A dedicated greedy pursuit algorithm for sparse spectral representation of music sound

A dedicated algorithm for sparse spectral representation of music sound is presented. The goal is to enable the representation of a piece of music signal as a linear superposition of as few spectral components as possible, without affecting the quality of the reproduction. A representation of this nature is said to be sparse. In the present context sparsity is accomplished by greedy selection of the spectral components, from an overcomplete set called a dictionary. The proposed algorithm is tailored to be applied with trigonometric dictionaries. Its distinctive feature being that it avoids the need for the actual construction of the whole dictionary, by implementing the required operations via the fast Fourier transform. The achieved sparsity is theoretically equivalent to that rendered by the orthogonal matching pursuit (OMP) method. The contribution of the proposed dedicated implementation is to extend the applicability of the standard OMP algorithm, by reducing its storage and computational demands. The suitability of the approach for producing sparse spectral representation is illustrated by comparison with the traditional method, in the line of the short time Fourier transform, involving only the corresponding orthonormal trigonometric basis.

SAFARI optical system architecture and design concept

SpicA FAR infrared Instrument, SAFARI, is one of the instruments planned for the SPICA mission. The SPICA mission is the next great leap forward in space-based far-infrared astronomy and will study the evolution of galaxies, stars and planetary systems. SPICA will utilize a deeply cooled 2.5m-class telescope, provided by European industry, to realize zodiacal background limited performance, and high spatial resolution. The instrument SAFARI is a cryogenic grating-based point source spectrometer working in the wavelength domain 34 to 230 μm, providing spectral resolving power from 300 to at least 2000. The instrument shall provide low and high resolution spectroscopy in four spectral bands. Low Resolution mode is the native instrument mode, while the high Resolution mode is achieved by means of a Martin-Pupplet interferometer. The optical system is all-reflective and consists of three main modules; an input optics module, followed by the Band and Mode Distributing Optics and the grating Modules. The instrument utilizes Nyquist sampled filled linear arrays of very sensitive TES detectors. The work presented in this paper describes the optical design architecture and design concept compatible with the current instrument performance and volume design drivers.

A novel latent factor model for recommender system

Matrix factorization (MF) has evolved as one of the better practice to handle sparse data in field of recommender systems. Funk singular value decomposition (SVD) is a variant of MF that exists as state-of-the-art method that enabled winning the Netflix prize competition. The method is widely used with modifications in present day research in field of recommender systems. With the potential of data points to grow at very high velocity, it is prudent to devise newer methods that can handle such data accurately as well as efficiently than Funk-SVD in the context of recommender system. In view of the growing data points, I propose a latent factor model that caters to both accuracy and efficiency by reducing the number of latent features of either users or items making it less complex than Funk-SVD, where latent features of both users and items are equal and often larger. A comprehensive empirical evaluation of accuracy on two publicly available, amazon and ml-100 k datasets reveals the comparable accuracy and lesser complexity of proposed methods than Funk-SVD.

Optimal control of non-linear systems through hybrid cell-mapping/artificial-neural-networks techniques

In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a control variable discretization. Furthermore, CM-based designs can be improved by the use of supervised feedforward artificial neural networks (ANNs), which have proved to be universal and efficient tools for function approximation, providing also very fast responses. The quantitative nature of the approximate CM solutions fits very well with ANNs characteristics. Here, we propose several control architectures which combine, in a different manner, supervised neural networks and CM control algorithms. On the one hand, different CM control laws computed for various target objectives can be employed for training a neural network, explicitly including the target information in the input vectors. This way, tracking problems, in addition to regulation ones, can be addressed in a fast and unified manner, obtaining smooth, averaged and global feedback control laws. On the other hand, adjoining CM and ANNs are also combined into a hybrid architecture to address problems where accuracy and real-time response are critical. Finally, some optimal control problems are solved with the proposed CM, neural and hybrid techniques, illustrating their good performance.

