In this study, the properties of inter-event times in probability for stochastic linear event-triggered control systems are explored. The analysis of inter-event intervals is conducted for three distinct classes of event-triggering mechanisms: the absolute, relative, and mixed ones. Given the inherent stochastic nature of systems, it is challenging to achieve a certain judgment on avoiding Zeno behavior, where events occur at an infinite frequency. To address this issue, a probabilistic approach to examine the inter-event times is employed. It enables us to quantify the likelihood of Zeno behavior occurring in different scenarios, providing valuable insights into the performance of stochastic event-triggered control systems. Our research reveals substantial differences in the likelihood on the occurrence of a positive minimum inter-event interval among different mechanisms. Specifically, the mixed event-triggering mechanism emerges as the most probable one to yield a positive minimum inter-event time, indicating its potential superiority in efficiency. Conversely, some solutions of relative event-triggered control are proved to exhibit Zeno behavior with a probability of 1. Finally, a numerical example is provided to illustrate the efficiency and feasibility of the obtained results.

The explosive growth of published articles in biomedical science field has led more research to focus on biomedical relationship extraction. However, there is relatively little investigation conducted on polarity analysis of these relationships, such as food (or nutrition) and disease relationships. In this paper, we investigate how to automatically identify the polarity of relationships between food and disease in biomedical text. In particular, we first analyze the characteristics and challenges of relation polarity analysis, and then propose an integrated approach, which utilizes background knowledge in terms of relation word and polarity class association, and refines this association by using any available domain specific training data. In addition, we propose several novel learning features and a computational approach to construct background knowledge base. Empirical results on real world datasets show that the proposed method is effective.

This paper proposes Easy Picker, a tool to help people find the exact location of densely distributed objects. We hope to use Easy Picker to help people quickly pick the specified objects among those are densely and regularly distributed, such as books on a shelf and garments on a hanger.

This paper investigates the automatic extraction of data from forums, blogs and news web sites. Web pages are increasingly dynamically generated using a common template populated with data from databases. This paper proposes a novel method that uses tree alignment to automatically extract data from these types of web pages. A new tree alignment algorithm is presented for determining the optimal matching structure of the input web pages. Based on the alignment, the trees are merged into one union tree whose nodes record statistical information obtained from multiple web pages. A heuristic method is employed for determining the most probable content block and the alignment algorithm detects repeating patterns on the union tree. A wrapper built on the most probable content block and the repeating patterns extracts data from web pages. Experimental results show that the method achieves high extraction accuracy and has steady performance.

MOTIVATION: An enormous number of protein-protein interaction relationships are buried in millions of research articles published over the years, and the number is growing. Rediscovering them automatically is a challenging bioinformatics task. Solutions to this problem also reach far beyond bioinformatics. RESULTS: We study a new approach that involves automatically discovering English expression patterns, optimizing them and using them to extract protein-protein interactions. In a sister paper, we described how to generate English expression patterns related to protein-protein interactions, and this approach alone has already achieved precision and recall rates significantly higher than those of other automatic systems. This paper continues to present our theory, focusing on how to improve the patterns. A minimum description length (MDL)-based pattern-optimization algorithm is designed to reduce and merge patterns. This has significantly increased generalization power, and hence the recall and precision rates, as confirmed by our experiments. AVAILABILITY: http://spies.cs.tsinghua.edu.cn.

MOTIVATION: Although there are several databases storing protein-protein interactions, most such data still exist only in the scientific literature. They are scattered in scientific literature written in natural languages, defying data mining efforts. Much time and labor have to be spent on extracting protein pathways from literature. Our aim is to develop a robust and powerful methodology to mine protein-protein interactions from biomedical texts. RESULTS: We present a novel and robust approach for extracting protein-protein interactions from literature. Our method uses a dynamic programming algorithm to compute distinguishing patterns by aligning relevant sentences and key verbs that describe protein interactions. A matching algorithm is designed to extract the interactions between proteins. Equipped only with a dictionary of protein names, our system achieves a recall rate of 80.0% and precision rate of 80.5%. AVAILABILITY: The program is available on request from the authors.

Images play a very important role in web content delivery. Many WWW images contain text information that can be used for web indexing and searching. A new text extraction and recognition algorithm is proposed in this paper. The character strokes in the image are first extracted by color clustering and connected component analysis. A novel stroke verification algorithm is used to effectively remove non-character strokes. The verified strokes are then used to build the binary text line image, which is segmented and recognized by dynamic programming. Since text in WWW image usually has close relationship with webpage content, approximate string matching is used to revise the recognition result by matching the content in the webpage with the content in the image. This effective post-processing not only improves the recognition performance, but also can be used in other applications such like image - webpage paragraph corresponding.

With the available resources on the Internet becoming plentiful, a large amount of harmful information is permeating in and has been seriously affecting people's normal work and living. Therefore, harmful data streams must be recognized and filtered out effectively. After analyzing some harmful contents in Internet information streams, we present a new method, which recognizes specific information by machine learning (ML). We extracted key information from a number of corpuses through the ML method to obtain the part of speech (POS) transfer-form for key information by learning from corpuses, which is based on the same pronunciation matching of key information. Furthermore, the testing value of key information will be obtained in a real corpus to examine the likelihood between matching rules from information streams and those learnt from corpuses through the average value of POS transfer probability of key information. Therefore, the testing value for the whole real data stream will be obtained The experiment proved that the method was efficient for recognizing certain Internet harmful information.