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This volume gathers contributions reflecting topics presented during an INDAM workshop held in Rome in May 2016. The event brought together many prominent researchers in both Mathematical Analysis and Numerical Computing, the goal being to promote interdisciplinary collaborations. Accordingly, the following thematic areas were developed: 1. Lagrangian discretizations and wavefront tracking for synchronization models; 2. Astrophysics computations and post-Newtonian approximations; 3. Hyperbolic balance laws and corrugated isometric embeddings; 4. “Caseology” techniques for kinetic equations; 5. Tentative computations of compressible non-standard solutions; 6. Entropy dissipation, convergence rates and inverse design issues. Most of the articles are presented in a self-contained manner; some highlight new achievements, while others offer snapshots of the “state of the art” in certain fields. The book offers a unique resource, both for young researchers looking to quickly enter a given area of application, and for more experienced ones seeking comprehensive overviews and extensive bibliographic references.
The concept of 'shape' is at the heart of image processing and computer vision, yet researchers still have some way to go to replicate the human brain's ability to extrapolate meaning from the most basic of outlines. This volume reflects the advances of the last decade, which have also opened up tough new challenges in image processing. Today's applications require flexible models as well as efficient, mathematically justified algorithms that allow data processing within an acceptable timeframe. Examining important topics in continuous-scale and discrete modeling, as well as in modern algorithms, the book is the product of a key seminar focused on innovations in the field. It is a thorough introduction to the latest technology, especially given the tutorial style of a number of chapters. It also succeeds in identifying promising avenues for future research. The topics covered include mathematical morphology, skeletonization, statistical shape modeling, continuous-scale shape models such as partial differential equations and the theory of discrete shape descriptors. Some authors highlight new areas of enquiry such as partite skeletons, multi-component shapes, deformable shape models, and the use of distance fields. Combining the latest theoretical analysis with cutting-edge applications, this book will attract both academics and engineers.
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
The first notable feature of this book is its innovation: Computational intelligence (CI), a fast evolving area, is currently attracting lots of researchers’ attention in dealing with many complex problems. At present, there are quite a lot competing books existing in the market. Nevertheless, the present book is markedly different from the existing books in that it presents new paradigms of CI that have rarely mentioned before, as opposed to the traditional CI techniques or methodologies employed in other books. During the past decade, a number of new CI algorithms are proposed. Unfortunately, they spread in a number of unrelated publishing directions which may hamper the use of such published resources. These provide us with motivation to analyze the existing research for categorizing and synthesizing it in a meaningful manner. The mission of this book is really important since those algorithms are going to be a new revolution in computer science. We hope it will stimulate the readers to make novel contributions or even start a new paradigm based on nature phenomena. Although structured as a textbook, the book's straightforward, self-contained style will also appeal to a wide audience of professionals, researchers and independent learners. We believe that the book will be instrumental in initiating an integrated approach to complex problems by allowing cross-fertilization of design principles from different design philosophies. The second feature of this book is its comprehensiveness: Through an extensive literature research, there are 134 innovative CI algorithms covered in this book.
Genrich Altshuller's The Innovation Algorithm is a milestone in the development of the Theory of Inventive Problem Solving (TRIZ). It is the result of more than 20 years of research and analysis. Here, Altshuller details ARIZ, TRIZ's problem solving algorythm that can produce innovation and creativity of the highest order. Saturated with profound thoughts, insights, and convincing examples, this book is regarded by many as Altshuller's magnum opus, his handbook for a creative and technological revolution. - Back cover.
