参考来源 （英文） Rick Durrett. Probability: theory and examples ,4th edition. Cambridge University Press. 2000. ISBN 0521765390 . ^ 2.0 2.1 2.2 （英文） Peter Mrters, Yuval Peres. Brownian Motion . Cambridge University Press Cambridge Series in Statistical and Probabilistic Mathematics. 2010. ISBN 9780521760188 . ^ （英文） Steven E. Shreve. Stochastic Calculus for Finance II: Continuous Time Models . Springer. 2008. ISBN 978-0-387-40101-0 . ^ Nizar Touzi, Peter Tankov. Calcul Stochastique en Finance . Les ditions de l'cole Polytechnique. 2010. Kleinert, Hagen , Path Integrals in Quantum Mechanics, Statistics, Polymer Physics, and Financial Markets , 4th edition, World Scientific (Singapore, 2004); Paperback ISBN 981-238-107-4 (also available online: PDF-files ) Stark,Henry, John W. Woods , Probability and Random Processes with Applications to Signal Processing , 3rd edition, Prentice Hall (New Jersey, 2002); Textbook ISBN 0-13-020071-9 Daniel Revuz and Marc Yor, Continuous martingales and Brownian motion , second edition, Springer-Verlag 1994.
工商管理硕士，即 MBA ， Master of Business Administration ，是源于欧美国家的一种专门培养中高级职业经理人员的专业硕士学位。工管硕是市场经济的产物，培养的是高素质的管理人员、职业经理人和创业者 工商管理硕士是商业界普遍认为是晋身管理阶层的一块垫脚石。现时不少学校为了开拓财源增加收入，都与世界知名的大学商学院学术合作，推销他们的 工商管理硕士课程 。 课程类型： 第一种是供任职公司高层管理者报读的课程，通常利用工作时间之余修习，名为高阶工商管理硕士（ Executive MBA ，简称 EMBA ）； 第二种是供现职管理者或大学毕业生报读，以增进其管理知识的普通工商管理硕士课程，此即工商管理硕士的原型； 第三种是供现职从业或具备两年以上工作经验者，以强化其企管专业基础的工商管理硕士课程，亦名工商管理硕士（ Special Master of Business Administration, 全称 SMBA) 编辑本段授课方式。 第一种是面授 MBA 项目， 1908 年诞生于美国 Harvard University ，至今已有百年历史了。就是通过面对面的形式进行授课。第二种是远程 MBA 项目， 1995 年由美国 McCann University 率先提出，至今也有 10 多年的历史了。就是通过网络多媒体形式进行授课。 学位特点： MBA 学位是一种注重复合型、综合型人才培养的学位，是能力培养重于知识传授的学位。它教授的是面对实战的“管理”，而不是注重研究的“管理学”，技巧和思维的培养重于理论分析能力。它要求其毕业生有一定的实践经验和管理经验（应变能力、预测能力、综合能力、组织能力等）。 其课程内容涉及管理类、经济学类、金融、财务、法律等等，能力训练不光讲究组织、领导才能，也涉及以口才为依托的沟通能力、运筹能力、判断和处理问题的能力等等。 MBA 是作为一种专业硕士学位，与一般硕士研究生有所不同。首先是培养目标不同， MBA 是培养能够胜任工商企业和经济管理部门高层管理工作需要的务实型、复合型和应用型高层次管理人才，而其他硕士研究生是培养具有扎实理论基础和较强的科研和教学能力的高层次科研型和教学型人才；其次是培养对象不同， MBA 的招生对象一般为大学本科毕业、大专毕业生、具有三年以上工作实践经验的国家机关事业单位干部和工商企业管理人员及技术人员，而其他硕士研究生可以招收应届毕业生，也可以招收在职人员；第三是培养方式不同， MBA 教育从本质上讲是一种职业训练，特别强调在掌握现代管理理论和方法的基础上，通过商业案例分析、实战观摩、分析与决策技能训练等培养学生的实际操作技能，使学生接受知识与技能、个性与心理、目标与愿望等方面的挑战，更具有职业竞争的实力，而其他研究生则侧重于理论学习、学术研究。 尽管 MBA 与其他研究生在上述方面存在区别，但在招生录取和在校的教学管理方面却是相同的：都必须参加全国统一的招生考试，按国家划定的录取线统一录取；在取得学籍后按正规研究生标准进行教学管理；做学位论文并通过论文答辩后取得学位。
Python Interview Question and Answers For the last few weeks I have been interviewing several people for Python/Django developers so I thought that it might be helpful to show the questions I am asking together with the answers. The reason is … OK, let me tell you a story first. I remember when one of my university professors introduced to us his professor – the one who thought him. It was a really short visit but I still remember one if the things he said. “Ignorance is not bad, the bad thing is when you do no want to learn.” So back to the reason – if you have at least taken care to prepare for the interview, look for a standard questions and their answers and learn them this is a good start. Answering these question may not get you the job you are applying for but learning them will give you some valuable knowledge about Python. This post will include the questions that are Python specific and I’ll post the Django question separately. How are arguments passed – by reference of by value? The short answer is “neither”, actually it is called “call by object” or “call by sharing”(you can check here for more info). The longer one starts with the fact that this terminology is probably not the best one to describe how Python works. In Python everything is an object and all variables hold references to objects. The values of these references are to the functions. As result you can not change the value of the reference but you can modify the object if it is mutable. Remember numbers, strings and tuples are immutable, list and dicts are mutable. Do you know what list and dict comprehensions are? Can you give an example? List/Dict comprehensions are syntax constructions to ease the creation of a list/dict based on existing iterable. According to the 3rd edition of “Learning Python” list comprehensions are generally faster than normal loops but this is something that may change between releases. Examples:# simple iterationa = # list comprehensiona = # dict comprehensiona = {x: x*2 for x in range(10)}# a == {0: 