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Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate ShiftJan 27 2019In this paper we revisit the method of off-policy corrections for reinforcement learning (COP-TD) pioneered by Hallak et al. (2017). Under this method, online updates to the value function are reweighted to avoid divergence issues typical of off-policy ... More

A Distributional Perspective on Reinforcement LearningJul 21 2017In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent. This is in contrast to the common approach to reinforcement learning which models the expectation ... More

Safe and Efficient Off-Policy Reinforcement LearningJun 08 2016Nov 07 2016In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorithm, Retrace($\lambda$), with three desired properties: (1) it has low variance; ... More

A Laplacian Framework for Option Discovery in Reinforcement LearningMar 02 2017Jun 16 2017Representation learning and option discovery are two of the biggest challenges in reinforcement learning (RL). Proto-value functions (PVFs) are a well-known approach for representation learning in MDPs. In this paper we address the option discovery problem ... More

A Comparative Analysis of Expected and Distributional Reinforcement LearningJan 30 2019Feb 21 2019Since their introduction a year ago, distributional approaches to reinforcement learning (distributional RL) have produced strong results relative to the standard approach which models expected values (expected RL). However, aside from convergence guarantees, ... More

A Comparative Analysis of Expected and Distributional Reinforcement LearningJan 30 2019Since their introduction a year ago, distributional approaches to reinforcement learning (distributional RL) have produced strong results relative to the standard approach which models expected values (expected RL). However, aside from convergence guarantees, ... More

Q($λ$) with Off-Policy CorrectionsFeb 16 2016Aug 11 2016We propose and analyze an alternate approach to off-policy multi-step temporal difference learning, in which off-policy returns are corrected with the current Q-function in terms of rewards, rather than with the target policy in terms of transition probabilities. ... More

Distributional Reinforcement Learning with Quantile RegressionOct 27 2017In reinforcement learning an agent interacts with the environment by taking actions and observing the next state and reward. When sampled probabilistically, these state transitions, rewards, and actions can all induce randomness in the observed long-term ... More

Safe and Efficient Off-Policy Reinforcement LearningJun 08 2016In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorithm, Retrace($\lambda$), with three desired properties: (1) low variance; ... More

The Arcade Learning Environment: An Evaluation Platform for General AgentsJul 19 2012Jun 21 2013In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 ... More

Approximate Exploration through State AbstractionAug 29 2018Jan 24 2019Although exploration in reinforcement learning is well understood from a theoretical point of view, provably correct methods remain impractical. In this paper we study the interplay between exploration and approximation, what we call approximate exploration. ... More

Unifying Count-Based Exploration and Intrinsic MotivationJun 06 2016Nov 07 2016We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across observations. Specifically, we focus on the problem of exploration in non-tabular reinforcement learning. Drawing inspiration from the intrinsic ... More

DeepMDP: Learning Continuous Latent Space Models for Representation LearningJun 06 2019Many reinforcement learning (RL) tasks provide the agent with high-dimensional observations that can be simplified into low-dimensional continuous states. To formalize this process, we introduce the concept of a DeepMDP, a parameterized latent space model ... More

Hyperbolic Discounting and Learning over Multiple HorizonsFeb 19 2019Feb 20 2019Reinforcement learning (RL) typically defines a discount factor as part of the Markov Decision Process. The discount factor values future rewards by an exponential scheme that leads to theoretical convergence guarantees of the Bellman equation. However, ... More

Increasing the Action Gap: New Operators for Reinforcement LearningDec 15 2015This paper introduces new optimality-preserving operators on Q-functions. We first describe an operator for tabular representations, the consistent Bellman operator, which incorporates a notion of local policy consistency. We show that this local consistency ... More

The Value Function Polytope in Reinforcement LearningJan 31 2019Feb 15 2019We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope (Aigner et al., 2010). To ... More

Compress and ControlNov 19 2014This paper describes a new information-theoretic policy evaluation technique for reinforcement learning. This technique converts any compression or density model into a corresponding estimate of value. Under appropriate stationarity and ergodicity conditions, ... More

An Introduction to Deep Reinforcement LearningNov 30 2018Dec 03 2018Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL ... More

Benchmarking Bonus-Based Exploration Methods on the Arcade Learning EnvironmentAug 06 2019This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We study the use of different reward bonuses that incentives exploration in reinforcement learning. We do so by fixing ... More

