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Local Loglinear Models for CaptureRecapture ; Loglinear models are often used to estimate the size of a closed population using capturerecapture data. When capture probabilities are related to auxiliary covariates, one may select a separate model based on each of several poststrata. We extend poststratification to its logical extreme by selecting a local loglinear model for each observed unit, while smoothing to achieve stability. Our local models serve a dual purpose In addition to estimating the size of the population, we estimate the rate of missingness as a function of covariates. A simulation demonstrates the superiority of our method when the generating model varies over the covariate space. Data from the Breeding Bird Survey is used to illustrate the method.

Bayesian analysis of measurement error models using INLA ; To account for measurement error ME in explanatory variables, Bayesian approaches provide a flexible framework, as expert knowledge about unobserved covariates can be incorporated in the prior distributions. However, given the analytic intractability of the posterior distribution, model inference so far has to be performed via timeconsuming and complex Markov chain Monte Carlo implementations. In this paper we extend the Integrated nested Laplace approximations INLA approach to formulate Gaussian ME models in generalized linear mixed models. We present three applications, and show how parameter estimates are obtained for common ME models, such as the classical and Berkson error model including heteroscedastic variances. To illustrate the practical feasibility, Rcode is provided.

An analysis of NK and generalized NK landscapes ; Simulated landscapes have been used for decades to evaluate search strategies whose goal is to find the landscape location with maximum fitness. Applications include modeling the capacity of enzymes to catalyze reactions and the clinical effectiveness of medical treatments. Understanding properties of landscapes is important for understanding search difficulty. This paper presents a novel and transparent characterization of NK landscapes. We prove that NK landscapes can be represented by parametric linear interaction models where model coefficients have meaningful interpretations. We derive the statistical properties of the model coefficients, providing insight into how the NK algorithm parses importance to main effects and interactions. An important insight derived from the linear model representation is that the rank of the linear model defined by the NK algorithm is correlated with the number of local optima, a strong determinant of landscape complexity and search difficulty. We show that the maximal rank for an NK landscape is achieved through epistatic interactions that form partially balanced incomplete block designs. Finally, an analytic expression representing the expected number of local optima on the landscape is derived, providing a way to quickly compute the expected number of local optima for very large landscapes.

The transfer principle A tool for complete case analysis ; This paper gives a general method for deriving limiting distributions of complete case statistics for missing data models from corresponding results for the model where all data are observed. This provides a convenient tool for obtaining the asymptotic behavior of complete case versions of established full data methods without lengthy proofs. The methodology is illustrated by analyzing three inference procedures for partially linear regression models with responses missing at random. We first show that complete case versions of asymptotically efficient estimators of the slope parameter for the full model are efficient, thereby solving the problem of constructing efficient estimators of the slope parameter for this model. Second, we derive an asymptotically distribution free test for fitting a normal distribution to the errors. Finally, we obtain an asymptotically distribution free test for linearity, that is, for testing that the nonparametric component of these models is a constant. This test is new both when data are fully observed and when data are missing at random.

Cosmographic reconstruction of fmathcalT cosmology ; A cosmographic reconstruction of fmathcal T models is here revised in a model independent way by fixing observational bounds on the most relevant terms of the fmathcal T Taylor expansion. We relate the fmathcal T models and their derivatives to the cosmographic parameters and then adopt a Monte Carlo analysis. The experimental bounds are thus independent of the choice of a particular fmathcal T model. The advantage of such an analysis lies on constraining the dynamics of the universe by reconstructing the form of fmathcal T, without any further assumptions apart from the validity of the cosmological principle and the analyticity of the fmathcal T function. The main result is to fix model independent cosmographic constraints on the functional form of fmathcal T which are compatible with the theoretical predictions. Furthermore, we infer a phenomenological expression for fmathcal T, compatible with the current cosmographic bounds and show that small deviations are expected from a constant fmathcal T term, indicating that the equation of state of dark energy could slightly evolve from the one of the LambdaCDM model.

Constraints on models with universal extra dimensions from dilepton searches at the LHC ; Models with universal extra dimensions predict that each Standard Model particle is accompanied by a tower of KaluzaKlein resonances. Canonical searches for the production and cascade decays of first KaluzaKlein modes through missing transverse momentum signatures suffer in general from low detection efficiencies because of the rather compressed KaluzaKlein particle mass spectrum. Here, instead we analyze signatures from the production of second KaluzaKlein states which can decay into Standard Model particles and thus do not result in any missing transverse momentum. Such signatures provide a strong sensitivity, and are of particular interest as they would allow for a clear distinction between extra dimension models and other models of new physics like supersymmetry. We constrain the production of second KaluzaKlein particles from recent LHC searches for dilepton resonances, and place limits on the compactification scale to be larger than 715GeV, and on the masses of the second KaluzaKlein particles to be larger than 1.4TeV.

Gelfand Models for Diagram Algebras ; A Gelfand model for a semisimple algebra A over C is a complex linear representation that contains each irreducible representation of A with multiplicity exactly one. We give a method of constructing these models that works uniformly for a large class of semisimple, combinatorial diagram algebras including the partition, Brauer, rook monoid, rookBrauer, TemperleyLieb, Motzkin, and planar rook monoid algebras. In each case, the model representation is given by diagrams acting via signed conjugation on the linear span of their vertically symmetric diagrams. This representation is a generalization of the Saxl model for the symmetric group, and, in fact, our method is to use the Jones basic construction to lift the Saxl model from the symmetric group to each diagram algebra. In the case of the planar diagram algebras, our construction exactly produces the irreducible representations of the algebra.

Little flavor A model of weakscale flavor physics ; We describe a model of quarks which identifies the large global symmetries of little Higgs models with the global flavor symmetries that arise in a deconstruction of the extradimensional 'topological insulator' model of flavor. The nonlinearly realized symmetries of little Higgs theories play a critical role in determining the flavor structure of fermion masses and mixing. All flavor physics occurs at the few TeV scale in this model, yet flavor changing neutral currents arising from the new physics are naturally smaller than those generated radiatively in the standard model, without having to invoke minimal flavor violation.

Two Loop Radiative Seesaw Model with Inert Triplet Scalar Field ; We propose a radiative seesaw model with an inert triplet scalar field in which Majorana neutrino masses are generated at the two loop level. There are fermionic or bosonic dark matter candidates in the model. We find that each candidate can satisfy the WMAP data when its mass is taken to be around the half of the mass of the standard model like Higgs boson. We also discuss phenomenology of the inert triplet scalar bosons, especially focusing on the doublycharged scalar bosons at Large Hadron Collider in parameter regions constrained by the electroweak precision data and WMAP data. We study how we can distinguish our model from the minimal Higgs triplet model.

Simple crystallizable beadspring polymer model ; We develop a simple coarsegrained beadspring polymer model exhibiting competing crystallization and glass transitions. For quench rates slower than the critical nucleation rate dotTcrit, systems exhibit a firstorder crystallization transition below a critical temperature TTcryst. Such systems form closepacked crystallites of FCC andor HCP order, separated by domain walls, twin defects, and an amorphous interphase. The size of amorphous regions grows continuously as the quench rate dotT increases, producing nearly amorphous structure for dotTdotTcrit. Our model exhibits many features observed in recent studies of crystallization of athermal polymer packings, but also critical differences arising from the softness of the pair interactions and the thermal nature of the phase transition. The model is considerably more computationally efficient than other recent crystallizable coarsegrained polymer models; while it sacrifices some features of real semicrystalline polymers such as lamellar structure and chain disentanglement, we anticipate that it will serve as a useful model for studying generic features related to semicrystalline order in polymer solids.

A unified model for the dynamics of driven ribbon with strain and magnetic order parameters ; We develop a unified model to explain the dynamics of driven one dimensional ribbon for materials with strain and magnetic order parameters. We show that the model equations in their most general form explain several results on driven magnetostrictive metallic glass ribbons such as the period doubling route to chaos as a function of a dc magnetic field in the presence of a sinusoidal field, the quasiperiodic route to chaos as a function of the sinusoidal field for a fixed dc field, and induced and suppressed chaos in the presence of an additional low amplitude near resonant sinusoidal field. We also investigate the influence of a low amplitude near resonant field on the period doubling route. The model equations also exhibit symmetry restoring crisis with an exponent close to unity. The model can be adopted to explain certain results on magnetoelastic beam and martensitic ribbon under sinusoidal driving conditions. In the latter case, we find interesting dynamics of a periodic one orbit switching between two equivalent wells as a function of an ac magnetic field that eventually makes a direct transition to chaos under resonant driving condition. The model is also applicable to magnetomartensites and materials with two order parameters.

A new approach to multimodal diffusions with applications to protein folding ; This article demonstrates that flexible and statistically tractable multimodal diffusion models can be attained by transformation of simple wellknown diffusion models such as the OrnsteinUhlenbeck model, or more generally a Pearson diffusion. The transformed diffusion inherits many properties of the underlying simple diffusion including its mixing rates and distributions of first passage times. Likelihood inference and martingale estimating functions are considered in the case of a discretely observed bimodal diffusion. It is further demonstrated that model parameters can be identified and estimated when the diffusion is observed with additional measurement error. The new approach is applied to molecular dynamics data in form of a reaction coordinate of the small Trpzipper protein, for which the folding and unfolding rates are estimated. The new models provide a better fit to this type of protein folding data than previous models because the diffusion coefficient is statedependent.

