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The Principles of Deep Learning Theory
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Holographic Models
Holographic correspondences provide models of strongly correlated systems whose thermodynamic and transport properties are computationally tractable. In this thesis we first provide a class of seemingly innocuous bottom-up holographic models which are argued to be inconsistent, violating microcausality. With such cautionary cases in mind, we go on to construct a variety of consistent top-down holographic models. In particular, we engineer holographic lattices, dimers, and dimer-glasses, using ingredients in type IIB string theory. Finally, we set up disordered holographic systems and develop technology which enables us to study renormalization group flows and thermodynamic properties in these strongly correlated systems with randomness.
Studies on Time-reversal Invariant Topological Insulators
This dissertation brings together a number of topics in the theory of time-reversal invariant topological insulators. The first four chapters are devoted to the transport properties of the two-dimensional (2D) quantum spin Hall state. We explain nonlocal transport measurements in mercury telluride (HgTe) quantum wells in terms of a Landauer-Büttiker theory of helical edge transport and confirm the discovery of the quantum spin Hall state in this material. We find that decoherence can lead to backscattering without breaking microscopic time-reversal symmetry. As an example of incoherent scattering, we study a Kondo impurity in an interacting helical edge liquid. A renormalization group analy...
String Theory and Its Applications
The book is based on lectures given at the TASI summer school of 2010. It aims to provide advanced graduate students, postdoctorates and senior researchers with a survey of important topics in particle physics and string theory, with special emphasis on applications of methods from string theory and quantum gravity in condensed matter physics and QCD (especially heavy ion physics).
Putting Ourselves Back in the Equation
Distant galaxies, dark matter, black holes – elusive, incomprehensible and inhospitable – these are the building blocks of modern physics. But where do we fit in this picture? For centuries, we have separated mind from matter. While physicists have pursued a theory of ‘everything’ with single-minded purpose, the matter of the mind, of human consciousness, has been conveniently sidestepped and ignored – consigned to priests, philosophers and poets. With the ambition of Stephen Hawking, Carlo Rovelli and Brian Cox, Putting Ourselves Back in the Equation sets out a bold new vision for theoretical physics, unrestricted by sleek equations and neat formulations. Combining cutting-edge neuroscience with the latest in quantum mechanics, acclaimed writer Musser offers a new interpretation of human consciousness. From bizarre cognitive phenomena, like lucid dreaming and self-taught synaesthesia, to the latest technological developments in AI, Musser asks: what can physics teach us about what it means to be human?
Data Science Concepts and Techniques with Applications
This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared ...
Advances in Selected Artificial Intelligence Areas
As new technological challenges are perpetually arising, Artificial Intelligence research interests are focusing on the incorporation of improvement abilities into machines in an effort to make them more efficient and more useful. Recent reports indicate that the demand for scientists with Artificial Intelligence skills significantly exceeds the market availability and that this shortage will intensify further in the years to come. A potential solution includes attracting more women into the field, as women currently make up only 26 percent of Artificial Intelligence positions in the workforce. The present book serves a dual purpose: On one hand, it sheds light on the very significant resear...
Mapping Texts
Mapping Texts is the first introduction to computational text analysis that simultaneously blends conceptual treatments with practical, hands-on examples that walk the reader through how to conduct text analysis projects with real data. The book shows how to conduct text analysis in the R statistical computing environment--a popular programming language in data science.
Gauge/String Duality, Hot QCD and Heavy Ion Collisions
Introduction to gauge/string duality and its applications to quark-gluon plasma for researchers in string theory and quantum field theory.
Machine Learning and Data Sciences for Financial Markets
Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.
