8,866 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    Developing International Mindedness through the Arts in the International Baccalaureate (IB) Diploma Programme (DP): An International Survey Design Conducted across all Continents

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    One distinct purpose of international education is to develop greater international understanding and intercultural competences. For the International Baccalaureate, this translates into students developing international mindedness throughout its programmes and courses. However, international mindedness is not measured and the impact of the programmes on the development of international mindedness remains mainly anecdotal. Furthermore, in the Diploma Programme, the choice of Arts courses is optional and the value of an Arts education, or specifically the value of taking a Diploma Programme Arts course in developing international mindedness, is equally unclear. This study investigated the development of international mindedness in students who opted for a Diploma Programme Arts course versus those who did not. The study followed a repeated measures, comparative and mixed-methods research design using a survey tool for data collection. The survey consisted of a quantitative section based on existing surveys and a qualitative section with six open-ended questions. The quantitative data showed an increase in intercultural knowledge and behaviours, while no change in attitudes, and a decrease in values was identified for both student groups, Diploma Programme Arts and Non-Arts-students. Furthermore, there was an increase in intercultural communication skills particularly in Diploma Programme Arts-students. Qualitative data analysis revealed a spectrum of categories of responses. The qualitative data also identified themes in addition to those identified in International Baccalaureate documentation and literature. Recommendations include for the International Baccalaureate Organization to integrate some of the emerging themes in their documentations, for example themes relating to adaptability and interconnectedness, which may also provide an interesting focus for curriculum design. Furthermore, curriculum and programme design should place a greater focus on the development of attitudes and values in the Diploma Programme and a reconsideration of the optionality of the Arts in this context

    Morphometric reorganization induced by working memory training: perspective from vertex and network levels

