Multi-task super resolution method for vector field critical points enhancement

It is a challenging task to handle the vector field visualization at local critical points. Generally, topological based methods firstly divide critical regions into different categories, and then process the different types of critical regions to improve the effect, which pipeline is complex. In the paper, a learning based multi-task super resolution (SR) method is proposed to improve the refinement of vector field, and enhance the visualization effect, especially at the critical region. In detail, the multi-task model consists of two important designs on task branches: one task is to simulate the interpolation of discrete vector fields based on an improved super-resolution network; and the other is a classification task to identify the types of critical vector fields. It is an efficient end-to-end architecture for both training and inferencing stages, which simplifies the pipeline of critical vector field visualization and improves the visualization effect. In experiment, we compare our method with both traditional interpolation and pure SR network on both simulation data and real data, and the reported results indicate our method lower the error and improve PSNR significantly

BAGEL: Backdoor Attacks against Federated Contrastive Learning

Federated Contrastive Learning (FCL) is an emerging privacy-preserving paradigm in distributed learning for unlabeled data. In FCL, distributed parties collaboratively learn a global encoder with unlabeled data, and the global encoder could be widely used as a feature extractor to build models for many downstream tasks. However, FCL is also vulnerable to many security threats (e.g., backdoor attacks) due to its distributed nature, which are seldom investigated in existing solutions. In this paper, we study the backdoor attack against FCL as a pioneer research, to illustrate how backdoor attacks on distributed local clients act on downstream tasks. Specifically, in our system, malicious clients can successfully inject a backdoor into the global encoder by uploading poisoned local updates, thus downstream models built with this global encoder will also inherit the backdoor. We also investigate how to inject backdoors into multiple downstream models, in terms of two different backdoor attacks, namely the \textit{centralized attack} and the \textit{decentralized attack}. Experiment results show that both the centralized and the decentralized attacks can inject backdoors into downstream models effectively with high attack success rates. Finally, we evaluate two defense methods against our proposed backdoor attacks in FCL, which indicates that the decentralized backdoor attack is more stealthy and harder to defend

Spatiotemporal heterogeneity and impact factors of hepatitis B and C in China from 2010 to 2018: Bayesian space–time hierarchy model

IntroductionViral hepatitis is a global public health problem, and China still faces great challenges to achieve the WHO goal of eliminating hepatitis.MethodsThis study focused on hepatitis B and C, aiming to explore the long-term spatiotemporal heterogeneity of hepatitis B and C incidence in China from 2010 to 2018 and quantify the impact of socioeconomic factors on their risk through Bayesian spatiotemporal hierarchical model.ResultsThe results showed that the risk of hepatitis B and C had significant spatial and temporal heterogeneity. The risk of hepatitis B showed a slow downward trend, and the high-risk provinces were mainly distributed in the southeast and northwest regions, while the risk of hepatitis C had a clear growth trend, and the high-risk provinces were mainly distributed in the northern region. In addition, for hepatitis B, illiteracy and hepatitis C prevalence were the main contributing factors, while GDP per capita, illiteracy rate and hepatitis B prevalence were the main contributing factors to hepatitis C.DisussionThis study analyzed the spatial and temporal heterogeneity of hepatitis B and C and their contributing factors, which can serve as a basis for monitoring efforts. Meanwhile, the data provided by this study will contribute to the effective allocation of resources to eliminate viral hepatitis and the design of interventions at the provincial level

Virulence Determinants Are Required for Brain Abscess Formation Through Staphylococcus aureus Infection and Are Potential Targets of Antivirulence Factor Therapy

Bacterial brain abscesses (BAs) are difficult to treat with conventional antibiotics. Thus, the development of alternative therapeutic strategies for BAs is of high priority. Identifying the virulence determinants that contribute to BA formation induced by Staphylococcus aureus would improve the effectiveness of interventions for this disease. In this study, RT-qPCR was performed to compare the expression levels of 42 putative virulence determinants of S. aureus strains Newman and XQ during murine BA formation, ear colonization, and bacteremia. The alterations in the expression levels of 23 genes were further confirmed through specific TaqMan RT-qPCR. Eleven S. aureus genes that persistently upregulated expression levels during BA infection were identified, and their functions in BA formation were confirmed through isogenic mutant experiments. Bacterial loads and BA volumes in mice infected with isdA, isdC, lgt, hla, or spa deletion mutants and the hla/spa double mutant strain were lower than those in mice infected with the wild-type Newman strain. The therapeutic application of monoclonal antibodies against Hla and SpA decreased bacterial loads and BA volume in mice infected with Newman. This study provides insights into the virulence determinants that contribute to staphylococcal BA formation and a paradigm for antivirulence factor therapy against S. aureus infections

Realization of Intelligent Household Appliance Wireless Monitoring Network Based on LEACH Protocol

