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[梦之队日记] 8.21二战,终于700了!回家以后上狗~(附件上传番茄工作法说明和软件)

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121#
 楼主| 发表于 2011-5-9 00:28:23 | 只看该作者
抓抓加油~~要注意身体不要熬得太辛苦啊~~真是佩服大家这么厉害呢~~
-- by 会员 xiaomakunkun (2011/5/8 23:32:12)

我这是典型的考前抱大腿啊。。。T T不要学我。。。
122#
 楼主| 发表于 2011-5-9 00:29:25 | 只看该作者
LZ 加油~~提高阅读能力那帖子借鉴了~~希望有用啊 我现在急需提高。。
-- by 会员 MarsTOF (2011/5/8 23:35:10)



恩恩,我觉得阅读还真是要高强度看和练,总结多思考逻辑结构和原文对应其实就够了。。不用固执于小安法。。。
123#
 楼主| 发表于 2011-5-9 00:30:01 | 只看该作者
LZ 加油~~提高阅读能力那帖子借鉴了~~希望有用啊 我现在急需提高。。
-- by 会员 MarsTOF (2011/5/8 23:35:10)



我CR看题也比较抓狂。。所以很纠结。。觉得看CR比看RC难。。
-- by 会员 xiaomakunkun (2011/5/8 23:35:51)

咱一起把杨鹏的好好吃透了!!就不信搞不定丫的阅读!!
124#
 楼主| 发表于 2011-5-9 00:30:56 | 只看该作者
唉。。。对啊,我基本没怎么看杨鹏难句,看来必须要连续坚持了。。。
-- by 会员 clumsy123 (2011/5/8 23:41:48)



我们来组成长难句小分队吧~哈~
125#
 楼主| 发表于 2011-5-9 00:33:49 | 只看该作者
鉴于对着电脑看文章的时候总不容易集中注意力,特下了长难句的电子版。。要感谢SharonFang的分享。。http://forum.chasedream.com/GMAT_RC/thread-519600-1-1.html长难句的电子版
接下来一边恢复长难句的训练,一边训练电脑上看文章的感觉了。。
明天开始就不到一周了,接下来几天还是应该保证语法,提高阅读,看例证和数学狗狗,努力努力努力!!
126#
 楼主| 发表于 2011-5-9 03:07:09 | 只看该作者
阅读水平提高练习(来自GTER,感谢草木姐姐!~~您这帖子对我的帮助不仅仅是一次GRE考试而已。。再看才发现自己骄躁了。。保持谦卑的心态 平和的心境 但必胜的决心 前行)
btw 如果大家感兴趣的话,我们也可以组成一个每天练习越障+速度的小分队,轮流上传这类的文章进行练习。。
===========================【SCI越障】=============================
来自http://bbs.gter.net/bbs/thread-982022-1-1.html
阅读要求:
没有别的要求,只要坚持读完就可以
如果你能坚持一个月,你会发现自己的阅读进化了~

