Sound objects in time, space and action

SOUND OBJECTS IN TIME, SPACE AND ACTIONThe term "sound object" describes an auditory experience that is associated with an acoustic event produced by a sound source. At cortical level, sound objects are represented by temporo-spatial activity patterns within distributed neural networks. This investigation concerns temporal, spatial and action aspects as assessed in normal subjects using electrical imaging or measurement of motor activity induced by transcranial magnetic stimulation (TMS).Hearing the same sound again has been shown to facilitate behavioral responses (repetition priming) and to modulate neural activity (repetition suppression). In natural settings the same source is often heard again and again, with variations in spectro-temporal and spatial characteristics. I have investigated how such repeats influence response times in a living vs. non-living categorization task and the associated spatio-temporal patterns of brain activity in humans. Dynamic analysis of distributed source estimations revealed differential sound object representations within the auditory cortex as a function of the temporal history of exposure to these objects. Often heard sounds are coded by a modulation in a bilateral network. Recently heard sounds, independently of the number of previous exposures, are coded by a modulation of a left-sided network.With sound objects which carry spatial information, I have investigated how spatial aspects of the repeats influence neural representations. Dynamics analyses of distributed source estimations revealed an ultra rapid discrimination of sound objects which are characterized by spatial cues. This discrimination involved two temporo-spatially distinct cortical representations, one associated with position-independent and the other with position-linked representations within the auditory ventral/what stream.Action-related sounds were shown to increase the excitability of motoneurons within the primary motor cortex, possibly via an input from the mirror neuron system. The role of motor representations remains unclear. I have investigated repetition priming-induced plasticity of the motor representations of action sounds with the measurement of motor activity induced by TMS pulses applied on the hand motor cortex. TMS delivered to the hand area within the primary motor cortex yielded larger magnetic evoked potentials (MEPs) while the subject was listening to sounds associated with manual than non- manual actions. Repetition suppression was observed at motoneuron level, since during a repeated exposure to the same manual action sound the MEPs were smaller. I discuss these results in terms of specialized neural network involved in sound processing, which is characterized by repetition-induced plasticity.Thus, neural networks which underlie sound object representations are characterized by modulations which keep track of the temporal and spatial history of the sound and, in case of action related sounds, also of the way in which the sound is produced.LES OBJETS SONORES AU TRAVERS DU TEMPS, DE L'ESPACE ET DES ACTIONSLe terme "objet sonore" décrit une expérience auditive associée avec un événement acoustique produit par une source sonore. Au niveau cortical, les objets sonores sont représentés par des patterns d'activités dans des réseaux neuronaux distribués. Ce travail traite les aspects temporels, spatiaux et liés aux actions, évalués à l'aide de l'imagerie électrique ou par des mesures de l'activité motrice induite par stimulation magnétique trans-crânienne (SMT) chez des sujets sains. Entendre le même son de façon répétitive facilite la réponse comportementale (amorçage de répétition) et module l'activité neuronale (suppression liée à la répétition). Dans un cadre naturel, la même source est souvent entendue plusieurs fois, avec des variations spectro-temporelles et de ses caractéristiques spatiales. J'ai étudié la façon dont ces répétitions influencent le temps de réponse lors d'une tâche de catégorisation vivant vs. non-vivant, et les patterns d'activité cérébrale qui lui sont associés. Des analyses dynamiques d'estimations de sources ont révélé des représentations différenciées des objets sonores au niveau du cortex auditif en fonction de l'historique d'exposition à ces objets. Les sons souvent entendus sont codés par des modulations d'un réseau bilatéral. Les sons récemment entendus sont codé par des modulations d'un réseau du côté gauche, indépendamment du nombre d'expositions. Avec des objets sonores véhiculant de l'information spatiale, j'ai étudié la façon dont les aspects spatiaux des sons répétés influencent les représentations neuronales. Des analyses dynamiques d'estimations de sources ont révélé une discrimination ultra rapide des objets sonores caractérisés par des indices spatiaux. Cette discrimination implique deux représentations corticales temporellement et spatialement distinctes, l'une associée à des représentations indépendantes de la position et l'autre à des représentations liées à la position. Ces représentations sont localisées dans la voie auditive ventrale du "quoi".Des sons d'actions augmentent l'excitabilité des motoneurones dans le cortex moteur primaire, possiblement par une afférence du system des neurones miroir. Le rôle des représentations motrices des sons d'actions reste peu clair. J'ai étudié la plasticité des représentations motrices induites par l'amorçage de répétition à l'aide de mesures de potentiels moteurs évoqués (PMEs) induits par des pulsations de SMT sur le cortex moteur de la main. La SMT appliquée sur le cortex moteur primaire de la main produit de plus grands PMEs alors que les sujets écoutent des sons associée à des actions manuelles en comparaison avec des sons d'actions non manuelles. Une suppression liée à la répétition a été observée au niveau des motoneurones, étant donné que lors de l'exposition répétée au son de la même action manuelle les PMEs étaient plus petits. Ces résultats sont discuté en termes de réseaux neuronaux spécialisés impliqués dans le traitement des sons et caractérisés par de la plasticité induite par la répétition. Ainsi, les réseaux neuronaux qui sous-tendent les représentations des objets sonores sont caractérisés par des modulations qui gardent une trace de l'histoire temporelle et spatiale du son ainsi que de la manière dont le son a été produit, en cas de sons d'actions.

