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Robust Super-Level Set Estimation using Gaussian ProcessesNov 25 2018This paper focuses on the problem of determining as large a region as possible where a function exceeds a given threshold with high probability. We assume that we only have access to a noise-corrupted version of the function and that function evaluations ... More

A Reachability Method for Verifying Dynamical Systems with Deep Neural Network ControllersMar 01 2019Mar 21 2019Deep neural networks can be trained to be efficient and effective controllers for dynamical systems; however, the mechanics of deep neural networks are complex and difficult to guarantee. This work presents a general approach for providing guarantees ... More

Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and ControlFeb 26 2019Koopman theory asserts that a nonlinear dynamical system can be mapped to a linear system, where the Koopman operator advances observations of the state forward in time. However, the observable functions that map states to observations are generally unknown. ... More

Model Primitive Hierarchical Lifelong Reinforcement LearningMar 04 2019Learning interpretable and transferable subpolicies and performing task decomposition from a single, complex task is difficult. Some traditional hierarchical reinforcement learning techniques enforce this decomposition in a top-down manner, while meta-learning ... More

Real-time Prediction of Automotive Collision Risk from Monocular VideoFeb 04 2019Many automotive applications, such as Advanced Driver Assistance Systems (ADAS) for collision avoidance and warnings, require estimating the future automotive risk of a driving scene. We present a low-cost system that predicts the collision risk over ... More

Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learningDec 01 2018Dec 05 2018Data I/O poses a significant bottleneck in large-scale CFD simulations; thus, practitioners would like to significantly reduce the number of times the solution is saved to disk, yet retain the ability to recover any field quantity (at any time instance) ... More

Verifying Aircraft Collision Avoidance Neural Networks Through Linear Approximations of Safe RegionsMar 02 2019The next generation of aircraft collision avoidance systems frame the problem as a Markov decision process and use dynamic programming to optimize the alerting logic. The resulting system uses a large lookup table to determine advisories given to pilots, ... More

Algorithms for Verifying Deep Neural NetworksMar 15 2019Deep neural networks are widely used for nonlinear function approximation with applications ranging from computer vision to control. Although these networks involve the composition of simple arithmetic operations, it can be very challenging to verify ... More

Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation LearningMar 14 2019Recent developments in multi-agent imitation learning have shown promising results for modeling the behavior of human drivers. However, it is challenging to capture emergent traffic behaviors that are observed in real-world datasets. Such behaviors arise ... More

On the Optimality of Ergodic Trajectories for Information Gathering TasksAug 20 2018Recently, ergodic control has been suggested as a means to guide mobile sensors for information gathering tasks. In ergodic control, a mobile sensor follows a trajectory that is ergodic with respect to some information density distribution. A trajectory ... More

Dynamic Real-time Multimodal Routing with Hierarchical Hybrid PlanningFeb 05 2019We introduce the problem of Dynamic Real-time Multimodal Routing (DREAMR), which requires planning and executing routes under uncertainty for an autonomous agent. The agent has access to a time-varying transit vehicle network in which it can use multiple ... More

Using Neural Networks to Generate Information Maps for Mobile SensorsSep 26 2018Target localization is a critical task for mobile sensors and has many applications. However, generating informative trajectories for these sensors is a challenging research problem. A common method uses information maps that estimate the value of taking ... More

Efficient and Low-cost Localization of Radio Signals with a Multirotor UAVAug 13 2018Localizing radio frequency (RF) sources with an unmanned aerial vehicle (UAV) has many important applications. As a result, UAV-based localization has been the focus of much research. However, previous approaches rely heavily on custom electronics and ... More

Burn-In Demonstrations for Multi-Modal Imitation LearningOct 13 2017Recent work on imitation learning has generated policies that reproduce expert behavior from multi-modal data. However, past approaches have focused only on recreating a small number of distinct, expert maneuvers, or have relied on supervised learning ... More

