b(N&w4Cu>jX(!I}^hlMh9VC1HRo1dZl~xRCh;(YZsq#oVxJhwQX$lA${lH=2h9V>cxu`fiY(o}CkANoM810YSoO zONZ!H-X4A(|6SIySNHpt%`cl{KAYpYqu9Aq%R350HS4h%3Y1n3F4sIfMMcBwB^FB` zJ$*8o%xa}gi%7QX>=@HnbAKqiNvrdgu}dxWt8aN|+$jY@NZ?n&Dv!RcfX!FGasCaMAQn~oxq2t30clS%qiA}#pZyZ-CS&pOP z*OO=|7qausDAYDxxCZCdloo7uG;DSyo~{qvyYx9fE^zrlbbMz+!G}^yklPSR_1MNyd~1!j-k7)>zl*edY|S& z`-|!E5xzEFb}b)CD6)2BLcn#TKt@WCYj<+|a9Xwr^qpIYLc(mXzOJ`BcnB_1UP zji&jIzI>Iaz(b)za_PKDzrWHn{`!R5$T_?@cdT|}zJ}|y=Qj&VtH#r}Te#5Rj#ahB z{hD${n)E#WKMvgQ1YBn7K=`>nf9X5%Phjqs&#INmJM)XR~YI#k{%f6eek2P<3 zT5U1BKF%LU98~nyBd%y9=~q7W_8Hw2&#vCTXfGXMCNoYlKHeR#Ha#htdBNd+ZS)e| zuh7|(t<&S6)0WeWvu|7{H$Qg+#H{fd!)?^XA{PhbjG-xf8JXXbcQEq}&*yl;#* zzq?F`e(b{|H@n9XH7Mj4vX>t<7st3iYBHy!JSeq}D-z~K7+&grRyEpCJB&iVyN#)- z7aJVrAj*3JLEG`gd?vQNdG-x$Gr1>G9l-DFJBTr53?TXW!Pq^(fw;Fc$ zx9ejTP~&cMs_lr6EE2mB^bbn}*Ru0B?D$FEcYnhaPgcgL>BoNXJ_nPnJgB5`!}^D3 z8nGYrEg0-O_s-i_vAL20Kq zct|#25!WYTy?VQ4!PKlT^?__3;>V?N>(x`6W1iWc+vVHjiI2NwgEAu#dzWW*f`)8P zD1zG$a9^js8*_b4$T^W`f2VabLFejU2|9t|iqjO+fwX5Yw{f-OXc@JP#Y2~i0%+Sh z$DVl3HZ&I2OA}kS>hXdi3oZOYa-=_0^&f_g`Y{YDL;8*E;eU?__Br zeOexwJTx=&jLbLl{AN7ur(|=)*fMn~V5EPy^K1oI%pz-|A@0;+K0`Xi^iI6g>{zF3 zY0P2;<|49CYDPiEq%!@SGOD9Ylt60 zs;REB2ualvGk%^oqu)zEFI7k(?FVDn%c{o8_<_%Rwi9a22(M*-kO=!wnO>1<`S5dm zWvO)uJK0C|duX(K_J9_m>^rR+75$XE0SP^CagpKl^WFqHzKJ3qNWX)fouBFq)~LFN ztOUwS-5nkV5UnjTt>L5ZfvD&^xAGse^UvPb&iCauJi4nU^JVsR$E0=W&90^7l8Tm5 zksTR-2wgSf!PC@+s2|Y6-X}Gw7#`E0ednO!#IvH_x!>!-Q*{A5b%z-_yZ28v##_e9 z-i@JcJKnv2w9r>_^4#WFpa+J zGR(C2us%9co@Ee&=)h$LoeeADj)d!ynVv?sbxL`uFU6P8_+=@b{h0fHQL`<|Sb+L{ zM+{QLQ#77}c~*g9QS(9rwlbNe(n#Hs?t?<+L^TX9=nsrz71-()@U1hjKJhGxrowK) z#IjOh%BPlfJXMUZo1Pc_T^!9)NC>9zJm9f^I3cMizBbTKgsMV_Iq`mu`0xygm4K>i(2DBEUv_`V%kMc-7LgzG zI5rm={=Gsx0(sAkqrlt=b+_D|rKr@_HFXlQfN>!?lk@%JnR z#WNY7w=&0~#}8+^_$I%M=zdsS4w}8Pw`X%|^LSJdVb$_%@8#LP&nX!fPGU|Nb-P-@ zu*7u##?tPJ{n=01Q+=-!OwRnJD)aiuj;I>iMqJ5hzRm9Yv;Oj9nd6zd-;O!BqggNe z`}P_JG8;a<@l7i)Z{SREB)HC;#h&C@8APiyZSs~=v<{FCWq0J)w{_0XhqP@Oj8Wgk zsYf@45GlKURh=jU`6wX<$+!K=w#tW3F|6iuQZDHl?NcW^6i9@RFS)?=pw*uIn$uc> z25efYGb=i>86wl$-OVfGzuRqgGhbv)ue?PS*`TEJg0ZioEI332{3BV`+tGw+TogHHm3MhKnW z*SzI;^s9*LB)RUfVk0^0+qRjyv*2c&%?HMAv;Evljn6x8474;hjPUK-MZI-1d`mKu zks{l2ly>+9w>@=7>36zp;O6o*p1uxpHy#Q;l}+!xqVkhTrEh9CHV=-xf^p-A`%1^o zfyX(=6t&I0qart2ubfHStR0-p&aK-k#ix9Fm}b~vzNQI3FXY;!uK5jGdOnFVApa?f@#>0�Hd!UJX%XU=8!@6Z?9O|;3-7^Ld|on9e3QMP&Y^XWH%5)? zWn){ojLHj-zBSEy`)P8S1Ims-DbM&S5#7C*s`x?{Bp)^Ecmba9xWjOCqMp$mbFcQj zm%~m(?{%aIHR@734<>fQc&sEDXL*sq>$!ds%1_0 z@Q2O!?C(4m>lS#*k4tTqFU+2;k4)SfQ|CM~%K3Hc#%Q87&S~r0t}xfnD4R8|Q{U?j zI*Pxle7`v#^#^U49^bY(n>&26bx4gn$-&((_v>{Fm@Eu@PIFvAv!f%KEsmV`t59^m zQ~$H=(Y^V|yBw)3HJBoP5{kuh)R~>Aze25{hdEZ(BhvWCV-HO`BE=dF{wG``y8dTK zE}#WJ(aa1gbpLpqK8;Cx^Tb((nj$=M`_7N!&C@yDY18U$uXi4qRkdRlV9{Lw0*cI+ zuJ|vSG)AX>@>MBPn~pt>LR0dyu7Tv;=)8~}x!u_Pt77|?q-XnMm-nOF`Ap2&;r_AP z?vSTM8j6=RRFn0f3}k=(LHRIkrTW^%u=8(UrhI76(Lr1@&3u57Dvo}==*OsALcLA7 z2K0qSZN~rk!e4)~t9*;|W&>Hbl6^y2bz^*lI_w&+xKAy)JH05Y0ZYGFGpx0X0kNV% z2FL+OR_1Ke?Tt0PX#Xdm1iW{itmEWIjP4Xjo)DMf&LgX3tde++RGh=wpyJ(I1 z`y;heh9U~3rb(>@QPlC|ho8eP@4e?=FMrc`OTDd~>TK9w$B(pEd^LN0ufjL>yUN}I z?Uw4r1%6LI6W}v}+q-La=6$Y;%-hYz-NUHu2Wv;qM0Hp6bgx`)tYX=EHTxQvq}};iSAFJfmb{iRXG-D zqvbF>)W{gMe|g!&ybvhxzqbALv0(zmRw#1*yBM{KVwD_50rmISw%h*Go;VhS-re!_ zT$?dG*Hqre{qx@gSZQMae)lhj-#&2@K|v>7-O9d;{-wlR*0KJ!kO2Z%_fjf2=KpRi z$AVqWi}rfP{^C@JqUcc&^E9!A7JdJ*WAO)CjTjJgx+T`r3vG|fQzQK47gfk=pq`@6 z|7^poX~*N&p8VbB|6-EAUl#HA0lpc%o%_MdPcNQAH}*jLdO3QV^SrGbsmr|oG+SP_ zt75}=qn|HKTeEFYj@UO;ao|Od#kN)uUhS8!p^`e!z*=0r3e|GPmE#&z_=`}nR zo4!|Y0H-#~E~)JUY}?Y=qh>FR`9FBZ-)1=Xm(_KG8o-XsvjMa3?GVLHdIXT zH<+G0n5p&PG#b4uWnbp#RQ(J%o>`3Z2>4iwne`kekTKCC_m~#>UvIMNy1*YK_tJk5 z|2v-lElU3pcac>#B?H?oJ&OJuV`sXrjG{n(;D!6*DVI>O2Y+E4k{2sh}P?mPT1VEK>U? zKK%Q8+P*Q1Nv-^{+RKV1HL1`^|A93K=-0WE=rJBt^BUA?PLGcTg0`Or zBy;xm`Ei@K`f6Xe#Is~lgvHp}NMCys+iRyNNR9l@hvR!N7>Py}S&EfKSJ#XI=)7fV z>0CJ61$KirbGtS*ez|B;(j-mH{^4J6a=3~NY)qeL`^jPyJuoy>bMC0#zw+xiI?DWE<7ifAG7tVr zBZ&FlNo@3Jcr0`hEMr{6o=57zpNyHX=r{dYWb(_e6Slg2P;_LvdzM|6 zT72#0hxLVtNDNjA)hr$A$KaM@8WSrsE&oJf? z=Il`iGpHSp=WEZAz`csAefimOB!BX(tX5bo_FyKJN6jKvrZmVjKZh!C#C* zM$o8bQ^itF24{`RYg6TEKdyo}MF8k2t$`xV(1LHrYdmC(Gt3^rW0ptXJ(#KSFio{m zPXe%Xr(&zYW2&^~g=0tHKPk-LT;`uIoCj;t**2_dUl`p5ohkc%zKmq*`3`(#pQ$%w zqKXUYWfuZ@JtOMr7Bpkn?FJr-Nj;dT%@yS7Fo!t9U+`B$Hy(RTEtQtN!X!ZYF9*f8 z50}W5L`6Jo%Jg{Y5Ea?mZMn)Do=*y|hH{!j`>XzgaQ)4^{(2#DOP!IA%x=zS>h66T z2$+no4gmnM%a5G1=&tq1t|oO8lUgjbLIYY#E2}z9isYMh4)d2s);=|sQlr~SErw1$ zHylAHd=U8TdqZO+p~47bJ_?OJw?PjjFVd~!)-3S<`tmXm6Zx*)MUEJ)2V$(6M#TTC z9sl-LTy4}mqJl}Pv>jB}*57ojcLDxomDV_zr9zvRw}KPPk3W>}_~16@M%QO3y0or~ z-dOvj)W#gV&3s%Myi+AnTT=PftrnJFmN7U+`Qu%kK)?ZRZ>D*PCMfHC3FTpv1}F&E zso3S=<{wTSdo$l%6^39UA{B_j19^`^~{R?ew;&Ue{>?cfz@t`jyDOOKxTO$V2W z+M+6rqVJY~f$!aHKBGA<#O>zz-zcju%+~4VD-`ka{lp%ynii0ecwDdRT@n+AR-uci zNsWR2#S9MG|3904`-c`w!)t_;W%cOT1h^B$MKMr<{rq(U>D*cVYw1A1U-`{lo zFVa7J`Aq#*cYInkK;?eeKm6pPsQ7~l@>qFt0;V#_n%H(>SrN0> zBtKRdEa2^9`7m@s!?NF5OMl^VaQ5{E=lc`)+dO%@vd1YX8EkX$2#Hw)pEzV#rnK9f zwA=hFn>$m@zIT>#6lvqjuGXu!NDGImKv2o*bj`R6<%?-xPQy_uv-KxpW~B@^k{8Lv z1gKdBjhBCR)2fV5LN2Zs5=!v*p8s`|xU0%=h=*hpxV}BR@cOA_^KpYshZ1fuSO*V@ zNC}g0b2~no04^D{v^@I-cie+}eW24#ZnNUQRa#m)@VKy;r8D{Zn8*Wp-A z2pXp9mcZ`Sn0(N*71+4n8T`WLxaQ#Jt4r5Wd29v}`uO;a7c#m&f2OehDHiaJj|(jM z6~Objyz%KjI;o!j-#afMK_3@>Zj2Wuev-ZU8p{(Pp%z7GstVzdYWM+C|&la7$tK*hn22A0-I(zH={-m4V zv^{4uQ9djT3Qdl%+E~Eo8*BS-!4_IW3GX1=8K>*_&ELLXV&gn)y&ws1!^o6LqrI>G zUo#*r!3=O7fhM2dZ6}3uXBYcUWAFPNUujGbDX)hJJ`o6!m^=8jHE$aH!}MsMxhW|w z`w>qWYK(33ulD`7(f%LTvKw@AAPCCl(PqG&BL=x^uo0vH5&gQ8b57Pg?#KGUkt$Z5MrAG+ndrN>-8mPdO z5ZRr>&LHCc#@`b}v==~E9#?xqkx+V~jj5KzlD8kw1MfF4wB&#*`bA}~Hs|u^6|Xc& zQ4+?rhfTP5oEvQ*`UYmy)TXz@dQaGT|6N*gM47vx@x}u6L(S}CvHp3o2=ND%kBj3jfNzXPdz2)5$9IkKrmkd z|Cz$vw4(9CZz}F+~>gNLx&M^(k7pw*#Ff3{|%7J z&sc9tW%gFsy||KI!-}rocy|lHY=BgM(Efd2a6*|s&1U}r5sVlgB8c=7Ae+*LO1xh! z$gE#0F2Png)6C(%Y<(}S>O-)Q_;2+%N%?PK3yKd_O#KD?DP7OH^@04`hD{4FgVa4tg-0^$b)SLro2n7>HPMHu^0)r|Eq5yK zeK)5p?&~ieeBrb{T{GR$wK;)UZ}(e&bWf*KGgiT?R3YtboTn;ZmM-_BD$GK%r3~_w zl!&B1VS8n}OA7h--Tm*qkC6#SdgOa|usZ)FRC;jH>rmF~TZX9;y`YW}ueSAeEZ0KN zK?9dp!0;0J{veucdFIFT4cWP0b23alg3|Mjl!W47fN_o?aO+QqR} z9HX7M1>Ga;9xfrrcc+DiG5_m>qFl(u`{_clWxOl@*3k*pXWl(4eNoDmcT4xKrQNwl zrYpCFYCQeX^4X_p*Ab)}G8l(}@IDu|U$^<_lHWnYT(5h7JX@@b%6~N*=qL5n9*ksk z#%+U@>(zPN1JPOSaW=mYvxgnBzoc6Zr779)ki3t#Z&~*cGuyO^SZ{|{kL6+q?4kHe z`;51ixD4-9kHMm$3^r}=6XnyI9v}Q_T)y$g(T8;ur~v-GC_;0SO$GcO&RMDpKCV0Z z8LbrGEgDHXIbg=VY{cl=CNZ`8Z~Y9_taK1aHJ41uFH*r~$+%(o62aV=`$x~xc9wGd zq;S7(o-y7W9`-w3$4Ptn4znLE90$=Q!?pWi>}-4m|HVK44CsXq%tfe3=FfI!tlS+J zW3KI}(0u)VCHNX(j_dU&?+OV8$nrxmO;!*KH+QYJnsMvyaIO*LAsceTq-P#)ml z#j((7`f6luvD|WU;=<6a%Zug!*k+s3e#Jyz=b&xCwqLhF(iDcYkw{3`%Aiyk5?Hy8 zP7~i9{A!B#NT^xs$)wX{v5TPNRt~|lix48V`%EAq>GCm`xGWZHrV05zVDL07gvkZ} z?vty|F|3q`6#SPV1h4eSiG@2JyhHexZLf6RAm=z+i_%q7HxAWipce7`uqxnrxLYW< z6?VyX>A}TZnymLr59Cwf@&-vuq%Rq?)L0~V%XoV4_6Yfqs8hrw8{7;r7|0#2On}Nj ztA#D_SnZhi$bQtG%j+hIlH^vxi`TdaQLZ(h9Muzph84S8y#^wN*U|W zs!i}9S4zSmqJk$rf_Od`&yGT^rvPsrE0pVPiBF)u(csAC^|&nfZaU@H;$()p z&2p9BSHwGt+W+}RYNd9eeTEU(VDM3r#&;ypZ=a$CmE+r;2_wMgAc!cVOvkfhz7V)e z7~JZp+Z0i2;X7tNLQt0ksl}HyTTIgBM1?LXdw5ZtT+)xaq^y zCx5KsJC@<5Bv#N~txN$Pq_jy9RFcKC{dXPN_?{8J9$;auw(SVJ7acJGC+_AB*TlqB zM_nzH7($3vVDm!=NOD&s*xK6M)G01E1YF@d8Q78#SH?(C&{9*ZYn$Ok;YHboO)zMS z`_w?OP9?JnDeRv0p$EZV2?OS8=5Cc;{h;aJo8heVhPv0xHWb+EFC(L63f&Uk%ikr` zCycGooaCq@1_mWANQ&?t(dSv$c3Q?-I8qJ^6YnRfwIT$+d5)w1$ zK@skJZX2Cai{z$v9$jm}J;o6e#Z1w1f33nTdmTk*9)R1(1YQQNBn`=+bQ%d$b0Dg2 z{i@1hH^dY{{8>=P%A3amubj#GXYT+i|Eoy7rwZ# zr?4>Th379VXvZovOM%ThB*$Xho}B+Uii$@HiB`l*zf14UpmE#qI=;3(X!B*X-%r5H zuUprSodCa$2_HONeuUK3d!g*VRXvu3$x0?V2OyM(|JLqoGDc5>>t->zc#}^tdE{$` zSERI+eH#%_v^co?2}nKeXpBki8{tQN)2T%jZ_{^Y-w3-`@Q|T@%1IhYR7ZKpz;{!1{_p1}yl@* zC?MChcguKCS;fLo3H{ufs7^XKtWBm8*0!Q&49mY8Lj4Sp&u}Y@p#Td?E<0XO0l?E? z3-$Eu5pwF>$IJb=lYYMqd#>&`h1szlP##pVDx}G$*8akv zpe@I;Hu}j(?uDtVz&CC}j3YgYW2uC0*ci7LKtYxR1=*$RtMGu6MK0%jVF6XF6NrX` z6pR34gl#6EYHgLSq~cU|?C_prQffFiv2-p2+{J^0VTNLp^1RJxmMHuw_fAmoK2D>@Zk0dnqPX1>(|Y5mYh1*TVOhG6iF7z^Z}?dl@p7uajbjjo#VlDxt;nbi{Zjlo0UHX(Z5&BVvevsv+nlzj0Dz=~| z3bH~g?T;})zFJ6PxXE|U(vY)$%Sf=ko1t-11t?K=sQ$;t)FDu$rPG^q7y=yuhX@Gq zFtXXPkaVHpeN6pB#waDb1wCrQkT7r~S5No@w{R5c(3^^nl5}Ruv08Zyg69q) zryXU7S5=0GU=OPp!5V4v(x%|srBVvZM1+$&v|*HaHaXqg+%_hvqKkQj{o7uF6^w4R z51tP=G~{j>cS3vPa1R9qI)Bw9r4kGX9HpR$CW%^ znj|F=y}_Cg>q84_G`A9-Wb~TA!#s-Fbo3&bimv7kl68I(r^E{2lexbX_AEQ-6Q_hB zQrcCP_Az!{V4T<7I}nu}df;M$l~-l7tb~LbTUq8*yF>=uQe?yg24R76KG#5YxGAAF_Dy|ar5_b`wuBreN)kn-DQ0uQq#_0fU|lu7$< zxfdSjxKRPo!4<|!hNg8=lQW2guT+OU6J~PbEAb$na@c@{J_|)|lv0ZLN=ROnD6kKQ ze#XP^Hp(>!SvB4m`O8*_m%>jRYE9iqEg52_?DXiA8Ej=D`H|x(n1W&i$4#9ZM-ClG zb&;?L`@9EH0!$zru6|9gCP}|nm`*yykU;7mVn;$9&3G${p^oRi8!(VDA7JxC@Y1rf zN|p1(vn-;+4Iu3X7driX+-DUSKV$Wo1MMJ#QH(vxH0->jkwU!mtb(yW;Jjohpo{P% z%td$@^pU1}(tE2_=tUtY&t*(&J*i&l!$ntZHlEd2i6L+ThJ{)p1}9#WWr^@3VRuPe zkPf8CZXpn@?zHS}CM4QeUU$1qPkL!LOzmm$UZ^e%+2 zxQT@rs8QMuPmCcQNCy@Yv~nAiOch|BJ0GgwbOn0Rm^2IF@H)cdH;XV~ug0~jZ1!Hc z#`3DJ93@a-`I`Xzj2L}1!BA*N2}cPJ-8Q6S=diWCgO@=^v&(?Ujh;$VExlD~hz8OM z+B=Oj;w32&;nL=xNJV2u089Y4ILH63EU0^-tF2yTdcRPlFtwzd5xIF!vA!{wsb@l$ zV8FP=o1G45?-0=v5MZwNj5dI4&oAiiymcfk^#=8CYP$~ zNdpKIOMUGgf$oqAhew(axSUM;Me=pZbUs$39=Sn8S?q_z+)+iX;prmbU`_n z_+|u{tE>D4gp5$B6rI^Xn}i!kV>cntBt7E|L?@I-9YO@m7W2|ItnV`mU*_@E!alXSAuIuG zHDth4V-`OtBVQ3{g50DmS5)5s;t!pU?T`T?CMnvbtdik8oqJzh<)bStr4(hNuyBLf z-QlnuGg)cUABDBP)GL$R7k5>cfcWH^NzxlmXOFSnZkKxkQze*!<<~@cKvlS9s#PH% zp23)Jr4qrlBhs-;zGe(Ph_|@K#9x~}%@kKZnLB=SVCqRHG!0z*uDBOg?(`aDbV}n8 zeH3RcKMD%VFNV>;|A!@@Jd6+q>8K0_CfYHwk|aU~k#Jj%qwhBU%;UJOPM0#6e9m`# z7nhA|M^Niu3tr#poM+QgiMPX;R@h?V5lIrn3@KWnJ%iBsF>sMS6u&)7;0XgY8IaJB zdcX!|tW7i4QCq)0kflvYNLn3W|@0T z*se#h2+F*|a#tCJT6?}%tCoGvw@0&S75CQODE{1)N974MhA#4TSjA;chAFrzT>IR!j#PZ*oy@Iesk)7M z_MGFgda-)k=~kjgzPMWgO6(P)5GX2eqKX9QBVIDN9x6f&?cE7d4gcWdq+T^Hf2L4v@#oyC6^%-@peP<|&nvPKr#_tGgi|N;eKzWcb2z2{MBAPLVkD-pY zR-8Z;>63&hfC8oN9FqsMX`jF^8Bsl@qzgWb4*p!;a%{@_SYn8UU(vFiv;J3X_xs(p zODbgif*cOHa!_JK)`Sj_PN1rgoue1$(xuTk83`b~ga>!$M^#9H@HGKGp(DQvZQjh7 zYZWdD*dPr49Q-@$068kvoIT9-K%gW}ysA>0i2MSpg8-8PFADnZ)dvFzN{Uww5=i4@uQgiHfYi67tycQW+0Fq$5@!^yE!{0JF!l6c~FdFEHj zNkq?*h~~vB#VWgU^9{AxZnaHE#p01XXQJgH5g2SOh!Kl7V;*CK&?-;A27``*Mv#hU z_L_+@86gYL(;c$UzBzt7^)z6~Ft}X-+~c)=X);aXg+DnDZyfhO<^nheZ3nW_lJJhP z60}9Y>7FFov84AeLsdwP>2iS;ZlZr4t(0=BD-GN&&CNrOO(ZT|qfub4Y%9EhBn-y+ zptaK>E|hP0rOoqB8GNARqpKA$U==sj)wxA0>dx@QuAboGiTaIg)v{?Y8X0 z+3mi$uWnZwLqy6OX`TDTE&1b#2=P@y2GY&=_{SI(k|WR{pAsPz8Q8DByINr+$|^`L zf|==-F^)VxG1DdY#E21HLB&F3963_a`)Gsi*~+T28f0_8Vv#P?lN{z-?zY{z0>u;iRCxSqgU zN~&C<(n&6q$RRfbzb)CJmJbw{wP=if>}-b)!{tF63kOvh-Q zUZ8hf8#o7@jBzYVFbUP8E>xRB1Suwmp94J}feJt?DUw)z!gjkzd+rD2JdDOt9UAG? zd1gQXEk^9dkZTcM*W(v0s^~N0FmiteV7C5#&kr*0&Gb-liGg~1C&YQ?skbx+1;GeW zL_0#oO$yyN2vP+-JOt3ibMpQ6>{361f_II z-#4we?UtYh0YvZ#C;*9`k@Aos9t-I+JTR30syt1WPoyS|psVb=nt-M7E)pi~ECnaU z27xR}4~IHk0zL%;>OME^1^flPQ4)iM!L|n`2Mlf(s0T=RaznUt&4hUuEBN5)!0pnZ z_()j^y%YPpW#>d%0C0rC$IiTDZTS{oA>Fy{)3ac~(m7N{CkKw0ya!~z(3LEGKWwBJ}e%O== zC5;A|uT!n0?c(GY_-+lIpGtRFwW0YfBst%97z~zBtoeZ~Z<2Bd;Fl5b3|ekf);Y&c zBBISi<1N4n40a_`UeU9Wskcw_JucSk5r11*gm3y1@ibY7Q2mWv&#N|-c7b+n-bYZ7 znqjJtoBU7m@v4mRDm7xM2&x#)6J|F!Ee5g~=&pwgM-^0*$*q)<5bpc=6KB^;&fl~; zo3g*&v^0jEoOUT5JZF{~UN#$KTcs!_=YQp6#Ni+lqwu;IROL5vhAr$tDdZ!h!wsyc z0#SGd=xdvk6%Pt3MLA`le3SOCW9{`ey8a5Qj4gypz zA!g*n3~+2x#Yb*#rXnmf6#`QhdYUhot4vHl6w~nHkM;x+0F@GOZjeE}-QprhpX2$+ zXW=o@a{G`lG8Qq+=&`}%UwbRU5#fN#i2|B<-U(-j8sRYDk)VJFk_!=3!ik8I;R|`E z+E!Naf(sNVcn}#NFGp*~a0};aCVCUOs%s1-tKLZp9Ru0DMpKO;g~a@$xr!?^l(Mxo z3n(biJR=So_ytM^apn31sCJtHM1agk1gI$I{w4!B5TNK6Z$34Fv7z+BBbjqUhsfcD z(iI70cCRc1kMJ()tViML?0EOBaz5!*D&K)3@A?>zt6XEnO zR{igKc5v&>IR{e;C?fJ2JyIen2K8kA$nLe#M`fC{ZD5;k{rD5Z*vQ1$CcrZ$LM z4YwK;e`M(O3_`y)K317F{*Ab_j+kCdOh^xH8?EZS{j=r+zs-_^1|J=Cig>*+_R?}i zu+o%SD6aqL>nuOaZDInDyGlCx^Lhtd9+sAQ6Ln(H>U!iKf)-6+KtocFB^bg9=xW2--vmu94oq-hSJWI{hUOzRa+;%32jXXbmEw zTo>h#-cVHr$mbCJa5+%2U%Tc&vfoO{s{)~xV!>*t>lqs{8lW>MwTnM~rM(o+O9{kw zazQ;hI&263=T=S!0Y|$qdQ^KeQpRDgJ{3>7`#mJ&MXIN zqByqj$X;mzA#UcqwHy~~ChDkUTFh!8i8mTxz=!_onVx2Ru zX#SbryBVt@Q>nm}XS6Hnh%{2y3lswro7r2e65EP@}^l{47B(zbIOVB3pt_w2+&UtySxH3~j!OQ!TXsi}K|`Nrhb z+cuX%a`UAcc}&F>rKYNKy|=Dz<(u_2#-U6G7+-GtJQgWkdBVrcX$30XGc?9f)CSkp*8xlrH zWrV)5=$%N61W^n@Y~(dMVpe(F%YbPRON*kJ>pHLIF#CpuVLVP zfJ;a`r6eqWT$wdKEGD;D@S`^JgqT(tPeqRO0V z72HrorxmGBs?Z(9B4OaMyu2*--f9c-c7Nt|U(sR_=H-EuLJ_?7hN-$;^o_5o z(05wVDSLx6Ag?eigp$f#?%pz52s3S_r>oN5>7?nrtTSJ~DuqO$gs~ngJRPrdYXPw&eTB%)|HQuXwwU*0Vn+M!q$m2 zvE1Y@bP2pNol$7x7~_wgCW2TNbujX`swLh^^odNhQ#9W ziwP8Gf+33y6lT(7L5>uZXVhL=V#5+9W;`XkP%^!w?i6ZI5cZLzA?3DU{2g}@{R_F-)}BHjvkwpNgN)rhihn*;mA>@sdVgLV{hhELldKUPWRw*I1&)u(B?_Td=1$ zm5@V}mq4{gTIx1^WX8pw!59mA>tRF^+kmrsxJhnDC1x)pm=d4pLYM|4cEgZ>^D8xF zF(IUQfAPBYy(DA14!ohek7BBMWZ()u_&tp*tSgQY0_u^H3lQX@_@iGx|VnMD`US z^d^`890gsAk!^uA%VVr)Igj&%2zADlQ#f9WlVQWnpyP=c#S@^SNlG>sufdXsr4X@8 zP^aXS&!Q~Pn~<=*AL{!`x7{?SG20<~S1$0!rrH2+)5*X+cU#wa8r)@?-med2N7GB^ z@~$>L-VNIGlyA;g+v7X=wrlx***m@rqPqWuaZ@{JgUX_+(q3@P!N6a{Nc$d6Xl&jp zhepaQ(N&YxFS@X$Q2NVshAOU;rZBNwlfGM@&6dvX6&}i3@Tb%xyFkA)RP;>ASXrKE zq@qUWh>KHNQO3vmC0gD?;iiM)rb`{)=_o>4HtnIbb~`MT{pf1|#&6eOtZsfjPnZ#u zcMS_TTRfHU+qb@vpyuCkasO>R@vZXue$#vQI1ad7WM<@zXHMPf)OsD4?3jpxs?wa_?_hqP`=%xB3rfcRjvY&TYjvq@^8y>9A-dpGLGa zitMlyc;&5_ve-w%xZrSUlLVW8zNs!yn=`-}0_*HwWFf>_=ri7@R#i8?>Jv)GJB+P-!h6 z^s9@#vidtkhAx>pq$AiWhn(4eM0n4p7lmbdaCanMZQwbNmqe-G^orC*MxW&Jl-JtF&8CjA9pDqSbz?rGSMrd23ie1HfN=_QoB~^EQaRCp$s1Nv z1P1Y|x}(YO$CcW-BZq{UzryaEIq4%FIwQXj@QRJx*t<`@ z9-v!zutsff#Fl)+JyjY(zPdq9ic5m93 z8!q9aHSgxa*D)*d9HO682s`dnMD0XWLP*WN+`bwxjsi1jA5fiL3f70!x2E zK2FFOyZPvCgDmgC-g4O~4j#|<&mVL@=hcnR6%JegiN3tP3;dig3KP(9M70#FX<`gD znX}3f(#ZEX5I$l zEZ-(aiB#n0HKd&cIlG=Do_oD&!iI!j-pSLFAvX_Tx5#x}d$^;bNF1MSQI@yZA0NCMvwmc7mwX@> zN+a3~Vc03$`yukWlm<-*6KA0pelfT6mS`q%xK~HL^I&Pm;psq^+JP#1yz^~RE|Sv8lM+aHcF-)J zpA&e_r+z-e=rnm)Ii3zUs>T&h2;>l$5!NHOjEYuQN7pw&yc@W$B)sf=B9>LweW$kA z^0!rHw^hV2!z|ozHtQYO=$Xcec6!!l8cYS&uGVKil-a~z%?%gU;EvQHqmEQr((32& zXr(>-?%9p9*YCYA$_?rdBbQnjA4*9cPhY(|-lb}wCBR7o8%kbb)l`jS`NwH>O)H@&4(gG>Qn7RqLs( z&-o$e09-f4rR#f4)rQd568K<|D%9Qy%{VD3T3cu&nU@Sg5s&JphrQ0-RkzEpe|afb z$Kpxd>`%AV)mQt2Y4u2RfB;+BVj}1M)=s#~cHL}B_D1UVsFfkee}cm>F$)nwxL_V#goOL-PuYw71*k1 z7VdRj0Xr5n)Ww!dmm6Ldj3O)7W>)N`WV$BWA4*4`5L?Wz4l&JUQI5WoIp*S>h^y&N z|K>8>K(U&%D)m>wmro_GYuf?4OILxjb?gC(2`uI!1g>`E4DQ+F)P#p{ zA=C3d7zIy7igl)|Gb`w3IQB4*UuF^1c=e`r+lubl9nC@hA@8Agj+i&B&WwW5VI7T^ zmxa%l4YbqLA?5VQZLDaJ>CG~JO`fql zr$h$R>d*fYRqOot)B`emcV~B_jZrP_DgD5C+}hX1Hake3bB7WBoM!@S_oMO&sZLaO zK`87Cj0V;LrVei}Z%f6Ity6d7GXmxhL4SMK906#M$#$dUZHyt zH&t`yows8~>8&-Jq#Zc9g_k&n!JTim^E|=j^@(r64C1*Yh6tqcVN8$c55BMWCWY| zu`8=wn$DsYf;KVIbT)%zaaM%V7eyo=Tz*w22l=F(pfq8rU4#a{!+~kWld+O>7;_^; znEBpyv~qL6EZ-x#hIB+is!*o-b+T{e{1=Xcx@4bja=+jIp6{_-NAy}X@RAvDma&@G z;GD|{qh?@7j8^Yp$-=nBT8%`E%7tSw;aDcyW)|jZ6a|ZL3o3-R8yXA;J1FLx zc(O{vucumCMS3;Kih_$0Z9B3whHQ*XLz&IrIGRx3km(`=E5ZJ;a-QrF z>bZLQGj&+3G(vNnB?==orX?a)7WOGJQO5?xLh@Fm(@)adcu}nN3?3i3-_5TJR8TuA zi$IoKSe?tfldY9=$=Svz@-&766Ns@iii(=tb@-!YOo|FLjt8KTxr6*Pane&v3tO@TBL1-y8z0Vsh2gr7;I ziby*Tt4%yjxpvstbB$GFzMTRNTPbSckLYW$AgK-h&9=-F(cnl!%5l>L+kO5CWDP z-pD4P!6Z(QW=95#Uj_==pHLCwdtPRk-Q2g~k!Z7(QULQetJ z_s5w=!IE@Gyx@mjH1LQ42bnz|wJw|=SDVnhbP>)_Zpbu3bx8A~UH%JJ0F^M#EVe*K z0JCdM>bHvxb_LrNG*lM{`zNKM3(p>mNmX^)&-`^;riAG#64}3v4JV6D%yqX1?`MO& z(fPb`Rw9u`IQXAL{SU37X#8-Ys%@6bBfia6;)e6lz-(j0_Ip2(<)g8-{dR3t=vm=d z3XnQ+7f;T)0Gt)A>WT?TnpemPjWY=uaQ^mDs4lmusTg3Gu?{&9hT@#W;W=>?`C#Kn z%HG6oro>8j>$})y^|#A|@%Yq$!+eJrPvVbuVgh4~ozc+Au~q6g)6m*Da$P%vF)ckH zQLWDtfcHdK%wi5o!C~7EP>SbDLnWLd=1wMutrb_82kSUa2S+HzhT(OZU&hnFwy7H7 zXh^;k8VZ#Ob9G^Ed74)gN!BB_+T8Ip?)nweNJ^M`MQMB90ankBqwU41m@`Q;v=HsM z)rzyO+bMDsAWdq#;1+N!QV6_&{mp)wI=P_ZYKtijD*xR6!Sldx_n#k}x_yyz8XTq&UEY`x&QY@Mr z7-m9I3vO=a4)Vn_C&Xlrr0OB(svAWBPffIr^lc-SFLMQ7uYJKX@gO1%{eLlAY}Se@ z&BvZ63m)J}ris%_GN1l>ull9<$y&qu@~gURU7V4CMHu2)IFg6__W#_@iYp$Sj|zt1 zB5yrO*h||DKoAE5f+Fv`eS9uG6%}|^Jj}5si78!0WC1PA4GBME7Rn4?c;O-i>KN7+ z%o~5}GpIX1`n}fQ`g|-?&|ChzouERBUTBi0X&0YXvjsE18CL324>M?=3^En8)qj5V z9bk#-Yo6=ld=9*E$-cJ$YAO)HkcWwT`zXA6=^84`Uf8MeH3n$>68E~kx0|i(jfgZO z!C-aKqH~;4z1X{S7owb5EDhd9v0-dt4dkS!_FuZ6Yp!56LlOU!>o}F_Sa%@TO+f=k(=_5_u3%iw(vY z5t591$WKPdeUAm!n_Uv9ZX**Pa1a2xqleKlUXi$w^{v5CsfCPdX*#2%gk<+%UvH4Q znFt|0=qBHM=2+9wXnwMGPiSWU>N=K!=gH4IF;4~<%w1z@2SY=`wDXJ7ZCa61x$d9! zhYL>CWbs7VvE&t{%M|_)>&IGmels%9_Na4TOnXoCZD;Vda^+(beg>cf@&VnAERL+R z8T71hV<4}d8*4tt^u*Oy=gC{rys{>z!hb&;pia8HKW4*?6KX8*(4 z?aG6%JT-Ywr@%S2x}Gz0{*q>gv!+tGf_Y;_v5|dwHiW5h=fqjgC6Yg#t(Ml{lBQEh}d;hp& z!pfwRxBK2$1H17}TP<~2^-$^8edC5)oRqo%kCZf@(0||S@*`n&>UYm@=Qi+{p7v@n zBNO_w^K2G_wZ8DMMCqauLhhodXlXeh0-Y|xxmGTegMMoi%3nO;!p2I?QK&**C86?6yam%p-_ayYEID(tR8x z9{$z@6SkJ`3DxUd-&WEX%v(cb+#t~2!k;fd{r^_T z>T<5JojS+oEQ41D&nDz6$p^X2L~d|CihFEQE@VVq%SYvj61uKymIM|_&Je_GSNNSJ z6p0#7_d^PXv`AIR1$75HSqyfrhOG4X+e}Q^m}Q za@m3$gdz|?!+1x)+b~eSXWIf5omMTl8MvFWXUu|D0EFZH2Qg_nZEQFiRuZq zFS(i;QEwZ6mrQM+Ux&anPL`~fbeaDCu#IM{)krmG-4ix*JBr7-No=CZbIH7in5N%cO+E9xyRgQjLf6fC#vs_> z{$I;^Od~O<3>yuB4-kPon7|+2*K=8n{_!&LO}ZoI@wE<^K<24JRvytIHly^jTj2$9 z+;65{78#}Y;{;4}-#z1jF%K}egen+K>(_=wT;sj&)w0jNm29}DOK9$>LMwhhQ?M0UfS>ti2`5%oWPm45MmYrEbNCkbMflBx)9If{qaF zKC|0(@K6kC|6w_AC$KY)Dqq(QTF|cj_2HuL*ae>V!~5eg#I%^th0uB{DCM0Y$~!*f*r`&MJ* zXCTZFQ=8WAsYxA4keyh9?bds~z>3*O!MeAaPus6J$D_ov`Eq$)9 zPy`Jb{zMYyqJ^7Wwl+3Y~t#&XT;=2@wlAaZd{!h z3JQPgCa2>LXV=y1QFMB3=HTW$BM`>r6Ggq_!9WYI0+h*#n(c|uYTn~s++a| zhn)>wh`N0BnX_7)4i46g76}9%-;*+`UK9f5^4gjt5fm_^F+nI;Nj9%eHdZmBq7sd$~2?7fnPY3WdrR?FxOTdRZW>M(vm9@G_~iYYEy&4u94Z z)D@&AF5P!+<)Fb!Er>TAqCT#_xgV%t@t%0xzx?2L@vVWl^D`m&s^1G?)O<+w_@oQi zyRwP>XRq>mfnuOa`opz4NlD*LzWSxA((n{n84&VzMM%K~WWR?AUhk2jGhs!xpgY1N zUv!4!*X$R2OA19EK4uj)yR3IUsXRKClS`gO^lCrqZM!sn{X+b!G4wYNJ9#=q`NfAa z4p?TnGq3~=IOioer>vvAo~qY@!yagr!QOaxN2vghgQ-34rvP> z+zHL5;G3G~*`4DwR7)Ud^cQR^BcS$tUos(&M*2<&T_;%x7(-!@!ZcSlkMV093iYBZH zqSFV!Te=r2`XuH-buh|3sLz;0v#)7u9+FV-#{w~$RF9t~YjxYld)?Zqy`bp<4Nv8v zZS7vtx(%c!FQJ&0t*0u6i~>fe4bP^SmZAPPz39?!%fwxRb!$dTJLSAO0jcmx8B@p)}M*n+#{wx|#I8t`WH|W4(XOlHoO6$MR2cn3j`0C`Sz(MoHpRWyv; zUP;PZog5Mi211TPU8S)iqtVF?hMj_=@RXd1X(>a0OYP4!x33KLo0?C1@VW9NQb^^CE}irR0kly!-(D?J?1!bLca@-9usJB|q$5mZcG3yg5-ub#y;%lBvU{qa8BzYS)*+s$j|Prnu5FuL9; z6Sxz`waL6y01mGrHJH)={Q71eh;zc-oJ5i%t@1vwAk|TqlEB>M68?GV$%ai2^a$nj zlUiSRj3TMT>xQ?Pe^@sdAsX9_pU`?$llHIyttZ+1*5;`FWk|*tS$-&2& zFhf`<{l70LdOBFdXK9-Pyidhw;e2uP^UVZb?Ap8F3>-z7Nmbuq_2mL$)e*XJcpG(ZU8rJSh2HMz3 zWLEUxH3(P?JZV91AiKY0PJ7&TP(^Vm-FD1=#T7vfO+-0n!v00C++Zw5CpRG&JSJg7 zBCW27AX`ievCK&970mx4KX|DhjY0&^{S|RU(e&Fq-fHxw&uumrIp}4!3;lQ;G!d4G z2J4Vi?3@aVqQR(_3GdZ@n6?wldPl;Slg93g-=4=Wt*{3^5kLm_Vs0B#T?G{T*K)KW zVr`lEhFcbCxyC#@^aL}g#VMt*<74VFQhhJ#f)k5>SUra&$I;UzKE4DucdLrlRy|7O zelQn26>wO`jERB4nBHaSuu=3>`HuJ9>bz)qa9J>d+Y>Gs@crVx#}hy|68-eOXM7!0 z561^9Vs9TU6zZd)DUnu}<)~xjWRobNcdSstcBCJ0o>x=HX?H&=4!n&qW~^UxW9Mzt zlZM@yEYe=+rq6VWyX8G(Vs3)c&~&c;$v4BC0a4=4dbbX$_A);jfrshJP$#Vk@=IT1 zAu&w>PKSR;rwm5`WF`?l!N|Un6@`$Z(=`pjVqwb1g@*Is7&X6hnfWR|?{FViKfm^= zp8_E^@mnD%gz@z;B02Kq=_oh>YsUm*f(66W5iS}_k2N``>gBw8(Bt}(xYorL$>p2t z57{G5dvLG^9&^RYSL(=r>W=1?BJV=mlgDYkP9LV_r=uQ`!YrQzS=t>RBo4qas8?g2 zR>dXN_F1mgU$SP81i2*4trEpfQ5M)sSWVVVC0g!~FX$S}Lgv-b-ypT1t}DZm); z?KStWS0OdalGDkk{X{znP+a~xB0_nA4>ZHcvGbg`lfQ|lQpRWX#%9$$e}Qc|E12&p_az5E@RrR07HlmVoiST{Xu`qGEL>$yJtQ(ej z`{-H{83BUOO{&F=Wy>YcO$6-sSuPT@?=p2Y&fir2u4{;av&cZhV(pUp1-!th+40?2 zbQ0_$`r0QTAwcVq1Vsd?jw2(V=FiYg(*Gy9Vi*Jy^IEcqo0t4|-VAPT-1MP%#y@1w zCK?Rm8`D7%VkwhggoPhW;%I_yJH8hkJZFDN0DX-KDP~MfD0w@PnXmSn(f8mA5#cCU zk*EP{!DKiKLR&Dk@*+SGwbc$hPQK@yB=W~80&$qryF|^YOLjA%XM`fg?)5b*tK2(D zFN7&6ap2GiPHKoqMwA?YZ39`ryup}LBC!H!I5e13c0v#hPr{)%(z);BFfx5J5$@z+(Ad1`c5u_N_3!_ z;ui41Y(6G`+W*3F=3)Ef_)jF`GH1}9KAQwrVlIU%+HS<e$Ako`U)Lu&L;u#n zhqSGVrajACT%;c5HLGS-;<_qm?nCS*+08S&*ZMNgI_GNM{s7#p_|~+~0U6?EuDVQe zNw5zvz&;oNT!zFDbovC`UjWoaf^H;x<7rd)`812p|7#DjKFlsATV7}GJsKsQezUZ6_^ODd^8qCs3n5}rbqyP z!;troVUXnMFp$j(68^v?RJ|+_k7$0|=!?ubXwTmVr!Fr{c{Rc`$dKxj2t-ddwP3m! z4T|jgW6XtqI4Ju{LhTQqat!=Tfv}*3j0*>2IN?cr_AqKWuK^3$(f+f49`sj{j)F>9 zGU7(~=%%}~;g_RQ|FB$LObRA?P#oZkl6b{~?Krb& zQDeeU;5AB@l7P`6ToND+9yLV3(h^372tX0@=nfqni&{d&;m=V2GnqfnFzRHw2y9tx z|3Z;WvM#QyFqZQl^Zn18TNrUtLDm2cCSsicgU}^nAR0_A*P5EotXuZwob-6r>`Im{@%Ru0p8C#J z>QE9O=h7FkO!W(cxB3ftPY^zn)_w@Wqfql18&;wET@~(!-9!xNPlh3vnJf1@k?>pF z0SrIVxAnwuZWd%yJz3uteTC9R>Bxa@ zdnb=>`W*94Z8E%dXbRZ<;_9xF<9S~%cKXDB?gnmRMgBRJVW z84~8xxXpovI_4(YCsNA|VaW#=ne{VssgraQ^a*yHeKo#-ez4><`=ku8005AJ{gysZ z9}0E^4cyQ)cnUW%&d3J&iS=vuOBaXZ1Lc4RSNwYA(%b#6Ab)ajJoWjlgVCl`@Qy4{ zt%npxF96`A!h0|jq4-OZ!8)BHhM*Ko7r7)Lx$hMhw7&W3Mim7zuWWZFets^*viZ8h zlaAz^Ba^ke5$DUg*8Dsd61BH}x(8+H;-rj?GweLW{%B%-lgPOu(6NI6|7M@4=d)cN z9}pYD7{~*h)Hi)WnQcGlz~=<=cf7w|a(~D#di8xbOx%0=_vrrA@`)tysvjF)XBn^{ z?~$B6oB@!9clr9DKc%`g4koGb*3c}L^OevQNUG#1< zA+!C~YU?*2xi9zxF8kEXpI-0g@mp=t6?)RnX6{A}K0bCQ|B2#!tn8RWPQcjXX&1kI zS6|}c?4afGh^0)FzJ?J8+IOk)Y0tFxSmNRII;4JMU-Bz6FA-BERe_OF$EtT*qrAdt z2q2I5C29RWOoQdr!IE?f#R;tBdZiY&xypI;;T6ZKW4;@%RU_Waf2H1VD*RuUMMbv_ z_8<>!2z5Ou{Vh(Ne*Qo2QER+>QM~HY<-Tf{zLi%a-p~Jj34ZtLh7sv6Th@$|Bl@uV zX;-(E)&u&9=QpZvn}Z*>q7iT1J87eqg^Pm?z*1I?{P(3wyl>d^%?R-Lw~_I;{;ZyS zuN(hYNp+TR9b5fy)r3>=&$G@|4Krq?x5QC%O3T^v^yLqKJ3%0$CFaY6sTF=EeMJ<- zY@aIpe%bP#H*JP^zp(rg9gUMX3iso8*#Shuads)>PII4oJaFMd1hQSb{ans{wozI8}KXA#q_Td zp&b7Th~Sq};skNX%zTpm2ci0gQ=M@k?GHlC#3Ma%*Q)7)Pg^g&zdehffj{Wxi+FI8 z)P4~PU8~=IFX5FjddcTJXV-A?4>zG{S5u|$ZbGJuFN*5rfn_Q0;wAFr{C?$71C$ON zUi0v`q~DPG;7-8$`JO&WVxuXoQ{iGbeVQK#Wlqcd^3U{BnNu4=uwH>}*oGou%egtn|I{VFg&u%!? z4ywkpZ_I_{tSGt}^{p9;yRwTbp1Tp(V6|ItbK>EV-^|Kq^V!yAN&Lv^P5Ad~N!N8WYHoj-n7{n$ zFsbz6qO*T{+h~pFVk&fIN&c-D@i^z3bQkwLP!XrAK2;R0x|JFM4oHo8H!0vco;v+A z&usFx#P-99+i5ppFlDe~uRrs}a0oy|7|f#1yF?+(Y?3#RBnt}0{|_!Tr45`T&xgY~ zg7M2w4>jwwtFlFxduu=JoD=?=UjYx-zrW$CAo36P1(MiDE3wBYi$o+e@76D?+$d2g zx@{hpN?ZMhKYud5nOOXN)u~qBb$O!J`}Gs&KMz0@wC_LDvfOKBKZiSo)S`Rp*-K^d zY2rDcr62PhlB76)cJgZ|xz%t3+YEGk7njZNA0trcYxB47bHs0xmd0sZv7fpOU3+oxtO9SK3O47(gog^0naY!ku=Ny$RFvMXHUN2p$|0- zlhACK6PS6!N$JjgZtbd7o{=plk_Owk@%V;>mnzd-Dw|7l7gUC%o>=wKYH^xt7)H0 z4a8-}r=67IxB&eLtmd#NS$~u0g{&elhQ}2Y15;*X*x?=W(7n zUF$zx-TlnTvj^-?cKCU|le+PaKDl2VGg0dlud;K^-6w1EVJr1|m6Q5SqdtM@l2p@0 zi^9c)R$58VK@}gLtQd-?K+Q`&@%^8<3BI30$PV>a;)q%`FY=_S_K&DRgYg z_vb#u)9h6-MfWeP6#UWsaZZQiKdXeRES9RA{wZBgoU2ACszykLjf|g?Z#sx5bgt0T zcsvdmx&IiB|9a-9@@C?ed-2s$HpB)1U7O+88Xi{R@hhK8wuK6T=_Y;dkRO=F@l@C1 z+jW7*58uu_bcR5vF?Xlg-k`L^S-#RohGnl$v!VLX7WvfO$ zySXmEn*n%}BCjaenm2Vw(HiK2vkrGt%(yF(<1625g_a zpPOBDWq*|V{9I8ft+&mwq4P;-0{++Xi|EM+**z-vGPd0qeQTME-Z72^Pp z0PCTq|Hr!Z>^APIkQVgtQ@-lR&$qKP)*P_BExRG;&{UhO(g8XtI8Qeq}vaJ%^S`9cwek?E&_2M+!x+m_!L&aXNy_4nTc zjJ(_37QbLat^SfctJE82m1d_QnrS*P$vD4!XNsdi#`}kdYH%W8@nCezC(E5GVz$TkHSAju` z5kW)ziHNlWDTm4wjdwq*eKzqzDjhyehn7cc0%_;&kl1PMJiFyrk~spP%Jq6R7)0^3 zYcWXwH{v2;nZBJ^SvB{nfAP_e*0Dg)FmP8>@*$~}`+F_aoXVPqA>%=^+Kwghx8=>I zz=r7(U>r)SW558#yKde6Fb${-4A#N;|Ic8>UG8)K^oK(ns~WyY)Ng1&sA1e_`nwUQ zy*FrnjsT`*C#iSewv-zuibWQmN{X+}c15;Cs_W>+@-@s6Xy1JLY*t(~!7D6O@=s|2 z!~b8fa8lHCrAzhX4#YtTenz^+X+%Ww-8VD}QceV>UV@1EK&?e?BVtv=sr+ z;MU8s#i9~{wtkk&I(!YklGg9k72K%1Dd(+p@EizT@z0lf;%T)7e+J!P{~sX+=I7B( zfa^V&;AdVW%xoI?cymuu0JJx9c~bNFVnN&=u_D1)K)zw_hu@w-p*)`5e7D#gp8CGT za zKR!)%W4~WlFsV5$ajMH3n3r1sLKcbulDbTKu2zgVksx>7ad5@RjvlZi5``pnpRX41 zPVzwu?G!+Iba!J|MW=z)Q~|r(hHrT>78TZQl%ub2t^`x@e5;;x6P+tEEuL~NZBX7B zvpt^KZFDfbZt7NKUN^0u;$2q&9xa^XNy;PjXHo^=Y!@)=stM71bp>@Le>MlNL_n?v zbQUB<*Noos^X)UAx!f#XeeFx;*`}Yxmd8$mpxbt7QIQ1+)?Ea%Hr<~s1(@oU2v8Zx zVW(WHxLy0!1J?bXKKL4?y4~zh)8;)%dxt^PznPqOl`rBXEJyX{?CFceBu+Ygk;F_+ zt_q_+!aW)QqZN-xhyYVu@gFX`ze+Lb`AiGETZG+I{K1T` z^XVUEf0TkpyZQ0bsN^RzU^{!pXsd6N2urdk?*FhTa`Q_PAWo<7l7Du`431?