Spatial integration and cortical dynamics.

Cells in adult primary visual cortex are capable of integrating information over much larger portions of the visual field than was originally thought. Moreover, their receptive field properties can be altered by the context within which local features are presented and by changes in visual experience. The substrate for both spatial integration and cortical plasticity is likely to be found in a plexus of long-range horizontal connections, formed by cortical pyramidal cells, which link cells within each cortical area over distances of 6-8 mm. The relationship between horizontal connections and cortical functional architecture suggests a role in visual segmentation and spatial integration. The distribution of lateral interactions within striate cortex was visualized with optical recording, and their functional consequences were explored by using comparable stimuli in human psychophysical experiments and in recordings from alert monkeys. They may represent the substrate for perceptual phenomena such as illusory contours, surface fill-in, and contour saliency. The dynamic nature of receptive field properties and cortical architecture has been seen over time scales ranging from seconds to months. One can induce a remapping of the topography of visual cortex by making focal binocular retinal lesions. Shorter-term plasticity of cortical receptive fields was observed following brief periods of visual stimulation. The mechanisms involved entailed, for the short-term changes, altering the effectiveness of existing cortical connections, and for the long-term changes, sprouting of axon collaterals and synaptogenesis. The mutability of cortical function implies a continual process of calibration and normalization of the perception of visual attributes that is dependent on sensory experience throughout adulthood and might further represent the mechanism of perceptual learning.

Self-recognition in primates: phylogeny and the salience of species-typical features.

Self-recognition has been explored in nonlinguistic organisms by recording whether individuals touch a dye-marked area on visually inaccessible parts of their face while looking in a mirror or inspect parts of their body while using the mirror's reflection. Only chimpanzees, gorillas, orangutans, and humans over the age of approximately 2 years consistently evidence self-directed mirror-guided behavior without experimenter training. To evaluate the inferred phylogenetic gap between hominoids and other animals, a modified dye-mark test was conducted with cotton-top tamarins (Saguinus oedipus), a New World monkey species. The white hair on the tamarins' head was color-dyed, thereby significantly altering a visually distinctive species-typical feature. Only individuals with dyed hair and prior mirror exposure touched their head while looking in the mirror. They looked longer in the mirror than controls, and some individuals used the mirror to observe visually inaccessible body parts. Prior failures to pass the mirror test may have been due to methodological problems, rather than to phylogenetic differences in the capacity for self-recognition. Specifically, an individual's sensitivity to experimentally modified parts of its body may depend crucially on the relative saliency of the modified part (e.g., face versus hair). Moreover, and in contrast to previous claims, we suggest that the mirror test may not be sufficient for assessing the concept of self or mental state attribution in nonlinguistic organisms.

Monetary Effervescence: A Sociological Theory of Religion Applied to Money

This project attempts to answer the question "What holds the construction of money together?" by asserting that it is money's religious nature which provides the moral compulsion for people to use, and continue to uphold, money as a socially constructed concept. This project is primarily descriptive and focuses on the religious nature of money by employing a sociological theory of religion in viewing money as a technical concept. This is an interdisciplinary work between religious studies, economics, and sociology and draws heavily from Emile Durkheim's 'The Elementary Forms of Religious Life' as well as work related to heterodox theories of money developed by Geoffrey Ingham, A. Mitchell Innes, and David Graeber. Two new concepts are developed: the idea of monetary sacrality and monetary effervescence, both of which serve to recharge the religious saliency of money. By developing the concept of monetary sacrality, this project shows how money acts to interpret our economic relations while also obfuscating complex power dynamics in society, making them seem naturally occurring and unchangeable. The project also shows how our contemporary fractional reserve banking system contributes to money's collective effervescence and serves to animate economic acting within a monetary network. The project concludes by outlining multiple implications for religious studies, economics, sociology, and central banking.

What TRIPS is and what is is not

For industry people, journalists, activists, lawyers, diplomats, national legislators, and students of the World Trade Organization's Agreement on Trade-related Aspects of Intellectual Property (TRIPS) has awesome proportions. These are magnified by the fact that these groups lack detailed knowledge of either IP as such or international trade law. IP involves a broad spread of academic specialists and practitioners covering heterogeneous complex regimes of patents, copyright, trade marks, design, undisclosed information (trade secrets), and geographical indications. IP, and subsequently TRIPS, is the meeting point of many stakeholders and actors with conflicting interests spread between market aspirations and concepts of public good. In a globalized economy with deep interconnections across sectors, national borders challenged by inchoate technologies, dynamic social stakeholders, and converging technologies, it is fundamental to have a clear and uncluttered understanding of this Agreement. That is because TRIPS impinges on trade in many products of daily life, from pharmaceuticals to entertainment electronics, as well as mitigating and adaptive technologies for climate change and sustainable development. Given its saliency and ubiquity in economic life, TRIPS has often generated misunderstanding and controversy in the public debate. To complicate matters, technical and legal issues at the interface of technology, IP, and trade remain the province of an eclectic band of specialists and on the radar of interest groups with goals on opposite poles.

