Procedural Fairness in Machine Learning

Fairness in machine learning (ML) has received much attention. However, existing studies have mainly focused on the distributive fairness of ML models. The other dimension of fairness, i.e., procedural fairness, has been neglected. In this paper, we first define the procedural fairness of ML models, and then give formal definitions of individual and group procedural fairness. We propose a novel metric to evaluate the group procedural fairness of ML models, called $GPF_{FAE}$, which utilizes a widely used explainable artificial intelligence technique, namely feature attribution explanation (FAE), to capture the decision process of the ML models. We validate the effectiveness of $GPF_{FAE}$ on a synthetic dataset and eight real-world datasets. Our experiments reveal the relationship between procedural and distributive fairness of the ML model. Based on our analysis, we propose a method for identifying the features that lead to the procedural unfairness of the model and propose two methods to improve procedural fairness after identifying unfair features. Our experimental results demonstrate that we can accurately identify the features that lead to procedural unfairness in the ML model, and both of our proposed methods can significantly improve procedural fairness with a slight impact on model performance, while also improving distributive fairness.Comment: 14 page

A Risk Model Developed Based on Homologous Recombination Deficiency Predicts Overall Survival in Patients With Lower Grade Glioma

The role of homologous recombination deficiency (HRD) in lower grade glioma (LGG) has not been elucidated, and accurate prognostic prediction is also important for the treatment and management of LGG. The aim of this study was to construct an HRD-based risk model and to explore the immunological and molecular characteristics of this risk model. The HRD score threshold = 10 was determined from 506 LGG samples in The Cancer Genome Atlas cohort using the best cut-off value, and patients with highHRDscores had worse overall survival. A total of 251 HRD-related genes were identified by analyzing differentially expressed genes, 182 of which were associated with survival. A risk score model based on HRD-related genes was constructed using univariate Cox regression, least absolute shrinkage and selection operator regression, and stepwise regression, and patients were divided into high- and low-risk groups using the median risk score. High-risk patients had significantly worse overall survival than lowrisk patients. The risk model had excellent predictive performance for overall survival in LGG and was found to be an independent risk factor. The prognostic value of the riskmodel was validated using an independent cohort. In addition, the risk score was associated with tumor mutation burden and immune cell infiltration in LGG. High-risk patients had higher HRD scores and “hot” tumor immune microenvironment, which could benefit from poly-ADP-ribose polymerase inhibitors and immune checkpoint inhibitors. Overall, this big data study determined the threshold of HRD score in LGG, identified HRD-related genes, developed a risk model based on HRD-related genes, and determined the molecular and immunological characteristics of the risk model. This provides potential new targets for future targeted therapies and facilitates the development of individualized immunotherapy to improve prognosis

EFFL: Egalitarian Fairness in Federated Learning for Mitigating Matthew Effect

Recent advances in federated learning (FL) enable collaborative training of machine learning (ML) models from large-scale and widely dispersed clients while protecting their privacy. However, when different clients' datasets are heterogeneous, traditional FL mechanisms produce a global model that does not adequately represent the poorer clients with limited data resources, resulting in lower accuracy and higher bias on their local data. According to the Matthew effect, which describes how the advantaged gain more advantage and the disadvantaged lose more over time, deploying such a global model in client applications may worsen the resource disparity among the clients and harm the principles of social welfare and fairness. To mitigate the Matthew effect, we propose Egalitarian Fairness Federated Learning (EFFL), where egalitarian fairness refers to the global model learned from FL has: (1) equal accuracy among clients; (2) equal decision bias among clients. Besides achieving egalitarian fairness among the clients, EFFL also aims for performance optimality, minimizing the empirical risk loss and the bias for each client; both are essential for any ML model training, whether centralized or decentralized. We formulate EFFL as a constrained multi-constrained multi-objectives optimization (MCMOO) problem, with the decision bias and egalitarian fairness as constraints and the minimization of the empirical risk losses on all clients as multiple objectives to be optimized. We propose a gradient-based three-stage algorithm to obtain the Pareto optimal solutions within the constraint space. Extensive experiments demonstrate that EFFL outperforms other state-of-the-art FL algorithms in achieving a high-performance global model with enhanced egalitarian fairness among all clients

A Probabilistic Approach In Long-Term Fatigue Analysis Of Onshore Wind Turbine Tower

To address the fatigue damage induced by wind on the wind turbine tower, the present work introduces a novel probabilistic fatigue assessment framework. The idea is based on the deterministic fatigue approach combining with various statistical and probabilistic techniques. The proposed framework is applied to a Design Load Case (DLC) given in IEC 61400-1 standard using a reference wind turbine and carry out a probability distribution of cumulative fatigue damage on the cross-section of wind turbine tower under a turbulent wind condition

