Deep learning-based clustering methods usually regard feature extraction and feature clustering as two independent steps. In this way, the features of all images need to be extracted before feature clustering, which consumes a lot of calculation. Inspired by the self-organizing map network, a self-supervised self-organizing clustering network (S 3 OCNet) is proposed to jointly learn feature extraction and feature clustering, thus realizing a single-stage clustering method. In order to achieve joint learning, we propose a self-organizing clustering header (SOCH), which takes the weight of the self-organizing layer as the cluster centers, and the output of the self-organizing layer as the similarities between the feature and the cluster centers. In order to optimize our network, we first convert the similarities into probabilities which represents a soft cluster assignment, and then we obtain a target for self-supervised learning by transforming the soft cluster assignment into a hard cluster assignment, and finally we jointly optimize backbone and SOCH. By setting different feature dimensions, a Multilayer SOCHs strategy is further proposed by cascading SOCHs. This strategy achieves clustering features in multiple clustering spaces. S 3 OCNet is evaluated on widely used image classification benchmarks such as Canadian Institute For Advanced Research (CIFAR)-10, CIFAR-100, Self-Taught Learning (STL)-10, and Tiny ImageNet. Experimental results show that our method significant improvement over other related methods. The visualization of features and images shows that our method can achieve good clustering results.In the research on image captioning, rich semantic information is very important for generating critical caption words as guiding information. However, semantic information from offline object detectors involves many semantic objects that do not appear in the caption, thereby bringing noise into the decoding process. To produce more accurate semantic guiding information and further optimize the decoding process, we propose an end-to-end adaptive semantic-enhanced transformer (AS-Transformer) model for image captioning. For semantic enhancement information extraction, we propose a constrained weaklysupervised learning (CWSL) module, which reconstructs the semantic object’s probability distribution detected by the multiple instances learning (MIL) through a joint loss function. These strengthened semantic objects from the reconstructed probability distribution can better depict the semantic meaning of images. Also, for semantic enhancement decoding, we propose an adaptive gated mechanism (AGM) module to adjust the attention between visual and semantic information adaptively for the more accurate generation of caption words. Through the joint control of the CWSL module and AGM module, our proposed model constructs a complete adaptive enhancement mechanism from encoding to decoding and obtains visual context that is more suitable for captions. Experiments on the public Microsoft Common Objects in COntext (MSCOCO) and Flickr30K datasets illustrate that our proposed AS-Transformer can adaptively obtain effective semantic information and adjust the attention weights between semantic and visual information automatically, which achieves more accurate captions compared with semantic enhancement methods and outperforms state-of-the-art methods.Metric-based methods achieve promising performance on few-shot classification by learning clusters on support samples and generating shared decision boundaries for query samples. However, existing methods ignore the inaccurate class center approximation introduced by the limited number of support samples, which consequently leads to biased inference. Therefore, in this paper, we propose to reduce the approximation error by class center calibration. Specifically, we introduce the so-called Pair-wise Similarity Module (PSM) to generate calibrated class centers adapted to the query sample by capturing the semantic correlations between the support and the query samples, as well as enhancing the discriminative regions on support representation. It is worth noting that the proposed PSM is a simple plug-and-play module and can be inserted into most metric-based few-shot learning models. Through extensive experiments in metric-based models, we demonstrate that the module significantly improves the performance of conventional few-shot classification methods on four few-shot image classification benchmark datasets. Codes are available at https//github.com/PRIS-CV/Pair-wise-Similarity-module.Previous blind or No Reference (NR) Image / video quality assessment (IQA/VQA) models largely rely on features drawn from natural scene statistics (NSS), but under the assumption that the image statistics are stationary in the spatial domain. Several of these models are quite successful on standard pictures. However, in Virtual Reality (VR) applications, foveated video compression is regaining attention, and the concept of space-variant quality assessment is of interest, given the availability of increasingly high spatial and temporal resolution contents and practical ways of measuring gaze direction. Distortions from foveated video compression increase with increased eccentricity, implying that the natural scene statistics are space-variant. Towards advancing the development of foveated compression / streaming algorithms, we have devised a no-reference (NR) foveated video quality assessment model, called FOVQA, which is based on new models of space-variant natural scene statistics (NSS) and natural video statistics (NVS). Specifically, we deploy a space-variant generalized Gaussian distribution (SV-GGD) model and a space-variant asynchronous generalized Gaussian distribution (SV-AGGD) model of mean subtracted contrast normalized (MSCN) coefficients and products of neighboring MSCN coefficients, respectively. We devise a foveated video quality predictor that extracts radial basis features, and other features that capture perceptually annoying rapid quality fall-offs. We find that FOVQA achieves state-of-the-art (SOTA) performance on the new 2D LIVE-FBT-FCVR database, as compared with other leading Foveated IQA / VQA models. we have made our implementation of FOVQA available at https//live.ece.utexas.edu/research/Quality/FOVQA.zip.