In the early 1990s when one of us was teaching his first bioinformatics class,8 k8 j' A, p9 y/ }$ }, ^8 g
he was not sure that there would be enough students to teach. Although$ U8 n! x8 q, @/ `2 Z4 X0 O* P3 \" I# I
the Smith-Waterman and BLAST algorithms had already been developed& c4 s* D9 U- N* f" }7 z7 F
they had not become the household names among biologists that they are
today. Even the term “bioinformatics” had not yet been coined. DNA arrays
were viewed by most as intellectual toys with dubious practical application,
except for a handful of enthusiasts who saw a vast potential in the technology.% R9 n2 ]1 V5 ~. w( \- Q
A few bioinformaticians were developing new algorithmic ideas for# d* \+ ~( C0 P- }, s
nonexistent data sets: David Sankoff laid the foundations of genome rearrangement( n4 u( P9 i# y6 b
studies at a time when there was practically no gene order data,9 J1 ~9 D7 A, b
Michael Waterman and Gary Stormo were developing motif finding algorithms: \+ L3 Y, k7 m) I  o3 w
when there were very few promoter samples available, Gene Myers- C0 b9 q: C( {4 `
was developing sophisticated fragment assembly tools when no bacterial
genome has been assembled yet, andWebbMiller was dreaming about comparing
billion-nucleotide-long DNA sequences when the 172, 282-nucleotide& X. e9 f9 v9 P* f
Epstein-Barr virus was the longest GenBank entry. GenBank itself just recently4 Y% y6 u- o& v. j7 N! l9 p) y6 Y
made a transition from a series of bound (paper!) volumes to an electronic
database on magnetic tape that could be sent to scientists worldwide.
One has to go back to the mid-1980s and early 1990s to fully appreciate the8 p3 j1 P, F  ?, P  g
revolution in biology that has taken place in the last decade. However, bioinformatics' s& ^  v* R. C8 @$ @1 p7 L
has affected more than just biology—it has also had a profound! I* x! F7 @$ V/ k3 q
impact on the computational sciences. Biology has rapidly become a large- q- V% E; _, j. D+ \1 B3 ^3 Y9 A
source of new algorithmic and statistical problems, and has arguably been% @  o7 E# `! o+ m
the target for more algorithms than any of the other fundamental sciences.3 v! l# k: Q+ u2 r) H/ l
This link between computer science and biology has important educational3 A  @4 t$ A& y" U4 T, F* }4 F9 q. u  V
implications that change the way we teach computational ideas to biologists,9 |. y0 b/ I# M) \2 O
as well as how applied algorithmics is taught to computer scientists.
For many years computer science was taught to only computer scientists,
and only rarely to students fromother disciplines. A biology student in an algorithms
classwould be a surprising and unlikely (though entirelywelcome)4 \' X  Y1 n+ Q) g8 N
guest in the early 1990s. But these things change; many biology students! e, t8 E& [) Z' {" y- C
now take some sort of Algorithms 101. At the same time, curious computer
science students often take Genetics 101 and Bioinformatics 101. Although
these students are still relatively rare, keep in mind that the number of bioinformatics8 a) I% `' s& @% c6 H( T* G. z
classes in the early 1990swas so small as to be considered nonexistent.
But that number is not so small now. We envision that undergraduate
bioinformatics classes will become a permanent component at every major' B0 g+ q% q3 X
university. This is a feature, not a bug.
This is an introductory textbook on bioinformatics algorithms and the computational
ideas that have driven them through the last twenty years. There
are many important probabilistic and statistical techniques that we do not
cover, nor do we cover many important research questions that bioinformaticians( ]9 [. V4 F4 ]/ k  _6 m6 e
are currently trying to answer. We deliberately do not cover all/ I* [2 ?* J# u7 @6 ]; d
areas of computational biology; for example, important topics like protein& z% h& ^& J; P4 y, @( `3 b& n- ?
folding are not even discussed. The very first bioinformatics textbooks were
Waterman, 1995 (108), which contains excellent coverage of DNA statistics
and Gusfield, 1997 (44) which includes an encyclopedia of string algorithms.9 G2 y0 H! G( w- }' k  y7 Y' B! K
Durbin et al., 1998 (31) and Baldi and Brunak, 1997 (7) emphasize Hidden% l1 @' i9 s. O! Y8 W* G
Markov Models and machine learning techniques; Baxevanis and Ouellette,
1998 (10) is an excellent practical guide to bioinformatics; Mount, 2001 (76)' Y9 \# \, o8 y9 I3 C: ~6 `
excels in showing the connections between biological problems and bioinformatics
techniques; and Bourne and Weissig, 2002 (15) focuses on protein
bioinformatics. There are also excellent web-based lecture notes for many
bioinformatics courses and we learned a lot about the pedagogy of bioinformatics
from materials on the World Wide Web by Serafim Batzoglou, Dick5 w, i) T1 k8 K, f" E: t
Karp, Ron Shamir, Martin Tompa, and others.3 V# K0 R- a1 k2 K7 Z. a
% _5 q  B) L0 ^; a2 M
Website0 c. I7 F  Q3 t" m1 A
We have created an extensive website to accompany this book at6 x3 F2 B1 b* T* Y6 O' x
http://www.bioalgorithms.info
This website contains a number of features that complement the book. For  }, u# W, c8 \- L
