EqCo: Equivalent Rules for Self-supervised Contrastive Learning

In this paper, we propose a method, named EqCo (Equivalent Rules for Contrastive Learning), to make self-supervised learning irrelevant to the number of negative samples in InfoNCE-based contrastive learning frameworks. Inspired by the InfoMax principle, we point that the margin term in contrastive loss needs to be adaptively scaled according to the number of negative pairs in order to keep steady mutual information bound and gradient magnitude. EqCo bridges the performance gap among a wide range of negative sample sizes, so that we can use only a few negative pairs (e.g. 16 per query) to perform self-supervised contrastive training on large-scale vision datasets like ImageNet, while with almost no accuracy drop. This is quite a contrast to the widely used large batch training or memory bank mechanism in current practices. Equipped with EqCo, our simplified MoCo (SiMo) achieves comparable accuracy with MoCo v2 on ImageNet (linear evaluation protocol) while only involves 4 negative pairs per query instead of 65536, suggesting that large quantities of negative samples might not be a critical factor in InfoNCE loss

Coordination Control of a Dual-Arm Exoskeleton Robot Using Human Impedance Transfer Skills

This paper has developed a coordination control method for a dual-arm exoskeleton robot based on human impedance transfer skills, where the left (master) robot arm extracts the human limb impedance stiffness and position profiles, and then transfers the information to the right (slave) arm of the exoskeleton. A computationally efficient model of the arm endpoint stiffness behavior is developed and a co-contraction index is defined using muscular activities of a dominant antagonistic muscle pair. A reference command consisting of the stiffness and position profiles of the operator is computed and realized by one robot in real-time. Considering the dynamics uncertainties of the robotic exoskeleton, an adaptive-robust impedance controller in task space is proposed to drive the slave arm tracking the desired trajectories with convergent errors. To verify the robustness of the developed approach, a study of combining adaptive control and human impedance transfer control under the presence of unknown interactive forces is conducted. The experimental results of this paper suggest that the proposed control method enables the subjects to execute a coordination control task on a dual-arm exoskeleton robot by transferring the stiffness from the human arm to the slave robot arm, which turns out to be effective

Facile synthesis of α-Fe2O3 micro-ellipsoids by surfactant-free hydrothermal method for sub-ppm level H2S detection

The α-Fe2O3 micro-ellipsoids were prepared using a facile hydrothermal process without any surfactant or template, and their morphological, structural and H2S sensing properties were investigated. The α-Fe2O3 showed uniform micro-ellipsoids with a long axis diameter of 1.7 μm and a short axis diameter of 1.2 μm. Detailed structural analysis confirmed that the synthesized α-Fe2O3 micro-ellipsoids were compact particles with a hexagonal structure. Gas sensor base on the α-Fe2O3 micro-ellipsoids showed excellent response, short response/recovery time (< 90 s and 30 s, respectively), low detection concentration (~ 0.5 ppm), good long-term stability and excellent selectivity towards H2S gas at the optimized operating temperature of 350 °C. The sensing mechanism of the sensor based on the α-Fe2O3 micro-ellipsoids towards H2S was discussed

Reliability and validity of the Mental Health Self-management Questionnaire among Chinese patients with mood and anxiety disorders

BackgroundSelf-management plays an important role in promoting and restoring mental health for individuals with mental health issues. However, there is no valid and reliable Chinese tool assessing the self-management behaviors of people with mood and anxiety disorders. This study aimed to develop a Chinese version of the Mental Health Self-management Questionnaire (MHSQ-C) and to verify its psychometric properties.MethodsA total of 440 potential participants were recruited by convenience sampling from June to August 2020. Item analysis and analyses of internal consistency, test-retest reliability, content validity, construct validity and criterion validity were performed.ResultsData from 326 participants were used. Three factors obtained via principal component analysis and varimax rotation explained 53.68% of the total variance. The average content validity index was 0.99. The Cronbach’s α coefficient (total: 0.874, clinical: 0.706, empowerment: 0.818, vitality: 0.830) and test-retest reliability (ICC: total: 0.783, 95% confidence interval (CI) [0.616, 0.882], clinical: 0.525, 95% CI [0.240, 0.725], empowerment: 0.786, 95% CI [0.622, 0.884], vitality: 0.748, 95% CI [0.564, 0.862]) were good. The MHSQ-C was well correlated with the Partners in Health scale and showed no floor or ceiling effect.DiscussionThe MHSQ-C is a reliable and valid tool to evaluate the self-management strategies of patients with mood and anxiety disorders

