Topic Distributions over Links on Web
It is well known that Web users create links with different intentions. However, a key question, which is not well studied, is how to categorize the links and how to quantify the strength of the influence of a web page on another if there is a link between the two linked web pages. In this paper, we focus on the problem of link semantics analysis, and propose a novel supervised learning approach to build a model, based on a training link-labeled and link-weighted graph where a link-label represents the category of a link and a link-weight represents the influence of one web page on the other in a link. Based on the model built, we categorize links and quantify the influence of web pages on the others in a large graph in the same application domain. We discuss our proposed approach, namely Pairwise Restricted Boltzmann Machines (PRBMs), and conduct extensive experimental studies to demonstrate the effectiveness of our approach using large real datasets.

title: ATID: a web-oriented database for collection of publicly available alternative translational initiation events
abstract: Alternative translational initiation is an important cellular mechanism contributing to the diversity of protein products and functions. We develop a database that provides a comprehensive collection of alternative translational initiation events. The purpose of this alternative translational initiation database (ATID) is to facilitate the systematic study of alternative translational initiation of genes. The current version of database contains 300 genes from Homo sapiens, Mus musculus and other species. Each of the genes has two or more isoforms due to alternative translational initiation. Resources in ATID, including gene information, alternative products of genes and domain structures of isoforms, are provided through a user-friendly web interface.


AVAILABILITY
The ATID database is available for public use at http://bioinfo.au.tsinghua.edu.cn/atie/.

title: A Mixture Model for Expert Finding
abstract: This paper addresses the issue of identifying persons with expertise knowledge on a given topic. Traditional methods usually estimate the relevance between the query and the support documents of candidate experts using, for example, a language model. However, the language model lacks the ability of identifying semantic knowledge, thus results in some right experts cannot be found due to not occurrence of the query terms in the support documents. In this paper, we propose a mixture model based on Probabilistic Latent Semantic Analysis (PLSA) to estimate a hidden semantic theme layer between the terms and the support documents. The hidden themes are used to capture the semantic relevance between the query and the experts. We evaluate our mixture model in a real-world system, ArnetMiner. Experimental results indicate that the proposed model outperforms the language models.

title: Arnetminer: expertise oriented search using social networks
abstract: Expertise Oriented Search (EOS) aims at providing comprehensive expertise analysis on data from distributed sources. It is useful in many application domains, for example, finding experts on a given topic, detecting the confliction of interest between researchers, and assigning reviewers to proposals. In this paper, we present the design and implementation of our expertise oriented search system, Arnetminer (http://www.arnetminer.net). Arnetminer has gathered and integrated information about a half-million computer science researchers from the Web, including their profiles and publications. Moreover, Arnetminer constructs a social network among these researchers through their co-authorship, and utilizes this network information as well as the individual profiles to facilitate expertise oriented search tasks. In particular, the co-authorship information is used both in ranking the expertise of individual researchers for a given topic and in searching for associations between researchers. We have conducted initial experiments on Arnetminer. Our results demonstrate that the proposed relevancy propagation expert finding method outperforms the method that only uses person local information, and the proposed two-stage association search on a large-scale social network is order of magnitude faster than the baseline method.

title: A unified framework for name disambiguation
abstract: Name ambiguity problem has been a challenging issue for a long history. In this paper, we intend to make a thorough investigation of the whole problem. Specifically, we formalize the name disambiguation problem in a unified framework. The framework can incorporate both attribute and relationship into a probabilistic model. We explore a dynamic approach for automatically estimating the person number K and employ an adaptive distance measure to estimate the distance between objects. Experimental results show that our proposed framework can significantly outperform the baseline method.

title: htSNPer1.0: software for haplotype block partition and htSNPs selection
abstract: BackgroundThere is recently great interest in haplotype block structure and haplotype tagging SNPs (htSNPs) in the human genome for its implication on htSNPs-based association mapping strategy for complex disease. Different definitions have been used to characterize the haplotype block structure in the human genome, and several different performance criteria and algorithms have been suggested on htSNPs selection. heuristic algorithm, generalized branch-and-bound algorithm, is applied to the searching of minimal set of haplotype tagging SNPs (htSNPs) according to different htSNPs performance criteria. We develop a software htSNPer1.0 to implement the algorithm, and integrate three htSNPs performance criteria and four haplotype block definitions for haplotype block partitioning. It is a software with powerful Graphical User Interface (GUI), which can be used to characterize the haplotype block structure and select htSNPs in the candidate gene or interested genomic regions. It can find the global optimization with only a fraction of the computing time consumed by exhaustive searching algorithm.ConclusionhtSNPer1.0 allows molecular geneticists to perform haplotype block analysis and htSNPs selection using different definitions and performance criteria. The software is a powerful tool for those focusing on association mapping based on strategy of haplotype block and htSNPs.

