17897427
Experimental Studies on a Compact Storage Scheme for Wavelet-Based Multiresolution Subregion Retrieval
Wavelet transforms, when combined with quantization and a suitable encoding, can be used to compress images eeectively. In order to use them for image library systems, a compact storage scheme for quantized coeecient wavelet data must be developed with a support for fast subregion retrieval. We have designed such a scheme and in this paper we provide experimental studies to demonstrate that it achieves good image compression ratios, while providing a natural indexing mechanism that facilitates fast retrieval of portions of the image at various resolutions.
36878064
A Compact Storage Scheme for Fast Wavelet-Based Subregion Retrieval
In an image browsing environment there is need for progressively viewing image subregions at various resolutions. We describe a storage scheme that accomplishes good image compression, while supporting fast image subregion retrieval. We evaluate analytically and experimentally the compression performance of our algorithm. We also provide results on the speed of the algorithm to demonstrate its effectiveness, and present an extension to a client/server environment.
14860992
Scalable Time-Parallelization of Molecular Dynamics Simulations in Nano Mechanics
Molecular dynamics (MD) is an important atomistic simulation technique, with widespread use in computational chemistry, biology, and materials. An important limitation of MD is that the time step size is small, requiring a large number of iterations to simulate realistic time spans. Conventional parallelization is not very effective for this. We recently introduced a new approach to parallelization, where data from related prior simulations are used to parallelize a new computation along the time domain. In our prior work, the size of the physical system in the current simulation needed to be identical to that of the prior simulations. The significance of this paper lies in demonstrating a strategy that enables this approach to be used even when the physical systems differ in size. Furthermore, this method scaled up to almost 1000 processors with close to ideal speedup in one case, where conventional methods scale to only 2-3 processors
8870978
Monte Carlo Techniques for Estimating the Fiedler Vector in Graph Applications
Determiningthe Fiedler vector of the Laplacian or adjacency matrices of graphs is the most computationally intensive component of several applications, such as graph partitioning, graph coloring, envelope reduction, and seriation. Often an approximation of the Fiedler vector is sufficient.We discuss issues involved in the use of Monte Carlo techniques for this purpose.
16971481
Image compression for fast wavelet-based subregion retrieval
In an image browsing environment there is need for progressively viewing image subregions at various resolutions. We describe a storage scheme that accomplishes good image compression, while supporting fast image subregion retrieval. We evaluate analytically and experimentally the compression performance of our algorithm. We also provide results on the speed of the algorithm to demonstrate its effectiveness, and present an extension to a client/server environment.
17105049
Data-Driven Time-Parallelization in the AFM Simulation of Proteins
Molecular dynamics is a popular technique to simulate the behavior of physical systems, with resolution at the atomic scale. One of its limitations is that an enormous computational effort is required to simulate to realistic time spans. Conventional parallelization strategies have limited effectiveness in dealing with this difficulty. We recently introduced a more scalable approach to parallelization, where data from prior, related, simulations are used to parallelize a simulation in the time domain. We demonstrated its effectiveness in nano-mechanics simulations. In this paper, we develop our approach so that it can be used in a soft-matter application involving the atomic force microscopy simulation of proteins. We obtain an order of magnitude improvement in performance when we combine time parallelization with conventional parallelization. The significance of this work lies in demonstrating the promise of data-driven time parallelization in soft-matter applications, which are more challenging than the hard-matter applications considered earlier.
