We strongly believe in open source and giving to our community. We work directly with researchers in academia and seek out new perspectives with our intern and fellowship programs. We generalize our solutions and release them to the world as open source projects. We host discussions and publish our results.


Proceedings of the Third Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, CA, August 1997

Mining multivariate time-series sensor data to discover behavior envelopes

This paper addresses large-scale regression tasks using a novel combination of greedy input selection and asymmetric cost. Our primary goal is learning envelope functions suitable for automated detection of anomalies in future sensor data.

We argue that this new approach can be more effective than traditional techniques, such as static red-line limits, variance-based error bars, and general probability density estimation.

Proceedings of IJCAI, the International Joint Conference in Artificial Intelligence. 1997

Notes Explorer: Toward Structured Retrieval in Semi-structured Information Spaces

A semi-structured information space consists of multiple collections of textual documents containing fielded or tagged sections. The space can be highly heterogeneous, because each collection has its own schema, and there are no enforced keys or formats for data items across collections.

Thus, structured methods like SQL cannot be easily employed, and users often must make do with only full-text search. In this paper, we describe an intermediate approach that provides structured querying for particular types of entities, such as companies, people, and skills.

Entity-based retrieval is enabled by normalizing entity references in a heuristic, type-dependent manner. To organize and filter search results, entities are categorized as playing particular roles (e.g., company as client, as vendor, etc.) in particular collection types (directories, client engagement records, etc.).

The approach can be used to retrieve documents and can also be used to construct entity profiles - summaries of commonly sought information about an entity based on the documents’ content. The approach requires only a modest amount of meta-information about the source collections, much of which is derived automatically. On a set of typical user queries in a large corporate information space, the approach produces a dramatic improvement in retrieval quality over knowledge-free methods like full-text search.

Proceedings of IEEE/EURASIP Workshop on Nonlinear Signal and Image Processing, Mackinac Island, Michigan, September, 1997

A method for ECT image reconstruction with uncertain MRI side information using asymptotic marginalization

Alfred O Hero III, Robinson Piramuthu

In [1] a methodology for incorporating extracted MRI anatomical boundary information into penalized likelihood (PL) ECT image reconstructions and tracer uptake estimation was proposed. This methodology used quadratic penalty based on Gibbs weights which enforced smoothness constraints everywhere in the image except across the MRI-extracted boundary of the ROI.

When high quality estimates of the anatomical boundary are available and MRI and ECT images are perfectly registered, the performance of this method was shown to be very close to that attainable using ideal side information, i.e. noiseless anatomical boundary estimates.

However when the variance of the MRI-extracted boundary estimates becomes significant this penalty function method performs poorly. We give a modified Gibbs penalty function implemented with a set of averaged Gibbs weights, where the averaging is performed with respect to a limiting form of the posterior distribution of the MRI boundary parameters.

Journal of Experimental Psychology: Learning, Memory and Cognition, Vol 23, No. 5, 1247-1260, 1997

An investigation into cued recall of multi-attribute stimuli

Geoff Ward, Elizabeth Churchill, Paul Musgrove

Memory performance for sequences of letters positioned in particular spatial locations in a 3 x 3 grid was examined by requiring participants to recall attributes of the target stimuli given 1 or 2 features of the stimuli as cues. Cuing asymmetry was observed between the serial-position curves of object and sequential-order information, and location and sequential-order information, when the stimuli were presented in both the same and different locations.

After correcting for response bias, this asymmetry was attenuated for the stimuli presented in different locations and was eliminated for the stimuli presented in the same location.

Contrary to the predictions of the fragmentation hypothesis (G. V. Jones, 1976), asymmetry was also observed between object and location information. The roles of spatial location and response bias are offered as explanations for previous contradictory claims for cuing symmetry between item and order information.

Proceedings of the Third Mississippi State Conference on Difference Equations and Computational Simulations. Mississippi State, MS. 1997

Two-sided Mullins-Sekerka flow does not preserve convexity

The (two-sided) Mullins-Sekerka model is a nonlocal evolution model for closed hypersurfaces, which was originally proposed as a model for phase transitions of materials of negligible specific heat. Under this evolution the propagating interfaces maintain the enclosed volume while the area of the interfaces decreases.

We will show by means of an example that the Mullins-Sekerka flow does not preserve convexity in two space dimensions, where we consider both the Mullins-Sekerka model on a bounded domain, and the Mullins-Sekerka model defined on the whole plane.

Proceedings of CIKIM. 1996

Entity-Based Retrieval in Shared Semi-Structured Information Spaces

Semi-structured information sharing systems are gaining in popularity because they allow users to easily create shared collections of textual documents, organized by a common set of fields. Unfortunately, in a large organization this freedom can result in an unwieldy space of shared information that is difficult to retrieve.

Standard tools like full-text search do not alleviate the problem, in part because they do not make any use of the structure within each document collection. In this paper, we describe an approach that goes beyond full-text search by taking advantage of both the structure of the document collections and a knowledge of what information types are important within the organization sharing the information.

We present an implemented indexing/browsing system called Notes Explorer that allows users to browse for entities (companies, people, etc.) across a large semi-structured information space. Notes Explorer incorporates three key components:

(1) automatic classification of document fields to recognize common entity and document collection types;

(2) entity-based browsing over multiple document collections, with type-dependent normalization;


(3) content-based filtering of browse results.

Machine Learning and Knowledge Acquisition. G. Tecuci, Y. Kodratoff. Eds. Academic Press. 1994

Increasing Levels of Assistance in Refinement of Knowledge-based Retrieval Systems (extended)

Catherine Baudin, Smadar Kedar, Barney Pell, Catherine Baudin, Smadar Kedar, Barney Pell

No Information

Knowledge Acquisition Journal Volume 6. 179-176. 1994

Increasing Levels of Assistance in Refinement of Knowledge-based Retrieval Systems

Catherine Baudin, Smadar Kedar, Barney Pell, Catherine Baudin, Smadar Kedar, Barney Pell

This paper is concerned with the task of incrementally acquiring and refining the knowledge and algorithms of a knowledge-based system in order to improve its performance over time. In particular, we present the design of DE-KART, a tool whose goal is to provide increasing levels of assistance in acquiring and refining indexing and retrieval knowledge for a knowledge-based retrieval system.

DE-KART starts with knowledge that has been entered manually, and increase its level of assistance in acquiring and refining that knowledge, both in terms of the increased level of automation in interacting with users, and in terms of the increased generality of the knowledge.

DE-KART is at the intersection of machine learning and knowledge acquisition: it is a first step towards a system which moves along a continuum from interactive knowledge acquisition to increasingly automated machine learning as it acquires more knowledge and experience.

Electronic J. Diff. Equ., 1993 no. 08, pp. 1-7 (1993)

One-sided Mullins-Sekerka flow does not preserve convexity

The Mullins-Sekerka model is a nonlocal evolution model for hypersurfaces, which arises as a singular limit for the Cahn-Hilliard equation. Assuming the existence of sufficiently smooth solutions we will show that the one-sided Mullins-Sekerka flow does not preserve convexity. The main tool is the strong maximum principle for elliptic second order differential equations.