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This work addresses the problem of quantifying fermionic non-Gaussianity, a key quantum resource beyond free-fermion (fermionic Gaussian) states.
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This paper addresses the problem of discovering partial differential equations (PDEs) from sparse and noisy observations, which requires both.
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This paper addresses the evolving security landscape when Large Language Model (LLM)-based web agents transition from single-agent systems (SAS) to.
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LEMUR addresses a key limitation in Multi-Objective Reinforcement Learning (MORL): the reliance on predefined, well-specified reward functions for each.
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OASIS addresses the challenging problem of reconstructing animatable 3D hand avatars from a single RGB image, where severe self-occlusion and highly.
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This paper addresses the challenging problem of predicting future susceptibility of social media users to automated "bot" interactions using longitudinal.
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The paper addresses a core challenge in large-scale vector similarity search: achieving high recall consistently across heterogeneous queries while.
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This paper addresses the longstanding challenge of performing quantum state tomography (QST) for inelastic electron scattering in transmission electron.
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This paper addresses a key challenge in phononic crystal cavities used for quantum information transfer: how to strongly couple them to microwave.
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This paper addresses the challenge of attributing AI-generated videos to their specific generative models, a task increasingly important given the rising.
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This paper addresses the longstanding challenge of quantitatively probing electron-electron interactions in one-dimensional (1D) systems, specifically.
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This paper addresses a critical but under-explored issue in continual learning (CL) systems: the degradation of out-of-distribution (OOD) detection.