0, 1: 2, 2: 4, 3: 6, 4: 8, 5: 10, 6: 12, 7: 14, 8: 16, 9: 18} What is PEP 8? PEP 8 is a coding convention(a set of recommendations) how to write your Python code in order to make it more readable and useful for those after you. For more information check PEP 8 . Do you use virtual environments? I personally and most(by my observation) of the Python developers find the virtual environment tool extremely useful. Yeah, probably you can live without it but this will make the work and support of multiple projects that requires different package versions a living hell. Can you sum all of the elements in the list, how about to multuply them and get the result? # the basic ways = 0for x in range(10): s += x# the right ways = sum(range(10))# the basic ways = 1for x in range(1, 10): s = s * x# the other wayfrom operator import mulreduce(mul, range(1, 10)) As for the last example, I know Guido van Rossum is not a fan of reduce, more info here , but still for some simple tasks reduce can come quite handy. Do you know what is the difference between lists and tuples? Can you give me an example for their usage? First list are mutable while tuples are not, and second tuples can be hashed e.g. to be used as keys for dictionaries. As an example of their usage, tuples are used when the order of the elements in the sequence matters e.g. a geographic coordinates, “list” of points in a path or route, or set of actions that should be executed in specific order. Don’t forget that you can use them a dictionary keys. For everything else use lists. Do you know the difference between range and xrange? Range returns a list while xrange returns a generator xrange object which takes the same memory no matter of the range size. In the first case you have all items already generated(this can take a lot of time and memory) while in the second you get the elements one by one e.g. only one element is generated and available per iteration. Simple example of generator usage can be find in the problem 2 of the “homework” for my presentation Functions in Python Tell me a few differences between Python 2.x and 3.x There are many answers here but for me some of the major changes in Python 3.x are: all strings are now Unicode, print is now function not a statement. There is no range, it has been replaced by xrange which is removed. All classes are new style and the division of integers now returns float. What are decorators and what is their usage? According to Bruce Eckel’s Introduction to Python Decorators “Decorators allow you to inject or modify code in functions or classes”. In other words decorators allow you to wrap a function or class method call and execute some code before or after the execution of the original code. And also you can nest them e.g. to use more than one decorator for a specific function. Usage examples include – logging the calls to specific method, checking for permission(s), checking and/or modifying the arguments passed to the method etc. The with statement and its usage. In a few words the with statement allows you to executed code before and/or after a specific set of operations. For example if you open a file for reading and parsing no matter what happens during the parsing you want to be sure that at the end the file is closed. This is normally achieved using the try… finally construction but the with statement simplifies it usin the so called “context management protocol”. To use it with your own objects you just have to define __enter__ and __exit__ methods. Some standard objects like the file object automatically support this protocol. For more information you may check Understanding Python’s “with” statement . Well I hope this will be helpful, if you have any question or suggestion feel free to comment. Update : Due to the lots of comments on Reddit and LinkedIn, I understood that there is some misunderstanding about the post. First, the questions I have published are not the only ones I ask, the interview also includes such related to general programming knowledge and logical thinking. Second the questions above help me get a basic understanding of your Python knowledge but they are not the only think that makes my decision. Not answering some of them does not mean that you won’t get the job, but it may show me on which parts we should to work. 总结了10道题的考试侧重点，供参考： 1.How are arguments passed – by reference of by value? 