16314241
End-to-end packet-scheduling in wireless ad-hoc networks
Packet-scheduling is a particular challenge in wireless networks due to interference from nearby transmissions. A distance-2 interference model serves as a useful abstraction here, and we study packet routing and scheduling under this model. The main focus of our work is the development of fully-distributed (decentralized) protocols. We present polylogarithmic/constant factor approximation algorithms for various families of disk graphs (which capture the geometric nature of wireless-signal propagation), as well as near-optimal approximation algorithms for general graphs. The packet-scheduling work by L eighton, Maggs and Rao (Combinatorica, 1994) and a basic distributed coloring procedure, originally due to Luby (J. Computer and System Sciences, 1993), underlie many of our algorithms. Experimental work of Finocchi, Panconesi, and Silvestri (SODA 2002) showed that a natural modification of Luby's algorithm leads to improved performance, and a rigorous explanation of this was left as an open question; we prove that the modified algorithm is provably better in the worst-case. Finally, using simulations, we study the impact of the routing strategy and the choice of parameters on the performance of our distributed algorithm for unit disk graphs.
6768945
Approximating hyper-rectangles: learning and pseudo-random sets
The PAC learning of rectangles has been studied because they have been found experimentally to yield excellent hypotheses for severaf applied learning problems. Also, pseudorandom sets for rectangles have been actively studied recently because (i) they are a subpmblem common to the derandomization of depth-2 (DIW) circuits and derandotnizing Randomized Logspace, and (ii) they approximate the distribution of n independent multivalued random variables. We present improved upper bounds for a class of such problems of “approximating” highdlmensional rectangles that arise in PAC learning and pseudorandomness.
13312992
Improved parallel approximation of a class of integer programming problems
We present a method to derandomizeRNC algorithms, converting them toNC algorithms. Using it, we show how to approximate a class of NP-hard integer programming problems inNC, to within factors better than the current-bestNC algorithms (of Berger and Rompel and Motwaniet al.); in some cases, the approximation factors are as good as the best-known sequential algorithms, due to Raghavan. This class includes problems such as global wire-routing in VLSI gate arrays and a generalization of telephone network planning in SONET rings. Also for a subfamily of the “packing” integer programs, we provide the firstNC approximation algorithms; this includes problems such as maximum matchings in hypergraphs, and generalizations. The key to the utility of our method is that it involves sums ofsuperpolynomially many terms, which can however be computed inNC; this superpolynomiality is the bottleneck for some earlier approaches, due to Berger and Rompel and Motwaniet al.
150268
Integrating a periodic and recurrent tasks on fair-scheduled multiprocessors
We propose two server implementations for multiplexing aperiodic and recurrent real-time tasks in fair-scheduled multiprocessor systems. We also provide admission-control tests for scheduling hard aperiodic tasks. Further, we consider several complexities, most of which arise because of the parallelism available in multiprocessor systems, and present techniques to handle them. Finally, we present experimental results that demonstrate the effectiveness of our implementations.
2075186
An assessment of submissions made to the Predictive Toxicology Evaluation Challenge
Constructing "good" models for chemical carcinogenesis was identified in IJCAI-97 as providing a substantial challenge to "knowledge discovery" programs. Attention was drawn to a comparative exercise which called for predictions on the outcome of 30 rodent carcinogenicity bioassays. This-the Predictive Toxicology Evaluation (or PTE) Challenge - was seen to provide AI programs with an opportunity to participate in an enterprise of scientific merit, and a yardstick for comparison against strong competition. Here we provide an assessment of the machine learning (ML) submissions made. Models submitted are assessed on: (1) their accuracy, in comparison to models developed with expert collaboration; and (2) their explanatory value for toxicology. The principal findings were: (a) using structural information available from a standard modelling package, layman-devised features, and outcomes of established biological tests, results from MLderived models were at least as good as those with expert-derived techniques. This was surprising; (b) the combined use of structural and biological features by ML-derived models was unusual, and suggested new avenues for toxicology modelling. This was also unexpected; and (c) significant effort was required to interpret the output of even the most "symbolic" of ML-derived models. Much of this could have been alleviated with measures for converting the results into a more "toxicology-friendly" form. As it stands, their absence is sufficient to prevent a whole-hearted acceptance of these promising methods by toxicologists. This suggests that ML techniques have been able to respond -not fully, but nevertheless substantially -to the PTE Challenge.