Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment

Anomaly segmentation plays a crucial role in identifying anomalous objects within images, which facilitates the detection of road anomalies for autonomous driving. Although existing methods have shown impressive results in anomaly segmentation using synthetic training data, the domain discrepancies between synthetic training data and real test data are often neglected. To address this issue, the Multi-Granularity Cross-Domain Alignment (MGCDA) framework is proposed for anomaly segmentation in complex driving environments. It uniquely combines a new Multi-source Domain Adversarial Training (MDAT) module and a novel Cross-domain Anomaly-aware Contrastive Learning (CACL) method to boost the generality of the model, seamlessly integrating multi-domain data at both scene and sample levels. Multi-source domain adversarial loss and a dynamic label smoothing strategy are integrated into the MDAT module to facilitate the acquisition of domain-invariant features at the scene level, through adversarial training across multiple stages. CACL aligns sample-level representations with contrastive loss on cross-domain data, which utilizes an anomaly-aware sampling strategy to efficiently sample hard samples and anchors. The proposed framework has decent properties of parameter-free during the inference stage and is compatible with other anomaly segmentation networks. Experimental conducted on Fishyscapes and RoadAnomaly datasets demonstrate that the proposed framework achieves state-of-the-art performance.Comment: Accepted to ACM Multimedia 202

Angular Reconstruction of a Lead Scintillating-Fiber Sandwiched Electromagnetic Calorimeter

A new method called Neighbor Cell Deposited Energy Ratio (NCDER) is proposed to reconstruct incidence position in a single layer for a 3-dimensional imaging electromagnetic calorimeter (ECAL).This method was applied to reconstruct the ECAL test beam data for the Alpha Magnetic Spectrometer-02 (AMS-02). The results show that this method can achieve an angular resolution of 7.36\pm 0.08 / \sqrt(E) \oplus 0.28 \pm 0.02 degree in the determination of the photons direction, which is much more precise than that obtained with the commonly-adopted Center of Gravity(COG) method (8.4 \pm 0.1 /sqrt(E) \oplus 0.8\pm0.3 degree). Furthermore, since it uses only the properties of electromagnetic showers, this new method could also be used for other type of fine grain sampling calorimeters.Comment: 6 pages, 8 figure

Different response to 1-methylcyclopropene in two cultivars of Chinese pear fruit with contrasting softening characteristics

In this study, the change in softening and its related genes expression under influence of 500 nl L-1 1-methylcyclopropene (1-MCP) was assessed in the two Chinese pear fruit, ‘Jingbaili’ (Pyrus ussuriensis Maxim) and ‘Yali’ (Pyrus bretschneideri Rehd), which exhibit different softening characteristics. ‘Jingbaili’ pear fruit softened rapidly after harvest, and was strongly inhibited by 1-MCP. In contrast, there was no obvious change of firmness compared to the control after 1-MCP treatment in ‘Yali’ pear fruit. The respiration and ethylene production rates were reduced by 1-MCP at early storage in both two cultivars. ‘Jingbaili’ pear fruit exhibited dramatically increased expression levels of the softening-related genes, i.e., polygalacturonase1 (PG1), polygalacturonase2 (PG2), β-Galactosidase4 (GAL4), α-arabinofuranosidase1 (ARF1) and α-arabinofuranosidase2 (ARF2), and these genes’ expression levels were significantly decreased by 1-MCP treatment. In contrast, ‘Yali’ pear fruit showed lower expression levels of the above-mentioned genes, as well as a relatively smaller inhibition effect by 1-MCP treatment before day 27. These results suggest that ‘Jingbaili’ pear fruit are more sensitive to 1-MCP/ethylene than ‘Yali’ pear fruit during ripening