Datasets:

sswt

/

arxiv_sample_50K

like 0

Finitesize corrections of the Entanglement Entropy of critical quantum chains ; Using the density matrix renormalization group, we calculated the finitesize corrections of the entanglement alphaRenyi entropy of a single interval for several critical quantum chains. We considered models with U1 symmetry like the spin12 XXZ and spin1 FateevZamolodchikov models, as well models with discrete symmetries such as the Ising, the BlumeCapel and the threestate Potts models. These corrections contain physically relevant information. Their amplitudes, that depend on the value of alpha, are related to the dimensions of operators in the conformal field theory governing the longdistance correlations of the critical quantum chains. The obtained results together with earlier exact and numerical ones allow us to formulate some general conjectures about the operator responsible for the leading finitesize correction of the alphaRenyi entropies. We conjecture that the exponent of the leading finitesize correction of the alphaRenyi entropies is palpha2Xepsilonalpha for alpha1 and p1nu, where Xepsilon is the dimensions of the energy operator of the model and nu2 for all the models.

Steady states in a structured epidemic model with Wentzell boundary condition ; We introduce a nonlinear structured population model with diffusion in the state space. Individuals are structured with respect to a continuous variable which represents a pathogen load. The class of uninfected individuals constitutes a special compartment that carries mass, hence the model is equipped with generalized Wentzell or dynamic boundary conditions. Our model is intended to describe the spread of infection of a vertically transmitted disease, for example Wolbachia in a mosquito population. Therefore the infinite dimensional nonlinearity arises in the recruitment term. First we establish global existence of solutions and the Principle of Linearised Stability for our model. Then, in our main result, we formulate simple conditions, which guarantee the existence of nontrivial steady states of the model. Our method utilizes an operator theoretic framework combined with a fixed point approach. Finally, in the last section we establish a sufficient condition for the local asymptotic stability of the positive steady state.

Arbitragefree Selforganizing Markets with GARCH Properties Generating them in the Lab with a Lattice Model ; We extend our studies of a quantum field model defined on a lattice having the dilation group as a local gauge symmetry. The model is relevant in the crossdisciplinary area of econophysics. A corresponding proposal by Ilinski aimed at gauge modeling in nonequilibrium pricing is realized as a numerical simulation of the oneasset version. The gauge field background enforces minimal arbitrage, yet allows for statistical fluctuations. The new feature added to the model is an updating prescription for the simulation that drives the model market into a selforganized critical state. Taking advantage of some flexibility of the updating prescription, stylized features and dynamical behaviors of realworld markets are reproduced in some detail.

Supersymmetric standard model inflation in the Planck era ; We propose a cosmological inflationary scenario based on the supergravityembedded Standard Model supplemented by the righthanded neutrinos. We show that with an appropriate Kahler potential the LHu direction gives rise to successful inflation that is similar to the recently proposed gravitationally coupled Higgs inflation model but is free from the unitarity problem. The mass scale MR of the righthanded neutrinos is subject to the seesaw relation and the present 2sigma constraint from the WMAP7BAOH0 data sets its lower bound MRgtrsim 1 TeV. Generation of the baryon asymmetry is naturally implemented in this model. We expect within a few years new observational data from the Planck satellite clearly discriminates this model from other existing inflationary models arising from the same Lagrangian, and possibly yields stringent constraints on MR.

Generalization of the JTZ model to open plane wakes ; The JTZ model C. Jung, T. T'el and E. Ziemniak, Chaos bf 3, 1993 555, as a theoretical model of a plane wake behind a circular cylinder in a narrow channel at a moderate Reynolds number, has previously been employed to analyze phenomena of chaotic scattering. It is extended here to describe an open plane wake without the confined narrow channel by incorporating a double row of shedding vortices into the intermediate and far wake. The extended JTZ model is found in qualitative agreement with both direct numerical simulations and experimental results in describing streamlines and vorticity contours. To further validate its applications to particle transport processes, the interaction between small spherical particles and vortices in an extended JTZ model flow is studied. It is shown that the particle size has significant influences on the features of particle trajectories, which have two characteristic patterns one is rotating around the vortex centers and the other accumulating in the exterior of vortices. Numerical results based on the extended JTZ model are found in qualitative agreement with experimental ones in the normal range of particle sizes.

Short and Long Range Population Dynamics of the Monarch ; The monarch butterfly annually migrates from central Mexico to southern Canada. During recent decades, its population has been reduced due to human interaction with their habitat. We examine the effect of herbicide usage on the monarch butterfly's population by creating a system of linear and nonlinear ordinary differential equations that describe the interaction between the monarch's population and its environment at various stages of migration spring migration, summer loitering, and fall migration. The model has various stages that are used to describe the dynamics of the monarch butterfly population over multiple generations. In Stage 1, we propose a system of coupled ordinary differential equations that model the populations of the monarch butterflies and larvae during spring migration. In Stage 2, we propose a predatorprey model with age structure to model the population dynamics at the summer breeding site. In Stages 3 and 4, we propose exponential decay functions to model the monarch butterfly's fall migration to central Mexico and their time at the overwintering site. The model is used to analyze the longterm behavior of the monarch butterflies through numerical analysis, given data available in the research literature.

Online Adaptive Statistical Compressed Sensing of Gaussian Mixture Models ; A framework of online adaptive statistical compressed sensing is introduced for signals following a mixture model. The scheme first uses nonadaptive measurements, from which an online decoding scheme estimates the model selection. As soon as a candidate model has been selected, an optimal sensing scheme for the selected model continues to apply. The final signal reconstruction is calculated from the ensemble of both the nonadaptive and the adaptive measurements. For signals generated from a Gaussian mixture model, the online adaptive sensing algorithm is given and its performance is analyzed. On both synthetic and real image data, the proposed adaptive scheme considerably reduces the average reconstruction error with respect to standard statistical compressed sensing that uses fully random measurements, at a marginally increased computational complexity.

Modeling of biological doses and mechanical effects on bone transduction ; Shear stress, hormones like parathyroid and mineral elements like calcium mediate the amplitude of stimulus signal which affects the rate of bone remodeling. The current study investigates the theoretical effects of different metabolic doses in stimulus signal level on bone. The model was built considering the osteocyte as the sensing center mediated by coupled mechanical shear stress and some biological factors. The proposed enhanced model was developed based on previously published works dealing with different aspects of bone transduction. It describes the effects of physiological doses variations of Calcium, Parathyroid Hormone, Nitric Oxide and Prostaglandin E2 on the stimulus level sensed by osteocytes in response to applied shear stress generated by interstitial fluid flow. We retained the metabolic factors Parathyroid Hormone, Nitric Oxide, and Prostaglandin E2 as parameters of bone cell mechanosensitivity because stimulationinhibition of induced pathways stimulates osteogenic response in vivo. We then tested the model response in term of stimulus signal variation versus the biological factors doses to external mechanical stimuli. Despite the limitations of the model, it is consistent and has physiological bases. Biological inputs are histologically measurable. This makes the model amenable to experimental verification.

The rate of convergence to stationarity for MG1 models with admission controls via coupling ; We study the workload processes of two restricted MG1 queueing systems in Model 1 any service requirement that would exceed a certain capacity threshold is truncated; in Model 2 new arrivals do not enter the system if they have to wait more than a fixed threshold time in line. For Model 1 we obtain several results concerning the rate of convergence to equilibrium. In particular we derive uniform bounds for geometric ergodicity with respect to certain subclasses. However, we prove that for the class of all Model 1 workload processes there is no uniform bound. For Model 2 we prove that geometric ergodicity follows from the finiteness of the momentgenerating function of the service time distribution and derive bounds for the convergence rates in special cases. The proofs use the coupling method.

Estimation for an additive growth curve model with orthogonal design matrices ; An additive growth curve model with orthogonal design matrices is proposed in which observations may have different profile forms. The proposed model allows us to fit data and then estimate parameters in a more parsimonious way than the traditional growth curve model. Twostage generalized leastsquares estimators for the regression coefficients are derived where a quadratic estimator for the covariance of observations is taken as the firststage estimator. Consistency, asymptotic normality and asymptotic independence of these estimators are investigated. Simulation studies and a numerical example are given to illustrate the efficiency and parsimony of the proposed model for model specifications in the sense of minimizing Akaike's information criterion AIC.

Orbital selective crossover and Mott transitions in an asymmetric Hubbard model of cold atoms in optical lattices ; We study the asymmetric Hubbard model at halffilling as a generic model to describe the physics of two species of repulsively interacting fermionic cold atoms in optical lattices. We use Dynamical Mean Field Theory to obtain the paramagnetic phase diagram of the model as function of temperature, interaction strength and hopping asymmetry. A Mott transition with a region of two coexistent solutions is found for all nonzero values of the hopping asymmetry. At low temperatures the metallic phase is a heavy Fermiliquid, qualitatively analogous to the Fermi liquid state of the symmetric Hubbard model. Above a coherence temperature, an orbitalselective crossover takes place, wherein one fermionic species effectively localizes, and the resulting bad metallic state resembles the nonFermi liquid state of the FalicovKimball model. We compute observables relevant to cold atom systems such as the double occupation, the specific heat and entropy and characterize their behavior in the different phases.