Profile of immunoglobulin G N-glycome in COVID-19 patients: A case-control study

Coronavirus disease 2019 (COVID-19) remains a major health challenge globally. Previous studies have suggested that changes in the glycosylation of IgG are closely associated with the severity of COVID-19. This study aimed to compare the profiles of IgG N-glycome between COVID-19 patients and healthy controls. A case-control study was conducted, in which 104 COVID-19 patients and 104 age- and sex-matched healthy individuals were recruited. Serum IgG N-glycome composition was analyzed by hydrophilic interaction liquid chromatography with the ultra-high-performance liquid chromatography (HILIC-UPLC) approach. COVID-19 patients have a decreased level of IgG fucosylation, which upregulates antibody-dependent cell cytotoxicity (ADCC) in acute immune responses. In severe cases, a low level of IgG sialylation contributes to the ADCC-regulated enhancement of inflammatory cytokines. The decreases in sialylation and galactosylation play a role in COVID-19 pathogenesis via the activation of the lectin-initiated alternative complement pathway. IgG N-glycosylation underlines the complex clinical phenotypes of SARS-CoV-2 infection

Lactic acid bacteria with a strong antioxidant function isolated from “Jiangshui,” pickles, and feces

Excessive free radicals and iron death lead to oxidative damage, which is one of the main causes of aging and diseases. In this field of antioxidation, developing new, safe, and efficient antioxidants is the main research focus. Lactic acid bacteria (LAB) are natural antioxidants with good antioxidant activity and can regulate gastrointestinal microecological balance and immunity. In this study, 15 LAB strains from fermented foods (“Jiangshui” and pickles) or feces were evaluated in terms of their antioxidant attributes. Strains with strong antioxidant capacity were preliminarily screened by the following tests: 2,2-diphenyl-1-picrylhydrazyl (DPPH), hydroxyl radical, superoxide anion radical scavenging capacity; ferrous ion chelating assay; hydrogen peroxide tolerance capacity. Then, the adhesion of the screened strains to the intestinal tract was examined using hydrophobic and auto-aggregation tests. The safety of the strains was analyzed based on their minimum inhibitory concentration and hemolysis, and 16S rRNA was used for molecular biological identification. Antimicrobial activity tests showed them probiotic function. The cell-free supernatant of selected strains were used to explore the protective effect against oxidative damage cells. The scavenging rate of DPPH, hydroxyl radicals, and ferrous ion-chelating of 15 strains ranged from 28.81–82.75%, 6.54–68.52%, and 9.46–17.92%, respectively, the scavenging superoxide anion scavenging activity all exceeded 10%. According to all the antioxidant-related tests, strains possessing high antioxidant activities J2-4, J2-5, J2-9, YP-1, and W-4 were screened, these five strains demonstrated tolerance to 2 mM hydrogen peroxide. J2-4, J2-5, and J2-9 were Lactobacillus fermentans and γ-hemolytic (non-hemolytic). YP-1 and W-4 were Lactobacillus paracasei and α-hemolytic (grass-green hemolytic). Although L. paracasei has been proven as a safe probiotic without hemolytic characteristics, the hemolytic characteristics of YP-1 and W-4 should be further studied. Due to the weak hydrophobicity and antimicrobial activity of J2-4, finally, we selected J2-5, J2-9 for cell experiment, J2-5 and J2-9 showed an excellent ability that resistant to oxidative damage by increasing SOD, CAT, T-AOC activity of 293T cells. Therefore, J2-5, and J2-9 strains from fermented foods “Jiangshui” could be used as potential antioxidants for functional food, health care, and skincare

Longitudinal Serum Proteome Characterization of COVID-19 Patients With Different Severities Revealed Potential Therapeutic Strategies

The COVID-19 pandemic caused by SARS-CoV-2 is exerting huge pressure on global healthcare. Understanding of the molecular pathophysiological alterations in COVID-19 patients with different severities during disease is important for effective treatment. In this study, we performed proteomic profiling of 181 serum samples collected at multiple time points from 79 COVID-19 patients with different severity levels (asymptomatic, mild, moderate, and severe/critical) and 27 serum samples from non-COVID-19 control individuals. Dysregulation of immune response and metabolic reprogramming was found in severe/critical COVID-19 patients compared with non-severe/critical patients, whereas asymptomatic patients presented an effective immune response compared with symptomatic COVID-19 patients. Interestingly, the moderate COVID-19 patients were mainly grouped into two distinct clusters using hierarchical cluster analysis, which demonstrates the molecular pathophysiological heterogeneity in COVID-19 patients. Analysis of protein-level alterations during disease progression revealed that proteins involved in complement activation, the coagulation cascade and cholesterol metabolism were restored at the convalescence stage, but the levels of some proteins, such as anti-angiogenesis protein PLGLB1, would not recovered. The higher serum level of PLGLB1 in COVID-19 patients than in control groups was further confirmed by parallel reaction monitoring (PRM). These findings expand our understanding of the pathogenesis and progression of COVID-19 and provide insight into the discovery of potential therapeutic targets and serum biomarkers worth further validation

