Fast supersymmetry phenomenology at the Large Hadron Collider using machine learning techniques

A pressing problem for supersymmetry (SUSY) phenomenologists is how to incorporate Large Hadron Collider search results into parameter fits designed to measure or constrain the SUSY parameters. Owing to the computational expense of fully simulating lots of points in a generic SUSY space to aid the calculation of the likelihoods, the limits published by experimental collaborations are frequently interpreted in slices of reduced parameter spaces. For example, both ATLAS and CMS have presented results in the Constrained Minimal Supersymmetric Model (CMSSM) by fixing two of four parameters, and generating a coarse grid in the remaining two. We demonstrate that by generating a grid in the full space of the CMSSM, one can interpolate between the output of an LHC detector simulation using machine learning techniques, thus obtaining a superfast likelihood calculator for LHC-based SUSY parameter fits. We further investigate how much training data is required to obtain usable results, finding that approximately 2000 points are required in the CMSSM to get likelihood predictions to an accuracy of a few per cent. The techniques presented here provide a general approach for adding LHC event rate data to SUSY fitting algorithms, and can easily be used to explore other candidate physics models.Comment: 20 pages, 7 figures, replaced to correct author contact detail

P300 Detection for Brain Computer Interface

P300 based brain computer interface (BCI) sometimes called brain machine interface (BMI) is a way of direct communication between human brain and external device which provides an alternative communication link with outside world to the people who are unable to communicate via conventional means because of sever motor disability. P300 wave is an event related potential which evoked in the process of decision making of human brain which can be generated using oddball paradigm. This thesis aims to detect the P300 wave as accurate as possible. To do that this study proposed discrete wavelet transforms (DWT) based feature extraction method from each P300 and No-P300 of EEG signal from the entire 64 channel. Principal component analysis (PCA) technique is further applied for the reduction of the dimension of the feature. Detection of P300 is achieved using support vector machine (SVM) and artificial neural network (ANN) classifier. Experimental result shows that the proposed method with SVM classifier yields better performance compared to the method with ANN

Science 2.0 : sharing scientific data on the Grid

Mestrado em Engenharia de Computadores e TelemáticaA computação assumese cada vez mais como um recurso essencial em ciência, levando ao surgimento do termo eCiência para designar a utilização de tecnologias de computação avançada para suportar a realização de experiências científicas, a preservação e partilha do conhecimento. Uma das áreas de aplicação do conceito de eCiência é o tratamento e análise de imagens médicas. Os processos que lidam com imagem médica, tanto ao nível clínico como de investigação, são exigentes em relação ao suporte computacional, devido aos algoritmos de processamento de imagem que requerem e à elevada capacidade de armazenamento relacionada com volume das imagens geradas. As políticas públicas e os avanços tecnológicos recentes orientados para a eCiência, têm vindo a apoiar o desenvolvimento da computação em Grid, tanto a nível dos middlewares como da instalação de capacidade de produção, como um sistema de computação avançado que permite a partilha de recursos, instrumentos científicos e boas práticas em comunidades virtuais. Este trabalho tem como objectivo desenvolver uma estratégia e um protótipo para o armazenamento de dados médicos na Grid, visando a sua utilização em investigação. Uma preocupação diferenciadora prendese com o objectivo de colocar as potencialidades da Grid ao serviço de utilizadores não técnicos (e.g. médicos, investigadores), que acedem a serviços de processamento e de armazenamento e catalogação de dados de forma transparente, através de um portal Web. O protótipo desenvolvido permite a investigadores na área das neurociências, sem conhecimentos específicos da tecnologia Grid, armazenar imagens e analisálas em Grids de produção existentes, baseadas no middleware gLite. ABSTRACT: Computing has become an essential tool in modern science, leading to the appearance of the term eScience to designate the usage of advanced computing technologies to support the execution of scientific experiments, and the preservation and sharing of knowledge. One of eScience domain areas is the medical imaging analysis. The processes that deal with medical images, both at clinical and investigation level, are very demanding in terms of computational support, due to the analysis algorithms that involve large volumes of generated images, requiring high storage capabilities. The recent public policies and technological advances are eScience oriented, and have been supporting the development of Grid computing, both at the middleware level and at the installation of production capabilities in an advanced computing system, that allows the sharing of resources, scientific instrumentation and good practices among virtual communities. The main objective of this work is to develop a strategy and a prototype to allow the storage of medical data on the Grid, targeting a research environment. The differentiating concern of this work is the ability to provide the nonexperts users (e.g: doctors, researchers) access to the Grid services, like storage and processing, through a friendly Web interface. The developed prototype allows researchers in the field of neuroscience, without any specific knowledge of Grid technology, to store images and analyse them in production Grid infrastructures, based on the gLite middleware

