Prediction- and simulation-error based perceptron training: Solution space analysis and a novel combined training scheme

Previous papers have noted the difficulty in obtaining neural models which are stable under simulation when trained using prediction-error-based methods. Here the differences between series-parallel and parallel identification structures for training neural models are investigated. The effect of the error surface shape on training convergence and simulation performance is analysed using a standard algorithm operating in both training modes. A combined series-parallel/parallel training scheme is proposed, aiming to provide a more effective means of obtaining accurate neural simulation models. Simulation examples show the combined scheme is advantageous in circumstances where the solution space is known or suspected to be complex. (c) 2006 Elsevier B.V. All rights reserved.

Conceptions of Teacher Identity: Creating Self-Boxes as Arts-Based Self-Study

This paper presents a palimpsest of ways in which self-study draws upon arts-based methods not just as processes towards teacher development, but also as means to problematize and inquire into conceptualizations of the self. It focuses on the creation of individual self-boxes that mediate teachers’ dynamic narratives of identity. Concepts of the unitary self, the decentred self and the relationship between inner and outer experience are challenged and illustrated through two interlapping stories made manifest through the creation of self-boxes. From time immemorial man has known that he is the subject most deserving of his own study, but he has also fought shy of treating this subject as a whole, that is, in accordance with its total character. (Buber, 1947, p. 140)

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.

Does subjective rating reflect behavioural coding? Personality in 2 month-old dog puppies: An open-field test and adjective-based questionnaire

A number of studies have recently investigated personality traits in non-human species, with the dog gaining popularity as a subject species for research in this area. Recent research has shown the consistency of personality traits across both context and time for adult dogs, both when using questionnaire based methods of investigation and behavioural analyses of the dogs' behaviour. However, only a few studies have assessed the correspondence between these two methods, with results varying considerably across studies. Furthermore, most studies have focused on adult dogs, despite the fact that an understanding of personality traits in young puppies may be important for research focusing on the genetic basis of personality traits. In the current study, we sought to evaluate the correspondence between a questionnaire based method and the in depth analyses of the behaviour of 2-month old puppies in an open-field test in which a number of both social and non-social stimuli were presented to the subjects. We further evaluated consistency of traits over time by re-testing a subset of puppies. The correspondence between methods was high and test-retest consistency (for the main trait) was also good using both evaluation methods. Results showed clear factors referring to the two main personality traits 'extroversion,' (i.e. the enthusiastic, exuberant approach to the stimuli) and 'neuroticism,' (i.e. the more cautious and fearful approach to the stimuli), potentially similar to the shyness-boldness dimension found in previous studies. Furthermore, both methods identified an 'amicability' dimension, expressing the positive interactions the pups directed at the humans stranger, and a 'reservedness' dimension which identified pups who largely chose not to interact with the stimuli, and were defined as quiet and not nosey in the questionnaire.

Combining WLAN fingerprint-based localization with sensor data for indoor navigation using mobile devices

This project proposes an approach for supporting Indoor Navigation Systems using Pedestrian Dead Reckoning-based methods and by analyzing motion sensor data available in most modern smartphones. Processes suggested in this investigation are able to calculate the distance traveled by a user while he or she is walking. WLAN fingerprint- based navigation systems benefit from the processes followed in this research and results achieved to reduce its workload and improve its positioning estimations.

High expectation in non-evidence-based smoking cessation interventions among smokers--the CoLaus study.

To assess the preferred methods to quit smoking among current smokers. Cross-sectional, population-based study conducted in Lausanne between 2003 and 2006 including 988 current smokers. Preference was assessed by questionnaire. Evidence-based (EB) methods were nicotine replacement, bupropion, physician or group consultations; non-EB-based methods were acupuncture, hypnosis and autogenic training. EB methods were frequently (physician consultation: 48%, 95% confidence interval (45-51); nicotine replacement therapy: 35% (32-38)) or rarely (bupropion and group consultations: 13% (11-15)) preferred by the participants. Non-EB methods were preferred by a third (acupuncture: 33% (30-36)), a quarter (hypnosis: 26% (23-29)) or a seventh (autogenic training: 13% (11-15)) of responders. On multivariate analysis, women preferred both EB and non-EB methods more frequently than men (odds ratio and 95% confidence interval: 1.46 (1.10-1.93) and 2.26 (1.72-2.96) for any EB and non-EB method, respectively). Preference for non-EB methods was higher among highly educated participants, while no such relationship was found for EB methods. Many smokers are unaware of the full variety of methods to quit smoking. Better information regarding these methods is necessary.