Contribution à l’optimisation évolutionnaire assistée par modèle de Krigeage : application à l’identification des paramètres en mécanique

In order to reduce the cost of solving expensive optimization problems, this thesis devoted to Kriging-Assisted Covariance Matrix Adaptation Evolution Strategy (KA-CMA-ES). Several algorithms of KA-CMA-ES were developed and a comprehensive investigation on KA-CMA-ES was performed. Then applications of the developed KA-CMA-ES algorithm were carried out in material parameter identification of an elastic-plastic damage constitutive model. The results of experimental studies demonstrated that the developed KA-CMA-ES algorithms generally are more efficient than the standard CMA-ES and that the KA-CMA-ES using ARP-EI has the best performance among all the investigated KA-CMA-ES algorithms in this work. The results of engineering applications of the algorithm ARP-EI in material parameter identification show that the presented elastic-plastic damage model is adequate to describe the plastic and ductile damage behavior and also prove that the proposed KA-CMA-ES algorithm apparently improve the efficiency of the standard CMA-ES. Therefore, the KA-CMA-ES is more powerful and efficient than CMA-ES for expensive optimization problems.Afin de réduire le coût de calcul pour des problèmes d'optimisation coûteuse, cette thèse a été consacrée à la Stratégie d'Evolution avec Adaptation de Matrice de Covariance assistée par modèle de Krigeage (KA-CMA-ES). Plusieurs algorithmes de KA-CMA-ES ont été développés et étudiés. Une application de ces algorithmes KA-CMA-ES développés est réalisée par l'identification des paramètres matériels avec un modèle constitutif d'endommagement élastoplastique. Les résultats expérimentaux démontrent que les algorithmes KA-CMA-ES développés sont plus efficaces que le CMA-ES standard. Ils justifient autant que le KA-CMA-ES couplé avec ARP-EI est le plus performant par rapport aux autres algorithmes étudiés dans ce travail. Les résultats obtenus par l'algorithme ARP-EI dans l'identification des paramètres matériels montrent que le modèle d'endommagement élastoplastique utilisé est suffisant pour décrire le comportement d'endommage plastique et ductile. Ils prouvent également que la KA-CMA-ES proposée améliore l'efficace de la CMA-ES. Par conséquent, le KA-CMA-ES est plus puissant et efficace que CMA-ES pour des problèmes d'optimisation coûteuse

Contribution à l’optimisation évolutionnaire assistée par modèle de Krigeage : application à l’identification des paramètres en mécanique

In order to reduce the cost of solving expensive optimization problems, this thesis devoted to Kriging-Assisted Covariance Matrix Adaptation Evolution Strategy (KA-CMA-ES). Several algorithms of KA-CMA-ES were developed and a comprehensive investigation on KA-CMA-ES was performed. Then applications of the developed KA-CMA-ES algorithm were carried out in material parameter identification of an elastic-plastic damage constitutive model. The results of experimental studies demonstrated that the developed KA-CMA-ES algorithms generally are more efficient than the standard CMA-ES and that the KA-CMA-ES using ARP-EI has the best performance among all the investigated KA-CMA-ES algorithms in this work. The results of engineering applications of the algorithm ARP-EI in material parameter identification show that the presented elastic-plastic damage model is adequate to describe the plastic and ductile damage behavior and also prove that the proposed KA-CMA-ES algorithm apparently improve the efficiency of the standard CMA-ES. Therefore, the KA-CMA-ES is more powerful and efficient than CMA-ES for expensive optimization problems.Afin de réduire le coût de calcul pour des problèmes d'optimisation coûteuse, cette thèse a été consacrée à la Stratégie d'Evolution avec Adaptation de Matrice de Covariance assistée par modèle de Krigeage (KA-CMA-ES). Plusieurs algorithmes de KA-CMA-ES ont été développés et étudiés. Une application de ces algorithmes KA-CMA-ES développés est réalisée par l'identification des paramètres matériels avec un modèle constitutif d'endommagement élastoplastique. Les résultats expérimentaux démontrent que les algorithmes KA-CMA-ES développés sont plus efficaces que le CMA-ES standard. Ils justifient autant que le KA-CMA-ES couplé avec ARP-EI est le plus performant par rapport aux autres algorithmes étudiés dans ce travail. Les résultats obtenus par l'algorithme ARP-EI dans l'identification des paramètres matériels montrent que le modèle d'endommagement élastoplastique utilisé est suffisant pour décrire le comportement d'endommage plastique et ductile. Ils prouvent également que la KA-CMA-ES proposée améliore l'efficace de la CMA-ES. Par conséquent, le KA-CMA-ES est plus puissant et efficace que CMA-ES pour des problèmes d'optimisation coûteuse