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本文原文来自LifeHacker，译者@小栾Cooper和@Pushing_Lap。

你是否知道每天在椅子或者沙发上坐超过六小时会带来以下一系列烦人的问题？看看这些令人忧心的事实：患心脏病的机率将提高了将近64%，减损七年高质量的生活，增加患上某种癌症的危险。换言之，久坐会要了你的命。这确实是个噩耗。不过好消息是，无论你有多懒，这个问题都不难解决。

我们从基本的开始分析吧。从小我们就知道整天坐在沙发上是伤身的。但是，这是为什么呢？简单地说，我们的身体并不适合久坐。长时间久坐，就算中间穿插一些锻炼，对身体健康还是有负面的影响。更糟糕的是，许多人一天坐着的时间长达15小时，这意味着，他们连散步的时间都花在沙发上，椅子上和车上了。

久坐并不是很难克服的坏习惯，关键在两个细节：日常活动、注意久坐时间。我们先来看看久坐是怎样影响身体的吧。

• 久坐对身体产生的伤害评估

要想准确地评估出久坐对身体带来多大的伤害是件困难的事，因为这还与个人的饮食习惯以及其他因素相关。因此，这里假设你身体相对健康（不酗酒，不抽烟，体重也不超标）。我们将从你坐下的那刻开始，评估每天坐六个小时以上对你身体的影响。为了使大家对这个评估有个整体的了解，请参考一下这张从医疗账务处理中得到的分项数据，对应不同的时间，可以看到久坐带来的不同影响（哪怕你是坐在桌子上、或使用站立式办公桌也会带来相应的问题）。

刚刚坐下那一刻

当你坐下的时候，肌肉里面的电流活动减弱，同时卡路里燃烧速度下降到每分钟一卡路里，这是你走路时所消耗卡路里的三分之一。如果久坐整整24个小时，供应给胰岛素的葡萄糖会减少40%，这最终可能会导致你患上第二型糖尿病。

每天坐六个小时，两周以后

当久坐变成生活方式的前5天时间里，你体内的甘油三酯（脂肪分子）、低密度脂蛋白胆固醇（又名坏胆固醇）和胰岛素耐受性将上升。这意味着肌肉不吸收脂肪，血糖浓度提高，从而增加肥胖风险。两个星期之后，肌肉将开始萎缩，最大耗氧量降低。也就是说，爬楼梯和跑步对你来说变得难了。即使你有每天健身，身体机能的退化还是会在你结束健身后出现。

每天坐六个小时，一年以后

一年之后，久坐带来的长期影响开始慢慢明显。根据Nature的研究，久坐有可能导致体重增加和胆固醇过高的问题。针对女人的研究表明，如果每天久坐超过6个小时，骨质每年减少可高达1%。

每天坐六个小时，十至二十年以后

久坐的生活方式持续10-20年之后，等于你失去7个高品质生活年（那种你想过的生活）。会让因心脏病死亡的几率上升64%，得前列腺癌或乳腺癌的几率增加30%。

是不是觉得这些很可怕？但是别担心，接下来我们将教你怎样在不完全改变生活方式的前提下应对久坐带来的负面影响

• 如何在不改变生活方式的前提下防止久坐伤身

幸运地是，你只需要做两件事来应对这些负面的影响：

1. 记住每小时要站立一次。
2. 每天要做大约30分钟的运动。

无论你是个整天黏在肥皂剧前的宅人还是坐电脑面前的白领，一个澳大利亚的研究表明每坐一个小时经过短暂休息可以减轻上面提到的危害。短暂休息不是指去健身，虽然健身有其好处，但是对于抵消久坐危害却没有多少帮助。你只需做些简单适度的活动，让你的身体从久坐中解放出来。

所以，什么是适度的活动呢？我曾与Brian Parr博士（美国南卡罗莱纳大学艾肯分校训练与健康科学学院院副教授）探讨过。他指出了适度活动与锻炼之间的差别：

我们常跟人们说适当活动就相当于快步走路。这包括了整理院子和清洁房间，任何能让你活动一下的事情都算。你并不需要做那些人们常说的锻炼。

当然，肥皂剧迷和白领难得有30分钟的空闲时间，但是，你可以把这30分钟分散到一整天，Brian Parr博士说：

我们经常告诉人们把活动时间分成10分钟的小片段，因为这是最实际的。如果我告诉你可以把这30分钟分散到一整天中，你会说，“我可以站起来经过房间走到茶水间”，十秒钟？在我看来，你不应该这样做（划分时间），但你可以这样试试（走动起来活动筋骨）。

