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Supercomputing: 5th Russian Supercomputing Days, RuSCDays 2019, Moscow, Russia, September 23–24, 2019, Revised Selected Papers (Communications in Computer and Information Science #1129)
by Vladimir Voevodin Sergey SobolevThis book constitutes the refereed post-conference proceedings of the 5th Russian Supercomputing Days, RuSCDays 2019, held in Moscow, Russia, in September 2019.The 60 revised full papers presented were carefully reviewed and selected from 127 submissions. The papers are organized in the following topical sections: parallel algorithms; supercomputer simulation; HPC, BigData, AI: architectures, technologies, tools; and distributed and cloud computing.
Supercomputing: 6th Russian Supercomputing Days, RuSCDays 2020, Moscow, Russia, September 21–22, 2020, Revised Selected Papers (Communications in Computer and Information Science #1331)
by Vladimir Voevodin Sergey SobolevThis book constitutes the refereed post-conference proceedings of the 6th Russian Supercomputing Days, RuSCDays 2020, held in Moscow, Russia, in September 2020.* The 51 revised full and 4 revised short papers presented were carefully reviewed and selected from 106 submissions. The papers are organized in the following topical sections: parallel algorithms; supercomputer simulation; HPC, BigData, AI: architectures, technologies, tools; and distributed and cloud computing. * The conference was held virtually due to the COVID-19 pandemic.
Supercomputing: 7th Russian Supercomputing Days, RuSCDays 2021, Moscow, Russia, September 27–28, 2021, Revised Selected Papers (Communications in Computer and Information Science #1510)
by Vladimir Voevodin Sergey SobolevThis book constitutes the refereed post-conference proceedings of the 7th Russian Supercomputing Days, RuSCDays 2021, held in Moscow, Russia, in September 2021.The 37 revised full papers and 3 short papers presented were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: supercomputer simulation; HPC, BigData, AI: architectures, technologies, tools; and distributed and cloud computing.
Supercomputing: 8th Russian Supercomputing Days, RuSCDays 2022, Moscow, Russia, September 26–27, 2022, Revised Selected Papers (Lecture Notes in Computer Science #13708)
by Vladimir Voevodin Sergey Sobolev Mikhail Yakobovskiy Rashit ShagalievThis book constitutes the refereed proceedings of the 8th Russian Supercomputing Days on Supercomputing, RuSCDays 2022, which took place in Moscow, Russia, in September 2022. The 49 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 94 submissions. The papers are organized in the following topical sections: Supercomputer Simulation; HPC, BigData, AI: Architectures, Technologies, Tools; Distributed and Cloud Computing.
Supercomputing: 9th Russian Supercomputing Days, RuSCDays 2023, Moscow, Russia, September 25–26, 2023, Revised Selected Papers, Part I (Lecture Notes in Computer Science #14388)
by Vladimir Voevodin Sergey Sobolev Mikhail Yakobovskiy Rashit ShagalievThe two-volume set LNCS 14388 and 14389 constitutes the refereed proceedings of the 9th Russian Supercomputing Days International Conference (RuSCDays 2023) held in Moscow, Russia, during September 25-26, 2023.The 44 full papers and 1 short paper presented in these proceedings were carefully reviewed and selected from 104 submissions. The papers have been organized in the following topical sections: supercomputer simulation; distributed computing; and HPC, BigData, AI: algorithms, technologies, evaluation.
Supercomputing: 9th Russian Supercomputing Days, RuSCDays 2023, Moscow, Russia, September 25–26, 2023, Revised Selected Papers, Part II (Lecture Notes in Computer Science #14389)
by Vladimir Voevodin Sergey Sobolev Mikhail Yakobovskiy Rashit ShagalievThe two-volume set LNCS 14388 and 14389 constitutes the refereed proceedings of the 9th Russian Supercomputing Days International Conference (RuSCDays 2023) held in Moscow, Russia, during September 25-26, 2023.The 44 full papers and 1 short paper presented in these proceedings were carefully reviewed and selected from 104 submissions. The papers have been organized in the following topical sections: supercomputer simulation; distributed computing; and HPC, BigData, AI: algorithms, technologies, evaluation.
Supercomputing Frontiers: 5th Asian Conference, SCFA 2019, Singapore, March 11–14, 2019, Proceedings (Lecture Notes in Computer Science #11416)
by David Abramson Bronis R. de SupinskiThis open access book constitutes the refereed proceedings of the 5th Asian Supercomputing Conference, SCFA 2019, held in Singapore in March 2019. The 6 full papers presented in this book were carefully reviewed and selected from 33 submissions. They cover a range of topics including memory fault handling, linear algebra, image processing, heterogeneous computing, resource usage prediction, and data caching.
