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"This book provides a comprehensive coverage of System-on-Chip (SoC) post-silicon validation and debug challenges and state-of-the-art solutions with contributions from SoC designers, academic researchers as well as SoC verification experts. The readers will get a clear understanding of the existing debug infrastructure and how they can be effectively utilized to verify and debug SoCs. "

"This book concerns non-linguistic knowledge required to perform computational natural language understanding (NLU). The main objective of the book is to show that inference-based NLU has the potential for practical large scale applications. First, an introduction to research areas relevant for NLU is given. "

"This book presents state-of-the-art probabilistic methods for the reliability analysis and design of engineering products and processes. It seeks to facilitate practical application of probabilistic analysis and design by providing an authoritative, in-depth, and practical description of what probabilistic analysis and design is and how it can be implemented. The text is packed with many practical engineering examples (e.g., electric power transmission systems, aircraft power generating systems, and mechanical transmission systems) and exercise problems. It is an up-to-date, fully illustrated reference suitable for both undergraduate and graduate engineering students, researchers, and professional engineers who are interested in exploring the fundamentals, implementation, and applications of probabilistic analysis and design methods."

"This book treats Modelling of CFD problems, Numerical tools for PDE, and Scientific Computing and Systems of ODE for Epidemiology, topics that are closely related to the scientific activities and interests of Prof. William Fitzgibbon, Prof. Yuri Kuznetsov, and Prof. O. Pironneau, whose outstanding achievements are recognised in this volume.It contains 20 contributions from leading scientists in applied mathematics dealing with partial differential equations and their applications to engineering, ab-initio chemistry and life sciences. It includes the mathematical and numerical contributions to PDE for applications presented at the ECCOMAS thematic conference "Contributions to PDE for Applications" held at Laboratoire Jacques Louis Lions in Paris, France, August 31- September 1, 2015, and at the Department of Mathematics, University of Houston, Texas, USA, February 26-27, 2016.This event brought together specialists from universities and research institutions who are developing or applying numerical PDE or ODE methods with an emphasis on industrial and societal applications.This volume is of interest to researchers and practitioners as well as advanced students or engineers in applied and computational mathematics. All contributions are written at an advanced scientific level with no effort made by the editors to make this volume self-contained.It is assumed that the reader is a specialist already who knows the basis of this field of research and has the capability of understanding and appreciating the latest developments in this field. "

"This monograph provides a concise overview of the main theoretical and numerical tools to solve homogenization problems in solids with finite elements. Starting from simple cases (linear thermal case) the problems are progressively complexified to finish with nonlinear problems. The book is not an overview of current research in that field, but a course book, and summarizes established knowledge in this area such that students or researchers who would like to start working on this subject will acquire the basics without any preliminary knowledge about homogenization. More specifically, the book is written with the objective of practical implementation of the methodologies in simple programs such as Matlab. The presentation is kept at a level where no deep mathematics are required.​"

"Grid computing adalah salah satu jenis komputasi paralel dan sistem terdistribusi yang berusaha menyatukan resource atau sumber daya yang tersebar secara geografis. Dengan adanya grid computing memungkinkan beberapa orang (peneliti) atau perusahaan yang terpisah secara geografis untuk melakukan suatu tujuan (penelitian) dengan memanfaatkan resource-resource yang ada. Fakultas Ilmu Komputer Universitas Indonesia (Fasilkom UI) hingga saat ini telah melakukan beberapa penelitian mengenai grid computing. Penelitian-penelitian yang ada di Fasilkom UI masih cenderung bersifat teoritis. Untuk mengimplementasikannya dibutuhkan suatu grid environment dimana grid service dapat berjalan di atasnya. Pada tulisan ini penulis berusaha untuk mengkaji tentang hal-hal dibutuhkan serta mekanisme untuk membangun suatu grid environment di lingkungan Fasilkom UI. Pembahasan diawali dengan pengenalan grid computing melalui definisi dan komponen-komponen yang menyusunnya. Setelah mengetahui konsep dasar grid, kemudian pembahasan dilanjutkan dengan menganalisis hal-hal yang diperlukan untuk membangun grid environment. Sebelum mengimplementasikan grid environment, penulis terlebih dahulu membahas tentang sumber daya pendukung grid yang dimiliki oleh Fasilkom UI. Dengan berlandaskan pada informasi mengenai halhal yang dibutuhkan dalam membangun grid environment serta kondisi di Fasilkom UI, kemudian penulis melakukan uji coba membangun prototype grid environment. Berdasarkan pembahasan dan uji coba yang telah dilakukan dapat disimpulkan bahwa untuk mengimplementasikan grid environment di lingkungan Fasilkom UI terdapat dua hal penting yang harus dilakukan. Kedua hal tersebut adalah membuat kebijakan (policy) mengenai grid environment yang akan dibangun dan melakukan instalasi grid middleware (misalnya Globus Toolkit 3)."

"This book includes high-quality, peer-reviewed research papers from the 6thInternational Conference on Innovations in Computer Science & Engineering (ICICSE 2018), held at Guru Nanak Institutions, Hyderabad, India from August 17 to 18, 2018. The book discusses a wide variety of industrial, engineering and scientific applications of the emerging techniques and offers a platform for researchers from academia and industry to present their original work and exchange ideas, information, techniques and applications in the field of computer science."

"This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras.This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming."