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On the variations of the principal eigenvalue with respect to a parameter in growthfragmentation models ; We study the variations of the principal eigenvalue associated to a growthfragmentationdeath equation with respect to a parameter acting on growth and fragmentation. To this aim, we use the probabilistic individualbased interpretation of the model. We study the variations of the survival probability of the stochastic model, using a generation by generation approach. Then, making use of the link between the survival probability and the principal eigenvalue established in a previous work, we deduce the variations of the eigenvalue with respect to the parameter of the model.

Spin Properties of Supermassive Black Holes with Powerful Outflows ; Relationships between beam power and accretion disk luminosity are studied for a sample of 55 HERG, 13 LERG, and 29 RLQ with powerful outflows. The ratio of beam power to disk luminosity tends to be high for LERG, low for RLQ, and spans the full range of values for HERG. Writing general expressions for the disk luminosity and beam power and applying the empirically determined relationships allows a function that parameterizes the spins of the holes to be estimated. Interestingly, one of the solutions that is consistent with the data has a functional form that is remarkably similar to that expected in the generalized BlandfordZnajek model with a magnetic field that is similar in form to that expected in MAD and ADAF models. Values of the spin function, obtained independent of specific outflow models, suggest that spin and AGN type are not related for these types of sources. The spin function can be used to solve for black hole spin in the context of particular outflow models, and one example is provided.

Flavor Changing Leptonic Decays of Heavy Higgs Bosons ; CMS has reported indications 2.4sigma of the decay of the Higgs boson into mutau. The simplest explanation for such a decay would be a general Two Higgs Doublet Model 2HDM. In this case, one would expect the heavy neutral Higgs bosons, H and A, to also decay in a similar manner. We study two specific models. The first is the type III 2HDM, and the second is a 2HDM, originally proposed by Branco et al., in which all flavorchanging neutral processes are given by the weak mixing matrix. In the latter model, since mixing between the second and third generations in the lepton sector is large, flavorchanging interactions are large. In this model it is found that the decays of H and A to mutau can be as high as 60 percent. This work has nothing to do with the 750 GeV diphoton resonance.

Bulk moduli of PbSxSe1x, PbSxTe1x, and PbSexTe1x from the combination of the cB model with the modified Born theory compared to generalized gradient approximation ; The bulk moduli of PbSxSe1x, PbSxTe1x, and PbSexTe1x have been recently determined E. Skordas, Materials Science in Semiconductor Processing 43 2016 6568 by employing a thermodynamical model, the so called cBOmega model, which has been found to give successful results in several applications of defects in solids. Here, we suggest an alternative procedure for this determination which combines the cBOmega model with the modified Born theory. The results are in satisfactory agreement with those deduced independently by the generalized gradient approximation approach.

Generalized Rabi models diagonalization in the spin subspace and differential operators of Dunkl type ; A discrete parity mathbbZ2 symmetry of a two parameter extension of the quantum Rabi model which smoothly interpolates between the latter and the JaynesCummings model, and of the twophoton and the twomode quantum Rabi models enables their diagonalization in the spin subspace. A more general statement is that the respective sets of 2times 2 hermitian operators of the FultonGouterman type and those diagonal in the spin subspace are unitary equivalent. The diagonalized representation makes it transparent that any question about integrability and solvability can be addressed only at the level of ordinary differential operators of Dunkl type. Braak's definition of integrability is shown i to contradict earlier numerical studies and ii to imply that any physically reasonable differential operator of FultonGouterman type is integrable.

Quantum machine learning with glow for episodic tasks and decision games ; We consider a general class of models, where a reinforcement learning RL agent learns from cyclic interactions with an external environment via classical signals. Perceptual inputs are encoded as quantum states, which are subsequently transformed by a quantum channel representing the agent's memory, while the outcomes of measurements performed at the channel's output determine the agent's actions. The learning takes place via stepwise modifications of the channel properties. They are described by an update rule that is inspired by the projective simulation PS model and equipped with a glow mechanism that allows for a backpropagation of policy changes, analogous to the eligibility traces in RL and edge glow in PS. In this way, the model combines features of PS with the ability for generalization, offered by its physical embodiment as a quantum system. We apply the agent to various setups of an invasion game and a grid world, which serve as elementary model tasks allowing a direct comparison with a basic classical PS agent.

Mechanism for the stabilization of protein clusters above the solubility curve the role of nonideal chemical reactions ; Dense protein clusters are known to play an important role in nucleation of protein crystals from dilute solutions. While these have generally been thought to be formed from a metastable phase, the observation of similar, if not identical, clusters above the critical point for the dilutesolutionstrongsolution phase transition has thrown this into doubt. Furthermore, the observed clusters are stable for relatively long times. Because protein aggregation plays an important role in some pathologies, understanding the nature of such clusters is an important problem. One mechanism for the stabilization of such structures was proposed by Pan, Vekilov and Lubchenko and was investigated using a DDFT model which confirmed the viability of the model. Here, we revisit that model and incorporate additional physics in the form of statedependent reaction rates. We show by a combination of numerical results and general arguments that the statedependent rates disrupt the stability mechanism. Finally, we argue that the statedepedent reactions correct unphysical aspects of the model with ideal stateindependent reactions and that this necessarily leads to the failure of the proposed mechanism.

A General World Model with Poiesis Poppers Three Worlds updated with Software ; With the famous Three Worlds of Karl Popper as template, the paper rigorously introduces the concept of software to define the counterpart of the physical subworld. Digesting the scientifictechnical view of biology and neurology on a high level, results in an updated Three Worlds scheme consistent with an information technical view. Chance and mathematics complete the world model. Some simple examples illustrate the move from Poppers view of the world with physics, psyche and World 3, to a new extended model with physics, extended software which we call Poiesis, and Geist the notion which embodies spirit, mind and soul.

A Lloydmodel generalization Conductance fluctuations in onedimensional disordered systems ; We perform a detailed numerical study of the conductance G through onedimensional 1D tightbinding wires with onsite disorder. The random configurations of the onsite energies epsilon of the tightbinding Hamiltonian are characterized by longtailed distributions For large epsilon, Pepsilonsim 1epsilon1alpha with alphain0,2. Our model serves as a generalization of 1D Lloyd's model, which corresponds to alpha1. First, we verify that the ensemble average leftlangle ln Grightrangle is proportional to the length of the wire L for all values of alpha, providing the localization length xi from leftlangleln Grightrangle2Lxi. Then, we show that the probability distribution function PG is fully determined by the exponent alpha and leftlangleln Grightrangle. In contrast to 1D wires with standard whitenoise disorder, our wire model exhibits bimodal distributions of the conductance with peaks at G0 and 1. In addition, we show that Pln G is proportional to Gbeta, for Gto 0, with betalealpha2, in agreement to previous studies.

Sentence Level Recurrent Topic Model Letting Topics Speak for Themselves ; We propose Sentence Level Recurrent Topic Model SLRTM, a new topic model that assumes the generation of each word within a sentence to depend on both the topic of the sentence and the whole history of its preceding words in the sentence. Different from conventional topic models that largely ignore the sequential order of words or their topic coherence, SLRTM gives full characterization to them by using a Recurrent Neural Networks RNN based framework. Experimental results have shown that SLRTM outperforms several strong baselines on various tasks. Furthermore, SLRTM can automatically generate sentences given a topic i.e., topics to sentences, which is a key technology for real world applications such as personalized short text conversation.

Quantile Processes for Semi and Nonparametric Regression ; A collection of quantile curves provides a complete picture of conditional distributions. Properly centered and scaled versions of estimated curves at various quantile levels give rise to the socalled quantile regression process QRP. In this paper, we establish weak convergence of QRP in a general series approximation framework, which includes linear models with increasing dimension, nonparametric models and partial linear models. An interesting consequence is obtained in the last class of models, where parametric and nonparametric estimators are shown to be asymptotically independent. Applications of our general process convergence results include the construction of noncrossing quantile curves and the estimation of conditional distribution functions. As a result of independent interest, we obtain a series of Bahadur representations with exponential bounds for tail probabilities of all remainder terms. Bounds of this kind are potentially useful in analyzing statistical inference procedures under divideandconquer setup.

Gruff Ultrafilters ; We investigate the question of whether mathbb Q carries an ultrafilter generated by perfect sets such ultrafilters were called gruff ultrafilters by van Douwen. We prove that one can consistently obtain an affirmative answer to this question in three different ways by assuming a certain parametrized diamond principle, from the cardinal invariant equality mathfrak dmathfrak c, and in the Random real model. Edit replace the Randor real model with the model obtained by adding omega1 Cohen reals to a model of mathsfCH, and subsequently forcing with the Random algebra; this is clarified in the corrigendum attached at the end of the paper.

A FirstOrder Electroweak Phase Transition from Varying Yukawas ; We show that the dynamics responsible for the variation of the Yukawa couplings of the Standard Model fermions generically leads to a very strong firstorder electroweak phase transition, assuming that the Yukawa couplings are large and of order 1 before the electroweak phase transition and reach their present value afterwards. There are good motivations to consider that the flavour structure could emerge during electroweak symmetry breaking, for example if the FroggattNielsen field dynamics were linked to the Higgs field. In this paper, we do not need to assume any particular theory of flavour and show in a modelindependent way how the nature of the electroweak phase transition is completely changed when the Standard Model Yukawas vary at the same time as the Higgs is acquiring its vacuum expectation value. The thermal contribution of the fermions creates a barrier between the symmetric and broken phase minima of the effective potential, leading to a firstorder phase transition. This offers new routes for generating the baryon asymmetry at the electroweak scale, strongly tied to flavour models.

