多年以前，在北大未名BBS上看到的一个关于Heroes in my heart的系列帖子，写了一些关于数学牛人们的传奇动人荒诞不经的轶事，后来我自己也搜集了一些物理学牛人们的逸事贴在侏罗纪BBS上，当时并没有什么宏伟的计划，只是随便乱贴，今天看到下面的一篇文章之后，突发奇想，上去找找我以前的帖子是不是还在，不知道是不是数据没有备份还是其他原因，这些帖子都已经不在了。

看起来，时间果然是我们最大的敌人，以至于在我们还没有反应过来的时候，很多事情都已经结束了。在这里，要感谢当时的版主icewater没有删掉我的帖子，也要感谢Tophill，Duan现在还记得我的ID下的真实身份。现在我也常怀念当年在宿舍跟Xingxing, Dabian, Zhiguo一起以一毛钱论输赢斗地主的日子，这样的日子不会再有了。也感谢He天天在宿舍玩儿吉他产生的噪声，它使我们的生活有了其它的色彩。感谢xiao Jiang的电脑，我在上面学会了星际并第一次QQ到天亮。感谢Ju, meidu, xiao Jiang一起在星语包夜的日子。恐怕我们以后都不会再有这种单纯的快乐了。

下面这篇文章让我联想到以往的这些日子，故将这篇文章转到blog上，以纪念在侏罗纪的那些荒诞不经的日子。但愿我们在隔了很长时间，在非常遥远的地方，还能够互相想念。

理论化学之华山论剑

理论化学长期以来有两个流派，正式名称可能叫做半经验计算的一派和从头计算的一派，但我怎么越看越象华山派的剑宗和气宗？很久很久以前，祖师爷开宗立派的时候（量子力学引进化学中的时候），由于当时计算条件的严重限制，对稍微大一点的分子的精确计算都是不可能的。于是大牛们就开始分裂。一派说别管精确性了，我们仍然可以得到许多定性的理解，——就像剑宗。另一派说计算机和算法都在不断进步，总有一天我们会算出来的，——就像气宗。

剑宗的领袖是1981年Nobel化学奖得主福井谦一（Kenichi Fukui，1918－1998）与Roald Hoffmann（1937－）。气宗的掌门是1998年Nobel化学奖得主Walter Kohn（1923－）和John A. Pople（1925－）。我想说Hoffmann是风清扬，但是实在不好意思把岳不群指派给Kohn或Pople（也许方证更合适？）。

剑宗的剑是分子轨道的组合规则。气宗的气是计算方法和程序。剑宗的理念是定性、图象、理解，气宗的精神是定量、数值、计算。剑宗肯定自称是理论化学家。气宗很可能自称为计算化学家。剑宗的文章被拒是因为充满解释却计算不可靠。气宗的文章被拒是因为充满计算结果却没有解释。

剑气孰为本？剑宗的立场是：“剑派剑派，自然是以剑为主。”强调自己是化学家，鼓吹一般物理学家没有的化学直觉。气宗的格言是：“气功一成，不论使拳脚也好，动刀剑也好，便都无往而不利之。”往往不觉得自己和物理学家有何区别，认为化学就是量子力学的应用。两者都认为化学是统一的。剑宗说化学统一在轨道图象中。气宗说化学统一在数值计算中。无机和有机在他们看来没区别。剑宗是知道它们的区别然后给出共通的图象。气宗是压根不知道它们的区别，反正算就是了。

