Category: 经济、IT观察与思考

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读书有感

英文写作笔记（二）：避免废话太多

依旧从BerkeleyX: ColWri2.2x Principles of Written English 抄过来的。我真的对这门课是相见恨晚！呜呜，我写英文各种罗嗦（楼下群众：你中文更罗嗦好不好？你看落园多少废话？）

ps 中间有段讲写作语气的我没抄过来，这个感觉和语言就无关了，更多是用词的精准...

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what causes wordiness?

Wordiness is using more words than you need to in order to write what you mean.  Everyone has a tendency to be too wordy at times. Some of the causes of this wordiness are:

- Trying to sound too formal or academic. Unfortunately, in academic writing, there are a lot of examples of wordy writing. This doesn't mean you have to model your writing on bad examples. Your readers will always appreciate if you state your ideas clearly, and using no more words than needed.

- Not knowing more precise vocabulary.  For example, saying, She ran quickly to the store can be made somewhat less wordy, and more precise, by saying: She raced to the store (race=run quickly). Finding the right vocabulary can help you cut down the number of words you use. Every reduction helps, even if it's only a word or two.

- Using too many unnecessary and vague modifiers.  Typically, modifiers like really, very, quite, and similar words add no meaning to your writing. If you need to modify a word, find precise modifiers. For example, instead of There's a really tall building near my house, write: There's a 50-story building near my house. 

- Using too many prepositional phrases or possessives. These types of phrases can add length to your sentences, often unnecessarily. So, instead of The car belonging to Mr. Wang is in the garage [10 words], write: Mr. Wang's car is in the garage [7 words].

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Writing less wordy prose

Continue reading "英文写作笔记（二）：避免废话太多"
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读书有感

英文写作笔记（一）：常见写作方向性错误

下面是抄过来的...来自：BerkeleyX: ColWri2.2x Principles of Written English。读完觉得我的英文写作真的是没受过正规教育...哎。

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There are two main sources of error in diction:

1. Choosing the wrong word. This can happen because of confusion between homonyms (words that sound alike but are spelled differently), or because the meaning of the word isn't fully understood.

2. Choosing colloquial, or less formal spoken language when standard or more formal language is called for (or vice versa). In academic writing, formal diction is generally expected.

Here are 21 common errors made in writing formal diction. How many of these usage errors have you made?

1. A lot/lots of

Colloquial: Diana likes her apartment a lot.
Formal: Diana likes her apartment very much.

Colloquial: There are lots of books in our library.
Formal: There are many books in our library.

2. Among/between
(Tip: Among involves more than two; between involves only two)

Colloquial: Discussions between our group members were often very lively.

Discussions Formal: Discussions among our group members were often very lively.

3. Around/ about
(Tip:  Don't use around to indicate time, distance, or other quantity.)

Colloquial: The class usually begins around nine.
Formal: The class usually begins at about nine.

4. Badly
(Tip: "Badly" is not a substitute for "very much.")

Colloquial: The team wanted to win really badly.
Formal: The team wanted desperately to win.

5. Based off of

Colloquial: Based off of that information,  we can move ahead with the plan.
Formal: Based on that information, we can move ahead with the plan.

6. Because
(Tip: Don't use "because" after " reason.")

Colloquial: The reason for our flight delay is because of bad weather.
Formal: The reason for our flight delay is the bad weather.

7. A bunch/bunches
(Tip: Use bunch or bunches only for things that are bound or grow together, like bananas and grapes; it is not used for other countable nouns.)

Colloquial: A bunch of us are going to the movies tonight.
Formal: A group of us are going to the movies tonight.
Formal: There are three bunches of bananas on the counter.

8. Each other/one other
(Tip: Each other refers to two, one another refers to more than two.)

Colloquial: Everyone at the party wished each other a happy new year.
Formal: Everyone at the party wished one another a happy new year.
Formal: My sister and I wished each other a happy new year.

9. Guys

Colloquial: I hope you guys can join me at the restaurant.
Formal: I hope all of you can join me at the restaurant.

10. In / Into
(Tip:  "In" means "within" or "inside", while "into" refers to the motion of going from outside to inside.)

Colloquial: Bill went in the bus station to buy a ticket.
Formal: Bill went into the bus station to buy a ticket.

