Current Developments in Fluorescent PET (Photoinduced Electron Transfer) Sensors and Switches

Following a brief introduction to the principle of fluorescent PET (photoinduced electron transfer) sensors and switches, the outputs of laboratories in various countries from the past year or two are categorized and critically discussed. Emphasis is placed on the molecular design and the experimental outcomes in terms of target-induced fluorescence enhancements and input/output wavelengths. The handling of single targets takes up a major fraction of the review, but the extension to multiple targets is also illustrated. Conceptually new channels of investigation are opened up by the latter approach, e.g. ‘lab-on-a-molecule’ systems and molecular keypad locks. The growing trends of theoretically-fortified design and intracellular application are pointed out.

Behavioral modeling optimization and enhancement for high-speed analog mixed-signal I/O interfaces

A integridade do sinal em sistemas digitais interligados de alta velocidade, e avaliada através da simulação de modelos físicos (de nível de transístor) é custosa de ponto vista computacional (por exemplo, em tempo de execução de CPU e armazenamento de memória), e exige a disponibilização de detalhes físicos da estrutura interna do dispositivo. Esse cenário aumenta o interesse pela alternativa de modelação comportamental que descreve as características de operação do equipamento a partir da observação dos sinais eléctrico de entrada/saída (E/S). Os interfaces de E/S em chips de memória, que mais contribuem em carga computacional, desempenham funções complexas e incluem, por isso, um elevado número de pinos. Particularmente, os buffers de saída são obrigados a distorcer os sinais devido à sua dinâmica e não linearidade. Portanto, constituem o ponto crítico nos de circuitos integrados (CI) para a garantia da transmissão confiável em comunicações digitais de alta velocidade. Neste trabalho de doutoramento, os efeitos dinâmicos não-lineares anteriormente negligenciados do buffer de saída são estudados e modulados de forma eficiente para reduzir a complexidade da modelação do tipo caixa-negra paramétrica, melhorando assim o modelo standard IBIS. Isto é conseguido seguindo a abordagem semi-física que combina as características de formulação do modelo caixa-negra, a análise dos sinais eléctricos observados na E/S e propriedades na estrutura física do buffer em condições de operação práticas. Esta abordagem leva a um processo de construção do modelo comportamental fisicamente inspirado que supera os problemas das abordagens anteriores, optimizando os recursos utilizados em diferentes etapas de geração do modelo (ou seja, caracterização, formulação, extracção e implementação) para simular o comportamento dinâmico não-linear do buffer. Em consequência, contributo mais significativo desta tese é o desenvolvimento de um novo modelo comportamental analógico de duas portas adequado à simulação em overclocking que reveste de um particular interesse nas mais recentes usos de interfaces de E/S para memória de elevadas taxas de transmissão. A eficácia e a precisão dos modelos comportamentais desenvolvidos e implementados são qualitativa e quantitativamente avaliados comparando os resultados numéricos de extracção das suas funções e de simulação transitória com o correspondente modelo de referência do estado-da-arte, IBIS.

Prediction of the solar radiation using RBF neural networks and ground-to-sky images

In this study, Artificial Neural Networks are applied to multistep long term solar radiation prediction. The networks are trained as one-step-ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and Auto-regressive with exogenous inputs solar radiationmodels are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images. The input-output structure of the neural network models is selected using evolutionary computation methods.

Evaluation of the economic impact of tourism expenditures in Portugal

Dissertação de Mestrado, Economia do Turismo e Desenvolvimento Regional, Faculdade de Economia, Universidade do Algarve, 2015

Three-level energy decoupling : energy decoupling at the primary, final and useful levels of energy use

Tese de doutoramento, Sistemas Sustentáveis de Energia, Universidade de Lisboa, Faculdade de Ciências, 2015

Synaptic dysfunction in early encephalopathies : from genes to function

Tese de doutoramento, Medicina (Neurologia), Universidade de Lisboa, Faculdade de Medicina, 2015

Program slice metrics and their potential role in DSL design, position paper

The advantages a DSL and the benefits its use potentially brings imply that informed decisions on the design of a domain specific language are of paramount importance for its use. We believe that the foundations of such decisions should be informed by analysis of data empirically collected from systems to highlight salient features that should then form the basis of a DSL. To support this theory, we describe an empirical study of a large OSS called Barcode, written in C, and from which we collected two well-known 'slice' based metrics. We analyzed multiple versions of the system and sliced its functions in three separate ways (i.e., input, output and global variables). The purpose of the study was to try and identify sensitivities and traits in those metrics that might inform features of a potential slice-based DSL. Results indicated that cohesion was adversely affected through the use of global variables and that appreciation of the role of function inputs and outputs can be revealed through slicing. The study presented is motivated primarily by the problems with current tools and interfaces experienced directly by the authors in extracting slicing data and the need to promote the benefits that analysis of slice data and slicing in general can offer.