Simultaneous Policy Learning and Latent State Inference for Imitating Driver BehaviorApr 19 2017In this work, we propose a method for learning driver models that account for variables that cannot be observed directly. When trained on a synthetic dataset, our models are able to learn encodings for vehicle trajectories that distinguish between four ... More

Analyzing Traffic Delay at Unmanaged IntersectionsMay 26 2018At an unmanaged intersection, it is important to understand how much traffic delay may be caused as a result of microscopic vehicle interactions. Conventional traffic simulations that explicitly track these interactions are time-consuming. Prior work ... More

Analytically Modeling Unmanaged Intersections with Microscopic Vehicle InteractionsApr 12 2018Sep 06 2018With the emergence of autonomous vehicles, it is important to understand their impact on the transportation system. However, conventional traffic simulations are time-consuming. In this paper, we introduce an analytical traffic model for unmanaged intersections ... More

Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement LearningOct 09 2018Teams of autonomous unmanned aircraft can be used to monitor wildfires, enabling firefighters to make informed decisions. However, controlling multiple autonomous fixed-wing aircraft to maximize forest fire coverage is a complex problem. The state space ... More

Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and PredictionOct 04 2018Small unmanned aircraft can help firefighters combat wildfires by providing real-time surveillance of the growing fires. However, guiding the aircraft autonomously given only wildfire images is a challenging problem. This work models noisy images obtained ... More

Belief State Planning for Autonomously Navigating Urban IntersectionsApr 14 2017Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty. The vehicle must plan in a stochastic environment with potentially rapid changes in driver behavior. Providing an efficient strategy to navigate ... More

A Reachability Method for Verifying Dynamical Systems with Deep Neural Network ControllersMar 01 2019Deep neural networks can be trained to be efficient and effective controllers for dynamical systems; however, the mechanics of deep neural networks are complex and difficult to guarantee. This work presents a general approach for providing guarantees ... More

Interpretable Categorization of Heterogeneous Time Series DataAug 30 2017Jan 26 2018Understanding heterogeneous multivariate time series data is important in many applications ranging from smart homes to aviation. Learning models of heterogeneous multivariate time series that are also human-interpretable is challenging and not adequately ... More

DropoutDAgger: A Bayesian Approach to Safe Imitation LearningSep 18 2017While imitation learning is becoming common practice in robotics, this approach often suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these issues by continually aggregating training data from both the ... More

A General Framework for Structured Learning of Mechanical SystemsFeb 22 2019Learning accurate dynamics models is necessary for optimal, compliant control of robotic systems. Current approaches to white-box modeling using analytic parameterizations, or black-box modeling using neural networks, can suffer from high bias or high ... More

Rethinking System Health ManagementMar 10 2019Health management of complex dynamic systems has traditionally evolved separately from automated control, planning, and scheduling (generally referred to in the paper as decision making). A goal of Integrated System Health Management has been to enable ... More

Deep Neural Network Compression for Aircraft Collision Avoidance SystemsOct 09 2018One approach to designing decision making logic for an aircraft collision avoidance system frames the problem as a Markov decision process and optimizes the system using dynamic programming. The resulting collision avoidance strategy can be represented ... More

Online algorithms for POMDPs with continuous state, action, and observation spacesSep 18 2017Sep 06 2018Online solvers for partially observable Markov decision processes have been applied to problems with large discrete state spaces, but continuous state, action, and observation spaces remain a challenge. This paper begins by investigating double progressive ... More

HG-DAgger: Interactive Imitation Learning with Human ExpertsOct 05 2018Imitation learning has proven to be useful for many real-world problems, but approaches such as behavioral cloning suffer from data mismatch and compounding error issues. One attempt to address these limitations is the DAgger algorithm, which uses the ... More

Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version)Nov 29 2015Feb 20 2016Many exact and approximate solution methods for Markov Decision Processes (MDPs) attempt to exploit structure in the problem and are based on factorization of the value function. Especially multiagent settings, however, are known to suffer from an exponential ... More

Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement LearningMar 08 2019To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement learning poses the ... More