Global prevalence of Cryptosporidium spp. in pigs: a systematic review and meta-analysis

Cryptosporidium spp. are significant opportunistic pathogens causing diarrhoea in humans and animals. Pigs are one of the most important potential hosts for Cryptosporidium. We evaluated the prevalence of Cryptosporidium in pigs globally using published information and a random-effects model. In total, 131 datasets from 36 countries were included in the final quantitative analysis. The global prevalence of Cryptosporidium in pigs was 16.3% (8560/64 809; 95% confidence interval [CI] 15.0–17.6%). The highest prevalence of Cryptosporidium in pigs was 40.8% (478/1271) in Africa. Post-weaned pigs had a significantly higher prevalence (25.8%; 2739/11 824) than pre-weaned, fattening and adult pigs. The prevalence of Cryptosporidium was higher in pigs with no diarrhoea (12.2%; 371/3501) than in pigs that had diarrhoea (8.0%; 348/4874). Seven Cryptosporidium species (Cryptosporidium scrofarum, Cryptosporidium suis, Cryptosporidium parvum, Cryptosporidium muris, Cryptosporidium tyzzeri, Cryptosporidium andersoni and Cryptosporidium struthioni) were detected in pigs globally. The proportion of C. scrofarum was 34.3% (1491/4351); the proportion of C. suis was 31.8% (1385/4351) and the proportion of C. parvum was 2.3% (98/4351). The influence of different geographic factors (latitude, longitude, mean yearly temperature, mean yearly relative humidity and mean yearly precipitation) on the infection rate of Cryptosporidium in pigs was also analysed. The results indicate that C. suis is the dominant species in pre-weaned pigs, while C. scrofarum is the dominant species in fattening and adult pigs. The findings highlight the role of pigs as possible potential hosts of zoonotic cryptosporidiosis and the need for additional studies on the prevalence, transmission and control of Cryptosporidium in pigs

Development of a prognostic nomogram and risk stratification system for upper thoracic esophageal squamous cell carcinoma

BackgroundThe study aimed to develop a nomogram model to predict overall survival (OS) and construct a risk stratification system of upper thoracic esophageal squamous cell carcinoma (ESCC).MethodsNewly diagnosed 568 patients with upper ESCC at Fujian Medical University Cancer Hospital were taken as a training cohort, and additional 155 patients with upper ESCC from Sichuan Cancer Hospital Institute were used as a validation cohort. A nomogram was established using Cox proportional hazard regression to identify prognostic factors for OS. The predictive power of nomogram model was evaluated by using 4 indices: concordance statistics (C-index), time-dependent ROC (ROCt) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI).ResultsIn this study, multivariate analysis revealed that gender, clinical T stage, clinical N stage and primary gross tumor volume were independent prognostic factors for OS in the training cohort. The nomogram based on these factors presented favorable prognostic efficacy in the both training and validation cohorts, with concordance statistics (C-index) of 0.622, 0.713, and area under the curve (AUC) value of 0.709, 0.739, respectively, which appeared superior to those of the American Joint Committee on Cancer (AJCC) staging system. Additionally, net reclassification index (NRI) and integrated discrimination improvement (IDI) of the nomogram presented better discrimination ability to predict survival than those of AJCC staging. Furthermore, decision curve analysis (DCA) of the nomogram exhibited greater clinical performance than that of AJCC staging. Finally, the nomogram fairly distinguished the OS rates among low, moderate, and high risk groups, whereas the OS curves of clinical stage could not be well separated among clinical AJCC stage.ConclusionWe built an effective nomogram model for predicting OS of upper ESCC, which may improve clinicians’ abilities to predict individualized survival and facilitate to further stratify the management of patients at risk