Principled Multilayer Network Embedding

Multilayer network analysis has become a vital tool for understanding different relationships and their interactions in a complex system, where each layer in a multilayer network depicts the topological structure of a group of nodes corresponding to a particular relationship. The interactions among different layers imply how the interplay of different relations on the topology of each layer. For a single-layer network, network embedding methods have been proposed to project the nodes in a network into a continuous vector space with a relatively small number of dimensions, where the space embeds the social representations among nodes. These algorithms have been proved to have a better performance on a variety of regular graph analysis tasks, such as link prediction, or multi-label classification. In this paper, by extending a standard graph mining into multilayer network, we have proposed three methods ("network aggregation," "results aggregation" and "layer co-analysis") to project a multilayer network into a continuous vector space. From the evaluation, we have proved that comparing with regular link prediction methods, "layer co-analysis" achieved the best performance on most of the datasets, while "network aggregation" and "results aggregation" also have better performance than regular link prediction methods

A logistic regression model for microalbuminuria prediction in overweight male population

Background: Obesity promotes progression to microalbuminuria and increases the risk of chronic kidney disease. Current protocols of screening microalbuminuria are not recommended for the overweight or obese.

Design and Methods: A cross-sectional study was conducted. The relationship between metabolic risk factors and microalbuminuria was investigated. A regression model based on metabolic risk factors was developed and evaluated for predicting microalbuminuria in the overweight or obese.

Results: The prevalence of MA reached up to 17.6% in Chinese overweight men. Obesity, hypertension, hyperglycemia and hyperuricemia were the important risk factors for microalbuminuria in the overweight. The area under ROC curves of the regression model based on the risk factors was 0.82 in predicting microalbuminuria, meanwhile, a decision threshold of 0.2 was found for predicting microalbuminuria with a sensitivity of 67.4% and specificity of 79.0%, and a global predictive value of 75.7%. A decision threshold of 0.1 was chosen for screening microalbuminuria with a sensitivity of 90.0% and specificity of 56.5%, and a global predictive value of 61.7%.

Conclusions: The prediction model was an effective tool for screening microalbuminuria by using routine data among overweight populations

HDMNet: A Hierarchical Matching Network with Double Attention for Large-scale Outdoor LiDAR Point Cloud Registration

Outdoor LiDAR point clouds are typically large-scale and complexly distributed. To achieve efficient and accurate registration, emphasizing the similarity among local regions and prioritizing global local-to-local matching is of utmost importance, subsequent to which accuracy can be enhanced through cost-effective fine registration. In this paper, a novel hierarchical neural network with double attention named HDMNet is proposed for large-scale outdoor LiDAR point cloud registration. Specifically, A novel feature consistency enhanced double-soft matching network is introduced to achieve two-stage matching with high flexibility while enlarging the receptive field with high efficiency in a patch-to patch manner, which significantly improves the registration performance. Moreover, in order to further utilize the sparse matching information from deeper layer, we develop a novel trainable embedding mask to incorporate the confidence scores of correspondences obtained from pose estimation of deeper layer, eliminating additional computations. The high-confidence keypoints in the sparser point cloud of the deeper layer correspond to a high-confidence spatial neighborhood region in shallower layer, which will receive more attention, while the features of non-key regions will be masked. Extensive experiments are conducted on two large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HDMNet.Comment: Accepted by WACV202

The Economics of Municipal Solid Waste Management

为减轻大量禽畜废弃物对环境的污染,将其资源化为优质的无害化有机肥料,研究了微生物F468对降低鸡粪N营养元素损失和促进其无害化进程的影响。结果表明,继代80次,F468降低鸡粪氨气挥发的能力无显著变化,能降低鸡粪79%的N元素损失。F468还能通过降低蛔虫卵和粪肠杆菌的数量,促进鸡粪的无害化进程。不添加F468,蛔虫卵和粪肠杆菌达到我国无害化标准（NY884-2004）的时间分别需要25d和20d。加入F468后,蛔虫卵和粪肠杆菌达到我国无害化标准的时间分别需要15d和10d,均比不加微生物提前10d达到我国无害化标准

Implementation of MP{_}Lite for the VI Architecture

MP{_}Lite is a light weight message-passing library designed to deliver the maximum performance to applications in a portable and user friendly manner. The Virtual Interface (VI) architecture is a user-level communication protocol that bypasses the operating system to provide much better performance than traditional network architectures. By combining the high efficiency of MP{_}Lite and high performance of the VI architecture, they are able to implement a high performance message-passing library that has much lower latency and better throughput. The design and implementation of MP{_}Lite for M-VIA, which is a modular implementation of the VI architecture on Linux, is discussed in this thesis. By using the eager protocol for sending short messages, MP{_}Lite M-VIA has much lower latency on both Fast Ethernet and Gigabit Ethernet. The handshake protocol and RDMA mechanism provides double the throughput that MPICH can deliver for long messages. MP{_}Lite M-VIA also has the ability to channel-bonding multiple network interface cards to increase the potential bandwidth between nodes. Using multiple Fast Ethernet cards can double or even triple the maximum throughput without increasing the cost of a PC cluster greatly

A numerical investigation into the effects of overweight and obesity on total knee arthroplasty

Overweight and obesity increase risks of knee osteoarthritis, which is a major cause of disability. Severe knee osteoarthritis can be treated by knee arthroplasty. Total knee arthroplasty has been used in overweight and obese patients; however, clinical reports showed that the outcome of this group of patients was not good as normal-weight patients. Two computer models were created in this paper to simulate the effect of excess loads on the distal femoral bone and contact pressures in total knee arthroplasty during a gait cycle. The numerical results showed increased stress in periprosthetic distal femoral bones and higher contact pressure on tibial polyethylene insert during the stance phase. Based on the computer simulation results and published research work, cementless total knee arthroplasty with thicker tibial polyethylene insert may be a better option for overweight patients