11. Infer/imply
(Tip: To infer is an act of thinking, to imply is an act of saying something.)

Incorrect:  I saw your memo about a noon meeting. Are you inferring that we should have lunch together?
Correct: I saw your memo about a noon meeting. Are you implying that we should have lunch together?

12. It's/its

 

Incorrect: The committee has reached it's goals this year.
Correct: The committee has reached its goals this year.

13. Kind of / sort of
(Tip: Don't use "kind of" or "sort of" when you mean "very" , "rather," or "somewhat." )

Colloquial: Jim was sort of angry when he learned we went to the park without him.
Formal: Jim was somewhat upset when he learned we went to the park without him.

14. Less than/fewer than
(Tip: Use less than only with uncountable nouns.)

Colloquial: There are less people in the store today than yesterday.
Formal: There are fewer people in the store today than yesterday.

15. Like / as
(Tip: Use "as" when comparing actions, "like" when comparing things.)

Colloquial: Alan wants to write a new essay, just like Sarah does.
Formal: Alan wants to write a new essay just as Sarah does.

16. Like / maybe
(Tip: Avoid using "like" and "maybe" when estimating; use "approximately", "perhaps" or "about" instead.)

Colloquial: There were like thirty people at my birthday party.
Formal: There were approximately thirty people at my birthday party.

17. Meantime

Colloquial: Meantime, the rain continued to pour.
Formal: In the meantime, the rain continued to pour.
Formal: Meanwhile, the rain continued to pour.

18. On account of

Colloquial: On the account of the weather, our plane was late.
Formal: Because of the weather, our plane was late.

19. Plenty

Colloquial: It has been plenty warm all week.
Formal: It has been very warm all week.

20. So
(Tip: Don't use "so" as a synonym for "therefore".)

Colloquial: Dmitry knew that I missed class, so he shared his notes.
Formal: Dmitry knew that I missed class; therefore, he shared his notes.

Colloquial: This week's homework is so difficult.
Formal: This week's homework is very difficult.

21. They're/their/there

 

Incorrect: Do you know if their going to the party?
Correct: Do you know if they're going to the party?

Incorrect: What is there address?
Correct: What is their address?

Categories
读书有感

Constitutional Law by Yale 听课笔记（三）

宪法修正过程。

第一次大的修正称之为Bill of Rights, 1791年通过。后面的修正案大都集中在某一段时间。

Bill of Rights 修正案1-12 十八世纪末十九世纪初

Civil War Amendments/Reconstruction Amendments 修正案13-15 十九世纪中期，civil war后

The Progressive Era Amendments 修正案16-18  二十世纪初

详细的列表在这里：http://en.wikipedia.org/wiki/List_of_amendments_to_the_United_States_Constitution

Bill of Rights主要的诉求是：

The Bill of Rights enumerates freedoms not explicitly indicated in the main body of the Constitution, such as freedom of religion, freedom of speech, a free press, and free assembly; the right to keep and bear arms; freedom from unreasonable search and seizure, security in personal effects, and freedom from warrants issued without probable cause; indictment by a grand jury for any capital or "infamous crime"; guarantee of a speedy, public trial with an impartial jury; and prohibition of double jeopardy. In addition, the Bill of Rights reserves for the people any rights not specifically mentioned in the Constitution and reserves all powers not specifically granted to the federal government to the people or the States.

Civil War Amendments主要是告别奴隶制度:

Their proponents saw them as transforming the United States from a country that was (in Abraham Lincoln's words) "half slave and half free" to one in which the constitutionally guaranteed "blessings of liberty" would be extended to the entire populace, including the former slaves and their descendants.

Progressive Era Amendments更多是现代化的标志：

The Sixteenth Amendment gave the federal government the power to lay and collect an income tax regardless of the source of that income.

The Seventeenth Amendment provided for the direct election of Senators by the people rather than by the state legislatures as the original Constitution called for.

The Eighteenth Amendment prohibited the import, export, transport, manufacture or sale of intoxicating beverages.

The Nineteenth Amendment gave women the right to vote.