A Representation of Uncertainty to Aid Insight into Decision Models ; Many real world models can be characterized as weak, meaning that there is significant uncertainty in both the data input and inferences. This lack of determinism makes it especially difficult for users of computer decision aids to understand and have confidence in the models. This paper presents a representation for uncertainty and utilities that serves as a framework for graphical summary and computergenerated explanation of decision models. The application described that tests the methodology is a computer decision aid designed to enhance the clinicianpatient consultation process for patients with angina chest pain due to lack of blood flow to the heart muscle. The angina model is represented as a Bayesian decision network. Additionally, the probabilities and utilities are treated as random variables with probability distributions on their range of possible values. The initial distributions represent information on all patients with anginal symptoms, and the approach allows for rapid tailoring to more patientspecific distributions. This framework provides a metric for judging the importance of each variable in the model dynamically.

On equilibration and coarsening in the quantum ON model at infinite N ; The quantum ON model in the infinite N limit is a paradigm for symmetrybreaking. Qualitatively, its phase diagram is an excellent guide to the equilibrium physics for more realistic values of N in varying spatial dimensions d1. Here we investigate the physics of this model out of equilibrium, specifically its response to global quenches starting in the disordered phase. If the model were to exhibit equilibration, the late time state could be inferred from the finite temperature phase diagram. In the infinite N limit, we show that not only does the model not lead to equilibration on account of an infinite number of conserved quantities, it also does emphnot relax to a generalized Gibbs ensemble consistent with these conserved quantities. Nevertheless, we emphstill find that the late time states following quenches bear strong signatures of the equilibrium phase diagram. Notably, we find that the model exhibits coarsening to a nonequilibrium critical state only in dimensions d2, that is, if the equilibrium phase diagram contains an ordered phase at nonzero temperatures.

Spin vortices, skyrmions and the KosterlitzThouless transition in the twodimensional antiferromagnet ; We investigate spinvortex excitations in the twodimensional antiferromagnet on the basis of the nonlinear sigma model. The model of twodimensional Heisenberg quantum antiferromagnet is mapped onto the 21D nonlinear sigma model. The 2D nonlinear sigma model has an instanton or skyrmion solution which describes an excitation of spinvortex type. Quantum fluctuations of instantons are reduced to the study of the Coulomb gas, and the gas of instantons of the 2D nonlinear sigma model is in the plasma phase. We generalize this picture of instanton gas to the 21D nonlinear sigma model. We show, using some approximation, that there is a KosterlitzThouless transition from the plasma phase to the molecular phase as the temperature is lowered.

Dissipative Dicke model with nonlinear atomphoton interaction ; We study a generalized Dicke model, as recently realized in an atomic quantum gas experiment, describing the collective interaction of N twolevel atoms with a single cavity mode. The model takes account of dissipation of the cavity field, and includes a nonlinear atomphoton coupling, not present in the conventional Dicke model. We extend previous theoretical investigations of a semiclassical model by including all quantum effects and considering finite atom number N . Our results show good agreement between quantum expectation values and the semiclassical model as N is increased, but also show exotic behaviour for the corresponding quantum state as the nonlinear atomphoton coupling is varied.

NoScale Supergravity Realization of the Starobinsky Model of Inflation ; We present a model for cosmological inflation based on a noscale supergravity sector with an SU2,1U1 Kahler potential, a single modulus T and an inflaton superfield Phi described by a WessZumino model with superpotential parameters mu, lambda. This model yields a scalar spectral index ns and a tensortoscalar ratio r that are compatible with the Planck measurements for values of lambda simeq mu3MP. For the specific choice lambda mu3MP, the model is a noscale supergravity realization of the RR2 Starobinsky model.

A reconstruction of modified holographic Ricci dark energy in fR,T gravity ; In this paper, we consider a recently proposed model of Dark Energy DE know as Modified Holographic Ricci DE MHRDE which is function of the Hubble parameter and its first derivative with respect to the cosmic time t in the light of the fR,T model of modified gravity, considering the particular model fR,T mu R nu T, with mu and nu constants. The equation of state EoS parameter omegaLambda approaches but never reaches the value 1, implying a quintessencelike behavior of the model. The deceleration parameter q passes from decelerated to accelerated phase at a redshift of zapprox 0.2, showing also a small dependence from the values of the parameters considered. Thanks to the statefinder diagnostic analysis, we observed that the LambdaCDM phase for the considered model is attainable. We observed that the fractional energy densities for DE and DM OmegaLambda and Omegam have, respectively, an increasing and a decreasing pattern with the evolution of the universe, indicating an evolution from matter to DE dominated universe. Finally, studying the squared speed of the sound vs2 for our model, we found that is classically stable.

Sparse Approximate Inference for SpatioTemporal Point Process Models ; Spatiotemporal point process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computa tionally challenging both due to the high resolution modelling generally required and the analytically intractable likelihood function. Here, we exploit the sparsity structure typical of spatially discretised logGaussian Cox process models by using approximate messagepassing algorithms. The proposed algorithms scale well with the state dimension and the length of the temporal horizon with moderate loss in distributional accuracy. They hence provide a flexible and faster alternative to both nonlinear filteringsmoothing type algorithms and to approaches that implement the Laplace method or expectation propagation on block sparse latent Gaussian models. We infer the parameters of the latent Gaussian model using a structured variational Bayes approach. We demonstrate the proposed framework on simulation studies with both Gaussian and pointprocess observations and use it to reconstruct the conflict intensity and dynamics in Afghanistan from the WikiLeaks Afghan War Diary.

Searching for New Physics through correlations of Flavour Observables ; The coming flavour precision era will allow to uncover various patterns of flavour violation in different New Physics scenarios. We discuss different classes of them. A simple extension of the Standard Model that generally introduces new sources of flavour and CP violation as well as righthanded currents is the addition of a U1 gauge symmetry to the SM gauge group. In such Z' models correlations between various flavour observables emerge that could test and distinguish different Z' scenarios. A concrete model with flavour violating Z' couplings is the 331 model based on the gauge group SU3C x SU3L x U1X. We also study treelevel FCNCs mediated by heavy neutral scalars andor pseudoscalars H0A0. Furthermore the implications of an additional approximate global U23 flavour symmetry is shortly discussed. Finally a model with vectorlike fermions and flavour violating Z couplings is presented. We identify a number of correlations between various observables that differ from those known from constrained minimal flavour violating CMFV models and that could test and distinguish these different scenarios.

Resurrecting power law inflation in the light of Planck results ; It is well known that a canonical scalar field with an exponential potential can drive power law inflation PLI. However, the tensortoscalar ratio in such models turns out to be larger than the stringent limit set by recent Planck results. We propose a new model of power law inflation for which the scalar spectra index, the tensortoscalar ratio and the nongaussianity parameter fmathbfNLmathrmequil are in excellent agreement with Planck results. Inflation, in this model, is driven by a noncanonical scalar field with an inverse power law potential. The Lagrangian for our model is structurally similar to that of a canonical scalar field and has a power law form for the kinetic term. A simple extension of our model resolves the graceful exit problem which usually afflicts models of power law inflation.

Domain wall and isocurvature perturbation problems in axion models ; Axion models have two serious cosmological problems, domain wall and isocurvature perturbation problems. In order to solve these problems we investigate the Linde's model in which the field value of the PecceiQuinn PQ scalar is large during inflation. In this model the fluctuations of the PQ field grow after inflation through the parametric resonance and stable axionic strings may be produced, which results in the domain wall problem. We study formation of axionic strings using lattice simulations. It is found that in chaotic inflation the axion model is free from both the domain wall and the isocurvature perturbation problems if the initial misalignment angle thetaa is smaller than O102. Furthermore, axions can also account for the dark matter for the breaking scale v simeq 101216 GeV and the Hubble parameter during inflation Hinf lesssim 101112 GeV in general inflation models.

Low High scale MSSM inflation, gravitational waves and constraints from Planck ; In this paper we will analyze generic predictions of an inflectionpoint model of inflation with Hubbleinduced corrections and study them in light of the Planck data. Typically inflectionpoint models of inflation can be embedded within Minimal Supersymmetric Standard Model MSSM where inflation can occur below the Planck scale. The flexibility of the potential allows us to match the observed amplitude of the TTpower spectrum of the cosmic microwave background radiation with low and high multipoles, spectral tilt, and virtually mild running of the spectral tilt, which can put a bound on an upper limit on the tensortoscalar ratio, r leq 0.12. Since the inflaton within MSSM carries the Standard Model charges, therefore it is the minimal model of inflation beyond the Standard Model which can reheat the universe with the right thermal degrees of freedom without any darkradiation.

Discrete Dynamical Modeling and Analysis of the RS FlipFlop Circuit ; A simple discrete planar dynamical model for the ideal logical RS flipflop circuit is developed with an eye toward mimicking the dynamical behavior observed for actual physical realizations of this circuit. It is shown that the model exhibits most of the qualitative features ascribed to the RS flipflop circuit, such as an intrinsic instability associated with unit set and reset inputs, manifested in a chaotic sequence of output states that tend to oscillate among all possible output states, and the existence of periodic orbits of arbitrarily high period that depend on the various intrinsic system parameters. The investigation involves a combination of analytical methods from the modern theory of discrete dynamical systems, and numerical simulations that illustrate the dazzling array of dynamics that can be generated by the model. Validation of the discrete model is accomplished by comparison with certain Poincar'e map like representations of the dynamics corresponding to threedimensional differential equation models of electrical circuits that produce RS flipflop behavior.

cAND A new graph model ; In this document, we study the scope of the following graph model each vertex is assigned to a box in a metric space and to a representative element that belongs to that box. Two vertices are connected by an edge if and only if its respective boxes contain the opposite representative element. We focus our study on the case where boxes and therefore representative elements associated to vertices are spread in the Euclidean line. We give both, a combinatorial and an intersection characterization of the model. Based on these characterizations, we determine graph families that contain the model e. g., boxicity 2 graphs and others that the new model contains e. g., rooted directed path. We also study the particular case where each representative element is the center of its respective box. In this particular case, we provide constructive representations for interval, block and outerplanar graphs. Finally, we show that the general and the particular model are not equivalent by constructing a graph family that separates the two cases.

