LyX
Pros:

• much more intuitive math input
• other WYSIWYG features: such as tables, etc.

Cons:

• Poor macro support: the math macro support is somewhat primitive and hard to use, and non-math macros are not supported at all (you can only embed them as LaTeX code, which defeats the purpose of WYSIWYG).
• Project management: it seems to me that LyX cannot easily separate a document into multiple files (the \input mechanism in LaTeX) and I cannot find a way to define mathmacros in a separate file and share it among multiple documents.
• Not all LaTeX features are not supported, such as “\bigl”, etc.

For a person converting from regular LaTeX to LyX (with many macros), I find the poor macro support a turn-off and will wait until it gets improved. But for a person starting to learn LaTeX, I think LyX will be a great tool.

TeXmacs
Pros:

• intuitive math input
• WYSIWYG
• Support with computer algebra systems, so you can call Maple or Maxima inside TeXmacs and the output will be nicely formated. Very cool!

Cons:

• Poor macro support
• Again, it seems that it is difficult to “\input” or “\include” any files into a document.

So for the same reason, I have to give it up.

In conclusion, the WYSIWYG LaTeX editing systems are very nice, but basically they are something different from LaTeX. You can only go one way or the other (at least for now), and any non-trivial migration from LaTeX to them will be very painful. I hope the interoperability of LyX and LaTeX will eventually be better, so that we can get the benefits of both worlds. For TeXmacs, it appears to be more difficult as the back-end is quite different from LaTeX.

Update: for users who do not want Compiz on their X, an alternative is: QuickTile

p.s.   For Mac users, they can get TwoUp or SplitUp.
p.s.2 For Windows users, I believe Windows 7 has this built-in. I can feel that Windows 7 will be a great product, and I think the hand-writing math-input will be a killer feature for people in academia.

And my laziness allows me only to write a few of the points that Dr. Bose made:

• MIT is one of the largest employer in Massachusetts.
  • As a large organization, it suffers from politics, which is everywhere in academia, industry, …
• Work takes most of the time in life. Work on something you are really excited at!
  • Have you bag packed. If you find a workplace unpleasant. Make a change.
• The worst job is the job that earns you money (only).
• Research is a kind of intellectual stumbling. However, this stumbling process cannot be taught, and can only be learned during the process.
  • When people write about their results, they usually take the direct route.
  • Al commented that this is a problem that faced by many new graduate students, who knew how to solve well structured problems from textbook, but don’t know how to get started in research.
  • It is not a bad thing to get further from the goal during the stumbling process, as long as you learn something.

Also check out other cool projects at the Mozilla Labs. Somehow Microsoft are never able to do these kind of cool things in their IE (maybe it is just that I don’t know, since I have not used IE for ages…)

• google-sparsehash
• google-glog
• google-coredumper
• google-all-pairs-similarity-search
• googlemock
• google-perftools
• google-ctemplate
• google-test

And you may want to take a look at Google Style Guide. My feeling is that learning the philosophy reflected by a style guide is usually helpful.

Student walks into elevator with random woman holding flowers.

Student: Nice Flowers, what’s the occasion?

Woman: I won the Turing award.

Read more or add yours at: http://qdb.mit.edu

The first question is quite important when you want to simulate really fast — writing data to the disk is very very slow so you don’t want that to slow down the entire program. A solution is to use a separate thread to do it, if your programming language supports it. For example, I use the Boost Thread library if I wrote my program in C++. The data sharing between the main program and the writer thread can, most of the time, be implemented in a producer-consumer queue.

The second question is equally important, because we want to automate this process as much as possible, when facing large volume of data. A good candidate would be sqlite, which can be accessed from many programming interfaces, and then you can manipulate the data via regular SQL statements.

In general, writing to a database may take longer as there is more work to do, so one may want to simply flush it to disk in files, and import it to a database later..

Above are some experience from myself, anyone has any other ideas on the tools or methods to deal with these kind of problems? That’ll be much appreciated!