example, though this book does not contain a glossary, we provide this service,
a searchable index, and a set of community message boards, at the* o8 I5 e/ Q8 m8 Q0 L: R* u. m' Z
above web address. Technically savvy students can also download practical bioinformatics exercises, sample implementations of the algorithms in this" K$ C& H* L5 }: d" p1 Z
book, and sample data to test them with. Instructors and students may find5 p/ @9 \! j+ ^# f& W. A( w% H9 D5 X$ c! i/ k
the prepackaged lecture notes on the website to be especially helpful. It is
our hope that this website be used as a repository of information that will
help introduce students to the diverse world of bioinformatics.
Acknowledgements
We are indebted to those who kindly agreed to be featured in the biographical
sketches scattered throughout the book. Their insightful and heartfelt) }0 p7 ]' s  w* n$ T# m
responses definitely made these the most interesting part of this book. Their
life stories and views of the challenges that lay ahead will undoubtedly inspire
students in the exploration of the unknown. There are many more scientists  o) U1 n; X5 F. P; m. c9 j; L" C
whose bioboxes we would like to have in this book and it is only
the page limit (which turned out to be 200 pages too small) that prevented
us from commissioning more of them. Special thanks go to Ethan Bier who
inspired us to include biographical sketches in this book.; v# z4 J9 N) x
This book would not have been possible without the diligent teaching assistants1 |3 ]; d9 Q$ u* m# d* I9 n) Y  ?
in bioinformatics courses taught during the winter and fall of 2003
and 2004: Derren Barken, Bryant Forsgren, Eugene Ke, ColemanMosley, and9 M% w4 j) q  I) p! K. D; f
Degui Zhi all helped find technical errors, refine practical exercises, and design0 ~1 V8 `% @6 O# G* D
problems in the book. Helen Wu and John Allison spent many hours4 u/ Q5 Z+ d% z* G/ o, F' y
making technical figures, which is a thankless task like no other. We are also1 \! E. P+ ?) [0 j1 U2 R
grateful to Vagisha Sharma who was kind enough to read the book from
cover to cover and provide insightful comments and, unfortunately, bugs in
the pseudocode. Steve Wasserman provided us with invaluable comments
from a biologist’s point of view that eventually led to new sections in the1 y! e1 ?7 h% Q" w, V/ {+ x& A# j
book. Alkes Price and Haixu Tang pointed out ambiguities and helped clarify6 Z0 U( v. M/ s$ Q3 B1 t, r9 i
the chapters on graphs and clustering. Ben Raphael and Patricia Jones3 ?  s  J, J7 c( k* x9 Y
provided feedback on the early chapters and helped avoid some potential- }7 r3 |) y, h9 R% |" u+ a. e
misunderstandings. Dan Gilbert, of Dan Gilbert Art Group, Inc. kindly provided
uswith Triazzles to illustrate the problems of DNAsequence assembly.
Our special thanks go to Randall Christopher, the artist behind the website
www.kleemanandmike.com. Randall illustrated the book and designed
many unique graphical representations of some bioinformatics algorithms.
It has been a pleasure to work with Robert Prior of The MIT Press. With
sufficient patience and prodding, he managed to keep us on track. We also& G; e+ O9 F' _$ b
appreciate the meticulous copyediting of G. W. Helfrich. Finally, we thank the many students in different undergraduate and graduate6 _) |% p+ h. |9 w- C4 J$ d
bioinformatics classes at UCSD who provided comments on earlier versions
of this book.: W  h  k- A4 h4 B- K4 O
PAP would like to thank several people who taught him different aspects
of computational molecular biology. Andrey Mironov taught him that common
sense is perhaps the most important ingredient of any applied research.. H+ E3 @9 D8 D2 J
Mike Waterman was a terrific teacher at the time PAP moved from Moscow" Z0 K6 L8 E% w4 n& Q. I
to Los Angeles, both in science and in life. PAP also thanks Alexander Karzanov,* Y$ N1 V9 y& m4 H9 b0 ?, C
who taught him combinatorial optimization, which, surprisingly, remains
the most useful set of skills in his computational biology research. He$ _4 K% T$ A9 c% R( z
especially thanks Mark Borodovsky who convinced him to switch into the
field of bioinformatics in 1985, when it was an obscure discipline with an
uncertain future.# c( @% e8 W1 f/ ~2 Y
PAP also thanks his former students, postdocs, and lab members who
taught him most of what he knows: Vineet Bafna, Guillaume Bourque, Sridhar; z4 E. |& y, V5 y' I7 ?. L& J
Hannenhalli, Steffen Heber, Earl Hubbell, Uri Keich, Zufar Mulyukov,: \2 y/ d: e$ D- }% v
Alkes Price, Ben Raphael, Sing-Hoi Sze, Haixu Tang, and Glenn Tesler.# P7 H6 z/ s8 \# ~: |
NCJwould like to thank hismentors during undergraduate school—Toshihiko
Takeuchi, Harry Gray, John Baldeschwieler, and Schubert Soares—for
patiently but firmly teaching him that persistence is one of the more important
ingredients in research. Also, he thanks the admissions committee at the
University of California, San Diego who gambled on a chemist-turned-programmer,/ u  _8 I7 x- @
hopefully for the best.$ B5 }% w% Z! C# z! U
Neil Jones and Pavel Pevzner
La Jolla, California, 2004

