116,143 research outputs found

    Linear and Affine Typing of Continuation-Passing Style

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    Submitted for the degree of Doctor of Philosophy, Queen Mary, University of Londo

    On the Relation of Interaction Semantics to Continuations and Defunctionalization

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    In game semantics and related approaches to programming language semantics, programs are modelled by interaction dialogues. Such models have recently been used in the design of new compilation methods, e.g. for hardware synthesis or for programming with sublinear space. This paper relates such semantically motivated non-standard compilation methods to more standard techniques in the compilation of functional programming languages, namely continuation passing and defunctionalization. We first show for the linear {\lambda}-calculus that interpretation in a model of computation by interaction can be described as a call-by-name CPS-translation followed by a defunctionalization procedure that takes into account control-flow information. We then establish a relation between these two compilation methods for the simply-typed {\lambda}-calculus and end by considering recursion

    Recursive Session Types Revisited

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    Session types model structured communication-based programming. In particular, binary session types for the pi-calculus describe communication between exactly two participants in a distributed scenario. Adding sessions to the pi-calculus means augmenting it with type and term constructs. In a previous paper, we tried to understand to which extent the session constructs are more complex and expressive than the standard pi-calculus constructs. Thus, we presented an encoding of binary session pi-calculus to the standard typed pi-calculus by adopting linear and variant types and the continuation-passing principle. In the present paper, we focus on recursive session types and we present an encoding into recursive linear pi-calculus. This encoding is a conservative extension of the former in that it preserves the results therein obtained. Most importantly, it adopts a new treatment of the duality relation, which in the presence of recursive types has been proven to be quite challenging.Comment: In Proceedings BEAT 2014, arXiv:1408.556

    Generalized Approximate Survey Propagation for High-Dimensional Estimation

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    In Generalized Linear Estimation (GLE) problems, we seek to estimate a signal that is observed through a linear transform followed by a component-wise, possibly nonlinear and noisy, channel. In the Bayesian optimal setting, Generalized Approximate Message Passing (GAMP) is known to achieve optimal performance for GLE. However, its performance can significantly degrade whenever there is a mismatch between the assumed and the true generative model, a situation frequently encountered in practice. In this paper, we propose a new algorithm, named Generalized Approximate Survey Propagation (GASP), for solving GLE in the presence of prior or model mis-specifications. As a prototypical example, we consider the phase retrieval problem, where we show that GASP outperforms the corresponding GAMP, reducing the reconstruction threshold and, for certain choices of its parameters, approaching Bayesian optimal performance. Furthermore, we present a set of State Evolution equations that exactly characterize the dynamics of GASP in the high-dimensional limit
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