你想把时间分成十分钟的主要原因在于这样可以在身体里产生小许的压力，帮助提高你的忍耐力。在现实生活中，这意味着你爬楼梯将不会爬到一半就觉得累了。试着这样子想：你不会每天快跑十分钟来锻炼马拉松。相反的，会通过长距离的慢跑来提高你的耐力。日常活动也是一样的道理，你需要维持一定的时间才能使效果有作用。

接下来我们将教你如何评估每天的活动量和确保你有离开你的座椅。

• 从利用计步器计算日常活动量基线开始

要做的第一件事情就是计算一下你现在正常一天的活动量是多少。对我而言，最简单的办法就是用计步器记录我一天走过的步数。你可以在亚马逊上花三美元就买到个便宜的计步器，或者使用iPhone或者Android手机上的应用程序。

你第一步要做的就是步行三十分钟，看看你走了多少步。我的总步数是略小于4000步。你的步伐的快慢和大小会最终影响你的结果。

下一步，你得找出日常活动量的基线。起床后把记步计打开，放在口袋里（或者打开手机里的应用），直到你晚上睡觉时结束记录。这样就可以从记步计上估量出你的日常活动量。

对我来说，如果不针对性地做运动，那么最后得到是数据将低得可怕。我总共走过的步数低于2000步。这个数字真的弱爆了，这相当于一天步行不超过一公里。毫无疑问，我需要站起来，到处多转转。

• 稍微改变你的行为达到目标活动量

如果你像我一样，步行数也是远远低于目标运动量。只需在你生活中进行一些小的改变来帮助你实现目标。以下是一些建议：

• 在停车场的远端停车
• 站起来走过去取文件夹，而不是滚动椅子滑过去
• 走过去跟你的同事面对面沟通问题而不是给他们发邮件
• 沿着景致路线去卫生间而不是最短路径

从离开家去上班开始，我就刻意去做这些来实现目标运动量。我开始在下午步行到附近的咖啡店，每天至少骑上10-20分钟的室内自行车。如果这两个都没做，那我会去洗衣服，因为这样我得走两次楼梯。

达到目标活动量只是第一步。第二步要做的要更加简单些，那就是不时的站起来。下面是一些小贴士，用于提示自己别忘了不时要站立一下。

• 设置定时提醒来使你站立一下

我们知道，如果每小时不时的站立一两分钟，这将有效抵消整天坐着带来的负面影响。从技术角度来说，你甚至不需要移动一下，只是站一下对你的帮助都很大。可是忙着工作的时候，我们就会常常忘记了。我在Mac OS X里发现有个定时提醒的功能：点击 设置 > 日期与时间 > 时间提醒。Windows的用户也可以设置类似的提醒，通过这个路径设置：控制面板 > 管理工具 > 任务安排。

如果电脑的定时提醒还不够，不妨下载一个专门的软件来提醒。Windows用户可以下载类似的免费软件，例如Workrave和Breakder，Mac用户就可以使用Time Out。这些软件会定时提醒你站立一下，并把电脑显示屏变暗从而强制你离开座位。

这取决于你怎么利用这些短时休息。你甚至可以不移动如果你不愿意的话。如果你那时确实想运动一下又不想离开座位，以下有一些小建议。

• 站起来
• 原地踏步20秒
• 伸出手，尝试着触摸到脚趾，持续20秒
• 远望一下，重复或变换刚才的运动

我还买了个iCade放在站立时的水平高度，这样我站立着的时候就有事可做。就我而言，我没办法只是呆呆地站立几分钟啥也不干，我得找点事情做。如果我站起来不想伸展一下的话，iCade会给我增加一些目标感。

• 把那些蹩脚的广告变成必须站立起来的理由

电视广告毫无意义。与其坐在那里浪费时间看那些过去20年你已经看过的广告，何不站起来，趁广告时间活动一下。

为了能在广告时间找到一些有建设性的事情来做，我会在咖啡桌那里贴着张的待办事项列表，不同于办公室里列表，这里面包括如：去丢垃圾，洗碗，清洗猫砂盘或者其他需要完成的事情。最重要的是？我不用去看那些无聊的广告了。

同样的，在玩网游的时候，我把每一局的结束当成是需要站立一下的提示。如果在玩单机游戏，我会在游戏加载间隔时候站立一下。

这些的关键在于，我们坐下时在享受的大部分活动包括了很多等待间隙。如果你是在看书，可以在每一个或者两个章节后停下来站立一下。如果你是在玩拼字游戏，可以在每一局结束后站立一下，而不是继续坐着发呆。站起来，这其实非常简单。

最后重复一遍两个要点：每小时站立一小会，每天至少活动30分钟。就这么简单。除非体型超重，否则你不需要进行锻炼或者去健身房来抵消久坐带来的负面影响，只需要保证一整天有活动一下，你甚至可以不用放弃长时间的看电视节目而只需要每小时花一点时间注意一下。

或许你也想阅读：
别折损了你的背！健康姿势贴士	你会休息吗？掌握最高效的休息方式	在工作时也享受美美的休息	男人二十岁后应该学会的习惯	如何培养细心的习惯
无觅

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Introduction

Suppose you ask a bunch of users to rate a set of movies on a 0-100 scale. In classical factor analysis, you could then try to explain each movie and user in terms of a set of latent factors. For example, movies like Star Wars and Lord of the Rings might have strong associations with a latent science fiction and fantasy factor, and users who like Wall-E and Toy Story might have strong associations with a latent Pixar factor.

