Saturday, December 8, 2012

關鍵詞:反讽、自黑、釣魚、stereotype

最近流行的一条joke《姐妹们,大家要小心所谓的xxx博士!相亲要谨慎》,其實本來是水木社區上的某個joke(原始出處也許不是joke),被人改編了無數個版本。而且這些版本一般都是本行的人把自己行業、生活裏的一些好笑現象寫出來反讽一般人对某类人的 stereotype 的(比如很多人以为读计算机的就是学修电脑的)。但是似乎大家都太低估網絡的傳播速度以及一般網民的理解能力,回復裏大部分看不出是個坑,反倒覺得『高級黑』、『入木三分』,甚至使 stereotype 更严重,以為貼中所說的博士、海歸、程序員真的就是那樣的。我相信很多数人都能读出里面的反讽成分,但让人惊讶的是,更多人没有得到那个点,并且信以为真、添油加醋地四处传播(比如这里)。最终的结果是,反讽不成,成了自黑。

有趣的是,這個結果跟之前一些釣魚文造成的后果非常相似(参见《高铁——悄悄开启群发性地质灾害的魔盒》,《假如一个国家穿了60年秋裤,就再也没可能脱下它了》等)。对于第一篇,由于设置的笑点太高、彩蛋太不明显,文章写得太像样了,于是无数网民上当,成为一个社会性的谣言,连中科院都出来辟谣了。我相信原作者的目的不是为了造谣,作者也已经发文澄清了。钓鱼文的初衷是好的,但是却犯了一个错误,即高估了普罗大众的智商。无可否认,写钓鱼文的人需要很好的文笔与知识水平,才能写出有质量的钓鱼文,但往往會產生自己智商高人一等的凌驾于绝大多数网民的优越感。怀疑、批判的精神是要通过训练才能得到的,我们从小都在『权威』的领导下长大,这点不管是天朝还是哪里没有例外,比如大家都会对教科书等给予较高的置信度,以及网络上一些看起来资料翔实,行文严谨的文章。不可能让每个人看完文章后都仔细研究一番,更多人是带着娱乐、汲取知识的目的去阅读、转发——这样的成本比做研究低多了。正如 Byvoid 的评论说:"這樣做的副作用要遠遠大於正面效果,因爲這並不一定會讓很多人變得有質疑精神,反而是進入什麼都不信的另一個極端。而且這種行爲是十分冒犯的,本質上與造謠無異。" 相信大家都討厭釣魚執法。釣魚文干的,其實也是類似的行為罷了。因此,我个人倾向于:通过钓鱼文来提高网民的critical thinking以及判断谣言的能力,出发点是好的,但是从实现过程、手法上几乎不可能实现(think about 共产主义)。


幾點建議:
  1. 帶有太多行話、jargon的自黑,限於同行之間傳播就好。如果要公開傳播,切記笑點不要設置得太高。
  2. 在隨手轉推之前,先想清楚自己有沒get到point。
  3. 如果你真要寫钓鱼文,最起碼应该在结尾注明真相。Anyway,我个人还是不同意釣魚文这种與人性相悖的手段。

Wednesday, November 14, 2012

My workflow of copying and pasting content between Mac and iOS device

I've always wanted to dumping contents from Mac to my iOS. For example, I subscribe to websites that reports Free for Limited Time apps everyday. Whenever I want to download, I have to download it to iTunes first, then sync to my iOS, or turn on the iCloud to allow simultaneous download, which is kinda cumbersome in my opinion.

One solution is Pastebot. Pastebot is good, but I always think that it's overpriced ($3.99, iPhone only), and you need to install server in your Mac. It's functionality is also rather limited.

My solution is to use droplr. It's pretty handy in all aspects. You capture the screen, drag a link, image, text or whatever file type to the menu bar icon, they get uploaded and the link is copied to your paste board immediately when upload is finished. 


Drag to the menu bar icon



Uploading

Thursday, September 20, 2012

转载:中年夫妻「相看兩相厭」?



轉載自親子天下雜誌



原文:

有些中年夫妻結婚許久,卻各自活在自己的世界,沒去理解對方應對問題的方式。這時,一個小小的溝通不良,就足以讓彼此覺得「真是夠了」...

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我想,很多夫妻情侶應該都有同感吧~



原文:

有些太太在工作上受挫,回家跟先生說今天工作不順,先生經常會答:「你就是怎樣怎樣不對……應該怎樣怎樣做才對……」通常太太希望聽到的是肯定與支持,所以當先生開始唸唸唸,太太覺得好厭煩:一是眼前這個男人何時變得這麼愛說教;二是他的觀點也許根本不正確。

=====================================

男人想法和女人真的不同,男人直覺想法就是想解決問題,所以才會碎碎念,說一堆分析和解決方式...

如果女人覺得不喜歡,也請直接或間接提醒一下,兩個人生活在一起就是要互相包容調整吧....





原文:

這種厭煩是一定會有的,幾乎沒有一個伴侶可以百分之百讓人滿意。面對先生的碎唸,這時請太太先問自己:「為什麼那麼厭煩?」是不是有些事情你必須倚賴先生,當他不能滿足時會讓人受不了;那麼這個東西,你有沒有辦法健全自己而不用去要求他。

說穿了,婚姻裡有兩種基本的安全感。第一是我跟你在一起,你喜不喜歡我,證明的方法是:我煮的湯很難喝你也要喝下去、我的屁很臭你也要聞、我媽要來你就得開門、我要你媽走你就要把她踢出去、我要做一件事你得認同我,不然就是看不起我。第二是我跟你在一起會不會被你害到,比方說你抽菸,我要你戒菸,否則十年後你生病了我要照顧你。這兩種安全感衍生出所有人跟人之間的問題,這是夫妻進入兩人生活最在意的事情。

太太跟先生抱怨工作不順時,若沒有得到他的支持,其實也不會怎樣;但若是得不到對方的支持,就會認為對方不夠關心、不夠肯定。這就是第一種安全感。

可是回頭去想,為什麼先生講不出好聽的話?我發現先生們常覺得太太如果受到挫折,自己也會感到挫折,夫妻之間的感覺是會流動分享的。所以先生會想趕快排除這個挫折感,他沒有辦法扛著你的挫折,跟你一起難過、罵人;他想趕快找個方法不要停留在挫折感裡面,結果他講出來的話就是急著排除感覺的方法。

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男人真的只會想著如何解除這些煩人的問題,不想一直聽著不停的抱怨...



