Enrique Pichon-Rivière : Los orígenes de la psicología social argentina

Enrique Pichon-Rivière (1907-1977) fue uno de los fundadores de la psicología social en Argentina. En este trabajo revisaremos su biografía y trayectoria institucional junto con sus aportes teóricos y epistemológicos. Sostenemos que Enrique Pichon-Rivière dio muestra de una actitud crítica con respecto a las divisiones disciplinares y a la distancia entre conocimiento académico y praxis social. Nuestro recorte privilegiará aquellos aspectos de su obra que consideramos de mayor relevancia para las ciencias sociales, incluyendo sus nociones de necesidad social, matrices de aprendizaje, esquema conceptual referencial operativo (E.C.R.O.) y epistemología convergente

Enrique Pichon-Rivière : Los orígenes de la psicología social argentina

Enrique Pichon-Rivière (1907-1977) fue uno de los fundadores de la psicología social en Argentina. En este trabajo revisaremos su biografía y trayectoria institucional junto con sus aportes teóricos y epistemológicos. Sostenemos que Enrique Pichon-Rivière dio muestra de una actitud crítica con respecto a las divisiones disciplinares y a la distancia entre conocimiento académico y praxis social. Nuestro recorte privilegiará aquellos aspectos de su obra que consideramos de mayor relevancia para las ciencias sociales, incluyendo sus nociones de necesidad social, matrices de aprendizaje, esquema conceptual referencial operativo (E.C.R.O.) y epistemología convergente

Enrique Pichon-Rivière : Los orígenes de la psicología social argentina

Enrique Pichon-Rivière (1907-1977) fue uno de los fundadores de la psicología social en Argentina. En este trabajo revisaremos su biografía y trayectoria institucional junto con sus aportes teóricos y epistemológicos. Sostenemos que Enrique Pichon-Rivière dio muestra de una actitud crítica con respecto a las divisiones disciplinares y a la distancia entre conocimiento académico y praxis social. Nuestro recorte privilegiará aquellos aspectos de su obra que consideramos de mayor relevancia para las ciencias sociales, incluyendo sus nociones de necesidad social, matrices de aprendizaje, esquema conceptual referencial operativo (E.C.R.O.) y epistemología convergente

Proyecto MATRIX : modalidades de aprendizaje telemático y resultados interuniversitarios extrapolables al Blended Learning.

Monográfico con el título: 'Web 2.0 : dispositivos móviles y abiertos para el aprendizaje'. Resumen basado en el de la publicación

Learning dependent sources using mixtures of Dirichlet: applications on hyperspectral unmixing

This paper is an elaboration of the DECA algorithm [1] to blindly unmix hyperspectral data. The underlying mixing model is linear, meaning that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. The proposed method, as DECA, is tailored to highly mixed mixtures in which the geometric based approaches fail to identify the simplex of minimum volume enclosing the observed spectral vectors. We resort then to a statitistical framework, where the abundance fractions are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. With respect to DECA, we introduce two improvements: 1) the number of Dirichlet modes are inferred based on the minimum description length (MDL) principle; 2) The generalized expectation maximization (GEM) algorithm we adopt to infer the model parameters is improved by using alternating minimization and augmented Lagrangian methods to compute the mixing matrix. The effectiveness of the proposed algorithm is illustrated with simulated and read data.

Recommending media content based on machine learning methods

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Aversive Stimuli and Functional Networks of Fear Learning in PTSD : 734

Background: One characteristic of post traumatic stress disorder is an inability to adapt to a safe environment i.e. to change behavior when predictions of adverse outcomes are not met. Recent studies have also indicated that PTSD patients have altered pain processing, with hyperactivation of the putamen and insula to aversive stimuli (Geuze et al, 2007). The present study examined neuronal responses to aversive and predicted aversive events. Methods: Twenty-four trauma exposed non-PTSD controls and nineteen subjects with PTSD underwent fMRI imaging during a partial reinforcement fear conditioning paradigm, with a mild electric shock as the unconditioned stimuli (UCS). Three conditions were analyzed: actual presentations of the UCS, events when a UCS was expected, but omitted (CS+), and events when the UCS was neither expected nor delivered (CS-). Results: The UCS evoked significant alterations in the pain matrix consisting of the brainstem, the midbrain, the thalamus, the insula, the anterior and middle cingulate and the contralateral somatosensory cortex. PTSD subjects displayed bilaterally elevated putamen activity to the electric shock, as compared to controls. In trials when USC was expected, but omitted, significant activations were observed in the brainstem, the midbrain, the anterior insula and the anterior cingulate. PTSD subjects displayed similar activations, but also elevated activations in the amygdala and the posterior insula. Conclusions: These results indicate altered fear and safety learning in PTSD, and neuronal activations are further explored in terms of functional connectivity using psychophysiological interaction analyses.

