Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning

In Federated Learning (FL) and many other distributed training frameworks, collaborators can hold their private data locally and only share the network weights trained with the local data after multiple iterations. Gradient inversion is a family of privacy attacks that recovers data from its generated gradients. Seemingly, FL can provide a degree of protection against gradient inversion attacks on weight updates, since the gradient of a single step is concealed by the accumulation of gradients over multiple local iterations. In this work, we propose a principled way to extend gradient inversion attacks to weight updates in FL, thereby better exposing weaknesses in the presumed privacy protection inherent in FL. In particular, we propose a surrogate model method based on the characteristic of two-dimensional gradient flow and low-rank property of local updates. Our method largely boosts the ability of gradient inversion attacks on weight updates containing many iterations and achieves state-of-the-art (SOTA) performance. Additionally, our method runs up to $100\times$ faster than the SOTA baseline in the common FL scenario. Our work re-evaluates and highlights the privacy risk of sharing network weights. Our code is available at https://github.com/JunyiZhu-AI/surrogate_model_extension.Comment: Accepted at ICML 202

The use of financial derivative, corporate governance, Shariah compliance and firm value: evidence from Malaysia, Indonesia, Bangladesh and Thailand

This study aims to examine the relationship between the use of financial derivatives and firm value with consideration of corporate governance and Shariah-compliance factors by analyzing the non-financial firms in Malaysia, Indonesia, Bangladesh and Thailand during the period between 2010 to 2017. Firstly, a general analysis is done in overall countries and found out that the use of financial derivatives has negative and insignificant relationship with the firm value. However, after taken account into corporate governance factor, the impact turned out to be positive. This shows that the corporate governance factor has some influence towards the impact of financial derivatives on the firm value. Secondly, the analysis is separately done in each country, and the results show that there is a positive and significant relationship between the use of financial derivatives and firm value under a strong corporate governance structure. In order to further analyze the different characteristic between the Shariah-compliant companies and non-Shariah-compliant companies. Under the assumption that the Shariah-compliant companies have a stronger corporate governance structure compared with non-Shariah-compliant companies. A total of 89 Shariah-compliance companies are chosen as the matching counterparties for the 89 conventional companies in Malaysia. And a total of 66 conventional companies are chosen as the matching counterparties for the 66 Shariah-complaint companies in Indonesia. The result shows that the Shariah-compliant companies with stronger corporate governance can obtain a derivative premium, however, the non-Shariah-compliant companies with weaker corporate governance obtain a derivative discount

A Survey on Consortium Blockchain Consensus Mechanisms

Blockchain is a distributed ledger that is decentralized, immutable, and transparent, which maintains a continuously growing list of transaction records ordered into blocks. As the core of blockchain, the consensus algorithm is an agreement to validate the correctness of blockchain transactions. For example, Bitcoin is a public blockchain where each node in Bitcoin uses the Proof of Work (PoW) algorithm to reach a consensus by competing to solve a puzzle. Unlike a public blockchain, a consortium blockchain is an enterprise-level blockchain that does not contend with the issues of creating a resource-saving global consensus protocol. This paper highilights several state-of-the art solutions in consensus algorithms for enterprise blockchain. For example, the HyperLedger by Linux Foundation includes implementing Practical Byzantine Fault Tolerance (PBFT) as the consensus algorithm. PBFT can tolerate a range of malicious nodes and reach consensus with quadratic complexity. Another consensus algorithm, HotStuff, implemented by Facebook Libra project, has achieved linear complexity of the authenticator. This paper presents the operational mechanisms of these and other consensus protocols, and analyzes and compares their advantages and drawbacks.Comment: under submissio

