Algorithms

Designed for portability and quick reference,Pocket Cardiology, Third Edition, provides the essential information needed by practitioners and trainees on a daily basis, and is an excellent resource for clinical board review. Written and edited by Dr. Marc S. Sabatine and a team of cardiologists from Brigham and Women’s Hospital and Harvard Medical School, this fully updated volume provides a concise and focused review of all areas of cardiology in one easy-to-navigate, pocket-sized notebook.Contains fully updated core clinical topics on cardiovascular evaluation tools, preventative cardiology, coronary disease, heart failure, critical care, valvular heard disease, pericardial heart disease, vascular disease, arrhythmias, and adult congenital heart diseaseProvides quick and thorough guidelines for each disease on how to work up initial diagnosis and follow-up stepsCovers the latest updates on pharmacologic management of obesity, algorithms for patients presenting with chest pain, and co

Designed for portability and quick reference, Pocket Anesthesia, Fifth Edition, provides the essential information needed by practitioners and trainees on a daily basis. Expertly edited by Drs. Richard D. Urman and Jesse M. Ehrenfeld, this fully updated volume provides a concise and focused review of all areas of anesthesiology in one easy-to-navigate, pocket-sized notebook, making it an ideal reference for residents, anesthesiologists, CRNAs, CAA's and students, in all clinical settings where anesthesia care is delivered. Follows the bestselling, looseleaf Pocket Notebook format, featuring bulleted lists, tables, diagrams, and algorithms that make essential facts easy to find and retain Contains fully updated core clinical topics on patient evaluation; pharmacology; airway management; regional anesthesia, POCUS, perioperative monitoring, ventilation techniques, acute and chronic pain management, ERAS, trauma and critical care management, pediatric and geriatric anesthesia, and much mo

Designed for easy transport and quick reference, Pocket Pediatrics, 4th Edition, provides essential pediatric information for practitioners, residents, interns, and medical students. Part of the popular Pocket Notebook series and edited by Dr. Paritosh Prasad and colleagues at the University of Rochester Medical Center, this fully updated volume provides a concise and focused review of all areas of pediatrics in one easy-to-navigate, pocket-sized notebook. Follows the bestselling, looseleaf Pocket Notebook format, featuring bulleted lists, tables, diagrams, and algorithms that make essential facts easy to find and retain Provides key clinical information about common pediatric problems in primary care, emergency medicine, allergy/immunology, cardiology, pulmonology, gastroenterology, genetics, nephrology, hematology-oncology, infectious diseases, endocrinology, rheumatology, dermatology, and neurology, as well as the well patient and the patient in ICU Contains a new chapter on Complex

Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state-of-the-art in hybrid electric vehicle system modelling and management. With a focus on learning-based energy management strategies, the book provides detailed methods, mathematical models, and strategies designed to optimize the energy management of the energy supply module of a hybrid vehicle.The book first addresses the underlying problems in Hybrid Electric Vehicle (HEV) modeling, and then introduces several artificial intelligence-based energy management strategies of HEV systems, including those based on fuzzy control with driving pattern recognition, multi objective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, the book also introduces State of Charge and State of Health prediction methods and real time driving pattern recognition. For each application, the detailed experimental process

電影除了在屏幕上的再現外，還有甚麼可能性？在數碼年代，電影與人工智能的關係是甚麼？本書回顧橫跨十三年的媒體藝術展覽CINEMA 2.0，展示電影作為思考與策展的方法。此展覽項目由媒體藝術家葉旭耀及策展人范可琪共同策劃，多年來嘗試探索當代科技與媒體文化，包括監控、演算法、後人類及遊戲等各種議題。 此書不單是展覽的紀錄，更試圖重溯電影，告別固有的概念，以跨學科及媒體考古學等方式重新探索電影與科技文化的關係。書中除了珍貴的展覽文獻，亦有重要的媒體藝術家邵志飛及羅海德等撰寫專文，擴展我們對當代文化的思考，同時引領我們走進新媒體藝術的世界。 Contemplating cinema beyond its traditional screen-based form, what other possibilities are there for cinema? In the digital age, what is the relationship between cinema, artificial intelligence, and algorithms? This book reviews the 13-year media art exhibition CINEMA 2.0, which showcases cinema as a method of thinking and curating. The exhibition, co-curated by media artist Ip Yuk Yiu and media art curator Kattie Fan, has covered key issues in contemporary technology and media culture, such as surveillance, algorithms, posthumanism and games. This book serves as more than a mere documentation of the exhibitions. It attempts to retrace cinema, bidding farewell to fixed concepts and exploring the relationships be

