3682795
Multibody grouping via orthogonal subspace decomposition
Multibody structure from motion could be solved by the factorization approach. However, the noise measurements would make the segmentation difficult when analyzing the shape interaction matrix. This paper presents an orthogonal subspace decomposition and grouping technique to approach such a problem. We decompose the object shape spaces into signal subspaces and noise subspaces. We show that the signal subspaces of the object shape spaces are orthogonal to each other. Instead of using the shape interaction matrix contaminated by noise, we introduce the shape signal subspace distance matrix for shape space grouping. Outliers could be easily identified by this approach. The robustness of the proposed approach lies in the fact that the shape space decomposition alleviates the influence of noise, and has been verified with extensive experiments.
2209704
Capturing natural hand articulation
Vision-based motion capturing of hand articulation is a challenging task, since the hand presents a motion of high degrees of freedom. Model-based approaches could be taken to approach this problem by searching in a high dimensional hand state space, and matching projections of a hand model and image observations. However, it is highly inefficient due to the curse of dimensionality. Fortunately, natural hand articulation is highly constrained, which largely reduces the dimensionality of hand state space. This paper presents a model-based method to capture hand articulation by learning hand natural constraints. Our study shows that natural hand articulation lies in a lower dimensional configurations space characterized by a union of liner manifolds spanned by a set of basis configurations. By integrating hand motion constraints, an efficient articulated motion-capturing algorithm is proposed based on sequential Monte Carlo techniques. Our experiments show that this algorithm is robust and accurate for tracking natural hand movements. This algorithm is easy to extend to other articulated motion capturing tasks.
296970
Integrating Web-based and Corpus-based Techniques for Question Answering
MIT CSAIL’s entry in this year’s TREC Question Answering track focused on integrating Web-based techniques with more traditional strategies based on document retrieval and named-entity detection. We believe that achieving high performance in the question answering task requires a combination of multiple strategies designed to capitalize on different characteristics of various resources. The system we deployed for the TREC evaluation last year relied exclusively on the World Wide Web to answer factoid questions (Lin et al., 2002). The advantages that the Web offers are well known and have been exploited by previous systems (Brill et al., 2001; Clarke et al., 2001; Dumais et al., 2002). The immense amount of freely available unstructured text provides data redundancy, which can be leveraged with simple pattern matching techniques involving the expected answer formulations. In many ways, we can utilize huge quantities of data to overcome many thorny problems in natural language processing such as lexical ambiguity and paraphrases. Furthermore, Web search engines such as Google provide a convenient front-end for accessing and filtering enormous amounts of Web data. We have identified this class of techniques as the knowledge mining approach to question answering (Lin and Katz, 2003). In addition to viewing the Web as a repository of unstructured documents, we can also take advantage of structured and semistructured sources available on the Web using knowledge annotation techniques (Katz, 1997; Lin and Katz, 2003). Through empirical analysis of real world natural language questions, we have noticed that large classes of commonly occurring queries can be parameterized and captured using a simple object–property–value data model (Katz et al., 2002). Furthermore, such a data model is easy to impose on Web resources through a framework of wrapper scripts. These techniques allow our system to view the Web as if it were a “virtual database” and use knowledge contained therein to answer user questions. While the Web is undeniably a useful resource for question answering, it is not without drawbacks. Useful knowledge on the Web is often drowned out by the sheer amount of irrelevant material, and statistical techniques are often insufficient to separate right answers from wrong ones. Overcoming these obstacles will require addressing many outstanding issues in computational linguistics: anaphora resolution, paraphrase normalization, temporal reference calculation, and lexical disambiguation, just to name a few. Furthermore, the setup of the TREC evaluations necessitates an extra step in the question answering process for systems that extract answers from external sources, typically known as answer projection. For every Web-derived answer, a system must find a supporting document from the AQUAINT corpus, even if the corpus was not used in the answer extraction process. This year’s main task included definition and list questions in addition to factoid questions. Although Web-based techniques have proven effective in handling factoid questions, they are less applicable to tackling definition and list questions. The datadriven approach implicitly assumes that each natural language question has a unique answer. Since a single answer instance is sufficient, algorithms were designed to trade recall for precision. For list and definition questions, however, a more balanced approach is required, since multiple answers are not only desired, but necessary. We believe that the best strategy is to integrate Web-based approaches with more traditional question answering techniques driven by document retrieval and named-entity detection. Corpusand Web-based strategies should play complementary roles in an overall question answering framework.
5082006
Mining motifs in massive time series databases
The problem of efficiently locating previously known patterns in a time series database (i.e., query by content) has received much attention and may now largely be regarded as a solved problem. However, from a knowledge discovery viewpoint, a more interesting problem is the enumeration of previously unknown, frequently occurring patterns. We call such patterns "motifs", because of their close analogy to their discrete counterparts in computation biology. An efficient motif discovery algorithm for time series would be useful as a tool for summarizing and visualizing massive time series databases. In addition it could be used as a subroutine in various other data mining tasks, including the discovery of association rules, clustering and classification. In this paper we carefully motivate, then introduce, a nontrivial definition of time series motifs. We propose an efficient algorithm to discover them, and we demonstrate the utility and efficiency of our approach on several real world datasets.
426181
Omnibase: Uniform Access to Heterogeneous Data for Question Answering
Although the World Wide Web contains a tremendous amount of information, the lack of uniform structure makes finding the right knowledge difficult. A solution is to turn the Web into a "virtual database" and to access it through natural language. We built Omnibase, a system that integrates heterogeneous data sources using an object-property-value model. With the help of Omnibase, our START natural language system can now access numerous heterogeneous data sources on the Web in a uniform manner, and answers millions of user questions with high precision.
11896424
Finding Time Series Discords Based on Haar Transform
The problem of finding anomaly has received much attention recently. However, most of the anomaly detection algorithms depend on an explicit definition of anomaly, which may be impossible to elicit from a domain expert. Using discords as anomaly detectors is useful since less parameter setting is required. Keogh et al proposed an efficient method for solving this problem. However, their algorithm requires users to choose the word size for the compression of subsequences. In this paper, we propose an algorithm which can dynamically determine the word size for compression. Our method is based on some properties of the Haar wavelet transformation. Our experiments show that this method is highly effective.
12592432
User simulations for evaluating answers to question series
Recently, question series have become one focus of research in question answering. These series are comprised of individual factoid, list, and ''other'' questions organized around a central topic, and represent abstractions of user-system dialogs. Existing evaluation methodologies have yet to catch up with this richer task model, as they fail to take into account contextual dependencies and different user behaviors. This paper presents a novel simulation-based methodology for evaluating answers to question series that addresses some of these shortcomings. Using this methodology, we examine two different behavior models: a ''QA-styled'' user and an ''IR-styled'' user. Results suggest that an off-the-shelf document retrieval system is competitive with state-of-the-art QA systems in this task. Advantages and limitations of evaluations based on user simulations are also discussed.