228217
Localized on-chip power delivery network optimization via sequence of linear programming
In this paper, we propose an efficient algorithm to reduce the voltage noises for on-chip power/ground (P/G) networks of VLSI. The new method is based on the sequence of linear programming (SLP) method as the optimization engine and a localized scheme via partitioning for dealing with large circuits. We show that by directly optimizing the decap area as the objective function and using the time-domain adjoint method, SLP can deliver much better quality than existing methods based on the merged time-domain adjoint method. The partitioning strategy further improves the scalability of the proposed algorithm and makes it efficient for large circuits. The resulting algorithm is general enough for any P/G network. Experimental results demonstrate the advantage of the proposed method over existing state-of-the-art methods in terms of solution quality at a mild computation cost increase
14087397
Statistical Model Order Reduction for Interconnect Circuits Considering Spatial Correlations
In this paper, we propose a novel statistical model order reduction technique, called statistical spectrum model order reduction (SS-MOR) method, which considers both intra-die and inter-die process variations with spatial correlations. The SSMOR generates order-reduced variational models based on given variational circuits. The reduced model can be used for fast statistical performance analysis of interconnect circuits with variational input sources, such as power grid and clock networks. The SSMOR uses statistical spectrum method to compute the variational moments and Monte Carlo sampling method with the modified Krylov subspace reduction method to generate the variational reduced models. To consider spatial correlations, we apply orthogonal decomposition to map the correlated random variables into independent and uncorrelated variables. Experimental results show that the proposed method can deliver about 100x speedup over the pure Monte Carlo projection-based reduction method with about 2% of errors for both means and variances in statistical transient analysis.
800122
Concept-oriented video skimming via semantic video classification
Effective video skimming requires a good understanding of the semantics of video contents. However, more existing systems for content-based video retrieval (CBVR) can only support low-level video analysis, but they have limited effectiveness on achieving semantic-sensitive video understanding. In this paper, we have developed a novel framework to achieve concept-oriented video skimming and it consists of three parts: (a) using salient objects for semantic-sensitive video content representation; (b) using finite mixture models for semantic video concept modeling and classification; (c) enabling concept-oriented video skimming via semantic video classification.
147992
A hierarchical access control model for video database systems
Content-based video database access control is becoming very important, but it depends on the progresses of the following related research issues: (a) efficient video analysis for supporting semantic visual concept representation; (b) effective video database indexing structure; (c) the development of suitable video database models; and (d) the development of access control models tailored to the characteristics of video data. In this paper, we propose a novel approach to support multilevel access control in video databases. Our access control technique combines a video database indexing mechanism with a hierarchical organization of visual concepts (i.e., video database indexing units), so that different classes of users can access different video elements or even the same video element with different quality levels according to their permissions. These video elements, which, in our access control mechanism, are used for specifying the authorization objects, can be a semantic cluster, a subcluster, a video scene, a video shot, a video frame, or even a salient object (i.e., region of interest). In the paper, we first introduce our techniques for obtaining these multilevel video access units. We also propose a hierarchical video database indexing technique to support our multilevel video access control mechanism. Then, we present an innovative access control model which is able to support flexible multilevel access control to video elements. Moreover, the application of our multilevel video database modeling, representation, and indexing for MPEG-7 is discussed.
1153368
Indirect anaphora resolution as semantic path search
Anaphora occur commonly in natural language text, and resolving them is essential for capturing the knowledge encoded in text. Indirect anaphora are especially challenging to resolve because the referring expression and the antecedent are related by unstated background knowledge. Such anaphora need to be resolved properly in order to automatically capture the knowledge expressed in natural language. Resolving indirect anaphora has been treated as a unique problem that requires special-purpose methods, and these methods have had limited success in precision and recall. In this study, we used a generic tool for finding semantic paths between two concepts to resolve these anaphora, and it achieved approximately twice the recall of the best previous system without loss of precision. A series of ablation study showed that the biggest increase in recall came from an abductive stopping criterion of the search.
4017008
Gene symbol disambiguation using knowledge-based profiles
MOTIVATION
The ambiguity of biomedical entities, particularly of gene symbols, is a big challenge for text-mining systems in the biomedical domain. Existing knowledge sources, such as Entrez Gene and the MEDLINE database, contain information concerning the characteristics of a particular gene that could be used to disambiguate gene symbols.


RESULTS
For each gene, we create a profile with different types of information automatically extracted from related MEDLINE abstracts and readily available annotated knowledge sources. We apply the gene profiles to the disambiguation task via an information retrieval method, which ranks the similarity scores between the context where the ambiguous gene is mentioned, and candidate gene profiles. The gene profile with the highest similarity score is then chosen as the correct sense. We evaluated the method on three automatically generated testing sets of mouse, fly and yeast organisms, respectively. The method achieved the highest precision of 93.9% for the mouse, 77.8% for the fly and 89.5% for the yeast.


AVAILABILITY
The testing data sets and disambiguation programs are available at http://www.dbmi.columbia.edu/~hux7002/gsd2006
1692629
Representing roles and purpose
Ontology designers often distinguish Entities (things that are) from Events (things that happen). It is not obvious how this division admits Roles (things that are, but only in the context of things that happen). For example, Person might be considered an Entity, while Employee is a Role. A Person remains a Person independent of the Events in which he participates. Someone is an Employee only by virtue of participating in an Employment Event. The problem of how to represent Roles is not new, but there is little consensus on a solution. In this paper, we present an ontology that finds a place for Roles as well as a representation that allows Roles to be related to Entities and Events to express the teleological notion of purpose.