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Separation logic is a popular approach for specifying properties of recursive mutable data structures. Several existing systems verify a subclass of separation logic specifications using static analysis techniques. Checking data structure specifications du ...
Autonomous mobile robots are promising tools for operations in environments that are difficult to access for humans. When these environments are dynamic and non-deterministic, like in collapsed buildings, the robots must coordinate their actions and the us ...
New extensions to programming languages are constantly being proposed. But implementing these extensions usually turns out to be a very difficult and expensive task, since conventional compilers often lack extensibility and reusability. In this paper we pr ...
A major problem for writing extensible software arises when recursively defined datatypes and operations on these types have to be extended simultaneously without modifying existing code. This paper introduces Extensible Algebraic Datatypes with Defaults w ...
This paper considers the problem of adaptive filtering in the presence of uncertainties in the regression data. A recursive procedure is derived that is based on solving local optimization problems that attemps to alleviate the worst-case effect of data un ...
Join calculus, usually presented as a process calculus, is suitable as a foundation of both sequential and concurrent programming. We give a new operational semantics of join calculus, expressed as a reduction system with a single reduction rule similar to ...
Separation logic is a popular approach for specifying properties of recursive mutable data structures. Several existing systems verify a subclass of separation logic specifications using static analysis techniques. Checking data structure specifications du ...
This paper deals with the important problem of parameter estimation in the presence of bounded data uncertainties. Its recent closed-form solution in leads to more meaningful results than alternative methods (e.g., total least-squares and robust estimation ...
This paper proposes an iterative scheme for worst-case parameter estimation in the presence of bounded model uncertainties. The algortihm distinguished itself from other estmation schemes, such as errors-in-variables and Hoo methors, in that it leads to le ...
Deals with the problem of worst-case parameter estimation in the presence of bounded uncertainties in a linear regression model. The problem has been formulated and solved in Chandrasekaran et al. (1997). It distinguishes itself from other estimation schem ...
