ppxscal/embedding_network huggingface dataset:
Solving the Classic Radiosity Equation Using Multigrid Techniques
Authors: Robert R. Lewis
Venue:  (1992)

We investigate the application of multigrid techniques to the solution of the ``classic'''' radiosity equation. After overviews of the global illumination problem and of radiosity, we describe the latter''s solution via multigrid methods. An implementation of the multigrid algorithm presented here is able to solve the classic radiosity equation in about 50% of the time required by the more commonly-used Gauss-Seidel approach. Although few researchers currently use classic radiosity, we discuss possibilities for the adaption of multigrid methods to more recent radiosity solution techniques. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Object Oriented Metrics: Generation and Application
Authors: Mark Lattanzi, Sallie M. Henry
Venue:  (1994)

Object oriented software has been cited by many [Cox86, Meye87, Coad90, Booc91] as a cure to some of the common problems occurring in software development. In particular, the object oriented paradigm, through inheritance, polymorphism, and encapsulation, allows for more reusable software components, easier software maintenance, and reduced software complexity. However, in order to make these claims, researchers will need to quantitatively measure various attributes of software (e.g., complexity, reusability, and maintainability). In order to efficiently and accurately use software metrics, it is necessary to automate the metric collection process. The metrics group at Virginia Tech has developed a tool (the Metrics Generator) that, through an interactive interface, allows software developers to generate various software metrics from Ada, C, Classic Ada, or C++ source. The most recent use of the tool was a study by Li and Henry [LiWe93] into the maintainability of object oriented source code. The next use of the tool involves the object oriented paradigm and software reusability.
Agent Usage Patterns: Bridging the Gap Between Agent-Based Applications and Middleware
Authors: Eugene Hung, Joseph Pasquale
Venue:  (1999)

The concept of agents -- programs that are capable of transporting themselves across a [heterogeneous] network to execute and return results -- is a fascinating if troubled area of research. While theoretical advantages of agents have been well-established, few agent-based applications have been commercially successful. We argue that the lack of applications stems from a lack of understanding essential agent usage patterns. In this paper, we identify a set of fundamental patterns that support the design of agent-based applications that scale performance, reliability, and security. To evaluate their performance, some of these patterns were implemented in Java to demonstrate customizable and scalable performance.
Average-Case Bounds on the Complexity of Path-Search
Authors: Nicholas Pippenger
Venue:  (1997)

A channel graph is the union of all paths between a given input and a given output in an interconnection network. At any moment in time, each vertex in such a graph is either idle or busy. The search problem we consider is to find a path (from the given input to the given output) consisting entirely of idle vertices, or to find a cut (separating the given input from the given output) consisting entirely of busy vertices. We shall also allow the search to fail to find either a path or a cut with some probability bounded by a parameter called the failure probability. This is to be accomplished by sequentially probing the idle-or-busy status of vertices, where the vertex chosen for each probe may depend on the outcome of previous probes. Thus a search algorithm may be modelled as a decision tree. For average-case analysis, we assume that each vertex is independently idle with some fixed probability, called the vacancy probability (and therefore busy with the complementary probability). For one commonly studied type channel graph, the parallel graph, we show that the expected number of probes is at most proportional to the length of a path, irrespective of the vacancy probability, and even if the allowed failure probability is zero. Another type of channel graph we study is the spider-web graph, which is superior to the parallel graph as regard linking probability (the probability that an idle path, rather than a busy cut, exists). For this graph we give an algorithm for which, as the vacancy probability is varied while the positive failure probability is held fixed, the expected number of probes reaches its maximum near the critical vacancy probability (where the linking probability make a rapid transition from a very small value to a substantial value). This maximum expected number of probes is about the cube-root of the diversity (the number of paths between the input and output).
Technical paper on measuring local change for image segmentation. Number 1.
Authors: Paul S Williams, Mike Alder
Venue:  (1996)

This technical paper examines the possible measures for identifying regions of an image which are highly homogeneous. In previous work the Euclidean distance between local feature vectors with scaled components is used to obtain this measurement and in turn to identify the initial segments for a subsequent segmentation algorithm. Although effective, the ability to reliably locate less homogeneous regions, particularly textured ones, requires improvement.
THE PERCEPTRON ALGORITHM VS WINNOW: LINEAR VS LOGARITHMIC MISTAKE BOUNDS WHEN FEW INPUT VARIABLES ARE RELEVANT
Authors: Jyrki Kivinen, Manfred Warmuth
Venue:  (1995)