剑宗的独孤九剑是前线轨道理论、扩展Huckel方法（这是Hoffmann在25岁时发明的一招，被引用过成千上万次，虽然有人称其为理论，他自己却永远都说这只是个方法）、等叶片性（isolobality），——当我读《化学中的轨道相互作用》到等叶片性那一章的时候，首先跳进我脑海的就是四个字“独孤九剑”！气宗的易筋经（紫霞神功猥琐了点，端不上台面，大概就相当于Gaussian软件使用说明之类的吧）是密度泛函理论、MP微扰论、耦合团簇方法。剑宗究竟干些什么？推公式？不，那是气宗高手干的。编程序？不，那是气宗少壮派干的。倒不是说这些事情剑宗一点都不干，只是它们对剑宗来说都是工具而非精髓。剑宗的代表性工作模式是黑板、粉笔、画图、联想。按说这画图的规则就那么几条，我也知道得差不多了，不见得比Hoffmann少多少，有时候推推公式还比他强那么一点点（见后文《本命年》）。但说到联想嘛……功力就表现在这上面。经常是Hoffmann说某某是显然的，然后我觉得当然是显然的，——可是在他说显然之前我怎么都想不到。所以说这年头，算得准能够发文章不奇怪，算不准还能够发文章才是大牛。可惜这种天马行空式的思维，我越来越觉得只有Hoffmann才能干。因为联想需要两样东西：一，巨大的数据库；二，快速的检索。我要达到同样的水平，不知这辈子有没有指望了。——但是没关系，路总是人走出来的。我曾经跟Hoffmann说：“今天您在几分钟之内就找到了和这个含有五配位的Al的分子相似的已知分子，画出了轨道结构，当时我简直是感动莫名。可是我没法做到同样的事。”他回答说：“哦，不奇怪，这样的事我做了四十多年了。你也会发展出你自己的风格的。”我再问：“您的风格是怎么来的？”回答是：“风格并不是特意培养出来的。我只是一件一件事地做下去，然后风格自然就有了。想起来，我的风格是我的老师们W. N. Lipscomb（1976年Nobel化学奖得主，Hoffmann是他第一个Harvard的博士生）、R. B. Woodward（1965年Nobel化学奖得主，Hoffmann的博士后老板）的风格的综合。你从你的老师们那里学到的，将来也会融合到你的风格中。”这话虽然是鼓励性的，可也令我有些哭笑不得：他的老师们都是这样的主儿，加上在有机化学方面对他影响很大的哥们E. J. Corey（1990年Nobel化学奖得主），这套豪华阵容比他本人还要吓人，我真不知道上哪儿找这么多大师来融合：－）

多年以前，气宗嘲笑剑宗算不准，剑宗讽刺气宗算不大，大家倒也是乌鸦不必说猪黑。九十年代以来，风云突变，计算机和算法的发展突破了瓶颈。好像一夜之间，冒出来无数的商业计算程序。以前想都不敢想的计算任务，现在随便一个新手拿起程序就能黑箱操作出结果来了，虽然很可能他自己压根不明白程序究竟做了些什么、结果有什么意义。所以我师兄double_mad在香港时，说他们推公式、编程序的（他们做量子统计物理的，也是很特别的一派，化学家看不懂他们的公式，理论物理学家认为他们的公式品味太低根本称不上理论，结果就是两边不讨好只能自娱自乐）整天焦头烂额苦思冥想，旁边有一群程序运行家（也许可以叫做runner？）却把任务一提交就上网打牌下棋，简直是新旧社会两重天。很多学问的发展史就是庸俗化的历史，从阳春白雪的科学（思想）转化为下里巴人的技术（教条），典型的例子是佛教、马克思主义，计算化学现在也来了。

剑宗入门不太容易，不先理解足够多的理论就什么都看不懂（虽然剑宗的理论是我见到的最简单的可以称为理论的东西）。气宗入门，多年以前也许是困难的，也要先苦读一大堆理论和程序；现在却变得轻松得象吃一盘白菜，什么理论都不要?只需要会用一个程序就行了。而只需要会用程序，就可以做很多具有实用意义的事情，有人愿意为此出钱。所以剑宗的人比气宗的少得多的多，少到甚至许多气宗的人都不知道剑宗的存在，－－反正我在科大没遇见懂得剑宗思维的。不过反过来说?神剑的奥妙岂是俗人随随便便就能领会的！当然，成为高手宗师还是很难的，无论剑宗或者气宗。

虽然我对气宗大师没有感觉，对气宗小混混的日子感觉可是太深了。其实气宗和剑宗最大的区别就在这一点上。作为气宗的学徒，每天干的事情就是用现成的程序，输入、输出、数据处理，不知道数据为什么是这样，不知道数据中最值得注意的什么，不知道数据能引出什么普遍结论。结果对了是程序的功劳，错了不知道去怪谁。总之就是我能干的别人同样能干，我们是革命的螺丝钉。常常想起《唐伯虎点秋香》中唐伯虎在华府编号9527那段：“死做活做像条狗，被人骂不能汪汪叫。像条狗，真好笑……”说某某人牛，一是他能推出一堆看不懂的公式，二是他的计算资源强大所以更加准确。无论怎样，美感、哲理都是谈不上的。如果科学就是这样无趣，那简直太恐怖啦。虽然可以跟自己说，在有创造性之前总要经过学徒、技术员的阶段，可怎么都看不到如何超越这个阶段。超越了又如何？难道就是推更多的公式，编更多的程序，用更多的计算机？最后就是不知道自己为什么要干这种莫名其妙的事情，“你说你感到万分沮丧，甚至开始怀疑人生……”

