Understanding design challenges for homeless’ sleeping situation in the US.

The living situations of the unsheltered America homeless vary across America. This paper highlights this difference by comparing two cities: New York City and San Francisco. The author considers the causes of homelessness, difficulty of being shelters, current living situation and the influence of the society. In both cities, the sleeping space has the biggest problems. It is critical and worth to improve. Different from bedding supplies for camping, disaster reliefs and military, bedding for unsheltered homeless people must meet their chronic needs and flexible requirements of changing sites. As in the New York City, the unsheltered homeless people prefer to stay in railway stations or bus stops to keep warm while in San Francisco most of the homeless live on the street, using tents. It is vital for designers to do some research about public transport sites, street management and street cleaning services to help them with sleeping space. When designing, designers need to take some references in terms of privacy design in public space, outdoor design products and temporary housing. Once the solutions meet the needs of a specific locate, designers must consider to make it more universal

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on three datasets of 579 proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively. Further, our contact-assisted models have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction. Finally, in recent blind CAMEO benchmark our method successfully folded 5 test proteins with a novel fold

The Impact of COVID-19 on Informal Employment and the Measures Taken by the Chinese Government: The Analysis of Street Vending Economy in Nanjing

To revitalize Chinese economic activities under the influence of the pandemic, the Chinese government adjusts the policies and attitudes about street vending. Taking Nanjing as a typical example of a city of street vending, the paper expounds in-depth on the positive impact of street vending on the economy after the pandemic. Meantime, it also describes the new management policies of street vending made by the Nanjing government to develop with a good trend. In addition, the paper directly reflects Nanjing people’s views on street vending and the resulting behavior through professional data. Finally, through the analysis of the advantages of street vending, it proves that the support from street vending in the Chinese economy is non-negligible after the pandemic

DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning

Causal mediation analysis can unpack the black box of causality and is therefore a powerful tool for disentangling causal pathways in biomedical and social sciences, and also for evaluating machine learning fairness. To reduce bias for estimating Natural Direct and Indirect Effects in mediation analysis, we propose a new method called DeepMed that uses deep neural networks (DNNs) to cross-fit the infinite-dimensional nuisance functions in the efficient influence functions. We obtain novel theoretical results that our DeepMed method (1) can achieve semiparametric efficiency bound without imposing sparsity constraints on the DNN architecture and (2) can adapt to certain low dimensional structures of the nuisance functions, significantly advancing the existing literature on DNN-based semiparametric causal inference. Extensive synthetic experiments are conducted to support our findings and also expose the gap between theory and practice. As a proof of concept, we apply DeepMed to analyze two real datasets on machine learning fairness and reach conclusions consistent with previous findings.Comment: Accepted by NeurIPS 202