Measurement of and Factors Associated with the Anterior Chamber Volume in Healthy Chinese Adults

Purpose. To measure the anterior chamber volume (ACV) and determine factors associated with the ACV in healthy Chinese adults. Methods. In this cross-sectional study, we used swept-source optical coherence tomography (SS-OCT) to measure ACV and other anterior segment parameters. Factors associated with ACV were also determined. Results. A total of 313 healthy Chinese adults were enrolled. The anterior segment parameters, including ACV, could be measured by SS-OCT with excellent repeatability and reproducibility. There was a significant difference between the horizontal and vertical anterior chamber widths (ACW) (P<0.05), with a mean difference of 390 μm. The ACV (mean 153.83±32.42 mm3) was correlated with most of the anterior segment parameters, especially anterior chamber depth (ACD), which accounted for about 85% of the variation of ACV. Most of the anterior segment parameters were significantly correlated with age, and the relative changes in ACV and ACD were greatest in subjects aged 41–50 years. Conclusion. ACV was correlated with most of the anterior segment parameters measured in this study, particularly ACD. The relatively large difference between horizontal and vertical ACW suggests that the ACV could and should be measured using multiple OCT scans

Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning

Machine Unlearning (MU) algorithms have become increasingly critical due to the imperative adherence to data privacy regulations. The primary objective of MU is to erase the influence of specific data samples on a given model without the need to retrain it from scratch. Accordingly, existing methods focus on maximizing user privacy protection. However, there are different degrees of privacy regulations for each real-world web-based application. Exploring the full spectrum of trade-offs between privacy, model utility, and runtime efficiency is critical for practical unlearning scenarios. Furthermore, designing the MU algorithm with simple control of the aforementioned trade-off is desirable but challenging due to the inherent complex interaction. To address the challenges, we present Controllable Machine Unlearning (ConMU), a novel framework designed to facilitate the calibration of MU. The ConMU framework contains three integral modules: an important data selection module that reconciles the runtime efficiency and model generalization, a progressive Gaussian mechanism module that balances privacy and model generalization, and an unlearning proxy that controls the trade-offs between privacy and runtime efficiency. Comprehensive experiments on various benchmark datasets have demonstrated the robust adaptability of our control mechanism and its superiority over established unlearning methods. ConMU explores the full spectrum of the Privacy-Utility-Efficiency trade-off and allows practitioners to account for different real-world regulations. Source code available at: https://github.com/guangyaodou/ConMU

An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification.

Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-scene classification, samples between source and target scenes are not drawn from the independent and identical distribution, resulting in significant performance degradation. To tackle this issue, a novel unsupervised domain adaptation (UDA) framework toward multilevel features and decision boundaries (ToMF-B) is proposed for the cross-scene HSIC, which can align task-related features and learn task-specific decision boundaries in parallel. Based on the maximum classifier discrepancy, a two-stage alignment scheme is proposed to bridge the interdomain gap and generate discriminative decision boundaries. In addition, to fully learn task-related and domain-confusing features, a convolutional neural network (CNN) and Transformer-based multilevel features extractor (generator) is developed to enrich the feature representation of two domains. Furthermore, to alleviate the harm even the negative transfer to UDA caused by task-irrelevant features, a task-oriented feature decomposition method is leveraged to enhance the task-related features while suppressing task-irrelevant features, and enabling the aligned domain-invariant features can be contributed to the classification task explicitly. Extensive experiments on three cross-scene HSI benchmarks have validated the effectiveness of the proposed framework

Unveiling nontrivial fusion rule of Majorana zero mode using a fermionic mode

Fusing Majorana zero modes leads to multiple outcomes, a property being unique to non-Abelian anyons. Successful demonstration of this nontrivial fusion rule would be a hallmark for the development of topological quantum computation.Here we show that this can be done by simply attaching a fermionic mode to a single Majorana zero mode. Through modulation of the energy level of this fermionic mode as well as its coupling with the Majorana mode in different sequences, we show that a zero or integer charge pumping can be realized when different fusion loops are chosen. Such fusion loops are intimately related with the nontrivial fusion rule of Majorana modes and are solely determined by the crossings at zero energy in the parameter space. Finally we demonstrate our proposal in a nanowire-based topological superconductor coupled to a quantum dot. We show that the charge pumping is robust for MZMs in the real system irrespective of the initial condition of FM state, contrary to the case for trivial Andreev bound states. This provides a feasible way to distinguish Majorana modes from trivial Andreev bound states.Comment: 5 pages, 5 figure

A Bayesian Method for Water Resources Vulnerability Assessment: A Case Study of the Zhangjiakou Region, North China

Water resources vulnerability (WRV) assessment is an important basis for maintaining water resources security in a basin. In this paper, considering the complexity of the water resources system and the uncertainty of the assessment information, a method based on the Bayesian theory was developed for performing WRV assessments while using the constructed indicator system. This system includes four subsystems, the hydrological subsystem, the socioeconomic subsystem, the ecoenvironmental subsystem and the hydraulic engineering subsystem. The WRV degree for each subsystem and the integrated water resources system were assessed. Finally, the assessment results and the characteristics of the Bayesian method were compared with those of the grey relational analysis method and the parametric-system method. The results showed the following