Inequivalent manifold ranking for content-based image retrieval
We propose to improve the effectiveness and scalability of graph based manifold ranking methods in image retrieval applications by emphasizing reliable images while damping the effect of noisy or irregular ones. Label information is firstly passed between most reliable data points, then propagated to less reliable ones on manifold structure. By treating these images inequivalently, undesirable effect of noisy samples is greatly reduced, thus effectiveness of manifold ranking algorithms is enhanced. Also, graph size in terms of number of nodes and edges is dramatically reduced, resulting in a great speed-up of the algorithm. Our experiment on real world image data set demonstrates the effectiveness of the proposed approach.

title: A New Multi-view Learning Algorithm Based on ICA Feature for Image Retrieval
abstract: In content-based image retrieval (CBIR), most techniques involve an important issue of how to efficiently bridge the gap between the high-level concepts and low-level visual features. We propose a novel semi-supervised learning method for image retrieval, which makes full use of both ICA feature and general low-level feature. Our approach can be characterized by the following three aspects: (1) The ICA feature, as proved to be representative of human vision, is adopted as a view to describe human perception; (2) A multi-view learning algorithm is introduced to make the most use of different features and dramatically reduce human relevance feedback needed to achieve a satisfactory result; (3) A new semi-supervised learning algorithm is proposed to adapt to the small sample problem and other special constrains of our multi-view learning algorithm. Our experimental results and comparisons are presented to demonstrate the effectiveness of the proposed approach.

title: Spectrum Sharing in Cognitive Radio Networks
abstract: In this paper, we present a novel joint power/channel allocation scheme that uses a distributed pricing strategy to improve the network's performance. According to this scheme, the spectrum allocation problem is modeled as a non-cooperative game. A price-based iterative water-filling (PIWF) algorithm is proposed, which allows users to converge to the Nash Equilibrium (NE). This PIWF algorithm can be implemented distributively, with CRs repeatedly negotiating their best transmission powers and spectrum. We propose a protocol that implements our price- based resource allocation algorithm. The proposed MAC protocol allows multiple CR pairs to first contend through an admission phase, and then to iteratively negotiate their transmission powers and spectrum via control-packet exchanges. Subsequently, CRs proceed concurrently with their data transmissions. Simulations are used to study the performance of our protocol and demonstrate its effectiveness in improving the overall network throughput and reducing the average transmission power.

title: An Adaptive MIMO System Based on Unified Belief Propagation Detection
abstract: An adaptive multiple input and multiple output (MIMO) system based on the unified belief propagation (BP) detection is proposed. A BP detector composed of a serial of component detectors is first presented. With a unified design of the BP detector, a multi-rate MIMO system adaptive to channel variations is then developed, which aims at achieving the maximal throughput performance. The usage of the unified BP detector eliminates the multiple detector modules at the receiver in conventional multi-mode adaptive MIMO systems. Further, the linear complexity to space time block length and parallel operation of the unified BP detection provide the advantages of low complexity, low detection delay and smooth switching between different data rates. With the average receiving signal-to-noise ratio (SNR) based switching mechanism, the proposed adaptive MIMO framework adaptively chooses the appropriate rate for the transmission. Simulation results are provided to show the BER and complexity performance of a specific BP detector as well as the throughput performance of the adaptive scheme.

title: Ab initio ro-vibrational structure of the C 2n isotopes of H 2 O +
abstract: The ro-vibrational structures of the C2" isotopes ofH20+ have been calculated from variational solution of the normal coordinate Eckart-Watson Hamiltonian. The calculations use the discrete ab initio potential energy surface of Weis et al. (1989). Where comparisons can be made, the assignment of the vibrational states is in excellent agreement with experiment and with the ab initio variational calculation of Weis et al., who utilised a different force field and an internal coordinate nuclear Hamiltonian (instead of the Eckart-Watson amiltonian). Furthermore, the calculated rotational levels of the ground and the first excited vibrational states of H20+ and D20+ are in excellent agreement with experiment.

title: Graph and Topological Structure Mining on Scientific Articles
abstract: In this paper, we investigate a new approach for literature mining. We use frequent subgraph mining, and its generalization topological structure mining, for finding interesting relationships between gene names and other key biological terms from the text of scientific articles. We show how we can find keywords of interest and represent them as nodes of the graphs. We also propose several methods for inserting edges between these nodes. Our study initially focused on comparing: 1) different methods for constructing edges, and 2) patterns found from sub-graph mining and topological structure mining. Subsequently, we analyzed several frequent topological minors reported by our experiments, and explained their scientific significance. Overall, our study shows the following. First, a simple method of constructing edges, which is based on sliding windows, seems to provide the best results. Second, we are able to find much larger number of well-known and meaningful topological patterns with high support values, as compared to sub-graphs. Overall, the frequent topological minors our algorithm found correspond well to known relationships between genes and biological terms. Thus, we believe that topological structure mining can be a very valuable tool for researchers who are not deeply familiar with the existing literature, and want to obtain a quick summary about known relationships among key scientific names or terms.

title: SNPMiner: A Domain-Specific Deep Web Mining Tool
abstract: In this paper, we propose a novel query-oriented, mediator-based biological data querying tool, SNPMiner. The system searches and queries single nucleotide polymorphisms (SNPs) data from eight widely used web accessible databases. The system provides a domain-specific search utility, which can access and collect data from the deep web. This is a web-based system, so any user can use the system by accessing our server from their own computers. The system includes three important components, which are the Web server interface, the dynamic query planner, and the Web page parser. The Web server interface can provide end users a unified and friendly interface. The dynamic query planner can automatically schedule an efficient query order on all available databases according to user's query request. The Web page parser analyzes the layout of HTML files and extracts desired data from those files. The final results of the query are organized in a tabular format, which can be reviewed by a biological researcher.