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  • 1
    Online Resource
    Online Resource
    Wiley ; 2023
    In:  Statistical Analysis and Data Mining: The ASA Data Science Journal Vol. 16, No. 3 ( 2023-06), p. 237-256
    In: Statistical Analysis and Data Mining: The ASA Data Science Journal, Wiley, Vol. 16, No. 3 ( 2023-06), p. 237-256
    Abstract: It is well recognized that historical biases exist in training data against a certain sensitive group (e.g., non‐White, women) which are socially unacceptable, and these unfair biases are inherited in trained artificial intelligence (AI) models. Various learning algorithms have been proposed to remove or alleviate unfair biases in trained AI models. In this paper, we consider another type of bias in training data so‐called covariate shift in view of fair AI. Here, covariate shift means that training data do not represent the population of interest well. Covariate shift occurs when special sampling designs (e.g., stratified sampling) are used when collecting training data, or the population where training data are collected is different from the population of interest. When covariate shift exists, fair AI models on training data may not be fair in test data. To ensure fairness on test data, we develop computationally efficient learning algorithms robust to covariate shifts. In particular, we propose a robust fairness constraint based on the L q norm which is a generic algorithm to be applied to various fairness AI problems without much hampering. By analyzing multiple benchmark datasets, we show that our proposed robust fairness AI algorithm improves existing fair AI algorithms much in terms of the fairness‐accuracy tradeoff to covariate shift and has significant computational advantages compared to other robust fair AI algorithms.
    Type of Medium: Online Resource
    ISSN: 1932-1864 , 1932-1872
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2416076-3
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  • 2
    Online Resource
    Online Resource
    Elsevier BV ; 2022
    In:  Neural Networks Vol. 154 ( 2022-10), p. 441-454
    In: Neural Networks, Elsevier BV, Vol. 154 ( 2022-10), p. 441-454
    Type of Medium: Online Resource
    ISSN: 0893-6080
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 1491372-0
    detail.hit.zdb_id: 740542-X
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  • 3
    Online Resource
    Online Resource
    The Electrochemical Society ; 2020
    In:  ECS Meeting Abstracts Vol. MA2020-02, No. 16 ( 2020-11-23), p. 1459-1459
    In: ECS Meeting Abstracts, The Electrochemical Society, Vol. MA2020-02, No. 16 ( 2020-11-23), p. 1459-1459
    Abstract: Additive manufacturing(AM) is a manufacturing technique that produces three-dimensional objects by sequentially stacking layer of material. Due to freedom of design, it is used in various industrial applications. There are many materials capable to use in AM. Among them, polymer-based AM is the most widely used, because the polymer is easy to sinter at low temperature. However, interests in metal-based AM have been increasing due to its advantages such as mechanical strength, electric conductivity and heat dissipation. Thus, many studies of metal-based AM method have been conducted. The most commonly methods for metal AM are selective laser melting(SLM), electron beam melting(EBM), direct metal deposition(DMD), and selective laser sintering(SLS). However, these methods need a high-temperature heat source to melt material. As a result, introducing the structural anisotropy and unfavorable microstructures are inevitable. Also, local defects caused by incomplete sintering, substrate delamination, and distortion are significant problems that degrades the quality of final products[1]. On the other hand, a new type of low-temperature electrochemical-based AM method has been developed. Localized electrochemical deposition(LECD) is a method utilizing electrochemical deposition on the specific area in the electrolyte. The electrochemical deposition has many advantages over other techniques because it is simple, damage-free from high temperature, and cost-effective . In LECD, instead of using conventional anode, wire type anode is used. This wire type anode with small area will produce strongly localized reduction reactions in the electrolyte. In this method, only the end of the tip of wire anode is involved in the electrochemical reaction. Theoretically, this technique can move micro anodes to produce flexible geometry features with a high aspect ratio. So far, the LECD has been used to fabricate a copper pillar, and not much research has been conducted on various structures[2-3]. Therefore, it is necessary to study the fabrication of other structures. In this research, we fabricated copper and nickel 3D structures through Electrochemical Additive Manufacturing (ECAM). We studied that morphologies of a deposited metal wall shape structures which could be obtained by controlling the applied current, temperature, and distance between cathode and anode. Additionally, the effect of organic additives on metal structure profiles was investigated. Additionally, nine(3x3) copper pin array was fabricated on the polymeric substrate, which cannot be manufactured by high-temperature laser-based metal 3D printing. Also, we confirmed that its bendability was well maintained. The micro anode used in this study was made of a Platinum wire of 100μm. This platinum wire was insulated by a capillary glass tube, then filled with epoxy resin. The surface of the anode was polished using sandpaper. The cathode was a copper wafer (1cm × 1cm × 1μm). The cathode was placed horizontally in the electrolytic cell, while anode was arranged perpendicular to the cathode. The entire experimental process was controlled by micropositioner and appropriate computer software. The changes in voltage and current during the process were monitored. The surface and microstructure were characterized by scanning electron microscopy (SEM) and optical microscope (OM). Reference [1] Y.K. Kim, D.H Kang, H.R. Kim, S.B. Kim and B.Y Yoo, “The Characteristics of Selective 3D Metal Additive Process Using Electrochemical Deposition and Nozzle Fluid Dynamics”, Frontiers in Mechanical Engineering , 6 (2020). [2] E. M. El-Giar, R. A. Said, G. E. Bridges and D. J. Thomson, “Localized Electrochemical Deposition of Copper Microstructures”, Journal of The Electrochemical Society , 147 (2000). [3] S.k. Seol, A. R. Pyun, Y. Hwu, G. Margaritondo and J.h. Je, “Localized Electrochemical Deposition of Copper Monitored Using Real-Time X-ray Microradiography”, Advanced Functional Materials , 15 , 934 (2005).