Matter inflation with A4 flavour symmetry breaking ; We discuss model building in tribrid inflation, which is a framework for realising inflation in the matter sector of supersymmetric particle physics models. The inflaton is a Dflat combination of matter fields, and inflation ends by a phase transition in which some Higgs field obtains a vacuum expectation value. We first describe the general procedure for implementing tribrid inflation in realistic models of particle physics that can be applied to a wide variety of BSM particle physics models around the GUT scale. We then demonstrate how the procedure works for an explicit lepton flavour model based on an A4 family symmetry. The model is both predictive and phenomenologically viable, and illustrates how tribrid inflation connects cosmological and particle physics parameters. In particular, it predicts a relation between the neutrino Yukawa coupling and the running of the spectral index alphas. We also show how topological defects from the flavour symmetry breaking can be avoided automatically.

Mixed Inflaton and Spectator Field Models after Planck ; We investigate the possibility that the primordial perturbation has two sources the inflaton and a spectator field, which is not dynamically important during inflation but which after inflation can contribute to the curvature perturbation. The recent Planck results on the power spectrum and nonGaussianity allow us to put constraints on such mixed models. In the generic case, where no specific model for the inflaton or the spectator is assumed, one finds that in the mixed scenario it is possible to have a large trispectrum with tauNL fNL2. The constraints on inflation models in the plane of the spectral index and tensortoscalar ratio are modified by the presence of a spectator and depend also on the ratio of the spectatortoinflaton power R. If one chooses the spectator to be the curvaton with a quadratic potential, nonGaussianities can be computed and imply restrictions on possible values of R. We also consider a mixed curvaton and chaotic inflation model and show that even quartic chaotic inflation is still feasible in the context of mixed models.

Objective Bayesian hypothesis testing in binomial regression models with integral prior distributions ; In this work we apply the methodology of integral priors to handle Bayesian model selection in binomial regression models with a general link function. These models are very often used to investigate associations and risks in epidemiological studies where one goal is to exhibit whether or not an exposure is a risk factor for developing a certain disease; the purpose of the current paper is to test the effect of specific exposure factors. We formulate the problem as a Bayesian model selection case and solve it using objective Bayes factors. To construct the reference prior distributions on the regression coefficients of the binomial regression models, we rely on the methodology of integral priors that is nearly automatic as it only requires the specification of estimation reference priors and it does not depend on tuning parameters or on hyperparameters within these priors.

Spectral Clustering on Subspace for Parameter Estimation of Jump Linear Models ; The problem of estimating parameters of a deterministic jump or piecewise linear model is considered. A subspace technique referred to as spectral clustering on subspace SCS algorithm is proposed to estimate a set of linear model parameters, the model input, and the set of switching epochs. The SCS algorithm exploits a block diagonal structure of the system input subspace, which partitions the observation space into separate subspaces, each corresponding to one and only one linear submodel. A spectral clustering technique is used to label the noisy observations for each submodel, which generates estimates of switching time epoches. A total least squares technique is used to estimate model parameters and the model input. It is shown that, in the absence of observation noise, the SCS algorithm provides exact parameter identification. At high signal to noise ratios, SCS attains a clairvoyant Cram'erRao bound computed by assuming the labeling of observation samples is perfect.

T7 Flavor Model in Three Loop Seesaw and Higgs Phenomenology ; We propose a new type of radiative seesaw model in which observed neutrino masses are generated through a threeloop level diagram in combination with treelevel typeII seesaw mechanism in a renormalizable theory. We introduce a Nonabelian flavor symmetry T7 in order to constrain the form of Yukawa interactions and Higgs potential. Although several models based on a Nonabelian flavor symmetry predict the universal coupling constants among the standard model like Higgs boson and charged leptons, which is disfavored by the current LHC data, our model can avoid such a situation. We show a benchmark parameter set that is consistent with the current experimental data, and we discuss multimuon events as a key collider signature to probe our model.

New Integrable Models from the GaugeYBE Correspondence ; We introduce a class of new integrable lattice models labeled by a pair of positive integers N and r. The integrable model is obtained from the GaugeYBE correspondence, which states the equivalence of the 4d N1 S1 times S3Zr index of a large class of SUN quiver gauge theories with the partition function of 2d classical integrable spin models. The integrability of the model starstar relation is equivalent with the invariance of the index under the Seiberg duality. Our solution to the YangBaxter equation is one of the most general known in the literature, and reproduces a number of known integrable models. Our analysis identifies the YangBaxter equation with a particular duality called the YangBaxter duality between two 4d N1 supersymmetric quiver gauge theories. This suggests that the integrability goes beyond 4d lens indices and can be extended to the full physical equivalence among the IR fixed points.

Mathematical modelling and optimal control of anthracnose ; In this paper we propose two nonlinear models for the control of anthracnose disease. The first is an ordinary differential equation ODE model which represents the withinhost evolution of the disease. The second includes spatial diffusion of the disease in a bounded domain. We demonstrate the wellposedness of those models by verifying the existence of solutions for given initial conditions and positive invariance of the positive cone. By considering a quadratic cost functional and applying a maximum principle, we construct a feedback optimal control for the ODE model which is evaluated through numerical simulations with the scientific software Scilab. For the diffusion model we establish under some conditions the existence of an optimal control with respect to a generalized version of the cost functional mentioned above. We also provide a characterization for this optimal control.

Computer Simulation of 3D FiniteVolume Liquid Transport in Fibrous Materials a Physical Model for Ink Seepage into Paper ; A physical model for the simulation inkpaper interaction at the mesoscopic scale is developed. It is based on the modified Ising model, and is generalized to consider the restriction of the finitevolume of ink and also its dynamic seepage. This allows the model to obtain the ink distribution within the paper volume. At the mesoscopic scale, the paper is modeled using a discretized fiber structure. The ink distribution is obtained by solving its equivalent optimization problem, which is solved using a modified genetic algorithm, along with a new boundary condition and the quasilinear technique. The model is able to simulate the finitevolume distribution of ink.

Asymptotic freedom, asymptotic flatness and cosmology ; Holographic RG flows in some cases are known to be related to cosmological solutions. In this paper another example of such correspondence is provided. Holographic RG flows giving rise to asymptoticallyfree betafunctions have been analyzed in connection with holographic models of QCD. They are shown upon Wick rotation to provide a large class of inflationary models with logarithmicallysoft inflaton potentials. The scalar spectral index is universal and depends only on the number of efoldings. The ratio of tensor to scalar power depends on the single extra real parameter that defines this class of models. The Starobinsky inflationary model as well as the recently proposed models of Tinflation are members of this class. The holographic setup gives a completely new and contrasting view to the stability, naturalness and other problems of such inflationary models.

Turbulence Accelerating Cosmology from an Inhomogeneous Dark Fluid ; Specific dark energy models with a linear inhomogeneous timedependent equation of state, within the framework of 4d FriedmanRobertsonWalker FRW cosmology, are investigated. It is demonstrated that such 4d inhomogeneous fluid models may lead to a turbulence FRW cosmology. Both onecomponent and twocomponent models from 4d inhomogeneous dark fluid models are considered. In the onecomponent model the universe may develop from a viscous era with, for instance, a constant bulk viscosity, into a turbulent era. In the twocomponent model the fluid can be decomposed into two components, one nonturbulent ideal and another turbulent part, obeying two different equations of state. Conditions for the appearance of the turbulent dark energy universe in terms of the parameters in the equation of state EoS without introducing the turbulence concept explicitly are are obtained. An equivalent description in terms of an inhomogeneous fluid for the viscous Little Rip LR cosmology is also developed.

Infrared Emission and the Destruction of Dust in HII regions ; The generation of infrared IR radiation and the observed IR intensity distribution at wavelengths of 8, 24, and 100 micron in the ionized hydrogen region around a young, massive star is investigated. The evolution of the HII region is treated using a selfconsistent chemicaldynamical model in which three dust populations are included large silicate grains, small graphite grains, and polycyclic, aromatic hydrocarbons PAHs. A radiative transfer model taking into account stochastic heating of small grains and macromolecules is used to model the IR spectral energy distribution. The computational results are compared with Spitzer and Herschel observations of the RCW 120 nebula. The contributions of collisions with gas particles and the radiation field of the star to stochastic heating of small grains are investigated. It is shown that a model with a homogeneous PAH content cannot reproduce the ringlike IRintensity distribution at 8 micron. A model in which PAHs are destroyed in the ionized region provides a means to explain this intensity distribution. This model is in agreement with observations for realistic characteristic destruction times for the PAHs.

Exact blocking formulas for spin and gauge models ; Using the example of the twodimensional 2D Ising model, we show that in contrast to what can be done in configuration space, the tensor renormalization group TRG formulation allows one to write exact, compact, and manifestly local blocking formulas and exact coarse grained expressions for the partition function. We argue that similar results should hold for most models studied by lattice gauge theorists. We provide exact blocking formulas for several 2D spin models the O2 and O3 sigma models and the SU2 principal chiral model and for the 3D gauge theories with groups Z2, U1 and SU2. We briefly discuss generalizations to other groups, higher dimensions and practical implementations.

On the existence of moments for high dimensional importance sampling ; Theoretical results for importance sampling rely on the existence of certain moments of the importance weights, which are the ratios between the proposal and target densities. In particular, a finite variance ensures square root convergence and asymptotic normality of the importance sampling estimate, and can be important for the reliability of the method in practice. We derive conditions for the existence of any required moments of the weights for Gaussian proposals and show that these conditions are almost necessary and sufficient for a wide range of models with latent Gaussian components. Important examples are time series and panel data models with measurement densities which belong to the exponential family. We introduce practical and simple methods for checking and imposing the conditions for the existence of the desired moments. We develop a two component mixture proposal that allows us to flexibly adapt a given proposal density into a robust importance density. These methods are illustrated on a wide range of models including generalized linear mixed models, nonGaussian nonlinear state space models and panel data models with autoregressive random effects.