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    Der sich beschleunigende globale Alterungsprozess und die Tatsache, dass sich die kog-nitiven Fähigkeiten mit dem Alter verschlechtern, was sich erheblich auf die Lebensquali-tät älterer Erwachsener auswirkt, insbesondere bei altersbedingten Störungen (z. B. kogni-tiver Beeinträchtigung, Demenz), weisen auf einen dringenden Bedarf an Ansätzen zum Schutz und zur Verbesserung der kognitiven Fähigkeiten sowie an Untersuchungen der neuronalen Substrate altersbedingter Veränderungen und der Neuroplastizität hin. Da man davon ausgeht, dass das Arbeitsgedächtnis (WM) die grundlegende Ursache für altersbe-dingte kognitive Beeinträchtigungen bei einer Vielzahl von kognitiven Fähigkeiten dar-stellt, ist das Arbeitsgedächtnistraining (WMT) zu einem aktuellen Thema und einem be-liebten Ansatz geworden. Frühere Studien haben gezeigt, dass das Arbeitsgedächtnistrai-ning (WMT) die kognitive Leistung verbessert. Die spezifischen Auswirkungen sowie die zugrunde liegenden neurobiologischen Mechanismen sind jedoch nach wie vor um-stritten. Ziel dieser Arbeit ist es, die durch das WMT induzierte neuronale strukturelle Plastizität auf mehreren Ebenen sowie die Verhaltenseffekte des WMT zu untersuchen. In der ers-ten Studie untersuchten wir die topographischen Veränderungen der Morphologie der grauen Substanz durch WMT, indem wir vier strukturelle Metriken (d.h. die kortikale Dicke, das kortikale Volumen, die kortikale Oberfläche und den lokalen Gyrifikationsin-dex, LGI) sowie die subkortikalen Volumina explorierten. Konkret wurden 59 gesunde Probanden mittleren Alters nach dem Zufallsprinzip entweder einem adaptiven WMT oder einer nicht-adaptiven Intervention zugewiesen. Alle Teilnehmer unterzogen sich vor und nach der 8-wöchigen WMT-Phase einer Neurobildgebung sowie kognitiven Tests. Vor und nach dem WMT wurden vier kortikale Metriken auf Scheitelpunktniveau und sieben subkortikale Volumina sowie die globale mittlere kortikale Dicke berechnet. Das wich-tigste Ergebnis war, dass die WMT-Gruppe im Vergleich zur aktiven Kontrollgruppe eine größere Zunahme der kortikalen Faltung in den bilateralen parietalen Regionen zeigte. Die Ergebnisse deuten darauf hin, dass strukturelle Veränderungen durch WMT in WM-bezogenen Regionen, insbesondere in parietalen Regionen, die Verarbeitung einer höhe-ren WM-Belastung erleichtern können. Darüber hinaus könnte die kortikale Faltung das relevanteste und plastischste Merkmal von WM und Lernen sein und WMT-Effekte stär-ker widerspiegeln als andere Metriken. Basierend auf den Ergebnissen der ersten Studie haben wir darüber hinaus untersucht, ob die trainingsinduzierten Effekte des WMT in der kortikalen Faltung auf Vertex-Ebene von topologischen Veränderungen begleitet werden. Zu diesem Zweck untersuchten wir in Studie zwei die durch WMT verursachte Plastizität auf Netzwerkebene mit Hilfe eines strukturellen Kovarianzansatzes (SC), der auf denselben Stichproben basiert. Es wurden gyrifikationsbasierte SC-Matrizen für jede Gruppe vor und nach dem Training sowie lon-gitudinale gyrifikationsbasierte SC-Matrizen erstellt. Innerhalb jeder Gruppe ergab die LGI-basierte SC-Analyse keine Hinweise auf WMT-induzierte Veränderungen der kor-tiko-kortikalen Verbindungen, weder in der WMT- noch in der aktiven Kontrollgruppe. Die Ergebnisse der longitudinalen SC-Analyse (unkorrigiert p < 0,005) zeigten, dass die trainingsinduzierten Veränderungen der kortikalen Faltungsintensität signifikante Unter-schiede zwischen Paaren von parietalen Regionen sowie Paaren von frontalen Regionen aufwiesen. Insgesamt deuten die kombinierten Ergebnisse dieser beiden Studien darauf hin, dass ers-tens WMT neuronale strukturelle Plastizität hervorrufen kann; zweitens die kortikale Fal-tung das relevanteste und plastischste Merkmal von WM und Lernen sein könnte, das die Auswirkungen von WMT besser widerspiegelt als andere Indikatoren auf Vertex-Ebene; und drittens die trainingsinduzierten lokalisierten Veränderungen der kortikalen Faltung von einem ähnlichen Muster vergleichbarer struktureller Veränderungen zwischen ROIs innerhalb der Regionen begleitet wurden. In Zukunft sind weitere Forschungen erforder-lich, um diese Ergebnisse zu wiederholen und zu validieren sowie um trainingsinduzierte topologische und topografische Veränderungen anhand einer breiteren Palette von Metri-ken und Eigenschaften zu untersuchen.The accelerating global aging process and the fact that cognitive abilities deteriorate with age, which has a significant impact on the quality of life of older adults, particularly those with age-related disorders (e.g., cognitive impairment, dementia), all point to an urgent need for approaches to protect and enhance cognitive abilities, as well as studies of the neural substrates of aging-related changes and neuroplasticity. Since working memory (WM) has been assumed to be the fundamental source of age-related cognitive impair-ments in a variety of cognitive abilities, working memory training (WMT) has become a hot topic as well as a popular approach. Previous studies have established that working memory training (WMT) improves cognitive performance. However, the specific effects, as well as the underlying neurobiological mechanisms, remain a matter of controversy. The purpose of this thesis is to investigate WMT-induced neural structural plasticity at multiple levels together with the behavioral effects of WMT. In study one, we investigated the topographic changes of grey matter morphology due to WMT by combining four structural metrics (i.e., cortical thickness (CT), cortical volume (CV), cortical surface area (CSA), and local gyrification index (LGI)) as well as subcortical volumes. Specifically, 59 healthy volunteers between the ages of 50 and 65 were randomly assigned to either an adaptive or a non-adaptive intervention. All participants underwent neuroimaging as well as cognitive testing before and after the 8-week intervention. Four cortical metrics at ver-tex level and seven subcortical volumes, as well as global mean cortical thickness, were calculated before and after the intervention. The most important finding was that the adap-tive WMT group showed greater increases in cortical folding in bilateral parietal regions in comparison to the active control group who performed the non-adaptive intervention. The results indicate that structural changes due to adaptive WMT in WM related regions, particularly parietal regions, may facilitate the processing of a higher WM load. In addi-tion, the cortical folding might be the most relevant and plastic feature of WM and learn-ing, reflecting WMT effects more than other metrics. Based on the findings of study one, we further asked whether the training-induced effects of WMT in cortical folding at vertex-level are accompanied by topological changes. To this end, study two investigated network-level plasticity due to WMT by using the struc-tural covariance (SC) approach based on the same samples. Gyrification based SC matri-ces for each group before and after training, together with longitudinal gyrification SC matrices, were constructed. Within each group, the LGI-based SC analysis revealed no evidence of WMT-induced changes in cortical-cortical connections, either in the WMT or the active control groups. The results of the longitudinal SC analysis (uncorrected p < 0.005) revealed that the training induced changes of cortical folding intensity showed sig-nificant difference between pairs of parietal regions as well as pairs of frontal regions. Overall, the combined findings of these two studies indicate that: firstly, WMT can pro-duce neural structural plasticity; secondly, cortical folding might be the most relevant and plastic feature of WM and learning, better reflecting the effects of WMT than other vertex-level indicators; and thirdly, the training induced localized changes in cortical folding were accompanied by the pattern of similar structural changes between ROIs within the regions. In the future, more research is required to replicate and validate these findings, as well as to investigate training-induced topological and topographic changes using a broader set of metrics and properties

