THEORY OF STEADY-STATE CURRENT AT MULTIPLE MICROCYLINDER ELECTRODES COUPLED WITH A PARALLEL PLANAR ELECTRODE

A novel device of multiple cylinder microelectrodes coupled with a parallel planar electrode was proposed. The feedback diffusion current at this device was studied using bilinear transformation of coordinates in the diffusion space, where lines of mass flux and equiconcentration are represented by orthogonal circular functions. The derived expression for the steady-state current shows that as the gap between cylindrical microelectrodes and planar electrode diminishes, greatly enhanced currents can be obtained with high signal-to-noise ratio. Other important geometrical parameters such as distance between adjacent microcylinders, cylinder radius, and number of microcylinders were also discussed in detail.

Integrated structure selection and parameter optimisation for eng-genes neural models

The eng-genes concept involves the use of fundamental known system functions as activation functions in a neural model to create a 'grey-box' neural network. One of the main issues in eng-genes modelling is to produce a parsimonious model given a model construction criterion. The challenges are that (1) the eng-genes model in most cases is a heterogenous network consisting of more than one type of nonlinear basis functions, and each basis function may have different set of parameters to be optimised; (2) the number of hidden nodes has to be chosen based on a model selection criterion. This is a mixed integer hard problem and this paper investigates the use of a forward selection algorithm to optimise both the network structure and the parameters of the system-derived activation functions. Results are included from case studies performed on a simulated continuously stirred tank reactor process, and using actual data from a pH neutralisation plant. The resulting eng-genes networks demonstrate superior simulation performance and transparency over a range of network sizes when compared to conventional neural models. (c) 2007 Elsevier B.V. All rights reserved.

Exoplanets or Dynamic Atmospheres? The Radial Velocity and Line Shape Variations of 51 Pegasi and tau Bootis

The stars 51 Pegasi and tau Bootis show radial velocity variations that have been interpreted as resulting from companions with roughly Jovian mass and orbital periods of a few days. Gray and Gray & Hatzes reported that the radial velocity signal of 51 Peg is synchronous with variations in the shape of the line lambda 6253 Fe I; thus, they argue that the velocity signal arises not from a companion of planetary mass but from dynamic processes in the atmosphere of the star, possibly nonradial pulsations. Here we seek confirming evidence for line shape or strength variations in both 51 Peg and tau Boo, using R = 50,000 observations taken with the Advanced Fiber Optic Echelle. Because of our relatively low spectral resolution, we compare our observations with Gray's line bisector data by fitting observed line profiles to an expansion in terms of orthogonal (Hermite) functions. To obtain an accurate comparison, we model the emergent line profiles from rotating and pulsating stars, taking the instrumental point-spread function into account. We describe this modeling process in detail. We find no evidence for line profile or strength variations at the radial velocity period in either 51 Peg or in tau Boo. For 51 Peg, our upper limit for line shape variations with 4.23 day periodicity is small enough to exclude with 10 sigma confidence the bisector curvature signal reported by Gray & Hatzes; the bisector span and relative line depth signals reported by Gray are also not seen, but in this case with marginal (2 sigma ) confidence. We cannot, however, exclude pulsations as the source of 51 Peg's radial velocity variation because our models imply that line shape variations associated with pulsations should be much smaller than those computed by Gray & Hatzes; these smaller signals are below the detection limits both for Gray & Hatzes's data and for our own. tau Boo's large radial velocity amplitude and v sin i make it easier to test for pulsations in this star. Again we find no evidence for periodic line shape changes, at a level that rules out pulsations as the source of the radial velocity variability. We conclude that the planet hypothesis remains the most likely explanation for the existing data.

