17,254 research outputs found

    Modelling uncertainties for measurements of the H → γγ Channel with the ATLAS Detector at the LHC

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    The Higgs boson to diphoton (H → γγ) branching ratio is only 0.227 %, but this final state has yielded some of the most precise measurements of the particle. As measurements of the Higgs boson become increasingly precise, greater import is placed on the factors that constitute the uncertainty. Reducing the effects of these uncertainties requires an understanding of their causes. The research presented in this thesis aims to illuminate how uncertainties on simulation modelling are determined and proffers novel techniques in deriving them. The upgrade of the FastCaloSim tool is described, used for simulating events in the ATLAS calorimeter at a rate far exceeding the nominal detector simulation, Geant4. The integration of a method that allows the toolbox to emulate the accordion geometry of the liquid argon calorimeters is detailed. This tool allows for the production of larger samples while using significantly fewer computing resources. A measurement of the total Higgs boson production cross-section multiplied by the diphoton branching ratio (σ × Bγγ) is presented, where this value was determined to be (σ × Bγγ)obs = 127 ± 7 (stat.) ± 7 (syst.) fb, within agreement with the Standard Model prediction. The signal and background shape modelling is described, and the contribution of the background modelling uncertainty to the total uncertainty ranges from 18–2.4 %, depending on the Higgs boson production mechanism. A method for estimating the number of events in a Monte Carlo background sample required to model the shape is detailed. It was found that the size of the nominal γγ background events sample required a multiplicative increase by a factor of 3.60 to adequately model the background with a confidence level of 68 %, or a factor of 7.20 for a confidence level of 95 %. Based on this estimate, 0.5 billion additional simulated events were produced, substantially reducing the background modelling uncertainty. A technique is detailed for emulating the effects of Monte Carlo event generator differences using multivariate reweighting. The technique is used to estimate the event generator uncertainty on the signal modelling of tHqb events, improving the reliability of estimating the tHqb production cross-section. Then this multivariate reweighting technique is used to estimate the generator modelling uncertainties on background V γγ samples for the first time. The estimated uncertainties were found to be covered by the currently assumed background modelling uncertainty

    Learning to listen: downstream effects of listening training on employees' relatedness, burnout, and turnover intentions

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    Abstract: The present work focuses on listening training as an example of a relational human resource practice that can improve human resource outcomes: Relatedness to colleagues, burnout, and turnover intentions. In two quasi‐field experiments, employees were assigned to either a group listening training or a control condition. Both immediately after training and after 3 weeks later, receiving listening training was shown to be linked to higher feelings of relatedness with colleagues, lower burnout, and lower turnover intentions. These findings suggest that listening training can be harnessed as a powerful human resource management tool to cultivate stronger relationships at work. The implications for Relational Coordination Theory, High‐Quality Connections Theory, and Self‐Determination Theory are discussed

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    Examination of a Brief, Self-Paced Online Self-Compassion Intervention Targeting Intuitive Eating and Body Image Outcomes among Men and Women

