44461941
Distributed sparse random projections for refinable approximation
42728551
Random distributed multiresolution representations with significance querying
1709003
On optimal permutation codes
Permutation codes are vector quantizers whose codewords are related by permutations and, in one variant, sign changes. Asymptotically, as the vector dimension grows, optimal Variant I permutation code design is identical to optimal entropy-constrained scalar quantizer (ECSQ) design. However, contradicting intuition and previously published assertions, there are finite block length permutation codes that perform better than the best ones with asymptotically large length; thus, there are Variant I permutation codes whose performances cannot be matched by any ECSQ. Along similar lines, a new asymptotic relation between Variant I and Variant II permutation codes is established but again demonstrated to not necessarily predict the performances of short codes. Simple expressions for permutation code performance are found for memoryless uniform and Laplacian sources. The uniform source yields the aforementioned counterexamples.
34993492
Microarray image compression: SLOCO and the effect of information loss
Microarray image technology is a powerful tool for monitoring the expression of thousands of genes simultaneously. Each microarray experiment produces immense amounts of image data, and efficient storage and transmission require compression that takes advantage of microarray image structure. In this paper we develop a compression scheme for microarray images which can be either lossless or lossy with successive refinements. Existing measures of distortion such as mean squared pixel-wise error and visual fidelity are not appropriate for microarray images. We introduce a new measure of distortion for lossy compression: the sensitivity of microarray information extraction to compression loss. Furthermore, our scheme has a coded data structure that allows fast decoding and reprocessing of image sub-blocks, and includes summary statistics and image segmentation information. The average lossless compression ratio is 1.83:1 for our cDNA test images and 2.43:1 for our inkjet test images, comparable or better than state-of-the-art lossless schemas, yet with additional structure and information. At an average lossy compression ratio of 8:1 for cDNA microarrays, we find that our scheme minimizes the effects of compression loss compared to other algorithms. We show that the variability in differential gene expression levels extracted from lossily vs. losslessly compressed microarray images is less than both the variability between different arrays and the variability between different extraction algorithms. In fact, lossy compression can improve the estimation of gene expression levels for cDNA images.
33099797
Compression of cDNA and inkjet microarray images
Microarray image technology is a powerful tool for monitoring the expression of thousands of genes simultaneously. Each microarray experiment produces immense amounts of image data, and efficient storage and transmission requires compression that utilizes the microarray image's structure and unique analysis goals. We have developed a progressive compression scheme for microarray images which can be either lossy or lossless. Our scheme has a coded data structure that allows fast decoding and reprocessing of image subsets, and includes summary statistics and image segmentation information. Since visual fidelity is not the end goal for microarray images, we introduce a new measure of distortion for lossy compression: the sensitivity of microarray information extraction to compression loss. We find that a lossy compression ratio of 8:1 for cDNA microarrays minimally affects downstream processing. The average lossless compression ratio is 1.83:1 for cDNA images and 2.43:1 for inkjet images, comparable to state-of-the-art lossless schemas, yet with added flexibility and information.
7654975
Using low-discrepancy sequences and the Crofton formula to compute surface areas of geometric models
The surface area of a geometric model, like its volume, is an important integral property that needs to be evaluated frequently and accurately in practice. In this paper, we present a new quasi-Monte Carlo method using low-discrepancy sequences for computing the surface area of a 3D object. We show that the new method is more efficient than a Monte Carlo method using pseudo-random numbers. This method is based on the Cauchy ‐ Crofton formula from integral geometry, and it computes the surface area of a 3D body B by counting the number of intersection points between the boundary surface of B and a set of straight lines in E 3 . Low discrepancy sequences are used to generate the set of lines in E 3 to reduce the estimation errors that would be caused by using statistically uniformly distributed lines. We study and compare two different methods for generating 3D random lines, and demonstrate their validity theoretically and experimentally. Experiments suggest that the new quasi-Monte Carlo method is also more efficient than the conventional approach based on surface tessellation. q 2002 Elsevier Science Ltd. All rights reserved.
8256320
Fast computation with neural oscillators
This paper studies new spike-based models for winner-take-all computation and coincidence detection. In both cases, fast convergence is achieved independent of initial conditions, and network complexity is linear in the number of inputs. Fully distributed versions can be modelled based on groups of interneurons connected through electrical synapses.
83220
Defending against wormhole attacks in mobile ad hoc networks
In ad hoc networks, malicious nodes can deploy wormhole attacks to fabricate a false scenario on the proximity relationship among mobile nodes. A classification of the attacks according to the format of the wormholes is proposed. This forms a basis to identify the detection capability of various approaches. An analysis shows that earlier approaches focus on the prevention of wormholes among neighbors that trust each other. As a more generic approach, we present an end-to-end scheme that can detect wormholes on a multi-hop route. Only the trust between the source and the destination is assumed. The mechanism uses geographic information to detect anomalies in neighbor relations and node movements. To reduce the computation and storage overhead, we present a scheme called cell-based open tunnel avoidance (COTA) to manage the information. COTA requires a constant space for every node on the path and the computation overhead increases linearly to the number of detection packets. We prove that the savings do not deteriorate the detection capability. Various schemes to control communication overhead are studied. The simulation and experiments on real devices show that the proposed mechanism can be combined with existent routing protocols to defend against wormhole attacks. Copyright © 2006 John Wiley & Sons, Ltd.
19868872
Management Assistance for Work First via a Dynamic Website
The purpose of the Work First project is to assist the state Dept. of Health and Human Services (DHHS) staff and each county Dept. of Social Services (DSS) across the state in the management of welfare reform initiatives. Work First is the cash assistance program in NC. One important objective is to encourage and support program managers to use data on outcomes to assess their performance. This is accomplished by providing them with easy to use and easy to understand comprehensive information on the experiences of families and individuals that have received assistance through their agency over time.
15806898
Market-driven bandwidth allocation in selfish overlay networks
Selfish overlay networks consist of autonomous nodes that develop their own strategies by optimizing towards their local objectives and self-interests, rather than following prescribed protocols. It is thus important to regulate the behavior of selfish nodes, so that system-wide properties are optimized. In this paper, we investigate the problem of bandwidth allocation in overlay networks, and propose to use a market-driven approach to regulate the behavior of selfish nodes that either provide or consume services. In such markets, consumers of services select the best service providers, taking into account both the performance and the price of the service. On the other hand, service providers are encouraged to strategically decide their respective prices in a pricing game, in order to maximize their economic revenues and minimize losses in the long run. In order to overcome the limitations of previous models towards similar objectives, we design a decentralized algorithm that uses reinforcement learning to help selfish nodes to incrementally adapt to the local market, and to make optimized strategic decisions based on past experiences. We have simulated our proposed algorithm in randomly generated overlay networks, and have shown that the behavior of selfish nodes converges to their optimal strategies, and resource allocations in the entire overlay are near-optimal, and efficiently adapts to the dynamics of overlay networks.