Popcorn
发表于 2025-3-23 09:44:32
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MAL
发表于 2025-3-23 13:58:29
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虚假
发表于 2025-3-23 18:04:42
Mthuli Ncube,Nombulelo Gumata,Eliphas Ndouility that a complex temporal property is satisfied by finite traces of the system. Desired temporal properties of the system are expressed using a fragment of linear temporal logic, called .. We propose to use barrier certificates for computations of such lower bounds, which is computationally much
背心
发表于 2025-3-23 23:26:29
Mthuli Ncube,Nombulelo Gumata,Eliphas Ndoun probability distributions. We study .-differential privacy in the setting of labelled Markov chains. While the exact differences relevant to .-differential privacy are not computable in this framework, we propose a computable bisimilarity distance that yields a sound technique for measuring ., the
syring
发表于 2025-3-24 03:48:55
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enfeeble
发表于 2025-3-24 10:03:58
https://doi.org/10.1007/978-3-030-97212-7ation of a concurrent program to a series of verification tasks of sequential programs. Our approach is modular in the sense that each sequential verification task roughly corresponds to the verification of a single thread, with some additional information about the environment in which it operates.
知道
发表于 2025-3-24 10:41:12
https://doi.org/10.1007/978-3-030-97212-7he transition system with theory of Arrays, further complicates the problem by requiring inference and reasoning with universally quantified formulas. In this paper, we present a new algorithm, ., that extends IC3 to infer universally quantified invariants over the combined theory of LIA and Arrays.
平淡而无味
发表于 2025-3-24 16:14:09
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截断
发表于 2025-3-24 21:43:08
Alan Shiell,Hannah Jackson,Penelope Hawethe ., that is closed under intersection and linear transformations, and allows to check inclusion, equality, and emptiness. The main feature of DCPs lies in their ability to represent concisely polyhedra that can be expressed as combinations of simpler sets, which can overcome combinatorial explosi
AMPLE
发表于 2025-3-24 23:47:27
Formal Specification for Deep Neural Networksc of verification of neural networks. However, verification is most meaningful when performed with high-quality formal specifications. In this paper, we survey the landscape of formal specification for deep neural networks, and discuss the opportunities and challenges for formal methods for this domain.