1783905
Project Halo: Towards a Digital Aristotle
Project Halo is a multistaged effort, sponsored by Vulcan Inc, aimed at creating Digital Aristotle, an application that will encompass much of the world's scientific knowledge and be capable of applying sophisticated problem solving to answer novel questions. Vulcan envisions two primary roles for Digital Aristotle: as a tutor to instruct students in the sciences and as an interdisciplinary research assistant to help scientists in their work. As a first step towards this goal, we have just completed a six-month pilot phase designed to assess the state of the art in applied knowledge representation and reasoning (KR&/R). Vulcan selected three teams, each of which was to formally represent 70 pages from the advanced placement (AP) chemistry syllabus and deliver knowledge-based systems capable of answering questions on that syllabus. The evaluation quantified each system's coverage of the syllabus in terms of its ability to answer novel, previously unseen questions and to provide human- readable answer justifications. These justifications will play a critical role in building user trust in the question-answering capabilities of Digital Aristotle. Prior to the final evaluation, a "failure taxonomy' was collaboratively developed in an attempt to standardize failure analysis and to facilitate cross-platform comparisons. Despite differences in approach, all three systems did very well on the challenge, achieving performance comparable to the human median. The analysis also provided key insights into how the approaches might be scaled, while at the same time suggesting how the cost of producing such systems might be reduced. This outcome leaves us highly optimistic that the technical challenges facing this effort in the years to come can be identified and overcome. This article presents the motivation and longterm goals of Project Halo, describes in detail the six-month first phase of the project -- the Halo Pilot -- its KR&R challenge, empirical evaluation, results, and failure analysis. The pilot's outcome is used to define challenges for the next phase of the project and beyond.
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.
5146657
Interpreting Loosely Encoded Questions
Knowledge-based question-answering systems have become quite competent and robust at answering a wide range of questions in different domains, however in order to ask questions correctly, one needs to have intimate knowledge of the structure of the knowledge base, and typical users lack this knowledge. We address this problem by developing a system that uses the content of the knowledge base to automatically align a user's encoding of a query to the structure of the knowledge base. Our preliminary evaluation shows the system detects and corrects most misalignments, and users are able to pose most questions quickly.
5975278
The Knowledge Required to Interpret Noun Compounds
This paper presents a novel approach to the estimation of user’s affective states in Human-Computer Interaction. Most of the present approaches divide emotions strictly between positive or negative. However, recent discoveries in the field of Emotional Intelligence show that emotions should be rather perceived as context-sensitive engagements with the world. This leads to a need to specify whether the emotions conveyed in a conversation are appropriate for a situation they are expressed in. In the proposed method we use a system for affect analysis on textual input to recognize users emotions and a Web mining technique to verify the contextual appropriateness of those emotions. On this basis a conversational agent can choose to either sympathize with the user or help them manage their emotions. Finally, the results of evaluation of the proposed method with two different conversational agents are discussed, and perspectives for further development of the method are proposed.
5651829
Utilizing Domain Knowledge in Neuroevolution
We propose a method called Rule-based ESP (RESP) for utilizing prior knowledge evolving Artificial Neural Networks (ANNs). First, KBANN-likete chniques are used to transform a set of rules into an ANN, then the ANN is trained using the Enforced Subpopulations (ESP) neuroevolution method. Empirical results in the Prey Capture domain show that RESP can reach higher level of performance than ESP. The results also suggest that incremental learning is not necessary with RESP, and it is often easier to design a set of rules than an incremental evolution scheme. In addition, an experiment with some of the rules deleted suggests that RESP is robust even with an incomplete knowledge base. RESP therefore provides a robust methodology for scaling up neuroevolution to harder tasks by utilizing existing knowledge about the domain.
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.
11303603
Wavelet deconvolution
This paper studies the issue of optimal deconvolution density estimation using wavelets. The approach taken here can be considered as orthogonal series estimation in the more general context of the density estimation. We explore the asymptotic properties of estimators based on thresholding of estimated wavelet coefficients. Minimax rates of convergence under the integrated square loss are studied over Besov classes B/sub /spl sigma/pq/ of functions for both ordinary smooth and supersmooth convolution kernels. The minimax rates of convergence depend on the smoothness of functions to be deconvolved and the decay rate of the characteristic function of convolution kernels. It is shown that no linear deconvolution estimators can achieve the optimal rates of convergence in the Besov spaces with p<2 when the convolution kernel is ordinary smooth and super smooth. If the convolution kernel is ordinary smooth, then linear estimators can be improved by using thresholding wavelet deconvolution estimators which are asymptotically minimax within logarithmic terms. Adaptive minimax properties of thresholding wavelet deconvolution estimators are also discussed.
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.
9742212
General Block Structure-Preserving Reduced Order Modeling of Linear Dynamic Circuits
In this paper, we propose a generalized block structure-preserving reduced order interconnect macromodeling method (BSPRIM). Our approach extends structure-preserving model order reduction (MOR) method SPRIM (Freund, 2004) into more general block forms. We first show how SPRIM-like structure-preserving MOR method can be extended to deal with admittance RLC circuit matrices and show the 2q moments are still matched and symmetry is preserved. We then show that 2q moment matching can't be achieved when the RLC circuits are driven by both current and voltage sources. The new approach also improves SPRIM by introducing the re-orthonormalization process on the partitioned projection matrix. Then we present our BSPRIM method to deal with more circuit partitions and show the resulting general block structure preserving MOR for linear dynamic circuits formulated in impedance and admittance forms. The reduced models by the proposed BSPRIM will still match the 2q moments and preserve the circuit structure properties like symmetry as SPRIM does. Experimental results demonstrate effectiveness of the proposed methods and it outperforms SPRIM in terms of accuracy with more partitions
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
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