Unifying Count-Based Exploration and Intrinsic MotivationJun 06 2016We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across observations. Specifically, we focus on the problem of exploration in non-tabular reinforcement learning. Drawing inspiration from the intrinsic ... More

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General AgentsSep 18 2017Dec 01 2017The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It supports a variety of different problem settings and it has been receiving increasing ... More

Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative DialogueJan 31 2019We consider the problem of designing an artificial agent capable of interacting with humans in collaborative dialogue to produce creative, engaging narratives. In this task, the goal is to establish universe details, and to collaborate on an interesting ... More

Statistics and Samples in Distributional Reinforcement LearningFeb 21 2019We present a unifying framework for designing and analysing distributional reinforcement learning (DRL) algorithms in terms of recursively estimating statistics of the return distribution. Our key insight is that DRL algorithms can be decomposed as the ... More

The Value Function Polytope in Reinforcement LearningJan 31 2019We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope (Aigner et al., 2010). To ... More

Count-Based Exploration with Neural Density ModelsMar 03 2017Jun 14 2017Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from a density model, to generalize count-based exploration to non-tabular reinforcement learning. This pseudo-count was used to generate an exploration bonus for a DQN agent and ... More

Automated Curriculum Learning for Neural NetworksApr 10 2017We introduce a method for automatically selecting the path, or syllabus, that a neural network follows through a curriculum so as to maximise learning efficiency. A measure of the amount that the network learns from each data sample is provided as a reward ... More

The Cramer Distance as a Solution to Biased Wasserstein GradientsMay 30 2017The Wasserstein probability metric has received much attention from the machine learning community. Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying geometry between ... More

Hyperbolic Discounting and Learning over Multiple HorizonsFeb 19 2019Feb 28 2019Reinforcement learning (RL) typically defines a discount factor as part of the Markov Decision Process. The discount factor values future rewards by an exponential scheme that leads to theoretical convergence guarantees of the Bellman equation. However, ... More

The Value Function Polytope in Reinforcement LearningJan 31 2019May 15 2019We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope (Aigner et al., 2010). To ... More

Distributional reinforcement learning with linear function approximationFeb 08 2019Despite many algorithmic advances, our theoretical understanding of practical distributional reinforcement learning methods remains limited. One exception is Rowland et al. (2018)'s analysis of the C51 algorithm in terms of the Cram\'er distance, but ... More

Dopamine: A Research Framework for Deep Reinforcement LearningDec 14 2018Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provide stable, comprehensive implementations for benchmarking. At the same time, recent deep RL research has become ... More

A Geometric Perspective on Optimal Representations for Reinforcement LearningJan 31 2019This paper proposes a new approach to representation learning based on geometric properties of the space of value functions. We study a two-part approximation of the value function: a nonlinear map from states to vectors, or representation, followed by ... More

A Geometric Perspective on Optimal Representations for Reinforcement LearningJan 31 2019Jun 26 2019We propose a new perspective on representation learning in reinforcement learning based on geometric properties of the space of value functions. We leverage this perspective to provide formal evidence regarding the usefulness of value functions as auxiliary ... More

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning AgentsDec 17 2018May 29 2019Much human and computational effort has aimed to improve how deep reinforcement learning algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, ... More

The Hanabi Challenge: A New Frontier for AI ResearchFeb 01 2019From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance ... More

The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement LearningApr 15 2017Jun 19 2018In this work we present a new agent architecture, called Reactor, which combines multiple algorithmic and architectural contributions to produce an agent with higher sample-efficiency than Prioritized Dueling DQN (Wang et al., 2016) and Categorical DQN ... More

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning AgentsDec 17 2018Much human and computational effort has aimed to improve how deep reinforcement learning algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, ... More

The Barbados 2018 List of Open Issues in Continual LearningNov 16 2018We want to make progress toward artificial general intelligence, namely general-purpose agents that autonomously learn how to competently act in complex environments. The purpose of this report is to sketch a research outline, share some of the most important ... More

Count-Based Exploration with the Successor RepresentationJul 31 2018Jan 25 2019In this paper we introduce a simple approach for exploration in reinforcement learning (RL) that allows us to develop theoretically justified algorithms in the tabular case but that is also extendable to settings where function approximation is required. ... More

Rejoinder of ``Cross-Covariance Functions for Multivariate Geostatistics''Jul 30 2015Rejoinder of ``Cross-Covariance Functions for Multivariate Geostatistics'' by Genton and Kleiber [arXiv:1507.08017].

