Empirical evidence for ultrametric structure in multi-layer perceptron error surfaces

Combinatorial optimization problems share an interesting property with spin glass systems in that their state spaces can exhibit ultrametric structure. We use sampling methods to analyse the error surfaces of feedforward multi-layer perceptron neural networks learning encoder problems. The third order statistics of these points of attraction are examined and found to be arranged in a highly ultrametric way. This is a unique result for a finite, continuous parameter space. The implications of this result are discussed.

Enriched behavioral prediction equation and its impact on structured learning and the dynamic calculus

This theoretical note describes an expansion of the behavioral prediction equation, in line with the greater complexity encountered in models of structured learning theory (R. B. Cattell, 1996a). This presents learning theory with a vector substitute for the simpler scalar quantities by which traditional Pavlovian-Skinnerian models have hitherto been represented. Structured learning can be demonstrated by vector changes across a range of intrapersonal psychological variables (ability, personality, motivation, and state constructs). Its use with motivational dynamic trait measures (R. B. Cattell, 1985) should reveal new theoretical possibilities for scientifically monitoring change processes (dynamic calculus model; R. B. Cattell, 1996b), such as encountered within psycho therapeutic settings (R. B. Cattell, 1987). The enhanced behavioral prediction equation suggests that static conceptualizations of personality structure such as the Big Five model are less than optimal.

Input-driven Language Learning

Input-driven models provide an explicit and readily testable account of language learning. Although we share Ellis's view that the statistical structure of the linguistic environment is a crucial and, until recently, relatively neglected variable in language learning, we also recognize that the approach makes three assumptions about cognition and language learning that are not universally shared. The three assumptions concern (a) the language learner as an intuitive statistician, (b) the constraints on what constitute relevant surface cues, and (c) the redescription problem faced by any system that seeks to derive abstract grammatical relations from the frequency of co-occurring surface forms and functions. These are significant assumptions that must be established if input-driven models are to gain wider acceptance. We comment on these issues and briefly describe a distributed, instance-based approach that retains the key features of the input-driven account advocated by Ellis but that also addresses shortcomings of the current approaches.

Learning about life and death in early childhood

Inagaki and Hatano (2002) have argued that young children initially understand biological phenomena in terms of vitalism, a mode of construal in which life or life-force is the central causal-explanatory concept. This study investigated the development of vitalistic reasoning in young children's concepts of life, the human body and death. Sixty preschool children between the ages of 3 years, 7 months and 5 years, 11 months participated. All children were initially given structured interviews to assess their knowledge of (1) human body function and (2) death. From this sample 40 children in the Training group were taught about the human body and how it functions to maintain life. The Control group (n = 20) received no training. All 60 children were subsequently reassessed on their knowledge of human body function and death. Results from the initial interviews indicated that young children who spontaneously appealed to vitalistic concepts in reasoning about human body functioning were also more sophisticated in their understanding of death. Results from the posttraining interviews showed that children readily learned to adopt a vitalistic approach to human body functioning, and that this learning coincided with significant development in their understanding of human body function, and of death. The overall pattern of results supports the claim that the acquisition of a vitalistic causal-explanatory framework serves to structure children's concepts and facilitates learning in the domain of biology. (C) 2003 Elsevier Science (USA). All rights reserved.