Análise não linear de séries temporais e aplicações à Psicologia

Nesta dissertação estudámos as séries temporais que representam a complexa dinâmica do comportamento. Demos especial atenção às técnicas de dinâmica não linear. As técnicas fornecem-nos uma quantidade de índices quantitativos que servem para descrever as propriedades dinâmicas do sistema. Estes índices têm sido intensivamente usados nos últimos anos em aplicações práticas em Psicologia. Estudámos alguns conceitos básicos de dinâmica não linear, as características dos sistemas caóticos e algumas grandezas que caracterizam os sistemas dinâmicos, que incluem a dimensão fractal, que indica a complexidade de informação contida na série temporal, os expoentes de Lyapunov, que indicam a taxa com que pontos arbitrariamente próximos no espaço de fases da representação do espaço dinâmico, divergem ao longo do tempo, ou a entropia aproximada, que mede o grau de imprevisibilidade de uma série temporal. Esta informação pode então ser usada para compreender, e possivelmente prever, o comportamento. ABSTRACT: ln this thesis we studied the time series that represent the complex dynamic behavior. We focused on techniques of nonlinear dynamics. The techniques provide us a number of quantitative indices used to describe the dynamic properties of the system. These indices have been extensively used in recent years in practical applications in psychology. We studied some basic concepts of nonlinear dynamics, the characteristics of chaotic systems and some quantities that characterize the dynamic systems, including fractal dimension, indicating the complexity of information in the series, the Lyapunov exponents, which indicate the rate at that arbitrarily dose points in phase space representation of a dynamic, vary over time, or the approximate entropy, which measures the degree of unpredictability of a series. This information can then be used to understand and possibly predict the behavior.

Using linear algebra for protein structural comparison and classification.

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

Effect of host genotype and prion strain on the phenotypic heterogeneity of Creutzfeldt-Jakob disease: the peripheral nervous system involvement and the spectrum of the genetic form

The first study was designed to assess whether the involvement of the peripheral nervous system (PNS) belongs to the phenotypic spectrum of sporadic Creutzfeldt-Jakob disease (sCJD). To this aim, we reviewed medical records of 117 sCJDVV2, 65 sCJDMV2K, and 121 sCJDMM(V)1 subjects for symptoms/signs and neurophysiological data. We looked for the presence of PrPSc in postmortem PNS samples from 14 subjects by western blotting and real-time quaking-induced conversion (RT-QuIC) assay. Seventy-five (41.2%) VV2-MV2K patients, but only 11 (9.1%) MM(V)1, had symptoms/signs suggestive of PNS involvement and neuropathy was documented in half of the VV2-MV2K patients tested. RT-QuIC was positive in all PNS samples, whereas western blotting detected PrPSc in the sciatic nerve in only one VV2 and one MV2K. These results support the conclusion that peripheral neuropathy, likely related to PrPSc deposition, belongs to the phenotypic spectrum of sCJDMV2K and VV2, the two variants linked to the V2 strain. The second study aimed to characterize the genetic/molecular determinants of phenotypic variability in genetic CJD (gCJD). To this purpose, we compared 157 cases of gCJD to 300 of sCJD. We analyzed: demographic aspects, neurological symptoms/signs, histopathologic features and biochemical characteristics of PrPSc. The results strongly indicated that the clinicopathological phenotypes of gCJD largely overlap with those of sCJD and that the genotype at codon 129 in cis with the mutation (i.e. haplotype) contributes more than the latter to the disease phenotype. Some mutations, however, cause phenotypic variations including haplotype-specific patterns of PrPSc deposition such as the “dense” synaptic pattern (E200K-129M), the intraneuronal dots (E200K-129V), and the linear stripes perpendicular to the surface in the molecular layer of cerebellum (OPRIs-129M). Overall, these results suggest that in gCJD PRNP mutations do not cause the emergence of novel prion strains, but rather confer increased susceptibility to the disease in conjunction with “minor” clinicopathological variations.

Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems.