A unifying energybased approach to stability of power grids with market dynamics ; In this paper a unifying energybased approach is provided to the modeling and stability analysis of power systems coupled with market dynamics. We consider a standard model of the power network with a thirdorder model for the synchronous generators involving voltage dynamics. By applying the primaldual gradient method to a social welfare optimization, a distributed dynamic pricing algorithm is obtained, which can be naturally formulated in portHamiltonian form. By interconnection with the physical model a closedloop portHamiltonian system is obtained, whose properties are exploited to prove asymptotic stability to the set of optimal points. This result is extended to the case that also general nodal power constraints are included into the social welfare problem. Additionally, the case of line congestion and power transmission costs in acyclic networks is covered. Finally, a dynamic pricing algorithm is proposed that does not require knowledge about the power supply and demand.

Rowless Universal Schema ; Universal schema jointly embeds knowledge bases and textual patterns to reason about entities and relations for automatic knowledge base construction and information extraction. In the past, entity pairs and relations were represented as learned vectors with compatibility determined by a scoring function, limiting generalization to unseen text patterns and entities. Recently, 'columnless' versions of Universal Schema have used compositional pattern encoders to generalize to all text patterns. In this work we take the next step and propose a 'rowless' model of universal schema, removing explicit entity pair representations. Instead of learning vector representations for each entity pair in our training set, we treat an entity pair as a function of its relation types. In experimental results on the FB15k237 benchmark we demonstrate that we can match the performance of a comparable model with explicit entity pair representations using a model of attention over relation types. We further demonstrate that the model per forms with nearly the same accuracy on entity pairs never seen during training.

An extension of the standard model in which parity is conserved at high energies ; To be compatible with general relativity, every fundamental theory should be invariant under general coordinate transformations including spatial reflection. This paper describes an extension of the standard model in which the action is invariant under spatial reflection, and the vacuum spontaneously breaks parity by giving a mean value to a pseudoscalar field. This field and the scalar Higgs field make the gauge bosons, the known fermions, and a set of mirror fermions suitably massive while avoiding flavorchanging neutral currents. In the model, there is no strongCP problem, there are no anomalies, fermion number quarkpluslepton number is conserved, and heavy mirror fermions form heavy neutral mirror atoms which are darkmatter candidates. In models with extended gauge groups, nucleons slowly decay into pions, leptons, and neutrinos.

Towards a Neural Statistician ; An efficient learner is one who reuses what they already know to tackle a new problem. For a machine learner, this means understanding the similarities amongst datasets. In order to do this, one must take seriously the idea of working with datasets, rather than datapoints, as the key objects to model. Towards this goal, we demonstrate an extension of a variational autoencoder that can learn a method for computing representations, or statistics, of datasets in an unsupervised fashion. The network is trained to produce statistics that encapsulate a generative model for each dataset. Hence the network enables efficient learning from new datasets for both unsupervised and supervised tasks. We show that we are able to learn statistics that can be used for clustering datasets, transferring generative models to new datasets, selecting representative samples of datasets and classifying previously unseen classes. We refer to our model as a neural statistician, and by this we mean a neural network that can learn to compute summary statistics of datasets without supervision.

SequencetoSequence Learning as BeamSearch Optimization ; SequencetoSequence seq2seq modeling has rapidly become an important generalpurpose NLP tool that has proven effective for many textgeneration and sequencelabeling tasks. Seq2seq builds on deep neural language modeling and inherits its remarkable accuracy in estimating local, nextword distributions. In this work, we introduce a model and beamsearch training scheme, based on the work of Daume III and Marcu 2005, that extends seq2seq to learn global sequence scores. This structured approach avoids classical biases associated with local training and unifies the training loss with the testtime usage, while preserving the proven model architecture of seq2seq and its efficient training approach. We show that our system outperforms a highlyoptimized attentionbased seq2seq system and other baselines on three different sequence to sequence tasks word ordering, parsing, and machine translation.

HoravaLifshitz gravity inspired BianchiII cosmology and the mixmaster universe ; We study different aspects of the HovravaLifshitz inspired BianchiII cosmology and its relations with the mixmaster universe model. First, we present exact solutions for a toy model, where only the cubic in spatial curvature terms are present in the action; then we briefly discuss some exotic singularities, which can appear in this toy model. We study also the toy model where only the quadratic in spatial curvature terms are present in the action. We establish relations between our results and those obtained by using the Hamiltonian formalism. Finally, we apply the results obtained by studying BianchiII cosmology to describe the evolution of the mixmaster universe in terms of the BelinskyKhalatnikovLifshitz formalism. Generally, our analysis gives some arguments in favour of the existence of the oscillatory approach to the singularity in a universe governed by the HovravaLifshitz type gravity.

Linear and nonlinear fractional Voigt models ; We consider fractional generalizations of the ordinary differential equation that governs the creep phenomenon. Precisely, two Caputo fractional Voigt models are considered a rheological linear model and a nonlinear one. In the linear case, an explicit Volterra representation of the solution is found, involving the generalized MittagLeffler function in the kernel. For the nonlinear fractional Voigt model, an existence result is obtained through a fixed point theorem. A nonlinear example, illustrating the obtained existence result, is given.

Parafermions in the tau2 model II ; Many years ago Baxter introduced an inhomogeneous twodimensional classical spin model, now called the tau2t model with free boundary conditions, and he specialized the resulting quantum spinchain Hamiltonian in a special limit to a simple clock Hamiltonian. Recently, Fendley showed that this clock Hamiltonian can be expressed in terms of free parafermions. Baxter followed this up by showing that this construction generalizes to the more general tau2t model, provided some conjectures hold. In this paper, we will compare the different notations and approaches enabling us to express the Hamiltonians in terms of projection operators as introduced by Fendley. By examining the properties of the raising operators, we are then able to prove the last unproven conjecture in Baxter's paper left in our previous paper. Thus the eigenvectors can all be written in terms of these raising operators.

Iterating Symmetric Extensions ; The notion of a symmetric extension extends the usual notion of forcing by identifying a particular class of names which forms an intermediate model of ZF between the ground model and the generic extension, and often the axiom of choice fails in these models. Symmetric extensions are generally used to prove choiceless consistency results. We develop a framework for iterating symmetric extensions in order to construct new models of ZF. We show how to obtain some wellknown and lesserknown results using this framework. Specifically, we discuss KinnaWagner principles and obtain some results related to their failure.

Reduced Order Podolsky Model ; We perform the canonical and path integral quantizations of a lowerorder derivatives model describing Podolsky's generalized electrodynamics. The physical content of the model shows an auxiliary massive vector field coupled to the usual electromagnetic field. The equivalence with Podolsky's original model is studied at classical and quantum levels. Concerning the dynamical time evolution we obtain a theory with two firstclass and two secondclass constraints in phase space. We calculate explicitly the corresponding Dirac brackets involving both vector fields. We use the Senjanovic procedure to implement the secondclass constraints and the BatalinFradkinVilkovisky path integral quantization scheme to deal with the symmetries generated by the firstclass constraints. The physical interpretation of the results turns out to be simpler due to the reduced derivatives order permeating the equations of motion, Dirac brackets and effective action.

Dynamic network models and graphon estimation ; In the present paper we consider a dynamic stochastic network model. The objective is estimation of the tensor of connection probabilities Lambda when it is generated by a Dynamic Stochastic Block Model DSBM or a dynamic graphon. In particular, in the context of the DSBM, we derive a penalized least squares estimator widehatLambda of Lambda and show that widehatLambda satisfies an oracle inequality and also attains minimax lower bounds for the risk. We extend those results to estimation of Lambda when it is generated by a dynamic graphon function. The estimators constructed in the paper are adaptive to the unknown number of blocks in the context of the DSBM or to the smoothness of the graphon function. The technique relies on the vectorization of the model and leads to much simpler mathematical arguments than the ones used previously in the stationary set up. In addition, all results in the paper are nonasymptotic and allow a variety of extensions.

Modeling of ItemDifficulty for Ontologybased MCQs ; Multiple choice questions MCQs that can be generated from a domain ontology can significantly reduce human effort time required for authoring administering assessments in an eLearning environment. Even though here are various methods for generating MCQs from ontologies, methods for determining the difficultylevels of such MCQs are less explored. In this paper, we study various aspects and factors that are involved in determining the difficultyscore of an MCQ, and propose an ontologybased model for the prediction. This model characterizes the difficulty values associated with the stem and choice set of the MCQs, and describes a measure which combines both the scores. Further more, the notion of assigning difficultlyscores based on the skill level of the test taker is utilized for predicating difficultyscore of a stem. We studied the effectiveness of the predicted difficultyscores with the help of a psychometric model from the Item Response Theory, by involving realstudents and domain experts. Our results show that, the predicated difficultylevels of the MCQs are having high correlation with their actual difficultylevels.

Two Iterative ProximalPoint Algorithms for the Calculus of Divergencebased Estimators with Application to Mixture Models ; Estimators derived from an EM algorithm are not robust since they are based on the maximization of the likelihood function. We propose a proximalpoint algorithm based on the EM algorithm which aim to minimize a divergence criterion. Resulting estimators are generally robust against outliers and misspecification. An EMtype proximalpoint algorithm is also introduced in order to produce robust estimators for mixture models. Convergence properties of the two algorithms are treated. We relax an identifiability condition imposed on the proximal term in the literature; a condition which is generally not fulfilled by mixture models. The convergence of the introduced algorithms is discussed on a twocomponent Weibull mixture and a twocomponent Gaussian mixture entailing a condition on the initialization of the EM algorithm in order for the later to converge. Simulations on mixture models using different statistical divergences are provided to confirm the validity of our work and the robustness of the resulting estimators against outliers in comparison to the EM algorithm.

