110 research outputs found

    Assessing Executive Function Impairments and Comorbidity between ADHD and Stuttering

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    Stuttering and ADHD are often considered ‘comorbid’ because different types of symptoms and processing issues occur in, for example, fluency, attention and working memory. This thesis addresses whether or not these shared factors signify fundamental similarities between stuttering and ADHD that distinguish them from typical controls. This is done in two main ways: First, a comparison is made of details of performance on attention capabilities using a range of behavioural and physiological measures in various test environments, including Web and VR approaches; Second, modelling analyses are conducted that compare networks representing participants’ performance across groups. Using the Load Theory of Attention methodology (Chapter 2), which addresses how to focus attention and ignore distractions up to a point where load exceeds perceptual capacity, it was observed that the performance of participants who stutter was significantly lower from the performance of controls in the auditory selective and divided attention tasks. The results showed that tasks in which attention demands enhanced were effective in detecting limitations in audio processing by PWS. Extending the task in the visual, audio and audio-visual domains in a virtual reality environment in people who stutter, (PWS) as well as people with ADHD (PWADHD) it was found that while audio was more affected in PWS, audio and audio-visual domains were affected in PWADHD. Lastly, Network Models (NMs) from the measures examined showed that comorbidity between PWS and PWADHD is limited. For better clinical assessments of attention, fluency and working memory problems, a Linear Mixed Model (LMM) was included in chapter 3 to understand if gender imbalance affected the results of PWS, PWADHD and controls in a selective attention task. LMM correctly determined that the gender imbalance did not affect the participants performance and PWS performed significantly worse from PWADHD showing that the groups were not comorbid and PWS is impaired in selective attention tasks. Further investigations were made in chapter 4 on PWS, PWADHD and controls in which data collected was extended to behavioural as well as physiological measures in a selective attention task implemented in a Virtual Reality (VR) environment. Although both PWS and PWADHD differed from controls with lower performance on the task, impulsive behaviours were only present in PWADHD (higher NOF) while inattentiveness was observed only in PWS (lower FD, higher theta activity). The architecture of NMs was different between PWS and PWADHD in task performance confirming again that comorbidity between groups is overstated while the frontal cortex is impaired in both groups as shown by NMs from EEG measures. While previous chapters showed that selective and divided attention tasks in Executive Function (EF) can correctly assess attention problems in PWS, chapters 5, 6 and 7 aimed at understanding which attention type in EF is impaired in PWADHD and can correctly assess attention problems in this group. An extensive investigation was made from 10 VR tasks that drew upon different attention types on behavioural measures and responses from questionnaires (chapter 5), eye measures (chapter 6) and brain activity (chapter 7). PWADHD were compared to controls on all the measures. NMs showed that sustained attention tasks in all domains and switched attention task only in the visual domain assessed ADHD traits in PWADHD on the measures examined. Furthermore, prefrontal cortex was impaired as shown from NMs in EEG measures. Finally, NMs were compared between controls, PWADHD and PWS in chapter 8 on cognitive factors including attention, fluency and working memory. NMs confirmed previous findings that the comorbidity of symptoms of both disorders is overstated. NM architecture between controls and PWADHD was similar, but both differed from PWS. Working memory was a strong factor that affected attention in all groups but the way it affected attention differed between PWS and PWADHD

    Delays in Reinforcement Learning

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    Delays are inherent to most dynamical systems. Besides shifting the process in time, they can significantly affect their performance. For this reason, it is usually valuable to study the delay and account for it. Because they are dynamical systems, it is of no surprise that sequential decision-making problems such as Markov decision processes (MDP) can also be affected by delays. These processes are the foundational framework of reinforcement learning (RL), a paradigm whose goal is to create artificial agents capable of learning to maximise their utility by interacting with their environment. RL has achieved strong, sometimes astonishing, empirical results, but delays are seldom explicitly accounted for. The understanding of the impact of delay on the MDP is limited. In this dissertation, we propose to study the delay in the agent's observation of the state of the environment or in the execution of the agent's actions. We will repeatedly change our point of view on the problem to reveal some of its structure and peculiarities. A wide spectrum of delays will be considered, and potential solutions will be presented. This dissertation also aims to draw links between celebrated frameworks of the RL literature and the one of delays

    Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

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    In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-

    Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

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    Review of Particle Physics

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    The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app.United States Department of Energy (DOE) DE-AC02-05CH11231government of Japan (Ministry of Education, Culture, Sports, Science and Technology)Istituto Nazionale di Fisica Nucleare (INFN)Physical Society of Japan (JPS)European Laboratory for Particle Physics (CERN)United States Department of Energy (DOE

    Social convergence in times of spatial distancing: The rRole of music during the COVID-19 Pandemic

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    Proceedings of the XXVIIIth TELEMAC User Conference 18-19 October 2022

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