Modelling and simulation of complex systems an approach based on multilevel agents ; A complex system is made up of many components with many interactions. So the design of systems such as simulation systems, cooperative systems or assistance systems includes a very accurate modelling of interactional and communicational levels. The agentbased approach provides an adapted abstraction level for this problem. After having studied the organizational context and communicative capacities of agentbased systems, to simulate the reorganization of a flexible manufacturing, to regulate an urban transport system, and to simulate an epidemic detection system, our thoughts on the interactional level were inspired by humanmachine interface models, especially those in cognitive engineering. To provide a general framework for agentbased complex systems modelling, we then proposed a scale of four behaviours that agents may adopt in their complex systems reactive, routine, cognitive, and collective. To complete the description of multilevel agent models, which is the focus of this paper, we illustrate our modelling and discuss our ongoing work on each level.

Cosmological models with YangMills fields ; Cosmological models with an SU2 YangMills field are studied. For a specific model with a minimally coupled YangMills Lagrangian, which includes an arbitrary function of the secondorder term and a fourthorder term, a corresponding reconstruction program is proposed. It is shown that the model with minimal coupling has no de Sitter solutions, for any nontrivial function of the secondorder term. To get de Sitter solutions, a gravitational model with nonminimally coupled YangMills fields is then investigated. It is shown that the model with nonminimal coupling has in fact a de Sitter solution, even in absence of the cosmological constant term.

Distributional exact diagonalization formalism for quantum impurity models ; We develop a method for calculating the selfenergy of a quantum impurity coupled to a continuous bath by stochastically generating a distribution of finite Anderson models that are solved by exact diagonalization, using the noninteracting local spectral function as a probability distribution for the sampling. The method enables calculation of the full analytic selfenergy and singleparticle Green's function in the complex frequency plane, without analytic continuation, and can be used for both finite and zero temperature at arbitrary fillings. Results are in good agreement with imaginary frequency data from continuoustime quantum Monte Carlo calculations for the single impurity Anderson model and the twoorbital Hubbard model within dynamical mean field theory DMFT as well as real frequency data for self energy of the single band Hubbard model within DMFT using numerical renormalization group. The method should be applicable to a wide range of quantum impurity models and particularly useful when highprecision real frequency results are sought.

Neutrinos from OffShell Final States and the Indirect Detection of Dark Matter ; We revisit the annihilation of dark matter to neutrinos in the Sun near the WW and tt kinematic thresholds. We investigate the potential importance of annihilation to WW in a minimal dark matter model in which a Majorana singlet is mixed with a vectorlike electroweak doublet, but many results generalize to other models of weaklyinteracting dark matter. We reevaluate the indirect detection constraints on this model and find that, once all annihilation channels are properly taken into account, the most stringent constraints on spindependent scattering for dark matter mass 60 GeV mX mt are derived from the results of the SuperKamiokande experiment. Moreover, we establish the modelindependent statement that Majorana dark matter whose thermal relic abundance and neutrino signals are both controlled by annihilation via an schannel Z boson is excluded for 70 GeV mX mW. In some models, annihilation to tt can affect indirect detection, notably by competing with annihilation to gauge boson final states and thereby weakening neutrino signals. However, in the minimal model, this final state is largely negligible, only allowing dark matter with mass a few GeV below the top quark mass to evade exclusion.

Generating a Performance Stochastic Model from UML Specifications ; Since its initiation by Connie Smith, the process of Software Performance Engineering SPE is becoming a growing concern. The idea is to bring performance evaluation into the software design process. This suitable methodology allows software designers to determine the performance of software during design. Several approaches have been proposed to provide such techniques. Some of them propose to derive from a UML Unified Modeling Language model a performance model such as Stochastic Petri Net SPN or Stochastic process Algebra SPA models. Our work belongs to the same category. We propose to derive from a UML model a Stochastic Automata Network SAN in order to obtain performance predictions. Our approach is more flexible due to the SAN modularity and its high resemblance to UML' statechart diagram.

Building a Model Astrolabe ; This paper presents a handson introduction to the medieval astrolabe, based around a working model which can be constructed from photocopies of the supplied figures. As well as describing how to assemble the model, I also provide a brief explanation of how each of its various parts might be used. The printed version of this paper includes only the parts needed to build a single model prepared for use at latitudes around 52degN, but an accompanying electronic file archive includes equivalent images which can be used to build models prepared for use at any other latitude. The vector graphics scripts used to generate the models are also available for download, allowing customised astrolabes to be made.

Kinetically constrained spin models on trees ; We analyze kinetically constrained 01 spin models KCSM on rooted and unrooted trees of finite connectivity. We focus in particular on the class of FriedricksonAndersen models FAjf and on an oriented version of them. These tree models are particularly relevant in physics literature since some of them undergo an ergodicity breaking transition with the mixed firstsecond order character of the glass transition. Here we first identify the ergodicity regime and prove that the critical density for FAjf and OFAjf models coincide with that of a suitable bootstrap percolation model. Next we prove for the first time positivity of the spectral gap in the whole ergodic regime via a novel argument based on martingales ideas. Finally, we discuss how this new technique can be generalized to analyze KCSM on the regular lattice mathbbZd.

Conceptual Level Design of Semistructured Database System Graphsemantic Based Approach ; This paper has proposed a Graph semantic based conceptual model for semistructured database system, called GOOSSDM, to conceptualize the different facets of such system in object oriented paradigm. The model defines a set of graph based formal constructs, variety of relationship types with participation constraints and rich set of graphical notations to specify the conceptual level design of semistructured database system. The proposed design approach facilitates modeling of irregular, heterogeneous, hierarchical and nonhierarchical semistructured data at the conceptual level. Moreover, the proposed GOOSSDM is capable to model XML document at conceptual level with the facility of documentcentric design, ordering and disjunction characteristic. A rule based transformation mechanism of GOOSSDM schema into the equivalent XML Schema Definition XSD also has been proposed in this paper. The concepts of the proposed conceptual model have been implemented using Generic Modeling Environment GME.

Constraints on the pMSSM from searches for squarks and gluinos by ATLAS ; We study the impact of the jets and missing transverse momentum SUSY analyses of the ATLAS experiment on the phenomenological MSSM pMSSM. We investigate sets of SUSY models with a flat and logarithmic prior in the SUSY mass scale and a mass range up to 1 and 3 TeV, respectively. These models were found previously in the study 'Supersymmetry without Prejudice'. Removing models with longlived SUSY particles, we show that 99 of 20000 randomly generated pMSSM model points with a flat prior and 87 for a logarithmic prior are excluded by the ATLAS results. For models with squarks and gluinos below 600 GeV all models of the pMSSM grid are excluded. We identify SUSY spectra where the current ATLAS search strategy is less sensitive and propose extensions to the inclusive jets search channel.

SparsityPromoting Bayesian Dynamic Linear Models ; Sparsitypromoting priors have become increasingly popular over recent years due to an increased number of regression and classification applications involving a large number of predictors. In time series applications where observations are collected over time, it is often unrealistic to assume that the underlying sparsity pattern is fixed. We propose here an original class of flexible Bayesian linear models for dynamic sparsity modelling. The proposed class of models expands upon the existing Bayesian literature on sparse regression using generalized multivariate hyperbolic distributions. The properties of the models are explored through both analytic results and simulation studies. We demonstrate the model on a financial application where it is shown that it accurately represents the patterns seen in the analysis of stock and derivative data, and is able to detect major events by filtering an artificial portfolio of assets.

A broadband flux scale for low frequency radio telescopes ; We present parameterized broadband spectral models valid at frequencies between 30300 MHz for six bright radio sources selected from the 3C survey, spread in Right Ascension from 024 hours. For each source, data from the literature are compiled and tied to a common flux density scale. These data are then used to parameterize an analytic polynomial spectral calibration model. The optimal polynomial order in each case is determined using the ratio of the Bayesian evidence for the candidate models. Maximum likelihood parameter values for each model are presented, with associated errors, and the percentage error in each model as a function of frequency is derived. These spectral models are intended as an initial reference for science from the new generation of low frequency telescopes now coming on line, with particular emphasis on the Low Frequency Array LOFAR.

A Short Note on Gaussian Process Modeling for Large Datasets using Graphics Processing Units ; The graphics processing unit GPU has emerged as a powerful and cost effective processor for general performance computing. GPUs are capable of an order of magnitude more floatingpoint operations per second as compared to modern central processing units CPUs, and thus provide a great deal of promise for computationally intensive statistical applications. Fitting complex statistical models with a large number of parameters andor for large datasets is often very computationally expensive. In this study, we focus on Gaussian process GP models statistical models commonly used for emulating expensive computer simulators. We demonstrate that the computational cost of implementing GP models can be significantly reduced by using a CPUGPU heterogeneous computing system over an analogous implementation on a traditional computing system with no GPU acceleration. Our small study suggests that GP models are fertile ground for further implementation on CPUGPU systems.

UPDATE February 2012 The Food Crises Predictive validation of a quantitative model of food prices including speculators and ethanol conversion ; Increases in global food prices have led to widespread hunger and social unrestand an imperative to understand their causes. In a previous paper published in September 2011, we constructed for the first time a dynamic model that quantitatively agreed with food prices. Specifically, the model fit the FAO Food Price Index time series from January 2004 to March 2011, inclusive. The results showed that the dominant causes of price increases during this period were investor speculation and ethanol conversion. The model included investor trend following as well as shifting between commodities, equities and bonds to take advantage of increased expected returns. Here, we extend the food prices model to January 2012, without modifying the model but simply continuing its dynamics. The agreement is still precise, validating both the descriptive and predictive abilities of the analysis. Policy actions are needed to avoid a third speculative bubble that would cause prices to rise above recent peaks by the end of 2012.