Scalartensor extension of the CDM model ; We construct a cosmological scalartensortheory model in which the BransDicke type scalar Phi enters the effective Jordanframe Hubble rate as a simple modification of the Hubble rate of the LambdaCDM model. This allows us to quantify differences between the background dynamics of scalartensor theories and general relativity GR in a transparent and observationally testable manner in terms of one single parameter. Problems of the mapping of the scalarfield degrees of freedom on an effective fluid description in a GR context are discused. Data from supernovae, the differential age of old galaxies and baryon acoustic oscillations are shown to strongly limit potential deviations from the standard model.

Viscosity and effective temperature of an active dense system of selfpropelled particles ; We obtain a nonequilibrium theory for a simple model of a generic class of active dense systems consisting of selfpropelled particles with a selfpropulsion force, f0, and persistence time, taup, of their motion. We consider two models of activity and find the system is characterized by an evolving effective temperature Tefftau, defined through a generalized fluctuationdissipation theorem. Tefftau is equal to the equilibrium temperature at very short time tau and saturates to TeffTefftautoinfty at long times; The transition time ttrans when Tefftau goes to the longtime limit depends on taup alone and ttranssim taup0.85 for both models. f0 reduces the viscosity with increasing activity, taup on the other hand, may increase or decrease viscosity depending on the details of how the activity is included. However, as a function of Teff, viscosity shows the same behavior for different models of activity and etasim TeffTgamma with gamma1.74. Our theory gives reasonable agreement when compared with experimental data and is consistent with several experiments on diverse systems.

Superpixelbased Twoview Deterministic Fitting for Multiplestructure Data ; This paper proposes a twoview deterministic geometric model fitting method, termed Superpixelbased Deterministic Fitting SDF, for multiplestructure data. SDF starts from superpixel segmentation, which effectively captures prior information of feature appearances. The feature appearances are beneficial to reduce the computational complexity for deterministic fitting methods. SDF also includes two original elements, i.e., a deterministic sampling algorithm and a novel model selection algorithm. The two algorithms are tightly coupled to boost the performance of SDF in both speed and accuracy. Specifically, the proposed sampling algorithm leverages the grouping cues of superpixels to generate reliable and consistent hypotheses. The proposed model selection algorithm further makes use of desirable properties of the generated hypotheses, to improve the conventional fitandremove framework for more efficient and effective performance. The key characteristic of SDF is that it can efficiently and deterministically estimate the parameters of model instances in multistructure data. Experimental results demonstrate that the proposed SDF shows superiority over several stateoftheart fitting methods for real images with singlestructure and multiplestructure data.

Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations ; Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, i.e., with a node's network partners being informative about the node's attributes and therefore its behavior. If the network grows according to either a latent community stochastic block model, or a continuous latent space model, then latent homophilous attributes can be consistently estimated from the global pattern of social ties. We show that, for common versions of those two network models, these estimates are so informative that controlling for estimated attributes allows for asymptotically unbiased and consistent estimation of socialinfluence effects in linear models. In particular, the bias shrinks at a rate which directly reflects how much information the network provides about the latent attributes. These are the first results on the consistent nonexperimental estimation of socialinfluence effects in the presence of latent homophily, and we discuss the prospects for generalizing them.

A Statistical Model for the Analysis of Beta Values in DNA Methylation Studies ; Background The analysis of DNA methylation is a key component in the development of personalized treatment approaches. A common way to measure DNA methylation is the calculation of beta values, which are bounded variables of the form M M U that are generated by Illumina's 450k BeadChip array. The statistical analysis of beta values is considered to be challenging, as traditional methods for the analysis of bounded variables, such as Mvalue regression and beta regression, are based on regularity assumptions that are often too strong to adequately describe the distribution of beta values. Results We develop a statistical model for the analysis of beta values that is derived from a bivariate gamma distribution for the signal intensities M and U. By allowing for possible correlations between M and U, the proposed model explicitly takes into account the datagenerating process underlying the calculation of beta values. Conclusion The proposed model can be used to improve the identification of associations between beta values and covariates such as clinical variables and lifestyle factors in epigenomewide association studies. It is as easy to apply to a sample of beta values as beta regression and Mvalue regression.

Nesting statistics in the On loop model on random maps of arbitrary topologies ; We pursue the analysis of nesting statistics in the On loop model on random maps, initiated for maps with the topology of disks and cylinders in mathph1605.02239, here for arbitrary topologies. For this purpose we rely on the topological recursion results of mathph0910.5896 and mathph1303.5808 for the enumeration of maps in the On model. We characterize the generating series of maps of genus g with k' marked points and k boundaries and realizing a fixed nesting graph. These generating series are amenable to explicit computations in the loop model with bending energy on triangulations, and we characterize their behavior at criticality in the dense and in the dilute phase.

A Smolinlike branching multiverse from multiscalartensor theory ; We implement a Smolinlike branching multiverse through a directed, acyclic graph of N metrics. Our gravitational and matter actions are indistinguishable from N decoupled statements of General Relativity, if one varies with respect to metric degrees of freedom. We replace N1 metrics with scalar fields by conformally relating each metric to its unique graph predecessor. Varying with respect to the N1 scalar fields gives a multiscalartensor model which naturally features dark matter candidates. Building atop an argument of Chapline and Laughlin, branching is accomplished with the emergence of order parameters during gravitational collapse we bootstrap a suitably defined N scalar field model with initial data from an N1 field model. We focus on the nearestneighbour approximation, determine conditions for dynamical stability, and compute the equations of motion. The model features a novel screening property where the scalar fields actively adjust to decouple themselves from the stress, oscillating about the requisite values. In the Newtonian limit, these background values for the scalar fields exactly reproduce Newton's law of gravitation.

Spatial Patterns of Wind Speed Distributions in Switzerland ; This paper presents an initial exploration of high frequency records of extreme wind speed in two steps. The first consists in finding the suitable extreme distribution for 120 measuring stations in Switzerland, by comparing three known distributions Weibull, Gamma, and Generalized extreme value. This comparison serves as a basis for the second step which applies a spatial modelling by using Extreme Learning Machine. The aim is to model distribution parameters by employing a high dimensional input space of topographical information. The knowledge of probability distribution gives a comprehensive information and a global overview of wind phenomena. Through this study, a flexible and a simple modelling approach is presented, which can be generalized to almost extreme environmental data for risk assessment and to model renewable energy.

Buttiker Probe Based Modeling of TDDB Application to Dielectric Breakdown in MTJs and MOS Devices ; Dielectric layers are gradually being downscaled in different electronic devices like MOSFETs and Magnetic Tunnel Junctions MTJ with shrinking device sizes. As a result, time dependent dielectric breakdown TDDB has become a major issue in such devices. In this paper we propose a generalized way of modeling the stress induced leakage current SILC and post breakdown current PBC due to time dependent wearout of the dielectric layer. We model the traps formed in dielectric layer using Buttiker probe and incorporate the Buttiker probe selfenergies in standard selfconsistent NonEquilibrium Green Function NEGF formalism in order to determine SILC and PBC. In addition, we have shown the impact of break down in the dielectric layer on the spin current and spin filtering characteristics of an MTJ. The proposed model is generic in nature. It can be extended from MTJs and conventional CMOS technology to any other devices with any type of single and multiple layers of dielectric materials.

Show and Tell Lessons learned from the 2015 MSCOCO Image Captioning Challenge ; Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. Finally, given the recent surge of interest in this task, a competition was organized in 2015 using the newly released COCO dataset. We describe and analyze the various improvements we applied to our own baseline and show the resulting performance in the competition, which we won exaequo with a team from Microsoft Research, and provide an open source implementation in TensorFlow.

Estimation of Graphical Models through Structured Norm Minimization ; Estimation of Markov Random Field and covariance models from highdimensional data represents a canonical problem that has received a lot of attention in the literature. A key assumption, widely employed, is that of em sparsity of the underlying model. In this paper, we study the problem of estimating such models exhibiting a more intricate structure comprising simultaneously of em sparse, structured sparse and em dense components. Such structures naturally arise in several scientific fields, including molecular biology, finance, and political science. We introduce a general framework based on a novel structured norm that enables us to estimate such complex structures from highdimensional data. The resulting optimization problem is convex and we introduce a linearized multiblock alternating direction method of multipliers ADMM algorithm to solve it efficiently. We illustrate the superior performance of the proposed framework on a number of synthetic data sets generated from both random and structured networks. Further, we apply the method to a number of real data sets and discuss the results.

Relativistic quantum clocks ; The conflict between quantum theory and the theory of relativity is exemplified in their treatment of time. We examine the ways in which their conceptions differ, and describe a semiclassical clock model combining elements of both theories. The results obtained with this clock model in flat spacetime are reviewed, and the problem of generalizing the model to curved spacetime is discussed, before briefly describing an experimental setup which could be used to test of the model. Taking an operationalist view, where time is that which is measured by a clock, we discuss the conclusions that can be drawn from these results, and what clues they contain for a full quantum relativistic theory of time.

