Long-Term Fairness with Unknown Dynamics

While machine learning can myopically reinforce social inequalities, it may also be used to dynamically seek equitable outcomes. In this paper, we formalize long-term fairness in the context of online reinforcement learning. This formulation can accommodate dynamical control objectives, such as driving equity inherent in the state of a population, that cannot be incorporated into static formulations of fairness. We demonstrate that this framing allows an algorithm to adapt to unknown dynamics by sacrificing short-term incentives to drive a classifier-population system towards more desirable equilibria. For the proposed setting, we develop an algorithm that adapts recent work in online learning. We prove that this algorithm achieves simultaneous probabilistic bounds on cumulative loss and cumulative violations of fairness (as statistical regularities between demographic groups). We compare our proposed algorithm to the repeated retraining of myopic classifiers, as a baseline, and to a deep reinforcement learning algorithm that lacks safety guarantees. Our experiments model human populations according to evolutionary game theory and integrate real-world datasets

Federated Learning with Reduced Information Leakage and Computation

Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized clients to collaboratively learn a common model without sharing local data. Although local data is not exposed directly, privacy concerns nonetheless exist as clients' sensitive information can be inferred from intermediate computations. Moreover, such information leakage accumulates substantially over time as the same data is repeatedly used during the iterative learning process. As a result, it can be particularly difficult to balance the privacy-accuracy trade-off when designing privacy-preserving FL algorithms. In this paper, we introduce Upcycled-FL, a novel federated learning framework with first-order approximation applied at every even iteration. Under this framework, half of the FL updates incur no information leakage and require much less computation. We first conduct the theoretical analysis on the convergence (rate) of Upcycled-FL, and then apply perturbation mechanisms to preserve privacy. Experiments on real-world data show that Upcycled-FL consistently outperforms existing methods over heterogeneous data, and significantly improves privacy-accuracy trade-off while reducing 48% of the training time on average

Fair Classifiers that Abstain without Harm

In critical applications, it is vital for classifiers to defer decision-making to humans. We propose a post-hoc method that makes existing classifiers selectively abstain from predicting certain samples. Our abstaining classifier is incentivized to maintain the original accuracy for each sub-population (i.e. no harm) while achieving a set of group fairness definitions to a user specified degree. To this end, we design an Integer Programming (IP) procedure that assigns abstention decisions for each training sample to satisfy a set of constraints. To generalize the abstaining decisions to test samples, we then train a surrogate model to learn the abstaining decisions based on the IP solutions in an end-to-end manner. We analyze the feasibility of the IP procedure to determine the possible abstention rate for different levels of unfairness tolerance and accuracy constraint for achieving no harm. To the best of our knowledge, this work is the first to identify the theoretical relationships between the constraint parameters and the required abstention rate. Our theoretical results are important since a high abstention rate is often infeasible in practice due to a lack of human resources. Our framework outperforms existing methods in terms of fairness disparity without sacrificing accuracy at similar abstention rates

Complex relationship between gut microbiota and thyroid dysfunction: a bidirectional two-sample Mendelian randomization study

BackgroundMany studies have reported the link between gut microbiota and thyroid dysfunction. However, the causal effect of gut microbiota on thyroid dysfunction and the changes in gut microbiota after the onset of thyroid dysfunction are not clear.MethodsA two-sample Mendelian randomization (MR) study was used to explore the complex relationship between gut microbiota and thyroid dysfunction. Data on 211 bacterial taxa were obtained from the MiBioGen consortium, and data on thyroid dysfunction, including hypothyroidism, thyroid-stimulating hormone alteration, thyroxine deficiency, and thyroid peroxidase antibodies positivity, were derived from several databases. Inverse variance weighting (IVW), weighted median, MR-Egger, weighted mode, and simple mode were applied to assess the causal effects of gut microbiota on thyroid dysfunction. Comprehensive sensitivity analyses were followed to validate the robustness of the results. Finally, a reverse MR study was conducted to explore the alteration of gut microbiota after hypothyroidism onset.ResultsOur bidirectional two-sample MR study revealed that the genera Intestinimonas, Eubacterium brachy group, Ruminiclostridium5, and Ruminococcaceae UCG004 were the risk factors for decreased thyroid function, whereas the genera Bifidobacterium and Lachnospiraceae UCG008 and phyla Actinobacteria and Verrucomicrobia were protective. The abundance of eight bacterial taxa varied after the onset of hypothyroidism. Sensitivity analysis showed that no heterogeneity or pleiotropy existed in the results of this study.ConclusionThis novel MR study systematically demonstrated the complex relationship between gut microbiota and thyroid dysfunction, which supports the selection of more targeted probiotics to maintain thyroid–gut axis homeostasis and thus to prevent, control, and reverse the development of thyroid dysfunction

Human mobility variations in response to restriction policies during the COVID-19 pandemic: An analysis from the Virus Watch community cohort in England, UK