Uncertainty assessment approach for composite structures based on global sensitivity indices

The problem of uncertainty propagation in composite laminate structures is studied. An approach based on the optimal design of composite structures to achieve a target reliability level is proposed. Using the Uniform Design Method (UDM), a set of design points is generated over a design domain centred at mean values of random variables, aimed at studying the space variability. The most critical Tsai number, the structural reliability index and the sensitivities are obtained for each UDM design point, using the maximum load obtained from optimal design search. Using the UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on supervised evolutionary learning. Finally, using the developed ANN a Monte Carlo simulation procedure is implemented and the variability of the structural response based on global sensitivity analysis (GSA) is studied. The GSA is based on the first order Sobol indices and relative sensitivities. An appropriate GSA algorithm aiming to obtain Sobol indices is proposed. The most important sources of uncertainty are identified.

Uncertainty propagation in inverse reliability-based design of composite structures

An approach for the analysis of uncertainty propagation in reliability-based design optimization of composite laminate structures is presented. Using the Uniform Design Method (UDM), a set of design points is generated over a domain centered on the mean reference values of the random variables. A methodology based on inverse optimal design of composite structures to achieve a specified reliability level is proposed, and the corresponding maximum load is outlined as a function of ply angle. Using the generated UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on an evolutionary learning process. Then, a Monte Carlo simulation using ANN development is performed to simulate the behavior of the critical Tsai number, structural reliability index, and their relative sensitivities as a function of the ply angle of laminates. The results are generated for uniformly distributed random variables on a domain centered on mean values. The statistical analysis of the results enables the study of the variability of the reliability index and its sensitivity relative to the ply angle. Numerical examples showing the utility of the approach for robust design of angle-ply laminates are presented.

Nearly Serial Sharing Methods

A group of agents participate in a cooperative enterprise producing a single good. Each participant contributes a particular type of input; output is nondecreasing in these contributions. How should it be shared? We analyze the implications of the axiom of Group Monotonicity: if a group of agents simultaneously decrease their input contributions, not all of them should receive a higher share of output. We show that in combination with other more familiar axioms, this condition pins down a very small class of methods, which we dub nearly serial.

The Transmission of Monetary Policy in a Multi-Sector Economy

This paper constructs and estimates a sticky-price, Dynamic Stochastic General Equilibrium model with heterogenous production sectors. Sectors differ in price stickiness, capital-adjustment costs and production technology, and use output from each other as material and investment inputs following an Input-Output Matrix and Capital Flow Table that represent the U.S. economy. By relaxing the standard assumption of symmetry, this model allows different sectoral dynamics in response to monetary policy shocks. The model is estimated by Simulated Method of Moments using sectoral and aggregate U.S. time series. Results indicate 1) substantial heterogeneity in price stickiness across sectors, with quantitatively larger differences between services and goods than previously found in micro studies that focus on final goods alone, 2) a strong sensitivity to monetary policy shocks on the part of construction and durable manufacturing, and 3) similar quantitative predictions at the aggregate level by the multi-sector model and a standard model that assumes symmetry across sectors.

Production Linkages Between Informal and Formal Activities Considering Domestic and Imported Inputs: an Application of the Minimal-Flow-Analysis Method to Senegal.

We apply to the Senegalese input-output matrix of 1990, disagregated into formal and informal activities, a recently designed structural analytical method (Minimal-Flow-Analysis) which permits to depict the direct and indirect production likanges existing between activities.

Durable Goods, Inter-Sectoral Linkages and Monetary Policy

Barsky, House and Kimball (2007) show that introducing durable goods into a sticky-price model leads to negative sectoral comovement of production following a monetary policy shock and, under certain conditions, to aggregate neutrality. These results appear to undermine sticky-price models. In this paper, we show that these results are not robust to two prominent and realistic features of the data, namely input-output interactions and limited mobility of productive inputs. When extended to allow for both features, the sticky-price model with durable goods delivers implications in line with VAR evidence on the effects of monetary policy shocks.

