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Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action SystemsJul 26 2016Oct 02 2016We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (1) local models are estimated only when the planner requires them; (2) ... More

Few-Shot Bayesian Imitation Learning with Logic over ProgramsApr 12 2019We describe an expressive class of policies that can be efficiently learned from a few demonstrations. Policies are represented as logical combinations of programs drawn from a small domain-specific language (DSL). We define a prior over policies with ... More

Every Local Minimum is a Global Minimum of an Induced ModelApr 07 2019For non-convex optimization in machine learning, this paper proves that every local minimum achieves the global optimality of the perturbable gradient basis model at any differentiable point. As a result, non-convex machine learning is theoretically as ... More

Accelerating EM: An Empirical StudyJan 23 2013Many applications require that we learn the parameters of a model from data. EM is a method used to learn the parameters of probabilistic models for which the data for some of the variables in the models is either missing or hidden. There are instances ... More

Learning to Acquire InformationApr 20 2017Jul 11 2017We consider the problem of diagnosis where a set of simple observations are used to infer a potentially complex hidden hypothesis. Finding the optimal subset of observations is intractable in general, thus we focus on the problem of active diagnosis, ... More

Adaptive Importance Sampling for Estimation in Structured DomainsJan 16 2013Sampling is an important tool for estimating large, complex sums and integrals over high dimensional spaces. For instance, important sampling has been used as an alternative to exact methods for inference in belief networks. Ideally, we want to have a ... More

Regret bounds for meta Bayesian optimization with an unknown Gaussian process priorNov 23 2018Bayesian optimization usually assumes that a Bayesian prior is given. However, the strong theoretical guarantees in Bayesian optimization are often regrettably compromised in practice because of unknown parameters in the prior. In this paper, we adopt ... More

Towards Understanding Generalization via Analytical Learning TheoryFeb 21 2018Oct 01 2018This paper introduces a novel measure-theoretic theory for machine learning that does not require statistical assumptions. Based on this theory, a new regularization method in deep learning is derived and shown to outperform previous methods in CIFAR-10, ... More

Learning to Rank for Synthesizing Planning HeuristicsAug 03 2016We investigate learning heuristics for domain-specific planning. Prior work framed learning a heuristic as an ordinary regression problem. However, in a greedy best-first search, the ordering of states induced by a heuristic is more indicative of the ... More

Learning Finite-State Controllers for Partially Observable EnvironmentsJan 23 2013Reactive (memoryless) policies are sufficient in completely observable Markov decision processes (MDPs), but some kind of memory is usually necessary for optimal control of a partially observable MDP. Policies with finite memory can be represented as ... More

Learning to guide task and motion planning using score-space representationJul 26 2018In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to ... More

Bayesian Optimization with Exponential ConvergenceApr 05 2016This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-cover sampling. Most Bayesian optimization methods require auxiliary optimization: an additional non-convex ... More

Learning Probabilistic Relational Dynamics for Multiple TasksJun 20 2012The ways in which an agent's actions affect the world can often be modeled compactly using a set of relational probabilistic planning rules. This paper addresses the problem of learning such rule sets for multiple related tasks. We take a hierarchical ... More

Backward-Forward Search for Manipulation PlanningApr 12 2016In this paper we address planning problems in high-dimensional hybrid configuration spaces, with a particular focus on manipulation planning problems involving many objects. We present the hybrid backward-forward (HBF) planning algorithm that uses a backward ... More

Deliberation Scheduling for Time-Critical Sequential Decision MakingMar 06 2013We describe a method for time-critical decision making involving sequential tasks and stochastic processes. The method employs several iterative refinement routines for solving different aspects of the decision making problem. This paper concentrates ... More

Look before you sweep: Visibility-aware motion planningJan 18 2019This paper addresses the problem of planning for a robot with a directional obstacle-detection sensor that must move through a cluttered environment. The planning objective is to remain safe by finding a path for the complete robot, including sensor, ... More

Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action SystemsJul 26 2016Oct 23 2016We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (1) local models are estimated only when the planner requires them; (2) ... More

Learning to Cooperate via Policy SearchAug 07 2014Cooperative games are those in which both agents share the same payoff structure. Value-based reinforcement-learning algorithms, such as variants of Q-learning, have been applied to learning cooperative games, but they only apply when the game state is ... More

On the Complexity of Solving Markov Decision ProblemsFeb 20 2013Markov decision problems (MDPs) provide the foundations for a number of problems of interest to AI researchers studying automated planning and reinforcement learning. In this paper, we summarize results regarding the complexity of solving MDPs and the ... More

Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action SystemsJul 26 2016Sep 22 2016We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (1) local models are estimated only when the planner requires them; (2) ... More

FFRob: Leveraging Symbolic Planning for Efficient Task and Motion PlanningAug 03 2016Mobile manipulation problems involving many objects are challenging to solve due to the high dimensionality and multi-modality of their hybrid configuration spaces. Planners that perform a purely geometric search are prohibitively slow for solving these ... More

Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samplesNov 04 2017In robotics, it is essential to be able to plan efficiently in high-dimensional continuous state-action spaces for long horizons. For such complex planning problems, unguided uniform sampling of actions until a path to a goal is found is hopelessly inefficient, ... More

Selecting Representative Examples for Program SynthesisNov 09 2017Jun 07 2018Program synthesis is a class of regression problems where one seeks a solution, in the form of a source-code program, mapping the inputs to their corresponding outputs exactly. Due to its precise and combinatorial nature, program synthesis is commonly ... More

Learning to Cooperate via Policy SearchMay 25 2001Cooperative games are those in which both agents share the same payoff structure. Value-based reinforcement-learning algorithms, such as variants of Q-learning, have been applied to learning cooperative games, but they only apply when the game state is ... More

The Thing That We Tried Didn't Work Very Well : Deictic Representation in Reinforcement LearningDec 12 2012Most reinforcement learning methods operate on propositional representations of the world state. Such representations are often intractably large and generalize poorly. Using a deictic representation is believed to be a viable alternative: they promise ... More

Combining Physical Simulators and Object-Based Networks for ControlApr 13 2019Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Most physics engines therefore employ . approximations that lead ... More

Hierarchical Solution of Markov Decision Processes using Macro-actionsJan 30 2013We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current models that combine both primitive actions and macro-actions and leave the state space unchanged, we propose a hierarchical ... More

Graph Element Networks: adaptive, structured computation and memoryApr 18 2019We explore the use of graph neural networks (GNNs) to model spatial processes in which there is a priori graphical structure. Similar to finite element analysis, we assign nodes of a GNN to spatial locations and use a computational process defined on ... More

Solving POMDPs by Searching the Space of Finite PoliciesJan 23 2013Solving partially observable Markov decision processes (POMDPs) is highly intractable in general, at least in part because the optimal policy may be infinitely large. In this paper, we explore the problem of finding the optimal policy from a restricted ... More

Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process MixturesDec 02 2015To accomplish tasks in human-centric indoor environments, robots need to represent and understand the world in terms of objects and their attributes. We refer to this attribute-based representation as a world model, and consider how to acquire it via ... More

CAPIR: Collaborative Action Planning with Intention RecognitionJun 26 2012We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described ... More

A General Method for Obtaining a Lower Bound for the Ground State Entropy Density of the Ising Model With Short Range InteractionsApr 14 2001We present a general method for obtaining a lower bound for the ground state entropy density of the Ising Model with nearest neighbor interactions. Then, using this method, and with a random coupling constant configuration, we obtain a lower bound for ... More

Learning Policies with External MemoryMar 02 2001In order for an agent to perform well in partially observable domains, it is usually necessary for actions to depend on the history of observations. In this paper, we explore a {\it stigmergic} approach, in which the agent's actions include the ability ... More