Effectively perform and interpret MR body imaging with this concise, highly illustrated resource! Fundamentals of Body MRI, 3rd Edition, covers the essential concepts residents, fellows, and practitioners need to know, laying a solid foundation for understanding the basics and making accurate diagnoses. This easy-to-use title covers all common body MR imaging indications and conditions, while providing new content on body MRI emergencies, physics, and noninterpretive skills with an emphasis on quality and safety. Covers all common body MR imaging content, along with discussion of how physics, techniques, hardware, and artifacts affect results-all summarized in an easy-to-read format with practical applications throughoutFeatures more than 1,600 detailed MRI images and 100 algorithms and diagrams that highlight key findings and help you grasp visual nuances of images you’re likely to encounterContains extensively revised content on liver lesions, including new coverage on LI-RADS system

Migrate from pandas and scikit-Learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Na鴳e Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary to apply these methods using

An empirical study and analysis of heart disease prediction involves using data analysis techniques to identify patterns and risk factors associated with cardiovascular disease. This approach utilizes machine learning algorithms to classify patients based on their likelihood of developing heart disease.The study involves collecting data on risk factors such as age, gender, family history, blood pressure, cholesterol levels, smoking, and diabetes. Feature selection techniques are used to identify the most important risk factors, and a classification model is trained using these factors. The accuracy of the model is evaluated using metrics such as sensitivity, specificity, and AUC.This empirical study and analysis has several advantages, including the ability to identify new risk factors associated with heart disease, improved accuracy in predicting cardiovascular risk, and the potential to develop more personalized prevention and treatment strategies. This approach has the potential to

A comprehensive introduction to the field of autonomous robotics aimed at upper-level undergraduates and offering additional online resources.Textbooks that provide a broad algorithmic perspective on the mechanics and dynamics of robots almost unfailingly serve students at the graduate level. Introduction to Autonomous Robots offers a much-needed resource for teaching third- and fourth-year undergraduates the computational fundamentals behind the design and control of autonomous robots. The authors use a class-tested and accessible approach to present progressive, step-by-step development concepts, alongside a wide range of real-world examples and fundamental concepts in mechanisms, sensing and actuation, computation, and uncertainty. Throughout, the authors balance the impact of hardware (mechanism, sensor, actuator) and software (algorithms) in teaching robot autonomy. Features:Rigorous and tested in the classroomWritten for engineering and computer science undergraduates with a soph

During the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master's students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book con

During the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master's students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book con

Understand the theory and implementation of simulation. This book covers simulation topics from a scenario-driven approach using Python and rich visualizations and tabulations. The book discusses simulation used in the natural and social sciences and with simulations taken from the top algorithms used in the industry today. The authors use an engaging approach that mixes mathematics and programming experiments with beginning-intermediate level Python code to create an immersive learning experience that is cohesive and integrated. After reading this book, you will have an understanding of simulation used in natural sciences, engineering, and social sciences using Python. What You Will LearnUse Python and numerical computation to demonstrate the power of simulationChoose a paradigm to run a simulationDraw statistical insights from numerical experimentsKnow how simulation is used to solve real-world problems Who This Book Is ForEntry-level to mid-level Python developers from various backg

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slide

How to use data as a tool for empowerment rather than oppression.Big data can be used for good, from tracking disease to exposing human rights violations, and for bad, implementing surveillance and control. Data inevitably represents the ideologies of those who control its use; data analytics and algorithms too often exclude women, the poor, and ethnic groups. In Data Action, Sarah Williams provides a guide for working with data in more ethical and responsible ways. Williams outlines a method that emphasizes collaboration among data scientists, policy experts, data designers, and the public. The approach generates policy debates, influences civic decisions, and informs design to help ensure that the voices of people represented in the data are neither marginalized nor left unheard.

