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Basic treatment principles for urinary tract infections with chinese herbal medicine: An application of text mining

Basic treatment principles for urinary tract infections with...

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International conference on computer Sciences and Convergence Information Technology (ICCIT)

作者： Yangping Cai Guanglei Wang Xiuchen Yu Guang Zheng Feng Cai Aiping Lu Miao Jiang Yunyu Huang Beijing Dongzhimen Hospital Beijing University of Chinese Medicine Beijing China School of Information Science-Engineering Lanzhou University Lanzhou China Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical Sciences Beijing China Hong Kong Baptist University School of Chinese Medicine Hong Kong China

ISBN: (纸本)9781467308946

Urinary tract infections (UTIs) are among the most common infectious diseases occurring in either the community or healthcare setting. Approximately one third of all women have had at least one physician-diagnosed uncomplicated UTI by the age of 26 years. A broad range of pathogens can cause complicated UTI. Escherichia coli remains the most common organism causing UTI. Antimicrobial resistance is one of the most common problems in complicated UTI which can cause treatment failure and serious complications. Antibiotic-resistance has been increasing in the past decades worldwide. chinese herbal medicine (CHM) has been an integral part of Traditional chinese Medicine (TCM) for thousands of years. Some chinese herbs in the study were proved to have antibacterial activity, which prompted their compound prescription use in the management of UTI. Many herbal formulations have been developed and used in the treatment of UTI and were proved to be effective and safe. Yet the principles of treating UTI with CHMs are hard to manage due to the complexity of TCM theory. In this study, a novel text mining method was development based on a comprehensive collection of literatures in order to explore the treatment principles more intuitively. Networks of TCM patterns and CHMs which are most frequently used in UTI treatment are built-up and analyzed, two major principles are explored in treating UTI from 44,662 records of literature: Clearing the damp-heat with strengthening healthy qi. these findings might guide the clinicians in treatment of obesity.

关键词： chinese herbal medicine pattern Text mining Traditional chinese medicine Urinary tract infection

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Alleviating Action Hallucination for LLM-based Embodied Agents via Inner and Outer Alignment

Alleviating Action Hallucination for LLM-based Embodied Agen...

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pattern recognition and Artificial Intelligence (PRAI), International conference on

作者： Kanxue Li Qi Zheng Yibing Zhan Chong Zhang Tianle Zhang Xu Lin Chongchong Qi Lusong Li Dapeng Tao Yunnan University Kunming China JD Explore Academy Beijing China Shenzhen University Shenzhen China Yunnan United Vision Technology Co. Ltd Kunming China Yunnan Key Laboratory of Media Convergence Kunming China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Large language models (LLMs) have demonstrated impressive potential in empowering embodied agents, fortifying them with task planning and reasoning capabilities closely akin to humans. However, there remain significant challenges in aligning LLM-based embodied agent actions with the executable and safe action space to reduce hallucination. In this paper, we propose a flexible and resource-efficient framework for aligning the action space of LLM-based embodied agents. the framework employs a parameter-efficient fine-tuning method for inner alignment and a retrieval-based generation approach for outer alignment. Specifically, when the inner aligned model generates an action, the outer alignment employs ROUGE to calculate the similarity between the action and all actions within a safe and valid action space, ultimately selecting the action with the highest similarity as the output. For situations with multiple alternative actions, the outer alignment introduces a policy model, which could be either open-source small LLMs or commercial LLMs, to determine the optimal action based on the current context of the agent's task execution. the retrieval-based outer alignment ensures all actions align with an executable action space, alleviating action hallucination for LLM-based agents and significantly improving the controllability and interpretability of their decision-making process. through extensive experimentation with the LlaMA-2, Bloomz, and OPT models on the ALFWorld benchmark, we validate the effectiveness and adaptability of our framework.

关键词： Large language models Decision making Process control Benchmark testing Aerospace electronics Controllability Stability analysis Planning Safety pattern recognition

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Coconut Internal Structure Feature Detection and Developmental Quality Inspection Based on YOLOv7

Coconut Internal Structure Feature Detection and Development...

