Weixin Li

We describe a novel and effective video enhancement algorithm for low lighting video. The algorithm works by first inverting an input low-lighting video and then applying an optimized image de-haze algorithm on the inverted video. To facilitate faster computation, temporal correlations between subsequent frames are utilized to expedite the calculation of key algorithm parameters. Simulation results show excellent enhancement results and 4× speed up as compared with the frame-wise enhancement algorithms.

We describe a procedure for model averaging of relaxed molecular clock models in Bayesian phylogenetics. Our approach allows us to model the distribution of rates of substitution across branches, averaged over a set of models, rather than conditioned on a single model. We implement this procedure and test it on simulated data to show that our method can accurately recover the true underlying distribution of rates. We applied the method to a set of alignments taken from a data set of 12 mammalian species and uncovered evidence that lognormally distributed rates better describe this data set than do exponentially distributed rates. Additionally, our implementation of model averaging permits accurate calculation of the Bayes factor(s) between two or more relaxed molecular clock models. Finally, we introduce a new computational approach for sampling rates of substitution across branches that improves the convergence of our Markov chain Monte Carlo algorithms in this context. Our methods are implemented under the BEAST 1.6 software package, available at http://beast-mcmc.googlecode.com.

Depression is a common and serious medical illness which has a wide negative impact on individuals, families, and society. Automatic Depression Detection (ADD) is increasingly demanded for human healthcare thanks to its objectiveness, convenience, and low cost. Considering that the duration of depressive symptoms varies among different identities and treatment phases, it is essential for ADD methods to have the capability to capture information at various temporal scales. However, most existing ADD methods cannot generate rich contextual cues or utilize long-range temporal dependency effectively. In this paper, we propose a novel approach for depression recognition based on visual behaviors, which employs Atrous Residual Temporal Convolutional Network (DepArt-Net) as well as temporal fusion to capture the long-range dynamic depressive cues. First, the proposed atrous temporal convolution generates multi-scale contextual features from low-level visual behaviors, which are further strengthened by residual blocks across different convolution groups. Second, we introduce the attention mechanism in temporal feature fusion stage, and with the learned attentive distribution, more discriminative video-level depression representation can be acquired. Experimental results on the DAIC-WOZ benchmark demonstrate the effectiveness of the proposed approach and its superiority over other state-of-the-art methods.