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1 Bhatawdekar, Ramesh M Environmental Issues of Blasting I11955 2021 eBook  
2 Dickinson, Jennet Elizabeth ATLAS Measurements of the Higgs Boson Coupling to the Top Quark in the Higgs to Diphoton Decay Channel I11910 2021 eBook  
3 Tosciri, Cecilia Higgs Boson Decays into a Pair of Bottom Quarks I11867 2021 eBook  
4 Schuld, Maria Machine Learning with Quantum Computers I11858 2021 eBook  
5 Tanaka, Akinori Deep Learning and Physics I11729 2021 eBook  
6 Rieger, Marcel Search for tt??H Production in the H ??? bb?? Decay Channel I11617 2021 eBook  
7 Sch??tt, Kristof T Machine Learning Meets Quantum Physics I09716 2020 eBook  
8 Helias, Moritz Statistical Field Theory for Neural Networks I09583 2020 eBook  
9 Czischek, Stefanie Neural-Network Simulation of Strongly Correlated Quantum Systems I09120 2020 eBook  
10 Zhao, Haitao Feature Learning and Understanding I09105 2020 eBook  
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TitleEnvironmental Issues of Blasting : Applications of Artificial Intelligence Techniques
Author(s)Bhatawdekar, Ramesh M;Armaghani, Danial Jahed;Azizi, Aydin
PublicationSingapore, Springer Nature Singapore, 2021.
DescriptionIX, 77 p. 9 illus., 8 illus. in color : online resource
Abstract NoteThis book gives a rigorous and up-to-date study of the various AI and machine learning algorithms for resolving environmental challenges associated with blasting. Blasting is a critical activity in any mining or civil engineering project for breaking down hard rock masses. A small amount of explosive energy is only used during blasting to fracture rock in order to achieve the appropriate fragmentation, throw, and development of muck pile. The surplus energy is transformed into unfavourable environmental effects such as back-break, flyrock, air overpressure, and ground vibration. The advancement of artificial intelligence and machine learning techniques has increased the accuracy of predicting these environmental impacts of blasting. This book discusses the effective application of these strategies in forecasting, mitigating, and regulating the aforementioned blasting environmental hazards
ISBN,Price9789811682377
Keyword(s)1. Computational Intelligence 2. EBOOK 3. EBOOK - SPRINGER 4. Engineering geology 5. ENVIRONMENTAL MANAGEMENT 6. Geoengineering 7. GEOPHYSICS 8. Geotechnical engineering 9. Geotechnical Engineering and Applied Earth Sciences 10. MACHINE LEARNING
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TitleATLAS Measurements of the Higgs Boson Coupling to the Top Quark in the Higgs to Diphoton Decay Channel
Author(s)Dickinson, Jennet Elizabeth
PublicationCham, Springer International Publishing, 2021.
DescriptionXIV, 223 p. 146 illus., 130 illus. in color : online resource
Abstract NoteDuring Run 2 of the Large Hadron Collider, the ATLAS experiment recorded proton-proton collision events at 13 TeV, the highest energy ever achieved in a collider. Analysis of this dataset has provided new opportunities for precision measurements of the Higgs boson, including its interaction with the top quark. The Higgs-top coupling can be directly probed through the production of a Higgs boson in association with a top-antitop quark pair (ttH). The Higgs to diphoton decay channel is among the most sensitive for ttH measurements due to the excellent diphoton mass resolution of the ATLAS detector and the clean signature of this decay. Event selection criteria were developed using novel Machine Learning techniques to target ttH events, yielding a precise measurement of the ttH cross section in the diphoton channel and a 6.3 $\sigma$ observation of the ttH process in combination with other decay channels, as well as stringent limits on CP violation in the Higgs-top coupling
ISBN,Price9783030863685
Keyword(s)1. EBOOK 2. EBOOK - SPRINGER 3. Elementary particles (Physics) 4. Elementary Particles, Quantum Field Theory 5. MACHINE LEARNING 6. PARTICLE PHYSICS 7. PARTICLES (NUCLEAR PHYSICS) 8. QUANTUM FIELD THEORY
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TitleHiggs Boson Decays into a Pair of Bottom Quarks : Observation with the ATLAS Detector and Machine Learning Applications
Author(s)Tosciri, Cecilia
PublicationCham, Springer International Publishing, 2021.
DescriptionXIX, 159 p. 84 illus., 76 illus. in color : online resource
Abstract NoteThis thesis presents the analysis that led to the observation of the Standard Model (SM) Higgs boson decay into pairs of bottom quarks. The analysis, based on a multivariate strategy, exploits the production of a Higgs boson associated with a vector boson. The analysis was performed on a dataset corresponding to a luminosity of 79.8/fb collected by the ATLAS experiment during Run-2 at a centre-of-mass energy of 13 TeV. An excess of events over the expected background is observed in a combination with complementary Hbb searches. The analysis was extended to provide a finer interpretation of the signal measurement. The cross sections of the V H(H ??? bb) process have been measured in exclusive regions of phase space and used to search for deviations from the SM with an effective field theory approach. The results are discussed in this book. A novel technique for the fast simulation of the ATLAS forward calorimeter response is also presented. The new technique is based on similarity search, a branch of machine learning that enables quick and efficient searches for vectors similar to each other
ISBN,Price9783030879389
Keyword(s)1. EBOOK 2. EBOOK - SPRINGER 3. Elementary particles (Physics) 4. Elementary Particles, Quantum Field Theory 5. MACHINE LEARNING 6. PARTICLE PHYSICS 7. PARTICLES (NUCLEAR PHYSICS) 8. QUANTUM FIELD THEORY
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TitleMachine Learning with Quantum Computers
Author(s)Schuld, Maria;Petruccione, Francesco
PublicationCham, Springer International Publishing, 2021.