Constrained Reinforcement Learning for Dynamic Material Handling

As one of the core parts of flexible manufacturing systems, material handling involves storage and transportation of materials between workstations with automated vehicles. The improvement in material handling can impulse the overall efficiency of the manufacturing system. However, the occurrence of dynamic events during the optimisation of task arrangements poses a challenge that requires adaptability and effectiveness. In this paper, we aim at the scheduling of automated guided vehicles for dynamic material handling. Motivated by some real-world scenarios, unknown new tasks and unexpected vehicle breakdowns are regarded as dynamic events in our problem. We formulate the problem as a constrained Markov decision process which takes into account tardiness and available vehicles as cumulative and instantaneous constraints, respectively. An adaptive constrained reinforcement learning algorithm that combines Lagrangian relaxation and invalid action masking, named RCPOM, is proposed to address the problem with two hybrid constraints. Moreover, a gym-like dynamic material handling simulator, named DMH-GYM, is developed and equipped with diverse problem instances, which can be used as benchmarks for dynamic material handling. Experimental results on the problem instances demonstrate the outstanding performance of our proposed approach compared with eight state-of-the-art constrained and non-constrained reinforcement learning algorithms, and widely used dispatching rules for material handling.Comment: accepted by the 2023 International Joint Conference on Neural Networks (IJCNN

Factors influencing nurses’ post-traumatic growth during the COVID-19 pandemic: Bayesian network analysis

ObjectiveDuring the COVID-19 pandemic, nurses, especially if females and working in intensive care units or emergencies unit, were much more at risk than other health-workers categories to develop malaise and acute stress symptoms. This study aimed to examine the nurses’ post-traumatic growth and associated influencing factors during the COVID-19 pandemic.MethodsA cross-sectional study using an online survey was conducted at Henan Provincial People’s Hospital to gather data from nurses. A set of questionnaires was used to measure the participants’ professional identity, organizational support, psychological resilience and post-traumatic growth. Univariate, correlation, and multiple linear regression analyses were used to determine significant factors influencing post-traumatic growth. A theoretical framework based on the Bayesian network was constructed to understand post-traumatic growth and its associated factors comprehensively.ResultsIn total, 1,512 nurses participated in the study, and a moderate-to-high level of post-traumatic growth was reported. After screening, the identified variables, including psychological counseling, average daily working hours, average daily sleep duration, professional identity, organizational support, and psychological resilience, were selected to build a Bayesian network model. The results of Bayesian network showed that professional identity and psychological resilience positively affected post-traumatic growth directly, which was particularly pronounced in low- and high-scoring groups. While organizational support positively affected post-traumatic growth indirectly.ConclusionAlthough this study identified a moderate-to-high level of nurses’ post-traumatic growth, proactive measures to improve psychological resilience fostered by professional identity and organizational support should be prioritized by hospitals and nursing managers

Immunosuppressive agents for frequently relapsing/steroid-dependent nephrotic syndrome in children: a systematic review and network meta-analysis

AimThis study aimed to systematically compare the efficacy of various immunosuppressive agents in treating pediatric frequently relapsing or steroid-dependent nephrotic syndrome (FRSDNS).MethodsWe conducted systematic searches of PubMed, Embase, the Cochrane Library, and the Web of Science up to May 23, 2023. Outcome measures included relapses within 1 year, mean cumulative exposure to corticosteroids, patients with treatment failure at 1 year, relapse-free survival during 1 year, and adverse events. The quality of the included studies was evaluated using the modified Jadad scale, the Methodological Index for Non-Randomized Studies (MINORS), and the modified Newcastle-Ottawa Scale (NOS).ResultsRituximab was found to be the most likely (92.44%) to be associated with the fewest relapses within 1 year and was also most likely (99.99%) to result in the lowest mean cumulative exposure to corticosteroids. Rituximab had the highest likelihood (45.98%) of being associated with the smallest number of patients experiencing treatment failure at 1 year. CsA was most likely (57.93%) to achieve the highest relapse-free survival during 1 year, followed by tacrolimus (26.47%) and rituximab (30.48%). Rituximab showed no association with serious side effects and had comparable adverse effects to ofatumumab and tacrolimus.ConclusionRituximab may be the most favorable immunosuppressive agent for treating pediatric FRSDNS. Nephrologists should consider this drug, along with their clinical experience, patient characteristics, and cost considerations, when choosing a treatment approach

Transcriptome and pan-cancer system analysis identify PM2.5-induced stanniocalcin 2 as a potential prognostic and immunological biomarker for cancers

