167 research outputs found
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 thyroidâgut 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 SelfâAssembled 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 MXeneâbased 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 gâ1 at 5 A gâ1, and more promisingly, demonstrates an exceptionally high rate performance with the capacitance of 165 F gâ1 even at 1000 A gâ1. 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 Airlinesâ 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
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