Detection of a sparse submatrix of a high-dimensional noisy matrix

We observe a $N\times M$ matrix $Y_{ij}=s_{ij}+\xi_{ij}$ with $\xi_{ij}\sim {\mathcal {N}}(0,1)$ i.i.d. in $i,j$, and $s_{ij}\in \mathbb {R}$. We test the null hypothesis $s_{ij}=0$ for all $i,j$ against the alternative that there exists some submatrix of size $n\times m$ with significant elements in the sense that $s_{ij}\ge a>0$. We propose a test procedure and compute the asymptotical detection boundary $a$ so that the maximal testing risk tends to 0 as $M\to\infty$, $N\to\infty$, $p=n/N\to0$, $q=m/M\to0$. We prove that this boundary is asymptotically sharp minimax under some additional constraints. Relations with other testing problems are discussed. We propose a testing procedure which adapts to unknown $(n,m)$ within some given set and compute the adaptive sharp rates. The implementation of our test procedure on synthetic data shows excellent behavior for sparse, not necessarily squared matrices. We extend our sharp minimax results in different directions: first, to Gaussian matrices with unknown variance, next, to matrices of random variables having a distribution from an exponential family (non-Gaussian) and, finally, to a two-sided alternative for matrices with Gaussian elements.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ470 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Effect of a single impurity on the local density of states in monolayer and bilayer graphene

We use the T-matrix approximation to analyze the effect of a localized impurity on the local density of states in mono- and bilayer graphene. For monolayer graphene the Friedel oscillations generated by intranodal scattering obey an inverse-square law, while the internodal ones obey an inverse law. In the Fourier transform this translates into a filled circle of high intensity in the center of the Brillouin zone, and empty circular contours around its corners. For bilayer graphene both types of oscillations obey an inverse law.Comment: 8 pages, 3 figures, version accepted for publicatio

Effects of decoherence on the shot noise in carbon nanotubes

We study the zero frequency noise in an interacting quantum wire connected to leads, in the presence of an impurity. In the absence of quasiparticle decoherence the zero-frequency noise is that of a non-interacting wire. However, if the collective, fractionally-charged modes have a finite lifetime, we find that the zero-frequency noise may still exhibit signatures of charge fractionalization, such as a small but detectable reduction of the ratio between the noise and the backscattered current (Fano factor). We argue that this small reduction of the Fano factor is consistent with recent observations of a large reduction in the experimentally-inferred Fano factor in nanotubes (calculated assuming that the backscattered current is the difference between the ideal current in a multiple-channel non-interacting wire and the measured current.Comment: 6 pages, 1 figur

Validation of the KC autotuning principle on a multi-tank pilot process

PIDs are the most widely used controllers in industrial applications. This particular interest generates on-going research regarding simplified tuning methods appealing to the industrial user. Such methods refer also to a fast design of PID controllers in the absence of a mathematical model of the process. Autotuners represent one way of achieving such a fast design. In this paper, the experimental validation of a previously presented direct autotuner is presented. The autotuning method requires only one simple sine test on the process to compute the PID controller parameters. The case study consists in the Quanser Six Tanks Process. Comparisons with other popular tuning methods are also presented. The results show that the proposed autotuning method is a valuable option for controlling industrial processes

Automatic speech recognition with deep neural networks for impaired speech

The final publication is available at https://link.springer.com/chapter/10.1007%2F978-3-319-49169-1_10Automatic Speech Recognition has reached almost human performance in some controlled scenarios. However, recognition of impaired speech is a difficult task for two main reasons: data is (i) scarce and (ii) heterogeneous. In this work we train different architectures on a database of dysarthric speech. A comparison between architectures shows that, even with a small database, hybrid DNN-HMM models outperform classical GMM-HMM according to word error rate measures. A DNN is able to improve the recognition word error rate a 13% for subjects with dysarthria with respect to the best classical architecture. This improvement is higher than the one given by other deep neural networks such as CNNs, TDNNs and LSTMs. All the experiments have been done with the Kaldi toolkit for speech recognition for which we have adapted several recipes to deal with dysarthric speech and work on the TORGO database. These recipes are publicly available.Peer ReviewedPostprint (author's final draft

The tunneling conductance between a superconducting STM tip and an out-of-equilibrium carbon nanotube

We calculate the current and differential conductance for the junction between a superconducting (SC) STM tip and a Luttinger liquid (LL). For an infinite single-channel LL, the SC coherence peaks are preserved in the tunneling conductance for interactions weaker than a critical value, while for strong interactions (g <0.38), they disappear and are replaced by cusp-like features. For a finite-size wire in contact with non-interacting leads, we find however that the peaks are restored even for extremely strong interactions. In the presence of a source-drain voltage the peaks/cusps split, and the split is equal to the voltage. At zero temperature, even very strong interactions do not smear the two peaks into a broader one; this implies that the recent experiments of Y.-F. Chen et. al. (Phys. Rev. Lett. 102, 036804 (2009)) do not rule out the existence of strong interactions in carbon nanotubes.Comment: 8 pages, 3 figure

Vulvodynia; an under-recognized disease

Vulvodynia is a chronic condition which affects an increasing number of women; it presents currently an incidence that is higher than had previously been estimated. Regarding pathogenesis, several (hormonal, infectious, inflammatory and psychological) factors have been proposed, but vulvodynia etiology remains still unclear. This disorder is a multifactorial condition with a significant impact on the patient’s quality of life, yet is difficult to diagnose (an under-estimated/ under-recognized affection). Certain medical investigations are required in order to exclude other diseases (the diagnosis of vulvodynia being one of exclusion), but anamnesis and physical examination are essential steps in the diagnosis. Although many therapies have been proposed, both pharmacological and non-pharmacological, a standardized therapy has not yet been established/ generally accepted. Accordingly, many therapeutic options have been studied with varying results. Vulvodynia remains a challenging disease and a multidisciplinary approach is needed to achieve satisfactory outcomes. Further studies are needed to completely understand its pathogenesis and to develop a standardized treatment

SEASONAL MIGRATION OF RETIREES: A REVIEW OF THE LITERATURE

Anecdotes suggest that tourism experiences may affect the migration decisions of retirees. Seasonal migration by retirees may be an intermediate step between tourism and permanent migration. A review of the literature on seasonal migration finds that some seasonal migrants become permanent migrants. In general, seasonal and permanent migrants come from two separate migration streams and are two different lifestyles. Seasonal migration generally does not lead to permanent migration.Resource /Energy Economics and Policy,