Discussion of "Breakdown and groups" by P. L. Davies and U. GatherAug 25 2005Discussion of ``Breakdown and groups'' by P. L. Davies and U. Gather [math.ST/0508497]

Multivariate Functional Data Visualization and Outlier DetectionMar 19 2017Apr 22 2018This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, ... More

Transport on coupled spatial networksMay 12 2012Sep 28 2012Transport processes on spatial networks are representative of a broad class of real world systems which, rather than being independent, are typically interdependent. We propose a measure of utility to capture key features that arise when such systems ... More

Efficient Maximum Approximated Likelihood Inference for Tukey's g-and-h DistributionJun 02 2015Tukey's $g$-and-$h$ distribution has been a powerful tool for data exploration and modeling since its introduction. However, two long standing challenges associated with this distribution family have remained unsolved until this day: how to find an optimal ... More

Diminishing functionals for nonclassical entropy solutions selected by kinetic relationsDec 21 2008We consider nonclassical entropy solutions to scalar conservation laws with concave-convex flux functions, whose set of left- and right-hand admissible states across undercompressive shocks is selected by a kinetic function \phi. We introduce a new definition ... More

An Analysis of Categorical Distributional Reinforcement LearningFeb 22 2018Distributional approaches to value-based reinforcement learning model the entire distribution of returns, rather than just their expected values, and have recently been shown to yield state-of-the-art empirical performance. This was demonstrated by the ... More

Cross-Covariance Functions for Multivariate GeostatisticsJul 29 2015Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists and statisticians. For many years, scientists developed ... More

A Copula-Based Linear Model of Coregionalization for Non-Gaussian Multivariate Spatial DataMar 12 2016We propose a new copula model for replicated multivariate spatial data. Unlike classical models that assume multivariate normality of the data, the proposed copula is based on the assumption that some factors exist that affect the joint spatial dependence ... More

Asymptotic-numerical study of supersensitivity for generalized Burgers equationsAug 11 1999This article addresses some asymptotic and numerical issues related to the solution of Burgers' equation, $-\epsilon u_{xx} + u_t + u u_x = 0$ on $(-1,1)$, subject to the boundary conditions $u(-1) = 1 + \delta$, $u(1) = -1$, and its generalization to ... More

Interdependent networks: the fragility of controlSep 26 2013Recent work in the area of interdependent networks has focused on interactions between two systems of the same type. However, an important and ubiquitous class of systems are those involving monitoring and control, an example of interdependence between ... More

A Copula Model for Non-Gaussian Multivariate Spatial DataMar 12 2016Oct 10 2018We propose a new copula model for replicated multivariate spatial data. Unlike classical models that assume multivariate normality of the data, the proposed copula is based on the assumption that some factors exist that affect the joint spatial dependence ... More

An algorithm (CoDeFi) for the curse of dimensionality in financeJun 30 2016We present a new algorithm (CoDeFi) to tackle the Curse Of Dimensionality In Finance and deal with a broad class of partial differential equations including the Kolmogorov equations as, for instance, the Black and Scholes equations. As a main feature, ... More

Bayesian linear regression with skew-symmetric error distributions with applications to survival analysisJan 10 2016We study Bayesian linear regression models with skew-symmetric scale mixtures of normal error distributions. These kinds of models can be used to capture departures from the usual assumption of normality of the errors in terms of heavy tails and asymmetry. ... More

On the asymptotic joint distribution of sample space--time covariance estimatorsMar 14 2008We study the asymptotic joint distribution of sample space--time covariance estimators of strictly stationary random fields. We do this without any marginal or joint distributional assumptions other than mild moment and mixing conditions. We consider ... More

Trajectory Functional BoxplotsApr 24 2019With the development of data-monitoring techniques in various fields of science, multivariate functional data are often observed. Consequently, an increasing number of methods have appeared to extend the general summary statistics of multivariate functional ... More

Comment on "Logarithmic Oscillators: Ideal Hamiltonian Thermostats" [arXiv 1203.5968]Jun 01 2012Jan 29 2013Campisi, Zhan, Talkner and H\"anggi have recently proposed a novel Hamiltonian thermostat which they claim may be used both in simulations and experiments [arXiv:1203.5968v4]. We show, however, that this is not possible due to the length and time scales ... More

