الصفحة 44
الصفحة 44
Defects in High-k Gate Dielectric Stacks : Nano-Electronic Semiconductor Devices
One of the key obstacles to high-k integration into Si nano-technology are the electronic defects in high-k materials. It has been established that defects do exist in high-k dielectrics and they play an important role in device operation. However, very little is known about the nature of the defects or about possible techniques to eliminate, or at least minimize them. Given the absence of a feasible alternative in the near future, well-focused scientific research and aggressive development programs on high-k gate dielectrics and related devices must continue for semiconductor electronics to remain a competitive income producing force in the global market.
Deep Learning with PyTorch Lightning : Build and train high-performance artificial intelligence and self-supervised models using Python
You’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide. In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning.
Deep learning and computer vision in remote sensing-II
Computer vision (CV) have seen a massive rise in popularity in the remote sensing field over the last few years. This success is mostly due to the effectiveness of deep learning (DL) algorithms. However, remote sensing data acquisition and annotation, as well as information extraction from massive remote sensing data, are still challenging. This reprint collected novel developments in the field of deep learning and computer vision methods for remote sensing. Papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems, have been published. With practical examples and real-world case studies, this reprint provides a valuable resource for researchers, professionals, and students seeking to harness the power of deep learning in the field of remote sensing.
Deep learning and computer vision in remote sensing-I
In the last few years, huge amounts of progress have been made regarding remote sensing in the field of computer vision. This success and progress is mostly due to the effectiveness of deep learning (DL) algorithms. In addition, the remote sensing community has shifted its attention to DL, and DL algorithms have been used to achieve significant success in many image analysis tasks. However, with regard to remote sensing, a number of challenges caused by difficulties in data acquisition and annotation have not been fully solved yet. This reprint is a collection of novel developments in the field of remote sensing using computer vision, deep learning, and artificial intelligence. The articles published involve fundamental theoretical analyses as well as those demonstrating their application to real-world problems.
Deep impact mission : Looking beneath the surface of a cometary nucleus
Deep Impact, or at least part of the flight system, is designed to crash into comet 9P/Tempel 1. This bold mission design enables cometary researchers to peer into the cometary nucleus, analyzing the material excavated with its imagers and spectrometers. The book describes the mission, its objectives, expected results, payload, and data products in articles written by those most closely involved. This mission has the potential of revolutionizing our understanding of the cometary nucleus.
Deep Brain Stimulation in Neurological and Psychiatric Disorders
Deep Brain Stimulation in Neurological and Psychiatric Disorders discusses today’s most current and cutting-edge applications of DBS. Initially used to treat Parkinson’s disease and essential tremor, DBS now has new clinical indications, new anatomic targets, and new technologies that enable an expanded role for DBS in the treatment of other movement disorders.
Declarative agent languages and technologies III ; 3rd International Workshop, DALT 2005, Utrecht, The Netherlands, July 25, 2005, Selected and Revised Papers
The workshop on Declarative Agent Languages and Technologies is a we- established venue for researchers interested in sharing their experiences in the areas of declarative and formal aspects of agents and multi-agent systems, and in engineering and technology. Today it is still a challenge to develop techno- gies that can satisfy the requirements of complex agent systems. The design and development of multi-agent systems still calls for models and technologies that ensure predictability, enable feature discovery, allow for the veri?cation of properties, and guarantee ?exibility. Declarative approaches are potentially a valuable means for satisfying the needs of multi-agent system developers and for specifying multi-agent systems.
Decision Procedures : An Algorithmic Point of View
Concentrates on decision procedures for first-order theories that are commonly used in automated verification and reasoning, theorem-proving, compiler optimization and operations research.
Dealing with Uncertainties : A Guide to Error Analysis
Dealing with Uncertainties proposes and explains a new approach for the analysis of uncertainties. Firstly, it is shown that uncertainties are the consequence of modern science rather than of measurements. Secondly, it stresses the importance of the deductive approach to uncertainties.
David Paul von Hansemann : Contributions to Oncology : Context, Comments and Translations
Presents translations of all the relevant German texts, and analyses the background and context of Hansemann's theories as well as the reasons why he was almost completely forgotten. It shows that some of Hansemann’s ideas may still be relevant to cancer research today, and that he deserves to be remembered in relation to cancer as Vordenker unter den führenden Denkern seiner Zeit - The foremost of the leading thinkers of his time.
