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.
Hybrid approach combines transformer and Bayesian filtering for robust multiple particle tracking.
Enhances particle filters with neural augmentation for multi-sub-state tracking.
CMS uses machine learning to improve particle flow reconstruction.
EggNet reconstructs particle tracks from hits using evolving graph attention networks.
Graph neural networks improve charged particle tracking on FPGAs.
Jointly estimates flow fields and particle properties from Lagrangian data.
In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interact…
New method uses cluster shapes to improve track finding in particle collisions.
NBF combines deep learning with classical filtering for better belief tracking.
Accurate and robust tracking of surrounding road participants plays an important role in autonomous driving. However, there is usually no prior knowledge of the number of tracking targets due to object emergence, object disappearance and false alarms. To overcome this challenge, we propose a generic vehicle tracking fr…
One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hi…
Online convex optimization is a sequential prediction framework with the goal to track and adapt to the environment through evaluating proper convex loss functions. We study efficient particle filtering methods from the perspective of such a framework. We formulate an efficient particle filtering methods for the non-st…
Generative Adversarial Networks generate PXD background noise efficiently.
The ability to track a moving vehicle is of crucial importance in numerous applications. The task has often been approached by the importance sampling technique of particle filters due to its ability to model non-linear and non-Gaussian dynamics, of which a vehicle travelling on a road network is a good example. Partic…
We study the tracking problem, namely, estimating the hidden state of an object over time, from unreliable and noisy measurements. The standard framework for the tracking problem is the generative framework, which is the basis of solutions such as the Bayesian algorithm and its approximation, the particle filters. Howe…
A computer vision approach improves neutral particle detection in particle flow algorithms.
One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hi…
The automatic reconstruction of three-dimensional particle tracks from Active Target Time Projection Chambers data can be a challenging task, especially in the presence of noise. In this article, we propose a non-parametric algorithm that is based on the idea of clustering point triplets instead of the original points.…
Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc. To apply particle filtering in practice, a critical challenge is to construct probabilistic system models, especially for systems with complex dynamics or r…
Improved particle-flow event reconstruction for future colliders using scalable neural networks.
A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspa…
We propose a new Bayesian tracking and parameter learning algorithm for non-linear non-Gaussian multiple target tracking (MTT) models. We design a Markov chain Monte Carlo (MCMC) algorithm to sample from the posterior distribution of the target states, birth and death times, and association of observations to targets, …
MWGraD solves multi-objective distributional optimization using particle-based gradient descent.
For regular particle filter algorithm or Sequential Monte Carlo (SMC) methods, the initial weights are traditionally dependent on the proposed distribution, the posterior distribution at the current timestamp in the sampled sequence, and the target is the posterior distribution of the previous timestamp. This is techni…
Bayesian methods improve tracking multiple objects through dynamic dependencies.
We present a frame-invariant method for detecting coherent structures from Lagrangian flow trajectories that can be sparse in number, as is the case in many fluid mechanics applications of practical interest. The method, based on principles used in graph coloring and spectral graph drawing algorithms, examines a measur…
Object tracking is an ubiquitous problem that appears in many applications such as remote sensing, audio processing, computer vision, human-machine interfaces, human-robot interaction, etc. Although thoroughly investigated in computer vision, tracking a time-varying number of persons remains a challenging open problem.…
Paper introduces a Bayesian nonparametric approach for tracking multiple objects with spawning events.
PFPN uses particle filtering to improve character control in physics-based simulations.
New method optimizes multiple objectives using particle dynamics and gradient flow.
Gaussian Process Hydrodynamics approximates fluid flow equations using probabilistic kernels.
New sampling-based approach for filtering problems using multiplicative Gaussian functions.
EnSF improves accuracy in tracking high-dimensional nonlinear systems.
Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In th…
We determine the sample complexity of pure exploration bandit problems with multiple good answers. We derive a lower bound using a new game equilibrium argument. We show how continuity and convexity properties of single-answer problems ensures that the Track-and-Stop algorithm has asymptotically optimal sample complexi…
New summary measures reveal geometric structure in weighted measures on manifolds.
HS-MoE selects sparse experts using adaptive priors and data-adaptive gating.
This paper presents a fast Bayesian filtering technique for state estimation.
New model predicts particle precipitation from magnetosphere to ionosphere.
Researchers develop multi-agent systems for quadcopters to collaborate in missions.
Study material evolution using groupoids to track intrinsic properties.
Study examines CSO algorithm for 3D swarming and tracking multiple targets.
Adaptive ML learns complex time-varying systems without new data.
Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics. However, this problem is also #P-complete, which leaves little hope for finding an exact solution that can be computed efficiently. While the problem admits a fully …
Factorial moments are convenient tools in particle physics to characterize the multiplicity distributions when phase-space resolution () becomes small. They include all correlations within the system of particles and represent integral characteristics of any correlation between these particles. In this letter, we sh…
Model tracks structural changes in Brownian particle configurations on a sphere.
Improved particle approximation for mean-field neural networks.
Develops an inverse particle filter for cognitive systems.