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WEQA addresses the challenging problem of answering natural language questions about wearable health data, which is continuous, high-dimensional, and temporally complex—unlike typical text data tha…
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This paper addresses the clinical and logistical challenges of multiphasic contrast-enhanced CT (CECT) for abdominal disease diagnosis, including risks of nephropathy, increased workload, and limit…
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Cache to the Future (CttF) addresses the challenge of maintaining access to web-hosted knowledge during internet blackouts caused by infrastructure failures or state censorship
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This paper addresses the challenging problem of exact classification of Canadian 10-digit Harmonized Tariff Schedule (HTS) codes from short, ambiguous product descriptions typical in maritime logis…
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This paper investigates the computational challenges of belief contraction in linear temporal logic (LTL), focusing on the construction and verification of epistemic preference relations that under…
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This paper addresses a fundamental challenge in quantum materials research — extracting and characterizing multi-spin dynamical correlations beyond conventional two-spin correlations accessible via …
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This paper addresses the challenge of improving large language model (LLM) reasoning capabilities through reinforcement learning (RL) when sparse reward signals are insufficient due to limited base…
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This paper addresses the increasingly cross-channel nature of SMS fraud, where a fraudulent text message directs a user to a webpage and risk depends on the joint consistency of the SMS claim, webp…
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This paper investigates the complex challenges and opportunities for integrating AI technologies into Emergency Medical Services (EMS), a high-stakes healthcare domain characterized by distributed …
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This paper addresses the emerging shift in web user tracking from traditional third-party tracking techniques to first-party tracking (FPT) and server-side tracking (SST)
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This paper addresses the gap between human dexterous grasping capabilities and multi-fingered robot grasping generality by directly learning from large-scale, in-the-wild human grasp data
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KVEraser addresses the computational challenge of post-hoc localized context erasing in long-context large language model (LLM) inference