Le financement public de l’enseignement supérieur et ses impacts au Pérou : une analyse longitudinale (1993 – 2003)

Cette recherche porte sur le financement public de l’enseignement supérieur au Pérou et ses impacts dans une perspective longitudinale couvant la période 1993-2003. Cette période est importante parce qu’elle a été témoin, dans ce pays, de changements majeurs aux plans du financement public et de la configuration du système d’enseignement supérieur. La recherche consiste principalement dans des analyses secondaires de données pertinentes publiées par des organismes nationaux et internationaux. Les analyses sont structurées à partir d’un schéma d’inputs et outputs. On considère comme inputs les ressources financières et les ressources humaines, lesquelles comprennent les professeurs et les étudiants, et comme outputs les taux de diplomation (efficacité interne) et la demande de diplômés par le marché du travail (efficacité externe). La théorie de la dépendance de ressources sert de cadre pour interpréter les rapports entre le financement public et ses incidences sur les réponses institutionnels et ses conséquences. Dans la période retenue, le financement du secteur public a décru de 32% en raison d’un désengagement progressif de l’État. Une conséquence majeure de la diminution du financement public a été la croissance rapide du secteur privé de l’enseignement supérieur. En effet, alors qu’en 1993 il y avait 24 institutions privées d’enseignement supérieur, il y en avait, en 2003, 46 institutions. La baisse du financement public et la croissance du secteur privé d’enseignement supérieur ont eu des incidences sur la sélectivité des étudiants, sur le statut des professeurs, sur l’implication des universités en recherche et sur les taux de diplomation. Le taux de sélectivité dans le secteur public a augmenté entre 1993 et 2003, alors que ce taux a diminué, dans la même période, dans le secteur privé. Ainsi, le secteur public répond à la diminution du financement en restreignant l’accès à l’enseignement supérieur. Le secteur privé, par contre, diminue sa sélectivité compensant ainsi l’augmentation de la sélectivité dans le secteur public et, par le fait même, augmente sa part de marché. Également, tant dans le secteur public que dans le secteur privé, les professeurs sont engagés principalement sur une base temporaire, ce qui se traduit, particulièrement dans le secteur privé, dans un moindre engagement institutionnel. Enfin, les universités publiques et privées du Pérou font peu de recherche, car elles favorisent, pour balancer leurs budgets, la consultation et les contrats au détriment de la recherche fondamentale. Paradoxalement, alors que, dans le secteur privé, les taux de sélectivité des étudiants diminuent, leurs taux de diplomation augmentent plus que dans le secteur public. Enfin, les formations avec plus d’étudiants inscrits, tant dans le secteur public que privé, sont les moins coûteuses en infrastructure et équipements. Dès lors, la pertinence de la production universitaire devient problématique. Cette recherche révèle que les organisations universitaires, face à un environnement où les ressources financières deviennent de plus en plus rares, développent des stratégies de survie qui peuvent avoir des incidences sur la qualité et la pertinence de l’enseignement supérieur.

Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks

Cette thèse étudie des modèles de séquences de haute dimension basés sur des réseaux de neurones récurrents (RNN) et leur application à la musique et à la parole. Bien qu'en principe les RNN puissent représenter les dépendances à long terme et la dynamique temporelle complexe propres aux séquences d'intérêt comme la vidéo, l'audio et la langue naturelle, ceux-ci n'ont pas été utilisés à leur plein potentiel depuis leur introduction par Rumelhart et al. (1986a) en raison de la difficulté de les entraîner efficacement par descente de gradient. Récemment, l'application fructueuse de l'optimisation Hessian-free et d'autres techniques d'entraînement avancées ont entraîné la recrudescence de leur utilisation dans plusieurs systèmes de l'état de l'art. Le travail de cette thèse prend part à ce développement. L'idée centrale consiste à exploiter la flexibilité des RNN pour apprendre une description probabiliste de séquences de symboles, c'est-à-dire une information de haut niveau associée aux signaux observés, qui en retour pourra servir d'à priori pour améliorer la précision de la recherche d'information. Par exemple, en modélisant l'évolution de groupes de notes dans la musique polyphonique, d'accords dans une progression harmonique, de phonèmes dans un énoncé oral ou encore de sources individuelles dans un mélange audio, nous pouvons améliorer significativement les méthodes de transcription polyphonique, de reconnaissance d'accords, de reconnaissance de la parole et de séparation de sources audio respectivement. L'application pratique de nos modèles à ces tâches est détaillée dans les quatre derniers articles présentés dans cette thèse. Dans le premier article, nous remplaçons la couche de sortie d'un RNN par des machines de Boltzmann restreintes conditionnelles pour décrire des distributions de sortie multimodales beaucoup plus riches. Dans le deuxième article, nous évaluons et proposons des méthodes avancées pour entraîner les RNN. Dans les quatre derniers articles, nous examinons différentes façons de combiner nos modèles symboliques à des réseaux profonds et à la factorisation matricielle non-négative, notamment par des produits d'experts, des architectures entrée/sortie et des cadres génératifs généralisant les modèles de Markov cachés. Nous proposons et analysons également des méthodes d'inférence efficaces pour ces modèles, telles la recherche vorace chronologique, la recherche en faisceau à haute dimension, la recherche en faisceau élagué et la descente de gradient. Finalement, nous abordons les questions de l'étiquette biaisée, du maître imposant, du lissage temporel, de la régularisation et du pré-entraînement.