Design of Novel Frequency Generation Circuit with Application to Ultra-Wideband Distributed Oscillators

Given the urgence of a new paradigm in wireless digital trasmission which should allow for higher bit rate, lower latency and tigher delay constaints, it has been proposed to investigate the fundamental building blocks that at the circuital/device level, will boost the change towards a more efficient network architecture, with high capacity, higher bandwidth and a more satisfactory end user experience. At the core of each transciever, there are inherently analog devices capable of providing the carrier signal, the oscillators. It is strongly believed that many limitations in today's communication protocols, could be relieved by permitting high carrier frequency radio transmission, and having some degree of reconfigurability. This led us to studying distributed oscillator architectures which work in the microwave range and possess wideband tuning capability. As microvave oscillators are essentially nonlinear devices, a full nonlinear analyis, synthesis, and optimization had to be considered for their implementation. Consequently, all the most used nonlinear numerical techniques in commercial EDA software had been reviewed. An application of all the aforementioned techniques has been shown, considering a systems of three coupled oscillator ("triple push" oscillator) in which the stability of the various oscillating modes has been studied. Provided that a certain phase distribution is maintained among the oscillating elements, this topology permits a rise in the output power of the third harmonic; nevertheless due to circuit simmetry, "unwanted" oscillating modes coexist with the intenteded one. Starting with the necessary background on distributed amplification and distributed oscillator theory, the design of a four stage reverse mode distributed voltage controlled oscillator (DVCO) using lumped elments has been presented. All the design steps have been reported and for the first time a method for an optimized design with reduced variations in the output power has been presented. Ongoing work is devoted to model a wideband DVCO and to implement a frequency divider.

An empirical hierarchical Bayesian unification of occupational exposure assessment methods.

In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.

Insufficient coping behavior under chronic stress and vulnerability to psychiatric disorders

Epidemiological data indicate that 75% of subjects with major psychiatric disorders have their onset in the age range of 17-24 years. An estimated 35-50% of college and university students drop out prematurely due to insufficient coping skills under chronic stress, while 85% of students receiving a psychiatric diagnosis withdraw from college/university prior to the completion of their education. In this study we aimed at developing standardized means for identifying students with insufficient coping skills under chronic stress and at risk for mental health problems. A sample of 1,217 college students from 3 different sites in the U.S. and Switzerland completed 2 self-report questionnaires: the Coping Strategies Inventory "COPE" and the Zurich Health Questionnaire "ZHQ" which assesses "regular exercises", "consumption behavior", "impaired physical health", "psychosomatic disturbances", and "impaired mental health". The data were subjected to structure analyses by means of a Neural Network approach. We found 2 highly stable and reproducible COPE scales that explained the observed inter-individual variation in coping behavior sufficiently well and in a socio-culturally independent way. The scales reflected basic coping behavior in terms of "activity-passivity" and "defeatism-resilience", and in the sense of stable, socio-culturally independent personality traits. Correlation analyses carried out for external validation revealed a close relationship between high scores on the defeatism scale and impaired physical and mental health. This underlined the role of insufficient coping behavior as a risk factor for physical and mental health problems. The combined COPE and ZHQ instruments appear to constitute powerful screening tools for insufficient coping skills under chronic stress and for risks of mental health problems.

Using self organizing maps on compositional data

Self-organizing maps (Kohonen 1997) is a type of artificial neural network developedto explore patterns in high-dimensional multivariate data. The conventional versionof the algorithm involves the use of Euclidean metric in the process of adaptation ofthe model vectors, thus rendering in theory a whole methodology incompatible withnon-Euclidean geometries.In this contribution we explore the two main aspects of the problem:1. Whether the conventional approach using Euclidean metric can shed valid resultswith compositional data.2. If a modification of the conventional approach replacing vectorial sum and scalarmultiplication by the canonical operators in the simplex (i.e. perturbation andpowering) can converge to an adequate solution.Preliminary tests showed that both methodologies can be used on compositional data.However, the modified version of the algorithm performs poorer than the conventionalversion, in particular, when the data is pathological. Moreover, the conventional ap-proach converges faster to a solution, when data is \well-behaved".Key words: Self Organizing Map; Artificial Neural networks; Compositional data

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Prediction of postpartum depression using multilayer perceptrons and pruning.