Novel gluino cascade decays in E6 inspired models ; We point out that the extra neutralinos and charginos, generically appearing in a large class of E6 inspired models, lead to distinctive signatures from gluino cascade decays involving longer decay chains, more visible transverse energy, softer jets and leptons and less missing transverse energy than in the Minimal Supersymmetric Standard Model MSSM. On the one hand, this makes the gluino harder to discover for certain types of conventional analysis. On the other hand, the E6 inspired models have enhanced 3 and 4lepton signatures, as compared to the MSSM, making the gluino more visible in these channels. After extensive scans over the parameter space, we focus on representative benchmark points for the two models, and perform a detailed Monte Carlo analysis of the 3lepton channel, showing that E6 inspired models are clearly distinguishable from the MSSM in gluino cascade decays.

Supersymmetrical Separation of Variables for Scarf II Model Partial Solvability ; Recently, a new quantum model twodimensional generalization of the Scarf II was completely solved analytically by SUSY method for the integer values of parameter. Now, the same integrable model, but with arbitrary values of parameter, will be studied by means of supersymmetrical intertwining relations. The Hamiltonian does not allow the conventional separation of variables, but the supercharge operator does allow, leading to the partial solvability of the model. This approach, which can be called as the first variant of SUSYseparation, together with shape invariance of the model, provides analytical calculation of the part of spectrum and corresponding wave functions quasiexactsolvability. The model is shown to obey two different variants of shape invariance which can be combined effectively in construction of energy levels and wave functions.

Anisotropic BianchiI Cosmological Models in String Cosmology with Variable Deceleration Parameter ; The present study deals with spatially homogeneous and anisotropic BianchiI cosmological model representing massive strings. The energymomentum tensor, as formulated by Letelier Phys. Rev. D 28 2414, 1983 has been used to construct massive string cosmological model for which we assume that the expansion scalar in the model is proportional to one of the components of shear tensor. The Einstein's field equations have been solved by considering time dependent deceleration parameter which renders the scale factor a sinhalpha tfrac1n, where alpha and n are constants. It has been detected that, for n 1, the presented model has a transition of the universe from the early decelerated phase to the recent accelerating phase at present epoch while for 0 n leq 1, this describes purely accelerating universe which is consistent with recent astrophysical observations. Moreover, some physical and geometric properties of the model along with physical acceptability of the solutions have been also discussed in detail.

On the Quantum Geometry of Multicritical CDT ; We discuss extensions of a recently introduced model of multicritical CDT to higher multicritical points. As in the case of pure CDT the continuum limit can be taken on the level of the action and the resulting continuum surface model is again described by a matrix model. The resolvent, a simple observable of the quantum geometry which is accessible from the matrix model is calculated for arbitrary multicritical points. We go beyond the matrix model by determining the propagator using the peeling procedure which is used to extract the effective quantum Hamiltonian and the fractal dimension in agreement with earlier results by Ambjorn et al. With this at hand a string field theory formalism for multicritical CDT is introduced and it is shown that the DysonSchwinger equations match the loop equations of the matrix model. We conclude by commenting on how to formally obtain the sum over topologies and a relation to stochastic quantisation.

A Universal Model of Commuting Networks ; We test a recently proposed model of commuting networks on 80 case studies from different regions of the world Europe and UnitedStates and with geographic units of different sizes municipality, county, region. The model takes as input the number of commuters coming in and out of each geographic unit and generates the matrix of commuting flows betwen the geographic units. We show that the single parameter of the model, which rules the compromise between the influence of the distance and job opportunities, follows a universal law that depends only on the average surface of the geographic units. We verified that the law derived from a part of the case studies yields accurate results on other case studies. We also show that our model significantly outperforms the two other approaches proposing a universal commuting model Balcan et al. 2009; Simini et al. 2012, particularly when the geographic units are small e.g. municipalities.

Longtime Behavior of a Twolayer Model of Baroclinic Quasigeostrophic Turbulence ; We study a viscous twolayer quasigeostrophic betaplane model that is forced by imposition of a spatially uniform vertical shear in the eastward zonal component of the layer flows, or equivalently a spatially uniform northsouth temperature gradient. We prove that the model is linearly unstable, but that nonlinear solutions are bounded in time by a bound which is independent of the initial data and is determined only by the physical parameters of the model. We further prove, using arguments first presented in the study of the KuramotoSivashinsky equation, the existence of an absorbing ball in appropriate function spaces, and in fact the existence of a compact finitedimensional attractor, and provide upper bounds for the fractal and Hausdorff dimensions of the attractor. Finally, we show the existence of an inertial manifold for the dynamical system generated by the model's solution operator. Our results provide rigorous justification for observations made by Panetta based on longtime numerical integrations of the model equations.

Statistical emulation of a tsunami model for sensitivity analysis and uncertainty quantification ; Due to the catastrophic consequences of tsunamis, early warnings need to be issued quickly in order to mitigate the hazard. Additionally, there is a need to represent the uncertainty in the predictions of tsunami characteristics corresponding to the uncertain trigger features e.g. either position, shape and speed of a landslide, or sea floor deformation associated with an earthquake. Unfortunately, computer models are expensive to run. This leads to significant delays in predictions and makes the uncertainty quantification impractical. Statistical emulators run almost instantaneously and may represent well the outputs of the computer model. In this paper, we use the Outer Product Emulator to build a fast statistical surrogate of a landslidegenerated tsunami computer model. This Bayesian framework enables us to build the emulator by combining prior knowledge of the computer model properties with a few carefully chosen model evaluations. The good performance of the emulator is validated using the LeaveOneOut method.

The scaling limit of the energy correlations in non integrable Ising models ; We obtain an explicit expression for the multipoint energy correlations of a non solvable twodimensional Ising models with nearest neighbor ferromagnetic interactions plus a weak finite range interaction of strength lambda, in a scaling limit in which we send the lattice spacing to zero and the temperature to the critical one. Our analysis is based on an exact mapping of the model into an interacting lattice fermionic theory, which generalizes the one originally used by Schultz, Mattis and Lieb for the nearest neighbor Ising model. The interacting model is then analyzed by a multiscale method first proposed by Pinson and Spencer. If the lattice spacing is finite, then the correlations cannot be computed in closed form rather, they are expressed in terms of infinite, convergent, power series in lambda. In the scaling limit, these infinite expansions radically simplify and reduce to the limiting energy correlations of the integrable Ising model, up to a finite renormalization of the parameters. Explicit bounds on the speed of convergence to the scaling limit are derived.

Models of transfinite provability logic ; For any ordinal Lambda, we can define a polymodal logic GLPLambda, with a modality xi for each xiLambda. These represent provability predicates of increasing strength. Although GLPLambda has no Kripke models, Ignatiev showed that indeed one can construct a Kripke model of the variablefree fragment with natural number modalities. Later, Icard defined a topological model for the same fragment which is very closely related to Ignatiev's. In this paper we show how to extend these constructions for arbitrary Lambda. More generally, for each Theta,Lambda we build a Kripke model ITheta,Lambda and a topological model TTheta,Lambda, and show that the closed fragment of GLPLambda is sound for both of these structures, as well as complete, provided Theta is large enough.

Diffusion of two molecular species in a crowded environment theory and experiments ; Diffusion of a two component fluid is studied in the framework of differential equations, but where these equations are systematically derived from a welldefined microscopic model. The model has a finite carrying capacity imposed upon it at the mesoscopic level and this is shown to lead to nonlinear cross diffusion terms that modify the conventional Fickean picture. After reviewing the derivation of the model, the experiments carried out to test the model are described. It is found that it can adequately explain the dynamics of two dense ink drops simultaneously evolving in a container filled with water. The experiment shows that molecular crowding results in the formation of a dynamical barrier that prevents the mixing of the drops. This phenomenon is successfully captured by the model. This suggests that the proposed model can be justifiably viewed as a generalization of standard diffusion to a multispecies setting, where crowding and steric interferences are taken into account.

Weighted Patterns as a Tool for Improving the Hopfield Model ; We generalize the standard Hopfield model to the case when a weight is assigned to each input pattern. The weight can be interpreted as the frequency of the pattern occurrence at the input of the network. In the framework of the statistical physics approach we obtain the saddlepoint equation allowing us to examine the memory of the network. In the case of unequal weights our model does not lead to the catastrophic destruction of the memory due to its overfilling that is typical for the standard Hopfield model. The real memory consists only of the patterns with weights exceeding a critical value that is determined by the weights distribution. We obtain the algorithm allowing us to find this critical value for an arbitrary distribution of the weights, and analyze in detail some particular weights distributions. It is shown that the memory decreases as compared to the case of the standard Hopfield model. However, in our model the network can learn online without the catastrophic destruction of the memory.

Communication Analysis modelling techniques ; This report describes and illustrates several modelling techniques proposed by Communication Analysis; namely Communicative Event Diagram, Message Structures and Event Specification Templates. The Communicative Event Diagram is a business process modelling technique that adopts a communicational perspective by focusing on communicative interactions when describing the organizational work practice, instead of focusing on physical activities1; at this abstraction level, we refer to business activities as communicative events. Message Structures is a technique based on structured text that allows specifying the messages associated to communicative events. Event Specification Templates are a means to organise the requirements concerning a communicative event. This report can be useful to analysts and business process modellers in general, since, according to our industrial experience, it is possible to apply many Communication Analysis concepts, guidelines and criteria to other business process modelling notations such as BPMN. Also, Message Structures can complement business process models created with other notations different than Communicative Event Diagram.

