On the existence of positive-definite maximum-likelihood estimates of structured covariance matrices
It is shown that a sufficient condition for the likelihood function of a zero-mean Gaussian random vector with covariance R from some class of covariances R to be unbounded above over the set of positive-definite matrices in R is that some singular R/sub o/ exists in R whose range space contains the data. The results obtained imply that, for the spectrum estimation problem in which R is the class of Toeplitz covariances and only one long observation vector is available, by constraining the maximum-likelihood estimation problem to the class of Toeplitz matrices with nonnegative definite circulant extensions, a positive-definite solution is guaranteed to exist.

title: Volumetric transformation of brain anatomy
abstract: Presents diffeomorphic transformations of three-dimensional (3-D) anatomical image data of the macaque occipital lobe and whole brain cryosection imagery and of deep brain structures in human brains as imaged via magnetic resonance imagery. These transformations are generated in a hierarchical manner, accommodating both global and local anatomical detail. The initial low-dimensional registration is accomplished by constraining the transformation to be in a low-dimensional basis. The basis is defined by the Green's function of the elasticity operator placed at predefined locations in the anatomy and the eigenfunctions of the elasticity operator. The high-dimensional large deformations are vector fields generated via the mismatch between the template and target-image volumes constrained to be the solution of a Navier-Stokes fluid model. As part of this procedure, the Jacobian of the transformation is tracked, insuring the generation of diffeomorphisms. It is shown that transformations constrained by quadratic regularization methods such as the Laplacian, biharmonic, and linear elasticity models, do not ensure that the transformation maintains topology and, therefore, must only be used for coarse global registration.

title: Individualizing Anatomical Atlases of the Head
abstract: This paper presents diffeomorphic transformations of 3D anatomical image data in human brains as imaged via magnetic resonance imagery. These transformations are generated in a hierarchical manner accommodating both global and local anatomical detail. The initial low-dimensional registration is accomplished by constraining the transformation to be in a low-dimensional basis. The basis is defined by the Green's function of the elasticity operator placed at predefined locations in the anatomy and the eigenfunctions of the elasticity operator. The high-dimensional, large-deformation registration is accomplished by minimizing the mismatch between the template (atlas) and target image volumes constrained by solving the Navier Stokes fluid model on a vector field.

title: Large deformation diffeomorphic metric mapping of fiber orientations
abstract: This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) 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 geodesies on the space of diffeomorphisms connecting two images. Two different distance function of vector fields are considered. 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 and brain images

title: Information measures for object recognition accommodating signature variability
abstract: 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.

title: Segmenting magnetic resonance images via hierarchical mixture modelling
abstract: We present a statistically innovative as well as scientifically and practically relevant method for automatically segmenting magnetic resonance images using hierarchical mixture models. Our method is a general tool for automated cortical analysis which promises to contribute substantially to the science of neuropsychiatry. We demonstrate that our method has advantages over competing approaches on a magnetic resonance brain imagery segmentation task.

title: Generalized Domination in Chordal Graphs
abstract: We discuss the computational complexity of generalized domination problems, which were introduced in  Complexity of domination-type problems in graphs, Nordic Journal of Computing 1 (1994), 157-171], restricted to chordal and interval graphs. The existence problem, parametrized by two sets of nonnegative integers σ and ρ, asks for the existence of a set S of vertices of a given graph such that for every vertex u ∈ S (or u ∉ S), the number of neighbors of u which are in S is in σ (in ρ, respectively). Telle proved that this problem is NP-complete for general graphs, provided both σ and ρ are finite and 0 ∉ ρ. One of our main results shows that in such cases, the existence problem is polynomially solvable for interval graphs. On the other hand, for chordal graphs, the complexity of the existence problem varies significantly even when σ and ρ contain one element each.

title: Concurrency Among Strangers
abstract: 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.

title: Training Teams with Collaborative Agents
abstract: 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.

title: Further results on almost Moore digraphs
abstract: The nonexistence of digraphs with order equal to the Moore bound Md k d d k for d k has lead to the study of the problem of the existence of almost Moore digraphs namely digraphs with order close to the Moore bound In it was shown that almost Moore digraphs of order Md k degree d diameter k d k contain either no cycle of length k or exactly one such cycle In this paper we shall derive some further necessary conditions for the existence of almost Moore digraphs for degree and diameter greater than As a consequence for diameter and degree d d we show that there are no almost Moore digraphs of order Md with one vertex in a C except the digraphs with every vertex in C

title: Almost Moore digraphs are diregular
abstract: An almost Moore digraph is a digraph of diameter k⩾2 , maximum out-degree d⩾2 and order n=d+d 2 +⋯+d k , that is, one less than the Moore bound. It is easy to show that the out-degree of an almost Moore digraph is constant (=d) . In this note we prove that also the in-degree of an almost Moore digraph is constant (=d) , that is, every almost Moore digraph is diregular of degree d .