Supercomputing Frontiers: 6th Asian Conference, SCFA 2020, Singapore, February 24–27, 2020, Proceedings (Lecture Notes in Computer Science #12082)
by Dhabaleswar K. PandaThis open access book constitutes the refereed proceedings of the 6th Asian Supercomputing Conference, SCFA 2020, which was planned to be held in February 2020, but unfortunately, the physical conference was cancelled due to the COVID-19 pandemic.The 8 full papers presented in this book were carefully reviewed and selected from 22 submissions. They cover a range of topics including file systems, memory hierarchy, HPC cloud platform, container image configuration workflow, large-scale applications, and scheduling.
Supercomputing Frontiers: 7th Asian Conference, SCFA 2022, Singapore, March 1–3, 2022, Proceedings (Lecture Notes in Computer Science #13214)
by Dhabaleswar K. Panda Michael SullivanThis open access book constitutes the refereed proceedings of the 7th Asian Conference Supercomputing Conference, SCFA 2022, which took place in Singapore in March 2022. The 8 full papers presented in this book were carefully reviewed and selected from 21 submissions. They cover a range of topics including file systems, memory hierarchy, HPC cloud platform, container image configuration workflow, large-scale applications, and scheduling.
Supercomputing Frontiers: 4th Asian Conference, Scfa 2018, Singapore, March 26-29, 2018, Proceedings (Lecture Notes in Computer Science #10776)
by Weigang Wu Rio YokotaIt constitutes the refereed proceedings of the 4th Asian Supercomputing Conference, SCFA 2018, held in Singapore in March 2018. Supercomputing Frontiers will be rebranded as Supercomputing Frontiers Asia (SCFA), which serves as the technical programme for SCA18. The technical programme for SCA18 consists of four tracks: Application, Algorithms & LibrariesProgramming System SoftwareArchitecture, Network/Communications & ManagementData, Storage & VisualisationThe 20 papers presented in this volume were carefully reviewed nd selected from 60 submissions.
Supercomputing in Engineering Analysis (New Generation Computing Ser. #1)
by Hojjat AdeliThe first volume in this new series has a companion in volume 2 (unseen), Parallel processing in computational mechanics . The first six contributions present general aspects of supercomputing from both hardware and software engineering points of view. Subsequent chapters discuss homotopy algorithms
The Superior Project Manager: Global Competency Standards and Best Practices (PM Solutions Research)
by Frank ToneyDescribes global best practices, competencies, and standards of superior project organizations based on research conducted by the Top 500 Project Management Forum. It emphasizes the selection process, performance evaluation, and personnel development to provide the key elements for adjusting and adapting to flexible conditions. The text also highli
The Superior Project Organization: Global Competency Standards and Best Practices (PM Solutions Research)
by Frank ToneyThis text describes global best practices, competencies, and standards of superior project organizations based on research conducted by the Top 500 Project Management Forum. It details the results of seven years of benchmarking and the bottom line value of project organizations in large functional enterprises. The text also highlights enhancements in professional image, job performance, and personal earnings.
Superminds: How Hyperconnectivity is Changing the Way We Solve Problems
by Thomas W. MaloneIs Apple conscious? Could a cyber–human system sense a potential terrorist attack? Or make diagnosing a rare and little-known disease routine? Computers are not replacing us: they are enhancing us. Different intelligences are joining together to do things we thought were impossible. Whether it&’s devising innovations to tackle climate change, helping job seekers and employers find one another, or identifying the outbreak of a serious disease, groups of humans and machines are already working together to solve all sorts of problems. And they will do a lot more. The future will be like another world – a place where we&’ll think differently. In many ways, we are already there.