LEARNING ENVIRONMENTS IN THE CONTEXT OF A MASTER DEGREE IN ANIMATION AND ILLUSTRATION

This paper aims to describe the processes of teaching illustration and animation, together, in the context of a masters degree program. In Portugal, until very recently, illustration and animation higher education courses, were very scarce and only provided by a few private universities, which offered separated programs - either illustration or animation. The MA in Illustration and Animation (MIA) based in the Instituto Politécnico do Cávado e Ave in Portugal, dared to join these two creative areas in a common learning model and is already starting it’s third edition with encouraging results and will be supported by the first international conference on illustration and animation (CONFIA). This masters program integrates several approaches and techniques (in illustration and animation) and integrates and encourages creative writing and critique writing. This paper describes the iterative process of construction, and implementation of the program as well as the results obtained on the initial years of existence in terms of pedagogic and learning conclusions. In summary, we aim to compare pedagogic models of animation or illustration teaching in higher education opposed to a more contemporary and multidisciplinary model approach that integrates the two - on an earlier stage - and allows them to be developed separately – on the second part of the program. This is based on the differences and specificities of animation (from classic techniques to 3D) and illustration (drawing the illustration) and the intersection area of these two subjects within the program structure focused on the students learning and competencies acquired to use in professional or authorial projects.

A Feedback Learning and Mental Models Perspective on Strategic Decision Making

This study aims to be a contribution to a theoretical model that explains the effectiveness of the learning and decision-making processes by means of a feedback and mental models perspective. With appropriate mental models, managers should be able to improve their capacity to deal with dynamically complex contexts, in order to achieve long-term success. We present a set of hypotheses about the influence of feedback information and systems thinking facilitation on mental models and management performance. We explore, under controlled conditions, the role of mental models in terms of structure and behaviour. A test based on a simulation experiment with a system dynamics model was performed. Three out of the four hypotheses were confirmed. Causal diagramming positively influences mental model structure similarity, mental model structure similarity positively influences mental model behaviour similarity, and mental model behaviour similarity positively influences the quality of the decision.

A Feedback Learning and Mental Models Perspective on Strategic Decision Making

This study aims to be a contribution to a theoretical model that explains the effectiveness of the learning and decision-making processes by means of a feedback and mental models perspective. With appropriate mental models, managers should be able to improve their capacity to deal with dynamically complex contexts, in order to achieve long-term success. We present a set of hypotheses about the influence of feedback information and systems thinking facilitation on mental models and management performance. We explore, under controlled conditions, the role of mental models in terms of structure and behaviour. A test based on a simulation experiment with a system dynamics model was performed. Three out of the four hypotheses were confirmed. Causal diagramming positively influences mental model structure similarity, mental model structure similarity positively influences mental model behaviour similarity, and mental model behaviour similarity positively influences the quality of the decision

Measuring and Controlling the Chaotic Motion of Profits

The study of economic systems has generated deep interest in exploring the complexity of chaotic motions in economy. Due to important developments in nonlinear dynamics, the last two decades have witnessed strong revival of interest in nonlinear endogenous business chaotic models. The inability to predict the behavior of dynamical systems in the presence of chaos suggests the application of chaos control methods, when we are more interested in obtaining regular behavior. In the present article, we study a specific economic model from the literature. More precisely, a system of three ordinary differential equations gather the variables of profits, reinvestments and financial flow of borrowings in the structure of a firm. Firstly, using results of symbolic dynamics, we characterize the topological entropy and the parameter space ordering of kneading sequences, associated with one-dimensional maps that reproduce significant aspects of the model dynamics. The analysis of the variation of this numerical invariant, in some realistic system parameter region, allows us to quantify and to distinguish different chaotic regimes. Finally, we show that complicated behavior arising from the chaotic firm model can be controlled without changing its original properties and the dynamics can be turned into the desired attracting time periodic motion (a stable steady state or into a regular cycle). The orbit stabilization is illustrated by the application of a feedback control technique initially developed by Romeiras et al. [1992]. This work provides another illustration of how our understanding of economic models can be enhanced by the theoretical and numerical investigation of nonlinear dynamical systems modeled by ordinary differential equations.

A especificidade do trabalho de Pontas no Ensino Artístico Especializado

Relatório Final de Estágio apresentado à Escola Superior de Dança com vista à obtenção do Grau de Mestre em Ensino de Dança.

