4,092 research outputs found
Interaction and observation: categorical semantics of reactive systems trough dialgebras
We use dialgebras, generalising both algebras and coalgebras, as a complement
of the standard coalgebraic framework, aimed at describing the semantics of an
interactive system by the means of reaction rules. In this model, interaction
is built-in, and semantic equivalence arises from it, instead of being
determined by a (possibly difficult) understanding of the side effects of a
component in isolation. Behavioural equivalence in dialgebras is determined by
how a given process interacts with the others, and the obtained observations.
We develop a technique to inter-define categories of dialgebras of different
functors, that in particular permits us to compare a standard coalgebraic
semantics and its dialgebraic counterpart. We exemplify the framework using the
CCS and the pi-calculus. Remarkably, the dialgebra giving semantics to the
pi-calculus does not require the use of presheaf categories
O(N) methods in electronic structure calculations
Linear scaling methods, or O(N) methods, have computational and memory
requirements which scale linearly with the number of atoms in the system, N, in
contrast to standard approaches which scale with the cube of the number of
atoms. These methods, which rely on the short-ranged nature of electronic
structure, will allow accurate, ab initio simulations of systems of
unprecedented size. The theory behind the locality of electronic structure is
described and related to physical properties of systems to be modelled, along
with a survey of recent developments in real-space methods which are important
for efficient use of high performance computers. The linear scaling methods
proposed to date can be divided into seven different areas, and the
applicability, efficiency and advantages of the methods proposed in these areas
is then discussed. The applications of linear scaling methods, as well as the
implementations available as computer programs, are considered. Finally, the
prospects for and the challenges facing linear scaling methods are discussed.Comment: 85 pages, 15 figures, 488 references. Resubmitted to Rep. Prog. Phys
(small changes
Structural operational semantics for stochastic and weighted transition systems
We introduce weighted GSOS, a general syntactic framework to specify well-behaved transition systems where transitions are equipped with weights coming from a commutative monoid. We prove that weighted bisimilarity is a congruence on systems defined by weighted GSOS specifications. We illustrate the flexibility of the framework by instantiating it to handle some special cases, most notably that of stochastic transition systems. Through examples we provide weighted-GSOS definitions for common stochastic operators in the literature
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.Comment: 232 page
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.Comment: 232 page
Probabilistic Numerics and Uncertainty in Computations
We deliver a call to arms for probabilistic numerical methods: algorithms for
numerical tasks, including linear algebra, integration, optimization and
solving differential equations, that return uncertainties in their
calculations. Such uncertainties, arising from the loss of precision induced by
numerical calculation with limited time or hardware, are important for much
contemporary science and industry. Within applications such as climate science
and astrophysics, the need to make decisions on the basis of computations with
large and complex data has led to a renewed focus on the management of
numerical uncertainty. We describe how several seminal classic numerical
methods can be interpreted naturally as probabilistic inference. We then show
that the probabilistic view suggests new algorithms that can flexibly be
adapted to suit application specifics, while delivering improved empirical
performance. We provide concrete illustrations of the benefits of probabilistic
numeric algorithms on real scientific problems from astrometry and astronomical
imaging, while highlighting open problems with these new algorithms. Finally,
we describe how probabilistic numerical methods provide a coherent framework
for identifying the uncertainty in calculations performed with a combination of
numerical algorithms (e.g. both numerical optimisers and differential equation
solvers), potentially allowing the diagnosis (and control) of error sources in
computations.Comment: Author Generated Postprint. 17 pages, 4 Figures, 1 Tabl
Recommended from our members
P colonies and kernel P systems
YesP colonies, tissue-like P systems with very simple components, have received constant attention from the membrane computing community and in the last years several new variants of the model have been considered. Another P system model, namely kernel P system, integrating the most successfully used features of membrane systems, has recently attracted interest and some important developments have been reported. In this paper we study connections among several classes of P colonies and kernel P systems, by showing how the behaviour of these P colony systems can be represented as kernel P systems. An example illustrates the way it is modelled by using P colonies and kernel P systems and some properties of it are formally proved in the latter approach.Grant No. K 120558 of the NKFIHâNational Research, Development, and Innovation Office, Hungary; Romanian National Authority for Scientific Research, CNCS-UEFISCDI (Project No. PN-III-P4-ID-PCE-2016-0210)
On Quantum Statistical Inference, I
Recent developments in the mathematical foundations of quantum mechanics have
brought the theory closer to that of classical probability and statistics. On
the other hand, the unique character of quantum physics sets many of the
questions addressed apart from those met classically in stochastics.
Furthermore, concurrent advances in experimental techniques and in the theory
of quantum computation have led to a strong interest in questions of quantum
information, in particular in the sense of the amount of information about
unknown parameters in given observational data or accessible through various
possible types of measurements. This scenery is outlined (with an audience of
statisticians and probabilists in mind).Comment: A shorter version containing some different material will appear
(2003), with discussion, in J. Roy. Statist. Soc. B, and is archived as
quant-ph/030719
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