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| nn little model: Models of Neural Networks III Eytan Domany, J. Leo van Hemmen, Klaus Schulten, 2012-12-06 One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, Global Analysis of Recurrent Neural Net works, by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and fire neurons with local interactions. The chapter, Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu ment since has been shown to be rather susceptible to generalization. |
| nn little model: Random Iterative Models Marie Duflo, 2013-03-09 Be they random or non-random, iterative methods have progressively gained sway with the development of computer science and automatic control theory. Thus, being easy to conceive and simulate, stochastic processes defined by an iterative formula (linear or functional) have been the subject of many studies. The iterative structure often leads to simpler and more explicit arguments than certain classical theories of processes. On the other hand, when it comes to choosing step-by-step decision algorithms (sequential statistics, control, learning, ... ) recursive decision methods are indispensable. They lend themselves naturally to problems of the identification and control of iterative stochastic processes. In recent years, know-how in this area has advanced greatly; this is reflected in the corresponding theoretical problems, many of which remain open. At Whom Is This Book Aimed? I thought it useful to present the basic ideas and tools relating to random iterative models in a form accessible to a mathematician familiar with the classical concepts of probability and statistics but lacking experience in automatic control theory. Thus, the first aim of this book is to show young research workers that work in this area is varied and interesting and to facilitate their initiation period. The second aim is to present more seasoned probabilists with a number of recent original techniques and arguments relating to iterative methods in a fairly classical environment. |
| nn little model: SUPERVISED LEARNING ALGORITHMS CLASSIFICATION AND REGRESSION ALGORITHMS Dr. Aadam Quraishi, ANIL WURITY, 2023-12-12 The branch of computer science known as machine learning is one of the subfields that is increasing at one of the fastest rates now and has various potential applications. The technique of automatically locating meaningful patterns in vast volumes of data is referred to as pattern recognition. It is possible to provide computer programs the ability to learn and adapt in response to changes in their surroundings via the use of tools for machine learning. As a consequence of machine learning being one of the most essential components of information technology, it has therefore become a highly vital, though not always visible, component of our day-to-day life. As the amount of data that is becoming available continues to expand at an exponential pace, there is good reason to believe that intelligent data analysis will become even more common as a critical component for the advancement of technological innovation. This is because there is solid grounds to believe that this will occur. Despite the fact that data mining is one of the most significant applications for machine learning (ML), there are other uses as well. People are prone to make mistakes while doing studies or even when seeking to uncover linkages between a lot of distinct aspects. This is especially true when the analyses include a large number of components. Data Mining and Machine Learning are like Siamese twins; from each of them, one may get a variety of distinct insights by using the right learning methodologies. As a direct result of the development of smart and nanotechnology, which enhanced people's excitement in discovering hidden patterns in data in order to extract value, a great deal of progress has been achieved in the field of data mining and machine learning. These advancements have been very beneficial. There are a number of probable explanations for this phenomenon, one of which is that people are currently more inquisitive than ever before about identifying hidden patterns in data. As the fields of statistics, machine learning, information retrieval, and computers have grown increasingly interconnected, we have seen an increase in the led to the development of a robust field that is built on a solid mathematical basis and is equipped with extremely powerful tools. This field is known as information theory and statistics. The anticipated outcomes of the many different machine learning algorithms are culled together into a taxonomy that is used to classify the many different machine learning algorithms. The method of supervised learning may be used to produce a function that generates a mapping between inputs and desired outputs. The production of previously unimaginable quantities of data has led to a rise in the degree of complexity shown across a variety of machine learning strategies. Because of this, the use of a great number of methods for both supervised and unsupervised machine learning has become obligatory. Because the objective of many classification challenges is to train the computer to learn a classification system that we are already familiar with, supervised learning is often used in order to find solutions to problems of this kind. The goal of unearthing the accessibility hidden within large amounts of data is well suited for the use of machine learning. The ability of machine learning to derive meaning from vast quantities of data derived from a variety of sources is one of its most alluring prospects. Because data drives machine learning and it works on a large scale, this goal will be achieved by decreasing the amount of dependence that is put on individual tracks. Machine learning functions on data. Machine learning is best suited towards the complexity of managing through many data sources, the huge diversity of variables, and the amount of data involved, since ML thrives on larger datasets. This is because machine learning is ideally suited towards managing via multiple data sources. This is possible as a result of the capacity of machine learning to process ever-increasing volumes of data. The more data that is introduced into a framework for machine learning, the more it will be able to be trained, and the more the outcomes will entail a better quality of insights. Because it is not bound by the limitations of individual level thinking and study, ML is intelligent enough to unearth and present patterns that are hidden in the data. |
| nn little model: Advances in Coastal Modeling V.C. Lakhan, 2003-10-24 This book unifies and enhances the accessibility of contemporary scholarly research on advances in coastal modeling. A comprehensive spectrum of innovative models addresses the wide diversity and multifaceted aspects of coastal research on the complex natural processes, dynamics, interactions and responses of the coastal supersystem and its associated subsystems. The twenty-one chapters, contributed by internationally recognized coastal experts from fourteen countries, provide invaluable insights on the recent advances and present state-of-the-art knowledge on coastal models which are essential for not only illuminating the governing coastal process and various characteristics, but also for understanding and predicting the dynamics at work in the coastal system. One of the unique strengths of the book is the impressive and encompassing presentation of current functional and operational coastal models for all those concerned with and interested in the modeling of seas, oceans and coasts. In addition to chapters modeling the dynamic natural processes of waves, currents, circulatory flows and sediment transport there are also chapters that focus on the modeling of beaches, shorelines, tidal basins and shore platforms. The substantial scope of the book is further strengthened with chapters concentrating on the effects of coastal structures on nearshore flows, coastal water quality, coastal pollution, coastal ecological modeling, statistical data modeling, and coupling of coastal models with geographical information systems. |
| nn little model: Knowledge Discovery and Data Mining. Current Issues and New Applications Takao Terano, Huan Liu, Arbee L.P. Chen, 2007-07-13 The Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000) was held at the Keihanna-Plaza, Kyoto, Japan, April 18 - 20, 2000. PAKDD 2000 provided an international forum for researchers and applica tion developers to share their original research results and practical development experiences. A wide range of current KDD topics were covered including ma chine learning, databases, statistics, knowledge acquisition, data visualization, knowledge-based systems, soft computing, and high performance computing. It followed the success of PAKDD 97 in Singapore, PAKDD 98 in Austraha, and PAKDD 99 in China by bringing together participants from universities, indus try, and government from all over the world to exchange problems and challenges and to disseminate the recently developed KDD techniques. This PAKDD 2000 proceedings volume addresses both current issues and novel approaches in regards to theory, methodology, and real world application. The technical sessions were organized according to subtopics such as Data Mining Theory, Feature Selection and Transformation, Clustering, Application of Data Mining, Association Rules, Induction, Text Mining, Web and Graph Mining. Of the 116 worldwide submissions, 33 regular papers and 16 short papers were accepted for presentation at the conference and included in this volume. Each submission was critically reviewed by two to four program committee members based on their relevance, originality, quality, and clarity. |
| nn little model: Recent Advances in Modeling Landslides and Debris Flows Wei Wu, 2014-09-12 Landslides and debris flows belong to the most dangerous natural hazards in many parts of the world. Despite intensive research, these events continue to result in human suffering, property losses, and environmental degradation every year. Better understanding of the mechanisms and processes of landslides and debris flows will help make reliable predictions, develop mitigation strategies and reduce vulnerability of infrastructure. This book presents contributions to the workshop on Recent Developments in the Analysis, Monitoring and Forecast of Landslides and Debris Flow, in Vienna, Austria, September 9, 2013. The contributions cover a broad spectrum of topics from material behavior, physical modelling over numerical simulation to applications and case studies. The workshop is a joint event of three research projects funded by the European Commission within the 7th Framework Program: MUMOLADE (Multiscale modelling of landslides and debris flows, www.mumolade.com), REVENUES (Numerical Analysis of Slopes with Vegetations, http://www.revenues-eu.com) and HYDRODRIL (Integrated Risk Assessment of Hydrologically-Driven Landslides, www.boku.ac.at/igt/). |
| nn little model: Machine Learning on Commodity Tiny Devices Song Guo, Qihua Zhou, 2022-12-13 This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization and hardware-level instruction acceleration. Analyzing the limitations of conventional in-cloud computing would reveal that on-device learning is a promising research direction to meet the requirements of edge intelligence applications. As to the cutting-edge research of TinyML, implementing a high-efficiency learning framework and enabling system-level acceleration is one of the most fundamental issues. This book presents a comprehensive discussion of the latest research progress and provides system-level insights on designing TinyML frameworks, including neural network design, training algorithm optimization and domain-specific hardware acceleration. It identifies the main challenges when deploying TinyML tasks in the real world and guides the researchers to deploy a reliable learning system. This book will be of interest to students and scholars in the field of edge intelligence, especially to those with sufficient professional Edge AI skills. It will also be an excellent guide for researchers to implement high-performance TinyML systems. |
| nn little model: Innovation in Medicine and Healthcare 2016 Yen-Wei Chen, Satoshi Tanaka, Robert J. Howlett, Lakhmi C. Jain, 2016-06-13 This proceedings volume includes 32 papers, which present recent trends and innovations in medicine and healthcare including Innovative Technology in Mental Healthcare; Intelligent Decision Support Technologies and Systems in Healthcare; Biomedical Engineering, Trends, Research and Technologies; Advances in Data & Knowledge Management for Healthcare; Advanced ICT for Medical and Healthcare; Healthcare Support System; and Smart Medical and Healthcare System. Innovation in medicine and healthcare is an interdisciplinary research area, which combines the advanced technologies and problem solving skills with medical and biological science. A central theme of this proceedings is Smart Medical and Healthcare Systems (modern intelligent systems for medicine and healthcare), which can provide efficient and accurate solution to problems faced by healthcare and medical practitioners today by using advanced information communication techniques, computational intelligence, mathematics, robotics and other advanced technologies. |
| nn little model: Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances Yanan Sun, Gary G. Yen, Mengjie Zhang, 2022-11-08 This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields. |
| nn little model: Statistical Models David Freedman, 2005-08-08 This lively and engaging textbook provides the knowledge required to read empirical papers in the social and health sciences, as well as the techniques needed to build statistical models. The author explains the basic ideas of association and regression, and describes the current models that link these ideas to causality. He focuses on applications of linear models, including generalized least squares and two-stage least squares. The bootstrap is developed as a technique for estimating bias and computing standard errors. Careful attention is paid to the principles of statistical inference. There is background material on study design, bivariate regression, and matrix algebra. To develop technique, there are computer labs, with sample computer programs. The book's discussion is organized around published studies, as are the numerous exercises - many of which have answers included. Relevant papers reprinted at the back of the book are thoroughly appraised by the author. |
| nn little model: Hands-On Generative Adversarial Networks with PyTorch 1.x John Hany, Greg Walters, 2019-12-12 Apply deep learning techniques and neural network methodologies to build, train, and optimize generative network models Key FeaturesImplement GAN architectures to generate images, text, audio, 3D models, and moreUnderstand how GANs work and become an active contributor in the open source communityLearn how to generate photo-realistic images based on text descriptionsBook Description With continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning. This book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples. This book starts by taking you through the core concepts necessary to understand how each component of a GAN model works. You'll build your first GAN model to understand how generator and discriminator networks function. As you advance, you'll delve into a range of examples and datasets to build a variety of GAN networks using PyTorch functionalities and services, and become well-versed with architectures, training strategies, and evaluation methods for image generation, translation, and restoration. You'll even learn how to apply GAN models to solve problems in areas such as computer vision, multimedia, 3D models, and natural language processing (NLP). The book covers how to overcome the challenges faced while building generative models from scratch. Finally, you'll also discover how to train your GAN models to generate adversarial examples to attack other CNN and GAN models. By the end of this book, you will have learned how to build, train, and optimize next-generation GAN models and use them to solve a variety of real-world problems. What you will learnImplement PyTorch's latest features to ensure efficient model designingGet to grips with the working mechanisms of GAN modelsPerform style transfer between unpaired image collections with CycleGANBuild and train 3D-GANs to generate a point cloud of 3D objectsCreate a range of GAN models to perform various image synthesis operationsUse SEGAN to suppress noise and improve the quality of speech audioWho this book is for This GAN book is for machine learning practitioners and deep learning researchers looking to get hands-on guidance in implementing GAN models using PyTorch. You’ll become familiar with state-of-the-art GAN architectures with the help of real-world examples. Working knowledge of Python programming language is necessary to grasp the concepts covered in this book. |
| nn little model: NASA Formal Methods Jyotirmoy V. Deshmukh, Klaus Havelund, Ivan Perez, 2022-05-19 This book constitutes the proceedings of the 14th International Symposium on NASA Formal Methods, NFM 2022, held in Pasadena, USA, during May 24-27, 2022. The 33 full and 6 short papers presented in this volume were carefully reviewed and selected from 118submissions. The volume also contains 6 invited papers. The papers deal with advances in formal methods, formal methods techniques, and formal methods in practice. The focus on topics such as interactive and automated theorem proving; SMT and SAT solving; model checking; use of machine learning and probabilistic reasoning in formal methods; formal methods and graphical modeling languages such as SysML or UML; usability of formal method tools and application in industry, etc. |
| nn little model: Inside Deep Learning Edward Raff, 2022-05-31 Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology Adapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped--you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. About the Technology Deep learning doesn't have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don't have to be a mathematics expert or a senior data scientist to grasp what's going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. About the Book Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You'll explore deep learning concepts and tools through plain language explanations, annotated code, and dozens of instantly useful PyTorch examples. Each type of neural network is clearly presented without complex math, and every solution in this book can run using readily available GPU hardware! What's Inside Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology About the Reader For Python programmers with basic machine learning skills. About the Author Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. Quotes Pick up this book, and you won't be able to put it down. A rich, engaging knowledge base of deep learning math, algorithms, and models--just like the title says! - From the Foreword by Kirk Borne Ph.D., Chief Science Officer, DataPrime.ai The clearest and easiest book for learning deep learning principles and techniques I have ever read. The graphical representations for the algorithms are an eye-opening revelation. - Richard Vaughan, Purple Monkey Collective A great read for anyone interested in understanding the details of deep learning. - Vishwesh Ravi Shrimali, MBRDI. |
| nn little model: Intracerebral Hemorrhage J. Ricardo Carhuapoma, Stephan A. Mayer, Daniel F. Hanley, 2009-11-12 Intracerebral Hemorrhage offers a review of the clinical and biological aspects of intracerebral hemorrhage. |
| nn little model: Neural Networks for Hydrological Modeling Robert Abrahart, P.E. Kneale, Linda M. See, 2004-05-15 A new approach to the fast-developing world of neural hydrological modelling, this book is essential reading for academics and researchers in the fields of water sciences, civil engineering, hydrology and physical geography. Each chapter has been written by one or more eminent experts working in various fields of hydrological modelling. The b |
| nn little model: Superconducting State Vladimir Kresin, Vladimir Z. Kresin, Sergei Ovchinnikov, Stuart A. Wolf, 2021 The book provides scientists with a detailed understanding of the nature of superconductivity and the most interesting superconducting materials. |
| nn little model: Time-Series Forecasting Chris Chatfield, 2000-10-25 From the author of the bestselling Analysis of Time Series, Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space |
| nn little model: Quantitative Psychology Research Roger E. Millsap, Daniel M. Bolt, L. Andries van der Ark, Wen-Chung Wang, 2014-11-26 The 78th Annual Meeting of the Psychometric Society (IMPS) builds on the Psychometric Society's mission to share quantitative methods relevant to psychology. The chapters of this volume present cutting-edge work in the field. Topics include studies of item response theory, computerized adaptive testing, cognitive diagnostic modeling, and psychological scaling. Additional psychometric topics relate to structural equation modeling, factor analysis, causal modeling, mediation, missing data methods, and longitudinal data analysis, among others. The papers in this volume will be especially useful for researchers in the social sciences who use quantitative methods. Prior knowledge of statistical methods is recommended. The 78th annual meeting took place in Arnhem, The Netherlands between July 22nd and 26th, 2013. The previous volume to showcase work from the Psychometric Society’s Meeting is New Developments in Quantitative Psychology: Presentations from the 77th Annual Psychometric Society Meeting (Springer, 2014). |
| nn little model: Business Process Modeling, Simulation and Design Manuel Laguna, Johan Marklund, 2018-12-07 Business Process Modeling, Simulation and Design, Third Edition provides students with a comprehensive coverage of a range of analytical tools used to model, analyze, understand, and ultimately design business processes. The new edition of this very successful textbook includes a wide range of approaches such as graphical flowcharting tools, cycle time and capacity analyses, queuing models, discrete-event simulation, simulation-optimization, and data mining for process analytics. While most textbooks on business process management either focus on the intricacies of computer simulation or managerial aspects of business processes, this textbook does both. It presents the tools to design business processes and management techniques on operating them efficiently. The book focuses on the use of discrete event simulation as the main tool for analyzing, modeling, and designing effective business processes. The integration of graphic user-friendly simulation software enables a systematic approach to create optimal designs. |
| nn little model: Stability Analysis of Neural Networks Grienggrai Rajchakit, Praveen Agarwal, Sriraman Ramalingam, 2021-12-05 This book discusses recent research on the stability of various neural networks with constrained signals. It investigates stability problems for delayed dynamical systems where the main purpose of the research is to reduce the conservativeness of the stability criteria. The book mainly focuses on the qualitative stability analysis of continuous-time as well as discrete-time neural networks with delays by presenting the theoretical development and real-life applications in these research areas. The discussed stability concept is in the sense of Lyapunov, and, naturally, the proof method is based on the Lyapunov stability theory. The present book will serve as a guide to enable the reader in pursuing the study of further topics in greater depth and is a valuable reference for young researcher and scientists. |
| nn little model: New Challenges and Future Perspectives in Nutrition and Sustainable Diets in Africa Hettie Carina Schönfeldt, Gloria Essilfie, Yunyun Gong, 2024-05-06 Africa is confronted with the triple burden of malnutrition; it is also faced with the triple challenges of poverty, inequality and unemployment. In many African countries, large proportions of the population rely on agriculture not only for their food - but also for their livelihoods. A transformed agricultural and food system is thus a necessary condition for addressing this double-triple challenge. Additionally, post harvest and food waste and losses reduce the availability of sufficient quantities of safe, edible and preferable foods. At least one third of food produced at farm level is lost due to inappropriate storage, infrastructure and agro-processing technologies in developing countries; and one third of food purchased is wasted at household and retail level. |
| nn little model: Introduction to Environmental Data Science William W. Hsieh, 2023-03-23 Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography; pattern recognition for satellite images from remote sensing; management of agriculture and forests; assessment of climate change; and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics is covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms and deep learning, as well as the recent merging of machine learning and physics. End‐of‐chapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data. |
| nn little model: Applied Positive School Psychology Andrea Giraldez-Hayes, Jolanta Burke, 2022-07-21 Applied Positive School Psychology is an essential guide to help teachers regain their own and assist the school community in rebuilding their health post-pandemic. While research in positive psychology is thriving, teachers and educational practitioners find it challenging to apply it in their daily practice. This practical book fills the gap between theory and practice and provides practitioners with an evidence-based toolkit on using the positive psychology in their school communities. With contributions from experts in their field, this important resource explores student wellbeing, teacher wellbeing, inclusion, developing positive relationships, creativity, and therapeutic art. Written with the practitioner in mind, Applied Positive School Psychology is a must read for the teaching community and those interested in positive education. It will also be of interest to academics specialising in wellbeing or education, educational psychologists, and education policy makers. |
| nn little model: Advances in mathematical and computational oncology, volume III George Bebis, Dinler Amaral Antunes, Ken Chen, Mohammad Kohandel, Kathleen Wilkie, Mamoru Kato, Jinzhuang Dou, 2023-10-25 |
| nn little model: Mining Scientific Papers: NLP-enhanced Bibliometrics Iana Atanassova, Marc Bertin, Philipp Mayr, 2019-10-09 |
| nn little model: New Analytical Advances in Transportation and Spatial Dynamics Aura Reggiani, 2018-05-08 This title was first published in 2001. A delightfully oriented selection of international state-of-the-art research in applied regional science, this informative volume places particular emphasis on the use of qualitative/quantitative methodologies in transportation and spatial dynamics. It presents new theoretical contributions in the context of spatial competition dynamics, particularly illustrating various combinations of methods and models regarding new measures of competition/cohesion in the two main fields of transportation and spatial dynamics. |
| nn little model: New Approaches to Macroeconomic Modeling Masanao Aoki, 1998-02-13 This book provides a method for modeling large collections of heterogeneous agents subject to non-pairwise externality called field effects. |
| nn little model: Superconducting State Vladimir Z. Kresin, Hans Morawitz, Stuart A. Wolf, 2014 This book describes fundamentals of the superconducting state and latest developments in the field. It represents the state of the art status of the theory, and key experiments for both historically important conventional superconductors and novel technologically significant superconductors. |
| nn little model: Coastal Engineering 2004 - Proceedings Of The 29th International Conference (In 4 Vols) Jane Mckee Smith, 2005-04-08 This comprehensive and up-to-date volume contains 367 papers presented at the 29th International Conference on Coastal Engineering, held in Lisbon, Portugal, 19-24 September 2004. It is divided into five parts: waves; long waves, nearshore currents, and swash; sediment transport and morphology; coastal management, beach nourishment, and dredging; coastal structures. The contributions cover a broad range of topics including theory, numerical and physical modeling, field measurements, case studies, design, and management. Coastal Engineering 2004 provides engineers, scientists, and planners state-of-the-art information on coastal engineering and coastal processes.The proceedings have been selected for coverage in: |
| nn little model: Insights in Educational Psychology 2021 Douglas F. Kauffman, Claudio Longobardi, Jesus de la Fuente, 2023-09-12 This Research Topic is part of the Insights in Psychology series. We are now entering the third decade of the 21st Century, and, especially in the last years, the achievements made by scientists have been exceptional, leading to major advancements in the fast-growing field of Psychology. Frontiers has organized a series of Research Topics to highlight the latest advancements in science in order to be at the forefront of science in different fields of research. This editorial initiative of particular relevance, led by Douglas Kauffman, Specialty Chief Editor of the section Educational Psychology, is focused on new insights, novel developments, current challenges, latest discoveries, recent advances and future perspectives in this field. Also, high-quality original research manuscripts on novel concepts, problems and approaches are welcomed. |
| nn little model: Neural Computing and Applications to Marine Data Analytics Jun Li, Zhengguang Zhang, Lulu Qiao, Andrei Herdean, 2022-03-30 |
| nn little model: Proceedings of the 2nd International Conference on Data Engineering and Communication Technology Anand J. Kulkarni, Suresh Chandra Satapathy, Tai Kang, Ali Husseinzadeh Kashan, 2018-10-03 This book features research work presented at the 2nd International Conference on Data Engineering and Communication Technology (ICDECT) held on December 15–16, 2017 at Symbiosis International University, Pune, Maharashtra, India. It discusses advanced, multi-disciplinary research into smart computing, information systems and electronic systems, focusing on innovation paradigms in system knowledge, intelligence and sustainability that can be applied to provide feasible solutions to varied problems in society, the environment and industry. It also addresses the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in a variety of disciplines of computer science and electronics engineering. |
| nn little model: MEDINFO 2019: Health and Wellbeing e-Networks for All L. Ohno-Machado, B. Séroussi, 2019-11-12 Combining and integrating cross-institutional data remains a challenge for both researchers and those involved in patient care. Patient-generated data can contribute precious information to healthcare professionals by enabling monitoring under normal life conditions and also helping patients play a more active role in their own care. This book presents the proceedings of MEDINFO 2019, the 17th World Congress on Medical and Health Informatics, held in Lyon, France, from 25 to 30 August 2019. The theme of this year’s conference was ‘Health and Wellbeing: E-Networks for All’, stressing the increasing importance of networks in healthcare on the one hand, and the patient-centered perspective on the other. Over 1100 manuscripts were submitted to the conference and, after a thorough review process by at least three reviewers and assessment by a scientific program committee member, 285 papers and 296 posters were accepted, together with 47 podium abstracts, 7 demonstrations, 45 panels, 21 workshops and 9 tutorials. All accepted paper and poster contributions are included in these proceedings. The papers are grouped under four thematic tracks: interpreting health and biomedical data, supporting care delivery, enabling precision medicine and public health, and the human element in medical informatics. The posters are divided into the same four groups. The book presents an overview of state-of-the-art informatics projects from multiple regions of the world; it will be of interest to anyone working in the field of medical informatics. |
| nn little model: Modeling and Mitigation Measures for Managing Extreme Hydrometeorological Events Under a Warming Climate Kasiviswanathan KS, Soundharajan Dr., Sandhya Patidar, Jianxun He, C.S.P. Ojha, 2023-05-10 Modeling and Mitigation Measures for Managing Extreme Hydrometeorological Events Under a Warming Climate explores the most recent computational tools, modeling frameworks, and critical data analysis measures for managing extreme climate events. Extreme climate events—primarily floods and droughts—have had major consequences in terms of loss of life and property around the world. Managing extreme occurrences, reducing their effects, and establishing adaptation strategies requires significant policy and planning improvements. This practical guide explores the latest research literature, recent advanced modeling approaches, and fundamental ideas and concepts to provide a variety of solutions for managing extreme events. - Discusses the impacts of climate change on the management of water resources - Provides flood and drought adaptation measures and strategies - Covers the latest research carried out in the modeling of extreme hydrometeorological variables |
| nn little model: Nuclear Methods and the Nuclear Equation of State Marcello Baldo, 1999 The theoretical study of the nuclear equation of state (EOS) is a field of research which deals with most of the fundamental problems of nuclear physics. This book gives an overview of the present status of the microscopic theory of the nuclear EOS. Its aim is essentially twofold: first, to serve as a textbook for students entering the field, by covering the different subjects as exhaustively and didactically as possible; second, to be a reference book for all researchers active in the theory of nuclear matter, by providing a report on the latest developments. Special emphasis is given to the numerous open problems existing at present and the prospects for their possible solutions.The general framework of the different approaches presented in the book is the meson theory of nuclear forces ? where no free parameter is introduced ? and the many-body treatment of nucleon-nucleon correlations. The ultimate hope of this world-wide effort is the understanding of the structure of nuclear matter, both in the ground state and at finite temperature.The main audience addressed is the community of theoretical nuclear physicists, but nuclear experimentalists and astrophysicists will also find in the book an extensive amount of material of direct interest for their everyday work, particularly for those studying heavy-ion collisions, where the nuclear EOS is of special relevance. Finally, theoretical physicists working on elementary particle theory could find in the book some stimulating ideas and problems directly related to their field. |
| nn little model: The Analysis of Time Series Chris Chatfield, Haipeng Xing, 2019-04-25 This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis. The book covers a wide range of topics, including ARIMA models, forecasting methods, spectral analysis, linear systems, state-space models, the Kalman filters, nonlinear models, volatility models, and multivariate models. |
| nn little model: A Companion to Economic Forecasting Michael P. Clements, David F. Hendry, 2008-04-15 A Companion to Economic Forecasting provides an accessible and comprehensive account of recent developments in economic forecasting. Each of the chapters has been specially written by an expert in the field, bringing together in a single volume a range of contrasting approaches and views. Uniquely surveying forecasting in a single volume, the Companion provides a comprehensive account of the leading approaches and modeling strategies that are routinely employed. |
| nn little model: New Vistas in Electro-Nuclear Physics Edward L. Tomusiak, Henry S. Caplan, Edward T. Dressler, 2013-11-11 The NATO Advanced Study Institute New Vistas in Electro-Nuclear Physics was held in Banff, Alberta, Canada from August 22 to September 4, 1985. This volume con tains the lecture notes from that Institute. The idea to organize this Institute coincided with the award of funding for a pulse stretcher ring at the University of Saskatchewan's Linear Accelerator Laboratory. This together with the high level of interest in electron accelerators worldwide convinced us that it was an appropriate time to discuss the physics to be learned with such machines. In particular that physics which requires high energy and/or high duty cycle accelerators for its extraction was intended to be the focus of the Institute. Thus the scope of the lec tures was wide, with topics ranging from the structure of the trinucleons to quark models of nucleons, QCD, and QHD. The theme however was that we are just trying to under stand the nucleus and that the electromagnetic probe can serve as a powerful tool in such a quest. |
| nn little model: The Amateur Photographer and Photographic News , 1917 |
| nn little model: Advances in Neuro-Information Processing Mario Köppen, Nikola Kasabov, George Coghill, 2009-07-30 The two volume set LNCS 5506 and LNCS 5507 constitutes the thoroughly refereed post-conference proceedings of the 15th International Conference on Neural Information Processing, ICONIP 2008, held in Auckland, New Zealand, in November 2008. The 260 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. 116 papers are published in the first volume and 112 in the second volume. The contributions deal with topics in the areas of data mining methods for cybersecurity, computational models and their applications to machine learning and pattern recognition, lifelong incremental learning for intelligent systems, application of intelligent methods in ecological informatics, pattern recognition from real-world information by svm and other sophisticated techniques, dynamics of neural networks, recent advances in brain-inspired technologies for robotics, neural information processing in cooperative multi-robot systems. |
Ñ - Wikipedia
It is a letter in the Spanish alphabet that is used for many words—for example, the Spanish word año "year" ( anno in Old Spanish) derived from Latin: annus. Other languages used the macron …
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Ñ - Wikipedia
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