Diverse Knowledge Distillation for End-to-End Person Search

Person search aims to localize and identify a specific person from a gallery of images. Recent methods can be categorized into two groups, i.e., two-step and end-to-end approaches. The former views person search as two independent tasks and achieves dominant results using separately trained person detection and re-identification (Re-ID) models. The latter performs person search in an end-to-end fashion. Although the end-to-end approaches yield higher inference efficiency, they largely lag behind those two-step counterparts in terms of accuracy. In this paper, we argue that the gap between the two kinds of methods is mainly caused by the Re-ID sub-networks of end-to-end methods. To this end, we propose a simple yet strong end-to-end network with diverse knowledge distillation to break the bottleneck. We also design a spatial-invariant augmentation to assist model to be invariant to inaccurate detection results. Experimental results on the CUHK-SYSU and PRW datasets demonstrate the superiority of our method against existing approaches -- it achieves on par accuracy with state-of-the-art two-step methods while maintaining high efficiency due to the single joint model. Code is available at: https://git.io/DKD-PersonSearch.Comment: Accepted to AAAI, 2021. Code is available at: https://git.io/DKD-PersonSearc

SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes

Self-supervised monocular depth estimation has shown impressive results in static scenes. It relies on the multi-view consistency assumption for training networks, however, that is violated in dynamic object regions and occlusions. Consequently, existing methods show poor accuracy in dynamic scenes, and the estimated depth map is blurred at object boundaries because they are usually occluded in other training views. In this paper, we propose SC-DepthV3 for addressing the challenges. Specifically, we introduce an external pretrained monocular depth estimation model for generating single-image depth prior, namely pseudo-depth, based on which we propose novel losses to boost self-supervised training. As a result, our model can predict sharp and accurate depth maps, even when training from monocular videos of highly-dynamic scenes. We demonstrate the significantly superior performance of our method over previous methods on six challenging datasets, and we provide detailed ablation studies for the proposed terms. Source code and data will be released at https://github.com/JiawangBian/sc_depth_plComment: Under Review; The code will be available at https://github.com/JiawangBian/sc_depth_p

Glutathione S-Transferase alpha 4 promotes proliferation and chemoresistance in colorectal cancer cells

Glutathion

Profiles and Bioinformatics Analysis of Differentially Expressed Circrnas in Taxol-Resistant Non-Small Cell Lung Cancer Cells

Background/Aims: Circular RNAs (circRNAs) act as microRNA (miRNA) sponges that regulate gene expression and are involved in physiological and pathological processes. In this study, we evaluated the roles of circRNAs in the chemoresistance of non-small cell lung cancer (NSCLC) to taxol. Methods: High-throughput circRNA microarrays were employed to investigate the circRNA profiles of parental A549 and taxol-resistant A549/Taxol cells. We predicted the miRNA targets of differentially expressed circRNAs via miRNA prediction software and then constructed a circRNA/miRNA network using Cytoscape. Bioinformatics analyses were performed to annotate dysregulated circRNAs in detail. Results: We detected 2909 significantly upregulated and 8372 downregulated circRNAs in A549/Taxol cells compared with A549 cells. The circRNA/miRNA network displayed their interactions, suggesting that circRNAs exert biological effects by absorbing and sequestering miRNA molecules. Computational Gene Ontology and pathway analyses were used to determine the biological function and signaling pathways of host genes of dysregulated circRNAs and to identify potential molecular mechanisms prompting the resistance of NSCLC to taxol. Conclusion: This study focusing on circRNAs related to taxol resistance provides a basis for clarifying the development and progression of drug resistance and for identifying therapeutic targets in NSCLC

The workload change and depression among emergency medical staff after the open policy during COVID-19: a cross-sectional survey in Shandong, China

IntroductionIn the middle of December 2022, the Chinese government adjusted the lockdown policy on coronavirus disease 2019 (COVID-19), a large number of infected patients flooded into the emergency department. The emergency medical staff encountered significant working and mental stress while fighting the COVID-19 pandemic. We aimed to investigate the workload change, and the prevalence and associated factors for depression symptoms among emergency medical staff after the policy adjustment.MethodsWe conducted a cross-sectional online survey of emergency medical staff who fought against COVID-19 in Shandong Province during January 16 to 31, 2023. The respondents’ sociodemographic and work information were collected, and they were asked to complete the 9-item Patient Health Questionnaire (PHQ-9) then. Univariate and multivariate logistic regression analyses were applied to identify the potential associated factors for major depression.ResultsNine hundred and sixteen emergency medical personnel from 108 hospitals responded to this survey. The respondents’ weekly working hours (53.65 ± 17.36 vs 49.68 ± 14.84) and monthly night shifts (7.25 ± 3.85 vs 6.80 ± 3.77) increased after the open policy. About 54.3% of the respondents scored more than 10 points on the PHQ-9 standardized test, which is associated with depressive symptoms. In univariate analysis, being doctors, living with family members aged ≤16 or ≥ 65 years old, COVID-19 infection and increased weekly working hours after the open policy were significantly associated with a PHQ-9 score ≥ 10 points. In the multivariate analysis, only increased weekly working hours showed significant association with scoring ≥10 points.ConclusionEmergency medical staff’ workload had increased after the open policy announcement, which was strongly associated with a higher PHQ-9 scores, indicating a very high risk for major depression. Emergency medical staff working as doctors or with an intermediate title from grade-A tertiary hospitals had higher PHQ-9 scores, while COVID-19 infection and weekly working hours of 60 or more after the open policy were associated with higher PHQ-9 scores for those from grade-B tertiary hospitals. Hospital administrators should reinforce the importance of targeted emergency medical staff support during future outbreaks

Auto-Rectify Network for Unsupervised Indoor Depth Estimation

Single-View depth estimation using the CNNs trained from unlabelled videos has shown significant promise. However, excellent results have mostly been obtained in street-scene driving scenarios, and such methods often fail in other settings, particularly indoor videos taken by handheld devices. In this work, we establish that the complex ego-motions exhibited in handheld settings are a critical obstacle for learning depth. Our fundamental analysis suggests that the rotation behaves as noise during training, as opposed to the translation (baseline) which provides supervision signals. To address the challenge, we propose a data pre-processing method that rectifies training images by removing their relative rotations for effective learning. The significantly improved performance validates our motivation. Towards end-to-end learning without requiring pre-processing, we propose an Auto-Rectify Network with novel loss functions, which can automatically learn to rectify images during training. Consequently, our results outperform the previous unsupervised SOTA method by a large margin on the challenging NYUv2 dataset. We also demonstrate the generalization of our trained model in ScanNet and Make3D, and the universality of our proposed learning method on 7-Scenes and KITTI datasets.Comment: Accepted to TPAMI. Find code at https://github.com/JiawangBia