>8Ca+< z4NNJ`KD@nFJs&5Agu9`V2Vh=XZ4?SRBuC=ON>gJeh-F3T+2_#{rt25W6gC|s1Emv$ zab&=+4A+rI(qsB+crtYprljZvskwhwzh%byT!14jy!UU{ALh*VBr`mqL6S?!M5RAN zw9uokj`RzRie%Q>agOF?bXz32h@`K$$t0t&4_1)cHVVmP;X0iXaY{cN8IHFvM8e%z zpe;dMDIbBa2Gkp}ghpDCW!Cx-x9dm|*90s|7a_xhgv*bDFYUE6-q^mxrdeiOS!_CpQCl6yac~j@((} zV%IT_wv_6mPgGw2ppvA-FMB0ee*5?7Z zwH#<0Re*OqhBeuNEj2F($r9T&T`FiTvXobrn%i-LnVLra3# z4=`f5kva-m$9KCU!7))9L4Xcpyu6wD5^%7KfU>a6ghzI%y1LUFE@@#ViI>c*l7lWGSAtv%}3wu(m z%_Y4&mg_5?oWW|nVRvb3-LkXvc%pQ3Xm%`xo%_hYlb&|UW~JmhS}lv@PqA9;O(E?+ zZ^wvN&#{(1py=WaQqDPEbr`pa5g&V4oRWKw1xen**lkh9d53WO$fq(RmbRsQ-Cu}P zcM<^+hjDv-GA2b`C{86JA_dXcI|nrE^O#`Cqaa}GGFBEW8MqmE`s*fgP{@3(F>NHxQr+CVC4HD*of0RE zqK8L4X;(c;!VIR&S+VOq6}_W(>am>g_(3=SFH2t~J{=*b3;!Glc`|vi_vg2)KF-Y* zc#ug(u7`5e+p*@|aU&&kp$XBLAHUB5h$vco_4m-|XxDEA|Ezwy{&eYk-rh3qGs^GY zy;B4mp@OlOe+kooSX75RX!<9BF1uvRVgd>&OfaB3iVJ@zvk4}p9|L$04Xwk8bn81+ z;bAcE)0$+h>5;iMS>QjHu8E5<4J2>teAD)MlB2R;>AnQ;2_UcD@)ei`j9dnRAXZ^uRTU^w0rM5c#7e!O65$(wVuAOtGN4}`Sh zxO=jdyuPii*@I(0xdZpU=%8hz;D7FL0=i!7ep>07sB*sE@bGN0S?R*z%IFm(r{2W+ z?I-JY!+Dz7zYdZ)Rc&CA4mcR=B%njop9aq0GK{8%2_{16jDw2Cy z>F1j;v0L@&N?zwUq-|gzBUE*@jkxc0G(mgo+J?p|lP-413N$OcIfjxpNfofwj<;U@ zCXM3Y=3W@Oz+l@MJF5=mqSHY&Q<6Sw1pdqRe@vnr#+=g2r1^ohqPP&13ggl-0v%(F zpBtcZQ7x1-*-_DdRt=+`8N#3($sCtM%@o1ALt`PR%`F`j&VmAUo^TgFQj-}uiWE7W z)z)-W*`vp+*UJ=y@6ceC2b3??OO3XK3p>*ER$!tD&nm>uL_^b}(9M+8Cj7EdnY95Q zwW24aNa+%roeGVKyi*6{6O)#@nsPnrR|pq#b8Uj@&q$$i$=VHW6X@VlI(T&rSRGBE ze2Xe7iOnGoE>fKkO6`*1Na8x3vO5W=Fbsk|IsRNy@0F1I`cj*b{7Or2<2!)+y86yt z0zFIzCES+;xVRIZXh1{Z9fj#;!sJp?#ylZJ7huu_%dc2vwtoc61R7G4nq&6>-}Xz; z?jWHVE68$IP+##ebp%f|J=JNM;DOzlt}n@!!MG3zq?(V;KuQNn#cC_U1U$WR!W2y6 zl$bI{MANvQx)HS(9a zGav$>rz|)yFcBBxG^Pb*U1h-+k`9-`pQ2Y!Zhxvn!5Vc_M^4A4Ac|RNi*za*$v;lV zz~DJ>cC-vwUx}t8&-W_379t<>4INqt6!(05yjrP+jEYAJbZiV|l7Z|ek@x}Br2s*8 zxsr$j8msJ*CKD@ABuOMzDAZA3Y@ynJm@Yw)Cb*Bl zg#r59Ox-)#l2q#sVN}&IG-MRL`g&oOczls`;>fd-l1Ka7%O9twFZuW=FbnEqnf@f! z0#yU30LGRF){*Y^pI6`{=O*s6fo-}J;-Cly4&x|Oheqk0qo(+diG~7QC=A5gZg1m@ zj#UUuelWoiREYw`bx8osTPE3Ph(%5S@dj4MWVM6AQUhg8&};EJ1L&ebA_V~fcqSnO zS7LQI$B+qy$}sa`i~++#bxCi$p30)BZl4F2LY?XrJCm1-rk3)nvx09EsO9Qqu&t03|Qu^hua*_3dmRP~&rRD0v;_whSEr9zd=#ri>2;d6A42 zCM+8f=xuldJ*pF&Ok6&qMYQ0PI9Q5)q%a=F#iGazh->!&p{Xvw91W%rncD)TxSonD z$-T@%Ay3a|45kjMU@x#(ZTDfK*vxUC{$6O3P5P5kt{;id6Mg_dQ^Hu7>r=jR+7Jzs z)xV7r<`f+q(+#6IBvVfw)5V8S3MgXbNO2ig5e-FVR?uwvO8F(+&}9}9{JKE+(iU;$ zOdk^~^z^VbxDA38%Bylq^NB}<>0nT%4+_yI? zxuNOqE(v@xZSwP-k8FXZxOeYfnND|ewtIRzls-=v ziXrJwVM4EMyjt%QAD6GylM35ow(*+_$%b3)$%Y<%2fzJ)SW4KpM18 zh;GKyGog$HUB)ZICf%E!_*GAJ#GO9BH*;WK;LxL)bHJ`~7VyO2ur}|&LQgWM%0m_= zVd%#NU?I>nI?ObvmR1bnQyxlI8by6(EQ@0B!fLBrRBGp<&1RsrczXXj= z2vmxs>W{!#Mhk0o>q}8WIbXqXc`sg#)-EJntl8}3;yn9*D0}aCs{inPJbQ;jW|_xK zWhJt8jAMnUH0)V~L@Imb$UG>UBx%`6B_n%8sE{&?ii}9M-~Bv#f4;wee!uVc`*@^B zgV%Yj=Y3z-bzk@Wyuh9$W;BlMcB2b4Jqf{~^5HHl?+O~hoG-M?wA8gkki5MYR6 z-YAqM;Esi|%TwuKZPFm}xDEPqW+=Fi53kSKuP^`l9Ju~V<#(&_xhp*n5cHYYy|=+s zcVAPoa6DS7Gz@OEVMa-0iD}~WjC$nZGDHm2Lh_vyEer&IfLh|_z%o<`2MlS|YnyVJuc zqq(r>9&HF2o$NG3PL_Nl0m(Fh5XwA}!H6*G1r=g}4gd1klrVtS!sUy#|;5{8{MB*-DNY4QDg987y*{j9$@rMMQfs+|T6{`!C z-1?XU5TADf1po$8T!!VK6BMM5TqLThs_34em5q4%eS#sOcfl!y)mmQV<(Zs@tk)T75ms8;mE>UBDLi`vm1d9an~b~-=@ zr>;!=Eq6({9Qd6$z^j)Bq$3s*T$>>R?Gg_%-ykP8px!)*r42R!Op2W`|Uufy(rn3tK%ZQgM)Zr1}R%x zfk%%X_3MF%p(^`|r;{i|-#b6Sznz-$_@wu*XZ@Ff^On2ziGtchCwe@))Ar`^@j5s- zuiD-_BDZNr31Zo!E$Uk`XDi*tw)nqvd=-v&XgAfp49~bn$wN&nYw3C<)8n4;a2Ckg zdG#~;<`_MmbN4jmQ_m9~q=||k7Nr-?qM%pe-Xd^h*pCgQ{>g%j+7_Vd&8mO5qM$Sx zd@t^3G##6$v|FR65VU|{4kkg4D7&`k1TVAJ(*vmr-&~N0X`Cg8P?#v6FYO*N+`D9I z5xt5k%02ZaPN(eVY&95d(;E*OCJk8ZV^J1i(thWVEyB|t{zsk5=1;m>QSH`s1&`s* zeOEs>N3jYkjzm-&&6^5T~zX-rakW{k`0^r9|D8h4`ROh@)=~rYU*}L0Og& z4cj_h;lg?%PK{W(%Navvxf`QRMp>DF;{hDIaiQY*b2({g4M)h0hFM2ba!%d2p-e$R zVXojdP={WxwtT_x!IWJ?qZld&ON=(Js9tV%$-=Vo_p{H^(RNc%aEpZ(hK)hg=x8Y5 zMA_?0F6QhGuZ`bdaCqGXyQ_5FK5%xR`kEZPWX|J>x?YX>ZJC#Uw@IZQxpS)a=V|(d zb9wa|qKXBy+O&4y->TE2KW)(iU+=( zw!_ue_R4N)V5N-1!Shq z%E|(*ptq-|eq~_-*4T0*kPDR14=I2=&;xiDa8~^+DN|PxXsKyvegL#s{r&C9>C#Zp za5BSoAB}3d#p%5~=q%^lwHLye4j?+fcA=^WS2;o|qDt|Z{)iw>ng8l#cr6W|p0o$T zp6S)hof)|H1A<*7tQ!Qw&VZcp>icdWLKtc*H{(L<9vF7y3$MiPDwh!%JC>91LS1azbO`*rUF zcZrMJ9q5HWQKDObZ{t%c%xBZ0l}QyjWqpI`@%8?9&nM|&Dng*K$~6wLEI<0BIK^mu zQru{6*lwXb>SyHr2Gr#aeotfTr5;M?^%H2@%W#oK8JDJJ-)2<7&3Uo*^YM!ti%w2& zk4E$zo$_287cum2>R?q)6Xn%PEubW4Nuk6@SH)8I*bt-PwZ-Ft1v`jQyXcY!L@hkG zDVWRPGEY988GCfGXtJb1%-zYVx%@}mP}O>u@@f}TzxP{P6uTJU1slh0bZ9mdC! zl&|m|%eeMpMUSfyOKIZn5!3X9LbUQK z9%=)$Bt`}6@?8smH8=d`_N%$^$+@C$pDq>^)J))yPIfx=xMp3+sZ21vw|ora=qf}m zjBO34O>o6972s#u0U=US&;z%)4Ii%X#Rt0LmP?$B_(!U@j{0qmSpVk*h*P{QxzC$Z z+WpghrOWLeUacL>jw^vMP=!DVJpKK&575EZ0x6nnfQV3pdG5X;c(K3eTO{wL(G*?2 z_BRm>KW2}{PQ>SgFtP?~k~4~5J88pZ?Xy$XO(2u>d#QVYbuwR#ew2}UD>;FY;^FavBE+ZO< z1K3*-cI36ZThC@SSiTjX3b1ZKQG8OmUH~8`WyMRyvz!_{J$Pjym zAT;d34-_Djh=^GRM7V_%utjgL#BbYl+&%zE9B#1X%PSJMI1f3^43wf+0w3CA2fPbu zjdwt21ir70c~u_kjuel)-vGV)Mn<~ZQ}+LW>wpSft~kB1;us}TalvJjsupmR6pj#I zLZ9mO#izdqp3BbqU}f;eS)=>ouWY%K)MzN(~>+dfdOt!cdJmeSVB1PXSfPej`7vTgAZ?@83* zW#IVbr4KO6FwR@mY=;s7tgNlA6@nzw2kGR{L09_aR(Xj*;SB0MDy6^h2e)RXR$Jl& zGAmhyQF;OnL;SVhb6%{Bcgt>Cw8&gsewgXk>$Y2cT--1= zipNK6cMle17^`B~-rYfz2tzRKd1W60Q*nO*5NQB9zR%YmDSi=c^n9e5zjhtAPfsuJkvA!f9O3$3KMdU!b7*aWx3+nRRNM^e3bdD$1xKp`UUu6+dyX6=+z0|J_s z>3J;138Qy!@{fUH#;LZva9sLFp+u+Jv#=9KCuOhwW~~~?=>#pIy{r*a>3Z?vJ;=F; zqwAZpJBZp~$7CghOm2^AR=U+BP1pD^jl5je|2Xnnb$Ge*`MmSo7MrNCKNW1tm{3nm z5bXgmwumw6L0T4(nkuDC%9Isr_v@37{|f9Z|I;HdDelFh(!y8EOuyd}G5fq({+Qw3 z*HNWhPXw4!DHvKN1}L;M|k@ z?=P^x;-Tin3KZsRk}qTw8K2k{a_zR?86GWjh2he)?LYDSQzJF=+D_S?LgkYONAr`6 z?KV2BbZ|`g+|V+R9dfS#L~;CLZoc0n9m5uBV#k}TCo~=Qo?LeN^RhKB?9a0QMc(r} zEwD4GdQf+cSJ%);^LNwr5LdG@-Q7Hmi;&7VFT?1D61PEFNmS*8Hvl?bJS0XXmx3; z@&e|^%uGIndVOHzfEYK%K?sQN%qBRi%-2!)&*Z4piyd6!wA%cXR{iVO)cjBG+GFo^ zOmxFPiOZznD?W&KXlAntvHX!r)YR5U@66}WR86YP(+FLQ)4bpw;_fCK=8)~bVOp~> zXFbZWy{7c(fzcSn_NEGlZ`UyuqZ!Sz$D`bnmilhOd$`0y9v8211pQ59_UdTl%sCB(^C;!r5@sL+65H4PQ~-UR&)haq?l_{xu`FI(ltN=3C)TGHr6q6I!tVF7p2$lu8ZqVUnAukhIZN zx0;&_>T7@f$8K5M4Zk@!{QKioX~%YRb9StdoWw&Q{R&J3&>qU90PzVE{tFIBFN7@_ zb;#W=ke?%3PU-#~ZkSSEx2#$C)1l}-rlK~#wcYjj!E<3>eF{2^k!IM+h#W9wNg10^ zk0&$VfÍ=41dtp4_NZj{L&D1LL9KlTC@OR^&Y3)(unb_hdhz`8HIdH^(KVS7R- zO-8#rKoL+j>CDq`S+BHnSKof{UzX$a?mmBYzD7NWc`AF+S0{d$Uk}QgR>$Ed3-B8S z2I-+w_6GYuhB6#P%9MGR`nEqX#OCe`kQ*Q)kV-C_Yky`MtlEI$!M8Ag^0TzOeEH9ZQ;V<+ z&C(8EGrul9Ek^TZ3?hALwbQUX?Y^2uq< z*H%-Ghi-7g?&rLy;~^Ce2MP6m;0IFJN?vv5V^}NDza`?3T6KmL*wXN(?4SK6V$WUw zaUVr8C>q^___VRQhyawqZ7Ev_COZPuf2axQDV!h>LO4HEu>~lHf)Wae%qsJ{;L@I0 zmPbNWtMbwpRHp-R%Vq=G^ngBs zpZ`hk|8H7I3Ree!A%a$$ox2moPZphP+x$Bck9B&R-UMwQIuHnfMhFWH+oX z%J%?b!ZP;Fj8;;=jjM4$S^??Z>m3<6uB%H^Ta_;{AiQ{PoEsXC@62b{`W;%F@`1%r zoz$kg&q$TR+~lYR)qOU+38kZw9!`LpRI`rCl`_DZ(g}oU36*1^vagNJ6CfK@yqf#D zKHXyf%98dr=%7l(Tk4wd;xy08|0L%8j_3}^J`}Vt;I>`X12WC~N8eBW+b)!Y3XWf1CR7kJT~pDgZZo)Fy~sJ1y~Mdg~b-D6g5 zJ#}zXHs{q!QuVjDL9N>=LEOT+yJ;x$+JL{gP|!#JDWB2V!N{aSlzQ!VH9O45_< z;kW*?i=1+`6Xu2l7WBSn=GyRQk9^eSpE@*rmGANOkMqmkBTYXHy6qab?8W6wOh>_^ zpg_dXN)mS^rkzHHidX`$Bv7*MAt)Wy5r>i^#L1%G%UucS!Jc14bY;Z%-!iU-}O8Sk( zW>7SMB>pdsKqX?Z(w+Av-*s{4OfPEmC6;vsqiqV+PRbjwBCA>c$x?_z=>xr)=>l;Y zj=pCIMGzD{iv7RsNB(w3UWFK>vuytj$`LWREq(c`-cHV_PKKZ502BtC2-TU9mP21Fj9lb0jNZ^7<0B3>D z<~FiOgpQb#@Dhm?f<`E{^B;(xoql!!uJXw!v{Ca_DnSJR@DI|Dl)NXU-3FuqAHAU9If(E&fEzRO?;M4;vjx27A~+)du;LX z`)va$8}Llr z<~^!^KQ9P15FiV=uoI!7?s4INgFZUYis&xhO_@f>YYy6+-#xrS6Qsw;+jbLL8TKmt z&?DAS3PchG!_F&lNopTURXsr$m5&K`)uS52Vc$^t@NKoluLfWXkDeH zcJ`S-ojg*!$rQrT3kgYt31dXJz40Pw3MTs)3@_<=OlD2h+$v zzyIH(Fc(*wDixA{)FmVq!6N)cX=e> zFLWVpxA0}wty=a^4R4< zYTCN4+F0h;Q8iQ6vGSofunD~KO>jY%!G5w$RQ>xo3B_0dR|j7&na$R$UxN6GK$Z7o zr|stIBstHga4BZGb)T-3VwLkAtgjkKFh z#YKbtov+&KkrY@Y0&4pKxPNq4A zKzlutAS5jl9{DP)7`bh19 z(Z=SD5yCFIJXbCeQ{evyLWhLF+s_A$G14`Ex3`GhK|fS{M*^;a#^`Bi zlqex|?N==)s&(bOZlAoK5S#JJTu=)UX1=~>&ThVC?JJ z5<(2|nu%;KCAphG>8^6Ti3rt@ygUDSZoxdqDqh>?Q^t-9R2@p|C-}!!`SE&WI~0|gn}jkJsUkd)9%0VaI<|N#4Co^rz6i|2ZdBjG)%7@qrVLg5+j_+&f9@Q3yp_#Q!|p_wJfEIhgozD zyOlkm2!?9fUJ(B;e@i)a85!b{Jr7Re5f}n*Mlf*7LNRao)5SxuA0sjVSRg-xIZ^!n zJ9#EmbTQ~79->3?{ubTH1{8?AOOFm;{3s3dCeT`+oUG%df2?r)(a7W3tFBNTw4RMq z%a4?KqX4HRDD<7Kxitn;AZstBApD4uTu&gF^jki?XBK1tVH>8a%h$gq%G9g!>MAaMDE_wn+3N`Z`U3ohsD^2(}C z*@!5v7@2LHwAH|icpnmO4!6CRv{l(O*i$u;?qS_Fv^A8WICU$>xl2>7PKQ=J#w-jB z6MlAldP~?8xkSnfg^Wn&onSCHIltPS^R_Q%;X$E0*AHUluX~C=WFahtdckHSL|q}e zN7A_t-b7D{s5t)Zc_*tID|e-?_bV%nF}2(^e2*TvBybyDOnd}9D=3m{_)NqeDQn@o zF>ehTVI34q3oliTQ5P7dy}5^w@pXvifRqK81l6|My63l;w~lvh^`7#ZymUgmf}Iy1 zb7zUtB0&Ws`?r*9LZC^!%|SF);FVU8b=a#n-9gK2ajQf36u*vg`+pC6(96}!Lhh`^ zg^!_wL`U)bOW=_(@2OH~GXGt~92ivn&EfRL3AViW|GF2wjL$p6w@Rs^dbi;AA`sl?wCRN zb_rYyN8^;%JXSj&bbn1abugDz5j>N+U!iH+5s11u>Lj_vabSGx6BOu>6(e*U)LxGk-okhjQWnO>Y<)kuilNAh1Xruy#5*<_ z`WRIR$|(^1gT9l11*B)aaWKk^g9i@=@pOL!%;GfONa)N|N`g5dnNY2fzV_Pz8T(>@ zge(md5K&q}e+C+XbIfR0KzY~{(y?Z|`=#1VU*xwg1)=%l6U&Cmc-40?|WL zbCH8V74D-=1|QxWRl7{}?_Nlcga?vW7K^PMm61X21SlLmgWf%4N3y^FjjqlO<2lxA zu{U6bcV)(MyR&Z5x=v3^(nUn4jO9X9fuNl0r`)o);^l-A4QkA9qta^w+rnpDy z9<}mWdA$1Je1re*z8uY8*eACs4OuWvbQ~#7;%qUOJ!=J0G17j6`NNESt<81A>ueLVE{Ej+b zP)GxhURUCG`i-AOH&T7xoy&f<&X;+)UBb_n4)+@$BXWm!9&Cg3reKopgNxR`mn3H!K#D~}Y=5=~`EefEUVi(|DC}6d~`@OZU zjmy|tyy?H}?JQ^8>##TA3iZSL$y%-9f;{$OdRTj+Ok1jhA$bUwy_6s?%VgkfZhMx$ zuIahcN7G#-6!LS)%p8_v{!rcU`luch($DYBc%OZ@^4CMI6w)yEO_cB`Sqosw8wJR_ z@UEi84EYr*oFAaK>+StJR_#zEMWgPv7x=eBum~er!Ag*WEW11s6a*co`(ef8O&|SK zsg1Fah$wJfIVJ$0`XNl~&W2TxfCB(SLJe7jhTmTr)~D!ykRCZ57CjL%hb1MGjYUz}=-8NNlKp>PfIl$P^0^**~543CgiRBQ#;eJ4PnV}0@{n@!*meR>pS!@!qlMsL)&<91Xv z>I3DT9c>SfzMv(`T3TMd5hw_%i#cccaDeyfYaIo%10XgOe$V?3M(86X3&!eb9{K7KuXa0VZDWv8WiEo9>H_Z>o^!-4Cvp+p1S8?r zUegF%ie1vKTP*f_qbSLhLhAHASvGvh%cghr=p@h%!P9_BkMI|1&36@SD|I)IPX)|f z9PE}ZoLXoV22pypYa{^bA zN>x5AvEF(Ls+1SjvT;?XLPI?@SqC6n>W2jU4dQn8{~5IKQ#j6HHNwE}$JQacWCm7p z3@v*-zp9+RUGl0=7p}+5r;g_My7<(g^Yxsy)!~F8_m<|~p2`Ep;v8|*`noW%Tg1kp z#L!WunTc5>MWwSJ3<3EBb<+6Zt*@#^rzXRSM3loBT2Da@O0}XQ%4_PRmWei>D zx}z4ZVZK$GZGG*G>{8V!|8M8E)}9qRO+S4;xp~W22og1FgGx4wD!9w##N6fF zubSO(VnabeO2_<*@Dnb^}^Vive}DRByCUb=av zPpQpOLA=SCSh0_H0&1{VWljLH5CK53h|H$Htn z+zyrzOCSo!?|Yx!_lwV|VHj$D8Ab!if+nqS2VyQMzgN0HguDR^apl-;_%{@`Di1)U zIDrRIII(f~#@ySQ_1TUe4|2K=_n&JxRl8=vamcC@j!vMR?9S;%obuyYd!fyr~;M3&xmRzx!G2Gz-N_T9pV2MRRvF@v}lN!DMM zgA`qwL;N?bKbG_}WZ&Yl=fskQgv4iV;k#bjNB|^~*~b)p+qhZbZe-E)_lrFG5~P=Z z{^DK@Y5jf7?e$@1ve~fAv_uF|k#@Gcv@=>kykA>&%p+HS-cjqmZ_PX2UTkg525Cn{ zMnrVwQ|-B~>A35v`%gZd#Gh4<7!zU-U^hgmYWnEf5YQuVY*SL645dgO9lZ;kQMplX zv6}I&L$?zXSsz6O%R|W(8*;|;E*+a?)#T7A&t8JZw-K<>Rwg~yQjvX-eO(fWN8ZtI z$tRFCnz(OsvXz@OP-(NpQ4wIx}%|SLK~?S$fE%{9n7ps(9er{di};2tfNSa zyxow*(GkRkSc1U+)&Q(zWT3EzbU=z(2$;4j^q=!)LXt07(k5@8y>$1Vd;xaWawK2C zs2N!Ha1O_yXCah?sLa)2vhwK~eK}V4q>SJb_!zNgA3KCcCma;*@9qI}f2y z#J@;VB!DS69})@5L)MMvSDVX2V1&AbzEG%DUu`>{(0I#w!wvSFPe2hFLa7&;I1a;DZAPObco9>l^)DZW?I?n=ra@?+ppLKk8W--zBJ})9UExMA1s_=l*7sOq8U1lQ3?D-=mVy`5O9L%RGgF z2Ecqtx4y~)!cFFyTA~4o8>8zZ-=b=dCT|D)u~6~8voG;ziN+hs?U7$sOV^Xhx>nw+ z|K4z2C;qgrns!+eh1ua>q#b&7Of1Liu`F<4(aF1~-Gr`VXa%5}O3-2ph)LF}*koj8 zmMg6lc@-BK_k3@V)S*u|B)2C5Zn=uLlq~n-*HfONXacW=t z<9J14tT5HH%>gI#^nzkXke*Q9*>&RnE(Q=Y98}UwIhd2Ug=Z&nov{o70Zb{Giprl{ za$zB98&oTTQ-rHZU2c$wug&MOt#Q zncs)UR!x1r8O<5POQN4Dn>eVWjr!yr$nc+a37xME9SJv&IVCqyuv5w23;o*9EwRg9 zPSmJ^X>iXd-`WSy`WM#UJ|7Aq_*^W`uZ_PULLF1}gXTGmmRv7g*9yKRlPdU53Htu2pZV}E+O^R><_Mi#cS1z=jSHsEX%o#L(Uiq5QxI^Mq%#f z0PC9QV_0d!A{>E;+Ai$EA7_bV4<{309?jLop6~C0V!jtDF98to*>^z{5Yhd2Rqrl@ z|IOSmuKeP(Uv2$|9E)mO>gZK3Xm%`f=@EK#!dNK+N)`*A?r>KjDnEL7;z1dh#(*(9 z!U~4Hf>_T5ZT?|oVnQli&FWhZGksgYCy2qLtPALzv<2RMeM1Dk>UUQ$2aOcno?ax=HE*g7+lb3kn?zJ0JzAJ?dyL zQ+T@nqfzXzEt99`U)@IA3ZpOnt@)cEoI601;+k|| zD~KMTL3@u76S%fGS?W2ag~l<G-BT7On5T z)vbI7#yfo(C@?4qKalr>BCRW;GoSvM`4_!j9KRxLZS6U4W2VAL-N_}akchNl4dEW2 zeVVfEAyoMJ8Rxm?i(6|ReiL*a)TIQ?g7)xQn1!$8vb@)-q<R>FgmF-|EI@# zA!VyT7^Cc8IzGM0dI?QBeG{QDx4K|D##EaaM%h+@V*)RWgoWUS75s9pG9Vi5t^Pa5 ziKbYA0lbQANMOuRQZSp=O<P8H5GG7pQ*No=@=M>M zOQzH|IgF_!MABS-C#<1Dzc)2kKXjd2npl!#Mh_-BqPXxrqDx-8_KzXJ>ZgU`OpLSjZ5 zyde^Lc~i4vtl3NXPZj?hz(<2Wre`E;uLZ)SUloR#q%(Yz+R7_jLZ zI)HWw*EB0E*Bm3mEYeiSUYl45(kNZB9qt%gz^DKN)`ma?SYBA)W0G~H7msqX|Mb~Y z_LZY_0&+hR8&Pvt1gBN&LJNO4ti#s#4^Jrl$WN+*K3v6xMmDr-7HV2Sd;nx$Ddk@k zY_LK}<)<_}XpEq%ml%nVDw2@@dGJt~MBl#(IJt?8W^Wxx1fA&Eh*zNfwNQW0-`ec} zHE>(Vg3T0X0&KwoXQE9>0_$LudI(F$%@`BJ2t)!cWVb$eL`adlPAW!JqKX1E%d5A) zJu=dXI(vt~4>Jz~2yY6HV0cEuXDUHZHifRU9tmv7@Lepev}vfSMu{S~Lx{gJK+Ih24pN(goiM6U6ARn~7z^u-5;W&IPcqc6bo|DrK1Fb8!c2`m zk{-#SVh7FClTd9DlKW8#R{BsgdR4IQU|3ZEVN4=D1&zO4LH(`J@z@4_tyXZm8X()U z44~fZ#mp(vUJwXkA>&!Q!rA#MVUIU+ZQuw8+*WV~1~PzsY&3DY?oIG}n`Q@cvSQV7 z7vJlXm-b%!%=`Uwf*Uk^?qk(VB4FvMajZ-DcV9GvX!UQo>$96}COci!p|9W(701yJ zoz|3R623B^C2vDPa2KrzNH+8It#I$q`$79`$$|Y*Nuh&|2m$*f*pP=E*U)+BBJb>O+Mu#0XG}blY<`&X1;W!g-vgglT7g!yi z=whmJVmQn_@jSC91@Ge&VXX^JGo=3F$FGaYD%N9DO-j0kEy-Bx6K^FK!I0YvgImLZC z*(A}J-Ycbvl|RIk1u8h<8ps+B_!= zfiiu&@H!m~H7>=R8fOzWfZKPkjEZc0n`g*vuW|NrjQY2{4|f(T_W9<$GkZ46q9ZGO zzSqzX1Ha=ivo5NJ6K`l?i~=i6;MrgAIfBKo{*026*FQ%r*mIstSG}n|e6ObLz6kxu zsK{{~25&;umP$@}nPRv86}>$6z7gs{pC#?z-@3QiMqY1?D0(b#CTfNnx{yO|bujEW zru66i3DlkU0X6>SY2WgzLd~Mkdi4Q9rDbiPYVH0VI|gVLc80Rq2PE+-T<}Mzo}gOr zIT9;Cjw+P1Cq#5;OJ6u7GhtX_oMBoRRH;a{4g^iFyDb)PuNC|Mx@c_=MIVULBX1PY zwrI#Rfyx~~y|R2iA@oo<6N>1`*_}xv!i0Yi$4?sI4Kj-$+y!zgh{;G5;la)dH~%l( z@L!pSgr-PNTWG|F4dO!Fk#J%4=g}!IAXN}qRA~Mcc62bL!U#zqf(wDes<6WqdhdWK zII@K(PP(0%V5*?^xmzWHEQG3=(#{)=2@wet21bKW7!slp1j|O9JL30QSYi9l56CQw z7~O9?RPLm`6;8ELzAYoiG^P};}b>*V6aUXKA*NV>r%@Nimf7N^cP0j)(|olh5w zMdSvrO=Vfa01BFYS6DzSbAm(;N67#??hbPNS5E>22N!@$l_YXVScjGk;HgNlC$MTw z22wY2EnI}QH@5`8_wcB9#OfHNmH}Xc?}B6QJRZ|QL||<}fr9ZAWlnE-V015E_V45e z(r{Tw7=vjrgFp>|t?j%+nL95JJUG&8w1f3M;nCJph`=Es9l_`UtfYPTcf8nLjnCkr zK?Ov(*?d69{jez4!6>PRsEYtKIX|h`Nc0ws=3pJ5rsEZ|v3EvtdF4-x+X-#THm)H7 z(_WUC%Vb9;ygz`&syWUn*+K)8r!g+ye0ifgTBh-OO(!-)B;y8 z9_?c>a8`zu&u<(@1+@lTFME>ixf+mECmpm*-etJHQ|5$3LmVA9NYpsr((5yW}8!Zc! zONpkNoiG(O4^QLkM#T`ag>}L3km=&>gV5*13|ovZN@z$>K_6@OUURb3c#f*sPZyQ` zZiocuGUP|P89J41ht^I8a~{{dB5x$E^)5C>`tCOVimL!EgR>^)eyT~w{FlG)fIPjN zNE`r{_O9d&AuAJOreHRqeo~3t_hVeap5)Q7taQ@A;7Vu%a2X3JKrm> zwPYGyM{YO-J%DLW$y~Z_2j%J`YIHy-d*?XzX}ZYq=VC!9@h7U?*4^pGe&Wy$GfS3| zH=>x`WxH_EY(C}CfyrjxqL%a0#Jcddn5w%!f-o(6*QtHi%iFG;*QD>o=SwI=3xKLi znV`uAb1cG3*pw4PZ}!SFeVL~sYZwvtA9ri6l5t~3Tqn+O#;T;Im_*Lu2YYpQFr>m!Rr z&mLe~lM}*O3Iqg_G*~b(8gEj=xzi*S89rN;e0t=(yKVG}Lj`&=C{^s@VfLdEH&G({^V*iW;x>5M-Q zjU*!@7EY(=dQ(LoUk!SJFXKjvA^5Q6&oFhWFfB$?57Dk<&0Ns}W zq|?+cBXXw!)55~^HmuulSi3w(OJ}h|RCPedPMFXyM1V7g ze+PZ509y(bE*Q&i%yIBsE5K@fn1fM?21TxP)NUA{7z*8&dZz4fXedb_S5gK9Ea=jV zDR~?9Z)OcV3sxeRY5(CJbFwh}uRNp;lHJR+zd&l|lQBdWc*@Yx{zicbkA}dZ5eVoD zjGZ*{V2j~L<93m=5f$MC2``Hh8>3KsZv_yDX8s`3P6}Frgh;FcsnAgAx^qu|Q2 zJ|$^JC1`rkN6lJPvvvVlHqksJ)c*qIg(GUqrSURg3fiqQuMwT6fk79HAiNP26hXlx zN(*R;;!3gyl7&1)qXpQ|0}&!bvV?75wEnt0(EFcuA- z@6KnS6jO9DTAT~rHF$uddpdQ<;TJEqq~2I4G_sM}$6W|b??R_hoenh$i6MD-m-O;j zS<$i3$wkq^yMl6chS5$gk_If{QLBm+RP+TZm_N)?nLwH(obS1-UwokJ7qSC#xVG%O zx)(CD7%`!-;O9#n40vTgr&hk`|Meoh;Zx~ zn5EMm{8DzGw&ur%D-~CN7pVG1_;jrPDZbQL8v%`+vN#ucyfp8lL-u-+6~R{AOLJ7gbp zG&=e~^Oc@1>G$};q+L>lauO#_yCql(a^kqnE!iA3&!3{qkPS+pXzaKkc4D|{zkAlc zjqfe-3%4njMPTi+3bMqGI#M@!pkXp1W;NMy-=uL>*^gxzDu1_mc~tiVSwW&kNunM(Y(072 z_CmB?<)<~Knb$4sN8gXfQ9Yl(QbR~GR*aL49CZ2&`vBf*_GGe5D{~2Z;(4;-c}f)d z16mCWt)2*XTpq1qEM7|s(?+`>OC#>eE*Tu!8C}9M6JYnB7eMJ}s*?ZItHnW>>P$-& zJNNmD+??|EaZUI4Potx9Y3mLP$&f{12kLA@4%aU$|I)YZ8$Q!;r>Khfu%$Xo;?=LF z@4f^6<)JSRYp}cLhY8XDyb!gVms`X};h6f8rU2pkSiI(; zu|FvdwhH~&nXQ^(AEy+FykB5!pFiW)mkaa0jdVaMOB&UE|Al=fy!;FgiV?AS zED3sG+28^%CZwbU9I~iJ+fd*k;qbz5r2N;3tZAP?fJc3?s99bf0;xJFyFe$e@1P?m zDWCHAoJXe%d`pN6L~qDVNQglRbo)69bs!}bixP|m3vByw$peSY$gpWF_186zk=tW! zW3&cz1a-v8DJ02R;0SgnL|wUr>3Da_Pv}e_vhIXZtf|EwM;PGzg`^rG;R;PdfGP~k zn}N|{*&v;kK?=?eShhqx%P7Ar58v`1xT_QZ2I_D4)8K`3D^BQ zj~6hoUc7?{!;umv;?=X!13AX5nhbr?*q(pL0YZIjR4&TB!U$myc?4q;DWmelA(=zC zX63p^+o|&d7a29`Xh#7zLYR%LGBM@=_5?f1LN0pa&q(h!ygkJnD4#&^N5_H(3QrH< z2cQatJ*0dH6U0zbg+iJEnly-l3hHC>hZ+Aw!-FRi*Znbq6={M`s06wB&r^!inCBjG{N%c!v}pU>OnQv+T@3o|N# z44-bLKL}d5m>py*bF(uOXp39J25<5GlhRYk>UOuDKG!s5!KR)n)0q4q%f|8}|LIJf zrEREd-$?sSwG}bcK@P+CqsJaKWnKO9R#$yX-36-?bxNOyj*|BS$1g{*KBv7}ElUqe zCPj+Yrlu8F+~t(M7TSMb5^%i8q+YdtusMdO;n*#{iBagfMHXc!F3i?W`{un4yB8m% ze5j@(m1gcm#xK}|t6?$|cZEG!AH(LPD##$sQ4HI0YP^eLi69=^trU)?08$jxa9gry z1rM~kVKI#!REjQodA^~cl=O7cQA~zJV=WP0E|y|JE?bQa8kX8U@;J|qrwl7~FF0*7 z;0=3Z$QX5socISrPAiWaU(9(O4qL5F z$dme#bFQa9-|HuR+vVc^hmWFRu_uzVv_0l#GgMw-`NVuQIB)J+(d${B0%P^mH1D{3 z``6#i^91^BhgDqlU8oaQcbdF=v$XQ6#E{34w4vFTS(S0damr)tR%>q=Hb$*y?b;8% zlG{vW6CZk2$eoKDsMaS3poJ9zjf`oC>pQgm0?Pki)fPW1?^g3q-3)yqx&HO6pZ?!Op65U@l6N}YTo)`FsopQ7uET<@n< z_Z_UZpHhu_a&7ERcgf{Hl3j`uj6A=T8YxCcyf?rPq3z)V3NAXNA+He2unWqX!l8f2_43iL9jvTp zhen4-V*g8Dto1O86QC{*wMKjfivcp`K?eYu*2RMa9W-zo23qZ2pQ}}84Z)&BiO3ZM z3laxH9|NDc1C~*85*kQ$hYt%5*@dB<;dwav43Qu7*^pi#-N>V#0v6`ot=bZMWVn^- z$Y*c_s%Y4w+4*_54vxcaKK2i?eDeeeXdDTAcOJs=@bI|c8O6ZISs9U%0{Kl7UEIi0X2HYtNf;o#V3h+{Tpru4b&!p{Ekf|>hg`HUo08}?aLA7b)b=W z;3PGM-EByNcVc<8agaesEEm`^{Mpr;M!xBMp&V1l($=@-XTOJ*u7Gko4;c1kweb13V#xGQm(Lk_HGP z(eK~|sF(;$jo0BBB33KpGT;=B<$=kq@WPR(eiW_Maeb@5;MfwRV}!Sg;ZSv5h}>>L zp+d9#P;rLKilwn`U(>Zjr#ZCGws~5ZCWrjo#hKh=Z1LgUN8>%x=bnkXdiB3OBD?+a z@!(?kRnte~oXlU|W-(lH`1`y%!qmQUl434E{nbnbTi^0X8vnsHnOlL&ylU%BwGS>? zBvV!Cvy(Z6*{I$+b&K}rQ9@pBbFizUQO|JK3f)leYX=!vt0J45V7gd*TA7~`S%!5F zjnr)0Psd=+AXNB;T3eX}$GSpw8U?~O_pgjQtLuJ{UFW;Z^7{Xw=}N$%?E1cCEF&^A zRF*Ip6DeUNNiht~2w6&d#*!sOMQO1U6T(ng3fWRgB$5_n&lXyUvPM!_vt{}I$Mb&I zd%2!^i)QY*?{og=w?29~-{!Pu|Mu?woacXP(o+|&D?hp0a-qwtzx->>Way<8n!j5` z>!yu+nF2@Mddo2}b=wuOo8$!#ztVRMV&1=-Jq|)T0Y+q9h<<>NJFh)V9wJJryz4YD z9Yz9NvY$l6aHEM)RSlSgl_w-go+s6M+K8dt_2(Id_hIf?3p>F)&R9Jyy?TBTG5K{d ztOLC!nL**~cprn&9g7WH4^8})ls0?Yzr#%KF03beO;2qaTiIKD zXVP(|(?denW?DYw^0dXCsp=LhC$0+D*ru|*?j7JgD(U;;Vfi_mzg5*JJc_`-e2h@G zmy3$&AaZKD6!=UK%(UGF4oWb!`};$OHOUZ#pYyOUG>Q(#XiA6y-fr z3P0~bLe>l2=;djpywV{fvS~uy^zNFe#E9kV7YD^|?4HmvdLS~)!SvgUi5k3UQVB

jH!3{-g*1Y@Jotg5V@HxQGEa~s@3?4ajvfMqlguNg8YtibBoL^)FG zgN?h`5eWMzHd0CPdQk3ZIRO(x1i@G-3h0#>dz#i?cJX}QX>GOb%8jzJ>4DrWUinq6aQh?@aNoasnc#gwL=*t; z_=KKwxZL{~K^FkKAmH)fI?}V-3SDMNjk9CRO0X-*y0wQZUfK;SwGrS_CqC-{|Fh-j zv+&n5Um{?sn1s;E^N?KzXM~qyZ+2};@sWcM;ke)YcL*+r2p`Ce_cwvm)(M#-#a9*Z z6~F;kf_SbbsB8UXmB@bcc)R9b_MP_8UoTC!*1nj^3k?r~pQ?U-$kyxP?D%63-!-S2 z(DXZ-UbS&3^hv>nl*+3_-w3Z@B!8-k*=2I0BV&W$g8B6iE=Dx}{gWxaa%AcJeF4mT?$~NhP$ZAKxUJ!InPk;UTE|BNJ z8mk5$u+q69`aOSsDET}rrl2C&52=E+jVTS#L)Dya7(EHqFv1MvVuIcalUrW1<4qn* zJzCYpabTC;)tr8|^OXNySi-MP%gdxs{PwVE0($^`HB}t^N8LczEY8*HLU<-b_0}Va zvDN4Umc>|yc zsMSObKj=LHZrTffb1wlr!^6G7!b2c{L=LF*0^rwI3q9a0_WibvnT05%sv}P0eF<2J zj)254u^xTUKUR#j%0VQwVW2`?6jyxW;@41j6i9_$F$kdNhjx^)NGQD%I))9A z1(cpX0Xcy2HHe(rx9xQZX;t5hX8qU0SI~(#{?smHe_nZDbqSR&atmKm( zg!fyn*}gWVIZ~W&YSc!z$BiCBi$I}xpT6}N7+^^Yl_53%ez{9QP@wq^l zC@3i}qEvZctM)^J7QO<>q>#G`3{{-GO>Kz}wb*D4`J69Smj68%O#&g=S?CQO2PE7Rt&g9# zBa#O$kv1EJ1USPJGY$bOF*Tpdmc79=6zGm_Db)+V4kI1|I@2NFW#gs6*k6zgqBB1j zht#?qLrorj6bK_Fp;$V}o;hB81w=eHAt8|j>KQqBuyqcC=S?g`Q6ilqfJhoh!LzC` zK+h~t17PD>31>hC(`7TfIUx`PnI?9jJDDJRZv)WXG-t=3bOADG{ zZP@SRv8i1`@ zU;#BcaR_Y*4rF(!-&cR^-xCs`{NxoI@@Q<6Q2{Orf!#G$8dod*PmK!=mowj-WKw(< z)ga_oMLllE*h^jXrcvMudedyNT^F{DUH<$szX>)oK%qLw6ezU$-}xzR&>KXCKB1zb zs-UHGwAZ|^sYS~;tpG%T=@C)E=WKX_+qVA7bW;X4rC3omxHJ!LS`Jjc zKlv926x&dZ|dX^Q)y2Zae z$LscpC_lXCt+QO!vU6x~z>`-ikeQKY`!U>sTOpx`zyRw$KCsXZVtQa%vJDQ%H5KLJ z<^>@k2VvqE!CN2C2&v!>XJ6MmP37aJyMKr$D&9%UG-L&6ayq^)2Y6|5rIMfYDew2{ z#*n?GODy;F0Zf^QhJ$8)hn|I{lVJ{#4%ld>sc~eRwz2Tj&&yWx5&M_7#+_4x5@bm9ES>x<(TZ!hmBWkA&1iZbw25as&u(Pu&^@I98y z;P}O{W?m%@5l*Dig5(eQkgkoU*(#`m8kZ5kDcQvoX*IGt{Y5rPl_9yF6i5mRdJQ{u z+4AUqIhL}@g6NeuBT>h%P8d!LSBmIrM_ZX`vkJd0TT5T^Jhc3a5*@dFln2YMo2HNq5) z4=pVzm#<~;Bs&o6X>-r!3S-3+tomn4Fl8BZ3sJ*Wm!K$y&w?g1!x}uG&hjoM#E$nMd=d~>!G9aaPh0Kw6x~+ zW`_$etZTS@#e%caz(8Z+-vTlxWd0bnG2nlAIpJH1G#3T(U6AN8Lm z7y&0Wu+KmS`aLyxhwh*U*$dn;`|$HKAY&~$3VC|K96|!Qkz-qXJ8<}=r7#7K*-p3jbe4iP2t^ZcCq{u23bMudZ+cxQYNu~P=@QNBwZsrn4ZSf? ztpSH|B;5hcwE5@lki;S{2mjt8KmY?Ulz_~7UZ7?7>_)bjuAdIdYXO|%I0#iW_WXUY zABE>gAT$CbbEPj|B4KwIO2LD8yoV3%AjB6g3S^#n{`|HHOcPsifJegs1OOCN1U1~AeR{!N*WJ+k;5w-@PT=gC zp7x5vg6;riZ$Pb0g=X+5uQOO6+Mx~39zX^%r2u_9oFLGLErGl`SAG|5fF&MbSzx-~ zec)jA`eSYtFefDLzM_fbNzfGw=m_exT2&1I{)a+D(e^u0y`c~!`KUkv`O?mVxDd)E z>T@hII^cq!BrqiDbwP0ga7$Qz-VV26nuCvXID{O*QwpJ?Mh6eR0*?#47yx{t;1ctO zg#jXmz61V_o7Ly-QJ~Yhd`qQ5)7RHm0~t}kXB#y!P}>f4_@6&#+zMgpZ_jUn`wTgj zi$QjUZf1Bo@D*?eM*(*NY25o@P=E4z23Q;k2|FnAqT4~f|92>r?t@&Q0hBfHVmM_0 zeWBljURUG9i4!I%I*E`GfJ*ghZb&tt#B6j$(dz)EJ4!Z*LaazQ)6+8nUdi!Ip~k92 z_$&85o|a|7wt$iprd56$Z5ET3&O~?e>fHq2>Wn_ruU~`RQAbinp6kiWBMSNAVq)dk z>&*X^1w7p%E+LT)%QW1t!J^oO|5+*pzX~0q-@)7H1Ywit5r*FMC}?5Y z6Nzqfm?QLH;j07!rT}JJC|=P|W;%c6Xm)^S65hejQpXeiy7>2}i_leMi%}c}q0#Q0UK84^XD5@A3_v7{dM2%eiMk&jdF)efbuSBj&{j# zM^GRf0<|e3?FB5OtHjN#lku;jO^(3(lhs*ow}>)y=eUp({HRLpV19X7qv)$jA6=|#lz~rM}OI(jd8(M zFkKU7(yQuaNT6y9Dpk)CPj z@_kUSH}a?`VZ}WVJaLy=kIOs#kUujGPZH{UwSJ%5W;(E_h*bR1no&T7A(EsS+pE`q zvC7}7KAQv>X$ZQqT3U4L?cUEQa`7ap7C((ZEDz+kug1d*8E65SIflsr5_fpKy$YJD zq_7_$ak9GV_kHu?DE-QH8Q=01>}x&tt)q73w0?LTf?21}2^=5jZ2~&$0(=zY*TDW* zgO)(ZQVxfRL)cc%2(^FvRt@+j-L3Fv;s0v^H1y@vsaTS{NlXy&?A`c;#t>Ydt1(1w zQo9`8)GkPp>_3mgVcU(l%9` z=ZDox&x<}v@xKA-*^w@LTe8o4ZIa?Y9xSj-0nnZa>apttk<2L34o7bBE~c%doH^#; zBVO7*mcB!)nJLv^`-g)}@24N<7!H`q$DCij1T%L1E-w|(-c^(Tcm2Rf$2nJZfiG(; zgoQ+~MN#RQ;pGQ3w!~iJ0FEA4ycbvngMx>yH2O{OYr- zaV;zi3U7Cq-$LyGi90-`qX2WcMQPRwMim86{a64&kq~pK;RYK3szXBZ+^EvL`EtHJ zdPp&Zlu(EO1VqsJ7oaaFz{S+t%L`eZfW%-{?bV&MkZ7jiR}XRMAP3roMu(b;_MJXo zyyOmV%Xhud;kQfWSB{M`>h^9tiwy4euqPqU zcqGgo&?^OJe09xJ$+|03x=vQb2&B*gWDo>z5R#7Uut1X2?Z8Psz}}}KE0@0_Gt-MX z+459pc4p<(pWzb>a|h`yhL3|pgS3daw8pi8dR*9km2|=8TiOTQUlx2nujeA$dq|o8 z*wL`fXQ&VPNkLR{B8|4$wBnxDc*d#la5=`Of8C$wA79yObJ!O5=Bs=NJrbZ`VYkddhkeLfwIUBf!JMa*NbmIbhQR%CJAd{stBJHX16q88`p; z3u0f#UhjZ6=#CEjX4)epi7pLfkO9b6OCDiEDD?mZexRLfq%CX9iPCs9R)Fe!;_2{z6t_*$QW4 z&kBdSY!IWR>(2`vkBRe%X8<2@n&^*9Y}Q0gO@K5b0QdCccuQyXID|+5CM&|wD8BTm zFr^xKe!SQyvju5uAj|EJ=UeP52FC&v zZ7oWpE51}I`AyG08NHJNfNbE!$5x?~Y9QsBKxtnB$PL1IUci45q6wDS?}#QuF%l3Q zv-6l+0gox{>_}|_7?NNkH{|1c%ygXhFmjt}blMXa2PaT61#jo3TGKDGAvnrFgN4EZ zjbJd23N+-Cc^kN9gGD-#sD@Y?=W|bg9 zivE^&0%yFuOT=`NgJQ^(v^OrlJgO6V#db>J!_+@^fK`VOCX)#v>L z32$j&r+*Ru87#mMkR~8_eDY?479sx&M6VvrP1NV5YrZAS6D_cSU^Mf=U8ywdA{bN)q?exeyc9$!I9 zHUfcefvG(9r4yUrX0Dzkhs~h}ftU13_JRCF(gdBM? zsTE!xy()_YY!~x-Rb6971Ckp#QvDv5!mvJrBe}AxO78;n0ncbhPpVVRE#5TFnAy`PW7kW*;H&R*#;bZTG z{;A=u9+%dj9x{{|-U%Y3PQVU)&~6W$f1(}3FvaVBH;mmryOmdx=Zc4S!iQHaXhKZ)AtLSon9c?PUGr>YQGPJ&IyO&Un!ppaa}Mr6^wa$_l#9)L8|0aYO! z2KaW86xCi&E%?69)Xwg6wYv`p<&<}{KOYLaxR{W_$#rrfDZ{7Q^+x$mdYWoQ1xbM% z$ic}q@n}O3%m8;0$oM#Q>bNKm+yVlsA(v=)s;MI0!J6MTBKu2blp<|iumCsbAM8PW zJ<`Zo^B9wGeY*vdACZMba)J$AB7zUvfFIyUMb@ZKtsbb$8VD*06~a^Px%tiYTQ@mw#GKn78%xW^SHwF&bgHqZs-Z?A>qkCYS>~+Yx*;ieygBOCW=nRZ)p|ql38p-?`0>_z+G$e9XEaeKC55 zXgX8jbtDftNAn;x`iGE(I*aG}~DcA{~;5N)~k*ne(uRzDr%|OqHEb zW&3QA3v?K`?_Mo@ri|}V#?HZyd(YNAicS|MOI`ke-w0m^&{Yny+0Iy0|M#ni@6hqf z;%nAR>3hV`Y?My~(DT)BcoOHea(EMOGnE|-)$1}%J#eAMh6Dw^_%|7fIqOKoNRk^x zj`hac_|T7kDT<4hY;%lRP69u_0siXW>kV4J{9g%5R*5n+`dN^{?~{YLp$%`wL(!=R zed)xb1;R+3DNQ7c!J{eQDj-@Bt|mqbOmK+A$zM-X1FbS7#zHfJG4ivZuH4&irWIiM z@Xzi9+~-nf!^s*nexn@%CXGuvXd!@A4;Ib-0(yTdTKYIi1h@}0LPBt$lR8EZgw5ba z7(0OkieA|X;zU5Q$H`{EUz>*a%eNJ)rLMnpcEEMc*XtkFa(I$AM6-o#FTjSLpI z31`TP!B@z~!X~p4eO`Rv;lB>!pq_%=lI5rVYtSH6f7f32z{V?2S_qga2tftSuT~>Yq-LF3F$=|Bf zYBxg4?dq@#w=Zlr@@Uqb-Tm3de>X}ckeMszgJn>Tg(?X^m5eg{@?H!