阅读情况:12min看完,第一段中间部分逐渐进入理解状态。。不过人名了什么的没有看的很细。。明天继续练。。

===========================【CET速度】=============================
来源:http://bbs.gter.net/bbs/viewthread.php?tid=986032&highlight=(感谢草木姐姐!)

阅读要求:
每篇文章只看一分钟,一分钟之后就一定要停下来,读到哪里算哪里,这篇就算过了
) H0 o: \- m! v. ~9 T* e9 `
D# O
如果上一篇没有读完,那么就要提醒自己在下一篇中加速,同时调整自己阅读的节奏感,找到最舒服的方式


Passage 1
4 I# h$ O" W* E+ [$ S6 Y% o
Britain almost more than any other country in the world must seriously face the problem of building upwards, that is to say of accommodation a considerable proportion of its population in high blocks of flats. It is said that the Englishman objects to this type of existence, but if the case is such, he does in fact differ from the inhabitants of most countries of the world today. In the past our own blocks of flats have been associated with the lower-income groups and they have lacked the obvious provisions, such as central heating, constant hot water supply, electrically operated lifts from top to bottom, and so on, as well as such details, important notwithstanding, as easy facilities for disposal of dust and rubbish and storage places for baby carriages on the ground floor, playgrounds for children on the top of the buildings, and drying grounds for washing. It is likely that the dispute regarding flats versus individual houses will continue to rage on for a long time as for as Britain is concerned. And it is unfortunate that there should be hot feelings on both sides whenever this subject is raised. Those who oppose the building of flats base their case primarily on the assumption that everyone prefers an individual home and garden and on the high cost per unit of accommodation. The latter ignores the higher cost of providing full services to a scattered community and the cost in both money and time of the journeys to work for the suburban resident.



Passage 2

Where do pesticides fit into the picture of environmental disease? We have seen that they now pollute soil, water, and food, that they have the power to make our streams fishless and our gardens and woodlands silent and birdless. Man, however much he may like to pretend the contrary, is part of nature. Can he escape a pollution that is now so thoroughly distributed throughout our world?% |) K7 w! [1 n* {* b
We know that even single exposures to these chemicals, if the amount is large enough, can cause extremely severe poisoning. But this is not the major problem. The sudden illness or death of farmers, farm workers, and others exposed to sufficient quantities of pesticides are very sad and should not occur. For the population as a whole, we must be more concerned with the delayed effects of absorbing small amounts of the pesticides that invisibly pollute our world.
Responsible public health officials have pointed out that the biological effects of chemicals are cumulative over long periods of time, and that the danger to the individual may depend on the sum of the exposures received throughout his lifetime. For these very reasons the danger is easily ignored. It is human nature to shake off what may seem to us a threat of future disaster. “Men are naturally most impressed by diseases which have obvious signs,” says a wise physician, Dr. Rene Dubos, “yet some of their worst enemies slowly approach them unnoticed.”



Passage 3

Space is a dangerous place, not only because of meteors but also because of rays from the sum and other stars. The atmosphere again acts as our protective blanket on earth. Light gets through, and this is essential for plants to make the food which we eat. Heat, too, makes our environment endurable. Various kinds of rays come through the air from outer space, but enormous quantities of radiation from the sun are screened off. As soon as men leave the atmosphere they are exposed to this radiation but their spacesuits or the walls of their kspacecraft, if they are inside, do prevent a lot of radiation damage.
Radiation is the greatest known danger to explorers in space. The unit of radiation is called “rem”. Scientists have reason to think that a man can put up with far more radiation than 0.1 rem without being damaged; the figure of 60 rems has been agreed on. The trouble is that it is extremely difficult to be sure about radiation damage-a person may feel perfectly well, but the cells of his or her sex organs may be damaged, and this will no be discovered until the birth of deformed children or even grandchildren.
Missions of the Apollo flights have had to cross belts of high amount of rems. So far, no dangerous amounts of radiation have had to cross belts of high amount of rems. So far, no dangerous amounts of radiation have been reported, but the Apollo missions have been quite short. We simply do not know yet how men are going to get on when they spend weeks and months outside the protection of the atmosphere, working in a space laboratory. Drugs might help to decrease the damage done by radiation, but no really effective ones have beenfound so far.
 u: ~, u$ ?( @