SuperShifts: Transforming How We Live, Learn, and Work in the Age of Intelligence
by Ja-Nae Duane Steve FisherForward-thinking exploration of the dawn of humanity's new age and the imminent technology-enabled transformation on society, business, and beyond. In SUPERSHIFTS, leading behavioral scientist Dr. Ja-Nae Duane and world-renowned entrepreneur and futurist Steve Fisher deliver an incisive overview of how we are at the end of one 200-year arc and embarking on another. With this new age of intelligence, Duane and Fisher highlight the various catalysts for change currently affecting individuals, businesses, and society as a whole. They also provide a model for transformation that expertly bridges the gap between theory and practice to provide a holistic view of making radical change through three lenses: you as a leader, your organization, and society. Drawing on Duane and Fisher's wealth of collective experience, this book pays particular attention to how emerging technologies, biological revolutions, energy abundance create opportunities for humanity's transformational purpose, and emergence of new intelligent species over the next two hundred years. Readers will find various case studies showing successful and failed responses to disruption, and learn about topics including: What is needed for mankind to thrive beyond the predictions of the singularity, and how that will shift our communications, beliefs, and values How can we create anti-fragile organizations and global systems based on nature's ecosystems Humanity's coexistence with technology, the fall of centralized systems, and the emergence of collective intelligence as a solution for prosperity A guide for change, SUPERSHIFTS earns a well-deserved spot on the bookshelves of executives, entrepreneurs, and leaders seeking to create a better world for themselves, their organizations, and society at large.
Supervised and Unsupervised Learning for Data Science (Unsupervised and Semi-Supervised Learning)
by Michael W. Berry Azlinah Mohamed Bee Wah YapThis book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018).Includes new advances in clustering and classification using semi-supervised and unsupervised learning;Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning;Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.
Supervised Descriptive Pattern Mining
by Sebastián Ventura José María LunaThis book provides a general and comprehensible overview of supervised descriptive pattern mining, considering classic algorithms and those based on heuristics. It provides some formal definitions and a general idea about patterns, pattern mining, the usefulness of patterns in the knowledge discovery process, as well as a brief summary on the tasks related to supervised descriptive pattern mining. It also includes a detailed description on the tasks usually grouped under the term supervised descriptive pattern mining: subgroups discovery, contrast sets and emerging patterns. Additionally, this book includes two tasks, class association rules and exceptional models, that are also considered within this field.A major feature of this book is that it provides a general overview (formal definitions and algorithms) of all the tasks included under the term supervised descriptive pattern mining. It considers the analysis of different algorithms either based on heuristics or based on exhaustive search methodologies for any of these tasks. This book also illustrates how important these techniques are in different fields, a set of real-world applications are described.Last but not least, some related tasks are also considered and analyzed. The final aim of this book is to provide a general review of the supervised descriptive pattern mining field, describing its tasks, its algorithms, its applications, and related tasks (those that share some common features).This book targets developers, engineers and computer scientists aiming to apply classic and heuristic-based algorithms to solve different kinds of pattern mining problems and apply them to real issues. Students and researchers working in this field, can use this comprehensive book (which includes its methods and tools) as a secondary textbook.
Supervised Learning: Mathematical Foundations and Real-world Applications
by Dalia ChakrabartyThis book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. The book provides methods for secured mechanistic learning of the function that represents this relationship between the output and input variables, where said learning is undertaken within the remit of real-world information that can be messy in different ways. For example, the available data may be highly multivariate or be high-dimensional, reflecting the nature of the output variable that could be a vector, or matrix, or even higher in dimension, as is often the case in a real-world application. Additionally, the data is noisy, and often it is small to moderately large in size in multiple applications. Another difficulty that regularly arises is that the training dataset – comprising pairs of values of the input and output –is such, that the sought function cannot be captured by a parametric shape, but is instead underlined by an inhomogeneous correlation structure. These difficulties notwithstanding, we desire a streamlined methodology that allows the learning of the inter variable relationship – to ultimately permit fast and reliable predictions of the output, at newly recorded values of the input. In fact, occasions arise when one seeks values of the input at which a new output value is recorded, and such a demand is also addressed in the book.The generic solution to the problem of secured supervised learning amidst real-world messiness, lies in treating the sought inter-variable relation as a (function-valued) random variable, which, being random, is ascribed a probability distribution. Then recalling that distributions on the space of functions are given by stochastic processes, the sought function is proposed to be a sample function of a stochastic process. This process is chosen as one that imposes minimal constraints on the sought function – identified as a Gaussian Process (GP) in the book. Thus, the sought function can be inferred upon, as long as the co-variance function of the underlying GP is learnt, given the available training set. The book presents probabilistic techniques to undertake said learning, within the challenges borne by the data, and illustrates such techniques on real data. Learning of a function is always followed by closed-form prediction of the mean and dispersion of the output variable that is realised at a test input. To help with the background, the book includes reviews on stochastic processes and basic probability theory. This will render the first half of the book useful for students across disciplines, while the latter half will be appreciated by students of numerate subjects at the postgraduate level or higher, including students of computational sciences, statistics and mathematics.