Neurobiological correlates of avatar identification processing and emotional inhibitory control in internet gaming disorder

Internet gaming disorder (IGD) is the most prevalent subcategory of internet addiction. It has been associated with self-concept deficits and related characteristics such as emotional as well as social competence deficits, increased social anxiety and a stronger identification with the own avatar (i.e. a graphical agent that often seems to be constructed according to gamers’ ideal). In addition, IGD seems to be linked with inhibitory control deficits, definable as impairments in the inhibition of reactions to irrelevant stimuli during the pursuit of cognitively represented goals. However, the neurobiological correlates of avatar compared to self and ideal-related identification processing as well as emotional inhibitory control in (socially) anxious contexts as potentially important factors in IGD development have not been explored yet. The brain region of the left angular gyrus (AG) has been associated with self-identification from a third-person perspective in healthy controls and showed avatar-related hyperactivation in long-term online gamers during a task on self and avatar reflection in functional magnetic resonance imaging (fMRI). The dorsal anterior cingulate cortex (dACC) seems to be involved in the integration of negative affect and cognitive control. Based on these observations, internet gaming addicts were neurobiologically examined by means of fMRI with a focus on the left AG as well as the dACC while completing specific tasks and compared to non-addicted controls as well as social media addicts. Hereby, participants’ concepts of self, ideal and avatar were assessed with a reflection task asking for the evaluation of characteristics regarding the self, ideal and own avatar. Emotional inhibitory control in a socially anxious context was neurobiologically explored by means of an emotional Stroop task (EST) assessing the inhibition on socially anxious words compared to positive, negative and neutral word stimuli under parallel reaction time recording. In addition, the emotional inhibitory control at anxious stimuli was examined neuropsychologically by means of an affective Go/No-Go task (AGN). Besides, psychometric questionnaires assessing impulsivity, emotional competence and social anxiety were applied. Internet gaming addicts showed significantly higher levels of impulsivity, social anxiety and emotional competence deficits relative to non-addicted controls in psychometric measures. Neurobiologically, internet gaming addicts exhibited left AG hyperactivations during the reflection on their own avatar relative to self and ideal reflection within their group as well as compared to non-addicted controls. In the EST, internet gaming addicts had longer reaction times during the inhibition on socially anxious compared to positive and negative words as well as compared to positive, negative and neutral words together. During the latter comparison, internet gaming addicts neurobiologically showed significant hypoactivations in the left middle and superior temporal gyrus (MTG and STG), which was also significantly lower relative to social media addicts. Functional alterations in the dACC were not observed. Neuropsychologically, no significant differences in emotional inhibitory control at anxious stimuli between internet gaming addicts and non-addicted controls were detected by means of the AGN. In summary, the virtually concretized avatar might replace the rather abstract ideal in IGD as a construct to identify with. The need for such a construct might arise from the urge to compensate dissatisfaction with the own person as a facet of self-concept deficits. The MTG and STG have previously been associated with the retrieval of words or expressions during communication, social perception and emotion regulation (based on a study in social anxiety disorder). The present finding of these regions’ hypoactivation in relation to socially anxious stimuli might indicate that 1) socially anxious words are less retrievable from the semantic storage of internet gaming addicts than positive, negative or neutral words, 2) in IGD, emotional inhibitory control in the socially anxious context is represented by brain regions involved in the processing of social information (such as the MTG and STG) and that 3) internet gaming addicts have deficiencies in the cognitive regulation of emotions as well as in the processing of social information, with the MTG and STG hypoactivation during socially anxious word blocks possibly serving as a neurobiological correlate of IGD-related social and emotional competence deficits as facets of self-concept impairments