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This paper addresses the challenge of continually personalizing neural surrogate models for cardiac electrophysiology (EP) simulations under sequentially arriving, unlabeled clinical data sources
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This manuscript compiles a carefully curated set of counterintuitive and challenging problems in discrete probability, along with fully worked solutions
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This paper addresses two core limitations of traditional prediction markets — they only aggregate beliefs without revealing underlying evidence or reasoning, and they require an event with a known e…
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This paper addresses the well-known conditioning challenges in the numerical solution of the Electric Field Integral Equation (EFIE) using the Boundary Element Method (BEM)
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This paper investigates the extent to which chord-symbol sequences—compact, interpretable symbolic representations of harmony—carry genre identity in multi-genre chord prediction
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This paper presents a detailed case study demonstrating how AI-assisted paired engineering combined with two key safeguards— a precise executable YAML specification as a single source of truth and …
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M3Exam addresses the lack of realistic benchmarks for evaluating multimodal conversational memory in user-agent interactions where heterogeneous multimodal data accumulate over multiple sessions
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This paper tackles the critical problem of poor generalization in deepfake speech detection when models trained on proxy resynthesized data (CoRS) encounter real-world synthesized speech (CodecFake…
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This paper addresses the fundamental problem of recovering hidden influence networks from observed cascade data sequences, such as disease spread, product adoption, or information diffusion
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This paper addresses the computational bottleneck in robot co-design, the joint optimization of robot morphology and control policies for locomotion tasks
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This paper addresses the challenging NLP problem of detecting Turkish idiomatic light verb constructions (LVCs), which are multiword expressions with ambiguous surface forms that can have either id…
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This paper identifies and rigorously studies sycophantic praise—a distinct alignment issue in large language models (LLMs) where models produce excessive or unwarranted praise towards users