Effective Caching for the Secure Content Distribution in Information-Centric NetworkingAug 01 2018The secure distribution of protected content requires consumer authentication and involves the conventional method of end-to-end encryption. However, in information-centric networking (ICN) the end-to-end encryption makes the content caching ineffective ... More

Discussion of: Brownian distance covarianceOct 05 2010Discussion on "Brownian distance covariance" by G\'{a}bor J. Sz\'{e}kely, Maria L. Rizzo [arXiv:1010.0297]

Perverse sheaves and the reductive Borel-Serre compactificationDec 04 2016We briefly introduce the theory of perverse sheaves with special attention to the topological situation where strata can have odd dimension. This is part of a project to use perverse sheaves on the topological reductive Borel-Serre compactification of ... More

Hypermap-Homology Quantum Codes (Ph.D. thesis)Oct 20 2013We introduce a new type of sparse CSS quantum error correcting code based on the homology of hypermaps. Sparse quantum error correcting codes are of interest in the building of quantum computers due to their ease of implementation and the possibility ... More

Geometric rationality of equal-rank Satake compactificationsNov 07 2002Sep 18 2004Satake has constructed compactifications of symmetric spaces D=G/K which (under a condition called geometric rationality by Casselman) yield compactifications of the corresponding locally symmetric spaces. The different compactifications depend on the ... More

Zero forcing and maximum nullity for hypergraphsAug 29 2018The concept of zero forcing is extended from graphs to uniform hypergraphs in analogy with the way zero forcing was defined as an upper bound for the maximum nullity of the family of symmetric matrices whose nonzero pattern of entries is described by ... More

Resonant Mirković-Vilonen polytopes and formulas for highest-weight charactersAug 30 2018Formulas for the product of an irreducible character $\chi_\lambda$ of a complex Lie group and a deformation of the Weyl denominator as a sum over the crystal $\mathcal{B}(\lambda+\rho)$ go back to Tokuyama. We study the geometry underlying such formulas ... More

L-modules and micro-supportDec 22 2001Jan 09 2005L-modules are a combinatorial analogue of constructible sheaves on the reductive Borel-Serre compactification of a locally symmetric space. We define the micro-support of an L-module; it is a set of irreducible modules for the Levi quotients of the parabolic ... More

A partial analogue of the Grothendieck-Springer resolution for symmetric spacesApr 19 2019Motivated by questions in the study of relative trace formulae, we construct a generalization of Grothendieck's simultaneous resolution over the regular locus of certain symmetric pairs. We use this space to prove a relative version of results of Donagi ... More

Modular meta-learningJun 26 2018Many prediction problems, such as those that arise in the context of robotics, have a simplifying underlying structure that could accelerate learning. In this paper, we present a strategy for learning a set of neural network modules that can be combined ... More

Finding Frequent Entities in Continuous DataMay 08 2018In many applications that involve processing high-dimensional data, it is important to identify a small set of entities that account for a significant fraction of detections. Rather than formalize this as a clustering problem, in which all detections ... More

A Generalized Theta lifting, CAP representations, and Arthur parametersMar 07 2017Jan 23 2018We study a new lifting of automorphic representations using the theta representation $\Theta$ on the $4$-fold cover of the symplectic group, $\overline{\mathrm{Sp}}_{2r}(\mathbb{A})$. This lifting produces the first examples of CAP representations on ... More

On The Group Algebra Decomposition of a Jacobian VarietyMar 11 2016Given a compact Riemann surface X with an action of a finite group G, the group algebra Q[G] provides an isogenous decomposition of its Jacobian variety JX, known as the group algebra decomposition of JX. We obtain a method to concretely build a decomposition ... More

On the Cohomology of Locally Symmetric Spaces and of their CompactificationsJun 27 2003Jul 19 2003This expository article is an expanded version of talks given at the "Current Developments in Mathematics, 2002" conference. It gives an introduction to the (generalized) conjecture of Rapoport and Goresky-MacPherson which identifies the intersection ... More

