arXiv:2603.03302v1 Announce Type: new Abstract: Effective knowledge management is critical for preserving institutional expertise and improving the efficiency of workforce training in state transportation agencies. Traditional approaches, such as static documentation, classroom-based instruction, and informal mentorship, often lead to fragmented knowledge transfer, inefficiencies, and the gradual loss of expertise as senior engineers retire. Moreover, given the enormous volume of technical manuals, guidelines, and research reports maintained by these agencies, it is increasingly challenging for engineers to locate relevant […]

Ver mais

arXiv:2601.09724v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in consequential decision-making settings, yet their robustness to benign prompt variation remains underexplored. In this work, we study whether LLMs maintain consistent ethical judgments across logically equivalent but syntactically different prompts, focusing on variations involving negation and conditional structure. We introduce Syntactic Framing Fragility (SFF), a robustness evaluation framework that isolates purely syntactic effects via Logical Polarity Normalization (LPN), enabling direct comparison of decisions across positive […]

Ver mais

arXiv:2508.06483v2 Announce Type: replace-cross Abstract: We study the self-normalized concentration of vector-valued stochastic processes. We focus on bounds for “sub-$psi$” processes, a well-known and quite general class of process that encompasses a wide variety of well-known tail conditions (including sub-exponential, sub-Gaussian, sub-gamma, sub-Poisson, and several heavy-tailed settings without a moment generating function such as symmetric or bounded 2nd or 3rd moments). Our results recover and generalize the influential bound of de la Pe~na et al. [20] (proved again […]

Ver mais

International shipping produces approximately 3% of global greenhouse gas emissions, yet voyage routing remains dominated by heuristic methods. We present PIER (Physics-Informed, Energy-efficient, Risk-aware routing), an offline reinforcement learning framework that learns fuel-efficient, safety-aware routing policies from physics-calibrated environments grounded in historical vessel tracking data and ocean reanalysis products, requiring no online simulator. Validated on one full year (2023) of AIS data across seven Gulf of Mexico routes (840 episodes per method), PIER reduces mean CO2 emissions by […]

Ver mais

arXiv:2603.28945v1 Announce Type: new Abstract: Increasing penetration of electric vehicles, heat pumps, and rooftop photovoltaics is creating thermal and voltage stress in low-voltage distribution grids. This work links three German energy transition pathways (2025-2045) with state estimation performance requirements, evaluated on two SimBench reference networks across three equipment quality levels (good, medium, poor) and three VDE Forum Netztechnik/Netzbetrieb (VDE FNN) measurement constellations that differ in the availability of transformer and feeder-level instrumentation. Congestion is caused exclusively by transformer […]

Ver mais

arXiv:2603.18483v1 Announce Type: new Abstract: In the present paper we study the performance of linear denoisers for noisy data of the form $mathbf{x} + mathbf{z}$, where $mathbf{x} in mathbb{R}^d$ is the desired data with zero mean and unknown covariance $mathbf{Sigma}$, and $mathbf{z} sim mathcal{N}(0, mathbf{Sigma}_{mathbf{z}})$ is additive noise. Since the covariance $mathbf{Sigma}$ is not known, the standard Wiener filter cannot be employed for denoising. Instead we assume we are given samples $mathbf{x}_1,dots,mathbf{x}_n in mathbb{R}^d$ from the true distribution. […]

Ver mais

arXiv:2601.02366v1 Announce Type: new Abstract: Graph-based recommendation has achieved great success in recent years. The classical graph recommendation model utilizes ID embedding to store essential collaborative information. However, this ID-based paradigm faces challenges in transferring to a new domain, making it hard to build a pre-trained graph recommendation model. This phenomenon primarily stems from two inherent challenges: (1) the non-transferability of ID embeddings due to isolated domain-specific ID spaces, and (2) structural incompatibility between heterogeneous interaction graphs across […]

Ver mais