We present a new independent pipeline for the Cosmic Microwave Background bispectrum estimation of primordial non-Gaussianity and release a public code for constraining bispectrum shapes of interest based on the Planc...
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We present a new independent pipeline for the Cosmic Microwave Background bispectrum estimation of primordial non-Gaussianity and release a public code for constraining bispectrum shapes of interest based on the Planck 2018 temperature and polarization data. The estimator combines the strengths of the conventional Komatsu-Spergel-Wandelt and modal estimators at the cost of increased computational complexity, which has been made manageable through intensive algorithmic and implementation optimization. We also detail some methodological advances in numerical integration over a tetrapyd—domain where the bispectrum is defined on—via new quadrature rules. The pipeline has been validated both internally and against Planck. As a proof-of-concept example, we constrain some highly oscillatory models that were out of reach in conventional analyses using a targeted basis with a fixed oscillation frequency, and no significant evidence for primordial non-Gaussianity of these shapes is found. The methodology and code developed in this work will be directly applicable to future surveys where we expect a notable boost in sensitivity.
Confining energetic ions such as alpha particles is a prime concern in the design of stellarators. However, directly measuring alpha confinement through numerical simulation of guiding-center trajectories has been con...
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Square and round magnetic nano-dots of varying dimensions exhibit a large amount of possible magnetization reversal processes, from domain wall nucleation and propagation to multi-vortex states. Clustering such single...
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This study examines double halide perovskites Rb2AgBiX6 (X = Cl, Br, I) using first-principles calculations, revealing their potential for various applications. The compounds demonstrate mechanical and thermodynamic s...
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In designing stellarators, any design decision ultimately comes with a trade-off. Improvements in particle confinement, for instance, may increase the burden on engineers to build more complex coils, and the tightenin...
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Advances in deep learning have significantly aided protein engineering in addressing challenges in industrial production,healthcare,and environmental *** review frames frequently researched problems in protein underst...
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Advances in deep learning have significantly aided protein engineering in addressing challenges in industrial production,healthcare,and environmental *** review frames frequently researched problems in protein understanding and engineering from the perspective of deep *** provides a thorough discussion of representation methods for protein sequences and structures,along with general encoding pipelines that support both pre‐training and supervised learning *** summarize state‐of‐the‐art protein language models,geometric deep learning techniques,and the combination of distinct approaches to learning from multi‐modal biological ***,we outline common downstream tasks and relevant benchmark datasets for training and evaluating deep learning models,focusing on satisfying the particular needs of protein engineering applications,such as identifying mutation sites and predicting properties for candidates'virtual *** review offers biologists the latest tools for assisting their engineering projects while providing a clear and comprehensive guide for computer scientists to develop more powerful solutions by standardizing problem formulation and consolidating data *** research can foresee a deeper integration of the communities of biology and computer science,unleashing the full potential of deep learning in protein engineering and driving new scientific breakthroughs.
We study a continuous-time expected utility maximization problem in which the investor at maturity receives the value of a contingent claim in addition to the investment payoff from the financial market. The investor ...
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We investigate the integrability of polynomial vector fields through the lens of duality in parameter spaces. We examine formal power series solutions annihilated by differential operators and explore the properties o...
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Traditional supervised learning aims to learn an unknown mapping by fitting a function to a set of input-output pairs with a fixed dimension. The fitted function is then defined on inputs of the same dimension. Howeve...
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We study the Carrollian limit of the (general) quadratic gravity in four dimensions. We find that in order for the Carrollian theory to be a modification of the Carrollian limit of general relativity, the parameters i...
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