Natural Flavour Conservation in a three Higgdoublet Model ; We consider an extension of the standard model SM with three SU2 scalar doublets and a discrete S3otimes mathbbZ2 symmetries. The irreducible representation of S3 has a singlet and a doublet, and here we show that the singlet corresponds to the SMlike Higgs and the two additional SU2 doublets forming a S3 doublet are inert. In general, in a three scalar doublet model, with or without S3 symmetry, the diagonalization of the mass matrices implies arbitrary unitary matrices. However, we show that in our model these matrices are of the tribimaximal type. We also analyzed the scalar mass spectra and the conditions for the scalar potential is bounded from below at the tree level. We also discuss some phenomenological consequences of the model.

Weightedindexed semiMarkov models for modeling financial returns ; In this paper we propose a new stochastic model based on a generalization of semiMarkov chains to study the high frequency price dynamics of traded stocks. We assume that the financial returns are described by a weighted indexed semiMarkov chain model. We show, through Monte Carlo simulations, that the model is able to reproduce important stylized facts of financial time series as the first passage time distributions and the persistence of volatility. The model is applied to data from Italian and German stock market from first of January 2007 until end of December 2010.

Products of Hidden Markov Models It Takes N1 to Tango ; Products of Hidden Markov ModelsPoHMMs are an interesting class of generative models which have received little attention since their introduction. This maybe in part due to their more computationally expensive gradientbased learning algorithm,and the intractability of computing the log likelihood of sequences under the model. In this paper, we demonstrate how the partition function can be estimated reliably via Annealed Importance Sampling. We perform experiments using contrastive divergence learning on rainfall data and data captured from pairs of people dancing. Our results suggest that advances in learning and evaluation for undirected graphical models and recent increases in available computing power make PoHMMs worth considering for complex timeseries modeling tasks.

A reduced integer programming model for the ferry scheduling problem ; We present an integer programming model for the ferry scheduling problem, improving existing models in various ways. In particular, our model has reduced size in terms of the number of variables and constraints compared to existing models by a factor of approximately On, where n being the number of ports. The model also handles efficiently loadunload time constraints, crew scheduling and passenger transfers. Experiments using real world data produced high quality solutions in 12 hours using CPLEX 12.4 with a performance guarantee of within 15 of optimality, on average. This establishes that using a general purpose integer programming solver is a viable alternative in solving the ferry scheduling problem of moderate size.

Daphnias from the individual based model to the large population equation ; The class of deterministic 'Daphnia' models treated by Diekmann et al. J Math Biol 61 277318, 2010 has a long history going back to Nisbet and Gurney Theor Pop Biol 23 114135, 1983 and Diekmann et al. Nieuw Archief voor Wiskunde 4 82109, 1984. In this note, we formulate the individual based models IBM supposedly underlying those deterministic models. The models treat the interaction between a general sizestructured consumer population 'Daphnia' and an unstructured resource 'algae'. The discrete, size and agestructured Daphnia population changes through births and deaths of its individuals and throught their aging and growth. The birth and death rates depend on the sizes of the individuals and on the concentration of the algae. The latter is supposed to be a continuous variable with a deterministic dynamics that depends on the Daphnia population. In this model setting we prove that when the Daphnia population is large, the stochastic differential equation describing the IBM can be approximated by the delay equation featured in Diekmann et al., l.c..

Model Driven Mutation Applied to Adaptative Systems Testing ; Dynamically Adaptive Systems modify their behav ior and structure in response to changes in their surrounding environment and according to an adaptation logic. Critical sys tems increasingly incorporate dynamic adaptation capabilities; examples include disaster relief and space exploration systems. In this paper, we focus on mutation testing of the adaptation logic. We propose a fault model for adaptation logics that classifies faults into environmental completeness and adaptation correct ness. Since there are several adaptation logic languages relying on the same underlying concepts, the fault model is expressed independently from specific adaptation languages. Taking benefit from modeldriven engineering technology, we express these common concepts in a metamodel and define the operational semantics of mutation operators at this level. Mutation is applied on model elements and model transformations are used to propagate these changes to a given adaptation policy in the chosen formalism. Preliminary results on an adaptive web server highlight the difficulty of killing mutants for adaptive systems, and thus the difficulty of generating efficient tests.

On Composite Two Higgs Doublet Models ; We investigate composite two Higgs doublet models realized as pseudo Goldstone modes, generated through the spontaneous breaking of a global symmetry due to strong dynamic at the TeV scale. A detailed comparative survey of two possible symmetry breaking patterns, SU5 SU4 x U1 and SU5 x SU4, is made. We point out choices for the Standard Model fermion representations that can alleviate some phenomenological constraints, with emphasis towards a simultaneous solution of anomalous Zbbarb coupling and Higgs mediated Flavor Changing Neutral Currents. We also write down the kinetic lagrangian for several models leading to Two Higgs Doublets and identify the anomalous contributions to the T parameter. Moreover, we describe a model based on the breaking SO9SO8 in which there is no treelevel breaking of custodial symmetry, discussing also the possible embeddings for the fermion fields.

Form factor approach to dynamical correlation functions in critical models ; We develop a form factor approach to the study of dynamical correlation functions of quantum integrable models in the critical regime. As an example, we consider the quantum nonlinear Schrodinger model. We derive longdistancelongtime asymptotic behavior of various twopoint functions of this model. We also compute edge exponents and amplitudes characterizing the powerlaw behavior of dynamical response functions on the particlehole excitation thresholds. These last results confirm predictions based on the nonlinear Luttinger liquid method. Our results rely on a first principles derivation, based on the microscopic analysis of the model, without invoking, at any stage, some correspondence with a continuous field theory. Furthermore, our approach only makes use of certain general properties of the model, so that it should be applicable, with possibly minor modifications, to a wide class of not necessarily integrable gapless one dimensional Hamiltonians.

Recommendation systems in the scope of opinion formation a model ; Aggregated data in real world recommender applications often feature fattailed distributions of the number of times individual items have been rated or favored. We propose a model to simulate such data. The model is mainly based on social interactions and opinion formation taking place on a complex network with a given topology. A threshold mechanism is used to govern the decision making process that determines whether a user is or is not interested in an item. We demonstrate the validity of the model by fitting attendance distributions from different real data sets. The model is mathematically analyzed by investigating its master equation. Our approach provides an attempt to understand recommender system's data as a social process. The model can serve as a starting point to generate artificial data sets useful for testing and evaluating recommender systems.

Human Mobility in a Continuum Approach ; Human mobility is investigated using a continuum approach that allows to calculate the probability to observe a trip to anyarbitrary region, and the fluxes between any two regions. The considered description offers a general and unified framework, in which previously proposed mobility models like the gravity model, the intervening opportunities model, and the recently introduced radiation model are naturally resulting as special cases. A new form of radiation model is derived and its validity is investigated using observational data offered by commuting trips obtained from the United States census data set, and the mobility fluxesextracted from mobile phone data collected in a western European country. The new modeling paradigm offered by this description suggests that the complex topological features observed in large mobility and transportation networks may be the result of a simple stochastic process taking place on an inhomogeneous landscape.

A model of competition among more than two languages ; We extend the AbramsStrogatz model for competition between two languages Nature 424, 900 2003 to the case of n2 competing states i.e., languages. Although the AbramsStrogatz model for n2 can be interpreted as modeling either majority preference or minority aversion, the two mechanisms are distinct when n3. We find that the condition for the coexistence of different states is independent of n under the pure majority preference, whereas it depends on n under the pure minority aversion. We also show that the stable coexistence equilibrium and stable monopoly equilibria can be multistable under the minority aversion and not under the majority preference. Furthermore, we obtain the phase diagram of the model when the effects of the majority preference and minority aversion are mixed, under the condition that different states have the same attractiveness. We show that the multistability is a generic property of the model facilitated by large n.

Group Sparse Additive Models ; We consider the problem of sparse variable selection in nonparametric additive models, with the prior knowledge of the structure among the covariates to encourage those variables within a group to be selected jointly. Previous works either study the group sparsity in the parametric setting e.g., group lasso, or address the problem in the nonparametric setting without exploiting the structural information e.g., sparse additive models. In this paper, we present a new method, called group sparse additive models GroupSpAM, which can handle group sparsity in additive models. We generalize the l1l2 norm to Hilbert spaces as the sparsityinducing penalty in GroupSpAM. Moreover, we derive a novel thresholding condition for identifying the functional sparsity at the group level, and propose an efficient block coordinate descent algorithm for constructing the estimate. We demonstrate by simulation that GroupSpAM substantially outperforms the competing methods in terms of support recovery and prediction accuracy in additive models, and also conduct a comparative experiment on a real breast cancer dataset.

Interacting agegraphic dark energy model in tachyon cosmology coupled to matter ; Scalarfield dark energy models for tachyon fields are often regarded as an effective description of an underlying theory of dark energy. In this paper, we propose the agegraphic dark energy model in tachyon cosmology by interaction between the components of the dark sectors. In the formalism, the interaction term emerges from the tachyon field nonminimally coupled to the matter Lagrangian in the model rather than being inserted into the formalism as an external source. The model is constrained by the observational data. Based on the best fitted parameters in both original and new agegraphic dark energy scenarios, the model is tested by Sne Ia data. The tachyon potential and tachyon field are reconstructed and coincidence problem is revisited.

