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  1. Free, publicly-accessible full text available August 10, 2025
  2. Crystalline materials are promising candidates as substrates or high-reflective coatings of mirrors to reduce thermal noises in future laser interferometric gravitational wave detectors. However, birefringence of such materials could degrade the sensitivity of gravitational wave detectors, not only because it can introduce optical losses, but also because its fluctuations create extra phase noise in the arm cavity reflected beam. In this paper, we analytically estimate the effects of birefringence and its fluctuations in the mirror substrate and coating for gravitational wave detectors. 
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    Free, publicly-accessible full text available January 1, 2025
  3. The broadcasting nature of wireless signals may result in the task offloading process of mobile edge computing (MEC) suffering serious information leakage. As a novel technology, physical layer security (PLS) combined with reconfigurable intelligent surfaces (RIS) can enhance transmission quality and security. This paper investigates the MEC service delay problem in RIS-aided vehicular networks under malicious eavesdropping. Due to the lack of an explicit formulation for the optimization problem, we propose a deep deterministic policy gradient (DDPG)-based communication scheme to optimize the secure MEC service. It aims to minimize the maximum MEC service time while reducing eavesdropping threats by jointly designing the RIS phase shift matrix and computing resource allocation in real-time. Simulation results demonstrate that 1) the DDPG-based scheme can help the base station make reasonable actions to realize secure MEC service in dynamic MEC vehicular networks; 2) deploying RIS can dramatically reduce eavesdropping threats and improve the overall MEC service quality. 
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    Free, publicly-accessible full text available October 29, 2024
  4. Free, publicly-accessible full text available September 11, 2024
  5. Free, publicly-accessible full text available August 4, 2024
  6. Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have been proposed and many fairness-aware methods are developed. However, most of existing definitions and methods focus only on single-label classification. Fairness for multi-label classification, where each instance is associated with more than one labels, is still yet to establish. To fill this gap, we study fairness-aware multi-label classification in this paper. We start by extending Demographic Parity (DP) and Equalized Opportunity (EOp), two popular fairness notions, to multi-label classification scenarios. Through a systematic study, we show that on multi-label data, because of unevenly distributed labels, EOp usually fails to construct a reliable estimate on labels with few instances. We then propose a new framework named Similarity s-induced Fairness (sγ -SimFair). This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp. Theoretical analysis and experimental results on real-world datasets together demonstrate the advantage of sγ -SimFair over existing methods on multi-label classification tasks.

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  7. Free, publicly-accessible full text available August 4, 2024
  8. Vlachos, Andreas ; Augenstein, Isabelle (Ed.)
    In this paper, we seek to improve the faithfulness of TempRel extraction models from two perspectives. The first perspective is to extract genuinely based on contextual description. To achieve this, we propose to conduct counterfactual analysis to attenuate the effects of two significant types of training biases: the event trigger bias and the frequent label bias. We also add tense information into event representations to explicitly place an emphasis on the contextual description. The second perspective is to provide proper uncertainty estimation and abstain from extraction when no relation is described in the text. By parameterization of Dirichlet Prior over the model-predicted categorical distribution, we improve the model estimates of the correctness likelihood and make TempRel predictions more selective. We also employ temperature scaling to recalibrate the model confidence measure after bias mitigation. Through experimental analysis on MATRES, MATRES-DS, and TDDiscourse, we demonstrate that our model extracts TempRel and timelines more faithfully compared to SOTA methods, especially under distribution shifts. 
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