10,968 research outputs found
Compactifications of topological groups
Every topological group has some natural compactifications which can be a
useful tool of studying . We discuss the following constructions: (1) the
greatest ambit is the compactification corresponding to the algebra of
all right uniformly continuous bounded functions on ; (2) the Roelcke
compactification corresponds to the algebra of functions which are both
left and right uniformly continuous; (3) the weakly almost periodic
compactification is the envelopping compact semitopological semigroup of
(`semitopological' means that the multiplication is separately continuous).
The universal minimal compact -space is characterized by the
following properties: (1) has no proper closed -invariant subsets; (2)
for every compact -space there exists a -map . A group is
extremely amenable, or has the fixed point on compacta property, if is a
singleton. We discuss some results and questions by V. Pestov and E. Glasner on
extremely amenable groups. The Roelcke compactifications were used by M.
Megrelishvili to prove that can be a singleton. They can be used to
prove that certain groups are minimal. A topological group is minimal if it
does not admit a strictly coarser Hausdorff group topology.Comment: 17 page
Minimization Strategies for Maximally Parallel Multiset Rewriting Systems
Maximally parallel multiset rewriting systems (MPMRS) give a convenient way
to express relations between unstructured objects. The functioning of various
computational devices may be expressed in terms of MPMRS (e.g., register
machines and many variants of P systems). In particular, this means that MPMRS
are computationally complete; however, a direct translation leads to quite a
big number of rules. Like for other classes of computationally complete
devices, there is a challenge to find a universal system having the smallest
number of rules. In this article we present different rule minimization
strategies for MPMRS based on encodings and structural transformations. We
apply these strategies to the translation of a small universal register machine
(Korec, 1996) and we show that there exists a universal MPMRS with 23 rules.
Since MPMRS are identical to a restricted variant of P systems with antiport
rules, the results we obtained improve previously known results on the number
of rules for those systems.Comment: This article is an improved version of [1
Finding All Solutions of Equations in Free Groups and Monoids with Involution
The aim of this paper is to present a PSPACE algorithm which yields a finite
graph of exponential size and which describes the set of all solutions of
equations in free groups as well as the set of all solutions of equations in
free monoids with involution in the presence of rational constraints. This
became possible due to the recently invented emph{recompression} technique of
the second author.
He successfully applied the recompression technique for pure word equations
without involution or rational constraints. In particular, his method could not
be used as a black box for free groups (even without rational constraints).
Actually, the presence of an involution (inverse elements) and rational
constraints complicates the situation and some additional analysis is
necessary. Still, the recompression technique is general enough to accommodate
both extensions. In the end, it simplifies proofs that solving word equations
is in PSPACE (Plandowski 1999) and the corresponding result for equations in
free groups with rational constraints (Diekert, Hagenah and Gutierrez 2001). As
a byproduct we obtain a direct proof that it is decidable in PSPACE whether or
not the solution set is finite.Comment: A preliminary version of this paper was presented as an invited talk
at CSR 2014 in Moscow, June 7 - 11, 201
Nonlinear Markov Processes in Big Networks
Big networks express various large-scale networks in many practical areas
such as computer networks, internet of things, cloud computation, manufacturing
systems, transportation networks, and healthcare systems. This paper analyzes
such big networks, and applies the mean-field theory and the nonlinear Markov
processes to set up a broad class of nonlinear continuous-time block-structured
Markov processes, which can be applied to deal with many practical stochastic
systems. Firstly, a nonlinear Markov process is derived from a large number of
interacting big networks with symmetric interactions, each of which is
described as a continuous-time block-structured Markov process. Secondly, some
effective algorithms are given for computing the fixed points of the nonlinear
Markov process by means of the UL-type RG-factorization. Finally, the Birkhoff
center, the Lyapunov functions and the relative entropy are used to analyze
stability or metastability of the big network, and several interesting open
problems are proposed with detailed interpretation. We believe that the results
given in this paper can be useful and effective in the study of big networks.Comment: 28 pages in Special Matrices; 201
A tool for model-checking Markov chains
Markov chains are widely used in the context of the performance and reliability modeling of various systems. Model checking of such chains with respect to a given (branching) temporal logic formula has been proposed for both discrete [34, 10] and continuous time settings [7, 12]. In this paper, we describe a prototype model checker for discrete and continuous-time Markov chains, the Erlangen-Twente Markov Chain Checker EĆMC2, where properties are expressed in appropriate extensions of CTL. We illustrate the general benefits of this approach and discuss the structure of the tool. Furthermore, we report on successful applications of the tool to some examples, highlighting lessons learned during the development and application of EĆMC2
Deterministic Sampling and Range Counting in Geometric Data Streams
We present memory-efficient deterministic algorithms for constructing
epsilon-nets and epsilon-approximations of streams of geometric data. Unlike
probabilistic approaches, these deterministic samples provide guaranteed bounds
on their approximation factors. We show how our deterministic samples can be
used to answer approximate online iceberg geometric queries on data streams. We
use these techniques to approximate several robust statistics of geometric data
streams, including Tukey depth, simplicial depth, regression depth, the
Thiel-Sen estimator, and the least median of squares. Our algorithms use only a
polylogarithmic amount of memory, provided the desired approximation factors
are inverse-polylogarithmic. We also include a lower bound for non-iceberg
geometric queries.Comment: 12 pages, 1 figur
Abstract Learning Frameworks for Synthesis
We develop abstract learning frameworks (ALFs) for synthesis that embody the
principles of CEGIS (counter-example based inductive synthesis) strategies that
have become widely applicable in recent years. Our framework defines a general
abstract framework of iterative learning, based on a hypothesis space that
captures the synthesized objects, a sample space that forms the space on which
induction is performed, and a concept space that abstractly defines the
semantics of the learning process. We show that a variety of synthesis
algorithms in current literature can be embedded in this general framework.
While studying these embeddings, we also generalize some of the synthesis
problems these instances are of, resulting in new ways of looking at synthesis
problems using learning. We also investigate convergence issues for the general
framework, and exhibit three recipes for convergence in finite time. The first
two recipes generalize current techniques for convergence used by existing
synthesis engines. The third technique is a more involved technique of which we
know of no existing instantiation, and we instantiate it to concrete synthesis
problems
Program Synthesis and Linear Operator Semantics
For deterministic and probabilistic programs we investigate the problem of
program synthesis and program optimisation (with respect to non-functional
properties) in the general setting of global optimisation. This approach is
based on the representation of the semantics of programs and program fragments
in terms of linear operators, i.e. as matrices. We exploit in particular the
fact that we can automatically generate the representation of the semantics of
elementary blocks. These can then can be used in order to compositionally
assemble the semantics of a whole program, i.e. the generator of the
corresponding Discrete Time Markov Chain (DTMC). We also utilise a generalised
version of Abstract Interpretation suitable for this linear algebraic or
functional analytical framework in order to formulate semantical constraints
(invariants) and optimisation objectives (for example performance
requirements).Comment: In Proceedings SYNT 2014, arXiv:1407.493
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