The intelligent household appliance wireless monitoring network can real-time monitor the apparent power and power factor of various household appliances in different indoor regions, and can realize the real-time monitoring on the household appliance working status and performance. The household appliance wireless monitoring network based on LEACH protocol is designed in the paper. Firstly, the basic idea of LEACH routing algorithm is proposed. Aiming at the node-distribution feature of intelligent home, the selection of cluster head in the routing algorithm and the data transmission method at the stable communication phase is modified. Moreover, the hardware circuit of power acquisition and power factor measurement is designed. The realization of wireless monitoring network based on CC2530 is described, each module and the whole system were conducted the on-line debugging. Finally, the system is proved to meet the practical requirement through the networking test

The Gender Gap in Household Bargaining Power: A Revealed-Preference Approach

When members of the same household have different risk preferences, whose preference matters more for investment decisions and why? We propose an intrahousehold model that aggregates individual preferences at the household level as a result of bargaining. We structurally estimate the model, analyze the determinants of bargaining power, and find a significant gender gap. The gap is partially explained by gender differences in individual characteristics, but it is also driven by gender effects. These patterns hold broadly across Australia, Germany, and the US. We further link the distribution of bargaining power to perceived gender norms in the cross-section of households.the British Academy (Grant SRG1920\101488) and the Keynes Fund at Cambridge for financial support. Also support from Cambridge Endowment for Research in Finance (CERF)

Insight into the C8 light hydrocarbon compositional differences between coal-derived and oil-associated gases

To analyze the C8 light hydrocarbon of absorbed gas in the source rock and natural gas, both the PY-GC and GC were applied. This is done in order to develop the discrimination parameters of different genetic gases. Eight samples, including six mudstones with type II1 and type I organic matter and two coals, were analyzed by PY-GC. On the other hand, the sixteen typical coal-derived gases and sixteen oil-associated gases were analyzed by GC. The results show that there exists a great difference in the ratio of 2-methylheptane and 1-cis-3-dimethylcyclohexane in coal-derived gases, oil-associated gases, and source rock absorbed gases. The ratio in coal-derived gases is less than 0.5, whereas it is higher than 0.5 in oil-associated gases. In addition, there are also differences in the relative composition of C8 normal alkanes, isoparaffin, and cycloparaffin in coal-derived and oil-associated gases. Coal-derived gas is characterized by high cycloparaffin content that is generally higher than 40%, while the oil-associated gas exhibits low cycloparaffin content that generally less than 40%, as well as high isoparaffin content. Therefore, these parameters can be used to identify a coal-derived gas from an oil-associated gas. Keywords: C8 light hydrocarbons, Coal-derived gas, Oil-associated ga

A Dynamic Hierarchical Evaluating Network for Real-Time Strategy Games

Researches of AI planning in Real-Time Strategy (RTS) games have been widely applied to human behavior modeling and combat simulation. State evaluation is an important research area for AI planning, which ensures the decision accuracy. Since complex interactions exist among different game aspects, the weighted average model usually cannot be well used to compute the evaluation of game state, which results in misleading player’s generation strategy. In this paper, we take dynamic changes and player’s preference into consideration, analyze player’s preference and units’ relationships base on game theory and propose a dynamic hierarchical evaluating network, denoted as DHEN. Experiments show that the modified evaluating algorithm can effectively improve the accuracy of task planning algorithm for RTS games

Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning

Abstract Premature ovarian insufficiency (POI) is a reproductive endocrine disorder characterized by infertility and perimenopausal syndrome, with a highly heterogeneous genetic etiology and its mechanism is not fully understood. Therefore, we utilized Oxford Nanopore Technology (ONT) for the first time to characterize the full-length transcript profile, and revealed biomarkers, pathway and molecular mechanisms for POI by bioinformatics analysis and machine learning. Ultimately, we identified 272 differentially expressed genes, 858 core genes, and 25 hub genes by analysis of differential expression, gene set enrichment, and protein–protein interactions. Seven candidate genes were identified based on the intersection features of the random forest and Boruta algorithm. qRT-PCR results indicated that COX5A, UQCRFS1, LCK, RPS2 and EIF5A exhibited consistent expression trends with sequencing data and have potential as biomarkers. Additionally, GSEA analysis revealed that the pathophysiology of POI is closely associated with inhibition of the PI3K-AKT pathway, oxidative phosphorylation and DNA damage repair, as well as activation of inflammatory and apoptotic pathways. Furthermore, we emphasize that downregulation of respiratory chain enzyme complex subunits and inhibition of oxidative phosphorylation pathways play crucial roles in the pathophysiology of POI. In conclusion, our utilization of long-read sequencing has refined the annotation information within the POI transcriptional profile. This valuable data provides novel insights for further exploration into molecular regulatory networks and potential biomarkers associated with POI