Many features that you see on the web require that the GD library be enabled on your PHP server. You may need the GD library if you are attempting to display CAPTCHA graphics somewhere on your site. In a default PHP installation, the GD library should already be enabled. The following How To explains how to determine if the GD library is enabled on your PHP server, and how you might be able to turn this on. What Do You Need to Start?
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Many simple programs that you have written up to now have undoubtedly consisted only of a main() function that performed all the necessary steps to solve that problem. Yet, you were always encouraged to break the problem down into one or more subprograms or functions. When you break a program down into a series of functions, you are doing what is called functional abstraction. This book begins with a formalization of this process of functional abstraction: its methods, techniques, benefits, and so on.
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Python was created by Guido van Rossum in 1990 to solve problems in the development of the Amoeba operating system. It is named after the British television show Monty Python’s Flying Circus. The use of Python has grown steadily over 1990’s to its current state where it is used in a variety of applications and environments.
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In this paper we describe a Python- and Tkinter-based visual-programming environment called ViPEr. This tool enables non-programmers to build computational and visualization networks interactively. Computational nodes can be placed onto a canvas and their input and output ports can be connected using the mouse. The connections between the nodes define a directed graph that will be used to propagate data and trigger the execution of nodes that have new input data. ViPEr is, in appearance, similar to programs such as AVS [Upson et al. 89] from Advanced Visual Simulations Inc, or OpenDX [DX 93] from IBM, but presents some fundamental differences which will be pointed out throughout this paper. Several examples of applications will be used to illustrate ViPEr’s design and current range of capabilities.
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Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.
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Web Services Description Language (WDSL), originally developed by IBM, Microsoft, and others, is an XML format for technical description of Web services. In this tutorial, Mike Olson and Uche Ogbuji introduce WSDL4Py, an open-source Python library for WSDL 1.1 hosted by IBM developerWork’s open-source zone. Usage of the library is explained, as well as discussion of its development.
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Control abstraction is the process by which programmers de ne new control constructs, specifying a statement ordering separately from an implementation of that ordering. We argue that control abstraction can and should play a central role in parallel programming. Control abstraction can be used to build new control constructs for the expression of parallelism. A control construct can have several implementations, representing the varying degrees of parallelism to be exploited on different architectures. Control abstraction also reduces the need for explicit synchronization, since it admits a precise specification of control ow. Using several examples, we illustrate these benefits of control abstraction. We also show that we can efficiently implement a parallel programming language based on control abstraction. We conclude that the enormous benefits and reasonable costs of control abstraction argue for its inclusion in explicitly parallel programming languages.
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Over the last several years, computing systems based on adaptive learning with fine-grained parallel architectures have moved from obscurity to front-page prominence. These systems derive some of their novel architecture from ideas gleaned from biology, hence the name “neural network”. Although many of the ideas behind this field are not new, improved computing hardware, better understanding of learning algorithms, and limitations of traditional approaches have combined to renew interest in neural nets.
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