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arXiv:2602.16181v1 Announce Type: new Abstract: Energy theft poses a significant threat to the stability and efficiency of smart grids, leading to substantial economic losses and...
arXiv:2602.16182v1 Announce Type: new Abstract: Autonomous inspection robots for monitoring industrial sites can reduce costs and risks associated with human-led inspection. However, accurate readings can...
arXiv:2602.16183v1 Announce Type: new Abstract: We study the \emph{Submodular Welfare Problem} (SWP), where items are partitioned among agents with monotone submodular utilities to maximize the...
arXiv:2602.16186v1 Announce Type: new Abstract: Operational disruptions in retail payments can induce behavioral responses that outlast technical recovery and may amplify liquidity stress. We propose...
arXiv:2602.16187v1 Announce Type: new Abstract: Robots executing iterative tasks in complex, uncertain environments require control strategies that balance robustness, safety, and high performance. This paper...
arXiv:2602.16188v1 Announce Type: new Abstract: LLM-for-time series (TS) methods typically treat time shallowly, injecting positional or prompt-based cues once at the input of a largely...
arXiv:2602.16189v1 Announce Type: new Abstract: Despite the continuous research and evolution of language models, they sometimes underperform previous versions. Existing approaches to overcome these challenges...
arXiv:2602.16191v1 Announce Type: new Abstract: We consider the eigenvalue problem $K x = \lambda x$. Our analysis focuses on the convergence rates of eigenvalue and...
arXiv:2602.16192v1 Announce Type: new Abstract: Driven by our mission of "uplifting the world with memory," this paper explores the design concept of "memory" that is...
arXiv:2602.16193v1 Announce Type: new Abstract: Several complex physical systems are governed by multi-scale partial differential equations (PDEs) that exhibit both smooth low-frequency components and localized...
arXiv:2602.16194v1 Announce Type: new Abstract: Permanent citizens' assemblies are ongoing deliberative bodies composed of randomly selected citizens, organized into panels that rotate over time. Unlike...
arXiv:2602.16196v1 Announce Type: new Abstract: Coordinating large populations of interacting agents is a central challenge in multi-agent reinforcement learning (MARL), where the size of the...
arXiv:2602.16197v1 Announce Type: new Abstract: Multimodal systems are vulnerable to partial or complete loss of input channels at deployment, which undermines reliability in real-world settings....
arXiv:2602.16198v1 Announce Type: new Abstract: Adaptation methods have been a workhorse for unlocking the transformative power of pre-trained diffusion models in diverse applications. Existing approaches...
arXiv:2602.16200v1 Announce Type: new Abstract: In this thesis, I refine our understanding as to what conclusions we can reach from coreference-based evaluations by expanding existing...
arXiv:2602.16201v1 Announce Type: new Abstract: Large language models (LLMs) are trained on web-scale corpora that exhibit steep power-law distributions, in which the distribution of knowledge...
arXiv:2602.16204v1 Announce Type: new Abstract: Neurochaos Learning (NL) has shown promise in recent times over traditional deep learning due to its two key features: ability...
arXiv:2602.16206v1 Announce Type: new Abstract: This paper proposes a nonplanar model predictive control (MPC) framework for autonomous vehicles operating on nonplanar terrain. To approximate complex...
arXiv:2602.16207v1 Announce Type: new Abstract: This article defines multi-twisted Goppa (MTG) codes as subfield subcodes of duals of multi-twisted Reed-Solomon (MTRS) codes and examines their...
arXiv:2602.16209v1 Announce Type: new Abstract: Neural operators offer an effective framework for learning solutions of partial differential equations for many physical systems in a resolution-invariant...
arXiv:2602.16213v1 Announce Type: new Abstract: This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure...
arXiv:2602.16216v1 Announce Type: new Abstract: Deep learning has improved automated electrocardiogram (ECG) classification, but limited insight into prediction reliability hinders its use in safety-critical settings....
arXiv:2602.16217v1 Announce Type: new Abstract: Surface extraction from implicit neural representations modelling a single class surface is a well-known task. However, there exist no surface...
arXiv:2602.16218v1 Announce Type: new Abstract: Bayesian quadrature is a probabilistic, model-based approach to numerical integration, the estimation of intractable integrals, or expectations. Although Bayesian quadrature...