IBM

About: IBM is a company organization based out in Armonk, New York, United States. It is known for research contribution in the topics: Layer (electronics) & Cache. The organization has 134567 authors who have published 253905 publications receiving 7458795 citations. The organization is also known as: International Business Machines Corporation & Big Blue.

Performance analysis of the CONFIDANT protocol

Abstract: Mobile ad-hoc networking works properly only if the participating nodes cooperate in routing and forwarding. However,it may be advantageous for individual nodes not to cooperate. We propose a protocol, called CONFIDANT, for making misbehavior unattractive; it is based on selective altruism and utilitarianism. It aims at detecting and isolating misbehaving nodes, thus making it unattractive to deny cooperation. Trust relationships and routing decisions are based on experienced, observed, or reported routing and forwarding behavior of other nodes. The detailed implementation of CONFIDANT in this paper assumes that the network layer is based on the Dynamic Source Routing (DSR) protocol. We present a performance analysis of DSR fortified by CONFIDANT and compare it to regular defenseless DSR. It shows that a network with CONFIDANT and up to 60% of misbehaving nodes behaves almost as well as a benign network, in sharp contrast to a defenseless network. All simulations have been implemented and performed in GloMoSim.

Intermittent turbulence in self-similar cascades: divergence of high moments and dimension of the carrier

Abstract: Kolmogorov’s “third hypothesis” asserts that in intermittent turbulence the average \( \bar \varepsilon \) of the dissipation e, taken over any domain D, is ruled by the lognormal probability distribution. This hypothesis will be shown to be logically inconsistent, save under assumptions that are extreme and unlikely. A widely used justification of lognormality due to Yaglom and based on probabilistic argument involving a self-similar cascade, will also be discussed. In this model, lognormality indeed applies strictly when D is “an eddy,” typically a three-dimensional box embedded in a self-similar hierarchy, and may perhaps remain a reasonable approximation when D consists of a few such eddies. On the other hand, the experimental situation is better described by considering averages taken over essentially one-dimensional domains D.

GraRep: Learning Graph Representations with Global Structural Information

Abstract: In this paper, we present {GraRep}, a novel model for learning vertex representations of weighted graphs. This model learns low dimensional vectors to represent vertices appearing in a graph and, unlike existing work, integrates global structural information of the graph into the learning process. We also formally analyze the connections between our work and several previous research efforts, including the DeepWalk model of Perozzi et al. as well as the skip-gram model with negative sampling of Mikolov et al. We conduct experiments on a language network, a social network as well as a citation network and show that our learned global representations can be effectively used as features in tasks such as clustering, classification and visualization. Empirical results demonstrate that our representation significantly outperforms other state-of-the-art methods in such tasks.

Decoherence-Free Subspaces for Quantum Computation

Abstract: Decoherence in quantum computers is formulated within the semigroup approach The error generators are identified with the generators of a Lie algebra This allows for a comprehensive description which includes as a special case the frequently assumed spin-boson model A generic condition is presented for errorless quantum computation: decoherence-free subspaces are spanned by those states which are annihilated by all the generators It is shown that these subspaces are stable to perturbations and, moreover, that universal quantum computation is possible within them

The DaCapo benchmarks: java benchmarking development and analysis

Abstract: Since benchmarks drive computer science research and industry product development, which ones we use and how we evaluate them are key questions for the community. Despite complex runtime tradeoffs due to dynamic compilation and garbage collection required for Java programs, many evaluations still use methodologies developed for C, C++, and Fortran. SPEC, the dominant purveyor of benchmarks, compounded this problem by institutionalizing these methodologies for their Java benchmark suite. This paper recommends benchmarking selection and evaluation methodologies, and introduces the DaCapo benchmarks, a set of open source, client-side Java benchmarks. We demonstrate that the complex interactions of (1) architecture, (2) compiler, (3) virtual machine, (4) memory management, and (5) application require more extensive evaluation than C, C++, and Fortran which stress (4) much less, and do not require (3). We use and introduce new value, time-series, and statistical metrics for static and dynamic properties such as code complexity, code size, heap composition, and pointer mutations. No benchmark suite is definitive, but these metrics show that DaCapo improves over SPEC Java in a variety of ways, including more complex code, richer object behaviors, and more demanding memory system requirements. This paper takes a step towards improving methodologies for choosing and evaluating benchmarks to foster innovation in system design and implementation for Java and other managed languages.