请原谅我哪些钻牛角尖的题目。。T,T 感激。。

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转载于economics。。。偶没有记住地址 随便点的
The science of civil war          What makes heroic strife      Computer models that can predict the outbreak and spread of civil conflict are being developedFOR the past decade or so, generals commanding the world’s most advanced armies have been able to rely on accurate forecasts of the outcomes of conventional battles. Given data on weather and terrain, and the combatants’ numbers, weaponry, positions, training and level of morale, computer programs such as the Tactical Numerical Deterministic Model, designed by the Dupuy Institute in Washington, DC, can predict who will win, how quickly and with how many casualties.
Guerrilla warfare, however, is harder to model than open battle of this sort, and the civil insurrection that often precedes it is harder still. Which, from the generals’ point of view, is a pity, because such conflict is the dominant form of strife these days. The reason for the difficulty is that the fuel of popular uprisings is not hardware, but social factors of a type that computer programmers find it difficult to capture in their algorithms. Analysing the emotional temperature of postings on Facebook and Twitter, or the telephone traffic between groups of villages, is always going to be a harder task than analysing physics-based data like a tank’s firing range or an army’s stocks of ammunition and fuel.


Harder, but not impossible. For in the war-games rooms and think-tanks of the rich world’s military powers, bright minds are working on the problem of how to model insurrection and irregular warfare. Slowly but surely they are succeeding, and in the process they are helping politicians and armies to a better understanding of the nature of rebellion.
SCARE tactics
One of the best-known projects in this field is SCARE, the Spatio-Cultural Abductive Reasoning Engine, developed at the United States Military Academy at West Point by a team led by Major Paulo Shakarian, a computer-scientist-turned-soldier. SCARE operates at the most militarily conventional end of the irregular-conflict spectrum: the point where an army of guerrillas is already in being and is making life hard for a notionally better-armed army of regular troops. That, of course, has been the experience of American forces in Vietnam, Iraq and Afghanistan. Major Shakarian and his team have analysed the behaviour of guerrillas in both Iraq and Afghanistan, and think they understand it well enough to build reliable models.


Their crucial insight is the local nature of conflict in these countries. In particular, bombs directed at occupying forces are generally planted close to the place where they were made, and on the territory of the bombmaker’s tribal kin or co-religionists. That is not a surprise, of course. Kin and co-religionists are the most reliable allies in wars where different guerrilla groups may not always see eye to eye about objectives, beyond the immediate one of driving out foreign troops. But it does give Major Shakarian and his team a convenient way in. Using the co-ordinates of previously bombed sites, data from topographical and street maps, and information on an area’s ethnic, linguistic and confessional “human terrain”, SCARE is able to predict where guerrillas’ munition dumps will be to within about 700 metres. That is not perfect, but it is close enough to be able to focus a search in a useful way.
Moreover, SCARE’s focus should soon become more precise. Major Shakarian’s latest trick is to include data on phone-traffic patterns in the calculations. An upgraded version of the program, employing this trick, will be created next month.
All of which is useful for dealing with a conflict once it has started. But it is better, if possible, to see what may happen before things get going. And for that, America’s navy has a project called RiftLand.
RiftLand is being developed on the navy’s behalf by Claudio Cioffi-Revilla, a professor of computational social science at George Mason University in Virginia. It is specific to the part of East Africa around the Great Rift Valley (hence the name). That this area includes Congo, Ethiopia, Rwanda, Somalia and Uganda, each of which has been the scene of present or recent civil strife, is no coincidence. But the ideas involved could be generalised to other parts of the world, with due alteration for local conditions.


Broadly, RiftLand works by chewing its way through a range of data collected by charities, academics and government agencies, and uses these to predict where groups of people will go and with whom they may clash in times of drought or armed conflict. Dr Cioffi-Revilla gives the example (though he will not name names specifically) of a tribe of nomadic herders known for sharing its notions of veterinary medicine with others. This tribe, the model predicts, will reckon it safer to cross the lands of groups who also rely on keeping their animals healthy. Another point is that tribes who own a radio or mobile phone will steer clear of roads after news reports of government atrocities against their kin. A third is that much of the movement of herdsmen can be predicted from satellite data on the condition of pasture lands, modified by knowledge of what Dr Cioffi-Revilla calls “the complex network of IOUs” between tribes: which are currently hostile to one another, and who owes whom favours.


