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| # |
Author | Title | Accn# | Year | Item Type | Claims |
| 21 |
Rieger, Marcel |
Search for tt??H Production in the H ??? bb?? Decay Channel |
I11617 |
2021 |
eBook |
|
| 22 |
Sch??tt, Kristof T |
Machine Learning Meets Quantum Physics |
I09716 |
2020 |
eBook |
|
| 23 |
Helias, Moritz |
Statistical Field Theory for Neural Networks |
I09583 |
2020 |
eBook |
|
| 24 |
Czischek, Stefanie |
Neural-Network Simulation of Strongly Correlated Quantum Systems |
I09120 |
2020 |
eBook |
|
| 25 |
Zhao, Haitao |
Feature Learning and Understanding |
I09105 |
2020 |
eBook |
|
| 26 |
Stark, Giordon |
The Search for Supersymmetry in Hadronic Final States Using Boosted Object Reconstruction |
I09004 |
2020 |
eBook |
|
| 27 |
Matthew Kirk |
Thoughtful machine learning: A Test-Driven Aproach |
026224 |
2014 |
Book |
|
| 28 |
Nikhil Buduma |
Fundamentals of deep learning: designing next generation machine intelligence algorithms |
PP0025 |
|
Book |
|
| 29 |
Christopher M. Bishop |
Pattern recognition and machine learning |
026213 |
2011 |
Book |
|
| 30 |
Ian Goodfellow |
Deep learning |
026211 |
2017 |
Book |
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21.
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| 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 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,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 |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I11617 |
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On Shelf |
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22.
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| Title | Machine Learning Meets Quantum Physics |
| Author(s) | Sch??tt, Kristof T;Chmiela, Stefan;von Lilienfeld, O. Anatole;Tkatchenko, Alexandre;Tsuda, Koji;M??ller, Klaus-Robert |
| 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 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,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
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09716 |
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On Shelf |
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23.
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| 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 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,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
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09583 |
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On Shelf |
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24.
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| Title | Neural-Network 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 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,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
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09120 |
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On Shelf |
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25.
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| 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 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,Price | 9783030407940 |
| 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
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09105 |
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On Shelf |
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26.
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| Title | The Search for Supersymmetry in Hadronic Final States Using Boosted Object Reconstruction |
| Author(s) | Stark, Giordon |
| Publication | Cham, Springer International Publishing, 2020. |
| Description | XVI, 257 p. 150 illus., 140 illus. in color : online resource |
| Abstract Note | This thesis represents one of the most comprehensive and in-depth studies of the use of Lorentz-boosted hadronic final state systems in the search for signals of Supersymmetry conducted to date at the Large Hadron Collider. A thorough assessment is performed of the observables that provide enhanced sensitivity to new physics signals otherwise hidden under an enormous background of top quark pairs produced by Standard Model processes. This is complemented by an ingenious analysis optimization procedure that allowed for extending the reach of this analysis by hundreds of GeV in mass of these hypothetical new particles. Lastly, the combination of both deep, thoughtful physics analysis with the development of high-speed electronics for identifying and selecting these same objects is not only unique, but also revolutionary. The Global Feature Extraction system that the author played a critical role in bringing to fruition represents the first dedicated hardware device for selecting these Lorentz-boosted hadronic systems in real-time using state-of-the-art processing chips and embedded systems |
| ISBN,Price | 9783030345488 |
| Keyword(s) | 1. EBOOK
2. EBOOK - SPRINGER
3. Elementary particles (Physics)
4. Elementary Particles, Quantum Field Theory
5. MACHINE LEARNING
6. Measurement Science and Instrumentation
7. Measurement??????
8. Particle acceleration
9. Particle Acceleration and Detection, Beam Physics
10. PHYSICAL MEASUREMENTS
11. QUANTUM FIELD THEORY
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09004 |
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On Shelf |
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