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Automatic Graph Drawing is concerned with the layout of relational structures as they occur in Computer Science (Data Base Design, Data Mining, Web Mining), Bioinformatics (Metabolic Networks), Businessinformatics (Organization Diagrams, Event Driven Process Chains), or the Social Sciences (Social Networks). In mathematical terms, such relational structures are modeled as graphs or more general objects such as hypergraphs, clustered graphs, or compound graphs. A variety of layout algorithms that are based on graph theoretical foundations have been developed in the last two decades and implemented in software systems. After an introduction to the subject area and a concise treatment of the technical foundations for the subsequent chapters, this book features 14 chapters on state-of-the-art graph drawing software systems, ranging from general "tool boxes'' to customized software for various applications. These chapters are written by leading experts, they follow a uniform scheme and can be read independently from each other.

Graphic methods --- Computer graphics. --- Computer software. --- Méthodes graphiques --- Infographie --- Logiciels --- Data processing. --- Informatique --- Computer graphics --- Computer software --- Data processing --- Méthodes graphiques --- Mathematics. --- Visualization. --- Bioinformatics. --- Applied mathematics. --- Engineering mathematics. --- Mathematical Software. --- Computer Graphics. --- Applications of Mathematics. --- Mathematical and Computational Engineering. --- Engineering --- Engineering analysis --- Mathematical analysis --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Automatic drafting --- Graphic data processing --- Graphics, Computer --- Computer art --- Graphic arts --- Electronic data processing --- Engineering graphics --- Image processing --- Software, Computer --- Computer systems --- Visualisation --- Imagination --- Visual perception --- Imagery (Psychology) --- Math --- Science --- Mathematics --- Digital techniques --- Graphic methods - Data processing

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This book constitutes the proceedings of the 16th International Conference on Algorithms and Computation, WALCOM 2022, which was held in Jember, Indonesia, during March 24-26, 2022. This proceedings volume contains 30 full papers which were carefully reviewed and selected from a total of 89 submissions and 3 invited papers. They cover diverse areas of algorithms and computation, such as approximation algorithms, computational complexity, computational geometry, graph algorithms, graph drawing and visualization, online algorithms, parameterized complexity and property testing.

Computer algorithms. --- Algorithms --- Computer science. --- Computer science—Mathematics. --- Discrete mathematics. --- Data structures (Computer science). --- Information theory. --- Computer graphics. --- Computer engineering. --- Computer networks. --- Theory of Computation. --- Discrete Mathematics in Computer Science. --- Data Structures and Information Theory. --- Computer Graphics. --- Computer Engineering and Networks. --- Informatics --- Science --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Electronic data processing --- Network computers --- Computers --- Automatic drafting --- Graphic data processing --- Graphics, Computer --- Computer art --- Graphic arts --- Engineering graphics --- Image processing --- Communication theory --- Communication --- Cybernetics --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- File organization (Computer science) --- Abstract data types (Computer science) --- Discrete mathematical structures --- Mathematical structures, Discrete --- Structures, Discrete mathematical --- Numerical analysis --- Distributed processing --- Design and construction --- Digital techniques

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Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 3 describes how the resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how machine learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from machine learning, for example by uncovering hidden characteristics of the wireless channel.

SCIENCE / Chemistry / General. --- Artificial Intelligence. --- Big Data and Machine Learning. --- Cyber-physical systems. --- Data mining for Ubiquitous System Software. --- Embedded Systems and Machine Learning. --- Highly Distributed Data. --- ML on Small devices. --- Machine learning for knowledge discovery. --- Machine learning in high-energy physics. --- Resource-Aware Machine Learning. --- Resource-Constrained Data Analysis.