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arXiv:2603.14028v1 Announce Type: new Abstract: A hybrid digital twin framework is presented for bridge condition monitoring using existing traffic cameras and weather APIs, reducing reliance …
arXiv:2603.13237v1 Announce Type: new Abstract: High-frequency banking environments face a critical trade-off between low-latency fraud detection and the regulatory explainability demanded by GDPR. Traditional rule-based …
arXiv:2603.13625v1 Announce Type: new Abstract: Twitter (now X) has become an important source of social media data for situational awareness during crises. Crisis informatics research …
arXiv:2603.13710v1 Announce Type: new Abstract: Home-based interventions like parent-child shared reading provide a cost-effective approach for supporting children with autism spectrum disorder (ASD). However, analyzing …
arXiv:2603.13673v1 Announce Type: new Abstract: Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage detection …
arXiv:2603.13260v1 Announce Type: new Abstract: Knowledge Distillation (KD) can transfer the reasoning abilities of large models to smaller ones, which can reduce the costs to …
arXiv:2603.13344v1 Announce Type: new Abstract: The prevailing paradigm in Automated Heuristic Design (AHD) typically relies on the assumption that a single, fixed algorithm can effectively …
arXiv:2603.13793v1 Announce Type: new Abstract: Low resource languages present unique challenges for natural language processing due to the limited availability of digitized and well structured …
arXiv:2603.13230v1 Announce Type: new Abstract: Slang interpretation has been a challenging downstream task for Large Language Models (LLMs) as the expressions are inherently embedded in …
arXiv:2603.13773v1 Announce Type: new Abstract: Web information extraction (WIE) is the task of automatically extracting data from web pages, offering high utility for various applications. …
arXiv:2603.13998v1 Announce Type: new Abstract: While graph-derived signals are widely used in tabular learning, existing studies typically rely on limited experimental setups and average performance …
arXiv:2603.13266v1 Announce Type: new Abstract: As large language models (LLMs) continue to grow in size, their abilities to tackle complex tasks have significantly improved. However, …