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Supercomputing: 10th Russian Supercomputing Days, RuSCDays 2024, Moscow, Russia, September 23–24, 2024, Revised Selected Papers, Part I (Lecture Notes in Computer Science #15406)
by Vladimir Voevodin Alexander Antonov Dmitry NikitenkoThe two-volume set LNCS 15406 and 15407 constitutes the refereed proceedings of the 10th Russian Supercomputing Days International Conference, RuSCDays 2024, held in Moscow, Russia, during September 2024. The 43 full papers presented in these two volumes were carefully reviewed and selected from 95 submissions. The papers are organized in the following topical sections: Part I: Supercomputer Simulation; HPC, BigData, AI: Algorithms, Technologies, Evaluation Part II: Distributed Computing; HPC Education.
Supercomputing: 10th Russian Supercomputing Days, RuSCDays 2024, Moscow, Russia, September 23–24, 2024, Revised Selected Papers, Part II (Lecture Notes in Computer Science #15407)
by Vladimir Voevodin Alexander Antonov Dmitry NikitenkoThe two-volume set LNCS 15406 and 15407 constitutes the refereed proceedings of the 10th Russian Supercomputing Days International Conference, RuSCDays 2024, held in Moscow, Russia, during September 2024. The 43 full papers presented in these two volumes were carefully reviewed and selected from 95 submissions. The papers are organized in the following topical sections: Part I: Supercomputer Simulation; HPC, BigData, AI: Algorithms, Technologies, Evaluation Part II: Distributed Computing; HPC Education.
Supercomputing: 4th Russian Supercomputing Days, RuSCDays 2018, Moscow, Russia, September 24–25, 2018, Revised Selected Papers (Communications in Computer and Information Science #965)
by Vladimir Voevodin Sergey SobolevThis book constitutes the refereed proceedings of the 4th Russian Supercomputing Days, RuSCDays 2018, held in Moscow, Russia, in September 2018.The 59 revised full papers and one revised short paper presented were carefully reviewed and selected from 136 submissions. The papers are organized in topical sections on parallel algorithms; supercomputer simulation; high performance architectures, tools and technologies.
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 International Conference, Isum 2018, Mérida, Mexico, March 5-9, 2018, Revised Selected Papers (Communications in Computer and Information Science #948)
by Jaime Klapp Isidoro Gitler Moises Torres Andrei TchernykhThis book constitutes the refereed proceedings of the 9th International Conference on Supercomputing, ISUM 2018, held in Mérida, Mexico, in March 2018. <P><P> The 19 revised full papers presented were carefully reviewed and selected from 64 submissions. The papers are organized in topical sections on scheduling, architecture, and programming; parallel computing; applications and HPC.
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.
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.
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 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.
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 Machine Learning for Text Analysis in R (Chapman & Hall/CRC Data Science Series)
by Julia Silge Emil HvitfeldtText 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.
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 and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data: MICCAI 2024 Challenges: ToothFairy 2024, 3DTeethLand 2024, and STS 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings (Lecture Notes in Computer Science #15571)
by Costantino Grana Shuai Wang Yaqi Wang Dahong Qian Achraf Ben-Hamadou Sergi Pujades Luca Lumetti Federico BolelliThis book constitutes three challenges that were held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024: ToothFairy challenge(ToothFairy2: Multi-Structure Segmentation in CBCT Volumes), Semi-supervised Teeth Segmentation (STS 2024), and the 3DTeethLand (3D Teeth Landmarks Detection Challenge). The 21 papers presented in this volume were carefully reviewed and selected from 28 submissions. ToothFairy challenges focused on the development of deep learning frameworks to segment anatomical structures in CBCTs by incrementally extending the amount of publicly available 3D-annotated CBCT scans and providing the first publicly available fully annotated datasets. The STS Challenge promoted the development of teeth segmentation in panoramic X-ray images and CBCT scans. It also provided instance annotations for different teeth, including pertinent category information. The 3DTeethLand24 Challenge played a key role in advancing automation and leveraging AI to optimize orthodontic treatments. It also aims to tackle the challenge of limited access to data, providing a valuable resource that encourages community engagement in this vital area with potential clinical implications.
