Research on the Influencing Factors of Farmers’ Choice of Medical Treatment in Health Stations under the Background of Rural Medical Insurance—Taking Guangdong Province as an Example

This paper examines the decision-making process of farmers when selecting medical treatments at health stations. The purpose is to establish a more rational and scientific rural medical insurance system and to adapt the system to the current external environment. This will enable the system to fully utilize its potential and benefit the rural medical insurance sector. Innovation. This paper adopts a combination of literature review, field investigation, descriptive research, and statistical analysis to examine the medical treatment choices made by farmers in Guangdong Province under the rural medical insurance system. The hardware and software facilities of village health stations have a significant impact on farmers’ choice of medical treatment. Among them, the number of doctors is significantly positively correlated, while the facility investment in health stations is significantly negatively correlated. From this, it can be seen that farmers are more concerned about whether they can access medical services in a timely manner, but they have a negative reaction to the high cost of medical expenses at health stations. This paper proposes a rational allocation of resources for medical and health stations through the reform of the rural medical insurance system. The aim is to improve the level of rural medical and social security

Enabling Large Language Models to Generate Text with Citations

Large language models (LLMs) have emerged as a widely-used tool for information seeking, but their generated outputs are prone to hallucination. In this work, our aim is to allow LLMs to generate text with citations, improving their factual correctness and verifiability. Existing work mainly relies on commercial search engines and human evaluation, making it challenging to reproduce and compare different modeling approaches. We propose ALCE, the first benchmark for Automatic LLMs' Citation Evaluation. ALCE collects a diverse set of questions and retrieval corpora and requires building end-to-end systems to retrieve supporting evidence and generate answers with citations. We develop automatic metrics along three dimensions -- fluency, correctness, and citation quality -- and demonstrate their strong correlation with human judgements. Our experiments with state-of-the-art LLMs and novel prompting strategies show that current systems have considerable room for improvement -- For example, on the ELI5 dataset, even the best models lack complete citation support 50% of the time. Our analyses further highlight promising future directions, including developing better retrievers, advancing long-context LLMs, and improving the ability to synthesize information from multiple sources.Comment: Accepted by EMNLP 2023. Code and data are available at https://github.com/princeton-nlp/ALC

EFFUSE: Efficient Self-Supervised Feature Fusion for E2E ASR in Multilingual and Low Resource Scenarios

Self-Supervised Learning (SSL) models have demonstrated exceptional performance in various speech tasks, particularly in low-resource and multilingual domains. Recent works show that fusing SSL models could achieve superior performance compared to using one SSL model. However, fusion models have increased model parameter size, leading to longer inference times. In this paper, we propose a novel approach of predicting other SSL models' features from a single SSL model, resulting in a light-weight framework with competitive performance. Our experiments show that SSL feature prediction models outperform individual SSL models in multilingual speech recognition tasks. The leading prediction model achieves an average SUPERB score increase of 135.4 in ML-SUPERB benchmarks. Moreover, our proposed framework offers an efficient solution, as it reduces the resulting model parameter size and inference times compared to previous fusion models.Comment: 7 pages, 2 figures, 7 table

Semantic-Aware Local-Global Vision Transformer

Vision Transformers have achieved remarkable progresses, among which Swin Transformer has demonstrated the tremendous potential of Transformer for vision tasks. It surmounts the key challenge of high computational complexity by performing local self-attention within shifted windows. In this work we propose the Semantic-Aware Local-Global Vision Transformer (SALG), to further investigate two potential improvements towards Swin Transformer. First, unlike Swin Transformer that performs uniform partition to produce equal size of regular windows for local self-attention, our SALG performs semantic segmentation in an unsupervised way to explore the underlying semantic priors in the image. As a result, each segmented region can correspond to a semantically meaningful part in the image, potentially leading to more effective features within each of segmented regions. Second, instead of only performing local self-attention within local windows as Swin Transformer does, the proposed SALG performs both 1) local intra-region self-attention for learning fine-grained features within each region and 2) global inter-region feature propagation for modeling global dependencies among all regions. Consequently, our model is able to obtain the global view when learning features for each token, which is the essential advantage of Transformer. Owing to the explicit modeling of the semantic priors and the proposed local-global modeling mechanism, our SALG is particularly advantageous for small-scale models when the modeling capacity is not sufficient for other models to learn semantics implicitly. Extensive experiments across various vision tasks demonstrates the merit of our model over other vision Transformers, especially in the small-scale modeling scenarios

Identifiable Cognitive Diagnosis with Encoder-decoder for Modelling Students' Performance

Cognitive diagnosis aims to diagnose students' knowledge proficiencies based on their response scores on exam questions, which is the basis of many domains such as computerized adaptive testing. Existing cognitive diagnosis models (CDMs) follow a proficiency-response paradigm, which views diagnostic results as learnable embeddings that are the cause of students' responses and learns the diagnostic results through optimization. However, such a paradigm can easily lead to unidentifiable diagnostic results and the explainability overfitting problem, which is harmful to the quantification of students' learning performance. To address these problems, we propose a novel identifiable cognitive diagnosis framework. Specifically, we first propose a flexible diagnostic module which directly diagnose identifiable and explainable examinee traits and question features from response logs. Next, we leverage a general predictive module to reconstruct response logs from the diagnostic results to ensure the preciseness of the latter. We furthermore propose an implementation of the framework, i.e., ID-CDM, to demonstrate the availability of the former. Finally, we demonstrate the identifiability, explainability and preciseness of diagnostic results of ID-CDM through experiments on four public real-world datasets