Estimation and Control Using Sampling-Based Bayesian Reinforcement LearningAug 01 2018Real-world autonomous systems operate under uncertainty about both their pose and dynamics. Autonomous control systems must simultaneously perform estimation and control tasks to maintain robustness to changing dynamics or modeling errors. However, information ... More

EnsembleDAgger: A Bayesian Approach to Safe Imitation LearningJul 22 2018While imitation learning is often used in robotics, this approach often suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these issues by aggregating training data from both the expert and novice policies, ... More

Optimized and Trusted Collision Avoidance for Unmanned Aerial Vehicles using Approximate Dynamic Programming (Technical Report)Feb 15 2016Feb 19 2016Safely integrating unmanned aerial vehicles into civil airspace is contingent upon development of a trustworthy collision avoidance system. This paper proposes an approach whereby a parameterized resolution logic that is considered trusted for a given ... More

A General Framework for Structured Learning of Mechanical SystemsFeb 22 2019Mar 01 2019Learning accurate dynamics models is necessary for optimal, compliant control of robotic systems. Current approaches to white-box modeling using analytic parameterizations, or black-box modeling using neural networks, can suffer from high bias or high ... More

Adaptive Stress Testing for Autonomous VehiclesFeb 05 2019This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte ... More

Closed-Loop Policies for Operational Tests of Safety-Critical SystemsJul 25 2017May 19 2018Manufacturers of safety-critical systems must make the case that their product is sufficiently safe for public deployment. Much of this case often relies upon critical event outcomes from real-world testing, requiring manufacturers to be strategic about ... More

Deep Dynamical Modeling and Control of Unsteady Fluid FlowsMay 18 2018Nov 10 2018The design of flow control systems remains a challenge due to the nonlinear nature of the equations that govern fluid flow. However, recent advances in computational fluid dynamics (CFD) have enabled the simulation of complex fluid flows with high accuracy, ... More

Visual Depth Mapping from Monocular Images using Recurrent Convolutional Neural NetworksDec 10 2018A reliable sense-and-avoid system is critical to enabling safe autonomous operation of unmanned aircraft. Existing sense-and-avoid methods often require specialized sensors that are too large or power intensive for use on small unmanned vehicles. This ... More

Predicting the behavior of interacting humans by fusing data from multiple sourcesJun 26 2012Multi-fidelity methods combine inexpensive low-fidelity simulations with costly but high-fidelity simulations to produce an accurate model of a system of interest at minimal cost. They have proven useful in modeling physical systems and have been applied ... More

Amortized Inference RegularizationMay 23 2018Jan 09 2019The variational autoencoder (VAE) is a popular model for density estimation and representation learning. Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate. However, ... More

Utility Decomposition with Deep Corrections for Scalable Planning under UncertaintyFeb 06 2018Decomposition methods have been proposed in the past to approximate solutions to large sequential decision making problems. In contexts where an agent interacts with multiple entities, utility decomposition can be used where each individual entity is ... More

Real-time Prediction of Intermediate-Horizon Automotive Collision RiskFeb 05 2018Advanced collision avoidance and driver hand-off systems can benefit from the ability to accurately predict, in real time, the probability a vehicle will be involved in a collision within an intermediate horizon of 10 to 20 seconds. The rarity of collisions ... More

A Comparison of Monte Carlo Tree Search and Mathematical Optimization for Large Scale Dynamic Resource AllocationMay 21 2014Dynamic resource allocation (DRA) problems are an important class of dynamic stochastic optimization problems that arise in a variety of important real-world applications. DRA problems are notoriously difficult to solve to optimality since they frequently ... More

Predicting the behavior of interacting humans by fusing data from multiple sourcesAug 09 2014Multi-fidelity methods combine inexpensive low-fidelity simulations with costly but highfidelity simulations to produce an accurate model of a system of interest at minimal cost. They have proven useful in modeling physical systems and have been applied ... More

