In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: Recent works on word representations mostly rely on predictive models. Distributed word representations (aka word embeddings) are trained to optimally predict the contexts in which the corresponding words tend to appear. Such models have succeeded in capturing word similarities as well as semantic and syntactic regularities. Instead, we aim at reviving interest in a model based on counts. We present a systematic study of the use of the Hellinger distance to extract semantic representations from the word co-occurrence statistics of large text corpora. We show that this distance gives good performance on word similarity and analogy tasks, with a proper type and size of context, and a dimensionality reduction based on a stochastic low-rank approximation. Besides being both simple and intuitive, this method also provides an encoding function which can be used to infer unseen words or phrases. This becomes a clear advantage compared to predictive models which must train these new words.

A: Rehabilitation of Count-based Models for Word Vector Representations
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Q: In this paper we present methods for attacking and defending k-gram statistical analysis techniques that are used, for example, in network traffic analysis and covert channel detection. The main new result is our demonstration of how to use a behavior's or process' k-order statistics to build a stochastic process that has those same k-order stationary statistics but possesses different, deliberately designed, (k + 1)order statistics if desired. Such a model realizes a "complexification" of the process or behavior which a defender can use to monitor whether an attacker is shaping the behavior. By deliberately introducing designed (k + 1)-order behaviors, the defender can check to see if those behaviors are present in the data. We also develop constructs for source codes that respect the k-order statistics of a process while encoding covert information. One fundamental consequence of these results is that certain types of behavior analyses techniques come down to an arms race in the sense that the advantage goes to the party that has more computing resources applied to the problem. Points of view in this document are those of the authors and do not necessarily represent the official position of the sponsoring agencies or the U.S. Government. V. Crespi is with the Department of Computer Science, California State University at Los Angeles, Los Angeles CA, 90032 USA. email: vcrespi@calstatela.edu. Crespi's work was partially supported by AFOSR Grant FA9550-07-1-0421 and by NSF Grant HRD-0932421. G. Cybenko is with the Thayer School of Engineering, Dartmouth College, Hanover NH 03755. email: gvc@dartmouth.edu. Cybenko's work was partially supported by Air Force Research Laboratory contracts FA8750-10-1-0045, FA8750-09-1-0174, AFOSR contract FA9550-07-1-0421, U.S. Department of Homeland Security Grant 2006-CS-001-000001 and DARPA Contract HR001-06-1-0033 A. Giani is with the Department of EECS, University of California at Berkeley, Berkeley CA 94720. email: agiani@eecs.berkeley.edu. Giani's's work was partially supported by U.S. Department of Homeland Security Grant 2006-CS001-000001 and DARPA Contract HR001-06-1-0033 when she was a Ph.D. student at Dartmouth April 28, 2011 DRAFT ar X iv :1 10 4. 50 71 v1 [ cs .L G ] 2 7 A pr 2 01 1 CRESPI, CYBENKO, GIANI 2

A: Attacking and Defending Covert Channels and Behavioral Models
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Q: In search engines, online marketplaces and other humancomputer interfaces large collectives of individuals sequentially interact with numerous alternatives of varying quality. In these contexts, individual trial and error (exploration) is crucial for uncovering novel high-quality items or solutions, but entails a high cost for individual agents [Frazier et al. 2014]. Self-interested decision makers, we will show, are often better off imitating the choices of individuals who have already incurred the costs of exploration. Although imitation makes sense at the individual level, it deprives the group of additional information that could have been gleaned by individual explorers [Rogers 1988]. Under these grim circumstances, certain non-monetary mechanisms can keep imitation forces in check and allow the collective to reap some of the benefits of the independent collection of information. For example, in simultaneous exploration problems, a natural equilibrium evolves between explorers and imitators [Conlisk 1980; Kameda and Nakanishi 2002]. Further, in some collective exploration settings, barriers to communication such as a sparser communication network among individuals can prove beneficial at the collective level. They encourage people to explore more, thus supplying useful information to the group [Fang et al. 2010; Lazer and Friedman 2007; Mason et al. 2008; Toyokawa et al. 2014]. Diversity is known to be a blessing for groups and collectives, as they can leverage the wealth of information possessed by different individuals [Conradt et al. 2013; Davis-Stober et al. 2014; MullerTrede et al. 2017] or take advantage of the complementarities between group members to solve complex problems [Clearwater et al. 1991; Hong and Page 2004]. Could some preference diversity be beneficial in problems where collectives sequentially explore numerous alternatives, and thus despite reducing the immediate value of social learning lead to an increase in collective welfare?

A:
Diversity of preferences can increase collective welfare in sequential exploration problems
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