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Athalye V: Quantum Modelling of Economic
and Financial Systems

Quantum Modelling of Economic and Financial Systems

Selected Topics

Why Men Still Win at Work

Patriarchy Inc

Economics and the Good Society

The Road to Freedom

How ideas from science can transform your life

The Art of Physics

Interface Transmission Tutorial Book Series

Long-Lived Resonances

Sensing

Mathematics and Biomedical Engineering in Medicine, Physiology and Health Sciences

Theory, Methods, and Computation

Elements of Stochastic Finance

Chalcogenide Nanophotonics

Modern Trends In Financial Engineering

Multiscale Financial Data Analytics and Machine Learning offers a systematic and comprehensive study on the multiscale approach to financial data analytics and machine learning. This book covers an array of multiscale methods to discover the properties of various timescales embedded in a financial time series, including noise-assisted empirical mode decomposition methods. Important interpretable multiscale outputs from the estimation are recognized as a new set of features that can be used for machine learning. The feature selection problem for machine learning is examined in this volume.This book offers an applied quantitative approach that combines novel analytical methodologies and practical applications to a wide array of examples with real-world data. It is self-contained and organized in its presentation. The explanations of the methodologies are both accessible and detailed enough to capture the interest of the curious student or researcher. Step-by-step descriptions of the algorithms are provided for straightforward implementation.

Multiscale Financial Data Analytics And Machine Learning

While the field of Deep Learning has been advancing at frightening speeds during the last ten years, we do not have a plausible theoretical explanation of its success yet. One can safely say that Deep Learning works, but nobody really understands why. Nevertheless, starting around five years before now, a decent number of theoretical papers specifically concerning neural nets started to emerge at top machine learning venues. This makes us think that the core of Deep Learning theory has already started to crystallize.The goal of the present book is to present these core concepts of Deep Learning theory to readers so that they could directly dive into recent papers of this area. For this purpose, each chapter elaborates a simple model or a classical result in details first and then discusses possible generalizations and more recent developments of the same idea.We have to warn the reader that the present book is not a mathematical manuscript. Not all results here are stated as formal theorems and not all theorems are provided with complete and rigorous proofs. Many of the theorems of the book are proven up to some technical lemmas, while for some there is only a proof sketch. This conforms with the main idea of the book: present and illustrate concepts rather than reproduce all the results.The book, in its present form, covers the following topics: uniform generalization bounds, PAC-bayesian generalization bounds, double descent phenomena, infinitely-wide networks, implicit bias of gradient descent, loss landscape, gradient descent convergence guarantees, and initialization strategies.

Elements Of Deep Learning Theory

This book covers complex analysis (the study of boundary behaviors of analytic functions) and its applications with engineering problems, especially control theory from the viewpoint of boundary functions. The metascience part is rather unique and illustrates a metascientific way of thinking engineering problems by examples of batteries which are cores of electric vehicles.This book gives a way of looking at the phenomena from a higher and brighter point of view through the windows of boundary behavior and metascience. In Chapter 1 which is meant for mathematics-oriented readers, we state materials in standard complex analysis with emphasis on similarity and Laurent expansions. Non-mathematical readers can also benefit by browsing through many examples and worked-out exercises. Chapter 2 gives some concrete examples of invoking novel ideas in the heap of stereo-typed contributions to a field. Possible replacement of LIB by ECaSS(R) is a typical example through which the reader can learn basics of electromagnetism and electrochemistry. The book also provides hints for a deeper understanding of environmental problems as well as how to manage and create a sustainable society.

From Complex Analysis To Metascience: A Stroll Around Boundary Behavior, Similarity And Duality

Molecular Collisions and Gaseous Kinetics covers elastic collisions, inelastic collisions leading to energy transfer and electronic transitions, reactive collisions, and photoinduced dynamics. Collisions with surfaces, thermal rates of those phenomena, as well as transport and relaxation phenomena mediated by molecular collisions are also discussed. A final chapter on many-atom scattering theory serves as an introduction to more advanced descriptions. The treatment is based on the quantal theory of scattering and its semiclassical limits, introduces realistic intermolecular forces, and includes computational aspects needed to calculate cross-sections and compare them with experimental data. It is motivated by a need to cover these subjects starting from the electronic structure and atomic conformation of molecules. The chapters provide an introduction to experimental methods where appropriate, and connect theory to applications for specific physical systems to obtain results that can be compared to experimental data.

Molecular Collisions and Gaseous Kinetics presents concepts, theory, and computational approaches to provide a modern treatment of molecular collisions, transport phenomena, and relaxation of importance in energy transfer reactions and photoinduced phenomena in gases.

Energy Transfer and Reactivity

Quantum Molecular Dynamics

Progress In Data Science

Ubiquitous Laplacian: An Introduction To Numerical Pdes With Applications In Data Science

Quantum Modelling of Economic and Financial Systems

Bibliografische Angaben

Schlagworte

Bibliografische Angaben

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