In this paper, we propose and validate an algorithm of extracting voxel-by-voxel time activity curves directly from inconsistent projections applied in dynamic cardiac SPECT. The algorithm was derived based on factor analysis of dynamic structures (FADS) approach and imposes prior information by applying several regularization functions with adaptively changing relative weighting. The anatomical information of the imaged subject was used to apply the proposed regularization functions adaptively in the spatial domain. The algorithm performance is validated by reconstructing dynamic datasets simulated using the NCAT phantom with a range of different input tissue time-activity curves. The results are compared to the spline-based and FADS methods. The validated algorithm is then applied to reconstruct pre-clinical cardiac SPECT data from canine and murine subjects. Images, generated from both simulated and experimentally acquired data confirm the ability of the new algorithm to solve the inverse problem of dynamic SPECT with slow gantry rotation.

Most of the prevalent anomaly detection systems use some training data to build models. These models are then utilized to capture any deviations resulting from possible intrusions. The efficacy of such systems is highly dependent upon a training data set free of attacks. "Clean" or labeled training data is hard to obtain. This paper addresses the very practical issue of refinement of unlabeled data to obtain a clean data set which can then train an online anomaly detection system.

In this paper we have studied the empirical relation between the hardness of the temporal 3-consistency problem with respect to its inherent problem structure. This is a sub-problem of the temporal constraint satisfaction problem (TCSP). The problem structure has been identified with some quantifiable parameters of the constraint graph. Our work is in line with the recent experimental studies of NPcomplete problems, except that in our case a richer repertoire of independent variables make a straight forward study impossible. Hence we have resorted to a deeper statistical analysis in order to develop a regression model as the sought after quantitative empirical relationship between the problem structure and the timecomplexity. Our model shows robustness against repeated experimentation and, a strong predictive power on independent verification. We also hope that this paper will contribute towards the development of a more rigorous experimental methodology in artificial intelligence than what is being practiced now.

The authors examine a specific scientific application that demonstrates the need for temporal concepts familiar to scientists. By concentrating on a particular (geophysical data) application, they are able to identify in some detail realistic and useful temporal concepts. They propose a framework for the specification of time sequence properties and define operator classes. They then work out a case study in detail, and demonstrate the usefulness of these properties and operator classes. They conclude with several examples of queries over time sequences which are not typically supported by existing query languages and commercial database systems.

The authors provide an efficient algorithm that obtains consistent singleton models of a temporal network by applying a forward pruning technique for the search space. It is important that an intelligent system should be able to capture the notion of time and be able to reason about it. The primitives to represent times are points and intervals. An interval representation for time is used. Obtaining all the satisfiable models of a constraint network. even if the constraints are limited to the pointisable subset of interval relations, takes exponential time in the worst case. Therefore, any success in this area depends on efficient pruning techniques and heuristics. A technique based on pruning the search space is described.>