Comparison of different methods in estimating potential evapotranspiration at Muda Irrigation Scheme of Malaysia

Evapotranspiration (ET) is a complex process in the hydrological cycle that influences the quantity of runoff and thus the irrigation water requirements. Numerous methods have been developed to estimate potential evapotranspiration (PET). Unfortunately, most of the reliable PET methods are parameter rich models and therefore, not feasible for application in data scarce regions. On the other hand, accuracy and reliability of simple PET models vary widely according to regional climate conditions. The objective of the present study was to evaluate the performance of three temperature-based and three radiation-based simple ET methods in estimating historical ET and projecting future ET at Muda Irrigation Scheme at Kedah, Malaysia. The performance was measured by comparing those methods with the parameter intensive Penman-Monteith Method. It was found that radiation based methods gave better performance compared to temperature-based methods in estimation of ET in the study area. Future ET simulated from projected climate data obtained through statistical downscaling technique also showed that radiation-based methods can project closer ET values to that projected by Penman-Monteith Method. It is expected that the study will guide in selecting suitable methods for estimating and projecting ET in accordance to availability of meteorological data.

Gradient-based reinforcement learning techniques for underwater robotics behavior learning

Darrerament, l'interès pel desenvolupament d'aplicacions amb robots submarins autònoms (AUV) ha crescut de forma considerable. Els AUVs són atractius gràcies al seu tamany i el fet que no necessiten un operador humà per pilotar-los. Tot i això, és impossible comparar, en termes d'eficiència i flexibilitat, l'habilitat d'un pilot humà amb les escasses capacitats operatives que ofereixen els AUVs actuals. L'utilització de AUVs per cobrir grans àrees implica resoldre problemes complexos, especialment si es desitja que el nostre robot reaccioni en temps real a canvis sobtats en les condicions de treball. Per aquestes raons, el desenvolupament de sistemes de control autònom amb l'objectiu de millorar aquestes capacitats ha esdevingut una prioritat. Aquesta tesi tracta sobre el problema de la presa de decisions utilizant AUVs. El treball presentat es centra en l'estudi, disseny i aplicació de comportaments per a AUVs utilitzant tècniques d'aprenentatge per reforç (RL). La contribució principal d'aquesta tesi consisteix en l'aplicació de diverses tècniques de RL per tal de millorar l'autonomia dels robots submarins, amb l'objectiu final de demostrar la viabilitat d'aquests algoritmes per aprendre tasques submarines autònomes en temps real. En RL, el robot intenta maximitzar un reforç escalar obtingut com a conseqüència de la seva interacció amb l'entorn. L'objectiu és trobar una política òptima que relaciona tots els estats possibles amb les accions a executar per a cada estat que maximitzen la suma de reforços totals. Així, aquesta tesi investiga principalment dues tipologies d'algoritmes basats en RL: mètodes basats en funcions de valor (VF) i mètodes basats en el gradient (PG). Els resultats experimentals finals mostren el robot submarí Ictineu en una tasca autònoma real de seguiment de cables submarins. Per portar-la a terme, s'ha dissenyat un algoritme anomenat mètode d'Actor i Crític (AC), fruit de la fusió de mètodes VF amb tècniques de PG.

Development of molecular-based techniques for the detection, identification and quantification of food-borne pathogens

