16608960
Objects in Concurrent Logic Programming Languages
Concurrent Prolog supports object-oriented programming with a clean semantics and additional programming constructs such as incomplete messages, unification, direct broadcasting, and concurrency synchronization [Shapiro 1983a]. While it provides excellent computational support, we claim it does not provide good notation for expressing the abstractions of object-oriented programming. We describe a preprocessor that remedies this problem. The resulting language, Vulcan, is then used as a behicle for exploring new variants of object-oriented programming which become possible in this framework.
40582600
Concurrency Among Strangers
Programmers write programs, expressing plans for machines to execute. When composed so that they may cooperate, plans may instead interfere with each other in unanticipated ways. Plan coordination is the art of simultaneously enabling plans to cooperate, while avoiding hazards of destructive plan interference. For sequential computation within a single machine, object programming supports plan coordination well. For concurrent computation, this paper shows how hard it is to use locking to prevent plans from interfering without also destroying their ability to cooperate.
12136431
Training Teams with Collaborative Agents
Training teams is an activity that is expensive, time-consuming, hazardous in some cases, and can be limited by availability of equipment and personnel. In team training, the focus is on optimizing interactions, such as efficiency of communication, conflict resolution and prioritization, group situation awareness, resource distribution and load balancing, etc. This paper presents an agent-based approach to designing intelligent team training systems. We envision a computer-based training system in which teams are trained by putting them through scenarios, which allow them to practice their team skills. There are two important roles that intelligent agents can play; these are as virtual team members and as coach. To carry out these functions, these agents must be equipped with an understanding of the task domain, the team structure, the selected decision-making process and their belief about other team members' mental states.
35339559
Two new families of large compound graphs
A question of special interest in graph theory is the design of large graphs. Specifically, we want to find constructions of graphs with order as large as possible for a given degree and diameter D . Two generalizations of two large compound graphs are proposed in this article. Three particular cases of these families of graphs presented here allow us to improve the order for the entries (15, 7), (13, 10), and (15, 10) in the table of the largest known ( , D)-graphs. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 47(3), 140–146 2006
1914740
Complete characterization of almost Moore digraphs of degree three
It is well known that Moore digraphs do not exist except for trivial cases (degree 1 or diameter 1), but there are digraphs of diameter two and arbitrary degree which miss the Moore bound by one. No 2004 Wiley Periodicals, Inc.
7046743
Large deformation diffeomorphic metric mapping of vector fields
This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRIs) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. We study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the Large Deformation Diffeomorphic Metric Mapping framework to this type of dataset, resulting in optimizing for geodesics on the space of diffeomorphisms connecting two images. Existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI heart images.
14414468
Information measures for object recognition accommodating signature variability
This paper presents measures characterizing the information content of remote observations of ground scenes imaged via optical and infrared sensors. Object recognition is posed in the context of deformable templates; the special Euclidean group is used to accommodate geometric variation of object pose. Principal component analysis of object signatures is used to represent and efficiently accommodate variation in object signature due to changes in the thermal state of the object surface. Mutual information measures, which are independent of the recognition system, are calculated quantifying both the information gain due to remote observations of the scene and the information loss due to signature variability. Signature model mismatch is quantitatively examined using the Kullback-Leibler divergence. Expressions are derived quadratically approximating the posterior conditional entropy on the orthogonal group for high signal-to-noise ratio. It is demonstrated that quadratic modules accurately characterize the posterior entropy for broad ranges of signal-to-mise ratio. Information gain in multiple-sensor scenarios is quantified, and it is demonstrated that the cost of signature uncertainty for the Comanche series of FLIR images collected by the US Army Night Vision Electronic Sensors Directorate is approximately 0.8 bits with an associated near doubling of mean-squared error uncertainty in pose.