Hostile sentiments
The sort of conflict dealt with by RiftLand—a war of all against all in countries where central government is light or non-existent—has been particularly characteristic of this part of Africa in recent years. Further north, where states are stronger, urban insurrection of the sort seen at the beginning of the Arab spring is a more common threat. Politicians faced with such uprisings may thus be interested in yet another piece of software, known as Condor, which has been developed by Peter Gloor of the Massachusetts Institute of Technology. Dr Gloor is certainly not in the business of saving the jobs of Middle-Eastern dictators. He is actually a consultant to the Christian Democratic Union, Germany’s largest political party. But all politicians in power, whether democrats or dictators, share a distaste for demonstrations and protests on the streets.
Condor works by sifting through data from Twitter, Facebook and other social media, and using them to predict how a public protest will evolve. It does so by performing what Dr Gloor calls “sentiment analysis” on the data.
Sentiment analysis first classifies protesters by their clout. An influential Twitter user, for instance, is one who has many followers but follows few people himself. His tweets are typically upbeat (containing words or phrases such as “great”, “fun”, “funny”, “good time”, “hilarious movie”, “you’ll love” and so forth), are rapidly retweeted, and appear to sway others. In a nod to the methods developed by Google, Dr Gloor refers to this process as “PageRanking for people”.
Having thus ranked protesters, Condor then follows those at the top of the list to see how their output changes. Dr Gloor has found that, in Western countries at least, non-violent protest movements begin to burn out when the upbeat tweets turn negative, with “not”, “never”, “lame”, “I hate”, “idiot” and so on becoming more frequent. Abundant complaints about idiots in the government or in an ideologically opposed group are a good signal of a movement’s decline. Complaints about idiots in one’s own movement or such infelicities as the theft of beer by a fellow demonstrator suggest the whole thing is almost over.
Condor, then, is good at forecasting the course of existing protests. Even better, from the politicians’ point of view, would be to predict such protests before they occur. Not surprisingly, several groups of researchers are trying to do this too.
 
             
Aptima, a firm based in Woburn, Massachusetts, is one. Its program, called E-MEME (Epidemiological Modelling of the Evolution of MEssages) uses sentiment analysis to see how opinions and states of mind flow across entire populations, not just activists. It employs data from online news sources, blogs and Twitter, and attempts to rank the “susceptibility” of certain parts of the populace to specific ideas. According to Robert McCormack, the project's chief technologist, E-MEME can determine things as different as which places in Egypt contain people who will care a lot about a border incident with Israel, and which parts of a country most need water in times of drought.
The Worldwide Integrated Crisis Early Warning System (W-ICEWS) project, led by Lockheed Martin, a large American defence contractor, goes even further. According to Lieutenant-Colonel Melinda Morgan of the office of the secretary of defence, in Washington, who is the government’s liaison officer for the project, it can crunch great quantities of data from digital news media, blogs and other websites, and also intelligence and diplomatic reports. It then uses all this to forecast—months in advance—riots, rebellions, coups, economic crises, government crackdowns and international wars. Colonel Morgan calls this process “social radar”.
Conflict forecasters are even joining the open-source bandwagon, in an attempt to improve their software. Last August IARPA, an American-government technology-development agency for the intelligence services, started the Open Source Indicators programme. This finances developers of software that can “beat the news”: forecasting political crises and mass violence in a reliable way. The programme’s manager, Jason Matheny, is now considering the proposals that have come in so far. These range from tracking Wikipedia edits to monitoring traffic with roadside cameras. The only proposals Mr Matheny will not consider are those designed to forecast conflict in America itself (the CIA is not supposed to spy on people in the United States), and those that rely on monitoring particular individuals, whether in America or elsewhere.
Guerrillas in the midst
Rather than just foretelling the future, however, the best technology should concentrate on shaping it. W-ICEWS offers a bit of that. It has a “what if” capability, which allows users to change the inputs and see how things might develop differently given different events in the real world. But Venkatramana Subrahmanian of the University of Maryland proposes something more specific. The Temporal-Probabilistic Rule System, a program his team has developed using $600,000 of American-army money, looks at 770 social and political indicators and uses them to predict attacks by Lashkar-e-Taiba, a guerrilla group based in Pakistan-administered Kashmir. If it works, this process might be applied, using a different set of indicators, to other groups of rebels.
The crucial point about Dr Subrahmanian’s model is that it not only predicts attacks, it also suggests how they might be countered. Dr Subrahmanian is understandably cagey about the details, but he does give one example: if an attack requires complex co-ordination between group members, the software might recommend “stoking paranoia” by forging false communications between them.
On April 2nd President Barack Obama announced a $10m bounty on Lashkar-e-Taiba’s leader, Hafiz Saeed. It would indeed mark the coming of age of civil-strife software if that bounty, or another like it, were one day claimed on behalf of a group of programmers half a world away.