7 69 6 A DESIGN APPROACH TO PROBLEM DIFFICULTY 71 1 Design and Problem Difficulty 71 2 Three Misconceptions 72 3 Hard Problems Exist 76 4 The 3-Way Decomposition and Its Core 77 The Core of Intra-BB Difficulty: Deception 5 77 6 The Core of Inter-BB Difficulty: Scaling 83 7 The Core of Extra-BB Difficulty: Noise 88 Crosstalk: All Roads Lead to the Core 8 89 9 From Multimodality to Hierarchy 93 10 Summary 100 7 ENSURING BUILDING BLOCK SUPPLY 101 1 Past Work 101 2 Facetwise Supply Model I: One BB 102 Facetwise Supply Model II: Partition Success 103 3 4 Population Size for BB Supply 104 Summary 5 106 8 ENSURING BUILDING BLOCK GROWTH 109 1 The Schema Theorem: BB Growth Bound 109 2 Schema Growth Somewhat More Generally 111 3 Designing for BB Market Share Growth 112 4 Selection Press ure for Early Success 114 5 Designing for Late in the Day 116 The Schema Theorem Works 6 118 A Demonstration of Selection Stall 7 119 Summary 122 8 9 MAKING TIME FOR BUILDING BLOCKS 125 1 Analysis of Selection Alone: Takeover Time 126 2 Drift: When Selection Chooses for No Reason 129 3 Convergence Times with Multiple BBs 132 4 A Time-Scales Derivation of Critical Locus 142 5 A Little Model of Noise-Induced Run Elongation 143 6 From Alleles to Building Blocks 147 7 Summary 148 10 DECIDING WELL 151 1 Why is Decision Making a Problem? 151
Analysis and Design of Algorithms provides a structured view of algorithm design techniques in a concise, easy-to-read manner. The book was written with an express purpose of being easy - to understand, read, and carry. It presents a pioneering approach in the teaching of algorithms, based on learning algorithm design techniques, and not merely solving a collection of problems. This allows students to master one design technique at a time and apply it to a rich variety of problems. Analysis and Design of Algorithms covers the algorithmic design techniques of divide and conquer, greedy, dynamic programming, branch and bound, and graph traversal. For each of these techniques, there are templates and guidelines on when to use and not to use each technique. Many sections contain innovative mnemonics to aid the readers in remembering the templates and key takeaways. Additionally, the book covers NP-completeness and the inherent hardness of problems. The third edition includes a new section on polynomial multiplication, as well as additional exercise problems, and an updated appendix. Written with input from students and professionals, Analysis and Design of Algorithms is well suited for introductory algorithm courses at the undergraduate and graduate levels. The structured organization of the text makes it especially appropriate for online and distance learning.
Innovation analytics is an emerging paradigm that integrates information/knowledge, analytics, digital twins and artificial intelligence to support and manage the entire lifecycle of a product and process from inception, through engineering design and manufacture, to service and disposal of manufactured products. Innovation analytics is set to become an integral part of the innovation lifecycle to help make smart, agile decisions and accelerate business growth.Innovation Analytics: Tools for Competitive Advantage provides a comprehensive overview of the challenges and opportunities behind the latest research surrounding technological advances driving innovation analytics; the transition of analytical ideas to interdisciplinary teams; the development of deep synchronicity of skills and production innovation; and the use of innovation analytics in multiple stages of product and process evolution.In exploring the impact of emerging developments in the current climate, researchers and academics will be able to gain insight into real-world usage of analytics for innovation and its contribution toward society. As such, students, scientists, engineers, academics, and management professionals alike will find this title beneficial.
While cognitive informatics and natural intelligence are receiving greater attention by researchers, multidisciplinary approaches still struggle with fundamental problems involving psychology and neurobiological processes of the brain. Examining the difficulties of certain approaches using the tools already available is vital for propelling knowledge forward and making further strides. Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence is a collection of innovative research that examines the enhancement of human cognitive performance using emerging technologies. Featuring research on topics such as parallel computing, neuroscience, and signal processing, this book is ideally designed for engineers, computer scientists, programmers, academicians, researchers, and students.
Innovative Food Analysis presents a modern perspective on the development of robust, effective and sensitive techniques to ensure safety, quality and traceability of foods to meet industry standards. Significant enhancements of analytical accuracy, precision, detection limits and sampling has expanded the practical range of food applications, hence this reference offers modern food analysis in view of new trends in analytical techniques and applications to support both the scientific community and industry professionals. This reference covers the latest topics across existing and new technologies, giving emphasis on food authenticity, traceability, food fraud, food quality, food contaminants, sensory and nutritional analytics, and more. - Covers the last ten years of applications across existing and new technologies of food analytics - Presents an emphasis on techniques in food authenticity, traceability and food fraud - Discusses bioavailability testing and product analysis of food allergens and foodomics