    A Connected World. Social Networks and Organizations

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    This is the submitted version. The final version is available from Cambridge University Press via the DOI in this recordThis Element synthesizes the current state of research on organizational social networks from its early foundations to contemporary debates. It highlights the characteristics that make the social network perspective distinctive in the organizational research landscape, including its emphasis on structure and outcomes. It covers the main theoretical developments and summarizes the research design questions that organizational researchers face when collecting and analyzing network data. Then, it discusses current debates ranging from agency and structure to network volatility and personality. Finally, the Element envisages future research directions on the role of brokerage for individuals and communities, network cognition, and the importance of past ties. Overall, the Element provides an innovative angle for understanding organizational social networks, engaging in empirical network research, and nurturing further theoretical development on the role of social interactions and connectedness in modern organizations

    Decision-making with gaussian processes: sampling strategies and monte carlo methods

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    We study Gaussian processes and their application to decision-making in the real world. We begin by reviewing the foundations of Bayesian decision theory and show how these ideas give rise to methods such as Bayesian optimization. We investigate practical techniques for carrying out these strategies, with an emphasis on estimating and maximizing acquisition functions. Finally, we introduce pathwise approaches to conditioning Gaussian processes and demonstrate key benefits for representing random variables in this manner.Open Acces

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Numerical methods for computing the discrete and continuous Laplace transforms

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    We propose a numerical method to spline-interpolate discrete signals and then apply the integral transforms to the corresponding analytical spline functions. This represents a robust and computationally efficient technique for estimating the Laplace transform for noisy data. We revisited a Meijer-G symbolic approach to compute the Laplace transform and alternative approaches to extend canonical observed time-series. A discrete quantization scheme provides the foundation for rapid and reliable estimation of the inverse Laplace transform. We derive theoretic estimates for the inverse Laplace transform of analytic functions and demonstrate empirical results validating the algorithmic performance using observed and simulated data. We also introduce a generalization of the Laplace transform in higher dimensional space-time. We tested the discrete LT algorithm on data sampled from analytic functions with known exact Laplace transforms. The validation of the discrete ILT involves using complex functions with known analytic ILTs

    Cell decision-making through the lens of Bayesian learning

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    Cell decision-making refers to the process by which cells gather information from their local microenvironment and regulate their internal states to create appropriate responses. Microenvironmental cell sensing plays a key role in this process. Our hypothesis is that cell decision-making regulation is dictated by Bayesian learning. In this article, we explore the implications of this hypothesis for internal state temporal evolution. By using a timescale separation between internal and external variables on the mesoscopic scale, we derive a hierarchical Fokker-Planck equation for cell-microenvironment dynamics. By combining this with the Bayesian learning hypothesis, we find that changes in microenvironmental entropy dominate cell state probability distribution. Finally, we use these ideas to understand how cell sensing impacts cell decision-making. Notably, our formalism allows us to understand cell state dynamics even without exact biochemical information about cell sensing processes by considering a few key parameters

    Bayesian Renormalization

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    In this note we present a fully information theoretic approach to renormalization inspired by Bayesian statistical inference, which we refer to as Bayesian Renormalization. The main insight of Bayesian Renormalization is that the Fisher metric defines a correlation length that plays the role of an emergent RG scale quantifying the distinguishability between nearby points in the space of probability distributions. This RG scale can be interpreted as a proxy for the maximum number of unique observations that can be made about a given system during a statistical inference experiment. The role of the Bayesian Renormalization scheme is subsequently to prepare an effective model for a given system up to a precision which is bounded by the aforementioned scale. In applications of Bayesian Renormalization to physical systems, the emergent information theoretic scale is naturally identified with the maximum energy that can be probed by current experimental apparatus, and thus Bayesian Renormalization coincides with ordinary renormalization. However, Bayesian Renormalization is sufficiently general to apply even in circumstances in which an immediate physical scale is absent, and thus provides an ideal approach to renormalization in data science contexts. To this end, we provide insight into how the Bayesian Renormalization scheme relates to existing methods for data compression and data generation such as the information bottleneck and the diffusion learning paradigm.Comment: 20 pages, no figures. V2: Citation format fixed, references adde
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