Effect of the choice of stagnation density in datafitted first and secondorder traffic models ; For a class of datafitted macroscopic traffic models, the influence of the choice of the stagnation density on the model accuracy is investigated. This work builds on an established framework of datafitted firstorder LighthillWhithamRichards LWR models and their secondorder AwRascleZhang ARZ generalizations. These models are systematically fitted to historic fundamental diagram data, and then their predictive accuracy is quantified via a version of the threedetector problem test, considering vehicle trajectory data and singleloop sensor data. The key outcome of this study is that with commonly suggested stagnation densities of 120 vehicleskmlane and above, information travels backwards too slowly. It is then demonstrated that the reduction of the stagnation density to 90100 vehicleskmlane addresses this problem and results in a significant improvement of the predictive accuracy of the considered models.

Nonlinear Model Reduction via an Adaptive Weighting of Snapshots ; In this paper, we propose a new approach to model reduction of parameterized partial differential equations PDEs based on the concept of adaptive reduced bases. The presented approach is particularly suited for largescale nonlinear systems characterized by parameter variations. Instead of using a global basis to construct a global reduced model, the proposed method approximates the original system by multiple lowerdimensional subspaces. Each localized reduced basis is generated by the SVD of a weighted snapshot ensemble; here, each weighting coefficient is a function of the input parameter. Compared with a global model reduction method, such as the classical POD, the adaptive model reduction method could yield a more accurate solution with a fixed subspace dimension. Moreover, we combine the adaptive reduced model with the chord iteration to solve elliptic PDEs in a computationally efficient fashion. The potential of the method for achieving large speedups, while maintaining good accuracy, is demonstrated for both elliptic and parabolic PDEs in a few numerical examples.

The restricted consistency property of leavenvout crossvalidation for highdimensional variable selection ; Crossvalidation CV methods are popular for selecting the tuning parameter in the highdimensional variable selection problem. We show the misalignment of the CV is one possible reason of its overselection behavior. To fix this issue, we propose a version of leavenvout crossvalidation CVnv, for selecting the optimal model among the restricted candidate model set for highdimensional generalized linear models. By using the same candidate model sequence and a proper order of construction sample size nc in each CV split, CVnv avoids the potential hurdles in developing theoretical properties. CVnv is shown to enjoy the restricted model selection consistency property under mild conditions. Extensive simulations and real data analysis support the theoretical results and demonstrate the performances of CVnv in terms of both model selection and prediction.

Nonparametric inference in hidden Markov models using Psplines ; Hidden Markov models HMMs are flexible time series models in which the distributions of the observations depend on unobserved serially correlated states. The statedependent distributions in HMMs are usually taken from some class of parametrically specified distributions. The choice of this class can be difficult, and an unfortunate choice can have serious consequences for example on state estimates, on forecasts and generally on the resulting model complexity and interpretation, in particular with respect to the number of states. We develop a novel approach for estimating the statedependent distributions of an HMM in a nonparametric way, which is based on the idea of representing the corresponding densities as linear combinations of a large number of standardized Bspline basis functions, imposing a penalty term on nonsmoothness in order to maintain a good balance between goodnessoffit and smoothness. We illustrate the nonparametric modeling approach in a real data application concerned with vertical speeds of a diving beaked whale, demonstrating that compared to parametric counterparts it can lead to models that are more parsimonious in terms of the number of states yet fit the data equally well.

Adaptive LASSO model selection in a multiphase quantile regression ; We propose a general adaptive LASSO method for a quantile regression model. Our method is very interesting when we know nothing about the first two moments of the model error. We first prove that the obtained estimators satisfy the oracle properties, which involves the relevant variable selection without using hypothesis test. Next, we study the proposed method when the multiphase model changes to unknown observations called changepoints. Convergence rates of the changepoints and of the regression parameters estimators in each phase are found. The sparsity of the adaptive LASSO quantile estimators of the regression parameters is not affected by the changepoints estimation. If the phases number is unknown, a consistent criterion is proposed. Numerical studies by Monte Carlo simulations show the performance of the proposed method, compared to other existing methods in the literature, for models with a single phase or for multiphase models. The adaptive LASSO quantile method performs better than known variable selection methods, as the least squared method with adaptive LASSO penalty, L1method with LASSOtype penalty and quantile method with SCAD penalty.

Model Checking Contest Petri Nets, Report on the 2013 edition ; This document presents the results of the Model Checking Contest held at Petri Nets 2013 in Milano. This contest aimed at a fair and experimental evaluation of the performances of model checking techniques applied to Petri nets. This is the third edition after two successful editions in 2011 and 2012. The participating tools were compared on several examinations state space generation and evaluation of several types of formulae reachability, LTL, CTL for various classes of atomic propositions run on a set of common models PlaceTransition and Symmetric Petri nets. After a short overview of the contest, this paper provides the raw results from the contest, model per model and examination per examination. An HTML version of this report is also provided httpmcc.lip6.fr.

Distributed Business Processes A Framework for Modeling and Execution ; Commercially available business process management systems BPMS still suffer to support organizations to enact their business processes in an effective and efficient way. Current BPMS, in general, are based on BPMN 2.0 andor BPEL. It is well known, that these approaches have some restrictions according modeling and immediate transfer of the model into executable code. Recently, a method for modeling and execution of business processes, named subjectoriented business process management SBPM, gained attention. This methodology facilitates modeling of any business process using only five symbols and allows direct execution based on such models. Further on, this methodology has a strong theoretical and formal basis realizing distributed systems; any process is defined as a network of independent and distributed agents i.e. instances of subjects which coordinate work through the exchange of messages. In this work, we present a framework and a prototype based on offtheshelf technologies as a possible realization of the SBPM methodology. We can prove and demonstrate the principal architecture concept; these results should also stimulate a discussion about actual BPMS and its underlying concepts.

Nonparametric identification of positive eigenfunctions ; Important features of certain economic models may be revealed by studying positive eigenfunctions of appropriately chosen linear operators. Examples include longrun riskreturn relationships in dynamic asset pricing models and components of marginal utility in external habit formation models. This paper provides identification conditions for positive eigenfunctions in nonparametric models. Identification is achieved if the operator satisfies two mild positivity conditions and a power compactness condition. Both existence and identification are achieved under a further nondegeneracy condition. The general results are applied to obtain new identification conditions for external habit formation models and for positive eigenfunctions of pricing operators in dynamic asset pricing models.

Determinantal Clustering Processes A Nonparametric Bayesian Approach to Kernel Based SemiSupervised Clustering ; Semisupervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input. Dirichlet process mixture models are appealing as they can infer the number of clusters from the data. However, these models do not deal with high dimensional data well and can encounter difficulties in inference. We present a novel nonparameteric Bayesian kernel based method to cluster data points without the need to prespecify the number of clusters or to model complicated densities from which data points are assumed to be generated from. The key insight is to use determinants of submatrices of a kernel matrix as a measure of how close together a set of points are. We explore some theoretical properties of the model and derive a natural Gibbs based algorithm with MCMC hyperparameter learning. The model is implemented on a variety of synthetic and real world data sets.

Analysis of the spatial and dynamical properties of a multiscale model of intestinal crypts ; The preliminary analyses on a multiscale model of intestinal crypt dynamics are here presented. The model combines a morphological model, based on the Cellular Potts Model CPM, and a gene regulatory network model, based on Noisy Random Boolean Networks NRBNs. Simulations suggest that the stochastic differentiation process is itself sufficient to ensure the general homeostasis in the asymptotic states, as proven by several measures.

Some issues with QuasiSteady State Model in Longterm Stability ; The Quasi SteadyState QSS model of longterm dynamics relies on the idea of timescale decomposition. Assuming that the fast variables are infinitely fast and are stable in the longterm, the QSS model replaces the differential equations of transient dynamics by their equilibrium equations to reduce complexity and increase computation efficiency. Although the idea of QSS model is intuitive, its theoretical foundation has not yet been developed. In this paper, several counter examples in which the QSS model fails to provide a correct approximation of the complete dynamic model in power system are presented and the reasons of the failure are explained from the viewpoint of nonlinear analysis.

Modeling with Normalized Random Measure Mixture Models ; The Dirichlet process mixture model and more general mixtures based on discrete random probability measures have been shown to be flexible and accurate models for density estimation and clustering. The goal of this paper is to illustrate the use of normalized random measures as mixing measures in nonparametric hierarchical mixture models and point out how possible computational issues can be successfully addressed. To this end, we first provide a concise and accessible introduction to normalized random measures with independent increments. Then, we explain in detail a particular way of sampling from the posterior using the FergusonKlass representation. We develop a thorough comparative analysis for locationscale mixtures that considers a set of alternatives for the mixture kernel and for the nonparametric component. Simulation results indicate that normalized random measure mixtures potentially represent a valid default choice for density estimation problems. As a byproduct of this study an R package to fit these models was produced and is available in the Comprehensive R Archive Network CRAN.