DescriptionXIV, 312 p. 104 illus., 74 illus. in color : online resource
Abstract NoteThis book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years
ISBN,Price9783030830984
Keyword(s)1. EBOOK 2. EBOOK - SPRINGER 3. MACHINE LEARNING 4. MATHEMATICS 5. Mathematics and Computing 6. QUANTUM COMPUTERS 7. Quantum computing
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TitleDeep Learning and Physics
Author(s)Tanaka, Akinori;Tomiya, Akio;Hashimoto, Koji
PublicationSingapore, Springer Nature Singapore, 2021.
DescriptionXIII, 207 p. 46 illus., 29 illus. in color : online resource
Abstract NoteWhat is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks. We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored
ISBN,Price9789813361089
Keyword(s)1. Computational Physics and Simulations 2. COMPUTER SIMULATION 3. EBOOK 4. EBOOK - SPRINGER 5. MACHINE LEARNING 6. MATHEMATICAL PHYSICS 7. STATISTICAL PHYSICS 8. Theoretical, Mathematical and Computational Physics
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TitleSearch for tt??H Production in the H ??? bb?? Decay Channel : Using Deep Learning Techniques with the CMS Experiment
Author(s)Rieger, Marcel
PublicationCham, Springer International Publishing, 2021.
DescriptionXIII, 217 p. 82 illus., 73 illus. in color : online resource
Abstract NoteIn 1964, a mechanism explaining the origin of particle masses was proposed by Robert Brout, Fran??ois Englert, and Peter W. Higgs. 48 years later, in 2012, the so-called Higgs boson was discovered in proton-proton collisions recorded by experiments at the LHC. Since then, its ability to interact with quarks remained experimentally unconfirmed. This book presents a search for Higgs bosons produced in association with top quarks tt??H in data recorded with the CMS detector in 2016. It focuses on Higgs boson decays into bottom quarks H ??? bb?? and top quark pair decays involving at least one lepton. In this analysis, a multiclass classification approach using deep learning techniques was applied for the first time. In light of the dominant background contribution from tt?? production, the developed method proved to achieve superior sensitivity with respect to existing techniques. In combination with searches in different decay channels, the presented work contributed to the first observations of tt??H production and H ??? bb?? decays
ISBN,Price9783030653804
Keyword(s)1. Data Analysis and Big Data 2. EBOOK 3. EBOOK - SPRINGER 4. Elementary particles (Physics) 5. Elementary Particles, Quantum Field Theory 6. MACHINE LEARNING 7. PARTICLE PHYSICS 8. PARTICLES (NUCLEAR PHYSICS) 9. Quantitative research 10. QUANTUM FIELD THEORY
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TitleMachine Learning Meets Quantum Physics
Author(s)Sch??tt, Kristof T;Chmiela, Stefan;von Lilienfeld, O. Anatole;Tkatchenko, Alexandre;Tsuda, Koji;M??ller, Klaus-Robert
PublicationCham, Springer International Publishing, 2020.
DescriptionXVI, 467 p. 137 illus., 125 illus. in color : online resource
Abstract NoteDesigning molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
ISBN,Price9783030402457
Keyword(s)1. Chemistry, Physical and theoretical 2. EBOOK 3. EBOOK - SPRINGER 4. MACHINE LEARNING 5. Numerical and Computational Physics, Simulation 6. PHYSICS 7. QUANTUM PHYSICS 8. Theoretical and Computational Chemistry
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8.     
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TitleStatistical Field Theory for Neural Networks
Author(s)Helias, Moritz;Dahmen, David
PublicationCham, Springer International Publishing, 2020.
DescriptionXVII, 203 p. 127 illus., 5 illus. in color : online resource
Abstract NoteThis book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra
ISBN,Price9783030464448
Keyword(s)1. EBOOK 2. EBOOK - SPRINGER 3. MACHINE LEARNING 4. Mathematical Models of Cognitive Processes and Neural Networks 5. MATHEMATICAL STATISTICS 6. Neural networks (Computer science)?? 7. Neurosciences 8. Probability and Statistics in Computer Science 9. STATISTICAL PHYSICS 10. Statistical Physics and Dynamical Systems
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9.     
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TitleNeural-Network Simulation of Strongly Correlated Quantum Systems
Author(s)Czischek, Stefanie
PublicationCham, Springer International Publishing, 2020.
DescriptionXV, 205 p. 51 illus., 48 illus. in color : online resource
Abstract NoteQuantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both
ISBN,Price9783030527150
Keyword(s)1. CONDENSED MATTER 2. CONDENSED MATTER PHYSICS 3. EBOOK 4. EBOOK - SPRINGER 5. MACHINE LEARNING 6. Mathematical Models of Cognitive Processes and Neural Networks 7. Neural networks (Computer science)?? 8. QUANTUM PHYSICS
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I09120     On Shelf    

10.    
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TitleFeature Learning and Understanding : Algorithms and Applications
Author(s)Zhao, Haitao;Lai, Zhihui;Leung, Henry;Zhang, Xianyi
PublicationCham, Springer International Publishing, 2020.
DescriptionXIV, 291 p. 126 illus., 109 illus. in color : online resource
Abstract NoteThis book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence
ISBN,Price9783030407940
Keyword(s)1. Computational Intelligence 2. Data-driven Science, Modeling and Theory Building 3. EBOOK 4. EBOOK - SPRINGER 5. ECONOPHYSICS 6. IMAGE PROCESSING 7. Image Processing and Computer Vision 8. MACHINE LEARNING 9. OPTICAL DATA PROCESSING 10. PATTERN RECOGNITION 11. SIGNAL PROCESSING 12. Signal, Image and Speech Processing 13. Sociophysics 14. Speech processing systems
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