Endoscopic thyroidectomy is popular among young patients because of its excellent cosmetic outcomes. But it takes a long time to become proficient and competent for surgeons. In addition, collaboration plays a critical role in endoscopic thyroidectomy. Our research aims to evaluate the learning curve of endoscopic thyroidectomy via breast areola approach, provide details of this approach, and demonstrate the importance of collaboration.

The authors retrospectively analyzed 100 cases of benign and malignant thyroid disease who underwent endoscopic thyroidectomy via breast areola approach between January 2015 and December 2020, which were performed by the same group of surgeons with little experience of endoscopic thyroidectomy. The learning curve was analyzed by moving average method. The mean operation time, blood loss, tumor size, postoperative complications were used to determine learning curve progression.

The learning curve in the first 30 cases were uplifted, stable at 30 to 60 cases and declined iequires a stable team.

Within the United States, the number of players participating in baseball increased by nearly 21% to 15.9 million between 2014 and 2019. Additionally, batting helmets with face-masks are encouraged yet optional in youth baseball as well as college baseball and softball. in light of inconsistencies in safety equipment enforcement and usage, this study aims to perform a comparative analysis of the number and frequency of baseball and softball-related craniofacial injuries (CFis).

Data regarding baseball and softball-related injuries were gathered from the National Electronic Injury Surveillance System database from 2011 to 2020. Craniofacial injuries were isolated and organized into 5-year age groups beginning with 5 to 9 years and ending with 25 to 29 years of age. Data was further stratified by location and type of injury. Injury types specifically reported in this study included concussion, contusion, fracture, and laceration.

Distribution of injuries across age groups differed significantly between baseball and softball ( P < 0.001). When comparing the 10 to 14 year old group to the 15 to 19 year old group, we found that concussions and head contusions comprise a significantly greater proportion of all injuries in softball than in baseball. Conversely, facial fractures, facial lacerations, and mouth lacerations comprise a significantly greater proportion ofinjuries in baseball than in softball.

Future prospective studies aiming to better characterize the within-game nature of these reported CFIs would certainly be beneficial in guiding the baseball and softball communities toward consideration of implementing maximally efficacious updates to current safety equipment standards.

Future prospective studies aiming to better characterize the within-game nature of these reported CFIs would certainly be beneficial in guiding the baseball and softball communities toward consideration of implementing maximally efficacious updates to current safety equipment standards.

Limited evidence is available on the real-world effectiveness of the BNT162b2 vaccine against coronavirus disease 2019 (Covid-19) and specifically against infection with the omicron variant among children 5 to 11 years of age.

Using data from the largest health care organization in Israel, we identified a cohort of children 5 to 11 years of age who were vaccinated on or after November 23, 2021, and matched them with unvaccinated controls to estimate the vaccine effectiveness of BNT162b2 among newly vaccinated children during the omicron wave. Vaccine effectiveness against documented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and symptomatic Covid-19 was estimated after the first and second vaccine doses. The cumulative incidence of each outcome in the two study groups through January 7, 2022, was estimated with the use of the Kaplan-Meier estimator, and vaccine effectiveness was calculated as 1 minus the risk ratio. Vaccine effectiveness was also estimated in age subgroups.

Ariant, two doses of the BNT162b2 messenger RNA vaccine provided moderate protection against documented SARS-CoV-2 infection and symptomatic Covid-19 in children 5 to 11 years of age. (Funded by the European Union through the VERDI project and others.).

Our findings suggest that as omicron was becoming the dominant variant, two doses of the BNT162b2 messenger RNA vaccine provided moderate protection against documented SARS-CoV-2 infection and symptomatic Covid-19 in children 5 to 11 years of age. (Funded by the European Union through the VERDI project and others.).Although an extrapolation from the clinical experience in adults can often be considered to support the pediatric use for most pharmaceutical compounds, differences in safety profiles between adult and pediatric patients can be observed. The developing immune system may be affected due to exaggerated pharmacological or non-expected effects of a new drug. Toxicology studies in juvenile animals could therefore be required to better evaluate the safety profile of any new pharmaceutical compound targeting the pediatric population. The Göttingen minipig is now considered a useful non-rodent species for non-clinical safety testing of human pharmaceuticals. However, knowledge on the developing immune system in juvenile minipigs is still limited. The objective of the work reported here was to evaluate across-age proportions of main immune cells circulating in blood or residing in lymphoid organs (thymus, spleen, lymph nodes) in Göttingen Minipigs. In parallel, the main immune cell populations from healthy and immunocompromised piglets were compared following treatment with cyclosporin A (CsA) at 10 mg/kg/day for 4 wk until weaning.