Leverage Business Analytics and OWA to Recommend Appropriate Projects in Crowdfunding Platform

Nowadays, crowdfunding is becoming more and more popular. Many studies have been published on the crowdfunding platform from different perspectives. However, among all these studies, few are concerned about the recommendation methods, which, in effect, are highly beneficial to crowdfunding websites and the participants. Having considered the situation talked above, this paper works out the several features from the relative projects of user’s current browsing project. Then we give different weights to each feature based on selective attention phenomenon, and adopt the method of OWA operator to calculate the final score of each relative project and accomplish our model by picking out the four projects with different outstanding characteristics. Finally, according to the statistics on China’s famous crowdfunding website, we conducted a group of contrast experiments and eventually testified that our proposed model could, to some extent, help classify and give recommendation effectively. Furthermore, the results of this research can give guidance to the management of crowdfunding websites and they are also very significant advices for the future crowdfunding website development

Part-aware Panoptic Segmentation

In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly used datasets: Cityscapes and Pascal VOC. Moreover, we present a single metric to evaluate PPS, called Part-aware Panoptic Quality (PartPQ). For this new task, using the metric and annotations, we set multiple baselines by merging results of existing state-of-the-art methods for panoptic segmentation and part segmentation. Finally, we conduct several experiments that evaluate the importance of the different levels of abstraction in this single task.Comment: CVPR 2021. Code and data: https://github.com/tue-mps/panoptic_part

Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos

Recently, the community has made tremendous progress in developing effective methods for point cloud video understanding that learn from massive amounts of labeled data. However, annotating point cloud videos is usually notoriously expensive. Moreover, training via one or only a few traditional tasks (e.g., classification) may be insufficient to learn subtle details of the spatio-temporal structure existing in point cloud videos. In this paper, we propose a Masked Spatio-Temporal Structure Prediction (MaST-Pre) method to capture the structure of point cloud videos without human annotations. MaST-Pre is based on spatio-temporal point-tube masking and consists of two self-supervised learning tasks. First, by reconstructing masked point tubes, our method is able to capture the appearance information of point cloud videos. Second, to learn motion, we propose a temporal cardinality difference prediction task that estimates the change in the number of points within a point tube. In this way, MaST-Pre is forced to model the spatial and temporal structure in point cloud videos. Extensive experiments on MSRAction-3D, NTU-RGBD, NvGesture, and SHREC'17 demonstrate the effectiveness of the proposed method.Comment: Accepted by ICCV 202

Relationship between serum irisin level, all-cause mortality, and cardiovascular mortality in peritoneal dialysis patients

Introduction: This study aimed to investigate the prospective role of serum irisin-a novel adipo-myokine-in all-cause mortality and cardiovascular (CV) mortality in patients on peritoneal dialysis (PD). Methods: A prospectively observational study was conducted with 154 PD patients. Baseline clinical data were collected from the medical records. Serum irisin concentrations were determined using enzyme-linked immunosorbent assay. Patients were divided into the high irisin group (serum irisin ≥ 113.5ng/mL) and the low irisin group (serum irisin < 113.5ng/mL) based on the median value of serum irisin. A Body Composition Monitor was used to monitor body composition. Cox regression analysis was utilized to find the independent risk factors of all-cause and CV mortality in PD patients. Results: The median serum irisin concentration was 113.5 ng/mL (interquartile range, 106.2–119.8 ng/mL). Patients in the high irisin group had significantly higher muscle mass and carbon dioxide combining power (CO2CP) than those in the low irisin group (p < 0.05). Serum irisin was positively correlated with pulse pressure, CO2CP, and muscle mass, while negatively correlated with body fat percentage (p < 0.05). During a median of follow-up for 60.0 months, there were 55 all-cause deaths and 26 CV deaths. Patients in high irisin group demonstrated a higher CV survival rate than those in low irisin group (p = 0.016). Multivariate Cox regression analysis showed that high irisin level [hazard ratio (HR), 0.341; 95% confidence interval (CI), 0.135–0.858; p = 0.022], age, and diabetic mellitus were independently associated with CV mortality in PD patients. However, serum irisin level failed to demonstrate a statistically significant relationship with all-cause mortality. Conclusion: Low serum irisin levels at baseline were independently predictive of CV mortality but not all-cause mortality in PD patients. Therefore, serum irisin could be a potential target for monitoring CV outcomes in PD patients