L-modules and the Conjecture of Rapoport and Goresky-MacPhersonDec 22 2001May 13 2005Consider the middle perversity intersection cohomology groups of various compactifications of a Hermitian locally symmetric space. Rapoport and independently Goresky and MacPherson have conjectured that these groups coincide for the reductive Borel-Serre ... More

L^2-cohomology of locally symmetric spaces, IDec 20 2004Jan 11 2006Let X be a locally symmetric space associated to a reductive algebraic group G defined over Q. L-modules are a combinatorial analogue of constructible sheaves on the reductive Borel-Serre compactification of X; they were introduced in [math.RT/0112251]. ... More

Residual Policy LearningDec 15 2018Jan 03 2019We present Residual Policy Learning (RPL): a simple method for improving nondifferentiable policies using model-free deep reinforcement learning. RPL thrives in complex robotic manipulation tasks where good but imperfect controllers are available. In ... More

Adaptive identification of coherent statesAug 18 2015Jan 07 2016We present methods for efficient characterization of an optical coherent state $|\alpha\rangle$. We choose measurement settings adaptively and stochastically, based on data while it is collected. Our algorithm divides the estimation into two distinct ... More

Planning for Decentralized Control of Multiple Robots Under UncertaintyFeb 12 2014We describe a probabilistic framework for synthesizing control policies for general multi-robot systems, given environment and sensor models and a cost function. Decentralized, partially observable Markov decision processes (Dec-POMDPs) are a general ... More

Logical Algorithms meets CHR: A meta-complexity result for Constraint Handling Rules with rule prioritiesJan 09 2009This paper investigates the relationship between the Logical Algorithms language (LA) of Ganzinger and McAllester and Constraint Handling Rules (CHR). We present a translation schema from LA to CHR-rp: CHR with rule priorities, and show that the meta-complexity ... More

Closed virial equations for hard parallel cubes and squaresMay 20 2011A correlation between maxima in virial coefficients (Bn), and "kissing" numbers for hard hyper-spheres up to dimension D=5, indicates a virial equation and close-packing relationship. Known virial coefficients up to B7, both for hard parallel cubes and ... More

Volatile transport on inhomogeneous surfaces: II. Numerical calculations (VT3D)Nov 18 2015Jul 09 2016Several distant icy worlds have atmospheres that are in vapor-pressure equilibrium with their surface volatiles, including Pluto, Triton, and, probably, several large KBOs near perihelion. Studies of the volatile and thermal evolution of these have been ... More

The Energy Balance Relation for Weak solutions of the Density-Dependent Navier-Stokes EquationsFeb 26 2016We consider the incompressible inhomogeneous Navier-Stokes equations with constant viscosity coefficient and density which is bounded and bounded away from zero. We show that the energy balance relation for this system holds for weak solutions if the ... More

Transparent Boundary Conditions for the Time-Dependent Schrödinger Equation with a Vector PotentialDec 11 2018We consider the problem of constructing transparent boundary conditions for the time-dependent Schr\"odinger equation with a compactly supported binding potential and, if desired, a spatially uniform, time-dependent electromagnetic vector potential. Such ... More

Norm-preserving discretization of integral equations for elliptic PDEs with internal layers I: the one-dimensional caseMay 29 2013We investigate the behavior of integral formulations of variable coefficient elliptic partial differential equations (PDEs) in the presence of steep internal layers. In one dimension, the equations that arise can be solved analytically and the condition ... More

Propagation time for probabilistic zero forcingDec 24 2018Zero forcing is a coloring game played on a graph that was introduced more than ten years ago in several different applications. The goal is to color all the vertices blue by repeated use of a (deterministic) color change rule. Probabilistic zero forcing ... More

Cyclical Learning Rates for Training Neural NetworksJun 03 2015Oct 26 2016It is known that the learning rate is the most important hyper-parameter to tune for training deep neural networks. This paper describes a new method for setting the learning rate, named cyclical learning rates, which practically eliminates the need to ... More

