3,934 research outputs found

    Towards Fair Disentangled Online Learning for Changing Environments

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    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

    Disorder in interacting quasi-one-dimensional systems: flat and dispersive bands

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    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

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    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

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    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

    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

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    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

    A PDEM-COM framework for uncertainty quantification of backward issues involving both aleatory and epistemic uncertainties

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    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
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