Improved algorithm tags B B B meson flavours in collider experiments.
problem Tagging neutral B B B mesons' flavours in collider environments. method Probabilistic model combining vertex and track info with machine learning.
result Increases overall performance in flavour tagging.
Equivariant neural network simplifies particle physics models.
problem Complexity and interpretability in particle physics classification.
method Lorentz group equivariant neural network architecture.
result Simplified, interpretable models with fewer parameters.
MLPF uses graph neural networks to improve particle-flow reconstruction in high-pileup conditions.
problem Improving particle-flow reconstruction in high-pileup conditions at high-luminosity LHC.
method End-to-end trainable machine-learned particle-flow algorithm based on graph neural networks.
result MLPF improves physics response and demonstrates scalable reconstruction in high-pileup environments.
Foundation models trained on collider data improve jet generation tasks.
problem Improving foundation models for jet generation tasks.
method Pre-training OmniJet- α \alpha α model on AspenOpenJets dataset. result Pre-trained model improves performance on jet generation tasks with domain shift.
NSBI approach detects Higgs trilinear coupling with high luminosity upgrade constraints.
problem Determining the Higgs trilinear self-coupling via off-shell Higgs production.
method Hybrid neural simulation-based inference (NSBI) incorporating SMEFT and quantum interference effects.
result NSBI achieves sensitivity close to theoretical optimum for Higgs trilinear self-coupling.
Tensor networks improve b-jet classification in high-energy physics.
problem Classifying jets from b-quarks in proton-proton collisions.
method Quantum-inspired machine learning using tensor networks.
result Optimized classification of b-jets with improved precision and speed.
Challenge uses unsupervised learning to detect new physics signals at LHC.
problem Detecting new physics signals at the LHC using unsupervised machine learning.
method Developed and evaluated anomaly detection algorithms on a large dataset.
result Benchmark dataset of >1 Billion simulated LHC events for future studies.
Introduces ML concepts to physicists, emphasizing connections to statistical physics.
problem Understanding machine learning for physicists.
method Fundamental ML concepts and tools explained, using Python Jupyter notebooks and physics datasets.
result Natural connections between ML and statistical physics highlighted.
Residual neural networks improve collision prediction in planetary simulations.
problem Accurate prediction of planetary collisions in N-body simulations.
method Residual neural networks trained on collision data.
result Residual neural networks outperform existing methods in prediction accuracy and generalization.
Study nonholonomic systems with collisions using variational principles.
problem Variational problems on nonholonomic systems with collisions.
method Extended variational principle, introduced connection on principal bundles, applied Lagrange–Poincaré–Pontryagin reduction.
result Implicit Lagrange–d'Alembert–Pontryagin equations for nonholonomic systems with collisions.
Optimal alarms detect vehicle collisions with theoretical and empirical validation.
problem Detecting dangerous vehicle collisions in real-time.
method Surveyed and compared three classes of collision detection techniques: Monte Carlo, deterministic approximations, and machine learning.
result Monte Carlo sampling is a robust solution for real-time collision detection despite its simplicity.
Model forecasts motor vehicle collision rates with high accuracy.
problem Forecasting motor vehicle collision rates with high accuracy.
method Adopted Heston Stochastic Volatility model and extended it to account for seasonality and accelerated safety periods.
result Short-term forecasts show high accuracy (over 95%) and outperform existing models.
Paper analyzes dynamics of nonholonomic systems with collisions using variational techniques.
problem Analyzing the dynamics of nonholonomic mechanical systems with impacts.
method Variational techniques extended to nonsmooth context for collisions.
result Variational formulation for implicit nonholonomic mechanical systems with energy-momentum preserving collisions.
Machine learning competition predicts spacecraft collision risks.
problem Predicting future collision risks between orbiting satellites.
method Machine learning models trained on satellite collision data.
result Models accurately predicted collision risks with high precision.
Deep neural network approximates collision avoidance table for aircraft systems.
problem High dimensionality of collision avoidance state space leads to large numeric tables.
method Used deep neural networks to approximate the numeric tables, using asymmetric loss functions and gradient descent.
result Reduced storage space by a factor of 1000, enabling current avionics systems.
New algorithms estimate and test collision probability with near-optimal sample complexity.
problem Estimating and testing collision probability in discrete distributions.
method Developed algorithms for ( α , β ) (α, β) ( α , β ) -local differential privacy and sequential testing. result Achieved nearly optimal sample complexity for estimating and testing collision probability.