Capability matrix – Identifying and evaluating the key capabilities of purchasing and supply management

Organizations gain resources, skills and technologies to find out the ultimate mix of capabilities to be a winner in the competitive market. These are all important factors that need to be taken into account in organizations operating in today's business environment. So far, there are no significant studies on the organizational capabilities in the field of PSM. The literature review shows that the PSM capabilities need to be studied more comprehensively. This study attempts to reveal and fill this gap by providing the PSM capability matrix that identifies the key PSM capabilities approached from two angles: there are three primary PSM capabilities and nine subcapabilities and, moreover, the individual and organizational PSM capabilities are identified and evaluated. The former refers to the PSM capability matrix of this study which is based on the strategic and operative PSM capabilities that complement the economic ones, while the latter relates to the evaluation of the PSM capabilities, such as the buyer profiles of individual PSM capabilities and the PSMcapability map of the organizational ones. This is a constructive case study. The aim is to define what the purchasing and supply management capabilities are and how they can be evaluated. This study presents a PSM capability matrix to identify and evaluate the capabilities to define capability gaps by comparing the ideal level of PSM capabilities to the realized ones. The research questions are investigated with two case organizations. This study argues that PSM capabilities can be classified into three primary categories with nine sub-categories and, thus, a PSM capability matrix with four evaluation categories can be formed. The buyer profiles are moreover identified to reveal the PSM capability gap. The resource-based view (RBV) and dynamic capabilities view (DCV) are used to define the individual and organizational capabilities. The PSM literature is also used to define the capabilities. The key findings of this study are i) the PSM capability matrix to identify the PSM capabilities, ii) the evaluation of the capabilities to define PSM capability gaps and iii) the presentation of the buyer profiles to identify the individual PSM capabilities and to define the organizational PSM capabilities. Dynamic capabilities are also related to the PSM capability gap. If a gap is identified, the organization can renew their PSM capabilities and, thus, create mutual learning and increase their organizational capabilities. And only then, there is potential for dynamic capabilities. Based on this, the purchasing strategy, purchasing policy and procedures should be identified and implemented dynamically.

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Learning from projects: a qualitative metasummary

The objective of this thesis was to form an understanding about the common gaps in learning from projects, as well as possible approaches to bridging them. In the research focus were the questions on how project teams create knowledge, which fac- tors affect the capture and re-use of this knowledge and how organizations can best capture and utilize this project-based knowledge. The method used was qualitative metasummary, a literature-based research method that has previously been mainly applied in the domains of nursing and health care research. The focus was laid on firms conducting knowledge-intensive business in some form of matrix organization. The research produced a theoretical model of knowledge creation in projects as well as a typology of factors affecting transfer of project-based knowledge. These include experience, culture and leadership, planning and controlling, relationships, project review and documentation. From these factors, suggestions could be derived as to how organizations should conduct projects in order not to lose what has been learned.

Developmental patterns of individiuals within a sensory-based learning environment in art education : insights emerging from a case study /

My research permitted me to reexamine my recent evaluations of the Leaf Project given to the Foundation Year students during the fall semester of 1997. My personal description of the drawing curriculum formed part of the matrix of the Foundation Core Studies at the Ontario College of Art and Design. Research was based on the random selection of 1 8 students distributed over six of my teaching groups. The entire process included a representation of all grade levels. The intent of the research was to provide a pattern of alternative insights that could provide a more meaningful method of evaluation for visual learners in an art education setting. Visual methods of learning are indeed complex and involve the interplay of many sensory modalities of input. Using a qualitative method of research analysis, a series of queries were proposed into a structured matrix grid for seeking out possible and emerging patterns of learning. The grid provided for interrelated visual and linguistic analysis with emphasis in reflection and interconnectedness. Sensory-based modes of learning are currently being studied and discussed amongst educators as alternative approaches to learning. As patterns emerged from the research, it became apparent that a paradigm for evaluation would have to be a progressive profile of the learning that would take into account many of the different and evolving learning processes of the individual. A broader review of the student's entire development within the Foundation Year Program would have to have a shared evaluation through a cross section of representative faculty in the program. The results from the research were never intended to be conclusive. We realized from the start that sensory-based learning is a difficult process to evaluate from traditional standards used in education. The potential of such a process of inquiry permits the researcher to ask for a set of queries that might provide for a deeper form of evaluation unique to the students and their related learning environment. Only in this context can qualitative methods be used to profile their learning experiences in an expressive and meaningful manner.

Sequential Machine learning Approaches for Portfolio Management

Cette thèse envisage un ensemble de méthodes permettant aux algorithmes d'apprentissage statistique de mieux traiter la nature séquentielle des problèmes de gestion de portefeuilles financiers. Nous débutons par une considération du problème général de la composition d'algorithmes d'apprentissage devant gérer des tâches séquentielles, en particulier celui de la mise-à-jour efficace des ensembles d'apprentissage dans un cadre de validation séquentielle. Nous énumérons les desiderata que des primitives de composition doivent satisfaire, et faisons ressortir la difficulté de les atteindre de façon rigoureuse et efficace. Nous poursuivons en présentant un ensemble d'algorithmes qui atteignent ces objectifs et présentons une étude de cas d'un système complexe de prise de décision financière utilisant ces techniques. Nous décrivons ensuite une méthode générale permettant de transformer un problème de décision séquentielle non-Markovien en un problème d'apprentissage supervisé en employant un algorithme de recherche basé sur les K meilleurs chemins. Nous traitons d'une application en gestion de portefeuille où nous entraînons un algorithme d'apprentissage à optimiser directement un ratio de Sharpe (ou autre critère non-additif incorporant une aversion au risque). Nous illustrons l'approche par une étude expérimentale approfondie, proposant une architecture de réseaux de neurones spécialisée à la gestion de portefeuille et la comparant à plusieurs alternatives. Finalement, nous introduisons une représentation fonctionnelle de séries chronologiques permettant à des prévisions d'être effectuées sur un horizon variable, tout en utilisant un ensemble informationnel révélé de manière progressive. L'approche est basée sur l'utilisation des processus Gaussiens, lesquels fournissent une matrice de covariance complète entre tous les points pour lesquels une prévision est demandée. Cette information est utilisée à bon escient par un algorithme qui transige activement des écarts de cours (price spreads) entre des contrats à terme sur commodités. L'approche proposée produit, hors échantillon, un rendement ajusté pour le risque significatif, après frais de transactions, sur un portefeuille de 30 actifs.