Epidemiological studies have shown that air pollution and particulate matter (PM) are closely related to the occurrence of cancer. However, the potential prognostic and immunological biomarkers for air pollution related cancers are lacking. In this study, we proved PM2.5 exposure was correlated with lung cancer through transcriptome analysis. Importantly, we identified STC2 as a key gene regulated by PM2.5, whose expression in epithelial cells was significantly increased after PM2.5 treatment and validated by using RT-qPCR and immunofluorescence. Kaplan-Meier OS curves suggested that high STC2 expression positively correlated with a poor prognosis in lung cancer. Furthermore, we discovered that STC2 was associated with multiple cancers and pathways in cancer. Next, Pan-Cancer Expression Landscape of STC2 showed that STC2 exhibited inconsistent expression across 26 types of human cancer, lower in KIRP in cancer versus adjacent normal tissues, and significantly higher in another cancers. Cox regression results suggested that STC2 expression was positively or negatively associated with prognosis in different cancers. Moreover, STC2 expression was associated with clinical phenotypes including age, gender, stage and grade. Mutation features of STC2 were also analyzed, in which the highest alteration frequency of STC2 was presented in KIRC with amplification. Meanwhile, the effects of copy number variation (CNV) on STC2 expression were investigated across various tumor types, suggesting that STC2 expression was significantly correlated with CNV in tumors. Additionally, STC2 was closely related to tumor heterogeneity, tumor stemness and tumor immune microenvironment like immune cell infiltration. In the meantime, we analyzed methylation modifications and immunological correlation of STC2. The results demonstrated that STC2 expression positively correlated with most RNA methylation genes and immunomodulators across tumors. Taken together, the findings revealed that PM2.5-induced STC2 might be a potential prognostic and immunological biomarker for cancers related to air pollution

Review of molten carbonate-based direct carbon fuel cells

Abstract Direct carbon fuel cell (DCFC) is a promising technology with high energy efficiency and abundant fuel. To date, a variety of DCFC configurations have been investigated, with molten hydroxide, molten carbonate or oxides being used as the electrolyte. Recently, there has been particular interest in DCFC with molten carbonate involved. The molten carbonate is either an electrolyte or a catalyst in different cell structures. In this review, we consider carbonate as the clue to discuss the function of carbonate in DCFCs, and start the paper by outlining the developments in terms of molten carbonate (MC)-based DCFC and its electrochemical oxidation processes. Thereafter, the composite electrolyte merging solid carbonate and mixed ionic–electronic conductors (MIEC) are discussed. Hybrid DCFC (HDCFCs ) combining molten carbonate and solid oxide fuel cell (SOFC) are also touched on. The primary function of carbonate (i.e., facilitating ion transfer and expanding the triple-phase boundaries) in these systems, is then discussed in detail. Finally, some issues are identified and a future outlook outlined, including a corrosion attack of cell components, reactions using inorganic salt from fuel ash, and wetting with carbon fuels

Distribution patterns and environmental risk assessments of microplastics in the lake waters and sediments from eight typical wetland parks in Changsha city, China

The quality of water in urban parks is closely related to people’s daily lives, but the pollution caused by microplastics in park water and sediments has not been comprehensively studied. Therefore, eight typical parks in the urban area of Changsha, China, were selected, and Raman spectroscopy was used to explore the spatial distributions and compositions of the microplastics in the water and sediments, analyze their influencing factors, and evaluate their environmental risks. The results showed that the abundances of surface water microplastics in all parks ranged from 150 to 525 n L−1, and the abundances of sediment microplastics ranged from 120 to 585 n kg−1. The microplastics in the surface water included polyethylene terephthalate (PET), chlorinated polyethylene (CPE), and fluororubber (FLU), while those in the sediments included polyvinyl chloride (PVC), wp-acrylate copolymer (ACR), and CPE. Regression analyses revealed significant positive correlations between human activities and the abundances of microplastics in the parks. Among them, the correlations of population, industrial discharge and domestic wastewater discharge with the abundance of microplastics in park water were the strongest. However, the correlations of car flow and tourists with the abundance of microplastics in park water were the weakest. Based on the potential ecological risk indices (PERI) classification assessment method, the levels of microplastics in the waters and sediments of the eight parks were all within the II-level risk zone (53–8,549), among which the risk indices for Meixi Lake and Yudai Lake were within the IV risk zone (1,365–8,549), which may have been caused by the high population density near the park. This study provides new insights into the characteristics of microplastics in urban park water and sediment