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    Ideals for appearance and body image are pervasive in Western culture in which men and women are portrayed with unrealistic and often unattainable standards (Ferguson, 2013; Martin, 2010). Exposure and reinforcement have created a culture of social acceptance and internalization of these ideals, contributing to pervasive body image disturbance (i.e., body dissatisfaction; Fallon et al., 2014; Stice, 2001; Thompson & Stice, 2001; Thompson et al., 1999). Research has suggested that body dissatisfaction is expressed differently across sexes (Grossbard et al., 2008), with attention to thin ideals among women and muscular ideals among men. Body dissatisfaction has been linked to numerous poor outcomes, including dieting, unhealthy weight control behaviors, disordered eating, and increased psychopathology. Although dieting is one of the primary mechanisms employed to reduce body dissatisfaction (Thompson & Stice, 2001), research has shown that such efforts are contraindicated as dieting predicts weight gain over time (Pietiläinen et al., 2012) as well as preoccupation with food, disordered eating, eating disorders, emotional distress, and higher body dissatisfaction (Grabe et al., 2007; Johnson & Wardle, 2005; Neumark- Sztianer et al., 2006; Paxton et al., 2006; Tiggemann, 2005). Restrictive dietary behaviors suppress physiological cues to eat (e.g., hunger) that presents a vulnerability to eating in response to alternative cues, both internal (e.g., emotions) and external (e.g., availability of food). Intuitive eating is a non-restrictive approach to eating that encourages adherence to internal physiological cues to indicate when, what, and how much to eat (Tylka, 2006) and has demonstrated an inverse relationship with disordered eating, restrained eating, food preoccupation, dieting, body dissatisfaction, and negative affect (Bruce & Ricciardelli, 2016). Self-compassion, relating to oneself in a caring and supportive manner (Neff, 2003a), has been proposed as a pathway to increase intuitive eating and reduce body dissatisfaction (Neff & Knox, 2017; Schoenefeld & Webb, 2013; Webb & Hardin, 2016). Research has highlighted the efficacy of self-compassion interventions in addressing weight-related concerns (Rahimi-Ardabili et al., 2018) as well as brief experiential exercises for reducing body dissatisfaction (Moffitt et al., 2018). Additionally, there is a growing body of evidence supporting the efficacy of internet-based self-compassion interventions (Mak et al., 2018; Kelman et al., 2018; Nadeau et al., 2020). The purpose of the current study was to examine the effectiveness of a brief, self-paced online self-compassion intervention targeting body image and adaptive eating behaviors and potential mechanisms of change (e.g., self-compassion and psychological flexibility) among undergraduate men and women. This study also examined outcomes among men and women in the area of self-compassion, body dissatisfaction, and intuitive eating as research has highlighted the need to determine who benefits more from self-compassion interventions (Rahimi-Ardabili et al., 2018). The study compared a one-hour, self-guided online self-compassion intervention to an active control condition. The intervention was comprised of psychoeducation, experiential exercises, and mindfulness practice designed to increase self-compassion surrounding body image and eating behaviors. In contrast, the active control condition consisted of self-care recommendations and self-assessments for nutrition, exercise, and sleep. The study was administered over three parts (e.g., baseline, intervention, and follow-up) in which variables of interest were assessed at each time point. Outcome variables included self-compassion, intuitive eating, disordered eating, body appreciation, muscle dysmorphia, internalized weight bias, fear of self-compassion, and psychological inflexibility. Participants were randomized on a 2:1 intervention to control ratio at the second time point in order to make comparisons between groups while simultaneously having sufficient power for examining mediation and moderation within the treatment condition. Overall, 1023 individuals (64% women, Mage = 18.9, 67.4% white) signed informed consent and participated in at least one part of the study whereas 101 participants (71% women, Mage = 19.3, 71% white) completed all three study portions. As predicted, self-compassion was correlated with all variables of interest, and all study variables were correlated with each other (p < .01). In contrast to hypothesized outcomes, the self-compassion condition failed to demonstrate improvements across time or between conditions on all study outcomes. These results persisted when participants were screened for levels of intuitive eating as well. Contrary to prediction, internalized weight bias, muscle dysmorphia, and fear of self-compassion demonstrated increased levels within the intervention condition and decreases in the control condition. There were significant gender differences on multiple outcome variables, with men demonstrating higher levels of self-compassion and body appreciation whereas women endorsed higher levels of disordered eating, internalized weight bias, muscle dysmorphia, and psychological inflexibility. Additionally, there were significant gender interactions for internalized weight bias, body appreciation, and muscle dysmorphia. The interactions existed such that men demonstrated increased internalized weight bias and muscle dysmorphia across time whereas women displayed decreased weight bias and muscle dysmorphia. The opposite pattern was found within body appreciation; women demonstrated increased body appreciation across time while men reported decreased levels of body appreciation. Despite this study’s intent to examine underlying mechanisms of change, the condition in which participants were randomly selected did not have any relationship, positive or negative, with the outcome variables of interest. As such, mediation within the current study was not conducted as it would violate statistical assumptions required to examine this hypothesis. Finally, upon examining the moderating relationship of fear of self-compassion between self-compassion and outcome variables, there were main effects for self-compassion on intuitive eating, emotional eating, internalized weight bias, body appreciation, and psychological inflexibility as well as main effects of fear of self-compassion on psychological inflexibility. There were significant interactions for intuitive eating and emotional eating, such that as fear of self-compassion increased, the effect of self-compassion on intuitive eating decreased, and the effect of self-compassion on reducing emotional eating behaviors decreased. Overall, the brief, self-paced online intervention delivered in the current study did not prove to be an effective means for improving self-compassion, intuitive eating, body appreciation, disordered eating, muscle dysmorphia, and psychological inflexibility. Nevertheless, the relationships between self-compassion and outcome variables of interest throughout the study mirror that of the existing literature. Findings from this study, in general, were also consistent with differences between men and women despite a gap in the research for intervention outcomes. Although fear of self-compassion demonstrated a moderating effect on the relationship between self-compassion and intuitive eating as well as emotional eating, this does not account for the lack of significant findings. The context surrounding this study, such as the COVID-19 pandemic, provided a considerable challenge to examining the efficacy of the current intervention. However, the findings of this study suggest future research will likely need to identify ways to enhance the delivery of experiential exercises that encourage engagement, provide a safe and warm environment for participants, and create flexibility and willingness surrounding painful and difficult experiences in order to undermine internalized and socially accepted beliefs about body image and eating behaviors

