Reporting program evaluations : two sample community mental health center annual evaluation reports /

Mode of access: Internet.

Bayesian Networks and the Value of the Evidence for the Forensic Two-Trace Transfer Problem

Forensic scientists face increasingly complex inference problems for evaluating likelihood ratios (LRs) for an appropriate pair of propositions. Up to now, scientists and statisticians have derived LR formulae using an algebraic approach. However, this approach reaches its limits when addressing cases with an increasing number of variables and dependence relationships between these variables. In this study, we suggest using a graphical approach, based on the construction of Bayesian networks (BNs). We first construct a BN that captures the problem, and then deduce the expression for calculating the LR from this model to compare it with existing LR formulae. We illustrate this idea by applying it to the evaluation of an activity level LR in the context of the two-trace transfer problem. Our approach allows us to relax assumptions made in previous LR developments, produce a new LR formula for the two-trace transfer problem and generalize this scenario to n traces.

Two-Dimensional Bin Packing Problem with Guillotine Restrictions

This thesis, after presenting recent advances obtained for the two-dimensional bin packing problem, focuses on the case where guillotine restrictions are imposed. A mathematical characterization of non-guillotine patterns is provided and the relation between the solution value of the two-dimensional problem with guillotine restrictions and the two-dimensional problem unrestricted is being studied from a worst-case perspective. Finally it presents a new heuristic algorithm, for the two-dimensional problem with guillotine restrictions, based on partial enumeration, and computationally evaluates its performance on a large set of instances from the literature. Computational experiments show that the algorithm is able to produce proven optimal solutions for a large number of problems, and gives a tight approximation of the optimum in the remaining cases.

Molecular orbital calculations of two-electron states for P-donor solid-state spin qubits

We theoretically study the Hilbert space structure of two neighboring P-donor electrons in silicon-based quantum computer architectures. To use electron spins as qubits, a crucial condition is the isolation of the electron spins from their environment, including the electronic orbital degrees of freedom. We provide detailed electronic structure calculations of both the single donor electron wave function and the two-electron pair wave function. We adopted a molecular orbital method for the two-electron problem, forming a basis with the calculated single donor electron orbitals. Our two-electron basis contains many singlet and triplet orbital excited states, in addition to the two simple ground state singlet and triplet orbitals usually used in the Heitler-London approximation to describe the two-electron donor pair wave function. We determined the excitation spectrum of the two-donor system, and study its dependence on strain, lattice position, and interdonor separation. This allows us to determine how isolated the ground state singlet and triplet orbitals are from the rest of the excited state Hilbert space. In addition to calculating the energy spectrum, we are also able to evaluate the exchange coupling between the two donor electrons, and the double occupancy probability that both electrons will reside on the same P donor. These two quantities are very important for logical operations in solid-state quantum computing devices, as a large exchange coupling achieves faster gating times, while the magnitude of the double occupancy probability can affect the error rate.

As imagens dos manuais escolares: representações mentais de professores e alunos relativamente à presença de imagens nos manuais escolares e à sua eficácia pedagógica

Sendo o manual escolar o recurso pedagógico mais utilizado por alunos e professores foi realizada uma investigação tendo por objectivo avaliar qualitativamente a forma comunicacional de um conjunto de manuais escolares do 2º ciclo do ensino básico1. A importância atribuída atualmente à imagem é patente nos manuais escolares, sob pretexto de se tornarem mais apelativos e mais eficazes. Tal como em outras áreas em que a quantidade de imagens acaba por se transformar em poluição visual porque o ser humano não tem capacidade para reagir, simultaneamente, a um demasiado número de estímulos, nas estratégias comunicacionais dos manuais escolares este problema deve ser acautelado porque delas depende a sua qualidade pedagógica. As investigações não comprovam que exista uma relação direta entre a presença de ilustrações num texto e o aumento da sua compreensão. Pelo contrário, há estudos que sugerem que muitas ilustrações falham como potenciadoras da aprendizagem. Neste sentido, da investigação referida, consta uma análise ao rácio imagem/texto e uma auscultação a dois grupos de amostra - professores e alunos – sobre alguns aspectos gerais relativos à presença e eficácia pedagógica das ilustrações dos manuais escolares. Destacamos que, no caso dos alunos, apesar do seu nível de literacia ter constituído um obstáculo ao tratamento das questões, optámos por apresentar as suas respostas.

