PADDLE: Proximal Algorithm for Dual Dictionaries LEarning

Recently, considerable research efforts have been devoted to the design of methods to learn from data overcomplete dictionaries for sparse coding. However, learned dictionaries require the solution of an optimization problem for coding new data. In order to overcome this drawback, we propose an algorithm aimed at learning both a dictionary and its dual: a linear mapping directly performing the coding. By leveraging on proximal methods, our algorithm jointly minimizes the reconstruction error of the dictionary and the coding error of its dual; the sparsity of the representation is induced by an $\ell_1$-based penalty on its coefficients. The results obtained on synthetic data and real images show that the algorithm is capable of recovering the expected dictionaries. Furthermore, on a benchmark dataset, we show that the image features obtained from the dual matrix yield state-of-the-art classification performance while being much less computational intensive

Directive Emission from Defect-Free Dodecagonal Photonic Quasicrystals: A Leaky-Wave Characterization

In this paper, we study the radiation from embedded sources in two-dimensional finite-size "photonic-quasicrystal" (PQC) slabs made of dielectric rods arranged according to a 12-fold symmetric aperiodic tiling. The results from our investigation, based on rigorous full-wave simulations, show the possibility of achieving broadside radiation at multiple frequencies, with high-directivity (e.g., 15 dB) and low-sidelobes (e.g., -12 dB). We also show that leaky waves are supported by a PQC slab, and that the beamwidth is directly proportional to the leaky-wave attenuation constant, which provides a physically-incisive interpretation of the observed radiation characteristics.Comment: 7 pages, 7 figures; slight change in the title, major revision in the text and figures. Accepted for publication in Phys. Rev.

Reconstruction of Underlying Nonlinear Deterministic Dynamics Embedded in Noisy Spike Trains

An experimentally recorded time series formed by the exact times of occurrence of the neuronal spikes (spike train) is likely to be affected by observational noise that provokes events mistakenly confused with neuronal discharges, as well as missed detection of genuine neuronal discharges. The points of the spike train may also suffer a slight jitter in time due to stochastic processes in synaptic transmission and to delays in the detecting devices. This study presents a procedure aimed at filtering the embedded noise (denoising the spike trains) the spike trains based on the hypothesis that recurrent temporal patterns of spikes are likely to represent the robust expression of a dynamic process associated with the information carried by the spike train. The rationale of this approach is tested on simulated spike trains generated by several nonlinear deterministic dynamical systems with embedded observational noise. The application of the pattern grouping algorithm (PGA) to the noisy time series allows us to extract a set of points that form the reconstructed time series. Three new indices are defined for assessment of the performance of the denoising procedure. The results show that this procedure may indeed retrieve the most relevant temporal features of the original dynamics. Moreover, we observe that additional spurious events affect the performance to a larger extent than the missing of original points. Thus, a strict criterion for the detection of spikes under experimental conditions, thus reducing the number of spurious spikes, may raise the possibility to apply PGA to detect endogenous deterministic dynamics in the spike train otherwise masked by the observational nois

Oral potentially malignant disorders in a large dental population

Objectives Oral cancer (OC) may be preceded by clinically evident oral potentially malignant disorders (OPMDs). Oral carcinogenesis is a multistep process that begins as epithelial hyperplasia and progresses to oral epithelial dysplasia and finally to fully malignant phenotypes. The aim of our study was to estimate the prevalence of OPMDs in a large population of dental patients. Methods Patients were seen in the Oral Diagnosis and Oral Medicine clinics at Boston University Henry M. Goldman School of Dental Medicine between July 2013 and February 2014 and received a comprehensive oral examination to identify any possible mucosal lesions. Patients with a suspected OPMD (submucous fibrosis, oral lichen planus, leukoplakia and erythroplakia) that did not resolve in 2–3 weeks received a biopsy for definitive diagnosis. Logistic regression models were used to explore the relationship between OPMDs and associated risk factors. Results A total of 3,142 patients received a comprehensive oral examination [median age: 43 (range: 18–97); 54.3% females]. Among these, 4.5% had an oral mucosal lesion with 0.9% being an OPMD (one submucous fibrosis, three epithelial dysplasias, fourteen with hyperkeratosis/epithelial hyperplasia and nine with oral lichen planus). Males and current smokers were associated with higher odds of having OPMD (OR 1.7, 95% CI 0.8–3.8; OR 1.9, 95%CI 0.8–4.1). Increasing age was associated with having OPMDs (

Flowmeter and Ground Penetrating Radar: comparison between hydrogeological and geophysical methods

We discuss a comparison between saturated hydraulic conductivity calculated with Electromagnetic Borehole Flowmeter (EBF) and water content obtained by Ground Penetrating Radar (GPR) Zero Offset Profile (ZOP

GPGPU for track finding in High Energy Physics

The LHC experiments are designed to detect large amount of physics events produced with a very high rate. Considering the future upgrades, the data acquisition rate will become even higher and new computing paradigms must be adopted for fast data-processing: General Purpose Graphics Processing Units (GPGPU) is a novel approach based on massive parallel computing. The intense computation power provided by Graphics Processing Units (GPU) is expected to reduce the computation time and to speed-up the low-latency applications used for fast decision taking. In particular, this approach could be hence used for high-level triggering in very complex environments, like the typical inner tracking systems of the multi-purpose experiments at LHC, where a large number of charged particle tracks will be produced with the luminosity upgrade. In this article we discuss a track pattern recognition algorithm based on the Hough Transform, where a parallel approach is expected to reduce dramatically the execution time.Comment: 6 pages, 4 figures, proceedings prepared for GPU-HEP 2014 conference, submitted to DESY-PROC-201

Social investment, labour market participation and public debt sustainability: An empirical analysis of European countries

This article explores the role of SI Stock, Flow and Buffer policies by shedding light on their relationships with active labour market participation and public debt sustainability for a panel of 22 European countries from 1997 to 2018. We find SI Stock, Flow and Buffer to be positively correlated with labour market participation and more sustainable public debt. When disaggregating the components of SI, we detect a small degree of heterogeneity, with Active Labour Market Policies (ALMPs) negatively associated with the activity rate and positively associated with the employment rate. This result is coherent with the idea that ALMPs make a significant contribution to increasing opportunities for those already in the labour market rather than creating new jobs for those excluded from the labour market, that is, inactive individuals. In this case, our findings indicate that measures to fight social exclusion and out-of-work expenditure (Buffer), as well as in-kind family benefits, are significantly associated with employability for those excluded from the labour market