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The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering ...
This paper studies the learning ability of consensus and diffusion distributed learners from continuous streams of data arising from different but related statistical distributions. Four distinctive features for diffusion learners are revealed in relation ...
We study the evolution of structural disorder under cooling in supercooled liquids, focusing on covalent networks. We introduce a model for the energy of networks that incorporates weak noncovalent interactions. We show that at low temperature these intera ...
An enantioselective total synthesis of (-)-terengganensine A, a heptacyclic monoterpene indole alkaloid, was performed. A short sequence allowed to obtain the enantio-enriched target in good overall yield. The synthesis featured a key asymmetric transfer h ...
Within the context of ancillary services for Active Distribution Networks (ADNs), application of intelligent control techniques is required in order to achieve specific operation objectives. Despite their differences, most control mechanisms proposed in th ...
Intra-session network coding has been shown to offer significant gains in terms of achievable throughput and delay in settings where one source multicasts data to several clients. In this paper, we consider a more general scenario where multiple sources tr ...
Institute of Electrical and Electronics Engineers2014
Part I of this paper examined the mean-square stability and convergence of the learning process of distributed strategies over graphs. The results identified conditions on the network topology, utilities, and data in order to ensure stability; the results ...
This paper contributes to the design and analysis of Quantize-Map-and-Forward (QMF) relaying by optimizing its performance for small relay networks. QMF was proved to achieve the capacity of arbitrary networks within a bounded gap, as well as the optimal d ...
Distributed graph signal processing algorithms require the network nodes to communicate by exchanging messages in order to achieve a common objective. These messages have a finite precision in realistic networks, which may necessitate to implement message ...
We consider distributed detection problems over adaptive networks, where dispersed agents learn continually from streaming data by means of local interactions. The requirement of adaptation allows the network of detectors to track drifts in the underlying ...