We give an adversary strategy that forces the Perceptron algorithm to make (N-k+1)/2 mistakes when learning k-literal disjunctions over N variables. Experimentally we see that even for simple random data, the number of mistakes made by the Perceptron algorithm grows almost linearly with N, even if the number k of relevant variable remains a small constant. In contrast, Littlestone''s algorithm Winnow makes at most O(k log N) mistakes for the same problem. Both algorithms use thresholded linear functions as their hypotheses. However, Winnow does multiplicative updates to its weight vector instead of the additive updates of the Perceptron algorithm.
Absolute Cost-Benefit Analysis for Predictive File Prefetching
Authors: Timothy Highley, Paul F. Reynolds, Vivekanand Vellanki
Venue:  (2002)

Cost-benefit analysis attempts to balance the benefits of file prefetching against the costs of giving up a buffer from the cache. This is a comprehensive approach to prefetching. It simultaneously answers the questions of what and when/whether to prefetch, and also addresses the interaction between prefetching and caching. Original research on cost-benefit analysis relied on hints about prefetching that were supplied by the program, though other research has considered the more general case of programs without hints. In this paper, we revisit previous research on cost-benefit analysis without hints, present new estimators for the costs and benefits of prefetching under a system model without hints, and generalize the system model to consider probability trees that may have the same block lie along multiple paths of the tree. We present a distinction between absolute and marginal cost-benefit analysis, two different approaches to file prefetching that previous researchers have used. The difference between the two is a subtlety that has not been previously noted or investigated. Our new estimators are based on absolute cost-benefit analysis, with modifications to address observed weaknesses in the absolute approach.
Assessing Aspect-Oriented Programming and Design: Preliminary Results
Authors: Robert J. Walker, Elisa L. A. Baniassad, Gail C. Murphy
Venue:  (1998)

Aspect-oriented programming is a new software design and implementation technique proposed by researchers at Xerox PARC. This project is assessing the claims of aspect-oriented programming to improve the software development cycle for particular kinds of applications. The project is divided into three experiments, the first of which has been completed. These experiments have been designed to investigate, separately, such characteristics of aspect-oriented development as the creation of new aspect-oriented programs and ease of debugging aspect-oriented programs.
The Expected Capacity of Concentrators
Authors: Nicholas Pippenger
Venue:  (1989)

We determine the ``expected capacity'''' of a class of sparse concentrators called ``modular concentrators''''. In these concentrators, each input is connected to exactly two outputs, each output is connected to exactly three inputs, and the ``girth'''' (the length of the shortest cycle in the connexion graph) is large. We consider two definitions of expected capacity. For the first (which is due to Masson and Morris), we assume that a batch of customers arrives at a random set of inputs and that a maximum matching of these customers to servers at the outputs is found. The number of unsatisfied requests is negligible if customers arrive at fewer than one-half of the inputs, and it grows quite gracefully even beyond this threshold. We also consider the situation in which customers arrive sequentially, and the decision as to how to serve each is made randomly, without knowledge of future arrivals. In this case, the number of unsatisfied requests is larger, but still quite modest.
An Inductive Solution to a Domino Tiling Problem
Venue:  (2001)

An inductive method of covering a mutilated chessboard (with two cells of opposite colors removed) with 2 X 1 dominoes is shown. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Photometric Method for Determining Shape from Shading
Authors: R. J. Woodham
Venue:  (1984)

A smooth opaque object produces an image in which brightness varies spatially even if the object is illuminated evenly and is covered by a surface material with uniform optical properties. Photometric methods relate image irradiance to object shape and surface material using physical models of the way surfaces reflect light. A reflectance map allows image irradiance to be written as a function of surface orientation, for a given surface material and light source distribution. Shape from shading algorithms use a reflectance map to analyze what is seen. The development of photometric methods for determining shape from shading is discussed, beginning with examples from lunar astronomy. The results presented delineate shape information that can be determined from geometric measurements at object boundaries from shape information that can be determined from intensity measurements over sections of smooth surface. Recent work of Ikeuchi and Horn is presented which relaxes the requirement that the image irradiance equation be satisfied exactly. Instead, the image irradiance equation specifies one constraint that is combined with another constraint derived from general surface smoothness criteria. Shape from shading is expressed as a constrained minimization problem. Another method uses multiple images in a technique called photometric stereo. In photometric stereo, the illumination is varied between successive images while the viewing direction remains constant. Multiple images obtained in this way provide enough information to determine surface orientation at each image point, without smoothness assumptions.
Machine learning by schedule decomposition: prospects for an integration of AI and OR techniques for job shop scheduling
Authors: Ulrich Dorndorf, Erwin Pesch, Toàn Phan Huy
Venue: Advances in evolutionary computing: theory and applications (2003)