最有趣的就是妇女投票权了。毕竟是需要男人们投票来决定妇女有权投票。一开始是从稀缺妇女的 Wyoming Territory州开始，然后扩展到全国。

最后的二战后的修正则主要是一些对于民主的更深入理解，比如总统任期的限制。

Categories
网络新发现

最新的一些经济学研究趋势...

今天闲着无聊抓了一下NBER最近一年的working paper数据看看。众所周知，econ现在发表周期越来越长，一两年都算少的，三五年也挺常见的。虽然跟跟AER什么的也是个比较好的指向，但多少还是“旧”了一点。

NBER覆盖的研究范围还是蛮广的，大部分发表的paper都能在这里找到working paper版本，所以一时没想到更好的抓数据的来源：

Aging(AG)
Asset Pricing(AP)
Children(CH)
Corporate Finance(CF)
Development Economics(DEV)
Development of the American Economy(DAE)
Economics of Education(ED)
Economic Fluctuations and Growth(EFG)
Environmental and Energy Economics (EEE)
Health Care(HC)
Health Economics(HE)
Industrial Organization(IO)
International Finance and Macroeconomics(IFM)
International Trade and Investment(ITI)
Labor Studies(LS)
Law and Economics(LE)
Monetary Economics(ME)
Political Economy(POL)
Productivity, Innovation, and Entrepreneurship Program(PR)
Public Economics(PE)

抓了一番之后，基本关键词热度如下。一些太没有意义的我就调透明了。(个人很讨厌word cloud这种东西，所以还是选择了bar chart)

[3/19更新] 和Bing里面的key words match了一下。貌似信息多了一些。

key_word

虽然数目不代表质量，但至少能看出来有多少人在某个领域耕耘。最突出的就是health这里了，很高（钱很多）。然后还有很多研究trade和growth的。然后risk和finance好像也蛮多的，crisis好像也挺多。Labor和IO一直也是热热的。研究方法上，随机试验还是最亮的。

没有进一步分析那些作者在高产，下次搞个“抱大腿”趋势好了。

代码在这里：

grab_url <- c("http://www.nber.org/new_archive/mar14.html",
              "http://www.nber.org/new_archive/dec13.html",
              "http://www.nber.org/new_archive/sep13.html",
              "http://www.nber.org/new_archive/jun13.html",
              "http://www.nber.org/new_archive/mar13.html")

library(RCurl)
require(XML)

grab_paper <- function (grab) {
  webpage <- getURLContent(grab)
  web_content <- htmlParse(webpage,asText = TRUE)
  paper_title <- sapply(getNodeSet(web_content, path="//li/a[1]"),xmlValue)
  author <- sapply(getNodeSet(web_content, path="//li/text()[1]") ,xmlValue)
  paper_author <- data.frame(paper_title = paper_title, author = author)
  return(paper_author)
}

library(plyr)
paper_all <- ldply(grab_url,grab_paper)

titles <- strsplit(as.character(paper_all$paper_title),split="[[:space:]|[:punct:]]")
titles <- unlist(titles)

library(tm)
library(SnowballC)
titles_short <- wordStem(titles)
Freq2 <- data.frame(table(titles_short))
Freq2 <- arrange(Freq2, desc(Freq))
Freq2 <- Freq2[nchar(as.character(Freq2$titles_short))>3,]
Freq2 <- subset(Freq2, !titles_short %in% stopwords("SMART"))
Freq2$word <- reorder(Freq2$titles_short,X = nrow(Freq2) - 1:nrow(Freq2))
Freq2$common <- Freq2$word %in% c("Evidenc","Effect","Econom","Impact","Experiment","Model","Measur","Rate","Economi",
                                  "High","Data","Long","Chang","Great","Estimat","Outcom","Program","Analysi","Busi"
                                  ,"Learn","More","What")
library(ggplot2)
ggplot(Freq2[1:100,])+geom_bar(aes(x=word,y=Freq,fill = common,alpha=!common))+coord_flip()