Restricted Boltzmann Machines essentially perform a binary version of factor analysis. (This is one way of thinking about RBMs; there are, of course, others, and lots of different ways to use RBMs, but I’ll adopt this approach for this post.) Instead of users rating a set of movies on a continuous scale, they simply tell you whether they like a movie or not, and the RBM will try to discover latent factors that can explain the activation of these movie choices.

More technically, a Restricted Boltzmann Machine is a stochastic neural network (neural network meaning we have neuron-like units whose binary activations depend on the neighbors they’re connected to; stochastic meaning these activations have a probabilistic element) consisting of:

• One layer of visible units (users’ movie preferences whose states we know and set);
• One layer of hidden units (the latent factors we try to learn); and
• A bias unit (whose state is always on, and is a way of adjusting for the different inherent popularities of each movie).

Furthermore, each visible unit is connected to all the hidden units (this connection is undirected, so each hidden unit is also connected to all the visible units), and the bias unit is connected to all the visible units and all the hidden units. To make learning easier, we restrict the network so that no visible unit is connected to any other visible unit and no hidden unit is connected to any other hidden unit.

For example, suppose we have a set of six movies (Harry Potter, Avatar, LOTR 3, Gladiator, Titanic, and Glitter) and we ask users to tell us which ones they want to watch. If we want to learn two latent units underlying movie preferences — for example, two natural groups in our set of six movies appear to be SF/fantasy (containing Harry Potter, Avatar, and LOTR 3) and Oscar winners (containing LOTR 3, Gladiator, and Titanic), so we might hope that our latent units will correspond to these categories — then our RBM would look like the following:

(Note the resemblance to a factor analysis graphical model.)

State Activation

Restricted Boltzmann Machines, and neural networks in general, work by updating the states of some neurons given the states of others, so let’s talk about how the states of individual units change. Assuming we know the connection weights in our RBM (we’ll explain how to learn these below), to update the state of unit :

• Compute the activation energy of unit , where the sum runs over all units that unit is connected to, is the weight of the connection between and , and is the 0 or 1 state of unit . In other words, all of unit ‘s neighbors send it a message, and we compute the sum of all these messages.
• Let , where is the logistic function. Note that is close to 1 for large positive activation energies, and is close to 0 for negative activation energies.
• We then turn unit on with probability , and turn it off with probability .
• (In layman’s terms, units that are positively connected to each other try to get each other to share the same state (i.e., be both on or off), while units that are negatively connected to each other are enemies that prefer to be in different states.)

For example, let’s suppose our two hidden units really do correspond to SF/fantasy and Oscar winners.

• If Alice has told us her six binary preferences on our set of movies, we could then ask our RBM which of the hidden units her preferences activate (i.e., ask the RBM to explain her preferences in terms of latent factors). So the six movies send messages to the hidden units, telling them to update themselves. (Note that even if Alice has declared she wants to watch Harry Potter, Avatar, and LOTR 3, this doesn’t guarantee that the SF/fantasy hidden unit will turn on, but only that it will turn on with high probability. This makes a bit of sense: in the real world, Alice wanting to watch all three of those movies makes us highly suspect she likes SF/fantasy in general, but there’s a small chance she wants to watch them for other reasons. Thus, the RBM allows us to generate models of people in the messy, real world.)
• Conversely, if we know that one person likes SF/fantasy (so that the SF/fantasy unit is on), we can then ask the RBM which of the movie units that hidden unit turns on (i.e., ask the RBM to generate a set of movie recommendations). So the hidden units send messages to the movie units, telling them to update their states. (Again, note that the SF/fantasy unit being on doesn’t guarantee that we’ll always recommend all three of Harry Potter, Avatar, and LOTR 3 because, hey, not everyone who likes science fiction liked Avatar.)

Learning Weights

So how do we learn the connection weights in our network? Suppose we have a bunch of training examples, where each training example is a binary vector with six elements corresponding to a user’s movie preferences. Then for each epoch, do the following:

• Take a training example (a set of six movie preferences). Set the states of the visible units to these preferences.
• Next, update the states of the hidden units using the logistic activation rule described above: for the th hidden unit, compute its activation energy , and set to 1 with probability and to 0 with probability . Then for each edge , compute (i.e., for each pair of units, measure whether they’re both on).
• Now reconstruct the visible units in a similar manner: for each visible unit, compute its activation energy , and update its state. (Note that this reconstruction may not match the original preferences.) Then update the hidden units again, and compute for each edge.
• Update the weight of each edge by setting , where is a learning rate.
• Repeat over all training examples.

