Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

For simple random walk on aN-vertex graph, the mean time to cover all vertices is at leastcN log(N), wherec>0 is an absolute constant. This is deduced from a more general result about stationary finite-state reversible Markov chains. Under weak conditions, the covering time for such processes is at leastc times the covering time for the corresponding i.i.d. process.

Lower bounds for covering times for reversible Markov chains and random walks on graphs

Vital nodes identification in complex networks

We prove equivalent conditions for two-sided sub-Gaussian estimates of heat kernels on metric measure spaces.

/pdf/two-sided-estimates-of-heat-kernels-on-metric-measure-spaces-29qc0pmh47.pdf

Two-sided estimates of heat kernels on metric measure spaces

We propose a new node centrality measure in networks, the lobby index, which is inspired by Hirsch’s h -index. It is shown that in scale-free networks with exponent α the distribution of the l -index has power tail with exponent α ( α + 1 ) . Properties of the l -index and extensions are discussed.

/pdf/lobby-index-in-networks-2aeygfnn36.pdf

Lobby index in networks

We show that the $\beta $
 -parabolic Harnack inequality for random walks on graphs is equivalent, on one hand, to the sub-Gaussian estimate for the transition probability and, on the other hand, to the conjunction of the elliptic Harnack inequality, the doubling volume property, and the fact that the mean exit time in any ball of radius R is of the order $R^{\beta }$
 . The latter condition can be replaced by a certain estimate of the resistance of annuli.

Harnack inequalities and sub-Gaussian estimates for random walks

We prove that a two-sided sub-Gaussian estimate of the heat kernel on an infinite weighted graph takes place if and only if the volume growth of the graph is uniformly polynomial and the Green kernel admits a uniform polynomial decay.

/pdf/sub-gaussian-estimates-of-heat-kernels-on-infinite-graphs-46zdomnxk3.pdf

Sub-Gaussian estimates of heat kernels on infinite graphs

Optimizing energy consumption in wireless sensor networks is of paramount importance. There is a recent trend to deal with this problem by introducing mobile elements (sensors or sink nodes) in the network. The majority of these approaches assume time-driven scenarios and/or single-hop communication between participating nodes. However, there are several real-life applications for which an event-based and multi-hop operation is more appropriate. In this paper we propose to adaptively move the sink node inside the covered region, according to the evolution of current events, so as to minimize the energy consumption incurred by the multi-hop transmission of the event-related data. Both analytical and simulation results are given for two optimization strategies: minimizing the overall energy consumption, and minimizing the maximum load on a specific sensor respectively. We show that by adaptively moving the sink, significant power saving can be achieved, prolonging the lifetime of the network.

András Telcs

Papers

Two-sided estimates of heat kernels on metric measure spaces

Lobby index in networks

Harnack inequalities and sub-Gaussian estimates for random walks

Sub-Gaussian estimates of heat kernels on infinite graphs

Adaptive sink mobility in event-driven multi-hop wireless sensor networks