How to find real-world applications for compressive sensingMay 06 2013Jun 26 2013The potential of compressive sensing (CS) has spurred great interest in the research community and is a fast growing area of research. However, research translating CS theory into practical hardware and demonstrating clear and significant benefits with ... More

A fast multipole method for the evaluation of elastostatic fields in a half-space with zero normal stressMar 31 2014In this paper, we present a fast multipole method (FMM) for the half-space Green's function in a homogeneous elastic half-space subject to zero normal stress, for which an explicit solution was given by Mindlin (1936). The image structure of this Green's ... More

Spectral edge detection in two dimensions using wavefrontsSep 29 2009A recurring task in image processing, approximation theory, and the numerical solution of partial differential equations is to reconstruct a piecewise-smooth real-valued function f(x) in multiple dimensions from its truncated Fourier transform (its truncated ... More

Auxiliary Variables in TLA+Mar 15 2017May 29 2017Auxiliary variables are often needed for verifying that an implementation is correct with respect to a higher-level specification. They augment the formal description of the implementation without changing its semantics--that is, the set of behaviors ... More

Fast elliptic solvers in cylindrical coordinates and the Coulomb collision operatorFeb 10 2011In this paper, we describe a new class of fast solvers for separable elliptic partial differential equations in cylindrical coordinates $(r,\theta,z)$ with free-space radiation conditions. By combining integral equation methods in the radial variable ... More

An adaptive fast Gauss transform in two dimensionsDec 01 2017A variety of problems in computational physics and engineering require the convolution of the heat kernel (a Gaussian) with either discrete sources, densities supported on boundaries, or continuous volume distributions. We present a unified fast Gauss ... More

Two-phase coexistence in the hard-disk modelJun 06 2008Jun 09 2008A two-dimensional system of 10000 hard disks with square periodic boundary conditions, at a density in the middle of the 2-phase region predicted from equation-of-state data, when subjected to a weak external uniform force, is seen to phase separate. ... More

Fluid phases of argonJun 13 2012A phase diagram of argon based upon percolation transition loci determined from literature experimental density-pressure isotherms, and simulation values using a Lennard-Jones model shows three fluid phases. The liquid phase spans all temperatures, from ... More

Pluto's Seasons: New Predictions for New HorizonsOct 29 2012Since the last Pluto volatile transport models were published (Hansen and Paige 1996), we have (i) new stellar occultation data from 2002 and 2006-2012 that have roughly twice the pressure as the discovery occultation of 1988, (ii) new information about ... More

Volatile transport on inhomogeneous surfaces: I. Analytic expressions, with application to Pluto's dayMay 07 2012Jun 22 2012An analytic expression for the variation in surface and sub-surface temperature is developed for worlds whose surface pressures are nearly constant with latitude and longitude and whose atmospheres are in vapor-pressure equilibrium with the dominant surface ... More

A new mixed potential representation for the equations of unsteady, incompressible flowSep 22 2018Sep 27 2018We present a new integral representation for the unsteady, incompressible Stokes or Navier-Stokes equations, based on a linear combination of heat and harmonic potentials. For velocity boundary conditions, this leads to a coupled system of integral equations: ... More

Modular meta-learning in abstract graph networks for combinatorial generalizationDec 19 2018Modular meta-learning is a new framework that generalizes to unseen datasets by combining a small set of neural modules in different ways. In this work we propose abstract graph networks: using graphs as abstractions of a system's subparts without a fixed ... More

Consensus on Transaction CommitAug 14 2004The distributed transaction commit problem requires reaching agreement on whether a transaction is committed or aborted. The classic Two-Phase Commit protocol blocks if the coordinator fails. Fault-tolerant consensus algorithms also reach agreement, but ... More

Universality for conditional measures of the Bessel point processApr 08 2019The Bessel point process is a rigid point process on the positive real line and its conditional measure on a bounded interval $[0,R]$ is almost surely an orthogonal polynomial ensemble. In this article, we show that if $R$ tends to infinity, one almost ... More