谢天谢地，如果说许多气宗低手的这条路象资本主义的流水线一样泯灭个性的话，剑宗就像马克思梦想的****那样解放人性。剑宗不会有不理解自己的计算结果这样的事，就像手工艺人不会不知道自己产品的来历。剑宗的理念是：“化学是一个整体。化学是简单的。化学是有趣的。”又要普适、又要简单、又要有趣、还得科学，真难以想象世界上还有这么好的事，－－可是居然还真有。普适，因为分子轨道理论是普适的。虽然电子关联是个问题，但密度泛函理论又堵上了这个缺口。简单，因为轨道构造规则是简单的。\”Orbital Interactions in Chemistry\”的前40页几乎就说尽了所有的规则，而且极其容易记忆。有趣，因为构造轨道就像画画，而这画既漂亮又含有丰富的信息。BeH2的直线形和H2O的折线形，过渡金属三维配合物的18电子规则，B12H12的正二十面体形状，聚乙炔不导电但少量掺杂后导电率激增，这种种现象都有简单而精妙的解释。大自然总是能给我们新的惊喜。归结到一句话就是：“化学是一门艺术！”剑宗是一门美学，思想美、方法美、结论美。Hoffmann上大学的时候，差点当了艺术家，后来出版了诗集（当然是得Nobel奖之后了：－）），现在还不断写才子书。风流的人，正与倜傥的学问交相映衬。我现在的同事们，都坚信科学在于人脑而不在电脑，趣味在于结构而不是数值，－－所以都“活得象个人样”。在这个意义上，选择剑宗不但是专业上的事情，甚至具有形而上的意义，事关后工业时代人的个性、自由、尊严、生命中不能不承受之重与不能承受之轻……：－）

我曾经不止一次问过Hoffmann：“理论化学的前途是什么？”他总要先来一句：“我不知道。我的年纪越来越大了……”然后才开始说正经的。不过现在在我看来，理论化学的前途就在于气剑结合、以气御剑、独孤九剑加易筋经。以前两派都是阳春白雪，能在哪一边绘修了。我相信这就是以后的趋势。两者结合，虽然已经有人在做，不过多半还是剑宗的人不得已使用气宗的程序，却只当作一个数字图书馆，就是从头计算完了再用扩展Huckel方法来一遍，还是很生硬的。虽然我说了气宗学徒许多坏话，但绝不是说气宗高手也是这样。气宗的长处在于严密、精确、循序渐进。毕竟，现在许多问题是没有计算就没有理解的。至于气宗更深入的妙处，我希望以后有机会学习。气剑之间高明的结合，应该是既保持精确性，又获得图象理解，两个目的可以在一个计算中毕其功于一役。这样的路已经有人在走了，参见\”Kohn-Sham Density Functional Theory: Predicting andUnderstanding Chemistry\”, by F. M. Bickelhaupt and E. J. Baerends（荷兰一位较大的牛，不是更深的蓝）,Reviews in Computational Chemistry, 2000, Vol.15, 1 - 86.这是最值得注意的阶级斗争新动向。这个目标，我觉得几乎可以说是理论化学的最后一战，——再不“洪哥，我们动手吧”以后就没得混了。在这个方向上创立模式的人，以后就等着被象A. D. Becke那样被引用个相思入骨吧（Becke在1993年发表的一篇文章提出了现在最流行的一种算法，已经被引用了超过9000次）！这是我遇到的第一个真正的机会。不过，如果懂得无招胜有招的道理，何时何处不可以是机会？

sobereva点评：把量化软件当作黑箱操作谁都会干，一个初中生就能完成，而且能得出很多成果。但是只会用而不学理论，没法做出更大的成就。只搞纯理论的日子不好过，随着计算机的发展而开始转型是必然的趋势。作为真正的大牛需要能将计算结果和理论解释融会贯通，真正的气宗高手的涉足范围绝不仅限于自己所处的领域。所以计算化学要用，理论研究也要深入。这样才能在研究过程中得到更多的发现，引出新的理论，将化学发展推进一步，对日后的研究工作产生更大的启发，起到更重要的引导作用。

