MMPL-Net: Multi-modal prototype learning for one-shot RGB-D segmentation

For one-shot segmentation, prototype learning is extensively used. However, using only one RGB prototype to represent all information in the support image may lead to ambiguities. To this end, we propose a one-shot segmentation network based on multi-modal prototype learning that uses depth information to complement RGB information. Specifically, we propose a multi-modal fusion and refinement block (MFRB) and multi-modal prototype learning block (MPLB). MFRB fuses RGB and depth features to generate multi-modal features and refined depth features, which are used by MPLB, to generate multi-modal information prototypes, depth information prototypes, and global information prototypes. Furthermore, we introduce self-attention to capture global context information in RGB and depth images. By integrating self-attention, MFRB, and MPLB, we propose the multi-modal prototype learning network (MMPL-Net), which adapts to the ambiguity of visual information in the scene. Finally, we construct a one-shot RGB-D segmentation dataset called OSS-RGB-D-5i. Experiments using OSS-RGB-D-5i show that our proposed method outperforms several state-of-the-art techniques with fewer labeled images and generalizes well to previously unseen objects.</p

A knowledge distillation-based multi-scale relation-prototypical network for cross-domain few-shot defect classification

Surface defect classification plays a very important role in industrial production and mechanical manufacturing. However, there are currently some challenges hindering its use. The first is the similarity of different defect samples makes classification a difficult task. Second, the lack of defect samples leads to poor accuracies when using deep learning methods. In this paper, we first design a novel backbone network, ResMSNet, which draws on the idea of multi-scale feature extraction for small discriminative regions in defect samples. Then, we introduce few-shot learning for defect classification and propose a Relation-Prototypical network (RPNet), which combines the characteristics of ProtoNet and RelationNet and provides classification by linking the prototypes distances and the nonlinear relation scores. Next, we consider a more realistic scenario where the base dataset for training the model and target defect dataset for applying the model are usually obtained from domains with large differences, called cross-domain few-shot learning. Hence, we further improve RPNet to KD-RPNet inspired by knowledge distillation methods. Through extensive comparative experiments and ablation experiments, we demonstrate that either our ResMSNet or RPNet proves its effectiveness and KD-RPNet outperforms other state-of-the-art approaches for few-shot defect classification.</p

Cohort profile: The prospective study on Chinese elderly with multimorbidity in primary care in Hong Kong

Acknowledgments We would like to thank the General Outpatient Clinics (Lek Yuen, Ma On Shan, Shatin (Tai Wai), Yuen Chau Kok) and Shatin Rhenish Neighbourhood Elderly Centre for the support and help in recruitment. We also greatly thank all the patients who joined in the cohort. Funding The staff working on this cohort received funding from the Hong Kong Jockey Club Charities Trust. Competing interests None declared. Patient consent for publication Not required. Provenance and peer review Not commissioned; externally peer reviewed. Data availability statement Data are available on reasonable request. The authors warmly welcome collaborations for future research based on this study. For those who would like to request for the data or propose new assessments into the follow-up assessments, they can email to: ([email protected]). For more information please see the website: http://cpcp.sphpc.cuhk.edu.hk/chi/.Peer reviewedPublisher PD

Comparative Effectiveness of Interventions for Global Cognition in Patients With Mild Cognitive Impairment: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

Background: There is a lack of study comprehensively comparing the effects of all existing types of interventions on global cognition among patients with mild cognitive impairment (MCI).Aims: To conduct a network meta-analysis to evaluate the effectiveness of different types of interventions in improving global cognition among MCI patients.Methods: Randomized controlled trials (RCTs) assessing the effects of pharmacological or non-pharmacological interventions on the Mini-Mental State Examination (MMSE) in MCI patients were included. Two authors independently screened the studies and extracted the data. Random-effects network meta-analysis was used to synthesize the data. Results were summarized as mean difference (MD) and corresponding 95% CIs of MMSE in forest plots.Results: Fifty RCTs with 5,944 MCI patients met the inclusion criteria and 49 were included in the network meta-analysis. Compared with the control group, cognition-based intervention (MD = 0.80, 95% CI 0.04–1.57), physical exercise (MD = 1.92, 95% CI 1.19–2.64), combined physical exercise and cognition-based intervention (MD = 1.86, 95% CI 0.60–3.12), and antioxidants (MD = 0.94, 95% CI 0.04–1.83) had positive effects on MMSE in participants with MCI. There was no significant difference between all other interventions included and the control group.Conclusions: This study suggested that cognition-based intervention, physical exercise, combined physical exercise and cognition-based intervention, and antioxidants could be among the most effective interventions on global cognition in older adults with MCI. The availability, acceptability, and cost-effectiveness of interventions should also be taken into consideration when selecting interventions.Registration: PROSPERO CRD42020171985

Distant Influence of Kuroshio Eddies on North Pacific Weather Patterns?

High-resolution satellite measurements of surface winds and sea-surface temperature (SST) reveal strong coupling between meso-scale ocean eddies and near-surface atmospheric flow over eddy-rich oceanic regions, such as the Kuroshio and Gulf Stream, highlighting the importance of meso-scale oceanic features in forcing the atmospheric planetary boundary layer (PBL). Here, we present high-resolution regional climate modeling results, supported by observational analyses, demonstrating that meso-scale SST variability, largely confined in the Kuroshio-Oyashio confluence region (KOCR), can further exert a significant distant influence on winter rainfall variability along the U.S. Northern Pacific coast. The presence of meso-scale SST anomalies enhances the diabatic conversion of latent heat energy to transient eddy energy, intensifying winter cyclogenesis via moist baroclinic instability, which in turn leads to an equivalent barotropic downstream anticyclone anomaly with reduced rainfall. The finding points to the potential of improving forecasts of extratropical winter cyclones and storm systems and projections of their response to future climate change, which are known to have major social and economic impacts, by improving the representation of ocean eddy–atmosphere interaction in forecast and climate models

Does It Matter Who You Live with during COVID-19 Lockdown? Association of Living Arrangements with Psychosocial Health, Life Satisfaction, and Quality of Life: A Pilot Study

Background: Living arrangements might greatly impact psychosocial health and quality of life, particularly during the COVID-19 lockdown. This pilot study aimed to examine the association of different common living arrangements with psychosocial health, life satisfaction, and quality of life among Chinese adults during the COVID-19 lockdown. Methods: An anonymous online survey was conducted using convenience sampling through the WeChat application in February 2020. Mental health (Patient Health Questionnaire-2, Generalized Anxiety Disorder-2, post-traumatic stress disorder symptoms, Patient Health Questionnaire-15, and meaning in life), social health (UCLA-3), quality of life (EQ5D and EQ-VAS), and life satisfaction were measured. Linear regression models were used. Result: The study included 1245 adults (mean age: 34.14 &plusmn; 10.71) in China. Compared to other living arrangements, participants who &ldquo;live with partner and children&rdquo; or &ldquo;live with partner, children and parents&rdquo; were more likely to have better outcomes of mental health, social health, quality of life, and life satisfaction. Participants who &ldquo;live with parents or grandparents&rdquo; or &ldquo;live with partner&rdquo; were more likely to have better health outcomes compared with those who &ldquo;live with children&rdquo; or &ldquo;live alone&rdquo;. Conclusion: Living with a partner, children, and/or parents could be a protective factor against poor psychosocial health during lockdown and quarantine