Dynamic Programming Generation of Curves on Brain Surfaces
Dynamic programming algorithms are presented for automated generation of length minimizing geodesics and curves of extremal curvature on the neocortex of the macaque and the visible human. Probabilistic models of curve variation are constructed in terms of the variability in speed, curvature, and torsion in the Frenet representation.

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: 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: 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: 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: Bayesian Segmentation via Asymptotic Partition Functions
abstract: Asymptotic approximations to the partition function of Gaussian random fields are derived. Textures are characterized via Gaussian random fields induced by stochastic difference equations determined by finitely supported, stationary, linear difference operators, adjusted to be nonstationary at the boundaries. It is shown that as the scale of the underlying shape increases, the log-normalizer converges to the integral of the log-spectrum of the operator inducing the random field. Fitting the covariance of the fields amounts to fitting the parameters of the spectrum of the differential operator-induced random field model. Matrix analysis techniques are proposed for handling textures with variable orientation. Examples of texture parameters estimated from training data via asymptotic maximum-likelihood are shown. Isotropic models involving powers of the Laplacian and directional models involving partial derivative mixtures are explored. Parameters are estimated for mitochondria and actin-myocin complexes in electron micrographs and clutter in forward-looking infrared images. Deformable template models are used to infer the shape of mitochondria in electron micrographs, with the asymptotic approximation allowing easy recomputation of the partition function as inference proceeds.

title: Objects in Concurrent Logic Programming Languages
abstract: 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.

title: Leveraging Channel Diversity for Key Establishment in Wireless Sensor Networks
abstract: As the use of sensor networks increases, security in this domain becomes a very real concern. One fundamental aspect of providing confidentiality and authentication is key distribution. While public-key encryption has provided these properties historically, sensors are resource constrained and benefit from symmetric key approaches. In this work, we propose a novel protocol for symmetric key distribution that uses the multiple channels available on sensor hardware. This channel diversity, along with spatial diversity of device locations, allows neighboring sensors to establish secure link keys from plaintext keys that are broadcast by sensors in their neighborhood. In particular, we show that using even one extra channel during the initialization procedure significantly improves the security of key establishment. Via analysis and simulation, we show that our protocol performs well in terms of network connectivity and resilience to colluding malicious devices, when compared to previous work. We show that our protocol can achieve over 90% connectivity among neighboring sensors with link keys that are uncompromised even when 80% of the devices in the network are malicious and collude. Finally, we present a thorough discussion of the comparative advantages and disadvantages of our approach compared to previous techniques.

title: Paradigm Regained: Abstraction Mechanisms for Access Control
abstract: Access control systems must be evaluated in part on how well they enable one to distribute the access rights needed for cooperation, while simultaneously limiting the propagation of rights which would create vulnerabilities. Analysis to date implicitly assumes access is controlled only by manipulating a system’s protection state – the arrangement of the access graph. Because of the limitations of this analysis, capability systems have been ”proven” unable to enforce some basic policies: revocation, confinement, and the *-properties (explained in the text).

title: Proving Facts about "|"
abstract: We study the Knights and Knaves problem, and find that for a proper treatment via theorem-proving, an interaction with natural language processing research is helpful. In particular, we discuss Ohlbach's claim that first-order logic is not well suited to handling this problem. Then we provide another interpretation of the problem using indexicals, and axiomatize it so that the desired result follows. We conclude by suggesting a broader context for dealing with "self-utterances" in automatic theorem-proving. Fuller details of automated proofs are given in a longer paper.

title: Consecutive magic graphs
abstract: Let G be a graph of order n and size e. A vertex-magic total labeling is an assignment of the integers 1,2,...,n+e to the vertices and the edges of G, so that at each vertex, the vertex label and the labels on the edges incident at that vertex, add to a fixed constant, called the magic number of G. Such a labeling is a-vertex consecutive magic if the set of the labels of the vertices is {a+1,a+2,...,a+n}, and is b-edge consecutive magic if the set of labels of the edges is {b+1,b+2,...,b+e}. In this paper we prove that if an a-vertex consecutive magic graph has isolated vertices then the order and the size satisfy (n-1)^2+n^2=(2e+1)^2. Moreover, we show that every tree with even order is not a-vertex consecutive magic and, if a tree of odd order is a-vertex consecutive then a=n-1. Furthermore, we show that every a-vertex consecutive magic graph has minimum degree at least two if a=0, or both 2e>=6n^2-2n+1 and 2a==10n^2-6n+1+4a and 2a=