Adaptive wavelet thresholding for image denoising and compression
The first part of this paper proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution (GGD) widely used in image processing applications. The proposed threshold is simple and closed-form, and it is adaptive to each subband because it depends on data-driven estimates of the parameters. Experimental results show that the proposed method, called BayesShrink, is typically within 5% of the MSE of the best soft-thresholding benchmark with the image assumed known. It also outperforms SureShrink (Donoho and Johnstone 1994, 1995; Donoho 1995) most of the time. The second part of the paper attempts to further validate claims that lossy compression can be used for denoising. The BayesShrink threshold can aid in the parameter selection of a coder designed with the intention of denoising, and thus achieving simultaneous denoising and compression. Specifically, the zero-zone in the quantization step of compression is analogous to the threshold value in the thresholding function. The remaining coder design parameters are chosen based on a criterion derived from Rissanen's minimum description length (MDL) principle. Experiments show that this compression method does indeed remove noise significantly, especially for large noise power. However, it introduces quantization noise and should be used only if bitrate were an additional concern to denoising.

title: Wavelet thresholding via MDL for natural images
abstract: We study the application of Rissanen's (1989) principle of minimum description length (MDL) to the problem of wavelet denoising and compression for natural images. After making a connection between thresholding and model selection, we derive an MDL criterion based on a Laplacian model for noiseless wavelet coefficients. We find that this approach leads to an adaptive thresholding rule. While achieving mean-squared-error performance comparable with other popular thresholding schemes, the MDL procedure tends to keep far fewer coefficients. From this property, we demonstrate that our method is an excellent tool for simultaneous denoising and compression. We make this claim precise by analyzing MDL thresholding in two optimality frameworks; one in which we measure rate and distortion based on quantized coefficients and one in which we do not quantize, but instead record rate simply as the number of nonzero coefficients.

title: Image subband coding using context-based classification and adaptive quantization
abstract: Adaptive compression methods have been a key component of many proposed subband (or wavelet) image coding techniques. This paper deals with a particular type of adaptive subband image coding where we focus on the image coder's ability to adjust itself "on the fly" to the spatially varying statistical nature of image contents. This backward adaptation is distinguished from more frequently used forward adaptation in that forward adaptation selects the best operating parameters from a predesigned set and thus uses considerable amount of side information in order for the encoder and the decoder to operate with the same parameters. Specifically, we present backward adaptive quantization using a new context-based classification technique which classifies each subband coefficient based on the surrounding quantized coefficients. We couple this classification with online parametric adaptation of the quantizer applied to each class. A simple uniform threshold quantizer is employed as the baseline quantizer for which adaptation is achieved. Our subband image coder based on the proposed adaptive classification quantization idea exhibits excellent rate-distortion performance, in particular at very low rates. For popular test images, it is comparable or superior to most of the state-of-the-art coders in the literature.

title: Density estimation by stochastic complexity
abstract: The results by P. Hall and E.J. Hannan (1988) on optimization of histogram density estimators with equal bin widths by minimization of the stochastic complexity are extended and sharpened in two separate ways. As the first contribution, two generalized histogram estimators are constructed. The first has unequal bin widths which, together with the number of the bins, are determined by minimization of the stochastic complexity using dynamic programming. The other estimator consists of a mixture of equal bin width estimators, each of which is defined by the associated stochastic complexity. As the main contribution in the present work, two theorems are proved, which together extend the universal coding theorems to a large class of data generating densities. The first gives an asymptotic upper bound for the code redundancy in the order of magnitude, achieved with a special predictive type of histogram estimator, which sharpens a related bound. The second theorem states that this bound cannot be improved upon by any code whatsoever.

title: On Model Selection Consistency of Lasso
abstract: Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences. Model selection is a commonly used method to find such models, but usually involves a computationally heavy combinatorial search. Lasso (Tibshirani, 1996) is now being used as a computationally feasible alternative to model selection. Therefore it is important to study Lasso for model selection purposes. 
 
In this paper, we prove that a single condition, which we call the Irrepresentable Condition, is almost necessary and sufficient for Lasso to select the true model both in the classical fixed p setting and in the large p setting as the sample size n gets large. Based on these results, sufficient conditions that are verifiable in practice are given to relate to previous works and help applications of Lasso for feature selection and sparse representation. 
 
