University of Central Florida

About: University of Central Florida is a education organization based out in Orlando, Florida, United States. It is known for research contribution in the topics: Laser & Population. The organization has 18822 authors who have published 48679 publications receiving 1234422 citations. The organization is also known as: UCF.

Mitochondrial fragmentation in neurodegeneration

Abstract: Mitochondria are remarkably dynamic organelles that migrate, divide and fuse. Cycles of mitochondrial fission and fusion ensure metabolite and mitochondrial DNA mixing and dictate organelle shape, number and bioenergetic functionality. There is mounting evidence that mitochondrial dysfunction is an early and causal event in neurodegeneration. Mutations in the mitochondrial fusion GTPases mitofusin 2 and optic atrophy 1, neurotoxins and oxidative stress all disrupt the cable-like morphology of functional mitochondria. This results in impaired bioenergetics and mitochondrial migration, and can trigger neurodegeneration. These findings suggest potential new treatment avenues for neurodegenerative diseases.

Recognizing realistic actions from videos .

Abstract: In this paper, we present a systematic framework for recognizing realistic actions from videos ldquoin the wildrdquo. Such unconstrained videos are abundant in personal collections as well as on the Web. Recognizing action from such videos has not been addressed extensively, primarily due to the tremendous variations that result from camera motion, background clutter, changes in object appearance, and scale, etc. The main challenge is how to extract reliable and informative features from the unconstrained videos. We extract both motion and static features from the videos. Since the raw features of both types are dense yet noisy, we propose strategies to prune these features. We use motion statistics to acquire stable motion features and clean static features. Furthermore, PageRank is used to mine the most informative static features. In order to further construct compact yet discriminative visual vocabularies, a divisive information-theoretic algorithm is employed to group semantically related features. Finally, AdaBoost is chosen to integrate all the heterogeneous yet complementary features for recognition. We have tested the framework on the KTH dataset and our own dataset consisting of 11 categories of actions collected from YouTube and personal videos, and have obtained impressive results for action recognition and action localization.