DFL: High-Performance Blockchain-Based Federated Learning

Many researchers are trying to replace the aggregation server in federated learning with a blockchain system to achieve better privacy, robustness and scalability. In this case, clients will upload their updated models to the blockchain ledger, and use a smart contract on the blockchain system to perform model averaging. However, running machine learning applications on the blockchain is almost impossible because a blockchain system, which usually takes over half minute to generate a block, is extremely slow and unable to support machine learning applications. This paper proposes a completely new public blockchain architecture called DFL, which is specially optimized for distributed federated machine learning. This architecture inherits most traditional blockchain merits and achieves extremely high performance with low resource consumption by waiving global consensus. To characterize the performance and robustness of our architecture, we implement the architecture as a prototype and test it on a physical four-node network. To test more nodes and more complex situations, we build a simulator to simulate the network. The LeNet results indicate our system can reach over 90% accuracy for non-I.I.D. datasets even while facing model poisoning attacks, with the blockchain consuming less than 5% of hardware resources.Comment: 11 pages, 17 figure

Metabolome response to temperature-induced virulence gene expression in two genotypes of pathogenic Vibrio parahaemolyticus

Relative concentration of metabolites identified in Vibrio parahaemolyticus ATCC17802. (XLSX 113 kb)

FOXO1 Inhibits Tumor Cell Migration via Regulating Cell Surface Morphology in Non-Small Cell Lung Cancer Cells

Background/Aims: Cell surface morphology plays pivotal roles in malignant progression and epithelial-mesenchymal transition (EMT). Previous research demonstrated that microvilli play a key role in cell migration of non-small cell lung cancer (NSCLC). In this study, we report that Forkhead box class O1 (FOXO1) is downregulated in human NSCLC and that silencing of FOXO1 is associated with the invasive stage of tumor progression. Methods: The cell proliferation, migration, and invasion were characterized in vitro, and we tested the expression of the Epithelial-mesenchymal transition (EMT) marker by immunofluorescence staining and also identified the effect of FOXO1 on the microvilli by scanning electron microscopy (SEM). Results: Functional analyses revealed that silencing of FOXO1 resulted in an increase in NSCLC cell proliferation, migration, and invasion; whereas overexpression of FOXO1 significantly inhibited the migration and invasive capability of NSCLC cells in vitro. Furthermore, cell morphology imaging showed that FOXO1 maintained the characteristics of epithelial cells. Immunofluorescence staining and western blotting showed that the E-cadherin level was elevated and Vimentin was reduced by FOXO1 overexpression. Conversely, the E-cadherin level was reduced and Vimentin was elevated in cells silenced for FOXO1. Furthermore, scanning electron microscopy (SEM) showed that FOXO1 overexpression increased the length of the microvilli on the cell surface, whereas FOXO1 silencing significantly reduced their length. Conclusions: FOXO1 is involved in human lung carcinogenesis and may serve as a potential therapeutic target in the migration of human lung cancer

Influencing factors for pediatric eye disorders and health related quality of life: a cross-sectional study in Shanghai, China

BackgroundMyopia, strabismus, and ptosis are common pediatric eye diseases, which have a negative impact on children and adolescents in terms of visual function, mental health, and health-related quality of life (HRQoL). Therefore, this study focused on those pediatric eye diseases by analyzing their risk factors and HRQoL for the comprehensive management of myopia, strabismus, and ptosis.MethodsA total of 363 participants (2–18 years old) were included in this study for risk factors analysis of myopia, strabismus, and ptosis. We collected demographic characteristics, lifestyle habits and eye care habits of these children and analyzed them by using univariable and multivariable logistic regression. In addition, we applied the Chinese version of Pediatric Quality of Life Inventory-Version 4.0 (PedsQL 4.0) to assess HRQoL in 256 children with strabismus and ptosis. Univariable and multivariable linear regression models were applied to evaluate potential influencing factors of HRQoL.ResultsOf all the participants, 140 had myopia, 127 had strabismus, and 145 had ptosis. Based on the multivariable logistic regression analysis model, we found that the history of parental myopia and daily average near-distance eye usage time were risk factors for myopia, and increased body mass index (BMI) was identified as a risk factor for strabismus and ptosis. Individuals with ptosis possessed decreased HRQoL. The multivariable linear regression model suggested that daily average near-distance eye usage time, light intensity during visual tasks, and daily average sleep duration had potential influences on HRQoL.ConclusionThis is the first study to assess the risk factors and HRQoL of myopia, strabismus, and ptosis together. We identified risk factors for these common pediatric eye diseases to help doctors, parents, and teachers better manage them. Our study discovered that children with eye disorders exhibit a notably diminished HRQoL. Consequently, it emphasizes the necessity for increased social attention and mental health assistance for these children