07年马上就过去了，总结一下还是很有必要的。受一些人的影响，也分以下几个部分：

一、所购图书

A. Topics on complex systems:

1 Scale-free networks: complex webs in nature and technology, G. Caldarelli, Oxford University Press, 2007
2 Systems Biology, Isidore Rigoutsos, Gergory Stephanopoulos, Volume I: Genomics, Oxford University Press, 2007
3 Systems Biology, Isidore Rigoutsos, Gergory Stephanopoulos, Volume I: Networks, Models and Applications, Oxford University Press, 2007
4 复杂网络，郭雷，许晓鸣，上海科技出版集团，2006
5 Complexity and Criticality, Kim Christensen, Nicholas R. Moloney, 复旦大学出版社，2006
6 Modeling Complex Systems, N. Boccara, Springer, 2004

B. Topics on my major.

1 Introducing Molecular Electronics, G. Cuniberti, G. Fagas, K. Richter, Springer, 2006
2 Electronic Structure: Basic Theory and Practical Methords, Richard M. Martin, Cambridge University Press, 2004
3 Molecular Electronic-Structure Theory, T. Helgaker, P. Jorgensen，J. Olsen, John Wiley & Sons. LTD, 2000
4 Atomic and Electronic Structure os Solids, E. Kaxiras, Cambridge University Press, 2003
5 Nano and Molecular Electronics Handbook, Sergey E. Lyshevski, CRC Press, 2007
6 Chemical Dynamics in Condensed Phases: Relaxation, Transfer, and Reactions in Condensed Molecular Systems, A. Nitzan, Oxford University Press, 2006
7 Charge Migration in DNA: Perspective from Physics, Chemistry and Biology, Springer, 2007
8 Charge Transfer in DNA: From Mechanism to Application, H.-A. Wagenknecht, Wiley-VCH, 2004
9 Transport in Nanostructure, D. K. Ferry, S. M. Goodniel，Cambridge University Press（世图影印），1998
10 Quantum Transport: Atom to Transistor, S. Datta, Cambridge University Press（世图影印），2006

C. Topics on Statistical Physics

1 A modern Approach to Critical Phenomena, I. Herbut, Cambridge University Press, 2007
2 Statistical Mechanics: From First Principles to Macroscopic Phenomena, Cambridge University Press, 2007
3 Topics in Statistical Mechanics, Brian Cowan, 复旦大学出版社，2006
4 Lectures on Statistical Physics and Protein Folding, 复旦大学出版社，2006

D. General Topics

1 Numerical Simulation in Molecular Dynamics: Numerics, Algorithms, Parallelization and Applications, Michael Griebel, Stephan Knapek, Gerhard Zumbusch, Springer, 2007
2 并行计算导论，张林波，迟学斌，莫则尧，李若，清华大学出版社，2006
3 数值并行算法与软件，李晓梅，吴建平，科学出版社，2007
4 数值分析与算法，徐士良，机械工业出版社，2007
5 发生函数论，Herbert S. Wilf著，王天明译，清华大学出版社，2003

E Other Topics

1 Writing for Science, R. Goldbort, Yale University Press, 2006
2 博尔赫斯诗选，河北教育出版社，2003
3 机遇与混沌，上海世纪出版集团，2005
4 天地有大美，上海世纪出版集团，2005
5 复杂性：一种哲学概观，上海世纪出版集团，2005
6 语言与因特网，上海世纪出版集团，2006
7 现代物理学前沿选讲，黄祖洽，科学出版社，2007
8 戴望舒经典，南海出版公司，2007