La presencia de microorganismos patógenos en alimentos es uno de los problemas esenciales en salud pública, y las enfermedades producidas por los mismos es una de las causas más importantes de enfermedad. Por tanto, la aplicación de controles microbiológicos dentro de los programas de aseguramiento de la calidad es una premisa para minimizar el riesgo de infección de los consumidores. Los métodos microbiológicos clásicos requieren, en general, el uso de pre-enriquecimientos no-selectivos, enriquecimientos selectivos, aislamiento en medios selectivos y la confirmación posterior usando pruebas basadas en la morfología, bioquímica y serología propias de cada uno de los microorganismos objeto de estudio. Por lo tanto, estos métodos son laboriosos, requieren un largo proceso para obtener resultados definitivos y, además, no siempre pueden realizarse. Para solucionar estos inconvenientes se han desarrollado diversas metodologías alternativas para la detección identificación y cuantificación de microorganismos patógenos de origen alimentario, entre las que destacan los métodos inmunológicos y moleculares. En esta última categoría, la técnica basada en la reacción en cadena de la polimerasa (PCR) se ha convertido en la técnica diagnóstica más popular en microbiología, y recientemente, la introducción de una mejora de ésta, la PCR a tiempo real, ha producido una segunda revolución en la metodología diagnóstica molecular, como pude observarse por el número creciente de publicaciones científicas y la aparición continua de nuevos kits comerciales. La PCR a tiempo real es una técnica altamente sensible -detección de hasta una molécula- que permite la cuantificación exacta de secuencias de ADN específicas de microorganismos patógenos de origen alimentario. Además, otras ventajas que favorecen su implantación potencial en laboratorios de análisis de alimentos son su rapidez, sencillez y el formato en tubo cerrado que puede evitar contaminaciones post-PCR y favorece la automatización y un alto rendimiento. En este trabajo se han desarrollado técnicas moleculares (PCR y NASBA) sensibles y fiables para la detección, identificación y cuantificación de bacterias patogénicas de origen alimentario (Listeria spp., Mycobacterium avium subsp. paratuberculosis y Salmonella spp.). En concreto, se han diseñado y optimizado métodos basados en la técnica de PCR a tiempo real para cada uno de estos agentes: L. monocytogenes, L. innocua, Listeria spp. M. avium subsp. paratuberculosis, y también se ha optimizado y evaluado en diferentes centros un método previamente desarrollado para Salmonella spp. Además, se ha diseñado y optimizado un método basado en la técnica NASBA para la detección específica de M. avium subsp. paratuberculosis. También se evaluó la aplicación potencial de la técnica NASBA para la detección específica de formas viables de este microorganismo. Todos los métodos presentaron una especificidad del 100 % con una sensibilidad adecuada para su aplicación potencial a muestras reales de alimentos. Además, se han desarrollado y evaluado procedimientos de preparación de las muestras en productos cárnicos, productos pesqueros, leche y agua. De esta manera se han desarrollado métodos basados en la PCR a tiempo real totalmente específicos y altamente sensibles para la determinación cuantitativa de L. monocytogenes en productos cárnicos y en salmón y productos derivados como el salmón ahumado y de M. avium subsp. paratuberculosis en muestras de agua y leche. Además este último método ha sido también aplicado para evaluar la presencia de este microorganismo en el intestino de pacientes con la enfermedad de Crohn's, a partir de biopsias obtenidas de colonoscopia de voluntarios afectados. En conclusión, este estudio presenta ensayos moleculares selectivos y sensibles para la detección de patógenos en alimentos (Listeria spp., Mycobacterium avium subsp. paratuberculosis) y para una rápida e inambigua identificación de Salmonella spp. La exactitud relativa de los ensayos ha sido excelente, si se comparan con los métodos microbiológicos de referencia y pueden serusados para la cuantificación de tanto ADN genómico como de suspensiones celulares. Por otro lado, la combinación con tratamientos de preamplificación ha resultado ser de gran eficiencia para el análisis de las bacterias objeto de estudio. Por tanto, pueden constituir una estrategia útil para la detección rápida y sensible de patógenos en alimentos y deberían ser una herramienta adicional al rango de herramientas diagnósticas disponibles para el estudio de patógenos de origen alimentario.

Model-based vehicle detection and classification using orthographic approximations

This paper reports the current state of work to simplify our previous model-based methods for visual tracking of vehicles for use in a real-time system intended to provide continuous monitoring and classification of traffic from a fixed camera on a busy multi-lane motorway. The main constraints of the system design were: (i) all low level processing to be carried out by low-cost auxiliary hardware, (ii) all 3-D reasoning to be carried out automatically off-line, at set-up time. The system developed uses three main stages: (i) pose and model hypothesis using 1-D templates, (ii) hypothesis tracking, and (iii) hypothesis verification, using 2-D templates. Stages (i) & (iii) have radically different computing performance and computational costs, and need to be carefully balanced for efficiency. Together, they provide an effective way to locate, track and classify vehicles.