Asteroseismology of DAV star HS05070434B, including the core composition profiles ; The DAV star, HS05070434B, was observed by Fu et al. 2013 in 2007 and from 2009 December to 2010 January. There were a total of six triplets with nearly equal split, which were identified as l 1 modes caused by rotation. In order to fit the six l 1 modes, grids of white dwarf models are generated by WDEC. For the core composition profiles, we choose the linear fittings to carbon profile of white dwarf models from MESA, which can be considered as results of real nuclear burning process. Coupled with diffusion equilibrium HHe and HeC mixtures, we make grids of models in WDEC and do asteroseismology works on HS05070434B. There is a total of 9.50 seconds error for our bestfitting model, which is smaller than the result a total of 22.1 seconds error of Fu et al. 2013, when fitting the six l 1 modes. The two other previous identified pulsation mode frequencies 286.1 s and 743.40 s may also be well fitted by our bestfitting model. The model parameters are Teff 11450 K, logg 8.088, M 0.640 Modot, logMHM 6, and logMHeM 3.

spBayes for large univariate and multivariate pointreferenced spatiotemporal data models ; In this paper we detail the reformulation and rewrite of core functions in the spBayes R package. These efforts have focused on improving computational efficiency, flexibility, and usability for pointreferenced data models. Attention is given to algorithm and computing developments that result in improved sampler convergence rate and efficiency by reducing parameter space; decreased sampler runtime by avoiding expensive matrix computations, and; increased scalability to large datasets by implementing a class of predictive process models that attempt to overcome computational hurdles by representing spatial processes in terms of lowerdimensional realizations. Beyond these general computational improvements for existing model functions, we detail new functions for modeling data indexed in both space and time. These new functions implement a class of dynamic spatiotemporal models for settings where space is viewed as continuous and time is taken as discrete.

On spontaneous breaking of conformal symmetry by probe flavour Dbranes ; We explore the possibilities of breaking conformal symmetry spontaneously by introducing flavour branes into conformal holographic backgrounds in the probe limit. A prototype model of such a mechanism is based on placing D7 antiD7 configuration in the KlebanovWitten conifold based model. In this paper we generalize this model. We conjecture on the required topology of the backgrounds and the corresponding probe brane embeddings. We identify several models that obey these requirements and admit spontaneous breaking of conformal invariance. These include type IIB conifold based examples, dual to defect field theories based on the conifold, and type IIA constructions based on the ABJM model. We identify the dilaton, the corresponding Goldstone boson, discuss its effective action and address the aterm. We briefly discuss the relevance of these models to the pseudo dilaton.

On the Topology of the Inflaton Field in Minimal Supergravity Models ; We consider global issues in minimal supergravity models where a single field inflaton potential emerges. In a particular case we reproduce the Starobinsky model and its description dual to a certain formulation of RR2 supergravity. For definiteness we confine our analysis to spaces at constant curvature, either vanishing or negative. Five distinct models arise, two flat models with respectively a quadratic and a quartic potential and three based on the SU1,1U1 space where its distinct isometries, elliptic, hyperbolic and parabolic are gauged. FayetIliopoulos terms are introduced in a geometric way and they turn out to be a crucial ingredient in order to describe the de Sitter inflationary phase of the Starobinsky model.

Consistency of the tachyon warm inflationary universe models ; This study concerns the consistency of the tachyon warm inflationary models. A linear stability analysis is performed to find the slowroll conditions, characterized by the potential slowroll PSR parameters, for the existence of a tachyon warm inflationary attractor in the system. The PSR parameters in the tachyon warm inflationary models are redefined. Two cases, an exponential potential and an inverse powerlaw potential, are studied, when the dissipative coefficient GammaGamma0 and GammaGammaphi, respectively. A crucial condition is obtained for a tachyon warm inflationary model characterized by the Hubble slowroll HSR parameter epsilonH, and the condition is extendable to some other inflationary models as well. A proper number of efolds is obtained in both cases of the tachyon warm inflation, in contrast to existing works. It is also found that a constant dissipative coefficient GammaGamma0 is usually not a suitable assumption for a warm inflationary model.

Learning Pairwise Graphical Models with Nonlinear Sufficient Statistics ; We investigate a generic problem of learning pairwise exponential family graphical models with pairwise sufficient statistics defined by a global mapping function, e.g., Mercer kernels. This subclass of pairwise graphical models allow us to flexibly capture complex interactions among variables beyond pairwise product. We propose two ell1norm penalized maximum likelihood estimators to learn the model parameters from i.i.d. samples. The first one is a joint estimator which estimates all the parameters simultaneously. The second one is a nodewise conditional estimator which estimates the parameters individually for each node. For both estimators, we show that under proper conditions the extra flexibility gained in our model comes at almost no cost of statistical and computational efficiency. We demonstrate the advantages of our model over stateoftheart methods on synthetic and real datasets.

Optimising Gaussian processes for reconstructing dark energy dynamics from supernovae ; Gaussian processes are a fully Bayesian smoothing technique that allows for the reconstruction of a function and its derivatives directly from observational data, without assuming a specific model or choosing a parameterization. This is ideal for constraining dark energy because physical models are generally phenomenological and poorly motivated. Modelindependent constraints on dark energy are an especially important alternative to parameterized models, as the priors involved have an entirely different source so can be used to check constraints formulated from models or parameterizations. A critical prior for Gaussian process reconstruction lies in the choice of covariance function. We show how the choice of covariance function affects the result of the reconstruction, and present a choice which leads to reliable results for present day supernovae data. We also introduce a method to quantify deviations of a model from the Gaussian process reconstructions.

Multiplerelaxationtime lattice Boltzmann modeling of incompressible flows in porous media ; In this paper, a twodimensional eightvelocity D2Q8 multiplerelaxationtime MRT lattice Boltzmann LB model is proposed for incompressible porous flows at the representative elementary volume scale based on the BrinkmanForchheimerextended Darcy formulation. In the model, the porosity is included into the pressurebased equilibrium moments, and the linear and nonlinear drag forces of the porous media are incorporated into the model by adding a forcing term to the MRTLB equation in the moment space. Through the ChapmanEnskog analysis, the generalized NavierStokes equations can be recovered exactly without artificial compressible errors. Numerical simulations of several typical twodimensional porous flows are carried out to validate the present MRTLB model. The numerical results of the present MRTLB model are in good agreement with the analytical solutions andor other numerical solutions reported in the literature.

Reconstruction of fG gravity with ordinary and entropycorrected m,n type Holographic dark energy model ; We have discussed the correspondence of the wellaccepted fG gravity theory with two dark energy models m,ntype holographic dark energy m,ntype HDE and entropycorrected m,ntype holographic dark energy. For this purpose, we have considered the power law form of the scale factor ata0tp, p1. The reconstructed fG in these models have been found and the models in both cases are found to be realistic. We have also discussed the classical stability issues in both models. The m,ntype HDE and its entropycorrected versions are more stable than the ordinary HDE model.

Equilibration in lowdimensional quantum matrix models ; Matrix models play an important role in studies of quantum gravity, being candidates for a formulation of Mtheory, but are notoriously difficult to solve. In this work, we present a fresh approach by introducing a novel exact model provably equivalent with lowdimensional bosonic matrix models. In this equivalent model significant local structure becomes apparent and it can serve as a simple toy model for analytical and precise numerical study. We derive a substantial part of the low energy spectrum, find a conserved charge, and are able to derive numerically the Regge trajectories. To exemplify the usefulness of the approach, we address questions of equilibration starting from a nonequilibrium situation, building upon an intuition from quantum information. We finally discuss possible generalizations of the approach.

The impact of dark energy perturbations on the growth index ; We show that in clustering dark energy models the growth index of linear matter perturbations, gamma, can be much lower than in LambdaCDM or smooth quintessence models and present a strong variation with redshift. We find that the impact of dark energy perturbations on gamma is enhanced if the dark energy equation of state has a large and rapid decay at low redshift. We study four different models with these features and show that we may have 0.33gammaleftzright0.48 at 0z3. We also show that the constant gamma parametrization for the growth rate, fdlndeltamdln aOmegamgamma, is a few percent inaccurate for such models and that a redshift dependent parametrization for gamma can provide about four times more accurate fits for f. We discuss the robustness of the growth index to distinguish between General Relativity with clustering dark energy and modified gravity models, finding that some fleftRright and clustering dark energy models can present similar values for gamma.

An effective quintessence field with a powerlaw potential ; In this paper, we consider an effective quintessence scalar field with a powerlaw potential interacting with a Pbxi qrhob barotropic fluid as a first model, where q is a deceleration parameter. For the second model we assume viscous polytropic gas interacting with the scalar field. We investigate problem numerically and analyze behavior of different cosmological parameter concerning to components and behavior of Universe. We also compare our results with observational data to fix parameters of the models. We find some instabilities in the first model which may disappear in the second model for the appropriate parameters. Therefore, we can propose interacting quintessence dark energy with viscous polytropic gas as a successful model to describe Universe.

Learning Negative Mixture Models by Tensor Decompositions ; This work considers the problem of estimating the parameters of negative mixture models, i.e. mixture models that possibly involve negative weights. The contributions of this paper are as follows. i We show that every rational probability distributions on strings, a representation which occurs naturally in spectral learning, can be computed by a negative mixture of at most two probabilistic automata or HMMs. ii We propose a method to estimate the parameters of negative mixture models having a specific tensor structure in their low order observable moments. Building upon a recent paper on tensor decompositions for learning latent variable models, we extend this work to the broader setting of tensors having a symmetric decomposition with positive and negative weights. We introduce a generalization of the tensor power method for complex valued tensors, and establish theoretical convergence guarantees. iii We show how our approach applies to negative Gaussian mixture models, for which we provide some experiments.

An inventory model with shortages for imperfect items using substitution of two products ; Inventory models with imperfect quality items are studied by researchers in past two decades. Till now none of them have considered the effect of substitutions to cope up with shortage and avoid lost sales. This paper presents an EOQ approach for inventory system with shortages and two types of products with imperfect quality by one way substitution. Our model provides significant advantage for substitution case while maintaining its inherent simplicity. We have provided numerical example and sensitivity analysis to justify the effectiveness of our model. It is observed that, presence of imperfect items affect the lot size of minor and major products differently. Under certain conditions, our model generalizes the previous existing models in this direction.