Continue until the network converges (i.e., the error between the training examples and their reconstructions falls below some threshold) or we reach some maximum number of epochs.

Why does this update rule make sense? Note that

• In the first phase, measures the association between the th and th unit that we want the network to learn from our training examples;
• In the “reconstruction” phase, where the RBM generates the states of visible units based on its hypotheses about the hidden units alone, measures the association that the network itself generates (or “daydreams” about) when no units are fixed to training data.

So by adding to each edge weight, we’re helping the network’s daydreams better match the reality of our training examples.

(You may hear this update rule called contrastive divergence, which is basically a funky term for “approximate gradient descent”.)

Examples

I wrote a simple RBM implementation in Python (the code is heavily commented, so take a look if you’re still a little fuzzy on how everything works), so let’s use it to walk through some examples.

First, I trained the RBM using some fake data.

• Alice: (Harry Potter = 1, Avatar = 1, LOTR 3 = 1, Gladiator = 0, Titanic = 0, Glitter = 0). Big SF/fantasy fan.
• Bob: (Harry Potter = 1, Avatar = 0, LOTR 3 = 1, Gladiator = 0, Titanic = 0, Glitter = 0). SF/fantasy fan, but doesn’t like Avatar.
• Carol: (Harry Potter = 1, Avatar = 1, LOTR 3 = 1, Gladiator = 0, Titanic = 0, Glitter = 0). Big SF/fantasy fan.
• David: (Harry Potter = 0, Avatar = 0, LOTR 3 = 1, Gladiator = 1, Titanic = 1, Glitter = 0). Big Oscar winners fan.
• Eric: (Harry Potter = 0, Avatar = 0, LOTR 3 = 1, Gladiator = 1, Titanic = 1, Glitter = 0). Oscar winners fan, except for Titanic.
• Fred: (Harry Potter = 0, Avatar = 0, LOTR 3 = 1, Gladiator = 1, Titanic = 1, Glitter = 0). Big Oscar winners fan.

The network learned the following weights:

 Bias Unit Hidden 1 Hidden 2 Bias Unit -0.08257658 -0.19041546 1.57007782 Harry Potter -0.82602559 -7.08986885 4.96606654 Avatar -1.84023877 -5.18354129 2.27197472 LOTR 3 3.92321075 2.51720193 4.11061383 Gladiator 0.10316995 6.74833901 -4.00505343 Titanic -0.97646029 3.25474524 -5.59606865 Glitter -4.44685751 -2.81563804 -2.91540988 

Note that the first hidden unit seems to correspond to the Oscar winners, and the second hidden unit seems to correspond to the SF/fantasy movies, just as we were hoping.

What happens if we give the RBM a new user, George, who has (Harry Potter = 0, Avatar = 0, LOTR 3 = 0, Gladiator = 1, Titanic = 1, Glitter = 0) as his preferences? It turns the Oscar winners unit on (but not the SF/fantasy unit), correctly guessing that George probably likes movies that are Oscar winners.

What happens if we activate only the SF/fantasy unit, and run the RBM a bunch of different times? In my trials, it turned on Harry Potter, Avatar, and LOTR 3 three times; it turned on Avatar and LOTR 3, but not Harry Potter, once; and it turned on Harry Potter and LOTR 3, but not Avatar, twice. Note that, based on our training examples, these generated preferences do indeed match what we might expect real SF/fantasy fans want to watch.

Modifications

I tried to keep the connection-learning algorithm I described above pretty simple, so here are some modifications that often appear in practice:

• Above, was determined by taking the product of the th and th units after reconstructing the visible units once and then updating the hidden units again. We could also take the product after some larger number of reconstructions (i.e., repeat updating the visible units, then the hidden units, then the visible units again, and so on); this is slower, but describes the network’s daydreams more accurately.
• Instead of using , where and are binary 0 or 1 states, we could also let and/or be activation probabilities. Similarly for .
• We could penalize larger edge weights, in order to get a sparser or more regularized model.
• When updating edge weights, we could use a momentum factor: we would add to each edge a weighted sum of the current step as described above (i.e., ) and the step previously taken.
• Instead of using only one training example in each epoch, we could use batches of examples in each epoch, and only update the network’s weights after passing through all the examples in the batch. This can speed up the learning by taking advantage of fast matrix-multiplication algorithms.

Further

If you’re interested in learning more about Restricted Boltzmann Machines, here are some good links.

