Assessing Accuracy with Locality-Sensitive Hashing in Multiple Source Environment

Accuracy assessment is a key issue in data quality management. Most of current studies focus on how to qualitatively analyze accuracy dimension and the analysis depends heavily on experts’ knowledge. Seldom work is given on how to automatically quantify accuracy dimension. Based on Jensen-Shannon Divergence (JSD) measure, we propose accuracy of data can be automatically quantified by comparing data with its entity’s most approximation in available context. To quickly identify most approximation in large scale data sources, Locality-Sensitive Hashing (LSH) is employed to extract most approximation at multiple levels, namely column, record and field level. Our approach can not only give each data source an objective accuracy score very quickly as long as context member is available but also avoid human’s laborious interaction. Theory and experiment show our approach performs well in achieving metadata on accuracy dimension

Reconstructive Neuron Pruning for Backdoor Defense

Deep neural networks (DNNs) have been found to be vulnerable to backdoor attacks, raising security concerns about their deployment in mission-critical applications. While existing defense methods have demonstrated promising results, it is still not clear how to effectively remove backdoor-associated neurons in backdoored DNNs. In this paper, we propose a novel defense called \emph{Reconstructive Neuron Pruning} (RNP) to expose and prune backdoor neurons via an unlearning and then recovering process. Specifically, RNP first unlearns the neurons by maximizing the model's error on a small subset of clean samples and then recovers the neurons by minimizing the model's error on the same data. In RNP, unlearning is operated at the neuron level while recovering is operated at the filter level, forming an asymmetric reconstructive learning procedure. We show that such an asymmetric process on only a few clean samples can effectively expose and prune the backdoor neurons implanted by a wide range of attacks, achieving a new state-of-the-art defense performance. Moreover, the unlearned model at the intermediate step of our RNP can be directly used to improve other backdoor defense tasks including backdoor removal, trigger recovery, backdoor label detection, and backdoor sample detection. Code is available at \url{https://github.com/bboylyg/RNP}.Comment: Accepted by ICML2

Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data. Then, manufacturers can predict customers' requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers' activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers' privacy and improve the test accuracy, we enforce differential privacy on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under differential privacy protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.Comment: This paper appears in IEEE Internet of Things Journal (IoT-J

Dynamics of Water Use Efficiency of Coniferous and Broad-Leaved Mixed Forest in East China

The aim of our study is to understand the patterns of variation in water use efficiency (WUE) in coniferous and broad-leaved mixed forest ecosystems across multiple scales and to identify its main controlling factors. We employ the eddy covariance method to gather data from 2017, 2018, and 2020, which we use to calculate the gross primary productivity and evapotranspiration of these forests in East China and to determine WUE at the ecosystem level. The mean daily variation in WUE ranges from 4.84 to 7.88 gC kg−1 H2O, with a mean value of 6.12 gC kg−1 H2O. We use ridge regression analysis to ascertain the independent effect of environmental factors on WUE variation. We find that WUE responds differently to environmental factors at different time scales. In mixed conifer ecosystems, temperature and relative humidity emerge as the most significant environmental factors influencing WUE variability. Especially at the seasonal scale, temperature and relative humidity can explain more than 51% of the WUE variation. Our results underscore the varied effects of environmental factors on WUE variation across different time scales and aid in predicting the response of WUE to climate change in coniferous and broad-leaved mixed forest ecosystems

Associations of Circulating Irisin with FNDC5 Expression in Fat and Muscle in Type 1 and Type 2 Diabetic Mice

Irisin is an exercise-induced myokine, suggested to exert beneficial effects on metabolism. However, the studies on the regulation of irisin secretion and the expression of its precursor FNDC5 have shown conflicting data. The discrepancies among previous correlation studies in humans are related to the heterogeneity of the study population. The fact that irisin is not only a myokine but also an adipokine leads to the further complexity of the role of irisin in metabolic regulation. In this study, we examined the regulation of FNDC5 expression and irisin in circulation in both type 1 and type 2 diabetic mice, and their potential relationships with metabolic parameters. In streptozotocin (STZ)-induced type 1 diabetic mice, high-fat diet (HFD)-induced obese mice and db/db mice, the circulating irisin as well as FNDC5 gene expression in subcutaneous fat was downregulated. Muscle FNDC5 expression was only significantly lower in STZ mice, and epididymal fat FNDC5 expression was unaltered. It is interesting to note that plasma irisin levels correlated positively with subcutaneous fat FNDC5 expression, but not epididymal fat or muscle. Moreover, both irisin levels and subcutaneous fat FNDC5 correlated negatively with markers of insulin resistance. These results suggest a regulatory role for subcutaneous fat-derived FNDC5/irisin in metabolic disease