Objective: Since the outbreak of COVID-19, public health and social measures to contain its transmission (e.g., social distancing and lockdowns) have dramatically changed people's lives in rural and urban areas globally. To facilitate future management of the pandemic, it is important to understand how different socio-demographic groups adhere to such demands. This study aims to evaluate the influences of restriction policies on human mobility variations associated with socio-demographic groups in England, UK. Methods: Using mobile phone global positioning system (GPS) trajectory data, we measured variations in human mobility across socio-demographic groups during different restriction periods from Oct 14, 2020 to Sep 15, 2021. The six restriction periods which varied in degree of mobility restriction policies, denoted as "Three-tier Restriction," "Second National Lockdown," "Four-tier Restriction," "Third National Lockdown," "Steps out of Lockdown," and "Post-restriction," respectively. Individual human mobility was measured with respect to the time period people stayed at home, visited places outside the home, and traveled long distances. We compared these indicators across the six restriction periods and across socio-demographic groups. Results: All human mobility indicators significantly differed across the six restriction periods, and the influences of restriction policies on individual mobility behaviors are correlated with socio-demographic groups. In particular, influences relating to mobility behaviors are stronger in younger and low-income groups in the second and third national lockdowns. Conclusions: This study enhances our understanding of the influences of COVID-19 pandemic restriction policies on human mobility behaviors within different social groups in England. The findings can be usefully extended to support policy-making by investigating human mobility and differences in policy effects across not only age and income groups, but also across geographical regions

Molecular cloning, characterization and expression analysis of CpCBF2 gene in harvested papaya fruit under temperature stresses

Background: C-repeat binding factors (CBFs) are transcription factors that regulate the expression of a number of genes related to abiotic stresses. Few CBF genes have been cloned from other plants but no report in papaya. In present study, a full-length cDNA, designated as CpCBF2, was cloned from papaya using in silico cloning and 5\u2019- rapid amplification cDNA ends (RACE). Sequence analysis was performed to understand the gene function. The expression pattern of CpCBF2 in papaya under low (7\ubaC) and high temperature (35\ubaC) stresses was examined using real-time quantitative polymerase chain reaction (RT-qPCR). Results: The full-length cDNA of CpCBF2 was 986-bp, with a 762-bp open reading frame (ORF) encoding a 254 amino acid polypeptide. CpCBF2 contained several major highly conserved regions including the CBF-family signature PKRRAGRKKFQETRHP and FADSAW in its amino acid sequence. Phylogenetic tree and three-dimensional structure analysis showed that CpCBF2 had a relatively close relationship with other plant CBFs. Gene expression analysis showed that high temperature stress had little effect on the expression of CpCBF2 but low temperature repressed CpCBF2 expression. Conclusion: The results showed that CpCBF2 may involve in different roles in temperature stress tolerance. This study provided a candidate gene potentially useful for fruit temperature stress tolerance, although its function still needs further confirmation

3D Macroscopic Architectures from Self‐Assembled MXene Hydrogels

Assembly of 2D MXene sheets into a 3D macroscopic architecture is highly desirable to overcome the severe restacking problem of 2D MXene sheets and develop MXene‐based functional materials. However, unlike graphene, 3D MXene macroassembly directly from the individual 2D sheets is hard to achieve for the intrinsic property of MXene. Here a new gelation method is reported to prepare a 3D structured hydrogel from 2D MXene sheets that is assisted by graphene oxide and a suitable reductant. As a supercapacitor electrode, the hydrogel delivers a superb capacitance up to 370 F g−1 at 5 A g−1, and more promisingly, demonstrates an exceptionally high rate performance with the capacitance of 165 F g−1 even at 1000 A g−1. Moreover, using controllable drying processes, MXene hydrogels are transformed into different monoliths with structures ranging from a loosely organized porous aerogel to a dense solid. As a result, a 3D porous MXene aerogel shows excellent adsorption capacity to simultaneously remove various classes of organic liquids and heavy metal ions while the dense solid has excellent mechanical performance with a high Young's modulus and hardness

The Airlines’ Recent Experience Under the Railway Labor Act

Silky-feather has been selected and fixed in some breeds due to its unique appearance. This phenotype is caused by a single recessive gene (hookless, h). Here we map the silky-feather locus to chromosome 3 by linkage analysis and subsequently fine-map it to an 18.9 kb interval using the identical by descent (IBD) method. Further analysis reveals that a C to G transversion located upstream of the prenyl (decaprenyl) diphosphate synthase, subunit 2 (PDSS2) gene is causing silky-feather. All silky-feather birds are homozygous for the G allele. The silky-feather mutation significantly decreases the expression of PDSS2 during feather development in vivo. Consistent with the regulatory effect, the C to G transversion is shown to remarkably reduce PDSS2 promoter activity in vitro. We report a new example of feather structure variation associated with a spontaneous mutation and provide new insight into the PDSS2 function