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

Planktonic Rotifers in a Subtropical Shallow Lake: Succession, Relationship to Environmental Factors, and Use as Bioindicators

Changes in the density and species composition of planktonic rotifers as well as their relationship to several environmental variables were studied at Dadian Lake, a shallow subtropical lake, which was completely dredged and reconstructed. Samples were taken monthly (2006–2009) at five stations. The total rotifer abundance exponentially declined and reached a relatively stable stage in 2009. Polyarthra dolichoptera and Trichocerca pusilla dominated the rotifer community in most seasons. TN, TP, and CODMn went down at the beginning of the monitoring period, rebounded in the second winter, and then decreased and reached a stable state in 2009. CCA showed that the most significant variations were caused by fluctuations in temperature, CODMn, SRP, and NO2-N. The rotifer community experienced a two-stage succession and the difference of species between the stages was exhibited during warm seasons. GAMs indicated that the selected factors were responsible for 64.8% of the total rotifer abundance variance and 16.5~64.3% of the variances of individual species abundance. Most of the environmental parameters had effects on rotifer abundance that could only be described by complicated curves, characterised by unimodality and bimodality instead of linearity. Our study highlighted the temperature influence on rotifer species composition and total abundance in subtropical lakes

Identifying disease associations via genome-wide association studies

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies prove to be a powerful approach to identify the genetic basis of different human diseases. We studied the relationship between seven diseases characterized in a previous genome-wide association study by the Wellcome Trust Case Control Consortium. Instead of doing a horizontal association of SNPs to diseases, we did a vertical analysis of disease associations by comparing the genetic similarities of diseases. Our analysis was carried out at four levels – the nucleotide level (SNPs), the gene level, the protein level (through protein-protein interaction network), and the phenotype level.</p> <p>Results</p> <p>Our results show that Crohn's disease, rheumatoid arthritis, and type 1 diabetes share evidence of genetic associations at all levels of analysis, offering strong molecular support for the current grouping of the diseases. On the other hand, coronary artery disease, hypertension, and type 2 diabetes, despite being considered as a natural group with potential aetiological overlap, do not show any evidence of shared genetic basis at all levels.</p> <p>Conclusion</p> <p>Our study is a first attempt on mining of GWA data to examine genetic associations between different diseases. The positive result is apparently not a coincidence and hence demonstrates the promising use of our approach.</p

Painterly Image Harmonization via Adversarial Residual Learning

Image compositing plays a vital role in photo editing. After inserting a foreground object into another background image, the composite image may look unnatural and inharmonious. When the foreground is photorealistic and the background is an artistic painting, painterly image harmonization aims to transfer the style of background painting to the foreground object, which is a challenging task due to the large domain gap between foreground and background. In this work, we employ adversarial learning to bridge the domain gap between foreground feature map and background feature map. Specifically, we design a dual-encoder generator, in which the residual encoder produces the residual features added to the foreground feature map from main encoder. Then, a pixel-wise discriminator plays against the generator, encouraging the refined foreground feature map to be indistinguishable from background feature map. Extensive experiments demonstrate that our method could achieve more harmonious and visually appealing results than previous methods.Comment: Accepted by WACV202

Evaluation of Arctic sea ice simulation of CMIP6 models from China

Nine coupled climate models from China participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were evaluated in terms of their capability in ensemble historical Arctic sea ice simulation in the context of 56 CMIP6 models. We evaluated these nine models using satellite observations from 1980 to 2014. This evaluation was conducted comprehensively using 12 metrics covering different aspects of the seasonal cycle and long-term trend of sea ice extent (SIE) and sea ice concentration (SIC). The nine Chinese models tended to overestimate SIE, especially in March, and underestimate its long-term decline trend. There was less spread in model skill in reproducing the spatial pattern of March SIC than in reproducing the spatial pattern of September SIC. The error of March SIC simulation was distributed at the margins of sea ice cover, such as in the Nordic Seas, the Barents Sea, the Labrador Sea, the Bering Sea, and the Sea of Okhotsk. However, the error of September SIC was distributed both at the margins of sea ice cover and in the central part of the Arctic Basin. Five of these nine models had capabilities comparable with the majority of the CMIP6 models in reproducing the seasonal cycle and long-term trend of Arctic sea ice