Network Model Selection Using Task-Focused Minimum Description Length

Networks are fundamental models for data used in practically every application domain. In most instances, several implicit or explicit choices about the network definition impact the translation of underlying data to a network representation, and the subsequent question(s) about the underlying system being represented. Users of downstream network data may not even be aware of these choices or their impacts. We propose a task-focused network model selection methodology which addresses several key challenges. Our approach constructs network models from underlying data and uses minimum description length (MDL) criteria for selection. Our methodology measures efficiency, a general and comparable measure of the network's performance of a local (i.e. node-level) predictive task of interest. Selection on efficiency favors parsimonious (e.g. sparse) models to avoid overfitting and can be applied across arbitrary tasks and representations. We show stability, sensitivity, and significance testing in our methodology

Combined flavor symmetry violation and lepton number violation in neutrino physics

Heavy singlet neutrinos admit Majorana masses which are not possible for the Standard Model particles. This suggest new possibilities for generating the masses and mixing angles of light neutrinos. We present a model of neutrino physics which combines the source of lepton number violation with the flavor symmetry responsible for the hierarchy in the charged lepton and quark sector. This is accomplished by giving the scalar field effecting the lepton number violation a nonzero charge under the horizontal flavor symmetry. We find an economical model which is consistent with the measured values of the atmospheric and solar neutrino mass-squares and mixing angles.Comment: 6 pages, no figures (published version

Network Model Selection for Task-Focused Attributed Network Inference

Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection to evaluate network representations from data, focusing on fundamental predictive tasks on networks. We present a modular methodology using general, interpretable network models, task neighborhood functions found across domains, and several criteria for robust model selection. We demonstrate our methodology on three online user activity datasets and show that network model selection for the appropriate network task vs. an alternate task increases performance by an order of magnitude in our experiments

Interjet Energy Flow/Event Shape Correlations

We identify a class of perturbatively computable measures of interjet energy flow, which can be associated with well-defined color flow at short distances. As an illustration, we calculate correlations between event shapes and the flow of energy, Q_Omega, into an interjet angular region, Omega, in high-energy two-jet e^+e^- -annihilation events. Laplace transforms with respect to the event shapes suppress states with radiation at intermediate energy scales, so that we may compute systematically logarithms of interjet energy flow. This method provides a set of predictions on energy radiated between jets, as a function of event shape and of the choice of the region Omega in which the energy is measured. Non-global logarithms appear as corrections. We apply our method to a continuous class of event shapes.Comment: 9 pages, 5 figures. Based on talk given by C.F. Berger at TH-2002, International Conference on Theoretical Physics, Theme 2: "QCD, Hadron dynamics, etc.", Paris, France, 2002. Slight changes to text, reference adde

Coulomb interacting Dirac fermions in disordered graphene

We study interacting Dirac quasiparticles in disordered graphene and find that an interplay between the unscreened Coulomb interactions and pseudo-relativistic quasiparticle kinematics can be best revealed in the ballistic regime, whereas in the diffusive limit the behavior is qualitatively (albeit, not quantitatively) similar to that of the ordinary 2DEG with parabolic dispersion. We calculate the quasiparticle width and density of states that can be probed by photoemission, tunneling, and magnetization measurements.Comment: Latex, 4 page

Dynamical electroweak symmetry breaking with superheavy quarks and 2+1 composite Higgs model

Recently, a new class of models describing the quark mass hierarchy has been introduced. In this class, while the t quark plays a minor role in electroweak symmetry breaking (EWSB), it is crucial in providing the quark mass hierarchy. In this paper, we analyze the dynamics of a particular model in this class, in which the b' and t' quarks of the fourth family are mostly responsible for dynamical EWSB. The low energy effective theory in this model is derived. It has a clear signature, a 2 + 1 structure of composite Higgs doublets: two nearly degenerate \Phi_{b'} and \Phi_{t'}, and a heavier top-Higgs resonance \Phi_t \sim \bar{t}_{R}(t,b)_L. The properties of these composites are described in detail, and it is shown that the model satisfies the electroweak precision data constraints. The signatures of these composites at the Large Hadron Collider are briefly discussed.Comment: 17 pages, 3 figures; v.2: references and clarifications added: PRD versio

Entropy-scaling search of massive biological data

Many datasets exhibit a well-defined structure that can be exploited to design faster search tools, but it is not always clear when such acceleration is possible. Here, we introduce a framework for similarity search based on characterizing a dataset's entropy and fractal dimension. We prove that searching scales in time with metric entropy (number of covering hyperspheres), if the fractal dimension of the dataset is low, and scales in space with the sum of metric entropy and information-theoretic entropy (randomness of the data). Using these ideas, we present accelerated versions of standard tools, with no loss in specificity and little loss in sensitivity, for use in three domains---high-throughput drug screening (Ammolite, 150x speedup), metagenomics (MICA, 3.5x speedup of DIAMOND [3,700x BLASTX]), and protein structure search (esFragBag, 10x speedup of FragBag). Our framework can be used to achieve "compressive omics," and the general theory can be readily applied to data science problems outside of biology.Comment: Including supplement: 41 pages, 6 figures, 4 tables, 1 bo