Comment on "Quantum key distribution for d-level systems with generalized Bell states" [Phys. Rev. A 65, 052331 (2002)]

In the paper [Phys. Rev. A 65, 052331(2002)], an entanglement-based quantum key distribution protocol for d-level systems was proposed. However, in this Comment, it is shown that this protocol is insecure for a special attack strategy.Comment: 4 pages, 4 figure

Comment on "Quantum secret sharing based on reusable Greenberger-Horne-Zeilinger states as secure carriers" [Phys. Rev. A 67, 044302 (2003)]

In a recent paper [S. Bagherinezhad and V. Karimipour, Phys. Rev. A 67, 044302 (2003)], a quantum secret sharing protocol based on reusable GHZ states was proposed. However, in this Comment, it is shown that this protocol is insecure if Eve employs a special strategy to attack.Comment: 2 pages, no figure

A Whole Process Prediction Method for Temperature Field of Fire Smoke in Large Spaces

AbstractBased on the fire development model for the whole process of localized fires in large-space buildings and assisted by the technology of FDS large eddy simulation, the temperature fields of fire smoke of localized fires in large spaces were investigated with different building heights, building areas and fire powers. It has been found that for large-space buildings with a height greater than 6 m and a building area more than 1500 m2, factors like building height and building area can slightly affect the curve trend of fire smoke, while such factor like fire power has more significant influence on the curve trend of fire smoke. Through the analysis of temperature rise curves of fire smoke in various fire scenarios, the paper proposed a whole-process prediction model for the temperature fields of fire smoke of localized fires in large-space buildings. As long as the model uses the appropriate shape coefficient, the prediction model can accurately predict the temperature fields of fire smoke of localized fires in large-space buildings

Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization

In the machine learning domain, research on anomaly detection and localization within image data has garnered significant attention, particularly in practical applications such as industrial defect detection. While existing approaches predominantly rely on Convolutional Neural Networks (CNN) as their backbone network, we propose an innovative method based on the Transformer backbone network. Our approach employs a two-stage incremental learning strategy. In the first stage, we train a Masked Autoencoder (MAE) model exclusively on normal images. Subsequently, in the second stage, we implement pixel-level data augmentation techniques to generate corrupted normal images and their corresponding pixel labels. This process enables the model to learn how to repair corrupted regions and classify the state of each pixel. Ultimately, the model produces a pixel reconstruction error matrix and a pixel anomaly probability matrix, which are combined to create an anomaly scoring matrix that effectively identifies abnormal regions. When compared to several state-of-the-art CNN-based techniques, our method demonstrates superior performance on the MVTec AD dataset, achieving an impressive 97.6% AUC