原文:

太太要克服厭煩的方式是,先告訴先生他的方法很好,然後說:「除了方法之外我想問你,在你心目中我是不是值得的人?我是不是特別笨?你可不可以給我一點鼓勵?」我也會鼓勵太太把感覺講清楚,比如說:「你剛剛的反應讓我發現,我今天工作的不愉快有感染到你,因為你看來也很不愉快,我剛剛講話是不是很不耐煩?」先生會說:「對,你的臉真的好臭。」太太可以說:「原來你很愛我,所以我感到挫折時,你馬上感到好像是自己的挫折,才有這麼強烈的反應。這樣我很安心,可是以後你不要這麼挫折,因為我講講就沒事了,你只要講一些無關痛癢的安慰話,反而我自己就會好了。」讓先生意識到「原來我的情緒會被太太感染」很重要。

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一直抱怨的壞心情,只會讓另一半覺得更心煩進而影響到心情,只是,很多人無法像作者這麼清楚的指出重點,當然就不可能解決這種問題...



原文:

有些夫妻會發展出一些暗號:「對不起,我沒有問你意見喔。」提醒另一半不用那麼辛苦的給意見。因為有些男人覺得給意見才是愛你跟認真,要是他像連續劇裡演的那樣摸摸老婆的頭、親親她,完全不費力,他就是個爛男人。

===========================

我也是屬於"覺得給意見才是愛妳跟認真的",只是,反而會讓女人覺得碎碎念(已經在改進了)...

有些女性的確也不需要男人的意見,純粹只是發洩情緒,

如果沒有發展出像作者說的一些暗號提醒另一半,

<真是夠了>這種心情就會常常出現,

其實,這就是需要雙方的包容和相互的調整,

沒有人是完美無瑕的....



只是,一般男人很難理解女人的情緒變化,如果這個男人能這麼了解女人的話,

我想,應該是情聖才辦的到吧,而情聖通常不會只有一個女人...

Saturday, June 30, 2012

quote

"为你流过两滴眼泪就心软啦,你为她也流过泪寒过心,早还给她了。况且这点眼泪对于她对你的残忍,算个p。 "

Saturday, May 12, 2012

Compressed Sensing

Compressed sensing is an interesting and hot topic recently. I wrote an report on it for my optimization course. I want to summarize it in a more succinct way in a blog post.

Overview

Consider an underdetermined linear system Ax = b, where A is a nxN matrix and n << N, x in R^N. If A is not degenerate (or has rank n), then there will be infinitely many solutions to this system. However, if we know the solution x is sparse, i.e., the number of non-zeros is only a few, then the solution is unique and under a certain condition we may find it exactly.

Application Background

Many application can be formulated in the above way. For example, in signal processing, to recover the signal x, one must have enough samples to do so. According to Nyquist-Shannon sampling theorem, the sampling rate must be no less than half of the frequency. The matrix A is usually called measurement matrix, which corresponds to the measurement, or sensing in our context. However, in compressed sensing we can still recover the signal even when the samples are not enough. This may seem contradicting, but it is not. The sampling theorem does not make any assumption to the signal, but we does – the signal is sparse.

Another example is image 'sensing'. When we are taking images using a digital camera, we must capture all of the pixels. If the resolution is high, the resulting image file will be very large, and people may want to compress it. The modern approach in image compression is wavelet transform, which is used in JPEG2000 standard. An image can be viewed as a 2D signal, and can be represented using a set of wavelets and coefficients. This is similar to Fourier transform, where the signal can be represented by a set of sinusoids and coefficients. The wavelet or sinusoids are called 'basis'. Interestingly, we may always find a basis such that only a few components in it are significant, while others are not. The compressions relies on such an idea, we may safely discard the unimportant components but keep those important components. This will not hurt the image quality too much, and human-eyes cannot see the difference. Intuitively, we can plot the coefficient of the components, and we will see some 'sparks' in the plot, while the others are very small magnitude that is close to zero. We threshold those small magnitude and keep only those sparks. This is roughly how the image compression works. Nevertheless, if we are to abandon lots of information anyway, why not just only capture the important ones from the very beginning?

Key Discoveries of CS

The following summarizes the keys:

  • If the solution x is sparse enough and matrix A satisfies 'Restricted Isometry Property' (RIP), then x is unique.
  • RIP itself is hard to check, but if we obtain A using some random process, it is almost always guaranteed (or, with high probability, with high confident).
  • We can formulate an optimization to find the solution: minimize ||x||_0 s.t. Ax=b, where ||x||_0 is the l0-norm of x. Note that the norm is not a true 'norm', but just counting the number of non-zeros in x. However, l0-norm is clearly not continuous, not differentiable and not convex, the minimization is combinatorially hard, or, NP-hard.
  • We can convexify the problem. Instead of minimizing l0-norm, we minimize l1-norm, which can be recast as linear program and solved efficiently.
  • When A satisfies some property, the l1-minimization has the exact solution as l0-minimization.

Disclaimer

The above description is based on the paper I read and my interpretation. Some description may not be very accurate or rigorous in math.