In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsystem is constrained to not deviate too much from its communicated reference trajectory. This difference between the plan trajectory and the communicated one is interpreted as a disturbance on the local level. Then, to ensure the satisfaction of both state and input constraints, they are tightened by considering explicitly the effect of these local disturbances. The proposed approach averages over all possible disturbances, handles tightened state and input constraints, while satisfies the compatibility constraints to guarantee that the actual trajectory lies within a certain bound in the neighborhood of the reference one. Each subsystem is optimizing a local arbitrary economic objective function in parallel while considering a local terminal constraint to guarantee recursive feasibility. In this framework, economic performance guarantees for a tube-based distributed predictive control (DPC) scheme are developed rigorously. It is presented that the closed-loop nominal subsystem has a robust average performance bound locally which is no worse than that of a local robust steady state. Since a robust algorithm is applying on the states of the real (with disturbances) subsystems, this bound can be interpreted as an average performance result for the real closed-loop system. To this end, we present our outcomes on local and global performance, illustrated by a numerical example.

Unified control system for three-phase electric drives operating in magnetic saturation region

The research project aims to study and develop control techniques for a generalized three-phase and multi-phase electric drive able to efficiently manage most of the drive types available for traction application. The generalized approach is expanded to both linear and non- linear machines in magnetic saturation region starting from experimental flux characterization and applying the general inductance definition. The algorithm is able to manage fragmented drives powered from different batteries or energy sources and will be able to ensure operability even in case of faults in parts of the system. The algorithm was tested using model-in-the-loop in software environment and then applied on experimental test benches with collaboration of an external company.

Linear and nonlinear thermal instability of Newtonian and non-Newtonian fluid saturated porous media

The present work aims to investigate the influence of different aspects, such as non-standard steady solutions, complex fluid rheologies and non-standard porous-channel geometries, on the stability of a Darcy-Bénard system. In order to do so, both linear and nonlinear stability theories are considered. A linear analysis focuses on studying the dynamics of the single disturbance wave present in the system, while its nonlinear counterpart takes into consideration the interactions among the single modes. The scope of the stability analysis is to obtain information regarding the transition from an equilibrium solution to another one, and also information regarding the transition nature and the emergent solution after the transition. The disturbance governing equations are solved analytically, whenever possible, and numerical by considering different approaches. Among other important results, it is found that a cylinder cross-section does not affect the thermal instability threshold, but just the linear pattern selection for dilatant and pseudoplastic fluid saturated porous media. A new rheological model is proposed as a solution for singular issues involving the power-law model. Also, a generalised class of one parameter basic solutions is proposed as an alternative description of the isoflux Darcy--Bénard problem. Its stability is investigated.

Distributed optimization and games over networks: a system theoretical perspective

Several decision and control tasks involve networks of cyber-physical systems that need to be coordinated and controlled according to a fully-distributed paradigm involving only local communications without any central unit. This thesis focuses on distributed optimization and games over networks from a system theoretical perspective. In the addressed frameworks, we consider agents communicating only with neighbors and running distributed algorithms with optimization-oriented goals. The distinctive feature of this thesis is to interpret these algorithms as dynamical systems and, thus, to resort to powerful system theoretical tools for both their analysis and design. We first address the so-called consensus optimization setup. In this context, we provide an original system theoretical analysis of the well-known Gradient Tracking algorithm in the general case of nonconvex objective functions. Then, inspired by this method, we provide and study a series of extensions to improve the performance and to deal with more challenging settings like, e.g., the derivative-free framework or the online one. Subsequently, we tackle the recently emerged framework named distributed aggregative optimization. For this setup, we develop and analyze novel schemes to handle (i) online instances of the problem, (ii) ``personalized'' optimization frameworks, and (iii) feedback optimization settings. Finally, we adopt a system theoretical approach to address aggregative games over networks both in the presence or absence of linear coupling constraints among the decision variables of the players. In this context, we design and inspect novel fully-distributed algorithms, based on tracking mechanisms, that outperform state-of-the-art methods in finding the Nash equilibrium of the game.

