Qualifying Chinese Medical Licensing Examination with Knowledge Enhanced Generative Pre-training Model

Generative Pre-Training (GPT) models like ChatGPT have demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. Although ChatGPT has been integrated into the overall workflow to boost efficiency in many domains, the lack of flexibility in the finetuning process hinders its applications in areas that demand extensive domain expertise and semantic knowledge, such as healthcare. In this paper, we evaluate ChatGPT on the China National Medical Licensing Examination (CNMLE) and propose a novel approach to improve ChatGPT from two perspectives: integrating medical domain knowledge and enabling few-shot learning. By using a simple but effective retrieval method, medical background knowledge is extracted as semantic instructions to guide the inference of ChatGPT. Similarly, relevant medical questions are identified and fed as demonstrations to ChatGPT. Experimental results show that directly applying ChatGPT fails to qualify the CNMLE at a score of 51 (i.e., only 51\% of questions are answered correctly). While our knowledge-enhanced model achieves a high score of 70 on CNMLE-2022 which not only passes the qualification but also surpasses the average score of humans (61). This research demonstrates the potential of knowledge-enhanced ChatGPT to serve as versatile medical assistants, capable of analyzing real-world medical problems in a more accessible, user-friendly, and adaptable manner

Shadow Datasets, New challenging datasets for Causal Representation Learning

Discovering causal relations among semantic factors is an emergent topic in representation learning. Most causal representation learning (CRL) methods are fully supervised, which is impractical due to costly labeling. To resolve this restriction, weakly supervised CRL methods were introduced. To evaluate CRL performance, four existing datasets, Pendulum, Flow, CelebA(BEARD) and CelebA(SMILE), are utilized. However, existing CRL datasets are limited to simple graphs with few generative factors. Thus we propose two new datasets with a larger number of diverse generative factors and more sophisticated causal graphs. In addition, current real datasets, CelebA(BEARD) and CelebA(SMILE), the originally proposed causal graphs are not aligned with the dataset distributions. Thus, we propose modifications to them

Connecting Multi-modal Contrastive Representations

Multi-modal Contrastive Representation learning aims to encode different modalities into a semantically aligned shared space. This paradigm shows remarkable generalization ability on numerous downstream tasks across various modalities. However, the reliance on massive high-quality data pairs limits its further development on more modalities. This paper proposes a novel training-efficient method for learning MCR without paired data called Connecting Multi-modal Contrastive Representations (C-MCR). Specifically, given two existing MCRs pre-trained on (A, B) and (B, C) modality pairs, we project them to a new space and use the data from the overlapping modality B to aligning the two MCRs in the new space. Meanwhile, since the modality pairs (A, B) and (B, C) are already aligned within each MCR, the connection learned by overlapping modality can also be transferred to non-overlapping modality pair (A, C). To unleash the potential of C-MCR, we further introduce a semantic-enhanced inter- and intra-MCR connection method. We first enhance the semantic consistency and completion of embeddings across different modalities for more robust alignment. Then we utilize the inter-MCR alignment to establish the connection, and employ the intra-MCR alignment to better maintain the connection for inputs from non-overlapping modalities. To demonstrate the effectiveness of C-MCR, we connect CLIP and CLAP via texts to derive audio-visual representations, and integrate CLIP and ULIP via images for 3D-language representations. Remarkably, without using any paired data, C-MCR for audio-visual achieves state-of-the-art performance on audio-image retrieval, audio-visual source localization, and counterfactual audio-image recognition tasks. Furthermore, C-MCR for 3D-language also attains advanced zero-shot 3D point cloud classification accuracy on ModelNet40.Comment: NeurIPS 202

Experimental study on mechanical damage characteristics of water-bearing tar-rich coal under microwave radiation

