, we show evidence that WWW traffic exhibits behavior that is consistent with self-similar traffic models. Then we show that the self-similarity insuch traffic can be explained based on the underlying distributions of WWW document sizes, the effects of caching and user preference in le transfer

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We conduct a field experiment in a large real-world social network to examine how subjects expect to be treated by their friends and by strangers who make allocation decisions in modified dictator games. We show that an allocator’s choice can be well predicted by (a) how much she passes to a random stranger (baseline altruism) and (b) the social distance between allocator and recipient, defined as the length of the shortest path connecting them within the social network (directed altruism). We find that recipients only take social distance into account when forming beliefs. Even direct friends are no better in predicting an allocator’s choice than the econometrician who knows the allocator’s demographic characteristics and social distance.

We define and measure social capital within a large social network where agents take actions which have externalities on other agents. Our concept of social capital measures the extent to which agents are able to internalize these externalities. We distinguish between preference-based social capital (directed altruism) and cooperative social capital based on repeated interac-tion between pairs or groups of agents. We find that preference-based social capital increases an agent’s weight on a friend’s utility by about 15 percent and cooperative social capital adds another 5 percent. 1

language models on different word factors and a discriminative word lexicon. This system is used to gen-erate a k-best list. In a second step, the list is reranked using SOUL language and translation models (Le et al., 2011). Originally, SOUL translation models were applied to n-gram-based translation sys

Sciences and Humanities Research Council of Canada and grants from le Fonds pour la Formation des Chercheurs et l'Aide a la Recherche and the University du Quebec a Montreal. We would like to thank Ed Deci, Andrew Elliott, Marc Blais, Stephane Perreault, and Luc Pelletier for their feedback

Everyone learns at a different rate, some people find they need a bit more time to absorb new ideas, whereas others like to steam ahead. If you find yourself struggling, just do the parts of the exercises marked e. If you’re speeding ahead, try doing the additional bits, marked!.

Using the network of university classmates among corporate directors and politicians and the regression discontinuity design of close gubernatorial elections from 1999 to 2010, we identify the positive and significant impact of social-network based political connections on firm value. Firms connected to elected governors increase value by 1.36 % on average surrounding the election date. Political connections are more valuable in a state with a higher level of regulation and corruption, in smaller firms, and in firms dependent on external finance. Firms connected to election winners invest more, earn better operating

and improving forecast performance. More generally, forecast evaluation figures prominently in many questions in empirical economics and finance, such as: Are expectations rational? (e.g., Keane and Runkle, 1990; Bonham and Cohen, 1995) Are financial markets efficient? (e.g., Fama, 1970, 1991) Do macroeconomic

Previous work on action recognition has focused on adaptinghand-designedlocalfeatures, suchasSIFTor HOG,fromstaticimagestothevideodomain. Inthispaper,weproposeusingunsupervisedfeaturelearningasa waytolearnfeaturesdirectlyfromvideodata.Morespecifically,wepresentanextensionoftheIndependentSubspace Analysisalgorithmtolearninvariantspatio-temporalfeaturesfromunlabeledvideodata.Wediscoveredthat,despite itssimplicity,thismethodperformssurprisinglywellwhen combined withdeeplearningtechniques suchasstackingandconvolutiontolearnhierarchicalrepresentations. Byreplacinghand-designedfeatureswithourlearnedfeatures,weachieveclassificationresultssuperiortoallpreviouspublishedresultsontheHollywood2,UCF,KTHand