116,143 research outputs found
Linear and Affine Typing of Continuation-Passing Style
Submitted for the degree of Doctor of Philosophy, Queen Mary, University of Londo
On the Relation of Interaction Semantics to Continuations and Defunctionalization
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
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
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
- …