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pattern recognition and Artificial Intelligence (PRAI), International conference on

作者： Mingwei Che Ziheng Fu Mengxing Huang Yu Zhang Jing Chen Chengxu Sun Chun Li School of Information and Communication Engineering Hainan University Haikou China School of Computer Science and Technology Hainan University Haikou China School of Medicine Affiliated Haikou Hospital Central South University Xiangya Haikou China Chinese Academy of Tropical Agricultural Sciences Coconut Research Institute Wenchang China

Coconut is one of the largest seeded fruits in the world. While its hard shell provides some protection for the growth and development of coconuts, it also hinders the observation of internal structural changes and quality inspection during early seedling selection. the cultivation of coconut seedlings takes about a year, and it takes several years for them to grow into mature coconut trees. therefore, early selection of coconuts is crucial. To address the issue of difficult observation of internal coconut features, we utilized CT scans for non-destructive scanning to obtain internal images of coconuts at different stages. We annotated the internal structures, such as coconut water, haustorium, germ and internal cracks, which exhibit significant changes during coconut growth and development. this led to the establishment of a dataset called Internal Structure Feature of Coconuts (ISFC), providing data resources for coconut image detection. In this paper, we propose an improved model based on YOLOv7 object detection specifically designed for the ISFC dataset, aiming to detect notable changes in internal coconut features and cracks. Experimental results demonstrate that this method accurately identifies and detects the targets, achieving an average precision of up to 97.4%. It satisfies the requirements for high-precision detection and provides a basis for observing internal coconut features and conducting quality inspections during development.

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FreeStyler: A Free-Form Stylization Method via Multimodal Vector Quantization 12th

FreeStyler: A Free-Form Stylization Method via Multimodal V...

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12th International conference on Computational Visual Media, CVM 2024

作者： Liu, WuQin Lin, MinXuan Huang, HaiBin Ma, ChongYang Dong, WeiMing School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Kuaishou Technology Beijing100085 China

ISBN: (纸本)9789819720910

Image stylization refers to the process of transforming an input image into a new one, while retaining its original content but in different styles. However, most existing works only support single-modal guidance, which is not ideal for real-world applications. To tackle this limitation, we propose FreeStyler, a flexible framework for image stylization that is capable of handling various input scenarios. Our approach goes beyond the traditional approach of relying on content and style images to generate a stylized image and supports situations where these references are absent. Specifically, in such cases, FreeStyler allows performing the stylization through text or audio information. the core of FreeStyler is a vector quantized style transfer framework that encodes content and style information into a shared discrete latent feature space, followed by a stylization transformer for style fusion and an image decoder for stylized image reconstruction. To enable free-form stylization, we introduce a novel pseudo-paired token predictor that can estimate tokens from varying input forms without the need for additional text or audio data. Specifically, we leverage Contrastive Language-Image Pre-training (CLIP) as prior knowledge to align discrete representations across different modalities and train the framework using an image and pseudo caption pair provided by Bootstrapping Language-Image Pre-training (BLIP). through qualitative and quantitative experiments, our method has demonstrated superior performance compared to state-of-the-art stylization methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Vector quantization

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Automatic Docking Design of Flange Pipe Based on Spatial Information

Automatic Docking Design of Flange Pipe Based on Spatial Inf...

