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Author  Title  Accn#  Year  Item Type  Claims 
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 
NeuralNetwork Simulation of Strongly Correlated Quantum Systems 
I09120 
2020 
eBook 

10 
Zhao, Haitao 
Feature Learning and Understanding 
I09105 
2020 
eBook 


1.


Title  Environmental Issues of Blasting : Applications of Artificial Intelligence Techniques 
Author(s)  Bhatawdekar, Ramesh M;Armaghani, Danial Jahed;Azizi, Aydin 
Publication  Singapore, Springer Nature Singapore, 2021. 
Description  IX, 77 p. 9 illus., 8 illus. in color : online resource 
Abstract Note  This book gives a rigorous and uptodate 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 backbreak, 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,Price  9789811682377 
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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Call#  Status  Issued To  Return Due On  Physical Location 
I11955 


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2.


Title  ATLAS Measurements of the Higgs Boson Coupling to the Top Quark in the Higgs to Diphoton Decay Channel 
Author(s)  Dickinson, Jennet Elizabeth 
Publication  Cham, Springer International Publishing, 2021. 
Description  XIV, 223 p. 146 illus., 130 illus. in color : online resource 
Abstract Note  During Run 2 of the Large Hadron Collider, the ATLAS experiment recorded protonproton 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 Higgstop coupling can be directly probed through the production of a Higgs boson in association with a topantitop 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 Higgstop coupling 
ISBN,Price  9783030863685 
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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Accession#  
Call#  Status  Issued To  Return Due On  Physical Location 
I11910 


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3.


Title  Higgs Boson Decays into a Pair of Bottom Quarks : Observation with the ATLAS Detector and Machine Learning Applications 
Author(s)  Tosciri, Cecilia 
Publication  Cham, Springer International Publishing, 2021. 
Description  XIX, 159 p. 84 illus., 76 illus. in color : online resource 
Abstract Note  This 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 Run2 at a centreofmass 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,Price  9783030879389 
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

Item Type  eBook 
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Accession#  
Call#  Status  Issued To  Return Due On  Physical Location 
I11867 


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4.


Title  Machine Learning with Quantum Computers 
Author(s)  Schuld, Maria;Petruccione, Francesco 
Publication  Cham, Springer International Publishing, 2021. 
Description  XIV, 312 p. 104 illus., 74 illus. in color : online resource 
Abstract Note  This book offers an introduction into quantum machine learning research, covering approaches that range from "nearterm" to faulttolerant 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 nearterm quantum machine learning seen over the past few years 
ISBN,Price  9783030830984 
Keyword(s)  1. EBOOK
2. EBOOK  SPRINGER
3. MACHINE LEARNING
4. MATHEMATICS
5. Mathematics and Computing
6. QUANTUM COMPUTERS
7. Quantum computing

Item Type  eBook 
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Accession#  
Call#  Status  Issued To  Return Due On  Physical Location 
I11858 


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5.


Title  Deep Learning and Physics 
Author(s)  Tanaka, Akinori;Tomiya, Akio;Hashimoto, Koji 
Publication  Singapore, Springer Nature Singapore, 2021. 
Description  XIII, 207 p. 46 illus., 29 illus. in color : online resource 
Abstract Note  What 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,Price  9789813361089 
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

Item Type  eBook 
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Accession#  
Call#  Status  Issued To  Return Due On  Physical Location 
I11729 


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6.


Title  Search for tt??H Production in the H ??? bb?? Decay Channel : Using Deep Learning Techniques with the CMS Experiment 
Author(s)  Rieger, Marcel 
Publication  Cham, Springer International Publishing, 2021. 
Description  XIII, 217 p. 82 illus., 73 illus. in color : online resource 
Abstract Note  In 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 socalled Higgs boson was discovered in protonproton 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,Price  9783030653804 
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

Item Type  eBook 
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Call#  Status  Issued To  Return Due On  Physical Location 
I11617 


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7.


Title  Machine Learning Meets Quantum Physics 
Author(s)  Sch??tt, Kristof T;Chmiela, Stefan;von Lilienfeld, O. Anatole;Tkatchenko, Alexandre;Tsuda, Koji;M??ller, KlausRobert 
Publication  Cham, Springer International Publishing, 2020. 
Description  XVI, 467 p. 137 illus., 125 illus. in color : online resource 
Abstract Note  Designing 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 firstprinciples 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 timescales, 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 stateoftheart 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,Price  9783030402457 
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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Call#  Status  Issued To  Return Due On  Physical Location 
I09716 


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8.


Title  Statistical Field Theory for Neural Networks 
Author(s)  Helias, Moritz;Dahmen, David 
Publication  Cham, Springer International Publishing, 2020. 
Description  XVII, 203 p. 127 illus., 5 illus. in color : online resource 
Abstract Note  This book presents a selfcontained 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 selfstudy 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,Price  9783030464448 
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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Call#  Status  Issued To  Return Due On  Physical Location 
I09583 


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9.


Title  NeuralNetwork Simulation of Strongly Correlated Quantum Systems 
Author(s)  Czischek, Stefanie 
Publication  Cham, Springer International Publishing, 2020. 
Description  XV, 205 p. 51 illus., 48 illus. in color : online resource 
Abstract Note  Quantum 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 highperformance 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 nonlocal 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 manybody 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,Price  9783030527150 
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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Accession#  
Call#  Status  Issued To  Return Due On  Physical Location 
I09120 


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10.
 
Title  Feature Learning and Understanding : Algorithms and Applications 
Author(s)  Zhao, Haitao;Lai, Zhihui;Leung, Henry;Zhang, Xianyi 
Publication  Cham, Springer International Publishing, 2020. 
Description  XIV, 291 p. 126 illus., 109 illus. in color : online resource 
Abstract Note  This book covers the essential concepts and strategies within traditional and cuttingedge 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 geometricalstructurebased methods, but also advanced feature learning methods, such as sparse learning, lowrank decomposition, tensorbased feature extraction, and deeplearningbased 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 realworld 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,Price  9783030407940 
Keyword(s)  1. Computational Intelligence
2. Datadriven 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

Item Type  eBook 
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Accession#  
Call#  Status  Issued To  Return Due On  Physical Location 
I09105 


On Shelf 



 