The coalescent and its descendantsJun 08 2010The coalescent revolutionised theoretical population genetics, simplifying, or making possible for the first time, many analyses, proofs, and derivations, and offering crucial insights about the way in which the structure of data in samples from populations ... More

Parity Sheaves and Smith TheoryAug 28 2017Sep 08 2017We develop a connection between parity complexes and Smith theory for varieties equipped with an action of a cyclic group of prime order $p$. We define a sheaf-theoretic Tate cohomology theory and study the corresponding notion of Tate-parity complex. ... More

Fast multi-particle scattering: a hybrid solver for the Maxwell equations in microstructured materialsApr 28 2011A variety of problems in device and materials design require the rapid forward modeling of Maxwell's equations in complex micro-structured materials. By combining high-order accurate integral equation methods with classical multiple scattering theory, ... More

Reconstructing Curves from Points and TangentsMar 10 2009Reconstructing a finite set of curves from an unordered set of sample points is a well studied topic. There has been less effort that considers how much better the reconstruction can be if tangential information is given as well. We show that if curves ... More

Error Estimation in Large Spreadsheets using Bayesian StatisticsAug 08 2009Spreadsheets are ubiquitous in business with the financial sector particularly heavily reliant on the technology. It is known that the level of spreadsheet error can be high and that it is often necessary to review spreadsheets based on a structured methodology ... More

Most 1.6 Earth-Radius Planets are not RockyJul 16 2014Mar 03 2015The Kepler Mission, combined with ground based radial velocity (RV) follow-up and dynamical analyses of transit timing variations, has revolutionized the observational constraints on sub-Neptune-size planet compositions. The results of an extensive Kepler ... More

Hybrid asymptotic/numerical methods for the evaluation of layer heat potentials in two dimensionsMar 20 2018We present a hybrid asymptotic/numerical method for the accurate computation of single and double layer heat potentials in two dimensions. It has been shown in previous work that simple quadrature schemes suffer from a phenomenon called "geometrically-induced ... More

Inverse Obstacle scattering in two dimensions with multiple frequency data and multiple angles of incidenceAug 22 2014We consider the problem of reconstructing the shape of an impenetrable sound-soft obstacle from scattering measurements. The input data is assumed to be the far-field pattern generated when a plane wave impinges on an unknown obstacle from one or more ... More

Variants on the minimum rank problem: A survey IIFeb 25 2011Oct 08 2014The minimum rank problem for a (simple) graph $G$ is to determine the smallest possible rank over all real symmetric matrices whose $ij$th entry (for $i\neq j$) is nonzero whenever $\{i,j\}$ is an edge in $G$ and is zero otherwise. This paper surveys ... More

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and BouncingAug 09 2018An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control. In this paper, we build such a simulator for two scenarios, planar pushing and ball bouncing, by ... More

Experimental investigation of the nebular formation of chondrule rims and the formation of chondrite parent bodiesSep 19 2011Jun 26 2012We developed an experimental setup to test the hypothesis that accretionary dust rims around chondrules formed in the solar nebula at elevated temperatures. Our experimental method allows us to form dust rims around chondrule-analogs while being levitated ... More

Deep Convolutional Neural Network Design PatternsNov 02 2016Recent research in the deep learning field has produced a plethora of new architectures. At the same time, a growing number of groups are applying deep learning to new applications and problems. Many of these groups might be composed of inexperienced ... More

Asynchronous Stochastic Approximation with Differential InclusionsDec 10 2011The asymptotic pseudo-trajectory approach to stochastic approximation of Benaim, Hofbauer and Sorin is extended for asynchronous stochastic approximations with a set-valued mean field. The asynchronicity of the process is incorporated into the mean field ... More

Conditions Implying Energy Equality for Weak Solutions to the Navier-Stokes EquationJun 08 2016Nov 03 2016When a Leray-Hopf weak solution to the NSE has a singularity set $S$ of dimension $d$ less than 3, for example a suitable weak solution, we find a family of new $L^q L^p$ conditions that guarantee validity of the energy equality. The conditions surpass ... More

Snowmass-2013 Cosmic Frontier 3 (CF3) Working Group Summary: Non-WIMP dark matterOct 31 2013Report of the CF-3 Working Group at Community Planning Study "Snowmass-2013".