不知道从什么时候开始穿阿迪 耐克
也忘记了什么时候开始用香水 唇膏
以前的冷狗现在叫鲜奶提子
以前的三色杯现在不知所终
你为什么会痴迷于打钩的东西
你又为什么想要一身产品牌子
再然后你会知道Prada和Gucci
然后会用LV的包穿Armani的衣服
直到你死去的那天
当你最后微笑着看着你身边哭泣的人们
然后慢慢回想起自己的过去
你的回忆里
是不是会充满这些牌子 牌子 牌子
还是会想起那一年 我们都没有钱的时候

我们穿着简单但是干净的衣服
略带皱褶但是整洁的鞋子
背着书包
也许路上会遇到顺路的同学
会聊起校园里的故事
那校园里的老槐树
还有透明的窗户狭窄的走道和嬉闹的孩子们
会在谈起某个同学的时候莫名地脸红
然后若无其事地岔开话题
会在体育课上尽情地欢呼
会在美术课上自由地涂鸦
然后
当班主任从窗口经过的时候赶紧收起抽屉里的漫画
抱背做好假装很认真但是内心很忐忑
会在每天做操的时候
踮起脚尖搜寻某个让自己魂牵梦萦的背影
然后在错身而过的瞬间
高傲的扬起头来
却在离开之后为自己的胆怯懊恼不已
会在收作业的时候
假装不经意的翻看检查下她的作业
其实无非就是想看看她一手漂亮的字
然后鸡蛋里挑骨头的告诉她
其实还有个简单的方法
会在早上进门的时候短暂地停留
不过就是为了能看见某个下一秒会出现在楼梯口的身影
又或者是系一系根本没松的鞋带
理一理没有被风吹乱的头发
趾高气扬的留下一个仓皇的背影
会在楼道上悄悄伸出身子
只为了能看清那个让你心跳停顿的女孩
然而当有人从你身边经过的时候
抬起头看远处的风景
把天边的云想像成她的样子

那一年 当我们没有钱的时候
每天只是幻想着某天长大了以后
背着和身体差不多大的旅行包
带着心中的那个人
走遍这个世界的每一个角落

那一年 我们还都没有钱
可是在许多年后
当我们开始注意到周围的人
他们背着LEVI‘S的、REEBOK的书包
穿G-STAR的衣服
出没在星巴克或者哈根达斯
周末的时候不是去打球而是去逛街
拿到衣服的第一反应不是适不适合
而是看看是什么牌子
会去鄙视那些穿干净的却不知道是什么牌子的孩子
会在手上挂满乱七八糟的丁丁坠坠
他们不再用激情洋溢到哀伤的情书去诉说一段感情
甚至他们会不断追求不同的女孩子去寻求刺激
然后在人群中大谈如何如何
去换取近乎嘲笑的惊叹

对DUNK了若指掌却不知道宫保鸡丁的做法
常常换着不同的发型然后假装自己很低调
又或者打电话的时候故意说些其实和自己无瓜葛的经济现象
然后再很正经地说SUPER-STAR黑灰版式多么好看

心中的对象开始划出价格
然后送名贵的香水和戒指
爱情在这个年龄被商品化
或者 商品被爱情化

男女孩子开始游弋于众多男女孩子之间
这样
人们会称之为
成熟

若当真如此
我情愿回到那一年
当我们都没有钱的时候

10 Lessons of an MIT Education

by Gian-Carlo Rota

Lesson One: You can and will work at a desk for seven hours straight, routinely. For several years, I have been teaching 18.30, differential equation, the largest mathematics course at MIT, with more than 300 students. The lectures have been good training in dealing with mass behavior. Every sentence must be perfectly enunciated, preferably twice. Examples on the board must be relevant, if not downright fascinating. Every 15 minutes or so, the lecturer is expected to come up with an interesting aside, joke, historical anecdote, or unusual application of the concept at hand. When a lecturer fails to conform to these inexorable requirements, the students will signify their displeasure by picking by their books and leaving the classroom.

