Time and spectral domain relative entropy: A new approach to multivariate spectral estimation

The concept of spectral relative entropy rate is introduced for jointly stationary Gaussian processes. Using classical information-theoretic results, we establish a remarkable connection between time and spectral domain relative entropy rates. This naturally leads to a new spectral estimation technique where a multivariate version of the Itakura-Saito distance is employed}. It may be viewed as an extension of the approach, called THREE, introduced by Byrnes, Georgiou and Lindquist in 2000 which, in turn, followed in the footsteps of the Burg-Jaynes Maximum Entropy Method. Spectral estimation is here recast in the form of a constrained spectrum approximation problem where the distance is equal to the processes relative entropy rate. The corresponding solution entails a complexity upper bound which improves on the one so far available in the multichannel framework. Indeed, it is equal to the one featured by THREE in the scalar case. The solution is computed via a globally convergent matricial Newton-type algorithm. Simulations suggest the effectiveness of the new technique in tackling multivariate spectral estimation tasks, especially in the case of short data records.Comment: 32 pages, submitted for publicatio

On the Achievable Error Region of Physical Layer Authentication Techniques over Rayleigh Fading Channels

For a physical layer message authentication procedure based on the comparison of channel estimates obtained from the received messages, we focus on an outer bound on the type I/II error probability region. Channel estimates are modelled as multivariate Gaussian vectors, and we assume that the attacker has only some side information on the channel estimate, which he does not know directly. We derive the attacking strategy that provides the tightest bound on the error region, given the statistics of the side information. This turns out to be a zero mean, circularly symmetric Gaussian density whose correlation matrices may be obtained by solving a constrained optimization problem. We propose an iterative algorithm for its solution: Starting from the closed form solution of a relaxed problem, we obtain, by projection, an initial feasible solution; then, by an iterative procedure, we look for the fixed point solution of the problem. Numerical results show that for cases of interest the iterative approach converges, and perturbation analysis shows that the found solution is a local minimum

"We-Diseases" and Dyadic Decision-Making Processes: A Critical Perspective.

This is a critical perspective paper discussing the theoretical bases and methodological issues regarding dyadic decision-making processes in the oncological domain. Decision-making processes are of a central interest when one partner in a couple has cancer, and patients and partners make decisions together under an interactive and dynamic process. Given that, the attention in research is progressively shifting from patient and partner considered as individuals to a more holistic view of patient-partner considered as a dyad. The consideration of the dyadic nature of the decision-making represents a challenge from a theoretical and methodological point of view. The Interdependence Theory and the Dyadic Model of decision-making provide the theoretical bases to consider, respectively, the interdependence of the dyadic decision-making and the mechanisms affecting the couple-based decision-making. Dyadic processes require also an appropriate data analysis strategy that is discussed in the study as well. Conclusions of the present critical review suggest to develop a new line of research on dyadic decision-making in the oncological domain, testing the Dyadic Model presented in the study and considering the interdependence of the data with appropriate levels of analysis

Multivariate moment problems with applications to spectral estimation and physical layer security in wireless communications

This thesis focuses on generalized moment problems and their applications in the framework of information engineering. Its contribution is twofold. The first part of this dissertation proposes two new techniques for tackling multivariate spectral estimation, which is a key topic in system identification: Relative entropy rate estimation and multivariate circulant rational covariance extension. The former provides a very natural multivariate extension of a state-of-the-art approach for scalar parametric spectral estimation with a complexity bound, known as THREE (Tunable High-Resolution Estimator). It allows to take into account available a priori information on the spectral density. It exhibits high resolution features and it is robust in case of short data records. As for multivariate circulant rational covariance extension, it is a new convex optimization approach to spectral estimation for periodic multivariate processes, in which the computation of the solution can be tackled efficiently by means of Fast Fourier Transform. Numerical examples show that this procedure also provides an efficient tool for approximating regular covariance extension for multivariate processes. The second part of this dissertation considers the problem of deriving a universal performance bound for a message source authentication scheme based on channel estimates in a wireless fading scenario, where an attacker may have correlated observations available and possibly unbounded computational power. Under the assumption that the channels are represented by multivariate complex Gaussian variables, it is proved that the tightest bound corresponds to a forging strategy that produces a zero mean signal that is jointly Gaussian with the attacker observations. A characterization of their joint covariance matrix is derived through the solution of a system of two nonlinear matrix equations. Based upon this characterization, the thesis proposes an efficient iterative algorithm for its computation: The solution to the matricial system appears as fixed point of the iteration. Numerical examples suggest that this procedure is effective in assessing worst case channel authentication performance

