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This book explores how machine learning can be used to improve the efficiency of expensive fundamental science experiments. The first part introduces the Belle and Belle II experiments, providing a detailed description of the Belle to Belle II data conversion tool, currently used by many analysts. The second part covers machine learning in high-energy physics, discussing the Belle II machine learning infrastructure and selected algorithms in detail. Furthermore, it examines several machine learning techniques that can be used to control and reduce systematic uncertainties. The third part investigates the important exclusive B tagging technique, unique to physics experiments operating at the Υ resonances, and studies in-depth the novel Full Event Interpretation algorithm, which doubles the maximum tag-side efficiency of its predecessor. The fourth part presents a complete measurement of the branching fraction of the rare leptonic B decay “B→tau nu”, which is used to validate the algorithms discussed in previous parts.
Combinatorial Kalman filters are a standard tool today for pattern recognition and charged particle reconstruction in high energy physics. In this thesis the implementation of the track finding software for the Belle II experiment and first studies on early Belle II data are presented. The track finding algorithm exploits novel concepts such as multivariate track quality estimates to form charged trajectory hypotheses combining information from the Belle II central drift chamber with the inner vertex sub-detectors. The eventual track candidates show an improvement in resolution on the parameters describing their spatial and momentum properties by up to a factor of seven over the former legacy implementation. The second part of the thesis documents a novel way to determine the collision event null time T0 and the implementation of optimisation steps in the online reconstruction code, which proved crucial in overcoming the high level trigger limitations.
This book constitutes the proceedings of the 19th International Symposium on Applied Reconfigurable Computing, ARC 2023, which was held in Cottbus, Germany, in September 2023. The 18 full papers presented in this volume were reviewed and selected from numerous submissions. The proceedings also contain 4 short PhD papers. The contributions were organized in topical sections as follows: Design methods and tools; applications; architectures; special session: near and in-memory computing; and PhD forum papers.
This book gathers a collection of selected works and new research results of scholars and graduate students presented at the 6th International Conference on Artificial Intelligence and Virtual Reality (AIVR 2022) via the Internet, during July 22-24 2022, hosted and organized by Sojo University in conjunction with other three universities and Beijing Huaxia Rongzhi Blockchain Technology Institute. The focus of the book is interdisciplinary in nature and includes research on all aspects of artificial intelligence and virtual reality, from fundamental development to the applied system. The book covers topics such as system techniques, performance, and implementation; content creation and modelling; cognitive aspects, perception, user behaviour; AI technologies; interactions, interactive and responsive environments; AI/VR applications and case studies.
Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons. This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods. Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.
The latest of the 'Lepton Photon' symposium, one of the well-established series of meetings in the high-energy physics community, was successfully organized at the South Campus of Sun Yat-sen University, Guangzhou, China, from August 7-12, 2017, where physicists around the world gathered to discuss the latest advancements in the research field.This proceedings volume of the Lepton Photon 2017 collects contributions by the plenary session speakers and the posters' presenters, which cover the latest results in particle physics, nuclear physics, astrophysics, cosmology, and plans for future facilities.
This book constitutes the refereed proceedings of the First International Conference on Big Scientific Data Management, BigSDM 2018, held in Beijing, Greece, in November/December 2018. The 24 full papers presented together with 7 short papers were carefully reviewed and selected from 86 submissions. The topics involved application cases in the big scientific data management, paradigms for enhancing scientific discovery through big data, data management challenges posed by big scientific data, machine learning methods to facilitate scientific discovery, science platforms and storage systems for large scale scientific applications, data cleansing and quality assurance of science data, and data policies.
The rapid advances of artificial intelligence (AI) in recent years have led to numerous creative applications in science. Accelerating the productivity of science could be the most economically and socially valuable of all the uses of AI.
This book offers essential insights into various management concepts for retail and consumer packaged goods companies. Addressing a range of topics in the field of performance management, it presents concepts for management control, management reporting, planning & forecasting, as well as digitization-related aspects. The contributing authors share valuable lessons learned from real-world consulting projects and present innovative approaches to successful and effective management control at retail and consumer packaged goods companies.