想写点什么

这几天不知道怎么了，总想在这里写点什么

也许真的心情不好，但不知道为什么

也许总想忙里偷点闲，这几天确实比较忙

也许脑子里确实有点什么想法，但是写不出来

train model,improve model

买奥运会门票

今天第三阶段售票，就几个人分头行动开始刷票，希望能买到几张，去感受感受奥运会，毕竟很难得的一次~~

早上七点多gx就打电话催我起床，说她已经在排队，排的很靠后，让我网上刷刷看能不能抢到，8点多Daisy又打电话让我赶快起来上网刷票，说银行已经排了很长队了。银行内部人士都催了，没办法，赶快上网刷。

到公司后赶快打开网页，并让旁边的同事帮我刷票，9点一到就刷到田径有票，赶快提交，选了200/张的，觉得不放心，就让同事刷400/张的。我RP不好，200/张的没买到，400/张的刷到两张。伤心啊。十来分钟的时间就没票了。

然后跟Daisy说只搞到了一张，^_^，另外要分别人一张嘛。Daisy好像很急，找了同事弄票，结果糊里糊涂搞了三张在上海的男子足球3-4名决赛的票，而且是A区800/张的。 哭…….这也太奢侈了……

最近要赶快把足球的这三张转出去，不然我21号22号这两天比赛都看不完了，还要上班……

五一

希格玛-机场-太月园-天安门-故宫-天坛-太月园-机场-钱柜-希格玛

This is my May Day!

第一次去机场；

第一次在公众场合帮助老外；

现在真的不想考虑那么多事情，还是好好工作吧，这个才是王道

天生一工作狂，谁嫁给我谁倒霉！

比较闲的一周

来到MSRA以后，难得有这么闲的一周。Mentor去美国出差一周，所以分配的任务相对来说不多，压力也就不大了。终于可以好好休息一下了。

但是我发现很怕自己闲下来。闲下来以后都不知道该做什么了。
周二拉个高中同学（一个PLMM）去看了场电影。周三周四好好干了两天活。周五晚上竟然打了四个小时桌球。
闲下来以后很多事情都开始想了，感情问题，以后的就业问题。哎，事情真多。

最近总结的几点：
不牵涉到自己的利益，朋友可以永远说在嘴里；
几个人干什么事之前，先把丑话说前头；
凡事靠自己，别相信任何人的空头支票

zz 恩杰二面面经

到了ask首先让我感到意外的是居然有三个男的面我，和我第一次去遭受的草草了事的面试截然不同，这次似乎ask是有备而来的。

ask: 熟悉正则表达式么？
我: 不熟（因为我知道他要问脚本的东西）。

ask: 熟悉脚本语言么？
我: 不熟悉。只是知道有bash, perl, python,昨天刚刚买了learning perl, 准备一个星期入门。

ask: 说一下你在实验室的工作？你的实验室就是那个和cgogo。。。。？
我: 是的。。。。。。

ask: 你们在实验室主要用win还是unix?
我: unix。

ask: 那你怎么不熟悉脚本呢？
我: 我一般只是编编C/C++程序，对系统管理和一块并不熟所以就不太写脚本，而且一些应用脚本网上都有的down的所以一般只是copy而已。
（那三人面面相觑，这个时候坐我右边的一个人出去接电话了，少了一个。）

ask: 那说说C吧，你用的比较多的。C里面如何优化分配内存的?如何提高内存分配和回首的效率？不停的malloc和不停free会产生什么问题？
我: 发呆状…突然提到一个“缓冲”，但是感觉不是用在内存上的，于是说“不清楚”。

ask: 在linux下面如何调试多线程和多进程？如何根据core.dump提供的信息调试？
我: 暗自想“土了土了”，——不知道

ask: 熟悉linux kernel么？你用linux用到什么层次？
我: 不太熟悉，理论懂一点，没有太多实践。分析过一些IPC，写过测试，分析它们的效率。我一般就用到系统API这种层面，在往下就不太接触了。（又被鄙视，脚本这种日常的维护不会，而底层高端的开发也不懂，我自我调侃到）

ask: 那说说unix下面有哪几种IPC？
我: ＆……％＊（流畅，难得啊，终于有流畅的了，多亏高级OS的课程作业）

ask: 如果有一堆url，它们指向一堆content，如何设计一种方法让它可以根据url找到对应的content？
我: hash.