On Recurrent and Deep Neural Networks

L'apprentissage profond est un domaine de recherche en forte croissance en apprentissage automatique qui est parvenu à des résultats impressionnants dans différentes tâches allant de la classification d'images à la parole, en passant par la modélisation du langage. Les réseaux de neurones récurrents, une sous-classe d'architecture profonde, s'avèrent particulièrement prometteurs. Les réseaux récurrents peuvent capter la structure temporelle dans les données. Ils ont potentiellement la capacité d'apprendre des corrélations entre des événements éloignés dans le temps et d'emmagasiner indéfiniment des informations dans leur mémoire interne. Dans ce travail, nous tentons d'abord de comprendre pourquoi la profondeur est utile. Similairement à d'autres travaux de la littérature, nos résultats démontrent que les modèles profonds peuvent être plus efficaces pour représenter certaines familles de fonctions comparativement aux modèles peu profonds. Contrairement à ces travaux, nous effectuons notre analyse théorique sur des réseaux profonds acycliques munis de fonctions d'activation linéaires par parties, puisque ce type de modèle est actuellement l'état de l'art dans différentes tâches de classification. La deuxième partie de cette thèse porte sur le processus d'apprentissage. Nous analysons quelques techniques d'optimisation proposées récemment, telles l'optimisation Hessian free, la descente de gradient naturel et la descente des sous-espaces de Krylov. Nous proposons le cadre théorique des méthodes à région de confiance généralisées et nous montrons que plusieurs de ces algorithmes développés récemment peuvent être vus dans cette perspective. Nous argumentons que certains membres de cette famille d'approches peuvent être mieux adaptés que d'autres à l'optimisation non convexe. La dernière partie de ce document se concentre sur les réseaux de neurones récurrents. Nous étudions d'abord le concept de mémoire et tentons de répondre aux questions suivantes: Les réseaux récurrents peuvent-ils démontrer une mémoire sans limite? Ce comportement peut-il être appris? Nous montrons que cela est possible si des indices sont fournis durant l'apprentissage. Ensuite, nous explorons deux problèmes spécifiques à l'entraînement des réseaux récurrents, à savoir la dissipation et l'explosion du gradient. Notre analyse se termine par une solution au problème d'explosion du gradient qui implique de borner la norme du gradient. Nous proposons également un terme de régularisation conçu spécifiquement pour réduire le problème de dissipation du gradient. Sur un ensemble de données synthétique, nous montrons empiriquement que ces mécanismes peuvent permettre aux réseaux récurrents d'apprendre de façon autonome à mémoriser des informations pour une période de temps indéfinie. Finalement, nous explorons la notion de profondeur dans les réseaux de neurones récurrents. Comparativement aux réseaux acycliques, la définition de profondeur dans les réseaux récurrents est souvent ambiguë. Nous proposons différentes façons d'ajouter de la profondeur dans les réseaux récurrents et nous évaluons empiriquement ces propositions.

Apprentissage des réseaux de neurones profonds et applications en traitement automatique de la langue naturelle

En apprentissage automatique, domaine qui consiste à utiliser des données pour apprendre une solution aux problèmes que nous voulons confier à la machine, le modèle des Réseaux de Neurones Artificiels (ANN) est un outil précieux. Il a été inventé voilà maintenant près de soixante ans, et pourtant, il est encore de nos jours le sujet d'une recherche active. Récemment, avec l'apprentissage profond, il a en effet permis d'améliorer l'état de l'art dans de nombreux champs d'applications comme la vision par ordinateur, le traitement de la parole et le traitement des langues naturelles. La quantité toujours grandissante de données disponibles et les améliorations du matériel informatique ont permis de faciliter l'apprentissage de modèles à haute capacité comme les ANNs profonds. Cependant, des difficultés inhérentes à l'entraînement de tels modèles, comme les minima locaux, ont encore un impact important. L'apprentissage profond vise donc à trouver des solutions, en régularisant ou en facilitant l'optimisation. Le pré-entraînnement non-supervisé, ou la technique du ``Dropout'', en sont des exemples. Les deux premiers travaux présentés dans cette thèse suivent cette ligne de recherche. Le premier étudie les problèmes de gradients diminuants/explosants dans les architectures profondes. Il montre que des choix simples, comme la fonction d'activation ou l'initialisation des poids du réseaux, ont une grande influence. Nous proposons l'initialisation normalisée pour faciliter l'apprentissage. Le second se focalise sur le choix de la fonction d'activation et présente le rectifieur, ou unité rectificatrice linéaire. Cette étude a été la première à mettre l'accent sur les fonctions d'activations linéaires par morceaux pour les réseaux de neurones profonds en apprentissage supervisé. Aujourd'hui, ce type de fonction d'activation est une composante essentielle des réseaux de neurones profonds. Les deux derniers travaux présentés se concentrent sur les applications des ANNs en traitement des langues naturelles. Le premier aborde le sujet de l'adaptation de domaine pour l'analyse de sentiment, en utilisant des Auto-Encodeurs Débruitants. Celui-ci est encore l'état de l'art de nos jours. Le second traite de l'apprentissage de données multi-relationnelles avec un modèle à base d'énergie, pouvant être utilisé pour la tâche de désambiguation de sens.

A comparative study of multilayered and single layered neural network based predictive controllers

The authors compare the performance of two types of controllers one based on the multilayered network and the other based on the single layered CMAC network (cerebellar model articulator controller). The neurons (information processing units) in the multi-layered network use Gaussian activation functions. The control scheme which is considered is a predictive control algorithm, along the lines used by Willis et al. (1991), Kambhampati and Warwick (1991). The process selected as a test bed is a continuous stirred tank reactor. The reaction taking place is an irreversible exothermic reaction in a constant volume reactor cooled by a single coolant stream. This reactor is a simplified version of the first tank in the two tank system given by Henson and Seborg (1989).