OBJECTIVE. The main goal of this paper is to obtain a classification model based on feed-forward multilayer perceptrons in order to improve postpartum depression prediction during the 32 weeks after childbirth with a high sensitivity and specificity and to develop a tool to be integrated in a decision support system for clinicians. MATERIALS AND METHODS. Multilayer perceptrons were trained on data from 1397 women who had just given birth, from seven Spanish general hospitals, including clinical, environmental and genetic variables. A prospective cohort study was made just after delivery, at 8 weeks and at 32 weeks after delivery. The models were evaluated with the geometric mean of accuracies using a hold-out strategy. RESULTS. Multilayer perceptrons showed good performance (high sensitivity and specificity) as predictive models for postpartum depression. CONCLUSIONS. The use of these models in a decision support system can be clinically evaluated in future work. The analysis of the models by pruning leads to a qualitative interpretation of the influence of each variable in the interest of clinical protocols.

New perspectives in the use of ink evidence in forensic science: Part II. Development and testing of mathematical algorithms for the automatic comparison of ink samples analysed by HPTLC

In the first part of this research, three stages were stated for a program to increase the information extracted from ink evidence and maximise its usefulness to the criminal and civil justice system. These stages are (a) develop a standard methodology for analysing ink samples by high-performance thin layer chromatography (HPTLC) in reproducible way, when ink samples are analysed at different time, locations and by different examiners; (b) compare automatically and objectively ink samples; and (c) define and evaluate theoretical framework for the use of ink evidence in forensic context. This report focuses on the second of the three stages. Using the calibration and acquisition process described in the previous report, mathematical algorithms are proposed to automatically and objectively compare ink samples. The performances of these algorithms are systematically studied for various chemical and forensic conditions using standard performance tests commonly used in biometrics studies. The results show that different algorithms are best suited for different tasks. Finally, this report demonstrates how modern analytical and computer technology can be used in the field of ink examination and how tools developed and successfully applied in other fields of forensic science can help maximising its impact within the field of questioned documents.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

Major: Electronics and Communication Engineering

Today, information technology is strategically important to the goals and aspirations of the business enterprises, government and high-level education institutions – university. Universities are facing new challenges with the emerging global economy characterized by the importance of providing faster communication services and improving the productivity and effectiveness of individuals. New challenges such as provides an information network that supports the demands and diversification of university issues. A new network architecture, which is a set of design principles for build a network, is one of the pillar bases. It is the cornerstone that enables the university’s faculty, researchers, students, administrators, and staff to discover, learn, reach out, and serve society. This thesis focuses on the network architecture definitions and fundamental components. Three most important characteristics of high-quality architecture are that: it’s open network architecture; it’s service-oriented characteristics and is an IP network based on packets. There are four important components in the architecture, which are: Services and Network Management, Network Control, Core Switching and Edge Access. The theoretical contribution of this study is a reference model Architecture of University Campus Network that can be followed or adapted to build a robust yet flexible network that respond next generation requirements. The results found are relevant to provide an important complete reference guide to the process of building campus network which nowadays play a very important role. Respectively, the research gives university networks a structured modular model that is reliable, robust and can easily grow.

Stereological analysis of the rat and monkey amygdala

The amygdala is part of a neural network that contributes to the regulation of emotional behaviors. Rodents, especially rats, are used extensively as model organisms to decipher the functions of specific amygdala nuclei, in particular in relation to fear and emotional learning. Analysis of the role of the nonhuman primate amygdala in these functions has lagged work in the rodent but provides evidence for conservation of basic functions across species. Here we provide quantitative information regarding the morphological characteristics of the main amygdala nuclei in rats and monkeys, including neuron and glial cell numbers, neuronal soma size, and individual nuclei volumes. The volumes of the lateral, basal, and accessory basal nuclei were, respectively, 32, 39, and 39 times larger in monkeys than in rats. In contrast, the central and medial nuclei were only 8 and 4 times larger in monkeys than in rats. The numbers of neurons in the lateral, basal, and accessory basal nuclei were 14, 11, and 16 times greater in monkeys than in rats, whereas the numbers of neurons in the central and medial nuclei were only 2.3 and 1.5 times greater in monkeys than in rats. Neuron density was between 2.4 and 3.7 times lower in monkeys than in rats, whereas glial density was only between 1.1 and 1.7 times lower in monkeys than in rats. We compare our data in rats and monkeys with those previously published in humans and discuss the theoretical and functional implications that derive from our quantitative structural findings.