ask: hash的算法有哪些？
我: (回答成冲突解决了，原来他说的hash算法就是hash function，我居然说不知道，但被鄙视了！该回家翻书了）

ask: 给你一个中序和后序遍历的结点序列，把它们还原成一棵树，在黑板上画。
我: (花了一分钟解决，还是觉得做得太慢，应该10秒钟解决才对，如果是考研那会的话估计肯定是秒杀的）

ask: ＊＆……％￥（说了一下实习的相关细节）
我: No problems。

ask: 你希望我们最迟什么时候给你答复？
我: 5月10号左右吧…

终。

感想：
这次面试给我留下深刻的印象，因为太出丑了。我在回来的路上仔细想了下，觉得C/C++还是很有区别的。比如百度和ebay，它们都问了STL，而且baidu还问道了stl内部的实现机制和存在的危险，多线程不安全等以及多线程的调试。多线程这一块看来是一定要去实践一下的，这个可能是众多c/c++程序员必备的素质，特别是搞搜索。数据结构三个公司都问到了hash, 百度和ebay还问到了b+树。显然ask对于c/c++和unix开发的基本功要求远远高于baidu和ebay,后两个都是大公司可能更在意综合能力，比如ebay会全程English面你，知道和我聊到ebay和google这种过于复杂的问题才让我插几句Chinese。而ask这样相对的小公司可能更能培养技术上的一些东西，这是我个人的想法，当然没有拿到baidu的offer挺遗憾的，其实我在这个领域也只能算是入门，以后的路还长，要坚定的走下去，别再东搞搞java西弄弄c/c++了。把c和linux API弄熟是我今后一段日子的主要目标，就是通过我们寝室的那个金融搜索作为我训练实战的舞台。
最后打算利用一个星期的时间把Perl入门了，然后如果有幸能进去就在ask那边一边做一边深入Perl和网页分析等技术。

买票买票

这几天被回家买票的事情搞的不能认真工作

今年是第一次没有在学校订票而且是这么晚回家，2月3号以后。

一票难求

今天不睡了，四点去车站排队买票去

想要的是什么？

自己想要的是什么？突然间很迷茫了

应该认真思考思考，不要整天忙碌，突然间又不知道了自己想要什么

趁寒假回家好好反省反省

Some challenges of speech synthesis

Text normalization challenges
The process of normalizing text is rarely straightforward. Texts are full of heteronyms, numbers, and abbreviations that all require expansion into a phonetic representation. There are many spellings in English which are pronounced differently based on context. For example, “My latest project is to learn how to better project my voice” contains two pronunciations of “project”.
Most text-to-speech (TTS) systems do not generate semantic representations of their input texts, as processes for doing so are not reliable, well understood, or computationally effective. As a result, various heuristic techniques are used to guess the proper way to disambiguate homographs, like examining neighboring words and using statistics about frequency of occurrence.
Deciding how to convert numbers is another problem that TTS systems have to address. It is a simple programming challenge to convert a number into words, like “1325″ becoming “one thousand three hundred twenty-five.” However, numbers occur in many different contexts; when part of an address, “1325″ should likely be read as “thirteen twenty-five”, or, when part of a social security number, as “one three two five”. A TTS system can often infer how to expand a number based on surrounding words, numbers, and punctuation, and sometimes the system provides a way to specify the context if it is ambiguous.[citation needed]
Similarly, abbreviations can be ambiguous. For example, the abbreviation “in” for “inches” must be differentiated from the word “in”, and the address “12 St John St.” uses the same abbreviation for both “Saint” and “Street”. TTS systems with intelligent front ends can make educated guesses about ambiguous abbreviations, while others provide the same result in all cases, resulting in nonsensical (and sometimes comical) outputs.

Text-to-phoneme challenges
Speech synthesis systems use two basic approaches to determine the pronunciation of a word based on its spelling, a process which is often called text-to-phoneme or grapheme-to-phoneme conversion (phoneme is the term used by linguists to describe distinctive sounds in a language). The simplest approach to text-to-phoneme conversion is the dictionary-based approach, where a large dictionary containing all the words of a language and their correct pronunciations is stored by the program. Determining the correct pronunciation of each word is a matter of looking up each word in the dictionary and replacing the spelling with the pronunciation specified in the dictionary. The other approach is rule-based, in which pronunciation rules are applied to words to determine their pronunciations based on their spellings. This is similar to the “sounding out”, or synthetic phonics, approach to learning reading.
Each approach has advantages and drawbacks. The dictionary-based approach is quick and accurate, but completely fails if it is given a word which is not in its dictionary.[citation needed] As dictionary size grows, so too does the memory space requirements of the synthesis system. On the other hand, the rule-based approach works on any input, but the complexity of the rules grows substantially as the system takes into account irregular spellings or pronunciations. (Consider that the word “of” is very common in English, yet is the only word in which the letter “f” is pronounced [v].) As a result, nearly all speech synthesis systems use a combination of these approaches.
Some languages, like Spanish, have a very regular writing system, and the prediction of the pronunciation of words based on their spellings is quite successful.[citation needed] Speech synthesis systems for such languages often use the rule-based method extensively, resorting to dictionaries only for those few words, like foreign names and borrowings, whose pronunciations are not obvious from their spellings. On the other hand, speech synthesis systems for languages like English, which have extremely irregular spelling systems, are more likely to rely on dictionaries, and to use rule-based methods only for unusual words, or words that aren’t in their dictionaries.

