检索结果-内蒙古大学图书馆

The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

来源：评论

学校读者我要写书评

暂无评论

Local Relationship Learning with Person-specific Shape Regularization for Facial Action Unit Detection

Local Relationship Learning with Person-specific Shape Regul...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Xuesong Niu Hu Han Songfan Yang Yan Huang Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology College of Electronics and Information Engineering Sichuan University TAL Education Group

ISBN: (纸本)9781728132945

Encoding individual facial expressions via action units (AUs) coded by the Facial Action Coding System (FACS) has been found to be an effective approach in resolving the ambiguity issue among different expressions. While a number of methods have been proposed for AU detection, robust AU detection in the wild remains a challenging problem because of the diverse baseline AU intensities across individual subjects, and the weakness of appearance signal of AUs. To resolve these issues, in this work, we propose a novel AU detection method by utilizing local information and the relationship of individual local face regions. Through such a local relationship learning, we expect to utilize rich local information to improve the AU detection robustness against the potential perceptual inconsistency of individual local regions. In addition, considering the diversity in the baseline AU intensities of individual subjects, we further regularize local relationship learning via person-specific face shape information, i.e., reducing the influence of person-specific shape information, and obtaining more AU discriminative features. The proposed approach outperforms the state-of-the-art methods on two widely used AU detection datasets in the public domain (BP4D and DISFA).

关键词： Gold African Union ASTRONOMICAL UNIT shape information Australia Face local information. Hypodynamia

来源：评论

学校读者我要写书评

暂无评论

Leaf Recognition Based on Capsule Network 15th

Leaf Recognition Based on Capsule Network

引用

15th International Conference on intelligent computing, ICIC 2019

作者： Zheng, Yang Yuan, Chang-An Shang, Li Huang, Zhi-Kai Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi330099 China

ISBN: (纸本)9783030267629

Plant is an indispensable part of human life, so it is very important to identify and protect plants. Convolutional neural network is a commonly used neural network for image recognition. However, convolutional neural network has huge defects. In order to avoid the defects of convolutional neural network, this paper adopts a new type of neural network: capsule neural network to complete plant leaf recognition. The experiment shows that the capsule neural network has a more effective recognition rate than the common neural network in plant leaf recognition. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Establishing Rigorous and Cost-effective Clinical Trials for Artificial Intelligence Models

arXiv

引用

arXiv 2024年

作者： Gao, Wanling Huang, Yunyou Cui, Dandan Yu, Zhuoming Liu, Wenjing Liang, Xiaoshuang Zhao, Jiahui Xie, Jiyue Li, Hao Ma, Li Ye, Ning Kang, Yumiao Luo, Dingfeng Pan, Peng Huang, Wei Liu, Zhongmou Hu, Jizhong Zhao, Gangyuan Jiang, Chongrong Huang, Fan Wei, Tianyi Tang, Suqin Xia, Bingjie Zhang, Zhifei Zhan, Jianfeng Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China International Open Benchmark Council China University of Chinese Academy of Sciences Beijing100086 China Guilin Medical University Guilin541100 China Affiliated Hospital of Guilin Medical University Guilin541000 China XuanJi Technology Co. Ltd. Guilin541000 China Xing An County People’s Hospital Guilin541300 China Meng Shan County People’s Hospital Wuzhou543000 China Guilin People’s Hospital Guilin541000 China Yong Fu County People’s Hospital Guilin541000 China Ling Chuan County People’s Hospital Guilin541000 China Quan Zhou County People’s Hospital Guilin541000 China Guan Yang County People’s Hospital Guilin541000 China The Second Affiliated Hospital of Guilin Medical University Guilin541000 China International College Guangxi University Nanning530004 China Capital Medical University Beijing100069 China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China

A profound gap persists between artificial intelligence (AI) and clinical practice in medicine, primarily due to the lack of rigorous and cost-effective evaluation methodologies. State-of-the-art and state-of-the-practice AI model evaluations are limited to laboratory studies on medical datasets or direct clinical trials with no or solely patient-centered controls. Moreover, the crucial role of clinicians in collaborating with AI, pivotal for determining its impact on clinical practice, is often overlooked. For the first time, we emphasize the critical necessity for rigorous and cost-effective evaluation methodologies for AI models in clinical practice, featuring patient/clinician-centered (dual-centered) AI randomized controlled trials (DC-AI RCTs) and virtual clinician-based in-silico trials (VC-MedAI) as an effective proxy for DC-AI RCTs. Leveraging 7500 diagnosis records from two-phase inaugural DC-AI RCTs across 14 medical centers with 125 clinicians, our results demonstrate the necessity of DC-AI RCTs and the effectiveness of VC-MedAI. Notably, VC-MedAI performs comparably to human clinicians, replicating insights and conclusions from prospective DC-AI RCTs. We envision DC-AI RCTs and VC-MedAI as pivotal advancements, presenting innovative and transformative evaluation methodologies for AI models in clinical practice, offering a preclinical-like setting mirroring conventional medicine, and reshaping development paradigms in a cost-effective and fast-iterative manner. Chinese Clinical Trial Registration: ChiCTR2400086816. © 2024, CC BY-NC-ND.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Motif Discovery via Convolutional Networks with K-mer Embedding 15th

Motif Discovery via Convolutional Networks with K-mer Embedd...