后面的几个部分先挖个坑放这吧

下午很困，照例冲一杯咖啡，乱其八糟的blog再写一篇。刚才看了一下访问情况，增加了法国的访问者，难道是Huang同学吗，不能确定。

风水和矩阵仿佛是风马牛不相及的事情，但是很不幸，它们可以通过洛书联系起来。龙马负图、神龟载书，远古时代河图洛书的传说，数千年来被认为是中华传统文化的源头。河图的“河”，是指黄河，洛书的“洛”，是指洛水。黄河与洛水之间，大概就是后来所谓的中原地带，也就是华夏文明的发祥地。据说黄帝统一天下之后，某日从黄河中跃出一匹龙马，马背上有奇异的图案，洛水中也浮出一只神龟，龟甲上也有些奇异的图案。大家认为是种异象，就把马背与龟甲上的图案记录下来，这就是河图与洛书。关于河图、洛书，古籍中有大量的记载，《周易》中提到：“河出图，洛出书，圣人则之。”《论语》中的：“凤鸟不至，河不出图，吾已矣夫。”我们所常见到关于河图与洛书的场景，仅仅是在风水师身上。

 其实洛书可以看成是一个3乘以3的矩阵，这个矩阵稍微有点特殊，矩阵的元素在1到N的平方之间，将其每行，每列，以及对角元的元素相加得到的和是完全一样的，事实上，这就是我们常常玩儿的幻方。对于洛书而言这个和值是15。为什么等于15呢？可以作一个简单的想象，将1到9这几个数字分成3行排列，每行和都相等，得到的的值为（1+2+……+9）/3=45/3=15。我无论如何也看不出来，圣人得之可以治天下的妙处来，可能这也是我不是圣人的原因吧。

 咖啡喝完了，可是我还是很困。

贴一张老照片，刺激一下神经。

9月份看到的Lawrence国家实验室一篇关于衬底可以使石墨的能带打开的文章（arXiv: 0709.1706)已经发表在Nature Materials上面了(Nature Materials, 6,770(2007))。这是不是意味着可以用它做一个接触式的开关，在实验上可以实现吗？对石墨本身是不是有什么要求，手性？层数等？对衬底有特殊要求吗？一切都不知道。时间总是太快了，我还没来得及把我手头上的事情做完，不过这个东西也给了我一个提醒，我可以考察一下手头上的这个关于石墨的东西是不是也有类似的效应。

光棍节肯定是过一个少一个了。有聚会要不要去呢？

 这一期prl上有一篇可能对分子自组织的第一性原理计算带来新视野的文章（Phys.Rev.Lett., 99, 106107)。

 看起来是该考虑长程相互作用的时候了, 最近的PRL和arXiv总是有一些长程相关的文献出来。

 发个美女图片：

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先占个位置，以后再补上。

website:  http://summerschool.ustc.edu.cn

Ten Simple Rules for Getting Published

Philip E. Bourne

 

Citation: Bourne PE (2005) Ten Simple Rules for Getting Published . PLoS Comput Biol 1(5): e57 doi:10.1371/journal.pcbi.0010057

Published: October 28, 2005

Copyright: © 2005 Philip E. Bourne. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

Philip E. Bourne is Editor-in-Chief of PLoS Computational Biology. E-mail: bourne@sdsc.edu

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The student council (http://www.iscbsc.org/) of the International Society for Computational Biology asked me to present my thoughts on getting published in the field of computational biology at the Intelligent Systems in Molecular Biology conference held in Detroit in late June of 2005. Close to 200 bright young souls (and a few not so young) crammed into a small room for what proved to be a wonderful interchange among a group of whom approximately one-half had yet to publish their first paper. The advice I gave that day I have modified and present as ten rules for getting published.

Rule 1: Read many papers, and learn from both the good and the bad work of others.

It is never too early to become a critic. Journal clubs, where you critique a paper as a group, are excellent for having this kind of dialogue. Reading at least two papers a day in detail (not just in your area of research) and thinking about their quality will also help. Being well read has another potential major benefit—it facilitates a more objective view of one’s own work. It is too easy after many late nights spent in front of a computer screen and/or laboratory bench to convince yourself that your work is the best invention since sliced bread. More than likely it is not, and your mentor is prone to falling into the same trap, hence rule 2.

Rule 2: The more objective you can be about your work, the better that work will ultimately become.

Alas, some scientists will never be objective about their own work, and will never make the best scientists—learn objectivity early, the editors and reviewers have.

Rule 3: Good editors and reviewers will be objective about your work.