考的是语法，基本功，虽说python程序员可以不用关心堆栈指针那些头疼的东东，但传引用和传值的区别还是必需清楚的。个人感觉从python中一切都是对象的角度看，第一题问传值还是传引用其实是考官有意看面试者是不是概念清楚，真正希望考生回答的是哪些对象传递到函数中是只读的或者说不可变的。 2.Do you know what list and dict comprehensions are? Can you give an example? 典型的pythonic用法 3.What is PEP 8? 是不是有良好的编码习惯 4.Do you use virtual environments? 是不是有独立配开发环境的能力 5.Can you sum all of the elements in the list, how about to multuply them and get the result? 期待你用pythonic方式解决 6.Do you know what is the difference between lists and tuples? Can you give me an example for their usage? python中两种最基本常用的数据结构 7.Do you know the difference between range and xrange? 期待你清楚generator及其是如何使用内存的，generator用法还是典型的pythonic 8.Tell me a few differences between Python 2.x and 3.x 希望你对新老产品都有所关注 9.What are decorators and what is their usage? 典型的pythonic用法 10.The with statement and its usage. 典型的pythonic语法 参考链接 http://ilian.i-n-i.org/python-interview-question-and-answers/ http://www.**.com/group/topic/38792398/
Hands-On Data Science with R Dr Graham Williams, PhD (ANU, Machine Learning), BSc (Maths, Hons) Chief Data Scientist, Togaware and Australian Taxation Office Adjunct Professor, Australian National University and University of Canberra International Visiting Professor, Chinese Academy of Sciences Our goal is to provide introductory material to cost effectively kick start an organization's entry into Data Science. To that end, we introduce the use of R for doing Data Science. In addition to the extensive material available on our web site we provide a unique offering of in-situ hands-on training . We offer traditional out-of-office training courses, but we find more effective learning can occur hands-on in-situ. We offer one of the world's leading Data Scientists to work alongside and mentor your staff over one or two weeks. We work confidentially on actual projects, with training "on-the-job" provided by a professional with 30 years experience in the industry and author of the best selling book on Data Mining with Rattle and R . Contact Togaware Training at training@togaware.com for details. Our on-line resources, including Hands-On Data Science , weave together a collection of freely available and open source tools for the Data Scientist. The tools are all part of the R Statistical Software Suite. Each chapter is made up of multiple pages, but each page within a chapter is a one page guide that covers a particular aspect of the topic (hence also refered to as the OnePageR guide). They are a great place to start, before engaging our hands-on training experts. Hands-On Data Science can be worked through as a hands-on guide and then used as a reference guide. Each page aims to be a bite sized chunk for hands-on learning, building on what has gone before. Many chapters also have a lecture pack and a laboratory session where a number of tasks can be completed. The R code sitting behind each chapter is also provided and can be easily run standalone to replicate the material presented in the chapter. The material is always under development ! Chapters will change (and hopefully improve) regularly. Links preceded with a * are more well developed. All of the material is provided under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License allowing access to everyone for any purpose (except commercial) and is provided at no cost. You can assist in helping cover the costs of providing this material through a $40 contribution using PayPal. Your support encourages further development of this resource as does feedback, suggestions, and ideas , which are always welcome. Refer to the Data Mining Survival Guide or my book on Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R) for related material. Many of the initial chapters were developed and tested whilst visiting the Shenzhen Institutes of Advanced Technology as an International Visiting Professor of the Chinese Academy of Sciences. The data used across the chapters is available for download as data.zip . Enjoy! Getting Started as a Data Scientist An Introduction to Data Mining: * Lecture Introducing Data Science, Rattle and R: * Lecture - * Chapter - * R Rattle to R: * Chapter - * R R for the Eager Data Scientist A Template for Preparing Data: * Chapter - * R A Template for Building Models: * Chapter - * R Case Studies: * Chapter - * R Basic R Tips and Tricks Chapter - R Dealing With Data Reading Data into R: * Chapter - * R