The Thyroid Hormone Receptor (TR) beta-Selective Agonist GC-1 Inhibits Proliferation But Induces Differentiation and TR beta mRNA Expression in Mouse and Rat Osteoblast-Like Cells

Previous studies showed anabolic effects of GC-1, a triiodothyronine (T3) analogue that is selective for both binding and activation functions of thyroid hormone receptor (TR) beta 1 over TR alpha 1, on bone tissue in vivo. The aim of this study was to investigate the responsiveness of rat (ROS17/2.8) and mouse (MC3T3-E1) osteoblast-like cells to GC-1. As expected, T3 inhibited cellular proliferation and stimulated mRNA expression of osteocalcin or alkaline phosphatase in both cell lineages. Whereas equimolar doses of T3 and GC-1 equally affected these parameters in ROS17/2.8 cells, the effects of GC-1 were more modest compared to those of T3 in MC3T3-E1 cells. Interestingly, we showed that there is higher expression of TR alpha 1 than TR beta 1 mRNA in rat (similar to 20-90%) and mouse (similar to 90-98%) cell lineages and that this difference is even higher in mouse cells, which highlights the importance of TR alpha 1 to bone physiology and may partially explain the modest effects of GC-1 in comparison with T3 in MC3T3-E1 cells. Nevertheless, we showed that TR beta 1 mRNA expression increases (similar to 2.8- to 4.3-fold) as osteoblastic cells undergo maturation, suggesting a key role of TR beta 1 in mediating T3 effects in the bone forming cells, especially in mature osteoblasts. It is noteworthy that T3 and GC-1 induced TR beta 1 mRNA expression to a similar extent in both cell lineages (similar to 2- to 4-fold), indicating that both ligands may modulate the responsiveness of osteoblasts to T3. Taken together, these data show that TR beta selective T3 analogues have the potential to directly induce the differentiation and activity of osteoblasts.

Design of H∞ control of neural networks with time-varying delays

This paper deals with the H∞ control problem of neural networks with time-varying delays. The system under consideration is subject to time-varying delays and various activation functions. Based on constructing some suitable Lyapunov-Krasovskii functionals, we establish new sufficient conditions for H∞ control for two cases of time-varying delays: (1) the delays are differentiable and have an upper bound of the delay-derivatives and (2) the delays are bounded but not necessary to be differentiable. The derived conditions are formulated in terms of linear matrix inequalities, which allow simultaneous computation of two bounds that characterize the exponential stability rate of the solution. Numerical examples are given to illustrate the effectiveness of our results.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

Reservoir Management For Flood And Drought Protection Using Infinite Horizon Model Predictive Control

Model Predictive Control (MPC) is a control method that solves in real time an optimal control problem over a finite horizon. The finiteness of the horizon is both the reason of MPC's success and its main limitation. In operational water resources management, MPC has been in fact successfully employed for controlling systems with a relatively short memory, such as canals, where the horizon length is not an issue. For reservoirs, which have generally a longer memory, MPC applications are presently limited to short term management only. Short term reservoir management can be effectively used to deal with fast process, such as floods, but it is not capable of looking sufficiently ahead to handle long term issues, such as drought. To overcome this limitation, we propose an Infinite Horizon MPC (IH-MPC) solution that is particularly suitable for reservoir management. We propose to structure the input signal by use of orthogonal basis functions, therefore reducing the optimization argument to a finite number of variables, and making the control problem solvable in a reasonable time. We applied this solution for the management of the Manantali Reservoir. Manantali is a yearly reservoir located in Mali, on the Senegal river, affecting water systems of Mali, Senegal, and Mauritania. The long term horizon offered by IH-MPC is necessary to deal with the strongly seasonal climate of the region.

Multidimensional polynomial powers of sigmoid (PPS) Wavelet neural networks

Wavelet functions have been used as the activation function in feedforward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical backpropagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem.

Human face verification based on multidimensional Polynomial Powers of Sigmoid (PPS)

In this paper, we described how a multidimensional wavelet neural networks based on Polynomial Powers of Sigmoid (PPS) can be constructed, trained and applied in image processing tasks. In this sense, a novel and uniform framework for face verification is presented. The framework is based on a family of PPS wavelets,generated from linear combination of the sigmoid functions, and can be considered appearance based in that features are extracted from the face image. The feature vectors are then subjected to subspace projection of PPS-wavelet. The design of PPS-wavelet neural networks is also discussed, which is seldom reported in the literature. The Stirling Universitys face database were used to generate the results. Our method has achieved 92 % of correct detection and 5 % of false detection rate on the database.