As a recognized special resource, tar-rich coal can extract the country's scarce oil and gas resources and generate semi-coke that can replace anthracite and coking coal. The tar-rich coal in northern Shaanxi is prominent, but due to the dense structure and high strength of tar-rich coal, it is easy to cause frequent dynamic disasters in coal mining. Therefore, the realization of pressure relief and disaster reduction has become the primary problem in mining tar-rich coal. There are many shortcomings in conventional pressure relief methods, so a new method of microwave-weakening coal is proposed. Through different water saturation treatments of tar-rich coal samples, the longitudinal wave velocity degradation trend and surface crack expansion law of water-bearing coal after microwave irradiation were analyzed, and the strength softening characterization and energy evolution relationship under the combined action of microwave and water were studied. Fractal dimension and its internal correlation based on the equivalent side length-mass of coal sample fragments. The experimental results show that: (1) Under the same microwave radiation condition, with the increase of water saturation, the deterioration trend of physical and mechanical parameters such as longitudinal wave velocity and peak strength is obvious. (2) After microwave radiation, the uniaxial compression results show that the coal sample is damaged by load, there is still a high residual strength, the ratio of elastic energy to dissipation energy decreases, and the possibility of rockburst of the coal sample decreases. The strength softening degree of coal specimen under the degradation of microwave and water is the highest, followed by microwave and water. (3) The fractal dimension is inversely proportional to the moisture content and microwave radiation intensity, and the fractal dimension has a significant positive correlation with the peak intensity and longitudinal wave velocity. The mechanical damage law of water-bearing tar-rich coal under microwave action is revealed, which aims to solve the problem of weakening and reducing the impact of hard coal on-site to a certain extent, ensure the safety of working face, and improve the mining efficiency of tar-rich coal. It provides basic theoretical support for microwave-assisted hydraulic fracturing technology and effective weakening measures for hard roof treatment

Evaluation of the effect of GSK-3β on liver cancer based on the PI3K/AKT pathway

The PI3K/AKT/GSK-3β signaling pathway plays a pivotal role in numerous physiological and pathological processes, including cell proliferation, apoptosis, differentiation, and metabolic regulation. Aberrant activation of the PI3K/AKT pathway is intricately linked to development of tumor. GSK-3β, belonging to the serine/threonine protein kinase family, is crucial in the pathogenesis of liver cancer. As a key rate-limiting enzyme in the glucose metabolism pathway, GSK-3β significantly impacts the growth, proliferation, metastasis, and apoptosis of liver cancer cells. It is also implicated in chemotherapy resistance. Elevated expression of GSK-3β diminishes the sensitivity of liver cancer cells to chemotherapeutic agents, thereby playing a substantial role in the development of drug resistance. Consequently, targeting of GSK-3β, particularly within the PI3K/AKT signaling pathway, is regarded as a promising therapeutic strategy for liver cancer. The precise identification and subsequent modulation of this pathway represent a substantial potential for innovative clinical interventions in the management of liver cancer

A multi-dimension traceable privacy-preserving prevention and control scheme of the COVID-19 epidemic based on blockchain

The outbreak of COVID-19 has brought great pain to people around the world. As an epidemic prevention and control measure, the health QR code (HC) has been designed to trace the confirmed cases and close contacts quickly. Although some existing health code schemes preserve the privacy, but most of them are either unsupported for fine-grained auditability or centralised health code storage. Therefore, we propose a multi-dimension traceable privacy-preserving HC scheme based on blockchain. It prevents health code information being tampered with and supports the traceability of virus transmission chain. We utilise attribute-based encryption to protect residents' privacy information and achieve fine-grained access control. Furthermore, to support the multi-dimension traceability by the epidemic prevention and control departments, the searchable encryption has been introduced. Finally, we give the security analysis and performance evaluation to verify the feasibility and practical significance of our scheme

Fine-Grained 3D Modeling and Semantic Mapping of Coral Reefs Using Photogrammetric Computer Vision and Machine Learning

Corals play a crucial role as the primary habitat-building organisms within reef ecosystems, forming expansive structures that extend over vast distances, akin to the way tall buildings define a city’s skyline. However, coral reefs are vulnerable to damage and destruction due to their inherent fragility and exposure to various threats, including the impacts of climate change. Similar to successful city management, the utilization of advanced underwater videography, photogrammetric computer vision, and machine learning can facilitate precise 3D modeling and the semantic mapping of coral reefs, aiding in their careful management and conservation to ensure their survival. This study focuses on generating detailed 3D mesh models, digital surface models, and orthomosaics of coral habitats by utilizing underwater coral images and control points. Furthermore, an innovative multi-modal deep neural network is designed to perform the pixel-wise semantic segmentation of orthomosaics, enabling the projection of resulting semantic maps onto a 3D space. Notably, this study achieves a significant milestone by accomplishing semantic fine-grained 3D modeling and rugosity evaluation of coral reefs with millimeter-level accuracy, providing a potent means to understand coral reef variations under climate change with high spatial and temporal resolution.ISSN:1424-822