A Multi-Resolution Spatio-Temporal Model for Brain Activation and Connectivity in fMRI DataFeb 07 2016Jun 15 2016Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate ... More

The connectivity at infinity of a manifold and $L^{q,p}$-Sobolev inequalitiesJul 11 2010May 16 2013The purpose of this paper is to give a self-contained proof that a complete manifold with more than one end never supports an $L^{q,p}$-Sobolev inequality ($2 \leq p$, $q\leq p^{*}$), provided the negative part of its Ricci tensor is small (in a suitable ... More

Bayesian Modeling of Air Pollution Extremes Using Nested Multivariate Max-Stable ProcessesMar 18 2018Capturing the potentially strong dependence among the peak concentrations of multiple air pollutants across a spatial region is crucial for assessing the related public health risks. In order to investigate the multivariate spatial dependence properties ... More

Odd-Parity Bipolar Spherical HarmonicsSep 13 2011Nov 18 2011Bipolar spherical harmonics (BiPoSHs) provide a general formalism for quantifying departures in the cosmic microwave background (CMB) from statistical isotropy (SI) and from Gaussianity. However, prior work has focused only on BiPoSHs with even parity. ... More

Factor Copula Models for Replicated Spatial DataNov 10 2015Dec 07 2016We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the ... More

Factor Copula Models for Replicated Spatial DataNov 10 2015Jul 10 2016We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the ... More

An algorithm (CoDeFi) for overcoming the curse of dimensionality in mathematical financeJun 30 2016Oct 06 2016We present an algorithm (CoDeFi) which overcomes the curse of dimensionality (CoD) in scientific computations and, especially, in mathematical finance (Fi). Our method applies a broad class of partial differential equations such as Kolmogorov-type equations ... More

A Multi-Resolution Spatial Model for Large Datasets Based on the Skew-t DistributionDec 06 2017Large, non-Gaussian spatial datasets pose a considerable modeling challenge as the dependence structure implied by the model needs to be captured at different scales, while retaining feasible inference. Skew-normal and skew-t distributions have only recently ... More

Physical properties of a very diffuse HI structure at high Galactic latitudeFeb 28 2007The main goal of this analysis is to present a new method to estimate the physical properties of diffuse cloud of atomic hydrogen observed at high Galactic latitude. This method, based on a comparison of the observations with fractional Brownian motion ... More

Diagonal Likelihood Ratio Test for Equality of Mean Vectors in High-Dimensional DataOct 27 2017Sep 24 2018We propose a likelihood ratio test framework for testing normal mean vectors in high-dimensional data under two common scenarios: the one-sample test and the two-sample test with equal covariance matrices. We derive the test statistics under the assumption ... More

Lexical Functions and Machine TranslationOct 20 1994This paper discusses the lexicographical concept of lexical functions and their potential exploitation in the development of a machine translation lexicon designed to handle collocations.

Modelling ultra-fast nanoparticle melting with the Maxwell-Cattaneo equationJun 06 2018Nov 13 2018The role of thermal relaxation in nanoparticle melting is studied using a mathematical model based on the Maxwell--Cattaneo equation for heat conduction. The model is formulated in terms of a two-phase Stefan problem. We consider the cases of the temperature ... More

Asymptotic analysis of the Guyer-Krumhansl-Stefan model for nanoscale solidificationApr 17 2018Nanoscale solidification is becoming increasingly relevant in applications involving ultra-fast freezing processes and nanotechnology. However, thermal transport on the nanoscale is driven by infrequent collisions between thermal energy carriers known ... More

The one-dimensionall Stefan problem with non-Fourier heat conductionMay 15 2019We investigate the one-dimensional growth of a solid into a liquid bath, starting from a small crystal, using the Guyer-Krumhansl and Maxwell-Cattaneo models of heat conduction. By breaking the solidification process into the relevant time regimes we ... More

The one-dimensional Stefan problem with non-Fourier heat conductionMay 15 2019May 16 2019We investigate the one-dimensional growth of a solid into a liquid bath, starting from a small crystal, using the Guyer-Krumhansl and Maxwell-Cattaneo models of heat conduction. By breaking the solidification process into the relevant time regimes we ... More

Rippled nanocarbons from periodic arrangements of reordered bivacancies in graphene or SWCNTsJan 20 2012We report on various nanocarbons formed from a unique structural pattern containing two pentagons, three hexagons and two heptagons, resulting from local rearrange- ments around a divacancy in pristine graphene or nanotubes. This defect can be inserted ... More