On Weighted MSE Model for MIMO Transceiver Optimization ; Meansquarederror MSE is one of the most widely used performance metrics for the designs and analysis of multiinputmultipleoutput MIMO communications. Weighted MSE minimization, a more general formulation of MSE minimization, plays an important role in MIMO transceiver optimization. While this topic has a long history and has been extensively studied, existing treatments on the methods in solving the weighted MSE optimization are more or less sporadic and nonsystematic. In this paper, we firstly review the two major methodologies, Lagrange multiplier method and majorization theory based method, and their common procedures in solving the weighted MSE minimization. Then some problems and limitations of the methods that were usually neglected or glossed over in existing literature are provided. These problems are fundamental and of critical importance for the corresponding MIMO transceiver optimizations. In addition, a new extended matrixfield weighted MSE model is proposed. Its solutions and applications are discussed in details. Compared with existing models, this new model has wider applications, e.g., nonlinear MIMO transceiver designs and capacitymaximization transceiver designs for general MIMO networks.

Ensembles of Generative Adversarial Networks ; Ensembles are a popular way to improve results of discriminative CNNs. The combination of several networks trained starting from different initializations improves results significantly. In this paper we investigate the usage of ensembles of GANs. The specific nature of GANs opens up several new ways to construct ensembles. The first one is based on the fact that in the minimax game which is played to optimize the GAN objective the generator network keeps on changing even after the network can be considered optimal. As such ensembles of GANs can be constructed based on the same network initialization but just taking models which have different amount of iterations. These socalled self ensembles are much faster to train than traditional ensembles. The second method, called cascade GANs, redirects part of the training data which is badly modeled by the first GAN to another GAN. In experiments on the CIFAR10 dataset we show that ensembles of GANs obtain model probability distributions which better model the data distribution. In addition, we show that these improved results can be obtained at little additional computational cost.

Knowing When to Look Adaptive Attention via A Visual Sentinel for Image Captioning ; Attentionbased neural encoderdecoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However, the decoder likely requires little to no visual information from the image to predict nonvisual words such as the and of. Other words that may seem visual can often be predicted reliably just from the language model e.g., sign after behind a red stop or phone following talking on a cell. In this paper, we propose a novel adaptive attention model with a visual sentinel. At each time step, our model decides whether to attend to the image and if so, to which regions or to the visual sentinel. The model decides whether to attend to the image and where, in order to extract meaningful information for sequential word generation. We test our method on the COCO image captioning 2015 challenge dataset and Flickr30K. Our approach sets the new stateoftheart by a significant margin.

convergence analysis of a generalized XY model fractional vortices and string defects ; We propose and analyze a generalized two dimensional XY model, whose interaction potential has n weighted wells, describing corresponding symmetries of the system. As the lattice spacing vanishes, we derive by Gammaconvergence the discretetocontinuum limit of this model. In the energy regime we deal with, the asymptotic ground states exhibit fractional vortices, connected by string defects. The Gammalimit takes into account both contributions, through a renormalized energy, depending on the configuration of fractional vortices, and a surface energy, proportional to the length of the strings. Our model describes in a simple way several topological singularities arising in Physics and Materials Science. Among them, disclinations and string defects in liquid crystals, fractional vortices and domain walls in micromagnetics, partial dislocations and stacking faults in crystal plasticity.

Large bar Oscillations from HighDimensional Lepton Number Violating Operator ; It is usually believed that the observation of the neutrinoantineutrino nubarnu oscillations is almost impossible since the oscillation probabilities are expected to be greatly suppressed by the square of tiny ratio of neutrino masses to energies. Such an argument is applicable to most models for neutrino mass generation based on the Weinberg operator, including the seesaw models. However, in the present paper, we shall give a counterexample to this argument, and show that large nubarnu oscillation probabilities can be obtained in a class of models in which both neutrino masses and neutrinoless double beta 0nubetabeta decays are induced by the highdimensional lepton number violating operator cal O7 baruR lcR barLL HdR rm H.c. with u and d representing the first two generations of quarks. In particular, we find that the predicted 0nubetabeta decay rates have already placed interesting constraints on the nue leftrightarrow barnue oscillation. Moreover, we provide an UVcomplete model to realize this scenario, in which a dark matter candidate naturally appears due to the new U1d symmetry.

Chaotic initial conditions for nonminimally coupled inflation via a conformal factor with a zero ; Nonminimally coupled inflation models based on a nonminimal coupling xi phi2 R and a phi4 potential are in excellent agreement with the scalar spectral index observed by Planck. Here we consider the modification of these models by a conformal factor with a zero. This enables a nonminimally coupled model to have a Planckscale potential energy density at large values of the inflaton field, which can account for the smooth, potentialdominated volume that is necessary for inflation to start. We show that models with a conformal factor zero generally predict a correlated increase of the spectral index ns and tensortoscalar ratio r. For values of ns that are within the present 2sigma bounds from Planck, modification by Delta r as large as 0.0013 is possible, which is large enough to be measured by next generation cosmic microwave background polarization satellites.

Partial chord diagrams and matrix models ; In this article, the enumeration of partial chord diagrams is discussed via matrix model techniques. In addition to the basic data such as the number of backbones and chords, we also consider the Euler characteristic, the backbone spectrum, the boundary point spectrum, and the boundary length spectrum. Furthermore, we consider the boundary length and point spectrum that unifies the last two types of spectra. We introduce matrix models that encode generating functions of partial chord diagrams filtered by each of these spectra. Using these matrix models, we derive partial differential equations obtained independently by cutandjoin arguments in an earlier work for the corresponding generating functions.

Optimizing the DrudeLorentz model for material permittivity method, program, and examples for gold, silver, and copper ; Approximating the frequency dispersion of the permittivity of materials with simple analytical functions is of fundamental importance for understanding and modeling the optical response of materials and resulting structures. In the generalized DrudeLorentz model, the permittivity is described in the complex frequency plane by a number of simple poles having complex weights, which is a physically relevant and mathematically simple approach By construction, it respects causality represents physical resonances of the material, and can be implemented easily in numerical simulations. We report here an efficient method of optimizing the fit of measured data with the DrudeLorentz model having an arbitrary number of poles. We show examples of such optimizations for gold, silver, and copper, for different frequency ranges and up to four pairs of Lorentz poles taken into account. We also provide a program implementing the method for general use.

Existence analysis for incompressible fluid model of electrically charged chemically reacting and heat conducting mixtures ; We deal with a three dimensional model based on the use of barycentric velocity that describes unsteady flows of a heat conducting electrically charged multicomponent chemically reacting nonNewtonian fluid. We show that under certain assumptions on data and the constitutive relations for such a fluid there exists a global in time and large data weak solution. The paper has two key novelties. The first one is that we present a model that is thermodynamically and mechanically consistent and that is able to describe the cross effects in a generality never considered before, i.e., we cover the socalled Soret effect, Dufour effect, Ohm law, Peltier effect, Joul heating, Thompson effect, Seebeck effect and also the generalized Fick law. The second key novelty is that contrary to the previous works on the similar topic, we do not need to deal with the energy equality method and therefore we are able to cover a large range of powerlaw parameters in the Cauchystress. In particular, we cover even the Newtonian case which is the most used model, for which the existence analysis was missing.

Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions ; Finding the main product of a chemical reaction is one of the important problems of organic chemistry. This paper describes a method of applying a neural machine translation model to the prediction of organic chemical reactions. In order to translate 'reactants and reagents' to 'products', a gated recurrent unit based sequencetosequence model and a parser to generate input tokens for model from reaction SMILES strings were built. Training sets are composed of reactions from the patent databases, and reactions manually generated applying the elementary reactions in an organic chemistry textbook of Wade. The trained models were tested by examples and problems in the textbook. The prediction process does not need manual encoding of rules e.g., SMARTS transformations to predict products, hence it only needs sufficient training reaction sets to learn new types of reactions.

Stable solutions of inflation driven by vector fields ; Many models of inflation driven by vector fields alone have been known to be plagued by pathological behaviors, namely ghost andor gradient instabilities. In this work, we seek a new class of vectordriven inflationary models that evade all of the mentioned instabilities. We build our analysis on the Generalized Proca Theory with an extension to three vector fields to realize isotropic expansion. We obtain the conditions required for quasi deSitter solutions to be an attractor analogous to the standard slowroll one and those for their stability at the level of linearized perturbations. Identifying the remedy to the existing unstable models, we provide a simple example and explicitly show its stability. This significantly broadens our knowledge on vector inflationary scenarios, reviving potential phenomenological interests for this class of models.

Simflowny 2 An upgraded platform for scientific modeling and simulation ; Simflowny is an open platform which automatically generates parallel code of scientific dynamical models for different simulation frameworks. Here we present major upgrades on this software to support an extended set of families of models, in particular i a new generic family for partial differential equations, which can include spatial derivatives of any order, ii a new family for agent based models to study complex phenomena either on a spatial domain or on a graph. Additionally we introduce a flexible graphical user interface GUI to accommodate these and future families of equations. This paper describes the new GUI architecture and summarizes the formal representation and implementation of these new families, providing several validation results.

Radiative TwoLoop Neutrino Masses with Dark Matter ; Using the Weinberg operator, we present a full collection of genuine twoloop models for neutrino mass generation, which contain a dark matter particle as one of the internal messengers. These models can be constructed simply by adding new fields that are singlets or doublets of SU2L. We ensure the stability of the dark matter candidate by the addition of a Z2 symmetry that will also be used to forbid tree level or oneloop diagrams. Thus we only present models where the main contribution for neutrinos masses is generated from the corresponding twoloop diagram. We also discuss a short outline corresponding to some phenomenological characteristics of these models.