Despite the lecturer’s best efforts, however, it becomes more difficult to hold the attention of the students as the term wears on, and they start falling asleep in class under those circumstances should be a source of satisfaction for a teacher, since it confirms that they have been doing their jobs. There students have been up half the night-maybe all night-finishing problem sets and preparing for their midterm exams.

Four courses in science and engineering each term is a heavy workload for anyone; very few students fail to learn, first and foremost, the discipline of intensive and constant work.

Lesson Two: You learn what you don’t know you are learning. The second lesson is demonstrated, among other places, in 18.313, a course I teach in advanced probability theory. It is a difficult course, one that compresses the material typically taught in a year into one term, and it includes weekly problem sets that are hard, even by the standards of professional mathematicians. (How hard is that? Well, every few years a student taking the course discovers a new solution to a probability problem that merits publication as a research paper in a refereed journal.)

Students join forces on the problem sets, and some students benefit more than others from these weekly collective efforts. The most brilliant students will invariably work out all the problems and let other students copy, and I pretend to be annoyed when I learn that this has happened. But I know that by making the effort to understand the solution of a truly difficult problem discovered by one of their peers, students learn more than they would by working out some less demanding exercise.

Lesson Three: By and large, “knowing how” matters more than “knowing what.” Half a century ago, the philosopher Gilbert Ryle discussed the difference between “knowing how” courses are those in mathematics, the exact sciences, engineering, playing a musical instrument, even sports. “Knowing what” courses are those in the social sciences, the creative arts, the humanities, and those aspects of a discipline that are described as having social value.

At the beginning of each term, students meet with their advisors to decide on the courses each will study, and much of the discussion is likely to resolve around whether a student should lighten a heavy load by substituting one or two “knowing what” courses in place of some stiff “knowing how” courses.

To be sure, the content of “knowing what” courses if often the most memorable. A serious study of the history of United States Constitution or King Lear may well leave a stronger imprint on a student’s character than a course in thermodynamics. Nevertheless, at MIT, “knowing how” is held in higher esteem than “knowing what” by faculty and students alike. Why?

It is my theory that “knowing how” is revered because it can be tested. One can test whether a student can apply quantum mechanics, communicate in French, or clone a gene. It is much more difficult to asses an interpretation of a poem, the negotiation of a complex technical compromise, or grasp of the social dynamics of a small, diverse working group. Where you can test, you can set a high standard of proficiency on which everyone is agreed; where you cannot test precisely, proficiency becomes something of a judgment call.

At certain liberal arts colleges, sports appear to be more important than classroom subjects, and with good reason. A sport may be the only training in “knowing how”-in demonstrating certifiable proficiency-that a student undertakes at those colleges. At MIT, sports are a hobby (however passionately pursued) rather than a central focus because we offer a wide range of absorbing “knowing how” activities.

Lesson Four: In science and engineering, you can fool very little of the time. Most of the sweeping generalizations one hears about MIT undergraduates are too outrageous to be taken seriously. The claim that MIT students are naive, however, has struck me as being true, at least in a statistical sense.

Last year, for example, one of our mathematics majors, who had accepted a lucrative offer of employment from a Wall Street firm, telephoned to complain that the politics in his office was “like a soap opera.” More than a few MIT graduates are shocked by their first contact with the professional world after graduation. There is a wide gap between the realities of business, medicine, law, or applied   enginering, for example, and the universe of scientific objectivity and theoretical constructs that is MIT.

An education in engineering and science is an education in intellectual honesty. Students cannot avoid learning to acknowledge whether or not they have really learned. Once they have taken their first quiz, all MIT undergraduates know dearly they will pay if they fool themselves into believing they know more than is the case.

On campus, they have been accustomed to people being blunt to a fault about their own limitations-or skills-and those of others. Unfortunately, this intellectual honesty is sometimes interpreted as naivete.