Exploring and Summarising Data: * Chapter - * R Visualising Data with GGPlot2: * Chapter - * R Transforming Data: * Chapter - * R Descriptive Analytics Cluster Analysis: * Lecture - Chapter - R Association Analysis: * Lecture - Chapter - R Predictive Analytics Decision Trees: * Lecture - * Chapter - * R - * Rattle Ensembles of Decision Trees: * Lecture - * Chapter - * R Support Vector Machines Neural Networks Naive Bayes: Chapter - R Multivariate Adaptive Regression Splines: Chapter - R Evaluating Models: * Chapter - * R Scoring (R) PMML (R) Exporting Models for Deployment Advanced Analytics Text Mining: * Chapter - * R Social Network Analysis: Chapter -R Genetic Programming: Chapter -R Advanced R Strings: Chapter , R Dates and Time: * Chapter - * R Spatial Data * Chapter - * R Big Data * Chapter - * R Exploring Different Plots: Chapter - R Writing Functions: Chapter - R Parallel Processing: Chapter - R Environments: * Chapter - R Expert R Packaging (R) Pulling it Together into a Package Doing R with Style: * Chapter - * R Literate Data Science with KnitR: * Lecture - * Chapter - * R
Was The Department Of Defense Behind Facebook’s Controversial Manipulation Study? Submitted by Tyler Durden on 07/01/2014 23:05 -0400 Reality Twitter Twitter University of California lang: en_U in Share 28 Submitted by Michael Krieger of Liberty Blitzkrieg blog , I’ve spent pretty much all day reading as much as possible about the extremely controversial Facebook “emotional contagion” study in which the company intentionally altered itsnews feed algorithm to see if it could manipulate its users’ emotions. In case you weren’t aware, Facebook is always altering your news feed under the assumption that there’s no way they could fill your feed with all of your “friends’” pointless, self-absorbed, dull updates (there’s just too much garbage). As such, Facebook filters your news feed all the time, something which advertisers must find particularly convenient. In any event, the particular alteration under question occurred duringone week in January 2012, and the company filled some people’s feeds with positive posts, while others were fed more negative posts. Once the data was compiled, academics from the University of California, San Francisco and Cornell University were brought in to analyzethe results. Their findings were thenpublished in the prestigiousProceedings of the National Academy of Sciences. They found that: For people who had positive content reduced in their News Feed, a larger percentage of words in people’s status updates were negative and a smaller percentage were positive. When negativity was reduced, the opposite pattern occurred. These results suggest that the emotions expressed by friends, via online social networks, influence our own moods, constituting, to our knowledge, the first experimental evidence for massive-scale emotional contagion via social networks. You probably know most of this already, but here is where it starts to get really strange. Initially, the press releasefrom Cornell highlighting the study said at the bottom: “The study was funded in part by the James S. McDonnell Foundation and the Army Research Office.” Once people started asking questions about this, Cornell claimed it had made a mistake, and that there was no outside funding. Jay Rosen, Journalism Professor at NYU, seems to find this highly questionable. He wrote on his Facebook page that: Strange little turn in the story of the Facebook “emotional contagion” study. Last month’s press release from Cornell highlighting the study had said at the bottom: “The study was funded in part by the James S. McDonnell Foundation and the Army Research Office.” Why would the military be interested? I wanted to know. So I asked Adam D.I. Kramer, the Facebook researcher, that question on his Facebook page, where he has posted what he called a public explanation. (He didn’t reply to my or anyone else’s questions.) See: https://www.facebook.com/akramer/posts/10152987150867796 Now it turns out Cornell was wrong! Or it says it was wrong. The press release now reads: “Correction: An earlier version of this story reported that the study was funded in part by the James S. McDonnell Foundation and the Army Research Office. In fact, the study received no external funding.” Why do I call this strange? Any time my work has been featured in an NYU press