• A Practical guide to training restricted Boltzmann machines, by Geoffrey Hinton.
• A talk by Andrew Ng on Unsupervised Feature Learning and Deep Learning.
• Restricted Boltzmann Machines for Collaborative Filtering. I found this paper hard to read, but it’s an interesting application to the Netflix Prize.
• Geometry of the Restricted Boltzmann Machine. A very readable introduction to RBMs, “starting with the observation that its Zariski closure is a Hadamard power of the first secant variety of the Segre variety of projective lines”. (I kid, I kid.)

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What types of students go to which schools? There are, of course, the classic stereotypes:

• MIT has the hacker engineers.
• Stanford has the laid-back, social folks.
• Harvard has the prestigious leaders of the world.
• Berkeley has the activist hippies.
• Caltech has the hardcore science nerds.

But how well do these perceptions match reality? What are students at Stanford, Harvard, MIT, Caltech, and Berkeley really interested in? Following the path of my previous data-driven post on differences between Silicon Valley and NYC, I scraped the Quora profiles of a couple hundred followers of each school to find out.

Topics

So let’s look at what kinds of topics followers of each school are interested in*. (Skip past the lists for a discussion.)

MIT

Topics are followed by p(school = MIT|topic).

• MIT Media Lab 0.893
• Ksplice 0.69
• Lisp (programming language) 0.677
• Nokia 0.659
• Public Speaking 0.65
• Data Storage 0.65
• Google Voice 0.609
• Hacking 0.602
• Startups in Europe 0.597
• Startup Names 0.572
• Mechanical Engineering 0.563
• Engineering 0.563
• Distributed Databases 0.544
• StackOverflow 0.536
• Boston 0.513
• Learning 0.507
• Open Source 0.498
• Cambridge 0.496
• Public Relations 0.493
• Visualization 0.492
• Semantic Web 0.486
• Andreessen-Horowitz 0.483
• Nature 0.475
• Cryptography 0.474
• Startups in Boston 0.452
• Adobe Photoshop 0.451
• Computer Security 0.447
• Sachin Tendulkar 0.443
• Hacker News 0.442
• Games 0.429
• Android Applications 0.428
• Best Engineers and Programmers 0.427
• College Admissions & Getting Into College 0.422
• Co-Founders 0.419
• Big Data 0.41
• System Administration 0.4
• Biotechnology 0.398
• Higher Education 0.394
• NoSQL 0.387
• User Experience 0.386
• Career Advice 0.377
• Artificial Intelligence 0.375
• Scalability 0.37
• Taylor Swift 0.368
• Google Search 0.368
• Functional Programming 0.365
• Bing 0.363
• Bioinformatics 0.361
• How I Met Your Mother (TV series) 0.361
• Operating Systems 0.356
• Compilers 0.355
• Google Chrome 0.354
• Management & Organizational Leadership 0.35
• Literary Fiction 0.35
• Intelligence 0.348
• Fight Club (1999 movie) 0.344
• Hip Hop Music 0.34
• UX Design 0.337
• Web Application Frameworks 0.336
• Startups in New York City 0.333
• Book Recommendations 0.33
• Engineering Recruiting 0.33
• Search Engines 0.329
• Social Search 0.329
• Data Science 0.328
• History 0.328
• Interaction Design 0.326
• Classification (machine learning) 0.322
• Startup Incubators and Seed Programs 0.321
• Graphic Design 0.321
• Product Design (software) 0.319
• The College Experience 0.319
• Writing 0.319
• MapReduce 0.318
• Database Systems 0.315
• User Interfaces 0.314
• Literature 0.314
• C (programming language) 0.314
• Television 0.314
• Reading 0.313
• Usability 0.312
• Books 0.312
• Computers 0.311
• Stealth Startups 0.311
• Daft Punk 0.31
• Healthy Eating 0.309
• Innovation 0.309
• Skiing 0.305
• JavaScript 0.304
• Rock Music 0.304
• Mozilla Firefox 0.304
• Self-Improvement 0.303
• McKinsey & Company 0.302
• AngelList 0.301
• Data Visualization 0.301
• Cassandra (database) 0.301

Stanford

Topics are followed by p(school = Stanford|topic).