Lesson Five: You don’t have to be a genius to do creative work. The idea of genius elaborated during the Romantic Age (late 18th and 19th centuries) has done harm to education. It is demoralizing to give a young person role models of Beethoven, Einstein, and Feynman, presented as saintly figures who moved from insight to insight without a misstep. Scientific biographies often fail to give a realistic description of personality, and thereby create a false idea of scientific work.

Young people will correct any fantasies they have about genius, however, after they come to MIT. As they start doing research with their professors, as many MIT undergraduates do, they learn another healthy lesson, namely, a professor may well behave like a fumbling idiot.

The drive for excellence and achievement that one finds everywhere at MIT has the democratic effect of placing teachers and students on the same level, where competence is appreciated irrespective of its provenance, Students learn that some of the best ideas arise in groups of scientists and engineers working together, and the source of these ideas can seldom be pinned on specific individuals. The MIT model of scientific work is closer to the communion of artists that was found in the large shops of the Renaissance than to the image of the lonely Romantic genius.

Lesson Six: You must measure up to a very high level of performance. I can imagine a propective student or parent asking, “Why should I (or my child) take calculus at MIT rather than at Oshkosh College? Isn’t the material practically identical, no matter where it is taught, while the cost varies a great deal?”

One answer to this question would be following: One learns a lot more when taking calculus from someone who is doing research in mathematical analysis than from someone who has never published a word on the subject. But this is not the answer; some teachers who is doing research in mathematical analysis than from someone who has never published a word on the subject. But this is not the answer; some teachers who have never done any research are much better at conveying the ideas of calculus than the most brilliant mathematicians.

What matters most is the ambiance in which the course is taught; a gifted student will thrive in the company of other gifted students. An MIT undergraduate will be challenged by the level of proficiency that is expected of everyone at MIT, students and faculty. The expectation of high standards is unconsciously absorbed and adopted by the students, and they carry it with them for life.

Lesson seven: The world and your career are unpredictable, so you are better off learning subjects of permanent value. Some students arrive at MIT with a career plan, many don’t, but it actually doesn’t matter very much either way. Some of the foremost computer scientists of our day received their doctorates in mathematical logic, a branch of mathematics that was once considered farthest removed from applications but that turned out instead to be the key to the development of present-day software. A number of the leading figures in experimental molecular biology received their doctorates in physics. Dramatic career shifts that only a few years ago were the exception are becoming common.

Our students will have a harder time finding rewarding jobs than I had when I graduated in the fifties. The skills the market demands, both in research and industry, are subject to capricious shifts. New professions will be created, and old professions will become obsolete with the span of a few years. Today’s college students have good cause to be apprehensive about future.

The curriculum that most undergraduates at MIT choose to follow focuses less on current occupational skills than on those fundamental areas of science and engineering that at least likely to be affected by technological changes.

Lesson Eight: You are never going to catch up, and neither is anyone else. MIT students often complain of being overworked, and they are right. When I look at the schedules of courses my advisees propose at the beginning of each term, I wonder how they can contemplate that much work. My workload was nothing like that when I was an undergraduate.

The platitudes about the disappearance of leisure are, unfortunately, true, and faculty members at MIT are as heavily burdened as students. There is some satisfaction, however, for a faculty member in encountering a recent graduate who marvels at the light work load they carry in medical school or law school relative to the grueling schedule they had to maintain during their four years at MIT.

Lesson Nine: The future belongs to the computer-literate-squared. Much has been said about computer literacy, and I suspect you would prefer not to hear more on the subject. Instead, I would like to propose the concept computer-literacy-squared, in other words computer literacy to second degree.