Mining protein structure data

The principal topic of this work is the application of data mining techniques, in particular of machine learning, to the discovery of knowledge in a protein database. In the first chapter a general background is presented. Namely, in section 1.1 we overview the methodology of a Data Mining project and its main algorithms. In section 1.2 an introduction to the proteins and its supporting file formats is outlined. This chapter is concluded with section 1.3 which defines that main problem we pretend to address with this work: determine if an amino acid is exposed or buried in a protein, in a discrete way (i.e.: not continuous), for five exposition levels: 2%, 10%, 20%, 25% and 30%. In the second chapter, following closely the CRISP-DM methodology, whole the process of construction the database that supported this work is presented. Namely, it is described the process of loading data from the Protein Data Bank, DSSP and SCOP. Then an initial data exploration is performed and a simple prediction model (baseline) of the relative solvent accessibility of an amino acid is introduced. It is also introduced the Data Mining Table Creator, a program developed to produce the data mining tables required for this problem. In the third chapter the results obtained are analyzed with statistical significance tests. Initially the several used classifiers (Neural Networks, C5.0, CART and Chaid) are compared and it is concluded that C5.0 is the most suitable for the problem at stake. It is also compared the influence of parameters like the amino acid information level, the amino acid window size and the SCOP class type in the accuracy of the predictive models. The fourth chapter starts with a brief revision of the literature about amino acid relative solvent accessibility. Then, we overview the main results achieved and finally discuss about possible future work. The fifth and last chapter consists of appendices. Appendix A has the schema of the database that supported this thesis. Appendix B has a set of tables with additional information. Appendix C describes the software provided in the DVD accompanying this thesis that allows the reconstruction of the present work.

Repertório Contemporâneo como disciplina de desenvolvimento técnico e artístico

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Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Physiotherapy and Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a severe, progressive disease first described by Meryon in 1852 and later by Guillaume Duchene. It is the most common and severe form of childhood muscular dystrophy, affecting 1 in 3500 live male births. Is caused by an X—linked recessive genetic disorder resulting in a deficiency of the dystrophin protein, responsible for linking contractile proteins to the sarcolemma. Diagnosis is not always easy and the first symptoms are often related to weakness and difficulty or delay in acquiring the ability to perform simple activities. Progressive weakness leads to the use of compensatory strategies in order to maintain the ability to walk and perform other activities. Respiratory muscles are also affected and the complications resulting from its impairments are frequently the cause of early death of these patients. The advances in DMD management has increased life expectancy of these children with the need for adequate care in adulthood. DMD manifestations include muscle weakness, contractures, respiratory and cardiac complications. Some authors also refer that one-third of patients have difficulties with learning and delayed global development because the gene that encodes dystrophyn expresses various dystrophin isoforms that are found in Schwann and Purkinje celis in the brain. Body functions and structure impairments like muscle weakness, contractures and reduced range of motion lead to limitations in activities, i.e., impairments affect the performance of tasks by the individual. In a physiotherapist’s point of view analysing these limitations is mandatory because physiotherapy’s final purpose is to restore or preserve the ability to perform ADL and to improve quality of life.

Liderança e estratégia em contexto de inovação nas organizações de saúde : estudos de caso

