3,952 research outputs found
Towards Fair Disentangled Online Learning for Changing Environments
In the problem of online learning for changing environments, data are
sequentially received one after another over time, and their distribution
assumptions may vary frequently. Although existing methods demonstrate the
effectiveness of their learning algorithms by providing a tight bound on either
dynamic regret or adaptive regret, most of them completely ignore learning with
model fairness, defined as the statistical parity across different
sub-population (e.g., race and gender). Another drawback is that when adapting
to a new environment, an online learner needs to update model parameters with a
global change, which is costly and inefficient. Inspired by the sparse
mechanism shift hypothesis, we claim that changing environments in online
learning can be attributed to partial changes in learned parameters that are
specific to environments and the rest remain invariant to changing
environments. To this end, in this paper, we propose a novel algorithm under
the assumption that data collected at each time can be disentangled with two
representations, an environment-invariant semantic factor and an
environment-specific variation factor. The semantic factor is further used for
fair prediction under a group fairness constraint. To evaluate the sequence of
model parameters generated by the learner, a novel regret is proposed in which
it takes a mixed form of dynamic and static regret metrics followed by a
fairness-aware long-term constraint. The detailed analysis provides theoretical
guarantees for loss regret and violation of cumulative fairness constraints.
Empirical evaluations on real-world datasets demonstrate our proposed method
sequentially outperforms baseline methods in model accuracy and fairness.Comment: Accepted by KDD 202
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Influence of substrate on corneal epithelial cell viability within ocular surface models
Corneal tissue engineering has improved dramatically over recent years. It is now possible to apply these technological advancements to the development of superior in vitro ocular surface models to reduce animal testing. We aim to show the effect different substrates can have on the viability of expanded corneal epithelial cells and that those which more accurately mimic the stromal surface provide the most protection against toxic assault. Compressed collagen gel as a substrate for the expansion of a human epithelial cell line was compared against two well-known substrates for modeling the ocular surface (polycarbonate membrane and conventional collagen gel). Cells were expanded over 10 days at which point cell stratification, cell number and expression of junctional proteins were assessed by electron microscopy, immunohistochemistry and RT-PCR. The effect of increasing concentrations of sodium lauryl sulphate on epithelial cell viability was quantified by MTT assay. Results showed improvement in terms of stratification, cell number and tight junction expression in human epithelial cells expanded upon either the polycarbonate membrane or compressed collagen gel when compared to a the use of a conventional collagen gel. However, cell viability was significantly higher in cells expanded upon the compressed collagen gel. We conclude that the more naturalistic composition and mechanical properties of compressed collagen gels produces a more robust corneal model
Disorder in interacting quasi-one-dimensional systems: flat and dispersive bands
We investigate the superconductor-insulator transition (SIT) in disordered
quasi-one dimensional systems using the density-matrix renormalization group
method. Focusing on the case of an interacting spinful Hamiltonian at
quarter-filling, we contrast the differences arising in the SIT when the parent
non-interacting model features either flat or dispersive bands. Furthermore, by
comparing disorder distributions that preserve or not SU(2)-symmetry, we unveil
the critical disorder amplitude that triggers insulating behavior. While
scaling analysis suggests the transition to be of a
Berezinskii-Kosterlitz-Thouless type for all models (two lattices and two
disorder types), only in the flat-band model with Zeeman-like disorder the
critical disorder is nonvanishing. In this sense, the flat-band structure does
strengthen superconductivity. For both flat and dispersive band models, i) in
the presence of SU(2)-symmetric random chemical potentials, the
disorder-induced transition is from superconductor to insulator of singlet
pairs; ii) for the Zeeman-type disorder, the transition is from superconductor
to insulator of unpaired fermions. In all cases, our numerical results suggest
no intermediate disorder-driven metallic phase.Comment: 10 pages, 13 figure
The effects of environmental inspection on air quality: Evidence from China
To address ecological and environmental issues, central environmental inspection (CEI) coordinated by the