{psHa|BWy&6 zWZuG0c0IR$HB@ZVP2l+_Qy%xpk05JX70(fvNR&lJ2zVcMAsAk_m~vjj7+ynJq4gMI zHu5mh;KUILdJtIV?X>^<6}s54n=wJ+a0+&np%Y`5H=v+rL{%c41^PlN=o6ojNE&Sd z{{wMZSe_ap`G`rZs(wHs5snL@L^4UMoENJmD@2H@s-$6}(VFhFg$=s^Tw_S7MVDEY z1=uF~xBG!C2ic<9iO=3B{seuYaUZIY9;3th9hhcUkGT-sPehXgIwzrsp|2OECG*Op z9j^rI!xCl5L>knwx zB(m(+mxTGPvo#+{8~uTO8z4}rQRF39$kblgm`*t0XfDnat`|6=CrcEuNXW!A%Cqw*2jP$a%cL2vJ%vCuVW6HSrGwYQ{ z10*P7S|fs5Ihzk`{>ZWud?c@C-BOZjGNx9_=`bTGZr^R+E6c5yU}Rz*L{KBF?u2Rz z;rzr{Q>9Ru9`6l3Z!YcJ);VlQBs0<(?V`0r!Op^Xk*?oRE~zUp_~ zrH`qf;t;@uW4SOqii<%+4z=pV&&Ra<_iO+*-a(h-;6@4Oa)DXr8p$VsJA&bBovw@v z!qFWB#hl2G6h${Z-2J+?PCeqsl^TIu&DT_D5CT_FPfkX`sLS7N zmP5z>o z1pRm<&P4&rNDqeO0?29tqkRAKg!pvwJG>V(SldwZ9l~c&IQ7(Ui__||%LWhvp&Verx`hNO zsFp1q2I~LDYPcA&;b~)6P*$NVYonJw z-h0A?z@5REVBtQiRPDHbett4`*E#I-p+L5a3B#zGTx0?v$jh#f7kfHsL_KcAZ7S7} zAfF}bR@iq<@*EPDpfkYBKcx{UdG4D;9 z=48hNH5z*j^fD8;uhVYNwkwIvnuywe*r?9n;`%_THo-?VW|CqOZu9hT7~JG z)Mj1DDpH}c?*ybl0rn_S{o09y2Ul_g4}U)*Xs5U1RdhJ>R3bH+zXpF(wJwlD{$bF= zv=1L?7-G_wHQeYZX2zCl;ghDS$D29chUkVSPWr(IJtsquf`y`s+Qpn1P0!? zsmCW+&QxQVsKt<|L~;oqm#7$S#iBmGw^gjf|JBop?Bs&Mx2JN;CbBafacayTDP&lF zV`VEftYffvoJ-Bav*8Cx;{A*z;~$O%ZIt{b!6zi`0ybxHVs%Vz{?kUHoZ^zNAjbDX z2=6prqYtAXd}f9b=u>bgo?*I{DU)krj7i@?|u(K(?FwWY_8H=neEK4PISmb zsSSM~FVkGPj~-5-I9-s(809Z7wT5FaoK1W(bIFIY|lin&c92$pv21LX;7M4 zrYt6$-@;Xn<$0=6c_gP>q|9Ig>%79b{a7*v0HjbAfkZ~>eMv#FwaURNBu1-X-gH$wuwT`7lK`=Ud&)UW5CRTk%(yTrYyG!B%~*evtV(=}x3rd@?d6Q3XH%$gTwRJ$Z$()#^?rBIi92y%7ACg-1be}zqsTAcauW6QZc9OLR=Q!J2xx!! zkW34|Ug$cD=1n?+S7D)%Wg%<^93tm{El0)&g9v^xn_>m}cY+n6D-r-*036nmKAF23g6!InAn<1PNsoQ~{07fu z_L`E#wuZyo-z0gbK3%)*>qQkb0fOcF6fD7+1*VP=z^4uCPU%r)zzX0AKyn8_Y2d)Y zf;hXou-=(oU7kusGK|&L3H~ezI2)gwqsqXENP$XNGj+bvsRST!CBt83J)B!~=7>XM zWdTK>FR6E!uoL31;o{&}-&=lQFhCWjp?R2$a|}Y>bv)ot2rjWvfVW_W+KgcTfSnM? zqU9(=)}SK+6nadwUl}{4s#%lra;)Ob)C{<5h6<_+pAW`DOu)2nn|I)e*s6?zr~|N7 zK?|oJ&yBXj=4>kARR8!paQ(y+?Z&u$2>&pGs)!O zbde@Y^aF(b-5YQER!zT^C9VsnVVSzJNJMc3F_Dut2@ zxyS;?7j8xznV(86=xW{tpblYi`BuxI`2L>AZGOvBUSh|wtlV`siE_@}hqz=}mI7vY zb(-zX8nMrUyg^hccA}QRcJ?=hsjRjMPgLRN&vpsiZ?V$qdE$}n76+8P72fLTVM)v+ zRY6{ExW78G4#riLgj#-XVcc_GYWT?`gv*H<_#ZHLhhBgr5b{!x(}IM4zT?YZylmf88&VB^`jN1j3MMx?qyY^5e->_Wyy6Vfmvcp11o^n@N& zhBZ)<;dP>xjvxRa7GPZFZ;h8*9ryrnv=46Lc56qn+aZsY8IPSn;dO$6`%*nL`Rv^@ ziBFloa?EqGeiL*9KS!wRG{EyFTU zps_?U!$wUz_y4s3gt&z_!;t;hyKT9DTZdi5R@4xH+T9TrGGFI%AGHBJb?Y?T=>5S0 zaY{4{Gzqz$g3&4*`UjGm6A)V2h<(u#ht>l+YW1Fl;iV-UNoePw!C1;nwHf zK?oE83LH)sw#kSVM@81)KZFmU3if3hyiXuD;68?_zr^x-hfD2-b8Y=1B@i$SD(QW5 z1Awf%em+t8=;Zun&2J6{YfnB4r|2$~&5yMB)sQ6h^#Zs>dYNp3nrtKKkOY>CD&ZtY zlmnPOkpUaTLk0lm_L8fMY!aL^nygY64&2b{=SMo6KW|3Ys=OYw{5Cn1@d9b<XnE@zqc3U;Wlzx}i+ z99Bsh1}VJ9d}pte{3&&P@jFbn-~a9m9z`)D&Cf*J?Kkm4JB}ke=45MhN;9CqoJyip z>tOB!M9gMlHva)Vd|IYOALn%rHCb^)1OOKc7Y_IXp03qq3Yq0TnK}3|A*7TXZNINC zf&}nPG97R;d<8!2br5CG!o{l#A~FNQ!n+6!33>{uOfH<3vtW2;p^IhAk$W=^Nhw5Q zSyA)2pT;zzLSp^l9TANVmR=t;vLww%wZ5jAQ>auWp3m1gA{a{LxU~|>^+#C&g!Hvn z{FV#<`Lw)Tk!jZYd!U9lG?NfsgO%C+TVuwr{`iHi^ZPZse>|A(q=_%R$Co!@Kk=>Z66#IX0T^|qHF+`_&cFs&!9W*&*7F7)zKqLhjQ@AbCQ}k z_{ms|7|dKmE3Ve?yAQ{6+y*x9t;28G;Aes5_zxNc5%!2f`(eO@L`b{~ zZ}y6PJgc(wuDbixV(QM}!c6Z-VvXbyh-4+%VEdK+6FUE9p96y# z;WM#sIK9o{5oc>=oQgo_A(CQipD0c&KRA7@GJ|MYk`zu%HxJv*rt@Tpo-2V+k58HL zH-25&h$G?CvswI4_X9gSR;#3-dXtPdRW)?9aKLd##C0wB|!plr1Cp{ zKll24ZT0y6O2NjiCN4$4i9~X+r^;=KNp#3|xK5BF*MAXFiwoqRa)n&!j%`f|45=q{sseUhrUUy@f+*0DJ(OREDAod*N`H2aJc)K%e&yR~8}{2} zm6k_DGkY$pDul`A#)P{BDPb}4{Vdsl=sTAMwsqL9-R3v4uVeaRYT!Y|Q+khC3Z4OM zrs=I8K+@Bt;L!ixN+G8F{VbxIIR)>ES&hOPZm#}dE$>&sjdK5_Rq-{N^jt+hTeFR{Mt=HSaLgLR}ji#^v0n@hlXiF$)|{=)BNURk5(Y# zRqhR~J&CY}!t!!m4^J`|kt@H(i6!J^TVV7fyZw7wUMx;F{C_8I&(fcZj7>=O@#NCq z*jGMfJP1q7ef>$$s14VuW@-Gs!w5ta`h@)$d_^!sM6qMaArnkUv{h1{Nu*00GRNEt z(2$Mw{f?GqkQ)xIv6Ba>#@7gnVJ^t6lMo@QTD1iPVcKv6tY}~&ZYb~Nm?OwTkAu}}%drc1-T8^v zg1n*V;Jia>$q1F-q%1$Xe~;gjhldD1Z#7^KP&b_eG*vlRp}{XT2#oAjllPl*#B+ z-RO$Q8<+n5sRh6Q$tU)oyy8w~5LNX}AgvlweflAfKje-H)(D0w7~ZS=S6in8N!bzn zNoeNIE;QYt`0LH^8TiV(;~6y9>i!(boDs@DBC|VcWwz44GWQ4tu=x!X&_>OCy=a5b z8#fUJv1}zgNnTFKvO)}JiYbfHXwIaK9|QiudG#bqQOZ(fTr<)9_qC_5=*bnKC%#`F zApxq*i$QfzV!wMa>>GQ&WmoFw*L_hCb9CcZ*^1vr&vGu13Jt3(=$Y{2K*z?QbFmq= z<&d39x*+F`4hO(!%CYeOR_uk%IHtEWy!2RE9`@Fy*ivL4rTxP3$LfVI(K@kQoDnAu z(4GZ6JAKpe;oPUK;Ri-7pLtU~UHXcO1&r4j60e=0#$dQ)^*GLQW7OTCQS`-=sg>_3 zXNjlX-T$3-`Ktx23W{USui(9NwnqHbZ+DLYke{T%js|6y8zv6dvxI|VGk)otTvWT;=3+e*sC z!i7!3=ROTDb9USDG?UWrwR3HH_1(Su*`(Q?>E|+`iU-yh2MaN{w<}rTv8U@yoCS)k zN%-7W5cKX-kSpIE|29uCQZTSCpb=w2GnazAvk1~dLostXN!2>tzK$Tbk()v1 zLdJRu*tg#dR(I!5AJyz}kT({kslTgGoQP<&s%l}>V0fz}crjw;Z4bPH-bxHH)|1Q+ zR|d@aZ@I({K08u{zdAGJH1Q{{r*`McWQfzE|?@Kiix397u; zcKH!|21_L6=a})wgwUS`kq!tfohCYCiWyY+;jyN*gu9ye;<-94Ni_Xpor$yZ_hxFA^Eo{r=%{J@Tdh`jMJwA-G!RlouVxQ`p?~EF5~_n@!|Tk8@9WVjwLAsY zCil52Bs80E@dt8=fOF;h;zZxxiJ~bL=918T`|WymlKg|l`$OH19hWe_=S?s(1_WR$ z85f%^Pc7?#sOZ2i5<94mZ&q+=smY1tX2$Egn{E>5Fyif^SwTx?cv@T}s%6|PNO^iW z!N6vr_Rjc*y;JwYWOzxqNJc(GAZ+6WjHF3-jL7n&`!hAVLX7EsAx`R9^>hV9Ao(sB*Ic`^+nPJNucVfQcN%>`)*7V7(-5=St_BkN+1U^i87 z{p|P8_}gUmK*>mrWI}1*LT=OQQF=K;Fgz8?tF`N`g6>B^=+5F_KC`|@HY>dT{zFa= zPSgLY@^nR7$^}<%3+F%m7$c`Y?Yd86w|bl$oL`jDVf+BCt5}fZ##`05n`tvjFr=%C z?1L>rD&oGDDx6F6Zk{B~_DspI{o)~$xBO{cW6E}smxg#fX8ARoLop45TRescFtFCq zhcn~M?4YoC@gXRuDqy@HLB31yqD_pwfUtQV#3aIK24|vF2&_kNg;tjrsTClenxc>K za+`@YIt8UI?|o;_ffA$e^ha)p@;?i|r0D20LOYKx1bIV7^&7oU@5+0L$sTWZZu6;F zgMf@qaPTESK3?2?)B8%e03kxgQ)`gnz(N%nM}we$A8mM_pBy3hB7i8uZVrgp7kap>=FlTwJ^l2?!yQbT8fw3dvXHqY(80sYprUlx#I9CIVyN zZn^Ryrx3CKvNT*9;26_$hUH!Z@eEy=8q~)|1dPr?$LT+%|IYj=^Itcst6BvtdnGt@ z#4hz?Pj{XB4SUUGl!qQ>^l{mqLPw9?ivb=GdJowcEe{^Kavutku zr*lLzJ(v_;=7XpNI6g=bMHdg9phV2H0viY8t;}p8ztt8kVXlvgERF9W$wbC+s@)yP ztRRZO3kiSecU2fMhlhOsoklWKv{L`9mEmJmM*D0>ei+aES_m9QfDk3y)amClC7ml7 zQHkoO3_c}-nS~TMe*}dny2DoU1eI4r7MkuQNC7`^8$LvB$Hw>XgW)rq2!82T1z_Pj zWX6qZI5b|zE&RUI_Ws?&p1pzb{7G1m4miJ|w<&3wS^?q432MIDR^2q5k~ra}gLhB_HwaLNAo*gzlLVFp)DU4t zSV=(*3kdjchSOsM@ZNLYE^{_SiiN1$VFMUau%4%8W+;~i3TizU&(@y((vw@_zO7R8dYW}GE$3cbf;E{} z;KREzLqg)Z*ewe+a?MMVw_l7bjB#Mia`kBj2omC%dlc6L?08FGZi|1)ZRrI8W0xOJ zgeGE8*9uiqAA6cLk_BbvR%ZSQslI^+H>)% z9@b%y256{ClC_;}_Fi?fcm}4x2nWAiQJ*TT+ICJqzPw!dp`!^TKgS1R)E{F^yweZ66tbu zoU2%(4V304vN*cxwmN+O?cuAut^490_>xg25=D&7jVs`}UU`|Q28Tjf*h91`SfY(R zPQq)yUGy-!+;wceyeCBJb-Lp}~2S#|B@?P0}x=i6;y`D#a zoAOg9uFXKl&UIm`|+*2O4 zW9x8Vn2clFvnZ5giaKfE#VeWI(TW9=^!w4jhyB0QG=7=C7P-NCpZIbBsp$AXT}=@LrEZ3p{51?Va{$Ze^yDq?-57189}VWW=GUoh;GIuceQ*q%>rA zH6m-({VA~hPXQPb2&Ndwtu6s2^O5_vA!ukV$N^hyK@a}xS><^I4_&I^9fh9vbeJe% z$Ooa8-{?-9E67tI+2MRclk$t@Yp0I;S4N?Bnk_bGMdanTwmUX5CkG#xq-ck_SUIV> zw2>|ca|>$h&OY-@eClNWj%4?OoswpEstj~5#Ymoc6C9zkqA9$4oH+Y+)*c*CtHB*+ zpbZCf`#NYzK||=)4m%5+ zxEeD-Rj31e>6T+>cm4Oewhk;vo2^>qPM%tUK~kpgitgMV z*vN)kZR39PFY!r(zIm6=TrA&<*%Xyvwy;}`(7wt6)fiVa8Z#(G{)(JT0E^_EAMJ z+^?&0I~^&F#r;kqSutW;NHG{Qqk5*#@Z0B8UuC*0L8)eOwLi`&f0>fPNf>gfXHqw- zy4J-o?B3$=4rDoMB!POA7G4E}l^a$FF+47^k#}iUIY(T+wiztGbX#xpEJ37a=E8d4 zB^^z_AyH2mhmarfmgNC#Y_L#Ii6{O_Q%|bj_HF*(`xIf0j8_jhQE#H}c$`b&@-z3; zz_twcy)hwy;Q_fZ*WShfewZI62DeyRQ>F!&ATcqKYD8`KE;$}qs;OvyAH;=)ACi6> z1d$sh_9w#x!!1x_>ckU93R{m!RVU9WarZOUyQ&RTm5~F}?DZBGln%?Qr4yT%`!_)U z;fAyQtg*qzWAj7^6_n${GaldkvHio*0#hpi@L;S<-%B%pkZC9u6yy>c{4mYJyvr6^ zoSSZuh#^NRBiZwZPLZV;T5(5}J+%Kge~HC|V0Iw#LFD!F&UCTfi#}{2{v2#Iv(L>! zrxedI}j=qea)ro`j4&+*(?^jP1xfpA~Kj>VnZd`D!M zh1cLo(}sl$7S7&7wtBg60T00SK7z$0+~oLabD_dzI72z5jX>p8ku{B@#_X~JF-{`~11+_xDuH^Du&<3* z*+)+#$=fu|be*Yw`K<`2B&$qkLS()=6^2?j+{$Cbl-eiP$89=47`wNv0A|bqxb<(q za2pJ3eB@Ek_HH=oBWv^T%jeaD#SA%F4G7la?#q%0gLXXgd@CAeGjsPTg+g^96l}`^ z0qAzVabmSh2cG1o3wRW4e&+RGYxK1T5@A4q{9ILM5wivgZw>jjG9hQr2||GAa{6AR zl=Z-(TGGZLD&>6jx}N@5Q_TbLWe?2?za5(#!ONlJG_&^Aw8A#~hR4tCKx&vJGioQ< z6>o{wH1xug&w^+}yZDmZkG&|U6HYIQ;Ha5kMf=7BLZp82{#~Eeh?7-8JpT$|J%{3G zQtK+S+trweBC|niH;*KDTVC4kMBzLQiIh%=Q`x!DF^~Yq;5hRBQ}F`D1+-1rn$B!_ ztW_+H3UUxRAE<_Ufhq-oLN2kL-+E^$>xt(-(;n0Ty0|>HvWIV@CkHqSHbF{vWFe5R z*|un{BWWkr57x(xz__FexiVi!tq9tA;``gR6QqCQ8}N%d=w$U@9~ks7#~?!WeA(qa zP??x5zL7aI(_;I`z1Xp5X=d2F_D!s5%4_kg&p3-500&Y9v~Sg!Tn{1DU3b48NqTz2 z^Ruu&DuZo&3@uBYHrinqQ|45xg}$~x3KW#0{b@n49y!_VZ3#lOfYIFntOIPd|KpO~ z+FyVsYUkLiDJ^JNcmW!n4zFaXL~z%2H6>Ml`zKuvhtNc=vvMCHMn55(2yG4oWEKyi z5ju}*H0v*m61YsUeqlBcn~O}! zsLcf7ba@LiMTiAz>^`#|fq*ynZmdViEZ5qzTjMz`V_eK_O<<0r5i(i)!~)Y8uj9%` zlrMIlm3*F8lGtUP*V`YVVp}~a>5_B}B9KvvOhIpeFSNBn_v#l?fhNL4UE``s4!!<) z@YV9)C&SUpKcW@0#=Yd8RCv@OvKcD1=wR`xMsbuqqTGp|A6!p1ps+}%JxjC5W1#cN z#5SJ5h=Ji#0R~cZ`zg%OrS>>4+U8Nt3WM?*@jCK7Jxho(U8IFJVbv2^HKJ*3k0N#{-x8z27j zZ?p(2V{=0jMcGpFEO&j2fS)9)vO!tGIglMM1v10HK+YCNdSD zxko^pC^Jgqde!y<_d!B%4YADj?9ESZ894Kc^7?e(m|O z6S{-@*IwDVY2z&Ka{rutIach$!1JVk0r?`;9J&h=OTP+c-~4*MLfv?NF_|86Fb9eo z0O$flETkSeH^Q&24vAc?XbuSwTbKUH_{sm?TNesfQT%X}mHoc{UC!T8Zb;1<*f{@F z9wqyy&JT{z0yqE~DCTZ7r^EUGEPlKAV&PcN;N(7)-Y*_D)oTkc z^p01atbo4HNTE-udXIgL@i=C%fIV9ctHjSqJ!QgSCsb$@6l5krGQz6*h5&H1kt!h( z$l(-$u{PjZGHKrBk565waw-{o>Y;xApL?fw%kIwesq=0-T=|>@4&YA{L+K=o?Lh~A zIAvLI@|C2^5nb?41Jbmpz7F*5z4}Jcb;eq(+?)hRxC(l7`($NSL|Mg608Q%bb^C;E zt`ckikEZXAr}};WcR0qe;*dQKj-4%KuVZE>6d745A|tZ*j$@O(^^UBpkiAzS5n0Iy znHgEX`_5KL0YdzdXE5e4@Xfy#_7LMVQgqfUOv}tzEq7Umq11vqE|RvZ%v+0yfX71 zc=WF29BK50NX8PnXoafFTnp@OcpeZYE^!Dw7=nYOkwnA5xseCxJ*dNZFX#psD-l-QsM*){l{ zVZK>X{Q_ym<#IJdgsy?eX5^FVV0Q)5PpiV(h~$)DuicM?O}{dYpYQ#ob?vcDtNN@e zc6)_0pnKEexYSYHnz%y<36wIZ4d90h&fYy%tM+t&R|b;dD$zCm`c>?E_=EMwChd(? zdSJ-9W_~c^Q+bgg?P*VE#+qz4?O}yX z%Y-NEk#gA#6f}x#vvRgoR$eCvqZ@4*J+uPLnF}ph(rrG{La*g><*x62F=TnF{c^M75%-ogN|fcXceX`q1VEjz$D_=-AU{5$8c((Ft6k6@#c z`FkRK_S=V|`FAz<7%)+rdg$q=S;;o5sf*2+WM9c^9H)3sM^rutu2&ODkl?QMDS`eT z=M9vKA|~^EmvG@nK@9El)1PTITOT;j=UcKRmv@a_r`d$8z9aA*iw4szwLSqH5tnqD z;?W!wq7KQ6fSsELfKE?rAy0g0EF}ervH`#A$5+l13qdh@T3GJs;ReLBfIPo}E)Fq= z)U^5WAOSNWZu9zyG8~g3NH$QgMB|Qw$|FOgbVL)3^8U5XfN%juuWHCTBhXMxMWa>2cv@M&9|H6!-{e|x z{}Tw|Jekk-+#T#9w*>&}eS5zkkFVJ{tDYy7nZj0Bp==L{&B|t$N|~*nb1(ZUNRL8m zbwd))umajt;>BvS^%!w={IKd*Z;DKD!N$&O!^J!UmK;KZM~fS{`alhUHU5QvZixdn z)UhAlfH7iu;2{)n`P%s4`|Pt-D1#oR34!}?$gcAw3C6yFDt{RD@pEz0lnYRB*g?2Y!-d!eMZeQ57 z*b?)ITYCbUFJDg>U{IVOwPd6KypJFX1HEjU*WeN1XJ~)}@qI=ab>dBS^}KHYCR7A` zLPd$Xh0;(&22?<&wQkvN0rN)RzZ0b#id=>0J$V(5#%BW_q-3&zus4fZs$rCR!TJsg z_>BK8yu3(B>nrGrKE$V>{Im!Po_Q_Y8{M8T#?h3T58l)K)b4-#Gy*BC!S~dB_w|L z@>*6W&8*{4CzY;UgWsy#z;$DnDcY;B!gzm5pf%$enb5cG(7-P%`BTN4M4j;5wPFSZ#$!V%i;^i zbrgo!U=Uff;Go=IHUN(@~@x2sL$Ex2jycpXmrl&36GGT3-_h>cA<@C=@%EUz19w3iJTfyI{*)>T7Q2(L&lGRu=T_e;#M}Uek+Em&GMFQ^7@$sMwGpCS53zaih`O2X4a`t!oG4$`%8)65n6femoR1Yg}iaKTmDg z^J$ie2lirmkBlc7n6T^x9d(?wJ?{>^!+xRPzJWFcxP&aX zWukj+xw1hw&1gSP$)2r%6C@yP=Dt^aD#b@1|aPpIs+ zfr7c3p0le|S3aeo^`fHXZyCpFKkrUqoJ^c+Xi4M)Q_-Zy#2Dqeb>k$s5gS7D(1lpP zAGacrvSCt~IziNl>*9gN6rcu}l2I0^;6@W@h-)ib)*SKT{-T<5?eaL^>mYPp+vYc^ zKYa2cr&bo%af*lsFOLA-OCN&AW69C6nkk?8w$1N4p7v56K1t*1AirK5#XAghiMDd2 z1l3ywadA8z9nK#by8Ww(*^1cd} z3UhS-V!iA9&j9!H^Xk6cpySrGYag#<)z7oQSPOI|M7==Q4Z?n>E%5}KPd2wMgPYDT zgU(!kOWISigiFQFrslLe6(=iIq(tU@qX^<8(uZbwHe3S?lx5UNaJD#cAxuE+O$6?! zVV6tWHA?sB{A%91;xvKQZ);cnf8EWG2!&QtgaUXiF4Kf+et*ET;exky1ti?>Di_ON z8d~$s;js8aO#X6!bvHxTm$V@sX|64VoJ(|K72R+MEc#Tbz{NJfkN;IO0Za8Y)f^G6 z5KKf_o)GmkObb(Bpq|HI$bf_?Vw7j=sS%>Q?@2$cJzdKhiMci%A)DJEW^C>*2Y8q) z%s|rwT`Xv7pl&^50F2X2fQ#GloIgjz#b?F2mP0=8Q}`>s5Ik8?sshE&fyDaW6l53v ztQyn^dzE6WLJ5ow0m*(KOacuSJR6a|01yi%3(t_>eKHjIS4P5J$XR;HOYS7c^WZc< zCdoR^pP6~WLA~3#l@5m%)gP)__4z$n)9-I@r@eZ^ncgqR`5r7gQ?PK=uqXNlz%|}y z(0wK)O^i&aHz30(i_8A~VyOJU1jdIh;6f`p5D?v5L`{C;GQod~h4aal2Wwg~3Xi5D zWD_p9p<}Z@m+6x4a?IjmSliX4L#QGy!tp>pn4WVHGLr?iF8Oz}c^GmMrvbaza536h z$f|j5c&TE?tpAe4d>EhhVaJ~HOWIjSY1%&cw@^(Ld{RT@4Kl2Fq!N{^V2a6kGX1!VYgpg22%8z1Zrjy4lfL`8_Q`?(K~z1d2ASKL3I|LKwWOfcqm zY2LD|Yd+En2HLm|AVMw}PBR@#g;FRNK#>u^*oeB*ajv7*L$&(sCVx$}oVBuo4Y*-x z@NL@uQhe5^yx+##^UFKeY%h4nY#IfMd<>PHv@7XRR9Zcn|0R+tJ5=R6nG$%*Lc6d# zI|+rmfhBR{rv$3c1!acZ7Z3EQCl7^TwtMz4HzAKZu`_abCJwJ`j26Ee$OmbRJrw*A76+Ol=`OF)I!Zvs4G;6{KaHtmZsB~=D zx)g}&qTXUjZ1dFQ&qDDeiE6`n4-tR+14DFp!yb_FY0bG13rVi%js6`s_PSc|4UG}J z03L{~0;RquKVs1~!T(xoK=k9vhV)c`^bc{@LFEMz6M!MHHK_l}QIrs*()-=>Dun5X zTWxRdp?SMYnA=!0#MxtL-f6uS60)f$P{VupP$SlHB|J}!&*2S5p+Xg&fw<7b(WY!1 z=iv!rQWW5-V-OH?-R zm_OUki1}(8`7_o zP9+ii5X@oWc7xHmo|&UgaRetin6q=h(t#)Z(UK#DB2@t|1+2o92@4ZQF=RJ>9m=5B z!+-#qVSJ`V1<@bu4BZU81U^*hHA{x%vRf`6_rDyNcOUDXn@?s|Xy^Zm+VXYAPXAvYUbPf-FMnMV*DXp+oqG%dCI|=lufpno1u@tH{k?d?XS#B2}bC2cQl33`Sc8)&J$2I@; z)vHtJ- z1Tx$Jh8$3;;Tie_&yq1pA35!^LKz_nvH<;$7F(Gb$8ydWfgdk(i3RDif%5*8NEA34 zK;<1fNdtg)P?oW?D<~oP6fvLZ9ZcC7Ugyi*r@#JWy&vHeqt3vmd-3b}B@jBUOc#>> zsJ!~+X9iTKCO&`e0Z>XEiy#;xF-LA+Y%BsKr2`$Fh=Zvmh?oG5@`R)8nrjqZb zikItd6jrg!WM?xLHU0J?lnW@-L?~gL>cQ_7@$vXzPzwNtEtwApsWaH`+SD-T_aFMe zoMPPChu>D(>})iPnfw($p1ZInm$8%&wsmwoh|mE5D`jA+}~T=E4mU@gZJk`Yi{rb;6`KflY>FKn1KbHTP#K`7~r(l zj!84)aMBbd@^JdEVh5|EOBK%k9BaNy?(fcjbNX&M>sW@FD*}`**`R8KP6O1UkQ^0Y z`%@;?WP+{hZ+&-9JooIKWfFBSzV~_kqK(i0kgUq3o+VJn%w|In0s~}#I44csX}ef7 zkch1%g*pM7S$9V&bYil}zC#$B+|{+*)u*&sLj$ar6s*AI-D@TP%%P0*CO#rMkh+fH&8IS0@aXPt zAm|KX(PUTh9Ckdo#64sED<=={V@&ZQf5P`&u@}ytWl9*S7mw(+=AHk{xC4 zNg1V|;PQnfFr>HBfO)Qx1FE~A^l7*hEPQ2yuh==6wleU%ro;v5nX7+OlEyRkA{YBEyfq9c%GVzt* z0cn)}>Q_-Pu7j$KgisvByS6ed`r&2t zq26V1gM1?ngtP{72*?&%$Ay8FW*Lm|ohm^Bd?3B3L)`ramI`5r`?@{&i8_YeKvC67 zL7BdRed1_3LMY%O(|5hQZAshNPr6e2kO&BSjp_Qer0VH`V;00@)9#aKa{v%J6jkI7 zIX8umiz|FefQM5Q`X5aT-QHd7^7`r%x7K}EuD6NSH~V4fj>*)bE(G$MTVD` zI51SbsZ&8ps4MpAp!>BV!=s6Af(%O#VyVq;$nam@*G-W!)#5(&*Vm2jr&!OQO9)q3l1~0jbxTw>N)Ph7s7TQP9CLT{Ual)AHQHqXPu{xH@7kh}+@Y1+ z-nHDd!`e}+((lJb_qy5(JQl}Gwdp2lNHB>r%fyQL`U+exiovFVv~HKs4>e=crV7&+ z<+(2yi0Wx0c1#Ke=T4pWlbc+Rc?}uN|GeHlS+UpsF((x8O6;QxjcJ=V;qUYdst2`M zFh(>=fqjHWL&m~x|BM|(^+J@kX*@$-=&+>-1wvK%(VIJy-2{tE!LmQJLUry@;wqn{FH!y`G9az3YDVQZm5UUVYs)t5Fo^Q zx{KJV|3NPECnncz>|yQg)ktN^D0LJ9KbxpO)JBqVSS*&F(<)O(WUKA?+1zR2tcmTi z*U_xot@*3po~}FJJ?}oblXTry)R0&fA?bj-rD=D^js*RJGo>hTO2FwgS{?W8Cw_MI ze$Y+yV+=b}b^|~1am3`T6jb_0`IYW;+RMM>%RL)jaO-$!zu%c+RK)GVrKG-kN=hee zc_|FWU>O5+AU6~}nksNBcQF3h1#H4|Hp)6)F8LOV+wF!qcd|(paR$A|&!vN(78?_b zg7y_^HXopa?o|=<$$kq((-}et81P`DNcV?N!j@p#PP`TEK9(Mz`Adm7nH9@-jax_=6U~r~ zn+3cD^&FtK;S19IWTymoGKI0>qyE#z1$|o36YJ|pEZKdBWO2>1uEvMM%nkGHjBzr_ z9OROb4$>dPS?67;Og}zy!{CChea{!*UclNE>>dERW<%gJxlJz3Np42mxZfD~y=1$c z7wq*1JIxU#g2j7*&$Sh#mD&(bDq^E8KNJIY6qH$ zMA=~9c;IMisl3AqcnE7%64ewpBH!sn3?y*+t`$_eEnfQURmc0LQ^}pk30_PYn>cUZ zBx<*lWOM|h0@Cya3r#~D;}`SKxy>5~NxbCjML~*9?(TS+)8Kee(@C>&&Fmo_&qWmJ zIO!S@eQ*huT#t>CQnBJXy|*?h(Eu>vU4YvNH4pd?kn0AkYUomkUbjXh0}=<^ND26g zm~EuU0S)sb&d=h8T6cOl^(MNC)tr|}ycP&`SHM=7rtABQb=vcao|K^E2X&ccyNoIB zKqaDzz8G!{ZKk{cJ>$h!cRYAA<{#I3ERSty+0M>78ndtd^?^~OKyDN;6*oRR)Q%te z7;+nYs@G@}Z{$)0yvC67{wUIV2X9#dq<~U*zowOfm%?)-xeFG~C9H^W(H2cA|HO_= zqO&PbfUEbi88SQ}nB93#NbA4WT;4JmlYTy)w%>K%vw%oJ9fVGpMFIS7SQHthxO__h z%m&fDphJml$Ia9qg@=l5m<9Z(aFp`6V`TAER`BB%vH720%qF7hkHcLK^AG9D`o^h^ z6yVwu6e^STDg-nAs%Un~>xLUexK!`cW0e?%Efo%5v9s~JvU79h+XVWcWpAi+)^C)- zqdUjzm)DbQneO=+D3>=Z-MM{sXYJsFa=c}O?9qywy)Ud*i|Aj?ih(ODD?@-Y0DFLp zA4(spMd-g(RDyBfxdFPA>#qg>M6t`Cj&feh1LFMUT2CS=2}eE=8^VFr+6awsPt+5V z*iS+l8kX3}Zw0F9r~+M};4Guo!v6ZqMU9}h!RFKA&nn~*eJeNQ{!9(Vov1QI@rIZ| zl4i#UMTnzbEw1zVM1$K>gW%HZ&1W{5#%8JP6l^H+nkW_Psw&@!|1W$%Xg2oX<38EZ z%oVDB){0@xV)v%hC>+k~IHA!l!8{9bVk|Qk>Hx(JjH{%8gI*Pkqx<4mzI)a55^*el z!b2Nyta&7BNkA{lM@EmYhW)JPq?K2qhRLrl8PU9_w;@vHLK&qG6hFWIxbRMP2ww6sq~F{_~Kh?faG@VaO;_lYgOd)ouFmV=#2tC4`vU znO?4w%DotjVrpD4_x(9!e{fQG;81p0Hn~)Mh{vEHQG-yBaE&ob;8$cfj@F`Dl*)^0 zuR{htEq;FIM=Ie?=1^Lj7njd(xmRPkM-Fj5eWs@~c*lG74IeYibt84yqjaKh;ovw@ zf5eWfj>gmAc^vK4XSy(~eDM3{SiBFd{l&yu#>b)2EWgza8J8WV0&+3xD0NMocUZ1j zni=XS1#Ah=V|F6w??43>6mc&xeG;OOT%?Vc=?|mL>gzb$89}g~yew1XE9fuvYr>|9#bZ-y6l+Ro9B=0-hJGf zyy1z+wedN{lfH;NlHzqLXlNT`DF!aK38dBy@pJjq$WOKD$#; z`q@}ZliLYMZR-5<)x2v|di&pKd-6etYOpHmfKV4$S%4WX!Fa5zZE+OU?LXGsVpo?w zGahTqw=ZRP7WD5 zO6nRX`X{&z9I=~-U&8XOYkgU#x~0{dakH*VUq{y_?_P%I&L3El%i9pN25PCQ1d=5} zna_JZV+5I_YVt>vU`GqYVHE8kP*JuACsfseVDrJd$OM8k6*(1v zHMN2^8%)EUILLrU8yi_fZKe1KFhe@wltHt_7$ZL1n*e0ORwX{sXQqoRq0sJ-0H(3l z3drq#VbH-Vj7z;CCg80ht==2D_2azKFJwvaXJ81qosFJ80-2ka9ESNEX3+H z(G8I*6RTws$x9)*eqPoy=@;2@*{05IA>u=bECg~IyfbgEhn_eMfi)UspF~$o)Xo!Z z>YNGKSik%FE{}e+Y_|5@4L4F~eO~0P=lve85rSsGrwqz^yuz#bkoa?<2kV_!*Fd~l zGrsnqmgF4FCeABv=J!tQhynLt`}A-HP=nQMT9cj?yg#dyr7mo4I8CP3y@1M9K*)0x79y&mkMP>t9mEAZX)LZlq?V5@L(z9UOxvLZux7fT@ zL((QWmlZ(Hiny!-2`Ba{z578=E$ zltF>7@xD$CQ)Pw>%uWs*rWwey8JN&@`?|&I_9w*{cqlqbHC!&znEx3Rfb>IsEN

@75hpETJMji17OJUimw25gNPsliz$FfwaR}x*GvoYZAurOybOkL}>%O{vuQPU= zAQQX)JZw*E!R^}ol{|43NM^C%87D_4%VrE`Wr>9k9Zv_TU|hn@K`BZi2KH-n>8h z;_X3b!s-|4?M^6QuRDr=q8c*`NFq;eCSu`eFjs?PK`ytQ*MNb)*IC7I%PPaW=A$Pp zJ}XuwJ0JPgSUu2ZTTO)76p*&2PeKU=%3nx1zYbQqHMl43|H7AXYqQ93(W(Mwjghru zk`W{yk361Tb~rhH?4faN>k>C_?K*0)`H?muPWDCV9XQVc*A4J({|OCfGW6r+FKeRf46YmGVvG2ndqJ|acRpJ7z;@5Jj@gv3>B;}0< zje!|JbN1(!S>L^dwnTH0v)`@Poc)I~r$J#uIhGhs0Z9NMlA!3cf>rW~vm^*F1h#Qh z>yTwOWXySPJOW1SAiC&NhLi^%Fd<%E(3HB*|Foix#`7GIYE7MPV%LevzMO+7hl~$B zsZ5^dgtr$LmEVY9>Ua;|Jmb|K|Ijn@N!$;xI|Mi%fEW(RtCpB)n|-bp?`HW9v<7~Z zBK$v%1$>QI9NCT&v%zd2L0SGQWYv>mpyYXd7uN2tMp@UO0VWO_y%?i-egyVK zh|8_29{7qPTT;F$&A8Q0TIb@VblQLIyNko)xB9&9|KY>S^yqpevgNwUZ=YN*4Oo(y znt9?}@^SxO>{p^N%ESQ95Pb2_kf=+^JGFfyp7NbT5`Ory4dPD*#+S7iZuMKORAs(kTV_d?&+rnd>j9xb{Sv-EhXX5lU^F4~W z8&6-qO_%l*tdu``l{|l2H8u9rQ-*RFrD6#d&RZa8^~gDtnj|I{s22Fy*GD)q+p}Tiw zPP+@wf6O$v4u4jwS^bY{;014qfw$~8@!J$pXw`9Xn25=+tmi@x7YJlSQWJ}s47e(? zqt43DQ*lX2T`ur`g`$Z}3+evO5jcs^=Ka^tsi?;&I#JymSJv0KZe>2Hn?D zn5`Gr{+|Od*i+g_pjHw1M}$H2bE*0by1mJ}G3g(ErZTxsi!fcRHi-74G;{Ser4a8O zUXS->P{#(+-pJ{qU-L`4cWX99py4~1N#`4@t^e`1sZj3~z1O9#dXrLl&h0n^7+|2e z%CjPEnF6c4;>z`K8IY!=L`7q=W)b(_FCyaPmWAz@-ph06 zo+zr$OkaNk9-qOwBZ#Q=J*v(9dA-sL|0=_&ekL3uNBtu+gB9Q79Nj|N#t8)t1JCtR zAYU-90xIHNM(fnO!DM_gnNFZ*ozhHJ>Z3_qpA40B3mAkFe>l_3djX5QJDR_u(|gky zOa3dwRZ*X z;m`Lt(_D924@{nB|1t3aAE2DOA8?^j-gNXJmQ_9ny?%T)+40ZDBIH=U9nw}qp}!p0 z#@0ZJNnnTY4`Z_gJd0*|?#n+B0P#jX(h?mE64R$cXGO8Qa7E(;$Gr?c>3Rg_x6t{y zyCY(aBiSOKN~WeKpV;OdOs8ld*60 zKi`z*fF%0NF}#D{YfEaYkbwrx`7aIg(?Mg`MRT8jp4Vgc>uMTH)L0SjrJGPQmp)OpMuvJM?b*$ZFe1I=E6c|JM z@W>B3uDRYdiZVJ@!Jbw&3;z4JqK=%u;yn`Z03-7ZfQsGf^IZ}Ua;uhpYe$a7|HSHd z+i~gnep;K&uSxA0`^t?i5PIi$F<7xqTgBEbMjCl#2#yw+T+rn>ru@XMVuQbnBE|+& z=n6gPJbu>_eMhi0_KwgMi~QM)b$RQ&H>4~mZy1#-2iDo;byE+ZC^!RR$_`6mOOR!A z4s6nw^CMW6g9z6|t~w$h@g$HqWP8qMK=%87M_Iif@kMdzr6c&d44@t=1A6tP_(YL+ zd7!ZRM}qGph+3m#acJ7>&729i@_YGfLq%_-tfa5|o{?3y$CUM8$z5Q{WnA^`myl|4 zgcFSr^D3aO+Ff4A|I7!8)6kda6Yi{Sl;+qpZ<}|``)m))`|OMiNz4;DqD2O;LvKP= z82XaT7~2j^FBV)O2eD2B_b17Xq9iDg=%f--0e}aSF9wvNc0f$hwnU7~{$gaB4TU9t zd-65`AOYFpfIzXcmPV4Z9K!7f3cO-AL}!rAc$Yi7_idHp@z9{+*c;4C0(~rf<`c(1 z|3t8c;t?eoV(#ei(33KezGi#PgpHT&?x>VczB?+HTX$<3d#{G`8|=C=(WIV0CkKzW z;a?jrw!4N7vU#X1jmkrZr%0eGe_!Q?2_VX zqUgk&O0y+b)2*iit}gDsOu0vAO^TGb!E88{`yQ4n|AWM_W`n=%N#5aOD#KVYhxH(S z%T%f`&e(~h;mZNPmdjV%uA^x}kGedJ!agLMBW&*0(Gg)3dA0BpK`zyC^~R*J%VBjN z@JrUO!FK42p#B0HNTxlBaz9bh3_C*U{g<*sjTkwB7sbMzrShj5Ip(|YwXBy?ut-}{ zo}j#>@IKZnd|HcrC-dVEU#pjisZN|AOAqLUod%|dJjI_xP^qaIuzm#=3y`d#Z``GUmA?qJ+E*o1O+7P6=7EV|Z zjMk{qt#Nlh^Y-?6M%G@KP|YKHww82;C`|c&q@h&A2-^X<7f|U4KbyM^=-W`QCM7ZF-{)Ae{gK<) z+>Ke8%BWx*tAOMI@)I5{ec-FxKh1^bo$hwI;r_1Gjw@Gs(@~VTbv#cB)e=f!=w0Mp zbVm9T!B5VzR2d&2?RcOw@7w+KJ}0gIw;r2~Bo)#u^#Wu50=2hFD%96_0P|!*GqyU+ z>2-;&osyG&I(t4-e&f$bAmn?v;{^(1el@&5)FT5-6=IxiL zS;uDP2o?|0jo>cBkUPpF=mJX;+YmEE9koDyW$DR4*8Q#HUa^y3r)yOm-`-L%DB{lE zHho>vaZ@9venU~(a`h*Wy}7mZ)7$3~7PHNja`0hIEEyCQQTSFAs3@HgIEaDX3>I^E z-7WzfXB??CqfOtn?h{{LI_oZR*X}4NK--Ij9b`Pc2OL!QzJ2{jqjCNY9L|uGkMUU} z8dEMXtB5j&1V@sx;j5-dZyYwrL0~ADP69z$ zpM?5kxw!s1m=*%@y;Hbm)d+usAoTrJhT49=aGjEVV(Tf9-b zwDQUXbSI!RDz6zIgwB*LX+K(toR)uzC?KOOp=L|er=dgh5D&K{3JuvsDq#-)7I6Hy%KuBg-Nv=)U$_ zj0J`_EQ*R7(%iq8bub0e_~ARZz6FBJnWX1G{uTks0}g^TGJx~)4tsDwJUU000FZWu zR6c1WDwGTRM}Ga?Ok4TgQ%FkphS%%NXDt~Z3@r(A3*W4~WVt4@ivu9@|K7eE1Ru?| zg|#ptZ96$@^MRpxZ=gcw4f{|WX-b%;dI)C1dD_3vX~uVGr0Mssr}?~k z{3bWfe~vYsrOB`TX8qp#bu=tL|N6A$zG^v8$^w5BBlDNG65a#hEv~t;(;$gx=5dHd zd3EnEaeUA_;~=|5?UFHSlwcyyWG5M=zkHlYYz;-Bf$q3FU5)jM(>~{b^P|UJu_42F zSTrwZ9v2san0Ypkhk6TEtPva?_(G5Ov-KjSD3zvF%2C?fWubJ_YOiU(9jAG1-=$;A zZ>jV3l1+N^T{^MKrr__!b7?c4msm0*i);{E|C+D*DpooD_>=Fm#${KoR-3yo=6neF zln?3hM_|#o;d)JPq8w%9XthJFF2rxV*YFTR8Kip@7ZUJlCewyK!AWk@|Ytq@lQ{)$q+B;W`Jt z*$K@DUmR~~hZ=}@c8lK(o2(I)Wg%w|`EG zrMK*u%N#`LR{x0{+2EQI^G;vX9m@TAcvttwd(Z4qp9LQYh*)mj8>@&+$pQk=%T|Dy zVgNdwK}q=!FvWZtAMb3Q|0DSKXnW}8T6Uj9X?E}KQkP;uW_O~k?(?s@zt{SX{RkQu z-ob-G<#{J~OTkhTuKI$z7=zKf6QtH#p-OM5Nj-?RP-}e~(!68QPE|2PC;G^gT<*k0 zLLk>_$$9mlbMX2NFH6`D&-qx+inct5B>!{w%dr`|S!SqwY1-*)sNH&FPv5XZ-TQl= zrZbzG{XR|*E*d2o{#iQ+Cte|#2}Z{${M(6&PR0=q`9wj%jBmiO-)k|5@bTlvdB9jg z0SV=$0JA-4LsCUD-cEcD`9HtA3y}ZglF9GwsN_hBr8r&`Q>R8jCR$W6hz;S%{WYKKfaGKoi&lzG|{1AelO^k zxNy02|0u_oB82eTYFuU|-c8p<{fTvX7oz;&tY&pOV!RZ?*gw=LR2m(D5suCVr^Ze6 z^C_SCp&r_R*ACn)5d*No8jv9lRh?jC>Z-9FDjQB035f(3@=65`;ZyPfTOxL5{*s)X zln6GAO`jHpjsdRbFdR0};lf}`7lrOBEE4Yf{^kw(*V{(t`R_NLoC)AXJnIR%8<+p| zI|GgU5y_Oy@r`Q>K0?+DHT>~H-qCWcnHg7$7mm%_sZ6(D%CBCWnCJNZI^Jr&3W{ZE z7W?BujGM$m$roF5!$G)9hA1RUEF5rQfk3`7s+DWI&$w-$^V!EgI&m`R+u+@y!0i|k zY8wVNU`<;!Lr|&3c=F|AOW+u9RI%#?^^aXL1K~6r_Y}O@@?_YAGmjp zS1aqgdKa%xOEOVj?tohUbOTp4 z)|VvdnpCpgP-w6yp^Cm9M&X6)BfPOdDj{Vb^J3Naz-B4wR|4 z#G(1Xoq|5*oYf+p!iAaAOrBfMflxVuB@w5T9pSkK@&cBl`9F4C9MAOt-->QvG={~5 zmW4{E%(wc8S=Nq#;8d`nJHLyFf%W}wVRM4@8HnK|`hc&F8ywwiZKwXoI`L!&9mPVa z2#nsLFYI7!lD1QgPX@N{7!*RR3;Z;AEF55r65olWbWdt0bup>mY5=L4kyBGfP)gu( zPt55T>o)i`3JS%V_Uu1QxbsK#vcP>{)(Zr5_R1ctCMHJWz>teDB*<^bdiIE;w-*ja zT?Sn}TCSvEgriViVH3*c09@tBtY0ZW7(F!hq07XHbK)vnqi;I)N zHTjV{^D&-G9`Mu-c6Gj8vqYOv(y(d~4!uBA_L?M)yG7m4xwG11?+BI@7pH_jV5f(x z5)u8lmjF{krqtZXkUfhycR9KS7+N^o*}k~kIpQhbd$&hDzJ70y;@0LMOfF7(a`TrY z7T2alhwi#P-dze?RkSO)62g$18lK_&@JMC#YK!vd_2CQ1e_{2zd@dBXvX*luHX!}- z_eRh8@@EUTiFnqP5$Bl{#mq^s1^QA^_gSZxL(iOl-fC+}>tj9ROtW}S&!VW0o@18L z6AjK=j{NFITIQo?0<-yhxZT%5$lM7T*pBA~!b$I~ zMzD0!g{^Q)vtKYkna-^~$0Z<8X?da0ys{RgJ1{UZZhU!5J2Em-l#C4pE2aElbLt>i zVkaxy@&opA2lV8!E0$3D7Ze5E-w3=@Zs(S(UmTnNtn&O>4t84@yZ6<6Ov3a~4L7(Al|^Af zC#h+m3AVC?VIDWGjOXK%4#PQq0>CJV6Rcm`c-f=&?;C7Uk&u59l$H%_S!KbiUY12r*7=@jc_Dj^gS+P-C441cZPj(mNq%L-C$_L!SAJAxTdV%N9shj& z=8N<5wD^PCzT72#i!%mZ40)dcp1GYl6^~aKl_9BtB7pn7UT|qm$LPM>w6uCk!Vykp zJ5|mK1-XfUG$gPlD}a*$MxYg}Q{SfZj*@Dtdvrk{j|ko`&V{CPewp)6xJPqmIM13dXnr?d+57(LBzfsN zfHZ1rOym@3D_AILy?tdNXtyHKHRHEcZzl1>&CKslonylV`DEF_<#E<%%GZU#DuMb= zH)+YQiZ^3173wQyYScWqN!ZxP_V3SQiAseN3|>*`z8)ODUn{x4+rWWvVY%oTJDqB1 z8v+~o#?}@F3B>>2|Mr`*ubb{KUX0Jr>-hLc;}Mb^H05h3`w$#Y zJ~HiB$(%*(iuWH?r}p`u^05A0v`_c#PdIn@LOd#SLHhCFRm}Yscf_WYwAZ60rK8!W z6ls1-2jy`DT(Jug3xell4d%BpM7?>z|KDALQ{NsSOT13MT{Txi5+<>%N^u?CRWe)XurM)}V6~T{t!R^!O z5lLF^Boez?DoA?lBCs`CEXj0ciUHzr}Ra@Ih1GjsPA*(`j^S}*5mW^ zw{2I1=*YkV;TLo_!}SYQMZ6CmtH!g8tbZQ6u;r2bM3IZ>B?Q zB};=BraGDdPPU+x^y(b&0!4v?0SmC$ps(;yFwCCKjbQapkM~_sp1b(r!xJ6+C#J~x z|F{6)IYmJhk&p+P-3SApovQ8jw=blO^^#Cb2Q=XvFVcWu>RYWF+U`k2d<4rLXiCRC z3M49$$U7ZGPJ`oAcv2(ACz6a4r!jYBL3T9-Y;E}noh0gj;S8ivE9o_)jMn9@(^ z{#Lhr<6%oEcdnVzojW8kGy(+`6|Z_?XeaLNbdt{HS68bR7uM=DCVJveucXV?8ei!|CQQ_>;aY~E&EWplzJfLj;_IHRxiDK6PUVrlEibs@g#*8Im5uKB_;RRr=k!T+Ofm-+O+b>}EbiA=P{QclJnCARO1&)~=-cOZ1Pzxt#j?T~*5{u*Dj74#BxMDXw z%6Zg*nDn7XWV;sB^YTWLYO=eAzq@(k*~{2g?%zYar8yl-YT5mOi`j06N8kd)jQeMs z0bLm&KiMt*yUJ4Q$;q0K?MbThqwR0Qxw#k2w0^(umYb)Yeu-ME-p?qVeul9aG~+G~ zCJT=CQmM*Gg6rF4Up4n=MCcu|COK`>}wW#&?J4yIoZiJ;|c?^qe zy+6C$67jbvcjI*S*>rjF&4X4|$BX;yw|v5mtSjwoEYm=bMN9h4?a{7y!JpY`EJKr? zI*a)c0Rw76O>qyH4Jap{;6MEHBKm>i?aG7+0n!zUMe@Y{rfzo7MqBn{*?d7j$?(n==D-+Kfw%)Fr$CQ;pxM{6WQa{NwI|?w!V6|3F2zlwPy4>W_H?UQyy8J;6!%r|g6xIb-}vd*T=ZmITI{Y-J|#wU zFgPlReb)uuWdj_l0UbG5cNLOVmc`zN-QEW{nJ+jgq@)GKC$oP0DoD&@2kxN4RN&(a%r&^((ug~7eapvF)l zAZ6$r;Lg>ogk3cLJQ5K<+8F!HozwZ}mVEF1wXDANOWn^a9g5t!J$u~QN}H{>4)#do z`}^eOL)g0N3;Z5t7wet}i~khBAEinIw{#3A=_AZ132jn4Ja%zJHh!hkk)L}&Ic%8zy5NwQkzA4b~9E5o7zqPoC zmOajDzau24+ZfnxV>34AKWi55B*}zpZvOvx`VM$1`~Q7AB;!y>a&V9lLRK=5O;!n& zQD&6YP-M@t$%s;9RJM#lW+FQ~$zEk<@BjPb`F;P->*aZR>Xf_t{@m|zy|4Fm?Qt}} z@`#3rj(Wf7^kz56?w`Qypi#;osvgarv-HxK&fjK0ZEkm#9tyk)eq>ltK5MezF!g(y zypaC%H7?