EM algorithm for GTM-FS

We propose a generative topographic mapping (GTM) based data visualization with simultaneous feature selection (GTM-FS) approach which not only provides a better visualization by modeling irrelevant features ("noise") using a separate shared distribution but also gives a saliency value for each feature which helps the user to assess their significance. This technical report presents a varient of the Expectation-Maximization (EM) algorithm for GTM-FS.

Data visualization with simultaneous feature selection

Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.

Novel visualization methods for protein data

Visualization of high-dimensional data has always been a challenging task. Here we discuss and propose variants of non-linear data projection methods (Generative Topographic Mapping (GTM) and GTM with simultaneous feature saliency (GTM-FS)) that are adapted to be effective on very high-dimensional data. The adaptations use log space values at certain steps of the Expectation Maximization (EM) algorithm and during the visualization process. We have tested the proposed algorithms by visualizing electrostatic potential data for Major Histocompatibility Complex (MHC) class-I proteins. The experiments show that the variation in the original version of GTM and GTM-FS worked successfully with data of more than 2000 dimensions and we compare the results with other linear/nonlinear projection methods: Principal Component Analysis (PCA), Neuroscale (NSC) and Gaussian Process Latent Variable Model (GPLVM).

Probabilistic methods for drug discovery

This thesis introduces a flexible visual data exploration framework which combines advanced projection algorithms from the machine learning domain with visual representation techniques developed in the information visualisation domain to help a user to explore and understand effectively large multi-dimensional datasets. The advantage of such a framework to other techniques currently available to the domain experts is that the user is directly involved in the data mining process and advanced machine learning algorithms are employed for better projection. A hierarchical visualisation model guided by a domain expert allows them to obtain an informed segmentation of the input space. Two other components of this thesis exploit properties of these principled probabilistic projection algorithms to develop a guided mixture of local experts algorithm which provides robust prediction and a model to estimate feature saliency simultaneously with the training of a projection algorithm.Local models are useful since a single global model cannot capture the full variability of a heterogeneous data space such as the chemical space. Probabilistic hierarchical visualisation techniques provide an effective soft segmentation of an input space by a visualisation hierarchy whose leaf nodes represent different regions of the input space. We use this soft segmentation to develop a guided mixture of local experts (GME) algorithm which is appropriate for the heterogeneous datasets found in chemoinformatics problems. Moreover, in this approach the domain experts are more involved in the model development process which is suitable for an intuition and domain knowledge driven task such as drug discovery. We also derive a generative topographic mapping (GTM) based data visualisation approach which estimates feature saliency simultaneously with the training of a visualisation model.

The relationship between transformational leadership and customer-orientated boundary-spanning behaviours:examining the role of compassion

Customer-oriented boundary-spanning behaviours (COBSBs) are critical to the success of service organisations. Transformational leadership, with its emphasis on the social elements of the leader-subordinate dyad, is a likely antecedent to COBSBs. Similarly, the interpersonal nature of services suggests leader compassion could have a significant effect on the saliency of the relationship between transformational leadership and COBSBs. This paper reports on a study of the moderating effect of leader compassion on the relationship between transformational leadership and COBSBs (service delivery behaviours, internal influence and external representation). Transformational leadership and compassion both have significant and positive influences on COBSBs. However, compassion plays no moderating role. These findings are discussed and avenues for further research are proposed.

Visualisation of bioinformatics datasets

Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset.

Investigation of skewing effects on the vibration reduction of three-phase switched reluctance motors

Switched reluctance motors (SRMs) are gaining in popularity because of their robustness, low cost, and excellent high-speed characteristics. However, they are known to cause vibration and noise primarily due to the radial pulsating force resulting from their double-saliency structure. This paper investigates the effect of skewing the stator and/or rotor on the vibration reduction of the three-phase SRMs by developing four 12/8-pole SRMs, including a conventional SRM, a skewed rotor-SRM (SR-SRM), a skewed stator-SRM (SS-SRM), and a skewed stator and rotor-SRM (SSR-SRM). The radial force distributed on the stator yoke under different skewing angles is extensively studied by the finite-element method and experimental tests on the four prototypes. The inductance and torque characteristics of the four motors are also compared, and a control strategy by modulating the turn-ON and turn-OFF angles for the SR-SRM and the SS-SRM are also presented. Furthermore, experimental results validate the numerical models and the effectiveness of the skewing in reducing the motor vibration. Test results also suggest that skewing the stator is more effective than skewing the rotor in the SRMs.

