ReSup: Reliable Label Noise Suppression for Facial Expression Recognition

Because of the ambiguous and subjective property of the facial expression recognition (FER) task, the label noise is widely existing in the FER dataset. For this problem, in the training phase, current FER methods often directly predict whether the label of the input image is noised or not, aiming to reduce the contribution of the noised data in training. However, we argue that this kind of method suffers from the low reliability of such noise data decision operation. It makes that some mistakenly abounded clean data are not utilized sufficiently and some mistakenly kept noised data disturbing the model learning process. In this paper, we propose a more reliable noise-label suppression method called ReSup (Reliable label noise Suppression for FER). First, instead of directly predicting noised or not, ReSup makes the noise data decision by modeling the distribution of noise and clean labels simultaneously according to the disagreement between the prediction and the target. Specifically, to achieve optimal distribution modeling, ReSup models the similarity distribution of all samples. To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression. Specifically, ReSup utilize the property that two networks are less likely to make the same mistakes, making two networks swap decisions and tending to trust decisions with high agreement. Extensive experiments on three popular benchmarks show that the proposed method significantly outperforms state-of-the-art noisy label FER methods by 3.01% on FERPlus becnmarks. Code: https://github.com/purpleleaves007/FERDenois

CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

Generalization to previously unseen images with potential domain shifts and different styles is essential for clinically applicable medical image segmentation, and the ability to disentangle domain-specific and domain-invariant features is key for achieving Domain Generalization (DG). However, existing DG methods can hardly achieve effective disentanglement to get high generalizability. To deal with this problem, we propose an efficient Contrastive Domain Disentanglement and Style Augmentation (CDDSA) framework for generalizable medical image segmentation. First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image. Second, to achieve better disentanglement, a contrastive loss is proposed to encourage the style codes from the same domain and different domains to be compact and divergent, respectively. Thirdly, to further improve generalizability, we propose a style augmentation method based on the disentanglement representation to synthesize images in various unseen styles with shared anatomical structures. Our method was validated on a public multi-site fundus image dataset for optic cup and disc segmentation and an in-house multi-site Nasopharyngeal Carcinoma Magnetic Resonance Image (NPC-MRI) dataset for nasopharynx Gross Tumor Volume (GTVnx) segmentation. Experimental results showed that the proposed CDDSA achieved remarkable generalizability across different domains, and it outperformed several state-of-the-art methods in domain-generalizable segmentation.Comment: 14 pages, 8 figure

A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

Disorder in Mn+1AXn phases at the atomic scale.

Atomic disordering in materials alters their physical and chemical properties and can subsequently affect their performance. In complex ceramic materials, it is a challenge to understand the nature of structural disordering, due to the difficulty of direct, atomic-scale experimental observations. Here we report the direct imaging of ion irradiation-induced antisite defects in Mn+1AXn phases using double CS-corrected scanning transmission electron microscopy and provide compelling evidence of order-to-disorder phase transformations, overturning the conventional view that irradiation causes phase decomposition to binary fcc-structured Mn+1Xn. With the formation of uniformly distributed cation antisite defects and the rearrangement of X anions, disordered solid solution γ-(Mn+1A)Xn phases are formed at low ion fluences, followed by gradual transitions to solid solution fcc-structured (Mn+1A)Xn phases. This study provides a comprehensive understanding of the order-to-disorder transformations in Mn+1AXn phases and proposes a method for the synthesis of new solid solution (Mn+1A)Xn phases by tailoring the disorder

Examining the generalizability of research findings from archival data

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

Would you choose to be a psychiatrist again? A large-sample nationwide survey of psychiatrists and psychiatry residents in China

Abstract Background The mental health workforce sustainability in China suffers high rates of attrition and the intention to leave. Among current professionals, the intention to choose the same career is an interesting way to gauge their job satisfaction and other factors, and it may affect the career choices of younger generations. We aimed to survey the intention of psychiatrists and psychiatry residents to choose the same career if they could start over and to identify associated factors. Methods We conducted an anonymous survey of psychiatrists in 41 tertiary psychiatric hospitals in China. We collected demographic data, work-related information, the sense of professional identity, job satisfaction, and burnout (Maslach Burnout Inventory), and we specifically asked each participant whether they would choose to be a psychiatrist again if they could. Results Among 3,783 psychiatrists we surveyed, one-quarter responded that they would not choose to be a psychiatrist again if they had a choice, with less than half (47.2%) saying they would. Those who would not choose psychiatry again were more likely to have a negative (relative to positive) professional identity (OR = 7.47, P＜0.001, 95%CI: 4.587–12.164); experience job burnout (OR = 2.945, P＜0.001, 95%CI: 2.356–3.681); be dissatisfied with their job (OR = 2.739, P＜0.001, 95%CI: 2.102–3.569) and excessive regulation (OR = 1.819, P＜0.001, 95%CI: 1.487–2.226); have a heavy workload (OR = 1.749, P＜0.001, 95%CI: 1.423–2.149) or a lower income (OR = 1.748, P＜0.001, 95%CI: 1.415–2.161); be married (relative to single) (OR = 1.604, P = 0.004, 95%CI: 1.165–2.208); be dissatisfied with strained doctor-patient relationship (OR = 1.333, P = 0.005, 95%CI: 1.089–1.632); have more night shifts per month (OR = 1.055, P = 0.021, 95%CI: 1.008–1.104) or work longer hours per week (OR = 1.016, P = 0.001, 95%CI: 1.006–1.025). Conclusion Among psychiatrists in tertiary hospitals in China, those with a heavier workload, poor sense of professional identity, job dissatisfaction, and burnout were less likely to choose psychiatry again. Policymakers and hospital administrators need to take effective measures to improve psychiatrists’ sense of professional identity and increase their intention to stay