People as Sensors: Imputing Maps from Human ActionsNov 03 2017Jan 08 2019Despite growing attention in autonomy, there are still many open problems, including how autonomous vehicles will interact and communicate with other agents, such as human drivers and pedestrians. Unlike most approaches that focus on pedestrian detection ... More

Multi-Agent Imitation Learning for Driving SimulationMar 02 2018Simulation is an appealing option for validating the safety of autonomous vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been shown to learn representative human driver models. These human driver models were learned through training ... More

The Value of Inferring the Internal State of Traffic Participants for Autonomous Freeway DrivingFeb 02 2017Safe interaction with human drivers is one of the primary challenges for autonomous vehicles. In order to plan driving maneuvers effectively, the vehicle's control system must infer and predict how humans will behave based on their latent internal state ... More

Towards Proving the Adversarial Robustness of Deep Neural NetworksSep 08 2017Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations. Manually crafting software controllers for these vehicles is difficult, but there has been some success in using deep neural networks generated ... More

Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position DataOct 22 2018Models for predicting aircraft motion are an important component of modern aeronautical systems. These models help aircraft plan collision avoidance maneuvers and help conduct offline performance and safety analyses. In this article, we develop a method ... More

Layer-wise synapse optimization for implementing neural networks on general neuromorphic architecturesFeb 20 2018Deep artificial neural networks (ANNs) can represent a wide range of complex functions. Implementing ANNs in Von Neumann computing systems, though, incurs a high energy cost due to the bottleneck created between CPU and memory. Implementation on neuromorphic ... More

Adaptive Stress Testing: Finding Failure Events with Reinforcement LearningNov 06 2018Finding the most likely path to a set of failure states is important to the analysis of safety-critical dynamic systems. While efficient solutions exist for certain classes of systems, a scalable general solution for stochastic, partially-observable, ... More

Deep Reinforcement Learning for Event-Driven Multi-Agent Decision ProcessesSep 19 2017The incorporation of macro-actions (temporally extended actions) into multi-agent decision problems has the potential to address the curse of dimensionality associated with such decision problems. Since macro-actions last for stochastic durations, multiple ... More

Learning Discrete Bayesian Networks from Continuous DataDec 08 2015Dec 15 2015Real data often contains a mixture of discrete and continuous variables, but many Bayesian network structure learning and inference algorithms assume all random variables are discrete. Continuous variables are often discretized, but the choice of discretization ... More

Simultaneous active parameter estimation and control using sampling-based Bayesian reinforcement learningJul 27 2017Robots performing manipulation tasks must operate under uncertainty about both their pose and the dynamics of the system. In order to remain robust to modeling error and shifts in payload dynamics, agents must simultaneously perform estimation and control ... More

Reluplex: An Efficient SMT Solver for Verifying Deep Neural NetworksFeb 03 2017May 19 2017Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their ... More

Learning Discrete Bayesian Networks from Continuous DataDec 08 2015Sep 18 2018Learning Bayesian networks from raw data can help provide insights into the relationships between variables. While real data often contains a mixture of discrete and continuous-valued variables, many Bayesian network structure learning algorithms assume ... More

Deep Stochastic Radar ModelsJan 31 2017Jun 16 2017Accurate simulation and validation of advanced driver assistance systems requires accurate sensor models. Modeling automotive radar is complicated by effects such as multipath reflections, interference, reflective surfaces, discrete cells, and attenuation. ... More

Imitating Driver Behavior with Generative Adversarial NetworksJan 24 2017The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts ... More

Toward Scalable Verification for Safety-Critical Deep NetworksJan 18 2018Feb 02 2018The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Formal verification can address these concerns by guaranteeing that a deep ... More

Stability of spherical stellar systems I : Analytical resultsNov 22 1995The so-called ``symplectic method'' is used for studying the linear stability of a self-gravitating collisionless stellar system, in which the particles are also submitted to an external potential. The system is steady and spherically symmetric, and its ... More