Testing for Proportional Hazards with Unrestricted Univariate Unobserved Heterogeneity

We develop tests of the proportional hazards assumption, with respect to a continuous covariate, in the presence of unobserved heterogeneity with unknown distribution at the individual observation level. The proposed tests are specially powerful against ordered alternatives useful for modeling non-proportional hazards situations. By contrast to the case when the heterogeneity distribution is known up to …nite dimensional parameters, the null hypothesis for the current problem is similar to a test for absence of covariate dependence. However, the two testing problems di¤er in the nature of relevant alternative hypotheses. We develop tests for both the problems against ordered alternatives. Small sample performance and an application to real data highlight the usefulness of the framework and methodology.

Measuring intergenerational earnings mobility in Spain: a selection-bias-free approach

This paper analyses intergenerational earnings mobility in Spain correcting for different selection biases. We address the co-residence selection problem by combining information from two samples and using the two-sample two-stage least square estimator. We find a small decrease in elasticity when we move to younger cohorts. Furthermore, we find a higher correlation in the case of daughters than in the case of sons; however, when we consider the employment selection in the case of daughters, by adopting a Heckman-type correction method, the diference between sons and daughters disappears. By decomposing the sources of earnings elasticity across generations, we find that the correlation between child's and father's occupation is the most important component. Finally, quantile regressions estimates show that the influence of the father's earnings is greater when we move to the lower tail of the offspring's earnings distribution, especially in the case of daughters' earnings.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Large-scale clustering through functional embedding

We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent. Compared to methods like spectral clustering our approach solves a single optimization problem, rather than an ad-hoc two-stage optimization approach, does not require a matrix inversion, can easily encode prior knowledge in the set of implementable functions, and does not have an ?out-of-sample? problem. Experimental results on both artificial and real-world datasets show the usefulness of our approach.

Sample preparation and heavy metal determination by atomic spectrometry /

Microwave digestions of mercury in Standards Reference Material (SRM) coal samples with nitric acid and hydrogen peroxide in quartz vessels were compared with Teflon® vessel digestion by using flow injection cold vapor atomic absorption spectrometry. Teflon® vessels gave poor reproducibiUty and tended to deliver high values, while the digestion results from quartz vessel show good agreement with certificate values and better standard deviations. Trace level elements (Ag, Ba, Cd, Cr, Co, Cu, Fe, Mg, Mn, Mo, Pb, Sn, Ti, V and Zn) in used oil and residual oil samples were determined by inductively coupled plasma-optical emission spectrometry. Different microwave digestion programs were developed for each sample and most of the results are in good agreement with certified values. The disagreement with values for Ag was due to the precipitation of Ag in sample; while Sn, V and Zn values had good recoveries from the spike test, which suggests that these certified values might need to be reconsidered. Gold, silver, copper, cadmium, cobalt, nickel and zinc were determined by continuous hydride generation inductively coupled plasma-optical emission spectrometry. The performance of two sample introduction systems: MSIS™ and gas-liquid separator were compared. Under the respective optimum conditions, MSIS^"^ showed better sensitivity and lower detection limits for Ag, Cd, Cu, Co and similar values for Au, Ni and Zn to those for the gas-liquid separator.