View-based approaches to spatial representation in human vision

In an immersive virtual environment, observers fail to notice the expansion of a room around them and consequently make gross errors when comparing the size of objects. This result is difficult to explain if the visual system continuously generates a 3-D model of the scene based on known baseline information from interocular separation or proprioception as the observer walks. An alternative is that observers use view-based methods to guide their actions and to represent the spatial layout of the scene. In this case, they may have an expectation of the images they will receive but be insensitive to the rate at which images arrive as they walk. We describe the way in which the eye movement strategy of animals simplifies motion processing if their goal is to move towards a desired image and discuss dorsal and ventral stream processing of moving images in that context. Although many questions about view-based approaches to scene representation remain unanswered, the solutions are likely to be highly relevant to understanding biological 3-D vision.

The role of biotic and abiotic factors in evolution of ant dispersal in the milkwort family (Polygalaceae)

A phylogenetic approach was taken to investigate the evolutionary history of seed appendages in the plant family Polygalaceae (Fabales) and determine which factors might be associated with evolution of elaiosomes through comparisons to abiotic (climate) and biotic (ant species number and abundance) timelines. Molecular datasets from three plastid regions representing 160 species were used to reconstruct a phylogenetic tree of the order Fabales, focusing on Polygalaceae. Bayesian dating methods were used to estimate the age of the appearance of ant-dispersed elaiosomes in Polygalaceae, shown by likelihood optimizations to have a single origin in the family. Topology-based tests indicated a diversification rate shift associated with appearance of caruncular elaiosomes. We show that evolution of the caruncular elaiosome type currently associated with ant dispersal occurred 54.0-50.5 million year ago. This is long after an estimated increase in ant lineages in the Late Cretaceous based on molecular studies, but broadly concomitant with increasing global temperatures culminating in the Late Paleocene-Early Eocene thermal maxima. These results suggest that although most major ant clades were present when elaiosomes appeared, the environmental significance of elaiosomes may have been an important factor in success of elaiosome-bearing lineages. Ecological abundance of ants is perhaps more important than lineage numbers in determining significance of ant dispersal. Thus, our observation that elaiosomes predate increased ecological abundance of ants inferred from amber deposits could be indicative of an initial abiotic environmental function.

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

A comparison between feature-based and EM-based contour tracking

Most active-contour methods are based either on maximizing the image contrast under the contour or on minimizing the sum of squared distances between contour and image 'features'. The Marginalized Likelihood Ratio (MLR) contour model uses a contrast-based measure of goodness-of-fit for the contour and thus falls into the first class. The point of departure from previous models consists in marginalizing this contrast measure over unmodelled shape variations. The MLR model naturally leads to the EM Contour algorithm, in which pose optimization is carried out by iterated least-squares, as in feature-based contour methods. The difference with respect to other feature-based algorithms is that the EM Contour algorithm minimizes squared distances from Bayes least-squares (marginalized) estimates of contour locations, rather than from 'strongest features' in the neighborhood of the contour. Within the framework of the MLR model, alternatives to the EM algorithm can also be derived: one of these alternatives is the empirical-information method. Tracking experiments demonstrate the robustness of pose estimates given by the MLR model, and support the theoretical expectation that the EM Contour algorithm is more robust than either feature-based methods or the empirical-information method. (c) 2005 Elsevier B.V. All rights reserved.

Software-based study of a non-sorting method for median calculation on a set of integer numbers

This paper presents a software-based study of a hardware-based non-sorting median calculation method on a set of integer numbers. The method divides the binary representation of each integer element in the set into bit slices in order to find the element located in the middle position. The method exhibits a linear complexity order and our analysis shows that the best performance in execution time is obtained when slices of 4-bit in size are used for 8-bit and 16-bit integers, in mostly any data set size. Results suggest that software implementation of bit slice method for median calculation outperforms sorting-based methods with increasing improvement for larger data set size. For data set sizes of N > 5, our simulations show an improvement of at least 40%.