    Network Slicing for Industrial IoT and Industrial Wireless Sensor Network: Deep Federated Learning Approach and Its Implementation Challenges

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    5G networks are envisioned to support heterogeneous Industrial IoT (IIoT) and Industrial Wireless Sensor Network (IWSN) applications with a multitude Quality of Service (QoS) requirements. Network slicing is being recognized as a beacon technology that enables multi-service IIoT networks. Motivated by the growing computational capacity of the IIoT and the challenges of meeting QoS, federated reinforcement learning (RL) has become a propitious technique that gives out data collection and computation tasks to distributed network agents. This chapter discuss the new federated learning paradigm and then proposes a Deep Federated RL (DFRL) scheme to provide a federated network resource management for future IIoT networks. Toward this goal, the DFRL learns from Multi-Agent local models and provides them the ability to find optimal action decisions on LoRa parameters that satisfy QoS to IIoT virtual slice. Simulation results prove the effectiveness of the proposed framework compared to the early tools

    Unraveling the effect of sex on human genetic architecture

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    Sex is arguably the most important differentiating characteristic in most mammalian species, separating populations into different groups, with varying behaviors, morphologies, and physiologies based on their complement of sex chromosomes, amongst other factors. In humans, despite males and females sharing nearly identical genomes, there are differences between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides the genome with a distinct hormonal milieu, differential gene expression, and environmental pressures arising from gender societal roles. This thus poses the possibility of observing gene by sex (GxS) interactions between the sexes that may contribute to some of the phenotypic differences observed. In recent years, there has been growing evidence of GxS, with common genetic variation presenting different effects on males and females. These studies have however been limited in regards to the number of traits studied and/or statistical power. Understanding sex differences in genetic architecture is of great importance as this could lead to improved understanding of potential differences in underlying biological pathways and disease etiology between the sexes and in turn help inform personalised treatments and precision medicine. In this thesis we provide insights into both the scope and mechanism of GxS across the genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits through the calculation of sex-specific heritability, genetic correlations, and sex-stratified genome-wide association studies (GWAS). We further investigated whether sex-agnostic (non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we studied the potential functional role of sex differences in genetic architecture through sex biased expression quantitative trait loci (eQTL) and gene-level analyses. Overall, this study marks a broad examination of the genetics of sex differences. Our findings parallel previous reports, suggesting the presence of sexual genetic heterogeneity across complex traits of generally modest magnitude. Furthermore, our results suggest the need to consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms

    Studies of strategic performance management for classical organizations theory & practice

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    Nowadays, the activities of "Performance Management" have spread very broadly in actually every part of business and management. There are numerous practitioners and researchers from very different disciplines, who are involved in exploring the different contents of performance management. In this thesis, some relevant historic developments in performance management are first reviewed. This includes various theories and frameworks of performance management. Then several management science techniques are developed for assessing performance management, including new methods in Data Envelopment Analysis (DEA) and Soft System Methodology (SSM). A theoretical framework for performance management and its practical procedures (five phases) are developed for "classic" organizations using soft system thinking, and the relationship with the existing theories are explored. Eventually these results are applied in three case studies to verify our theoretical development. One of the main contributions of this work is to point out, and to systematically explore the basic idea that the effective forms and structures of performance management for an organization are likely to depend greatly on the organizational configuration, in order to coordinate well with other management activities in the organization, which has seemingly been neglected in the existing literature of performance management research in the sense that there exists little known research that associated particular forms of performance management with the explicit assumptions of organizational configuration. By applying SSM, this thesis logically derives some main functional blocks of performance management in 'classic' organizations and clarifies the relationships between performance management and other management activities. Furthermore, it develops some new tools and procedures, which can hierarchically decompose organizational strategies and produce a practical model of specific implementation steps for "classic" organizations. Our approach integrates popular types of performance management models. Last but not least, this thesis presents findings from three major cases, which are quite different organizations in terms of management styles, ownership, and operating environment, to illustrate the fliexbility of the developed theoretical framework