HighScale SUSY Breaking Models in light of the BICEP2 Result ; The large value of the tensortoscalar ratio in the cosmic microwave background radiation reported by the BICEP2 collaboration gives strong impact on models of supersymmetry SUSY. The large ratio indicates inflation with a highenergy scale and thus a high reheating temperature in general, and various SUSY models suffer from the serious gravitino and Polonyi problems. In this article, we discuss a class of the highscale SUSY breaking models which are completely free from those problems. With especially focusing on the dark matter relic abundance, we examine how the BICEP2 result narrows down the parameter space of the models, assuming the simplest chaotic inflation model. We find that the mass of the dark matter is predicted to be less than about 1 TeV thanks to the nonthermal production in the early universe through the decay of abundant gravitinos produced after the reheating process. We also discuss implications in some details to dark matter searches at collider and indirect dark matter detection experiments.

Dark energy model selection with current and future data ; The main goal of the next generation of weak lensing probes is to constrain cosmological parameters by measuring the mass distribution and geometry of the low redshift Universe and thus to test the concordance model of cosmology. A future allsky tomographic cosmic shear survey with design properties similar to Euclid has the potential to provide the statistical accuracy required to distinguish between different dark energy models. In order to assess the model selection capability of such a probe, we consider the dark energy equationofstate parameter w0. We forecast the Bayes factor of future observations, in the light of current information from Planck by computing the predictive posterior odds distribution. We find that Euclid is unlikely to overturn current model selection results, and that the future data are likely to be compatible with a cosmological constant model. This result holds for a wide range of priors.

Distributional Analysis for Model Predictive Deferrable Load Control ; Deferrable load control is essential for handling the uncertainties associated with the increasing penetration of renewable generation. Model predictive control has emerged as an effective approach for deferrable load control, and has received considerable attention. In particular, previous work has analyzed the averagecase performance of model predictive deferrable load control. However, to this point, distributional analysis of model predictive deferrable load control has been elusive. In this paper, we prove strong concentration results on the distribution of the load variance obtained by model predictive deferrable load control. These concentration results highlight that the typical performance of model predictive deferrable load control is tightly concentrated around the averagecase performance.

Downlink Analysis for a Heterogeneous Cellular Network ; In this paper, a comprehensive study of the the downlink performance in a heterogeneous cellular network or hetnet is conducted. A general hetnet model is considered consisting of an arbitrary number of openaccess and closedaccess tier of base stations BSs arranged according to independent homogeneous Poisson point processes. The BSs of each tier have a constant transmission power, random fading coefficient with an arbitrary distribution and arbitrary pathloss exponent of the powerlaw pathloss model. For such a system, analytical characterizations for the coverage probability and average rate at an arbitrary mobilestation MS, and average pertier load are derived for both the maxSINR connectivity and nearestBS connectivity models. Using stochastic ordering, interesting properties and simplifications for the hetnet downlink performance are derived by relating these two connectivity models to the maximum instantaneous received power MIRP connectivity model and the maximum biased received power MBRP connectivity models, respectively, providing good insights about the hetnets and the downlink performance in these complex networks. Furthermore, the results also demonstrate the effectiveness and analytical tractability of the stochastic geometric approach to study the hetnet performance.

Large Deviations of a SpatiallyStationary Network of Interacting Neurons ; In this work we determine a processlevel Large Deviation Principle LDP for a model of interacting neurons indexed by a lattice mathbbZd. The neurons are subject to noise, which is modelled as a correlated martingale. The probability law governing the noise is strictly stationary, and we are therefore able to find a LDP for the probability laws Pin governing the stationary empirical measure hatmun generated by the neurons in a cube of length 2n1. We use this LDP to determine an LDP for the neural network model. The connection weights between the neurons evolve according to a learning rule neuronal plasticity, and these results are adaptable to a large variety of neural network models. This LDP is of great use in the mathematical modelling of neural networks, because it allows a quantification of the likelihood of the system deviating from its limit, and also a determination of which direction the system is likely to deviate. The work is also of interest because there are nontrivial correlations between the neurons even in the asymptotic limit, thereby presenting itself as a generalisation of traditional meanfield models.

Towards Verifying Safety Properties of RealTime Probabilistic Systems ; Using probabilities in the formalmethodsbased development of safetycritical software has quickened interests in academia and industry. We address this area by our modeldriven engineering method for reactive systems SPACE and its toolset Reactive Blocks that provide an extension to support the modeling and verification of realtime behaviors. The approach facilitates the composition of system models from reusable building blocks as well as the verification of functional and realtime properties and the automatic generation of Java code. In this paper, we describe the extension of the toolset to enable the modeling and verification of probabilistic realtime system behavior with the focus on spatial properties that ensure system safety. In particular, we incorporate descriptions of probabilistic behavior into our Reactive Blocks models and integrate the model checker PRISM which allows to verify that a realtime system satisfies certain safety properties with a given probability. Moreover, we consider the spatial implication of probabilistic system specifications by integrating the spatial verification tool BeSpaceD and give an automatic approach to translate system specifications to the input languages of PRISM and BeSpaceD. The approach is highlighted by an example.

A Stochastic Temporal Model of Polyphonic MIDI Performance with Ornaments ; We study indeterminacies in realization of ornaments and how they can be incorporated in a stochastic performance model applicable for music information processing such as scoreperformance matching. We point out the importance of temporal information, and propose a hidden Markov model which describes it explicitly and represents ornaments with several state types. Following a review of the indeterminacies, they are carefully incorporated into the model through its topology and parameters, and the state construction for quite general polyphonic scores is explained in detail. By analyzing piano performance data, we find significant overlaps in interonsetinterval distributions of chordal notes, ornaments, and interchord events, and the data is used to determine details of the model. The model is applied for score following and offline scoreperformance matching, yielding highly accurate matching for performances with many ornaments and relatively frequent errors, repeats, and skips.

Modeling Massive Amount of Experimental Data with Large Random Matrices in a RealTime UWBMIMO System ; The aim of this paper is to study data modeling for massive datasets. Large random matrices are used to model the massive amount of data collected from our experimental testbed. This testbed was developed for a realtime ultrawideband, multiple input multiple output UWBMIMO system. Empirical spectral density is the relevant information we seek for. After we treat this UWBMIMO system as a black box, we aim to model the output of the black box as a large statistical system, whose outputs can be described by large random matrices. This model is extremely general to allow for the study of nonlinear and nonGaussian phenomenon. The good agreements between the theoretical predictions and the empirical findings validate the correctness of the our suggested data model.

Natural Inflation in Supergravity and Beyond ; Supergravity models of natural inflation and its generalizations are presented. These models are special examples of the class of supergravity models proposed in arXiv1008.3375 and arXiv1011.5945, which have a shift symmetric Kahler potential, superpotential linear in goldstino, and stable Minkowski vacua. We present a class of supergravity models with arbitrary potentials modulated by sinusoidal oscillations, similar to the potentials associated with axion monodromy models. We show that one can implement natural inflation in supergravity even in the models of a single axion field with axion parameters O1. We also discuss the irrational axion landscape in supergravity, which describes a potential with infinite number of stable Minkowski and metastable dS minima.

Reconstruction of fG Gravity with New Agegraphic Dark Energy Model ; In this work, we consider the reconstruction scenario of new agegraphic dark energy NADE model and fG theory of gravity with G representing the GaussBonnet invariant in the flat FRW spacetime. In this context, we assume a solution of the scale factor in powerlaw form and study the correspondence scenario. A new agegraphic fG model is constructed and discussed graphically for the evolution of the universe. Using this model, we investigate the different eras of the expanding universe and stability with the help of the equation of state EoS parameter omegaeff and squared speed of sound vs2, respectively. It is mentioned here that the reconstructed model represents the quintessence era of the accelerated expansion of the universe with instability. Moreover, the statefinder trajectories are studied and we find out that the model is not capable of reaching the LambdaCDM phase of the universe.

The Simple Single Field Inflation Models and the Running of Spectral Index ; The BICEP2 experiment confirms the existence of primordial gravitational wave with the tensortoscalar ratio r0 ruled out at 7sigma level. The consistency of this large value of r with the em Planck data requires a large negative running n's of the scalar spectral index. Herein we propose two types of the single field inflation models with simple potentials to study the possibility of the consistency of the models with the BICEP2 and em Planck observations. One type of model suggested herein is realized in the supergravity model building. These models fail to provide the needed n's even though both can fit the tensortoscalar ratio and spectral index.

Evaluation of the capability of local helioseismology to discern between monolithic and spaghetti sunspot models ; The helioseismic properties of the wave scattering generated by monolithic and spaghetti sunspots are analyzed by means of numerical simulations. In these computations, an incident f or p1 mode travels through the sunspot model, which produces absorption and phase shift of the waves. The scattering is studied by inspecting the wavefield, computing traveltime shifts, and performing FourierHankel analysis. The comparison between the results obtained for both sunspot models reveals that the differences in the absorption coefficient can be detected above noise level. The spaghetti model produces an steep increase of the phase shift with the degree of the mode at short wavelengths, while modemixing is more efficient for the monolithic model. These results provide a clue for what to look for in solar observations to discern the constitution of sunspots between the proposed monolithic and spaghetti models.