Supervised Learning with Python: Concepts and Practical Implementation Using Python
by Vaibhav VerdhanGain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and images datasets.You’ll start with an introduction to machine learning, highlighting the differences between supervised, semi-supervised and unsupervised learning. In the following chapters you’ll study regression and classification problems, mathematics behind them, algorithms like Linear Regression, Logistic Regression, Decision Tree, KNN, Naïve Bayes, and advanced algorithms like Random Forest, SVM, Gradient Boosting and Neural Networks. Python implementation is provided for all the algorithms. You’ll conclude with an end-to-end model development process including deployment and maintenance of the model.After reading Supervised Learning with Python you’ll have a broad understanding of supervised learning and its practical implementation, and be able to run the code and extend it in an innovative manner.What You'll LearnReview the fundamental building blocks and concepts of supervised learning using PythonDevelop supervised learning solutions for structured data as well as text and images Solve issues around overfitting, feature engineering, data cleansing, and cross-validation for building best fit modelsUnderstand the end-to-end model cycle from business problem definition to model deployment and model maintenance Avoid the common pitfalls and adhere to best practices while creating a supervised learning model using PythonWho This Book Is ForData scientists or data analysts interested in best practices and standards for supervised learning, and using classification algorithms and regression techniques to develop predictive models.
Supervised Learning with Quantum Computers (Quantum Science and Technology)
by Maria Schuld Francesco PetruccioneQuantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
The Supervised Learning Workshop: A New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition
by Benjamin Johnston Ishita Mathur Blaine Bateman Ashish Ranjan JhaCut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms Key Features Ideal for those getting started with machine learning for the first time A step-by-step machine learning tutorial with exercises and activities that help build key skills Structured to let you progress at your own pace, on your own terms Use your physical print copy to redeem free access to the online interactive edition Book Description You already know you want to understand supervised learning, and a smarter way to do that is to learn by doing. The Supervised Learning Workshop focuses on building up your practical skills so that you can deploy and build solutions that leverage key supervised learning algorithms. You'll learn from real examples that lead to real results. Throughout The Supervised Learning Workshop, you'll take an engaging step-by-step approach to understand supervised learning. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend learning how to predict future values with auto regressors. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Supervised Learning Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your book. Fast-paced and direct, The Supervised Learning Workshop is the ideal companion for those with some Python background who are getting started with machine learning. You'll learn how to apply key algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead. What you will learn Get to grips with the fundamental of supervised learning algorithms Discover how to use Python libraries for supervised learning Learn how to load a dataset in pandas for testing Use different types of plots to visually represent the data Distinguish between regression and classification problems Learn how to perform classification using K-NN and decision trees Who this book is for Our goal at Packt is to help you be successful, in whatever it is you choose to do. The Supervised Learning Workshop is ideal for those with a Python background, who are just starting out with machine learning. Pick up a Workshop today, and let Packt help you develop skills that stick with you for life.
Supervised Machine Learning: Optimization Framework and Applications with SAS and R
by Tanya Kolosova Samuel BerestizhevskyAI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesn’t ensure building classifiers that generalize well for new data Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework are available on GitHub
Supervised Machine Learning for Text Analysis in R (Chapman & Hall/CRC Data Science Series)
by Emil Hvitfeldt Julia SilgeText data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
Supervised Machine Learning with Python: Develop rich Python coding practices while exploring supervised machine learning
by Taylor SmithTeach your machine to think for itself!Key FeaturesDelve into supervised learning and grasp how a machine learns from dataImplement popular machine learning algorithms from scratch, developing a deep understanding along the wayExplore some of the most popular scientific and mathematical libraries in the Python languageBook DescriptionSupervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood.This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning.By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems.What you will learnCrack how a machine learns a concept and generalize its understanding to new dataUncover the fundamental differences between parametric and non-parametric modelsImplement and grok several well-known supervised learning algorithms from scratchWork with models in domains such as ecommerce and marketingExpand your expertise and use various algorithms such as regression, decision trees, and clusteringBuild your own models capable of making predictionsDelve into the most popular approaches in deep learning such as transfer learning and neural networksWho this book is forThis book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are expected.
Superwrite: Alphabetic Writing System, Office Professional (Volume 1, 2nd Edition)
by A. James Lemaster John BaerVolume One introduces SuperWrite theory and beginning transcription principles. The step-by-step approach to writing principles guides students to alphabetic writing success. Eight lessons develop personal success factors such as goal-setting, problem-solving, time management, attendance, and promptness. Updated keyboarding style references reflect current business practices.