改良穴位图谱在中医护理操作中运用效果评价

Objective To find an effective study method to master the nursing procedures of acupoints that will be applied in the clinical nursing work. Methods Based on the Chinese medicine hospital of traditional Chinese medicine nursing guidelines (trial version) published by the bureau of traditional Chinese medicine and given full expression to the advantages of the Traditional Chinese Medicine, acupoints map was improved and was applied in clinical work in August 2011 in our hospital. The nursing operation workload and learning effect in the continuity two years were compared by retrospective investigation combined with questionnaire survey. Results 1, The operation workload increases obviously and the overall growth rate in five work targets is 88.66%, after improving acupoints map of TCM nursing. 2, There are significant differences (P < 0.001) on learning interest, learning degree, memory speed, knowledge of diseas, method of operation and clinical application in our comparison.Conclusion The improved acupoints map can inspire the learning interest of the nursing stuff, help them to master common nursing acupoints of traditional Chinese medicine quickly and apply it to clinical disease. There are good social and economic benefits of this method, so it is worth promoting.目的 寻找一种有效掌握中医护理穴位的学习方法并运用于临床护理工作中。 方法 以中医药局印发《中医医院中医护理工作指南(试行)》文件的内容为指导思想，全面体现中医特色和优势。本院积极开展中医护理操作，于2011年8月设计改良穴位图谱并运用于临床。采用回顾性调查并结合问卷式调查，比较实施前后一年期间中医护理操作工作量及学习效果。结果 （1）2011年8月采用改良穴位图谱后中医护理操作工作量明显增长，五项工作量的总体增长率达88.66%；（2）采用改良穴位图谱前后护士在学习兴趣、易学程度、记忆速度、疾病腧穴知识、操作方法、临床应用等方面比较均有显著性差异（P＜0.001）。 结论 采取改良穴位图谱学习方法能提高护理人员对穴位的学习兴趣，快速掌握中医护理疾病的常用腧穴并应用于临床，赢得了较好的社会效益和经济效益，值得推广

DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures

Diffusion models have recently exhibited remarkable performance on synthetic data. After a diffusion path is selected, a base model, such as UNet, operates as a denoising autoencoder, primarily predicting noises that need to be eliminated step by step. Consequently, it is crucial to employ a model that aligns with the expected budgets to facilitate superior synthetic performance. In this paper, we meticulously analyze the diffusion model and engineer a base model search approach, denoted "DiffNAS". Specifically, we leverage GPT-4 as a supernet to expedite the search, supplemented with a search memory to enhance the results. Moreover, we employ RFID as a proxy to promptly rank the experimental outcomes produced by GPT-4. We also adopt a rapid-convergence training strategy to boost search efficiency. Rigorous experimentation corroborates that our algorithm can augment the search efficiency by 2 times under GPT-based scenarios, while also attaining a performance of 2.82 with 0.37 improvement in FID on CIFAR10 relative to the benchmark IDDPM algorithm

Can GPT-4 Perform Neural Architecture Search?

We investigate the potential of GPT-4~\cite{gpt4} to perform Neural Architecture Search (NAS) -- the task of designing effective neural architectures. Our proposed approach, \textbf{G}PT-4 \textbf{E}nhanced \textbf{N}eural arch\textbf{I}tect\textbf{U}re \textbf{S}earch (GENIUS), leverages the generative capabilities of GPT-4 as a black-box optimiser to quickly navigate the architecture search space, pinpoint promising candidates, and iteratively refine these candidates to improve performance. We assess GENIUS across several benchmarks, comparing it with existing state-of-the-art NAS techniques to illustrate its effectiveness. Rather than targeting state-of-the-art performance, our objective is to highlight GPT-4's potential to assist research on a challenging technical problem through a simple prompting scheme that requires relatively limited domain expertise\footnote{Code available at \href{https://github.com/mingkai-zheng/GENIUS}{https://github.com/mingkai-zheng/GENIUS}.}. More broadly, we believe our preliminary results point to future research that harnesses general purpose language models for diverse optimisation tasks. We also highlight important limitations to our study, and note implications for AI safety

Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection

Human-Object Interaction (HOI) detection is a challenging computer vision task that requires visual models to address the complex interactive relationship between humans and objects and predict HOI triplets. Despite the challenges posed by the numerous interaction combinations, they also offer opportunities for multimodal learning of visual texts. In this paper, we present a systematic and unified framework (RmLR) that enhances HOI detection by incorporating structured text knowledge. Firstly, we qualitatively and quantitatively analyze the loss of interaction information in the two-stage HOI detector and propose a re-mining strategy to generate more comprehensive visual representation.Secondly, we design more fine-grained sentence- and word-level alignment and knowledge transfer strategies to effectively address the many-to-many matching problem between multiple interactions and multiple texts.These strategies alleviate the matching confusion problem that arises when multiple interactions occur simultaneously, thereby improving the effectiveness of the alignment process. Finally, HOI reasoning by visual features augmented with textual knowledge substantially improves the understanding of interactions. Experimental results illustrate the effectiveness of our approach, where state-of-the-art performance is achieved on public benchmarks. We further analyze the effects of different components of our approach to provide insights into its efficacy.Comment: ICCV202

CoNe: Contrast Your Neighbours for Supervised Image Classification

Image classification is a longstanding problem in computer vision and machine learning research. Most recent works (e.g. SupCon , Triplet, and max-margin) mainly focus on grouping the intra-class samples aggressively and compactly, with the assumption that all intra-class samples should be pulled tightly towards their class centers. However, such an objective will be very hard to achieve since it ignores the intra-class variance in the dataset. (i.e. different instances from the same class can have significant differences). Thus, such a monotonous objective is not sufficient. To provide a more informative objective, we introduce Contrast Your Neighbours (CoNe) - a simple yet practical learning framework for supervised image classification. Specifically, in CoNe, each sample is not only supervised by its class center but also directly employs the features of its similar neighbors as anchors to generate more adaptive and refined targets. Moreover, to further boost the performance, we propose ``distributional consistency" as a more informative regularization to enable similar instances to have a similar probability distribution. Extensive experimental results demonstrate that CoNe achieves state-of-the-art performance across different benchmark datasets, network architectures, and settings. Notably, even without a complicated training recipe, our CoNe achieves 80.8\% Top-1 accuracy on ImageNet with ResNet-50, which surpasses the recent Timm training recipe (80.4\%). Code and pre-trained models are available at \href{https://github.com/mingkai-zheng/CoNe}{https://github.com/mingkai-zheng/CoNe}

Protective Effects of Li-Fei-Xiao-Yan Prescription on Lipopolysaccharide-Induced Acute Lung Injury via Inhibition of Oxidative Stress and the TLR4/NF- κ

Li-Fei-Xiao-Yan prescription (LFXY) has been clinically used in China to treat inflammatory and infectious diseases including inflammatory lung diseases. The present study was aimed at evaluating the potential therapeutic effects and potential mechanisms of LFXY in a murine model of lipopolysaccharide- (LPS-) induced acute lung injury (ALI). In this study, the mice were orally pretreated with LFXY or dexamethasone (positive drug) before the intratracheal instillation of LPS. Our data indicated that pretreatment with LFXY enhanced the survival rate of ALI mice, reversed pulmonary edema and permeability, improved LPS-induced lung histopathology impairment, suppressed the excessive inflammatory responses via decreasing the expression of proinflammatory cytokines (TNF-α, IL-1β, and IL-6) and chemokine (MIP-2) and inhibiting inflammatory cells migration, and repressed oxidative stress through the inhibition of MPO and MDA contents and the upregulation of antioxidants (SOD and GSH) activities. Mechanistically, treatment with LFXY significantly prevented LPS-induced TLR4 expression and NF-κB (p65) phosphorylation. Overall, the present study suggests that LFXY protected mice from acute lung injury induced by LPS via inhibition of TLR4/NF-κB p65 activation and upregulation of antioxidative enzymes and it may be a potential preventive and therapeutic agent for ALI in the clinical setting