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Without established design patterns to guide them, developers have had to build distributed systems from scratch, and most of these systems are very unique indeed. Today, the increasing use of containers has paved the way for core distributed system patterns and reusable containerized components. This practical guide presents a collection of repeatable, generic patterns to help make the development of reliable distributed systems far more approachable and efficient. Author Brendan Burns—Director of Engineering at Microsoft Azure—demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system. Understand how patterns and reusable components enable the rapid development of reliable distributed systems Use the side-car, adapter, and ambassador patterns to split your application into a group of containers on a single machine Explore loosely coupled multi-node distributed patterns for replication, scaling, and communication between the components Learn distributed system patterns for large-scale batch data processing covering work-queues, event-based processing, and coordinated workflows
A fast and easy way to write winning white papers! Whether you’re a marketing manager seeking to use white papers to promote your business, or a copywriter keen to break into this well-paying field, White Papers For Dummies gives you a wealth of practical, hands-on advice from one of the world’s leading experts in the field. The fact-based documents known as white papers have been called the “king of content.” No other B2B marketing piece can do more to generate leads, nurture prospects, and build mindshare. Where white papers were once used only by technology firms, they are becoming “must-have” items in the marketing toolkit for almost any B2B firm. Practically every startup must produce a white paper as part of its business planning. But writing effective white papers is a big challenge. Now you can benefit from the experience of a white paper specialist who’s done more than 200 projects for clients from Silicon Valley to Finland, from mighty Google to tiny startups. Author Gordon Graham—also known as That White Paper Guy—provides dozens of tips and tricks to help your project come together faster and easier. White Papers For Dummies will help you to: Quickly determine if your B2B firm could benefit from a white paper Master the three phases of every white paper project: planning, production, and promotion Understand when and how to use the three main types of white paper Decide which elements to include and which to leave out Learn the best practices of seasoned white paper researchers and writers Choose from 40 different promotional tactics to get the word out Avoid common mistakes that many beginners make
Includes its Reports, which are also issued separately.
This publication provides guidance and recommendations on arrangements to be made at the preparedness stage, as part of overall emergency preparedness, for the termination of a nuclear or radiological emergency and the subsequent transition from the emergency exposure situation to either a planned exposure situation or an existing exposure situation. It elaborates the prerequisites that need to be fulfilled so that responsible authorities can declare the nuclear or radiological emergency ended and it gives detailed guidance on adapting and lifting protective actions. This publication, jointly sponsored by 10 international organizations (FAO, IAEA, ICAO, ILO, IMO, INTERPOL, OECD/NEA, UN OCHA, WHO and WMO) is intended to assist Member States in the application of IAEA Safety Standards Series Nos GSR Part 3 and GSR Part 7.
It is one thing to write a good scientific paper; it is quite another thing to get it published. Don Harris draws upon nearly a quarter of a century of experience as an author and reviewer of research papers, and ultimately as a journal editor. By his own admission, it contains all the things he wished that his mentors had told him 25 years ago, but did not. The material in the book is drawn from many years of finding all these things out for himself.
The motivation for the research that is described in these volumes is the wish to explain things in terms of their underlying causes, rather than merely being satisfied with phenomenological descriptions. When this reductionist approach is applied to information processing it allows the internal structure of information to be analysed, so information processing algorithms can then be derived from first principles. One of the simplest examples of this approach is the diagonalisation of a data covariance matrix – there are many variants of this basic approach, such as singular value decomposition – in which the assumed independent components of high-dimensional data are identified and extracted. The main limitation of this type of information analysis approach is that it is based on linear algebra applied globally to the data space, so it is unable to preserve information about any local data structure in the data space. For instance, if the data lives on a low-dimensional curved manifold embedded in the data space, then only the global properties of this manifold would be preserved by global linear algebra methods. In practice, data whose high-dimensional structure is non-trivial typically lives on a noisy version of a curved manifold, so techniques for analysing such data must automatically handle this type of structure. For instance, a blurred image of a point source is described by its underlying degrees of freedom – i.e. the position of the source – and as the source moves about it generates a curved manifold that lives in the high-dimensional space of pixel values of the sampled image. The basic problem is then to deduce the internal properties of this manifold by analysing examples of such images. A more challenging problem would be to extend this analysis to images that contain several overlapping blurred images of point sources, and so on. There is no limit to the complexity of the types of high-dimensional data that one might want to analyse. These methods then need to be automated so that they do not rely on human intervention, which would then allow them to be inserted as “components” into information processing networks. The purpose of the research that is described in these volumes is to develop principled information processing methods that can be used for such analysis. Self-organising information processing networks arise naturally in this context, in which ways of cutting up the original manifold into simpler pieces emerge automatically.