### get some keywords from Bing academic
start_id_Set = (0:5)*100+1
require(RCurl)
require(XML)
# start_id =1
# 

get_keywords_table <- function (start_id) {
  end_id = start_id+100-1
  keyword_url <- paste0("http://academic.research.microsoft.com/RankList?entitytype=8&topDomainID=7&subDomainID=0&last=0&start=",start_id,"&end=",end_id)
  keyword_page <- getURLContent(keyword_url)
  keyword_page <- htmlParse(keyword_page,asText = TRUE)
  keyword_table <- getNodeSet(keyword_page, path="id('ctl00_MainContent_divRankList')//table")
  table_df <- readHTMLTable(keyword_table[[1]])
  names(table_df) <- c("rowid","Keywords"   ,  "Publications" ,"Citations")
  return (table_df)
}

require(plyr)
keywords_set <- ldply(start_id_Set,get_keywords_table)

save(keywords_set, file="keywords_set.rdata")

最后更新的部分代码。效率偏低，见谅。

### map keywords
load("keywords_set.rdata")
load("NBER.rdata")
keys <- strsplit(as.character(keywords_set$Keywords), split=" ")
require(SnowballC)
keys_Stem <- lapply(keys,wordStem)

#get edges 
edge_Set <- data.frame()
for (word in Freq2$word){
#   print(word)
  for (key_id in 1:length(keys_Stem)){
#     print(keys_Stem[[key_id]])
    if (word %in% keys_Stem[[key_id]]) {
      edge <- data.frame(keywords = keywords_set[key_id,]$Keywords, kid = word)
      edge_Set <- rbind(edge_Set,edge)}
  }
}

#edge_Set
require(ggplot2)
kid_freq <- as.data.frame(table(edge_Set$kid))
require(plyr)
kid_freq <- arrange(kid_freq, desc(Freq))

edge_Set_sub <- subset(edge_Set, kid %in% Freq2[1:100,]$word)
edge_Set_sub$keywords <- as.character(edge_Set_sub$keywords)
# edge_Set_sub$kid <- as.character(edge_Set_sub$kid)

link_keys <- function(x) {paste(x$keywords,collapse = ", ")}

linked <- ddply(edge_Set_sub, .(kid), link_keys)

show_keys <- merge(Freq2[1:100,],linked, by.x="word",by.y="kid", all.x=T)
names(show_keys)[5] <- "linked"

ggplot(show_keys[!is.na(show_keys$linked),],aes(x=word,y=Freq))+
  geom_bar(aes(fill = common,alpha=!common),stat="identity",ymin=10)+coord_flip()+
  geom_text(aes(label=substr(linked,1,200),y = Freq, size = 1),hjust=0)

 

Categories
读书有感

读大学读什么？

最近一直在想这个问题：花费了那么多时间读书，究竟读了一些什么？

知识这东西，但凡肯花时间，大部分都是能学会的。应付考试什么的就更不是特别难的事情了。

可是成绩单上满满的，都是知识、知识。让人看起来都觉得疲倦。

除了知识，上学的时候还学会了什么？更多是培养性情？养成一颗好奇心，养成探索事物的兴趣，广泛的接纳各个领域的思维冲击。说起来工作了之后，太多东西都是可以现用现学的，没有什么那么困难的。

前段时间在看美国LAC(Liberal Arts College)的教育模式，培养精英的气质。因为有幸接触过一些top LAC出来的精英，确实气质上稍胜一筹。

A "liberal arts" institution can be defined as a "college or university curriculum aimed at imparting broad general knowledge and developing general intellectual capacities, in contrast to a professional, vocational, or technical curriculum."

越往后走，这种积淀的力量越能超越知识课程什么的，支撑着前行。而我的大学，确实缺少这样的时间。被无辜的填了太多鸭，被GPA逼得去竞争分数，缺少了太多太多思考的广度和深度。而那些知识，考过了试，又有多少受用至今？了了。

说回语言。学西班牙语的时候，很多人说，拉丁语系学两门以上，其他的就都很容易了。现在深以为然——计算机语言也是如此。R和Matlab用的熟了，加上C和PHP的一些基础，现在去看Python真的没什么难度。估计去学Java也不会花太多功夫。

我曾经试图说服无数周围的人，数学也是一门语言(统计学不是，它是一种思维方式，可以用多种语言表述)，学了那么多公式什么的表达的其实是人们对于逻辑推理的极致追求。看似复杂高深的课程，其实大都还是可以，读书百变、其意自现的。

想到这里就说到这里。是的，我是在有些可惜那些匆匆错过的时光。