This Irrepresentable Condition, which depends mainly on the covariance of the predictor variables, states that Lasso selects the true model consistently if and (almost) only if the predictors that are not in the true model are "irrepresentable" (in a sense to be clarified) by predictors that are in the true model. Furthermore, simulations are carried out to provide insights and understanding of this result.

title: A rate of convergence result for a universal D-semifaithful code
abstract: The problem of optimal rate universal coding is considered in the context of rate-distortion theory. A D-semifaithful universal coding scheme for discrete memoryless sources is given. The main result is a refined covering lemma based on the random coding argument and the method of types. The average codelength of the code is shown to approach its lower bound, the rate-distortion function, at a rate O(n/sup -1/log n), and this is conjectured to be optimal based on a result of A.J. Pilc (1968). Issues of constructiveness and universality are addressed.

title: Processing partially specified queries over high-dimensional databases
abstract: In recent years, high-dimensional database applications deal with multidimensional ad hoc queries that refer to an arbitrary number of arbitrarily (unpredictably) chosen dimensions of high-dimensional data. This paper thoroughly and systematically investigates possible secondary-storage based solutions to the problems of processing multidimensional ad hoc query in transactional or semi-transactional environments. Then a complementary solution called the indexed and transposed access method (ITAM) is proposed. This method is based on two complementary measures - a multidimensional access method and a proposed access method called the Opus-path. The performance of multidimensional access methods deteriorates rapidly as the ratio of query dimensionality to data dimensionality decreases. On the other hand, the Opus-path shows retrieval performance that is actually better when the ratio is low.

title: Synergistic Integration of Agent Technologies for Military Simulation
abstract: To perform large-scale coordination in real-world environments requires that many individually complex technologies come together to form integrated solutions. In this paper, we present an application where several key technologies are integrated into a unified system via a multiagent infrastructure. We show how the synergistic behavior among heterogeneous technologies results in a significant improvement over the performance of the individual technologies acting alone. Critical extensions were required to the language describing required behavior to allow the pieces to work together. Initial experimental results show system performance on a task of coordinating a military convoy in an adversarial environment was significantly improved when all technologies worked together. However, experiments with a human user in the loop showed that significant advances must still be made before such systems can be fielded in the real-world

title: Scalable and reliable data delivery in mobile ad hoc sensor networks
abstract: This paper studies scalable data delivery algorithms in mobile ad hoc sensor networks with node and link failures. Many algorithms have been developed for data delivery and fusion in static microsensor networks, but most of them are not appropriate for mobile sensor networks due to their heavy traffic and long latency. In this paper we propose an efficient and robust data delivery algorithm for distributed data fusion in mobile ad hoc sensor networks, where each node controls its data flows and learns routing decisions solely based on their local knowledge. We analyze the localized algorithm in a formal model and validate our model using simulations. The experiments indicate that controlled data delivery processes significantly increase the probability of relevant data being fused in the network even with limited local knowledge of each node and relatively small hops of data delivery.

title: A Social Mechanism of Reputation Management in Electronic Communities
abstract: Trust is important wherever agents must interact. We consider the important case of interactions in electronic communities, where the agents assist and represent principal entities, such as people and businesses. We propose a social mechanism of reputation management, which aims at avoiding interaction with undesirable participants. Social mechanisms complement hard security techniques (such as passwords and digital certificates), which only guarantee that a party is authenticated and authorized, but do not ensure that it exercises its authorization in a way that is desirable to others. Social mechanisms are even more important when trusted third parties are not available. Our specific approach to reputation management leads to a decentralized society in which agents help each other weed out undesirable players.

title: Scalable QSF-Trees: Retrieving Regional Objects in High-Dimensional Spaces
abstract: Many database applications require effective representation of regional objects in high-dimensional spaces. By applying an original query transformation, a recently proposed access method for regional data, called the simple QSF-tree (sQSF-tree), effectively attacks the limitations of traditional spatial access methods in spaces with many dimensions. Nevertheless, sQSF-trees are not immune to all problems associated with high data dimensionality. Based on the analysis of sQSF-trees, this paper presents a new variant of sQSF-trees, called the scalable QSF-tree (cQSF-tree), which relies on a heuristic optimization to reduce the number of false drops into pages that contain no object satisfying the query. By increasing the selectivity of search predicates, cQSF-trees improve the performance of multi-dimensional selections. Experimental evidence shows that cQSF-trees are more scalable than sQSF-trees to the growing data dimensionality. The performance improvements also increase with more skewed data distribution.