release, the PR officers involved show me drafts and coordinate closely with me, for the simple reason that they don’t want to mischaracterize scholarly work. So now we have to believe that Cornell’s Professor of Communication and Information Science, Jeffrey Hancock, wasn’t shown or didn’t read the press release in which he is quoted about the study’s results (weird) or he did read it but somehow failed to notice that it said his study was funded by the Army when it actually wasn’t (weirder). I think I would notice if my university was falsely telling the world that my research was partially funded by the Pentagon… but, hey, maybe there’s an innocent and boring explanation that I am overlooking. It gets even more interesting from here. TheProfessor of Communication and Information Science, Jeffrey Hancock, who Mr. Rosen mentions above, has a history of working with the U.S. military, specifically the Minerva Institute. In case you forgot what this is, the Guardian reported on it earlier this year. It explained: A US Department of Defense (DoD) research program is funding universities to model the dynamics, risks and tipping points for large-scale civil unrest across the world, under the supervision of various US military agencies. The multi-million dollar program is designed to develop immediate and long-term “warfighter-relevant insights” for senior officials and decision makers in “the defense policy community,” and to inform policy implemented by “combatant commands.” Launched in 2008 – the year of the global banking crisis – the DoD ‘Minerva Research Initiative’ partners with universities “to improve DoD’s basic understanding of the social, cultural, behavioral, and political forces that shape regions of the world of strategic importance to the US.” SCG News has written one of the best articles I have seen yet on the links between the Facebook study and the Department of Defense. It notes: In the official credits for the study conducted by Facebook you’ll find Jeffrey T. Hancock from Cornell University. If you go to the Minerva initiative website you’ll find that Jeffery Hancock received funding from the Department of Defense for a study called“Cornell: Modeling Discourse and Social Dynamics in Authoritarian Regimes”. If you go to the project site for that study you’ll find a visualization program that models the spread of beliefs and disease. Cornell University is currently being funded for another DoD study right now called“Cornell: Tracking Critical-Mass Outbreaks in Social Contagions”(you’ll find the description for this project on the Minerva Initiative’s funding page). So I went ahead and looked at the study mentioned above, and sure enough I found this: There he is, Jeff Hancock, the same guy who analyzed the Facebook data for Cornell, which initially claimed funding from the Pentagon and then denied it. I call bull*****. Stinking bull*****. So it seems that Facebook and the U.S. military are likely working together to study civil unrest and work on ways to manipulate the masses into apathy or misguided feelings of contentment in the face of continued banker and oligarch theft. This is extremely disturbing, but this whole affair is highly troubling in spite of this. For one thing, although governments and universities need to take certain precautions when conducting such “research,” private companies like Facebook apparently do not. Rather, allthey have to do is get people to click “I accept” to a terms of service agreement they never read, which allows companies to do almost anything they want to you, your data and your emotions. What we basically need to do as a society is completely update our laws. For starters, if a private corporation is going to lets say totally violate your most basiccivil liberties as defined under the Bill of Rights, a simple terms of service agreement should not be sufficient. For more invasive violations ofsuchrights, perhaps a one page simple-to-read document explaining clearly whichof your basic civil libertiesyou are giving away should be mandatory. For example, had Facebook not partnered at the university level to analyze thisdata, we wouldn’t even know this happened at all. So what sort of invasive, mind-****ing behavior do you think all these large corporations with access to your personal data are up to. Every. Single. Day. The Faculty Lounge blog put it perfectly