Chinese Ministry of Ecology and Environment has been implemented since 2016. This paper aims to comprehensively evaluate how and how much CEI affects air quality. The results of the difference-in-differences models
show that CEI improved the air quality and reduced the concentrations of PM2.5, PM10, NO2, and SO2 by 8.8%,
8.1%, 7.9%, and 2.4%, respectively. Moreover, environmental effectiveness was strengthened over the course of
four rounds of inspection. The mediating model results indicate that effectiveness was achieved through active
public participation, administrative punishments from the central inspectors, and positive rectification actions
from the local governments. The greatest improvement in air quality occurred during the on-site inspection
period, after which the effects gradually weakened. A review inspection was carried out to supervise the rectification tasks. The adoption of review inspection made the effects on air quality improvement reappear, which
verifies that CEI in China is not just a temporary campaign-style enforcement but a normalized and effective
governance of air pollution
Air pollution control or economic development? Empirical evidence from enterprises with production restrictions
Production restriction is an environmental regulation adopted in China to curb the air pollution of industrial enterprises. Frequent production restrictions may cause economic losses for enterprises and further hinder their green transformation. Polluting enterprises are faced with the dilemma of choosing environmental protection or economic development. Using panel data on industrial enterprises in China from 2016 to 2019, this paper evaluates the impact of production restrictions on both enterprises' environmental and economic performance with regression models. The results show that production restrictions significantly drop the concentrations of SO2 and NOx emitted from polluting enterprises. Meanwhile, production restrictions have significant negative effects on operating income, financial expenses, net profit, and environmental protection investment. The mechanism analysis reveals that production restrictions mitigate air pollutant concentrations by increasing the number of green patents and improving total factor productivity, which also verifies the Porter hypothesis. However, there is a masking mediating effect of environmental investment, which indicates that the reduction of environmental investment hinders the enterprise's efforts to control air pollution. In addition, heterogeneous analysis shows that the economic shock on microenterprises is larger than that on small enterprises. Implementing production restrictions for microenterprises may be a way to eliminate their backwards production capacity
A PDEM-COM framework for uncertainty quantification of backward issues involving both aleatory and epistemic uncertainties
Uncertainties that exist in nature or due to lack of knowledge have been widely recognized by researchers and engineering practitioners throughout engineering design and analysis for decades. Though great efforts have been devoted to the issues of uncertainty quantification (UQ) in various aspects, the methodologies on the quantification of aleatory uncertainty and epistemic uncertainty are usually logically inconsistent. For instance, the aleatory uncertainty is usually quantified in the framework of probability theory, whereas the epistemic uncertainty is quantified mostly by non-probabilistic methods. In the present paper, a probabilistically consistent framework for the quantification of both aleatory and epistemic uncertainty by synthesizing the probability density evolution method (PDEM) and the change of probability measure (COM) is outlined. The framework is then applied to the backward issues of uncertainty quantification. In particular, the uncertainty model updating issue is discussed in this paper. A numerical example is presented, and the results indicate the flexibility and efficiency of the proposed PDEM-COM framework
Structural Characterization of Rapid Thermal Oxidized Si\u3csub\u3e1−x−y\u3c/sub\u3eGe\u3csub\u3ex\u3c/sub\u3eC\u3csub\u3ey\u3c/sub\u3e Alloy Films Grown by Rapid Thermal Chemical Vapor Deposition
The structural properties of as-grown and rapid thermal oxidized Si1−x−yGexCy epitaxial layers have been examined using a combination of infrared, x-ray photoelectron, x-ray diffraction, secondary ion mass spectroscopy, and Raman spectroscopy techniques. Carbon incorporation into the Si1−x−yGexCy system can lead to compressive or tensile strain in the film. The structural properties of the oxidized Si1−x−yGexCy film depend on the type of strain (i.e., carbon concentration) of the as-prepared film. For compressive or fully compensated films, the oxidation process drastically reduces the carbon content so that the oxidized films closely resemble to Si1−xGex films. For tensile films, two broad regions, one with carbon content higher and the other lower than that required for full strain compensation, coexist in the oxidized films
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