Helicobacter pylori may participate in the development of inflammatory bowel disease by modulating the intestinal microbiota

Abstract. Inflammatory bowel disease (IBD) is a non-specific inflammatory disease of the gastrointestinal (GI) tract that is generally accepted to be closely related to intestinal dysbiosis in the host. GI infections contribute a key role in the pathogenesis of IBD; however, although the results of recent clinical studies have revealed an inverse correlation between Helicobacter pylori (H. pylori) infection and IBD, the exact mechanism underlying the development of IBD remains unclear. H. pylori, as a star microorganism, has been a focus for decades, and recent preclinical and real-world studies have demonstrated that H. pylori not only affects the changes in the gastric microbiota and microenvironment but also influences the intestinal microbiota, indicating a potential correlation with IBD. Detailed analysis revealed that H. pylori infection increased the diversity of the intestinal microbiota, reduced the abundance of Bacteroidetes, augmented the abundance of Firmicutes, and produced short-chain fatty acid-producing bacteria such as Akkermansia. All these factors may decrease vulnerability to IBD. Further studies investigating the H. pylori-intestinal microbiota metabolite axis should be performed to understand the mechanism underlying the development of IBD

Sensing Mechanism of Nanofibrous Membranes for Fluorescent Detection of Metal Ion

We have reported a hexaphenylsilole/polymethyl methacrylate (HPS/PMMA) composite film with a lotus-leaf-like structure shows highly stability and excellent sensitivities for metal ions (Fe3+ and Hg2+) a few years ago. In the above paper, it was considered that the special surface morphology is the important aspects which enhance the stability of the sensor. But it is still a lack of research on the sensing mechanism. In this paper, the sensing mechanism of fiber membrane is continuing to study in detail. The experimental results show that the electron-transfer mechanism is a crucial factor for the fluorescent quenching

Nucleolin down-regulation is involved in ADP-induced cell cycle arrest in S phase and cell apoptosis in vascular endothelial cells.

High concentration of extracellular ADP has been reported to induce cell apoptosis, but the molecular mechanisms remain not fully elucidated. In this study, we found by serendipity that ADP treatment of human umbilical vein endothelial cells (HUVEC) and human aortic endothelial cells (HAEC) down-regulated the protein level of nucleolin in a dose- and time-dependent manner. ADP treatment did not decrease the transcript level of nucloelin, suggesting that ADP might induce nucleolin protein degradation. HUVEC and HAEC expressed ADP receptor P2Y13 receptor, but did not express P2Y1 or P2Y12 receptors. However, P2Y1, 12, 13 receptor antagonists MRS2179, PSB0739, MRS2211 did not inhibit ADP-induced down-regulation of nucleolin. Moreover, MRS2211 itself down-regulated nucleolin protein level. In addition, 2-MeSADP, an agonist for P2Y1, 12 and 13 receptors, did not down-regulate nucleolin protein. These results suggested that ADP-induced nucleolin down-regulation was not due to the activation of P2Y1, 12, or 13 receptors. We also found that ADP treatment induced cell cycle arrest in S phase, cell apoptosis and cell proliferation inhibition via nucleolin down-regulation. The over-expression of nucleolin by gene transfer partly reversed ADP-induced cell cycle arrest, cell apoptosis and cell proliferation inhibition. Furthermore, ADP sensitized HUVEC to cisplatin-induced cell death by the down-regulation of Bcl-2 expression. Taken together, we found, for the first time to our knowledge, a novel mechanism by which ADP regulates cell proliferation by induction of cell cycle arrest and cell apoptosis via targeting nucelolin