Redundancy gain : manifestations, causes and predictions

Les temps de réponse dans une tache de reconnaissance d’objets visuels diminuent de façon significative lorsque les cibles peuvent être distinguées à partir de deux attributs redondants. Le gain de redondance pour deux attributs est un résultat commun dans la littérature, mais un gain causé par trois attributs redondants n’a été observé que lorsque ces trois attributs venaient de trois modalités différentes (tactile, auditive et visuelle). La présente étude démontre que le gain de redondance pour trois attributs de la même modalité est effectivement possible. Elle inclut aussi une investigation plus détaillée des caractéristiques du gain de redondance. Celles-ci incluent, outre la diminution des temps de réponse, une diminution des temps de réponses minimaux particulièrement et une augmentation de la symétrie de la distribution des temps de réponse. Cette étude présente des indices que ni les modèles de course, ni les modèles de coactivation ne sont en mesure d’expliquer l’ensemble des caractéristiques du gain de redondance. Dans ce contexte, nous introduisons une nouvelle méthode pour évaluer le triple gain de redondance basée sur la performance des cibles doublement redondantes. Le modèle de cascade est présenté afin d’expliquer les résultats de cette étude. Ce modèle comporte plusieurs voies de traitement qui sont déclenchées par une cascade d’activations avant de satisfaire un seul critère de décision. Il offre une approche homogène aux recherches antérieures sur le gain de redondance. L’analyse des caractéristiques des distributions de temps de réponse, soit leur moyenne, leur symétrie, leur décalage ou leur étendue, est un outil essentiel pour cette étude. Il était important de trouver un test statistique capable de refléter les différences au niveau de toutes ces caractéristiques. Nous abordons la problématique d’analyser les temps de réponse sans perte d’information, ainsi que l’insuffisance des méthodes d’analyse communes dans ce contexte, comme grouper les temps de réponses de plusieurs participants (e. g. Vincentizing). Les tests de distributions, le plus connu étant le test de Kolmogorov- Smirnoff, constituent une meilleure alternative pour comparer des distributions, celles des temps de réponse en particulier. Un test encore inconnu en psychologie est introduit : le test d’Anderson-Darling à deux échantillons. Les deux tests sont comparés, et puis nous présentons des indices concluants démontrant la puissance du test d’Anderson-Darling : en comparant des distributions qui varient seulement au niveau de (1) leur décalage, (2) leur étendue, (3) leur symétrie, ou (4) leurs extrémités, nous pouvons affirmer que le test d’Anderson-Darling reconnait mieux les différences. De plus, le test d’Anderson-Darling a un taux d’erreur de type I qui correspond exactement à l’alpha tandis que le test de Kolmogorov-Smirnoff est trop conservateur. En conséquence, le test d’Anderson-Darling nécessite moins de données pour atteindre une puissance statistique suffisante.

Fast Sorting on a Distributed-Memory Architecture

We consider the often-studied problem of sorting, for a parallel computer. Given an input array distributed evenly over p processors, the task is to compute the sorted output array, also distributed over the p processors. Many existing algorithms take the approach of approximately load-balancing the output, leaving each processor with Θ(n/p) elements. However, in many cases, approximate load-balancing leads to inefficiencies in both the sorting itself and in further uses of the data after sorting. We provide a deterministic parallel sorting algorithm that uses parallel selection to produce any output distribution exactly, particularly one that is perfectly load-balanced. Furthermore, when using a comparison sort, this algorithm is 1-optimal in both computation and communication. We provide an empirical study that illustrates the efficiency of exact data splitting, and shows an improvement over two sample sort algorithms.

Problem-based learning approaches in meteorology

Problem-Based Learning, despite recent controversies about its effectiveness, is used extensively as a teaching method throughout higher education. In meteorology, there has been little attempt to incorporate Problem-Based Learning techniques into the curriculum. Motivated by a desire to enhance the reflective engagement of students within a current field course module, this project describes the implementation of two test Problem-Based Learning activities and testing and improvement using several different and complementary means of evaluation. By the end of a 2-year program of design, implementation, testing, and reflection and re-evaluation, two robust, engaging activities have been developed that provide an enhanced and diverse learning environment in the field course. The results suggest that Problem-Based Learning techniques would be a useful addition to the meteorology curriculum and suggestions for courses and activities that may benefit from this approach are included in the conclusions.

Solution of two-body bound state problems with confining potentials

The homogeneous Lippmann-Schwinger integral equation is solved in momentum space by using confining potentials. Since the confining potentials are unbounded at large distances, they lead to a singularity at small momentum. In order to remove the singularity of the kernel of the integral equation, a regularized form of the potentials is used. As an application of the method, the mass spectra of heavy quarkonia, mesons consisting from heavy quark and antiquark (Υ(bb̄), ψ(cc̄)), are calculated for linear and quadratic confining potentials. The results are in good agreement with configuration space and experimental results. © 2010 American Institute of Physics.