Parsimonious Kernel Fisher Discrimination

By applying recent results in optimization transfer, a new algorithm for kernel Fisher Discriminant Analysis is provided that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The algorithm is simple, easily programmed and is shown to perform as well as or better than a number of leading machine learning algorithms on a substantial benchmark. It is then applied to a set of extreme small-sample-size problems in virtual screening where it is found to be less accurate than a currently leading approach but is still comparable in a number of cases

Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength

International audienceWhole-History Rating (WHR) is a new method to estimate the time-varying strengths of players involved in paired comparisons. Like many variations of the Elo rating system, the whole-history approach is based on the dynamic Bradley-Terry model. But, instead of using incremental approximations, WHR directly computes the exact maximum a posteriori over the whole rating history of all players. This additional accuracy comes at a higher computational cost than traditional methods, but computation is still fast enough to be easily applied in real time to large-scale game servers (a new game is added in less than 0.001 second). Experiments demonstrate that, in comparison to Elo, Glicko, TrueSkill, and decayed-history algorithms, WHR produces better predictions

Differentially Private Exponential Random Graphs

We propose methods to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network. Proposed techniques aim at fitting and estimating a wide class of exponential random graph models (ERGMs) in a differentially private manner, and thus offer rigorous privacy guarantees. More specifically, we use the randomized response mechanism to release networks under $\epsilon$-edge differential privacy. To maintain utility for statistical inference, treating the original graph as missing, we propose a way to use likelihood based inference and Markov chain Monte Carlo (MCMC) techniques to fit ERGMs to the produced synthetic networks. We demonstrate the usefulness of the proposed techniques on a real data example.Comment: minor edit

Ab initio many-body calculations on infinite carbon and boron-nitrogen chains

In this paper we report first-principles calculations on the ground-state electronic structure of two infinite one-dimensional systems: (a) a chain of carbon atoms and (b) a chain of alternating boron and nitrogen atoms. Meanfield results were obtained using the restricted Hartree-Fock approach, while the many-body effects were taken into account by second-order M{\o}ller-Plesset perturbation theory and the coupled-cluster approach. The calculations were performed using 6-31$G^{**}$ basis sets, including the d-type polarization functions. Both at the Hartree-Fock (HF) and the correlated levels we find that the infinite carbon chain exhibits bond alternation with alternating single and triple bonds, while the boron-nitrogen chain exhibits equidistant bonds. In addition, we also performed density-functional-theory-based local density approximation (LDA) calculations on the infinite carbon chain using the same basis set. Our LDA results, in contradiction to our HF and correlated results, predict a very small bond alternation. Based upon our LDA results for the carbon chain, which are in agreement with an earlier LDA calculation calculation [ E.J. Bylaska, J.H. Weare, and R. Kawai, Phys. Rev. B 58, R7488 (1998).], we conclude that the LDA significantly underestimates Peierls distortion. This emphasizes that the inclusion of many-particle effects is very important for the correct description of Peierls distortion in one-dimensional systems.Comment: 3 figures (included). To appear in Phys. Rev.

The Multidimensional Study of Viral Campaigns as Branching Processes

Viral campaigns on the Internet may follow variety of models, depending on the content, incentives, personal attitudes of sender and recipient to the content and other factors. Due to the fact that the knowledge of the campaign specifics is essential for the campaign managers, researchers are constantly evaluating models and real-world data. The goal of this article is to present the new knowledge obtained from studying two viral campaigns that took place in a virtual world which followed the branching process. The results show that it is possible to reduce the time needed to estimate the model parameters of the campaign and, moreover, some important aspects of time-generations relationship are presented.Comment: In proceedings of the 4th International Conference on Social Informatics, SocInfo 201

High-precision determination of the critical exponents for the lambda-transition of 4He by improved high-temperature expansion

We determine the critical exponents for the XY universality class in three dimensions, which is expected to describe the $\lambda$-transition in ${}^4$He. They are obtained from the analysis of high-temperature series computed for a two-component $\lambda\phi^4$ model. The parameter $\lambda$ is fixed such that the leading corrections to scaling vanish. We obtain $\nu = 0.67166(55)$, $\gamma = 1.3179(11)$, $\alpha=-0.0150(17)$. These estimates improve previous theoretical determinations and agree with the more precise experimental results for liquid Helium.Comment: 8 pages, revte

Receptor tyrosine kinase activation of RhoA is mediated by AKT phosphorylation of DLC1

We report several receptor tyrosine kinase (RTK) ligands increase RhoA-guanosine triphosphate (GTP) in untransformed and transformed cell lines and determine this phenomenon depends on the RTKs activating the AKT serine/threonine kinase. The increased RhoA-GTP results from AKT phosphorylating three serines (S298, S329, and S567) in the DLC1 tumor suppressor, a Rho GTPase-activating protein (RhoGAP) associated with focal adhesions. Phosphorylation of the serines, located N-terminal to the DLC1 RhoGAP domain, induces strong binding of that N-terminal region to the RhoGAP domain, converting DLC1 from an open, active dimer to a closed, inactive monomer. That binding, which interferes with the interaction of RhoA-GTP with the RhoGAP domain, reduces the hydrolysis of RhoA-GTP, the binding of other DLC1 ligands, and the colocalization of DLC1 with focal adhesions and attenuates tumor suppressor activity. DLC1 is a critical AKT target in DLC1-positive cancer because AKT inhibition has potent antitumor activity in the DLC1-positive transgenic cancer model and in a DLC1-positive cancer cell line but not in an isogenic DLC1-negative cell line

Pervasive sensing to model political opinions in face-to-face networks

Exposure and adoption of opinions in social networks are important questions in education, business, and government. We de- scribe a novel application of pervasive computing based on using mobile phone sensors to measure and model the face-to-face interactions and subsequent opinion changes amongst undergraduates, during the 2008 US presidential election campaign. We nd that self-reported political discussants have characteristic interaction patterns and can be predicted from sensor data. Mobile features can be used to estimate unique individ- ual exposure to di erent opinions, and help discover surprising patterns of dynamic homophily related to external political events, such as elec- tion debates and election day. To our knowledge, this is the rst time such dynamic homophily e ects have been measured. Automatically esti- mated exposure explains individual opinions on election day. Finally, we report statistically signi cant di erences in the daily activities of individ- uals that change political opinions versus those that do not, by modeling and discovering dominant activities using topic models. We nd people who decrease their interest in politics are routinely exposed (face-to-face) to friends with little or no interest in politics.U.S. Army Research Laboratory (Cooperative Agreement No. W911NF-09-2-0053)United States. Air Force Office of Scientific Research (Award No. FA9550-10-1-0122)Swiss National Science Foundatio