when it stated: Academic researchers’ status as academics already makes it more burdensome for them to engage in exactly the same kinds of studies that corporations like Facebook can engage in at will. If, on top of that, IRBs didn’t recognize our society’s shifting expectations of privacy (and manipulation) and incorporate those evolving expectations into their minimal risk analysis, that would make academic research still harder, and would only serve to help ensure that those who are most likely to study the effects of a manipulative practice and share those results with the rest of us have reduced incentives to do so. Would we have ever known the extent to which Facebook manipulates its News Feed algorithms had Facebook not collaborated with academics incentivized to publish their findings? We can certainly have a conversation about the appropriateness of Facebook-like manipulations, data mining, and other 21st-century practices. But so long as we allow private entities freely to engage in these practices, we ought not unduly restrain academics trying to determine their effects. Recall those fear appeals I mentioned above. As one social psychology doctoral candidate noted on Twitter, IRBs make it impossible to study the effects of appeals that carry the same intensity of fear as real-world appeals to which people are exposed routinely, and on a mass scale, with unknown consequences. That doesn’t make a lot of sense. What corporations can do at will to serve their bottom line, and non-profits can do to serve their cause, we shouldn’t make (even) harder—or impossible—for those seeking to produce generalizable knowledge to do. If you read Liberty Blitzkrieg, you know I strongly dislike Facebook as a company. However, this is much bigger thanjust one experiment by Facebook with what appears to be military ties. What this is really about is thefrightening reality that these sorts of things are happening every single day, and we have no idea it’s happening. We need to draw the lines as far as to what extentwe as a society wish to be data-mined and experimented onby corporations with access to all of our private data. Until we do this, we will continue to be violated and manipulated at will. For some of my Facebook critical articles from earlier this year, read: The Chief Operating Officer of Facebook Wants to Ban the Word “Bossy” How UK Prime Minister David Cameron Paid Thousands of Dollars for Facebook “Likes” How Facebook Exploits Underage Girls in its Quest for Ad Revenue This Man’s $600,000 Facebook Disaster is a Warning For All Small Businesses Average: 4.92 Your rating: None Average: 4.9 ( 25 votes)
The list below details the 40 journals used by the Financial Times in compiling the Business School research rank, included in both the Global MBA and EMBA rankings. Accounting 1. Accounting, Organisations and Society (Elsevier) 2. Journal of Accounting and Economics (Elsevier) 3. Journal of Accounting Research (University of Chicago) 4. The Accounting Review (American Accounting Association) Entrepreneurship 5. Entrepreneurship Theory and Practice (Baylor University, Waco, Texas) 6. Journal of Business Venturing (Elsevier) General Management 7. Academy of Management Journal (Academy of Management, Ada, Ohio) 8. Academy of Management Review (Academy of Management) 9. Journal of International Business Studies (Academy of International Business) 10. Management International Review (Gabler) 11. Strategic Management Journal (John Wiley and Sons) Marketing 12. Journal of Marketing (American Marketing Association) 13. Journal of Marketing Research (American Marketing Association) 14. Journal of Consumer Research (University of Chicago) 15. Marketing Science (Informs) Organisational Behaviour 16. Journal of Applied Psychology (American Psychological Association) 17. Organization Science (Informs) 18. Organizational Behaviour and Human Decision Processes (Academic Press) Economics 19. Econometrica (Econometric Society, University of Chicago) 20. Journal of Political Economy (University of Chicago) 21. The American Economic Review (American Economic Association, Nashville) 22. The Rand Journal of Economics (The Rand Corporation) Finance 23. Journal of Finance (Blackwell) 24. Journal of Financial and Quantitative Analysis - NEW 25. Journal of Financial Economics (Elsevier) 26. Review of Financial Studies (Oxford