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 Test-Time Training for Supervised Causal Learning (TTT-SCL), a novel framework that dynamically generates training data aligned with specific test instances to significantly improve…

A consensus anomaly detection framework was used to identify atypical malaria transmission patterns in Ghana (2014-2023), revealing spatial distinctions between anomaly burden and frequency.

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 introduces StellarTTS, a mobile-optimized non-autoregressive text-to-speech framework with sparse temporal embeddings and a semantic-aware codec, achieving lower latency and stronger robust…

This paper investigates the feasibility of using 5G networks for teleoperated driving (ToD) and identifies the impact of bandwidth and TDD frame structure on ToD performance.

This paper proposes a source-free continual test-time adaptation methodology using a controlled teacher adaptation and class prototype estimation.

This paper is an invited commentary on Ying Cheng's Psychometrika focus article comparing test fairness and algorithmic fairness. The commentary discusses the distinction between equality and equity a…

This paper analyzes the near-field to far-field transition for an elevated access point with downward tilt, showing that it can occur more than once along a user's ground path, leading to an unexpecte…

The paper presents teLLMe, a system for exploratory causal analysis of urban driving datasets using structured event tables, causal structure learning, and query-specific effect estimation.

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 introduces SafetyDrift, a predictive model that forecasts when AI agents will violate safety protocols by analyzing the cumulative risk across sequences of individually safe actions.

The paper establishes the first theoretical framework for analyzing the learnability of Test-Time Adaptation (TTA) under non-stationary data streams by introducing Recovery Complexity, which quantifie…

ETSI ISG MAT report compares 3GPP-specified and candidate downlink multiple access techniques for 3GPP-based 6G systems, assessing their spectral efficiency and implications.

IstGPT introduces a novel LLM-based framework for real-time, fine-grained anomaly detection in complex industrial cyber-physical systems, achieving state-of-the-art performance across multiple benchma…

The paper introduces QuITE, a plug-and-play embedding module that uses learnable query tokens to effectively embed irregular multivariate time series data into latent representations compatible with e…