No-collision maps improve manifold learning for image data.
problem Lack of geometric feature sensitivity in traditional distance measures.
method Developed no-collision transportation maps and distances.
result No-collision distances provide isometry for translations and dilations.
A new metric for uncertainty quantification using class collisions.
problem Fine-grained uncertainty quantification in classification problems.
method Introducing the collision matrix and estimating it from one-hot labeled data.
result The collision matrix uniquely recovers the posterior class probability distribution.
Reduces necessary conditions for collision avoidance on curved spaces.
problem Finding non-intersecting trajectories for multiple agents on curved spaces.
method Reduction by Lie group symmetries of variational collision avoidance problems.
result Derives necessary conditions for reduced extremals.
New algorithm for multi-player bandits with collision-dependent rewards.
problem Stochastic multi-player multi-armed bandits with collision-dependent reward distributions.
method Error-Correction Collision Communication (EC3) algorithm.
result EC3 algorithm achieves optimal regret approaching centralized MP-MAB regret.
Study multiplayer bandits without collision info, achieving regret bounds.
problem Multiplayer bandits without collision info.
method Two feedback models considered; algorithms for both models.
result First theoretical guarantees for second model with square-root regret.
Develops machine learning classifiers for better centrality estimation in proton-nucleus and nucleus-nucleus collisions.
problem Direct measurement of centrality in A-A and p-A collisions is challenging due to limited data access.
method Uses machine learning techniques to classify centrality based on information from multiple detector subsystems.
result Improved centrality resolution can reduce volume fluctuations impact on physical observables.
Algorithm reduces regret in multi-player bandits with unknown collision rewards.
problem Reducing regret in multi-player multi-armed bandits with unknown collision rewards.
method Proposes an algorithm that combines a modified successive elimination strategy with a communication protocol to estimate suboptimality gaps and coordinate among players.
result Achieves logarithmic regret for the problem when collision reward is unknown.
New algorithm reduces regret in multi-player bandits with collision information.
problem Optimizing decisions in multi-player bandits with collision penalties.
method Developed an algorithm with optimal T \sqrt{T} T regret under collision announcements, and sublinear regret without collision info. result First T \sqrt{T} T -type regret guarantee for non-stochastic multi-player multi-armed bandits with collision information. A new algorithm reduces regret in multi-player bandits without collision info.
problem Decentralized multi-player multi-armed bandits with no collision info.
method EC-SIC algorithm using optimal error correction coding for reward statistics.
result Regret approaches that of centralized with collision info.
This study uses reinforcement learning to mitigate imminent collisions by controlling car speed and direction.
problem Mitigating imminent collisions on roads.
method Constructed a model using camera images to predict obstacle dynamics. Trained reinforcement learning policies to control braking and steering.
result Both reinforcement learning policies outperform a baseline policy, with the injury model-based policy showing the highest performance.
A new algorithm RESYNC for defenders against malicious attackers in multi-player bandits.
problem Malicious players colliding with cooperative players to prevent rewards.
method Decentralized and robust algorithm RESYNC for defenders.
result RESYNC algorithm is order-optimal, performing gracefully as the number of collisions increases.
Examining orbits ending in binary collisions for three equal masses under an inverse cube force.
problem Analyzing orbits ending in binary collisions for three equal masses under an inverse cube force.
method Reparametrizing orbits as geodesics on a negatively curved metric on a pair of pants.
result Visibility properties of negatively curved surfaces describe orbits beginning or ending in binary collisions.
A new algorithm reduces regret in multiplayer bandits with minimal communication.
problem Maximizing rewards in multiplayer multi-armed bandits with collisions.
method DPE (Decentralized Parsimonious Exploration) algorithm.
result Achieves the same regret as optimal centralized algorithms with less communication.
Bayesian deep learning predicts satellite collisions.
problem Space debris poses planetary risk.
method Bayesian deep learning with LSTM networks.
result Predicts conjunction event evolution with uncertainties.
New algorithms tackle adversarial multi-player bandits with forced-collision communication.
problem No-sensing adversarial multi-player multi-armed bandits (MP-MAB) problem.
method Adversary-Adaptive Collision-Communication (A2C2) algorithms, attackability-aware and unaware settings, information-theoretic tools, error-correction coding.
result Asymptotic attackability-dependent sublinear regret achieved, with or without knowing attackability.
