Results for "Mykel J. Kochenderfer"

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Satellite Image Tasking Under Orbit Prediction UncertaintyMay 04 2019Small satellites have proven to be viable Earth observation platforms. These satellites operate in regimes of increased trajectory uncertainty where traditional planning approaches can lead to sub-optimal task plans, limiting science return. Previous ... More
Dynamic Environment Prediction in Urban Scenes using Recurrent Representation LearningApr 28 2019A key challenge for autonomous driving is safe trajectory planning in cluttered, urban environments with dynamic obstacles, such as pedestrians, bicyclists, and other vehicles. A reliable prediction of the future environment state, including the behavior ... More
Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban EnvironmentsApr 25 2019Navigating urban environments represents a complex task for automated vehicles. They must reach their goal safely and efficiently while considering a multitude of traffic participants. We propose a modular decision making algorithm to autonomously navigate ... More
Pedestrian Collision Avoidance System for Scenarios with OcclusionsApr 25 2019Safe autonomous driving in urban areas requires robust algorithms to avoid collisions with other traffic participants with limited perception ability. Current deployed approaches relying on Autonomous Emergency Braking (AEB) systems are often overly conservative. ... 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
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous DrivingMay 06 2019Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. This paper introduces a general framework for tactical decision ... More
Object Exchangeability in Reinforcement Learning: Extended AbstractMay 07 2019Although deep reinforcement learning has advanced significantly over the past several years, sample efficiency remains a major challenge. Careful choice of input representations can help improve efficiency depending on the structure present in the problem. ... 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
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
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
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
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 2018Mar 01 2019Small 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
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
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
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
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
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 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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 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
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
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
Reinforcement Learning with Probabilistic Guarantees for Autonomous DrivingApr 15 2019Designing reliable decision strategies for autonomous urban driving is challenging. Reinforcement learning (RL) has been used to automatically derive suitable behavior in uncertain environments, but it does not provide any guarantee on the performance ... 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
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
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
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
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
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
Decomposition Methods with Deep Corrections for Reinforcement LearningFeb 06 2018Apr 22 2019Decomposition methods have been proposed to approximate solutions to large sequential decision making problems. In contexts where an agent interacts with multiple entities, utility decomposition can be used to separate the global objective into local ... 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
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
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
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
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
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
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
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
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
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
Absence of pairing in atomic Fermi gasesMay 11 2010May 18 2010Some thoughts regarding pairing in atomic Fermi gases were considered, meant for starting discussion on the topic.
Massive stars in their death-throesSep 07 2008Sep 11 2008The study of the stars that explode as supernovae used to be a forensic study, working backwards from the remnants of the star. This changed in 1987 when the first progenitor star was identified in pre-explosion images. Currently there are 8 detected ... More
Testing Skyrme energy-density functionals with the QRPA in low-lying vibrational states of rare-earth nucleiMay 19 2011Although nuclear energy density functionals are determined primarily by fitting to ground state properties, they are often applied in nuclear astrophysics to excited states, usually through the quasiparticle random phase approximation (QRPA). Here we ... More
Self-consistent description of multipole strength: systematic calculationsMar 26 2006We use the quasiparticle random phase approximation with a few Skyrme density functionals to calculate strength functions in the Jpi = 0+, 1-, and 2+ channels for even Ca, Ni, and Sn isotopes, from the proton drip line to the neutron drip line. We show ... More
A Plasma Instability Theory of Gamma-Ray Burst EmissionApr 02 1999A plasma instability theory is presented for the prompt radiation from gamma-ray bursts. In the theory, a highly relativistic shell interacts with the interstellar medium through the filamentation and the two-stream instabilities to convert bulk kinetic ... More
Optical linewidth of a low density Fermi-Dirac gasFeb 11 1999We study propagation of light in a Fermi-Dirac gas at zero temperature. We analytically obtain the leading density correction to the optical linewidth. This correction is a direct consequence of the quantum statistical correlations of atomic positions ... More
Three-dimensional photography by holographyMar 22 2007Color encoding of depth is shown to occur naturally in holograms that are reconstructed under white light illumination. It can be registered in a common color photograph, allowing a simple method of visual decoding by means of ordinary colored 3-D spectacles. ... More
Asymptotic step profiles from a nonlinear growth equation for vicinal surfacesMay 23 2000Aug 28 2000We study a recently proposed nonlinear evolution equation describing the collective step meander on a vicinal surface subject to the Bales-Zangwill growth instability [O. Pierre-Louis et al., Phys. Rev. Lett. (80), 4221 (1998)]. A careful numerical analysis ... More
Asymmetric particle systems on RSep 02 1999Dec 22 1999We study interacting particle systems on the real line which generalize the Hammersley process [D. Aldous and P. Diaconis, Prob. Theory Relat. Fields 103, 199-213 (1995)]. Particles jump to the right to a randomly chosen point between their previous position ... More
Improved description of charged Higgs boson production at hadron collidersSep 08 2004Jan 13 2005We present a new method for matching the two twin-processes gb->H+/-t and gg->H+/-tb in Monte Carlo event generators. The matching is done by defining a double-counting term, which is used to generate events that are subtracted from the sum of these two ... More
Virtual photon structure from jet productionJul 08 1997Some aspects of extracting the information on the structure of virtual photons from jet production in ep and e^+e^- collisions are discussed.
Virtual Photon Structure from Jet Production at HERANov 21 1996The feasibility of measuring parton distribution functions of of virtual photons via the jet production at HERA is investigated.