Computational methods applied to undeciphered scripts from the Aegean and Cyprus: Linear A and Cypro-Minoan

The study of ancient, undeciphered scripts presents unique challenges, that depend both on the nature of the problem and on the peculiarities of each writing system. In this thesis, I present two computational approaches that are tailored to two different tasks and writing systems. The first of these methods is aimed at the decipherment of the Linear A afraction signs, in order to discover their numerical values. This is achieved with a combination of constraint programming, ad-hoc metrics and paleographic considerations. The second main contribution of this thesis regards the creation of an unsupervised deep learning model which uses drawings of signs from ancient writing system to learn to distinguish different graphemes in the vector space. This system, which is based on techniques used in the field of computer vision, is adapted to the study of ancient writing systems by incorporating information about sequences in the model, mirroring what is often done in natural language processing. In order to develop this model, the Cypriot Greek Syllabary is used as a target, since this is a deciphered writing system. Finally, this unsupervised model is adapted to the undeciphered Cypro-Minoan and it is used to answer open questions about this script. In particular, by reconstructing multiple allographs that are not agreed upon by paleographers, it supports the idea that Cypro-Minoan is a single script and not a collection of three script like it was proposed in the literature. These results on two different tasks shows that computational methods can be applied to undeciphered scripts, despite the relatively low amount of available data, paving the way for further advancement in paleography using these methods.

In-car sound linear simulation

In recent years, vehicle acoustics have gained significant importance in new car development: increasingly advanced infotainment systems for spatial audio and sound enhancement algorithms have become the norm in modern vehicles. In the past, car manufacturers had to build numerous prototypes to study the sound behaviour inside the car cabin or the effect of new algorithms under development. Nowadays, advanced simulation techniques can reduce development costs and time. In this work, after selecting the reference test vehicle, a modern luxury sedan equipped with a high-end sound system, two independent tools were developed: a simulation tool created in the Comsol Multiphysics environment and an auralization tool developed in the Cycling ‘74 MAX environment. The simulation tool can calculate the impulse response and acoustic spectrum at a specific position inside the cockpit. Its input data are the vehicle’s geometry, acoustic absorption parameters of materials, the acoustic characteristics and position of loudspeakers, and the type and position of virtual microphones (or microphone arrays). The simulation tool can also provide binaural impulse responses thanks to Head Related Transfer Functions (HRTFs) and an innovative algorithm able to compute the HRTF at any distance and angle from the head. Impulse responses from simulations or acoustic measurements inside the car cabin are processed and fed into the auralization tool, enabling real-time interaction by applying filters, changing the channels gain or displaying the acoustic spectrum. Since the acoustic simulation of a vehicle involves multiple topics, the focus of this work has not only been the development of two tools but also the study and application of new techniques for acoustic characterization of the materials that compose the cockpit and the loudspeaker simulation. Specifically, three different methods have been applied for material characterization through the use of a pressure-velocity probe, a Laser Doppler Vibrometer (LDV), and a microphone array.

GEANT4 Radioprotection studies of the ERHA system for protontherapy treatment

This work is focused on the radiation protection for a protontherapy facility. The aim is to simulate with the best accuracy the prompt radiation field of the proton accelerator situed in Ruvo di Puglia, owned by Linearbeam s.r.l. company. In order to simulate it, is used Geant4, a software for interaction simulations of particles with matter. Thanks to internship work, thesis speaks about cancer therapy with a new method for particle acceleration, a linear beam. For a complete overview of the therapy, this work starts with a crush course on interactions of particle with matter, goes specifically to biological matter, then is shown a brief introduction to shielding studies for a particle acceleration facility, and then a presentation of Geant4. At the end, the main aspects of the proton accelerator are simulated, from proton hitting material of beam-pipe to detectors used to measure dose.

Obesity And Inflammation And The Effect On The Hematopoietic System.

Bone marrow is organized in specialized microenvironments known as 'marrow niches'. These are important for the maintenance of stem cells and their hematopoietic progenitors whose homeostasis also depends on other cell types present in the tissue. Extrinsic factors, such as infection and inflammatory states, may affect this system by causing cytokine dysregulation (imbalance in cytokine production) and changes in cell proliferation and self-renewal rates, and may also induce changes in the metabolism and cell cycle. Known to relate to chronic inflammation, obesity is responsible for systemic changes that are best studied in the cardiovascular system. Little is known regarding the changes in the hematopoietic system induced by the inflammatory state carried by obesity or the cell and molecular mechanisms involved. The understanding of the biological behavior of hematopoietic stem cells under obesity-induced chronic inflammation could help elucidate the pathophysiological mechanisms involved in other inflammatory processes, such as neoplastic diseases and bone marrow failure syndromes.