^u^}b!EjT(?gq$8wqmu*t~^mMTa@!rn*`0t&?TZ8kz>yz1r@RL6WipoYR zXq>*6|NiQ8rYWJdGtm$tXD z!Q`*J_8-0S_@{1i?V53!6FbtmD=vOMEVp-r!xwu?1RbCh*(Xfm4ouAls{%J}+~2KZuaL~Zwh{qtB5tJH|(0Yz8Brr#@a``+H z?iJ;$-8@=DRzv1u%5@~vYotnrU`&Vv`eVKZGm&5dKps>ykeV#s5)F(=r_Bf?{Ua%e zeAh^734+KY`>}o`7TA4p4*77RU@bj{%N*6AeTWj2vNxyPU9Q2$EF5ySr2p_!@4j^Cv00O8_;4Mf4 zTn)=1EXo%RrT3?4!$R9mj-Nk&+TOX74!5VIM0|hqn;bMh%+?M07Ii3s^ygd1?93H; z8*G@jh!36v?U40Xk|73{qym13V(`_BUz{l5<=cYnHVJbqT%(8ARL@-bFz#~zu}TCM zOF+S%e2j`*oLqi)@usg{T7&RGO^S$Z2R2fHB)VXh`-xGJ^)YDea%^jfld&AM)J%eD z7#D^S7tAH_<@xn8788Busmf;;M(YOCol}kHC4M*krspZFD_cJ0F%w0_@>yy!#ZxuB znsSu;c36^#QzJR0>r;|FGKWh!pV*rEIx?F!pImM5aq8scASb1MYhGDRdIIn$eGRu* zTAwKAhkw?jhs*!)Jw7j?Xer|xsla5Rc$!>Qk|$a67=#RR8u(ya=x;l!t_gXbbXou6JarRVz5b&Bmb3 zsW~>0yJP#O+;^pnlpJf15#KiC$P=~g?|o}RudRHu%d~ZsRm3;^q=P=myWa|LLizQa zgK$cezQ-77ZxWOG`^lf45HZjUteBX*BIL4qA!6!vQ;bom_4$`DR?qqS+H}!!?N86d z^-Oqz({TsPGyo#P<1mh!NCuHCK?v{G^}%$ji^C~ZDwyYVoGMOhJy*LrI~5>;+V|zA z7Zt^r+?e z2i`v2j~zAQkFVbS?cW+iMM%N_`cqsZ>pX2Dz!ev~lnE41ki2driPYz)uxx!FKQmY= z!~&Ygz#^4h4$pK*qY#>uAAmpzHbrmE?P%OBp%j)$8MPL&-DKHB)0R{h6~skV`s zqo?1a{)ADE{F;`l0UH_3DJ{y(PYT^6B>1*6tJ}r+Hm=law=g2#J}5sF&N81+XuI(- z??$Vf*%D!!+(YS1#_$R zADen^S$!|8za3U)gKHd6=e3ZhKGV`Dl}5nDiE~MO*LR`DkK;TM5k6Y2SS9@NG=;}% zEsgUejc23a_{Mly%x|4ru55~=hB)6#Z!bM|6i?9JSxXy{61US@Wt9wd2;tCpED`+q zh%v1^o^}++%uXk$=z|LmEsvxSgya^I_oaReD5faa}A6BNrAACWly|5hD9! zTNr3)fTJL@TQy$fUEERr(DCHSlL;Wq388N}N-Q z)VLPCw?3RQAR%a;0y3`C1)NfIjip7i)$G|f=woq;A>3A!zHhEMs9p{J-P%g^XE>s} zg5rM2REBSE@qDA`vEq|2JE^CP#6#EW|DKsSkjY3mk{N?j~5LMxJD0Ak&_=0b#Cnet`S|LyXP)Fj5*}N)-7|ca>EqzTA&lpk%Z$= zp%Jl_jmWe~jthv-52i>|SzUcFbmt)gdA~pAfkDMBu=)P@@nimK({LdeKAcWC!2a#c z4G`u_y3-qR?pA}a%Y@p4pLRSTpv4L%*21qF1=FUTXSht4sN zQlEgfiS9B^4>m`ZOHcgwBfjYV1h_l^2mXVjxAqpj+QHk%&ffkO(1t#il>F%JZFqVd zo-1GF;pMm&j*@YtOlp&DQm+CZwdP3bx*XhDRK}YMKAMMD$ggeS!p3U=Ub@B@MW<`(lAWx z`Lww|^+%)5J>?j+3ZV;rebc+hhxAP7(+M#KGE@D<>&+3OT_B<(XYX*SDJEomj$ZMqcpTr}_S$&L{%i_7dsHeO2aII1QB}4UCJrjf zn1wblv#Eg`*c85iuEk>4ZA?#Qw=r*X;6~|E!R+GAO&UQ?iS-8U%h{ccq+(({`6P55 zm&Ib}!h;(b0`!{N%%Bp3V$|GvqVTA?*a?-~hmp}`lNoI19{sa?3_V65)Q+g7!tvDw z(M*F>-*}k5M#Y_7HxQ~5r@lC5d-5r_^yl7AUHe+jE>04XJcHf~b^@W-9aX2gUDb^g z^cCWHp8mAHzBH=iyg57V+OFf(@QcN9{MJ#nshz7HRK^Oi^J3!ixqYlTOd8AM!|M(!Qu><>O=peT|$N2CG8DR_D@_QMq0*5M3X&;{t)kcK7D z527EfOrZuAnI$8~PY7I5t_=u>5;m_UX!|^3ko;&WG3$&Pbs^R`wS`FZ^`+Tx)ksQC zA{Y56O8xgysnr^<`8)eN2qvZdi`o@}oHl;DFE1%QbeSGnNtE3?mHTpwSO!1KOwsbX zz_y;&EmA-A45h{q&9I46{>h*Hn)6cw^;OB$4yCQtOgU{Xjx4oQpdHA4%Q| z?BBLrik1)et|p)(3Lp$8@)dlGjX6Us9Gp!4SNlMr@{@I2gzX*U#x%Lax`#r00`_O5 zM)Ge7_XsEFX3>*IVw$kmW79d0R#W&0=*CWnq?sIhsQQj%tRMtLkAF8e!DuIhp_aG$6tJs~DIjG3Iu*8WCIp68Z5pZ-l~ ze@?kH)`YT_bJEBNrvN-2G;QWV$N@61_DC3g`i7G%qt`-Fh2!=;WcHEq~%XwSf zRYefY9DYh2su6@t3RSIQ%qmx11b6z_E>?JN(a1Ea-mM}&M7+RrJWM@ieLM&3a=-^m;>q@E#!YKltf=qnpu>sr zVd_-_>jP}aQ)rTds{w`Kd-?LEz_pT4=-a{nNdp1Oj6&Aneh71|6EN@eBRh5z$Ofl? zjEUS%s7}iyFq(NpK{q$mp3)AI%~@g+;st-phRV>QQ(3#I7L(j7?Va^>XcCD+pI*4` z{Pj4hT4}sy6mN$G9>L3tz(|Y@vw+R~-Sai#?u$A(?UyRlJWb@JJ~Q0Hp{K_VjzZpb zwE2a2{vv>v2 z-$xHytR}xtGYi`iJSQiYPfemvsIOAry)w3_gl-n zmJ~-B1udamK}0^dE8sRwUDCx5SPP4Emi)mQSI12QSRno3-5%1iITj6w5bX{l=ibEe+mtbJTn?lbd4*fd5L zU2*VhgP80lu@B((4P;|pJZLs8jIT6R#azVoTa@4F_e!bUtSRnu`onB9?ko6EUvDHz zAspV?BSQj4?F33uHG|6);du3gQErz&N6+a&ncx3N%qmu&h5lS_mDw6Ro_(Y1(nS#~ z$?qX#_PC%k#J?zIFj1luXL{6z!h+?QZwFZ1wanwB2QCc!d7D<2hW$7n7>yvNkbu52=j2VFRM34zb13Ln~Y8K^D7nbI_s%|wdoegdj z6YDa=0fDSj@yN_hd;x% zBKAeWI5_nj1?N(?mR%*5h3>Tpr!nb5xYRwe?Ae*Owpmf60bCHd{3rsPVC?09zqf*k z5w=1N(gTnmY?v<;1&}4U(c8zLHVy-SRz~4aQptFCm(DFPBti$iLJtl=6*o#-hh!); zci^1XLiZU8-3cJCPG+Tr-y9iHV2Hztkj$->3^9M27jG#ar~bVk&M9oqQLdz+R(r8= zAPSm;SL_tm1D~22y^p%d8YZ81N_2ek!F0B0yY;^d;gkNibj%}IOQs4`Pg~y)GEm{r z<8gVJ+jwsjPttkirgir09QJtFL^vi|Z$p}w>(3`V#!J0I50GH*LVMP*VqYsw#+44= zBV}7Z9mf^`p$R7cviVggT3->(M>u@JDG^y#h(b1o&VnR*vp;}sa;R&#{mr6e^ip(f z=-hpr9t9PtzZ(D}3ztOnBGpg05v5@(Mnb-7ZcfuA1E!vy*I=nsA zC$;gU%kJ)7vQxsS=YthOpw*AUnu#S~jV;Mi^rE+J@pAnFmB=w*Y*@z}6`JzdS+Gta zrsg(Qz>g5Bt0ejfTaXztMK2|*2j}A-q{a1hs2w?`?D+i`X>j;D^>6py#ES9AJ*nA= zx&e!C&N55=-;7G{vZmrkNhl)7mbAE3!yc0xn^Q=;nird@rM@BfaAbii001EZ#ai5B zH5GzUtioBYiNum`Ddx+m0TuRtv~ilWi4fJM`^O{DS6XG|OGNWT$>+mqaZFH>0h6Pxu9W@yJEcQtho*Yy;czR=W)DYL5y9q5)r86ScAc-g_FPFm z5MtE-aK7_OURQ@vn&~1(NZ(OT0@A;v>9;L?fpm7WGFLj`jNL@i@E&!Q!=vat4+}E- z2h#DGDUSjt+jDTnoD)xOG*5aL9xsoQOVD0{W%kPxM`>kI?SA@l+!lbV8+K6&TM~CaPk4M(syxY0T6aWrV*i+xkUKN9Q>cb%NhlzO?LDR7=L1t! z&+gzl-xn8xaLQC`(zlKdT5zN>ht&h1t|g`HPVfMW7o^23;lbTPU(nuL{F`V(?K;#Q zD~+5HOY@FiN!J`JpX)sNWAF?79{XWPa}MzbD>r^8B^Fud&-V^?NW4#xzpSLE`;vha z(`=x&X>f-`aL z2@bAyqfx|_U}*qmJB-f zt%_Xa(a4(=m#$wdb`%y#LN`2l7p2C?Nz`^te2JsvNA1QW;F_kvwWu^2&kx_nE4%;VN&1NcmYTWKk(`{AWRZ?S>v@+VeFSIp9+Q$gEXm=e!tC=V|;OG(K zJH_XXF4cM)jlPXYuD&ERoOssk^YLqUD=%ty>GXMH9q)8|X4@`2duIUR}?16*x}6(Kb!&;GdTdjRD~w#fSk ziAoC^lSEoN`9j6QWMXjNnj1Yb`}c2MuDpmM7#0K;*ylCS1yv={oZ2&O+xf-F#8gC9 zcY|JA?!7vD0UtlE`4hpDtn8;h?y-4!OH|%w(eE(u(fi=skZ9vNB3te{rYhG!%J{OK zQU zgIN~)R|AYCFyjfSCr{k-lSHX6U?G4pje-Vux4Gk)|+OP6f2CSjEo%XHn=o#-+jLIqg~g4 zQ_;J7(tG#HZv+RPVZA;7L#>~?kjTgCwwS;Jxw^{QPi#BbY0q8UYt5{G4Ht(SO?O(S zV(Bl9J!8CHop*v}<)3k$#h=*tjvTp>8u#gCpN%gu581ZGPwj~bt|}91%`WQRh}@oK z?nah`OlJgaLY{NwycP@ic_R>adtT%{ugv-kN4V7XlZt~ZPQ)GMkEutKFb>~1@XLb? zJBC2w=XoAnu!0|K(h4&?%3hXoYMUox_rg673h?)I8Q!;2*7}n8+T7#XCK;VrzgAV( zdrd1{Rj#Z~h2>6lU+(8ffW)7ce%U|fkl$(Px&4f1tK&#Gp4h%<^zGTL&CGU-dryee zaSFYPX2WOR$B#DX8d{WzXVH-hGf>v{lNDUYkVN8YL~(k!MnSXkDgIv%{+`|4OJ}_0 zClHN0T=%mGJKqZ)EHu+M79TTKn$p1P!}CLF9p^t3i_=4@74f_V+#+|=!W53F$On-V zQ{-aDPpkRNcFa@UCmU(naqF@#`FihNr%&5^kAK;s170bmPJh(DIX8V}Tzw>a)k!`fV(<=J=|@Av4bd@xA3um5t@L0`yT@e2$e z*+b&s426Igu(l>f2#N3pzqU#O0M)tQA_#pQZat;3?FGX_g3afTic|eGr|MSEmRgmw z+7ynrmFu0N1WR8+Xaxx z8E#E7D`H^|@agHD2u<6tDZV7^`I0bP#zRi_UaemD{`oh8ZVC~wH>%A6=Fz4#$mC$xh%8VL5xHr%9d)6 zO3l3X^TvsO+Sz$10-a7njzqYjGzIC()PXm$5QRnL&UeD%kR3S!{#=%@Xpfp)Fb6&D zv813vMMZ^-5X6T)T)8hLHzJ=)D07jolesx)*?vPv0@@z?vN!Lz0!ag{N>6`8PSOa) zM?8O+j=ME!nks^?Ggh>gNl#cMc#;a!E^H2-cPAh{lrL1o#8*1-J49BKQF#7~VOvRE z9yd-ri}QKpsbOgCJI%T8u9YJD5>~uW47dp#v|StR|NR79F%${A1(%}@X+PHIB@S!v zk|6`9t$}vWhRx;iCM^@tE1oYMZiBhKaab4L8<%vTge&6C&;HotY7Bm$K-^5Z3`Ez3(X%Q%NPgKA)> zHvw=`vp8WcwJb0-Q18mcPvWHk3rDYezwd1fw~vfqQH`^|y9ImYhlHQACjNI!K4ceC z{?8~Dax~hShA|S{UNti_Hh{Y)-NB=Dcz?YVaSqDh%WbaUn{6d@KvR=KUQ(jqu{!&W1@nY8c4nfg5zpq%szKas^G)0T+XOxZ)GJh>>)bnP<1xPob()raa$8* zjmub{uUsG+P9ux$UUi{U+dg3+)xtm9E<<8Qhl@Gj#${aw5w^Wal#{BX?V7XzVl5#+JCOe#<_WO zr8S&O7M7=(#ALrcK0ruBZ0H+p_=D0HpXl+gCBkLpSf6WM>AqY;ZY3F=0MDzN@)w15 zD6sM};2i%*#r%~;(J#UytlxLtz3OcQ%Yxfxda|gcPefW?)-6o-dr$rKxu@KvcNe88 z3!j?K>NdZZF;>+}%eQza8zEZ|>&~{%MQk5oQ@kerCgOX-Xv#|0&H`6McI!zNhXzbo zhM92woP{*|>DlbILZ+{uHQCaqaXT!Z$&SVbs2t1H^_$E-ErO*#(JXaFV}bR>H-(~& zQG@B^uXCken3;7IPu7B66SZQtxjY3)J2D}90ysQ~QJq8P!nvRc)FpNqID<~4A=zW8 z%~kb!iwDAtrjMmq97RH?ES~98g2?h0?nW)kxT4oe5k9|fjeO51a}py-<%vmuvmV&Q z6Yn(qM9!l4zJ%4h^SJTzBaL%Q9d%V>1>e|!y2+@=_4#x__nSPy^bkvmI{a+f;Eezk z)(#Ox?sM%t6ecCS12r9+|yNr|0$D$%pkPoj;>=NcY}J{U`&Ii)E?tUsKh*gZ^n%|tvrI0r?Sm9 z&YG>B;GZz-MMdX`AU2QC6uF%hqTidU4xoxi-_TPE-g&0k@xx|0j63qr?AA3VoKy?9ms;g)D$!K{!B62yGUdrd+ zNZZ`~E!xPBo8O*VuBDLY^yCp5nOeTT4vjO%xI5l_JK21CA}Zy;KSfS&;NHJ`C+t{N zJ{6kC2IEeAl~g?T;Q<>vrHrBBH@%r_B?oDJrmu-vi z+PoFFmecl3{FV1M(OTTtH@5K_w!O%Lq)Ts#{@4a;%7NaNJ4VX*msmnz#*vr$GoLv^ zR)rM^*G3ui894>iKs6JO!42GKC{V46u+rI$_A1-fP|5qDAj5SGGmGP3I^-72>Uh0C zvox=EG^~XmY5avB*nG$-8LQH$ODRyyPu?sxx5BjR8;z#q9oU?HJVR)5Xvlh0u*{p7 zS`x{X_y6O%R#ZsateqsCShX|uHQD2bsw9}TNFMC2AOp;P$Ev-}>Laq%P@;m=grPOu zC|NQxvIH>PE;h{lG<9-+lx^P!ikIJe3lEDHzw8{+yC8+31_>HXuY(P*c5vr83M
q}J_oHOOuw}i^f;Q?P$j%pY-Uo=F8t0{ zF?Ku;McyfY`tZ!&?ZPEL`iAOIK+Dt5HhiaKA^zu9$VAWv_Ue*G!v zgUL-wm`|Ucg1PV%Ogt??o$41@ydsZ6JV}y6Aw{11(BA}*_d+c=ETwZH9aWK-IGlVa zEkF6P*s=o=E-r?a)AyOs)`d{v1~T=xtyb}7htsw1I78Yh<$RH4n_b2;B<-5JL124M zR}=qbl}jXMdb^hKOEt=(nwVf4y7tITR^R8~fs%vtW6+s}COKb(a2UKDC1E1F)gbYu z2Cl>H;GC*+x1Q=bV8*M&>f5T{91oM2VpgzrSwFZCFQ(&K`>@KL$ItI5sw=9VSBoc1 z;yN)0ugu~8*Dr!%aCXa~z=#Q~W8*pt|4&{gwP*eTs#c)oMXTSEl8yhDYX=IgK03fe z!U!!Sn}P{nEU!TV;MOrbI@ZAZ!(+}4%7DBSieUiNBqP*o@NRh+?5*ESLcI5O*&Sim zDJ@jIpta^#5jYdN8gliI^+miVO)ymom>m1uPk|=op&*Tn*3aaCOQxagQ*J^~bxY9xyU45MR z;`VH3#EZ^;vLkN@?%Q>bGyY_vbP%>vGFcy28>0wI&;5MDAd}>5-#5KrD-+P=w@OgC zaoWCtBV^)m&WWcOl31GDdUk!)Z>v)epk=hc>2Qb_&>MoOPO77B(6tM;@MvY*Hgyb zz8{UpFpyVYlD0a>Ix_u)rPd^W*<|;ph{>}HfAiNT3gBi})0-RhGPmaA&DLj{d5rY8 zo3ti;-85dbyk(>yECG3xpV}P0`B}vnk?6AhKhLBlV+}Uvq^6fjO6MrV)#T-u#0W9% zv^o@gSS`zl<0QeMzCH~UBo}?ai<_j*mm&0KK=5m>kmu@STVR0nfuKZzEW}W3>h<1w zhVx_xTzpez@^^l-;v;oE_*Ht!+d09$$^98(0e8<@+FZPygYAsQ=Nvc2hjKJzfA>Gz ztKi#WwoLMYj?3tl-xVW+zeQp(y52RpRPnNbgm&I{^Yl*|xbjBl6ZHt@*n-}L5$T|s zF0CO~ve<%%x^w=)lb#P-BkqgioAf#s#XWs*-|L>$4LYr>Ay;SY^V&K6uikE*xOrno>ABUc%DST6!U}X}P-pZ7rV;&%UQTi=qG%bhKD_cXGPe0Qby1r`# zD{5zT>e6N%sT52n`7Y>^&Td?C?;KHKJs8LIk(m{N=uR&V9E| z69FEuEZe=^{keYgKGP9_D5D-7?~$fL?i@W6GJ#uVvuWq~f|^WyU#ryM+JKs?#4Xmz zUZkno(UJbH>BhrfZOd#jff8T#?8;$uX}o@l&@tUOEC1J z?lL*C2M`CAf|6=bzJ{zkCAOL@@s+eceo8p%hO}}Pbr;01JbGmiyGOOAO4Z)yDv{5Ik6SJZM1TQi~iEgQ)+57OM+s z*(HNjDY#xgy6&<3wF0&ory$^4CEY1u7yVDC4_diSI7rA6bq)1XT{?0v^8iH7F8O); z6gVuyKHH+juQKOQ_$#-37kj#c$oR3Vf5ggQdXqCZwOK-2owFr=9OGiqYxMv$OQcwG?Xn#5)o|sOF`fm zFM2aAO#CLaY^UyZDE#k=AiII}=M4lOsHGMIu!-na*zN^)d2=wEzV1F>e2AO{;Q;mkH^k1e};SWbtaU_+7lADz4loOOJfG6tr!*P__ zU8+ljM~0wXu&Wb1i?G4y&AbQTqT2Q6@84T)ZjiGZI{vO94oSW?>MgUiFb9LmBxeVA zyX5xhiw#W<3+zUJayWrQhbHAqBT8l4e?Ca9ryay2R}PGVb*gQni5|t{O3;^!yJM0O zeTf0QcQ|V^N`XUyeiqf zG&H{tIPZS3X9xV~sC_8q-Ks}|5u#7$t?xFo`QobKkQ(#rNw>VJWMjGekpkQanas9# z_+hRWQLz4a-1dc@nnGS*9Xms6?`eJAAtgOXmmu4OZXRtjxavg`iM&g^r{P0xNzYCh z@XFoa@6YL%wllqgVJF}7f5&nVbh0e`SiKK&V;oNN7Ff&An&63`^Qx)ms%cGPX3Fyq{sW!Y_tP z%oj68&se2*-J4m9`_Z?m*VXuZ|BGzg*=s5Wlp#pJ#l#)U9+DY=)#m5Orw6N37z6!Y zIEE22Vn}YH1FBa;lJ?Y6l_%R(vz7DVdmTZ>Hou-VYLQ7F>$xbx{#-2G9JCl8%-Wv> zJky8N@~iwC-FNp=mD2TT9|YV!^O}kQ5Z8}5J^QcX+FiQ#@fcR!=mmDHqUCy|zi^vI zL18^>!MSEya6)wY z$%`u4-It(+0CNa_Bo|2luqv)o8tPUALV4Yu$Xrq_glMw@eta>#P;z_&`H$}h_aKm$ z4q5>)N3SCjH69`N3X;k4A_{)Rh8ny8F$ROYZ9LStNvdp5VgcZJjy}lxpx+1RROr#c z_wqHg<_Lk%5RN8TfAgwdfe7BSvb_rAZ6AL-xC6 zqhxnK&vK&$5FifbYj=quB}lZ(jWlPWviIDqWBZ3fA;M5mSOhwfMUo+J0Qz-ceIl$9 zg+u5T5e*xmZHE!dXaHey(6N<)zHcJ9cn`n zLo@C%2H9$WzE4olcMlu0}t(*M&deA1dCmjb<6 z*Uy{J&X&Q^{O=aQ*_3@abf59ow{R{Frl`a-zH!H3R3=AwdiX z9bO~V4l+HTE|yRYR)Trd1{&-|KN$4vl^Fo4J)8@$hiKj6L91JopN z3&6&OgVZ0C!(itQxnh@r_e-N?UbCOv79Hc0QN{gbwlCkKR8Kb*sIn@+!+;DvCRgiy zOrL}AUdqTn&8il5)mODZ(Wvgg_|e$as)?t~ zLetUDge-eoShu=AP3QNTe4tOL&Zhl{kn;N-3P+sxx2C&h@7bz@nc{A@^SE&FM6wmnA^5JV9t)$eW{j;#OvvJ z$ya2ckYWdC^y75d<{~kMdNr5i@xgOk;ZJdD29}R2Efy;F%*r-X+gJ9=7bXCpZ8dfO z@@h^Y(+3`aq5zL6EsPM3VRrw8fHU4LwrSCnO7GJ7N}(beN$-NcIXWD{WbXjfG2!zS z>rn@$ffzhlOT8O*sc+&hKBXjStQE=S2H53m7mgiyd%x-XfDZ<%IPEU;BoqEUi|a9} zY&YuTg{ndU?NXjA*!i$oKg(t_bl*@_CJm~gfH-4?-4oWI?qTSdvMNt&+f@hB@`Ebl z`xxC3D4Eo-!dMeP@!6v9$YYsum$rni*Y8B^(FzSu9zW&QQKR)j6*pun8m;5cGtS6c832 zfKtEv)ohm(isy4LF9o5YCRFP%M1r&b9=*FzK;cP{G%X}xAxHPY!Oi6eua>i=zYg_S z5CQzl&d{O=P=27xGK7f^+LnZDRx!aPWC7nnD_JrM%x6NOLxEx)pgsqv?op|Zq!eL| z45pE?8oY><3#_AjQ2T++6#*pDxV^JuoCsHfnm%M449TgUEgGj{$=%Ks0_pF051RQy zi(>#!jp{wA8oPz3@dT6y^PloOPS``3Z z5yJ=mXdT%t949#xgvn!s%5}dpKsOv=m8c;=FDedVDA+}M9PA9+Kqf)x95gP6 z<4roG!aE1u$Zn6>-f@_HjUaweIs=?4JW0D%5?%D>EmS0Ygxgcligsk^=w#R+!!1`O zLh~CuQrIkb3pV+9{wr?qk)Z#Vxz{>;s^;3pKzuvw{ha!E@Y|tc*7}sw2>V>&-@B`8 zVT!n+Y=+~v=k;+!!s;2ptGN}?2+Y>%pZE%{q^kLSrtY0)ZZ8fSQD6eGgLt`N2Cp*o zvElr=WuYunzuVBs=5O2cs@T8pXueLkQ#5N$AEm%Ywec7)hV5is_%S@JQ#zO)OTcne zk|!pS%|&4SA^wj(;jx<26LwJx(HTyIbjT1g}0eUU~w zbn?&m^#?syRzLDxG%dS_p@hE8(h&X?I^$T+%fkw4L~Qk$Z97zsgMY&+R;{kBtPd0o zJm?j```syg5stU!!rflC)p)ZWZ3wZ34&K!f5+ERB67XaJk-m2yCS-t9i({E8yevSN zAR*Eqe^?&+hv9nsxy`*t*B{JsdBDQgt%$ygr+FS*A|=DtELxb4{&Wo_k#Gh8e?~Ls zC^)Tn5;XJ%_(4YEw;Lyn8L)N(8OPa8MCS5`3_TXZ2RHx4=Z(#Q@IyB^^XwlvRsYKI z(NRQy%DwoR`b7pi$IOFrv2@)Vv-jQcv)B+5el;UP_!cN>r@pcNiUZ*OyH>TBo^8+E z4?fC5Ze_)LbYo3iI$#pz`5fKj;=%Dsuf5Z`pT|TRvVZCjq!Y4m_)fO41^N?Gh**Sm z5_^n&jFFreA%;YlG8N`t@pS${Le1PB&4kr8N9|cEU3%~iy*Os+W zV@LZpa4A;8^33X*OMWGT#}vq#=4gQK^|ja)FS*%>XdRIZnF z**8q+j^)f<;qa}0t8LUDDbIVXhufQ^uulm*$!tGy(?Rl%2>S>HZgue-!1UKjU3wC)C%$9{_DGn=a zFd_LLio^FIyB3v0NXt>Ma#de;yEEb+i15Zi#kc}OO$nuy^LH${VNO&wMhO~nVhAAv z8pIZwW&k-)6AoPtL_1p;bsUzUD6pMX{|KR39o5Tdp4cUZ5Il+lId>0WM1$4Xjie_lLvkb zux0xDw`p&lS$p6ymYIbG>!Hv01#Wzf74jH8dbqqy#UdUG)p-Xjc7Waz;&yWRqbJ z8kX-)oOT-_MD19K!$2)+n~+2%Pz~RG_;3q0(JYbVJIY7EMFzrx6--@zK^qFs$L|2VcwNqrLIHqMleEwdJ0dV} z?Y6*YMF!!!Xyaj-NClI4M+AP}=>IyOTJfEY#pxALSLrkJx=3?zmDVQNnGDe!&^jXT zQ(u@EMdFR)7?hDgwFieUQu>3Tp(T7f4GML&RFIs(3&#TRc3A^fN5mme6rzv7|CpbF z@AEG6)lIn14!_ExKmp_P=g+X*PkgQPZhBr`L@+kJ9zZ~}ue<2>?Sk#yyBQ$71|uKQ z+udhydU0GJv={?Cbz^+wK!!<<3sf8VE|VGnn4tB~e%Qd~&btp( zoP?J~Xq6d^LUbcz)zo7z`D;g?xg061?~N}+w;2XA*Rxb6XPVknmpRfTioZ*>!zgDC+eyvElk8<$U1sdeM2-IM;GUg&FEC_q%d%H(ho{KLw7ef+P;CK5y zhFajvW!=wsF#Xh3D3+1m%a{J{cZH8nA*AqUJAGwkcG(l=(&7D!al@V$x-u_~7Cx+c zCgHi4!gMZ`kP3s&J-V9^yxrIZoP0GHMo6fLNlDFJ)mV+0^oedt%DU0^xN7+m-BRRu z27h6V$Z8cna`T^d^q=-$$W7|xZ4;PnmM4{sjU@2SZFsmQ;d!64_UKK1wm#!NLXx0VdKLE2Yn=n`3wF%a@{0W|f5 z&ue+Q&vh;3NpRI(?XYvJ>c0QClEsON6|<&PuIWN2=L=zJs!^$m8X=cor0TcV+)mXu zQ!O^(B7{ozsqbw&jh6iH!tZnS&xR?HnDdA*JNRMlKPS4X`Bbq?Ku=^RR*<0Tf)-Fy2-(;f`$L?j`+p$2g`vH6+Y#0XRWqFn_xGKiVvxJHiDcX34{yC zKP52rw@(nW!vLl+nW-KS?<8_JdG}Ixy-}lJb!8VOsp-=g;o| zY)OG6aR~V;Lg#w>| zifR1Gg{wJUXgcL!D`o3L)i%F_DDVvtaRbN>a3URkS0tkS6u2Jt5Ex%AuYgelNV6s= zKRa;_CiM_`I5c_b=#ZL?Lk!%+Xn1=B&^r?Xq9~33Wt{Nmu^44N^CSUq!Pa9yAB6ym zx~O{1%|veaAPWBf_b3u^x6u=^GS+7x{pJg=98?-O=#A>8WjCY{Wd!~~+xn=)L=yi% zFVrn*dYX#mZ;sYTuRTgf*Nqa7h`J>tAxJ5&1vHHHl^+On0|hK9xdMit_EZlDz1=|gKuMD zQ}oS$hYCE(Yam_LgZ6bn>;B#jpv5TjZZ2klpc9fxf;oL<81MuM)v2tK2r(Gqr;3(z zwACzvdtAR)12qRK-Gzp?+p3m}z4jSpm#cEEv<&F)xK4?DxGygHA}LX}C;(O;e)J8i z>b)Z-VHzL_c`c7~$ntmAxJ*Q(ca{e8UY8{H$PO<@)O#+NqYJbC?ahBQt|Bd-J^s6V zaWppOo9nW2-h|hax!XS(&%VvgRn*{?#Qop9b9jTXG4%z&6N#n30fUs+$In2Axm|UC zl~4BH=m(?9k{GW~Yi3(=DSN}$9}GBxnN4%Tzz;2ZdnBocu+7hbWctSGC)dvSPGB7P zERr#TnjC-5oT2bhZ_Lo;NUaue;jmm^pL<(wPdvmTJ<#9t_2>ByZkB^e6=SA8*!#(xZAdL0ltK+4=?0XAE zcAq=dRFHWvk<}dhxNsKJZ{^^@{d;Y_bh)F1)ng@Tc*3k|`K;`&kK9yulL6uyi6&i3 z5WgjT=Z>o_$Ur!Z5ERyk9KMU_k>IrY-^MoLIIEkeo{hZAT+hGXWhnhw zy1%Km(761;*=yo=EkP(C+pIhss#(ELo5y!Y7xx6w z3;2y~*!x%wcJMMK+;PN+tuMfkQIPa=ThfkTP9-~e5LW>$Y^m+x7r&s>p8nSk)~@X~ z=N!}?$=kk#O0kwMoy?r{u-VqG#3AV<3(mkOYQuMje}C^7cu&anXy5)JR8%Cg)Yo9R zDW9nE=P6P7TgPG95{Ew-_DVc2S)^y?<}B+z7qaf0?l=2Dc7GFH~mQZvFtu~ zA|qR(LMcca$eqiuqJB_~zW2wFL(mmeAiAFZ{xKi0(}g3w2^=DsEalC~Lh4_Se2paV zJBEQUW$xXGQC8mlmma^gttn*ju-){_&dWsz_#V%yHtX+)GIiB6XI6#w8n?D2{MH&w z9tb^oKCn2H79~IsxIr`Z>z-SacoLz@sQTte_s8MR zLG`V-Qcth5R9>}HzPmSka1jFTyUT8WuML0akzJ;MqoxoBL9`pnUwEaAu$2G*od(J_LV$n53H8|fYc^e+ z_v9=ruu0g!5E7aj^=_1tJWmuC7k7fv2c zmjnKiu*(k;zKL*UC_O<(bmz{UWSFE~<~xp>J(Nb!U3^pGINSz`kj6lY;5W=RZ+&sT zKO;9c9Ef{GaNAMQfs9OGvK4ulAxohC&^@eJgi(Kn)kKF5odY?$!=#Lo?Fc{vV=x26 zLKO1l9N>ms)nXd2&ARJA7_em9?g%P_p_AJikOZ`~x93cPun8N{Q)=kb4y!YrAVZ9l zdZBi>d1kO%jiX4Ln?m%=xh$=vYQGO|OEp(yHb-uwuKUjwdu9;7gEBm|z>eycN?5w{ zs+x*KxJ7q@ii+5&Aj+&fsV;JhVJ(;zMuDL%0>g~Cv4>lIYg4|OqH^S!~etrze z$l$gvE_P~Qwh?yxJzKFmDK!1jfG3hmNtudPiHWuyy5rk$qZoNW=p$7z4l1WCNW=QR z(jWu0Qbp9V#ZvqeH8@-K>F*GAI#u8D+m_(Z`k|kp+BTBR{_K*lEk+N4N zVrlI?%a^!@$c-}`<(l*G*Q+|PZV-Ww%fUs=y@)qy2)yXz7v1dGGl zzC*_?=!C@)q@>g;q5`Q+km4J~7r}3lT@wxAW1CVUa)^|DFDKEFXj<&@tzWN-f8LFr ziaGM#VUzMMdmkSI^)&~824XVQ;#)4Qf7RCZ1Tc3QJ^$rr&z{}#{gm(fC^5Mk+bC&} znEIx*JvgdrU|=xt?oI?w;mgcihblqC2ppz}o3ni!s5 zyc{wHe!vgNN8g{Af8gy6=hxro>@4#)cyP>L^T_wZ>Gg46>%>76V?a8+KGv-@wIxQ{ zSI$gK!t5j~X&-y%wm^up`#zLp;6)C;H}m#Zec=D28|tTdx2QO(XWvLuHmLYw=gONm z#*W;8brz6rKTXIdUx{SUVNMP{dAsEu=3ivAOE;7F<>=<8nAD+G+iC1>eP8z1=*_KN z&#v9M{_XoWr^Bz@H)i?>U0NUKI?(33N;|VIf3vf@r|0F0im>~CkDaF_-`5>AoL;pU zPy7c1z4lkHHh2#nJQfrb2<;;Q1M%zA)cqEWe93G&-|mHsEWf5T8TI0uve)4%MUnnF z@^G)qxG;drU;tkgs&n~iwvZrgCxdJnewdI$yrsBRVd+-$t$(8hl`pQn<}zn%HEUtE z3ps2JGc23=M2MynwzS;cl`Ep)8Bu3lPhxD~@bSHWa7<+0+QKuF@^|B2Uw`wWe7X{U54^J)%=|F$<3nxJ zg^s?yK1b&Z?N%eZIk|pWw6cC^2qtiKs^)6zjR2FGNXYX(+KbzlqcdL zr+E1A;qhOIS)Z_5s~t9I!LirZ172UZdjyR4r*4LmJ8^Vj@Ykckr@Ll;bmf6w*guQz z!HkzQ1Lz6SN|+snTs2Hd*f~%hwAv;ikOn0=$X~!C0+`l*d%w7>wr4gN{C}|1x9{Jb zW@o^Ug9ZW4A%t#nY^cH>EZ{Z3CW>A zvL`4MAV~GG_B3Fct)3mFXEB!s$}^A0aPWh!=c^Yls$gUnqEWfIC&BJxqwuyHJF4eb zj+^nw^?&~!g9dmxP@}E^*eVbje>XZiE2=Dpya(0`j~6(gv&>i}hEBpiG{^!ybpi~o zxPM>wX|X9r#PI+Yf$oT1P4jI`i=co2u>?AtVu0143@Hmj34kJvLigkd6xGhdpiNCE zNe%yfTbYUe3x$l(ziZ(5^Ys8XL4++2>$mI-LVS=|#iJ)cCWSR7MIbkasx{9uR+oSa zLj)97_{515t>=&bYQW{eG#Zq{U=)z?fdk91QJsdsX0RN-KA?hlTkTNF+F9x;gBBQI zP6P!9KzqUsXpK`1SyL1s!F+^J$rU!PDM?FZF+5#fJ_=3+!u=dxDE;rj1HC{4ZqU$V zvqURMHCzk>34AH+22Li=7_$uU*zK5yeQVNkMuoSH|LhaC(07_ zpPz>%mb#tUyt#9rA1qSExD7~p#M>MR&SGrjCy2I$`C%5<8#_KH2fKj|WAT|Nr z9yr3~jDU-tT+&K~NNSG{h>%7S1kwVA|7T_8>IEraKcfhH7#jRWF z>G4~y1N-q^eW_PR+rtN^e?0InJTSR&(L}YaQ@f$bQ31WEUWEvowE(TV-euLlb!MfwwCceW-}o3$WCXiELnZ=icA)I_4If%uItA zxj@FBKO*^|5V(L;cg6ngx&OljNY%fRJltE4Xb~!wBhPB+FHx3RB`C88n9i^?1b$)$ zCWM@eXd4i~XGly1R1;*0f5!|Tl~wx9^SW7*s(V5|Zq{Mhvp0tIW?xnFf=T)_5vO-n zxM~*M9oo7|JCvywQKwL?{*}foG?KHic8(y`lWMe?;$kdfM#-1wpKk>EeOx`WWan&k z!UesfSh4UQXnS;P?}Gr(Nm2hF$74jU%U=e(9?}DF%OKPoPEEEjP+XshJv8oVx@kpU z8{ME(5wlS5+|K8k`(c)ra%5;EC6XOwk^t|)r*VcL%M=8lSBbM+hA9X!Eo{uL+3BwJ zG>js%q$KTXiXce_xQMwKA|VV=dD#i?;PaAyXvx7Ix%V)R4gs0+nLh;v@ar00xLABTWmU@q*)@m!atg@kHzOlDGSU9;8zV} z!J@XgLY9X>==;O3tlGW+B~o*{UEpGX%2+&_1Vq+Ig9Fc_lfX;BAbkY=3|?&joU$Uv zlC~2oKW}gC^=vB)1JPLs{5K))`Q~OXXb!?aDe%-l54i-Poc;Jo`)qKLpfQH7I7&Uc)@MdoI4F*!C32n)~x7;mcW*`@|KYbevMh5&_?6N5qWitA$Vb|5U1G^=h|97Twc zNHr2@25zdDTte#McL7Qlv@<`^LrEln60S8&eb4A@>L%b#wK42~2xN&OpySbzuD!-8xAkPpRL23eV0}Of&8vnN5@afx%Q7uTJA(z|C z2P1;jSx&GF5wtVi=8Bti!&0&gvKoc2EimQ^D~$J7-s2oO@OEUlzo0stK%+<0Y1UC` zX)?@VMW@h+OPfL2!Pk=A%@WE~5KLtWC)8z|D)~l*(XUJ793u!LFN2vX8_AgM z(i^Xm-}~r6$k(?!j!ljQJ{z>(@HJx0$7Xu?eZ}v|@;|=}KhL~Au>X7;nJGxN z3Bx@Nq3{|^VRKzwbNC#83=UAVrU;Dlk-_F43t3^gm zp~hPcDj--20P zJcM9Rpn(vhXkNu0&l!uRg^sg$c>};{X4L?9*Zlp{4}LKRY7GBj;6Pml6b1~0i3vXl z)3`ue`qX#eS_8fSs#=L~_~7jD4jwP;L$Cq!=fsV=93(g6=MBVSvcUmh@61$3<+LpH z+$@2h$r(OfO0Q!2i3C?@q6gDH`lAE9vL5y=0{8%h5?M$`3Id-WK;Z=w3l4-0KSAdh zr+Hx|_p9M-Ao7$M;mBtOK_i5Yuw((DIYhG>|7@EJmRx$wc~Hj`2=(5dpFqsrJ<%MiiZG|G$;+KK?o4Fxhk_^rZCS?VnZYV7XPd8 zaSA$lG&;~+|NMFI&5fP4c~%)bQ1X<>K<7hzo^>!k{t2*~b)cUXW$YmzK-{J=)#$_t z1Bg2}JHylFVL$)$VtAq0-@kwFVuvb}Xi$~_*barurf~mFUEH(4ciUwjzdOi!5&nm&XO;G{CJ(aN$d=|n&vFn_msZNZA0kx59t-;uPwCt!h z1*JAalOCBKbB^0$QD>I0TC)Z{wuwj4W&#WYxu6o~MrCCdPaGEeA-ve`!DD?>M7u7 zp1k_uXRq?nn}d0=CNKFq#|=X+ysh~2BPmp;E|f4Qiq9#(yNxWU!xE;RmR`=~Q#YS7 zA2MEJMB*p?9{u_GdfAlX1OJ0=?ZqAOK=k4Fgi=FlZJN=SR0sx`Jo-6O-~bMo$DX>K zKSYaN%$BpVfC!uy{X?><0PGB)RDe`FZzKqGQAe*@wF=SnL~JTMf9(5J^yrZIY?ct% z3DRe)KO7e4A#fm20`_+10P;JBu;mrIp&$|g*NdAWAs-VgFy#AoCLDpWU{L!4_Fe;K znUJale!qGJ56CKQZ4@nSo_^ol^6s6*e<0(brz(IFz@~BmkBhsGmq0+Ysr1i;@gDam zz@vU3=~9Go$*nyNz`ytgx=z|yL<0m}5$a-CM}+kB{(}ctWBBml1EzNcA=CZXP7S~j zoJ1TK0L=m~-@k7J29c zo(e#1&D-u`@NPKS5~(n7{2?rbBm?V~yn-Ar4d&suktsuoP#(l25CTs?2~4>kp!mXz zZ=in*I|!$M<)J=!?Ck?-!(e@_f8`gfEMltm-^yYQYB|_&kAK`3|L!V2Kyd4TLv(kf zNiVR6vqT31(mp6Wf`wepkOpH2)v)rPM+z{=L9RWlIjE-q-KQ4mF|kYz#3&H7nL|8= z;qL&Hfj%|q4ZDF^i=YL#gbin zTnE@YOCUM6BPQ&C#2Q3LFDnc20_OSRE;&lR(sNGF6%>e|T_l*0)t-FG1KUmX>lmwy zewGkb)u2p-(;`hjMWzqmATdSfySQ@4*>f`!m&qmT==w&I*z|acGAShF)%>c(_-E>5 zQJg|BGp(B%-Pj`oh!z+huCxUyZH>06RVaU*7$B_F(-Ko#z=TZclyWV1ZsX+8g&b-1 zkwO*Y^Fr1yo%ic=M&*+`Z>MBb78gf#@8n#XD;QN9CBNi}Fqw<_J+~dzxgi3#*}nZW zgm9)LmBrwWzA}TEIthX2p2O)JLi9e2gS%3 zKyd`v5D*+dnK=a^_v5|L7y*@>PoQ~kh?@M6r<&*Fq5p*%w0VH)6*k@Q0FU z$rdNk2EYvfMse25g9q&ZL?1=(@9Vn=B_&+pDf`iG(!0U?6(h4Fe`cl*jU+Zf{78vFWHlV#8@&ZFF$1`7^cW$l=PP8tET zcS%yQ2tHal`2oq5wW4~$kZ2mlk<$}S*n~&$Ifd@0HE-?_lHk)W#l|rx<$_=Ftv)%Q zkq-5Q^AwL43=cl$1A!nlI<->4lU$YaeelYv1@EE13iIezgr9MDB-Ic&=^x@4p$_wi za@nLJ+rnu6Izo{=S&g2VsBdpA?qD}Jd~f4H3wo$2%>nz5l_^qi!GbC&*1OH-mu<7IRhYnP@x+j6A^y(7^3} zZ{kY^3aUieNIL+KRg}n|)J<5d!wTgqisl=Bs%a%yr^*QZ^a#9jY2awP+db&sYOq9< z{LQTsQO@;oW>N6Qn-d|)gx{mGhCRFG zVqFD^TP|lww}Tbrx3qn^Wqd*d^cF;DtKa+Z-?8?YEURpIFx$H?pCGC+87~whL=STa zIpFQ>hIIhg`)|Vq=L{Vxw6dZFCDv7#=X{2XaYb=ms9)61Gdz#uDzWt*rd&#ccY!tn zk6Z~s*eAOHP}{$1L2Il|4;d9=kQR(R88eVaobd*mh4?SlMHNg=AsrT2z3k2hbt64+YgA%z!6H6N8ubeYgM0Fk(Kiku;p^g<-0vRkYVGxYu-S2Qwwz!A47H z{ZNDdgqJie^y<)tA`UofR~XlZ0_VU8MiMh*BJS{W!f1e?W&p)q1b*OBA?rewwBz0U z^4YM#b$L_F7H>#}9vJz_w#QLq3l$98FqMMRH?({6E&>i*{$aPuaKO+JQ)&5A;m+3U+jz2SP(FsL#Lglofjc<33CeOKS3>??L1|c& z@bCWYyoet)RXjZ7IJQihUmQZsR=hG*c_K&=F1|O?nZdrKQ})v9X3a1O>&s}_hb!mnLZ&czl92vi?yBd#+#jsy zwQD_jyARf{Ix+pGW4z{kl~^JR*vr@sakckkm+cy!E?q}2nlnO6Q^Ib#YHbV~v>+$DlT*!CE{A)fJQX+I)~%0F=nr%qp2r3M zR;)iO`?zcBdDjpSS+ihUSc6IY=RSD52{6HHOBb9Kywiwt!SGx`s<-Xtu1CP_2pp=O zgYeQ02;8@BRd=c*Qo`#JvK5rb$R`?vtjxPyy41YyOzjwBn9R=;))bH`|`_6aL- zcB!0{La<7UjllydTfo7^ty&puuyw01ybY+(x((roF0coOK%NpJ?{%gxaEpRi!7fI) z6vW)F8R<+99{86gETa*Lunm5udwC?_TsUy5Q0^{<5r~kmk3;W6Jpw4u_lE?Re>2Na zx*s54OdkLz%7xvL8T@xYB9cJr=K*Q0SJplnXfL^X0X0f|>v`ov&1U^X#aJCwg2B2N zTtXe6T-A(QMzuyFcB}zas4^Q60U#`j$JVWzEiSrdoQul7w$wre zOoLZPX_3e4rI2f(J%AcDjOyS5u#y@0MFxyE$}U2x6!=xIs{uN<-Jui-xfSS)LKP8P z<-KqgA(jYRd7WQiWdBJ){X5jKtspLquS13o+!4+)MC}Y5O>hVO1k!xaPk_DjdC6V^ z)wcg2i6A)jU}UYKP~iZpY*zp|5|)p2LXnxwgcL^~zH^EfK1?0wv+xO}kv&{D3D)^F zuH;o5)22RjO)t!qxONS;6B1-msUAZM_(oFr-pz>#u!TomSo-Tj`I8*%^pb?pI54SJ zf-bCqi`SNd)X9eDAi4rlCs>yTeh)-AhGKk7tLh3SwVc4;b7VzWc0*`ewCM^9K7QJr z85|{PD zWt5>n;jf?iRGquLf0v+wPvMb%{|euZvb(>YK5K|n4r@!~E1J*Wv)T^M=5Z+X=C!!L zfN;$VTJw0xr?i@>*b1AWYllK5S0Pr-Gh@(~9}0xOi8~FW8EjSzUG9Xt|y@oTIhSPn`Iz3Uf?lf)l0i%J4@#K9Kb(7BVtSNTG;ei@KBqVdx5ISL3Gr z9vc%E+p^W{QcIO~t^^)Nh)cnQoJ(tkfC$jH#L2ij}6*ClWfp&!Nu~PG&)`Mm+&{&bOS6mV0`#5Ejab((s0%`s`r3a9@GJ9c(O21 z)V>5sJCrHluwz-J?EN9=qlxr_!-MsESQb2v4U1FMK$sQ+2h^8?9QzY2J&MJ(x%Sj? zF!;gv1LEUYgV7yu2d4PUM7s&2^U(i+u$&Jb=;vB*k^KdNr=}H9y&Ac7^x<5#xsx~C z{!M5u^+?F~0w@`-1>Xm9d9*}-{uDvO3x;1PRsn_*rwgH~DAJih=Tua0K+RJVECd7y z7}9&ekAh!;)aD^X8gRT@AWlLk6BiUn-bIif!6=eOFz4}gfJv-DWkC1@BB>Opx*?My z8Xh}XNVHK9M^*phrIubrMm=nLZ6*p!s)yVdR z1e_S7+e|G*raEXaFRbrmq7HKrVA0Is4VppZuuwu>MLWI4#kcl|AIDwsy3m%*?-uMC z`+Q(xdYS4R8EUG5d8iQ)gm~FLh0y_K98&n#xppxDeT?w+ivlB&X_02i%G+6Se9hSO z(46!BPp@KD5FaU=y7w{(deF%( z&QY$e9#NLDB&l)|c47M%6Kj=-op%0xQ)XlF@jvH3VhktV0b*?|pW+N>M<~9X59r|> z|8c;+7g6-H>mtAfGqAGgAy$n;#UPvi?-YS?{15wE0Y8TNAz%}bZx30hMNp6)HiZLS z5aWfcncr6?kOT^r^j^=IW#fs46XG*N^JclIed1VqYV$Ko=H_UKt-)RKHX?(eg(r9A=S1p2+LTI>Oh?QzywgliSwh=j^h} z_#ZC76a(i6@RXJT8odiOUuf>~i?74{0goh#%s{U4pT`Fj1eSYI;3qKvk1m>=)&M~f z#41p;6UMAoqg)HHDxlDror@tuTb>yW0)KO-!p6&Q+*je&0DB7vNvr282B$jn*mo6- zU&00EVw7eG`CZ%nBg+!7=Oo2Gcnz4_9KbjL&M8(+kVYgGEgU|fU7)@La&qV;(8b%u z;jH#IH+N$iTW~pKBQ@oX>TWQz-nxpEMhMq3>*ZK4*SHtEU2|&OE1v?AYDkH7*DV3U;r1+F}wPFxk=7^lVwX ztxe(r*p?8x@bK1WE6UU$=-@?(pd*5r1v6MOhduCU6@{6<)b>mvaJnbuO7~y=JIRY| z!P8+Lm&R6Zi`cJW<+^&YPQV{X=jXvI4uQu0$;Qj)D6}@-J;D%J;C7hje6a+y!+%3M zZSw|tTY)fHLcmr*NQ+-m(#ZM@fuBOUb2*1P@O%{~h10&IB!h2QZe%X6mk5WO+}K-k zYA^Hr7px@WQI8_iu>_&XY9K6DT@5e(8M@yeX%}dT1V2wxYDpMSf$Oq)DbWVCP3s=Z z%LG|t9dRg15}mg= z5w@%ljt8RqhyU<;nl>Gk2ZpIW^dXT8mx(UWN_W0umFxG$a&6miR+@I;Qtvefb;V<4 z*30Ndrc*rFwuV$eN@Tv)0+G(i&aq!GyFI1DyYj-O<2~Y2hVoO*Qw_5-hEvt+4QuAC z9pYPeRyS&wQ)D5*$jLYd5&-b&172{>00W3bZ|3mP7|C_lW=zG-AMP2Rx-$Da1g?K0 z);wN)gvr@?SBb_>^EE*hcRssZLabVK18tJQdC1N&!{KS4fP3~8J|7Em`1Jfeoq-gL zkQGo3UfuDO0dm{>L$99eKOEjKN6d(yv(OZb7@KnziI#Y6!360 zA@4_{gGIqVw>70TE{kyXdXm#)fJn}ukT~jAO-x4BDwXOydjY=sRn!;F1lcXp-4648 z394B_ezQlvel9w8EM=tbm8_A#X&v`g z_q@u$r63Ws60#Qn3z8x6fe{vu)o&}*ZM;PYBX+PbRmvrsGyOGcSQnKb%{N~sO-;DE zb*mZJ&pMJ*XykqiOJ(X1a-POJClLi1TfXZFAV%cZqVEMZoB(n4KOhfxj^e0VgOi0H$bILmZ%i5z?g8ZS$N_hzW|J z)Zzh~%n^#)fITOim0kVo!