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This paper studies numerical solution strategies for the ghost-Gutzwiller (ghost-GA) approximation applied to Mott insulating phases of the single-band.
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This paper challenges the dominant assumption in reinforcement learning (RL) that pretraining the Q-function alongside a pretrained policy improves sample.
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To comply with strict privacy regulations like GDPR, the authors propose PACE, a novel Pseudo-Anonymized Contextual patch Extraction algorithm.
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This paper addresses the critical bottleneck in inversion-based watermarking for diffusion models, where reconstructing the initial noise latent from a.
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This paper addresses the growing need in face anti-spoofing (FAS) systems to go beyond simple bona fide/spoof classification and provide attack semantics.
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This paper tackles the challenging problem of privately releasing continuous functions representing personal data (e.g., trajectories or 1D density.
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GraphQAG addresses the problem of generating high-quality question-answer (QA) pairs from long documents, where important knowledge is distributed across.
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HumanCLAW addresses the fundamental challenge of evaluating whether vision-language models (VLMs) can exhibit action intelligence by controlling a.
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This paper addresses the challenge of learning reliable mechanistic models for Chikungunya virus (CHIKV) transmission dynamics from noisy, incomplete, and.
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This paper addresses a key challenge in quantum error correction: accurately estimating logical error rates at extremely low physical error probabilities.
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This paper addresses the mathematical analysis of interface and initial-boundary value problems for the Barenblatt-Zheltov-Kochina pseudoparabolic.
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This paper investigates how increasing reliance on large language models (LLMs) to assist in writing shapes population-level linguistic variation, a.
