A Survey on Dropout Methods and Experimental Verification in Recommendation

Overfitting is a common problem in machine learning, which means the model too closely fits the training data while performing poorly in the test data. Among various methods of coping with overfitting, dropout is one of the representative ways. From randomly dropping neurons to dropping neural structures, dropout has achieved great success in improving model performances. Although various dropout methods have been designed and widely applied in past years, their effectiveness, application scenarios, and contributions have not been comprehensively summarized and empirically compared by far. It is the right time to make a comprehensive survey. In this paper, we systematically review previous dropout methods and classify them into three major categories according to the stage where dropout operation is performed. Specifically, more than seventy dropout methods published in top AI conferences or journals (e.g., TKDE, KDD, TheWebConf, SIGIR) are involved. The designed taxonomy is easy to understand and capable of including new dropout methods. Then, we further discuss their application scenarios, connections, and contributions. To verify the effectiveness of distinct dropout methods, extensive experiments are conducted on recommendation scenarios with abundant heterogeneous information. Finally, we propose some open problems and potential research directions about dropout that worth to be further explored.Comment: 26 page

Graph-based Village Level Poverty Identification

Poverty status identification is the first obstacle to eradicating poverty. Village-level poverty identification is very challenging due to the arduous field investigation and insufficient information. The development of the Web infrastructure and its modeling tools provides fresh approaches to identifying poor villages. Upon those techniques, we build a village graph for village poverty status identification. By modeling the village connections as a graph through the geographic distance, we show the correlation between village poverty status and its graph topological position and identify two key factors (Centrality, Homophily Decaying effect) for identifying villages. We further propose the first graph-based method to identify poor villages. It includes a global Centrality2Vec module to embed village centrality into the dense vector and a local graph distance convolution module that captures the decaying effect. In this paper, we make the first attempt to interpret and identify village-level poverty from a graph perspective.Comment: 5 pages, accepted by theWebConf 202

Experimental study on combustion optimization to alleviate fouling on heating surface of a Zhundong coal Boiler

In 2021, the price of most of the domestic coal skyrocketed, affecting the stable supply of electric power supply in some regions, while the price of Xinjiang Zhundong coal remains stable at a low level. Thus, the study of safe and stable large-scale combustion of Zhundong coal in boilers is more and more important for the energy supplyHowever, fouling and slagging often occur on the heating surfaces of the boiler due to the characteristics of Zhundong coal and the high temperature of the flue gas. The effects of the operating parameters, including the primary air velocity, operating oxygen content, air staging and coal fineness on the combustion temperature and NOx emission were investigated on a four-corner tangentially fired boiler of 660 MW capacity, which burned 95% Zhundong coal to get the relevant control criterion. Based on the research, the improvement of control function for contamination of heating surfaces was implemented under steady and dynamic working conditions. Finally, the influence of the improvement on the contamination of the heating surfaces was proved by the method of the contamination monitoring of heated surfaces. The results show that, at 660 MW load condition, the operating oxygen content had the most obvious influence on the flue gas temperature at the outlet of the furnace. The flue gas temperature decreased 99 °C when it increased from 2.0% to 3.5%. The reduction of the operating oxygen content and the local mean stoichiometric ratio of the main combustion zone could lead to a significant reduction of NOx emission and the change of the coal fineness had no obvious effect on NOx emission. After the improved control function was implemented, the rate of flue gas temperature decreased from 6.18 to 4.26 °C min−1 during the load increase process, the maximum temperature decreased from 1104 to 1023 °C under 660 MW. The heat absorption ratio of the platen superheater, low-temperature superheater and low-temperature reheater increased by 0.6%, 1.6% and 0.9%, respectively, indicating that the fouling and slagging was significantly reduced. At 330 MW load condition, increasing the coal fineness R90 to around 4.9% of the uppermost mill could effectively reduce the flue gas temperature near the bottom of the high-temperature reheater, reduce the deposition of combustible matter and fly ash, and inhibit the formation of fouling and slagging

Gene Delivery to Nonhuman Primate Preimplantation Embryos Using Recombinant Adeno-Associated Virus

Delivery of genome editing tools to mammalian zygotes has revolutionized animal modeling. However, the mechanical delivery method to introduce genes and proteins to zygotes remains a challenge for some animal species that are important in biomedical research. Here, an approach to achieve gene delivery and genome editing in nonhuman primate embryos is presented by infecting zygotes with recombinant adeno-associated viruses (rAAVs). Together with previous reports from the authors of this paper and others, this approach is potentially applicable to a broad range of mammals. In addition to genome editing and animal modeling, this rAAV-based method can facilitate gene function studies in early-stage embryos

Modeling Rett Syndrome Using TALEN-Edited MECP2 Mutant Cynomolgus Monkeys

Gene-editing technologies have made it feasible to create nonhuman primate models for human genetic disorders. Here, we report detailed genotypes and phenotypes of TALEN-edited MECP2 mutant cynomolgus monkeys serving as a model for a neurodevelopmental disorder, Rett syndrome (RTT), which is caused by loss-of-function mutations in the human MECP2 gene. Male mutant monkeys were embryonic lethal, reiterating that RTT is a disease of females. Through a battery of behavioral analyses, including primate-unique eye-tracking tests, in combination with brain imaging via MRI, we found a series of physiological, behavioral, and structural abnormalities resembling clinical manifestations of RTT. Moreover, blood transcriptome profiling revealed that mutant monkeys resembled RTT patients in immune gene dysregulation. Taken together, the stark similarity in phenotype and/or endophenotype between monkeys and patients suggested that gene-edited RTT founder monkeys would be of value for disease mechanistic studies as well as development of potential therapeutic interventions for RTT