Pediatric blood cancer survivors and tobacco use across adolescence and emerging adulthood: A narrative review

Scholars underline the pivotal role of tobacco cigarette smoking in carcinogenesis process for blood tumors. A controversial debate is represented by the diffusion of tobacco use in young cancer survivors that had a previous diagnosis of blood tumor during the childhood. Compared with their peers, scientific evidence highlights that pediatric survivors have more difficult to give-up cigarette smoking. Furthermore, tobacco-smoking is frequently linked with others risk behaviors as drinking or substance abuse. In reviewing the main knowledge on this topic, authors affirm the need for increasing research on blood cancer survivors in order to depict psychological characteristics of pediatric blood cancer survivors. Improving health decision-making skills in young survivors could reduce the risk to adopt un-healthy behaviors and increase psychological wellbeing. Furthermore, authors propose tailored antismoking interventions based on the knowledge of the psychological and cognitive factors that support smoking during the transition toward emerging-adulthood

a deep learning approach for anomaly detection with industrial time series data a refrigerators manufacturing case study

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a convolutional autoencoder approach for feature extraction in virtual metrology

Abstract Exploiting the huge amount of data collected by industries is definitely one of the main challenges of the so-called Big Data era. In this sense, Machine Learning has gained growing attention in the scientific community, as it allows to extract valuable information by means of statistical predictive models trained on historical process data. In Semiconductor Manufacturing, one of the most extensively employed data-driven applications is Virtual Metrology, where a costly or unmeasurable variable is estimated by means of cheap and easy to obtain measures that are already available in the system. Often, these measures are multi-dimensional, so traditional Machine Learning algorithms cannot handle them directly. Instead, they require feature extraction, that is a preliminary step where relevant information is extracted from raw data and converted into a design matrix. Features are often hand-engineered and based on specific domain knowledge. Moreover, they may be difficult to scale and prone to information loss, affecting the effectiveness and maintainability of machine learning procedures. In this paper, we present a Deep Learning method for semi-supervised feature extraction based on Convolutional Autoencoders that is able to overcome the aforementioned problems. The proposed method is tested on a real dataset for Etch rate estimation. Optical Emission Spectrometry data, that exhibit a complex bi-dimensional time and wavelength evolution, are used as input

A CYCLOIDEA-like gene mutation in sunflower determines an unusual floret type able to produce filled achenes at the periphery of the pseudanthium

The pseudanthium of sunflower (Helianthus annuus L.) consists of two floret types: zygomorphic sterile ray florets and actinomorphic hermaphrodite disc florets. In the tubular ray flower (turf) mutant, the loss-of-function of a CYCLOIDEA (CYC) gene generates hermaphrodite tubular-like ray florets that replace the normal sterile ray florets. We evaluated whether tubular-like ray florets have a multifaceted set of floral traits and the presence of heteromorphic seeds in the turf inflorescence. During early stages of floral ontogeny, primordia of both tubular-like ray florets and typical ray florets displayed a comparable shape. In contrast, during later stages of development, the form of tubular-like ray floret primordia was most similar to disc floret primordia. In mature tubular-like ray florets, corolla and ovary had both ray and disc floret characteristics but also displayed distinct identity traits. In open-pollinated tubular-like ray florets, the seed set was low, but a noteworthy increase of filled achenes was obtained by hand pollination. Wild type ray achenes were always empty. Embryos of tubular-like ray florets were shorter and lighter than the embryos of disc florets but able to produce fertile plants. In conclusion, the different identity characteristics combined in tubular-like ray florets of the mutant evolved a capitulum type not described in the genus Helianthus

deep learning based production forecasting in manufacturing a packaging equipment case study

Abstract We propose a Deep Learning (DL)-based approach for production performance forecasting in fresh products packaging. On the one hand, this is a very demanding scenario where high throughput is mandatory; on the other, due to strict hygiene requirements, unexpected downtime caused by packaging machines can lead to huge product waste. Thus, our aim is predicting future values of key performance indexes such as Machine Mechanical Efficiency (MME) and Overall Equipment Effectiveness (OEE). We address this problem by leveraging DL-based approaches and historical production performance data related to measurements, warnings and alarms. Different architectures and prediction horizons are analyzed and compared to identify the most robust and effective solutions. We provide experimental results on a real industrial case, showing advantages with respect to current policies implemented by the industrial partner both in terms of forecasting accuracy and maintenance costs. The proposed architecture is shown to be effective on a real case study and it enables the development of predictive services in the area of Predictive Maintenance and Quality Monitoring for packaging equipment providers