Identificação e controle de sistemas dinâmicos utilizando redes wavelets

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Uso de modelo de regressão aleatória na análise de produção de leite em bubalinos

Dados referentes a 1.719 controles de produção de leite de 357 fêmeas predominantemente da raça Murrah, filhas de 110 reprodutores, com partos distribuídos entre os anos de 1974 e 2004, obtidos do Programa de Melhoramento Genético de Bubalinos (PROMEBUL) com adição de registros do rebanho pertencente à EMBRAPA Amazônia Oriental - EAO, localizada em Belém, Pará. Os registros foram usados para comparar modelos de regressão aleatória na estimação de componentes de variância e predição de valores genéticos dos reprodutores utilizando a. função polinomial de Legendre, variando de segunda à quarta ordem. O modelo de regressão aleatória incluiu os efeitos de rebanho-ano, mês de parto, coeficientes de regressão para idade da fêmea (para descrever a parte fixa da curva de lactação) e coeficientes de regressão relacionados ao efeito genético direto e de ambiente permanente. A comparação entre modelos foram realizadas por meio do Critério de Informação de Akaike. O modelo de regressão aleatória que utilizou a terceira ordem de polinômio de Legendre, com quatro classes de resíduo para o ambiente temporário, foi o que melhor descreveu a variação genética aditiva da produção de leite. A herdabilidade estimada variou entre 0,08 a 0,40. A correlação genética entre produções mais próximas foram próximas da unidade, mas em idades mais distantes a correlação foi baixa. A correlação de Spearman e de Pearson entre os valores genéticos preditos em todas as situações foram próximas da unidade.

Myc-mediated control of gene transcription in cancer cells

The Myc oncoproteins belong to a family of transcription factors composed by Myc, N-Myc and L-Myc. The most studied components of this family are Myc and N-Myc because their expressions are frequently deregulated in a wide range of cancers. These oncoproteins can act both as activators or repressors of gene transcription. As activators, they heterodimerize with Max (Myc associated X-factor) and the heterodimer recognizes and binds a specific sequence elements (E-Box) onto gene promoters recruiting histone acetylase and inducing transcriptional activation. Myc-mediated transcriptional repression is a quite debated issue. One of the first mechanisms defined for the Myc-mediated transcriptional repression consisted in the interaction of Myc-Max complex Sp1 and/or Miz1 transcription factors already bound to gene promoters. This interaction may interfere with their activation functions by recruiting co-repressors such as Dnmt3 or HDACs. Moreover, in the absence of , Myc may interfere with the Sp1 activation function by direct interaction and subsequent recruitment of HDACs. More recently the Myc/Max complex was also shown to mediate transcriptional repression by direct binding to peculiar E-box. In this study we analyzed the role of Myc overexpression in Osteosarcoma and Neuroblastoma oncogenesis and the mechanisms underling to Myc function. Myc overexpression is known to correlate with chemoresistance in Osteosarcoma cells. We extended this study by demonstrating that c-Myc induces transcription of a panel of ABC drug transporter genes. ABCs are a large family trans-membrane transporter deeply involved in multi drug resistance. Furthermore expression levels of Myc, ABCC1, ABCC4 and ABCF1 were proved to be important prognostic tool to predict conventional therapy failure. N-Myc amplification/overexpression is the most important prognostic factor for Neuroblastoma. Cyclin G2 and Clusterin are two genes often down regulated in neuroblastoma cells. Cyclin G2 is an atypical member of Cyclin family and its expression is associated with terminal differentiation and apoptosis. Moreover it blocks cell cycle progression and induces cell growth arrest. Instead, CLU is a multifunctional protein involved in many physiological and pathological processes. Several lines of evidences support the view that CLU may act as a tumour suppressor in Neuroblastoma. In this thesis I showed that N-Myc represses CCNG2 and CLU transcription by different mechanisms. • N-Myc represses CCNG2 transcription by directly interacting with Sp1 bound in CCNG2 promoter and recruiting HDAC2. Importantly, reactivation of CCNG2 expression through epigenetic drugs partially reduces N-Myc and HDAC2 mediated cell proliferation. • N-Myc/Max complex represses CLU expression by direct binding to a peculiar E-box element on CLU promoter and by recruitment of HDACs and Polycomb Complexes, to the CLU promoter. Overall our findings strongly support the model in which Myc overexpression/amplification may contribute to some aspects of oncogenesis by a dual action: i) transcription activation of genes that confer a multidrug resistant phenotype to cancer cells; ii), transcription repression of genes involved in cell cycle inhibition and cellular differentiation.