¿A quién le importa Europa? La UE en las elecciones generales españolas de 2011

Este artículo versa sobre el papel de la Unión Europea (UE) en las elecciones generales en España. Emplea los siguientes materiales: la base de datos del Manifesto Project, los programas de los partidos que obtuvieron representación en los comicios de 2011, así como, también para 2011, la transcripción del único debate televisado y las cuentas en la red social twitter de los candidatos a la Presidencia del Gobierno del Partido Popular y del Partido Socialista. La metodología empleada es el análisis de contenido. Los datos confirman las expectativas derivadas de la teoría de la importancia (saliency theory). Los partidos españoles han desenfatizado los asuntos de la UE, incluso en 2011, cuando medidas impulsadas por el gobierno anterior, incluida una reforma constitucional, fruto de decisiones adoptadas a escala europea, motivaron la convocatoria anticipada de elecciones. La evolución del énfasis y posición sobre la UE de los partidos españoles contrasta con los cambios observados en estas variables en otros Estados miembros como Francia o Italia. Los hallazgos de esta investigación tienen implicaciones desde el punto de vista de la legitimidad democrática de la UE en España.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Detecção de região saliente em imagens usando dissimilaridade de cor e amostragem por pixeis aleatórios

The purpose of this work is to demonstrate and to assess a simple algorithm for automatic estimation of the most salient region in an image, that have possible application in computer vision. The algorithm uses the connection between color dissimilarities in the image and the image’s most salient region. The algorithm also avoids using image priors. Pixel dissimilarity is an informal function of the distance of a specific pixel’s color to other pixels’ colors in an image. We examine the relation between pixel color dissimilarity and salient region detection on the MSRA1K image dataset. We propose a simple algorithm for salient region detection through random pixel color dissimilarity. We define dissimilarity by accumulating the distance between each pixel and a sample of n other random pixels, in the CIELAB color space. An important result is that random dissimilarity between each pixel and just another pixel (n = 1) is enough to create adequate saliency maps when combined with median filter, with competitive average performance if compared with other related methods in the saliency detection research field. The assessment was performed by means of precision-recall curves. This idea is inspired on the human attention mechanism that is able to choose few specific regions to focus on, a biological system that the computer vision community aims to emulate. We also review some of the history on this topic of selective attention.

Estimation de cartes d'énergie de hautes fréquences ou d'irrégularité de périodicité de la marche humaine par caméra de profondeur pour la détection de pathologies

Ce travail présente deux nouveaux systèmes simples d'analyse de la marche humaine grâce à une caméra de profondeur (Microsoft Kinect) placée devant un sujet marchant sur un tapis roulant conventionnel, capables de détecter une marche saine et celle déficiente. Le premier système repose sur le fait qu'une marche normale présente typiquement un signal de profondeur lisse au niveau de chaque pixel avec moins de hautes fréquences, ce qui permet d'estimer une carte indiquant l'emplacement et l'amplitude de l'énergie de haute fréquence (HFSE). Le second système analyse les parties du corps qui ont un motif de mouvement irrégulier, en termes de périodicité, lors de la marche. Nous supposons que la marche d'un sujet sain présente partout dans le corps, pendant les cycles de marche, un signal de profondeur avec un motif périodique sans bruit. Nous estimons, à partir de la séquence vidéo de chaque sujet, une carte montrant les zones d'irrégularités de la marche (également appelées énergie de bruit apériodique). La carte avec HFSE ou celle visualisant l'énergie de bruit apériodique peut être utilisée comme un bon indicateur d'une éventuelle pathologie, dans un outil de diagnostic précoce, rapide et fiable, ou permettre de fournir des informations sur la présence et l'étendue de la maladie ou des problèmes (orthopédiques, musculaires ou neurologiques) du patient. Même si les cartes obtenues sont informatives et très discriminantes pour une classification visuelle directe, même pour un non-spécialiste, les systèmes proposés permettent de détecter automatiquement les individus en bonne santé et ceux avec des problèmes locomoteurs.