Interpreting the WW mass anomaly in the vectorlike quark models

The new measurement of $W$-boson mass from the CDF collaboration depicts a remarkable $7\sigma$ disagreement with the Standard Model (SM) prediction. This highly implies that there exist new particles or fields beyond the SM. In this work, we explore the possibility of explaining the $W$ mass anomaly in the simple extension of the SM with the vector-like quarks. Confronting with the current LHC data and the electroweak precision measurements, we find that the vector-like quark models can reconcile SM theory with the $W$ mass anomaly

SynFundus-1M: A High-quality Million-scale Synthetic fundus images Dataset with Fifteen Types of Annotation

Large-scale public datasets with high-quality annotations are rarely available for intelligent medical imaging research, due to data privacy concerns and the cost of annotations. In this paper, we release SynFundus-1M, a high-quality synthetic dataset containing over one million fundus images in terms of \textbf{eleven disease types}. Furthermore, we deliberately assign four readability labels to the key regions of the fundus images. To the best of our knowledge, SynFundus-1M is currently the largest fundus dataset with the most sophisticated annotations. Leveraging over 1.3 million private authentic fundus images from various scenarios, we trained a powerful Denoising Diffusion Probabilistic Model, named SynFundus-Generator. The released SynFundus-1M are generated by SynFundus-Generator under predefined conditions. To demonstrate the value of SynFundus-1M, extensive experiments are designed in terms of the following aspect: 1) Authenticity of the images: we randomly blend the synthetic images with authentic fundus images, and find that experienced annotators can hardly distinguish the synthetic images from authentic ones. Moreover, we show that the disease-related vision features (e.g. lesions) are well simulated in the synthetic images. 2) Effectiveness for down-stream fine-tuning and pretraining: we demonstrate that retinal disease diagnosis models of either convolutional neural networks (CNN) or Vision Transformer (ViT) architectures can benefit from SynFundus-1M, and compared to the datasets commonly used for pretraining, models trained on SynFundus-1M not only achieve superior performance but also demonstrate faster convergence on various downstream tasks. SynFundus-1M is already public available for the open-source community

Medical service unity: an effective approach for medical care in rural areas in China

Medical care in rural China has long suffered because of a concentration of medical resources in major hospitals in cities. The patients in rural areas thus do not have affordable access to quality medical services. To tackle such issues, a tiered medical scheme (TMS) was promoted by the Chinese State Council in 2015. It divides hospitals into three tiers and encourages collaborations among different tiers within a region in order to provide better accessibility to medical care for patients in rural areas. The implementation of the TMS policy has not been successful, because the previous funding model, which allocated funding to each hospital according to the number of patients treated, did not facilitate close collaborations between different hospitals. In this report, the medical service unity (MSU) approach, which has been piloted in Funan county, is reported. The MSU organises the tiered hospitals as a unity in terms of medical capabilities and financial abilities. With the radical reform of financial decentralisation, three flows are thereby enabled: the funding flow binds together the hospitals into a unity, the patient flow shares the load across the providers and eases barriers to access, and the resource flow ensures accessibility and affordability for patients. The MSU approach has been shown by the pilot project in Funan to be effective for the realisation of the TMS policy, benefiting hospitals, doctors and patients. The successful experience of the Funan MSU could be introduced to other regions across China and other countries. In particular, future finance reform policies for the health system would largely benefit the health reforms and especially the decentralisation of medical resources to rural areas

Discovering medication patterns for high-complexity drug-using diseases through electronic medical records

An Electronic Medical Record (EMR) is a professional document that contains all data generated during the treatment process. The EMR can utilize various data formats, such as numerical data, text, and images. Mining the information and knowledge hidden in the huge amount of EMR data is an essential requirement for clinical decision support, such as clinical pathway formulation and evidence-based medical research. In this paper, we propose a machine-learning-based framework to mine the hidden medication patterns in EMR text. The framework systematically integrates the Jaccard similarity evaluation, spectral clustering, the modified Latent Dirichlet Allocation and cross-matching among multiple features to find the residuals that describe additional knowledge and clusters hidden in multiple perspectives of highly complex medication patterns. These methods work together, step by step to reveal the underlying medication pattern. We evaluated the method by using real data from EMR text (patients with cirrhotic ascites) from a large hospital in China. The proposed framework outperforms other approaches for medication pattern discovery, especially for this disease with subtle medication treatment variances. The results also revealed little overlap among the discovered patterns; thus, the distinct features of each pattern are well studied through the proposed framework

INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion

Computer-aided translation (CAT) aims to enhance human translation efficiency and is still important in scenarios where machine translation cannot meet quality requirements. One fundamental task within this field is Word-Level Auto Completion (WLAC). WLAC predicts a target word given a source sentence, translation context, and a human typed character sequence. Previous works either employ word classification models to exploit contextual information from both sides of the target word or directly disregarded the dependencies from the right-side context. Furthermore, the key information, i.e. human typed sequences, is only used as prefix constraints in the decoding module. In this paper, we propose the INarIG (Iterative Non-autoregressive Instruct Generation) model, which constructs the human typed sequence into Instruction Unit and employs iterative decoding with subwords to fully utilize input information given in the task. Our model is more competent in dealing with low-frequency words (core scenario of this task), and achieves state-of-the-art results on the WMT22 and benchmark datasets, with a maximum increase of over 10% prediction accuracy.Comment: EMNLP202