Murphy Berzish

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The organization of neural systems reflects the specific complexities of the physical environments in which they operate. In order to address this relationship more directly, there is increasing interest in testing real-time neural simulations that interface with the physical world. We describe a new simulation approach that allows us to run large,(More)
In this paper we present a solver for a many-sorted first-order quantifier-free theory T w,bv of string equations, string length represented as bit-vectors, and bit-vector arithmetic aimed at formal verification, automated testing, and security analysis of C/C++ applications. Our key motivation for building such a solver is the observation that existing(More)
In recent years there has been considerable interest in theories over string equations, length function, and string-number conversion predicate within the formal verification and computer security communities. SMT solvers for these theories, such as Z3str2, CVC4, and S3, are of immense practical value in exposing security vulnerabilities in string-intensive(More)
We present a new string SMT solver, Z3str3, that is faster than its competitors Z3str2, Norn, CVC4, S3, and S3P over a majority of three industrial-strength benchmarks, namely Kaluza, PISA, and IBM AppScan. Z3str3 supports string equations, linear arithmetic over length function, and regular language membership predicate. The key algorithmic innovation(More)
Models of neural systems often use idealized inputs and outputs , but there is also much to learn by forcing a neural model to interact with a complex simulated or physical environment. Unfortunately, sophisticated interactions require models of large neural systems, which are difficult to run in real time. We have prototyped a system that can simulate(More)
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