Cascaded 3D Full-body Pose Regression from Single Depth Image at 100 FPS

There are increasing real-time live applications in virtual reality, where it plays an important role in capturing and retargetting 3D human pose. But it is still challenging to estimate accurate 3D pose from consumer imaging devices such as depth camera. This paper presents a novel cascaded 3D full-body pose regression method to estimate accurate pose from a single depth image at 100 fps. The key idea is to train cascaded regressors based on Gradient Boosting algorithm from pre-recorded human motion capture database. By incorporating hierarchical kinematics model of human pose into the learning procedure, we can directly estimate accurate 3D joint angles instead of joint positions. The biggest advantage of this model is that the bone length can be preserved during the whole 3D pose estimation procedure, which leads to more effective features and higher pose estimation accuracy. Our method can be used as an initialization procedure when combining with tracking methods. We demonstrate the power of our method on a wide range of synthesized human motion data from CMU mocap database, Human3.6M dataset and real human movements data captured in real time. In our comparison against previous 3D pose estimation methods and commercial system such as Kinect 2017, we achieve the state-of-the-art accuracy

Multi-omics integration reveals molecular networks and regulators of psoriasis.

BackgroundPsoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis.MethodsTo achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk.ResultsThis integrative genomics study identified both well-characterized (e.g., the IL17 pathway in both GWAS and EWAS) and novel biological processes (e.g., the branched chain amino acid catabolism process in GWAS and the platelet and coagulation pathway in EWAS) involved in psoriasis. Finally, by utilizing tissue-specific gene regulatory networks, we unraveled the interactions among the psoriasis-associated genes and pathways in a tissue-specific manner and detected potential key regulatory genes in the psoriasis networks.ConclusionsThe integration and convergence of multi-omics signals provide deeper and comprehensive insights into the biological mechanisms associated with psoriasis susceptibility

Comparative Analysis on Energy Consumption of Commercial Buildings Based on Sub-metered Data

With energy use growing rapidly around the world, building energy conservation is becoming a great concern especially for large commercial buildings. Therefore, it is of great significance to develop appropriate methods for energy use assessment of commercial buildings. In recent years, energy monitoring system (EMS) has been applied in some large-scale commercial buildings, which has laid the foundation for exhaustive and authentic evaluation. However, most of the current studies are only focused on annual or monthly aggregated energy consumption. Though end-use data are monitored in some buildings, only major categories or equipment are included. Little has been done to analyze the energy performance of numerous buildings with detailed hourly end-use data. With the access to hourly sub-metered data of detailed end uses, this study aims to introduce a comparing method to evaluate building energy performance through a case study. Information on selected buildings in the case was introduced. The research intends to compare energy use intensity (EUI) of the 19 malls based on a uniform energy data model, from total energy to detailed end-uses. It was shown that there is a significant discrepancy on the total energy use among these buildings, mainly due to HVAC (Heating, Ventilation and Air Conditioning) and public lighting. Then an in-depth comparative study was conducted on the energy consumption of public lighting and HVAC respectively. An unexpectedly remarkable discrepancy was illustrated on the EUI of public lighting. Thus the daily and hourly energy of public lighting were compared to identify the discrepancy in management mode. The study on HVAC was focused on the comparison of daily and hourly EUI in terms of four subordinate end uses (chillers, chilled water pumps, fans and cooling systems). The result showed that chillers accounts for larger proportions of total energy use, and the daily and hourly data were compared between buildings with similar climate. At last, the methods were summarized and challenges were discussed