Datatype-Generic Programming ; International Spring School, SSDGP 2006, Nottingham, UK, April 24-27, 2006, Revised Lectures
A leitmotif in the evolution of programming paradigms has been the level and extent of parametrisation that is facilitated — the so-called genericity of the paradigm. The sorts of parameters that can be envisaged in a programming language range from simple values, like integers and fioating-point numbers, through structured values, types and classes, to kinds (the type of types and/or classes).Datatype-generic programming is about parametrising programsby the structure of the data that they manipulate. To appreciate the importance of data type genericity,one need look no further than the internet. The internet is a massive repository of structured data, but the structure is rarely exploited. For example, compression of data can be much more efiective if its structure is known, but most compression algorithms regard the input data as simply a string of bits, and take no account of its internal organisation. Datatype-generic programming is about exploiting the structure of data when it is relevant and ignoring it when it is not. Programming languages most c- monly used at the present time do not provide efiective mechanisms for do- menting and implementing datatype genericity.
Database performance at scale: a practical guide
Optimizing database performance at the scale required for today’s data-intensive applications often requires more than performance tuning and scaling out. This book shares commonly overlooked considerations, pitfalls, and opportunities that have helped many teams break through database performance plateaus. It’s neither a definitive guide to distributed databases nor a beginner’s resource. Rather, it’s a look at the many different factors that impact performance, and our top field-tested recommendations for navigating them. Chapter 1 provides two (fun and fanciful) tales that surface some of the many roadblocks you might face and highlight the range of strategies for navigating around them.
Data Warehousing and Data Mining Techniques for Cyber Security
It provide techniques for collecting information from distributed databases and for performing data analysis. The ever expanding, tremendous amount of data collected and stored in large databases has far exceeded our human ability to comprehend--without the proper tools. There is a critical need for data analysis that can automatically analyze data, summarize it and predict future trends. In the modern age of Internet connectivity, concerns about denial of service attacks, computer viruses and worms are extremely important. Data Warehousing and Data Mining Techniques for Cyber Security contributes to the discipline of security informatics. The author discusses topics that intersect cyber security and data mining, while providing techniques for improving cyber security. Since the cost of information processing and internet accessibility is dropping, an increasing number of organizations are becoming vulnerable to cyber attacks. This volume introduces techniques for applications in the area of retail, finance, and bioinformatics, to name a few.
Data structure and algorithms using C++ : A practical implementation
Intended to flow from the basic concepts of C++ to technicalities of the programming language, its approach and debugging. The chapters of the book flow with the formulation of the problem, it's designing, finding the step-by-step solution procedure along with its compilation, debugging and execution with the output. Keeping in mind the learner’s sentiments and requirements, the exemplary programs are narrated with a simple approach so that it can lead to creation of good programs that not only executes properly to give the output, but also enables the learners to incorporate programming skills in them. The style of writing a program using a programming language is also emphasized by introducing the inclusion of comments wherever necessary to encourage writing more readable and well commented programs. As practice makes perfect, each chapter is also enriched with practice exercise questions so as to build the confidence of writing the programs for learners.
Data Science and Classification
This volume provides new methodological developments in data analysis and classification. A wide range of topics is covered that includes the measurement of similarity and dissimilarity, methods for classification and clustering, network and graph analyses, analysis of symbolic data, and web mining. Apart from structural and theoretical results the book shows how to apply the proposed to a variety of problems, for example in medicine, microarray analysis, social network structures, and music. The combination of new methodological advances with the wide range of real applications collected in this volume is of special value for researchers when choosing the appropriate among newly developed analytical tools for their research problems in classification and data analysis.
Data Processing in Precise Time and Frequency Applications
The book describes the data processing at various levels: design of the time and frequency references, characterization of the time and frequency references, applications involving precise time and/or frequency references. The metrological properties stability, accuracy and reproducibility are defined and the processes leading to their characterization are shown.
Data management in a connected world : Essays dedicated to Hartmut Wedekind on the occasion of his 70th birthday
Data management systems play the most crucial role in building large application s- tems. Since modern applications are no longer single monolithic software blocks but highly flexible and configurable collections of cooperative services, the data mana- ment layer also has to adapt to these new requirements. Therefore, within recent years, data management systems have faced a tremendous shift from the central management of individual records in a transactional way to a platform for data integration, fede- tion, search services, and data analysis. This book addresses these new issues in the area of data management from multiple perspectives, in the form of individual contributions, and it outlines future challenges in the context of data management.
Data driven : Harnessing data and AI to reinvent customer engagement
Data Driven will show you how to: Target and delight your customers with unprecedented accuracy and success, Bring customers closer to your brand and inspire them to engage, purchase, and remain loyal, Capture, organize, and analyze data from every source and activate it across every channel, Create a data-powered marketing strategy that can be customized for any audience, Serve individual consumers with highly personalized interactions, Deliver better customer service for the best customer experience, Improve your products and optimize your operating systems, Use AI and IoT to predict the future direction of markets. Also you’ll discover the three principles for building a successful data strategy and the five sources of data-driven power.
Data assimilation fundamentals : A unified formulation of the state and parameter estimation problem
This textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method.
Data Assimilation : The Ensemble Kalman Filter
Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples.It presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page.The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time.