General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv ; The R package frailtySurv for simulating and fitting semiparametric shared frailty models is introduced. Package frailtySurv implements semiparametric consistent estimators for a variety of frailty distributions, including gamma, lognormal, inverse Gaussian and power variance function, and provides consistent estimators of the standard errors of the parameters' estimators. The parameters' estimators are asymptotically normally distributed, and therefore statistical inference based on the results of this package, such as hypothesis testing and confidence intervals, can be performed using the normal distribution. Extensive simulations demonstrate the flexibility and correct implementation of the estimator. Two case studies performed with publicly available datasets demonstrate applicability of the package. In the Diabetic Retinopathy Study, the onset of blindness is clustered by patient, and in a large hard drive failure dataset, failure times are thought to be clustered by the hard drive manufacturer and model.

DeepCloak Masking Deep Neural Network Models for Robustness Against Adversarial Samples ; Recent studies have shown that deep neural networks DNN are vulnerable to adversarial samples maliciouslyperturbed samples crafted to yield incorrect model outputs. Such attacks can severely undermine DNN systems, particularly in securitysensitive settings. It was observed that an adversary could easily generate adversarial samples by making a small perturbation on irrelevant feature dimensions that are unnecessary for the current classification task. To overcome this problem, we introduce a defensive mechanism called DeepCloak. By identifying and removing unnecessary features in a DNN model, DeepCloak limits the capacity an attacker can use generating adversarial samples and therefore increase the robustness against such inputs. Comparing with other defensive approaches, DeepCloak is easy to implement and computationally efficient. Experimental results show that DeepCloak can increase the performance of stateoftheart DNN models against adversarial samples.

On Store Languages of Language Acceptors ; It is well known that the store language of every pushdown automaton the set of store configurations state and stack contents that can appear as an intermediate step in accepting computations is a regular language. Here many models of language acceptors with various data structures are examined, along with a study of their store languages. For each model, an attempt is made to find the simplest model that accepts their store languages. Some connections between store languages of oneway and twoway machines generally are demonstrated, as with connections between nondeterministic and deterministic machines. A nice application of these store language results is also presented, showing a general technique for proving families accepted by many deterministic models are closed under right quotient with regular languages, resolving some open questions and significantly simplifying proofs for others that are known in the literature. Lower bounds on the space complexity for recognizing store languages for the languages to be nonregular are obtained.

Exact Synthesis of Reversible Logic Circuits using Model Checking ; Synthesis of reversible logic circuits has gained great atten tion during the last decade. Various synthesis techniques have been pro posed, some generate optimal solutions in gate count and are termed as exact, while others are scalable in the sense that they can handle larger functions but generate suboptimal solutions. Although scalable synthe sis is very much essential for circuit design, exact synthesis is also of great importance as it helps in building design library for the synthesis of larger functions. In this paper, we propose an exact synthesis technique for re versible circuits using model checking. We frame the synthesis problem as a model checking instance and propose an iterative bounded model checking calls for an optimal synthesis. Experiments on reversible logic benchmarks shows successful synthesis of optimal circuits. We also illus trate optimal synthesis of random functions with as many as 10 variables and up to 10 gates.

Consistent Alignment of Word Embedding Models ; Word embedding models offer continuous vector representations that can capture rich contextual semantics based on their word cooccurrence patterns. While these word vectors can provide very effective features used in many NLP tasks such as clustering similar words and inferring learning relationships, many challenges and open research questions remain. In this paper, we propose a solution that aligns variations of the same model or different models in a joint lowdimensional latent space leveraging carefully generated synthetic data points. This generative process is inspired by the observation that a variety of linguistic relationships is captured by simple linear operations in embedded space. We demonstrate that our approach can lead to substantial improvements in recovering embeddings of local neighborhoods.

Dynamic Walking over Rough Terrains by Nonlinear Predictive Control of the Floatingbase Inverted Pendulum ; We present a realtime pattern generator for dynamic walking over rough terrains. Our method automatically finds step durations, a critical issue over rough terrains where they depend on terrain topology. To achieve this level of generality, we consider a Floatingbase Inverted Pendulum FIP model where the center of mass can translate freely and the zerotilting moment point is allowed to leave the contact surface. This model is equivalent to a linear inverted pendulum with variable centerofmass height, but its equations of motion remain linear. Our solution then follows three steps i we characterize the FIP contactstability condition; ii we compute feedforward controls by solving a nonlinear optimization over recedinghorizon FIP trajectories. Despite running at 30 Hz in a modelpredictive fashion, simulations show that the latter is too slow to stabilize dynamic motions. To remedy this, we iii linearize FIP feedback control into a constrained linearquadratic regulator that runs at 300 Hz. We finally demonstrate our solution in simulations with a model of the HRP4 humanoid robot, including noise and delays over state estimation and foot force control.

Quantum Surface and Intertwiner Dynamics in Loop Quantum Gravity ; We introduce simple generic models of surface dynamics in loop quantum gravity LQG. A quantum surface is defined as a set of elementary patches of area glued together. We provide it with an extra structure of locality nearest neighbors, thought of as induced by the whole spin network state defining the 3d bulk geometry around the quantum surface. Here, we focus on classical surface dynamics, using a spinorial description of surface degrees of freedom. We introduce two classes of dynamics, to be thought as templates for future investigation of LQG dynamics with in mind the dynamics of quantum black holes. The first defines global dynamics of the closure defect of the surface, with two basic toymodels, either a dissipative dynamics relaxing towards the closure constraint or a Hamiltonian dynamics precessing the closure defect. The second class of dynamics describes the isolated regime, when both area and closure defect are conserved throughout the evolution. The surface dynamics is implemented through UN transformations and generalizes to a BoseHubbard Hamiltonian with a local quadratic potential interaction. We briefly discuss the implications of modeling the quantum black hole dynamics by a surface BoseHubbard model.

Custom Hypergraph Categories via Generalized Relations ; Process theories combine a graphical language for compositional reasoning with an underlying categorical semantics. They have been successfully applied to fields such as quantum computation, natural language processing, linear dynamical systems and network theory. When investigating a new application, the question arises of how to identify a suitable process theoretic model. We present a conceptually motivated parameterized framework for the construction of models for process theories. Our framework generalizes the notion of binary relation along four axes of variation, the truth values, a choice of algebraic structure, the ambient mathematical universe and the choice of proof relevance or provability. The resulting categories are preorderenriched and provide analogues of relational converse and taking graphs of maps. Our constructions are functorial in the parameter choices, establishing mathematical connections between different application domains. We illustrate our techniques by constructing many existing models from the literature, and new models that open up ground for further development.

EX2 Exploration with Exemplar Models for Deep Reinforcement Learning ; Deep reinforcement learning algorithms have been shown to learn complex tasks using highly general policy classes. However, sparse reward problems remain a significant challenge. Exploration methods based on novelty detection have been particularly successful in such settings but typically require generative or predictive models of the observations, which can be difficult to train when the observations are very highdimensional and complex, as in the case of raw images. We propose a novelty detection algorithm for exploration that is based entirely on discriminatively trained exemplar models, where classifiers are trained to discriminate each visited state against all others. Intuitively, novel states are easier to distinguish against other states seen during training. We show that this kind of discriminative modeling corresponds to implicit density estimation, and that it can be combined with countbased exploration to produce competitive results on a range of popular benchmark tasks, including stateoftheart results on challenging egocentric observations in the vizDoom benchmark.

Dual wavefunction of the symplectic ice ; The wavefunction of the freefermion sixvertex model was found to give a natural realization of the Tokuyama combinatorial formula for the Schur polynomials by BumpBrubakerFriedberg. Recently, we studied the correspondence between the dual version of the wavefunction and the Schur polynomials, which gave rise to another combinatorial formula. In this paper, we extend the analysis to the reflecting boundary condition, and show the exact correspondence between the dual wavefunction and the symplectic Schur functions. This gives a dual version of the integrable model realization of the symplectic Schur functions by Ivanov. We also generalize to the correspondence between the wavefunction, the dual wavefunction of the sixvertex model and the factorial symplectic Schur functions by the inhomogeneous generalization of the model.

Local charges in involution and hierarchies in integrable sigmamodels ; Integrable sigmamodels, such as the principal chiral model, mathbbZTcoset models for T in mathbbZgeq 2 and their various integrable deformations, are examples of nonultralocal integrable field theories described by cyclotomic rssystems with twist function. In this general setting, and when the Lie algebra mathfrakg underlying the rssystem is of classical type, we construct an infinite algebra of local conserved charges in involution, extending the approach of Evans, Hassan, MacKay and Mountain developed for the principal chiral model and symmetric space sigmamodel. In the present context, the local charges are attached to certain regular' zeros of the twist function and have increasing degrees related to the exponents of the untwisted affine KacMoody algebra widehatmathfrakg associated with mathfrakg. The Hamiltonian flows of these charges are shown to generate an infinite hierarchy of compatible integrable equations.

