In this paper we study problems with critical and sandwich-type growth represented by − div (|∇u|p−2∇u + a(x)|∇u|q−2∇u) = λw(x)|u|s−2u + θB (x, u) in Ω, u = 0 on ∂Ω, where Ω ⊂ RN is a bounded domain with Lipschitz b...
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Core-collapse supernovae (CCSNe) are the terminal explosions of massive stars. While most massive stars explode as iron-core-collapse supernovae (FeCCSNe), slightly less massive stars explode as electron-capture super...
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Morphological segmentation of words is the process of dividing a word into smaller units called morphemes;it is tricky especially when a morphologically rich or polysynthetic language is under question. In this work, ...
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作者：
Childs, Andrew M.Wang, DaochenDepartment of Computer Science
Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland Applied Mathematics
Statistics and Scientific Computation Joint Center for Quantum Information and Computer Science University of Maryland
Quantum computers can sometimes exponentially outperform classical ones, but only for problems with sufficient structure. While it is well known that query problems with full permutation symmetry can have at most poly...
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作者：
Cangiani, AndreaDong, ZhaonanGeorgoulis, Emmanuil H.SISSA
International School for Advanced Studies via Bonomea 265 TriesteI-34136 Italy Inria
2 rue Simone Iff Paris75589 France CERMICS
Ecole des Ponts Marne-la-Vallée77455 France The
Maxwell Institute for Mathematical Sciences Department of Mathematics School of Mathematical and Computer Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom Department of Mathematics
School of Applied Mathematical and Physical Sciences National Technical University of Athens Zografou15780 Greece IACMFORTH
Greece
We present a new residual-type energy-norm a posteriori error analysis for interior penalty discontinuous Galerkin (dG) methods for linear elliptic problems. The new error bounds are also applicable to dG methods on m...
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Recent calculations in both flat and de Sitter spacetimes have highlighted a tension between the decoupling of high-energy physics from low-energy degrees of freedom and the expectation that quantum systems decohere d...
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To develop low-power, non-volatile computing-in-memory device using ferroelectric transistor technologies, ferroelectric channel materials with scaled thicknesses are required. Two-dimensional semiconductors, such as ...
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We consider the problem of the absence of backscattering in the transport of Manakov solitons on a line. The concept of transparent boundary conditions is used for modeling the reflectionless propagation of Manakov ve...
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We consider the problem of the absence of backscattering in the transport of Manakov solitons on a line. The concept of transparent boundary conditions is used for modeling the reflectionless propagation of Manakov vector solitons in a one-dimensional domain. Artificial boundary conditions that ensure the absence of backscattering are derived and their numerical implementation is demonstrated.
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective...
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective...
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