• Stanford Computer Science 0.951
• Stanford Graduate School of Business 0.939
• Stanford 0.896
• Stanford Football 0.896
• Stanford Cardinal 0.896
• Social Dance 0.847
• Stanford University Courses 0.847
• Romance 0.769
• Instagram 0.745
• College Football 0.665
• Mobile Location Applications 0.634
• Online Communities 0.621
• Interpersonal Relationships 0.585
• Food & Restaurants in Palo Alto 0.572
• Your 20s 0.566
• Men’s Fashion 0.548
• Flipboard 0.537
• Inception (2010 movie) 0.535
• Tumblr 0.531
• People Skills 0.522
• Exercise 0.52
• Joel Spolsky 0.516
• Valuations 0.515
• The Social Network (2010 movie) 0.513
• LeBron James 0.506
• Northern California 0.506
• Evernote 0.5
• Quora Community 0.5
• Blogging 0.49
• Downtown Palo Alto 0.487
• The College Experience 0.485
• Consumer Internet 0.477
• Restaurants in San Francisco 0.477
• Chad Hurley 0.47
• Meditation 0.468
• Yishan Wong 0.466
• Arrested Development (TV series) 0.463
• fbFund 0.457
• Best Engineers at X Company 0.451
• Language 0.45
• Words 0.448
• Happiness 0.447
• Path (company) 0.446
• Color Labs (startup) 0.446
• Palo Alto 0.445
• Woot.com 0.442
• Beer 0.442
• PayPal 0.441
• Women in Startups 0.438
• Techmeme 0.433
• Women in Engineering 0.428
• The Mission (San Francisco neighborhood) 0.427
• iPhone Applications 0.416
• Asana 0.413
• Monetization 0.412
• Repetitive Strain Injury (RSI) 0.4
• IDEO 0.398
• Spotify 0.397
• San Francisco Giants 0.396
• Fortune Magazine 0.389
• Love 0.387
• Human-Computer Interaction 0.382
• Hip Hop Music 0.378
• Self-Improvement 0.378
• Food in San Francisco 0.375
• Quora (company) 0.374
• Quora Infrastructure 0.373
• iPhone 0.371
• Square (company) 0.369
• Social Psychology 0.369
• Network Effects 0.366
• Chris Sacca 0.365
• Walt Mossberg 0.364
• Salesforce.com 0.362
• Sex 0.361
• Etiquette 0.361
• David Pogue 0.361
• Gowalla 0.36
• iOS Development 0.354
• Palantir Technologies 0.353
• Mobile Computing 0.347
• Sports 0.346
• Video Games 0.345
• Burning Man 0.345
• Engineering Management 0.343
• Cognitive Science 0.342
• Dating & Relationships 0.341
• Fred Wilson (venture investor) 0.337
• Taiwan 0.333
• Natural Language Processing 0.33
• Eric Schmidt 0.329
• Social Advice 0.329
• Engineering Recruiting 0.328
• Job Interviews 0.325
• Mobile Phones 0.324
• Twitter Inc. (company) 0.321
• Engineering in Silicon Valley 0.321
• San Francisco Bay Area 0.321
• Google Analytics 0.32
• Fashion 0.315
• Interaction Design 0.314
• Open Graph 0.313
• Drugs & Pharmaceuticals 0.312
• Electronic Music 0.312
• Facebook Inc. (company) 0.309
• Fitness 0.309
• YouTube 0.308
• TED Talks 0.308
• Freakonomics (2005 Book) 0.307
• Jack Dorsey 0.306
• Nutrition 0.305
• Puzzles 0.305
• Silicon Valley Mergers & Acquisitions 0.304
• Viral Growth & Analytics 0.304
• Amazon Web Services 0.304
• StumbleUpon 0.303
• Exceptional Comment Threads 0.303

Harvard

• Harvard Business School 0.968
• Harvard Business Review 0.922
• Harvard Square 0.912
• Harvard Law School 0.912
• Jimmy Fallon 0.899
• Boston Red Sox 0.658
• Klout 0.644
• Oprah Winfrey 0.596
• Ivanka Trump 0.587
• Dalai Lama 0.569
• Food in New York City 0.565
• U2 0.562
• TwitPic 0.534
• 37signals 0.522
• David Lynch (director) 0.512
• Al Gore 0.508
• TechStars 0.49
• Baseball 0.487
• Private Equity 0.471
• Classical Music 0.46
• Startups in New York City 0.458
• HootSuite 0.449
• Kiva 0.442
• Ultimate Frisbee 0.441
• Huffington Post 0.436
• New York City 0.433
• Charlie Cheever 0.433
• The New York Times 0.431
• Technology Journalism 0.431
• McKinsey & Company 0.427
• TweetDeck 0.422
• How Does X Work? 0.417
• Ashton Kutcher 0.414
• Coldplay 0.402
• Conan O’Brien 0.397
• Fast Company 0.397
• WikiLeaks 0.394
• Michael Jackson 0.389
• Guy Kawasaki 0.389
• Journalism 0.384
• Wall Street Journal 0.384
• Cambridge 0.371
• Seattle 0.37
• Cities & Metro Areas 0.357
• Boston 0.353
• Tim Ferriss (author) 0.35
• The New Yorker 0.343
• Law 0.34
• Mashable 0.338
• Politics 0.335
• The Economist 0.334
• Barack Obama 0.333
• Skiing 0.329
• McKinsey Quarterly 0.325
• Wired (magazine) 0.316
• Bill Gates 0.31
• Mad Men (TV series) 0.308
• India 0.306
• TED Talks 0.306
• Netflix 0.304
• Wine 0.303
• Angel Investors 0.302
• Facebook Ads 0.301

