2105049
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-regularized MLE
We consider the problem of estimating the graph structure associated with a Gaussian Markov random field (GMRF) from i.i.d. samples. We study the performance of study the performance of thel1-regularized maximum likelihood estimator in the high-dimensional setting, where the number of nodes in the graph p, the number of edges in the graph s and the maximum node degree d, are allowed to grow as a function of the number of samples n. Our main result provides sufficient conditions on (n, p, d) for thel1-regularized MLE estimator to recover all the edges of the graph with high probability. Under some conditions on the model covariance, we show that model selection can be achieved for sample sizes n = Ω(d log(p)), with the error decaying as O(exp(−c log(p))) for some constant c. We illustrate our theoretical results via simulations and show good correspondences between the theoretical predictions and behavior in simulations.
828878
Image denoising via lossy compression and wavelet thresholding
Some past work has proposed to use lossy compression to remove noise, based on the rationale that a reasonable compression method retains the dominant signal features more than the randomness of the noise. Building on this theme, we explain why compression (via coefficient quantization) is appropriate for filtering noise from signal by making the connection that quantization of transform coefficients approximates the operation of wavelet thresholding for denoising. That is, denoising is mainly due to the zero-zone and that the full precision of the thresholded coefficients is of secondary importance. The method of quantization is facilitated by a criterion similar to Rissanen's minimum description length principle. An important issue is the threshold value of the zero-zone (and of wavelet thresholding). For a natural image, it has been observed that its subband coefficients can be well modeled by a Laplacian distribution. With this assumption, we derive a threshold which is easy to compute and is intuitive. Experiments show that the proposed threshold performs close to optimal thresholding.
6762008
Perceptual audio coding using adaptive pre- and post-filters and lossless compression
This paper proposes a versatile perceptual audio coding method that achieves high compression ratios and is capable of low encoding/decoding delay. It accommodates a variety of source signals (including both music and speech) with different sampling rates. It is based on separating irrelevance and redundancy reductions into independent functional units. This contrasts traditional audio coding where both are integrated within the same subband decomposition. The separation allows for the independent optimization of the irrelevance and redundancy reduction units. For both reductions, we rely on adaptive filtering and predictive coding as much as possible to minimize the delay. A psycho-acoustically controlled adaptive linear filter is used for the irrelevance reduction, and the redundancy reduction is carried out by a predictive lossless coding scheme, which is termed weighted cascaded least mean squared (WCLMS) method. Experiments are carried out on a database of moderate size which contains mono-signals of different sampling rates and varying nature (music, speech, or mixed). They show that the proposed WCLMS lossless coder outperforms other competing lossless coders in terms of compression ratios and delay, as applied to the pre-filtered signal. Moreover, a subjective listening test of the combined pre-filter/lossless coder and a state-of-the-art perceptual audio coder (PAC) shows that the new method achieves a comparable compression ratio and audio quality with a lower delay.
12165073
On the Convergence of Boosting Procedures
A boosting algorithm seeks to minimize empirically a loss function in a greedy fashion. The resulted estimator takes an additive function form and is built iteratively by applying a base estimator (or learner) to updated samples depending on the previous iterations. This paper studies convergence of boosting when it is carried out over the linear span of a family of basis functions. For general loss functions, we prove the convergence of boosting's greedy optimization to the infinimum of the loss function over the linear span. As a side product, these results reveal the importance of restricting the greedy search step sizes, as known in practice through the works of Friedman and others.
17350574
Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images
We propose a novel hierarchical, nonlinear model that predicts brain activity in area V1 evoked by natural images. In the study reported here brain activity was measured by means of functional magnetic resonance imaging (fMRI), a noninvasive technique that provides an indirect measure of neural activity pooled over a small volume (≈ 2mm cube) of brain tissue. Our model, which we call the V-SPAM model, is based on the reasonable assumption that fMRI measurements reflect the (possibly nonlinearly) pooled, rectified output of a large population of simple and complex cells in V1. It has a hierarchical filtering stage that consists of three layers: model simple cells, model complex cells, and a third layer in which the complex cells are linearly pooled (called "pooled-complex" cells). The pooling stage then obtains the measured fMRI signals as a sparse additive model (SpAM) in which a sparse nonparametric (nonlinear) combination of model complex cell and model pooled-complex cell outputs are summed. Our results show that the V-SPAM model predicts fMRI responses evoked by natural images better than a benchmark model that only provides linear pooling of model complex cells. Furthermore, the spatial receptive fields, frequency tuning and orientation tuning curves of the V-SPAM model estimated for each voxel appears to be consistent with the known properties of V1, and with previous analyses of this data set. A visualization procedure applied to the V-SPAM model shows that most of the nonlinear pooling consists of simple compressive or saturating nonlinearities.
