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On the Future of Problem Solving Environments

, 2000
"... In this paper we review the current state of the problem solving environment (PSE) field and make projections for the future. First we describe the computing context, the definition of a PSE and the goals of a PSE. The stateoftheart is summarized along with sources (books, bibliographics, web sit ..."
Abstract

Cited by 19 (2 self)
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In this paper we review the current state of the problem solving environment (PSE) field and make projections for the future. First we describe the computing context, the definition of a PSE and the goals of a PSE. The stateoftheart is summarized along with sources (books, bibliographics, web sites) of more detailed information. The principal components and paradigms for building PSEs are presented. The discussion of the future is given in three parts: future trends, scenarios for 2010/2025, and research
Fast FloatingPoint Processing in Common Lisp
 ACM Trans. on Math. Software
, 1995
"... this paper we explore an approach which enables all of the problems listed above to be solved at a single stroke: use Lisp as the source language for the numeric and graphical code! This is not a new idea  it was tried at MIT and UCB in the 1970's. While these experiments were modestly succe ..."
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Cited by 5 (1 self)
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this paper we explore an approach which enables all of the problems listed above to be solved at a single stroke: use Lisp as the source language for the numeric and graphical code! This is not a new idea  it was tried at MIT and UCB in the 1970's. While these experiments were modestly successful, the particular systems are obsolete. Fortunately, some of those ideas used in Maclisp [37], NIL [38] and Franz Lisp [20] were incorporated in the subsequent standardization of Common Lisp (CL) [35]. In this new setting it is appropriate to reexamine the theoretical and practical implications of writing numeric code in Lisp. The popular conceptions of Lisp's inefficiency for numerics have been based on rumor, supposition, and experience with early and (in fact) inefficient implementations. It is certainly possible to continue to write inefficient programs: As one example of the results of deemphasizing numerics in the design, consider the situation of the basic arithmetic operators. The definitions of these functions require that they are generic, (e.g. "+" must be able to add any combination of several precisions of floats, arbitraryprecision integers, rational numbers, and complexes), The very simple way of implementing this arithmetic  by subroutine calls  is also very inefficient. Even with appropriate declarations to enable more specific treatment of numeric types, compilers are free to ignore declarations and such implementations naturally do not accommodate the needs of intensive numbercrunching. (See the appendix for further discussion of declarations). Be this as it may, the situation with respect to Lisp has changed for the better in recent years. With the advent of ANSI standard Common Lisp, several active vendors of implementations and one active universi...
University ofWaikato
"... Lisp, one of the oldest higherlevel programming languages [29] [21] has rarely been used for fast numerical ( oatingpoint) computation. We explore the bene ts of Common Lisp [35], an emerging new language standard with some excellent implementations, for numerical computation. We compare it to For ..."
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Lisp, one of the oldest higherlevel programming languages [29] [21] has rarely been used for fast numerical ( oatingpoint) computation. We explore the bene ts of Common Lisp [35], an emerging new language standard with some excellent implementations, for numerical computation. We compare it to Fortran in terms of the speed of e ciency of generated code, as well as the structure and convenience of the language. There are a surprising number of advantages to Lisp, especially in cases where a mixture of symbolic and numeric processing is needed. Categories and Subject Descriptors: G.4 [Mathematics of Computing]: MathematicalSoftware{ e ciency, portability � D.3.4 [Programming Languages]: Processors{compilers, interpreters,
Robust SymbolicNumericGraphic Software
"... Issues in the field of intelligent scientific computing are addressed from the point of view of the development of a problem solving environment. These issues include how symbolic, numeric and graphic code should be linked, how a system might be constructed so that it is robust and survives a changi ..."
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Issues in the field of intelligent scientific computing are addressed from the point of view of the development of a problem solving environment. These issues include how symbolic, numeric and graphic code should be linked, how a system might be constructed so that it is robust and survives a changing environment, and whether language translators have a role in the retention of the algorithmic content of existing code. The need for a substantial numerical library in Lisp is underlined. Experience has led us to the conclusion that more homogeneity rather than less makes for stronger software.
CRICKET BOWLING: A TWOSEGMENT LAGRANGIAN MODEL
"... In this study, a Lagrangian forward solution of the bowling arm in cricket is made using a twosegment rigid body model, coupled with projectile equations for the free flight of the ball. For given initial arm positions and constant joint torques, the equations are solved numerically to determine th ..."
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In this study, a Lagrangian forward solution of the bowling arm in cricket is made using a twosegment rigid body model, coupled with projectile equations for the free flight of the ball. For given initial arm positions and constant joint torques, the equations are solved numerically to determine the ball speed and arm angle at release so that the ball can land on a predetermined position on the pitch. The model was driven with kinematic data from video obtained from an elite bowler. The model can be analysed in order to study the biomechanics of the bowling arm as well as to quantify the effects of changing input parameters on the trajectory and speed of the ball. KEY WORDS: cricket, bowling biomechanics, Lagrangian model INTRODUCTION: The function of bowling in cricket is to deliver the ball to the batter. In the majority of deliveries, the ball bounces once off the ground. The laws of cricket specify that any straightening of the bowling arm must occur well before the time of ball release. The basic bowling action can be described as a series of sequential steps (Figure 1).