Modeling Images using Transformed Indian Buffet Processes ; Latent feature models are attractive for image modeling, since images generally contain multiple objects. However, many latent feature models ignore that objects can appear at different locations or require presegmentation of images. While the transformed Indian buffet process tIBP provides a method for modeling transformationinvariant features in unsegmented binary images, its current form is inappropriate for real images because of its computational cost and modeling assumptions. We combine the tIBP with likelihoods appropriate for real images and develop an efficient inference, using the crosscorrelation between images and features, that is theoretically and empirically faster than existing inference techniques. Our method discovers reasonable components and achieve effective image reconstruction in natural images.

Flexible Modeling of Latent Task Structures in Multitask Learning ; Multitask learning algorithms are typically designed assuming some fixed, a priori known latent structure shared by all the tasks. However, it is usually unclear what type of latent task structure is the most appropriate for a given multitask learning problem. Ideally, the right latent task structure should be learned in a datadriven manner. We present a flexible, nonparametric Bayesian model that posits a mixture of factor analyzers structure on the tasks. The nonparametric aspect makes the model expressive enough to subsume many existing models of latent task structures e.g, meanregularized tasks, clustered tasks, lowrank or linearnonlinear subspace assumption on tasks, etc.. Moreover, it can also learn more general task structures, addressing the shortcomings of such models. We present a variational inference algorithm for our model. Experimental results on synthetic and realworld datasets, on both regression and classification problems, demonstrate the effectiveness of the proposed method.

Holographic dark energy described at the Hubble length ; We consider holographic cosmological models of dark energy in which the infrared cutoff is set by the Hubble's radius. We show that any interacting dark energy model with a matter like term able to alleviate the coincidence problem i.e., with a positive interaction term, regardless of its detailed form can be recast as a noninteracting model in which the holographic parameter evolves slowly with time. Two specific cases are analyzed. First, the interacting model presented in 1 is considered, and its corresponding noninteracting version found. Then, a new noninteracting model, with a specific expression of the timedependent holographic parameter, is proposed and analyzed along with its corresponding interacting version. We constrain the parameters of both models using observational data, and show that they can be told apart at the perturbative level.

Empar EMbased algorithm for parameter estimation of Markov models on trees ; The goal of branch length estimation in phylogenetic inference is to estimate the divergence time between a set of sequences based on compositional differences between them. A number of software is currently available facilitating branch lengths estimation for homogeneous and stationary evolutionary models. Homogeneity of the evolutionary process imposes fixed rates of evolution throughout the tree. In complex data problems this assumption is likely to put the results of the analyses in question. In this work we propose an algorithm for parameter and branch lengths inference in the discretetime Markov processes on trees. This broad class of nonhomogeneous models comprises the general Markov model and all its submodels, including both stationary and nonstationary models. Here, we adapted the wellknown ExpectationMaximization algorithm and present a detailed performance study of this approach for a selection of nonhomogeneous evolutionary models. We conducted an extensive performance assessment on multiple sequence alignments simulated under a variety of settings. We demonstrated high accuracy of the tool in parameter estimation and branch lengths recovery, proving the method to be a valuable tool for phylogenetic inference in real life problems. empar is an opensource C implementation of the methods introduced in this paper and is the first tool designed to handle nonhomogeneous data.

'Say EM' for Selecting Probabilistic Models for Logical Sequences ; Many real world sequences such as protein secondary structures or shell logs exhibit a rich internal structures. Traditional probabilistic models of sequences, however, consider sequences of flat symbols only. Logical hidden Markov models have been proposed as one solution. They deal with logical sequences, i.e., sequences over an alphabet of logical atoms. This comes at the expense of a more complex model selection problem. Indeed, different abstraction levels have to be explored. In this paper, we propose a novel method for selecting logical hidden Markov models from data called SAGEM. SAGEM combines generalized expectation maximization, which optimizes parameters, with structure search for model selection using inductive logic programming refinement operators. We provide convergence and experimental results that show SAGEM's effectiveness.

A Hierarchical Graphical Model for Record Linkage ; The task of matching coreferent records is known among other names as rocord linkage. For large recordlinkage problems, often there is little or no labeled data available, but unlabeled data shows a reasonable clear structure. For such problems, unsupervised or semisupervised methods are preferable to supervised methods. In this paper, we describe a hierarchical graphical model framework for the linakgeproblem in an unsupervised setting. In addition to proposing new methods, we also cast existing unsupervised probabilistic recordlinkage methods in this framework. Some of the techniques we propose to minimize overfitting in the above model are of interest in the general graphical model setting. We describe a method for incorporating monotinicity constraints in a graphical model. We also outline a bootstrapping approach of using singlefield classifiers to noisily label latent variables in a hierarchical model. Experimental results show that our proposed unsupervised methods perform quite competitively even with fully supervised recordlinkage methods.

NonGaussianities in multifield DBI inflation with a waterfall phase transition ; We study multifield DBI inflation models with a waterfall phase transition. This transition happens for a D3 brane moving in the warped conifold if there is an instability along angular directions. The transition converts the angular perturbations into the curvature perturbation. Thanks to this conversion, multifield models can evade the stringent constraints that strongly disfavour single field ultraviolet DBI inflation models in string theory. We explicitly demonstrate that our model satisfies current observational constraints on the spectral index and equilateral nonGaussianity as well as the bound on the tensor to scalar ratio imposed in string theory models. In addition we show that large local type nonGaussianity is generated together with equilateral nonGaussianity in this model.

Modelling Epistemic Systems ; In this Chapter, I will explore the use of modeling in order to understand how Science works. I will discuss the modeling of scientific communities, providing a general, noncomprehensive overview of existing models, with a focus on the use of the tools of AgentBased Modeling and Opinion Dynamics. A special attention will be paid to models inspired by a Bayesian formalism of Opinion Dynamics. The objective of this exploration is to better understand the effect that different conditions might have on the reliability of the opinions of a scientific community. We will see that, by using artificial worlds as exploring grounds, we can prevent some epistemological problems with the definition of truth and obtain insights on the conditions that might cause the quest for more reliable knowledge to fail.

Radiative Two Loop Inverse Seesaw and Dark Matter ; Seesaw mechanism provides a natural explanation of light neutrino masses through suppression of heavy seesaw scale. In inverse seesaw models the seesaw scale can be much lower than that in the usual seesaw models. If terms inducing seesaw masses are further induced by loop corrections, the seesaw scale can be lowered to be in the range probed by experiments at the LHC without fine tuning. In this paper we construct models in which inverse seesaw neutrino masses are generated at two loop level. These models also naturally have dark matter candidates. Although the recent data from Xenon100 put stringent constraint on the models, they can be consistent with data on neutrino masses, mixing, dark matter relic density and direct detection. These models also have some interesting experimental signatures for collider and flavor physics.

P A Model of PilotAbstractions ; PilotJobs support effective distributed resource utilization, and are arguably one of the most widelyused distributed computing abstractions as measured by the number and types of applications that use them, as well as the number of production distributed cyberinfrastructures that support them. In spite of broad uptake, there does not exist a welldefined, unifying conceptual model of PilotJobs which can be used to define, compare and contrast different implementations. Often PilotJob implementations are strongly coupled to the distributed cyberinfrastructure they were originally designed for. These factors present a barrier to extensibility and interoperability. This pa per is an attempt to i provide a minimal but complete model P of PilotJobs, ii establish the generality of the P Model by mapping various existing and well known PilotJob frameworks such as Condor and DIANE to P, iii derive an interoperable and extensible API for the P Model PilotAPI, iv validate the implementation of the PilotAPI by concurrently using multiple distinct PilotJob frameworks on distinct production distributed cyberinfrastructures, and v apply the P Model to PilotData.

Consistent selection of tuning parameters via variable selection stability ; Penalized regression models are popularly used in highdimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature, their performances largely depend on the tuning parameters that balance the tradeoff between model fitting and model sparsity. Existing tuning criteria mainly follow the route of minimizing the estimated prediction error or maximizing the posterior model probability, such as crossvalidation, AIC and BIC. This article introduces a general tuning parameter selection criterion based on a novel concept of variable selection stability. The key idea is to select the tuning parameters so that the resultant penalized regression model is stable in variable selection. The asymptotic selection consistency is established for both fixed and diverging dimensions. The effectiveness of the proposed criterion is also demonstrated in a variety of simulated examples as well as an application to the prostate cancer data.

Epidemics on networks with large initial conditions or changing structure ; Background Recently developed techniques to study the spread of infectious diseases through networks make assumptions that the initial proportion infected is infinitesimal and the population behavior is static throughout the epidemic. The models do not apply if the initial proportion is large and fail whenever R01, and cannot measure the impact of an intervention. Methods In this paper we adapt edgebased compartmental models to situations having finitesized initial conditions. Results The resulting models remain simple and accurately capture the effect of the initial conditions. It is possible to generalize the model to networks whose partnerships change in time. Conclusions The resulting models can be applied to a range of important contexts. The models can be used to choose between different interventions that affect the disease or the population structure.

Using LocationBased Social Networks to Validate Human Mobility and Relationships Models ; We propose to use social networking data to validate mobility models for pervasive mobile adhoc networks MANETs and delay tolerant networks DTNs. The Random Waypoint RWP and ErdosRenyi ER models have been a popular choice among researchers for generating mobility traces of nodes and relationships between them. Not only RWP and ER are useful in evaluating networking protocols in a simulation environment, but they are also used for theoretical analysis of such dynamic networks. However, it has been observed that neither relationships among people nor their movements are random. Instead, human movements frequently contain repeated patterns and friendship is bounded by distance. We used social networking site Gowalla to collect, create and validate models of human mobility and relationships for analysis and evaluations of applications in opportunistic networks such as sensor networks and transportation models in civil engineering. In doing so, we hope to provide more humanlike movements and social relationship models to researchers to study problems in complex and mobile networks.