A survey of recent solution approaches as well as a class of approximation algorithms is provided for solving the minimum makespan problem of job shop scheduling. We briefly review most recent exact approaches as well as neighbourhood search methods and evolution based heuristics. Genetic algorithm-based meta-strategies serve to guide an optimal design of scheduling decision sequences. Simple sequences of dispatching rules for job assignment as well as learning of promising sequences of one machine and multiple job decompositions are considered. Finally, a number of ways for introducing problem-specific knowledge through constraint consistency tests for propagation will be presented. These ideas are applied in a subproblem based constraint propagation approach that learns to find the best bounds for fixing arc directions. Calculation of the initial lower bounds for the subproblems' "best bounds" uses a branch and bound search. Whenever some problem-specific knowledge through constraint propagation leads to a partial solution of the job shop problem, a complete solution can be obtained with either a branch and bound procedure or some heuristic neighbourhood or priority rule based search. Computational experiments show that the approach can find shorter makespans than other local search approaches. The chapter provides an initial framework for a unified solution approach to many combinatorial optimization problems incorporating techniques from artificial intelligence, e.g. evolutionary algorithms and constraint propagation, and operations research, e.g. metaheuristics and branch and bound.
The Hardness of Minimum Capacity Network Design
Authors: H. Regev
Venue:  (1995)

We define the Minimum Capacity Network Design problem (MCND) and present a taxonomy of its variants. We consider all variants s.t.&#x00A0;their input does not admit a general compressed form, and prove that each such variant, provided that it is not ``trivial'''', is NP-complete. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Algorithmic Aspects of Constrained Unit Disk Graphs
Authors: Heinz Breu
Venue:  (1996)

Computational problems on graphs often arise in two- or three- dimensional geometric contexts. Such problems include assigning channels to radio transmitters (graph colouring), physically routing traces on a printed circuit board (graph drawing), and modelling molecules. It is reasonable to expect that natural graph problems have more efficient solutions when restricted to such geometric graphs. Unfortunately, many familiar NP-complete problems remain NP-complete on geometric graphs. Indifference graphs arise in a one-dimensional geometric context; they are the intersection graphs of unit intervals on the line. Many NP-complete problems on arbitrary graphs do have efficient solutions on indifference graphs. Yet these same problems remain NP-complete for the intersection graphs of unit disks in the plane (unit disk graphs), a natural two-dimensional generalization of indifference graphs. What accounts for this situation, and how can algorithms be designed to deal with it? To study these issues, this thesis identifies a range of subclasses of unit disk graphs in which the second spatial dimension is gradually introduced. More specifically, tau-strip graphs ``interpolate'''' between unit disk graphs and indifference graphs; they are the intersection graphs of unit-diameter disks whose centres are constrained to lie in a strip of thickness tau. This thesis studies algorithmic and structural aspects of varying the value tau for tau-strip graphs. The thesis takes significant steps towards characterizing, recognizing, and laying out strip graphs. We will also see how to develop algorithms for several problems on strip graphs, and how to exploit their geometric representation. In particular, we will see that problems become especially tractable when the strips are ``thin'''' (tau is small) or ``discrete'''' (the number of possible y-coordinates for the disks is small). Note again that indifference graphs are the thinnest (tau=0) and most discrete (one y-coordinate) of the nontrivial tau-strip graphs. The immediate results of this research concern algorithms for a specific class of graphs. The real contribution of this research is the elucidation of when and where geometry can be exploited in the development of efficient graph theoretic algorithms.
A Layered Approach to Parallel Software Performance Prediction: A Case Study
Authors: E. Papaefstathiou, Darren J. Kerbyson, Graham R. Nudd
Venue:  (1994)

An approach to the characterisation of parallel systems using a structured layered methodology is described here. The aim of this is to produce accurate performance predictions which maybe used to influence the choice of machines and investigate implementation trade-offs. The methodology described enables the separate characterisation of both application, and parallel machine to be developed independently but integrated through an intermediary layer encompassing mapping and parallelisation techniques. The layered approach enables characterisations which are modular, re-usable, and can be evaluated using analytical techniques. The approach is based upon methods introduced in Software Performance Engineering (SPE) and structural model decomposition but due to its modular nature, takes less time for development. A case study in image synthesis is considered in which factors from both the application and parallel system are investigated, including the accuracy of predictions, the parallelisation strategy, and scaling behaviour.
Development of a Modern OPAC: From REVTOLC to MARIAN
Authors: Edward A. Fox, Robert K. France, Eskinder Sahle, Amjad M. Daoud, Ben E. Cline
Venue:  (1993)

In the Retrieval Experiment -- Virginia Tech OnLine Catalog (REVTOLC) study we carried out a large pilot test in 1987 and a larger, controlled investigation in 1990, with 216 users and roughly 500,000 MARC records. Results indicated that a forms-based interface coupled with vector and relevance feedback retrieval methods would be well received. Recent efforts developing the Multiple Access and Retrieval of Information with ANnotations (MARIAN) system have involved use of a specially developed object-oriented DBMS, construction of a client running under NeXTSTEP, programming of a distributed server with a thread assigned to each user session to increase concurrency on a small network of NeXTs, refinement of algorithms to use objects and stopping rules for greater efficiency, usability testing and iterative interface refinement.
AspectBrowser: Tool Support for Managing Dispersed Aspects
Authors: W. G. Griswold, Y. Kato, J. J. Yuan
Venue:  (1999)