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Manufacturing          The third industrial revolution      The digitisation of manufacturing will transform the way goods are made—and change the politics of jobs too

THE first industrial revolution began in Britain in the late 18th century, with the mechanisation of the textile industry. Tasks previously done laboriously by hand in hundreds of weavers’ cottages were brought together in a single cotton mill, and the factory was born. The second industrial revolution came in the early 20th century, when Henry Ford mastered the moving assembly line and ushered in the age of mass production. The first two industrial revolutions made people richer and more urban. Now a third revolution is under way. Manufacturing is going digital. As this week’s special report argues, this could change not just business, but much else besides.
A number of remarkable technologies are converging: clever software, novel materials, more dexterous robots, new processes (notably three-dimensional printing) and a whole range of web-based services. The factory of the past was based on cranking out zillions of identical products: Ford famously said that car-buyers could have any colour they liked, as long as it was black. But the cost of producing much smaller batches of a wider variety, with each product tailored precisely to each customer’s whims, is falling. The factory of the future will focus on mass customisation—and may look more like those weavers’ cottages than Ford’s assembly line.
Towards a third dimension
The old way of making things involved taking lots of parts and screwing or welding them together. Now a product can be designed on a computer and “printed” on a 3D printer, which creates a solid object by building up successive layers of material. The digital design can be tweaked with a few mouseclicks. The 3D printer can run unattended, and can make many things which are too complex for a traditional factory to handle. In time, these amazing machines may be able to make almost anything, anywhere—from your garage to an African village.
The applications of 3D printing are especially mind-boggling. Already, hearing aids and high-tech parts of military jets are being printed in customised shapes. The geography of supply chains will change. An engineer working in the middle of a desert who finds he lacks a certain tool no longer has to have it delivered from the nearest city. He can simply download the design and print it. The days when projects ground to a halt for want of a piece of kit, or when customers complained that they could no longer find spare parts for things they had bought, will one day seem quaint.
Other changes are nearly as momentous. New materials are lighter, stronger and more durable than the old ones. Carbon fibre is replacing steel and aluminium in products ranging from aeroplanes to mountain bikes. New techniques let engineers shape objects at a tiny scale. Nanotechnology is giving products enhanced features, such as bandages that help heal cuts, engines that run more efficiently and crockery that cleans more easily. Genetically engineered viruses are being developed to make items such as batteries. And with the internet allowing ever more designers to collaborate on new products, the barriers to entry are falling. Ford needed heaps of capital to build his colossal River Rouge factory; his modern equivalent can start with little besides a laptop and a hunger to invent.
Like all revolutions, this one will be disruptive. Digital technology has already rocked the media and retailing industries, just as cotton mills crushed hand looms and the Model T put farriers out of work. Many people will look at the factories of the future and shudder. They will not be full of grimy machines manned by men in oily overalls. Many will be squeaky clean—and almost deserted. Some carmakers already produce twice as many vehicles per employee as they did only a decade or so ago. Most jobs will not be on the factory floor but in the offices nearby, which will be full of designers, engineers, IT specialists, logistics experts, marketing staff and other professionals. The manufacturing jobs of the future will require more skills. Many dull, repetitive tasks will become obsolete: you no longer need riveters when a product has no rivets.
The revolution will affect not only how things are made, but where. Factories used to move to low-wage countries to curb labour costs. But labour costs are growing less and less important: a $499 first-generation iPad included only about $33 of manufacturing labour, of which the final assembly in China accounted for just $8. Offshore production is increasingly moving back to rich countries not because Chinese wages are rising, but because companies now want to be closer to their customers so that they can respond more quickly to changes in demand. And some products are so sophisticated that it helps to have the people who design them and the people who make them in the same place. The Boston Consulting Group reckons that in areas such as transport, computers, fabricated metals and machinery, 10-30% of the goods that America now imports from China could be made at home by 2020, boosting American output by $20 billion-55 billion a year.
The shock of the new
Consumers will have little difficulty adapting to the new age of better products, swiftly delivered. Governments, however, may find it harder. Their instinct is to protect industries and companies that already exist, not the upstarts that would destroy them. They shower old factories with subsidies and bully bosses who want to move production abroad. They spend billions backing the new technologies which they, in their wisdom, think will prevail. And they cling to a romantic belief that manufacturing is superior to services, let alone finance.
None of this makes sense. The lines between manufacturing and services are blurring. Rolls-Royce no longer sells jet engines; it sells the hours that each engine is actually thrusting an aeroplane through the sky. Governments have always been lousy at picking winners, and they are likely to become more so, as legions of entrepreneurs and tinkerers swap designs online, turn them into products at home and market them globally from a garage. As the revolution rages, governments should stick to the basics: better schools for a skilled workforce, clear rules and a level playing field for enterprises of all kinds. Leave the rest to the revolutionaries.