    Type of Medium: Online Resource
    ISSN: 2151-2043
    Language: Unknown
    Publisher: The Electrochemical Society
    Publication Date: 2020
    detail.hit.zdb_id: 2438749-6
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  • 4
    In: Open Biology, The Royal Society, Vol. 11, No. 12 ( 2021-12)
    Abstract: MLC1 is a membrane protein mainly expressed in astrocytes, and genetic mutations lead to the development of a leukodystrophy, megalencephalic leukoencephalopathy with subcortical cysts disease. Currently, the biochemical properties of the MLC1 protein are largely unknown. In this study, we aimed to characterize the transmembrane (TM) topology and oligomeric nature of the MLC1 protein. Systematic immunofluorescence staining data revealed that the MLC1 protein has eight TM domains and that both the N- and C-terminus face the cytoplasm. We found that MLC1 can be purified as an oligomer and could form a trimeric complex in both detergent micelles and reconstituted proteoliposomes. Additionally, a single-molecule photobleaching experiment showed that MLC1 protein complexes could consist of three MLC1 monomers in the reconstituted proteoliposomes. These results can provide a basis for both the high-resolution structural determination and functional characterization of the MLC1 protein.
    Type of Medium: Online Resource
    ISSN: 2046-2441
    Language: English
    Publisher: The Royal Society
    Publication Date: 2021
    detail.hit.zdb_id: 2630944-0
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  • 5
    In: IBRO Reports, Elsevier BV, Vol. 6 ( 2019-09), p. S382-
    Type of Medium: Online Resource
    ISSN: 2451-8301
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
    detail.hit.zdb_id: 2893627-9
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  • 6
    In: Advanced Materials, Wiley
    Abstract: Living cells efflux intracellular ions for maintaining cellular life, so intravital measurements of specific ion signals are of significant importance for studying cellular functions and pharmacokinetics. In this work, de novo synthesis of artificial K + ‐selective membrane and its integration with polyelectrolyte hydrogel‐based open‐junction ionic diode (OJID) is demonstrated, achieving a real‐time K + ‐selective ion‐to‐ion current amplification in complex bioenvironments. By mimicking biological K + channels and nerve impulse transmitters, in‐line K + ‐binding G‐quartets are introduced across freestanding lipid bilayers by G‐specific hexylation of monolithic G‐quadruplex, and the pre‐filtered K + flow is directly converted to amplified ionic currents by the OJID with a fast response time at 100 ms intervals. By the synergistic combination of charge repulsion, sieving, and ion recognition, the synthetic membrane allows K + transport exclusively without water leakage; it is 250× and 17× more permeable toward K + than monovalent anion, Cl − , and polyatomic cation, N‐methyl‐ d ‐glucamine + , respectively. The molecular recognition‐mediated ion channeling provides a 500% larger signal for K + as compared to Li + (0.6× smaller than K + ) despite the same valence. Using the miniaturized device, non‐invasive, direct, and real‐time K + efflux monitoring from living cell spheroids is achieved with minimal crosstalk, specifically in identifying osmotic shock‐induced necrosis and drug‐antidote dynamics.
    Type of Medium: Online Resource
    ISSN: 0935-9648 , 1521-4095
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 1474949-X
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