(eaD7;8h{AmSC6bhZ_j2Kp?2}9^{=EZ+XDuKscXA z*a>#*$ra;=JiH-v$K9btfVetfJ0NCKP*jId0Ct-nXu`l?sO@5iR~p)@vC1vNr&SpF zCm`QbU`WHeVr?F89mr8ruRhG0ZWZk8G*}Q~ohpFE<8R6;9&t+*S|Oz5Av?g4#Aa^jj4&rXOn>Gm)8fXlvA<}JVrD9TbYGcgqI>-+B7KYFuGSXy(`93OWGB#HZWt)KHs zPd8Qfh=%iHIrdgllQ2RDIgI8O9=R`4&B27s^a&5=(>BlXhpAJ3IC(0rt1<&NO5$-~ zdws;3$?SAx2v2DgA;Mi-w>m_TO%o9{+_Lo%NVGl0ss#E&$bNyb%tBFst5C*xymnL_ zPkJAG4P6%bC#%xT!}f-mu5hPS?)2ogAAIZU-?x(o*iXT% zd(a(Nf7MK_8wbQa;VMi)U3Xa*Si>lC&TSm3;rHdn&cpth)hH=*@$@1g*U*2N7{uVx z3XJft@l-3L4c(R!0-o7wEPxOc?abAxl-R~$=#%{c+>PW@xOR;Q=scu;kQi>7_^p-I zgJg&ff6uQkH>iH@m7moCWe*}5!}6z+C!j+k0!daFP zjYCJ#iM+#>ZoiWJfg-IlP`Fym!hfzXGeXiwpl1z?K-?Z>330xl#u5XJi7q}qVc~IU-r6Xn&KORyU^iUHNd|4^z5buWgP&jGe9 z;MFW^@fxCVloH>Ux3Peh}L&{e&gBJ|K zkPjVD_#6MxHRFZG9bCyJTI=PSgA~$(s4go-zGDkD;Dn=nJ;+<OFElS zZ32&Gyz^rlgjOQC3e+?XBY%y!8Mv^M!JZ0~<#ebSJI&{JqP~Kyk}^M3+H_!XKHHk` zfMKjtc8N@{f|~q31ogxO8bcwhpZTSk^%+KHYm`nr?3y%KS^jMy+V1y0(KY?(t_ zpv9J}S_wsrGrKOeuxMZz#Ke+pu++dG@LV|;P~}!2qoQGXhSIy`HgJ7Xc(!>M_O2J* zN9sZY);=Qx1+10HR}&h#iX1Oklt?gH%;l5x96q@wt?f_IH+QG888W)bNZ%z?KgtlXXSYGZFkSxEG)TZ`5Xb2j-rzhYS1b$n(n z!q=(YnpqPYq*mNdS-e52#IaVK1)Y2?2xvhP4UfL>oBNd(cL%J=fnm%EJsK)xWfUz! z3u4p9tz2NSvDCKLv(b+kNl;+Sx_t!EndLzH8mY0k@FmebmM?4 z__=517FbjqRHtvmuGK&YzbvN8Ca4NW)6!iNo`vv<=`O}CeCPk6gvLL1=vGQ zAl?BpARzG@T@8l;fYgMx@<7cyHbPLf{fER49m9&>3Pd!So3~18!E@BYjtia8X$wF` z*s#xG*JG)27YNmE?|_k5@RKbJ4lvYsbO_jeHgJTb#2_{{gRABFcc52StwdM`DN+FA z!U0mwwZ4v)V9roFyboLouVV4$&6`t54rgQOoju^4kPnUx^Vf+Nr`KW&Ea1}17EHa{ z0H|4bbz`PwqB{+=>k^{~lnlQ8^r}z+!^yEF5Ii~md7AwDH(;R?hmO6ywQnTPt*v<9 z+@y<#$9{(sEruFP7skM-N7srUp0Ob#hhgI)=;Gxi;K>Y|3?r!16ZxPl2L1*tmkx{S zbcGZyC2-k@$Xu|rHPT3MiL|wm5Z#xD9PfwAhwh|27}kXRl54LI1Poj~8Q( z2PFDbwS)RNp#!ScSXOgh65}>Cekopv)@E!5QGr$m-Ip-AnONOvX$wIcO#dS;r#6dS ze|y92^LH&2JxN}+{pH)YkFRN62e8=ZJk}z2xB^Q(S1@ zcUMixj!bpnf-TnoOd(#7ow_R&rjHpr@Nc|KfUqKgHQ;3V&XffH29U}H3cJt|lbU~i zb9;1VD^?Sn9tmx`v8NV#C&pm}&QqY5^nbMG z@?mboRkl;8X`(fA3q_1I)Nct32_kTSgC+zlMzPebTcHq+B6HdR9q~5(zy7>IUTqrW znK2?pm>xSQkX#|^Q2GBCQH7z714Gr}cuXcDF$qG)@lQ((d9t4G-o1k=M=6|hJ9Zj- z1%hu_@4yqL@DF#Z>qpWtyqKqLi4`n3%4>p%hg{Y5Ye~i%U{;)q5)Z(Kg22DXxcp`C zRIhvqgw0r3t2tm6(M7WG8F~gL4?B;=RH%jff((y#O>a6rdn@DZQ)=hhvw)8%_RD|yxc*JivCm5@3`4~1 z@6RY@c8)W>4g0+?<3k-FOP%jz&j=$Tg_-Ud!*x%PrYrhe(4~1r$wFYsXvx8`3w(pf`T3nPbL60*fp?TJ~$H2+OLNp zyQ;n)@b12N=Q?PP0)ML~CwgU1%}BFzYkvGc(#977<^4jU-P@B`g{l{!JV-kUe4xzE zyPOAuGfRHn?G^obH)zrHbVJKyxmxJSLJn$4=Zxq|J&)zk8} zUU-$`;GIA=)x~fS%q0X}t=A#}F7x}kx9e+1*S(07(J5-9-<*+5yFS(+eYkgtPff!W zfWM^y65%lMG02LpNdl~V_*3GhLLdOXxwZ4rS^I~{cMfbX@&Drijf{1AO?p{#SL$SG zZq!RwFd-Htx+nM~Q~APcHafAAtr@HaKDwZ<8cSH2Z>up0SP^D)e-C~W!xwKKoI!CT z@6H)7q*;l|Yo5bQ{L7O|C|&MtkegWhSgsl@BwRiogmy>>H+i;ILCeHWpiIpED%g9& zZe-1y8@rz0^qX*5uj|cN-f(Io8QYG@RS($)28)$BJr z_#m1y@M`Tv$DeQe_hmFWcvL#X5ZbMY=UGFRbPnp-VtL)opnoUW>rL8#=h|90x~oaQ z=o_0vEajUiDN))D@_TWy!#uOHkbM{eu_y=HCJOs7_)j`R8rnFd(D{dmiW#{~-K%>{mp5w3vU5NYfyNyh7G7f_8}>L^cI+Xy^9@0+DE38p~n zNv&gmyb;(u5CXol?G(H<7&09805X4|ss_PG$2rhO2AY+!dq5tw^06g#_F+^uP@RPZ zzdWt1JX6wS5EpWP!oJNX;%q=)y{zBp#7IEP_XAK7+@>f)_IS0V;|#NKr}N+X*3#Uh zn)H6@2Q-?McuZ{UuW0+J`Sz84N^67u%ukyhJvuy9xhM0((GO=M=B|&?9tr^!GGsAt({cd6a45(VE0Uy8cfDcyZ7P1 znwbw*3f~JYGnRN~1d_yzQVHZb&k`(_Iet|3DlULP6h6`4w%s&FwLy6xs1yx!@2zz$a$*(?;=OLd9N zdg+sN5nANk*%^Dk>c=ZTVe+*hn^j+c)Y>>zB-H*O`N+HrYKuwtTT0( zYo;bG?4EuKJU1;J?xB+dY@4T)6>U>kxu;jiVb6IQ$tm}1!A6y82Q`Oen;qVQE~Tr4BwP<4 zPQtOP1toxo5O&JB;vzI@8x94+fv__iJ{3O5317fkb0VzBIc{FdEnnfge3w zO)_8;ugJ5Rv!ZMIXW6~sQw2V4@sEF9>Ii<~-*NEK#nTnZUTz1?E`~0%p)Z2mOaFq4 z62d@U9ly3fA3bkw&z)`X_+It@*^$aT1xNEy8dH}Sg@bFyNl-vYVwAJuCS`bj1B#JH`0XE!$3jII2@gQza-{Xb zKiE#@fclvJdr_v+u$vnkJqRiys$n!B&P0N;AEwK7i4Y}xGsjU!<=@OyF&dGQ6lr$o zi4$uZ%II4AWa6uRRiEG2;;E8tPFW|$zW&y1-pAQ@PLOz=t1W2_(K`=S zLImK&kF^(4xK)x=<*Jy(Dt$&>DoNJNS)DS<(lc=^ZXvU6*$3gh@Mu>CMO_I ztjk@0B)C)Ls})V#8-9Z@z7JkinZBZn~!zt;w+eFu7r7 z_-}vUSI!dOx`8s^M)k*qU7bNib+i+@ui0c0NG>l>3*iO`#&f0Js$9?4-HjrEtRY=p z!s$G-{^Z?}TeF{P!4-$c**~e*w5$KPzB1x0jN{2OJZZ`_%4O~MAziwZ{Bp2Gl%^GC z^MaJdNRH(HbIe63=?Qn~4GpiYc7=m{18WY*3tpNxyvhC{XVY<~wIP1*IdZ<^j^%%{ z`F8^5NOi7bX3F(!uG8Tkdq26XY3-Q)m&_yDs&;-}o#nID=gTI(#QeVEBye-Vd9IM> z#~Y@7nm)OlMJtl6O*Yu%tj|r5R1TLovsNpya{T(%Hm~M~?MKE8d=?HBtnO;O0+RS^ z22aS(SyX6v`r#kyw!j%bnWvVo>Zdk7O!EU9>S(Qtp;r9g;Zj{9mJr zL$zlI?)U#ZA5?vO+uk3Kr~)|`y>=@byyQynZljZyUM6YD>_~Er{4T}L?|b#`iPACK zc85z=__^lL=;rs6&+L6Z+{tpDjhN@$clAUkto%S}e}CWD0xI_uRhnuZqe)jXB38@0 z0D;knZ+7ar_ejN978uv5xPjtlw_w=b$WEY~b6#G0wLs2@sI6?OWbQC$zhZVCMPO@< z_G|hd?gFRF&A&xfyH1N}e<{2a`!Osir25F)6Jh+K!OJ$RR6kkvJo9;G&_I*9c+?d` z*zo{&;c;SSdLqrN1)?k@&BIKolW;hA<6M67B6h$`i>#Jtg5-s%8l>2kJ?z)3L>8j3 z5#!r!uMKcQ&*C|*O9V};Z{|6EFj|AoxfqoVl>0HC{+ngRE$d_*Gh&&UmheMd?D~w# zesc!i8$EUa@a9|K1;8*UayF`*#P?1dFG;AYfm3cOLf2^gA1=TP4jJEt%ghu?*wF@n z1^kxO;_6p+glaAyzmswpX*<0-R3XuKo=ErQM8hh-EW@)0Dg*fCb$jL3PNyvke00Fy z;p)CDv#ln%7a2AJw&qo|bMD4ZI2V+KrIV-*GS7C@hu>z$2uPXQ?l@X2Q$E)EYW#G6 zg>j#1^}39M^&9mD>lYrKNm@0p9uZm_r30~-L6V*>v>(!k-Ti?r(b#h3HeX9+wE$fzS`Cc_ih+t1w<@)J`gU({u?&tf$OMfes;$>kH% zB|%**HTxxZpU_X$=Nndcotk&y$fE6gj;t`8epImIz{8Z{u^as36RJNiidUz)oEKpx zREP5M5lNANL74+iAx<+tIMGy=TGW1SpuGQ9dBBIhJx6P|ulaIUJgu}|w^jP$R+9tS ztJ(y&8ndlcEW(-QpDdn`D7Q#-S&jJTwMp5RUlzVE$$GXjs3I-o?$2d%0pC2E^p7rD zR_SwO?b=xyl}hB_DMrz*w{+U`<|)^!KDRnldhEyG@i>_b@8=r}y3T6BiqD@NR*E$^ zD5@GT?BODQTjOR-?G{U?<%!J5qL^eIVMn=oi>1k162h0di%~8MZ9TaqPBL>^-#@Tc zcH;Dbg0Dl0U4Z=C*V*4J)|G!aq~Q^s#!b^_R3|^4&l>vbnsE__u6<6ihu(Px=L(?N zEk#ythYw4zV`5?o)!2tER4j>XVRjCnONW`m0Zetyl^8f^J9Yr3b(pEnH~9D}n6juh%S5I?tc2;ZXc+HJyZR^W8=+EXD@f1lr&3ZGxp zbWB!l(0P8m>mHIzxYtVxiRus$$qcsdvGgGrSq*=@_q742PY{@=#==(XF6Hih9^{^nvm5 zC5F%6`+fC&y5FsM`nvz8rUx_P&N(WU=EPo0C8MN8kT)er&nH%gx18(kJVNX6922s8 z*xR&cGPhu;{=__ozR%ko9R7Rs+;9mdLx6IbE~CgVCVjTm!^xhI#qD9oYzg!0RE6XwuaOokZSTHGQ;=;q%S_6fDF^3NWn?5*ze zYySATqcUB-D|r6G270`d;7f-vb+{%;a}A>w?kV}sV|p3hYhb$9-SeC51GP#z52@{Q zp)5U_*B8IvQ@N#9Dvc`fi9jUR80l2;ffuL>4NRpa3E%PBu%4Di5u+OIK1<2Fd0#1I z&oP0H%I{i@ZO50bJzT%%-6|zDt~9^kyHyKiL1Q~}CgCK7N@s=c4Xe}MEEMsMmK#b6 zpS?fE=l{V^>h!{C|IUzidtyVz8kXOiI+LGPldZ*eAd0gHMl|8YH6)Ij5yO&87H8s@ zVT+eLQWW25Z{A-h+`6~zBc{9&FA;~3!2O0ny)A&*@Ut@s;V!^s5w+E*Jb+FiLD<3 zea|x&8$VrD;xv8xrhFl|vY817WeM1&i%||hssRU+w9!=Uc8$<7>=^R|JiJ)|=6?FP~X=tNiaS|IW`MTO_XuMMMhaOWZKdn96i; z6pUG{E7fc1P_GkUI;1v7CzL>B=d0+ide`%^wxy6h_j*OZ3JlkF2K^QEoGMNkemFQw zV0tQ>ZK(n=w|`k4RL<{CWRw-Q%Pk_$p()hDX;zye&%H{jt%pCtRIFlJ|IHBU-HDa~g-HJwb zv=p!qOQRRHVM9pz%NIo-WA%dTPidB#PA?uGc%9`IA(Ii`qPW++#2A7xs9rr5vWcPm zUmsl9ZAIB#gT3jdBuQt<9K#y-n|FWbe=qs5%&zuz)-2JmV&saark~!=$=Cd>Q0Ww` zH<#SF80X>Yg znBT`B^yGpcRag4IK8B?=qDYGuZlLDeWL$bIsHxpsvOOd-KKGpSw9Iaw#~%zI#cVe6 zkUryZo+Rk0X(a9Y=WBs;#}E5QvSWHtag7h6B(oj}tuWXX0i94@9Sqjt1hu(hm75OJ z%yo?(3oS1UkCkLO2vB**mUbBJ_SXF0QJrM1bvoZ(DBc_Dd8YHDu|%imTbY7D8imGP zKDzuR;9%LrsOFI2WsUEHgF1B%7&fIHPd8s^HTcfm4;{qa8jz4=^9^8|hjwu%+w(xovEK*uAAocEZ!3fJ@Hg`w zPz7{@;$EI2$INW#uv-?oBM>#mEhY?b`0MV z=lWfvyti(w@L8s}YsvGnkEL1ftro;oe{s}qBXjF?s&c zG|a?O&FJMaC26WKnFc9=bC{UMlNfaeA{lPmbmJ$}jqoRaFhh4OgP@{x#X|9zQuZp5 z-+4nhciuS8EK&Ws0is@o)AAILRO<4d##B(QbL{rBAJ_11>$hq&7$f?>`z&&Ko%>$% zIj^~v?&*R&XJJIKunvcJ1iukRqNzF110o50#)2{kWET`>FMOmR=VYWuPp`?Jd;Mn7 z%9K5SRUnY;mpe4>DM%6YTx1fu&+ZU zSZT|7ilniO&$E^-*NW`(9m**gvwWaDmEo;!{{2G3`tRAbcEtfqs7>F`^fr|ALm-^; zSyVFPXm{&{cEW~@v6wXRMHm+u4gm&50#|CiS=Vz|HtK$l_l+-kT=UI$?_N7Xya{=K zGo4o)JB9^4H(!>?oflDa=QNgj`&M?G%NG^sXkqeINPmRSDMF>i)a8>@T@gv#h zOV3p5yUtJi{#HB*n%}*Gbij}yPnx}^6L$Jq;S1q~!kgN^p0oSh_GEymB!m_y>rc79xy21IFl9_&QA_4ii?2GM*oQshL^De)QukQResO104(EyoqWBq}oVjQ-t~t}-B%bb;Ar2MTN* z>>2i1FpHZemI@fWCK`=f8+pt2Kt&<@hBC>b?jWf;n;YkllKj03;M z#0<(lF7O#0{m_sFQBm3Fcdp(1f9<)y90XT=S zb345{V8wPnT-yj+aldu%=6u$6RHAP~1OuOzU<{n`e)h#@w|t$YPaBx&!lb~d6%?2( z-~{yVH~p)*7+H96L?q}V5s2_?y)5m;X!N7oVuGw$l39mHs@9$;z%yJ68BGfOsp_QQ zgz7V8DG&Lg7z3gA5MiY10l@Uv)mbBvizl)k?P0ffKPLtMc>ZXeJ0F zwP0H#T5Ea-3l3`Lno34KTopKxn9gz60>ETfX4Bk3X@?#%JlXzW*87u__oW`T1q`@M zBw1fzIYE62>V6K|9!~oR#sJZ?oOS89eMp(6l(=8f=x^#MBZMAO!iYb|IWz?HmL$Op z+;Bj9HC!sQH<`q-Uo1S;Ua+MVy`g=B^P@^AU$%DTP-UNKKGyH#kaU@^6Bb$hd>D@*cPbXcl<%3+ zB0tW`)N=0$?;HokJ?IS6CMgx%3Yk5Z+xGjivJ6RBecy=>l8M#`@S)aBNh>kIZFpJPCMpz6nh5;;mXR+W;;<{2}RwaVw00yP% zgbzgWaK22-W*q=p+zQ?fGoO3;zBmy0ZgDuP=3<9GxVHvOJF-WaV z48MDFFw-$C-dyld`bnog*!SZ}S-3A(8x)WQcM1!98&5@28oK+Xb!6 zoA@$pE`As!)<)z#b3Qsf9ZL0=8Oo3;VV5c~u{!(f_^n>0I?LyzhB(6#S&7uLE(pDy zHHtx*qFY;H2Bol`nkAnt<@Z6AR^4(=WU)1{glF`cq$gP$YZ2@>xw!={r|73XS}Uon zp}IWnxO7eDPQ-KoQ|xiSSlRAzq7CGuF$z>XMmX?fCm@|;KuT09-$H&a!~^fzEoKR! zXUxnM&!K}T5Zj{ZVq`5vUgoFl+h=ZGe(ij=$C_rj4Oo4#8B7IO zGrBqvGb=n4eops=F`tG%VmOxjtI)fI5=$>*R--m4(00ofsuob-cLXzrXk-?}IK3u@ z=?fxDsG=1kelJ|=R=;-6(f*H!Ai{^*Qm9v?c)P4>Ou1W|<^dnB)vX)T#E{{SG~xyN zrH{K^5$t|Lm(Iv9WZ5Jh+A8qZA#PC!L{hBOc~|He-Ak6}V*#c7i9)J=$#_cAy=ZXy z%t$DQNR<*kD=C#bO7Y+t;D`VYCO(3Q^ejr+D298MtjK%4-5sdsf}~cpegs`@S!T1; z7pp<2MiZy^&%WH7J~kdWBg)%ptr}|E8013_wjqu5w$pHg6|+JU7OiXiSz_f}M#4z^ z7ubCT{c||nES?S1ZHv^0Rf$Yl^4O;2+_B_!PQT6au)GT#DJ4AfIv#m9$6eS8Ry-!8 zE-(QqNHA$_*!_k%Y1b{*jp4=R=KOb@1>@$JVbarAeBs<%Jn)q6VrcOqpdH0@>MZU* zy$_a!S&nd&V010lSzCcm55zq6Q!^5ieH>HI>Z>Mlb4D!bHHl0Tb+23dB$w)Dv7pUj z!L!`f%V^h;6ivl2I1uC1Q1NmBk6w3;lY;E8?E93&U60m z#6x9+>7Lt)^LRoEZn<|HiLslvov!|+I2g4$a}+Z8Q0h@hL!h~pj93c3misXtk`s3U za>8~}ZSM3mmAswyVi+SNoKgKA4| zgL@m=M>ARrA?&xL@SUcGjO!cn#D=@G2n;32$8AG~){d@gp_T`N5wRbz3ek>iQp7e> z@0jDBF|dr{r(K&~@|7R;&Z8D>QLGK^U~A;}!T#~mWLx=4vSxm6n}M)(TtZ-Y{e;2S zgDcUF=cnCR56*%Mk>SNimD6K>>XSLf^D}mj*hXx_GxOqXSjJ4{d2yC8N<@_O&@qdW zDkcti!ttu;nEyJXV>)vsl`U%dYRr1@{4BYW4ZG)tp*&Tlw4aCkK&i~k1K&C2nxVw& z>1B+KK3<5)Mu0WtFP{1NET+!FpP7+bPFrVkF%R*sb{QtF)aRX>?QpNk7vL?Vy~h~U z_UJs<8nZLV3>u|%95|N2R=?rO*$f-2ypJR%5>c!)tS6WzPlfb99xPF(-C{uAK8!{~ zp$fW4Gu{BfOL{{DJl5LMm!OLTwdLOfZL^QF4QbAj6&#!t(0!1gX^P-dV{^Met5Ed< z%|P3dqjuATe~1e2GAYv|>APv}j(PT@(W6dkp2pfn?;)%OdbPMDDcQR#uW6YJY5zPg z2&CXD>1$mvE2k4f*zCz>{QSeO{^Gp)ar-0|g zX+*xWXw+@1F7neN)y9N_a~5AAQeVN%$xsNY@rNblBPP1s2n5OrjmVqd1uYiPp##l$ z9%I3PUXj8Zf4z6^>--gM5Z9$cT8Ll#(rMuy8=Xj3FhDDr0{?sXY<}o1yFTZqD`lHC zW)IB3j*R{p2YU)mV^ka4z8iu}`5uW4{AaVQbLaEpGX0>s=MyW3JPf3a;>|xQ8DZ26@tWzuD(Loj@GG4w=IBSm|A@hiY}_7%rm}ceh)&_rjN2_w6vltJ_|n z$XK*DwSvxh1r!#UGUZ#3VLkVZ2|!8|MJ9t=moK#9QWz%$W-X#9Sus@0TSG3c%+hO} z0TMGX<71EbIpaUEd^2F&-HsT8QID6*2)3et@{F_nd5|l0L$MuGZw9w46@@Tjfbxw@ zXX+~8WaOHU>F()CVoFFzU}E@)SvM7-;PDG9)erb{2Sp&hpmiLPbzGMmQ6Qy@A4-u)OHDzG&YfwES#|C=hiQ0RgU& zE8vyU246{-N>D_Bg$4(%IQX${DPGDJChB_JozV;;>WolIt}7Sk<{l3m)26FVCwjah zcJcqT0J_*rsGGXEC|r;ji?9!Jh8%lpnoc29XM;j)0;Y4DZvEM1W{mm^ekM!|pC9(5 zT}>&@0Pm4uj`3q)aWyd$g#gP0+G=`2(Z*4qkNAOzI z!fQ)70fa(K1WmGh`!IC#gL)}U#OA+mEMd}*xRl6##b3-VWi8WeDe!U;GXfo!L^wMS z%ab)I(^Wzg%CePyc*|%TtX^+M&HzG3P`VP#zJ9VR1`>cbcf%sXg@K5N`1zf{nELd> zsJSXbHmYloq$|p6{a!|^ouZ;>D@EZpjg4tTB$h}$L^L$;+-2mV7=s`XgRx ztys=DHkvM_X2*+w&bY&{8a&$ib#+kVt{07)$Q4cZ5sr$cBj15RkYVs8FL0+d5zHM1F!RNaaN&gvL1c9!GST6WBEgv=%6=PthF<!AQ)(aP6Sgsaf9 ztklN$OTz~Oq;WQ}pbJWs?-p_7JKl0CRyi0yGvWmvUhz2i-jVR^X-OvA&2 zib)>Fomc#_?QGZN#RP$J%pLa zi6Ge6aA1v560tQuP=*9#ipN5?G1bOzR{eP$&=<*@CJ- zH#(Qp`{fL#32gGvP75%zq_uF?=F;(KypLPacRIuvQIrodK#1nBhA5)clP;vEV)SBi zNmw!KuIq)CDPcqEH6o`82#{T%%EwXm4Z&+G-Hx&21}g_4S`j=KCFk8zA@p9f=oYYp zEYh6AEbQzb|50qcpS9%NZ2opK2*?Nw81t549AI*2%u>#Ij}!)l<+dqTX7Y;Sk#R<* zAEkpn5D`*}oU&sCrQ8zBHe)U=6dupyR6>T^k{xw9OMuW!(#j94j(M7rpbrHqSNOb( z1p^mvBAR6kygF{4tVB#!;&EpYGYJV3KLZGcSW_Cu%LIuzhtqu(jo`TiQ5HktDBQZR zS%jBSZ2Jp6ewOkWf`VAI5_BnV08pw8RYc;7=D_0h zhNBJ6fYdnM>lFA6%L`R2z(^z++(3u0mCQU5g!w44&_5-}68^?Nt8p)BTuK+Ye66hrod>7*-DsaQX zXJAZEUvHgp4nMqG1&%cStfdOEk7l0(|K7QtlV)+fN}K?XnvIQ zcvfj5*PO0TdHj4L9oeJm%tXUxsMei`AJQ+^9-r7qXOH4YieZ-9my*7tldhmdsA&Ie zgwBSQahWREqhfu&9Ksf&Ry41^Tx9I_o>MS&XEZKl8JyJ@K18`O&+F!!fX+Ji7j@mZ zhg=c)62!!xR=#nb7j&`4WW0y<2lz31lS&5;j4`fXsyUzw065YPA!%2?P|Y<4fcqsF zOFy>=swX@`9W2U8rd&{#Zlidxe&HzQ$>e1??r3AokkdKxhmQYjojz!X2vx^ps7i`m zKAAz7z^QlV!gu%O6y)XG{a8R*gZ?o0eY827r{|=>{k9hsgtP`-zII&lU>Ea(D_&F5#U3<(tV35Bin=j0QVC@e}>svK&;9!7R_LP;iOgYVpRIfVX~#)T=! zzei`4+wS$C1z2J3<&UM2AHyOt#%uK8fE;JkwY8RZRo?ZlXAD8m&E2&|>DbOhm52(~bf57JuLItxiH z$3_CvMD79_+#xi%;pneR*oPEl@q1fWBEPXlnb7pXvA{9p4K((HfTUB#q|RxIyhkmk zNsuMuX$}C!PUeAxlvA$~ZOk=d-xw%qeR&du=z!)eY*q|Lw&GQ& z46n}|Vr@9>i!Xl_y|!-&J9D1Dy}e98`w3SE?peUKkwAHinLrAd+Th$5Eg@rLvSNra zyaDY^ki@3w z_JCo3*CjGvt|$r)#=Yqwf#V@QskOuR_#BivI?_V`Cnxdl>Pr>#MC%$|O=CPiXv@(A z<+Rz^6*3G-(u31nX&JeI1O@8Udp$y7sWh1P9ng84C7BG#Pvjai@=p;=(z4KelQ(+rkH@E9G(tgl zcQ@|^RT>@Bp*dJy^ggo#FwJhvFDC|D>q+|Dhbj+pLp%iNUBlq8BmO)N2sj)XxwbL# zs3+59Jjs?|z$ifUR;W@=78TpuxZo0*RKqe!3_{V zj!}o`fhI?KrxIAKTQ0IsL{sv(L1C;L!zo*HY1#1K`v9^)-g=MLdi1Lc1^-5y_{(~=W#ei>eg|MrIy8Hx)}~t>zjLR?Z8ZzK4{K<$xkvt>;271j(thD%A8W>G&E>SdC-fKrpJn0TP7hCBJ_=AE`9Z9x4 zCW^61rl=Y-b+57^x<|5RF_nB}7L5@`6m>5Q z9IzZ6iV9;C<=f+3T5@N057HE@4Bpx6VFt^}-5^&eU1L|}M5h!cqsSJA#YgP7?(Lu7 z`f|kK!E!DkjJ3rS;fjuf|K+k6^5$Pc<=XCjisNSmOURc8UgS3!IE*EWZdbHqIT^KR z@9Ib~J=?kTazkxr$j)!^$IPtkwQ#MBJ|)g}CHXE>P8)9Bp7Am!6FgP|%F)Db#Sste zw3YGX2Z@r5^2S8`zBC^Py@x=0P>C~Xy>os*bMLloUi`+qHOtfNN=a4vs-`=c$3K`FDGGjTTE3sF8=5GAn-+d> zLi1Urm*>QBAfy2e2QUI3@i$jpx21t=?`Y3iV&u){jN6syT^*tAa;NF%_{f0DosoN^ zo#6yf96sPQ)4$*L)Ru6cuAl};^hoF1W(l06{FlH~HWab#(IE8W-Lr^zfeqY1=mFoR zrvvU4SBnR9LX|nM#0X>KDOpkTxl&xW)k4moH4pMM%hhq)3-)geU$Kef0k!7GyciDi zVYIpWuI<3lJBH@$oEQ_FQ)b%N zE>!42V+dFA-(P_aH~wmj-5f7n!oR2T#hfi}FbmaJ?1j0Dv9MX0KpNwKu*c>d?!|dq zLRYyHf6}spYnU0{qe6-G-~DnC#o+{U|@u{Yq&bz(G7w@b_ay(zy5db$%WP;+U7KK zuMy`xBEzQs6UF9u19j_^MlJy9W*uzL6f+r#SHSLhBFbav#3gn@@}l_Goiilc@#mR_ zt0=q=as6$TJw3aJ+fi-(D%0IGwCar?pC>~WPNt^HxDJoGdZeX-9Ar(y6EdWryDekh zt!kSIw@}cBL#t_AoIX=^@n&g0-;4G}e&5e-tPAbGGNMzL{$hJ7#h1B) zMF;4?9A^amVaO`{b!+Z1tjVPkECG@MP6+E6s7kOi1jN8Pf)t$4P#c^jgKg8+f5QG# zkFuR_elvU0e2Bg3d|gyuUvVsCK^`piWB$|5^MfwE&AZGFH#*-aX;6W!>R|j@g|D>l z>8E}7e~qrM{bijD@A(W2s5vjvo?Kdf>uulYA-lKQ$bL((E|q>;C;Ba$HU&_a@=xB# zjfX0LX2CySV~GtJLeB|wHUF@*)q)}4(`o4hpx~vcM_`~0S#&Cf2(}R$Or?ZKS2PsH zBLUHQxhfJF+@EHD{eac^=7H4Jrb8^)T7lTp{pNg{4C!kxuSS2t`~7sz8&SIuYJLlS z#iqUgyFBhci*{}W0AEKdtlXj`#Vg zwnx9t2Yh554gA*MI8>mPV(@z99Y~LLP`wt%zm}mJFbs#$z96{0G?_Yk|`A@N`1ibyD zqNqK6dHcc*T0kpRIB(EAa6wT)x%=?en?euI+)6L|_5ViQ1V&Ak8-1oXnLuvt*R8Pg zwd=#TpExw;+!h#FA8OgI9etnUH{1SR6`T}RH*55RyF_lutE&sM^kLu?aE2Qx97Ymk zFAo8E*_e`#|J*h-JK9AV{)-)NHm0KHfBxvnfI$JzCLzD=#SnZOuIu5-d<)l}(Xw@E zu_z^P{OON7uUFdBZU<~>OusWd7aEzW@yx||uhZjA#D;}^-3EHgQB$ueVB(}o;9!f+ z*SO4!?+4sccK>5=DZv&V=G>GId$613=_LayXsrxw9rn{;7`n(XkaPd8@cY&9?=!_cHJ%+oq5$afBLTlZaF;DCnwG>07|+9o%r*JvPCQSBcc)f za}!Gp@U6vxZX}Gc^T>iFvG|>`5qK!rcXq~CI}oOp-Ub!mph~AWOT?R+6uPd0%Ld#^ zm`B00Tdy4&*Ewm?4EkQV#&!QC2rCRfbG|useMv$2-AY(caaW~t;(ZLu0XJrH(-0JKGUr8&~E4u;O8!E;BTveR*sP)N=yDYI? z(ZR~Y#(u=kn{W2CrhgCI{P+xgA6jNdrY3&?k1{x-#j>*DmRh#8Wu_wHUPk7gZaj)U z)+p3gFc8VCXyTqAywLsY&6$Sze1R2@AdwDcm_$V7&zR*dIa}O<^{vJ9iwzy_UG=}M@~MrkDPl* zm5cbDbY^#pXU6rGTK&uaj@i`%jD^sJ)|k7?4H|b{LKu=lmL0BrZ{HK14V!5uPu*R5 z3%}e9m-hUmFu7Tk4Gx1eJ0_V5e&-|BE2{IkZaXVCTi^fwab_6k2FOvINl&=4K2Uhj zbfB$`g+myxoUTYb5liDZ4+w>|6t2g8P%?ckYX%$agFFhlqrEknd)@i$)DB1S(VU&? zeD<@spIk2ela*0N&dBFAd|Z z0k8nXH8Kw}CMC|6XPVD}$ZS^HPqhvQ^5fSj&1 zAXv&X0>GRqS4BFD@_E;!PL|Oe0DtiVDC>at)hhmdKkU}lb&i;YLVrTcwRcG%el!Ri z-fg=LPjDb#jXafn>028@#b z!F7R_ou)Jd-(W>UI|K0wFNcM^4O!AP10ul0VPA8-BYgYT%t9Hn+`RYQ@NAGnS$k9$ zt2W!_E*Q2i*}SI`RnW3MnOW&oL+C+dv_WZy)k4tZGM38lGwZNKOFSCO4qFXN6u4^N zrFC;^U}k^n@-Dk{RB@jETfw9JOx2A|Mb*WfZ{z;A_pBXC+g)6jz2Q`M;w-y!_lJn>ZwEcaz1n-H+V!Z0k!85SnAaJ484Z7}M{9sNJ1Jiajne>ZV481yb$`wAaHTnFHpl6z>+bLw z5XtcDD`>2_9YtI;#}X2kKRW_XsI&o;1&s81qB)q5!Qaz+cDq%cexFM@NXym?V6Apd9p5V8i@y2UJLLoLCe+>C%y*%YVB z=--E4$*mf)DgNrS`-Nd4a6JA*%?#hQTMdI#c2NZ>a(@-x-%-mbJ$YhHV0D*n4^W?n zkI8+_2k@Okvzr@977_g0(wqtCLY~e+Op-a6Z+>#LIuArz`ddz5#>?4Wc}B>6Wk24Y zA-f~k3C!`}MSammB}xouc@-}<;s|24iw|l`cHnha=LfY9qlhV@E+Lp-qmw;q!bgFg z($eRb`d_F__P=y_lh~DV<7-vL>)&^Ub?)|PrYO#Ah9=aZ(&%II(DWu`M56h>L&0(S zB#}!4<{EW3{#Z~5BRLYf9%W1f@jRrdu{stv3+Q`DXXDe)+bH5C)HXPhz7oo+Y_EAQ zLo#Nk3y_r!ndk*ZbUG$?nNj?OI89CJND>^w$%Yy)62#9MeNn1VYBDn_<<8^KXRU9X ze3yOVPv5o|wI-@Us$vmA+AXF-v^k57%EH7te;)m-8hgtwN z%f*7YRv*{H4l7d*NM7a0ab!Y)kT2F3OK5k1h*WN}mJ+>_Bb9Rbg7Oi!_rf>GDNVC; zDQa_B^+x0{yNKdvkw1i0_s8D;T*lpEZ(day58R$o{hKJ}eDjaaM$0c1{{ff7RXos2 z+5sR(l8BF+lnsE)_eq8-CV16bqd}3bIdUyL>kdX}d{aV$TBL7Q!kviW0Eas3P*v#( z7$r_Oh@5!o?=tMIgI9hB!6`{-Q@mmJ2G_#=XrQGin^PoAU__JpKxf@6;Fp_V;H1ct zSAUe>1h;oBX)1RQ@7?+R@Mud-`P0eZDW{7TZl2-|9GpQi!^UKDUP~mCkFzwOg+jeJ zt#Y+4v!!YNm05iM=S!pqYODw@h{jxr3YCN&vn$cVXUBrtl7INi)(`kd!a zf+r{u+r|tLNG6uV%kMmL*BiusepJFutC-P`&k#&~=0O|iauqk*g-DFR=waLu0N-U= z_;_^59}Ar5f4tLr>P@ni;Z4%6w&;FZ+7YL~FVl-Rw;VPV`jTXg-ueu3qkt8#jPfGQ zWN2$ItMW{BwlzZcZQ-~EI12P9N&>+kmjMyqtU3m?7(nTfNY|M~GKmgOmIzvTwa~M_ zs}|Jmg|0^b2`@=~bLvQ80o+nLj)h*`X7);Z(Ts@TeeaZ)LlFzshdy>&#!& z<)TCWgj+y)Pv3rYV$K*8EGO5)p}^MW`PiX8Qfp!Vv&(v8)W*#!B^vgsv3Xs z9EbXw<(8iJIDi+V(by)`7AcuniO2HPkYSGaJnp0qFwi|WEn$)|;-9AqyX`)co%{LC z0)uF(e&W)YaP7jbMfldniM5!SzrH!kty%05YX{9BK~KL04|VHk|ClQ|cb8D3m9BXK zv;ri^FlDWVcu*em06d=HO4FVQs3BN-iv6Ii2VuhQ>t$Bu9V6UT97rdHBo8pi=M`Th z1Qg1~jlKHe57Jws<{qtv-#Xel zQCx1LYH3|G2MV-1X-g_?rUzPlYiqdIt8X-DL@iV_F>KD* zBN;}{etdPVwRpq$O;CG$>Z$2$TK=)lex}Y3z+e^27xA&Oq3xu?Tnc%{Riz_f1PgJ^ zfb-x!sp~3!x_`~`V{~bC62vR<-p!eU+NrIwxb`EtFhA&12ZtiBy;sHC7iOMbI7daH zQ2Gv>Syu9BD8B)u-8BG~*MUmtR2#0Ul_xj}-bbs>Mf00I_D@7JaH~RGkqu^GqJz@ry8UY25{4$t_!&{xZn2V42abYq=y@!p)9;Io^AOrCqjBAX1dfC?)Cgp2`|2- z`CUYCZ76o<>aP z3q03x-zL7-T7E8D!9$TSI7VeAT+yZJuhgm6`vHf)d7im_Gp230-+)KhquxxVgViJ^ zu|u=|PR4ceb@B`xz!hC-%v{k4`)*0?-?bJrEbRKY^wYVR)+3_O-yxk@^7g%s5seii z0s}e67%EFv6X29!c=0x`stvap^y0(ylLMXH+*n_-9>S8?87-Njabx-$L}tQ`oh_|I zZ1dy9?%au|^B;>}rE*z>BWQt{;zfQkQQ?1Wng)NLY>UAq(;=BUFEEJG5W{$+@NVxJ zdk)V1hN^uwV|^w|fXt9!m>}4qdJB7J z=!~b$YgENN-@xYOsG`1zsB3D^f`5$fuPJZrN}GQL_6bb~HqO1dE4Ekc=um6Yew zJvZd&_MPrUjE`1jz&^Yu@H-dVkCD@jeiwr@4~=`)Z}n(Jzkj-8SM+y3Q0?g1#esnW zv%J@Tj=#ClucFpFB-u9E7n2Oi{}58N#-Do%BzejlJ4h_ebLa36h5O80$$lZ zkooSDzbxC6*IgQ%8dyUn_)j&H#J1ne=#P0lsNOkjZ0*0>y2&o+H!C9JsBjhvNn`+B z{B1FgRFY?IBC1T`dCbK8JWUP29Y^mkp4t0^$EXj7Nx34%l`K^e#8O?G^_glocyMDX zwpDdQY*Mtf=e^6W)JjD01+~5GXYR4WQMct>4%FB*{__`HZ*tom9encrv|JRNWRZ2} z`-%aq`ZO(CB7@f*ds>Q9*ZVf;4;RuWzl(}ZBu3;B3r6^}P+;z+pAbaLDyq_TMI-9G z#;dHn^6XDqsi)$ym8y8nj(M24KbyL-@Z>}*)5G66ckgvYtZsxgFD=~-`1L9L@V}42 zOIBwBsl)7ZXOH|`igxRwN={2KyiymrC1m`3gaVse;2>g#x3#R)^95d<_o*jdy?W>A zud3@ElXMxiX4PDdXQ4Ir()Qw0zRy3KJFU|y%R5J1T@ufIDLZ% z`FWlxmfGJhpV6i)3Rqk#0kXQ0ym}w~n&B;W6`?U$me(3U$fdM%OyfPW4 z)17qkUvtGz_E=e5Z2#}`W3@(M)x%uzzz?;@{C_N*`s#lvSh*;{#E({zHXqtQUIAl(~C!l@rC};Vm^FWD7z2vaG?`>;PS3d}v ziZj)R$#R`3g$RMOTOM5b6c0D%wyBM51{tmwBc{OMPWH(+lV#A*^(Y9NDiGHS;p@5Q zr_eYR$}DXcnQ@}^`wz`!zQOMgE!1v5D2zlDT&shNL~8x-Pndrf_5Jj-2-y=CpJ((u zQ6-PIE>WlX@&9xT^hd`YS909@nO;KSp-U zYcPz9?({0x129lgnohGfjvu7*nm%dcg&@*T&7veZbXO%$t0#8c*?uV}z8auouJ(uJ z>cKGk%QZLQuaZ%lN`8YM4|iD1)pju;#i|n|MzJKm=zJCp#H|wTIQ-&lK5(!H4n)u| zxU89Pg$%vX+zW|nf3v3zGoa(CPt=aBx_yba>N;nGS9PhvC3TE7u>cuA5{k|o$v~b| z!g;IVh#4Wso0bMheXMm?b?xHu39zn7tFBRa#^%4sx%6v*OyG+FThbv^H?|)kgCWYV zr>Wgh}iaO&eK2ZZ$Tv^MT}Gb_mF;E1QT8Fktdg8 zSQ=hxyn~m*zY-WH;-vbiDOIz{*p%_0_r9o9U)$sLpOrOixIDyD#ym5tbMvO6jdNL# zUnl9HYR{8RDTy#vdpJh2rk^I`y^rsu)`KyxxBaUiNil?|@xB5qx434v9RvjdLvtMn zMl;meIlNsM`Nd{ur|U#I*}|nc`J?%c{?(>k!%!3*A8vysw;0!|d^SghmSnYgAjk_bq>yAHsoELDWpgMdSB+I(qnD`Z z{OhBbEHUO$Lly|B6?lq_0PUELcc%Z5)+chh?0uHqui-G>-B#De$=5R2<4dVPbeY zPy%G^?u1xBIYWES>7XJR0R3Fwhb_R>0a-U#s8`IBLyi`@k7l0(p~ko3W*D>f^~H+9 zfDgvbN5U%UZ9g8L`td2#{QKXd9c_B%jG5=OYXDGK3Uy{RcYGuHIOD0E$}kc(uLQ}$ zbW@;aS6Kr`%tv`B6W=edm_h3m+pz-I`aV<$WXQYU@OS?i2K75{>0XqPE|>^Ia^!#z zP|&mjcbkZ*8+Z$8@jU5@4yc&UYen>eWAgx%>iU*dg@6x5#Lh)^Mx$`iZ$qdKy5XHb5155HEr;_2AbgSAp6OsGT2w4$0 zH_u(RvsK)o%#SIECnXLX*<$tRvn~tF5$eVq-DE^^(@Rdm2{sw^CulAa*P}Pf?(cjm@ZMlp#b&C zCET(mRc|Jw>dlxEbP*pQk60dgddTnT%%wLkUs#==0_=#H*NexN-Yn4XyssbdxT;QU zdm@JG<~1^G;1lb9l9`8x_8qY{?1uQ%YtwtJ%$gn_(?fdCGamNjMRi>aTYb+S86>@W z)OIHL+r)Gq;QEeuz$RBQ^^eLV3@S(s zRLJOr8q2u6DSVyL$*MBh7JuSmFCg0EyvLb*N!~U1xOGjdKpBPld1P3s;=3$45tZ0;5Z> zGBa@1Ff{Oud&Kyz5cT0cilY`GBj83lrX8#^Ld{D%JdnB=HZU=E^XSYaS&`H4XHDL6 zc=+j8;4n%j^mzC`F+mg2j%-0zy{0rDU$S9UDNdrczZVHCok<=d2y+{;4QhA%nv?2#+NeH$H2(O?&)EV+#WKt+*maA{*P7)GZ$tHa1ESy%b0aXPMDdHYM! zWu5Ka@cCppzcx{}dD?@expOIP)&&e8fkzla8S@~uJ)vRV=K)sl2Ozbg?7DW1d%YPPe+ltk*`hjJQnTM+Ymp$|t$K*MqUXXjFE5=(`LjF8#9PHT? zR8>%_mX4Q7ebTA?Sr1NIZ3&$ETQW5}c|P>U=kNj`l=b&=)SgNMpwO8}J|7ti&b%#Q z5Wf9-CdL#6H_d%PRfi`QV*@-1wqPtw!delq&qqX)Y-fQb1;2`Lm!}Y5SWiZU^e|NV z%UG&rO2|VZCJ79pjM+`E^gt(i4UwZ40$D-clR0tY=vEALEPCB2Qbk_;Z$s01C(hnZ}$vkdR;?^7($U0Ed`(&ej)-JONP7%U^R&)oy-eS*pQib$1*EqS`lz8 z^qlP~PY={JR%)~|ENOpI-u{i6cG@mUs6i?68M}T$UTyK6l^YC&hv|5_2;Gw@;zzY&=l@0C0T=lI{tcinl~ZFQNz(Y73jMC-YPfvCokeqNhAE6mK=F~RSrr8 zR^GRn{9+T6WigjhKvs#K>j#RJkyI@6>m~oFi+__x{SIFrB{aS{fNqU%)4lmC=h73+ z%2eZzl>oEA$W!3KKK^_J_^=JZ#bk|$ZH=6IIeD{DYy8T@@=mA0-GIrY2LJ@*G{io{ zP2#qWuR0miI|2w2PTUA)vL1lI4zbgp|4;{}p2`O3`k0K)^8PB;N>JnI@mL#`InW9h zB-e`%4@#*+8e<+kKlXQ-U8u)&w&14@HhGrnCWM?1#~*v*`6=K5gC+rbo=7U6F+JCn-j|}8Iel6cX z-FN%1L{0L#o>cC@pHJ$S)K4Z>+3O+E0Dq^cgy@jotvQlj(w)*gU}t%YvrMKC zem%$nCP@>&%)jo)W#{c&plPh zq{ZI9Sp{lcP-!vcq?r%lsFRuu^NGa zq7nWITSqdy5dALBaMn7{dhGJit>3RUrm62Q9*k=4y^I;P;bCCRC$n?f|z++$kJR;)Gog63+ z7!SuV@g_i+eYmXJu@b3YC~*j7qjsZ9OPHyeOmN@nAr}*Xt$c+4K6jQiNBkw;n!L*e zGATdn>(=L;erG8bb-z0+cmHM0`>U@vavEDxayYDNS&{cg_?5>1rzn-iCnaLww9AF@ zQOxLkX5yic5r-MM3p#Q4{~^kJ=;4_AjNw0Ap8XIxCfKXC&~m%6buXfMbIGpu*G2Y{ zo0Q@7|7zk)F5!DExe2TwU`{n&BDZAZ9gfAavx>lw0{*l_A}=FG{z|S@X$iOJ1T03T z5Y15$M2f8}k@U3vBs-VbBmDN5&c46U)>>@HT4Yq)#>E|-+=5^7y55G!`#HDMeQArx zZI$Un5DYzoZC|CE7^C!^v?cRMpZ8mQlp=YB(pPQ^sze%*1)WjCzyi^41-B1J>VJ1bqLRMl+|) zn%>b2XLx+d0Vv5>!R2`wE0$4gX~8{$l#S&k$mbcusuFDnp||`JQ%el+xut)Al5EyU z0-Xgz9ZQebXd+A7<8k~6F?ml>Ntb|k8eCwnrhfnNM`u;%)_6$R0aA=T2j1@i9U00* zVPSGcNf}r=NMoaOPutS-{FFLrnRy)sj<$HV`6QQ@vpR}3bMm(Wb|0PVO)^ptFwiqc z=_X~Dz{DgVfPaDVvMe!BAnA+IeSGZZM|t!-`uJ^KdI}S-CMwAq8&~qe(8`kI`S%rC z^+3Z;gU93f2uk4YGsmt=Y1%LWiXXN?{w5L?xeW&`2Nc}L5}l_n9Ki&>z<}t(bnXc# zIPXzWJU$}y3cdCnpeW#>X5p(v3UC{OrzWUCZi!bOb4FBDh8F+i_(<@cVZ$F zxVx@*Ug$%``occZP1;}YGhx#SXHXvV00sN}_I4x+{06u=HWQ(&i!}G60XnRQxb2B5 zONQz-QhCO_1YKK1ydMW!a%nlrz>*Tj5>GzosT}kFXu9rrs`vk&gVC`A0;=YAi*Kf3itH=Of1@AvEVoc+*w zDiHoh)=d>Dbg02|zR}xhqaZMxtyr1~uVTasMR}}LI!L?v0pJ8AwP17`n2vn4z zlFzvwEfcd*`POOi5UKO`hoez$L;GC&f5;XG&*Xpk%KS45{#c^i7*eND24chFMXoxb z6$xF{kW#2|V{4nd(qfXoVnRk!3Uq`#YB?&3u*hj4=ZHGiS9A^b$gwd4p*2jk!Y7GN zZ4t&&dgU#(8x8zcasH1+{T_FOypTT|mH1Jgv?06q?;$__HSgr~WQEkhK<^M7UZsNo z#1r^~4X5vmX3!l3(sRI>iNy*v41;8Iqr%=cOSv*K#8CLUM`1LH z_cD(EEq6HKHFYf8JjPJ@?ThhbMv%3Ynv8us{%~wK)i(02gLT2-`HlU=;|q$%0o(JE z7o`SzcUkG?IKk+!dVV;H|2~np6coO|55)T@PT$DZ!;T@^gAcFy{kpjJJu)wNXvy4* zC#vcRzmh4|DY6UcgzrmsQ=+qM(BiR7S0^%do|5XKe~|HJx*vHLqKI!9)5QFY9v~mq z1^IoMYTZzQH13aR4OpaRmBs`cI8R;DuD@dT6rlS-er?uXUhaH9&ZyLQNs6{C^+@6@Qi1nqBak{rNG2zbYnZus2W%rIz9- zwoXZ1qMR{r=1>x@R=wvK27Rw_V5tJ=I$U8&al(z#tBf^u`j|%35b`>-h6cPr=y;`Y z-eOV`9=eHMo9?EZQhobOw{;`I74UNL0p`Q`$=}8TEe2fzpmj=DBvAyFQvhio!mpD+ zk^+wB7(KfKbKATW)b1xSa9MyI2i$vDgoTO#W8uXREgk{$n_`PPxac_J9`u<@tGbgt zsOW^_IjSCa;g7zOGHX|J`zcW`^dUL1x(y{p32dSH6}R8kARx`)0A|0+epQ1#Z<_12 z6d-mKojFTVVX+zv`k_QIF~+*j#$H6{l*w#`r=4zc39cWSpCGQX1klM}>^qeJl2=V5 zIt5HRc|d96P`y8oJYm+yHAC!0zWDE*c@8E8^}nt;BSsEPTjwE|t8cqdQ%>8vN2O;s zWWVInMDj#pFh$uQk|q)Ne7VA=5>rMqZahjAM`x9JpMf%(M1_TcNLYiTqhE*|3+3pP zWEH<}>LHvY`wPrV<#?jD~^d z^sNPNW@uX_I2Qbi;3Dn}_|0c-wY6&G zW7bYxMneOHlzH#IM}7rmgj29`jA9@6l~AoKljwwIa*k~KX}J9Gd2Dnca(7J=C2>$e zYtnX**7_Sb=4RJ7)N^Qny&q(mCh+65XYIWFUHb1fV$~tpBE^QPHxl|)KJt6+TF@0K zq$hT<#+a*M$BfyL+1f9>O#6h$j{tFApDX525F zv*?7*F{C~@Qb<}Yyx2cn@EJ@Zxkt&fpeDuu!ebJ#8PClYTA<7 zlOBMG20G@zHQ+)!Et^t{bW8#ecRfOF8n$i}Zp6p)xhcfasFx|DJDswhpqD>cP34CJ zy)K5MRe((3y53ZO{zM;nO>5~+t14`?$B0!{mS1peH)?_X z8q`WQM_vK_o#v;Kf4*3Yx6FF6u20T%WdB`VY@$~7HKHWH+XifMJ&!h}uU5`#8PZ={(Haj?H4E9WbN7d$c2qn|AStg)@=J_$#Y6Q6G@-M|Nt;|_KIPER_0SZE z4LjrfP#mb5v~(yR?fe?64vOzz@)?zY5ksV%J6O3AVLAgU zCaWG-R=2Dcg;63!0bsSpRJZbx@v&XUzndR>Zu*f3`hTy?