Observations of Lyman-alpha and O VI: Signatures of Cooling and Star Formation in a Massive Central Cluster GalaxyDec 25 2016Dec 28 2016We report new HST COS and STIS spectroscopy of a star-forming region (~100 solar masses/year) in the center of the X-ray cluster RXJ1532.9+3021 (z=0.362), to follow-up the CLASH team discovery of luminous UV filaments and knots in the central massive ... More

Non-Gaussianity from Self-Ordering Scalar FieldsMar 02 2010The Universe may harbor relics of the post-inflationary epoch in the form of a network of self-ordered scalar fields. Such fossils, while consistent with current cosmological data at trace levels, may leave too weak an imprint on the cosmic microwave ... More

Evolution of the stripe phase as a function of doping from a theoretical analysis of angle-resolved photoemission dataMar 01 2001Mar 20 2001By comparing single-particle spectral functions of t-J and Hubbard models with recent angle-resolved photoemission (ARPES) results for LSCO and Nd-LSCO, we can decide where holes go as a function of doping, and more specifically, which type of stripe ... More

The Velocity Field from Type Ia Supernovae Matches the Gravity Field from Galaxy SurveysJul 24 1997We compare the peculiar velocities of nearby SNe Ia with those predicted by the gravity fields of full sky galaxy catalogs. The method provides a powerful test of the gravitational instability paradigm and strong constraints on the density parameter beta ... More

Improving Bayesian Local Spatial Models in Large Data SetsJul 16 2019Environmental processes resolved at a sufficiently small scale in space and time will inevitably display non-stationary behavior. Such processes are both challenging to model and computationally expensive when the data size is large. Instead of modeling ... More

Bayesian model averaging over tree-based dependence structures for multivariate extremesMay 30 2017Jul 22 2018Describing the complex dependence structure of extreme phenomena is particularly challenging. To tackle this issue we develop a novel statistical algorithm that describes extremal dependence taking advantage of the inherent hierarchical dependence structure ... More

Non-local energetics of random heterogeneous latticesMar 16 2010Nov 09 2010In this paper, we study the mechanics of statistically non-uniform two-phase elastic discrete structures. In particular, following the methodology proposed in (Luciano and Willis, Journal of the Mechanics and Physics of Solids 53, 1505-1522, 2005), energetic ... More

Dark-Matter Decays and Self-Gravitating HalosMar 01 2010May 26 2010We consider models in which a dark-matter particle decays to a slightly less massive daughter particle and a noninteracting massless particle. The decay gives the daughter particle a small velocity kick. Self-gravitating dark-matter halos that have a ... More

Fabrication of High Aspect Ratio Micro-Penning-Malmberg Gold Plated Silicon Trap ArraysJul 09 2013Acquiring a portable high density charged particles trap might consist of an array of micro-Penning-Malmberg traps (microtraps) with substantially lower end barriers potential than conventional Penning-Malmberg traps [1]. We report on the progress of ... More

Full likelihood inference for max-stable dataMar 25 2017Jul 13 2018We show how to perform full likelihood inference for max-stable multivariate distributions or processes based on a stochastic Expectation-Maximisation algorithm, which combines statistical and computational efficiency in high-dimensions. The good performance ... More

A Stochastic Generator of Global Monthly Wind Energy with Tukey $g$-and-$h$ Autoregressive ProcessesNov 10 2017Quantifying the uncertainty of wind energy potential from climate models is a very time-consuming task and requires a considerable amount of computational resources. A statistical model trained on a small set of runs can act as a stochastic approximation ... More

Dark energy from the string axiverseSep 01 2014Sep 22 2014String theories suggest the existence of a plethora of axion-like fields with masses spread over a huge number of decades. Here we show that these ideas lend themselves to a model of quintessence with no super-Planckian field excursions and in which all ... More

Functional Outlier Detection and Taxonomy by Sequential TransformationsAug 16 2018Functional data analysis can be seriously impaired by abnormal observations, which can be classified as either magnitude or shape outliers based on their way of deviating from the bulk of data. Identifying magnitude outliers is relatively easy, while ... More