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International conference on Information Science and Control Engineering (ICISCE)

作者： Xingxun Li Xiuhua Zhang Jinmeng Wu Jian Huang Pianpian Chen Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan China Hubei Engineering Research Center of Video Image and HD Projection Wuhan Institute of Technology Wuhan China

With the development of ship industry, pipeline system has been more and more widely used. However, the pipeline site space is narrow. Mechanical and electrical equipments are of all kinds. the pipeline cable is densely distributed. All problems mentioned above seriously affects the pipeline construction. In order to solve the problem of pipeline docking, the method of making model and comprehensive lofting is usually adopted. However, this method is technically backward. the operation is inconvenient due to the narrow onsite space. the required period is long. With low information utilization and accuracy, the method of making model and comprehensive lofting has a great influence on the development cycle and design quality. In order to solve these problems, this paper presents a design method and system of flange pipe docking. A binocular camera is used to obtain the image of the pipe joint. the center position of the flange surface and array holes on the flange surface are extracted from the image, which is bias of subsequent work. First, the path of the intermediate connecting pipe is calculated. then, according to the pipe standards of intermediate connection pipe and production specification of enterprise, the model of intermediate connecting pipe is automatically generated by using secondary development technology of CAD. In this paper, the binocular vision technology is combined with pipeline docking design, which significantly improves the utilization rate of field space information and design efficiency, and solves the problems of inconvenient operation and safety risk of traditional profiling method.

关键词： Industries Solid modeling Design methodology Pipelines Production Flanges Feature extraction

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Automatic extraction of important objects for an MPEG-7 compliant video database system

Automatic extraction of important objects for an MPEG-7 comp...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Muhammet Bastan Ugur Gudukbay Ozgur Ulusoy Bilgisayar Muhendisligi Bolumu Bilkent Universitesi Ankara Turkey

We describe a method to automatically extract video objects, which are important for object-based indexing of videos in an MPEG-7 compliant video database system. Most of the existing salient object detection approaches detect visually conspicuous image structures, while our method aims to find regions that may be important for indexing in a video database system. Our method works on a shot basis. We first segment each frame to obtain homogeneous regions in terms of color and texture. then, we extract a set of local and global color, shape, texture and motion features for each region. Finally, the regions are classified as being salient or non-salient using SVMs trained on a few hundreds of example regions. Experimental results from news video segments show that the proposed method is more effective in extracting the important regions in terms of human visual perception.

关键词： Transform coding conferences pattern recognition Support vector machines computer vision Image segmentation Database systems

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Text Detection of Power Equipment Nameplates Based on Deep Learning

Text Detection of Power Equipment Nameplates Based on Deep L...

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IEEE International conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Chenjun Guo Xianlong Ma Yue Shu Yuting Duan Hong Yu Yi Ma Jiayu Wang Linlin Zhong Bingtuan Gao Electric Power Research Institute of Yunnan Power Grid Co. Ltd. Kunming Yunnan Province P. R. China School of Electrical Engineering Southeast University No 2 Sipailou Nanjing Jiangsu Province P. R. China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

recognition of the nameplate text in power equipment by image recognition technology can effectively improve the equipment management in power system. Text detection is the first step of text recognition, which also affects significantly the accuracy of text recognition. In this paper, an Efficient and Accuracy Scene Text Detector (EAST) based text detection model is proposed for detecting the characters on power equipment nameplates. Data augmentation technology is used to expand the data set of nameplate images. the experiment results show that the proposed model achieves a detection precision of 95.48% and a recall of 97.08%. Compared with the traditional text detection method, the EAST-based model performs better on the nameplate images of power equipment and has stronger generalization ability. Moreover, the nameplate text detection model runs at 7 frames per second (FPS) on average, which is faster than the traditional methods.

关键词： computer vision pattern recognition conferences Erbium Automation Control systems Intelligent systems

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Exploring the rules of Li-Fa-Fang-Yao on diabetes mellitus within traditional chinese medicine through text mining

Exploring the rules of Li-Fa-Fang-Yao on diabetes mellitus w...