UC Berkeley

• Berkeley 0.978
• UC Riverside 0.92
• California Golden Bears 0.91
• Internships 0.717
• Web Marketing 0.484
• Google Social Strategy 0.453
• Southwest Airlines 0.451
• WordPress 0.429
• Stock Market 0.429
• BMW (automobile) 0.428
• Web Applications 0.423
• Flickr 0.422
• Snowboarding 0.42
• Electronic Music 0.404
• MySQL 0.401
• Internet Advertising 0.399
• Search Engine Optimization (SEO) 0.398
• Yelp 0.396
• Groupon 0.393
• In-N-Out Burger 0.391
• The Matrix (1999 movie) 0.389
• Trading (finance) 0.385
• jQuery 0.381
• Hedge Funds 0.378
• Social Media Marketing 0.377
• San Francisco 0.376
• Stealth Startups 0.362
• Yahoo! 0.36
• Cascading Style Sheets 0.359
• Angel Investors 0.355
• UX Design 0.35
• StarCraft 0.348
• Los Angeles Lakers 0.347
• Mountain View 0.345
• How I Met Your Mother (TV series) 0.338
• Google+ 0.337
• Ruby on Rails 0.333
• Reading 0.333
• Social Media 0.326
• China 0.322
• Palantir Technologies 0.319
• Facebook Platform 0.315
• Basketball 0.315
• Education 0.314
• Business Development 0.312
• Online & Mobile Payments 0.305
• Restaurants in San Francisco 0.302
• Technology Companies 0.302
• Seth Godin 0.3

Caltech

• Pasadena 0.969
• Chess 0.748
• Table Tennis 0.671
• UCLA 0.67
• MacBook Pro 0.618
• Physics 0.618
• Haskell 0.582
• Los Angeles 0.58
• Electrical Engineering 0.567
• Star Trek (movie 0.561
• Disruptive Technology 0.545
• Science 0.53
• Biology 0.526
• Quantum Mechanics 0.521
• LaTeX 0.514
• Mathematics 0.488
• xkcd 0.488
• Genetics & Heredity 0.487
• Chemistry 0.47
• Medicine & Healthcare 0.448
• Poker 0.445
• C++ (programming language) 0.442
• Data Structures 0.434
• Emacs 0.428
• MongoDB 0.423
• Neuroscience 0.404
• Science Fiction 0.4
• Mac OS X 0.394
• Board Games 0.387
• Computers 0.386
• Research 0.385
• Finance 0.385
• The Future 0.379
• Linux 0.378
• The Colbert Report 0.376
• The Beatles 0.374
• The Onion 0.365
• Ruby 0.363
• Cars & Automobiles 0.361
• Quantitative Finance 0.359
• Academia 0.359
• Law 0.355
• Cooking 0.354
• Psychology 0.349
• Eminem 0.347
• Football (Soccer) 0.346
• Computer Programming 0.343
• Algorithms 0.343
• Evolutionary Biology 0.337
• Behavioral Economics 0.335
• California 0.329
• Machine Learning 0.326
• Futurama 0.324
• Social Advice 0.324
• StarCraft II 0.319
• Job Interview Questions 0.318
• Game Theory 0.316
• This American Life 0.315
• Economics 0.314
• Vim 0.31
• Graduate School 0.309
• Git (revision control) 0.306
• Computer Science 0.303

What do we see?

