Optical interconnect with densely integrated plasmonic modulator and germanium photodetector arrays

We demonstrate the first chip-to-chip interconnect utilizing a densely integrated plasmonic Mach-Zehnder modulator array operating at 3 x 10 Gbit/s. A multicore fiber provides a compact optical interface, while the receiver consists of germanium photodetectors

Optical interconnect solution with plasmonic modulator and Ge photodetector array

We report on an optical chip-to-chip interconnect solution, thereby demonstrating plasmonics as a solution for ultra-dense, high-speed short-reach communications. The interconnect comprises a densely integrated plasmonic Mach-Zehnder modulator array that is packaged with standard driving electronics. On the receiver side, a germanium photodetector array is integrated with trans-impedance amplifiers. A multicore fiber provides a compact optical interface to the array. We demonstrate 4 × 20 Gb/s on-off keying signaling with direct detection.ISSN:1041-1135ISSN:1941-017

Increased left ventricular mass is a risk factor for the development of a depressed left ventricular ejection fraction within five years The Cardiovascular Health Study

AbstractObjectivesOur aim in this study was to determine whether increased left ventricular mass (LVM) is a risk factor for the development of a reduced left ventricular ejection fraction (LVEF).BackgroundPrior studies have shown that increased LVM is a risk factor for heart failure but not whether it is a risk factor for a low LVEF.MethodsAs part of the Cardiovascular Health Study, a prospective population-based longitudinal study, we performed echocardiograms upon participant enrollment and again at follow-up of 4.9 ± 0.14 years. In the present analysis, we identified 3,042 participants who had at baseline a normal LVEF and an assessment of LVM (either by electrocardiogram or echocardiogram), and at follow-up a measurable LVEF. The frequency of the development of a qualitatively depressed LVEF on two-dimensional echocardiography, corresponding approximately to an LVEF <55%, was analyzed by quartiles of baseline LVM. Multivariable regression determined whether LVM was independently associated with the development of depressed LVEF.ResultsBaseline quartile of echocardiographic LVM indexed to body surface area was associated with development of a depressed LVEF (4.8% in quartile 1, 4.4% in quartile 2, 7.5% in quartile 3, and 14.1% in quartile 4 [p < 0.001]). A similar relationship was seen in the subgroup of participants without myocardial infarction (p < 0.001). In multivariable regression that adjusted for confounders, both baseline echocardiographic (p < 0.001) and electrocardiographic (p < 0.001) LVM remained associated with development of depressed LVEF.ConclusionsIncreased LVM as assessed by electrocardiography or echocardiography is an independent risk factor for the development of depressed LVEF

Disorder-induced trapping versus Anderson localization in Bose-Einstein condensates expanding in disordered potentials

We theoretically investigate the localization of an expanding Bose-Einstein condensate with repulsive atom-atom interactions in a disordered potential. We focus on the regime where the initial inter-atomic interactions dominate over the kinetic energy and the disorder. At equilibrium in a trapping potential and for small disorder, the condensate shows a Thomas-Fermi shape modified by the disorder. When the condensate is released from the trap, a strong suppression of the expansion is obtained in contrast to the situation in a periodic potential with similar characteristics. This effect crucially depends on both the momentum distribution of the expanding BEC and the strength of the disorder. For strong disorder, the suppression of the expansion results from the fragmentation of the core of the condensate and from classical reflections from large modulations of the disordered potential in the tails of the condensate. We identify the corresponding disorder-induced trapping scenario for which large atom-atom interactions and strong reflections from single modulations of the disordered potential play central roles. For weak disorder, the suppression of the expansion signals the onset of Anderson localization, which is due to multiple scattering from the modulations of the disordered potential. We compute analytically the localized density profile of the condensate and show that the localization crucially depends on the correlation function of the disorder. In particular, for speckle potentials the long-range correlations induce an effective mobility edge in 1D finite systems. Numerical calculations performed in the mean-field approximation support our analysis for both strong and weak disorder.Comment: New Journal of Physics; focus issue "Quantum Correlations in Tailored Matter - Common perspectives of mesoscopic systems and quantum gases"; 30 pages, 10 figure

Small doubling in groups

Let A be a subset of a group G = (G,.). We will survey the theory of sets A with the property that |A.A| <= K|A|, where A.A = {a_1 a_2 : a_1, a_2 in A}. The case G = (Z,+) is the famous Freiman--Ruzsa theorem.Comment: 23 pages, survey article submitted to Proceedings of the Erdos Centenary conferenc

Detecting change via competence model

In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine "when" and "how" the concept changes. In this paper, we developed a competence-based empirical distance between case chunks and then proposed a change detection method based on it. As a main contribution of our work, the change detection method provides an approach to measure the distribution change of cases of an infinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change. © 2010 Springer-Verlag

Tailoring Anderson localization by disorder correlations in 1D speckle potentials

We study Anderson localization of single particles in continuous, correlated, one-dimensional disordered potentials. We show that tailored correlations can completely change the energy-dependence of the localization length. By considering two suitable models of disorder, we explicitly show that disorder correlations can lead to a nonmonotonic behavior of the localization length versus energy. Numerical calculations performed within the transfer-matrix approach and analytical calculations performed within the phase formalism up to order three show excellent agreement and demonstrate the effect. We finally show how the nonmonotonic behavior of the localization length with energy can be observed using expanding ultracold-atom gases

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Career Success in Different Countries : Reflections on the 5C Project

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A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed