الصفحة 132
الصفحة 132
Macrophytes in Aquatic Ecosystems : from Biology to Management ; Proceedings of the 11th International Symposium on Aquatic Weeds, European Weed Research Society
Macrophytes and macrophytes ecology have gained an added importance with the introduction of, and legal requirement to implement, the European Water Framework Directive. This reflects the valuable role that aquatic plant communities play in assessing the ecological status of water bodies. Significant changes in the status and general abundance of these communities has been recorded in the last three decades, consequent on such factors as global warming and, increasingly, on the spread of aggressive alien species. These changes have been most felt in those waters that are exploited for amenity and recreational pursuits. The increased biomass of aquatic vegetation in water bodies has adversely impacted on leisure use of these systems but has also impacted on their use for agricultural, flood relief, municipal and domestic purposes. The loss to national economies resulting from this excessive plant biomass has been appreciable and has put pressure on water managers to develop weed control procedures that are efficient, environmentally sensitive and cost-effective.
Macroeconomic Risk Management Against Natural Disasters : Analysis focussed on governments in developing countries
Natural disasters cause considerable economic damage. While developed countries usually are able to cope with the impacts of natural hazards, developing countries are faced with severe consequences for their resources. In order to prevent long-term macroeconomic repercussions, governments need a comprehensive disaster risk management strategy.Budgetary resources are allocated to pre-disaster risk management strategies to reduce the probability of financing gaps. The framework and model approach allows cross country comparisons as well as the assessment of financial vulnerability, macroeconomic risk, and risk management strategies.
Macroeconomic modelling of R&D and innovation policies
This book encompasses a collection of in-depth analyses showcasing the challenges and ways forward for macroeconomic modelling of R&D and innovation policies. Based upon the proceedings of the EC-DG JRC-IEA workshop held in Brussels in 2017, it presents cutting-edge contributions from a number of leading economists in the field. It provides a comprehensive overview of the current academic and policy challenges surrounding R&D as well as of the state-of-the-art modelling techniques.
Macroeconomic analysis for economic growth
Discusses the essential principles that guide macroeconomic policy formulation and implementation to stimulate strong economic growth for sustainable development, especially for emerging economies. it includes twelve chapters over three sections: "macroeconomics of economic growth", "labour market and employment", and "the financial system and macroeconomic performance". key conclusions illustrate that the efficacy of regulatory frameworks to create enabling conditions for nurturing and bolstering robust value-adding production structures anchored on appropriate macroeconomic management are the fundamental building blocks of strong economic growth.
MacLaurins Physical Dissertations
The Scottish mathematician Colin MacLaurin (1698-1746) is best known for developing and extending Newton’s work in calculus, geometry and gravitation; his 2-volume work "Treatise of Fluxions" (1742) was the first systematic exposition of Newton’s methods. It is well known that MacLaurin was awarded prizes by the Royal Academy of Sciences, Paris, for his earlier work on the collision of bodies (1724) and the tides (1740); however, the contents of these essays are less familiar – although some of the material is discussed in the Treatise of Fluxions - and the essays themselves often hard to obtain.
Mack Scogin Merrill Elam : Knowlton Hall
Mack Scogin Merrill Elam Architects/Knowlton Hall provides a comprehensive look at this impressive new work using sketches, models, renderings, working drawings, and photographs. it is accompanied by commentaries from the architects and critics who explore both the technical and contextual elements of the work
Machining: Fundamentals and Recent Advances
Machining is one of the most important manufacturing processes. Parts manufactured by others processes often require further operations before the product is ready for application. Machining is the broad term used to describe the removal of material from a work-piece. Machining processes can be applied to work metallic and non-metallic materials such as polymers, wood, ceramics and composites.
Machines, Computations, and Universality ; 5th International Conference, MCU 2007, Orleans, France, September 10-13, 2007, Proceedings
The 18 revised full papers presented together with nine invited papers cover Turing machines, register machines, word processing, cellular automata, tiling of the plane, neural networks, molecular computations, BSS machines, infinite cellular automata, real machines, and quantum computing.
Machine Learning: ECML 2007 ; 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007, Proceedings
The two premier annual European conferences in the areas of machine learning and data mining have been collocated ever since the ?rst joint conference in Freiburg, 2001. The European Conference on Machine Learning (ECML) traces its origins to 1986, when the ?rst European Working Session on Learning was held in Orsay, France. The European Conference on Principles and Practice of KnowledgeDiscoveryinDatabases(PKDD) was?rstheldin1997inTrondheim, Norway.