Discovery:
Statistical Multiplexing Gain of Link Scheduling Algorithms in QoS Networks
Authors: Boorstyn Robert, Burchard Almut, Liebeherr Jorg, Oottamakorn Chaiwat
Venue:  (1999)

A statistical network service which allows a certain fraction of traffic to not meet its QoS guarantees can extract additional capacity from a network by exploiting statistical properties of traffic. Here we consider a statistical service which assumes statistical independence of flows, but does not make any assumptions on the statistics of traffic sources, other than that they are regulated, e.g., by a leaky bucket. Under these conditions, we present functions, so-called local effective envelopes and global effective envelopes, which are, with high certainty, upper bounds of multiplexed traffic. We show that these envelopes can be used to obtain bounds on the amount of traffic on a link that can be provisioned with statistical QoS. A key advantage of our bounds is that they can be applied with a variety of scheduling algorithms. In fact, we show that one can reuse existing admission control functions that are available for scheduling algorithms with a deterministic service. We present numerical examples which compare the number of flows with statistical QoS guarantees that can be admitted with our empirical envelope approach to those achieved with existing methods.
Third Generation Cellular Automaton for Modeling Excitable Media
Authors: Joerg R Weimar, John J. Tyson, Layne T. Watson
Venue:  (1991)

This paper introduces a new cellular automaton model of excitable media with improved treatments of (1) diffusion and wave propagation, and (2) slow dynamics of the recovery variable. The automaton is both computationally efficient and faithful to the underlying partial differential equations.
Bar-Representable Visibility Graphs and a Related Network Flow Problem
Authors: Stephen K Wismath
Venue:  (1989)

A bar layout is a set of vertically oriented non-intersecting line segments in the plane called bars. The visibility graph associated with a layout is defined as the graph whose vertices correspond to the bars and whose edges represent the horizontal visibilities between pairs of bars. \n This dissertation is concerned with the characterization of bar-representable graphs: those graphs which are the visibility graphs of some bar layout. A polynomial time algorithm for determining if a given graph is bar-representable, and the subsequent construction of an associated layout are provided. Weighted and directed versions of the problem are also formulated and solved; in particular, polynomial time algorithms for the layout of such graphs are developed. \n The Planar Full Flow problem is to determine a plane embedding and an (acyclic) orientation of an undirected planar network that admits a feasible flow, that uses all arcs (except those incident upon the source or sink) to full capacity and maintains planarity. The connection of this flow problem to bar-representable graphs is exploited to solve the weighted case of the latter. As evidence that both the acyclicity and planarity constraints are necessary to obtain a polynomial algorithm for this problem, two natural variants of the Full Flow problem are shown to be strongly NP-Complete.
Static Data Association with a Terrain-Based Prior Density
Authors: Allen L. Barker, Donald E. Brown, Worthy N. Martin
Venue:  (1994)

We consider the problem of estimating the states of a static set of targets given a collection of densities, each representing the state of a single target. We assume there is no a-priori knowledge of which of the given densities represent common targets, but that a prior density for the target locations is available. For a two-dimensional location estimation problem we construct a prior density model based on known features of the terrain. For a simple Gaussian association-estimation algorithm using a prior density we consider when the prior is most effective in data association, or correlation, and when it is most effective in state estimation. We present some simulation results and discuss some issues involved in measuring algorithm performance and in the algorithm implementation. We briefly discuss extensions to higher dimensional state spaces and non-static models.
CALCULATION OF THE LEARNING CURVE OF BAYES OPTIMAL CLASSIFICATION ALGORITHM FOR LEARNING A PERCEPTRON WITH NOISE
Authors: David Haussler, Manfred Opper
Venue:  (1991)

The learning curve of Bayes optimal classification algorithm when learning a perceptron from noisy random training examples is calculated exactly in the limit of large training sample size and large instance space dimension using methods of statistical mechanics. It is shown that under certain assumptions, in this "thermodynamic" limit, the probability of misclassification of Bayes optimal algorithm is less than that of a canonical stochastic learning algorithm, by a factor approaching the square root of 2 as the ratio of number of training examples to instance space dimension grows. Exact asymptotic learning curves for both algorithms are derived for particular distributions. In addition, it is shown that the learning performance of Bayes optimal algorithm can be approximated by certain learning algorithms that use a neural net with a layer of hidden units to learn a perceptron. (supersedes 91-03) (NOTE: 4 pages of diagrams are not in the postscript file. Request trs@cse.ucsc.edu to have them sent to you by regular mail.)