The quality of the editorial board is an early indicator of the review process. Look at the masthead of the journal in which you plan to publish. Outstanding editors demand and get outstanding reviews. Put your energy into improving the quality of the manuscript before submission. Ideally, the reviews will improve your paper. But they will not get to imparting that advice if there are fundamental flaws.

Rule 4: If you do not write well in the English language, take lessons early; it will be invaluable later.

This is not just about grammar, but more importantly comprehension. The best papers are those in which complex ideas are expressed in a way that those who are less than immersed in the field can understand. Have you noticed that the most renowned scientists often give the most logical and simply stated yet stimulating lectures? This extends to their written work as well. Note that writing clearly is valuable, even if your ultimate career does not hinge on producing good scientific papers in English language journals. Submitted papers that are not clearly written in good English, unless the science is truly outstanding, are often rejected or at best slow to publish since they require extensive copyediting.

Rule 5: Learn to live with rejection.

A failure to be objective can make rejection harder to take, and you will be rejected. Scientific careers are full of rejection, even for the best scientists. The correct response to a paper being rejected or requiring major revision is to listen to the reviewers and respond in an objective, not subjective, manner. Reviews reflect how your paper is being judged—learn to live with it. If reviewers are unanimous about the poor quality of the paper, move on—in virtually all cases, they are right. If they request a major revision, do it and address every point they raise both in your cover letter and through obvious revisions to the text. Multiple rounds of revision are painful for all those concerned and slow the publishing process.

Rule 6: The ingredients of good science are obvious—novelty of research topic, comprehensive coverage of the relevant literature, good data, good analysis including strong statistical support, and a thought-provoking discussion. The ingredients of good science reporting are obvious—good organization, the appropriate use of tables and figures, the right length, writing to the intended audience—do not ignore the obvious.

Be objective about these ingredients when you review the first draft, and do not rely on your mentor. Get a candid opinion by having the paper read by colleagues without a vested interest in the work, including those not directly involved in the topic area.

Rule 7: Start writing the paper the day you have the idea of what questions to pursue.

Some would argue that this places too much emphasis on publishing, but it could also be argued that it helps define scope and facilitates hypothesis-driven science. The temptation of novice authors is to try to include everything they know in a paper. Your thesis is/was your kitchen sink. Your papers should be concise, and impart as much information as possible in the least number of words. Be familiar with the guide to authors and follow it, the editors and reviewers do. Maintain a good bibliographic database as you go, and read the papers in it.

Rule 8: Become a reviewer early in your career.

Reviewing other papers will help you write better papers. To start, work with your mentors; have them give you papers they are reviewing and do the first cut at the review (most mentors will be happy to do this). Then, go through the final review that gets sent in by your mentor, and where allowed, as is true of this journal, look at the reviews others have written. This will provide an important perspective on the quality of your reviews and, hopefully, allow you to see your own work in a more objective way. You will also come to understand the review process and the quality of reviews, which is an important ingredient in deciding where to send your paper.

Rule 9: Decide early on where to try to publish your paper.

This will define the form and level of detail and assumed novelty of the work you are doing. Many journals have a presubmission enquiry system available—use it. Even before the paper is written, get a sense of the novelty of the work, and whether a specific journal will be interested.

Rule 10: Quality is everything.

It is better to publish one paper in a quality journal than multiple papers in lesser journals. Increasingly, it is harder to hide the impact of your papers; tools like Google Scholar and the ISI Web of Science are being used by tenure committees and employers to define metrics for the quality of your work. It used to be that just the journal name was used as a metric. In the digital world, everyone knows if a paper has little impact. Try to publish in journals that have high impact factors; chances are your paper will have high impact, too, if accepted.

When you are long gone, your scientific legacy is, in large part, the literature you left behind and the impact it represents. I hope these ten simple rules can help you leave behind something future generations of scientists will admire.

