Stephen J. Hartley Oxford University Press, New York, 1998, 260 pp. ISBN 0-19-511315-2, $45.00 Concurrent Programming  is a thorough treatment of Java multi-threaded programming for both a stand-alone and distributed environment. Designed mostly for students in concurrent or parallel programming classes, the text is also an excellent reference for the practicing professional developing multi-threaded programs or applets. Hartley first provides a complete explanation of the features of Java necessary to write concurrent programs, including topics such as exception handling, interfaces, and packages. He then gives the reader a solid background to write multi-threaded programs and also presents the problems introduced when writing concurrent programs—namely race conditions, mutual exclusion, and deadlock. Hartley also provides several software solutions that do not require the use of common process and thread mechanisms. Once the groundwork is laid for writing concurrent programs, Hartley then takes a different approach than most Java references. Rather than presenting how Java handles mutual exclusion with the  synchronized  keyword (although it is covered later), he first looks at semaphore-based solutions to classic concurrent problems such as bounded-buffer, readers-writers, and the dining philosophers. Hartley also uses the same approach to develop Java classes for monitors and message passing. This unique approach to introducing concurrency allows the readers to both understand how Java threads are synchronized and how the basic synchronization mechanism can be used to construct more abstract tools such as semaphores. If there is a shortcoming with the text it is with the lack of sufficient coverage of remote method invocation (RMI), although there is a section covering RMI. This is quite understandable as RMI is a fairly recent phenomenon with the Java community. Also, the classes that Hartley provides could easily implement RMI rather than sockets to handle communication. The strengths of the book include its ease in reading, several examples at the end of chapters, a package similar to Xtango that provides algorithm animation, and a supportive web site by the author (see  www.mcs.drexel.edu/~shartley/ConcProgJava/index.html ) including compressed source code. As Java becomes more dominant on the server side of multi-tier applications, writing thread-safe concurrent applications becomes even more important.  Concurrent Programming  is a strong step towards teaching students and professionals such skills. Greg Gagne, Westminster College of Salt Lake City Salt Lake City, Utah

Distributed Computing: Fundamentals, Simulations and Advanced Topics

Preface vii Acknowledgments xi Chapter 1. What Is Language? 1 Chapter 2. Do Animals Have Language? 21 Chapter 3. In the Beginning Was the Gesture 41 Chapter 4. On Our Own Two Feet 66 Chapter 5. Becoming Human 82 Chapter 6. Signed Language 102 Chapter 7. It's All Talk 126 Chapter 8. Why Are We Lopsided? 159 Chapter 9. From Hand to Mouth 184 Chapter 10. Synopsis 213 References 221 Index 247

Gestural Gab@@@From Hand to Mouth: The Origins of Language

In this Topical Review we consider stochastic processes under resetting, which have attracted a lot of attention in recent years. We begin with the simple example of a diffusive particle whose position is reset randomly in time with a constant rate r, which corresponds to Poissonian resetting, to some fixed point (e.g. its initial position). This simple system already exhibits the main features of interest induced by resetting: (i) the system reaches a nontrivial nonequilibrium stationary state (ii) the mean time for the particle to reach a target is finite and has a minimum, optimal, value as a function of the resetting rate r. We then generalise to an arbitrary stochastic process (e.g. Levy flights or fractional Brownian motion) and non-Poissonian resetting (e.g. power-law waiting time distribution for intervals between resetting events). We go on to discuss multiparticle systems as well as extended systems, such as fluctuating interfaces, under resetting. We also consider resetting with memory which implies resetting the process to some randomly selected previous time. Finally we give an overview of recent developments and applications in the field. PACS numbers: 05.40.-a, 05.70.Fh, 02.50.Ey, 64.60.-i arXiv:1910.07993v2 [cond-mat.stat-mech]

https://iopscience.iop.org/article/10.1088/1751-8121/ab7cfe/pdf

Stochastic Resetting and Applications

Publisher Summary This chapter discusses machine models and simulations. The existence of many different machine models does not necessarily mean that there are no uniform underlying notions of computational complexity. In the theory of computation, a large variety of models and formal calculi has been proposed to capture the notion of effective computation. This has not lead to a proliferation of computation theories, because of the basic fact that the proposed formalisms have all been shown to be equivalent in the following sense—each computation in one formalism can be simulated by a computation in the other formalism. From the equivalence, it can be concluded that if a problem is unsolvable in one particular model, then it is also unsolvable for all other formalized computing devices to which this particular model is related by mutual simulation. Consequently the notion of solvability does not really depend on the model used. The basic models of computation themselves remain on the shelf, to be used if and when needed.

