10126460
Mining common topics from multiple asynchronous text streams
Text streams are becoming more and more ubiquitous, in the forms of news feeds, weblog archives and so on, which result in a large volume of data. An effective way to explore the semantic as well as temporal information in text streams is topic mining, which can further facilitate other knowledge discovery procedures. In many applications, we are facing multiple text streams which are related to each other and share common topics. The correlation among these streams can provide more meaningful and comprehensive clues for topic mining than those from each individual stream. However, it is nontrivial to explore the correlation with the existence of asynchronism among multiple streams, i.e. documents from different streams about the same topic may have different timestamps, which remains unsolved in the context of topic mining. In this paper, we formally address this problem and put forward a novel algorithm based on the generative topic model. Our algorithm consists of two alternate steps: the first step extracts common topics from multiple streams based on the adjusted timestamps by the second step; the second step adjusts the timestamps of the documents according to the time distribution of the discovered topics by the first step. We perform these two steps alternately and a monotone convergence of our objective function is guaranteed. The effectiveness and advantage of our approach were justified by extensive empirical studies on two real data sets consisting of six research paper streams and two news article streams, respectively.
15012237
A topical PageRank based algorithm for recommender systems
In this paper, we propose a Topical PageRank based algorithm for recommender systems, which aim to rank products by analyzing previous user-item relationships, and recommend top-rank items to potentially interested users. We evaluate our algorithm on MovieLens dataset and empirical experiments demonstrate that it outperforms other state-of-the-art recommending algorithms.
1961988
An Area-Efficient LDPC Decoder Architecture and Implementation for CMMB Systems
This paper presents an area-efficient LDPC decoder architecture for the China Multimedia Mobile Broadcasting (CMMB) standard. Several techniques are adopted to reduce memory size, including the min-sum algorithm (MSA), optimal bit-width quantization of the iterative messages and reduced complexity for the interconnect network. The decoder for the rate-1/2 9216-bit code is implemented using the 90nm 1.0V CMOS technology. It achieves the decoding throughput of 48Mbps at 5 iterations when operating at 60MHz and the power dissipation is only 34mW.
20822692
Fuzzy Predictive Control in Czochralski Crystal Growth with Power Input as the Unique Manipulation
This paper presents a study to Czochralski crystal growth process based on Hurle’s model linearized at the equilibrium points. In this study, we do the attempt to get single crystal with constant diameter using the power input as the unique manipulation by removing pull rate manipulation which was suspected to be contributing to structure defects in the crystal from the system. Compared with the traditional PID, fuzzy predictive control algorithm is more effective to handle the Czochralski growth process which enhances the tracking capability and system robustness. Simulation results indicate that we can get the constant diameter of crystal by manipulating the power input only and fuzzy predictive control is more effective to the time-lagging and inertia system.
26966022
Term Committee Based Event Identification within News Topics
Most previous research focus on organizing news set into flat collections of stories. However, a topic in news is more than a mere collection of stories: it is characterized by a definite structure of inter-related events. Stories within a topic usually share some terms which are related to the topic other than a specific event, so stories of different events are usually very similar to each other within a topic. To deal with this problem, we propose a new event identification method based on the term committee. We first capture some tight term clusters as term committees of potential events, and then use them to reweight the key terms in a story. The experimental results on two Linguistic Data Consortium (LDC) datasets show that the proposed method for event identification outperforms previous methods significantly.
2754791
Interference Suppression for Block Diagonalization MIMO Downlink Systems over Time-Varying Channels
The performance of multiuser MIMO downlink systems with block diagonalization (BD) relies on the channel state information (CSI) at the transmitter to a great extent. For time division duplex TDD systems, the transmitter estimates the CSI while receiving data at current time slot and then uses the CSI to transmit at the next time slot. When the wireless channel is time-varying, the CSI for transmission is imperfect due to the time delay between the estimation of the channel and the transmission of the data and severely degrades the system performance. In this paper, we propose a linear method to suppress the interferences among users and data streams caused by imperfect CSI at transmitter. The transmitter first sends pilot signals through a linear spatial precoding matrix so as to make possible that the receiver can estimate CSI of other users, and then the receiver exploits a linear prefilter to suppress the interference. The numerical results show that the proposed schemes achieve obvious performance enhancement in comparison to the BD scheme with imperfect CSI at the transmitter.
207113548
A Hybrid Two-Phase Algorithm For Face Recognition
Scientists have developed numerous classifiers in the pattern recognition field, because applying a single classifier is not very conducive to achieve a high recognition rate on face databases. Problems occur when the images of the same person are classified as one class, while they are in fact different in poses, expressions, or lighting conditions. In this paper, we present a hybrid, two-phase face recognition algorithm to achieve high recognition rates on the FERET data set. The first phase is to compress the large class number database size, whereas the second phase is to perform the decision-making. We investigate a variety of combinations of the feature extraction and pattern classification methods. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are examined and tested using 700 facial images of different poses from FERET database. Experimental results show that the two combinations, LDA+LDA and LDA+SVM, outperform the other types of combinations. Meanwhile, when classifiers are considered in the two-phase face recognition, it is better to adopt the L1 distance in the first phase and the class mean in the second phase.