`Rd?lVy|}ZX`G!fYMGVl zxo(%3GN;;(O5;oKZXp!#wj*)qQj7@8Bl}ezrDvA6!nOcTQhgfq3oj zwnN1rz@YP_S>@L$l9mpD3$)Bm z&55_^*JP#pJxl`=8DPScV53~~OT1`b;)i-_(8WpT=a3!g*;x$_(Xf3~^TJD1m-MBD z+e-Vpy#fElV}e^}M;j+;_Huc|V4((+#dG|72-3qq0&UKK8hHA!(93(^3C6!-AgB>6 zX7tL?oB?h?=V08nYztQ_`s6|n7fENqv#1isbrZw^=P*?KnKnK{mpWzx;gvQH93``;ysfX9=cukh&D4yfdL{4JD8i>GH&5Zip^ zP)%TE<9?N%pIV#q2z_H(h|F8DX`cHLh*C_%JwzZrsZ=NEEnOYIx3I1TK@)h}P?B&H ziKKA~lMZpipSuZ-QmCtyG>6u7`?h;C>qwlBFL?DFZ#pSJ$X|bz>*GA0jU^L2rO9)B z&-{{|6m8v&7AtN3m@3k@ z9Qvjqdr`rj8bxL!>csn?NDRy#)q5b&1BjrbS))Q@h|MnKFH^O$ZmoZ=eZoVRAEQwx z*O=H^Y4fa8Obr+qf!QB2K}C$~r7H%1B3$Jnzkb+Tdk3Px;4|l{Q*d@JI_JOiuXre# zQr9{?TJJn~t&417!}HnYW#8fNi-7i-+tzBco2)M`eiUk5olsD0{!;okLrX*Br+#?# zlt!8-Z&dyr8lU;PY9;pAbZcySyd{$oZ?5o=khNXS-tl3OZ17>u7B6$lpX2$9*I)iE z?NudL>4H70HXWyuVJ3hintTg;OIJ5$4IbEU!rcR@OfS+iQ}5`ZH61Vt=;zAmYEs6! z$=UK)gzOB6mP{)EiS3UIFCPaScGCmPns_Lb@vd4J)Jh*m*jw1E!syxyT9UWEg3KNl z0NI@{eQ|i6kS*&k_J00x$1|Whn>(LDQ5})bvc}uoCjq(fGl#EcTza+_|CqVJ$BWRh z8r2ze@p{f2R2q{s;I~*dz67asyC9%=eIC+$in)6q ziGg6zQ;C}BJ9q(Sw4ZaPUwMZ>>7W|(KX@2{WI3nAWD2pBQdiuy01My6qaRsyGiLs5 zF-yR{dTyy+4bo%;@v{1-_zK^#MlBr#u=W;y)d~J{W%TQbvPsZy?Xrz7 zbES|+@jZ?P2mz`Dm-^jANP(9`>S<`*u!lCWrCig@Zrjtrtb_4uKNA|2KuIK@BguXg zekEjb>m)d$lsmDYlvuuU(Fhv5hrt_(eGGDT?0~fPyaf$1OF+fZ+Vn$vP>@s!hhr={ zBRl5Z?NvSp{EdEIM6YBUzf!>t!v6$V^}K92sp7M2gmOne&n)&|+coQ-2cF}PpQ2aU za@466reLX8XkTefqWkSYzmv?L&46iBUsG_xeukvB(Dax3DA5R{)};Z!?R_-sBv@Zt zwv;Dzoj;&b9k!!6dwc{QbxFjen%Jzln50KY!!}s0cFw7Dq%MvHBri zXXtyAX#1zh!eb{fztihzZys0X@iB}Rx57rR)|hRBl&8@Y?W4DWw;1Jot;@Of5+AUs zDNLemm@OHzI>kU5gcKe$t9iT?4Sf8d8exufquRei-!G}K#-A~vGi|`Oz z&VZ5qvk%gX`;<@d>6>_qL(RKgsYOuvKGvA9i6NqHb7(aDm73L&D*NX`$f-{~#xg6t zrAti2>ud7MZ{NtXw8t_dW2pvy&zOC{H^PnVeWkDh-)iKqWUn%EV$FCJEH&2j(_PL7 z=*N`ew~XTw!~z&>(F#2zb801q8pYdENQm^n4oQq|7zLc7IWZY)1*C5=^$FxQBHTou z*>0E$h4oO*75eYJO}JOsglNSXyG*8m*a`p{f);ki$#O7xfSGp_2z%_G`Lf+yv^Q@* ze{BApN9Q3ZyiVX~v(`x?VqC=jgo$yt&H9#(si$dqHBy5oUWFZr zGmGhLelF!sZ(*T33^c;nDGerVB0`({*H+Dq1 z`M<@YShLe}j=*#W%J`Jb{!U~a{M!pIT)YX*Mi^qDq=%!Z(F31d``@9eGuir z8Uw_O;7G>*&gMD8{M!BE&Bm{;_s8A0w|H>s>odK34G{wOsl>2#CQ!cc2W;?xnBL%CVtORbn2%nutV`?xe~h~dy2-LP`3FIUyKzoG z{5CPUXi*;PHlo3i?rj(R!MPqjB?Wx-Q8D2E2ZxK?2d?tqe08Ay?e$oPrJs*x$>`|8&AmC*z#8RaA}4VZ~EcpI_0!0804@9KNM@eA;Y)|54o>-_}u zGSO`T^)ZejoE7F3$Gy7L6uxK1ZyQAP^*y73ETaU2@ZrG8Yaq5Xo;~>Vf>Js=j51#v zU|V#_5Bz_=IQ;8sJ~*z*JW>R6At+CWi1cfwMhGQi03iL!68VwtZ7~7y-3CJR6>luG zq^z25{N`*@8Cv_G5 zcyltd6H09yQ^rE|swtlCUfQ^o0ya!$q@qDnkuS59&WSW3a!Sf-li*P~j?XpT%|{b4 z$h}2hrzwc9Ob5)gI=GW5#4mmk`vmU!hhX8!-BlKhD3C)O^c~a{*nsjVe8;fsQ}Lyz zj3ADI9^8<0XzRN-RA^0zALvrMOb$ZTxfwjrR-MW39{qzaz1ewJT2x-I^^nyQTUmM! znR?}iL$5mi|x#@2qTD*JY-n` z{j}@R?DBV{#fVDSz6eX@v!nnW_R#}#w zkC`hrrVrQsd2-Zs)%~Y$ZK-!j75X|G+z6JWt1aZfi|xWEXoJNU{G4ctoS58h>I^_z zaJZ2_un(g+eGzzU{rBi!{V7Ea|4fTO%bS0H=KVfbntjNzFBot@gLoljs4Tu4Bsd;C z)G657=}**i<-ULB>>GP1rGGsGLU?1o_xf1;ZTD=&dh*&b{uljrjm|wK4mu+$iq!r` zYAMNJGHTIdR(KuleCvJo{NCFn4)zKjB-RqkNc)D}#>xIJag#UP(DZ;JQDU_*n^?Fc zri@0Vi36_wGm^$oG5J>f0xOP*1NK0XBe@ea1lYJK8__4IV4+x{S9Sqk(j<0%v7av7 zXzvF`X7CNakG236=_T6$K2F2#fwOk~{J7p>U;oefa#TzgjjqtE zauEGulRT7OFC2M?3lx%SsuAT3nEEb^WMrAE%y@e>TQQE0KjASzmr!OQK%zo9%_ldu zzCaHtLlaLz(}+;MZ!8lPsivxWV-%`Q1UCB2t?}@(55U*OOz~g0JVp#G%wYnhIE_1o zX|I;G9tV*L&IgcbpDf%6xdJvRyo}-EY;ObT{_Xz#p4kAl#u5^j)6EiVbL z0imdQ6s54{a}p#(xd%0mfs|vT+QR$RX+J`0bu@$nGLtw4 z4HM%Yx4POw&-i%rZDMrMV627(BTqkYTl^0WbKE3H3tSC4(7koJ`*{Ik%sWh&4T z!FPZQW5IjI6h8@>$1ZnB&L5!&LCiBNHR{PoqqxPWtj|Vr7$t58uZW1okaBV;KyJqq z#dHE8e&`UT+71>`6T^h+=DnNe1abr=H~CA2K|2Tt_D|uv72_Jq5Y)l?!J~zMUBRwz zW<)kBB1ynvDQSnWM$Muz-{wzY`%T}k?u=Q18}sKu1W<|Ou=ErRZm5^vfI)=qE=b5U z`lw;UjzCFizR0xHq9H)sBNzvC$dOGlp`?%kDTIUz9*V*%d*BLL_&4zCZioEU-Afo= zm03bkCtPly2PKa-16gIsN2pLNgNRn0rcV`BX_sBj70A2iu(b2k&^1`9JjWs6`m&c; zVecBgJ$!8+_IrAzN${CsH7!9yGTSB*$e4#x^($Zru;A=4&5c%+sMDN)dtvVb|I_tS zp5_VN7^}Oz6`Z#OBr2YrT7&X<5^$RUm1CZDqGV)8;7_qOr$NIj>N;?>?sd?IcyXx~ zGVC#e(jZ8!Zh0d4cwzT9)9FhkdJF6a$r2wWMHR7>3}j3tKj_B|qhorV4cEtC zSqx$X8 zxZyU&LG_U!vxvro)fI>1>(_;(Ub!lpV++2(GingcI$NHg$gJho$tBkX>$}9!KBbq}4P3ChB)8w$#EA;jpRLaQw^eY*x!r}dzxKd~|WyKMBDGpnNMd7P` zKS0QkxEe-}F@nP7R%^MzwEH=cqDI;!S+G>hfO%?k(FTYeyTcX5>_Zpav3*9DCqh&b z3W}JDaQD`PGJc z-TLv%h|j!hpRsfGhbXxdg-7kDb|Pu&eN3L@^XETXWCDLStB1qhLhtJGl!pCKREe9i zxJo=l;sJ3~ixJ*Ye8`Z`tXvLF9}l?oe$z!;>W`?2+lUdr-wu4YWBdO4`BC43`y|Er z+8?6@&uy&*zdK#K=wN|Fl1y}&M2VUc7NAyw4d#M`{G>NCnmuffy_q5#Kgc2P27p3e>PD& zS2th3d3nab!`lPb+c=5UBb^ZsPe!%x6Ht*5{5@GuB9?!O@ zbwKpGwElRv^zDY{O{;nFHi8nidv*AaLuxYA8w*p@^iI920S`l$F-(>aD-vQ=f6;yI z6>L3vxZ%E9uj={7aCBwAk8^YVGJih!h;V(xFMQAJw=9eQ+Qx$0@7LLZhs{i$Q`@EV1 z+J)91J{sQdyQwoOmZmNFod)y~fUIS}taZCd^ny!n6sp4XWUOmvKlHs2g{fBLa}7Z8 zLOCmvGj^mjcrYQPsj$$uYzcf?P&Pee*PWbhE0(}bv-YR!@}8^veeS@UJEldRjnOoHz_n{;H!Ra)tELz8)`tBT2d-BJMh2D2l~z4UTuu1N75# z+B`TAkB)%RKn^I)6!A&9ARBRL4bZEqJS|VZXL^}Vh!HZCOlyqhsFkB3_a-uuQ_@Nj z%kPfSxRHHMRzZxdJOY#A36r+Oy1|gdhPhG0FX8L5Z$t8GI|`H!*WHH8vg#ZzOxlA_ z%Z4txe&;`#S~2-k9Y-hI9!+MK-?!h#)FaSnmkPFW+DQ&Cy=)D-Q zL>h;!>8B!@LGh_YD41$~gFE%TB#2df`&>_Ts%)rzD=J{W_uJ{jhWzM1`SvvZ8y=It z4ePyDAT@8!Jy>!)ug#cy@*IDajWcL>W4b&q6BG(w3d<*?RB7dVuW#DBzNKGkT~=%fJp2;^4B8s3oJOpyM4;^sf#-KX0}&@1 zTa0dTWkz}Q1bWJtXWSz0ZU`T!0@x}=m)P~OeysSc`vkH-&(E$@ozcer>C+2zstf9)J`FkBv2;zUi$|dZ}L38 zHqX?}-7zD+?)tDF9olI@F2+NE_c5fBkRTZr?tS6xzvcc?E!2gzo=_dgG}UtT`_Uq- znZm4~Dx-Khwl_EyG`nobwV!-2Xg!UZ0Rh0N=CDlvPd_R>kH%F+1OIwm{M$LCf9S}Y z1Iu=U5M&p8poXSmkgmCi{!m$~H|SzTLGHV($O^rew_y+|B|d}!6;TR&>Wc#UoDCd` z#Pg-Y^?fXZhvJLbKQCv*@W2I-yQTDA@-RZd8(wxLrdI?XYCsJE&hKPUd5wKM{Gtk= zEiyQ;-y{XgA1e|Y7~;^f1nb9Xgo;!VPNMfo-+QT&rjJo!kT8)LlJ4XjXvuUbSPh=0 zaz|G&r*3j2lMabMhC~1|0}KWrCBb9Wz3(u8Lney(VmBe$ydj6CO9UnsEiavCJA}X1 z=*>?>D&*`WlK!>V*lAUodxRr}Gf?EkiX85onL;I8F<_dGE$PDPWuY}< zd%uxw92UQq>l7_`L#gBU_ie!Hx0}|-*LFVS1~*G34LwsDfFCdUUX#z2jPvZc&=T|8 zx6{lMOE}(Z;bW9JbaZLiifcVD&l!6A+ecesmrgOFhEXq@jfg$dGa1yR-BYkbdNU&x*)fy009t6RRr z%Kn)R6K~&70I`kx!K=klEURD#vjYF~{=R#;_OnkXClUtbnwCOzbrTwV_EB|~Y``Gkp+S(}T*i$1Upc{VT)tmH1Xc#gx@ zWttW!-*98Q>WoQjam|*=@8pH#&pR)Ewb|Dy{SLXdbEF3bk8Eb-ZI-o;4BSRU3tFlr1=e^_c95YN2KdtwA8?~<&RT>~ zoK)T}{516Nk?mr>`LC60|JaY0g5~kY+$>mrDr?(wLFg*@d5!>T+#63XI{WC|&u9mb zbu7H2%RWV;R+nH}&qvA*L%1m2&sTa2PoL1ATFz%>*i=z!KRLBNy|}$(J6L@YdD!72 z2C#c$x%$1Jg|lz(9{l_a)CQGSQ(`w5Rthsng};y+KH@bOXr_611W zK&%oC6_7!YmPHSh?nko@Kna^U3AsLfN(wqr`t$Ea(&3kmKx(qi1W)d_O$q{-JnmaVDNmPEb>HWnQ!4KPW+c zRZyWP*dLtX3OeVU^YIkD^p^po^KCv4@0)uAr|g^C&0^fR(y=sYn@qkkUBC^2JVxMG zX&}}ggQ2*a^`Y7+A>5@}3dtGCYK+hb+&hdIj}oT7J^@$<&}9Ih8_&c+bJ!V%*sqO# zm)#4v>ff>VwJl(>!!9DS008&cy~k$1H5N~OYweRvTQ;U9FEhBRTZo^NOJTXyQ)_^m zQyO6Ed=GRP47RBdG>T9d4TMoQEHpCY7SE4P)=W5>KaEyMY|tp97YMBqCb7^h8yaiS zKgn~~SgPdK%EP6$WP_xCG=}Ehl0-^$_Q=~HLF#>X>9Q{gR82;qh~#P4W8ShW5oVV3 z^@=No*k3PD1(BGhlp*BqgNgx01+W@BlD51Q z~Wnl-4b4o4Mj%fMOvw=_fYaLQnI z>)GCFx99TA3VMK3$-F)< zA&rDo;)=K8QA4CedgKmJoY&YLQq>=y+fa?6BWkuWkUS)u zWqj&W&ZP(MQqDdt-X7CU44V>CmkI4H%)1g}ix;8yx|I-Kt>~#iMR43vjn~PE{6zQa zBmZ+OrXuw-8m<+ zlK8h~*D@X+?2vT~^IbeOUHtQE!?W*N`{1P5mt4Zc3z*ABwuzpn%Ddgevntqet12aZ&5T_5q*JRo9inU`~lb3howp49q0F%o(g! zn8c!14ks$q%R~p#o=0kqSaF7f#`)c4+PwGaraqTfYW+caoag7?ZOa++2Mc6^-@lKV zCy8GUA7)D)YD_;_oYs{8b66#L$+`IF-(KBm;?33Oz1e~p6EDuf^PTakwLUr#BGqJC zV%}NW$rX}UT4PAqLzFrxj+MsC8)DyZkaqw3P_SgeV{;GTR*`{oRfZkt5Urs}*F8g% z0Vn6G5&4}KzO$XiYtAx$-kH>VQ*78UxS9of%vF{rA%xdtlJ!ZO?>`0NNIMaA1pcAs zd?mTP<0)~tbNFPsQEcs{bL}vhSrdKh1h&|-3|nj$g-nK$+*Ly$2hbE$_Z}#yBtu}x z;iCKH@z>q=cJlnT2F-i?ea=Yc|9$N-@&5j~s%_oiWS)6(q}u#_%2%IY`NK!8*$S^k zZpt3S$ymRqFskbxDm**P*V^r7bKRx zAYGV2y(sggfuZGiiLz!x0>Bifi~-|$1h5Kw*=-|`i?}#j&J{z-Z_<=qpHU7U4zVHf z%|K89nVJN(x|)I@sf~|X#(jk}g@JDFqBvN2#q+JqgW2<_9Qk8QpRc}G#TH$h%^E&b zj+gkgc^!KDjl`MtzZLYO2BojJ&Ck0sWUQZSolK2KHU8RXTk6=?ZVCSO@Z^ly?2~V8 zdjVmROwv9qy8J`PovWx6!f>ICCV-=mmdw~9G%%Reka`|M?em3=*3G;d2ea(g06z@k z-C0GqhO>pk=tb>-B?|zgV4Gi@dTB7{Qu(vOse)3GqM6cBBlHoLT`lJa3a!E^q1CFZ zh{nv{>f*QSl_-m@(3Lt&%iFW4%yJnR*_%(RUXlWD{>YlTvs|2byPs<09ej<{Y-mnn zjs4=RO1{@xz004bzJb0y%k}fmr?W5Q`(BaRHBKhgthW@7*a!T)jN*9{u0YQgSCP>I zYw+PFo+S?NuDGvgC23ekLR49GZgXA0L}L#iD;vGkNgHc|0R=$gOP>a&^jdJL=K~4W z5ExS<$Zj0WYs>%hntu3L7NCu0-UujSkVAzo1@xB+ipE@dc!b&q%)!BcPEe}os7xBj zaMjf)w$EEM^ZyAX-%I8~OJo^2ry5Hph))&vhbsyVuu#4zBYp^*3=O z?Z5KXvG4P3k1I5J$^vZY*KVF2(O0!kiyAeYuu02k9a5`9Yi5Ff!w=mT=nKKF{2Ac2NBGJyIcWJXtMu{O*lVF_}e+eoM9aR z@q{l|{KDfK*%kQ>(}eJe+<=t@R+jUqqmH>S>Jak{smsgJnKMt*r8BuGYsm&wyb3pi zKrWAl?Mtk6403x#B15eK+b?32D^hkZZs*R~lrBP@%OJJbHd3PlNy??#kng#>=N-6M z+p#U?>v%DIzJ9s)8f}oIDxwubm%XGFk_=(YEiaqyMZhC^)sXq&n#s$t`2qW6=F6qj zi{1AOO+Nq-F$7ho2c1L_gR0>$eNv!5K>hjZYrgn*`Ze%`#y4!mA=Vs$Zf&W3Kb)xOX`MK-^FOw9@OzFb!?Wu@Z4 zXfs%pbVkXa1dYfaGu4=$+`Dfc=zRedN+M|xFN;0|O-kO=Pa$M~=CdAcDM|R}QoWK@ z)^%33)4^9yfQlm|?lL)5AsGroDTufg+Hexi&whlcS3m>7dme2ub}Wl0tBl?MK-b$m zM^<(kN9|U+BV;UxZ=clmMz-(qlnK>+U=8V$eRK=dHr_B5SB8y)%Ckvsy4ytarTz>= zT9Z^9FKg7SB11H}o%%Id089)Dss-(81OKelnm5tTn4J0nNK)x3X-@eUujW;fX=7&r zjZl4KyWxUir^@O`is$2~2Wlmt<2AJ&tT0;DvSl5r z!e=eB4Bf~3xB43B$Yw05<(7w15n5A9?+C3A=k@V=8d^rWmS+$PrB-}av-;KaLN;M& z4Nw1rAAJF2*epEVt`69cURuE?#)7vGf61v?;hCNE?xOVWm8g!CjYln#an}WquVz(5OTUdVxU*Gxx{X3@a0eaQ0jJ;O5W9Uclm+c5*x6KB}6UKe|MkxeAcR4zaV z!NWT-jihw^$Ym^eO7pqhgJ7N^yGdbAS8Sd3B53McY5WeX)n0!}kmi z)+h?FJQ2L(Xa&x@y}8cSN<0zcYf$iXIak#a2j&jUHCBj`s&SR~)!17LLdjU>PPPb?hH3d5n`(2a!4;~RAagv_vLdWiv&ymE`)YMZo z_Eb*5rw2+B&sda;rSt$v3O?5W5>MB6h)wPVPf+-0L-UM7!PacR|7PKd%K1m{z${~B z!3aR4hmYUizRKiw1*irX>0%9bW4=B6Fy@3%aa9_09ArRwNyN$N7QF>#sg|DQ14q-z^ijE~0>o!@>=4 zU@t3eI`q zmN)`WDhO>=UbhE=2B%8dLWR|CFY{+%j;p?&L=b|9E*CDh z0EF-Ix@QCgh^x2Ry!%;X6#BBp-pcB_>5QI6GBIrmH1moz)Kf0{QOI-#i zTQSmw9bvCuGWM{jy9lqmIF?P|_3)T^<_p*Wxt;eLl@eRw;?yA%MNlXYDm21{3b=9{ z85X@iZZWy9jqfxb?*?pEc|Q2J?>s(JP!~;NqI|D}Dn^fFVz%6o;cq+Fi7CQm4ay-= z_ar=fo%KVLuC8l?PDvBprgM}Af#w~ZX)LNbRVV1&x^4UD^17MGpnEy0fJT}-dyikp&cZ?U#M47 zw#PS4laCYzttl@{V{nZ<-a`qOM*@^je?mqix0Tug`m)$A!j}wUY&Mw9aI2c@!@;-r z0*=p428RpcrY5j=3So{kp~8V#=^!2iVAP#f#=iT_yrtQT|0dJq9bh#dQI@5N)CuGaqA$zONWKpr0()02s4MVk~~$TYZj=9h{m|~ ztn@wW4k9xf_BOvj6`n7AZ#z}(SeCsh5k;p3ymw8k!roG8!DZS zwQ*@Y_*7X3#;nGr_QP4PNv)UK8a&{;UTn_~`snXBe#cCaW}M?FkORrBmqMBWg6C`W zAsTQGWvyb~I<0iYpTiYAy8ardJl94t!$!l`KR9%2DLoSMdyEH1K6V&VO(9M`i8$ z&b6SB@d{h3CL~`KItsKJ2@uZXZ}_3;5PP7*YXfNh9B52GIrJ_hA-0A}P%E>>0Qo0I z(>XT#zoM?SH5C42u>$wDHET@Q_bhWzSqf8kdH1sgpE-%gi-(>`-%$QFS@;#G%O7_G zgqfYM_N~;jp$eX(oFdS;6_Z(t&=XR@*ojo~tWDS!2@^89eBk@Bw zmt8tvwf7rT@UNfdTGYveHQZEE%#1W9or=wm23hs`3NyYH7)Qpxw;Wa2?@HT{?9(Cr z(y2A1LmO2|oxr_`)^b~vy!oB^v1GbFDWN*~;_n(?Aj_7S2G~hIo+Sb;P&sU=_q*B; z|EzSAjePfhEWuP+B!uk>5uq#9M1yO2#v9})3mW(v5Wa$Ngd684BUl7R;e7?YHMMf_ zg64(?2af2C7Qzku$v9gg6C;U_Y@uO?2gJ$t*hgJmAn zX2iYm+yJ^vK%`k(tilIq8CT!*@<64qq?7-EJQ$GjGf7S#l$}VS42Lww+5@{evaL?Iu?;(+K`_ zyHs=%NnHxI8iV19sQQRe=^{}sHa>!B8MtX_@PupHaV+SaC6WnBBsIzhfcxssz+Q@}g<$_=IFPayh2(J3 zQ{~7KT7GHyj%QWyeORk;g6j{L{$9slv0-3!k0AjpHIStz`clDVGF=PKZ;S$wOVS1y z)mFDg&+mN1*jXuI!zi?Mp(N=8&*M=OvN`H+qpl$7lEPYs?0(;#`Cf8IZsb}=u;}GU znU348|58`aZNG8zJ(z8HZjp25^CWl~4QWzzWIJa9juWVyUrrgvl$XT-&C^{WmEDvC z07*X}VsE&g%e|~o&1}?{9}L#-d3!;K?iHhY;0?faoo*D4;<i(+K>iu*ga2{+x>I6OSA>b`djb!7ifZ|sNy=L z8OJUmw2G$-B7;SOi_ZRhv@hMD!i`Z{u!;bPnb1TX|( zKYEKC8Hf{>pS|cvF#OrVCw>0=&gsHZaBP3#{US{U1)z2Y0}`07s>%5Yx)H4WESAKX zU0of5KqNQrfKZ>c(=Fi;rRUBXpV;qw@9RG1eBEtv&fb0av*G@cRzWu)2YNemhU!j1 z0RY~chbItrCzn39z4zG&+>?3sXif>8A|c(60@dr_^h3Ge{SNJWr-2D)1?A5=d5DSN z#D5bb!@rEnp0mCvd`f!ty$`_+b2l-;rG49Imd#V@ARXGxxqVx~l~uBl^=NUH?w4Ez znB!kztv9LXL`lb?G=RL;4kdNmKl=m#T`f(nB|GkgW zi+{xN=!n8RRMh1;$%OzT!~r6A5fgV3mr&SEIsa(5+0a~J$~Ih@fj-93^rt$RvJxn@ z1;T8Rw5kl%nBXK7st)9~fU%a88Xn!<1R5PvNf0v{&p*lfE_@8~|2ZPw{+IT;**}hg z)<3z}D`LSAN`+{1QO%aFK0&EMdpTEjpM_mzld}xIm>|->Jl*OR#5J;_8a(*SW}t)% z{eX^hyxyT4S<(quc)&;jsVZ=mV4(!jP{Vv?tx$Zmg5jQgQe|x$F2c}!;O%ymx&ah5 zdtMBPs~57a)GE~Ayq>lD^>?PDAj14zu5{2}>bLs{JFP?#)^`Qb#8kk+L6KTV!S?Ze zXwk6k5O1=Go}HHY-JLfZi~NBOJguvbNMYqnN4Yk48BEw{ zB}j7yu$Y9RZ3;FG^eksS9vDafS#mK7OTJC61cpdz6CUl6CmYg|>zm@jl9JtGu@;di zl&}&a^$Fk{2Jp=qvd0TOARSUpEO5G!4)loc#~QZhN3pafZMVA%G&8cNuEv_wYYmtB z;P>Z{Foi<91&$VnO-B#<1jG!AR;IP8;kfPJNz|a+sMvg4HhZE>tdGa_Yl5cT6|=h zT>sg4qq63$2x!x=N0bTSUADJxf50b)g4PW3N0$SxCc*!_ncPp{GVWiwMEZ?^VdE~u z8oWbOP{MU_JU9h34OSpi6<`~Hl)E}2du)R5;=^lF)<+9R0h|^?0zdideqjs4c z(BU=ADH&h9XMuWu{z#iRCWSN*Liwtz46qH@Tef0UX2iVbPL8&t|I7z%3ooylotNAI z1@2cH0hjD~naaaW!OokI_NwRw7}HB%YQL2ej&R`IdN%cC9Gm<;C5JO?s6!-)6s$55 z${+`<0@m*FJ*(oMh|d>CE|98OuJX_e9 zwX3X_IIrt@0Cw#s%b{1ZW%bn3T#N+atD5i|-!D~nCqb$<0 zc~;Qk3JXIu_=NCiLk=Sb<@y*Zt3QFJj!<%tBgq2vhiLpi-gY=ngOSgFnGqTB3=KA z#BmGgWr+~9Ch`nN*OTpC_Rlf-Y<9UcUA%2u`F(YN$Nuq0!98=u$tD`92Y;^Ixg95* z1_}!+cx{^06yys^Xy9;i5fJ1pI`mwGTAhl*v=GbFGz_pz2sbdcd2^Y+`Fn@yZ=@2{ zvL?vj!h(;>;=f&APf&h+5LLd?AtgL~E{$rqUqXX{{>mDi4G%uu)nYm=e?IH?u3B=x zmwN86vS9I^G#G+%am(-$29zGfZGR-L1l+60Iz~ht8$lW>%qH2f#IcBErniQ~GAFv| zp02YFb;%zC8ZO^|J1>~L*HUa=r*GH%v4~G!Yque|e z>Q(E(6b=Hz{M}A(-1^p=rfrvSd!|b)khn#Uhm}A9@{t?+1ynX)QAe!Gm!qD97o!#A zo5>?mfY8~M$Hv@oVgK}wAjpE(29-4%?sH4QxVf|lO>Tl%i(CwcT09kEmUVwA7ex%X zUwrj~=$}Pbu9ihB(LZ^?S8Bg_U)A=T-*P#bN>C8^wnIXTsi1K&aJ2!YHMJ7Eq9GRJuEqMoPMoP9+8q2?Z%>l~MueZn(eK z?|%Q?weDJnh06oZn@{X#@BQF$T**rB84TGZmQ@vK1*(M(>{1pqNo}4Uy?VIw2e>Sx z51$Y_VGeKUnShSm+8nXVc9A`n0&hF`^k7NxVZa+4GLg_X&c_J#)4AsTLF-E@a+VLS zV+}05jV#|rL~Ru)Ml^Pf?_31rVK?egk|t8e^+9Pynv(U~-BI^-GAH2ie$)E!zZGCo z_~A8MRlN{4uyF3f5-Sw@+ zZWXA>mR$bP6Z1kg}CZ*1n-dfuwk1w;4r%2eyBg7CqA` z_+Ku-O5*<4m3Hf_uRvMUr4_-T0E?Tj_% z^8Ll$u=|E98=v*>|I+@Zk{3yj#slvYCLW-G3zs(I(*F0+N(2akAO}F_42cYve!)jT zWmnl>EEoSm)i^ZE#Iq|b%d2#??h%;S!sE zEU%~v)@CfOtoiPsE_!PDXS0><_TxRzwes%UK@J#G6UEv%@F;wW-n-iX0p zIn}MfLpf)18SqQbK_d-sM9$UuaNQ~Y)bfAT-_!f5Pp!XSRur`zMJ}heW=(sxPXH8P zTfWgc-74V+Uj~n#ZRs;POU1d5$z0Fek>k#V$_>dCF_A{OID@p_xL$ghmYT^DiG?8Csas8ox!J+EKDhPT#-}1-Zad_+h}Y4)Jt9XlH_R4g6*~F zF6?Xjs@PUX08J*w(p!1t0*N5|rR$0~N!)n3ZQ_~CUVDWVhxpGGNx^7yXLMpw4a zRum+O9T4M;EMCYch2bb!2E$kxNfCw8Y5%i;nI?UJkvYv3xG4jG$^k%s%)x*#M&9;& z+G5%v=61~%#gxU=k7M(E0o93~Mp+^}K=ZOg7CD0L#G7$XJfYkxG&1!J*+n#~be`_p@UH?_G9SBV5T#Q>8-Q1zyR zEo|ZmD-zeI@9K{}0JKltarqXmi+W^=UrzJ{I9>Cs`3J5hyyBvXp_!K(e^}v?%oY|~pqaMb% z%T5ROh(FmMdbk21mCy}_VB~ePYZ0(!0qo;6|G#5i1X!Gn`eq&rC+{LrvXlwsF%N+e z0frl>WPhg`|8oU}oI$%Bn}76zKR~;vN&aU?8RJcpnn3rj!f{}15mxZiO>C`j_-0Bf zjk;9u4PZ{_8A+A)mzd$vp@GECYn?9fk=kXhm%lqt&s`61-?VJK?Wn|W?!TC`4a!dD;3Abbbzs>Vv|==X{g>iSKtI%aLWG0` z5YV22D&1^l1{U|S%VdH12n%D!LZaZ2EB9Yu*=X3mzk#{-)iZK;%<9IT zLwAvA)u=_!Gtt1VC1@aOL4uT&m4@^*G&D0gS@eLWPi|XYD>bL}IblN$@NsajXz*gm znJJWa6#uV`UXk??`lIkl_VA)2{U^OS2SBwPw!S2Nn6GFctp3+MCK5@688bX}5mlPQ)Q+WstYnI5q40v{8k zmUF1TfmxVvXd4(WBPu6O#vbLl8%GmHZEz7DaxL&5iM6fJ(NRHI4}HU!6y)z1Mk3=5|oq2ew9Ciu-KTGx%(@ zKj0dC99BjY9c~v-1iW(ik{F{1%0FGJf^AmNBTN#FEt&wUA~6x|@SItBwDOLu{$fMv ztK|7TA@9#;swz4^bd+g`VgW3Q@yzyK%@Q1~Xqvq-yy79p89Ae6>+KW%`cC zgNiS&7i?VsB8b`{l8PM$jw+ISk?bTN?C{8gOgIpY4CA*JooMt;{Dm2=`tj2Rd6$;l zhyQSB7&4H&L|_46T8tBnt5H|((9qfKVz&=0-78Fbbak?PI{e}EPv#6z4bnK&4WRyw z-#ox9rD@iV>$TV(8WNhzU}IY#M=gF{W|Qq4d1QBm-S*3BoHUHfh<_$*x9Osbd~(|X zN1R){z8#izz^kOe7-Cz>n?G#s^tfmE){nm7fcLR%@6M4ZeFMVZ+Njn~-@WGIXogI~ z`oyoIVg-Sk`}bS~%`v^7VOXuP-lD{ZjDyicPEZL?@!DaJA)_b_aF2mb;GMV{jvMRQ z>m&_{^liW6Ip4LIH??P+TzqhI{lf0f3$9EcAW5-AX-gy0=AUvV@^tk{oYLGBJ0S%) zt^15kQi(;3YdVtOHYUDDSKoFnwIf4jII@WHIG*{=h(PQQ9snqg-JM+Z@5NC`bzRrk zw)!-Y-T7T`D&$VRFD7lysXffl{chiP!Ry#(ymS+vKSLfr_c9HlIGV-29`jd?2!<&$ zF_%78G$v}QWh773B*F@krh2daH;wo~TYOyh;ghPzo`UO_GZfU1z#uRjloGli)KSCZ zT!tCz5xmm0$h%rWH=^DZQ}UO>@M!%%3h^U(!OI49(Oo2vZsMO-bpc=U!`l_{Yur1Y zJ9!%&QxXQ!+rhZnU<6)J3|QXGyst!2>mLQuvQwYOGkMb}=DigEF%|)k;MTPIJ)?KK zCogQ4*y1Vfeej_n6K@G+t7=9~gulGymobS-HW1f~_lx;HLJ3AgoC6kycs&*ac>1)Q z^Y7R2Oj{Rjd=*_`+1ROb$MTD}pe;uzxb@a$wCMIJ z)w;x5nW}2!fai@vvfauYTrO4hy03Oz<6|MckBI!<=5jp!1rYn9II1!`t%H905UJ;g zL`JTzPRJver*5q{)%9CK1aIT7C-*TM+v(z*3oyjD_;fIA1B=1IGxFu14G4&W-JKrS zzpYGbCR83BwNT7B_0em_uAb#Gvr9#qspQS_+UFN+r^1D$wNy57hmb0|>|1Fcaqf97 z_&c?`le^z-zQt9&yV9^_(9xQCetZ67egvm|bu-o%fj6nV6qoI#FtR#6_1dRma@vMQ zdb#Df{)7d^=CYMkJc+fm(LD{SE=B#YKWEiuU%K$=`>iGSIhuUCtYC39G-y11%`X&Lt4U6$)k~<2t;)!CZt&41F?WRUk z!v~cih`C@aF<}D4K+_OF$pFs0wp<|H^K?X3qZ4HR+D zfKkK>an89wEJLwYQ34HBg5NN34gj8|y6`Bp970_Vfs|%O%=E)ybuNO^Ja(wk$IqBK zAI_hsz4pCapMKG|Y51Z@*mt{_?$J@{Lp~y4%|{u}hzo;TVly!gsKVh(Q;5*cpc~Kl zd~_e3hAy{kF;qW!Kd?~r;Adh1nPe!#kG;qF_X~=~@H9oS`)8RFd9EMmnd1U4ewu#9 z5E8y$o~pGRGkG6KUZ|Y>`n0k)^}vs5+i$Au&PfK&$xja_%YAD}Vk}?+%MGAhk~xx4 zYWBeZ4`6VL2LNU{(5f0r6Kh!kl8T!W0p8`*=3qSFZdF_D>sgdYtZmD|9k-6l*#2Ye=oo<_gv=|-W_sYs+tQ>U49*f?xz37(VZg0oQVo%1TtyH z^d~{MdWqD4j^H=1lckz0)hIhta3|@@-Cq~=5ekvu-NXA$uhY$Wze-^-Wo&g2l-VfWI({Ku0xus2_;O*lyOIoo0+^419hYh$o#|i|Be_ejZS|rBgD@Zs_g3#G zU{2s$&Ztt7BM`PHAOi?FU}VGBiNY(x(V^iGSNOq5DCBHOTX2v$a8T?#xoxo|qoHK&wNYz#N1HE}1QfiVi#g9mM?@IqGE=O~ZxD}h(?eC& z%n?WMMzfgbHBQ^+yXKwum~-UR;}^m{TOGs}4nVl}E8QXc7Wp(Ps3+D{1(w@`#StHi zo(*NkKJKEz@nhuoLMf@%s3{`eg5m5cRL4UNf;xM@>YuGfe6Th`Fz2F2@$|>MaE#(z z1C2cS8Mb5ra!W@J^*Z_@0?x36w9kL|QcAwZ3zm*nr}pC@<{$7LEjG1UfgO1+RVli1m60ThX$y_dk_eSG_P?f87Ucp1iA!$cBr z+9kRCAKQba+h(qaH~yy#c|QitmAVU(;fCjwp~(CLf|v{(v7)uun}g~oktB1roc>(;UdZ1>sT}(j+9JN6-OZWhDnkGR~39TceVCC!vRg4JW{NOE~NDsQ%=$L9WMxN+nfShdlbIhs&Zic7`58nY(m{v&oebydiY{Mqov^&I zw*gKeUcWzXu)lNsM1H0;LV59jR<=S=Peda* zQKXJ@z)WZoq-?<;Foy)>D|9p$tD>#g=QsD;0;f9u>iTcfTkp0ypVLEmJ0VQ~6rKaB zBHNHhfXEBZ^nqatFJgZMh((sjsT4quxUx8fZyfxbH)cOvLrDUFAxQ~fIihHhL6MG<5xiRp|H?hDK%gX0m~?Kw7l z@5Tzvou^E_cs0ap6Fk~BH``9Rou96+>2xyg;t&cwk^;Lo<7=dKkyt^Fp`C#@>cgb5 zA(V=`KeUe_APaLgJ@0mPy25YKx@p+50J|x=L~v7N)x2D-`XSm_jYxGz^_GPkF^s~p zH&j}xOYEXQ#Ke7q>R#&>ZB@sf8_=CIUM(EYe*#<#*^q=DerOdq4-O`hb&wsn75;Kv*lDwUmJw!zk`` zc0Izf3SLDnCw?tv!wesONp=1BV#@V&3e)>~?&|{C&qMXEVwa5qV3F_uHGU+o5c(p5 zhL-lJr)Sz%uv{^nAAn_*d3j9X5fN+ej2fc>6Jf7DF&u6IZX`RHK2u9$)FiGy#hmPz zW%WH?-(GIP1uH;#`&?F;B6v6Dje|lchgo=rHDZ%E>(&|ySiaY67*(~O*#TE#w+xi$ z)b{0l%iaVm)qanF zd`@_8wP4yJE?qT8;%#KOe`TovQo0ZOj%p1x3ve<3VtXx{b@n)k3;2}D3taujuDD;S zTB9j$-R;}PtUJ6;3t(TuCz*!h(Lv8DxX6EIdNlrgcOQlrxX%Ie=?=6|O~Vdcb8w|a zAkHC|-n)hO;8%h(olU#?`w8&rpYzCw#U#p6yY5$3oW6Q^g>uYn+x83SHa#DdZx0{M zkaG^4MUew{4=m+(a=_%Ay@pgML@LG>$A}GA>)VZPA@g2BnFi{!5*XG{jqWX;(-8gshp<$JaL;Fn*_bzt+`7q`e6gP>5z5=9tAE^`qNg%^-^Ucskx zVT1$@JTAj$_+I$?6D5_O2Z?iIAF1IDh0u3 zfG0S%1pkI405(8ciT;_%d{TO=VSsQMLx9hoK*OT?h&<~1$_M+ZMZcA{H*fB?FffR3 zS9|WNi^7R&nB>@?Hyo@0`uD0k?0>;re97|uTh?}U2>e83V#zH?kS;@wQ@h0vuTi4g zO?20r?JG5NTlFJt1@$FW^LMpS)_q~&XxV_t8UTt3T(6s~iKcyLka`PY}x`z^2%&PhK5u;#SkJ z+@y$PW?^&tUH2t*r*B&l&~Qb9$pUt8Hd-!8ly@4y$9qEQst>T9!S|jPQiq>ezQF## z{Ms)fP2AmQ3v=#N^|&^iLfxQWFUfdv{>6i-%9fBYVs*=2N~!pAYVuo&C)bdt2L0S~ zB$i)k_?S8Ve=a{2CB80QANv7P-h=(g=Jr=s=7`aQ(p$FDFeUTMAk}+M(t8YWEK%)G zHwsyB)t_YE3l@-9>bJyIdJYo+?-yr{6jGfDw^Xy9O#V@xR!HeI%Ci5K@O!@@<*T{Y zYpx`puIrPYzDMTQOei26U_!88i~uZAwR*||_@O=r%@MxRMK+8f&QhYER^xHub>J}Upjgr+vOB}voBe~ z`;VaLRYHrblisXI^+%4b3J!emR!{wDlt*;Hhqs#+?Ii8JW1Jq{mM$ zwsoi0SH?~s(w*;NvK8-oMJ~S<2F#EpU3K9#IS^qV!YpM&`x_qs_PM;cv!`(jv+=} zEE~EsrhADvt6{5=Ik8^;OYalaS$Q;1n0U9!)L2YjOn{FodgC%sWcE9XG&3#W8~Fxi zJ_xMOa)S7RZ$0KxOsgY+hVS6q4-i2J9q0CTK9aev%)a;zKJ}f+`$T8oWxDT}D+zUp zT?5Dg=hoZj%&i+O^o7#ctc8qnePWp>!=J!AcmIE2K=5dQv>ZZ;n9l7E&0q|IVe>Hs z^QQBNh^x8;>Fl=_BKmOr!yF#DT{bSv_=g!sPoq&toEVn&o=$QcR0)7n`)w7lE=qUH% zN`g)7*q^Qqwf=#8b(j^W~o-!739b2NqmlU*H_yto-m?%}#?|MQ(uj6|H@@lU$l@pU@dput;yJE;2?t;74p^VcjLr$f$l+jqy`@9^g= zs%>AebnL&~=CO|GuFiJFTYpHlr(t%<_h2cEcey9@S48mZpiC!2U?w1$`5)NX{f7qA zy#~C3fMbc``dtg<_*-)E(AGP^h|&auPu?aD8iLYKV1yrxJ@^P5oe+Fva!3Ft$>toD zALKw7`iwu|H2i{+T3{4;rWWuI| zAm`Opc1Rxr4EaP8JyBpBqLXt*dHeBm^4n&m8nEUug1P^x){j0rhzJ7;ggY% zrxRx9FK!ASxR~J8c$<%=Dx(BAAIY;Zm(D~8u!d6D1S4&D-=arZ(2jW6@It~+Y&(=( zuEXjhBNVVVnv)}M)a#D2dGC0rEOEZ3;WnfHU~zEVtS{$!Er-YNOqclv-2fD=Zv@}& zu0mteu``}bHaq`m9u~=Lotz)8zL>4v`8(B?N;Bv5%C^S4K~M{Ls^GsBI+q&<+^tu% zBG_Z$h=X^PNArHqIBR#drl(ICFK`glI*(9^f&Z zelqjsuD+fyv!424oj*2$Vy7)n%Iy-$Lpmx~_ksJNrc={}i0UU&gU+UiyoHrS`vxlO z`51QSCu*f{MudRE{{#utf^s3H{V5LTCcB4UT6Thm-RAxpd(AI>YCC+_3`(x&kV*kz z(L+Y>vr*f2Sm_K@;p)q5ya89fsSnK5SHPg{7x1)PnvZG2m1F^OtEV82K2N>>_%q;X zdo6-opuxf$lm=uuAdBO$u=rJVK*J|jT>GgXUFCw!^-e!T6co+|XZ>!CmuQqsaiN!j z@V70NdtZq)TuCr8Ve?7j@&6e#lL#Tce{zq>rHMVbq==dVDked$8GhCqq)EFg9)4Bp(3V6tq*Lhsb^gd z&anpNOdK!_1T@q59vFDs--CQ7M;vMG!W<<9j_a+eW~Y`S{l~gzUtEHjYj6?MQX{JA z-3M#NwdaFB(gb+Y6RxbXXHSsxt>^dAke-sy=ja|}>s|{)vr&C_29nE?XjppFTzd(2 zRhMW;BCp{fV3LrLjT^Rm3qrZFL{LDW2gK30|5fPm0xH8*LE4J7Dr0XNuZELv^p`5b zUPw{;;RQJLqnnde?WARpvVrf}>~lOkJUDJLNH0PT)18;=+ZN;fR#sab`0huuNkE3> z0u*X)OS{Bgqk1~FJ>znF4qb5;**Tt7h5X8zz;G%LHmwl(niuqIliy$Bo3&o7fA<~d ze%Ikhx<2`1;PLVIp`*F&&YPl}XbM~z)w@{$K6}|PTo5POd*-wCY%f_F()WR`aA2g( z@6lGvvghd+vZpnHq}^z293cyt3v0;Qd>Fdk0Kxf}yMnmTv9)Djk?UYRA!x(q2!hm} zB7=Gs7A(&$B@7sYf{LXobGyG)He3DuRt5;Nrr%~u9Fy=Fykdc_ZWzeM(?qQwSPjp< z%JVyZ(#5OjzIqX2n-BF9L*`NrO#aHCO6wB_+BQh$_7)_m2q3K7Polkh*UH8w@+%hp zoz7?~F3BXD3FTB{bNAV4$KhB3u*$ES@1{3X2F{c?NXI^b~XyZbgy z+g1brBd0}~qwH3)p-x689OhEKC@dzT6lGL0UR{O^6QIISDg;mdo708!m|N(pst5!d zfsNTU9P;dHLlW{`%W)Ee6Z)oIpWc{uM{oPbJiG!}=j6OUvCq(VQj-LtYaiC@7)tab ziWEi)%GkALzYgyiZHa?L%u+Gc$F@I$C9dDQDLe{Fmi7nLB(R56}F{#0RlRKoG zM3r(wuCbO8m~zLb6;i;l5l`}Iu@t6Jyz*G;v_a^N&*7wW1|7b_x6Sr|k;R^ydcY6+ zllErJ+smUL#7uHKGSn`?mU6hxc(e*n5pSiPgN>0~U``1Ha=W-ZF;;%L`& z%G+n<0_3o6LtZ#JkaCP4pjGFZ4p2bs5W~-ou*~P7rwJ~k->hE}onP83dgD5J8`GH( zF}yzBQ15AG zeh&GCLPlmHmPXcs5f6ZoQFogwBoG&xT% zjoI~<)tKwf#~wch0OMWn>Pz)CZEKsSXcoLHZ4epE3y4Ut2Xaq=c`-G<6>-<$7w4r1 z!{@|ThlwixM)0nq19!x(*^5;()idMMrOY$g!MOx+X9dF(*Q{A(lNCm)1)%o0PDz!> zfg(H-_#k(3lLr9@*kTL)D$JF4mFeEIj*H&`f6@wGFVSy%(h&pWNNg}+zL986EMk@A znI{yCZ$l$R7)B0{?PbKnyF|hrp7J47`HYb%J$5#pix3D10oI_m~yAdAf?%A^Ri`i<5Oy}!+}{V2H| z_LmXe?dPy)0}p#$_8j7s5W>n^nv%6kFD05QY5qhPnXTkMFzLVl;3oj}E-SOHX3d_C znUjq%27$dT(~Lo2fD0NVFeK?IE6M&m+h|X7=gzPHM`BX>7~uA&j~^8KGH8XTF@-Fd zN+Dsud@KVpAVZ2!fla{=-TLV5y#Uo=K=pph0(Np{%i__0Qo%68HG&{&_ib?x*UP%~ zX^)>04*VAO=n(4LL5{s>>KZJN9<#Wk%}qv>LSTutY9E-1qPwYamTbwfge;I*QJ^wv zo_|TlJmdK0EsYJ4F~$Z^+}gyO=lw;wI=sZE?}?mRHvP1_eb@hjH}(GM&C)>9g4mcq zyHen9{aQY@o-0u*kgHVDDj)s_A4x*ZgtcV*QXZnV?yE!Ycp&fOvqY)Offd^jl8hjS zdMzbT@gSs!l;s#A$~~N{QWt36Z^#+=DHu@lrC%BG|D(=E0{-QP(lIdoLbEonX-c z1d)`eSUtVFLPI!PKgUgsK9e_2;>>Tdvh^)n_G}L*Kypq_=J$#pViHWN`x25$KB;AqnaLkUEfJ#X==UnB>sV__2n0h|<8^&H70-ITH|dBxMZ)gb=q zyl!)HeCG0#sN2-pOR~!MT_!t4nwB(N0w&?WvVV9 zE#C;4mU$Z6Nf5=rCz){~X(YE(@Q)P%J;5NzB6$_UnjFET0JkU*Dvkj-21sUvbL?`A zlhtO!8G`oHFMB7&b~$#jClP~+Q7kD529eJUUR9y)7_rohlF;5MX;C8KtgH*)%!21k}K@-;&y^ySG+sZN@(zswsYus z{h%q?pXL8*{wdn{c&S~!j1wdivkc$XVYNGoU{b&Fut zngNkP9V(!d6JNu~%xJ7jv!J1y>R}3DE_NcV%4{N=qJSb0I_xGB7P&q(~Fh%nz<7Wz0s?J;-;>916kgm2i}?1 zrw;q?@n7sNT}K=qS6g2tD7y7@FAG~f3BSb!YIbUIv^qh}2MxBaCFc$qyz_ zq}T`qOZGNgrDN>uC8E;vB+9mgi-8?ysSHC*%mRJ!i1a zcLp%xIb>I-IlGWk3i0*Ua@)G2^I|8C+2`6m?q53vanJskx-;Q%u)?G2MRc&+uE0{&E>MZHQEL{3Wv5 zt`#l0Cla;oZ$aa;DM&#l6I&~dVB~`kJ0wWq5<5X;JK4u=5$gZ&HmE2~La8kQL8UCB ziJYn|P@aH~35Na#3rpv-4XpQHAG$blZcneMHtH>ry!2>0oif$n5PxmrDWG`ApAsJ^ zQX0yPS*U`gI17`A8X8%3l5N=4;+h;2R41b|J8kn1R-21U$4HJ!8*yXv$D9?}Uqzwo znhq3&_}iAi2HC%~RqacN$!bj8^6h)S!~3U0wvC}@I|D>j5Efs ztUWlN2QEIA(g1DHg-KiHnA)z!IRL%6CR07G8g>X3W6i6#&qJkCL}p7El{;mR=f5;N zw)g+&CI(gyH+3t}beewe&GrQK0F zShE%IeCz!50(kEN7%&rAQX&E3NiW z2+Ro3d^k#syQ)Iqv%p|)+!YmlBRIzcBprT0HTs{?bumN{`q%itknm|Lf~}5oC%wv! zSD+uMb^EQXS50dL#v=F(o`Hl|1Yj*mluTv{bi;N(-L{CiJZ4z>>9cJC243dDj>yG~ z(i=jWp0`+spP^vGtXCE!q@=uJVyf1Mej#6kI(fgjUWf4Q$t=!W#;S% zoWb!#fSW_>c(OV8XKJ3{h;E%ct$M#@chl>%3u?p$k(7W!EEKv1`N*30MlBc)KH9za z=QN6e6--^3X;A>hE(#>2RDE{E6ydtu+z~~Z_iu%f<5!XOe;wKFjvZX5mPN3FoQaL^llP}+#Cv?5sTMVfQDVSRc~A~|GseQ4$mqNJ8` zSKy5~bn&uk;euh$t)SCpa!F;r`zNlDiVaMjR966`_5_TgE(*bv4-EhE-8-@HYo2~0 z@=qR=<7@KzNcXc7YZA1C6*6YlZ-`a^S;%&2@W| zPnlLC1X9GJGQe!jhTdiLA*bd1%+Z<9|b?r6q&^~O;Ke*`$3+T-u< z9Wa+i4#!LMG!A%LYrt|QG4ckhU>8hV+BO4aGp7}7`EgleXXKL1Wu5z>>&n}8ARs$8 zeE(N(%fLrewA~XQz@>8P!ly{cwf&$hi5#1bDO-M^9{2hdz|QBI1k^@caZ}7?tnvv& zun{3)uzvuXJL~Y$4LX@LS0M`_N7`oq+c5E7(|>`$`b5H_77IVe#pepI)lvKz`=dL5hJM6>jbQbC zck9!D3yRNhdS64ODeQ^DT%j%MNv1+)YLR(CVoE}P22`p@4eqx|K8z5-#YX? zrTsVq%!vmrtA*XCj{|XL>@po^ta|k*VH`wZl!c_q(YfW)h+phYOn9ruTyYWyjL~XL5XJDyS;@AwA@#H%GPar!r`8IG8S;^!mme0TvZIBhSD37~m{?k9cvF zR3UYuG(z$-*oI>WTJpDaAZLZ#6p0LYuy^Ys_MTBgB&$m5h~bCl9FUQ07$0MHS1iTw z)7~g+eyCJOW@}g>Mn?uf+x9w0wdE>&{SjdGq)YgT#EI<@IQy*|pPuwd-W1Z{FYWC6 zdd=Rt)j`)>{T|GGXskf|1;Pbxhz1l(T&$kc4TAR2h{QRRsX!0?5rk!