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Titlebook: Numerical Software Verification; 12th International W Majid Zamani,Damien Zufferey Conference proceedings 2019 Springer Nature Switzerland

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書目名稱Numerical Software Verification
副標(biāo)題12th International W
編輯Majid Zamani,Damien Zufferey
視頻videohttp://file.papertrans.cn/670/669205/669205.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Numerical Software Verification; 12th International W Majid Zamani,Damien Zufferey Conference proceedings 2019 Springer Nature Switzerland
描述.This book constitutes the proceedings of the 12th International Workshop on Numerical Software Verification, NSV 2019, held in New York City, NY, USA, in July 2019 - colocated with the International Conference on Computer Aided Verification, CAV 2019...The 5 full papers presented together with 2 short papers, 3 abstracts of invited talks, and 2 tutorial papers were carefully reviewed and selected from numerous submissions...The NSV 2017 workshop is dedicated to the development of logical and mathematical techniques for the reasoning about programmability and reliability..
出版日期Conference proceedings 2019
關(guān)鍵詞artificial intelligence; decision theory; learning algorithms; model checking; numerical simulation; sema
版次1
doihttps://doi.org/10.1007/978-3-030-28423-7
isbn_softcover978-3-030-28422-0
isbn_ebook978-3-030-28423-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

書目名稱Numerical Software Verification影響因子(影響力)




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Rémi Delmas,Thomas Loquen,Josep Boada-Bauxell,Mathieu Carton
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Luca Geretti,Sanja ?ivanovi? Gonzalez,Pieter Collins,Davide Bresolin,Tiziano Villa
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ered aromatic compound. NMR titration experiments and paramagnetic relaxation enhancement observation of PAC3 homodimer were performed with synthesized TB1 and its molecular probe, respectively. The results of the above NMR studies and additional .in silico. docking studies suggested that TB1 promot
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Reinforcement Learning and Formal Requirementsled limit-reachability, that bridges this gap by faithfully translating logic-based requirements into the scalar reward form needed in model-free reinforcement learning. This technique enables the synthesis of controllers that maximize the probability to satisfy given logical requirements using off-
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