Black hole as a magnetic monopole within exponential nonlinear electrodynamics ; We perform the gauge covariant quantization of the exponential model of nonlinear electrodynamics. Magnetically charged black holes, in the framework of our model are considered, and the regular black hole solution is obtained in general relativity. The asymptotic black hole solution at rrightarrow infty is found. We calculate the magnetic mass of the black hole and the metric function which are expressed via the parameter beta of the model and the magnetic charge. The thermodynamic properties and thermal stability of regular black holes are analysed. We calculate the Hawking temperature of black holes and their heat capacity at the constant magnetic charge. We find a point where the temperature changes the sign that corresponds to the firstorder phase transition. It is shown that at critical point, where the heat capacity diverges, there is a phase transition of the secondorder. We obtain the parameters of the model when the black hole is stable.

LHC phenomenology and baryogenesis in supersymmetric models with a U1R baryon number ; We study the phenomenology of a supersymmetric extension of the Standard Model with an Rsymmetry under which Rcharges correspond to the baryon number. This identification allows for the presence in the superpotential of the Rparity violating term lambda''Uc Dc Dc without breaking baryon number, which loosens several bounds on this operator while changing considerably the phenomenology. However, the Rsymmetry cannot remain exact as it is at least broken by anomaly mediation. Under these conditions, we investigate the constraints coming from baryon number violating processes and flavour physics and find that, in general, they are lessened. Additionally, we examine recent ATLAS and CMS experimental searches and use these to place limits on the parameter space of the model. This is done for both stop production, which now features both pair and resonant production, and pair production of the first two generations of squarks. Finally, we study the implications this model has on baryogenesis. We find that successful baryogenesis can potentially be achieved, but only at the cost of breaking the Rsymmetry by a significant amount.

Generalized BornInfeldlike models for kinks and branes ; In this work we deal with a noncanonical scalar field in the twodimensional spacetime. We search for a generalized model that is twin of the standard model, supporting the same defect structure with the same energy density. We also study the stability of the defect solution under small fluctuations, which is governed by a SturmLiouville equation, and show how to make it stable. The model is then modified and used in the fivedimensional spacetime to construct a thick brane that engenders the first order framework and preserves the twinlike behavior, under tensorial fluctuations of the metric in its gravitational sector.

High scale flavor alignment in twoHiggs doublet models and its phenomenology ; The most general twoHiggs doublet model 2HDM includes potentially large sources of flavor changing neutral currents FCNCs that must be suppressed in order to achieve a phenomenologically viable model. The flavor alignment ansatz postulates that all Yukawa coupling matrices are diagonal when expressed in the basis of masseigenstate fermion fields, in which case treelevel Higgs mediated FCNCs are eliminated. In this work, we explore models with the flavor alignment condition imposed at a very high energy scale, which results in the generation of Higgsmediated FCNCs via renormalization group running from the high energy scale to the electroweak scale. Using the current experimental bounds on flavor changing observables, constraints are derived on the aligned 2HDM parameter space. In the favored parameter region, we analyze the implications for Higgs boson phenomenology.

Efficient optimization for Hierarchicallystructured Interacting Segments HINTS ; We propose an effective optimization algorithm for a general hierarchical segmentation model with geometric interactions between segments. Any given tree can specify a partial order over object labels defining a hierarchy. It is wellestablished that segment interactions, such as inclusionexclusion and margin constraints, make the model significantly more discriminant. However, existing optimization methods do not allow full use of such models. Generic expansion results in weak local minima, while common binary multilayered formulations lead to nonsubmodularity, complex highorder potentials, or polar domain unwrapping and shape biases. In practice, applying these methods to arbitrary trees does not work except for simple cases. Our main contribution is an optimization method for the Hierarchicallystructured Interacting Segments HINTS model with arbitrary trees. Our PathMoves algorithm is based on multilabel MRF formulation and can be seen as a combination of wellknown aexpansion and Ishikawa techniques. We show stateoftheart biomedical segmentation for many diverse examples of complex trees.

Modeling Temporal Dynamics and Spatial Configurations of Actions Using TwoStream Recurrent Neural Networks ; Recently, skeleton based action recognition gains more popularity due to costeffective depth sensors coupled with realtime skeleton estimation algorithms. Traditional approaches based on handcrafted features are limited to represent the complexity of motion patterns. Recent methods that use Recurrent Neural Networks RNN to handle raw skeletons only focus on the contextual dependency in the temporal domain and neglect the spatial configurations of articulated skeletons. In this paper, we propose a novel twostream RNN architecture to model both temporal dynamics and spatial configurations for skeleton based action recognition. We explore two different structures for the temporal stream stacked RNN and hierarchical RNN. Hierarchical RNN is designed according to human body kinematics. We also propose two effective methods to model the spatial structure by converting the spatial graph into a sequence of joints. To improve generalization of our model, we further exploit 3D transformation based data augmentation techniques including rotation and scaling transformation to transform the 3D coordinates of skeletons during training. Experiments on 3D action recognition benchmark datasets show that our method brings a considerable improvement for a variety of actions, i.e., generic actions, interaction activities and gestures.

Register automata with linear arithmetic ; We propose a novel automata model over the alphabet of rational numbers, which we call register automata over the rationals RAQ. It reads a sequence of rational numbers and outputs another rational number. RAQ is an extension of the wellknown register automata RA over infinite alphabets, which are finite automata equipped with a finite number of registersvariables for storing values. Like in the standard RA, the RAQ model allows both equality and ordering tests between values. It, moreover, allows to perform linear arithmetic between certain variables. The model is quite expressive in addition to the standard RA, it also generalizes other wellknown models such as affine programs and arithmetic circuits. The main feature of RAQ is that despite the use of linear arithmetic, the socalled invariant problema generalization of the standard nonemptiness problemis decidable. We also investigate other natural decision problems, namely, commutativity, equivalence, and reachability. For deterministic RAQ, commutativity and equivalence are polynomialtime interreducible with the invariant problem.

Common adversaries form alliances modelling complex networks via antitransitivity ; Antitransitivity captures the notion that enemies of enemies are friends, and arises naturally in the study of adversaries in social networks and in the study of conflicting nation states or organizations. We present a simplified, evolutionary model for antitransitivity influencing link formation in complex networks, and analyze the model's network dynamics. The Iterated Local AntiTransitivity or ILAT model creates anticlone nodes in each timestep, and joins anticlones to the parent node's nonneighbor set. The graphs generated by ILAT exhibit familiar properties of complex networks such as densification, short distances bounded by absolute constants, and bad spectral expansion. We determine the cop and domination number for graphs generated by ILAT, and finish with an analysis of their clustering coefficients. We interpret these results within the context of realworld complex networks and present open problems.

Dark matter, dark energy, and alternate models A review ; The nature of dark matter DM and dark energy DE which is supposed to constitute about 95 of the energy density of the universe is still a mystery. There is no shortage of ideas regarding the nature of both. While some candidates for DM are clearly ruled out, there is still a plethora of viable particles that fit the bill. In the context of DE, while current observations favour a cosmological constant picture, there are other competing models that are equally likely. This paper reviews the different possible candidates for DM including exotic candidates and their possible detection. This review also covers the different models for DE and the possibility of unified models for DM and DE. Keeping in mind the negative results in some of the ongoing DM detection experiments, here we also review the possible alternatives to both DM and DE such as MOND and modifications of general relativity and possible means of observationally distinguishing between the alternatives.

Starobinskylike Inflation, Supercosmology and Neutrino Masses in NoScale Flipped SU5 ; We embed a flipped rm SU5 times rm U1 GUT model in a noscale supergravity framework, and discuss its predictions for cosmic microwave background observables, which are similar to those of the Starobinsky model of inflation. Measurements of the tilt in the spectrum of scalar perturbations in the cosmic microwave background, ns, constrain significantly the model parameters. We also discuss the model's predictions for neutrino masses, and pay particular attention to the behaviours of scalar fields during and after inflation, reheating and the GUT phase transition. We argue in favor of strong reheating in order to avoid excessive entropy production which could dilute the generated baryon asymmetry.

Punny Captions Witty Wordplay in Image Descriptions ; Wit is a form of rich interaction that is often grounded in a specific situation e.g., a comment in response to an event. In this work, we attempt to build computational models that can produce witty descriptions for a given image. Inspired by a cognitive account of humor appreciation, we employ linguistic wordplay, specifically puns, in image descriptions. We develop two approaches which involve retrieving witty descriptions for a given image from a large corpus of sentences, or generating them via an encoderdecoder neural network architecture. We compare our approach against meaningful baseline approaches via human studies and show substantial improvements. We find that when a human is subject to similar constraints as the model regarding word usage and style, people vote the image descriptions generated by our model to be slightly wittier than humanwritten witty descriptions. Unsurprisingly, humans are almost always wittier than the model when they are free to choose the vocabulary, style, etc.

Dynamic interdependence and competition in multilayer networks ; From critical infrastructure, to physiology and the human brain, complex systems rarely occur in isolation. Instead, the functioning of nodes in one system often promotes or suppresses the functioning of nodes in another. Despite advances in structural interdependence, modeling interdependence and other interactions between dynamic systems has proven elusive. Here we define a broadly applicable dynamic dependency link and develop a general framework for interdependent and competitive interactions between general dynamic systems. We apply our framework to studying interdependent and competitive synchronization in multilayer oscillator networks and cooperativecompetitive contagions in an epidemic model. Using a meanfield theory which we verify numerically, we find explosive transitions and rich behavior which is absent in percolation models including hysteresis, multistability and chaos. The framework presented here provides a powerful new way to model and understand many of the interacting complex systems which surround us.

A Polya Contagion Model for Networks ; A network epidemics model based on the classical Polya urn scheme is investigated. Temporal contagion processes are generated on the network nodes using a modified Polya sampling scheme that accounts for spatial infection among neighbouring nodes. The stochastic properties and the asymptotic behaviour of the resulting network contagion process are analyzed. Unlike the classical Polya process, the network process is noted to be nonstationary in general, although it is shown to be timeinvariant in its first and some of its secondorder statistics and to satisfy martingale convergence properties under certain conditions. Three classical Polya processes, one computational and two analytical, are proposed to statistically approximate the contagion process of each node, showing a good fit for a range of system parameters. Finally, empirical results compare and contrast our model with the wellknown discrete time SIS model.

On Hierarchical Statistical Static Timing Analysis ; Statistical static timing analysis deals with the increasing variations in manufacturing processes to reduce the pessimism in the worst case timing analysis. Because of the correlation between delays of circuit components, timing model generation and hierarchical timing analysis face more challenges than in static timing analysis. In this paper, a novel method to generate timing models for combinational circuits considering variations is proposed. The resulting timing models have accurate inputoutput delays and are about 80 smaller than the original circuits. Additionally, an accurate hierarchical timing analysis method at design level using precharacterized timing models is proposed. This method incorporates the correlation between modules by replacing independent random variables to improve timing accuracy. Experimental results show that the correlation between modules strongly affects the delay distribution of the hierarchical design and the proposed method has good accuracy compared with Monte Carlo simulation, but is faster by three orders of magnitude.

Gradient Estimators for Implicit Models ; Implicit models, which allow for the generation of samples but not for pointwise evaluation of probabilities, are omnipresent in realworld problems tackled by machine learning and a hot topic of current research. Some examples include data simulators that are widely used in engineering and scientific research, generative adversarial networks GANs for image synthesis, and hotoffthepress approximate inference techniques relying on implicit distributions. The majority of existing approaches to learning implicit models rely on approximating the intractable distribution or optimisation objective for gradientbased optimisation, which is liable to produce inaccurate updates and thus poor models. This paper alleviates the need for such approximations by proposing the Stein gradient estimator, which directly estimates the score function of the implicitly defined distribution. The efficacy of the proposed estimator is empirically demonstrated by examples that include metalearning for approximate inference, and entropy regularised GANs that provide improved sample diversity.

Cosmological constraints from a joint analysis of cosmic growth and expansion ; Combining measurements on the expansion history of the Universe and on the growth rate of cosmic structures is key to discriminate between alternative cosmological frameworks and to test gravity. Recently, Linder 2017 proposed a new diagram to investigate the joint evolutionary track of these two quantities. In this letter, we collect the most recent cosmic growth and expansion rate datasets to provide the stateoftheart observational constraints on this diagram. By performing a joint statistical analysis of both probes, we test the standard LambdaCDM model, confirming a mild tension between cosmic microwave background predictions from Planck mission and cosmic growth measurements at low redshift z2. Then we test alternative models allowing the variation of one single cosmological parameter at a time. In particular, we find a larger growth index than the one predicted by general relativity gamma0.650.050.04. However, also a standard model with total neutrino mass of 0.26pm0.10 eV provides a similarly accurate description of the current data. By simulating an additional dataset consistent with nextgeneration darkenergy mission forecasts, we show that growth rate constraints at z1 will be crucial to discriminate between alternative models.

A variant of 331 model for the generation of the SM fermion mass and mixing pattern ; We propose an extension of the 331 model with an additional symmetry group Z2times Z4 times U1Lg and an extended scalar sector. To our best knowledge this is the first example of a renormalizable 331 model, which allows explanation of the SM fermion mass hierarchy by a sequential loop suppression treelevel top and exotic fermion masses, 1loop bottom, charm, tau and muon masses; 2loop masses for the light up, down, strange quarks as well as for the electron. The light active neutrino masses are generated from a combination of linear and inverse seesaw mechanisms at two loop level. The model also has viable fermionic and scalar dark matter candidates.

Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes ; Often in machine learning, data are collected as a combination of multiple conditions, e.g., the voice recordings of multiple persons, each labeled with an ID. How could we build a model that captures the latent information related to these conditions and generalize to a new one with few data We present a new model called Latent Variable Multiple Output Gaussian Processes LVMOGP and that allows to jointly model multiple conditions for regression and generalize to a new condition with a few data points at test time. LVMOGP infers the posteriors of Gaussian processes together with a latent space representing the information about different conditions. We derive an efficient variational inference method for LVMOGP, of which the computational complexity is as low as sparse Gaussian processes. We show that LVMOGP significantly outperforms related Gaussian process methods on various tasks with both synthetic and real data.

Freefermion descriptions of parafermion chains and stringnet models ; Topological phases of matter remain a focus of interest due to their unique properties fractionalisation, ground state degeneracy, and exotic excitations. While some of these properties can occur in systems of free fermions, their emergence is generally associated with interactions between particles. Here we quantify the role of interactions in general classes of topological states of matter in all spatial dimensions, including parafermion chains and stringnet models. Using the interaction distance Nat. Commun. 8, 14926 2017, we measure the distinguishability of states of these models from those of free fermions. We find that certain topological states can be exactly described by free fermions, while others saturate the maximum possible interaction distance. Our work opens the door to understanding the complexity of topological models and to applying new types of fermionisation procedures to describe their lowenergy physics.

On approximating copulas by finite mixtures ; Copulas are now frequently used to construct or estimate multivariate distributions because of their ability to take into account the multivariate dependence of the different variables while separately specifying marginal distributions. Copula based multivariate models can often also be more parsimonious than fitting a flexible multivariate model, such as a mixture of normals model, directly to the data. However, to be effective, it is imperative that the family of copula models considered is sufficiently flexible. Although finite mixtures of copulas have been used to construct flexible families of copulas, their approximation properties are not well understood and we show that natural candidates such as mixtures of elliptical copulas and mixtures of Archimedean copulas cannot approximate a general copula arbitrarily well. Our article develops fundamental tools for approximating a general copula arbitrarily well by a copulas based on finite mixtures. We show the asymptotic properties as well as illustrate the advantages of our methodology empirically on a financial data set and on some artificial data.

Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications ; Markov random fields area popular model for highdimensional probability distributions. Over the years, many mathematical, statistical and algorithmic problems on them have been studied. Until recently, the only known algorithms for provably learning them relied on exhaustive search, correlation decay or various incoherence assumptions. Bresler gave an algorithm for learning general Ising models on bounded degree graphs. His approach was based on a structural result about mutual information in Ising models. Here we take a more conceptual approach to proving lower bounds on the mutual information through setting up an appropriate zerosum game. Our proof generalizes well beyond Ising models, to arbitrary Markov random fields with higher order interactions. As an application, we obtain algorithms for learning Markov random fields on bounded degree graphs on n nodes with rorder interactions in nr time and log n sample complexity. The sample complexity is information theoretically optimal up to the dependence on the maximum degree. The running time is nearly optimal under standard conjectures about the hardness of learning parity with noise.

Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling ; Conventional methods of estimating latent behaviour generally use attitudinal questions which are subjective and these survey questions may not always be available. We hypothesize that an alternative approach can be used for latent variable estimation through an undirected graphical models. For instance, nonparametric artificial neural networks. In this study, we explore the use of generative nonparametric modelling methods to estimate latent variables from prior choice distribution without the conventional use of measurement indicators. A restricted Boltzmann machine is used to represent latent behaviour factors by analyzing the relationship information between the observed choices and explanatory variables. The algorithm is adapted for latent behaviour analysis in discrete choice scenario and we use a graphical approach to evaluate and understand the semantic meaning from estimated parameter vector values. We illustrate our methodology on a financial instrument choice dataset and perform statistical analysis on parameter sensitivity and stability. Our findings show that through nonparametric statistical tests, we can extract useful latent information on the behaviour of latent constructs through machine learning methods and present strong and significant influence on the choice process. Furthermore, our modelling framework shows robustness in input variability through sampling and validation.

A Flexible Modeling Approach for Robust MultiLane Road Estimation ; A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of selfdriving vehicles. In this paper, a flexible method for modeling multiple lanes in a vehicle in real time is presented. Information about traffic lanes, derived by cameras and other environmental sensors, that is represented as features, serves as input for an iterative expectationmaximization method to estimate a lane model. The generic and modular concept of the approach allows to freely choose the mathematical functions for the geometrical description of lanes. In addition to the current measurement data, the previously estimated result as well as additional constraints to reflect parallelism and continuity of traffic lanes, are considered in the optimization process. As evaluation of the lane estimation method, its performance is showcased using cubic splines for the geometric representation of lanes in simulated scenarios and measurements recorded using a development vehicle. In a comparison to ground truth data, robustness and precision of the lanes estimated up to a distance of 120 m are demonstrated. As a part of the environmental modeling, the presented method can be utilized for longitudinal and lateral control of autonomous vehicles.

A generalization of the quantum Rabi model exact solution and spectral structure ; We consider a generalization of the quantum Rabi model where the twolevel system and the singlemode cavity oscillator are coupled by an additional Starklike term. By adapting a method recently introduced by Braak Phys. Rev. Lett. bf 107, 100401 2011, we solve the model exactly. The lowlying spectrum in the experimentally relevant ultrastrong and deep strong regimes of the Rabi coupling is found to exhibit two striking features absent from the original quantum Rabi model avoided level crossings for states of the same parity and an anomalously rapid onset of twofold neardegenerate levels as the Rabi coupling increases.