A large fraction of MIT undergraduates major in computer science or at least acquire extensive computer skills that are applicable in other fields. In their second year, they catch on to the fact that their required courses in computer science do not provide the whole story. Not because of deficiencies in the syllabus; quite the opposite. The undergraduate curriculum in computer science at MIT is probably the most progressive and advanced such curriculum anywhere. Rather, the students learn that side by side with required courses there is another, hidden curriculum consisting of new ideas just coming into use, new techniques and that spread like wildfire, opening up unsuspected applications that will eventually be adopted into the official curriculum.

Keeping up with this hidden curriculum is what will enable a computer scientist to stay ahead in the field. Those who do not become computer scientists to the second degree risk turning into programmers who will only implement the ideas of others.

Lesson Ten: Mathematics is still the queen of the sciences. Having tried in lessons one through nine to take an unbiased look at the big MIT picture, I’d like to conclude with a plug for my own field, mathematics.

When an undergraduate asks me whether he or she should major in mathematics rather than in another field that I will simply call X, my answer is the following: “If you major in mathematics, you can switch to X anytime you want to, but not the other way around.”

Alumni who return to visit invariably complain of not having taken enough math courses while they were undergraduates. It is a fact, confirmed by the history of science since Galileo and Newton, that the more theoretical and removed from immediate applications a scientific topic appears to be, the more likely it is to eventually find the most striking practical applications. Consider number theory, which only 20 years ago was believed to be the most useless chapter of mathematics and is today the core of computer security. The efficient factorization of integers into prime numbers, a topic of seemingly breathtaking obscurity, is now cultivated with equal passion by software desigers and code breakers.

I am often asked why there are so few applied mathematicians in the department at MIT. The reason is that all of MIT is one huge applied mathematics department; you can find applied mathematicians in practically every department at MIT except mathematics.

The first semester was intense and very challenging for me.. it is very different from undergrad…  and it requires very good time management to be able to take heavy-loaded courses and do research at the same time… In retrospection, I could have done better in many places, but at least now I know what are the right things to do…

Time to take a break now~

Dining

So far I mainly cook for myself. I usually cook a big meal (with a few dishes and rice) on weekend so I can have a few days’ supply during the weekdays. Then on Thursday, our group meeting will provide lunch. Moreover, at MIT, there are various kinds interesting activities that provide food (seminars, talks, etc.), so I can easily fill some blanks  (but I would never go to an event just for the sake of food). Finally, if there indeed is a blank, I will simply eat out, which happens once or twice a week.

Residence

I live in Edgerton House, a graduate residence located in the northwest part of MIT campus. It takes about 8 minutes to walk from my residence to my office, so it is quite convenient. If I want, I can go back to dorm and take a nap at noon.

What I got is a 3-bedroom apartment, together with two other graduate students. We get our own room, but share the kitchen and two bathrooms. The apartment is unfurnished (comes with a stove and fridge only), so it took me a while to settle down (getting furnitures, assembling them, cleaning up, arranging stuff, etc.). But in the end all work out nicely, and I am comfortably living there.

Transportation

In campus, I walk all the time so far, as it is a good exercise. In the winter, I may take our campus shuttle from dorm to office, if there is heavy snow.

Outside campus, I take the subway/buses most of the time. Boston is a small city with good public transportation. So by taking the “T”, you can get to anywhere you want with a little bit walking.

Fitness

I am trying to pick up working out at graduate school, and MIT offers excellent environment and facility for that. There are two gyms, with one right located in Stata Center, which takes me 4 minutes to get to. So it is very convenient for me to go walk out in the afternoon, or go swim a while to make me feel refreshed. I will try to keep exercising everyday, either by swmming or working out.

Fun

There are many fun things to do on and around campus, as Boston is a lively city. This part is not very MIT specific so I wouldn’t elaborate further. But the most fun thing is to hang out with my girlfriend, of course!

