arXiv research

Study shows how transformers classify symbols without naming them, proving a margin-versus-collision criterion.

We design an algorithm writing down presentations of graph braid groups. Generators are represented in terms of actual motions of robots moving without collisions on a given graph. A key ingredient is a new motion planning algorithm whose complexity is linear in the number of edges and quadratic in the number of robots…

Residual neural networks improve collision prediction in planetary simulations.

Study nonholonomic systems with collisions using variational principles.

Paper analyzes dynamics of nonholonomic systems with collisions using variational techniques.

Model forecasts motor vehicle collision rates with high accuracy.

Machine learning competition predicts spacecraft collision risks.

New algorithms estimate and test collision probability with near-optimal sample complexity.

One 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 as a numeric table. This methodology has been used in the developme…

Quantum GNNs outperform classical GNNs in jet tagging.

No-collision maps improve manifold learning for image data.

Reduces necessary conditions for collision avoidance on curved spaces.

A new metric for uncertainty quantification using class collisions.

New algorithm for multi-player bandits with collision-dependent rewards.

Improved particle-flow event reconstruction for future colliders using scalable neural networks.

We investigate 3-dimensional globally hyperbolic AdS manifolds containing "particles", i.e., cone singularities of angles less than $2π$ along a time-like graph $Γ$. To each such space we associate a graph and a finite family of pairs of hyperbolic surfaces with cone singularities. We show that this data is sufficient …

The paper addresses the Multiplayer Multi-Armed Bandit (MMAB) problem, where $M$ decision makers or players collaborate to maximize their cumulative reward. When several players select the same arm, a collision occurs and no reward is collected on this arm. Players involved in a collision are informed about this collis…

Algorithm reduces regret in multi-player bandits with unknown collision rewards.

An important application of intelligent vehicles is advance detection of dangerous events such as collisions. This problem is framed as a problem of optimal alarm choice given predictive models for vehicle location and motion. Techniques for real-time collision detection are surveyed and grouped into three classes: ran…

A new algorithm reduces regret in multi-player bandits without collision info.

A new algorithm RESYNC for defenders against malicious attackers in multi-player bandits.

Bayesian deep learning predicts satellite collisions.

New algorithms tackle adversarial multi-player bandits with forced-collision communication.

New strategy achieves optimal regret without communication or collisions in multi-player bandit.

We study multiplayer stochastic multi-armed bandit problems in which the players cannot communicate and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider two feedback models: a model in which the players can observe whether a collision has occurred an…

The configuration manifold $M$ of a mechanical system consisting of two unconstrained rigid bodies in $\mathbb{R}^n$, $n\geq 1$, is a manifold with boundary (typically with singularities.) A complete description of the system requires boundary conditions that specify how orbits should be continued after collisions. A b…

Study motion planning for points avoiding obstacles in a plane.

Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of nucleus-nucleus and proton-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Common methods for centrality estimation in A-A and p-A collisions usually rely on a single…

Recovering manifold geometry from geodesic intersections.

Zero-energy orbits in the Kepler-Heisenberg problem are self-similar and stratify into three families.

We consider the non-stochastic version of the (cooperative) multi-player multi-armed bandit problem. The model assumes no communication at all between the players, and furthermore when two (or more) players select the same action this results in a maximal loss. We prove the first $\sqrt{T}$-type regret guarantee for th…

New algorithm for multi-player bandits without needing lower bounds or scaling inversely.

Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments, etc.) and edges characterize the relationships between these primitives. However, these …

We investigate 3-dimensional globally hyperbolic AdS manifolds containing "particles", i.e., cone singularities along a graph $Γ$. We impose physically relevant conditions on the cone singularities, e.g. positivity of mass (angle less than $2π$ on time-like singular segments). We construct examples of such manifolds, d…

Unified approach detects traffic conflicts across various interactions.

Rolling systems limit to billiard models with no-slip collisions.

Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, which train a classifier to distinguish coll…

This work examines the role of reinforcement learning in reducing the severity of on-road collisions by controlling velocity and steering in situations in which contact is imminent. We construct a model, given camera images as input, that is capable of learning and predicting the dynamics of obstacles, cars and pedestr…

Up to symmetries, the orbits of three equal masses under an inverse cube force with zero angular momentum and constant moment of inertia can be reparametrized as the geodesics of a complete, negatively curved metric on a pair of pants. The ends of the pants represent binary collisions. Here we will examine the visibili…

Graph neural networks improve charged particle tracking on FPGAs.

Stochastic approach improves neural network training for kinetic simulations.

Multipeakons are special solutions to the Camassa-Holm equation described by an integrable geodesic flow on a Riemannian manifold. We present a bi-Hamiltonian formulation of the system explicitly and write down formulae for the associated first integrals. Then we exploit the first integrals and present a novel approach…

Generalizes Landau-Ginzburg mirrors for Frobenius manifolds in Dynkin type A.

This work improves motion planning for quadcopters by learning and reasoning about controller performance.

This work optimizes signal estimation for sparse MRA with collision-free signals.

CriticSMC improves planning efficiency in constrained environments.

CARML uses meta-learning to avoid obstacles in 2D vehicle navigation.

A fast method learns plasma collision kernels from simulations, improving kinetic models.