This paper presents a spiking neural network architecture for generating polar trajectories using a winner-take-all architecture and accessory populations, achieving significant reductions in step tim…

A fully unsupervised Fast-Slow DSVDD detector is presented for continuous State-of-Polarization monitoring on subsea cables, identifying five confirmed trawler contacts and additional corroborated eve…

The paper proposes a new paradigm for trajectory forecasting by training models with metric-agnostic probabilistic objectives and optimizing metrics as downstream tasks.

This paper proposes a method to describe dynamical systems using molecular and reaction concepts, making three key decisions: number of places, species determination, and transitions.

The paper introduces STEP, a self-supervised method that learns interpretable, structured embeddings for progressive time series, allowing the state progression and active mode to be read out using po…

This paper shows that finding the minimum geodetic set in chordal graphs is fixed parameter tractable, implying a polynomial-time algorithm for k-trees. It also proves that the problem is NP-hard on i…

This paper introduces Path Consistency Scoring (PCS), a framework that evaluates router geolocation as a path-level consistency problem and produces a path consistency score based on a Hidden Markov M…

This paper proposes a motion planning framework for UAV navigation in non-convex urban air corridors using a mixed-integer tracking model predictive control formulation.

The paper introduces Compass, an expert-guided LLM agent framework that successfully extracts and integrates thousands of previously inaccessible marine lead records from vast corpora of scientific pa…

This paper proposes a hierarchical analytical framework to characterize region-level latency differences in Low-Earth orbit satellite Internet using Starlink RTT measurements.

The paper proposes HTP, a novel framework that leverages Large Language Models (LLMs) to first generate abstract travel patterns and then synthesize realistic GPS points, significantly improving traje…

This paper compares the performance of open-loop and closed-loop filters in inertial navigation systems using simulations.

This paper proposes STOIC, a framework that integrates graph-based forecasting with tabular foundation models for uncertainty quantification in energy demand forecasting using spatial-temporal graph n…

The paper introduces 'probe trajectories'—a continuous measure of a concept's probability across a model's reasoning process—to improve the monitoring of Large Reasoning Models' future behavior, showi…

This paper proves convergence of belief propagation algorithms for multipath data association to a unique fixed point.

The paper proposes a novel active learning framework using Linearized Optimal Transport to strategically select measurement timepoints, thereby minimizing uncertainty when inferring continuous probabi…

This paper models the changes in graph adjacency matrices over time as a low-rank matrix and develops a method for jointly interpolating signals and estimating graph adjacency matrices using this assu…

The paper proposes BitTP, a lightweight bitlinear architecture that quantizes LLM-based trajectory predictors to 1.58-bit weights while keeping activations full-precision, enabling high-performance de…