University Press) Human Resource Management 27. Human Resource Management (John Wiley and Sons) 28. International Journal of Human Resource management (Routledge) Operations and IT 29. Information Systems Research (Informs) 30. Journal of Operations Management (Elsevier) 31. MIS Quarterly (Management Information Systems Research Centre, University of Minnesota) 32. Management Science (Informs) 33. Operations Research (Informs) Practitioners 34. Academy of Management Perspectives (AMP) 35. California Management Review (UC Berkeley) 36. Harvard Business Review (Harvard Business School Publishing) 37. Sloan Management Review (MIT) Other 38. Administrative Science Quarterly (Cornell University) 39. Journal of the American Statistical Association (American Statistical Association) 40. The Journal of Business Ethics (Kluwer Academic)
http://www.huffingtonpost.com/2013/02/12/top-one-percent-income-gains_n_2670455.html In the aftermath of the worst recession in decades, the richest Americans have been getting richer -- a lot richer -- while most Americans have gone the other direction. Add it all together, and the top 1 percent of households by income captured 121 percent of all income gains between 2009 and 2011, during the first two years of the economic recovery, according to new research by Emmanuel Saez, an economics professor at the University of California at Berkeley. (Saez is a renowned income inequality expert and winner of the prestigious John Bates Clark Medal, an award that the American Economic Association gives every year to the top economist under age 40.) How was the top 1 percent able to capture more than all of the recovery's income gains? They became 11.2 percent richer while the bottom 99 percent got 0.4 percent poorer, when accounting for inflation, according to Saez. Saez released the updated figures in late January after finding last year that the top 1 percent had captured 93 percent of all income gains in 2010, the first full year of the economic recovery. Overall, between 1993 and 2011, the top 1 percent's incomes surged 57.5 percent, while the incomes of the bottom 99 percent grew just 5.8 percent, according to Saez. (Hat tip: The New Republic's Timothy Noah .)
Computer Animation " There is no particular mystery in animation...it's really very simple, and like anything that is simple, it is about the hardest thing in the world to do. "Bill Tytla at the Walt Disney Studio, June 28, 1937. Introduction Types of Animation Control Systems Low level Motion Control Camera Animation Character Animation Particle Systems Artificial Life WWW Animation Book by Rick Parent, Ohio State University WWW Animation Book by Rick Parent, Ohio State University - local version Post - Production in Computer Animation HyperGraph Table of Contents. HyperGraph Home page. Last changed February 08, 2000, G. Scott Owen, owen@siggraph.org
Even Slaves in 1774 Colonial America Experienced Less Economic Inequality than We Have Today Submitted by George Washington on 10/16/2012 10:46 -0500 University of California We’ve repeatedly noted that inequality in America today is worse than it was in the Gilded Age , worse than in Egypt, Tunisia or Yemen , and worse than in many banana republics in Latin America . We’ve also noted that inequality in modern America is twice as bad as in ancient Rome ... which was built on slave labor. A newly-updated study by professors Peter Lindert of the University of California at Davis and Jeffrey Williamson of Harvard shows that inequality is now worse than in the slave-holding colonies. As the Atlantic notes : American income inequality may be more severe today than it was way back in 1774 — even if you factor in slavery. This is not a partisan issue: both conservatives and liberals are concerned about runaway inequality and the collapse of social mobility . Indeed, we’ve known for thousands of years that: An imbalance between rich and poor is the oldest and most fatal ailment of all republics. Obviously, we do not condone slavery. As the Atlantic notes in a footnote: Please don’t interpret anything in this article as suggesting that American slavery was anything less than horrific. The paper only suggests that on a strictly dollars and cents basis, income was skewed less towards the rich during the colonial era than it is today. Average:
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