Stability of the lepton bag model based on the KerrNewman solution ; We show that the considered in previous paper citeBurGrBag lepton bag model, generating the external gravitational and electromagnetic fields of the KerrNewman KN solution is supersymmetric and represents a BPSsaturated soliton, interpolating between internal vacuum state and external KN solution. We obtain Bogomolnyi equations for this phase transition, and show that Bogomolnyi bound determines all important features of this bag model, including its stable shape. In particular, for stationary KN solution the BPSbound provides stability of the ellipsoidal form of the bag and formation of the ringstring structure at its border, while for the periodic electromagnetic excitations of the KN solution, the BPSbound controls deformation of the surface of the bag, reproducing the known flexibility of bag models.

Twisted Quantum Double Model of Topological Orders with Boundaries ; We generalize the twisted quantum double model of topological orders in two dimensions to the case with boundaries by systematically constructing the boundary Hamiltonians. Given the bulk Hamiltonian defined by a gauge group G and a threecocycle in the third cohomology group of G over U1, a boundary Hamiltonian can be defined by a subgroup K of G and a twocochain in the second cochain group of K over U1. The consistency between the bulk and boundary Hamiltonians is dictated by what we call the Frobenius condition that constrains the twocochain given the threecocyle. We offer a closedform formula computing the ground state degeneracy of the model on a cylinder in terms of the input data only, which can be naturally generalized to surfaces with more boundaries. We also explicitly write down the groundstate wavefunction of the model on a disk also in terms of the input data only.

Engineering Inertial and Primaryfrequency Response for Distributed Energy Resources ; We propose a framework to engineer syntheticinertia and droopcontrol parameters for distributed energy resources DERs so that the system frequency in a network composed of DERs and synchronous generators conforms to prescribed transient and steadystate performance specifications. Our approach is grounded in a secondorder lumpedparameter model that captures the dynamics of synchronous generators and frequencyresponsive DERs endowed with inertial and droop control. A key feature of this reducedorder model is that its parameters can be related to those of the originating higherorder dynamical model. This allows one to systematically design the DER inertial and droopcontrol coefficients leveraging classical frequencydomain response characteristics of secondorder systems. Timedomain simulations validate the accuracy of the modelreduction method and demonstrate how DER controllers can be designed to meet steadystateregulation and transientperformance specifications.

Multifractality in the generalized AubryAndre quasiperiodic localization model with powerlaw hoppings or powerlaw Fourier coefficients ; The nearestneighbor AubryAndr'e quasiperiodic localization model is generalized to include powerlaw translationinvariant hoppings Tlpropto tla or powerlaw Fourier coefficients Wm propto wmb in the quasiperiodic potential. The AubryAndr'e duality between Tl and Wm is manifest when the Hamiltonian is written in the realspace basis and in the Fourier basis on a finite ring. The perturbative analysis in the amplitude t of the hoppings yields that the eigenstates remain powerlaw localized in real space for a1 and are critical for ac1 where they follow the Strong Multifractality linear spectrum, as in the equivalent model with random disorder. The perturbative analysis in the amplitude w of the quasiperiodic potential yields that the eigenstates remain delocalized in real space powerlaw localized in Fourier space for b1 and are critical for bc1 where they follow the Weak Multifractality gaussian spectrum in real space or Strong Multifractality linear spectrum in the Fourier basis. This critical case bc1 for the Fourier coefficients Wm corresponds to a periodic function with discontinuities, instead of the cosinus of the standard selfdual AubryAndr'e model.

Families of Distributed Memory Parallel Graph Algorithms from SelfStabilizing KernelsAn SSSP Case Study ; Selfstabilizing algorithms are an important because of their robustness and guaranteed convergence. Starting from any arbitrary state, a selfstabilizing algorithm is guaranteed to converge to a legitimate state.Those algorithms are not directly amenable to solving distributed graph processing problems when performance and scalability are important. In this paper, we show the Abstract Graph Machine AGM model that can be used to convert selfstabilizing algorithms into forms suitable for distributed graph processing. An AGM is a mathematical model of parallel computation on graphs that adds work dependency and ordering to selfstabilizing algorithms. Using the AGM model we show that some of the existing distributed Single Source Shortest Path SSSP algorithms are actually specializations of selfstabilizing SSSP. We extend the AGM model to apply more finegrained orderings at different spatial levels to derive additional scalable variants of SSSP algorithms, essentially enabling the algorithm to be generated for a specific target architecture. Experimental results show that this approach can generate new algorithmic variants that outperform standard distributed algorithms for SSSP.

Violation of Positivity Bounds in Models of Generalized Parton Distributions ; As with parton distributions, flexible phenomenological parameterizations of generalized parton distributions GPDs are essential for their extraction from data. The large number of constraints imposed on GPDs make simple Lorentz covariant models viable; but, such models are often incomplete in that they employ the impulse approximation. Using the GPD of the pion as a test case, we show that the impulse approximation can lead to violation of the positivity bound required of GPDs. We focus on a particular model of the pion boundstate vertex that was recently proposed and demonstrate that satisfying the bound is not guaranteed by Lorentz covariance. Violation of the positivity bound is tied to a problematic mismatch between the behavior of the quark distribution at the endpoint and the crossover value of the GPD.

Reconstructing fR Gravity from a Chaplygin Scalar Field in de Sitter Spacetimes ; We present a reconstruction technique for models of fR gravity from the Chaplygin scalar field in flat de Sitter spacetimes. Exploiting the equivalence between fR gravity and scalartensor theories, and treating the Chaplygin gas as a scalar field model in a universe without conventional matter forms, the Lagrangian densities for the fR action are derived. Exact fR models and corresponding scalar field potentials are obtained for asymptotically de Sitter spacetimes in early and late cosmological expansion histories. It is shown that the reconstructed fR models all have General Relativity as a limiting solution.

Generalized LipkinMeshkovGlick models of HaldaneShastry type ; We introduce a class of generalized LipkinMeshkovGlick gLMG models with sum interactions of HaldaneShastry type. We have computed the partition function of these models in closed form by exactly evaluating the partition function of the restriction of a spin chain Hamiltonian of HaldaneShastry type to subspaces with welldefined magnon numbers. As a byproduct of our analysis, we have obtained strong numerical evidence of the Gaussian character of the level density of the latter restricted Hamiltonians, and studied the distribution of the spacings of consecutive unfolded levels. We have also discussed the thermodynamic behavior of a large family of su2 and su3 gLMG models, showing that it is qualitatively similar to that of a twolevel system.

Neutrino mass generation and leptogenesis via pseudoNambuGoldstone Higgs portal ; We consider an extension of the Standard Model with the global symmetry breaking pattern SO5SO4, where the Higgs boson arises as a pseudoNambuGoldstone boson. The scalar content of the theory consists of a StandardModellike Higgs field and an extra real scalar field. The flavour sector of the model is extended by two righthanded neutrinos compatible with the observed lightneutrino phenomenology, and we find that the correct vacuum alignment determines the mass of the heavier neutrino eigenstate to be around 80 TeV. The new singletscalar state generates dynamically a Majorana mass term for the heavy neutrino states. We show how the model leads to the correct baryon asymmetry of the universe via leptogenesis in the case of two degenerate or hierarchical heavy neutrinos.

Bridging the Gap between Probabilistic and Deterministic Models A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model ; The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The paper concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder and for free action.

CharManteau Character Embedding Models For Portmanteau Creation ; Portmanteaus are a word formation phenomenon where two words are combined to form a new word. We propose characterlevel neural sequencetosequence S2S methods for the task of portmanteau generation that are endtoendtrainable, language independent, and do not explicitly use additional phonetic information. We propose a noisychannelstyle model, which allows for the incorporation of unsupervised word lists, improving performance over a standard sourcetotarget model. This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task. Experiments find our approach superior to a stateoftheart FSTbased baseline with respect to ground truth accuracy and human evaluation.

Choosing Requirements for Experimentation with User Interfaces of Requirements Modeling Tools ; When designing a new presentation frontend called FlexiView for requirements modeling tools, we encountered a general problem designing such an interface requires a lot of experimentation which is costly when the code of the tool needs to be adapted for every experiment. On the other hand, when using simplified user interface UI tools, the results are difficult to generalize. To improve this situation, we are developing a UI experimentation tool which is based on socalled ImitGraphs. ImitGraphs can act as a simple, but an accurate substitute for a modeling tool. In this paper, we define requirements for such a UI experimentation tool based on an analysis of the features of existing requirements modeling tools.

Estimation of Component Reliability in Coherent Systems ; The first step in statistical reliability studies of coherent systems is the estimation of the reliability of each system component. For the cases of parallel and series systems the literature is abundant. It seems that the present paper is the first that presents the general case of component inferences in coherent systems. The failure time model considered here is the threeparameter Weibull distribution. Furthermore, neither independence nor identically distributed failure times are required restrictions. The proposed model is general in the sense that it can be used for any coherent system, from the simplest to the more complex structures. It can be considered for all kinds of censored data; including intervalcensored data. An important property obtained for the Weibull model is the fact that the posterior distributions are proper, even for noninformative priors. Using several simulations, the excellent performance of the model is illustrated. As a real example, boys first use of marijuana is considered to show the efficiency of the solution even when censored data occurs.

SaltiNet Scanpath Prediction on 360 Degree Images using Saliency Volumes ; We introduce SaltiNet, a deep neural network for scanpath prediction trained on 360degree images. The model is based on a temporalaware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by backpropagation computed from a binary cross entropy BCE loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. Our source code and trained models available at httpsgithub.commassenssaliency360salient2017.