Supervised and Unsupervised Learning for Data Science (Unsupervised and Semi-Supervised Learning)
by Michael W. Berry Bee Wah Yap Azlinah MohamedThis 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.
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.
Supplement to Inside the Microsoft® Build Engine: Using MSBuild and Team Foundation Build
by William Bartholomew Sayed Ibrahim HashimiGet the supplement that helps you drill even further into MSBuild--and maximize your control over the software build and deployment process. Designed as a companion to the popular book Inside the Microsoft Build Engine: Using MSBuild and Team Foundation Build, Second Edition, this supplement extends your knowledge by covering what's new in Visual Studio 2012 for MSBuild and Team Foundation Build. You'll also gain a fresh cookbook of examples to help you get productive with UI changes, batching, Team Foundation Server, offline apps, database publishing, and other essential topics. Extends your knowledge of MSBuild with all-new coverage of Visual Studio 2012 Shares additional hands-on insights and guidance from two expert authors Provides a cookbook of examples to study and reuse
Supplier Selection
by Krishnendu MukherjeeThe purpose of this book is to present a comprehensive review of the latest research and development trends at the international level for modeling and optimization of the supplier selection process for different industrial sectors. It is targeted to serve two audiences: the MBA and PhD student interested in procurement, and the practitioner who wishes to gain a deeper understanding of procurement analysis with multi-criteria based decision tools to avoid upstream risks to get better supply chain visibility. The book is expected to serve as a ready reference for supplier selection criteria and various multi-criteria based supplier's evaluation methods for forward, reverse and mass customized supply chain. This book encompasses several criteria, methods for supplier selection in a systematic way based on extensive literature review from 1998 to 2012. It provides several case studies and some useful links which can serve as a starting point for interested researchers. In the appendix several computer code written in MatLab and VB. NET is also included for the interested reader. Lucid explosion of various techniques used to select and evaluate suppliers is one of the unique characteristic of this book. Moreover, this book gives in depth analysis of selection and evaluation of suppliers for traditional supply chain, closed loop supply chain, supply chain for customized product, green supply chain, sustainable supply chain and also depicts methods for supply base reduction and selection of large number of suppliers.
Supply Chain Analytics: An Uncertainty Modeling Approach (Springer Texts in Business and Economics)
by Işık BiçerThis textbook offers a detailed account of analytical models used to solve complex supply chain problems. It introduces a unique risk analysis framework that helps the reader understand the sources of uncertainties and use appropriate models to improve decisions in supply chains. This framework illustrates the complete supply chain for a product and demonstrates the supply chain's exposure to demand, supply, inventory, and financial risks. Step by step, this book provides a detailed examination of analytical methods that optimize operational decisions under different types of uncertainty. It discusses stochastic inventory models, introduces uncertainty modeling methods, and explains methods for managing uncertainty. To help readers deepen their understanding, it includes access to various supplementary material including an online interactive tool in Python. This book is intended for undergraduate and graduate students of supply chain management with a focus on supply chain analytics. It also prepares practitioners to make better decisions in this field.
Supply Chain Analytics: Concepts, Techniques and Applications
by Kurt Y. LiuThis innovative new core textbook, written by an experienced professor and practitioner in supply chain management, offers a business-focused overview of the applications of data analytics and machine learning to supply chain management. Accessible yet rigorous, this text introduces students to the relevant concepts and techniques needed for data analysis and decision making in modern supply chains and enables them to develop proficiency in a popular and powerful programming software. Suitable for use on upper-level undergraduate, postgraduate and MBA courses in supply chain management, it covers all of the major supply chain processes, including managing supply and demand, warehousing and inventory control, transportation and route optimization. Each chapter comes with practical real-world examples drawn from a range of business contexts, including Amazon and Starbucks, case study discussion questions, computer-assisted exercises and programming projects.