Physical Sciences and Mathematics Commons^™

Packaging Predictable Assembly With Prediction-Enabled Component Technology, Scott A. Hissam, Gabriel A. Moreno, Judith Stafford, Kurt C. Wallnau

Gabriel A. Moreno

Formalization Of Computer Supported Cooperative Work Applications, Carl Chang, Jia Zhang, Tsang Ming Jiang

Jia Zhang

No abstract provided.

Applicability Of General Scenarios To The Architecture Tradeoff Analysis Method, Len Bass, Mark H. Klein, Gabriel A. Moreno

Gabriel A. Moreno

Oral History Interview With Mark P. Mccahill, Philip L. Frana

Philip L Frana

In this oral history Mark P. McCahill, Assistant Director of Academic and Distributed Computing Services at the University of Minnesota, recounts his role as leader of the team that created the popular client/server software for organizing and sharing information on the Internet. McCahill also describes his work in the development of Pop Mail, Gopher VR, Forms Nirvana, the Electronic Grants Management System, and the University of Minnesota Portal.

Insufficiency Of Piecewise Evolution, Sanza Kazadi, Yan Qi, Isaac Park, Nancy Huang, Paul Hwu, Brian Kwan, Wayne Lue, Hubert Li

Sanza Kazadi

Verifying Properties Of Neural Networks, Pedro Rodriques, J. Félix Costa, Hava Siegelmann

Hava Siegelmann

In the beginning of nineties, Hava Siegelmann proposed a new computational model, the Artificial Recurrent Neural Network (ARNN), and proved that it could perform hypercomputation. She also established the equivalence between the ARNN and other analog systems that support hypercomputation, launching the foundations of an alternative computational theory. In this paper we contribute to this alternative theory by exploring the use of formal methods in the verification of temporal properties of ARNNs. Based on the work of Bradfield in verification of temporal properties of infinite systems, we simplify his tableau system, keeping its expressive power, and show that it is …

Oral History Interview With Dick Hedger, Philip L. Frana

Philip L Frana

Richard Hedger begins the interview describing his education in electrical engineering at the University of Minnesota. Following graduation he accepted a position at IBM Rochester in the optical character recognition group. He then discusses his shift to software development. Eventually he joined the Service and Support group developing application software supporting facilities infrastructures for the IBM System/3, and System/360. In various places in the interview, he describes some of the programming techniques in ...

Symbolic Dynamics And Computation In Model Gene Networks, R. Edwards, Hava Siegelmann, K. Aziza, L. Glass

Hava Siegelmann

We analyze a class of ordinary differential equations representing a simplified model of a genetic network. In this network, the model genes control the production rates of other genes by a logical function. The dynamics in these equations are represented by a directed graph on an n-dimensional hypercube (n-cube) in which each edge is directed in a unique orientation. The vertices of the n-cube correspond to orthants of state space, and the edges correspond to boundaries between adjacent orthants. The dynamics in these equations can be represented symbolically. Starting from a point on the boundary between neighboring orthants, the equation …

Does Sei Level 5 Lead To High Quality Software, Robert Yacobellis

Robert H Yacobellis

No abstract provided.

Configuring Client Software Using Remote Notification Us:6219698, Robert Iannucci, Chris Weikart

Robert A Iannucci

No abstract provided.

Distinguishing Natural Language Processes On The Basis Of Fmri-Measured Brain Activation, Francisco Pereira, Marcel Just, Tom Mitchell

Marcel Adam Just

No abstract provided.

Metafam: A Unified Classification Of Protein Families. Ii. Schema And Query Capabilities, Elizabeth Shoop, K. Silverstein, Et Al

Elizabeth Shoop

No abstract provided.

Mental Rotation Of Objects Retrieved From Memory: An Fmri Study Of Spatial Processing, Marcel Just, Patricia Carpenter, Mandy Maguire, Vaibhav Diwadkar, Stephanie Mcmains

Marcel Adam Just

No abstract provided.

Metafam: A Unified Classification Of Protein Families I. Overview And Statistics, Elizabeth Shoop, K. Silverstein, Et Al

Elizabeth Shoop

No abstract provided.

The Metafam Server: A Comprehensive Protein Family Resource, Elizabeth Shoop, K. Silverstein, Et Al

Elizabeth Shoop

No abstract provided.

Interdependence Of Non-Overlapping Cortical Systems In Dual Cognitive Tasks, Marcel Adam Just, Patricia A. Carpenter, Timothy A. Keller, Lisa Emery, Holly Zajac, Keith R. Thulborn

Marcel Adam Just

No abstract provided.

Oral History Interview With Edsger W. Dijkstra, Philip L. Frana

Philip L Frana

In this oral history Edsger Dijkstra recounts his early education and training as a theoretical physicist and as a'programmer'. Dijkstra describes his work developing software, and his activities at several early information processing conferences. Dijkstra also discourses on the development of ALGOL 60 and the origins of computing science in Europe and America.

