Operational Rate-Distortion Performance of Single-source and Distributed Compressed Sensing

We consider correlated and distributed sources without cooperation at the encoder. For these sources, we derive the best achievable performance in the rate-distortion sense of any distributed compressed sensing scheme, under the constraint of high--rate quantization. Moreover, under this model we derive a closed--form expression of the rate gain achieved by taking into account the correlation of the sources at the receiver and a closed--form expression of the average performance of the oracle receiver for independent and joint reconstruction. Finally, we show experimentally that the exploitation of the correlation between the sources performs close to optimal and that the only penalty is due to the missing knowledge of the sparsity support as in (non distributed) compressed sensing. Even if the derivation is performed in the large system regime, where signal and system parameters tend to infinity, numerical results show that the equations match simulations for parameter values of practical interest.Comment: To appear in IEEE Transactions on Communication

Graded quantization for multiple description coding of compressive measurements

Compressed sensing (CS) is an emerging paradigm for acquisition of compressed representations of a sparse signal. Its low complexity is appealing for resource-constrained scenarios like sensor networks. However, such scenarios are often coupled with unreliable communication channels and providing robust transmission of the acquired data to a receiver is an issue. Multiple description coding (MDC) effectively combats channel losses for systems without feedback, thus raising the interest in developing MDC methods explicitly designed for the CS framework, and exploiting its properties. We propose a method called Graded Quantization (CS-GQ) that leverages the democratic property of compressive measurements to effectively implement MDC, and we provide methods to optimize its performance. A novel decoding algorithm based on the alternating directions method of multipliers is derived to reconstruct signals from a limited number of received descriptions. Simulations are performed to assess the performance of CS-GQ against other methods in presence of packet losses. The proposed method is successful at providing robust coding of CS measurements and outperforms other schemes for the considered test metrics

Exact Performance Analysis of the Oracle Receiver for Compressed Sensing Reconstruction

A sparse or compressible signal can be recovered from a certain number of noisy random projections, smaller than what dictated by classic Shannon/Nyquist theory. In this paper, we derive the closed-form expression of the mean square error performance of the oracle receiver, knowing the sparsity pattern of the signal. With respect to existing bounds, our result is exact and does not depend on a particular realization of the sensing matrix. Moreover, our result holds irrespective of whether the noise affecting the measurements is white or correlated. Numerical results show a perfect match between equations and simulations, confirming the validity of the result.Comment: To be published in ICASSP 2014 proceeding

Gender diversity on corporate boards: an empirical investigation of Italian listed companies

In recent years, there has been a growing consensus that gender diversity could improve the economic and social performance of companies—although progress has been painfully slow. In Italy, Law n. 120/2011 has required that since 2012 the composition of corporate boards must comprise at least 1/5 women and, from 2015, 1/3 women. Here, our primary aim was to study the proportion and the progression of female directors on the boards of Italian companies over the period between 2009–2014, among a sample of 60 companies listed on the Milan Stock Exchange. We additionally studied the characteristics of the women on the companies’ boards, in order to assemble a typical profile. To do this, we verified the boards’ compositions, the number of female directors, women’s power on the boards and their levels of education. Our results confirm that Italian law has produced significant effects on the composition of corporate boards. Moreover, we found that female directors are generally of Italian ethnic origin and have high levels of education, with a master’s, and sometimes, post-master’s degree. Furthermore, female directors are more likely to be professional figures with experience—the percentages of female directors with professional qualifications as well as the percentage of female directors on boards of other companies have consistently increased during the period mentioned. Our findings provide a descriptive analysis of female directors during the period between 2009–2014, thereby presenting a picture of gender equality in the Italian context. To our knowledge, this is the first study that has focused on examining gender equality among Italian-listed companies over such a long period. Future development of this research could examine in more depth the differences between male and female presence, behaviour and characteristics on boards, as well as how these issues affect the performance of firms. This article is published as part of a collection on the role of women in management and the workplace

An application of the option-pricing model to the valuation of football player in the ‘Serie A League’

Football is perhaps the most popular sport in the world. The market of football players is one of the most popular factors of the sport that makes the fans dream of each team which increases the interest around the sport. In 2013 the player Gareth Bale was sold from Tottenham to Real Madrid for 100 million Euros. Someone argues that the market for football players is inherently irrational precisely because of the sale price of certain players. This paper is based on Tunaru et al. model that is real option based model. The aim of the paper is the financial valuation of a goalkeeper of Serie A League club. The model depends on relationship of player’s and team’s performance and the club’s turnover

A novel approach to robust radar detection of range-spread targets

This paper proposes a novel approach to robust radar detection of range-spread targets embedded in Gaussian noise with unknown covariance matrix. The idea is to model the useful target echo in each range cell as the sum of a coherent signal plus a random component that makes the signal-plus-noise hypothesis more plausible in presence of mismatches. Moreover, an unknown power of the random components, to be estimated from the observables, is inserted to optimize the performance when the mismatch is absent. The generalized likelihood ratio test (GLRT) for the problem at hand is considered. In addition, a new parametric detector that encompasses the GLRT as a special case is also introduced and assessed. The performance assessment shows the effectiveness of the idea also in comparison to natural competitors.Comment: 28 pages, 8 figure

Distributed Learning from Interactions in Social Networks

We consider a network scenario in which agents can evaluate each other according to a score graph that models some interactions. The goal is to design a distributed protocol, run by the agents, that allows them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which scores and states are associated to probabilistic events with unknown parameters and hyperparameters, respectively. We show that each agent can learn its state by means of a local Bayesian classifier and a (centralized) Maximum-Likelihood (ML) estimator of parameter-hyperparameter that combines plain ML and Empirical Bayes approaches. By using tools from graphical models, which allow us to gain insight on conditional dependencies of scores and states, we provide a relaxed probabilistic model that ultimately leads to a parameter-hyperparameter estimator amenable to distributed computation. To highlight the appropriateness of the proposed relaxation, we demonstrate the distributed estimators on a social interaction set-up for user profiling.Comment: This submission is a shorter work (for conference publication) of a more comprehensive paper, already submitted as arXiv:1706.04081 (under review for journal publication). In this short submission only one social set-up is considered and only one of the relaxed estimators is proposed. Moreover, the exhaustive analysis, carried out in the longer manuscript, is completely missing in this versio

Joint recovery algorithms using difference of innovations for distributed compressed sensing

Distributed compressed sensing is concerned with representing an ensemble of jointly sparse signals using as few linear measurements as possible. Two novel joint reconstruction algorithms for distributed compressed sensing are presented in this paper. These algorithms are based on the idea of using one of the signals as side information; this allows to exploit joint sparsity in a more effective way with respect to existing schemes. They provide gains in reconstruction quality, especially when the nodes acquire few measurements, so that the system is able to operate with fewer measurements than is required by other existing schemes. We show that the algorithms achieve better performance with respect to the state-of-the-art.Comment: Conference Record of the Forty Seventh Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 201