*uNj{f3Y9K2 z;|J0RYPjf?R7)oyDPOI3N?%W%6NQ2rXE7PO8H;bzAoeCf<0V3z1qU4!Y(+?{= zy6oxB6ky8;xy$ylJ^WWo?GIm56V=cFK9eXIkj3%wjVXe)+MbMH9R^I1{spBXgP(xt z_TJO%FVr;1T~%m14PZpWC8xk#7?Onin-gCEYr4qX`R)3bn?_LD*s0_)XtX1(&?Bw4 zo1hhP@+CrwfKu)0Y{TtZ*JTAE$5}aR&^$Q!E)k+AbCVY^07iBJHV6d-KTqhK0ibWT2%xAQqxrT(kMOV6PtguAE;V zUR|gQ&GZMSDI_b!qu-6&P~?4vJ^P^9+tAn`!o8`>@!z_n7GC9KWszNZYPnxD=lJ^~ z*0R4mWr!Iq)pyc}8F&Br?a)cyzwkt6OO+zZi5v2vlf;!&-u9*6unNcGrRejSq{x}#DaYcRFtQ9M zomkNXSmj}pB5Nva&`3wimU80m+pAKASG25BTG1kblzENs8snLfR1@)*O7hyPf;OZo zb(He-?0?~BNqFqcx;VNq1dY>r9P;ZH#;9*Lp+c3}p%H)LRXW0aqUZ z>m#vBgIXC*rn$f4%^2^4)jW9>)0DYP?a;Hs^%B;05N(zrAcwxm9 z>Qc`AK?zO7!95hPg)6?d%HqA6pm+ct*uP)_*g?1Zux(ejJfPyd!E@l#48bGO%`x@+ z)~e4yKDqb%OXKQ9kx`pxo?fLvc?y@l-Nfhrw2LVd<0;EzCC;=i4^4gS&o>q9GqqSI z>J>dEu*-6%a&M9w^`+8!t7(z8Yn6U7s#tw>PpeQ9|KP)jQJ`I*GDrCgxTrGBg($Tb zG2z&iTHg|x49B=0a8Ckf^mh?={n7l^gCpM!>kpS=hz|y1hAVvHNCnJy6lOgaP_s1} zW`}=&%2K81J)ArIJNDbC@o5-<_^XPl?`!$^wCYtGaU^IHDwXZFZdYN>TQTFST64Co zo4uvZIi`Ht#^|6)8ErH+N4yLIT<(g}@Sp4l=UBSLVKr1xfvU{GryVL1dPRNDL7oF4 zFK96V4mX}Q?`BgWGq-7-47ll|EOBD_ig*2V`5F9lhsDLs9<*QN>;0!jN-c)Q!MiyL z%(hA;#C?GI5G^UUL5sBy=oh~#R`=Ju=i;76E3kX?PM+%PU8$J)Ar!yobCChqJ1B|$G6Ej z#|r5XA#K@2?iBFxuN_mE!K<$%AIK~dfNuIvIl*#*k&+x6{Kp|vSEP-Ef&}wHs$hb0 zsVrkk{EaBxp@S%N7mQ1|Rl1Js!7V=50)bOkovZjS)N{o>u_KKt%aLR#LZp~{rwhsTS8|0g%`nKdcpd zgDXrqI^xsYt~h1l{z`~JNVRdW*#Dy1gERX1vf9-8FCEM~BU=Gv^q88U@H6^0rAJ0};K@0&!LMK=MF{-TQH=9@0=-i(7egomLw--{?k9vPb9NtO6^wv_BAP z=Y>19t)aqXzM9e!a}n~+oXEW92yXdO*-$wxQaI5> zO@r?zBF7qOhDxJGWzDki!sXYO!C%k%>H(ObL8K&~_oe!z>i$VDZZv|q(uPtAJS0qE zAWw3ZWAWu_ALN8hvj3GD%_)e~9BN{g`FAnF>!tMtm}VXV?xOAF#`_Fm_hPHz^fHj* z0aR=TJhA{O=sH4oko4TKFbKTK zrA*8R&^Sb7yKVnly|u>#a6ow=m0reG)gl9o9}8%Un1VF5?(=7664av&}Q@_LiUfmTp%7cSLZ)69RLY@)IPCzE*9XqK~ zs;;g*rS0^HnxMmK@oWGliqnXt2t*JgkW5W|ZG%j`U4u*?H^}|>pY4nK$117!Cj_TV zDrU$eOKl7aCkew~%!L)0bB({Fm92a_MWbp2VKU^2@yQ&5wA!mL7*m)ByuWlOMt_je zwyBKNv<9V%6g}-|yyZZuvPX~{T3$N=gj&+Z;h#P>Iqhr&nx#xZ?0KM6UJH9;Iud-_ zbfiu{m7TxwgHa{xm(;&L^XDB~Z;zbqxbg&y*zOr>qu)Gs%L65AR z)Ph3>n~CFKl92<+s>c#-N6usojtrg~sR=7jiFe=tu`s|>UJDsSnaZV0OUm($NoHM6 zI&UP z8ayUO6?ySWerqu~Q@~8QQj;B7RHTq_BZXk1hN=inUJgA+lLUmCa-~NH;~MLw1+T{{ zn}3!|_??x4I*XFL9E-E4bkv4GQ*IT9;UsXYZC~AQqvj(Ne6>O?L6ih8N?#LA#?bYWuzQ4PexT(4@R?mw=E=Zh-Qt5uiBl2U+Un=7i6 zA&!E|nP@KM1b!+~N}?2YgmwXW;yZ;v;`V}td5F+;t@nN^FzD(*$*B%+gBj*_h#Dl_sTXr!E{82QyP6lKZ#w*w*gT7MzVW~nC% zC{ln)@k8ebA|7*7i%AgCehsn4OD#E!J@cTmav)b^w(LCr3X`QWIK)5D`#}Jxl`# zO@)4SamC;-4J9jZ{;6QG)3De+3FKNk55&wT-&Z91AP@DCJXzo-%M|7G;{&hTFEr$5 zr2c3T29fDri~MEp-EI;U_c>HRfTgt;zg;9Q%P=c{E2_+|k}z2MQo&qC5@)p^S4Z7( z?w^-%^}{l63VlBuZ{;Y;1+E1LG;!Me@q~B|leJvHSIugYFMc2s&DeF(Oe5@u1Z)wI z8@;#`Uf%&dD=)~AS1kxM&w5Jww?U&nFPh4pp_9-q7$cY1`D5ibWw-3bF0kCmKlr z6zq2{XDmv~iEym5T=9sQqOfrA$A6kW>BOt|FRq6HnVKyx&?BA(L^zsy{icGpBLNf1 zUZ7>@lQ`e_So7rLzkj5mv9WfuY0eFJGPwa~;N7AJeo-1=k6J9x;FCB)(^m92Re{i98Dd}vlC&2jY4e+d{KLQrH%-io4-*Ge4S)y@Rsn?{?%8F5 zCnd0p5YOpy(a{0a3930)JWaN>hHRu(q&RqUX+203@Yde5OMJ=bic5_~b2367f`?<< zUwk@%xpMwld#Xv9KMFkw+Ow;Z1)>ye>c1Lv)PI|8wLr%E=DAV4_CRe=PKaz$QcVdo z?1O%tB+UrA@)y=>|8wnvdZ=r-Hli~e9bD?vMb%U(%2Cu~npjamp#p_nYA&LJnK#qh z?!T&ljWdxx{QjKf^87GQD-;QKm4fw5!`T2&EP@E889|?wGNx!sGc*Oes049rC!za- zg;Sgg-RH;uNo5Ek(01u3AP&#Fb zii&6up@b4rGL#IJ(toY{alY^G|9M|$lIOYa>)O}eYp=cbzA!0WKipWSv!E&k^ESSG zyXb#(vCG51J=|g}iuBQWnw;#IA9qK4zTu+Lfs*oZ;XS(bi0qRQm|L+^vT$(7`Uyz| zqxUZ7CC4W6X1z}aB?jVuITxSD2txm^qnsq8m)Y`OWJOS`dg~)b$d3a)aTF8 zFw@v+f}wS=mbzkG{W;4vJ?%h0nUBW(wAU!gln3;STag_ut>0h}Ym6b=irOn;`>a;Z z>3=2BWk@%ptj~Vaeq1dnb=Oa|a?NO6mVaQ_UN`}dXL`TFbCpVSL;9h`j#ZSGefrPu znxu*aHh;O4wpQlulx;j1Cm$x^F-cLoI&_HVl>^}#y+VVA<-7OoArn)8Th3;i?l_+@ zcxs!*n%i^-Iezg4rwjPPe}a!ebaFN zc5l3m{>`?>7uUUsnDyn;=B!v8Bw6f#WL;I35q0yBrHde<8;c}x2$H!j6RWoAB845w zn_N9SJWM4|DQrRjHT=HUzUE2g+IPDK4tS6g(C<=^d(VkDr#<5R9lACr`0{k0%*l4x zqZE=DIQy7<+=efQ$FDdxo9o{7Q6D{vQ8-etp}{>Y)ulxSIR*ZQBSy}GL&D(P!JmurZ_L`o|Tt`bnQkOxhOxt!crn+nFIc$jH3CB!AQI&TI^fXpie3F8zTk-TK88WCwQB zJmrN;x)&H)>aM&fS z0BrKrh6~qQYA0qDa3o$!(`$~ogAJ+#y}YqC4HMeFG==%cr4>)wi-GEY|NNYlfcxuW z#%+7`2UYQSRV*z>2SZe9@!ap{$1zKn4U zCBv_tt!>?_ssrtx-$_=TlWpa-%VJb^fbUV7hoW{mcc!)Eh_QqP=GoytiQtfRqBhFt z4;vMEa#&pW1HC^3EW9R*=69{=qnEmW(QV^PGbIWp^?*M}@hKfWwC!?F%v)4e!2@jh zRvs|__?eufA$?yR+hKG|^|0UGL20;(+C0w6tKTJw$i?0N%q$kZMI3cD*mg}iWDvW- zCHAT6qV#UktKC+=>>(}E;)8|vNL%QF!3zKUZ7dOzCjIY@*y>Hn<3pBpMfe(zr(Pu? z-9ugROu+q%*N*sgOf*rI>i?|lnCpq2Gj_WNnH-eu*7ogf$Ip%lt_c%DG=KcsXFGbK zV{EbTdN4G&@(-mVZm79LO`x4q<)2$1kA?W z=R@k9EAK-`x4wx;Sdkknr_g;wDfrd4ZQVK9{A?GPI!+fMSR7h1FP>`Se436 zj=o%s9a$QP_^=}ONT8k~Ecc&?G0QGRPyY4o&NfWQsqnqCtZw*Jsr1tGrNeg3m-^dy z6c5Lgrt;V2{llw!EuF5Wcti*kg=;PaSt^gPn)5cdOx}F4%*jQgdyAaAV+ECcTVB=w zGj~DLq{@Gt)zS;!Tpl!e(-@#e=%-p{$orcDp#TeAfs(g%eQK58PwUy!am#=RC&Xe- zTE&|OGy1|+uRWf2cERZ{zkXyXS-AgOI#cLN!;PhIz<+y8x^D?TJfyO0`L-rAPC~ue z@zJw+*SKwg^uFkGiVHTDkJLCNiy5~|)?QVmnx*R3$Ev?jUdo`uFnAL_<1dJiwV>Bl zN9&QudbR^DAvtv@29K;2V_|1&+f)evtIL^8L!laH;r5_Su+-rFJw9Xlm%*!@{V-u> zvQO*V!>d!Rv~&S_VNjVdh0fb`q6T1ILPHuV*u%Hfjld0Ri+Nd6pDB&TGsA{kU0(YF z-!~#8t7UK2iisP4^pHz9k+E!pR>oWA}X9{#qGlD{ALe+>ICsH&|PI^!^g& z2lz(tE{&(}>f`l1FikV5bQO{h6L}CIdRiI2=iDDHvbD_|R9WVpHZ7*$9%_6oHG2o? zI7`XpT(9a_%e>ot!F|gMS8E6Nnfi5pa(+t-q(w zG{AFWB_Lg%zOC(el-d{o*=@?}Q2-m}RNdT;1P0SU6}1CE&o!T&w>|Wau~!0kWBHVC zWefha&7*t&bgiLy`PANjYI^_C3$-`C|FhVra~tpI=6&6`hUZx6D&ESpz7P$^ zxQkW?3U-~SLxpdO$~nTXNZ&J`ADpK@fggRu!_C&#It~AM0@k?M#eKogoW6e9h<Gd%q(3q z7AaM3Sjn#uHq$Y70Df2a4I(w}2n_Y(u7S&|aq$U8OXP4&a&mIb#H`ldRru$YLf2_X z2v6|pA*4f17dGG6+i$Kxpi+BvPjs_+aF^ zE{)YWaT}j3628_hrU3P?E3hHM>VR%_m6yjwB4J{#Wf7$|9%~7M3|{e}JQWS_{e(j; zULMyNi@kgWT|Tas?&<=|#b})4ixlY#_&wFi4hEids>1yXJcW0u*+6gP(&L@)%Hk6P zyY>7M*mqmN#qA&0w0}9Ab@IlIqy6ShiLmJvsi^BB)7Pk7oA!y1F4E81kME(C3cGYD zM%bfk_beB?H(xKOIXrgPuP=l_;~6HUSYh zt64^8zbS~~Zp3T$qj=q+yZ7CO{DI#o} zq{W7YC;Q==4!Fkq?ykz`*QoM^@59m!TB>){KRG`nQc>Ss@iS(h)t8>=2|uYEa(u>~ zOS}>5YRUEkbH-lX2^!=yvz0iX8qzZuHK`ne-|quU{%v(QDv>cTLfRP{$k98T<#kW< zG{(7JFHLiuhm+0gX12XwkWkas0P$se>z}q}b6jlBCHJb_faMncU+%Jf`IdUsBs7*V zXUElZU^+h+A!o<^dZN-*dWzGw87Q?q*kjQ8ChF?@lX65(gq`ACfC|Nb|JfrI-p z^fwBIQp?B;8+;$;%O7WS;{7|AL{~Y2e*S0$=KNtnU3l`+P#UA4PXDEklPDD{v<~0n z06SUijtc###qR5GNvmUWH*kKi6d48gN0Bj^FObO5EePH*Yp(}SF}6zV&M9b^_}r@- zgI4&AJ`QDPtiRTkGF6OJoU}m$7hw%V`{o#i4Bh~ZE1AoaRu;z@F7<1D%R77BQ}YJ! zXSG*!K`l^;DFsT&%Fu7!!b|M;csZA*gU4a^UIX3vErf)EPAgs*gvCMb()=^pa6P+% zP(AiJ$Gr(3F{2z7*jhi&f3_W7e!mA!0^RvVwy+4Uc_G*0g z@BBGPZkt zXO?T-gX7#_oD9qbmtnq$o;BQ~P~qR%5s`b+B5IYV=er`zGsOlm63v0B7M>hk`A--v zZAfK%0_W<3ZyB>BV;?Ug_x<9mHa8AMv<}3gjOXj-^f7Ap5;``wes>r9UJCx@au&HB zHQTy%GF=y#NpTd&gNrP%e7VoVZ(t<=5YE+v%75m$L?S}u3%@0xwHR6q7S{(q*FvPw(36ii zj-cSaz_vedT8sr&Y^B;VTU#^nF*-!)CT5wt@xH8tmRxmkudcq6g<$^MsixQa*PXut zBKWbynS&~o*MKS~tbT}b!@bRRqSq`*DD<8$T?PJ{1*Nsu>ecXYPLlx8Ho^hFzxBts zGAMn{j@w>8XYjObSHNbwpIbN^Wwc{9p(re(q8R+(pG9dKFp#C;$eIh*nr7JhopYx& znR@4%lr7^&;e=75 zPBz;)shffvlq;i7p&Wu*F3PI&wR9h^(sN#d>itu&ohNWg?b6}&roz=9nY1z44_ACO zpuw?hbJovgU7tY*l@9#thFa(mZHJ(Yms{50y*Hv#hbl2v!*~LUU#AoN7PH2UchCcH zqb`Rf&^hC7oIHfAdJR0%Gki}vnDABL3%CZm`zl=kYADEnA|n0rJP2=pB_ z1G65Wb=tNPSbBoPEy?{uCeCG54`8FahKeM_G`4>~d-jaAoEx?fGMrLi(HF1j79zC_ z7fJEE9``>M{Gjte!WcXrON_HMuf5tmq)cA!()nj^58Gc@FxSv5;ANjv3M-Qa@85k5 z+7|IEt-^J(ZQn0voxkYc(ZG@&UC~ioe>QmH+PKB;=kUh-z_8&{CC|Ktl%T8)cQJgM zgh8RUY3FT1g{?i9c==WaK6NpQgTbt(v)=OOx#ZcP4&L_?|Eq%B;sjLXR-xPe$VAV0 zYrdCxK!FRmrsXWG zgTzjT1zTiH0I=bLp|(9R9ieq~rN?F4AD>rW?Kz^J3-tv^0ZLN4_9vZp=wwNd)3$fFJyFklIA5`V9_`o zGnkjtQ~V3JVHH1RQARq{6%Dw{-qz#cNek$kQfF%(fwZda$LEU#O{`YPEuL1dYUj<`*>^iM^P{k8e zV@@wFiJ$uI@kKw|Qwr=*-SX+`yFX`_u0DM$OQq`YRZ$-Ftf(JG$NROd?mbw~eB8~JM7+Ul&B6Na(Y!Mk zy4I?P0K*%Q$i7EmDh6OI5zxsBY|aDR9(g|a0qzt1r>}p@ZMQXR9^v+z6H31t_q26Z z?D(Oa)zH@dv!vl$tIPWGxWiTfLnk^!6;1CtLwQe84=LpVQ_^j6SD!tx@a!3l0di(z z%w`_5`D56cbGQGBJAPnc(!s^~f5}I_=q>6xZ>PKdt1%iq^Nb5aoU<-A+^oVFxQfX; zK6d=f*$1 z({_obb=1#m{&IhuE?UUhYoP0xEA{Ks=U)eMa;B>U@ym;g=K^VdPDOXV$QvkNJ+AE> zudSWUc1uvRw~_Psz`eABATGo|hvy#DT;Nl(_2q3?WIkff9t^nNe4}BRWwdr6$z$R= zh0l;!aE^3Du2N@{QZulC7hbCgA?B#?_G_=9eR0#Kb@8y5bmPxvKm!8tCl^f35~1BeRoh0chnOX2X$ zYv9N9e*XA;I(ZZuVrTAC%(2UAv)%OZnVHL%)UfUQ;DA#g7^VzrWXR>rw_41~-77Po zhQ7M12A=2*9{UZLho$i5=8mYqx98cnch6vP+!qgmiZ=l$Qd*idFj%`}r9eLI>^FA> zrWzpAYyTsrAO<5kaAn#;dRl@cuqFV1j%OWiD^dlu?PO|dj1XB}{Ue#JY^ zw>GA{HgxobY9RXZT25?0@O*tZt6kOp=d-85Qe&^xsjw*@Qn);%8t5ab@$lR_WX+R* z94a$BsQ)&1gu;mJ2Pjxh<;|zKb7;+)*U9HDBwndKvZe)&$;{LX7vqlit}!K9#oJR= zf;h~YJcxhi5C3=M>LzdcQ2mE}i^Soj>!g{4`L~_+pKWW~eqnXvq~-fbS|W+=Q@M0~ z(FEdK=wcmXF+n5(%h?NZgk8dzL|k`(v0KPH(K<2U*Ozgfk=$c~6h3O^&ljI-QJ#2> zy%MQtuqC#<_Ht`!m|reCdC0{rUc9)a{LVCC5EBsbpbM}vEdQj*lg}bQrD9<6=1*@h zW$7KeD-h&gzccDI+|y^6iF{0fP~yhLM`$B4wzC_^JPMPZSOD^Voj)SvmB<>C&L&tW z#8}wc>cv_iTSaGSsNBez4_>^m+Prxpa^ZRYZ3`eb&qY8MnA|6qZ<@KR`&v4S8S$1a zTbQ+|$9YNYpSj~%&W@hGsCp?XDz11}d=y=GDQljN-SnY62fuOF>gc4ud8uIW=9^I% zS&#lJ4YN8f_OHa#Glng^xV)&kTA8U5`7EY_K%l2ntVV>G$X|TD`3Ns( zz|{km00qT=+bu6ZPXN6Y8w={|>m7h{+i~le3rabe3$Yw`EklEkiT^bAF~zlH_|31U z#x?zYaoesJ*Eg?6U=x^>sJW9uf^q4MhR7N(!kv%B9TkDRU@F*Nt;zb{Ue7u<_regwC8E)NDkelAr-dvKl{pTjGV)4<* z6&W5wcI?&;j;tLnXa#4X8&LoF&u6k&{ErX31cS?&Vv!&xm#eV)sw=T{ZjI1y6-S!Fb4 zmw_mUmiZ8wvJD$Hfcdtym6KCI;^GKeu{3>N>S(6}IpDfWOG^ViKm!JdLXiTb!8TNa09kD;Vd%)x zoMHhQt(oy2IM62)WGF6ZK>ZFsx`03N4E_&$H1h;5wP0EOWqa*JK0stN7)6w2N2sjw z@KEJn-dcHX>4!BSzsc*zJt{N2Uu=BZ1eUfL`h9f!9G=fl=IxzuG=43&mDbgSsILQC zjWyN;Ey+~==+UFjYCH?k+71$U*nE9-Q-Uyr4SoMmfhiL^r4=&R^SSl=hY_fhgu=mH z^6&I>fAs8GsA%z$C7jVImjluvg%mxzo+3d%;D+-0x;oagK>FeCBm9)k+UEv+l%`pp zC^eSz4F%OB+G1+bTf`@mU^ zZ)(xe89WLSGTe94zave{P^(^M~c*(^7oC<1z+rn^TP9c ze#zx^APCa{kA`pmY8`s$&;SVaiaFU2nSL&Q1V8)^n#(SN2gW&xR2=UpnTPkl)*8#7 zn7JRP8cZbJd`tmG1(}V9*qM$DC^g8#9jx3?ov7qw4Uso(Zk3(}JSQbkbOkV6oF&nC z7zIWKS@F3=r4Jw8VwJ{p$&w+F8f@x=8;1U_SLIOTkV6f;0)kh7dnk8-QNmkb8YoUt zVtV@DI7vEDjIS1jafVTN5%9VeBSN6rJ|f*uO&bW2J79%9g?G56?{w!t9{;iu=i5{g zX)7+tfX!fWG3m_11MU@TiMh)*<+YG5o5^QXq&oGi`t|*CD_~v`9EgEeb(cJ(d3SLG z>%0@C$I{ZGcuNO{8>Vdiu{M7D_eG(gQF&~2T?&s?xI4xI2zMX2G+Vgt=b^hkP_IlL z&J5*#*fh{xg4qZQhCjIgZc`gJB*jrFW0GJ^uE zjAc1+cz$+wJJo*~tpn<1>sTmen#JPj8mj_5mix7?L>;mS@{?>*?p_FBjU1Jjj#&UL zXu&LCrF0!;LVf|dZU(0sgUm7QpTAN$EWjWV({U>+@25Xqejf=`H#MsxxwM#sj*0}q z5}jfh7+q{^`{US4TgbR_XcmVXljp1d`0*_$epetexab|ru5W6>T)?OpOzGxIki|TA zZZBXA3kpHq{~(WPE{sp|79pPS2*PfXlYvHbqB!^X=FMqawr=Ief@{6450iAfcTjWR zP{YU@TYr3k=x#m%7mJ`^^XuNfcLdDYuyJE5yg+`K(!3(^0=iW^%eVuseSix{M;Eylew`byr)LTSaEXP2XYcxw zsfW?x7XTut5uBxY&1Qt*y?{N(%CD|hr<3x3`ujg94jt-@ib(qfyvpwT_=q=+6 zBFHLktLc{j*nr<#J{pa^oV|SU4NG@>?b}tLeecXhfBB-mN)#GZk&h6nv=Zn z!{D3ohdv^ouEq|!qRvXK;StQX=I5x2Cn|;tlOJ&7qeA2vz7H60^6LA?$~zD|)ipGnz$y}@0b;C( z1%E_!ivYqdEG@v3;H~iUDxmspZN;bv4=$IpMoYI9<>Y(psq|~TZ?6MVT7XTkPmH;0 z@B9cV^_76uK}fI^)Z*{9VnqPPm0D*u?e~0xQ52e;ldeIxgY@0gd5rj#uzsxCx7S z|of0!YEd!^dA8(-y(T5|ddX#mMic#8ywSe#-` zA7E99n~!(}zl1PkYfCd^yQ_5?Um^+%1Q#(2qmc24M?8ArU$8s#VHy`j*b=u4O~7Uo zd2FCV?4IFUf^k3@z5iX^k3)g-a4fl2FJ8RhTv3d%c!)s5n@sb8#ZYg@-Ex4VW_Yzs zd9ms9JGF$(#&4ng!sCKrI)WMCyZ&$Ia_JgjdU%rG$DNCarB*Cv1z1RpGCZq_nF1k8 z|0~OH>d)^VPgJ;bkOngjbYW6tf{-FM#-wmK4Z}#hr!B%IsH{V0keE}Z~HY;|ykfy=nOh2N5qJ~8im?br+q*Or)4gke)xk@hmP!UH}B@b$-!G-5-iSOjCvD1g`f z>x$P1Oi6pU-*ekYL2Uzb6uzMsDw<3%%aeS*6sT9EuNlZs^YRJ@>NyDajrqY$_9+Fy zi-IC~?tLe9TkE=)Uk+XN0-VzZ3>SPJKTt*27Z*5|V|WH2o%^$YL%;%s0$|KFe5-(o zGNZ0tzfLac{Q2_=fO9HAJnVuoj*k;stW#iI!PSq!nc};4c>o;aNQ5$X^BI=a3_p~F zyJ+V-3)YLd9@cn++%-AK&tMV!sT8Xwbg$q_7yp=xU}=#nuqr(5XGIB(K_?2Q4k-?U z5fVq9IAqsCC?ll0Xe!SSqM<(K!A2S=_$N^)%YNuOixkT7SY-FAn@0l!yJCEcAs|RP z!ChAWl392Ix_qh9ch6>GZNFR|B%}fScg8+l|*Er8`OF0odUcv}sFIevH7!;IP;Vv&n?8~u*2%-Zk6{ysp=e>VO%l*Z* zzq8uTW#OkBV}XPytm14=6a+ag0I(Jg5{U_(7M8o|(BNu77}F6U_*V=;z%j)Gmf^<{Fsxw* zILj)0*6D&v;?~18$_YOayaLo52M+U>0vNw{AbXubz&%F+XB(Kv*W@5IkqvRA0+z}N zz?kKC_vcq4w3cMdN6UkETm9+gn38R~k?+O=b}I8Z&Y)b%B@m(#Py^lozF7DRZWa|z zr{Hs8)-?J^IJ{lZmkAJpmhXj%&|5wWjS7d2f(lYxj)2f`G%VRdTV)1n2?-A06Nidn z=zpJ#obF`uC^sw&oTxpDerqH_=q` zHcby}?Z&_G4dBvIEXF$x_iJ%LFu%il;rR<}p0TMms@j~92f4Q%{WQuMR$LCMBSh8= zNPJIB5(|$FDt-r5z*^BTR_n+UgI>64w3%Wi zGl(7m9u%RxTy$XAWTc8cK@0)T2{bjFaJNbY z9GT-tS$vyon|4pk3?$yh&>Eh7mB_KwNQ_fIeGLw-32q-{q>2iqK;<7V-47u2rEgMy+%jtoUfk3D^rT;S$k zzFdwtUibO)J2I1zOmADiyv3~&pPe2j+ft8>NQh;n?I5iNMTe4qx=R{D1`L-Wau;29 z_2YZ!=wPHmvIuB{X>GIyx^13G!Om2IkcIL(xH7@#H@PvF;HQTazCn?*mQb1ZxW*JD zV79XtN?eaBDl8BYj)j|w4K-3j_>%%5IRPv#{%8$SY&C!93)qgDW8u!BP=_ zWw43?555v)4UsI%CZ>`wdk0z;YRyDL5N2sZ80!|PAz<;}0S|6n{8dB<|532zN%}&b z_kUC_b{r=gM@5J^0;m>nzYrRCMjWr0+0>Y|PxFfgbHUY^K-s5TU3fM5jM?hcx2Ktr| zZBpg9 z10jxyU?W0CTGW`wSl}hF={a7)njK3XB^(O6OS$R7IRU`~_k>en+y6GQN_b~w>?Cw& zAyR%s`Q)>a>?20fxacX7I`hGHrxlHcpCm9NvPR57Sy!EqnzCqihn5(1P9djQjhYby zv2L-!4*08Zvfz*&Ss;2c$-MYsay0SNku`>4JL~-l;C9H%1se?;hRBSH&^ac zGKy#D$FCukIP%_*xu^OzyMQ*iif+i3pIMz-LrYK0!nqX6Z}@WNEKsYuAOq$(#TK#b zU@a7a0Hgrqur74DViZJ>(CD`EcVdSS2;e97&dcxk{~=yNI7|qtWaWlV+omX_DUg~k z#TtUU zOpMbEk;52f44Sxh=xDnOCy{FanQ+=9ycqm9R{H-^OflHQ5(zNs2wqL7g8WW~`M28@ zNjS_j0KT>wp)8#ltrK>{c#VU~*D)oQ#1vPaJc2pRv69+QuMGC}ZQRCXRYk#4A z0y9%ypE|r8Bm0N107nPb!yB_YE`cIs@<@CLA!anH9O+E-gzv+t#V5U#3Af!i^Yg!de+a&3N8f4YVq zZB>E*E#YkFncT#=f!NEFY~pdisT&cJoJ@Bw6fcD*njd8fS15jIF*(#vNF?~<$V9Nq z0>0)Fy}}9_BdZpmxsNId4!InHMi5b`77J{K5zP~xsE6|n{GRIh3?ny*KZ4UOL<$(A z8oV^)8=oFk3{<3ULx30o%C#Es;=_yUhd~tRAT|TzU~9y=1GWMu#rvd?m+wcDviMWQ zJjrh&ZT@}B@1Ltm&@VtL1L@34h+kP@!1srvYBf>==MB|z90&YNd6vIX&pty};AcKm zUJl#Qvo9}n!j}-@JH+1J)0eb|A4bBZZ2nBLY@f)TdK8g&-GdRLRQw*l3{P%|FT?iC z7vmaJFS6PL7@35V5b2|3CTV8cXdfa4*EvUSIuZ;9Cl1AAycArGkc)9E8S_o#^RlzM z*tgso*n;}`46K4uN8PNebe|}3V^c?S^@Km-7U`pkYz!C<7pwCy8NaCErbL*q%fK1>iHm^ySp_gZ~pxB+VfYfC$C>hDPqI}k3dS3S1{ohOG>x&FXr;JTLGg~n@1{+C6FVl%!E|@ z+zi}hFahDAN?*QYGxZ%dR5t;lq^^FXMk;~F>B~KA0}7X1Kn7Om!+47%@;YoG&j_UB zg3rUQ3dsxbQLq$UT(A_r0tp_yFaB<)B6fK~coni_ngwv6nD0fgg~@yF{c%}^&@a*1 z@tPwx6SLT401c-hfCW9Ema}?zi-5nZL<6U`z^7-E~pzgQFubf~w*b z3)o`%h9!tyq5~pW1E8yUjXA#s9tkHvu#Z`hbWi?jD%kzKSQ8o0rh)+oQ_yh;1+G91 zC{^HdXKew^$=E~i$h%RHza~ZNtn4*)>eQvaZ~x|1lHkq2)2oEoA@Qdjb?(DHDvl*K zr6cJK#pk!pRuAm40apHN5{yT7q%{_!%b&>|ll_u!vCn-Ng(H8!I4x z+@OL{rcQ)8;`F>6#3}320E<|36f>PskE2idRvqy!RnF9n5C^UM(rrz7Ae2&2=^s2~h=pdvdH_u; z*i@!0v+U61WQ-Y!w`Wlg1as&t|p)owN0u zxDY9B7Ti3`H3C^MxN_hA{mjQo5gg$xyLIa}bc#=S4a(LhLFHY}>fq-!HsFln4~ky~ z5(*W**!YkHa@++lF!Mw_ses456KUKY?(5;ye+#&!Qb%1sBJo3W=)D$vr*nKKn}Nf9_+8TtB!q6V?7U4t+J>n+vhOPuf zR|S_L*d&}53Okyrp%BJ;CBjq-%gYr>$3wNJ#y9`pLZW2Pb*E1+E6c?liHi@7S!A+) zy)KePl3=t+)|xse)a#(RnsQ}cqKJwqsSj|qu?Gp_VLl21CCWrm>_`bWMNRjW0Stg|If<%%j{Utk z_H8!QG(LFx^bUlcn_p}k$Ugn>T&k{cZ(76Ci>@7Q-$+EG@47_+(X?gAY^teRrQ&(WJW@zL|=g+pF8_7Fh>KJNi?KoHh2FIMGL-W5U~WDF;Q z)untKQuPSIt>U4Ak3q|5Gh2L)z(A`pU`bohv9baV(gR={LKIvkraUWb_$lZ=IZEA7 z?2o)4?^FpHTv>)c&otv8-bXtmVGc}-;8}yOI)Ti>n>Y(iW`lX80!c)YB4VzL+H*<~ zJV^cf_cgseeY!!yI)W^#rYat8Fsh8umjkKqS{?O=N;+%*v#2bU3i zGdW2uqP~_qJA)e+h%cvr$}GpPJO|as1>ukuYVOZnym^Eg{6aKElG_#%{nYd9z{H(r&r0s+EII<>hzr zHU)+#BUv8AA`1>$C^#`XGnk_?G9EP|@b>wy!;Fd%pfdzzES(aX1elqyOIIAV8zIX+ zh<*a<&IS)2jMYDW{CHq~{!FT?O%XJ3SV>k=QjcHq7OZIVXd!L2w^yPbnOc)b*7rd~ zVfP;*m%z<~n)Jim6`lf3)pf-PB_biD(Y+XL+_({D+`JS&1t>rUpOiG65SIot1BYiM zpo4J-Z%t>E50kV3>+cpp2s{hYC9+UJy67{}UGrp&yKV$*mS+QoRN@FPQ=(3b7GpVR zE$Lt??k~@$^DB;F%t9>&%pw)n91Vd%kscUi?7>5e$eOz4n;V{ECLWMEkeJFLXc6oH zFY+!5c}1KC0gRgv%SG{fMF)mYosC6dZc4R2RQsn5U?f*DqKJ|P6oTGh?uYUzcym}j zV+=lv3ZS~!p&`6@V7aEcodxg7IC`@@Pnj{~4U9J`Pd939P8@3V`pG@8Q z9gXCD8-)@o8+*!KQ0>;!*S`d+ld?FFFZ=iJkHc-#{3f#G;bi0JS_M2MEGlByeh+N- zJ>=&+2S|{y7L5=WShJMMLJx2nlW%wfLsvM)*RNl1`&n5uatvC(ilLUlG$Q1=5)BeXK({Cwkw zJg5r@U1~Ts7(Ryd&i{i?6@X*r9z>!bgu=)cTlJ6-tUF}6Ar@1z0PZRY>w9r zRFD%5owM(73xzKG5=%2Zdy+FPzqx&K!q4aLsDHkNj%TzNIf3f|4Mo7cY}qizFRa|V zeON;tWLWN$Dxs7~TI=LNEUr5a2~aRQvog_AVkX~-=Ac`69f$%>T)upm_h!Vdy%uti zgUkN0&x(hBFcs+OeM18b4JM1C48Th3O1NE|M-<9`XG`$O1P&`=VMUm!Gxp|}zb`&O zS`Vv;WG)A4npVvd!Utq9K~^E)_FyTsdyJOlNn&@7R1X}<`0Z^QSXTiaM%o80?{CBm z6_?N_l|g|!mfaD!3o!5!JR=G9b%JMy4Pgu;_*Ba8nOvfBgv=S&6vd$|YHo|Kma5I6onQd89wGw`t91h+W$(9^a_B@odK$|)u|6w5$)gYKJ zT{RlQUZLaW9XjBgA&X-(+HWu+L_Xp*N~cUEqkN{HagxuH!ub{QDo|0vmW>Pnj`f0u zqY*8~5imy1K!5o?8tJSp6OKo8{HEVW%hS1|*<($DfcSMcw+GuFNs z-Gs{_W(pONDE&tF2)4}9cJSy3>U%G&^J(Rfkd127|214+DgO1O%br|mTl~h^*I5G2P$1bu_|B< zxGzpnpeBSYDGD_}Fig)sJWJmTGKj@UWNm`plRewm13f*x2Qcvk3;Gdb>FYm4Wdpn_ z>rF@(GhYw;eC$f+ub&k-An3Qj`E?$pilnKL*EAmXZ#~RLJLtIj!wHDRLPDmGHK9vj{TYs?44HNl z#)4?;=-fh^lP`wI41!ySPX(86qYFz`2}1J+NZh%<^CNUMD@3;V3czRt!4U*o!N*#7 zNe6gRkStC(koyiCaEwJh!2|dU*TNoz?`C5LxOYNrG?XE5AvMf3{e6=7@0sC1MMz;C zw{43f>vWh06odu1bD_aY42a9ho<2>3Jc2VMF=l916UMNwgD=y8o`gcC!#BUGQcW~6 z1V!)^{2(SMuqp|!z+OonP(cG=iUR}il_16%^pvnm0YnN^f@u-@Jg#B_27hVx88(SM zmq`6s1%pP(hEIWQVNAvskSHNPj#y7+!C^D`34Bk0MNj>*n=l>xxevB#dT;f`ozv@Wg>Pg2A%#@>-IZNV#BuWS$&BB>=Pu zBpxp{gJJ~aQy9RMz=RwW&=B1U;e{Zk_`P^)O}b#20c6muhO+)KgH0b4fs@!J@Zv=g zWblTd&NNkF2fptnR~4X-448XOG;9KHQs92No zA}$ZEWa7RG*9Df~gy;!2`$n0{w^0`yQh1I`CW zcbBpJ1ehMtCGlOBnDfM)<>(SxE_^qB3w?DaAS`Loe%y_ymRH-lO=|* zenVF_R{mDI}byir~h95<3`Q+zvqix!QaE&}!L zn>TuS&gkV$y+S!?fn^v(R+7PN#>T}3v#sszOK`54OOcnvgb{Y3!peP`_S#ztpG3Yb zlItrF&B$XRCBTaB6oG{%vLQBny$dAv9012M3(gptNzFCcfASKv$|bA_ zP#Ajw|MRGGsx_&BLkM zfa-=5$?&Hc7Wk)4=fepJ)BuPub4L*0kjd*Hw|7Q@3O*9Cl_l)o&P1HSao9V#mom=m zBN6m}h=u+3E^wOEP9teXv3drW8O|=C3ugNSofwdIDRNXa=nDLc9Qc3)92cw-+^KLK zDkn=&*a5zY!y~$$ z_%g5%@NPNd=Fpo00OBS2b7c1jEG+e44=kobrV}b+FA~fJ1x1du0J}q>bU!%&$cLq+ zITRUy{wHr)V;GBgL9?I0n>X8}Z{XzfpxYyuH23iogZuYPMc7VGFULxy(^*<7)D-7CX z6DgTQa0?Wo3B&7zvxXL^b;yo>r!@-V$|De)Fc<(RQsk#*!CVPj%c2>O8tO7h)RE#! zq22^N;19_M^s~67Z}wlEY_15APGCSNuoW#2f8WyI^+1&2+@Y>>=LhaQD;wf!n4+_* zrE{jFVj-?99NnwApMFWCsqOwI46+DBRi}Bs=ag&^lC6;Q7BlaiJ8AW#2^q2_;b-Ec zyVnfrp(E?{byePszPRP^Qfx%2_3k~h2EFJ|MmeJPZp7_??Aoust}I{vZP(tXNJ~f% z%AfNSQEbrQNnu{}`Fj2KZIF(=#mF9YyQSXUHC464QI|$kP3gi32-1}Tp9rrFR?sjE z0O|GX7-X|swzMhMDJ3E~o~5*Pr{kyRh8s3+SXY$pnY3nE&w)!*4z_7-H1=AG;w#ua zGx>fNHKvg*Kj8i%qtC$dfFuxx#Su1a>UTwQ$Y}Tjqr_~%Zf$24WdG$A)} zcV67n4a4^r6=i<=*g1OKw0A=>Mo1~eX+`kIZk4TfJDdE!WcgQjlYLNDZUT-C_=m*G z_lj{ft_6csTE-7KS?HaUrgL|Cxbj~A+wFQyN{;!OrgG&b^2N$sYwTD0p?2h5YY^yh zX{XF_nI4h`*#X&uPs?`gGrxyKA%3H2OgC^NPX7#6cS;HGvfoL^v6tKmJEk|(g>xpu zhZjF(i;Aq;!#bN*<5Fb5J#fzl?gw=BUaFj%mOsKKplj1VJ9^$MK^{~wLFeAxyHdCN zej;n`*-`aVQxq)QyLay~iyjt&rk@T@OH7*6BgZePy<#lyyd(GEHDluo`Pu!$W#M>7 zNR~TGTNuLzsVKK^)dJKI=k$du09hWm5#&Ug zs=X`_h*-=LK%oegv=C8Kr}~gw?;fJ~2+dKU%3ZqFo-Sx#s&gmA_3>k?yaCv8dr93s zk~M?+_*qW5@4Qb=+9^dU&|{~uWzDWR6bOmDRTvl;#%Ic_MVD5R*QATh*(Eak9zRz**!L4LJ5mI+f3!vaWs*FI;x2CC;a0r zeRIWriXpCwLzPo{Cu`rDy>q(U3gg$|ql32)Xqm_xn4O!GQK@mypQUGgqiL{NZSSpKb(X6%Kzkca36N4R+ z!AfcSoqK8GTUfrN%VAjIsRFA?B^!a|(4m~R0@iarTQT5Wzp%>;P=qO@qMZ>Txkxz> zm(>P!4lPWRF`wcj)6KEhot4T?`^?K{&IW7D0w}36xWskEQ^3Ch0;PdC29?o@>B+{a z$O$goY;$9NN}#~WO`Ss`0dh&|J0?Pm0J`0>ru+E=1G_ZIG(D13jMJSxWrl)E)0m#+ z%JFi>JtXcLcNrw5+^ws#asV!I4@41^HJ+5wmL?KIM2bU{AQh`rZ%s4WYSh;#lSBR* z>pOpGUfA+@bhx}+`$4G#X|1mw?J1w2Q}`*udbiT^yq7onrmb;~ri?D`PN1@i0j9Em zzpRejJMqDTf1^R;fVz2V=vzC+Mii%uS*^8KqFl|cJd=O(tsc7C_uSRnQ-f;;m4 zalNX@cAS6r1q}@`cj1}u8nSjNP7e@wS;Il7!3?E7WF|g;K7ILe0-AJtNiF}HIMBOv z_aEdu)6N}D+C9^Kfri9gA+M}4nv7&ShcZ5lQsFG432Mzd)DzI^Gu-h<pLu5=za_Hc=gr$fXVaI6zFqNgt|@*UW*atT zJbIiSuR7<*nNd-Z!PR%@yvx-j+>gtKkMA$nzC@yM+&(!IF#GcZdOOM+g=OoTn5%SY zmg%X{>{Mgl{SN+TWY)gZxc%Hxx#)ee+VRK7%IBpzy+&8j9Zd7nq6!lgoMtF-4uA<+ zX+CrIpN|zat-9)f_4ZbIuN}TVwr!Kqt^*9@uJ7Jj4eM;D)#>5y^f<#el3pS&aY z-_^Q*T{;<(KFp(i+?*=&84E^Q_1?SM-Ca$!YoK%Ty^|vMuKzQQ9+EpXR8(b#4jIx- z`tBR=ej7vLMo6dxxQB5R@I1=d z0bRN|N_6cZsVKW5egYIPV{d%5VMe$%vuUOv9|XZl#R)`x92mluWs*?`8Ij-NJdu0Vu`O9MVb!|h6;QAfuO^1!&q*51L#N3pW~*~N~*ta?FIN8JglTOhTW zuh~mB%QTN^lJQ=ue<=J|vhsUe%`EB)VtN9k5=fN=iW{QxK#$O-CU0&y`~tIN^ezFE zDG1k`z>9HK=I_Qy#-TFv8cx=?y||@6Y0*fQc(Ip!?cRze!*psRkctA+;d z#iq1?DVvxi4uz-vuOx;ircjt+l5-AN0tB$5l8bpcmTiT6R7=>|*LE>1!gm^cYJ4@a9hXx^v(2zNb(Ye~ zZ}0zZ_w;YFZ~a*H%j0Brgyy>8@5tz21O(GOohh6OHR$Dpdro0SyH&i3EsqXn$!l9{ z_x4X~>@6Po&LMw+x>woLdEEtL#v@8zm>(dDUpZ3xZh4a5y+2>#Ub8Pf(Ry`F%`#lXMn^>pCM=P+ch0LI4jszV>A#0Gicv?)&xz2P%VR&fJvdx?vo$u&-6$rNMnlN@}cs_WWvlo9EJO3-uy2_|0g4v}|)@ zeM$Rfg`7CU(_ou5*U^RE)A#3w z#W0d|(UJZCg%>IL5>=RIW4|2HuinigPfGTgf2&7`>sy(~E+ZQ{9r`Y>&b-+$xhm^r z!e{?0J4N!Uc2Tm^XWZ?7%VYPT70Nry7hnH)V#^1^sr6>pGk$7cJNF%}glQMfp09MW z+Hij6GP`*jOPb2!xBqk9ZU2aDZEaa)joHd>VPe4{!J?bfTx=g;n&egp&7!?yqY#VZkWbQ^uKMOkeT{qtDMQl+lznf51n>b9G~Lt- z5<-e)P!ORK7e#^B{SsQhYM*L^JTM$=78!i@szXp4V{z_rqMV!_rQH~}G5VB(TJXsk z({J}zP|iPP9pGUiEg2)*RYluEqN_;On16{gm*^(17;X@VZWt2%E=7aUkpjUzO32i_ zK#&V1UJY(89LL-8G){Tt*&a^YRFwYp;$Owp_a1{?>sM($Gw9^Q`3f>0T5k=fE5oma z9LYIVC!g{t$h@<@Ey>D5nc)rwQyvhhnJ3~lJczmV3PW=HMWX$|$09s}RlN4@ zRy=InrH|Bs2pK76m%=pxY_f%Zi{VsII(VZL{^cCT4p1kJk_$zzl9gqPHE~x0gv*G) zbC>UR{X^Bj$7V&Obm)M@In(!aZIVHNG+HVpGhu|p{2cwBef%`q|eaFPt1L{4KYS zh&@odspK=qA>COQo~_(1aiog&JlWvgtIXq!9pkk7VP9}pLRSfQNr>kiUUvTK%&vU- zUH@jsQO{JAx29oyF{+5?QeWMTu;w^Ka&fG%u>cF2hT~m`Q!-YwGq&)0yCVm$*4j@A zBfaZ8?pidN&m&F;HH;fiN=lCm8jxSUVus|f7}=chk#+LR66b7CbT?O)n60UN=CN|% ze$ikw0X3q8vzMm?ZVh!A5{O}c0zq+hBL*5>!lEo(_>da7x}I| zRO-xkN(EgwHoC4?yeNCh4V)yp4h|LzH+6bnc9y*+Xloce>%dS4%C}I2Dq6DJ=jrvT zg379_BYn!F!n_&gWj z#W|G*aHwo?L@xUFTV7OkEJL0JNXbrQ@V^Gj&;M=H3mY2FZeGmhsqu-y2#?ZWDOS)y za$ObLjHue7$Hvq_>by9?mQ~UJrz3@D~RQt6k z+n6?9lMrdH({+$_@U1BKaN`Zq+UE1uocjN$dJC|qyDwT;5hVo#q*S^?Vn_w)ZjhAj z#-SyZPN|_wS}Exi5b2>o8blcprAs=#^TYppzk45WnCB63=5WqFd#|+p@q|~1u(%mMq zlR)pSEoG5T{qHs``24@CNDK{xxW8&dcp@IDqNj-ha7Y93J6DqZ)2W^RpA=&xU94}) z1EVv2=m5=a7SIIZJpL31nM{lrUz|ZHyy7q8LCE=7&w4m7o?L2Fz4u*hLWP)^nFLxw zD6s|DdMcG*Xa^wy5xr>;d%Gqt*YRvd1|8Gk7PuOyKT|h=JH<3ZTlL2|&M@kH zKW7BoW6*_%M=9I~hMCN^cyaj^p{0xQ5eC9YtrgWoJ|jWUoc5F~oZBz<^oYf6Fr9*f~K zf1lV+G{Wt6uVNszJS!{LlnS1RcQ8oBte_dSZTmZ&fSL$3_L*%r1wt9$U$C5COIK%} z&=?ws^VYPNCX4~>s|#?E-^FQv`3UmvfaqSpenSOCBjXrr>f`u=dBpDye!@qPz6J-E z<}=zGV9i8FYXh#wD{2NpVrp30_}ZJ`s(eSO*WaPF2BIy%M1i%2R3PFnk(vQV;jf9DXhEv~7=0c&%x!n#gzE7Wb z?_7;AZB(;}25nl5SS9^87YJ7bF=97|r5Gw>zR**nLoo`g1{{dqC=jARW}*RR@<1k7 zplKPjqxM66ZeWks+d^msM9Bd*#baxsALH51O=y*910i`XKyHBENf8h`0MfI#HhTnz z)}%o!uhS$5ozM_3kVR}+NkY66`*S$e8*wyTLX)@~lGn61$l1V35Gk-IK`yM2p9&rx zT0crQ7O)#xVBb~k9x8*nsy!v+8yt2wkzl!2GpDoJo%2^t>p_a1&nCt2Gfxuv@w~9> zq>46BMv6@|(F4QjVG%cqS=2v&2X`Hmfw_R@5N-fMgX@7D zaBtxDydGf!X?uGE&U+!lGZ1*8rW&v;SP5Pw8rUGq0oDboP_f(zdi8dp|74ASd?l>&v>(H`V75&k_d>Tw=8DxkduT>MZI{vT{=n;B|`dUaw( zvbaAQuW#X+WfEdizqxV!kPx7I0}DM)?B7HGM0Gg9FuK;exH>|PH)KrX1bO+|V1MG* zz)_n}Oad(LE-=|n(q{uE>-$g_BCtOO73d@s8^YgR2WPZ-kO~5I$i0r>>neBGW|!SW zOc-|So|}aPI)XNV{zg0*LB!4YRGD$;%fIKZZ_HC4Pl#SxLY!c8c3{H@?XL?~x+%Q4O-A2 z4T08Tz+>>UlTnNaV zX;7>}L?4uijKN|m(BIyOr9od7L_=38AThj2yukvMDWTV#uyCo6+6~8Ww1}HymEy}` zgsNQB{;%&6&Jf&8Ab`}+CSvEh;g5-gSvn-{@v?Kth!xW4Lw6rby{(*CAXsdmXrd@2 zhSD;EdrEOq`BMrkA))k;R}VNjuYgidg$@QRn(-mZ7HBzu!^E|M4Jriyg$JajP(v@I zotf0av8_YD^ML+|!)LefV6@zKra|<$edjhxDSvo+|0M|5EaEQMNuiD#Kezm#u3JaQ ziUo9}@c=38!G{S*=ahRJ8rv1tcI9{3-hTFU>tsA-wr(Hq&YcvPU}J*b2=KoJuN}hf zLcyQ{&{YMc9=%_vak>P7w16aL5Ts(|NrWPCuf=XsDn1LChDbx?zfkhypW6|D@ge^u z(6Y(^Bq)$N9Jo8vMxf-OHM#a(xbEqF$O$qdXlcHtZHoM& z;jKPf{WT8`0IXTau^n{Y(ovmY#;gLHQm97-v{ZrctC(`g<2d&myyov89O8agcig!R zGSt0;|5JYd#DG%Py=eN(2yg`@*+~wbJV6`9;CnYW1uuZlb^A=UL`;S{H#4DXG$OnXF`ur(7Dlrc>lFb ziufgMolW@TF$M^MxK7ox{O54Nkrr?Pjct$}G*rL?*dkT*ipL1_{LzAj9B6I$QG^ak zbRaum{(PUF3xTF9g~`r^il*PV!2p?_1k%ak?T!Xv06q<;1l(QB{Yu!k$7_p-fx3xB z+JX_!JVOK9+&oE}O>l+SYyQO*-$(z+D7-AAgmUel**Xc4d*Cp`~p+ zVmpJ%0|y6Og;C(vaS!+ga6|*}U!P6gyHc4Ek!