• First, in a nice validation of this approach, we find that each school is interested in exactly the locations we’d expect: Caltech is interested in Pasadena and Los Angeles; MIT and Harvard are both interested in Boston and Cambridge (Harvard is interested in New York City as well); Stanford is interested in Palo Alto, Northern California, and San Francisco Bay Area; and Berkeley is interested in Berkeley, San Francisco, and Mountain View.
• More interestingly, let’s look at where each school likes to eat. Stereotypically, we expect Harvard, Stanford, and Berkeley students to be more outgoing and social, and MIT and Caltech students to be more introverted. This is indeed what we find:
  • Harvard follows Food in New York City; Stanford follows Food & Restaurants in Palo Alto, Restaurants in San Francisco, and Food in San Francisco; and Berkeley follows Restaurants in San Francisco and In-N-Out Burger. In other words, Harvard, Stanford, and Berkeley love eating out.
  • Caltech, on the other hand, loves Cooking, and MIT loves Healthy Eating – both signs, perhaps, of a preference for eating in.
• And what does each university use to quench their thirst? Harvard students like to drink wine (classy!), while Stanford students prefer beer (the social drink of choice).
• What about sports teams? MIT and Caltech couldn’t care less, though Harvard follows the Boston Red Sox, Stanford follows the San Francisco Giants (as well as their own Stanford Football and Stanford Cardinal), and Berkeley follows the Los Angeles Lakers (and the California Golden Bears).
• For sports themselves, MIT students like skiing; Stanford students like general exercise, fitness, and sports; Harvard students like baseball, ultimate frisbee, and skiing; and Berkeley students like snowboarding. Caltech, in a league of its own, enjoys table tennis and chess.
• What does each school think of social? Caltech students look for Social Advice. Berkeley students are interested in Social Media and Social Media Marketing. MIT, on the more technical side, wants Social Search. Stanford students, predictably, love the whole spectrum of social offerings, from Social Dance and The Social Network, to Social Psychology and Social Advice. (Interestingly, Caltech and Stanford are both interested in Social Advice, though I wonder if it’s for slightly different reasons.)
• What’s each school’s relationship with computers? Caltech students are interested in Computer Science, MIT hackers are interested in Computer Security, and Stanford students are interested in Human-Computer Interaction.
• Digging into the MIT vs. Caltech divide a little, we see that Caltech students really are more interested in the pure sciences (Physics, Science, Biology, Quantum Mechanics, Mathematics, Chemistry, etc.), while MIT students are more on the applied and engineering sides (Mechanical Engineering, Engineering, Distributed Databases, Cryptography, Computer Security, Biotechnology, Operating Systems, Compilers, etc.).
• Regarding programming languages, Caltech students love Haskell (hardcore purity!), while MIT students love Lisp.
• What does each school like to read, both offline and online? Caltech loves science fiction, xkcd, and The Onion; MIT likes Hacker News; Harvard loves journals, newspapers, and magazines (Huffington Post, the New York Times, Fortune, Wall Street Journal, the New Yorker, the Economist, and so on); and Stanford likes TechMeme.
• What movies and television shows does each school like to watch? Caltech likes Star Trek, the Colbert Report, and Futurama. MIT likes Fight Club (I don’t know what this has to do with MIT, though I will note that on my first day as a freshman in a new dorm, Fight Club was precisely the movie we all went to a lecture hall to see). Stanford likes The Social Network and Inception. Harvard, rather fittingly, likes Mad Men and Ted Talks.
• Let’s look at the startups each school follows. MIT, of course, likes Ksplice. Berkeley likes Yelp and Groupon. Stanford likes just about every startup under the sun (Instagram, Flipboard, Tumblr, Path, Color Labs, etc.). And Harvard, that bastion of hard-won influence and prestige? To the surprise of precisely no one, Harvard enjoys Klout.

Let’s end with a summarized view of each school:

• Caltech is very much into the sciences (Physics, Biology, Quantum Mechanics, Mathematics, etc.), as well as many pretty nerdy topics (Star Trek, Science Fiction, xkcd, Futurama, Starcraft II, etc.).
• MIT is dominated by everything engineering and tech.
• Stanford loves relationships (interpersonal relationships, people skills, love, network effects, sex, etiquette, dating and relationships, romance), health and appearance (fashion, fitness, nutrition, happiness), and startups (Instagram, Flipboard, Path, Color Labs, etc.).
• Berkeley, sadly, is perhaps too large and diverse for an overall characterization.
• Harvard students are fascinated by famous figures (Jimmy Fallon, Oprah Winfrey, Invaka Trump, Dalai Lama, David Lynch, Al Gore, Bill Gates, Barack Obama), and by prestigious newspapers, journals, and magazines (Fortune, the New York Times, the Wall Street Journal, the Economist, and so on). Other very fitting interests include Kiva, classical music, and Coldplay.

*I pulled about 400 followers from each school, and added a couple filters, to try to ensure that followers were actual attendees of the schools rather than general people simply interested in them. Topics are sorted using a naive Bayes score and filtered to have at least 5 counts. Also, a word of warning: my dataset was fairly small and users on Quora are almost certainly not representative of their schools as a whole (though I tried to be rigorous with what I had).

可从此处完成的操作：

• 使用 Google 阅读器订阅Edwin Chen’s Blog
• 开始使用 Google 阅读器，轻松地与您喜爱的所有网站保持同步更新

• Level15：溶解
• 有难度的一关，卡了很久。txt改jpg得到一个包含windings字体的图片，原txt用base64解密后得到一幅图显示网址。

• Level16：猴子/扭伤
• 有两关，1）Mozart，major，不要搜成火车了；2）（去咖啡）

• Level19：符号
• doc的F7，拼写错误

• Level20：孤独的
• 和Math没关，10^100=googol，这一关的google有什么不一样？查看源码可能不容易直接看到，chrome用F12.