Center Disorder in the 3D Georgi-Glashow ModelApr 15 1998We present a number of arguments relating magnetic disorder to center disorder, in pure Yang-Mills theory in D=3 and D=4 dimensions. In the case of the D=3 Georgi-Glashow model, we point out that the abelian field distribution is not adequatedly represented, ... More

Recoil Polarization for Delta Excitation in Pion ElectroproductionMay 23 2005We measured angular distributions of recoil-polarization response functions for neutral pion electroproduction for W=1.23 GeV at Q^2=1.0 (GeV/c)^2, obtaining 14 separated response functions plus 2 Rosenbluth combinations; of these, 12 have been observed ... More

Spinons and helimagnons in the frustrated Heisenberg chainFeb 23 2012We investigate the dynamical spin structure factor S(q,w) for the Heisenberg chain with ferromagnetic nearest (J1<0) and antiferromagnetic next-nearest (J2>0) neighbor exchange using bosonization and a time-dependent density-matrix renormalization group ... More

Non-adiabatic primordial fluctuationsNov 10 2009Apr 19 2010We consider general mixtures of isocurvature and adiabatic cosmological perturbations. With a minimal assumption set consisting of the linearized Einstein equations and a primordial perfect fluid we derive the second-order action and its curvature variables. ... More

Nuclear time-reversal violation and the Schiff moment of 225RaMar 22 2005May 12 2005We present a comprehensive mean-field calculation of the Schiff moment of the nucleus 225Ra, the quantity which determines the static electric dipole moment of the corresponding atom if time-reversal (T) invariance is violated in the nucleus. The calculation ... More

Solution of the Skyrme-Hartree-Fock equations in the Cartesian deformed harmonic-oscillator basis. (III) HFODD (v1.75r): a new version of the programMar 01 2000We describe the new version (v1.75r) of the code HFODD which solves the nuclear Skyrme-Hartree-Fock problem by using the Cartesian deformed harmonic-oscillator basis. Three minor errors that went undetected in the previous version have been corrected. ... More

On Zero-Mass Ground States in Super-Membrane Matrix ModelsJan 23 1997We recall a formulation of super-membrane theory in terms of certain matrix models. These models are known to have a mass spectrum given by the positive half-axis. We show that, for the simplest such matrix model, a normalizable zero-mass ground state ... More

Measurement of CP violation at a Neutrino FactoryMay 29 2001The prospects of measuring CP violation in the leptonic sector using the intense neutrino beams arising from muon decay in the straight sections of a muon accumulator ring (the so-called neutrino factory) are discussed.

Thermalization of Quantum Fields from Time-Reversal Invariant Evolution EquationsJun 14 2000We study the time evolution of correlation functions in closed quantum systems for nonequilibrium ensembles of initial conditions. For a scalar quantum field theory we show that generic time-reversal invariant evolutions approach equilibrium at large ... More

Permutation Group Symmetry and CorrelationsJun 11 2013Correlation factors are constructed that are consistent with the permutation symmetry group of N Fermions at given value of the filling factor.

Horizontal symmetry in the algebraic approach of genetic codeFeb 14 2013Using concepts of physics of elementary particles concerning the breaking of symmetry and grannd unified theory we propose to study with the algebraic approximation the degeneracy finded in the genetic code with the incorporation of a horizontal symmetry ... More

The winds of the hot massive first starsSep 07 2005We study dynamical aspects of circumstellar environment around massive zero-metallicity first stars. For this purpose we apply our NLTE wind models. We show that the hydrogen-helium stellar wind from stationary massive first generation (Population III) ... More

Inverted crossover resonance aiding laser cooling of $^{171}$YbMar 03 2016Apr 19 2016We observe an inverted crossover resonance in $\pi$-driven four-level systems, where $F'-F=0,+1$. The signal is observed through saturated absorption spectroscopy of the $(6s^{2})$ $^{1}S_{0}$ $-$ $(6s6p)$ $^{3}P_{1}$ transition in $^{171}$Yb, where the ... More