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yiayia

孩子 悠着点。。 1周10篇很容易看完的。。

199249712

yiayia哥我现在处于暴力阶段 嘻嘻。。。悠着悠着就3战了 你说10篇啥。。？

199249712

转载于NYT。。。 也是首页随便点的。。。
THE complimentary wine and fruit platter was sent up to Jessica Griffin and her family moments after they strolled into their roomy suite. They were accompanied by a bellhop who placed their bags near a tidy crib made up with luxurious, high thread-count sheets for Ms. Griffin’s 1-year-old daughter.                Enlarge This Image

Fred R. Conrad/The New York TimesAgent Emily Meredith Prentiss secures exclusive rates and special treatment for her clients. Still, the travel tip she offers is simple: plan ahead for better rates.                            

 
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The V.I.P. treatment at the Cheeca Lodge and Spa in the Florida Keys last month hadn’t come with an extra cost. In fact, Ms. Griffin said, she paid about $100 a night less than the standard rate for her room. And the deal wasn’t the result of hours of tedious online research either. She had finagled her savings the old-fashioned way: through a travel agent.        
“I needed recommendations and someone to steer me in the right direction,” said Ms. Griffin, who opted to work with an agent after years of making her own reservations because she needed a getaway suitable for a toddler and had little interest in scrolling through endless and conflicting user hotel reviews online. “There are so many,” she said. And with every site displaying beautiful pictures and tantalizing offers, “it can be overwhelming.”        
“I wanted somebody from a reputable agency who could say yes, you’ll enjoy this stay,” she said.        
According to those in the travel agent industry, clients like Ms. Griffin are not alone, and are in fact helping to stanch the bloodletting the industry has experienced since the onset of D.I.Y. booking more than a decade ago. Nearly one in three leisure agencies is hiring, according to PhoCusWright, a travel research firm. And in 2011 travel agencies experienced a second consecutive year of growth; their bookings account for a third of the $284 billion United States travel market.        
This comes after years during which all signs seemed to be suggesting that travel agents would soon go the way of telex operators. And it’s true that the numbers are stark: During the industry’s peak years of the mid-1990s, there were about 34,000 retail locations booking trips. Today, there are 14,000 to 15,000, according to PhoCusWright. In 2009 alone, in the throes of the recession, bookings through traditional agencies plummeted by 23 percent.        
But now, some green shoots. An improving economy and the corporate travel that goes with it seem to be converging with a population for whom booking travel online has become increasingly onerous and time-consuming. Just how time-consuming? Steve Peterson, the global travel and transportation leader for the I.B.M. Institute for Business Value, set out to answer that very question. In a survey of more than 2,000 travelers worldwide, 20 percent said it took them more than five hours to search and book travel online. Nearly half said it required more than two hours.        
No one expects agency business to rebound to pre-Internet levels, but recent signs — like the fact that leisure travelers accounted for a 10 percent bump in sales in 2010 (a bit less in 2011) — suggest that agents can still play a relevant role. And though no one has been keeping track of the reasons travelers are turning to actual human beings, Mr. Peterson suspected it might have something to do with the drawbacks of the Web. “It’s come to a point that it’s too much information to be confident that they have the ability to book the lowest fare,” or uncover the best place to stay, he said of the respondents. “Consumers are hungry for that one-and-done shopping experience.”        
As it turns out, after years of losing ground to online sites, a new breed of tech savvy, specialized and collaborative agent has emerged.

babybearmm

wow~ this thread seems to become your diary! nice~
keep going.

i think yiayia meant that a non-subscriber has limited access (10 articles per month)
but don't worry, even without subscription there are a bunch of valuable stuffs on the web (I can name a few great websites), and you can turn to me when you need an article (i am a subscriber to economist).

yiayia

yiayia哥我现在处于暴力阶段 嘻嘻。。。悠着悠着就3战了 你说10篇啥。。？

-- by 会员 199249712 (2012/4/22 22:52:51)

the economist 网站 1周只能看10篇

199249712

哇真的吗。。谢谢姐姐~！！！！ 太感谢了mua~!!!

199249712

昨天周天 早知道先下10篇再说了