11765401
Looking at Markov samplers through cusum path plots: a simple diagnostic idea
In this paper, we propose to monitor a Markov chain sampler using the cusum path plot of a chosen one-dimensional summary statistic. We argue that the cusum path plot can bring out, more effectively than the sequential plot, those aspects of a Markov sampler which tell the user how quickly or slowly the sampler is moving around in its sample space, in the direction of the summary statistic. The proposal is then illustrated in four examples which represent situations where the cusum path plot works well and not well. Moreover, a rigorous analysis is given for one of the examples. We conclude that the cusum path plot is an effective tool for convergence diagnostics of a Markov sampler and for comparing different Markov samplers.
41743230
Automatically wrapping legacy software into services: A grid case study
The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing for solving large-scale data and computationally intensive problems. Peer-to-peer (P2P) systems have emerged as an infrastructure enabling technologies for enhanced scalability and reliability in file sharing and content distribution. It is envisioned that P2P enabled service-oriented grid systems would virtualize various resources as services with high scalability and reliability. Many legacy software resources exist nowadays, but making them grid aware services for effective resource sharing has become an issue of vital importance. This paper presents GSLab, a toolkit for automatically wrapping legacy software into services that can be published, discovered and reused in grid environments. GSLab employs Sun Grid Engine (SGE) to enhance its performance in execution of wrapped services. Using GSLab, we have automatically wrapped a legacy computer animation rendering code written in C as a service that can be discovered and accessed in a SGE environment. The evaluation results show that the performance of GSLab improves with an increase in the number of computing nodes involved.
16677111
A Verifiable Visual Cryptography Scheme
To solve the problem of cheating in the reconstructing process of visual cryptography, a verifiable visual cryptography was proposed by adding a verifier to common visual cryptography scheme (VCS). This scheme can be used in any access structure and can check every share to prevent attackers from spoiling and grabbing the secret information.
15309050
Prediction of Transmembrane Helical Segments in Membrane Proteins Using Back Propagation Neural Network with Wavelet Analysis
Transmembrane proteins possess important physiological function. The increasing transmembrane protein sequences from genome projects raise the needs for theoretical methods to predict their structures, especially transmembrane helical segments (TMHs). wnnTM, a new method of BP neural network based on wavelet multiresolution analysis (MRA) is initially developed to predict the number and location of TMHs in membrane proteins. 80 proteins with known 3D-structure randomly selected from Mptopo database are used as test set to evaluate the prediction accuracy of the method. The final results indicate that the proposed method is more effective than BP neural network model only.
11512625
Hourglass multimedia content and service composition framework for smart room environments
Pervasive environments with their ubiquitous multimedia devices such as video-enabled cell-phones, laptops, PC tablets, digital cameras, HDTV plasma displays are becoming integral part of our workplaces and homes. Furthermore, these digital multimedia devices make it easier and easier to create and view volumes of content that can be stored throughout the Internet. The big challenge is to organize the distributed content, get the distributed multimedia content composed, produced and distributed to users in customized fashion according to users' preference, and their resource availabilities in pervasive environments. Basically, the challenge is to create Internet Interactive Television where the user/interest group, residing in a pervasive environment, corresponds to the TV program director who decides and/or influences what content(s) will be viewed, how it will be composed, presented and delivered. In this paper, we present a vision how this challenge can be solved. We propose a novel hourglass-based system that delivers composite multimedia content to users in pervasive environments, which considers both users' interest in the content and the presentation capabilities of various display devices. This hourglass-based system relies on our content and service composition framework that allows for customized production, distribution and presentation of multimedia content for pervasive environments. We present the hourglass design, possible solutions for some content composition scenarios and validate the solutions via selected experiments.
34534588
Lane boundary detection using a multiresolution Hough transform
Lane boundary detection is the problem of estimating the geometric structure of the lane boundaries of a road based on the images grabbed by a camera on board a vehicle. We use the Hough transform to detect lane boundaries with a parabolic model under a variety of road pavement types, lane structures and weather conditions. In the three-dimensional Hough space, a parabolic curve is represented as a straight line. To simplify the computation, the parametric space can be divided into (i) a two-dimensional space measured by the parameters which are shared by all the lane edges, and (ii) a one-dimensional space of the parameter which makes a distinction among different edges in an image. A multiresolution strategy is used to improve both the speed and accuracy of the Hough transform. Experimental results show that the proposed method is relatively less prone to the image noise and is computationally tractable.