A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Residual neural networks improve collision prediction in planetary simulations.
Machine learning competition predicts spacecraft collision risks.
Bayesian deep learning predicts satellite collisions.
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…
Study shows how transformers classify symbols without naming them, proving a margin-versus-collision criterion.
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.
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…
Stochastic approach improves neural network training for kinetic simulations.
New algorithms estimate and test collision probability with near-optimal sample complexity.
Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world. In Quebec only, in 2018, road accidents are responsible for 359 deaths and 33 thousands of injuries. In this paper, we show how one can leverage open datasets of a city like Montreal, …
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…
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.
Navigating complex urban environments safely is a key to realize fully autonomous systems. Predicting future locations of vulnerable road users, such as pedestrians and cyclists, thus, has received a lot of attention in the recent years. While previous works have addressed modeling interactions with the static (obstacl…
The paper addresses the Multiplayer Multi-Armed Bandit (MMAB) problem, where 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.
This work improves motion planning for quadcopters by learning and reasoning about controller performance.
A new algorithm RESYNC for defenders against malicious attackers in multi-player bandits.
Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This pap…
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 of a mechanical system consisting of two unconstrained rigid bodies in , , 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…
The Kepler-Heisenberg problem is that of determining the motion of a planet around a sun in the Heisenberg group, thought of as a three-dimensional sub-Riemannian manifold. The sub-Riemannian Hamiltonian provides the kinetic energy, and the gravitational potential is given by the fundamental solution to the sub-Laplaci…
Study motion planning for points avoiding obstacles in a plane.
The decentralized stochastic multi-player multi-armed bandit (MP-MAB) problem, where the collision information is not available to the players, is studied in this paper. Building on the seminal work of Boursier and Perchet (2019), we propose error correction synchronization involving communication (EC-SIC), whose regre…
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.
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 -type regret guarantee for th…
New algorithm for multi-player bandits without needing lower bounds or scaling inversely.
We present an approach for designing correct-by-construction neural networks (and other machine learning models) that are guaranteed to be consistent with a collection of input-output specifications before, during, and after algorithm training. Our method involves designing a constrained predictor for each set of compa…
Unified approach detects traffic conflicts across various interactions.
Rolling systems limit to billiard models with no-slip collisions.
Naturalistic driving trajectories are crucial for the performance of autonomous driving algorithms. However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms. When considering safety, testing algorithms in near-miss…
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…
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…
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.
Models 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 for learning a probabilistic generative model of aircraft motion i…
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.
Develops scalable differentiable physics for complex object interactions.