Modelbased clustering of Gaussian copulas for mixed data ; Clustering task of mixed data is a challenging problem. In a probabilistic framework, the main difficulty is due to a shortage of conventional distributions for such data. In this paper, we propose to achieve the mixed data clustering with a Gaussian copula mixture model, since copulas, and in particular the Gaussian ones, are powerful tools for easily modelling the distribution of multivariate variables. Indeed, considering a mixing of continuous, integer and ordinal variables thus all having a cumulative distribution function, this copula mixture model defines intracomponent dependencies similar to a Gaussian mixture, so with classical correlation meaning. Simultaneously, it preserves standard margins associated to continuous, integer and ordered features, namely the Gaussian, the Poisson and the ordered multinomial distributions. As an interesting byproduct, the proposed mixture model generalizes many wellknown ones and also provides tools of visualization based on the parameters. At a practical level, the Bayesian inference is retained and it is achieved with a MetropoliswithinGibbs sampler. Experiments on simulated and real data sets finally illustrate the expected advantages of the proposed model for mixed data flexible and meaningful parametrization combined with visualization features.

Topic words analysis based on LDA model ; Social network analysis SNA, which is a research field describing and modeling the social connection of a certain group of people, is popular among network services. Our topic words analysis project is a SNA method to visualize the topic words among emails from Obama.com to accounts registered in Columbus, Ohio. Based on Latent Dirichlet Allocation LDA model, a popular topic model of SNA, our project characterizes the preference of senders for target group of receptors. Gibbs sampling is used to estimate topic and word distribution. Our training and testing data are emails from the carbonfree server Datagreening.com. We use parallel computing tool BashReduce for word processing and generate related words under each latent topic to discovers typical information of political news sending specially to local Columbus receptors. Running on two instances using paralleling tool BashReduce, our project contributes almost 30 speedup processing the raw contents, comparing with processing contents on one instance locally. Also, the experimental result shows that the LDA model applied in our project provides precision rate 53.96 higher than TFIDF model finding target words, on the condition that appropriate size of topic words list is selected.

General model of phospholipid bilayers in fluid phase within the single chain mean field theory ; Coarsegrained model for saturated DCPC, DLPC, DMPC, DPPC, DSPC and unsaturated POPC, DOPC phospholipids is introduced within the Single Chain Mean Field theory. A single set of parameters adjusted for DMPC bilayers gives an adequate description of equilibrium and mechanical properties of a range of saturated lipid molecules that differ only in length of their hydrophobic tails and unsaturated POPC, DOPC phospholipids which have double bonds in the tails. A double bond is modeled with a fixed angle of 120 degrees, while the rest of the parameters are kept the same as saturated lipids. The thickness of the bilayer and its hydrophobic core, the compressibility and the equilibrium area per lipid correspond to experimentally measured values for each lipid, changing linearly with the length of the tail. The model for unsaturated phospholipids also fetches main thermodynamical properties of the bilayers. This model is used for an accurate estimation of the free energies of the compressed or stretched bilayers in stacks or multilayers and gives reasonable estimates for free energies. The proposed model may further be used for studies of mixtures of lipids, small molecule inclusions, interactions of bilayers with embedded proteins.

Beyond the GinzburgLandau theory of freezing Anisotropy of the interfacial free energy in the PhaseField Crystal model ; This paper revisits the weakly fourth order anisotropic GinzburgLandau GL theory of freezing. First we determine the anisotropy of the interfacial free energy in the PhaseField Crystal PFC model analytically, and prove that it remains finite at the critical point as a direct consequence of the onemode dominance of the model. Next, we derive the leading order PFC amplitude model and show the formal analogy to traditional weakly 4th order anisotropic GL theories. We conclude that the materialindependent anisotropy appearing in emergent GL theory coincides with the remnant anisotropy of the generating PFC model. As a result, we show that the reduced temperature epsilon does not enter into the interfacial free energy anisotropy for metallic materials in both the PhaseField Crystal model and the emerging GinzburgLandau theories. Finally, we investigate the possible pathways of calibrating anisotropic GinzburgLandau theories.

Models for SmallScale Structure on Cosmic Strings I. Mathematical Formalism ; We describe the formalism of a quantitative analytic model for the evolution of realistic wiggly as opposed to GotoNambu cosmic strings. The model is particularly suited for describing the evolution of smallscale structure on string networks. We discuss model solutions in the extreme limit where the wiggles make up a high fraction of the total energy of the string network which physically corresponds to the tensionless limit and also provide a brief discussion of the opposite linear limit where wiggles are a small fraction of the total energy. A companion paper will discuss the detailed modelling and scaling behavior of the smallscale wiggles in the general model, together with a basic comparison with numerical simulations.

Charged black holes in stringinspired gravity I. Causal structures and responses of the BransDicke field ; We investigate gravitational collapses of charged black holes in stringinspired gravity models, including dilaton gravity and braneworld model, as well as fR gravity and the ghost limit. If we turn on gauge coupling, the causal structures and the responses of the BransDicke field depend on the coupling between the charged matter and the BransDicke field. For Type IIA inspired models, a Cauchy horizon exists, while there is no Cauchy horizon for Type I or Heterotic inspired models. For Type IIA inspired models, the nohair theorem is satisfied asymptotically, while it is biased to the weak coupling limit for Type I or Heterotic inspired models. Apart from string theory, we find that in the ghost limit, a gravitational collapse can induce inflation by itself and create oneway traversable wormholes without the need of other special initial conditions.

Variations of the finestructure constant in exotic singularity models ; Various classes of exotic singularity models have been studied as possible mimic models for the observed recent acceleration of the universe. Here we further study one of these classes and, under the assumption that they are phenomenological toy models for the behavior of an underlying scalar field which also couples to the electromagnetic sector of the theory, obtain the corresponding behavior of the finestructure constant alpha for particular choices of model parameters that have been previously shown to be in reasonable agreement with cosmological observations. We then compare this predicted behavior with available measurements of alpha, thus constraining this putative coupling to electromagnetism. We find that values of the coupling which would provide a good fit to spectroscopic measurements of alpha are in more than threesigma tension with local atomic clock bounds. Future measurements by ESPRESSO and ELTHIRES will provide a definitive test of these models.

The genealogy of a solvable population model under selection with dynamics related to directed polymers ; We consider a stochastic model describing a constant size N population that may be seen as a directed polymer in random medium with N sites in the transverse direction. The population dynamics is governed by a noisy traveling wave equation describing the evolution of the individual fitnesses. We show that under suitable conditions the generations are independent and the model is characterized by an extended WrightFisher model, in which the individual i has a random fitness etai and the joint distribution of offspring nu1,ldots,nuN is given by a multinomial law with N trials and probability outcomes etai's. We then show that the average coalescence times scales like log N and that the limit genealogical trees are governed by the BolthausenSznitman coalescent, which validates the predictions by Brunet, Derrida, Mueller and Munier for this class of models. We also study the extended WrightFisher model, and show that, under certain conditions on etai, the limit may be Kingman's coalescent, a coalescent with multiple collisions, or a coalescent with simultaneous multiple collisions.

Constant Factor Approximation for Balanced Cut in the PIE model ; We propose and study a new semirandom semiadversarial model for Balanced Cut, a planted model with permutationinvariant random edges PIE. Our model is much more general than planted models considered previously. Consider a set of vertices V partitioned into two clusters L and R of equal size. Let G be an arbitrary graph on V with no edges between L and R. Let Erandom be a set of edges sampled from an arbitrary permutationinvariant distribution a distribution that is invariant under permutation of vertices in L and in R. Then we say that G Erandom is a graph with permutationinvariant random edges. We present an approximation algorithm for the Balanced Cut problem that finds a balanced cut of cost OErandom n textpolylogn in this model. In the regime when Erandom Omegan textpolylogn, this is a constant factor approximation with respect to the cost of the planted cut.

Search Strategies for Top Partners in Composite Higgs models ; We consider how best to search for top partners in generic composite Higgs models. We begin by classifying the possible group representations carried by top partners in models with and without a custodial SU2times SU2 rtimes mathbbZ2 symmetry protecting the rate for Z rightarrow boverlineb decays. We identify a number of minimal models whose top partners only have electric charges of frac13, frac23, or frac43 and thus decay to top or bottom quarks via a single Higgs or electroweak gauge boson. We develop an inclusive search for these based on a top veto, which we find to be more effective than existing searches. Less minimal models feature light states that can be sought in final states with likesign leptons and so we find that 2 straightforward LHC searches give a reasonable coverage of the gamut of composite Higgs models.

Scalable Topical Phrase Mining from Text Corpora ; While most topic modeling algorithms model text corpora with unigrams, human interpretation often relies on inherent grouping of terms into phrases. As such, we consider the problem of discovering topical phrases of mixed lengths. Existing work either performs post processing to the inference results of unigrambased topic models, or utilizes complex ngramdiscovery topic models. These methods generally produce lowquality topical phrases or suffer from poor scalability on even moderatelysized datasets. We propose a different approach that is both computationally efficient and effective. Our solution combines a novel phrase mining framework to segment a document into single and multiword phrases, and a new topic model that operates on the induced document partition. Our approach discovers high quality topical phrases with negligible extra cost to the bagofwords topic model in a variety of datasets including research publication titles, abstracts, reviews, and news articles.

Verifying Component and Connector Models against Crosscutting Structural Views ; The structure of component and connector CC models, which are used in many application domains of software engineering, consists of components at different containment levels, their typed input and output ports, and the connectors between them. CC views, presented in 24, can be used to specify structural properties of CC models in an expressive and intuitive way. In this work we address the verification of a CC model against a CC view and present efficient polynomial algorithms to decide satisfaction. A unique feature of our work, not present in existing approaches to checking structural properties of CC models, is the generation of witnesses for satisfactionnonsatisfaction and of short naturallanguage texts, which serve to explain and formally justify the verification results and point the engineer to its causes. A prototype tool and an evaluation over four example systems with multiple views, performance and scalability experiments, as well as a user study of the usefulness of the witnesses for engineers, demonstrate the contribution of our work to the stateoftheart in component and connector modeling and analysis.