 J# P! `' ^% z$ H
Passage 48 M) @! U" p* f9 R2 E' [' f

Taste is such a subjective matter that we don’t usually conduct preference tests for food. The most you can say about anyone’s preference, is that it’s one person’s opinion. But because the two big cola companies-Coca-Cola and Pepsi Cola are marketed so aggressively, we’ve wondered how big a role taste preference actually plays in brand loyalty. We set up a taste test that challenged people who identified themselves as either Coca-Cola or Pepsi fans: Find your brand in a blind tasting.
We invited staff volunteers who had a strong liking for either Coca-Cola Classic or Pepsi, Diet Coke, or Diet Pepsi. These were people who thought they’d have no trouble telling their brand from the other brand.
We eventually located 19 regular cola drinkers and 27 diet cola drinkers. Then we fed them four unidentified samples of cola one at a time, regular colas for the one group, diet versions for the other. We asked them to tell us whether each sample was Coke or Pepsi; then we analyzed the records statistically to compare the participants’ choices with what mere guess-work could have accomplished.
Getting all four samples right was a tough test, but not too tough, we thought, for people who believed they could recognize their brand. In the end, only 7 out of 19 regular cola drinkers correctly identified their brand of choice in all four trials. The diet-cola drinkers did a little worse-only 7 to 27 identified all four samples correctly.- i; y/ S8 ^8 f$ f" m
While both groups did better than chance would predict, nearly half the participants in each group made the wrong choice two or more times. Two people got all four samples wrong. Overall, half the participants did about as well on the last round of tasting as on the first, so fatigue, or taste burnout, was not a factor. Our preference test result suggest that only a few Pepsi participants and Coke fans may really be able to tell their favorite brand by taste and price.
! e( c4 e2 ?3 V) N" A. P



Passage 5% U  \5 j1 v+ H$ y1 o
% x1 r6 {$ {) n( o, S5 l, i& `
Cancer is feared by everyone. And this fear is reaching epidemic(流行性) proportions. Not the disease itself — there is no such thing as a cancer epidemic. Except for lung cancer, mostly caused by cigarette smoking ,the incidence rates are leveling off, and in the case of some kinds of cancer are decreasing. But the fear of cancer is catching, and the country stands at risk of an anxiety, The earth itself is coming to seem like a huge carcinogen(致癌物). The ordinary, more or less scientific statement that something between 80 and 90 percent of all cancers are dun to things in the environment is taken to mean that none of us will be safe until the whole environment is “cleaned up.” This is not at all the meaning.# G( d* d7 t* x. {) V! S
The 80-percent calculation is based on the unthinkable differences in the incidence of cancer in various societies around the world — for example, the high proportion of liver cancer in Africa and the Far East, stomach cancer in Japan, breast cancer in Western Europe and North America, and the relatively low figures for breast cancer in Japan and parts of Africa and for liver cancer in America. These data indicate there may be special and specific environmental influences, largely based on personal life-style, that determine the incidence of various forms of cancer in different communities — but that is all the data suggest. The overall incidence of cancer, counting up all the cases, is probable roughly the same everywhere.*


阅读情况:
时隔半年 重新拾起来~!
PASSAGE 1 差四行
PASSAGE 2 差三行
PASSAGE 3 差三分之一><
PASSAGE 4 差快一半啊啊啊
PASSAGE 5 差一行



明天继续。。。
127#
 楼主| 发表于 2011-5-9 03:53:01 | 只看该作者
明天开始按照自己的方法抱大腿了。。必须相信适合自己的就是最好的,坚持一种模式复习到考试结束。。

明天开始,上午阅读,中午数学狗狗+作文,下午语法,晚上逻辑

【5.9】
上午精读一篇,连做4篇VERBAL分册。。
中午看数学狗狗,然后看黄金80篇的AI和AA,AA明天先补全模板,AI侧重对着提纲想自己的论据,晚上11点多回来开始整理出英文的文字
下午一口气做40题PREP,还是好好做PREP吧,把MANHATTAN当做工具书,解释回归到MANHATTAN上。
做完40题看两章MANHATTAN    MANHATTAN3th会不会比4th差很多?我在看3th,刚看了一些,不行的话马上转4th,急求解答哇~~~
晚上做逻辑大全40题(一口气~) 然后错的不看答案再做一遍,还错的看答案自己想,自己想不通的看解释,解释都不明白的就扔掉吧~!
睡觉之前google整理论据

就这样吧~~
128#
发表于 2011-5-9 07:59:32 | 只看该作者
监督来咯…加油
129#
发表于 2011-5-9 08:11:49 | 只看该作者
抓抓加油!用最适合自己的方法是最好的选择!坚持住!
130#
发表于 2011-5-9 10:57:49 | 只看该作者
抓抓~~看我的名字~~知道我谁不~~~哈哈哈~~~加油啊~!!!
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