These companies, arguably, attract the brightest people in the world, mainly by their extraordinary pay. Still, they collapse in the end and their stock become pieces of crap… Why? I think Prof. Ho has a concise but insightful comment here:

Wall Street Crisis

As he points out, basically, it is all about greed. And as some others point out, people there may not care about whether their company fails in the end or not—getting stellar bonuses in the good years is more important… This is a very interesting mechanism. If it does not gets corrected, and risk management is not totally separated from trading, these kinds of collapses will repeat again and again…

• Aug 17: drove from Toronto to Boston, with my parents. Actually it was the first time for my to drive on highway, but it wasn’t as hard as I expected. I found myself quite comfortable with the car running at 110km/hr.
• Aug 18: brief tour around Boston with my parents. We walked along Newbury St., visited Prudential Center and took the Duck Tour
• Aug 19: drove to NYC with my parents. We arrived in the afternoon and stayed at a Howard Johnson Inn in Queens. We visited American Museum of Natural History and walked around Times Square and Broadway. We had dinner with my friend George, in a nice restaurant that is not far from Morgan Stanley‘s midtown office, where he worked.
• Aug 20: We drove across lower Manhattan to the second place we would stay at, a Ramada in Jersey City. Then we took PATH to the WTC station, glanced at the site, and went to midtown for Guggenheim Museum and Metropolitan Museum of Arts. The first one is unique, and the second one is HUGE! Then we went to the UN Headquarter. Afterwards, we went up the Empire State Building to enjoy the nightview of NYC there. Oh, one more thing, we tasted Pinkberry before that.
• Aug 21: We took PATH to WTC again, walked to Battery Park, then took the ferry to Liberty Island to visit Statue of Liberty. After that, we had a walk around the Wall St. area, and then headed back to our hotel and departed NYC.
• Aug 22: Drove to IKEA and Walmart, which are 30 miles south to the Boston downtown. I bought all kinds of stuff for my dorm, which is unfurnished.
• Aug 23: Move in work. Some more shopping. Some organization of the dorm.
• Aug 24: A little bit shopping and more organization work.
• Aug 25 – Sep 1: MIT Orientation. A fun week. We got lots of good free food, and had many fun activities, such as Boston Scavenger Hunt, Edicott House Retreat, etc.. It was also great to get to know more people around.
• Sep 2: Registration day. I had a very nice meeting with Prof. Alan Oppenheim, who is my graduate counselor. Besides giving me helpful course selection advices, he told me that when thinking about research topics, don’t restrict one to new future like “next generation cell-phone” or something like that, but rather think in the long term. Looking back 20 years, many things at that time seem very stupid, and many modern technologies were virtually non-existent at that time. It is quite likely that we get the same feeling if we look back again 20 years later…
• Sep 3: My first class at MIT
• Sep 4: Life goes on…. I hope it will be fun

Basically, nowadays most (if not all) laptops use Li-ion batteries, which does NOT have the memory effect, but discharges itself at a rate of 5% per month, and it seems that this would result a permanent loss of capacity. So to slow down (and not to speed up) this self-discharging effect, we need to:

1. recharge the battery early, unless it won’t be used for a long time. (avoid frequent full recharges)

2. keep the battery cool. The higher the temperature, the faster they self-discharges

3. do not store batteries for long periods plugged into or attached to any power source. This includes AC adapters and laptop docking stations that are plugged in.

For more information, please see the article about Li-ion Battery at Wikipedia and its references. DELL provides an FAQ here too.

To select books, I mainly rely on reviews on amazon. In general, I will look for books with a overall rating above 4 stars and some good reviews, then happily check them out from the library. However, after finishing a few books, I found that this selection method is quite problematic. Many people just blindly give out reviews and some books get much better rating than it really is. Seems the wisdom of crowd does not happen here.

So how do I evaluate a book now? Luckily amazon allows us to read the most helpful reviews. Read the first page of the “Most Helpful Reviews” and get an average of those ratings — I found it a much better guide than the simplistic overall rating.

对于中国来说, 今年真是坎坷的一年, 但勤劳的我们一定会渡过这个难关的. 身在海外, 除了捐款, 只能在心底默默地, 祝祖国安好!