title: MDDAS 2008 Workshop Organization
abstract: A combination capacitor and transistor structure is described wherein the capacitor is formed integrally with the emitter electrode of the transistor. The transistor is formed in a monolithic integrated circuit using generally known techniques and constitutes a vertically integrated PNP device. The emitter electrode of the transistor which comprises a P+ diffusion region is of a predetermined area which is large enough to form the bottom plate of the capacitor. The top plate of the capacitor is formed by growing a dielectric material over the diffused emitter region and then forming metallization thereover. The combination capacitor and transistor structure may be utilized in a bias network for biasing the output stage of an operational amplifier in a class AB mode. The capacitor formed in the combination structure may be utilized as the compensation capacitor in such operational amplifier which utilizes pole splitting techniques. The improvement provided by the invention reduces the surface area of the semiconductor die chip required to form the capacitor and transistor which facilitates greater device density on a particular die chip.

title: Sequential Proximity-Based Clustering for Telecommunication Network Alarm Correlation
abstract: Alarm correlation for fault management in large telecommunication networks demands scalable and reliable algorithms. In this paper, we propose a clustering based alarm correlation approach using sequential proximity between alarms. We define two novel distance metrics appropriate for measuring similarity between alarm sequences obtained from interval-based division: 1) the two-digit binary metric that values the occurrences of two alarms in neighboring intervals to tolerate the false separation of alarms due to interval-based alarm sequence division, and 2) the sequential ordering-based distance metric that considers the time of arrival for different alarms within the same interval. We validate both metrics by applying them with hierarchical clustering using real-world cellular network alarm data. The efficacy of the proposed sequential proximity based alarm clustering is demonstrated through a comparative study with existing similarity metrics.

title: PC-Filter: A Robust Filtering Technique for Duplicate Record Detection in Large Databases
abstract: In this paper, we will propose PC-Filter (PC stands for Partitio n Comparison), a robust data filter for approximately duplicate record detection in large databases. PC-Filter distinguishes itself from all of existing methods by using the notion of partition in duplicate detection. It first sorts the whole database and splits the sorted database into a number of record partitions. The Partition Comparison Graph (PCG) is then constructed by performing fast partition pruning. Finally, duplicate records are effectively detected by using internal and external partition comparison based on PCG. Four properties, used as heuristics, have been devised to achieve a remarkable efficiency of the filter based on triangle inequity of record similarity. PC-Filter is insensitive to the key used to sort the database, and can achieve a very good recall level that is comparable to that of the pair-wise record comparison method but only with a complexity of O(N 4/3). Equipping existing detection methods with PC-Filter, we are able to well solve the ”Key Selection” problem, the ”Scope Specification” problem and the ”Low Recall” problem that the existing methods suffer from.

title: HOS-Miner: A System for Detecting Outlying Subspaces of High-dimensional Data
abstract: We identify a new and interesting high-dimensional outlier detection problem in this paper, that is, detecting the subspaces in which given data points are outliers. We call the subspaces in which a data point is an outlier as its Outlying Subspaces. In this paper, we will propose the prototype of a dynamic subspace search system, called HOS-Miner (HOS stands for High-dimensional Outlying Subspaces), that utilizes a sample-based learning process to effectively identify the outlying subspaces of a given point.

title: A Novel Composite Kernel Approach to Chinese Entity Relation Extraction
abstract: Relation extraction is the task of finding semantic relations between two entities from the text. In this paper, we propose a novel composite kernel for Chinese relation extraction. The composite kernel is defined as the combination of two independent kernels. One is the entity kernel built upon the non-content-related features. The other is the string semantic similarity kernel concerning the content information. Three combinations, namely linear combination, semi-polynomial combination and polynomial combination are investigated. When evaluated on the ACE 2005 Chinese data set, the results show that the proposed approach is effective.