Machine Learning, Image Processing, Network Security and Data Sciences ; 2nd International conference, MIND 2020, Silchar, India, July 30 - 31, 2020, Proceedings, Part II
This two-volume set (CCIS 1240-1241) constitutes the refereed proceedings of the Second International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2020, held in Silchar, India. Due to the COVID-19 pandemic the conference has been postponed to July 2020. The 79 full papers and 4 short papers were thoroughly reviewed and selected from 219 submissions. The papers are organized according to the following topical sections: data science and big data; image processing and computer vision; machine learning and computational intelligence; network and cyber security.
Machine learning, image processing, network security and data sciences ; 2nd International conference, MIND 2020, Silchar, India, July 30 - 31, 2020, Proceedings, Part I
This two-volume set (CCIS 1240-1241) constitutes the refereed proceedings of the Second International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2020, held in Silchar, India. Due to the COVID-19 pandemic the conference has been postponed to July 2020. The 79 full papers and 4 short papers were thoroughly reviewed and selected from 219 submissions. The papers are organized according to the following topical sections: data science and big data; image processing and computer vision; machine learning and computational intelligence; network and cyber security.
Machine Learning Techniques for Multimedia : Case Studies on Organization and Retrieval
This book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in machine learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the machine learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific machine learning issues in domains .
Machine Learning Techniques and Analytics for Cloud Security
covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions
Machine learning refined : Foundations, algorithms, and applications
Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization
Machine Learning in Document Analysis and Recognition
The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphicalcomponents of a document and to extract information. With ?rst papers dating back to the 1960’s, DAR is a mature but still gr- ing research?eld with consolidated and known techniques. Optical Character Recognition (OCR) engines are some of the most widely recognized pr- ucts of the research in this ?eld, while broader DAR techniques are nowadays studied and applied to other industrial and o?ce automation systems. In the machine learning community, one of the most widely known - search problems addressed in DAR is recognition of unconstrained handwr- ten characters which has been frequently used in the past as a benchmark for evaluating machine learning algorithms, especially supervised classi?ers.
Machine Learning in Computer Vision
The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.
Machine Learning for Multimodal Interaction ; Vol.4299 ; 3rd International Workshop, MLMI 2006, Bethesda, MD, USA, May 1-4, 2006, Revised Selected Papers
This book contains a selection of refereed papers presented at the 3rd Workshop on Machine Learning for Multimodal Interaction (MLMI 2006), held in Bethesda MD, USA during May 1–4, 2006.
Machine Learning for Multimodal Interaction ; Vol.3869 ; 2nd International Workshop, MLMI 2005, Edinburgh, UK, July 11-13, 2005, Revised Selected Papers
The papers are organized in topical sections on multimodal processing, HCI and applications, discourse and dialogue, emotion, visual processing, speech and audio processing, and NIST meeting recognition evaluation
Machine Learning for Multimodal Interaction ; Vol.3361 : 1st International Workshop, MLMI 2004, Martigny, Switzerland, June 21-23, 2004, Revised Selected Papers
his book contains a selection of refereed papers presented at the 1st Wo- shop on Machine Learning for Multimodal Interaction (MLMI 2004), held at the “Centre du Parc,” Martigny, Switzerland, during June 21–23, 2004. The workshop was organized and sponsored jointly by three European projects, – AMI, Augmented Multiparty Interaction, http://www.amiproject.org – PASCAL, Pattern Analysis, Statistical Modeling and Computational Learning, http://www.pascal-network.org – M4, Multi-modal Meeting Manager, http://www.m4project.org as well as the Swiss National Centre of Competence in Research (NCCR): – IM2: Interactive Multimodal Information Management, http://www.im2.ch MLMI 2004 was thus sponsored by the European Commission and the Swiss National Science Foundation.
Machine Learning for Multimodal Interaction ; 5th International Workshop, MLMI 2008, Utrecht, The Netherlands, September 8-10, 2008. Proceedings
The 12 revised full papers and 15 revised poster papers presented together with 5 papers of a special session on user requirements and evaluation of multimodal meeting browsers/assistants were carefully reviewed and selected from 47 submissions. The papers cover a wide range of topics related to human-human communication modeling and processing, as well as to human-computer interaction, using several communication modalities. Special focus is given to the analysis of non-verbal communication cues and social signal processing, the analysis of communicative content, audio-visual scene analysis, speech processing, interactive systems and applications.