Nonlinear filtering and optimal investment under partial information for stochastic volatility models ; This paper studies the question of filtering and maximizing terminal wealth from expected utility in a partially information stochastic volatility models. The special features is that the only information available to the investor is the one generated by the asset prices, and the unobservable processes will be modeled by a stochastic differential equations. Using the change of measure techniques, the partial observation context can be transformed into a full information context such that coefficients depend only on past history of observed prices filters processes. Adapting the stochastic nonlinear filtering, we show that under some assumptions on the model coefficients, the estimation of the filters depend on a priorimodels for the trend and the stochastic volatility. Moreover, these filters satisfy a stochastic partial differential equations named KushnerStratonovich equations. Using the martingale duality approach in this partially observed incomplete model, we can characterize the value function and the optimal portfolio. The main result here is that the dual value function associated to the martingale approach can be expressed, via the dynamic programmingapproach, in terms of the solution to a semilinear partial differential equation. We illustrate our results with some examples of stochastic volatility models popular in the financial literature.

Cosmic acceleration in noncanonical scalar field model An interacting scenario ; In this paper we have studied the dynamics of accelerating scenario within the framework of scalar field models possessing a noncanonical kinetic term. In this toy model, the scalar field is allowed to interact with the dark matter component through a source term. We have assumed a specific form for the coupling term and then have studied the dynamics of the scalar field having a constant equation of state parameter. We have also carried out the dynamical system study of such interacting noncanonical scalar field models for power law potentials for some physically relevant specific values of the model parameters. It has been found that the only for two particular stable fixed points of the system, an accelerating solution is possible and the universe will settle down to a LambdaCDM universe in future and thus there is no future singularity in this model.

On the particlehole symmetry of the fermionic spinless Hubbard model in D1 ; We revisit the particlehole symmetry of the onedimensional D1 fermionic spinless Hubbard model, associating that symmetry to the invariance of the Helmholtz free energy of the onedimensional spin12 XXZ Heisenberg model, under reversal of the longitudinal magnetic field and at any finite temperature. Upon comparing two regimes of that chain model so that the number of particles in one regime equals the number of holes in the other, one finds that, in general, their thermodynamics is similar, but not identical both models share the specific heat and entropy functions, but not the internal energy per site, the firstneighbor correlation functions, and the number of particles per site. Due to that symmetry, the difference between the firstneighbor correlation functions is proportional to the zcomponent of magnetization of the XXZ Heisenberg model. The results presented in this paper are valid for any value of the interaction strength parameter V, which describes the attractivenullrepulsive interaction of neighboring fermions.

Toward Using Surrogates to Accelerate Solution of Stochastic Electricity Grid Operations Problems ; Stochastic unit commitment models typically handle uncertainties in forecast demand by considering a finite number of realizations from a stochastic process model for loads. Accurate evaluations of expectations or higher moments for the quantities of interest require a prohibitively large number of model evaluations. In this paper we propose an alternative approach based on using surrogate models valid over the range of the forecast uncertainty. We consider surrogate models based on Polynomial Chaos expansions, constructed using sparse quadrature methods. Considering expected generation cost, we demonstrate the approach can lead to several orders of magnitude reduction in computational cost relative to using Monte Carlo sampling on the original model, for a given target error threshold.

MobilityAware Uplink Interference Model for 5G Heterogeneous Networks ; To meet the surging demand for throughput, 5G cellular networks need to be more heterogeneous and much denser, by deploying more and more small cells. In particular, the number of users in each small cell can change dramatically due to users' mobility, resulting in random and time varying uplink interference. This paper considers the uplink interference in a 5G heterogeneous network which is jointly covered by one macro cell and several small cells. Based on the L'evy flight moving model, a mobilityaware interference model is proposed to characterize the uplink interference from macro cell users to small cell users. In this model, the total uplink interference is characterized by its moment generating function, for both closed subscriber group CSG and open subscriber group CSG femto cells. In addition, the proposed interference model is a function of basic step length, which is a key velocity parameter of L'evy flights. It is shown by both theoretical analysis and simulation results that the proposed interference model provides a flexible way of evaluating the system performance in terms of success probability and average rate.

The Chromospheric Solar Millimeterwave Cavity; a Common Property in the Semiempirical Models ; The semiempirical models of the solar chromosphere are useful in the study of the solar radio emission at millimeter infrared wavelengths. However, current models do not reproduce the observations of the quiet sun. In this work we present a theoretical study of the radiative transfer equation for four semi empirical models at these wavelengths. We found that the Chromospheric Solar Milimeterwave Cavity CSMC, a region where the atmosphere becomes locally optically thin at millimeter wavelengths, is present in the semiempirical models under study. We conclude that the CSMC is a general property of the solar chromosphere where the semiempirical models shows temperature minimum.

Cosmological perturbations in nonlocal higherderivative gravity ; We study cosmological perturbations in a nonlocal higherderivative model of gravity introduced by Biswas, Mazumdar and Siegel. We extend previous work, which had focused on classical scalar perturbations around a cosine hyperbolic bounce solution, in three ways. First, we point out the existence of a Starobinsky solution in this model, which is more attractive from a phenomenological point of view even though it has no bounce. Second, we study classical vector and tensor perturbations. Third, we show how to quantize scalar and tensor perturbations in a de Sitter phase for choices of parameters such that the model is ghostfree. Our results show that the model is wellbehaved at this level, and are very similar to corresponding results in local fR models. In particular, for the Starobinsky solution of nonlocal higherderivative gravity, we find the same tensortoscalar ratio as for the conventional Starobinsky model.

Local models and hidden nonlocality in Quantum Theory ; This Master's thesis has two central subjects the simulation of correlations generated by local measurements on entangled quantum states by local hiddenvariables models and the revelation of hidden nonlocality. We present and detail the Werner's local model and the hidden nonlocality of some Werner states of dimension dgeq5, the GisinDegorre's local model for a Werner state of dimension d2 and the local model of Hirsch et al. for mixtures of the singlet state and noise, all of them for projective measurements. Finally, we introduce the local model for POVMs of Hirsch et al. for a state constructed upon the singlet with noise, that still violates the CHSH inequality after local filters are applied, hence presenting the socalled genuine hidden nonlocality.

Stellar Populations and the Star Formation Histories of LSB Galaxies III. Stellar Population Models ; A series of population models are designed to explore the star formation history of gasrich, low surface brightness LSB galaxies. LSB galaxies are unique in having properties of very blue colors, low Halpha emission and high gas fractions that indicated a history of constant star formation versus the declining star formation models used for most spirals and irregulars. The model simulations use an evolving multimetallicity composite population that follows a chemical enrichment scheme based on Milky Way observations. Color and time sensitive stellar evolution components i.e., BHB, TPAGB and blue straggler stars are included, and model colors are extended into the Spitzer wavelength regions for comparison to new observations. In general, LSB galaxies are well matched to the constant star formation scenario with the variation in color explained by a fourfold increasedecrease in star formation over the last 0.5 Gyrs i.e., weak bursts. Earlytype spirals, from the S4G sample, are better fit by a declining star formation model where star formation has decreased by 40 in the last 12 Gyrs.

Spectral goodness of fit for network models ; We introduce a new statistic, 'spectral goodness of fit' SGOF to measure how well a network model explains the structure of an observed network. SGOF provides an absolute measure of fit, analogous to the standard Rsquared in linear regression. Additionally, as it takes advantage of the properties of the spectrum of the graph Laplacian, it is suitable for comparing network models of diverse functional forms, including both fitted statistical models and algorithmic generative models of networks. After introducing, defining, and providing guidance for interpreting SGOF, we illustrate the properties of the statistic with a number of examples and comparisons to existing techniques. We show that such a spectral approach to assessing model fit fills gaps left by earlier methods and can be widely applied.

Walking dynamics are symmetric enough ; Many biological phenomena such as locomotion, circadian cycles, and breathing are rhythmic in nature and can be modeled as rhythmic dynamical systems. Dynamical systems modeling often involves neglecting certain characteristics of a physical system as a modeling convenience. For example, human locomotion is frequently treated as symmetric about the sagittal plane. In this work, we test this assumption by examining human walking dynamics around the steadystate limitcycle. Here we adapt statistical cross validation in order to examine whether there are statistically significant asymmetries, and even if so, test the consequences of assuming bilateral symmetry anyway. Indeed, we identify significant asymmetries in the dynamics of human walking, but nevertheless show that ignoring these asymmetries results in a more consistent and predictive model. In general, neglecting evident characteristics of a system can be more than a modeling convenienceit can produce a better model.

SimLex999 Evaluating Semantic Models with Genuine Similarity Estimation ; We present SimLex999, a gold standard resource for evaluating distributional semantic models that improves on existing resources in several important ways. First, in contrast to gold standards such as WordSim353 and MEN, it explicitly quantifies similarity rather than association or relatedness, so that pairs of entities that are associated but not actually similar Freud, psychology have a low rating. We show that, via this focus on similarity, SimLex999 incentivizes the development of models with a different, and arguably wider range of applications than those which reflect conceptual association. Second, SimLex999 contains a range of concrete and abstract adjective, noun and verb pairs, together with an independent rating of concreteness and free association strength for each pair. This diversity enables finegrained analyses of the performance of models on concepts of different types, and consequently greater insight into how architectures can be improved. Further, unlike existing gold standard evaluations, for which automatic approaches have reached or surpassed the interannotator agreement ceiling, stateoftheart models perform well below this ceiling on SimLex999. There is therefore plenty of scope for SimLex999 to quantify future improvements to distributional semantic models, guiding the development of the next generation of representationlearning architectures.