Hartree shift in unitary Fermi gasesMar 24 2011The Hartree energy shift is calculated for a unitary Fermi gas. By including the momentum dependence of the scattering amplitude explicitly, the Hartree energy shift remains finite even at unitarity. Extending the theory also for spin-imbalanced systems ... More

Coulomb blockade in electron transport through a C$_{60}$ molecule from first principlesMay 23 2005We present results of spin-unrestricted first-principles quantum transport for a gated C$_{60}$ molecule weakly contacted to Al electrodes, making emphasis on the role played by the electronic localization and the spin degree of freedom. As expected, ... More

Nuclear suppression of dileptons at forward rapiditiesDec 14 2010Data from E772 and E866 experiments on the Drell-Yan process exhibit a significant nuclear suppression at large Feynman xF. We show that a corresponding kinematic region does not allow to interpret this as a manifestation of coherence or a Color Glass ... More

Asymmetric Wolf-Rayet winds: implications for GRB afterglowsJan 25 2007Recent observations of Wolf-Rayet (WR) binaries WR151 and WR155 infer that their stellar winds are asymmetric. We show that such asymmetries can alter the stellar-wind bubble structure, bringing the wind-termination shock closer to the WR star. If the ... More

Unbiased estimators in Quantum Monte Carlo methods: application to liquid $^4$HeJan 18 1995A Monte Carlo algorithm for computing quantum mechanical expectation values of coordinate operators in many body problems is presented. The algorithm, that relies on the forward walking method, fits naturally in a Green's Function Monte Carlo calculation, ... More

Asymptotic cones and Assouad-Nagata dimensionOct 10 2006Oct 20 2006We prove the dimension of any asymptotic cone over a metric space X does not exceed the asymptotic Assouad-Nagata dimension of X. This improves a result of Dranishnikov and Smith who showed that dim(Y) does not exceed asymptotic Assouad-Nagata dimension ... More

A Plasma Instability Theory of Gamma-Ray Burst EmissionApr 02 1999A new theory for gamma-ray burst radiation is presented. In this theory, magnetic fields and relativistic electrons are created through plasma processes arising as a relativistic shell passes through the interstellar medium. The gamma-rays are produced ... More

Pi^0_1 ordinal analysis beyond first order arithmeticDec 11 2012In this paper we give an overview of an essential part of a Pi^0_1 ordinal analysis of Peano Arithmetic (PA) as presented by Beklemishev. This analysis is mainly performed within the polymodal provability logic GLP. We reflect on ways of extending this ... More

Fazendo 3D com uma camera soDec 14 2010Jan 29 2012A simple system to make stereo photography or videos based in just two mirrors was made in 1989 and recently adapted to a digital camera setup.

Enlarging Holograms Under White Light, a Way to Save Holographic MaterialMay 08 2009We developed techniques for employing holographic screens under white light, first exhibited to the public in 1989 (3) (4) which demonstrated the possibility of enlarging holograms already in 1990 (5). Fig. 1 shows how we encode views in a first step. ... More

White Light Colour Photography for Rendering Holoimages in a Diffractive ScreenApr 16 2009The capability of color encoding the continuous sequence of views from a scene was demonstrated previously by the author (1990). In the present work, the scheme for this process is shown where white light from a black and white object is diffracted at ... More

On the quality of the Olmec mirrors and its utilizationJan 29 2007Archaeological mirrors from the Olmec civilization were analyzed in the context of bibliography produced in the last two decades. Photographs of its images are showed as a proof of its good quality. Some suggestions are made on its probable utilization. ... More

Lagrangian and Hamiltonian for the Bondi-Sachs metricsMar 31 2004Feb 03 2015We calculate the Hilbert action for the Bondi-Sachs metrics. It yields the Einstein vacuum equations in a closed form. Following the Dirac approach to constrained systems we investigate the related Hamiltonian formulation.