Evaluation challenges
It is very difficult to evaluate speech synthesis systems consistently because there is no subjective criterion and usually different organizations use different speech data. The quality of a speech synthesis system highly depends on the quality of recording. Therefore, evaluating speech synthesis systems is almost the same as evaluating the recording skills.
Recently researchers start evaluating speech synthesis systems using the common speech dataset.[19] This may help people to compare the difference between technologies rather than recordings.

Speech Synthesis

Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech.
Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely “synthetic” voice output.

The quality of a speech synthesizer is judged by its similarity to the human voice, and by its ability to be understood. An intelligible text-to-speech program allows people with visual impairments or reading disabilities to listen to written works on a home computer. Many computer operating systems have included speech synthesizers since the early 1980s.

A text-to-speech system (or “engine”) is composed of two parts: a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization. The front-end then assigns phonetic transcriptions to each word, and divides and marks the text into prosodic units, like phrases, clauses, and sentences. The process of assigning phonetic transcriptions to words is called text-to-phoneme or grapheme-to-phoneme conversion .[3] Phonetic transcriptions and prosody information together make up the symbolic linguistic representation that is output by the front-end. The back-end—often referred to as the synthesizer—then converts the symbolic linguistic representation into sound.

The most important qualities of a speech synthesis system are naturalness and Intelligibility. Naturalness describes how closely the output sounds like human speech, while intelligibility is the ease with which the output is understood. The ideal speech synthesizer is both natural and intelligible. Speech synthesis systems usually try to maximize both characteristics.
The two primary technologies for generating synthetic speech waveforms are concatenative synthesis and formant synthesis. Each technology has strengths and weaknesses, and the intended uses of a synthesis system will typically determine which approach is used.

Facebook怎样改变互联网

转自http://www.keso.cn

互联网自从商业化以后，依次经历了接入时代（AOL）、门户时代（Yahoo!）、搜索时代（Google），接下来会是人际关系时代吗？如果是，代表性企业会是Facebook吗？

每一个过去的巨头被替代，都不是在旧战场上进行的战争，换句话说，你不能在一个已经有垄断者的市场上，用过去的游戏规则，去跟垄断者较量，但你却可以在一个全新的战场上，间接地把老巨头拉下马。如果Google当初也跟Lycos、Excite一样，去跟Yahoo!拼门户，就没有今天的Google。那些期望在搜索市场上成为下一个Google的公司，我完全不看好，无论他们的新武器是垂直搜索、社区搜索、移动搜索，还是自然语言搜索，只要还是做搜索，他们都在Google强大火力的射程之内，存活的希望都很渺茫，更不要说成为下一个Google。

但互联网最迷人的地方就在于，总是在看似不可能再有突破的时候，新的巨头出现了。他们把老巨头看不上或者做不了的新游戏逐渐做大，直到有一天老巨头忽然发现，自己过去的权势、声望甚至用户，都被新巨头悄悄地拿去了，连一向热情的媒体，也开始冷落他们。

回到本文标题提出的话题，Facebook怎样改变游戏规则？没人可以在巨人成长为巨人之前，就断言他会成为巨人，但我们仍然可以通过某些发展中的现象，去试着理解未来。

在Facebook之前，没有一家互联网公司如此深入细致地去分析人和人之间复杂而微妙的现实关系（不是空泛的六度分隔理论），并让原本很难把握的这种社会图景（Social Graph），变得可分析、可计量、可把控、可管理。这还只是刚刚打开的一道门缝，但我们已经隐约可以看到门后那一片广阔的蓝天。从这一点上看，Google的OpenSocial根本未能触及Facebook的核心。因为Google并不掌握这种细致的人际关系模型，它的合作伙伴也未必愿意把自己的用户关系数据交给它。

如果说，过去Google是电子商务最大的助推力量，无论是eBay、Amazon这样的大型电子商务网站，还是中小型的制造和零售企业，都不得不依附于Google，无论是SEO，还是直接在Google上投放广告，说明它们极端地依赖Google给它们带来买家。那么未来呢？Facebook会不会将大量的买家源源不断地输送到电子商务网站？同时又会有多少原本应该流向Gogole的钱，流向了Facebook？

腾讯是中国最大的维护人与人之间关系的企业，昨晚和马化腾吃饭的时候，我问他如何看待Facebook？很显然，他也非常重视Facebook正在进行的探索，并试图将Facebook的某些成功经验，移植到腾讯。不过我仍然认为，腾讯是一家缺乏大理想的公司。改变互联网，甚至改变世界，永远都是别人的事，我们只要把能赚的钱赚到手，就相当满足了。