Agent Asha is back in the second installment of this action-packed spy adventure series. Top-secret Children's Spy Agency newest recruit, Asha Joshi is back with a brand new mission. Asha is suspicious when evil teenage trillionaire Shelly Belly proposes to cut down all the trees in Asha's favourite park to make room for CyberOaks: the new tech trees.Asha's a top coder and super spy so she should be able to face robot security parrots, navigate a suspicious internship and travel to Shelly's super-confidential base in Scotland to crack the case. Will Asha and her robo-hamster sidekick Tumble be able to protect the park and save the day?The story is a great way to engage young readers in coding and critical-thinking as it combines STEM with exciting adventures. It is mapped onto key National Curriculum Computing KS1 and KS2 concepts including algorithms, conditionals and debugging.

By adopting a multi-disciplinary approach, this book provides a comprehensive analysis of the legality of the use of autonomous weapons systems under international law. It examines different arguments presented by States, roboticists and scholars to demonstrate the challenges such systems will create for the laws of war. This study examines how technology of warfare seeks to increase the dissociation of risk and communication between weapons and their human operators. Furthermore, it explains how algorithms might give rise to 'errors' on the battlefield that cannot be directly attributed to human operators. Against this backdrop, Dr Seixas-Nunes examines three distinct legal frameworks: the distinction between the legality of weapons and the laws of targeting; different mechanisms of individual accountability and the importance of recovering the category of 'dolus eventualis' for programmers and technicians and, finally, State responsibility for violations of the laws of war caused by

A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.

Major enterprises shape our lives in countless ways: big tech and 'surveillance media' that affect democratic debate, algorithms that influence online shopping, transport to work and home, energy and agriculture corporations that drive climate damage, and public services that provide our education, health, water, and housing. The twentieth century experienced swings between private and public ownership, between capitalism and socialism, without any settled, principled outcome, and without settling major questions of how enterprises should be financed, governed and the rights we have in them. This book's main question is 'are there principles of enterprise law', and, if they are missing, 'what principles of enterprise law should there be'? Principles of Enterprise Law gives a functional account of the 'general' enterprise laws of companies, investment, labour, competition and insolvency, before moving into specific enterprises, from universities to the military. It is an original guide

Differential Equations with Mathematica, Fifth Edition uses the fundamental concepts of the popular platform to solve (analytically, numerically, and/or graphically) differential equations of interest to students, instructors, and scientists. Mathematica's diversity makes it particularly well suited to performing calculations encountered when solving many ordinary and partial differential equations. In some cases, Mathematica's built-in functions can immediately solve a differential equation by providing an explicit, implicit, or numerical solution. In other cases, Mathematica can be used to perform the calculations encountered when solving a differential equation. Because one goal of elementary differential equations courses is to introduce students to basic methods and algorithms so that they gain proficiency in them, nearly every topic covered this book introduces basic commands, also including typical examples of their application. A study of differential equations relies on concep

An introduction to computational modeling for cognitive neuroscientists, covering both foundational work and recent developments. Cognitive neuroscientists need sophisticated conceptual tools to make sense of their field’s proliferation of novel theories, methods, and data. Computational modeling is such a tool, enabling researchers to turn theories into precise formulations. This book offers a mathematically gentle and theoretically unified introduction to modeling cognitive processes. Theoretical exercises of varying degrees of difficulty throughout help readers develop their modeling skills. After a general introduction to cognitive modeling and optimization, the book covers models of decision making; supervised learning algorithms, including Hebbian learning, delta rule, and backpropagation; the statistical model analysis methods of model parameter estimation and model evaluation; the three recent cognitive modeling approaches of reinforcement learning, unsupervised learning, and

What happens to risk as the economic horizon goes to zero and risk is seen as an exposure to a change in state that may occur instantaneously at any time? All activities that have been undertaken statically at a fixed finite horizon can now be reconsidered dynamically at a zero time horizon, with arrival rates at the core of the modeling. This book, aimed at practitioners and researchers in financial risk, delivers the theoretical framework and various applications of the newly established dynamic conic finance theory. The result is a nonlinear non-Gaussian valuation framework for risk management in finance. Risk-free assets disappear and low risk portfolios must pay for their risk reduction with negative expected returns. Hedges may be constructed to enhance value by exploiting risk interactions. Dynamic trading mechanisms are synthesized by machine learning algorithms. Optimal exposures are designed for option positioning simultaneously across all strikes and maturities.