RESUMO - A presente investigação procura descrever e compreender como a estratégia influencia a liderança e como esta por sua vez interage nos processos de inovação e mudança, em organizações de saúde. Desconhecem-se estudos anteriores, em Portugal, sobre este problema de investigação e da respectiva problemática teórica. Trata-se de um estudo exploratório e descritivo que envolveu 5 organizações de saúde, 4 portuguesas e 1 espanhola, 4 hospitais (dois privados e uma unidade local de saúde). Utilizou-se uma abordagem mista de investigação (qualitativa e quantitativa), que permitiu compreender, através do estudo de caso, como se articulam a estratégia, a liderança e a inovação nessas cinco organizações de saúde. Os resultados do estudo empírico foram provenientes da recolha de dados efectuada através de observação directa e estruturada, entrevistas com actores-chave, documentos em suporte de papel e digital, e ainda inquérito por questionário de auto-resposta a uma amostra (n=165) de actores do line e do staff (Administradores, Directores de Serviço/Departamento, Enfermeiros Chefe e Técnicos Coordenadores) das cinco organizações de saúde. Tanto o modelo de Miles & Snow (estratégia organizacional), como o modelo dos valores contrastantes de Quinn (cultura organizacional e liderança), devidamente adaptados, mostram-se heurísticos e provam poder aplicar-se às organizações de saúde, apesar a sua complexidade e especificidade. Tanto as organizações do sector público como do sector privado e organizações públicas concessionadas (parcerias público privadas) podem ser acompanhadas e monitorizadas nos seus processos de inovação e mudança, associados aos tipos de cultura, liderança ou estratégia organizacionais adoptadas. As organizações de saúde coabitam num continuum, onde o ambiente (quer interno quer externo) e o tempo são factores decisivos que condicionam a estratégia a adoptar. Também aqui, em função da realidade dinâmica e complexa onde a organização se move, não há tipologias puras. Há, sim, uma grande plasticidade e flexibilidade organizacionais. Quanto aos líderes, exercem habitualmente a autoridade formal, pela via da circular normativa. Não são pares (nem primi inter pares), colocam-se por vezes numa posição de superioridade, quando o mais adequado seria a relação de parceria, cooperação e procura de consensos, com todos os colaboradores, afim de serem eles os verdadeiros protagonistas e facilitadores da mudança e das inovações. Como factores facilitadores da inovação e da mudança, encontrámos nas organizações de saúde estudadas o seguinte: facilidade de aprender; visão/missão adequadas; ausência de medo de falhar; e como factores inibidores: falta de articulação entre serviços/departamentos; estrutura organizacional (no sector público muito verticalizada e no sector privado mais horizontalizada); resistência à mudança; falta de tempo; falha no tempo de reacção (o tempo útil para a tomada de decisão é, por vezes, ultrapassado). --------ABSTRACT - The present research seeks to describe and understand how strategy influences leadership and how this in turn interacts in the process of innovation and change in health organizations. Previous studies on these topics are unknown in Portugal, about this research problem and its theoretical problem. This is an exploratory and descriptive study that involved 5 health organizations, 4 Portuguese and 1 Spanish. We used a mixed approach of research (qualitative and quantitative), which enabled us to understand, through case study, how strategy and leadership were articulated with innovation in these five health organizations. The results of the empirical study came from data collection through direct observation, interviews with key actors, documents and survey questionnaire answered by 165 participants of line and staff (Administrators, Medical Directors of Service /Department, Head Nurses and Technical Coordinators) of the five health organizations. Despite their complexity and specificity, both the model of Miles & Snow (organizational strategy) and the model of the Competing Values Framework of Quinn (organizational culture and leadership), suitably adapted, have proven heuristic power and able to be apply to healthcare organizations. Both public sector organizations, private and public organizations licensed (public-private partnerships) can be tracked and monitored in their processes of innovation and change in order to understand its kind of culture, leadership or organizational strategy adopted. Health organizations coexist in a continuum, where the environment (internal and external) and time are key factors which determine the strategy to adopt. Here too depending on the dynamic and complex reality where the organization moves, there are no pure types. There is indeed a great organizational plasticity and flexibility. Leaders usually carry the formal authority by circular normative. They are not pairs (or primi inter pares). Instead they are, sometimes, in a position of superiority, when the best thing is partnership, collaboration, cooperation, building consensus and cooperation with all stakeholders, in order that they are the real protagonists and facilitators of change and innovation. As factors that facilitate innovation and change, we found in health organizations studied, the following: ease of learning; vision / mission appropriate; absence of fear of failure, and as inhibiting factors: lack of coordination between agencies / departments; organizational structure (in the public sector it is too vertical and in the private sector it is more horizontal); resistance to change; lack of time and failure in the reaction time (the time for decision making is sometimes exceeded).