Numeration-automatic sequencesMay 17 2006We present a base class of automata that induce a numeration system and we give an algorithm to give the n-th word in the language of the automaton when the expansion of n in the induced numeration system is feeded to the automaton. Furthermore we give ... More
Generating conjecture and Einstein-Maxwell field of plane symmetryApr 10 2000For the plane symmetry we have found the electro-vacuum exact solutions of the Einstein-Maxwell equations and we have shown that one of them is equivalent to the McVittie solution of a charged infinite thin plane. The analytical extension has been accomplished ... More
Interaction of antiproton with nucleiFeb 19 2015We performed fully self-consistent calculations of $\bar{p}$-nuclear bound states within the relativistic mean-field (RMF) model. The G-parity motivated $\bar{p}$-meson coupling constants were adjusted to yield potentials consistent with $\bar{p}$-atom ... More
2 and 3-point functions in the ENJL-modelSep 06 1994We discuss the extended Nambu-Jona-Lasinio model as a low energy expansion, all two-point functions and an example of a three-point function to all orders in momenta and quark masses. The model is treated at leading level in $1/N_c$ but otherwise exact. ... More
Quantum Space-time and Classical GravityJul 24 1996A method has been recently proposed for defining an arbitrary number of differential calculi over a given noncommutative associative algebra. As an example a version of quantized space-time is considered here. It is found that there is a natural differential ... More
Spin diffusion of the t-J modelDec 12 1994Dec 13 1994The spin-diffusion constant of the 2D $t-J$ model is calculated for the first time using an analytical approach at high temperatures and a recently-developed numerical method based on the Lanczos technique combined with random sampling in the intermediate ... More
Computational Ergodicity of $s^4$Nov 07 1994It is known that there are four-manifolds which are not algorithmically recognizable. This implies that there exist triangulations of these manifolds which are separated by large barriers from the point of view of the computer algorithm. We have not observed ... More
On the exponential bound in four dimensional simplicial gravityMay 11 1994Simplicial quantum gravity has been proposed as a regularization for four dimensional quantum gravity. The partition function is constructed by performing a weighted sum over all triangulations of the 4-sphere. The model is well-defined only if the number ... More
Distance growth of quantum states due to initial system--environment correlationsMay 03 2010Intriguing features of the distance between two arbitrary states of an open quantum system are identified that are induced by initial system-environment correlations. As an example, we analyze a qubit dephasingly coupled to a bosonic environment. Within ... More
Effective fermion kinematics from a modified quantum gravityJun 11 2015Sep 05 2016We consider a classical fermion and a classical scalar, propagating on two different kinds of 4-dimensional diffeomorphism breaking gravity backgrounds, and we derive the one-loop effective dispersion relation for matter, after integrating out gravitons. ... More
Negative Screenings in Conformal Field Theory and 2D Gravity: The Braiding MatrixApr 20 1999We consider an extension of the Coulomb gas picture which is motivated by Liouville theory and contains negative powers of screening operators on the same footing as positive ones. The braiding problem for chiral vertex operators in this extended framework ... More
High-Order Coupled Cluster Method (CCM) Formalism 2 -- "Generalised" Expectation Values: Spin-Spin Correlation Functions for Frustrated and Unfrustrated 2D AntiferromagnetsNov 27 2009Recent developments of high-order CCM have been to extend existing formalism and codes to $s \ge \frac 12$ for both the ground and excited states. In this article, we describe how "generalised" expectation values for a wide range of one- and two-body ... More
Solution of the Skyrme-Hartree-Fock equations in the Cartesian deformed harmonic oscillator basis. (I) The methodNov 17 1996We describe a method of solving the nuclear Skyrme-Hartree-Fock problem by using a deformed Cartesian harmonic oscillator basis. The complete list of expressions required to calculate local densities, total energy, and self-consistent fields is presented, ... More
A term-rewriting system for computer quantum algebraSep 25 2008Existing computer algebra packages do not fully support quantum mechanics calculations in Dirac's notation. I present the foundation for building such support: a mathematical system for the symbolic manipulation of expressions used in the invariant formalism ... More
Symmetry and Stability of Homogenuous Flocks. A Position PaperOct 24 2018The study of the movement of flocks, whether biological or technological is motivated by the desire to understand the capability of coherent motion of a large number of agents that only receive very limited information. In a biological flock a large group ... More
Applications harmoniques et hyperbolicité des domaines tubesOct 26 2006An application of the Zalcman renormalization theorem to harmonic functions shows that the limit functions are nonconstant affine. Extensions of this method are given for maps with values in a torus or in a complex Lie groups. As an application, we give ... More
Metastability and paramagnetism in superconducting mesoscopic disksAug 24 1999Dec 22 1999A projected order parameter is used to calculate, not only local minima of the Ginzburg-Landau energy functional, but also saddle points or energy barriers responsible for the metastabilities observed in superconducting mesoscopic disks (Geim et al. Nature ... More
On Weierstraß semigroups at one and two points and their corresponding Poincaré seriesMar 30 2009Jun 29 2011The aim of this paper is to introduce and investigate the Poincar\'e series associated with the Weierstra{\ss} semigroup of one and two rational points at a (not necessarily irreducible) non-singular projective algebraic curve defined over a finite field, ... More
Unified treatment of multisymplectic 3-forms in dimension 6May 06 2004On a 6-dimensional real vector space $V$ there are three types of multisymplectic 3-forms. We present in this paper a unified treatment of these three types. Forms of each type represent a subset of $\Lambda^3 V^*$. In two cases they are open subsets, ... More