Develops a neural network for precise vehicle trajectory prediction.
problem Improving situational awareness in vehicular networks for safety applications.
method Two-layer neural network predicting vehicle parameters and trajectory points.
result Significantly improved prediction accuracy compared to existing methods.
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.
Space debris warnings follow a predictable pattern, allowing timely satellite maneuvers.
problem Estimating when fresh information about space debris will arrive.
method Statistical learning model of the message arrival process, specifically a Bayesian Poisson process.
result The average prediction error for the next message arrival time is smaller than baseline predictions.
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.
This paper presents first steps toward robust models for crisis prediction. We conduct a horse race of conventional statistical methods and more recent machine learning methods as early-warning models. As individual models are in the literature most often built in isolation of other methods, the exercise is of high rel…
New method avoids surface self-collision in geometric optimization.
problem Avoiding self-collision in surface optimization.
method Developed a numerical framework using tangent-point energy and fractional Sobolev inner product.
result Successfully accelerated collision avoidance scheme for triangle meshes.
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.
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.
New model predicts financial market abnormalities using stock index uncertainties.
problem Forecasting abnormal financial fluctuations in the market.
method Quantitative analysis of mean and volatility uncertainties, constructing early warning indicators.
result Established a new abnormal fluctuations warning model.
Study shows negative stock returns after Moroccan companies issue profit warnings.
problem Impact of profit warnings on stock returns in Moroccan market.
method Event study methodology, analyzing Casablanca Stock Exchange, 2009-2016.
result Negative average abnormal return after profit warning announcements, greater for qualitative than quantitative warnings.
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.
Novel approach detects early warning indicators in complex systems.
problem Detecting abrupt transitions in complex systems.
method Directed anisotropic diffusion map and latent stochastic dynamical systems.
result Early warning indicators can detect tipping points in state transitions.
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.
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.
Study proposes a new early-warning framework for high-dimensional complex systems.
problem Predicting critical transitions in complex systems like epileptic seizures.
method Integrates manifold learning with stochastic dynamical system modeling, using Schrödinger bridge theory.
result Demonstrates higher sensitivity and robustness in epilepsy prediction.
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…
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. Quantifier elimination enhances safety assurance of deep neural networks.
problem Rigorously assure safe operation of sophisticated, autonomous systems like DNNs.
method Use quantifier elimination as a formal method to enhance safety assurance.
result Initial results show QE can precisely analyze robustness of DNNs.
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.
The refugee crisis is perhaps the single most challenging problem for Europe today. Hundreds of thousands of people have already traveled across dangerous sea passages from Turkish shores to Greek islands, resulting in thousands of dead and missing, despite the best rescue efforts from both sides. One of the main reaso…
TDA detects financial bubbles through early warning signals.
problem Detecting financial bubbles early.
method Using Log-Periodic Power Law Singularity (LPPLS) model to fit financial time series data.
result TDA generates early warning signals when LPPLS model fits the data.
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.
Study improves early warning models for currency and stock market crises.
problem Predicting currency and stock market crises.
method Synthetic review and comparison of early warning models, focusing on crisis identifications and predictive models.
result SWARCH model with elastic thresholding methodology most accurately classifies crisis observations.
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.
This study uses high-frequency data to identify early warning signals for bank crises.
problem Identifying early warning signals for impending bank crises.
method Constructing multiple recurrence networks (MRNs) based on high-frequency stock returns to monitor nonlinear dynamics.
result Key indicators of MRNs, particularly average mutual information, provide valuable insights into periods of extreme volatility.
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.
Machine learning detects regime shifts in online game-experiments with high accuracy.
problem Detecting regime shifts in online social systems.
method Gradient-boosted decision trees with memory-retaining features.
result Significantly outperforms standard early warning indicators.
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.
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.
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.