.................................................................................................................... viii ACKNOLWEDGEMENTS .................................................................................................... x ABBREVIATIONS ................................................................................................................. xi CHAPTER 1 INTRODUCTION ...

Several deep learning approaches have been applied to the autonomous driving task, many employing end-toend deep neural networks. Autonomous driving is complex, utilizing multiple behavioral modalities ranging from lane changing to turning and stopping. However, most existing approaches do not factor in the different behavioral modalities of the driving task into the training strategy. This pap...

The MITRE Corporation’s Evaluation Working Group has developed a methodology for evaluating multi-modal groupware systems and capturing data on human-human interactions. The methodology consists of a framework for describing collaborative systems, a scenario-based evaluation approach, and evaluation metrics for the various components of collaborative systems. We designed and ran two sets of exp...

To develop theories to specify and reason about various aspects of multi-agent systems, many researchers have proposed the use of modal logics such as belief logics, logics of knowledge, and logics of norms. As multi-agent systems operate in dynamic environments, there is also a need to model the evolution of multi-agent systems through time. In order to introduce a temporal dimension to a beli...

The contribution of this paper is twofold: (1) to formulate a fusion problem encountered in the design of a multi-modal identity veriication system as a particular classiication problem, (2) to propose a simple classiier to solve this problem. The multi-modal identity veriication system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, call...

Multi-modal propositional logics with operators representing both time and knowledge are particularly effective to describe the interaction of agents throughout the flow of time ([1, 2, 3, 4, 5, 6]). These systems are usually generated by adding to an existing propositional logic two sets of modalities: one to model the flow of time and one to describe agents’ knowledge. The interaction of such...

In this paper, we present a Bayesian framework for both generating inter-subject large deformation transformations between two multi-modal image sets of the brain and for forming multi-class brain atlases. In this framework, the estimated transformations are generated using maximal information about the underlying neuroanatomy present in each of the different modalities. This modality independe...

This paper motivates and outlines multi-modal multi-threaded online forums. The basic idea is that rather than a single “reply” mode as in current multi-threaded online forums, where the user can only indicate that a current input statement is a reply to an existing statement, the user can select from a menu among a range of ways the input statement relates to a particular existing statement. F...

A robust multi-modal tool, for automated registration, bias correction, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. This work focused on improving the an iterative optimization framework between bias-correction, registration, and tissue classification inspired from previous work. The primary contributions are robustness...

We introduce stable canonical rules and prove that each normal modal multi-conclusion consequence relation is axiomatizable by stable canonical rules. We apply these results to construct finite refutation patterns for modal formulas, and prove that each normal modal logic is axiomatizable by stable canonical rules. We also define stable multi-conclusion consequence relations and stable logics a...