    Working in ministries or public organizations in Saudi Arabia : A study of career development and job satisfaction of the Saudi Arabian middle managers

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    Career development and job satisfaction studies carried out in developing countries are very limited in number. Saudi Arabia is one of those developing countries which appeared on the political scene quite recently, but striving hard to develop its human resources due to its heavy dependence on expatriate labour to initiate and execute its development plans. The genesis of the study began when General Civil Service Bureau officials noticed a large movement of employees from ministries to other sectors (i.e. public organizations and the private sector). The purpose of this dissertation is to examine and analyze the factors behind this movement and relate this to the studies of career development and job satisfaction. The position of government organizations in Saudi Arabia is rather unique. Most of their employees are drawn from Universities due to the regulations of the GCSB of compelling them to work in ministries for a period equivalent to that spent in their University education until graduation. This situation has prevented such graduates from choosing their own occupations and seem to hinder their career development. As a consequence, this study, not only analyzes career development and job satisfaction in Saudi Arabia, but (v) job satisfaction in Saudi Arabia, but also makes a comprehensive evaluation of economic, social and organisational environments which seem to have an effect of the occupational choice of the Saudis. We take the assumption that the ideology of free occupational choice is not properly applied in Saudi Arabia due to some cultural variables (e.g. nepotism and strong family ties). Hence, this thesis will develop a definition of the concept of occupational choice and career development and the process of personnel flow and the ways in which such movement can be influenced within the Saudi context. The study will be primarily concerned with middle managers in two types of organization - government ministries and public organizations. This will hopefully give a profile of the Saudi situation as far as occupational choice, career development and job satisfaction are concerned

    Anytime algorithms for ROBDD symmetry detection and approximation

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    Reduced Ordered Binary Decision Diagrams (ROBDDs) provide a dense and memory efficient representation of Boolean functions. When ROBDDs are applied in logic synthesis, the problem arises of detecting both classical and generalised symmetries. State-of-the-art in symmetry detection is represented by Mishchenko's algorithm. Mishchenko showed how to detect symmetries in ROBDDs without the need for checking equivalence of all co-factor pairs. This work resulted in a practical algorithm for detecting all classical symmetries in an ROBDD in O(|G|³) set operations where |G| is the number of nodes in the ROBDD. Mishchenko and his colleagues subsequently extended the algorithm to find generalised symmetries. The extended algorithm retains the same asymptotic complexity for each type of generalised symmetry. Both the classical and generalised symmetry detection algorithms are monolithic in the sense that they only return a meaningful answer when they are left to run to completion. In this thesis we present efficient anytime algorithms for detecting both classical and generalised symmetries, that output pairs of symmetric variables until a prescribed time bound is exceeded. These anytime algorithms are complete in that given sufficient time they are guaranteed to find all symmetric pairs. Theoretically these algorithms reside in O(n³+n|G|+|G|³) and O(n³+n²|G|+|G|³) respectively, where n is the number of variables, so that in practice the advantage of anytime generality is not gained at the expense of efficiency. In fact, the anytime approach requires only very modest data structure support and offers unique opportunities for optimisation so the resulting algorithms are very efficient. The thesis continues by considering another class of anytime algorithms for ROBDDs that is motivated by the dearth of work on approximating ROBDDs. The need for approximation arises because many ROBDD operations result in an ROBDD whose size is quadratic in the size of the inputs. Furthermore, if ROBDDs are used in abstract interpretation, the running time of the analysis is related not only to the complexity of the individual ROBDD operations but also the number of operations applied. The number of operations is, in turn, constrained by the number of times a Boolean function can be weakened before stability is achieved. This thesis proposes a widening that can be used to both constrain the size of an ROBDD and also ensure that the number of times that it is weakened is bounded by some given constant. The widening can be used to either systematically approximate an ROBDD from above (i.e. derive a weaker function) or below (i.e. infer a stronger function). The thesis also considers how randomised techniques may be deployed to improve the speed of computing an approximation by avoiding potentially expensive ROBDD manipulation
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