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

Hyperbolicity is a property of a graph that may be viewed as being a “soft” version of a tree, and recent empirical and theoretical work has suggested that many graphs arising in Internet and related data applications have hyperbolic properties. Here, we consider Gromov’s notion of δ-hyperbolicity, and we establish several positive and negative results for small-world and tree-like random graph models. In particular, we show that small-world random graphs built from underlying grid structures do not have strong improvement in hyperbolicity, even when the rewiring greatly improves decentralized navigation. On the other hand, for a class of tree-like graphs called ringed trees that have constant hyperbolicity, adding random links among the leaves in a manner similar to the small-world graph constructions may easily destroy the hyperbolicity of the graphs, except for a class of random edges added using an exponentially decaying probability function based on the ring distance among the leaves. Our study provides the first significant analytical results on the hyperbolicity of a rich class of random graphs, which shed light on the relationship between hyperbolicity and navigability of random graphs, as well as on the sensitivity of hyperbolic δ to noises in random graphs.

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On the Hyperbolicity of Small-World and Tree-Like Random Graphs

Learning recursive functions: A survey

We extend the theory of cellular automata to arbitrary, time-varying graphs. In other words we formalise, and prove theorems about, the intuitive idea of a labelled graph which evolves in time - but under the natural constraint that information can only ever be transmitted at a bounded speed, with respect to the distance given by the graph. The notion of translation-invariance is also generalised. The definition we provide for these 'causal graph dynamics' is simple and axiomatic. The theorems we provide also show that it is robust. For instance, causal graph dynamics are stable under composition and under restriction to radius one. In the finite case some fundamental facts of cellular automata theory carry through: causal graph dynamics admit a characterisation as continuous functions, and they are stable under inversion. The provided examples suggest a wide range of applications of this mathematical object, from complex systems science to theoretical physics.

Causal graph dynamics

Graph automata were first introduced by P. Rosenstiehl [10], under the name of intelligent graphs, surely because a network of finite automata is able to know some properties about its own structure. Hence P. Rosenstiehl created some algorithms that find Eulerian paths or Hamiltonian cycles in those graphs, with the condition that every vertex has a fixed degree [11]. Those algorithms are called “myopic” since each automaton has only the knowledge of the state of its immediate neighborhood. This hypothesis seems to be absolutely essential for the modelisation of the physical reality. A. Wu and A. Rosenfeld ([12] [13]) developed ideas of P. Rosenstiehl, using a simpler and more general formalism: the d-graphs. For each of these authors, a graph automata is formed by synchronous finite automata exchanging information according to an adjacent graph.

Hyperbolic Recognition by Graph Automata

The intrinsic complexity of learning compares the difficulty of learning classes of objects by using some reducibility notion. For several types of learning recursive functions, both natural complete classes are exhibited and necessary and sufficient conditions for completeness are derived. Informally, a class is complete iff both its topological structure is highly complex while its algorithmic structure is easy. Some self-describing classes turn out to be complete. Furthermore, the structure of the intrinsic complexity is shown to be much richer than the structure of the mind change complexity, though in general, intrinsic complexity and mind change complexity can behave “orthogonally”.

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On the intrinsic complexity of learning recursive functions

The intrinsic complexity of learning compares the difficulty of learning classes of objects by using some reducibility notion. For several types of learning recursive functions, both natural complete classes are exhibited and necessary and sufficient conditions for completeness are derived. Informally, a class is complete iff both its topological structure is highly complex while its algorithmic structure is easy. Some self-describing classes turn out to be complete. Furthermore, the structure of the intrinsic complexity is shown to be much richer than the structure of the mind change complexity, though in general, intrinsic complexity and mind change complexity can behave "orthogonally".

Many mathematical results exist about continuous topological surfaces of negative curvature. We give here some properties of discrete regular tessellations on such objects and explain a characterization of discrete geodesics and areas that shows how such hyperbolic networks can be seen as intermediary structures between Euclidean infinite tessellations (like square grid) and regular infinite trees.

We do not use some possible group structures of this networks (Cayley graphs) but only geometrical arguments in our constructive proofs. Hence we can see that there are few geodesics in hyperbolic networks and that large areas have very unsmooth borders.

/pdf/some-properties-of-hyperbolic-networks-57ena6545y.pdf

Some Properties of Hyperbolic Networks

Graph automata were first introduced by P. Rosenstiehl [5], under the name of intelligent graphs, surely because a network of finite automata is able to know some properties about its own structure. Hence P. Rosenstiehl created some algorithms that find Eulerian paths or Hamiltonian cycles in those graphs, with the condition that every vertex has a fixed degree [6]. Those algorithms are called ”myopic” since each automaton has only the knowledge of the state of its immediate neighborhood. A. Wu and A. Rosenfeld ([7] [8]) developed ideas of P. Rosenstiehl, using a simpler and more general formalism: the d-graphs. Hence, a graph automata is formed from synchronous finite automata exchanging information according to an adjacency graph. A. Wu and A. Rosenfeld gave a linear algorithm allowing a graph automata to know if its graph is a rectangle or not, then, E. Remila [3] extended this result to other geometrical structures.

Christophe Papazian

Papers

Hyperbolic Recognition by Graph Automata

On the intrinsic complexity of learning recursive functions

On the intrinsic complexity of learning recursive functions

Some Properties of Hyperbolic Networks

Linear Time Recognizer for Subsets of Z2