81034
Co-clustering by block value decomposition
Dyadic data matrices, such as co-occurrence matrix, rating matrix, and proximity matrix, arise frequently in various important applications. A fundamental problem in dyadic data analysis is to find the hidden block structure of the data matrix. In this paper, we present a new co-clustering framework, block value decomposition(BVD), for dyadic data, which factorizes the dyadic data matrix into three components, the row-coefficient matrix R, the block value matrix B, and the column-coefficient matrix C. Under this framework, we focus on a special yet very popular case -- non-negative dyadic data, and propose a specific novel co-clustering algorithm that iteratively computes the three decomposition matrices based on the multiplicative updating rules. Extensive experimental evaluations also demonstrate the effectiveness and potential of this framework as well as the specific algorithms for co-clustering, and in particular, for discovering the hidden block structure in the dyadic data.
13915860
Object detection in aerial imagery based on enhanced semi-supervised learning
Object detection in aerial imagery has been well studied in computer vision for years. However, given the complexity of large variations of the appearance of the object and the background in a typical aerial image, a robust and efficient detection is still considered as an open and challenging problem. In this paper, we present the enhanced semi-supervised learning (ESL) framework and apply this framework to revising an object detection methodology we have developed in a previous effort. Theoretic analysis and experimental evaluation using the UCI machine learning repository clearly indicate the superiority of the ESL framework. The performance evaluations of the revised object detection methodology against the original one clearly demonstrate the promise and superiority of this approach
14148380
Discovering strong skyline points in high dimensional spaces
Current interests in skyline computation arise due to their relation to preference queries. Since it is guaraneed that a skyline point will not lose out in all dimensions when compared to any other point in the data set, this means that for each skyline point, there exists a set of weight assignments to the dimensions such that the point will become the top user preference.We believe that the usefulness of skyline points is not limited to such application and can be extended to data analysis and knowledge discovery as well. However, since the skyline of high dimensional datasets (which are common in data analysis applications) can contain too many points, various means must be developed to filter off the less interesting skyline points in high dimensions. In this paper, we will propose algorithms to find a set of interesting skyline points called strong skyline points. Extensive experiments show that our proposal is both effective and efficient.
14590836
Eye gaze correction with stereovision for video-teleconferencing
The lack of eye contact in desktop video teleconferencing substantially reduces the effectiveness of video contents. While expensive and bulky hardware is available on the market to correct eye gaze, researchers have been trying to provide a practical software-based solution to bring video-teleconferencing one step closer to the mass market. This paper presents a novel approach: based on stereo analysis combined with rich domain knowledge (a personalized face model), we synthesize, using graphics hardware, a virtual video that maintains eye contact. A 3D stereo head tracker with a personalized face model is used to compute initial correspondences across two views. More correspondences are then added through template and feature matching. Finally, all the correspondence information is fused together for view synthesis using view morphing techniques. The combined methods greatly enhance the accuracy and robustness of the synthesized views. Our current system is able to generate an eye-gaze corrected video stream at five frames per second on a commodity 1 GHz PC.
29068146
PseudoPipe: an automated pseudogene identification pipeline
MOTIVATION
Mammalian genomes contain many 'genomic fossils' i.e. pseudogenes. These are disabled copies of functional genes that have been retained in the genome by gene duplication or retrotransposition events. Pseudogenes are important resources in understanding the evolutionary history of genes and genomes.


RESULTS
We have developed a homology-based computational pipeline ('PseudoPipe') that can search a mammalian genome and identify pseudogene sequences in a comprehensive and consistent manner. The key steps in the pipeline involve using BLAST to rapidly cross-reference potential "parent" proteins against the intergenic regions of the genome and then processing the resulting "raw hits" -- i.e. eliminating redundant ones, clustering together neighbors, and associating and aligning clusters with a unique parent. Finally, pseudogenes are classified based on a combination of criteria including homology, intron-exon structure, and existence of stop codons and frameshifts.
17900866
Robust extraction of specific signals with temporal structure
There is a trend to develop blind or semi-blind source extraction algorithms based on second-order statistics, due to its low computation load and fast processing speed. An important and primary work is done by Barros and Cichocki, who propose an extraction algorithm based on a time delay. The algorithm is simple and fast, but its performance is not satisfying. The paper extends their work and proposes a robust algorithm based on eigenvalue decomposition of several delayed covariance matrices. It is faster and has better performance, which is confirmed by theoretical analysis and computer simulations.
32938113
Slotted Optical Burst Switching (SOBS) Networks
In this paper we study Optical Burst Switching (OBS) networks. Since OBS still suffers high loss ratio due to the lack of buffer at the OBS core routers, we study methods to reduce the loss without using optical buffers. We consider time-slotted OBS called Slotted OBS (SOBS), where routers are synchronized and only send fixed length bursts at the beginning of time slots. Our simulation results show that SOBS reduces the packet loss probability significantly. Moreover, we show that SOBS can be implemented with little or no additional cost and has other advantages such as the better supporting of Quality of Service (QoS).