Kinesin Motor Transport is Altered by Macromolecular Crowding and Transiently Associated Microtubule-Associated ProteinsSep 11 2014Intracellular transport of vesicular cargos, organelles, and other macromolecules is an essential process to move large items through a crowded, and inhomogeneous cellular environment. In an effort to dissect the fundamental effects of crowding and an ... More

Approximating Pairwise Correlations in the Ising ModelOct 13 2018In the Ising model, we consider the problem of estimating the covariance of the spins at two specified vertices. In the ferromagnetic case, it is easy to obtain an additive approximation to this covariance by repeatedly sampling from the relevant Gibbs ... More

The complexity of approximating complex-valued Ising and Tutte partition functionsSep 19 2014Jan 22 2017We study the complexity of approximately evaluating the Ising and Tutte partition functions with complex parameters. Our results are partly motivated by the study of the quantum complexity classes BQP and IQP. Recent results show how to encode quantum ... More

Local and Nonlocal Dispersive TurbulenceSep 18 2007Feb 20 2009We consider the evolution of a family of 2D dispersive turbulence models. The members of this family involve the nonlinear advection of a dynamically active scalar field, the locality of the streamfunction-scalar relation is denoted by $\alpha$, with ... More

The complexity of approximating complex-valued Ising and Tutte partition functionsSep 19 2014Feb 25 2015We study the complexity of approximately evaluating the Ising and Tutte partition functions with complex parameters. Our results are partly motivated by the study of the quantum complexity classes BQP and IQP. Recent results show how to encode quantum ... More

Young Stellar Groups Around Herbig Ae/Be Stars: A Low-Mass YSO CensusJan 11 2007We present NIR and MIR observations of eight embedded young stellar groups around Herbig Ae/Be stars (HAEBEs) using archived Spitzer IRAC data and 2MASS data. These young stellar groups are nearby ($\leq$ 1 kpc) and still embedded within their molecular ... More

Deep Convolutional Neural Network Design PatternsNov 02 2016Nov 14 2016Recent research in the deep learning field has produced a plethora of new architectures. At the same time, a growing number of groups are applying deep learning to new applications. Some of these groups are likely to be composed of inexperienced deep ... More

Adaptive Drift AnalysisAug 01 2011Sep 27 2011We show that, for any c>0, the (1+1) evolutionary algorithm using an arbitrary mutation rate p_n = c/n finds the optimum of a linear objective function over bit strings of length n in expected time Theta(n log n). Previously, this was only known for c ... More

Approximating the Tutte polynomial of a binary matroid and other related combinatorial polynomialsJun 27 2010Apr 02 2012We consider the problem of approximating certain combinatorial polynomials. First, we consider the problem of approximating the Tutte polynomial of a binary matroid with parameters q>= 2 and gamma. (Relative to the classical (x,y) parameterisation, q=(x-1)(y-1) ... More

The Complexity of Computing the Sign of the Tutte PolynomialFeb 01 2012Oct 08 2014We study the complexity of computing the sign of the Tutte polynomial of a graph. As there are only three possible outcomes (positive, negative, and zero), this seems at first sight more like a decision problem than a counting problem. Surprisingly, however, ... More

The complexity of counting locally maximal satisfying assignments of Boolean CSPsSep 11 2015Apr 06 2016We investigate the computational complexity of the problem of counting the maximal satisfying assignments of a Constraint Satisfaction Problem (CSP) over the Boolean domain {0,1}. A satisfying assignment is maximal if any new assignment which is obtained ... More