A Binary Entropy Measure To Assess Nonrigid Registration Algorithms, Simon K. Warfield, Jan Rexilius, Petra S. Huppi, Terrie E. Inder, Erik G. Learned-Miller, William M. Wells Iii, Gary P. Zientara, Ferenc A. Jolesz, Ron Kikinis

Erik G Learned-Miller

Assessment of normal and abnormal anatomical variability requires a coordinate system enabling inter-subject comparison. We present a binary minimum entropy criterion to assess affine and nonrigid transformations bringing a group of subject scans into alignment. This measure is a data-driven measure allowing the identification of an intrinsic coordinate system of a particular group of subjects. We assessed two statistical atlases derived from magnetic resonance imaging of newborn infants with gestational age ranging from 24 to 40 weeks. Over this age range major structural changes occur in the human brain and existing atlases are inadequate to capture the resulting anatomical variability. …

Color Eigenflows : Statistical Modeling Of Joint Color Changes, Erik G. Learned-Miller, Kinh Tieu

Erik G Learned-Miller

We develop a linear model of commonly observed joint color changes in images due to variation in lighting and certain non-geometric camera parameters. This is done by observing how all of the colors are mapped between two images of the same scene under various “real-world” lighting changes. We represent each instance of such a joint color mapping as a 3-D vector field in RGB color space. We show that the variance in these maps is well represented by a low dimensional linear subspace of these vector fields. We dub the principal components of this space the color eigenflows. When applied …

An Intrinsic Coordinate System Of The Developing Human Brain, Simon K. Warfield, Petra S. Huppi, Terrie E. Inder, Erik G. Learned-Miller, William M. Wells Iii, Gary P. Zientara, Ferenc A. Jolesz, Ron Kikinis

Erik G Learned-Miller

No abstract provided.

A Service Paradigm For Reconfigurable Agents, Gary Holness, Deepak Karuppiah, Subramanya Uppala, Roderic Grupen, S. Chandu Ravela

Roderic Grupen

Applications of multiple processors embedded in the systems involved with entertainment, informatics, climate control, communication, transportation, and food preparation are already commonplace. A network of these embedded processors presents application development challenges since the state space grows exponentially as new devices attach to the network. The programming model for such systems will need to change if reliable systems are to be realized. By observing information about sensorimotor activity, such systems can gather information useful to programming the network. Through such interaction, a network can build hierarchies of shareable computational structures for representing various activities. This application domain presents additional challenges …

Lighting Invariance Through Joint Color Change Models, Erik G. Learned-Miller, Kinh Tieu, Eric Grimson

Erik G Learned-Miller

In [9], we introduced a linear statistical model of joint color changes in images due to variation in lighting and certain non-geometric camera parameters. We did this by measuring the mappings of colors in one image of a scene to colors in another image of the same scene under different lighting conditions. In this paper, we extend our model in several ways and examine its applicability to several important problems in machine vision. The extensions to our model include incorporating a model of image noise and a prior on the color flows used to explain a particular image difference. In …

The Neural Bases Of Sentence Comprehension: A Fmri Examination Of Syntactic And Lexical Processing, Timothy A. Keller, Patricia A. Carpenter, Marcel Adam Just

Marcel Adam Just

No abstract provided.

An Fmri Investigation Of Sentence Comprehension By Eye And By Ear: Modality Fingerprints On Cognitive Processes, Erica B. Michael, Timothy A. Keller, Patricia A. Carpenter, Marcel Adam Just

Marcel Adam Just

No abstract provided.

Modeling Score Distributions For Combining The Outputs Of Search Engines, R. Manmatha, T. Rath, F. Feng

R. Manmatha

In this paper the score distributions of a number of text search engines are modeled. It is shown empirically that the score distributions on a per query basis may be fitted using an exponential distribution for the set of non-relevant documents and a normal distribution for the set of relevant documents. Experiments show that this model fits TREC-3 and TREC-4 data for not only probabilistic search engines like INQUERY but also vector space search engines like SMART for English. We have also used this model to fit the output of other search engines like LSI search engines and search engines …

Teacher’S Stages Of Development In Using Visualization Tools For Inquiry-Based Science: The Case Of Project Vism, M. Charles, Robert A. Kolvoord

Robert A Kolvoord

No abstract provided.

Learning Object-Independent Modes Of Variation With Feature Flow Fields, Erik G. Learned-Miller, Kinh Tieu, Chris Stauffer

Erik G Learned-Miller

We present a unifying framework in which object-independent modes of variation are learned from continuous-time data such as video sequences. These modes of variation can be used as generators to produce a manifold of images of a new object from a single example of that object. We develop the framework in the context of a well-known example: analyzing the modes of spatial deformations of a scene under camera movement. Our method learns a close approximation to the standard affine deformations that are expected from the geometry of the situation, and does so in a completely unsupervised (i.e. ignorant of the …