Machine models and simulations

We survey and summarize the literature on the computational aspects of neural network models by presenting a detailed taxonomy of the various models according to their complexity theoretic characteristics. The criteria of classification include the architecture of the network (feedforward versus recurrent), time model (discrete versus continuous), state type (binary versus analog), weight constraints (symmetric versus asymmetric), network size (finite nets versus infinite families), and computation type (deterministic versus probabilistic), among others. The underlying results concerning the computational power and complexity issues of perceptron, radial basis function, winner-take-all, and spiking neural networks are briefly surveyed, with pointers to the relevant literature. In our survey, we focus mainly on the digital computation whose inputs and outputs are binary in nature, although their values are quite often encoded as analog neuron states. We omit the important learning issues.

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General-purpose computation with neural networks: a survey of complexity theoretic results

The importance of algorithms is now recognized in all mathematical sciences, thanks to the development of computability and computational complexity theory in the 20th century. The basic understanding of computability theory developed in the nineteen thirties with the pioneering work of mathematicians like Godel, Church, Turing and Post. Their work provided the mathematical basis for the study of algorithms as a formalized concept. The work of Hartmanis, Stearns, Karp, Cook and others in the nineteen sixties and seventies showed that the refinement of the theory to resource-bounded computations gave the means to explain the many intuitions concerning the complexity or ‘hardness’ of algorithmic problems in a precise and rigorous framework.

/pdf/the-turing-machine-paradigm-in-contemporary-computing-1qik493gdb.pdf

The Turing machine paradigm in contemporary computing

A finite automaton—the so-called neuromaton, realized by a finite discrete recurrent neural network, working in parallel computation mode, is considered. Both the size of neuromata (i.e., the number of neurons) and their descriptional complexity (i.e., the number of bits in the neuromaton representation) are studied. It is proved that a constraint time delay of the neuromaton output does not play a role within a polynomial descriptional complexity. It is shown that any regular language given by a regular expression of length n is recognized by a neuromaton with Θ(n) neurons. Further, it is proved that this network size is, in the worst case, optimal. On the other hand, generally there is not an equivalent polynomial length regular expression for a given neuromaton. Then, two specialized constructions of neural acceptors of the optimal descriptional complexity Θ(n) for a single n-bit string recognition are described. They both require O(n1/2) neurons and either O(n) connections with constant weights or O(n1/2) edges with weights of the O(2√n) size. Furthermore, the concept of Hopfield languages is introduced by means of so-called Hopfield neuromata (i.e., of neural networks with symmetric weights). It is proved that the class of Hopfield languages is strictly contained in the class of regular languages. The necessary and sufficient so-called Hopfield condition stating when a regular language is a Hopfield language, is formulated. A construction of a Hopfield neuromaton is presented for a regular language satisfying the Hopfield condition. The class of Hopfield languages is shown to be closed under union, intersection, concatenation and complement, and it is not closed under iteration. Finally, the problem whether a regular language given by a neuromaton (or by a Hopfield acceptor) is nonempty, is proved to be PSPACE-complete. As a consequence, the same result for a neuromaton equivalence problem is achieved.

Theory of neuromata

Characterizing the super-Turing computing power and efficiency of classical fuzzy Turing machines

Classical models of computation no longer fully correspond to the current notions of computing in modern systems. Even in the sciences, many natural systems are now viewed as systems that compute. Can one devise models of computation that capture the notion of computing as seen today and that could play the same role as Turing machines did for the classical case? We propose two models inspired from key mechanisms of current systems in both artificial and natural environments: evolving automata and interactive Turing machines with advice. The two models represent relevant adjustments in our apprehension of computing: the shift to potentially non-terminating interactive computations, the shift towards systems whose hardware and/or software can change over time, and the shift to computing systems that evolve in an unpredictable, non-uniform way. The two models are shown to be equivalent and both are provably computationally more powerful than the models covered by the old computing paradigm. The models also motivate the extension of classical complexity theory by non-uniform classes, using the computational resources that are natural to these models. Of course, the additional computational power of the models cannot in general be meaningfully exploited in concrete goal-oriented computations.

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Jiří Wiedermann

Papers

The Turing machine paradigm in contemporary computing

Theory of neuromata

Characterizing the super-Turing computing power and efficiency of classical fuzzy Turing machines

How We Think of Computing Today

On the power of synchronization