TY - CHAP AB - As autism spectrum disorder (ASD) is largely regarded as a neurodevelopmental condition, long-time consensus was that its hallmark features are irreversible. However, several studies from recent years using defined mouse models of ASD have provided clear evidence that in mice neurobiological and behavioural alterations can be ameliorated or even reversed by genetic restoration or pharmacological treatment either before or after symptom onset. Here, we review findings on genetic and pharmacological reversibility of phenotypes in mouse models of ASD. Our review should give a comprehensive overview on both aspects and encourage future studies to better understand the underlying molecular mechanisms that might be translatable from animals to humans. AU - Schroeder, Jan AU - Deliu, Elena AU - Novarino, Gaia AU - Schmeisser, Michael ED - Schmeisser, Michael ED - Boekers, Tobias ID - 634 T2 - Translational Anatomy and Cell Biology of Autism Spectrum Disorder TI - Genetic and pharmacological reversibility of phenotypes in mouse models of autism spectrum disorder VL - 224 ER - TY - CONF AB - A Rapidly-exploring Random Tree (RRT) is an algorithm which can search a non-convex region of space by incrementally building a space-filling tree. The tree is constructed from random points drawn from system’s state space and is biased to grow towards large unexplored areas in the system. RRT can provide better coverage of a system’s possible behaviors compared with random simulations, but is more lightweight than full reachability analysis. In this paper, we explore some of the design decisions encountered while implementing a hybrid extension of the RRT algorithm, which have not been elaborated on before. In particular, we focus on handling non-determinism, which arises due to discrete transitions. We introduce the notion of important points to account for this phenomena. We showcase our ideas using heater and navigation benchmarks. AU - Bak, Stanley AU - Bogomolov, Sergiy AU - Henzinger, Thomas A AU - Kumar, Aviral ED - Abate, Alessandro ED - Bodo, Sylvie ID - 633 SN - 978-331963500-2 TI - Challenges and tool implementation of hybrid rapidly exploring random trees VL - 10381 ER - TY - CONF AB - Memory-hard functions (MHFs) are hash algorithms whose evaluation cost is dominated by memory cost. As memory, unlike computation, costs about the same across different platforms, MHFs cannot be evaluated at significantly lower cost on dedicated hardware like ASICs. MHFs have found widespread applications including password hashing, key derivation, and proofs-of-work. This paper focuses on scrypt, a simple candidate MHF designed by Percival, and described in RFC 7914. It has been used within a number of cryptocurrencies (e.g., Litecoin and Dogecoin) and has been an inspiration for Argon2d, one of the winners of the recent password-hashing competition. Despite its popularity, no rigorous lower bounds on its memory complexity are known. We prove that scrypt is optimally memory-hard, i.e., its cumulative memory complexity (cmc) in the parallel random oracle model is Ω(n2w), where w and n are the output length and number of invocations of the underlying hash function, respectively. High cmc is a strong security target for MHFs introduced by Alwen and Serbinenko (STOC’15) which implies high memory cost even for adversaries who can amortize the cost over many evaluations and evaluate the underlying hash functions many times in parallel. Our proof is the first showing optimal memory-hardness for any MHF. Our result improves both quantitatively and qualitatively upon the recent work by Alwen et al. (EUROCRYPT’16) who proved a weaker lower bound of Ω(n2w/ log2 n) for a restricted class of adversaries. AU - Alwen, Joel F AU - Chen, Binchi AU - Pietrzak, Krzysztof Z AU - Reyzin, Leonid AU - Tessaro, Stefano ED - Coron, Jean-Sébastien ED - Buus Nielsen, Jesper ID - 635 SN - 978-331956616-0 TI - Scrypt is maximally memory hard VL - 10212 ER - TY - CONF AB - Signal regular expressions can specify sequential properties of real-valued signals based on threshold conditions, regular operations, and duration constraints. In this paper we endow them with a quantitative semantics which indicates how robustly a signal matches or does not match a given expression. First, we show that this semantics is a safe approximation of a distance between the signal and the language defined by the expression. Then, we consider the robust matching problem, that is, computing the quantitative semantics of every segment of a given signal relative to an expression. We present an algorithm that solves this problem for piecewise-constant and piecewise-linear signals and show that for such signals the robustness map is a piecewise-linear function. The availability of an indicator describing how robustly a signal segment matches some regular pattern provides a general framework for quantitative monitoring of cyber-physical systems. AU - Bakhirkin, Alexey AU - Ferrere, Thomas AU - Maler, Oded AU - Ulus, Dogan ED - Abate, Alessandro ED - Geeraerts, Gilles ID - 636 SN - 978-331965764-6 TI - On the quantitative semantics of regular expressions over real-valued signals VL - 10419 ER - TY - GEN AB - This book constitutes the refereed proceedings of the 9th InternationalWorkshop on Numerical Software Verification, NSV 2016, held in Toronto, ON, Canada in July 2011 - colocated with CAV 2016, the 28th International Conference on Computer Aided Verification. The NSV workshop is dedicated to the development of logical and mathematical techniques for the reasoning about programmability and reliability. ED - Bogomolov, Sergiy ED - Martel, Matthieu ED - Prabhakar, Pavithra ID - 638 SN - 0302-9743 TI - Numerical Software Verification VL - 10152 ER -