Quartz Tuning Forks and Acoustic Phenomena - Application to Superfluid HeliumApr 09 2013Jan 30 2016Immersed mechanical resonators are well suited for probing the properties of fluids, since the surrounding environment influences the resonant characteristics of such oscillators in several ways. Quartz tuning forks have gained much popularity in recent ... More

Infrared dynamic polarizability of HD+ rovibrational statesJun 07 2011A calculation of dynamic polarizabilities of rovibrational states with vibrational quantum number $v=0-7$ and rotational quantum number $J=0,1$ in the 1s$\sigma_g$ ground-state potential of HD$^+$ is presented. Polarizability contributions by transitions ... More

Curvettes and clusters of infinitely near pointsMar 30 2009Jul 03 2011The aim of this paper is to revise the theory of clusters of infinitely near points for arbitrary fields. We describe in particular the intersection matrix of such a cluster, we introduce the notion of curvette over an arbitrary field and we relate it ... More

Phase transitions in systems of magnetic dipoles on a square lattice with quenched disorderSep 14 2008We study by Monte Carlo simulations the effect of quenched orientational disorder in systems of interacting classical dipoles on a square lattice. Each dipole can lie along any of two perpendicular axes that form an angle psi with the principal axes of ... More

Effective realization of random magnetic fields in compounds with large single-ion anisotropyJun 20 2016We show that spin $S=1$ system with large and random single-ion anisotropy can be at low energies mapped to a $S=1/2$ system with random magnetic fields. This is for example realized in Ni(Cl$_{1-x}$Br$_{x}$)$_2$-4SC(NH$_2$)$_2$ compound (DTNX) and therefore ... More

Building a linguistic corpus from bee dance dataJun 28 2004This paper discusses the problems and possibility of collecting bee dance data in a linguistic \textit{corpus} and use linguistic instruments such as Zipf's law and entropy statistics to decide on the question whether the dance carries information of ... More

Application of Jitter Radiation: Gamma-ray Burst Prompt PolarizationSep 20 2013A high-degree of polarization of gamma-ray burst (GRB) prompt emission has been confirmed in recent years. In this paper, we apply jitter radiation to study the polarization feature of GRB prompt emission. In our framework, relativistic electrons are ... More

Equivalence between the Morita categories of etale Lie groupoids and of locally grouplike Hopf algebroidsMar 13 2007Any etale Lie groupoid G is completely determined by its associated convolution algebra C_c(G) equipped with the natural Hopf algebroid structure. We extend this result to the generalized morphisms between etale Lie groupoids: we show that any principal ... More

Superlogarithmic estimates on pseudoconvex domains and CR manifoldsOct 15 2004This paper is concerned with proving superlogarithmic estimates for the operator $\Box_b$ on pseudoconvex CR manifolds and using them to establish hypoellipticity of $\Box_b$ and of the $\bar{\partial}$-Neumann problem. These estimates are established ... More

Chaplygin's sphereSep 01 2004Chaplygin proved the integrability by quadratures of a round sphere, rolling without slipping on a horizontal plane, with center of mass at the center of the sphere, but with arbitrary moments of inertia. Although the system is integrable in every sense ... More

An addendum to `A rigidity property for the set of all characters induced by valuations'May 28 2013This corrects and amplifies some comments made in a paper by Robert Bieri and the author with the above title.

A remark on the structure of the Busemann representative of a polyconvex functionSep 23 2008Let W be a polyconvex function defined on the 2 x 2 real matrices. The Busemann representative f, say, of W is the largest possible convex representative of W. Writing L for the set of affine functions on R^{5} such that a(A, det A) is less than or equal ... More

A note on Positivity of the CM line bundleMay 11 2006Aug 09 2006We show that positivity of the CM line associated to a family of polarised varieties is intimately related to the stability of its members. We prove that the CM line is nef on any curve which meets the stable locus, and that it is pseudoeffective (i.e. ... More

Social Balance and the Bernoulli EquationJan 14 2017Dec 13 2017Since the 1940's there has been an interest in the question why social networks often give rise to two antagonistic factions. Recently a dynamical model of how and why such a balance might occur was developed. This note provides an introduction to the ... More