一篇文章被IEEE接收，昨天弄了大半天才搞好。立此存照

这篇文章很有意思！

The Cult of Genius    

While some physicists are known for their hearty support of atheism, even they can have some personal dieties. High in the physicist’s pantheon sits Richard Feynman, due not only to his obvious smarts and good work, but also to an outsized personality chonicled in a wealth of popular writings (and even a movie!). I’ve always had mixed feelings about Feynman as a cult figurehead, however. It’s nothing personal against Feynman in particular, but about the hero worship he represents. During high school or college, many aspiring physicists latch onto Feynman or Einstein or Hawking as representing all they hope to become. The problem is, the vast majority of us are just not that smart. Oh sure, we’re plenty clever, and are whizzes at figuring out the tip when the check comes due, but we’re not Feynman-Einstein-Hawking smart. We go through a phase where we hope that we are, and then reality sets in, and we either (1) deal, (2) spend the rest of our career trying to hide the fact that we’re not, or (3) drop out. It’s always bugged the crap out of me that physicists’ worship of genius conveys the simultaneous message that if you’re not F-E-H smart, then what good are you? In physics recommendation land, there is no more damning praise than saying someone is a “hard worker”. 

Well, screw that. Yes, you have to be clever, but if you have good taste in problems, an ability to forge intellectual connections, an eye for untapped opportunities, drive, and yes, a willingness to work hard, you can have major impacts on the field. While my guess is that this is broadly understood to be true by those of us clever-but-not-F-E-H-smart folks who’ve survived the weeding of graduate school, postdoctoral positions, and assistant professorhood, we do a lousy job of communicating this fact to our students. I’ve always suspected that we lose talent from the field because people opt for Door #3 (drop out) when they face up to the fact that physics is frequently hard, even for very clever people. The idea that you have to be F-E-H smart to succeed gives little encouragement to continue when the going gets rough. (I have no idea if other fields have this same problem — my guess is that physicists are particularly prone to it, since we are trained early on to think that physicists are simply smarter than chemists or biologists. Those other fields are for the hard workers. We don’t put mathemeticians on this scale, because we secretly believe they’re smarter than us. Note to the biologist lynch mob: tounge is in cheek.) 

Anyways, I’ve been thinking about this again in light of Po Bronson’s excellent article in New York Magazine about Carol Dweck’s research (which I read via Nordette in Blogher is coming out in a popular book Mindset: The New Psychology of Success). The article is focused on how to effectively handle praise for smart kids. The upshot (verified by a number of clever experiments), is that when you praise a kid for being smart in general, rather than for specific accomplishments or efforts, you risk paralyzing the kid with a fear of not looking smart, to the point where they will tend to shun challenges. 

In follow-up interviews, Dweck discovered that those who think that innate intelligence is the key to success begin to discount the importance of effort. I am smart, the kids’ reasoning goes; I don’t need to put out effort. Expending effort becomes stigmatized—it’s public proof that you can’t cut it on your natural gifts. 

Repeating her experiments, Dweck found this effect of praise on performance held true for students of every socioeconomic class. It hit both boys and girls—the very brightest girls especially (they collapsed the most following failure). 

While Dweck is working primarily with preK-12 students, everything covered in the article rings true for what I’ve seen at the higher levels (both for myself, my colleagues, and students). Those of us who are fortunate enough to sail through high school often crumple when the stuff we’re allegedly good at finally becomes hard. Whether you “make it” as a physicist after that has a lot to do with how you respond at that moment. Do you take it as a sign that you’re not cut out for the game? Do you feel like a failure, and stop enjoying physics as a whole? Do you buck up and forge ahead? (Like a neutrino, you’ll probably wind up oscillating among the three mixed states for a while, before collapsing into one of them.) 

I was most struck in Bronson’s article by a description of an experiment by Lisa Blackwell and Dweck on the impact on performance of how one perceives intelligence. In a science magnet school with low acheiving students, Blackwell studied 700 students, all of whom were taught a multi-session unit on study skills. One half of the group, however, also received a “special module on how intelligence is not inate”: 

The teachers—who hadn’t known which students had been assigned to which workshop—could pick out the students who had been taught that intelligence can be developed. They improved their study habits and grades. In a single semester, Blackwell reversed the students’ longtime trend of decreasing math grades. 

The only difference between the control group and the test group were two lessons, a total of 50 minutes spent teaching not math but a single idea: that the brain is a muscle. Giving it a harder workout makes you smarter. That alone improved their math scores. 

These studies have lots of implications for higher ed in the sciences. Physics, with its strong cult of genius, is probably the canary in the coal mine.

假期结束了。

给自己定的一些事情基本做了一些，但并没有完成！

特别是论文一字儿没写。

运气不是太好，甚至连机票都没买到这两天的。