Sparse Distributed Learning Based on Diffusion Adaptation

This article proposes diffusion LMS strategies for distributed estimation over adaptive networks that are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to enhance the detection of sparsity via a diffusive process over the network. The resulting algorithms endow networks with learning abilities and allow them to learn the sparse structure from the incoming data in real-time, and also to track variations in the sparsity of the model. We provide convergence and mean-square performance analysis of the proposed method and show under what conditions it outperforms the unregularized diffusion version. We also show how to adaptively select the regularization parameter. Simulation results illustrate the advantage of the proposed filters for sparse data recovery.Comment: to appear in IEEE Trans. on Signal Processing, 201

qq-independent slow-dynamics in atomic and molecular systems

Investigating million-atom systems for very long simulation times, we demonstrate that the collective density-density correlation time ($\tau_{\alpha}$) in simulated supercooled water and silica becomes wavevector independent ($q^0$) when the probing wavelength is several times larger than the interparticle distance. The $q$-independence of the collective density-density correlation functions, a feature clearly observed in light-scattering studies of some soft-matter systems, is thus a genuine feature of many (but not all) slow-dynamics systems, either atomic, molecular or colloidal. Indeed, we show that when the dynamics of the density fluctuations is due to particle-type diffusion, as in the case of the Lennard Jones binary mixture model, the $q^0$ regime does not set in and the relaxation time continues to scale as $\tau_{\alpha} \sim q^{-2}$ even at small $q$.Comment: Includes the supplementary materia

Full counting statistics of weak measurement

A weak measurement consists in coupling a system to a probe in such a way that constructive interference generates a large output. So far, only the average output of the probe and its variance were studied. Here, the characteristic function for the moments of the output is provided. The outputs considered are not limited to the eigenstates of the pointer or of its conjugate variable, so that the results apply to any observable \Hat{o} of the probe. Furthermore, a family of well behaved complex quantities, the normal weak values, is introduced, in terms of which the statistics of the weak measurement can be described. It is shown that, within a good approximation, the whole statistics of weak measurement is described by a complex parameter, the weak value, and a real one.Comment: Expanded version: 9 pages, 3 Figs. Now the validity of the expansion for the moments is analysed. Introduced a one-parameter family of weak values, useful to express the correct characteristic function. More figures added. Thanks to Referee C of PRL for asking stimulating question

Utilisation of wheat bran as a substrate for bioethanol production using recombinant cellulases and amylolytic yeast

Wheat bran, generated from the milling of wheat, represents a promising feedstock for the production of bioethanol. This substrate consists of three main components: starch, hemicellulose and cellulose. The optimal conditions for wheat bran hydrolysis have been determined using a recombinant cellulase cocktail (RCC), which contains two cellobiohydrolases, an endoglucanase and a beta-glucosidase. The 10% (w/v, expressed in terms of dry matter) substrate loading yielded the most glucose, while the 2% loading gave the best hydrolysis efficiency (degree of saccharification) using unmilled wheat bran. The ethanol production of two industrial amylolytic Saccharomyces cerevisiae strains, MEL2[TLG1-SFA1] and M2n [TLG1-SFA1], were compared in a simultaneous saccharification and fermentation (SSF) for 10% wheat bran loading with or without the supplementation of optimised RCC. The recombinant yeasts. cerevisiae MEL2[TLG1-SFA1] and M2n[TLG1-SFA1] completely hydrolysed wheat bran's starch producing similar amounts of ethanol (5.3 +/- 0.14 g/L and 5.0 +/- 0.09 g/L, respectively). Supplementing SSF with RCC resulted in additional ethanol production of about 2.0 g/L. Scanning electron microscopy confirmed the effectiveness of both RCC and engineered amylolytic strains in terms of cellulose and starch depolymerisatio

Tuning non-Markovianity by spin-dynamics control

We study the interplay between forgetful and memory-keeping evolution enforced on a two-level system by a multi-spin environment whose elements are coupled to local bosonic baths. Contrarily to the expectation that any non-Markovian effect would be buried by the forgetful mechanism induced by the spin-bath coupling, one can actually induce a full Markovian-to-non-Markovian transition of the two-level system's dynamics, controllable by parameters such as the mismatch between the energy of the two-level system and of the spin environment. For a symmetric coupling, the amount of non-Markovianity surprisingly grows with the number of decoherence channels.Comment: 7 pages, 6 figures, PRA versio

Constraining galaxy cluster temperatures and redshifts with eROSITA survey data

The nature of dark energy is imprinted in the large-scale structure of the Universe and thus in the mass and redshift distribution of galaxy clusters. The upcoming eROSITA mission will exploit this method of probing dark energy by detecting roughly 100,000 clusters of galaxies in X-rays. For a precise cosmological analysis the various galaxy cluster properties need to be measured with high precision and accuracy. To predict these characteristics of eROSITA galaxy clusters and to optimise optical follow-up observations, we estimate the precision and the accuracy with which eROSITA will be able to determine galaxy cluster temperatures and redshifts from X-ray spectra. Additionally, we present the total number of clusters for which these two properties will be available from the eROSITA survey directly. During its four years of all-sky surveys, eROSITA will determine cluster temperatures with relative uncertainties of Delta(T)/T<10% at the 68%-confidence level for clusters up to redshifts of z~0.16 which corresponds to ~1,670 new clusters with precise properties. Redshift information itself will become available with a precision of Delta(z)/(1+z)<10% for clusters up to z~0.45. Additionally, we estimate how the number of clusters with precise properties increases with a deepening of the exposure. Furthermore, the biases in the best-fit temperatures as well as in the estimated uncertainties are quantified and shown to be negligible in the relevant parameter range in general. For the remaining parameter sets, we provide correction functions and factors. The eROSITA survey will increase the number of galaxy clusters with precise temperature measurements by a factor of 5-10. Thus the instrument presents itself as a powerful tool for the determination of tight constraints on the cosmological parameters.Comment: accepted for publication in A&A; 17 pages, 20 figure