Automated Medical History Taking in Primary Care: A Reinforcement Learning Approach

Online searching for healthcare information has gradually become a widely used internet case. Suppose a patient suffers the symptom but is unsure of the action he needs to take, a self-diagnosis tool can help the patient identify the possible conditions and whether this patient needs to seek immediate medical help. However, the accuracy and quality of the service provided by those self-diagnosis tools are still disappointing and need further improvement. This thesis focuses on an automatic differential diagnosis task with a comprehensive evaluation of reinforcement learning methods. Also, we present a systematic method to simulate medically correct patients records, which integrates a standard symptom modeling approach called NLICE. In this way, we can bridge the gap between limited available patients records and data-driven healthcare methodologies. This project investigates both flat-RL methods and hierarchical RL in an automatic differential diagnosis setting and evaluates the performance of those two kinds of methods on simulated patients records. More specifically, the action space for the differential diagnosis task is inevitably large, so the flat-RL performs relatively poorly in complicated scenarios. The hierarchical RL method can split a complex diagnosis task into smaller tasks: it contains two-level of policy learning, and each low-layer policy imitates one medical specialty. Therefore hierarchical RL method increases the Top 1 success rate from 23.1\% in flat-RL method to 45.4\%.Besides the advanced policy learning strategy, this thesis explores the ability of NLICE symptom modeling in distinguishing conditions that share similar symptoms. The experimental results experience increases in flat-RL and hierarchical RL models and finally achieve 36.2\% and 71.8\% Top 1 success rates, respectively. To further solve the sparse action space problem in the automatic diagnosis domain, the reward shaping algorithm is implemented in the reward configuration part. The average gained reward of hierarchical RL increases from -3.65 to 0.87. Additionally, we model the general demographic background of patients and utilize contextual information to perform the policy transformation strategy, which eliminates the miss classification problem in highly sex-age related diseases.Electrical Engineerin

Machine learning and whale optimization algorithm based design of energy management strategy for plug‐in hybrid electric vehicle

Abstract In this paper, a novel energy management strategy with the improved adaptability to various conditions for plug‐in hybrid electric vehicle (PHEV) is proposed. The control parameters, derived from the benchmark test, are optimized offline for different driving conditions. The optimized parameters are implemented according to different driving behaviours identified online. The offline and online cooperation improves performance of energy management strategy in different driving conditions. Three main efforts have been made: Firstly, the valuable features that describe different driving conditions are extracted by random forest (RF) and the features are used for determining driving condition categories, utilized for online driving condition identification by support vector machine (SVM). Secondly, the control thresholds in the developed control strategy are optimized by whale optimization algorithm (WOA) under different driving conditions. The optimal control thresholds for different driving conditions will be called online after certain traffic condition is categorized. At last, simulation‐based evaluation is performed, validating the enhanced performance of the proposed methods in energy‐saving in different driving conditions

A Hierarchical Energy Management Strategy for 4WD Plug-In Hybrid Electric Vehicles

In the field of new energy vehicles, 4WD PHEVs show strong energy-saving potential. A single energy management strategy, nevertheless, has difficulty achieving the energy-saving potential due to the complex, nonlinear energy system of the 4WD PHEV. To cope with it, a hierarchical energy management strategy (H-EMS) for 4WD PHEVs is proposed in this paper to achieve energy management optimization. Firstly, the future speed information is predicted by the speed prediction method, and the upper energy management strategy adopts the model predictive control (MPC) based on the future speed information to carry out the power source distribution between the engine and the battery. Secondly, the lower energy management strategy performs the power component distribution of the front motor and the rear motor based on an equivalent consumption minimization strategy (ECMS). Finally, the simulation based on MATLAB/Simulink is performed, validating that the proposed method has more energy-saving capabilities, and the economy is improved by 11.87% compared with the rule-based (RB) energy management strategies

A Hierarchical Energy Management Strategy for 4WD Plug-In Hybrid Electric Vehicles

In the field of new energy vehicles, 4WD PHEVs show strong energy-saving potential. A single energy management strategy, nevertheless, has difficulty achieving the energy-saving potential due to the complex, nonlinear energy system of the 4WD PHEV. To cope with it, a hierarchical energy management strategy (H-EMS) for 4WD PHEVs is proposed in this paper to achieve energy management optimization. Firstly, the future speed information is predicted by the speed prediction method, and the upper energy management strategy adopts the model predictive control (MPC) based on the future speed information to carry out the power source distribution between the engine and the battery. Secondly, the lower energy management strategy performs the power component distribution of the front motor and the rear motor based on an equivalent consumption minimization strategy (ECMS). Finally, the simulation based on MATLAB/Simulink is performed, validating that the proposed method has more energy-saving capabilities, and the economy is improved by 11.87% compared with the rule-based (RB) energy management strategies