Simultaneous Model Selection and Estimation for Mean and Association Structures with Clustered Binary Data ; This paper investigates the property of the penalized estimating equations when both the mean and association structures are modelled. To select variables for the mean and association structures sequentially, we propose a hierarchical penalized generalized estimating equations HPGEE2 approach. The first set of penalized estimating equations is solved for the selection of significant mean parameters. Conditional on the selected mean model, the second set of penalized estimating equations is solved for the selection of significant association parameters. The hierarchical approach is designed to accommodate possible model constraints relating the inclusion of covariates into the mean and the association models. This twostep penalization strategy enjoys a compelling advantage of easing computational burdens compared to solving the two sets of penalized equations simultaneously. HPGEE2 with a smoothly clipped absolute deviation SCAD penalty is shown to have the oracle property for the mean and association models. The asymptotic behavior of the penalized estimator under this hierarchical approach is established. An efficient twostage penalized weighted least square algorithm is developed to implement the proposed method. The empirical performance of the proposed HPGEE2 is demonstrated through MonteCarlo studies and the analysis of a clinical data set.

Homotopy Theory of Labelled Symmetric Precubical Sets ; This paper is the third paper of a series devoted to higher dimensional transition systems. The preceding paper proved the existence of a left determined model structure on the category of cubical transition systems. In this sequel, it is proved that there exists a model category of labelled symmetric precubical sets which is Quillen equivalent to the Bousfield localization of this left determined model category by the cubification functor. The realization functor from labelled symmetric precubical sets to cubical transition systems which was introduced in the first paper of this series is used to establish this Quillen equivalence. However, it is not a left Quillen functor. It is only a left adjoint. It is proved that the two model categories are related to each other by a zigzag of Quillen equivalences of length two. The middle model category is still the model category of cubical transition systems, but with an additional family of generating cofibrations. The weak equivalences are closely related to bisimulation. Similar results are obtained by restricting the constructions to the labelled symmetric precubical sets satisfying the HDA paradigm.

Interacting viscous ghost tachyon, Kessence and dilaton scalar field models of dark energy ; We study the correspondence between the interacting viscous ghost dark energy model with the tachyon, Kessence and dilaton scalar field models in the framework of Einstein gravity. We consider a spatially nonflat FRW universe filled with interacting viscous ghost dark energy and dark matter. We reconstruct both the dynamics and potential of these scalar field models according to the evolutionary behavior of the interacting viscous ghost dark energy model, which can describe the accelerated expansion of the universe. Our numerical results show that the interaction and viscosity have opposite effects on the evolutionary properties of the ghost scalar filed models.

Dark energy, matter creation and curvature ; The most studied way to explain the current accelerated expansion of the universe is to assume the existence of dark energy; a new component that fill the universe, does not clumps, currently dominates the evolution, and has a negative pressure. In this work I study an alternative model proposed by Lima et al. citelima96, which does not need an exotic equation of state, but assumes instead the existence of gravitational particle creation. Because this model fits the supernova observations as well as the LambdaCDM model, I perform in this work a thorough study of this model considering an explicit spatial curvature. I found that in this scenario we can alleviate the cosmic coincidence problem, basically showing that these two components, dark matter and dark energy, are of the same nature, but they act at different scales. I also shown the inadequacy of some particle creation models, and also I study a previously propose new model that overcome these difficulties.

A Detailed Analytical Derivation of the GN Model of NonLinear Interference in Coherent Optical Transmission Systems ; Recently, a perturbative model of nonlinear fiber propagation in uncompensated optical transmission systems has been proposed, called GNmodel 1. Here, an extended and more detailed version of the GNmodel derivation 1 is reported, providing deeper insight into the model. Some straightforward generalizations of the model are also proposed.

Screening fifth forces in kessence and DBI models ; New fifth forces have not yet been detected in the laboratory or in the solar system, hence it is typically difficult to introduce new light scalar fields that would mediate such forces. In recent years it has been shown that a number of nonlinear scalar field theories allow for a dynamical mechanism, such as the Vainshtein and chameleon ones, that suppresses the strength of the scalar fifth force in experimental environments. This is known as screening, however it is unclear how common screening is within nonlinear scalar field theories. kessence models are commonly studied examples of nonlinear models, with DBI as the best motivated example, and so we ask whether these nonlinearities are able to screen a scalar fifth force. We find that a Vainshteinlike screening mechanism exists for such models although with limited applicability. For instance, we cannot find a screening mechanism for DBI models. On the other hand, we construct a large class of kessence models which lead to the acceleration of the Universe in the recent past for which the fifth force mediated by the scalar can be screened.

Learning ModelBased Sparsity via Projected Gradient Descent ; Several convex formulation methods have been proposed previously for statistical estimation with structured sparsity as the prior. These methods often require a carefully tuned regularization parameter, often a cumbersome or heuristic exercise. Furthermore, the estimate that these methods produce might not belong to the desired sparsity model, albeit accurately approximating the true parameter. Therefore, greedytype algorithms could often be more desirable in estimating structuredsparse parameters. So far, these greedy methods have mostly focused on linear statistical models. In this paper we study the projected gradient descent with nonconvex structuredsparse parameter model as the constraint set. Should the cost function have a Stable ModelRestricted Hessian the algorithm produces an approximation for the desired minimizer. As an example we elaborate on application of the main results to estimation in Generalized Linear Model.

Raw Report on the Model Checking Contest at Petri Nets 2012 ; This article presents the results of the Model Checking Contest held at Petri Nets 2012 in Hambourg. This contest aimed at a fair and experimental evaluation of the performances of model checking techniques applied to Petri nets. This is the second edition after a successful one in 2011. The participating tools were compared on several examinations state space generation and evaluation of several types of formulae structural, reachability, LTL, CTL 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 context, model per model and examination per examination.

Traditional and novel approaches to palaeoclimate modelling ; Palaeoclimate archives contain information on climate variability, trends and mechanisms. Models are developed to explain observations and predict the response of the climate system to perturbations, in particular perturbations associated with the anthropogenic influence. Here, we review three classical frameworks of climate modelling conceptual, simulatorbased including general circulation models and Earth system models of intermediate complexity, and statistical. The conceptual framework aims at a parsimonious representation of a given climate phenomenon; the simulatorbased framework connects physical and biogeochemical principles with phenomena at different spatial and temporal scales; and statistical modelling is a framework for inference from observations, given hypotheses on systematic and random effects. Recently, solutions have been proposed in the literature to combine these frameworks, and new concepts have emerged the emulator a statistical, computing efficient surrogate for the simulator and the discrepancy, which is a statistical representation of the difference between the simulator and the real phenomenon. These concepts are explained, with references to implementations for both timeslices and dynamical applications.

Storage Workload Modelling by Hidden Markov Models Application to FLASH Memory ; A workload analysis technique is presented that processes data from operation type traces and creates a Hidden Markov Model HMM to represent the workload that generated those traces. The HMM can be used to create representative traces for performance models, such as simulators, avoiding the need to repeatedly acquire suitable traces. It can also be used to estimate directly the transition probabilities and rates of a Markov modulated arrival process, for use as input to an analytical performance model of Flash memory. The HMMs obtained from industrial workloads are validated by comparing their autocorrelation functions and other statistics with those of the corresponding monitored time series. Further, the performance model applications are illustrated by numerical examples.

Spatiotemporal spike trains analysis for large scale networks using maximum entropy principle and MonteCarlo method ; Understanding the dynamics of neural networks is a major challenge in experimental neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main statistics of the data is required. In a first part, we present a review on recent results dealing with spike train statistics analysis using maximum entropy models MaxEnt. Most of these studies have been focusing on modelling synchronous spike patterns, leaving aside the temporal dynamics of the neural activity. However, the maximum entropy principle can be generalized to the temporal case, leading to Markovian models where memory effects and time correlations in the dynamics are properly taken into account. In a second part, we present a new method based on MonteCarlo sampling which is suited for the fitting of largescale spatiotemporal MaxEnt models. The formalism and the tools presented here will be essential to fit MaxEnt spatiotemporal models to large neural ensembles.

Finite Local Models for the GHZ Experiment ; Some years ago Szab'o and Fine proposed a it local hidden variable theory for the GHZ experiment based on the assumption that the detection efficiency is not only the effect of random errors in the detector equipment, but it is a more fundamental phenomenon, the manifestation of a predetermined hidden property of the particles. Szab'o and Fine, however, did not provide a general approach to quantum phenomena which avoids nonlocality. Such an approach, based on the same assumption, was instead recently supplied by some of us and called it extended semantic realism it ESR model. We show here that one can extract from the ESR model several local finite models referring to the specific physical situation considered in the GHZ experiment, and that these models can be converted into the toy models for the GHZ experiment worked out by Szab'o and Fine.

High Energy Neutrinos from Dissipative Photospheric Models of Gamma Ray Bursts ; We calculate the high energy neutrino spectrum from gammaray bursts where the emission arises in a dissipative jet photosphere determined by either baryonically or magnetically dominated dynamics, and compare these neutrino spectra to those obtained in conventional internal shock models. We also calculate the diffuse neutrino spectra based on these models, which appear compatible with the current IceCube 4059 constraints. While a reanalysis based on the models discussed here and the data from the full array would be needed, it appears that only those models with the most extreme parameters are close to being constrained at present. A multiyear operation of the full IceCube and perhaps a next generation of large volume neutrino detectors may be required in order to distinguish between the various models discussed.

