278 research outputs found
The Buffered \pi-Calculus: A Model for Concurrent Languages
Message-passing based concurrent languages are widely used in developing
large distributed and coordination systems. This paper presents the buffered
-calculus --- a variant of the -calculus where channel names are
classified into buffered and unbuffered: communication along buffered channels
is asynchronous, and remains synchronous along unbuffered channels. We show
that the buffered -calculus can be fully simulated in the polyadic
-calculus with respect to strong bisimulation. In contrast to the
-calculus which is hard to use in practice, the new language enables easy
and clear modeling of practical concurrent languages. We encode two real-world
concurrent languages in the buffered -calculus: the (core) Go language and
the (Core) Erlang. Both encodings are fully abstract with respect to weak
bisimulations
Symbolic bisimulation for quantum processes
With the previous notions of bisimulation presented in literature, to check
if two quantum processes are bisimilar, we have to instantiate the free quantum
variables of them with arbitrary quantum states, and verify the bisimilarity of
resultant configurations. This makes checking bisimilarity infeasible from an
algorithmic point of view because quantum states constitute a continuum. In
this paper, we introduce a symbolic operational semantics for quantum processes
directly at the quantum operation level, which allows us to describe the
bisimulation between quantum processes without resorting to quantum states. We
show that the symbolic bisimulation defined here is equivalent to the open
bisimulation for quantum processes in the previous work, when strong
bisimulations are considered. An algorithm for checking symbolic ground
bisimilarity is presented. We also give a modal logical characterisation for
quantum bisimilarity based on an extension of Hennessy-Milner logic to quantum
processes.Comment: 30 pages, 7 figures, comments are welcom
ResMatch: Residual Attention Learning for Local Feature Matching
Attention-based graph neural networks have made great progress in feature
matching learning. However, insight of how attention mechanism works for
feature matching is lacked in the literature. In this paper, we rethink cross-
and self-attention from the viewpoint of traditional feature matching and
filtering. In order to facilitate the learning of matching and filtering, we
inject the similarity of descriptors and relative positions into cross- and
self-attention score, respectively. In this way, the attention can focus on
learning residual matching and filtering functions with reference to the basic
functions of measuring visual and spatial correlation. Moreover, we mine intra-
and inter-neighbors according to the similarity of descriptors and relative
positions. Then sparse attention for each point can be performed only within
its neighborhoods to acquire higher computation efficiency. Feature matching
networks equipped with our full and sparse residual attention learning
strategies are termed ResMatch and sResMatch respectively. Extensive
experiments, including feature matching, pose estimation and visual
localization, confirm the superiority of our networks
Proving Expected Sensitivity of Probabilistic Programs with Randomized Variable-Dependent Termination Time
The notion of program sensitivity (aka Lipschitz continuity) specifies that
changes in the program input result in proportional changes to the program
output. For probabilistic programs the notion is naturally extended to expected
sensitivity. A previous approach develops a relational program logic framework
for proving expected sensitivity of probabilistic while loops, where the number
of iterations is fixed and bounded. In this work, we consider probabilistic
while loops where the number of iterations is not fixed, but randomized and
depends on the initial input values. We present a sound approach for proving
expected sensitivity of such programs. Our sound approach is martingale-based
and can be automated through existing martingale-synthesis algorithms.
Furthermore, our approach is compositional for sequential composition of while
loops under a mild side condition. We demonstrate the effectiveness of our
approach on several classical examples from Gambler's Ruin, stochastic hybrid
systems and stochastic gradient descent. We also present experimental results
showing that our automated approach can handle various probabilistic programs
in the literature
Characterising Testing Preorders for Finite Probabilistic Processes
In 1992 Wang & Larsen extended the may- and must preorders of De Nicola and
Hennessy to processes featuring probabilistic as well as nondeterministic
choice. They concluded with two problems that have remained open throughout the
years, namely to find complete axiomatisations and alternative
characterisations for these preorders. This paper solves both problems for
finite processes with silent moves. It characterises the may preorder in terms
of simulation, and the must preorder in terms of failure simulation. It also
gives a characterisation of both preorders using a modal logic. Finally it
axiomatises both preorders over a probabilistic version of CSP.Comment: 33 page
Generalizing Deep Models for Overhead Image Segmentation Through Getis-Ord Gi* Pooling
That most deep learning models are purely data driven is both a strength and a weakness. Given sufficient training data, the optimal model for a particular problem can be learned. However, this is usually not the case and so instead the model is either learned from scratch from a limited amount of training data or pre-trained on a different problem and then fine-tuned. Both of these situations are potentially suboptimal and limit the generalizability of the model. Inspired by this, we investigate methods to inform or guide deep learning models for geospatial image analysis to increase their performance when a limited amount of training data is available or when they are applied to scenarios other than which they were trained on. In particular, we exploit the fact that there are certain fundamental rules as to how things are distributed on the surface of the Earth and these rules do not vary substantially between locations. Based on this, we develop a novel feature pooling method for convolutional neural networks using Getis-Ord Gi* analysis from geostatistics. Experimental results show our proposed pooling function has significantly better generalization performance compared to a standard data-driven approach when applied to overhead image segmentation
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