Optimal information transmission in organizations: Search and congestion

We propose a stylized model of a problem-solving organization whoseinternal communication structure is given by a fixed network. Problemsarrive randomly anywhere in this network and must find their way to theirrespective specialized solvers by relying on local information alone.The organization handles multiple problems simultaneously. For this reason,the process may be subject to congestion. We provide a characterization ofthe threshold of collapse of the network and of the stock of foatingproblems (or average delay) that prevails below that threshold. We buildupon this characterization to address a design problem: the determinationof what kind of network architecture optimizes performance for any givenproblem arrival rate. We conclude that, for low arrival rates, the optimalnetwork is very polarized (i.e. star-like or centralized ), whereas it islargely homogenous (or decentralized ) for high arrival rates. We also showthat, if an auxiliary assumption holds, the transition between these twoopposite structures is sharp and they are the only ones to ever qualify asoptimal.

Structural covariance of superficial white matter in mild Alzheimer's disease compared to normal aging.

INTRODUCTION: Interindividual variations in regional structural properties covary across the brain, thus forming networks that change as a result of aging and accompanying neurological conditions. The alterations of superficial white matter (SWM) in Alzheimer's disease (AD) are of special interest, since they follow the AD-specific pattern characterized by the strongest neurodegeneration of the medial temporal lobe and association cortices. METHODS: Here, we present an SWM network analysis in comparison with SWM topography based on the myelin content quantified with magnetization transfer ratio (MTR) for 39 areas in each hemisphere in 15 AD patients and 15 controls. The networks are represented by graphs, in which nodes correspond to the areas, and edges denote statistical associations between them. RESULTS: In both groups, the networks were characterized by asymmetrically distributed edges (predominantly in the left hemisphere). The AD-related differences were also leftward. The edges lost due to AD tended to connect nodes in the temporal lobe to other lobes or nodes within or between the latter lobes. The newly gained edges were mostly confined to the temporal and paralimbic regions, which manifest demyelination of SWM already in mild AD. CONCLUSION: This pattern suggests that the AD pathological process coordinates SWM demyelination in the temporal and paralimbic regions, but not elsewhere. A comparison of the MTR maps with MTR-based networks shows that although, in general, the changes in network architecture in AD recapitulate the topography of (de)myelination, some aspects of structural covariance (including the interhemispheric asymmetry of networks) have no immediate reflection in the myelination pattern.

Identificación de puntos de pesca en los viajes pesqueros de anchoveta monitoreados por satélites mediante redes neuronales artificiales

La sostenibilidad de los recursos marinos y de su ecosistema hace necesario un manejo responsable de las pesquerías. Conocer la distribución espacial del esfuerzo pesquero y en particular de las operaciones de pesca es indispensable para mejorar el monitoreo pesquero y el análisis de la vulnerabilidad de las especies frente a la pesca. Actualmente en la pesquería de anchoveta peruana, se recoge información del esfuerzo y capturas mediante un programa de observadores a bordo, pero esta solo representa una muestra de 2% del total de viajes pesqueros. Por otro lado, se dispone de información por cada hora (en promedio) de la posición de cada barco de la flota gracias al sistema de seguimiento satelital de las embarcaciones (VMS), aunque en estos no se señala cuándo ni dónde ocurrieron las calas. Las redes neuronales artificiales (ANN) podrían ser un método estadístico capaz de inferir esa información, entrenándose en una muestra para la cual sí conocemos las posiciones de calas (el 2% anteriormente referido), estableciendo relaciones analíticas entre las calas y ciertas características geométricas de las trayectorias observadas por el VMS y así, a partir de las últimas, identificar la posición de las operaciones de pesca. La aplicación de la red neuronal requiere un análisis previo que examine la sensibilidad de la red a variaciones en sus parámetros y bases de datos de entrenamiento, y que nos permita desarrollar criterios para definir la estructura de la red e interpretar sus resultados de manera adecuada. La problemática descrita en el párrafo anterior, aplicada específicamente a la anchoveta (Engraulis ringens) es detalllada en el primer capítulo, mientras que en el segundo se hace una revisión teórica de las redes neuronales. Luego se describe el proceso de construcción y pre-tratamiento de la base de datos, y definición de la estructura de la red previa al análisis de sensibilidad. A continuación se presentan los resultados para el análisis en los que obtenemos una estimación del 100% de calas, de las cuales aproximadamente 80% están correctamente ubicadas y 20% poseen un error de ubicación. Finalmente se discuten las fortalezas y debilidades de la técnica empleada, de métodos alternativos potenciales y de las perspectivas abiertas por este trabajo.