Multi-View Learning and Link Farm Discovery

The first part of this abstract focuses on estimation of mixture models for problems in which multiple views of the instances are available. Examples of this setting include clustering web pages or research papers that have intrinsic (text) and extrinsic (references) attributes. Mixture model estimation is a key problem for both semi-supervised and unsupervised learning. An appropriate optimization criterion quantifies the likelihood and the consensus among models in the individual views; maximizing this consensus minimizes a bound on the risk of assigning an instance to an incorrect mixture component. An EM algorithm maximizes this criterion. The second part of this abstract focuses on the problem of identifying link spam. Search engine optimizers inflate the page rank of a target site by spinning an artificial web for the sole purpose of providing inbound links to the target. Discriminating natural from artificial web sites is a difficult multi-view problem

Primary Facets Of Order Polytopes

Mixture models on order relations play a central role in recent investigations of transitivity in binary choice data. In such a model, the vectors of choice probabilities are the convex combinations of the characteristic vectors of all order relations of a chosen type. The five prominent types of order relations are linear orders, weak orders, semiorders, interval orders and partial orders. For each of them, the problem of finding a complete, workable characterization of the vectors of probabilities is crucial---but it is reputably inaccessible. Under a geometric reformulation, the problem asks for a linear description of a convex polytope whose vertices are known. As for any convex polytope, a shortest linear description comprises one linear inequality per facet. Getting all of the facet-defining inequalities of any of the five order polytopes seems presently out of reach. Here we search for the facet-defining inequalities which we call primary because their coefficients take only the values -1, 0 or 1. We provide a classification of all primary, facet-defining inequalities of three of the five order polytopes. Moreover, we elaborate on the intricacy of the primary facet-defining inequalities of the linear order and the weak order polytopes

Manipulating the Capacity of Recommendation Models in Recall-Coverage Optimization

Traditional approaches in Recommender Systems ignore the problem of long-tail recommendations. There is no systematic approach to control the magnitude of long-tail recommendations generated by the models, and there is not even proper methodology to evaluate the quality of long-tail recommendations. This thesis addresses the long-tail recommendation problem from both the algorithmic and evaluation perspective. We proposed controlling the magnitude of long-tail recommendations generated by models through the manipulation with capacity hyperparameters of learning algorithms, and we dene such hyperparameters for multiple state-of-the-art algorithms. We also summarize multiple such algorithms under the common framework of the score function, which allows us to apply popularity-based regularization to all of them. We propose searching for Pareto-optimal states in the Recall-Coverage plane as the right way to search for long-tail, high-accuracy models. On the set of exhaustive experiments, we empirically demonstrate the corectness of our theory on a mixture of public and industrial datasets for 5 dierent algorithms and their dierent versions.Traditional approaches in Recommender Systems ignore the problem of long-tail recommendations. There is no systematic approach to control the magnitude of long-tail recommendations generated by the models, and there is not even proper methodology to evaluate the quality of long-tail recommendations. This thesis addresses the long-tail recommendation problem from both the algorithmic and evaluation perspective. We proposed controlling the magnitude of long-tail recommendations generated by models through the manipulation with capacity hyperparameters of learning algorithms, and we dene such hyperparameters for multiple state-of-the-art algorithms. We also summarize multiple such algorithms under the common framework of the score function, which allows us to apply popularity-based regularization to all of them. We propose searching for Pareto-optimal states in the Recall-Coverage plane as the right way to search for long-tail, high-accuracy models. On the set of exhaustive experiments, we empirically demonstrate the corectness of our theory on a mixture of public and industrial datasets for 5 dierent algorithms and their dierent versions

SO(10) SUSY GUTs with mainly axion cold dark matter: implications for cosmology and colliders

Supersymmetric grand unified theories based on the gauge group SO(10) are highly motivated. In the simplest models, one expects t-b-\tau Yukawa coupling unification, in addition to gauge, matter and Higgs unification. Yukawa unification only occurs with very special GUT scale boundary conditions, leading to a spectra with ~10 TeV first and second generation scalars, TeV-scale third generation scalars, and light gauginos. The relic density of neutralino cold dark matter is calculated to be 10^2-10^4 times higher than observation. If we extend the theory with the PQWW solution to the strong CP problem, then instead a mixture of axions and axinos comprises the dark matter, with the measured abundance. Such a solution solves several cosmological problems. We predict a rather light gluino with m(gluino)~300-500 GeV that should be visible in either Tevatron or forthcoming LHC run 1 data. We would also expect ultimately a positive result from relic axion search experiments.Comment: 6 pages plus 2 .eps figures; invited talk given at Axions 2010 meeting, University of Florida, Jan. 15-17, 201

Numerical Study and Geometric Investigation of the Influence of Rectangular Baffles over the Mixture of Turbulent Flows into Stirred Tanks

The present work aims to define strategies for numerical simulation of the mixture of turbulent flows in a stirred tank with a low computational effort, and to investigate the influence of the geometry of four rectangular baffles on the problem of performance. Two computational models based on momentum source and sliding mesh are validated by comparison with experimental results from the literature. For both models, the time‐averaged conservation equations of mass, momentum and transport of the mixture are solved using the finite volume method (FVM) (FLUENT® v.14.5). The standard k–ε model is used for closure of turbulence. Concerning the geometrical investigation, constructal design is employed to define the search space, degrees of freedom and performance indicators of the problem. More precisely, seven configurations with different width/length (L/B) ratios for the rectangular baffles are studied and compared with an unbaffled case. The momentum source model leads to valid results and significantly reduces the computational effort in comparison with the sliding mesh model. Concerning the design, the results indicate that the case without baffles creates the highest magnitude of turbulence kinetic energy, but poorly distributes it along the domain. The best configuration, (L/B)o = 1.0, leads to a mixture performance nearly two times superior than the case without baffles