A Flexible Mixed Integer Programming framework for Nurse Scheduling ; In this paper, a nursescheduling model is developed using mixed integer programming model. It is deployed to a general care ward to replace and automate the current manual approach for scheduling. The developed model differs from other similar studies in that it optimizes both hospitals requirement as well as nurse preferences by allowing flexibility in the transfer of nurses from different duties. The model also incorporated additional policies which are part of the hospitals requirement but not part of the legislations. Hospitals key primary mission is to ensure continuous ward care service with appropriate number of nursing staffs and the right mix of nursing skills. The planning and scheduling is done to avoid additional non essential cost for hospital. Nurses preferences are taken into considerations such as the number of night shift and consecutive rest days. We will also reformulate problems from another paper which considers the penalty objective using the model but without the flexible components. The models are built using AIMMS which solves the problem in very short amount of time.

Modeling of causality with metamaterials ; Hyperbolic metamaterials may be used to model a 21 dimensional Minkowski spacetime in which the role of time is played by one of the spatial coordinates. When a metamaterial is built and illuminated with a coherent extraordinary laser beam, the stationary pattern of light propagation inside the metamaterial may be treated as a collection of particle world lines, which represents a complete history of this 21 dimensional spacetime. While this model may be used to build interesting spacetime analogs, such as metamaterial black holes and big bang, it lacks causality since light inside the metamaterial may propagate back and forth along the timelike spatial coordinate, events in the future may affect events in the past. Here we demonstrate that a more sophisticated metamaterial model may fix this deficiency via breaking the mirror and temporal PT symmetries of the original model and producing oneway propagation along the timelike spatial coordinate. Resulting 21 Minkowski spacetime appears to be causal. This scenario may be considered as a metamaterial model of the WheelerFeynman absorber theory of causality.

Topseesaw assisted technicolor model and a m126 GeV scalar ; We consider a model of strong dynamics able to account for the origin of the electroweak symmetry breaking and heavy quark masses. The model is based on a technicolor sector, augmented with topcolor and topseesaw mechanism to assist in the generation of heavy quark masses. The low energy effective theory is a particular three Higgs doublet model. The additional feature is the possibility of the existence of composite higher spin states beyond the scalars, which are shown to be essential in this model to provide extra contributions in the higgs decays into two photons. We provide a detailed strategy and analysis how this type of models are to be constrained with the present data.

Managing sparsity, time, and quality of inference in topic models ; Inference is an integral part of probabilistic topic models, but is often nontrivial to derive an efficient algorithm for a specific model. It is even much more challenging when we want to find a fast inference algorithm which always yields sparse latent representations of documents. In this article, we introduce a simple framework for inference in probabilistic topic models, denoted by FW. This framework is general and flexible enough to be easily adapted to mixture models. It has a linear convergence rate, offers an easy way to incorporate prior knowledge, and provides us an easy way to directly trade off sparsity against quality and time. We demonstrate the goodness and flexibility of FW over existing inference methods by a number of tasks. Finally, we show how inference in topic models with nonconjugate priors can be done efficiently.

Modeling charge transport in Swept Charge Devices for Xray spectroscopy ; We present the formulation of an analytical model which simulates charge transport in Swept Charge Devices SCDs to understand the nature of the spectral redistribution function SRF. We attempt to construct the energydependent and position dependent SRF by modeling the photon interaction, charge cloud generation and various loss mechanisms viz., recombination, partial charge collection and split events. The model will help in optimizing event selection, maximize event recovery and improve spectral modeling for Chandrayaan2 slated for launch in 2014. A prototype physical model is developed and the algorithm along with its results are discussed in this paper.

Comment on Evidence of NonMeanFieldLike LowTemperature Behavior in the EdwardsAnderson SpinGlass Model ; A recent interesting paper Yucesoy et al. Phys. Rev. Lett. 109, 177204 2012, arXiv12060783 compares the lowtemperature phase of the 3D EdwardsAnderson EA model to its meanfield counterpart, the SherringtonKirkpatrick SK model. The authors study the overlap distributions PJq and conclude that the two models behave differently. Here we notice that a similar analysis using stateoftheart, larger data sets for the EA model generated with the Janus computer leads to a very clear interpretation of the results of Yucesoy et al., showing that the EA model behaves as predicted by the replica symmetry breaking RSB theory.

Applying geometric Kcycles to fractional indices ; A geometric model for twisted Khomology is introduced. It is modeled after the MathaiMelroseSinger fractional analytic index theorem in the same way as the BaumDouglas model of Khomology was modeled after the AtiyahSinger index theorem. A natural transformation from twisted geometric Khomology to the new geometric model is constructed. The analytic assembly mapping to analytic twisted Khomology in this model is an isomorphism for torsion twists on a finite CWcomplex. For a general twist on a smooth manifold the analytic assembly mapping is a surjection. Beyond the aforementioned fractional invariants, we study Tduality for geometric cycles.

Queues and risk models with simultaneous arrivals ; We focus on a particular connection between queueing and risk models in a multidimensional setting. We first consider the joint workload process in a queueing model with parallel queues and simultaneous arrivals at the queues. For the case that the service times are ordered from largest in the first queue to smallest in the last queue we obtain the LaplaceStieltjes transform of the joint stationary workload distribution. Using a multivariate duality argument between queueing and risk models, this also gives the Laplace transform of the survival probability of all books in a multivariate risk model with simultaneous claim arrivals and the same ordering between claim sizes. Other features of the paper include a stochastic decomposition result for the workload vector, and an outline how the twodimensional risk model with a general twodimensional claim size distribution hence without ordering of claim sizes is related to a known Riemann boundary value problem.

The DD3anyon chain integrable boundary conditions and excitation spectra ; Chains of interacting nonAbelian anyons with local interactions invariant under the action of the Drinfeld double of the dihedral group D3 are constructed. Formulated as a spin chain the Hamiltonians are generated from commuting transfer matrices of an integrable vertex model for periodic and braided as well as open boundaries. A different anyonic model with the same local Hamiltonian is obtained within the fusion path formulation. This model is shown to be related to an integrable fusion interaction round the face model. Bulk and surface properties of the anyon chain are computed from the Bethe equations for the spin chain. The low energy effective theories and operator content of the models in both the spin chain and fusion path formulation are identified from analytical and numerical studies of the finite size spectra. For all boundary conditions considered the continuum theory is found to be a product of two conformal field theories. Depending on the coupling constants the factors can be a Z4 parafermion or a mathcalM5,6 minimal model.

Search for new physics in events with photons, jets, and missing transverse energy in pp collisions at sqrts 7 TeV ; A search for physics beyond the standard model involving events with one or more photons, jets, and missing transverse energy has been performed by the CMS experiment. The data sample corresponds to an integrated luminosity of 4.93 inverse femtobarns of protonproton collisions at sqrts 7 TeV, produced at the Large Hadron Collider. No excess of events with large missing transverse energy is observed beyond expectations from standard model processes, and upper limits on the signal production cross sections for new physics processes are set at the 95 confidence level. The results of this search are interpreted in the context of three models of new physics a general model of gaugemediated supersymmetry breaking, Simplified Models, and a theory involving universal extra dimensions. In the absence of evidence for new physics, exclusion regions are derived in the parameter spaces of the respective models.

Heterogeneous Enterprises in a Macroeconomic AgentBased Model ; We present a macroeconomic agentbased model that combines several mechanisms operating at the same timescale, while remaining mathematically tractable. It comprises enterprises and workers who compete in a job market and a commodity goods market. The model is stockflow consistent; a bank lends money charging interest rates, and keeps track of equities. Important features of the model are heterogeneity of enterprises, existence of bankruptcies and creation of new enterprises, as well as productivity increase. The model's evolution reproduces empirically found regularities for firm size and growth rate distributions. It combines probabilistic elements and deterministic dynamics, with relative weights that may be modified according to the considered problem or the belief of the modeler. We discuss statistical regularities on enterprises, the origin and the amplitude of endogeneous fluctuations of the system's steady state, as well as the role of the interest rate and the credit volume. We also summarize obtained results which are not discussed in detail in this paper.

ANDNOT logic framework for steady state analysis of Boolean network models ; Finite dynamical systems e.g. Boolean networks and logical models have been used in modeling biological systems to focus attention on the qualitative features of the system, such as the wiring diagram. Since the analysis of such systems is hard, it is necessary to focus on subclasses that have the properties of being general enough for modeling and simple enough for theoretical analysis. In this paper we propose the class of ANDNOT networks for modeling biological systems and show that it provides several advantages. Some of the advantages include Any finite dynamical system can be written as an ANDNOT network with similar dynamical properties. There is a onetoone correspondence between ANDNOT networks, their wiring diagrams, and their dynamics. Results about ANDNOT networks can be stated at the wiring diagram level without losing any information. Results about ANDNOT networks are applicable to any Boolean network. We apply our results to a Boolean model of Thcell differentiation.

