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Abstract-We consider the problem of routing in a mobile ad-hoc network (MANET) for which the planned mobilities of the nodes are partially known a priori and the nodes travel in groups. This situation arises commonly in military and emergency response scenarios. Optimal routes are computed using the most reliable path principle in which the negative logarithm of a node pair's adjacency probability is used as a link weight metric. This probability is estimated using the mobility plan as well as dynamic information captured by table exchanges, including a measure of the social tie strength between nodes. The latter information is useful when nodes deviate from their plans or when the plans are inaccurate. We compare the proposed routing algorithm with the commonly-used optimized link state routing (OLSR) protocol in ns-3 simulations. As the OLSR protocol does not exploit the mobility plans, it relies on link state determination which suffers with increasing mobility. Our simulations show considerably better throughput performance with the proposed approach as compared with OLSR at the expense of increased overhead. However, in the high-throughput regime, the proposed approach outperforms OLSR in terms of both throughput and overhead.

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Abstract. Preliminary results of a longitudinal study aimed at understanding the neurocognitive correlates of learning in a visual object recognition task are reported. The experimental task used real-world novel stimuli, whereas the control task used real-world familiar stimuli. Participants practiced the tasks over 10 weeks and reached a high level of accuracy. Brain imaging data was acquired in weeks 2, 6, and 10 and eye-tracking data was acquired in the other seven weeks. Quantitative and qualitative changes in brain activity were observed over the course of learning and skill acquisition. Generally, in the experimental task, brain activity increased at week 6 and decreased at week 10, whereas in the control task, brain activity decreased at week 6 and further decreased at week 10 compared to week 2. New clusters of brain activity emerged at week 6 in the experimental task. Eye-fixation and pupil-dilation data showed that fast learners tend to inspect the stimuli more thoroughly even after a response was given. These results are used to inform the development of computational cognitive models of visual object recognition tasks.

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