New strategy achieves optimal regret without communication or collisions in multi-player bandit.
problem Cooperative multi-player stochastic multi-armed bandit with shared randomness.
method Combination of combinatorial approach to generalize geometric intuition.
result Achieves near-optimal regret i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) for any number of players and arms without collisions. Novel method decomposes configuration space for improved collision checking.
problem Improving collision checking in high-degree-of-freedom robot motion planning.
method Proposes a configuration space decomposition method to build a composite classifier.
result Composite classifier outperforms state-of-the-art single classifier methods.
New configuration space accounts for spatial linkages and collisions.
problem Modeling spatial linkages considering collisions.
method Constructed completed and simplified configuration spaces.
result New configuration spaces account for linkages touching each other.
Study shows how transformers classify symbols without naming them, proving a margin-versus-collision criterion.
problem How transformers classify symbols without naming them.
method Logistic classification analysis of transformer-kernel regime, colored collision graph.
result Decomposes learned predictor into ideal template-level classifier and finite-sample perturbation.
PUMML uses ML to remove pileup contamination in particle collisions.
problem Contamination from pileup affects the energy distribution of primary collisions.
method Developed a machine learning algorithm using convolutional neural networks.
result The PUMML algorithm effectively removes pileup distortion on various jet observables.
The configuration manifold M M M of a mechanical system consisting of two unconstrained rigid bodies in R n \mathbb{R}^n R n , n ≥ 1 n\geq 1 n ≥ 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.
problem Avoiding collisions for multiple points in a plane with unknown obstacles.
method Algebraic and topological tools for motion planning.
result New topological complexity for planar motion planning.
New algorithm syncs multi-player bandits by deliberately causing collisions.
problem Stochastic multiplayer multi-armed bandit problem with collisions.
method Decentralized algorithm that exploits communication between players to share information.
result Achieves performance of centralized algorithm with negligible cost.
Recovering manifold geometry from geodesic intersections.
problem Recovering the geometry of a Riemannian manifold from geodesic intersection lengths.
method Applying stitching data to solve the delayed collision data problem.
result Geometry of the manifold can be recovered from geodesic intersection lengths.
Zero-energy orbits in the Kepler-Heisenberg problem are self-similar and stratify into three families.
problem Determining the motion of a planet around a sun in the Heisenberg group.
method Analysis of the sub-Riemannian Hamiltonian and sub-Laplacian dynamics.
result Zero-energy orbits are self-similar and stratify into future collision, past collision, and quasi-periodic families.
New algorithm for multi-player bandits without needing lower bounds or scaling inversely.
problem Multi-player bandits without collision sensing information.
method Proposes a novel algorithm that circumvents two problems of existing algorithms.
result Proves a theoretical regret upper bound and shows superior performance in practice.
SafeCritic predicts safe trajectories for pedestrians and cyclists avoiding collisions.
problem Predicting safe trajectories for pedestrians and cyclists in urban environments.
method Generative adversarial networks and reinforcement learning to generate safe trajectories, evaluated by a Discriminator.
result Significant improvement over state-of-the-art models in safety classification.
A policy for near-optimal multi-player bandits with non-zero collision rewards.
problem Decentralized multi-player bandits with heterogeneous rewards and collisions.
method A policy achieving near-optimal regret in a non-communicative setting.
result Near order-optimal expected regret of O ( log 1 + δ T ) O(\log^{1 + δ} T) O ( log 1 + δ T ) for 0 < δ < 1 0 < δ< 1 0 < δ < 1 . Deep learning identifies QCD transition properties from particle spectra.
problem Detecting the nature of the QCD transition from heavy-ion collision data.
method Supervised deep learning with a convolutional neural network.
result A neural network acts as an 'EoS-meter' for QCD transition properties.
Unified approach detects traffic conflicts across various interactions.
problem Inconsistent detection of traffic conflicts across different interactions.
method Unified probabilistic approach decomposes conflicts into statistical learning tasks.
result Effective collision warnings across diverse datasets and environments.
Rolling systems limit to billiard models with no-slip collisions.
problem Understanding how rolling systems behave as billiard models with no-slip collisions.
method Showed that no-slip billiards arise as limits of non-holonomic rolling systems.
result Rolling systems limit to billiard models with no-slip collisions.
Counterexamples show Marchal's lemma fails for certain N-body systems.
problem Understanding when Marchal's lemma for N-body collisions holds or fails.
method Using metric geometry and the Jacobi-Maupertuis reformulation of mechanics, the team created counterexamples.
result Counterexamples demonstrate Marchal's lemma does not always apply to N-body systems.