Artificial Intelligence for Healthcare Applications and Management introduces application domains of various AI algorithms across healthcare management. Instead of discussing AI first and then exploring its applications in healthcare afterward, the authors attack the problems in context directly, in order to accelerate the path of an interested reader toward building industrial-strength healthcare applications. Readers will be introduced to a wide spectrum of AI applications supporting all stages of patient flow in a healthcare facility. The authors explain how AI supports patients throughout a healthcare facility, including diagnosis and treatment recommendations needed to get patients from the point of admission to the point of discharge while maintaining quality, patient safety, and patient/provider satisfaction. AI methods are expected to decrease the burden on physicians, improve the quality of patient care, and decrease overall treatment costs. Current conditions affected by COVI

The fifth edition of this bestselling Oxford Medical Handbook is now even better than ever, with the inclusion of three new chapters on day case surgery, remote and rural surgery, and emergency surgery. It has been thoroughly revised with the latest guidelines, management algorithms, and guidance on decision-making.Now with the editorial team expanded to include four new associate editors, this new edition provides an accessible pocketreference for surgical trainees, medical students, and all those involved in the care of the surgical patient. It is thoroughly comprehensive, without sacrificing the clear, concise, and quick-referencestyle the Oxford Medical Handbooks are known for. It provides surgically relevant anatomy and physiology, quick reference symbols, key diagrams, and a focus on evidence-based practice with key references throughout. This essential guide will ensure you have the information you need at your fingertips.

《計算流體力學基礎算法》譯自Springer出版的Fundamental Algorithms in Computational Fluid Dynamics一書。《計算流體力學基礎算法》介紹了在計算流體力學中已發展成熟而又應用廣泛的基礎算法，包括流體力學的基本原理、基本方程和實用化的技術，例如，有限差分法和隱式近似分解、廣義曲線坐標系變換、人工耗散和穩定性、基於顯式多階時間推進和多重網格的有限體

Using easy-to-follow mathematics, this textbook provides comprehensive coverage of block codes and techniques for reliable communications and data storage. It covers major code designs and constructions from geometric, algebraic, and graph-theoretic points of view, decoding algorithms, error control additive white Gaussian noise (AWGN) and erasure, and dataless recovery. It simplifies a highly mathematical subject to a level that can be understood and applied with a minimum background in mathematics, provides step-by-step explanation of all covered topics, both fundamental and advanced, and includes plenty of practical illustrative examples to assist understanding. Numerous homework problems are included to strengthen student comprehension of new and abstract concepts, and a solutions manual is available online for instructors. Modern developments, including polar codes, are also covered. An essential textbook for senior undergraduates and graduates taking introductory coding courses,

Jonathan Stalling’s experimental approach to bridging art, poetics, and linguistics imagines a world where individual value systems are no longer translated into the language of other mediums, but foster conscious “interlanguages.” Stalling writes, “Meeting in the middle, ‘interlanguages’ are spaces where one learns a new language without having left one’s home fully behind; these situations can result in richly generative interlingual and intercultural estuaries which provide new ways of imagining.” Stalling’s conceptual language art fuses Classical Chinese poetics and linguistics with modern algorithms to create art installations and poetry that transform Chinese into English and English into Chinese in new and surprising ways.With a visual gallery of Stalling’s work, an interview with the artist, a critical introduction by the editor, and critical chapters written by the comparative literature scholar Timothy Billings and Chinese Linguist Liu Nian, the volume provides readers with a

Structured population models are transport-type equations often applied to describe evolution of heterogeneous populations of biological cells, animals or humans, including phenomena such as crowd dynamics or pedestrian flows. This book introduces the mathematical underpinnings of these applications, providing a comprehensive analytical framework for structured population models in spaces of Radon measures. The unified approach allows for the study of transport processes on structures that are not vector spaces (such as traffic flow on graphs) and enables the analysis of the numerical algorithms used in applications. Presenting a coherent account of over a decade of research in the area, the text includes appendices outlining the necessary background material and discusses current trends in the theory, enabling graduate students to jump quickly into research.