Multi-shot Pedestrian Re-identification via Sequential Decision Making

Multi-shot pedestrian re-identification problem is at the core of surveillance video analysis. It matches two tracks of pedestrians from different cameras. In contrary to existing works that aggregate single frames features by time series model such as recurrent neural network, in this paper, we propose an interpretable reinforcement learning based approach to this problem. Particularly, we train an agent to verify a pair of images at each time. The agent could choose to output the result (same or different) or request another pair of images to verify (unsure). By this way, our model implicitly learns the difficulty of image pairs, and postpone the decision when the model does not accumulate enough evidence. Moreover, by adjusting the reward for unsure action, we can easily trade off between speed and accuracy. In three open benchmarks, our method are competitive with the state-of-the-art methods while only using 3% to 6% images. These promising results demonstrate that our method is favorable in both efficiency and performance

The Expected Fitness Cost of a Mutation Fixation under the One-dimensional Fisher Model

This paper employs Fisher’s model of adaptation to understand the expected fitness effect of fixing a mutation in a natural population. Fisher’s model in one dimension admits a closed form solution for this expected fitness effect. A combination of different parameters, including the distribution of mutation lengths, population sizes, and the initial state that the population is in, are examined to see how they affect the expected fitness effect of state transitions. The results show that the expected fitness change due to the fixation of a mutation is always positive, regardless of the distributional shapes of mutation lengths, effective population sizes, and the initial state that the population is in. The further away the initial state of a population is from the optimal state, the slower the population returns to the optimal state. Effective population size (except when very small) has little effect on the expected fitness change due to mutation fixation. The always positive expected fitness change suggests that small populations may not necessarily be doomed due to the runaway process of fixation of deleterious mutations

Temporal and Spatial Features of Single-Trial EEG for Brain-Computer Interface

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface

Quantifying the major mechanisms of recent gene duplications in the human and mouse genomes: a novel strategy to estimate gene duplication rates

By studying two mechanisms of gene duplication, unequal crossover and retrotranspostion, and looking at both small gene families and the entire genome, a new estimate for the rate of gene duplication is made which is more accurate for both small and large gene families

Methods for detecting inter-protein covarying sites

Covarying sites are defined to be sites in a protein whose rate of evolution changes over time. We design software to group protein sites into three rate pools: conserved, variant, and temporary invariant. Other software is written to find sites which are closely correlated. The algorithms used by the software require a multiple sequence alignment and phylogenetic tree as input and rely heavily on tree-corrected information entropy. Through a study of the protein Cu, Zn Superoxide Dimutase it is shown that temporary invariant sites have interactions with at least one site which is either closely correlated or binary-switching. From this result it is reasonable to assume that temporary invariant sites which interact with no such intra-protein sites must be sites of protein-protein interaction. Temporary invariant sites are also shows to reflect the animal plant divergence