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International conference on computer Sciences and Convergence Information Technology (ICCIT)

作者： Yunyu Huang Shidong Wang Lianjie Wang Xiuchen Yu Miao Jiang Junping Zhan Aiping Lu Guang Zheng Beijing Dongzhimen Hospital Beijing University of Chinese Medicine Beijing China Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical Sciences Beijing China Shanghai University of Traditional Chinese Medicine Shanghai China Hong Kong Baptist University School of Chinese Medicine Hong Kong China School of Information Science-Engineering Lanzhou University Lanzhou China

ISBN: (纸本)9781467308946

Diabetes mellitus, or simply diabetes, is a group of metabolic diseases in which a person has high blood sugar, either because the body does not produce enough insulin, or because cells do not respond to the insulin that is produced. In traditional chinese medicine (TCM), diabetes mellitus is called “Sweet Urine Disease”. Over several thousand years of clinical research and theoretical thoughts, some treatment on diabetes has been accumulated. these treatment rules can be demonstrated through the networks of Li-Fa-Fang-Yao which is of critical importance in clinical practices of TCM. Li-Fa-Fang-Yao, which means principles, methods, formulae, and chinese herbal medicines respectively, indicate the four basic steps of diagnosis and treatment: determining the cause, mechanism and location of the disease according to the medical theories and principles, then deciding the treatment principle and method, and finally selecting a formula as well as proper chinese herbal medicines. In this paper, focused on disease of diabetes, we explored the rules of Li-Fa-Fang-Yao within the framework of traditional chinese medicine. through calculation, four networks i.e., symptom, pattern/syndrome, treatment principle, herbal formula, and chinese herbal medicine were constructed. Based on these four networks, their connections were also demonstrated by the professional knowledge of TCM and thus a whole network of Li-Fa-Fang-Yao was constructed and demonstrated. this Li-Fa-Fang-Yao network might be meaningful for both clinical practise and medical research.

关键词： text mining Li-Fa-Fang-Yao network diabetes mellitus traditional chinese medicine chinese Traditional Medicine Diabetes Mellitus text mining chinese herbal medicine Rule trellis coded modulation Hyperglycemia Network formula clinical practice Insulin Insulins

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Input Collapse of CMOS Logic Gates with a Series-Connected MOSFET Chain

Input Collapse of CMOS Logic Gates with a Series-Connected M...

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2004 7th International conference on Solid-State and Integrated Circuits Technology（ICSICT 2004）

作者： Anura P.Jayasumana Dept of Electrical and Computer Engineering Colorado State UniversityFort CollinsCO 80523USA

We propose a generic series-connected MOSFET chain model(SMCM)extending the PDSMM[2],to describe the peculiar DC characteristics of a series MOSFET *** state propagation delays(ISPDs) are defined as propagation delays due to initial states of intermediate nodes of a series-connected MOSFET chain. Two extreme and important ISPDs,called as fast ISPD and slow ISPD,are *** effect of input patterns of CMOS logic gates on propagation delays in the two ISPDs is ***,an efficient mapping algorithm for every possible input pattern to an equal input ramp is introduced.

关键词： MOSFET Input Collapse of CMOS Logic Gates with a Series-Connected MOSFET Chain

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4D unsupervised object discovery 22

4D unsupervised object discovery

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Proceedings of the 36th International conference on Neural Information Processing Systems

作者： Yuqi Wang Yuntao Chen Zhaoxiang Zhang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) and School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics HKISI_CAS Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) and School of Artificial Intelligence University of Chinese Academy of Sciences and Centre for Artificial Intelligence and Robotics HKISI_CAS

ISBN: (纸本)9781713871088

Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. therefore, discovering objects without annotations has great significance. However, this task seems impractical on still-image or point cloud alone due to the lack of discriminative information. Previous studies underlook the crucial temporal information and constraints naturally behind multi-modal inputs. In this paper, we propose 4D unsupervised object discovery, jointly discovering objects from 4D data - 3D point clouds and 2D RGB images with temporal information. We present the first practical approach for this task by proposing a ClusterNet on 3D point clouds, which is jointly it- eratively optimized with a 2D localization network. Extensive experiments on the large-scale Waymo Open Dataset suggest that the localization network and ClusterNet achieve competitive performance on both class-agnostic 2D object detection and 3D instance segmentation, bridging the gap between unsupervised methods and full supervised ones.

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