Modeling the resilience of large and evolving systems ; This paper summarizes the state of knowledge and ongoing research on methods and techniques for resilience evaluation, taking into account the resiliencescaling challenges and properties related to the ubiquitous computerized systems. We mainly focus on quantitative evaluation approaches and, in particular, on modelbased evaluation techniques that are commonly used to evaluate and compare, from the dependability point of view, different architecture alternatives at the design stage. We outline some of the main modeling techniques aiming at mastering the largeness of analytical dependability models at the construction level. Actually, addressing the model largeness problem is important with respect to the investigation of the scalability of current techniques to meet the complexity challenges of ubiquitous systems. Finally we present two case studies in which some of the presented techniques are applied for modeling web services and General Packet Radio Service GPRS mobile telephone networks, as prominent examples of large and evolving systems.

Modelorder reduction of biochemical reaction networks ; In this paper we propose a modelorder reduction method for chemical reaction networks governed by general enzyme kinetics, including the massaction and MichaelisMenten kinetics. The modelorder reduction method is based on the Kron reduction of the weighted Laplacian matrix which describes the graph structure of complexes in the chemical reaction network. We apply our method to a yeast glycolysis model, where the simulation result shows that the transient behaviour of a number of key metabolites of the reducedorder model is in good agreement with those of the fullorder model.

Stochastic complexity of Bayesian networks ; Bayesian networks are now being used in enormous fields, for example, diagnosis of a system, data mining, clustering and so on. In spite of their wide range of applications, the statistical properties have not yet been clarified, because the models are nonidentifiable and nonregular. In a Bayesian network, the set of its parameter for a smaller model is an analytic set with singularities in the space of large ones. Because of these singularities, the Fisher information matrices are not positive definite. In other words, the mathematical foundation for learning was not constructed. In recent years, however, we have developed a method to analyze nonregular models using algebraic geometry. This method revealed the relation between the models singularities and its statistical properties. In this paper, applying this method to Bayesian networks with latent variables, we clarify the order of the stochastic complexities.Our result claims that the upper bound of those is smaller than the dimension of the parameter space. This means that the Bayesian generalization error is also far smaller than that of regular model, and that Schwarzs model selection criterion BIC needs to be improved for Bayesian networks.

A radiofrequency sheath model for complex waveforms ; Plasma sheaths driven by radiofrequency voltages occur frequently, in contexts ranging from plasma processing applications to magnetically confined fusion experiments. These sheaths are crucial because they dominantly affect impedance, power absorption, ion acceleration and sometimes the stability of the nearby plasma. An analytical understanding of sheath behavior is therefore important, both intrinsically and as an element in more elaborate theoretical structures. In practice, these radiofrequency sheaths are commonly excited by highly anharmonic waveforms, but no analytical model exists for this general case. In this letter we present a mathematically simple sheath model that can be solved for essentially arbitrary excitation waveforms. We show that this model is in good agreement with earlier models for single frequency excitation, and we show by example how to develop a solution for a complex wave form. This solution is in good agreement with simulation data. This simple and accurate model is likely to have wide application.

Proceedings of the Second International Workshop on DomainSpecific Languages and Models for Robotic Systems DSLRob 2011 ; Proceedings of the Second International Workshop on DomainSpecific Languages and Models for Robotic Systems DSLRob'11, held in conjunction with the 2011 IEEERSJ International Conference on Intelligent Robots and Systems IROS 2011, September 2011 in San Francisco, USA. The main topics of the workshop were DomainSpecific Languages DSLs and Modeldriven Software Development MDSD for robotics. A domainspecific language DSL is a programming language dedicated to a particular problem domain that offers specific notations and abstractions that increase programmer productivity within that domain. Models offer a highlevel way for domain users to specify the functionality of their system at the right level of abstraction. DSLs and models have historically been used for programming complex systems. However recently they have garnered interest as a separate field of study. Robotic systems blend hardware and software in a holistic way that intrinsically raises many crosscutting concerns concurrency, uncertainty, time constraints, ..., for which reason, traditional generalpurpose languages often lead to a poor fit between the language features and the implementation requirements. DSLs and models offer a powerful, systematic way to overcome this problem, enabling the programmer to quickly and precisely implement novel software solutions to complex problems

The War of Attrition in the Limit of Infinitely Many Players ; The War of Attrition is a classical game theoretic model that was first introduced to mathematically describe certain nonviolent animal behavior. The original setup considers two participating players in a oneshot game competing for a given prize by waiting. This model has later been extended to several different models allowing more than two players. One of the first of these Nplayer generalizations was due to J. Haigh and C. Cannings Acta Appl. Math.14 where two possible models are mainly discussed; one in which the game starts afresh with new strategies each time a player leaves the game, and one where the players have to stick with the strategy they chose initially. The first case is well understood whereas, for the second case, much is still left open. In this paper we study the asymptotic behavior of these two models as the number of players tend to infinity and prove that their time evolution coincide in the limit. We also prove new results concerning the second model in the Nplayer setup.

Estimation of Dynamic Mixed Double Factors Model in High Dimensional Panel Data ; The purpose of this article is to develop the dimension reduction techniques in panel data analysis when the number of individuals and indicators is large. We use Principal Component Analysis PCA method to represent large number of indicators by minority common factors in the factor models. We propose the Dynamic Mixed Double Factor Model DMDFM for short to re ect cross section and time series correlation with interactive factor structure. DMDFM not only reduce the dimension of indicators but also consider the time series and cross section mixed effect. Different from other models, mixed factor model have two styles of common factors. The regressors factors re flect common trend and reduce the dimension, error components factors re ect difference and weak correlation of individuals. The results of Monte Carlo simulation show that Generalized Method of Moments GMM estimators have good unbiasedness and consistency. Simulation also shows that the DMDFM can improve prediction power of the models effectively.

Random cascade model in the limit of infinite integral scale as the exponential of a nonstationary 1f noise. Application to volatility fluctuations in stock markets ; In this paper we propose a new model for volatility fluctuations in financial time series. This model relies on a nonstationary gaussian process that exhibits aging behavior. It turns out that its properties, over any finite time interval, are very close to continuous cascade models. These latter models are indeed well known to reproduce faithfully the main stylized facts of financial time series. However, it involve a large scale parameter the socalled integral scale where the cascade is initiated that is hard to interpret in finance. Moreover the empirical value of the integral scale is in general deeply correlated to the overall length of the sample. This feature is precisely predicted by our model that turns out, as illustrated on various examples from daily stock index data, to quantitatively reproduce the empirical observations.

Toy model studies of tuning and typicality with an eye toward cosmology ; We investigate a number of simple toy models to explore interesting relationships between dynamics and typicality. We start with an infinite model that has been proposed as an illustration of how nonergodic dynamics can produce interesting features that are suggestive for cosmological applications. We consider various attempts to define the infinite model more rigorously as a limit of a finite system. None of our attempts at such rigor were able to preserve the attractive properties. We hope our work will challenge others to find more successful toy models. The difficulty of finding such models suggests that connections between dynamics and typicality we hope for in cosmological theories such as eternal inflation may not be so easy to achieve.

Markovian acyclic directed mixed graphs for discrete data ; Acyclic directed mixed graphs ADMGs are graphs that contain directed rightarrow and bidirected leftrightarrow edges, subject to the constraint that there are no cycles of directed edges. Such graphs may be used to represent the conditional independence structure induced by a DAG model containing hidden variables on its observed margin. The Markovian model associated with an ADMG is simply the set of distributions obeying the global Markov property, given via a simple path criterion mseparation. We first present a factorization criterion characterizing the Markovian model that generalizes the wellknown recursive factorization for DAGs. For the case of finite discrete random variables, we also provide a parameterization of the model in terms of simple conditional probabilities, and characterize its variation dependence. We show that the induced models are smooth. Consequently, Markovian ADMG models for discrete variables are curved exponential families of distributions.

Fractional EinsteinHilbert Action Cosmology ; We propose a new type of cosmological model derived from the fractional variational principle when it is applied to the gravitational sector of action functional. In contrast to the fractional cosmological model developed earlier by the author from a fractional total action, in our new model the continuity equation remains valid in its usual form. For this model, a lot of exact solutions are obtained from a specific ansatz which is proposed for the cosmological term in this paper. Several examples arising from the given variations of the Hubble parameter with time are provided. Besides, we suggest an original interpretation of the main equations for our model. It supposes that the effective cosmological term could arise as a result of kinematical induction through the nonzero Hubble parameter. With the help of particular example, we demonstrate how this approach could lead our model quite closer to the real behavior of the universe.

Probing exotic Higgs sectors from the precise measurement of Higgs boson couplings ; We study coupling constants of the standard model like Higgs boson with the gauge bosons hZZ and hWW and fermions hfbarf in the general Higgs sector which contains higher isospin representations with arbitrary hypercharge. In Higgs sectors with exotic Higgs representations, the hZZ and hWW coupling constants can be larger than those in the standard model. We calculate deviations in the Higgs boson couplings from standard model values in the model with a real or complex triplet field, the GeorgiMachacek model and the model with a septet scalar field. We also study deviations in the event rates of hto ZZ, hto WW, hto gammagamma, hto bbarb and hto tautau channels.

Graphical Models and Exponential Families ; We provide a classification of graphical models according to their representation as subfamilies of exponential families. Undirected graphical models with no hidden variables are linear exponential families LEFs, directed acyclic graphical models and chain graphs with no hidden variables, including Bayesian networks with several families of local distributions, are curved exponential families CEFs and graphical models with hidden variables are stratified exponential families SEFs. An SEF is a finite union of CEFs satisfying a frontier condition. In addition, we illustrate how one can automatically generate independence and nonindependence constraints on the distributions over the observable variables implied by a Bayesian network with hidden variables. The relevance of these results for model selection is examined.