A Meta-Theory of Boundary Detection Benchmarks

Human labeled datasets, along with their corresponding evaluation algorithms, play an important role in boundary detection. We here present a psychophysical experiment that addresses the reliability of such benchmarks. To find better remedies to evaluate the performance of any boundary detection algorithm, we propose a computational framework to remove inappropriate human labels and estimate the intrinsic properties of boundaries.Comment: NIPS 2012 Workshop on Human Computation for Science and Computational Sustainabilit

Linear and Order Statistics Combiners for Pattern Classification

Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the "added" error. If N unbiased classifiers are combined by simple averaging, the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the ith order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.Comment: 31 page

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

A to Z of the Muon anomalous magnetic moment in the MSSM with Pati-Salam at the GUT scale

We analyse the low energy predictions of the minimal supersymmetric standard model (MSSM) arising from a GUT scale Pati-Salam gauge group further constrained by an A4 × Z5 family symmetry, resulting in four soft scalar masses at the GUT scale: one left-handed soft mass m0 and three right-handed soft masses m1, m2, m3, one for each generation. We demonstrate that this model, which was initially developed to describe the neutrino sector, can explain collider and non-collider measurements such as the dark matter relic density, the Higgs boson mass and, in particular, the anomalous magnetic moment of the muon (g − 2)μ. Since about two decades, (g − 2)μ suffers a puzzling about 3σ excessoftheexperimentallymeasuredvalueoverthetheoreticalprediction,whichour model is able to fully resolve. As the consequence of this resolution, our model predicts specific regions of the parameter space with the specific properties including light smuons and neutralinos, which could also potentially explain di-lepton excesses observed by CMS and ATLAS

The No-Scale Multiverse at the LHC

We present a contemporary perspective on the String Landscape and the Multiverse of plausible string, M- and F-theory vacua, seeking to demonstrate a non-zero probability for the existence of a universe matching our own observed physics within the solution ensemble, arguing for the importance of No-Scale Supergravity as an essential common underpinning. Our context is a highly detailed phenomenological probe of No-Scale F-SU(5), a model representing the intersection of the F-lipped SU(5) X U(1)_X Grand Unified Theory (GUT) with extra TeV-Scale vector-like multiplets derived out of F-theory, and the dynamics of No-Scale Supergravity. We present a highly constrained "Golden" region with tan(beta) \sim 15, m_t = 173.0 - 174.4 GeV, M_1/2 = 455 - 481 GeV, and M_V = 691 - 1020 GeV, which simultaneously satisfies all known experimental constraints. We supplement this bottom-up phenomenological perspective with a top-down theoretical analysis of the one-loop effective Higgs potential, achieving a striking consonance via the dynamic determination of tan(beta) and M_1/2 at the local secondary minimization of the spontaneously broken electroweak Higgs vacuum V_min. We present the distinctive signatures of No-Scale F-SU(5) at the LHC, where a light stop and gluino are expected to generate a surplus of ultra-high multiplicity (>= 9) hadronic jet events. We propose modest alterations to the canonical background selection cut strategy which would enhance resolution of these events, while readily suppressing the contribution of all Standard Model processes, and allowing a clear differentiation from competing models of new physics. Detection by the LHC of the ultra-high jet signal would constitute a suggestive evocation of the intimately linked stringy origins of F-SU(5), and could provide a glimpse into the fundamental string moduli, and possibly even the workings of the No-Scale Multiverse.Comment: A review of recent work, submitted to the DICE 2010 Workshop proceedings, based on the invited talk by D.V.N. (20 Pages, 5 Tables, 18 Figures

Cosmological Phase Transitions and their Properties in the NMSSM

We study cosmological phase transitions in the Next-to-Minimal Supersymmetric Standard Model (NMSSM) in light of the Higgs discovery. We use an effective field theory approach to calculate the finite temperature effective potential, focusing on regions with significant tree-level contributions to the Higgs mass, a viable neutralino dark matter candidate, 1-2 TeV stops, and with the remaining particle spectrum compatible with current LHC searches and results. The phase transition structure in viable regions of parameter space exhibits a rich phenomenology, potentially giving rise to one- or two-step first-order phase transitions in the singlet and/or $SU(2)$ directions. We compute several parameters pertaining to the bubble wall profile, including the bubble wall width and $\Delta\beta$ (the variation of the ratio in Higgs vacuum expectation values across the wall). These quantities can vary significantly across small regions of parameter space and can be promising for successful electroweak baryogenesis. We estimate the wall velocity microphysically, taking into account the various sources of friction acting on the expanding bubble wall. Ultra-relativistic solutions to the bubble wall equations of motion typically exist when the electroweak phase transition features substantial supercooling. For somewhat weaker transitions, the bubble wall instead tends to be sub-luminal and, in fact, likely sub-sonic, suggesting that successful electroweak baryogenesis may indeed occur in regions of the NMSSM compatible with the Higgs discovery.Comment: 49 pages + 2 appendices, 6 figures. v2: Minor corrections; matches version published in JHE