@?hyJ|)Y{(I^W2|y+m8-mj({AivT z7f&KCt$-T6$V>{gV;)PS2UpJ1dQVbBXs7NPtz9Z@1*{gSVYb&cQlpK>(yp(5ERa@i z&Dk>-#-)9I(gRLqjNjQ|6_A>E1UTVC-C>YRZK}u_8s4K*%DZq{B3>yMoCgy;g2Uw$4tlKLuK)SWsaK? z#@lB+3Nh-#!guq;F!e=tjyu8{U5{zD7S8nu1aFFC4hl*t+uWB6dM!s*{&H=97=>f} z><0SXrvwD|+-{HlLR}rnzWU1+5T;iwiSzz8dMz8`2D;u&@lr8m0u!;N=+ zzTa<2-XfKp9w1eN=A}W7mjfxBtAVRIu@=P3sl3c@#K^eJGP`0W%acIBfY9f+kRceP zm4d`vvj9%YY5l9|3)1ZFn}CBC0uw3PEdZZ)aKvE^Jk_74>9aw?PRR!XH7AEL=f+g0 z)Z3Tqo(Z8)Aq-GfE`XH)ga@!4HMZCIHuT!TlZ4+~q7?fF#AIrt>O{1`Hy?zfg=++M zhQ(IeuR8JLJ6a4R4?)Td;k}FPF=$LXROLaorJ%nQ7}--tgwns&u`fhO{6mc(obNgw z{X2>v=>Z`Ra_0gYRxS@A0a8~m1Oj@CX`oY%*FjUEgCDdvUmK%>Zg-A^Yc(p+K4i$uX!9NBzma@njl^i;lAL!Y)q1mJ5ddCVVDWh{PJ6ueGHu!wve0~Mx^W3mx&09?WFp&YrI1maPn9#(NyXt;fL30 zU9hL#pcn7ixk$aEFUC&+HTqnVItl9LgwXr+h8@9??9>2vh%(oRsHyyMm<6mK(14W< z?*=!tVs41+akH}e_%{S^-m63gcEyGMd*Y$3udYTy`A8(>(*5c|0nIxKY>-Y=##n59 zYY$7gsCo9ytMjAUns_xCIeQ!p_Zu~=HQfhEVs~&XDxrrywCA_p{1&f;X3|3~H=tD% z4=?{l3#|?<+H?Ve(ypABUNUvCfVon-6ToKlb`QS7vhe+XqZV46NnaBG&q)JpbFX?u zrYKS)Wb4`13c~gsnl1CqwAa7m{t%kHWhv@(EBsvvpsqU^=GhasKAvPP{dB)=p%N(_ zrj&A1oQzPe;|Q%ATMse z)(du{+HhF*nL%a*F#+0OY|wQ=1%ekHNFJae2;h)hw?RO02C$Xaqa8S#wn&=}yQizX zhxCVmmgc22p`aK6W}Vdbxa9%I=8Ed&aqRp2{EfNR3`poTzqt-+kdTlrke(Z0qEM>{ z-?Tv|6mme{Fi4;0GyjEqz*PR9CvUl#s(*a%kcIS%6Tm(7e=4w0?yDNsO^| zH&Ps+I!)gLaUkTScK|YxlOil629OxQj6ik%uLiseonL?tfyAdc60%+ng=BZU4ij-* z!Qc?SuhRgLCcy^G&46TrK(Gh(2*yZaP?>@_fa|>210WzTfCYCOUrZX0d5uLGb^WtR zDWll120c`u49C>GM|~NA1ufa;gD|qW%J?4~lA!eh$Dv3$2Z>AyI-mM`Ey^b`ZVlIF zv-t{SGf-U#-4Gy;g%U!n1OTUlbWj3nrHGY{vJL^zDJ%IX%6Hj(+jMf$dQWkhqv)8M zo({|UhZhMGq~`}(?$7*n+6pYG+!kEo5$_D4PMhLHg}0=&Mn_#zmLmv9RtiyqHX9TQ{O&+eG4JX@=9a; zKDf-@0hc52G8-BB#0ZJ#^NB(FVah7MqohPljVo^=Zpk`XypIvE=RtY)Ehrp45DyFF zu|Qx^o7jae|8N8yjsCM+Ft%a8XWK5py7u4?XQva%qTf&7@jt@*d4qu|pPB+M9vUQ2 zIkfvFle#;$`%Dxb*J^xD_&yK~fRHK(UM1>mPzM-jiXh92bdJ5ak5qa4!H_@Tn8FB`$RZD z`L$O@$28N%Z^@-nqo;x-a_k3pcEB)8X22ntrW^W##J{LeN*kmVfBkR3gUpKv4dOSh z#Cw>o!+v;w_+JJ;N3)V3vxsB6JD?k1loQEpl1&c<1*rr<@C4f5pzxssSv`o{Uaa!MAUy{N z-u0hp|4kj+=MA$pt5Fb7A06$mI{egg54aJAz9k06zuZs(WaP^?fN|QiG0v9yqz)rq z_|c1Ng(x^pP)7bElJ>?#+`IZ-Xy6V+Pk`}V=75vePh`>bhOmH--Yd?on|0GJKnQ*- zB=k=LwuiJrGaAjiK=e@H+%w?qqc=4_@ZjqE->>>73#-q10c8Od80J?a0n2_33xVkS zzYHO2<$vC*H6ub8nE-2Npno4K_B!tKYxE-1@giox{mIjReuInI1_bv&Hg(M^CUek1 z%@w>VY5YN;Qs0#g*a-uf2Y02JP;vINAhjDD6rfPd`v)KY18*3vt0b^gHURI1R`4y; zH3Ad4#gkQS5oblYGcwX@?Ge7zh0=!2LZO;ZOLjaLr3n0QD!x6!PH5JJ);xfTbVsFAi5c z%E@00Wf9RN)2+$`97={>jrV;pF(9|tnpN#{k6C7wGD5-(?H;HC>BVj;>f;> zOY&%0YFJ7=jk|7WxpcVLv!e;547Lrjw$iyVnAESl!S4(NQviBH2^=xQ-3Ja(y)jud zX&0aNa3tO>?eW-;cQgrH-$@7x-erkICrvc@Ug{knfY~KF8C@5?9nq6PVoKAJHhIM* z+2?`uWUHSs*~F5}*(uQ2>4Fq+rm^p!@g!o>+~Pry#JEY{DUJ`OeP;*{XTloj`@&QG z-jp^3lj6=?`fzIO`0`nB?`!lxYrb0wFF`IJ`f_|3UbL2>G&zmM`UpGthAR)^wUi^% zJqenPNTA!vqhU-9m2VDAyjM$LF81K}k1)mNKzTP7aRTq?2;LNWb{n?VO@|^cbmSk)EMPa9f(f6kvJ(8?na0W;%l&@N+=5^0ahx# z{2Qz}VGQQl*YEDq3)=KSB?F`SSF;~lIE~&N5|<~XM#}Dcz>xyJI&28;I2DMq0Rv}- zmwXR8*LJpXQAe#9E6pGK&$$=qt0BK-C2L9ds47@Nv5; zn}02`>q|R#=FH7ETGt=*FZe&r07^@!3CZcz5(WWa$A>Kcey3*BDK{Hho6L0m_KH{H z&5c|2;vm$a0fLcc(9mbw%m!rz`Gph>N&0Tu8+TsInLx0ChV+*GHtq+8EvZk+r8}MS zzo+W60p|{I3Uiq5nXrzrvK!V%1J|TH0rVJ*t*4(M_o^p6U#2$akDYT)=?gJ5Dt-HT z0L%%2Y5@EY26iEmJa=1Yv07;FgJ=i=K!9WHSE7LOS#AK!KsqAOl>{15Ac%Qvj9b6_ zOZ7K~Pk)r1m(%&C$3T-jBKw9G$jPvWrS8c^6L~zqsuY7zrWO73K}7--!1_%CLls8J z9*KNGu!M}Ne`ltVWK@sFrO8hIg3yjCOTrvu1zyZ^Wcge(U-fO1SS%QGC2XE6LAkmD?WK1 zFQhUWN6uvN-rO899$03gsn4C z8$?b-^vqRgpyGb>n=t0Pb(UN++Cw)?Tse7cT=b-6fSW`-)a{p#AeljAlT?^L*8%p8 zN#P{)D0K8VH)e79Fd>!>vb?)wa@4=MRO3_Sg{7YcQkZ~?)`=zxkBRvWy1p9&k18g8 zrMN-~TS1(d0>O1bP#1_kSxc-|q}Dqpm6}L{{e#*T9Z;I;dqIJra!M{d3ik`OUSrou zh?y|ooUffDA`Dt?#6cQv49w!MgP63s z1SM~fi|f$t6Th)gKyOrzAdwPlxqGkCF!!a4lnypQ{2O!`vN)PSqCh5c0+29`aAVwM z3J<=KN!;}ZrUE*+wSa95wgxZd`L?m;QN`2QC)GiZ<~_lj#%*hdjgKGdcz}@vG?4&Y zCy*Kn>`%z_4U|!ag!$%4pa%ug7;iY{m|j>rn=f7ZnFwy5uevTq1;pD|7!E`M*~R{F zrZn}S-~)O9odgF~@Y0A=5K}YX1`ye25J1dvnWbng#Kh^i3;9AyNeqC0xX%8mz$H!H z;x8PZ_P(7_D}IN$zTT@X(fv#OOIbO{_bvJ8u*zx^jG*M}YXWms<9^Gj-M}*6ckU;AE+NKpc%15buU+V<;bchXf*%u_*jnS)`v?;2IId#~EFP#_F1DYE z+Cr_%Ktl(iXiy&y$cZ%|_=+Y%1G_#RqP<`dHe*f=P;W3~Dh0+HK(wzuTOFkGy$U$| z&Sx{-U50bE9k6(`U(s6aO1;W~k?-PCDZ>^+p72^N@;>^#((XWwQk(KGUhh$)_M0}M zwOPB8XC3;onN$3=RS(N1hhLj_za|L&Dj66^!Kh5*SV$9R!DQb$;%BV0wCXoCXTO=H zjJY9)cXn}c9xu8_b(+U{N(=c8h@581R;Ds`*E>(SPcD{_ppY8%S*(N_rM9eqeMEg; zBhtthcF-GWTjV}dpDxtbsm~%uA-%j|R?b#~Tw75J8}sUf>)p2(3kuEN%6>>7x84mV zPh>2&Qc`%pXVD`ceU{9+=(*{K=<4TX!SAXQUr4;iG4V`pPVbd&PhGVAK=m3*y*!G4 zBgM{8x#5rYfcWU5TTKOzGQQSK8to?i@NyfRV+es8qBOuownCqM`-_b=ioP#S@BzB5 zb%;!^x(zYKzb_Li-Yo@r8Y&6Doh;!~!(R~g|L->-W|DTF6Y)45j~;TV+*v6TrXdpz zVr0|ze1VVAx;5 z?|<9@gBQ9z{iZ!nwzmBfTRfXC9=7e^7_Z}BUW%fkfHczYaP=G;ANSipZ9yNk3R1+j zD4Y$aMk6Zi-SecxY;Bf&21lzD0?!B^87w$Ihwk*Az5n0hGrsoIiHls(gBAkki?FND z50;K)mi{(edM@n>w9N`!tz1pv=3M^aJPI*B4uSMF-rx4KSAhhe1{x{ScH|Q9KW z(RYP&_i6j7qTl6eK-+Zd;lA);q)sEhP6fU)xF)-89V6QqteuuILPhx5Ro23sH0gQefjmqYD; zT7Y?q2$+6cf`pt}mUiwgeTNpzw}aF-SC*=kLvOTsaYCaz+MT1eAagLTk{$i5mQagZ zc{%g}=#G3g93%1SU1nEOMW*rv8Op<<7k?T_Q1Z+AUS=7M%?Lr^I56T2A|40`R5PKx@m~tvb=*F?&m%csATOi%k zmk_8+CnuWf3^Csh@4SBH|GmmYnRu&IFY2c#eKr)kDZxtUgY53WS-*jGSXi)6-_b}N9$Za}Ucf~UNBkEW&hY~#o2wn^4Tu{!8~M&J zwoh9wJC;sbE@1)V{kQ{P+F+g4qeDMFuP|s;3{{dYqJz%nfOFOJn*qzJSEL#Z(H(C! zWNNE2nhw@RE*^>=OnG|s-(GwkKe{{~ zJkdS#?E{b&ztZ(AWgWTGpGrfreHoGC(sWVFYGw9elE1}L&wXMsA({M_$%h~jPsbN$ zWh3WqITw04E1tfaI{8q~~LeM4QuUe%_h&T5rE z^jvSAJ5bPDN$}I_WH`8Y+b;+Ac8)RUaD5T9MI=&8BWDGpTu8u4`0ta)HIBU}Rqr5r ztDZe7+i4PR zdiqrGq1lE4EMqb}nLIjzjeB@mL0|dA1l5L1WTy}Z?@ZdIM=2PvFp*o62SxjQX(UlK zNA}Y*pAEY-7Fru@(KW@bd_FY#?`?qhqn+&^zk>Cj7Jz_plU~zb8{T<$#0*B)BYL&! z{I{j;5a(h{^gM%Oe**9EV4FGYe*W|Fgt5iu`DS(%tNBUO$wieCAkdTyee>)&NSsg3od1@LTqIHYu<+S5_kZDk{^|?G zVGM(Vw8WR)zV6$tEmK1vD)=ICWHL!#IM9XaOV}~I`x$Lwyr!L z6&{quvcl7TL`TSaD`U>|d^K|+3=pG(2Yq%pp z4)qr=>WXN#^4gwY`E^7gsXAkewb^NtH@6p;^KlqiWV2l#hBXkE!UPeo}w z_GvJ9sMJ2P+PYSAQN(%Xj(6XxcPlNNkprcVsA0sV7-+24eWE1PfBY(goaqYHaQ?3S z3jZ?RNM`=;c;mYJ#}fnN%Lo2he&3wa@_+W#GUpRyRSyqy*-roZt{+;9p-^O@plpFR z_{Pg4ip_DHkvlz(hG?J>tm77zOlIvF&k8$Zs+OlZskfPE1%DKKX?94K@JhGErsN0`v@_M$bw+^w0T~MJvX^(TNbVs z;kcxglm@S^DD_)nsB&yc3Fx`qzG2BId4+zToDoGI;W+;g599h+m|K;t+bwy@yERzq z4Mry}#^KB_4k>20_*id^d%mCd)~)_s^o5tjV*fXCz_`+DcRxUb@MtKSzC^)O(QL^x ztg~a+>$#SSm5kOH%EAhaPI)!^@seJv@XRkkaQ0~NM`d~|`0l}ql0LIge!_ykaQc<3JkTZ1fB6Gub|5A4t{Me05bRnP*~ zKwS$u$Ao8uib+bdoUzH4@wroihfb_-v$sE9{B1a$U)onqTU44&q1F~y*r{^*yX!=- z+$KYA{lO(6uf&4IuL-_ms;A+W;iN}$nB{Icf8G~+9`5Yjx$fRjmpThSm>J04#`i}} zM`;RG|WiI)uaqq zdK9qLe&YJ==$pp|7sk}QlJCX#t2&)e%lDHl#}hX)& zi*U%S(?Cs>`1$vN4WNqST2?3JDo9-B% zpAM>wH(ZQGG}zu&6pB;|+Ak~Q3r`kUM%LEar)?CDSV?3Sx~3A&I925@krL!! zD)|S&G|Dj6#m$#hb8oldRS0^O<`+^;Je|$I{d5LOjtsT_IN2{-Sd~dDZ`8cdpnqO7 zVXCKn)|NrJPEkW(S#QXZQSe^+#YW0@9iBW?SO}((Gl9VWz`E+iFTW}8lps%ib#agC znPPd<8e$loy_#DMV%CE+%*8R%OYZ#`Oog)5&`Rb`#H@!N35nGbgK2s&U&T=46lzL; zu#ugokg%?qM914x?QzVYq5=DM-2p?5;v*@C9;lLrQXE5V>&-Gu@@dA9$$qD4{yLdE zMA3;@B9EgtvV4REA*y=~D=o$L>8 zw2qS@@9H;1z(;j`Eu^065hLqrv9df8BGR>bk_@XKL|HHAv|R|b?fV=rUR`o}?#iuG z+H+9)xm8V%nOV^Ki95UKD)%TQnl7iMZZJNWeW{3G3LeW- zFN#7(KYB*3N4~%7R^eQdSt>zyCG&LP#$jb~DxuPxo>0G+w=KS6I(e^mw()5i=K-Jb z{>R3?mgiKHjT(dPT=RWD#9FG;BmI^*2J)Jvw{p93Z{43TuHu-vM`Xhpz0_XdiQvcK!-3$oU`L?0OCZPUFc&h2d;x z6@gu~_F2gcX7N_tR-Ohua7$3ZSJL~p$>e8tiFAT}l3E)keIf+FqUbWbnFF4B2g*M~ z-5WA#V5Q|~&rD3MBgKpQC8#h_;rx>-NN~9+ay)Suc&s@dwW0Kl>2ZPf6K|pPsMNTvL(qjT_jw7OfVP5D?KN#EHtlf zV?DO`__5c}dfR8xPix$hqC*-U@Tc3g;NKBQV@peHFSBb_bcgZ!JKcegP(KTPr=~)} z6tY(--XjSz7l}-Su`ZF4l1vd<7p%JI)Av`tlg^iRVeooxG7j_qaewaaQ7yi0BriosYHhTAzuCNJTGdyH`%hbA8s8ax-)eh--|+CvrvGt` zyvKyO-a!AxQ1mzc0g4aZZH}x#{K!s5i&R|wUQON(MDfI=%$b~=;Y_{k*Hg5#{B-4U z)^fa=x8aWZL%N)lRaCJFCJO?{TPyvcyc97#$8u0eUDKySkaZ=Qa7ikZ__Aa7U<@43 zDmU?C@{w(9{)9b4!Oo|a4kwK&TL(Bi-#0D@pt354djt}Q+&WULFp z{&YH)%ZQ_ab=5h}YGzcm#r+v&Jm5dxV;dN*SZOQuP5{1v|QB zrc$3E4v~&RPw%@ux8>|ztdcCHzBqVcbX8ettLFfr_j5Dv#OAoafq1=HtnwR+B;o^& z+LFchh{44q{Io1PhDYh#z%tb&%UW(VW1byLxWPt=;|-9FtB|zCt_I3UF5oMg5U3{7T`yapvSqnI`MI{=k1`>GbW@jEl*9 zV`F#DrD|Ij$e~>g=mQ=3|HRX*(+&+*QgL)g=Zpo*&zFkOP>!Q(4yW_-Cl1-9O?EQw z9w7CevYE3le>_)p-hH%mLL%{UJy3K%=hFM*<-$nS!6QXEv!OSYi%s@IWz7PK?=q8^ z9@N&zcVZPd8Yg1yqFfyxG;RS<5^EpgK$^*Y(@j1{vEli$g> zb4Dxlnt}aJ8YMxuX8oSuU7n*J?x~S1%5>Ybd6Z0}sl?^2Z&+4;VKWPMl8g9u^T39q zo^~Vv&(tR!f=(1T=>N65d2JwHUuU{RuHZq?yU}PiIv68-DL9%9zH}@^5$r_)FU&;8>5&`< zcNGn?vqwIHZ5OA&P;p(Y)51d7JWT)7#inn!&iTE%%Djw^=Ov`aolD1m3&`DE1?9GJ zN!>f5xt)&RuQf6~(^a$KMHZ+Brm5HccI?zmp5cbIoht@}EFGO)aePp$PJOkYXk7o^ z5_a}pDm46S<+jWRR+!{=0@>01xqiWQL7Z`BDtbyD3*Ul=#TPwG2W%H@AFmvH9&guw<)aW9VtCbC3D6*j!QX*TBx!`jl;&akyvpIHkPgAa1nv1DG+9*qxNj3Uf1f zLFg^#tCZ@Jz_O9ein8S}YmEnUEe)77&y+_`= zI22D-M_X^p+iE#9r@hI93nP)1Zi2ZhF$s@P$$&iVp49uiZLmigGd$X>jXrQ)eu zEd6JH46RaFXQRkT28RjHYI!WU@}eaykp00bAIQ@*V&EL62xe8SPTVv?%Vqr@5!9+# z>0aNaS2q(U$pgNEF2h-Ya=s}`Q<)>H57ko~t#=0m0d$5x;3b!<#$2S9U1fxIf4b*Z zg#BW9By))4TteP*{>SKdWHRxG&xuq^PruLACWKY15FBMw9=NxQQ3HO*%$6Q3Mfcls7QJbO$Z?Fx2w?}hs< zO%k?J4b{I$mL8E??j)iTilOa9kKwU>#0c)MBaQ*_P}S1{QUHsQHYdr(EYkmMZPrV< zMV{3N_Ph{ThP`bOS2#uw?4EUyIByW5m_n+F|5@-9wk0`kpjV{KNlIx}Ok%nRp>itA zf5hB1KUaeM@p-`LFm-+;7RT|hrKd1)ajd!Q@EhifSV@wpBy&(c|KDU@&t1^~B0m2+ zbe+Kf-CUSpuB-U9$GmR$HpPY6?^W%uaSho4!gZI*zZI-S=H*&0-(6g-n^s<8_VzLq z+SMrojA#@ftV{(}?``k#F;pnsQzh$~hzPSh|MY0jKT5hVjUSe5nM4VbPBL_pb$ARi zA+n_LzDDaVJ&-IhDY1mOXlGY7PAN|ot%Xl!)3WnxnIjSfp9HIWls%h6jyMSGok#Pi zJb{nfTAJ!T-vzDBO9RG-QJ;QS3m0!aIaDW{U-2nhr>nnwU8rrv-O43vo)QeV%(M_L zcF6QeaYUrol;il3cWD?PQ9fz122ZW6)C!|GI_<&skQc=v+VKu4Vu;@*fZMaZE=(4pAV2&)Cn0_ERjJxTCFd6;sIvqA7i z!=}-O(^55g|MK#V`QgBjQG?R9Mn<(>a!-%c`PD|p-I=Wj5vt>nqoeli)y%a12d_xw zyYlAr3w3qWKZ-|09p*Jhdd=2Kpxeq7f|{OfPR=u8t=Zw8WB&XmYpdF1o>62nPRzL0 z3(k1C(NBY!Y5NODS4Z=!Z8<{LRy^`{u6eLD;T+Z6#YJ88Dhg*Y3gtONq~RR1_(Md# zV2Vt{G~Ksw2^LT6<>up{ufs{xA0OToFHVg!lZeD(VJ!IXNtw^tuUaa*34yet>XLfwR+%*3^>1h0}Od^>{PVOlWd%LWHQp$ZK zRWq-6f1)~KtBU3E-}_fP+ZVE&mtn?s?u-ugpH_W#j@ovG9&a9p1nf`v|Dk_&e6chw z`MBFk#J!o&I?#zYl7%QCD@a#;aM@&mB|1nZe?c=3sWY(6gTxzJs!U@6)H5OV>ms38QhxZKjo* z7&{Bnv?rDadEt^%ak(W`=2sM)S3^s?LG3#Vz;6`8=b(Sn|FGD9e0ziH$1TzCA_j9y zYoz{bOsy_{1xF`|+bbRL9E$SJ0%bE^@>(CZkM_!Jbe&xyw^`V{1LU`z>p#ji=6{^C z)u6O;ZeFo4nB6oid2!%h=WuwytW37bvsD}Cv4{u~gjtRQZ}>n_ITl#{lW;;_z498Q z<>gO|)&_jv5!vS2fFw&In9tctWLX1`K(3mNyt%vTN_zBJN}F9x8RY@jaIcf` zk6#ORPgvPnHEBtTloZ0M36Gj0@@-@_@s{7IGgja%4+?5_y=av9Lovw_B(A-B2i}`> zT(SMC9REDH*Sm#DMBK1xNSVAQ(|d-4sd~PuqME>*k;gR7g*e)kh;KL==dl+^zrSzs zRDd|_EUw!*;D4e`^;bM6FP*~f2aUik>}oCTXLU;Z-dE3nBhRy_oRi`|Pg7~)ySA6S zP;-V9n@4m(h#(66zV|HTt0nB@>sr~nq-_gUy4bm$qyZh~ehWD#UdBhB?PJcZ8y?^{ zaCX`kdrXkB?lJLH{Ej%qN;;N!(I|6mOBB2UXd~uCqFq^5=bB`MD5jMkO2aHAGNW8$ zx<$JAR`6At6op8GY49T9jBo#+4?MelyY^Ey-{Ua<_zQ(l<;D~7^W#|G5ND8@9Tj+b zW#XS#9@V}$X$e?;;rDITzk=^${6<>$Sl9pkaa+Kl+7rQ_9>>y?l`h_gh=7H77$#j@ z;$VSA7CG8KQ}+036m zkH()TDU_XMbsI7cE600OD3fr^rEzT$uB3~P=AxV^EG0tw`?@SF%y@W|`nYI13oMsU zM$XJGr=~W~6s=s-BG)vTwsww;4;(44QhNdU>7C4ZUd_30wEfrU+mCq58ul{fZfYRJ z2$6WGq>Yg^Wt&L5IYa}m$YA$BHol0v`ok%@qYTr*Ze6q5aT*|h+ z|IBxP=@{*5V7m2lalp4?@2~k(>!kjF1X_Qt`cE-k&f>dPca@=-H*GRm61i!SBGFHi zTvrSr@&32WF9(9*5)Uoe@3)R4JHG0MRk70b%rq9mRagG99pT5D8h*%pKQ~&KoEvY| zCrE+exP0jtSv&nvosysklMgJ93fzN*o%wkj2oxcGt8uQy8g$1eU+TFyRJ z+i?@;j??y~fA&FY`Jh(6;4~g%hD+TfwNdT3(A0aj(MWIWK{QF zX-rRlnbll+jESwac1bj>LvR95MkrjAOnu8Vuc$ykBP!eKy*`(jJKg2i-gDFT1MYyn zCbN2*L^i7{iuUt_l+)~hL;LKVt?jMku!UuYcDK@tE%AD$26HA6Cn>;fPP;XfxNyO` zZaa5;L#b{YvHe}vsl-NIf$%x8$-2T z9k+P*749awC(brF>TW0@pP1W8eHEPGiNZ>Jr}R*{=SitiYGa1W*I}*vy5z!xXcp_a z4sguUcx(~!Pg4cc1=D%8l85ZwV+ZE%y{K8WU=h&~`I{&@$J5|-vVHk>uEwxlVclIz z%SwjHj=&mw#XybO&aLjEd3s>RS}Bu7z>a)X<12$VYQ%5O`s8@TE7YTMAOEsnv}TUg zT7?qm0M^gS>w7LfM@u6B{{GutE5n2vhQ~9En69%uikllS;m56iI^uIldK@{#V2?Dj zPD_5A=Jj{L6NqVkOs!lUE^uCz8tty7oK7xzg;ePlD@n-QB`>%;WmaiOijioA}@IE4@_OD7`)CUG~^9&qF6uF!An$9gB!0CeLs%%eYNe{b0{+QsGKR;m^_wi-4d=k~6dVRDng# zX!H>ai{$Z%W3wK~ggH-M+HPE!$e`;L()H6$z!hViP}R~${yK(S9W6`kD=qowN%U4d zgL2CT^1q#GOYS10VIeN~niT`72;3fZ3fs^K*CxC5uT7Ip5~)NvdmnO^`Cyj=kGR?9 zH>U`bxjKWHQIE4c)UejGgITS}z)uoPr`>u!oOIR96N<>!+jRD++rH|c+6()(uj+Ni z^laGs%-(Ms|FV+lV(jQ}c>CCk^K8<1H$2R>^FJ-XcEIJT-)Gr?%%PR?+i z8MK>s|9rDAPj=xF;h}ReZpk8 zB!5PXyP;tsA}B4si;VP>cuj0@n(nj~N7Uk<;rjX#{sR`7P%QFkOEa$Df|@*XDArl| zScdLqb0O2aT9jv9y(e|Xm(pi-4BYl?=HAwJRflr-QARRccvkImP7S;rHX`gSJxzl8 z)^+Sr6YlOQJaAr?+KBv!r}yVXl7jxQ=cauVW3Pf^_rqm{ z7Y+s(xO3SC1t~mV--bp^ZJdT_%bna4g~4p8%naNaG)$g(ZXx-qs`zlX*be>rzj2O4G@m+h8*I8xQ? zPCs>E!zC2)!KmVqTxjSSE8&16Lfj8oSKet@Ietj!Qs<%{jO*%Xt^jHc7+u6!9m_|1 zM3B5*F$dCPHeF=oRFM<$qVbdYyzt?plhgJSw^+ZKFBFR$5nEO)Y5aJ!sAii&LvS4| z^}I-g}*hW zix18iBZyscdmyt9cdn_s<9Yu7SKfE{MU`yp-g9S03@9Tgk`%-S1<6qmWe`MiZjdYp zO@;v^r!z1j86;=Kmek~&VU(aWG&v(Mf=vz*o7C{u#&h1CbML$N54g|v2lNiLt5&V> zt*Tx16`Cx@ak>&*)t9exJ?BLAvponh{~(UKlP7g^M(LB9LdKfybs_DzpX_9)2cC{c zC%QPCOVJ$+F?UR0IqT076cljp+I+l}LQe#}BPJ?*`lwRJvwP&|hm2jZo=y7`^6cs| zI{K{22pVS_c8^A>mPdMF>N0MDKJ+Q3lLMppIZ8?>S&EOy!thccNKpF2(&w3`;q62+ zXBDlKSqMF+z}l6t`V{L}XQ|imCitYG-zl>S=#9AVJ^3m{tGv0;LCGfMoeNes=$HoQA% z<3rPo{R-8s8|ym}+cxN*UN$<1283ai#1h99X&(^OQXYrueyl6%t`wEOGg1-o%ABOt3XR?4)z=#Lxvvr@tnPA-hI#F$x>TczWN9HqNrdOLomtrbrI&lYw=Fm>O3_G zOX||%bQ9*K#;WGDDdYWgW=vU0PKNgxMr6*=W49}~Lf_>*%EcHKnv^#ave!$L1OtAp zd+zmtQjjiypN=Al|5I7%J31vrT8cv>J8P+TiRUVmI%6LM;aUqtoGae85iV}fkmT{S%U^znrD zy0sV8eFAJXFUqoXXhq*uKKg7G;yxRAe%1F`dl^^sJzRf%njS5Vue)tom}r!(luA!Q z|Ad};BwSDt=DlgA*G!H-s_wBElL^zr#%a+@9wCH8OOOvFM*ji zsf;Av=gpFp9Pv$)bHAsGSMx*<*8PKh1{p6B;xYR2R@!0!WTmzTb zmG~})Dg|R8^uOy9FSMn!w&OjBxOPZt?$UXLu^i;tnr$X0>g1hs)7+mPLUd2!{AD{*yy5#;UyR-9lSkk##wL=YlR9n<;e|wjzL~ zJh4~!Qv4-_I<`XV=Y|tywuOg{=3^!2D)(rZ53j8I4oI7w=%tLQcT-MqA3i`+1~~Q> z(L44wmTTW%$`%>E(OE{vjhEu;lFjZ4v!%8Bv`419X;f@meXy;?t>0KBKK5(yyl;n~ z_m_kzrL=_|kqK)>S5NNHwxTCG92VI@_l2z}mg_Vp!l$b7!Gor|_sboYE{Wn|YcxX~ zMcZNnS{q{ze=AwPuDEE0zMxpnl|6CRQ4}$os&3&xp^*2#UzvIKp?}sOv5Zhiz3Wj) zwO>NoY?R!^-dG7))O=#{h+SaznLv=bcvj#gq6&=$CM4G8OdcTZ3saQYu7Kr}_^lM9 z9Lu}H>1iRWq`= ze}CrTP$u6bC*zq120n3q$Gm>6*y+`PhOL+z#Lc+rE#BjOD)&Ru-mj|eubz1ApB!e8 zH+zmNNc*{w8*ftVqxqyCeTVGFw#@|sT!sRlmUzthJ*#i)yRD3Rl?+IHusIy8($-11 zOPhmpC{NP2OUU0g+EFfenyTxPSV1Y=#-;Rn=pV4%r95^SaR0z8i=(vZ-M1sJ!&J0H z%HGUgrfTlcISJ?0a#8Sa^KBm2>Qlz6k2gLpdm0~J8rQn4(5{4K(uxZpHz|vZTHY42 zN#@9}=b{N4X8*)0Ehaeo?B}kM;i>o50lDAWddRF9Cs_`&IK{Gxv1!bb2({h(v(n39 z7`m4W<8rKuC|D-o#9C{X*7i=#a-M;BG1wV3OPfuFsGZQt$_VJfkKeGsHXz&bGN#(uz<6T zIzGCLhg0K5;*P}K#+VqI`#O(fO%-zDFo6~54tuGjv87sh5vr=;h*-v}^N_a-wzMWu zk5ph|1D<^*wVS3z6QfJ3r`LzdDpj^+sAK!r@~)dzU1HCdzja6bZK7-vpR~~UJGH4f zJBMBWW-^oGE3Kn&-V;*_v`$wloke>ZgI(|Ww%%^a)vj143`1zrXmJ6}xk)XtCt! z{jT!#5o?d%u}<=i_H5J00B068h2XpP^`@D)^xcY_{m;dFp6lD&syiV%O`k3Nx-C4~ zjmfgAeg~T5W#;3{Jsxud<+_4jk+-x~%D+uANd^JfXayo3a0B z`2#V|oVZQh<2%Q!o|7|0JQukk#k^_nDcM9!X(G(~b_<5uW5Jo^xSNjhJaah!) zXgOLAOki+*%02wO5K-F?UBSO+Y0rny4~zH=m6f_2d{OGcfbEp^G-I?i}BhA0b}<@+tgje7RK-EP3AGZDAu@L zww7wCjuXK&+Kyx;bY6-d%f<9xV$W*n?-LYXv9Q&^@YH&`nDF!^P?`qFij8!~NnLTw zzTR4!6eU-yIT&cqnfUDH+Ms`<+>B-F1)3*)-EpEBB6#6ald5b{Ja@c=-1;mFO})MD zOyEUzjNVEi9U~V;mTT9gJPDnJUhiN#91Cg-PxL6n2L_{tUAPprVAwzH?zI{XMqaOr zC%e~-7BvhjpM0>k=;-cxn_VGj71J1?l*!eYTv54u3En8!<%mrh(lGBW*Fg)ckaP^k zZ3|s7Z_7Cs50;?3!ze zS~*5JX7gLpJX!4Q4u|14z5D!PrIWo6&Txze{d}6#;_}f6*ef(JwYJ*v_ON;Mt2T{5 zn@nAnO5>{Z-Ur@eZ@+bYqi17blXc~bPvbjX-BQ8$`JIOhL+JY>*y%`lloO5SWIIvw z*XILt4RPPzY{VJw)RuWLN*)#aeZF;Lm*tS$&8GD>{IEZoFW@D;?#!QGRK0IEA8$@q zcFO+RNi#L%=ed}lx~rWmd19ryGcUOlvzpNM{>_Iltb1paQS56girF`2GjBv%j$NsI zTH7O!M~$7b3oJ*$Zz(ug{?sJ)wo+(y$?(B9^7_$+`cUA80)CD#?kV7`|pc1=AUqZunelgtboSu zsY0=Bxrtpz)G)qw_duzzJulw|*)RsCYFUD@^doE$*3X+6r#IeS^BOB|3JAa3E>-a; zlSzqzd%W)oUN2lj^OCq%R#LQy#wE`Dq|2d6iBTLKOMku?>ul|3RJ=B31M?YKY59qj z+=?qB;oU7q->Aqqs{I<%pB)BSE{)fkhSb=nRlQ@<;-BsMKpDKIO}t}zR6V$6o~nEF zxrbc0ew@DU>`ac}!Ke-e-Y{I1Ezs_Gn{Af7!lXny5Q>Kv5tTE|{f4sN<})dM42NZi zHA5}T`!dvmT#8JP&hfZoZyW?YIeZSYS3YApzG*2-gVztMY(?5RtddOeD7v)9faFKI zfhs8#0p?6;n1G-mR`D*%amo~~x(U-eMdP`tboonF?;cNt2FQ1+eNvmX)G;u0*m%uM z&Kx#5Cx~L}=2oP8;x|J+dFQvCHa(Nybl`_|sJ<*}+K)*bKQFRuolPiv8m?<iuYP1fgtnom59#TYGNe^N#yZo-Y$)< zZdYBqlJWdZ+@dM%m;vbIR|>K*@3CXgo=s2hBxIb+9F7^BCV@4Y_i^D?RVJ8E@n7Dh zG7io>Q?t}!a;b);l#=-z71#=Of$l2-JD1`GTqw$&2dI-w4LeNhBM4mfP211>matQu z7mW4@$D7U>vk{rjV|N@ee2J{eQL<2XimGsmFsGvHZMF7Gn8?Exzj5Qeeuv#ZDNZU- zupEG7Omu04eMffk{1$!sA~2s!F_7s8C3U8gU~{gGPfrCU2P$}u>9hUIKF&5-(4%ASxd@betZ&( zmni3TibKK;^Kx1mE~V1xy(Yg6!Qih=^usHJq`W-BFA?VQ)_o5b5&oy(QfB zvl6|2;Akc~A)sT)fS;{fxv<-g-qdk0%!Gr|>d?T2__f)+naW4CnOrD2pVM@B+ctGn zH?`*7q)MS>e?KEve*%giL@#8Ln$-5*x>66Vx9dd_0I5Xv6-KIG^PIkYa5$1hR?V`Y zFJ(KzTWoQjLERYG_a-6qs=kkIHr;%meeCRfyh`6 zNqKDmkUMn1j}bsia31FbI85U2KClG$H;V_$x|iS-G?pS78K&q$p6w*~`+0O0PK&mD zqEq8(+TE})=UIa3XOnW16>clRTiS;%wj!cQHvk%{U)o((umihJX{$@G%P}=HFQ<#W z$6)H%B5=w2Gh6q?`U!`v9o{4L;<~Q#jQ!fFS$T=|yXkhiPf1>td_gw;jsz{UX1AzT z-)0@fE?NHd)-cSg@i((OwY1El@mN+_WG4E-@|8`;^5%;n3Uuk z_=T?}Nl*gyUYh%gD*u@n#}S_TAnteXHLfCB1CrMtJj4y8JIXU2cNOT_+dBNR>Q`0he6LJNurEadN9{cuDa^^+7 z#<#Vb60Ly}Qrs#gl=s5~5V{L})Y@`E{TE z*`7#0l%YONM2_!~#}Ae1(QxluW*q05+SQ2B&})(?NS+7m}>Q zU%r%LT=-#N$0f!tVC)4>fR4Z^%bgolq$9fhe&4dNVn>iRB&#F2lVmWHLGHglNnVu1 zcxo+C)?GB$OdaYNEO5bY7DO#^b}t#>urt zR!><3n($Eu^x29CCMkvC@bMtivaCu>Z0oWib)oQg5l@}P;o$VZ?gS*EnVXW99CJR;HqCscNXjHV%Qc9c@?B@iM&$|H+}oksG>KN z*0b*suinDr3yqeU*SHhYt4i!TPL7+&7Z%Me9Xkt8(oS}nr;2bk>`xz&Qmq#(`*YPw zGj&82#PT^PD-D&puGD4r-7oA)+7jn;d~buZ1(x|c(XgKZvqj6FV&%G^vgNvz((<`w znK)Z{&_*17*$_>?GZl`*Gd`wn;iIb@Hn8+u*R#RlL)p~PuzkAo-*B9~@iNd8#cqvm zg~A|ubws{A<}DTR65Ba`#K_|<{J(T1Pz=U@BKgOB^M$roGHOf?zI`!Z*ba*8Eaii-@K zeUsI3w-z+ItvRzuN!gT(ft-zTfG3Z5n<9C!M|`Z1i_H{hWwN+W^qlg8>qYDbgC z=%;mqyc|9#wpilTkN^)#@-Ni{=<;aVDJC7KWaqm5+?UU`aS||-|!f)U} z+Ou{u|32b1q8B5B2(xmyAsvNCnG^dvCnGoj`LOTyzN|})1%H9V-wDQT%0{*3;R5tS>27lpQrn)9}m_d^fhgWKSGxE5|Z$8`? zR!2XwQ3jU?R_f6rm$W-4dD0sxX^fa@{S1l3|2Q(+3>p&Z2#3Vp5|gi zLZ|RbN?j=a{NR@_e^=!Z?i-eloDHIOAh&H;0v(0^r4;)79L@N#)N^a%cP7%&Fj}wc6?zmQm z6Ig1@=PVTdhvtuzBTgN>xdR_~CRlPxEnhL*FvZ4?I3@@H(7j0i=;UY5G9Lv@kR_>( zb<9IlLKxJ}3b#>SzxV?~_P;3Hg5|yE(hS1Aoq@Ch*i@{ob$X1wv{=4;mHZj%!sH+g z1z5yyHFVT|vN2&MOX%(G`|C%pIx|^WkXNYXm-EUm&>}xwO%Z3nXOH&YFYJ@HIeUIW zEs+jP7@FZg7p%2am9g(|6nV0pad2|uf&HgT10lo7;7mDXuUfb?j;SsFbKeML6A3wH z%9dwBomyzCLa<+OU*mHIdF%^A;b%0l3W7^ReOk#1&qai7Q)*UnroKElX}PYa|EN6k zRpxh7xX1!(N=Y<8`>sF*sBsdvGx33^(GT%C29^+KJg{AmfL3dzH}4&wym{585q> zDBCWHRPb=1e|S&{vO>}l47TLY(EjsXfTfe8!jn$E$B26!(=uWa ziCb%U7p$%o$0Mt3Yu$Y{rA|4(UQ;k6Pnm{T@Pc}rPw8nA@L7R+#Lep2twf?cC314Dta>Tk%#uJJ3Snf-60o=-onHJcO$WUtXF9nM><+0 zmYR}~%{gz44;)n!zNsZ9Nfbja5XM^nc%AsT-}Vk~zbywLkW)j?SzCu#pn;yMsq1@# zzji^iT;K1v0U^U}FCqp>1z?(-yx@Wl6_R8ikZ1^0!+C3B8#s>1(6-!6dSM}<6b+HMa)xC0 zQdxLS{#|B_eG&ateEE4|fr<+bdzi_|h^P9Q@jQ|3_eYC!U1E}m7@-F0r+V&HsMK&K znlA9Yt;m4hfR`vkAaDT!VBv#+2h99ZE-QZp9tidIcILb!?EY|D3JYLi>yR##rm zd2k?I?{lgQe3a32=r{-$AaOwBsAve~D1Hm@Rg#qQ4Y5=8I*@n(fKsOhBTL$D)MXo56vl%hUAJst8b3vC{zCokW4S~>Dl+j%R zl`h~8v^i8UYA96u7C_mH7h|~}mh;#?<&xS9Q_#cx}AN|~8?Y|&ix20pMIPxL| zpG%W6^O(VEp-M>^fr-SX}v4Z=Kn-z-{SPvBsmDa_|0cZtBN`IG-pt6uu7(?>4v;Jy# zVnsAN2}^YNLy6AWMVjg8%I-t#%A0Ml@13p9z60fj^a za9kCQ9NyV+;eE0ttqrXvU<4sDL0~-XR*ZDna`X=FzrY2BG$sJg3zRZyC56bcH2)Tr}yYdmf<;2#f2y&Jsv!rIZXZ)pjKD>HY2S0g=2 z0of1_<>AJr1O(*7f~qvcA_oy+tbeau1PUS$;x+Ybs2T(L13pS3<-Ap`Itffr9=jF)oWRo{xHtRj}>!< z^AhP(BelwJ5-w;}=iS$7yFlsFm)jMF8OjR6NGqdLv3Ww+c+Bu{TIAA1xMvE-WK>>X zLGqQj2wC zA&&ws?O>o^`;;mD%D`>Xg{S$*snM-RVk->nwcfVphP=d9m*dHLhWCJ<--O-q(nfPt zT^&MF<7&{*GBYDWj0n!MyTJt4<|rzr*^BV_k%O` z&iMhD1x!^2obA8|k^7O^@AKnphGsw%b;D;{or3DdHt-W|H66Cn@J!Zrmfm_|=)H02 zg5ret>N1r$X#aW1Ln^=FjN=i>odxW4X@z^~p$Ad5?l`bo46M{5g1OpeMCD^7@{7m#XePmSqKXh|=sISOkV5CEq&Xe!Io7+b zmVp808ZYB-i0k*~z*E4+wZy%R zS)lM*JU(2ncAi&R_a)Uo*l4BE*4Acv=nbTx6I{H!UJIG8n8aUvQbZWiSA%cEcyJkT z_O`%TjEBdVG}VKzKpra#BpKVJj7SyDjwIG$t?UD6`W5`^HOH-}@e+p_DHuDjIM0Yu3zB5%bi|4RkH+(*NLK0So)@pNz&6iRO)NfEY?^ zYAl7)RO}!N0ykk|B4tCc712cN+4#O`2()h|@3P3Df0Fw7$N!!Gk5_t*xp>V*Qiw!^ zhcFp;-k$}1^?z4mqx{IlCoZ1r9vqFNR2yij{`7%%6tq+ejr>G_-~U(tKmGOtg;UQ1 zk{LK+sVG(N8Jc|{pkVlM|Jy6c)JVUhS*a*0zrB|Iy^oSN%mB`a{rcxG56O$F^FXC{ zULZ+nvoz9&E8}1=#~;W?;*hgIJU`Cz3HIeq2li~q(9lpR@R#jC6TIGGCUC9{c$PkU ztvBjEeR}pUe}4kc=mf-z-UkC2-QeM`mQ`56nl*PS|e%dXuO!LhvWPNlz7QrYqht5yV4j8Hma?y z4SDYV{K-*9P9-fumDjaSo?0i)`>xG95#w~`fcCL=cibj`1^9Zs)|yX_AdFBNg=n#ig(5UTYnAyh0QXUwm>wy?G9oXaUpWO^r^=4%zFFXmT7~V|k@n&b<|Ma9^??PEkO(JyV{S24}3SdFH zX02v6g5@l%7&@L8b6L_#_ulLX13VN1K~zBX;l3HLkS0P@K*HsIKG0RC0zYLV3F6#6 zP{z*AzN4s}DM%h*(e^V%09dEkUoVdnKorK@ybY~mm_@P&KnKI3bHo~86GW9}RkHsB z0{bx*3hN_K1Kj;oy)%r>S5*ufxD25oFmnWR%LDf1L()iR5pUvMV@C*G`b}GD>rZuc zHx#@#LXBSl>V`P6bisJfAKBwpTSteRi;J!iu6ePTmB8Qww0gT!q^6Pa@#MaYqmB&V zu>Cv4l?p&J(|hfrP;$E^K$)hEaEE)&b79^Y^D0`xSYG4>l8;SAbo;81e+5R84DZdrT$q?eE;v z+oqXdRQ)AdISAx7fAS*9%^sNODfVbp-ITVKM99p}C%lFohQ zWFWDB@5`&}kPB6g7}>m-bQu9<`#Yp?aEB~XX=Ed7S>OpPud7>f&Nxh+s_z4lQ`~U> z1Gue0Atye3@b9}ny|0pBhi<*x9C^st`bQsZI5$>u3I`FO*t z>rU`#V#N(fmG8zizdi)WDk8~f!t(SnEH12DF4}6PQMD|DVIx_X(`rnCq&Ky>5cV9o z?hB*QM##0yW`O(<$do}|v4D&W6n8iou1i#6hrtcO`S7)+{9;$~&Pe@oQ9U>?hDji2 z=c!*209Lac@G4#-^;3{wbi=+yK8LRBo+I@Y!wBUGf<=)#XTwG7jy5|HJuU=HR_|6^ zzgE}ThkbHg%&C<^a!-o+p!XF3e8C%jM~>65ck*?BoVmEvWuQCR`gAMO{p4`k!OE(` zu6l9~0g}h0=4rMB1B96Dzb_jBi}tld+Rn~S*zn~HPz}3m5k%_&qFroxRsrxEj_mwK z<^4yIVg1Tv{RmR{T#ADT%$-e>>-|PjrjIb|uq!QsO=}V6a>*S{N#gE=Lp8J!Y#e}S zK91>sXaJ&D>r;)#eUb-Hz}R4Ja(lSCY3Q%z0_EK5!E&)1=o^F`p}NKF6a&Zw)*7}{ zfIypMs$=X^iP)T>+oU#}=N$OYZ00@5nfk2VMv- zr5sH~MMd8o9lxOr@>oXg{#**V&G_ggf~{R)ca>4lgA|9RvBK&p$P%!I^{zk*T}P@PZVsr$#9UVrVaTe5@2Q@i0A&EKtfb^m1NpEB zCNbnv=(&n*u}b-_78D+lv5RZ{Pn0LqjFbtwfm3jR9! z9;E0dbdc&Q&Mfp{15HXA8n}X|>LC}`Gd8YnYO)6wXBbw4L5KgQy=$MXV2%^U7SkU3 zj3a&q$&$TD_5+Jr>yzMMzt*4ld3~$Eay!uE&&9=H ztK>UbC6o3XSuDX#>!k!$NpdS6<>$#sN5P0&s;a8qKupfn`QebXzQ6I2`L)b*#tHAe zZ@&#JwqnY^IMu2td~ui10OIlip^feB){Nur+pPZWB5-3vQ`08E%cQ1tD_iGUgF9Kj zO5E5NyYBaQ7ekILptBdcEXDd-?noWAQHxM`$@Gq9$9~|w84T1gmZuzUa4bp=Mcw=o z^-#x#jnxw6_&fz@@9b;`veJNz%(tVXE~E&<5nz@&Qnwu#)EiqT0skIwyRMMD6gV2# z{_&WZ`O$2Tlj8@5Bc;>pVmBhUeR?E%gsQ9#nsN}Ln(BCSDQY0UWPz4eE!IU~n4Zje zWXC?5CXHq<9;WNv&AKwB7yl9n-bf_Upk7io^ia^*^o$I#G?iG5J%$M(pJ`2C*ZiR0 zSq6NWax*}qri`@S(sql~e3GV`@o)nYi9XVUX7soF<;tgRf$jOJgJou>J4ApXhe2Mb zYM%`u+sy_!vii%gzTTV8tu#$ZK|10V)GHh{6)hw4>+=-G36QbOjKvtRf?6WY!XZ4d#ettcea+7q6IgfS`2jv0KkDIKP&==)nKVSaO zw|$F8p+GuG^Yjx!(R3d#%WwNcqFn2*cb0wAE{p>ev#*{g<+@O1WxMsIh2o*lflOgs zLc%px|5evc=;IVZphtSrd=jR4T2PtBVMuRZ*mlcC?(NvBYplkoeKlpvADD6}>XGHE z2vcJ8KWlNsBww(sFybO1AyG2DK4vQdy#m}OWMAkYkVWgg2)VcGUdcgk*1g&t&C17E zfIWCw9R8#`!vOrv@UR|1xPw39R`fz05GeEV^2+=;u^NAP=p~YJaZ3VDOFp5Fh8Hut zw!%QnVY5M~gQkxnKGR?|quIJ6ai_F>@#bH^?%<}X3GQ$^-_VHZBou@EpkDCVc=OC< z@nIJ>Rz$U@V^9w*z)qrppA$q5#-R?q3>;HXWf#NXvq;B`M9To9VWl=03^2p8o}2g# zJc1c9zuUn{*5B-Gx8f0VnOR$lFxL$_i*j^xgN1i8KMn|$j@dqi>O9k>y}`^Z#&iiO z!{1!@PFAl$u?{?KSv=WXx5j`6MD^|90MADWI_={F4%yb(xzT9Mz8VB61B=NbBO_tU zXM0;{AW+y@A!N5(5?gzFpT7M12IwRaYknI4(pypMR8(?h{$ zK^E|c+{#S|p6Uu;WU1ZU+<e-NF$ zr(u&-s$5=lmx_vN+efu9t{r%TTZ{WP?w5z35>gtD^u=nNIx~t>Voy}E#`$I~aEQ?I ztSd-2SL47_Aqrx<1c{%ddaBOR$2&2lo=mENO~}<9qeCHa9=qix|ZuvxD=D zE+GghL!?0U4eHez6~6%TqbEaT9{9R-a`O7e z*~{`m^^*}GOytpr}j^|@AX(J;e#Ba!%GCc`2`Gr~(vGr32 zV88t_+Ur8qL_#=ksml##LVYY3Qf zGnncLBm(YxXAg&<*4EbU-H1fINw(F@?8it@!^`hmT7K}E9)M8vmu>cJ%o(HL( zJ{S?JGRI4hoJ5ZtxXxbEgK!kI)!o;3PBG%+EvV4)4j#HMA?oBeaNLO6eE)TlYL`sK z{1Bonv>bHwH&m0Ep$@2$v1y8nAq1LskyG1(ucv2y5yn6;puW*;M4?U{02B=!EP3?p z2V+f}Hi#9dht)-Qp+3B&%7t+n=lz6ESq5dku~mUHD_VA6-q?6uv5E$8h38TQWxq%u!-2w%ED+sIrf-xWEQEJeiHgHQg+;?;zsH8uPAUAT#dB;AI`Ihm_P$%2!Ek{<#n62Cq zRTU@-Kopso6Uu$&|LCpnoz}#GV4(log>Z0#aPD8qg>w!n2n_m1FMofSGZM4^dT=I% z=3g&CQBS9`{L3W}Skvp=0N|2Oz2|5!z)=+QI( F{{t{vpH~0? diff --git a/images/README.md b/images/README.md deleted file mode 100644 index 8b13789..0000000 --- a/images/README.md +++ /dev/null @@ -1 +0,0 @@ -