Likelihood Approximation With Hierarchical Matrices For Large Spatial DatasetsSep 08 2017Sep 12 2018We use available measurements to estimate the unknown parameters (variance, smoothness parameter, and covariance length) of a covariance function by maximizing the joint Gaussian log-likelihood function. To overcome cubic complexity in the linear algebra, ... More

Quantum supremacy with analog quantum processors for material science and machine learningJun 10 2019Quantum supremacy is the ability of quantum processors to outperform classical computers at certain tasks. In digital random quantum circuit approaches for supremacy, the output distribution produced is described by the Porter-Thomas (PT) distribution. ... More

Multi-Level Restricted Maximum Likelihood Covariance Estimation and Kriging for Large Non-Gridded Spatial DatasetsApr 01 2015Mar 28 2016We develop a multi-level restricted Gaussian maximum likelihood method for estimating the covariance function parameters and computing the best unbiased predictor. Our approach produces a new set of multi-level contrasts where the deterministic parameters ... More

Mineral bridges in nacre revisitedJul 20 2012We confirm with high-resolution techniques the existence of mineral bridges between superposed nacre tablets. In the towered nacre of both gastropods and the cephalopod Nautilus there are large bridges aligned along the tower axes, corresponding to gaps ... More

Gravitational-wave phasing for low-eccentricity inspiralling compact binaries to 3PN orderMay 01 2016Jun 29 2016[abridged] Although gravitational radiation causes inspiralling compact binaries to circularize, a variety of astrophysical scenarios suggest that binaries might have small but nonnegligible orbital eccentricities when they enter the low-frequency bands ... More

Interrelation of Superconducting and Antiferromagnetic Gaps in High-Tc Compounds: a Test Case for a Microscopic TheoryAug 12 1999Recent angle resolved photoemission (ARPES) data, which found evidence for a d-wave-like modulation of the antiferromagnetic gap, suggest an intimate interrelation between the antiferromagnetic insulator and the superconductor with its d-wave gap. This ... More

A Non-Gaussian Spatio-Temporal Model for Daily Wind Speeds Based on a Multivariate Skew-t DistributionMar 13 2017Feb 13 2019Facing increasing domestic energy consumption from population growth and industrialization, Saudi Arabia is aiming to reduce its reliance on fossil fuels and to broaden its energy mix by expanding investment in renewable energy sources, including wind ... More

Incorporating geostrophic wind information for improved space-time short-term wind speed forecastingDec 05 2014Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, ... More

Interrelation of Superconducting and Antiferromagnetic Gaps in High-Tc Compounds: a Test Case for the SO(5) TheoryDec 09 1999Jul 17 2000Recent angle resolved photoemission data, which found evidence for a d-wave-like modulation of the antiferromagnetic gap, suggest an intimate interrelation between the antiferromagnetic insulator and the superconductor with its d-wave gap. It is shown ... More

Superconducting Pb stripline resonators in parallel magnetic field and their application for microwave spectroscopyMay 13 2016Oct 10 2016Planar superconducting microwave resonators are key elements in a variety of technical applications and also act as sensitive probes for microwave spectroscopy of various materials of interest. Here superconducting Pb is a suitable material as a basis ... More

Likelihood estimators for multivariate extremesNov 13 2014Jun 16 2015The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high ... More

Site selection for the 3.4m optical telescope of the Iranian National ObservatoryMar 05 2019The Results of the site selection campaign conducted for the proposed 3.4m optical telescope of the Iranian National Observatory are reported. During the first 3 years, among 33 nominated regions throughout the country, the potential regions were confined ... More

Quantum state transfer via acoustic edge states in a 2D optomechanical arrayJul 09 2019We propose a novel hybrid platform where solid-state spin qubits are coupled to the acoustic modes of a two-dimensional array of optomechanical nano cavities. Previous studies of coupled optomechanical cavities have shown that in the presence of strong ... More

Effective thermal conductivity of rectangular nanowires based on phonon hydrodynamicsMay 31 2018Jun 03 2018A mathematical model is presented for thermal transport in nanowires with rectangular cross sections. Expressions for the effective thermal conductivity of the nanowire across a range of temperatures and cross-sectional aspect ratios are obtained by solving ... More

On the Cartesian Skeleton and the Factorization of the Strong Product of DigraphsJan 20 2014The three standard products (the Cartesian, the direct and the strong product) of undirected graphs have been wellinvestigated, unique prime factor decomposition (PFD) are known and polynomial time algorithms have been established for determining the ... More