Exemplar-based Video Colorization with Long-term Spatiotemporal Dependency

Exemplar-based video colorization is an essential technique for applications like old movie restoration. Although recent methods perform well in still scenes or scenes with regular movement, they always lack robustness in moving scenes due to their weak ability in modeling long-term dependency both spatially and temporally, leading to color fading, color discontinuity or other artifacts. To solve this problem, we propose an exemplar-based video colorization framework with long-term spatiotemporal dependency. To enhance the long-term spatial dependency, a parallelized CNN-Transformer block and a double head non-local operation are designed. The proposed CNN-Transformer block can better incorporate long-term spatial dependency with local texture and structural features, and the double head non-local operation further leverages the performance of augmented feature. While for long-term temporal dependency enhancement, we further introduce the novel linkage subnet. The linkage subnet propagate motion information across adjacent frame blocks and help to maintain temporal continuity. Experiments demonstrate that our model outperforms recent state-of-the-art methods both quantitatively and qualitatively. Also, our model can generate more colorful, realistic and stabilized results, especially for scenes where objects change greatly and irregularly

Exploring Unsupervised Cell Recognition with Prior Self-activation Maps

The success of supervised deep learning models on cell recognition tasks relies on detailed annotations. Many previous works have managed to reduce the dependency on labels. However, considering the large number of cells contained in a patch, costly and inefficient labeling is still inevitable. To this end, we explored label-free methods for cell recognition. Prior self-activation maps (PSM) are proposed to generate pseudo masks as training targets. To be specific, an activation network is trained with self-supervised learning. The gradient information in the shallow layers of the network is aggregated to generate prior self-activation maps. Afterward, a semantic clustering module is then introduced as a pipeline to transform PSMs to pixel-level semantic pseudo masks for downstream tasks. We evaluated our method on two histological datasets: MoNuSeg (cell segmentation) and BCData (multi-class cell detection). Compared with other fully-supervised and weakly-supervised methods, our method can achieve competitive performance without any manual annotations. Our simple but effective framework can also achieve multi-class cell detection which can not be done by existing unsupervised methods. The results show the potential of PSMs that might inspire other research to deal with the hunger for labels in medical area.Comment: MICCAI 2023. arXiv admin note: substantial text overlap with arXiv:2210.0786

Promoting Rural Tourism in Inner Mongolia: Attributes, Satisfaction, and Behaviors among Sustainable Tourists

With the growth of rural tourism in China, this study aims to determine the destination attributes, tourism satisfaction, and intention of revisiting Inner Mongolia. This study also investigated the mean comparison of tourist satisfaction and revisit intention across domestic tourists’ demographic characteristics. Structural analysis revealed that destination attributes have a positive influence on satisfaction and revisit intention. In addition, the result of the mean difference test showed that satisfaction is significantly different between male and female tourists, and revisit intention significantly varies across the season. Our findings have an excellent directive significance to bring forward rural tourism in Inner Mongolia

The E3 ubiquitin ligases regulate PD-1/PD-L1 protein levels in tumor microenvironment to improve immunotherapy

The tumor microenvironment (TME) is the tumor surrounding environment, which is critical for tumor development and progression. TME is also involved in clinical intervention and treatment outcomes. Modulation of TME is useful for improving therapy strategies. PD-L1 protein on tumor cells interacts with PD-1 protein on T cells, contributing to T cell dysfunction and exhaustion, blockage of the immune response. Evidence has demonstrated that the expression of PD-1/PD-L1 is associated with clinical response to anti-PD-1/PD-L1 therapy in cancer patients. It is important to discuss the regulatory machinery how PD-1/PD-L1 protein is finely regulated in tumor cells. In recent years, studies have demonstrated that PD-1/PD-L1 expression was governed by various E3 ubiquitin ligases in TME, contributing to resistance of anti-PD-1/PD-L1 therapy in human cancers. In this review, we will discuss the role and molecular mechanisms of E3 ligases-mediated regulation of PD-1 and PD-L1 in TME. Moreover, we will describe how E3 ligases-involved PD-1/PD-L1 regulation alters anti-PD-1/PD-L1 efficacy. Altogether, targeting E3 ubiquitin ligases to control the PD-1/PD-L1 protein levels could be a potential strategy to potentiate immunotherapeutic effects in cancer patients

Primary sternal tumour resection and reconstruction with LARS mesh-bone cement sandwich by 3D-printing: Case reports

Background: There are many reconstruction methods after sternal tumor resection, but the method that LARS mesh combines with bone-cement has not been reported.Case report: A 54-year-old female patient and a 55-year-old male patient admitted to our department all presented with sternum masses, but neither presented with respiratory disorders. In women with limited manubrium sternum lesions, we resected the manubrium sternum completely. In men with sternal lesions, we removed part of the sternum and part of the sternocostal joint. The patients recovered well after surgery, and there were no respiratory complications and no tumor recurrence during the 1-year follow-up respectively.Conclusion: We report two cases of sternal defect repair using LARS mesh combined with bone cement. This method is safe and stable, and can achieve satisfactory results