引用

15th International Conference on intelligent computing, ICIC 2019

作者： Wang, Dailun Zhang, Qinhu Yuan, Chang-An Qin, Xiao Huang, Zhi-Kai Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi330099 China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030269685

With the rapid development of deep learning, some discriminative motif discovery methods based on deep neural network are gradually becoming the mainstream, which also bringing huge improvement of prediction accuracy. In this paper, we propose a convolutional neural network based architecture (eCNN), combining embedding layer with GloVe. Firstly, eCNN divides each single sequence of ChIP-seq datasets into multiple subsequences called k-mers by a sliding window, and then encoding k-mers into a relatively low dimension vectors by GloVe, and finally scores each vector using multiple convolutional networks. The experiment shows that our architecture can get good results on the task of motif discovery. © 2019, Springer Nature Switzerland AG.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Rotation differential invariants of images generated by two fundamental differential operators

arXiv

引用

arXiv 2019年

作者： Mo, Hanlin Li, Hua Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

In this paper, we design two fundamental differential operators for the derivation of rotation differential invariants of images. Each differential invariant obtained by using the new method can be expressed as a homogeneous polynomial of image partial derivatives, which preserve their values when the image is rotated by arbitrary angles. We produce all possible instances of homogeneous invariants up to the given order and degree, and discuss the independence of them in detail. As far as we know, no previous papers have published so many explicit forms of high-order rotation differential invariants of images. In the experimental part, texture classification and image patch verification are carried out on popular real databases. These rotation differential invariants are used as image feature vector. We mainly evaluate the effects of various factors on the performance of them. The experimental results also validate that they have better performance than some commonly used image features in some cases. Copyright © 2019, The Authors. All rights reserved.

关键词： Rotation

来源：评论

学校读者我要写书评

暂无评论

Spectral-Spatial Weighted Sparse Nonnegative Tensor Factorization for Hyperspectral Unmixing

Spectral-Spatial Weighted Sparse Nonnegative Tensor Factoriz...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shaoquan Zhang Guorong Zhang Chengzhi Deng Jun Li Shengqian Wang Jun Wang Antonio Plaza Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University Guangzhou China Hyperspectral Computing Laboratory Escuela Politécnica University of Extremadura Cáceres Spain

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Hyperspectral unmixing aims to decompose a hyperspectral image (HSI) into a collection of constituent materials, or end-members, and their corresponding abundance fractions. Recently, nonnegative tensor factorization (NTF)-based spectral unmixing methods have attracted significant attention owing to their outstanding performance when representing an HSI without any information loss. However, tensor factorization-based HSI methods do not fully exploit the spatial contextual information present in the scene. Besides, these approaches are sensitive to low signal-to-noise ratio (SNR) in HSIs. To address this limitation, we propose a new spectral-spatial weighted sparse nonnegative tensor factorization (SSWNTF) method to preserve the spatial details in the abundance maps via the spectral and spatial weighting factors. Our experiments with simulated data sets certified that the proposed method outperforms other advanced methods.

关键词： Hyperspectral imaging Tensors Signal to noise ratio Signal processing algorithms Matrix decomposition Sparse matrices Noise measurement

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Attention Network for Predicting DNA-Protein Binding Sites 15th

Hierarchical Attention Network for Predicting DNA-Protein Bi...

引用

15th International Conference on intelligent computing, ICIC 2019

作者： Yu, Wenbo Yuan, Chang-An Qin, Xiao Huang, Zhi-Kai Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi330099 China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030269685

Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analyses of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) and recurrent neural networks (RNN) are introduced to motif discovery task and have achieved state-of–art performance. However, these methods still have limitations such as neglecting the context information in large-scale sequencing data. Thus, inspired by the similarity between DNA sequence and human language, in this paper we propose a hierarchical attention network for predicting DNA-protein binding sites which is based on a natural language processing method for document classification. The proposed method is tested on real ChIP-seq datasets and the experimental results show a considerable improvement compared with two well-tested deep learning-based sequence model, DeepBind and Deepsea. © 2019, Springer Nature Switzerland AG.

关键词： Transcription factors

来源：评论

学校读者我要写书评

暂无评论

***: Unveiling Intricate Interactions Between AI and Clinicians through an Open-Access Database

arXiv

引用

arXiv 2024年

作者： Gao, Wanling Liu, Yuan Yu, Zhuoming Cui, Dandan Liu, Wenjing Liang, Xiaoshuang Zhao, Jiahui Xie, Jiyue Li, Hao Ma, Li Ye, Ning Kang, Yumiao Luo, Dingfeng Pan, Peng Huang, Wei Liu, Zhongmou Hu, Jizhong Huang, Fan Zhao, Gangyuan Jiang, Chongrong Wei, Tianyi Zhang, Zhifei Huang, Yunyou Zhan, Jianfeng Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China International Open Benchmark Council University of Chinese Academy of Sciences Beijing100086 China Guilin Medical University Guilin541100 China Affiliated Hospital of Guilin Medical University Guilin541000 China Xing An County People’s Hospital Guilin541300 China Meng Shan County People’s Hospital Wuzhou543000 China XuanJi Technology Co. Ltd. Guilin541000 China Guilin People’s Hospital Guilin541000 China Yong Fu County People’s Hospital Guilin541000 China Ling Chuan County People’s Hospital Guilin541000 China The Second Affiliated Hospital of Guilin Medical University Guilin541000 China Quan Zhou County People’s Hospital Guilin541000 China Guan Yang County People’s Hospital Guilin541000 China International College Guangxi University Nanning530004 China Capital Medical University Beijing100069 China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China

Artificial Intelligence (AI) plays a crucial role in medical field and has the potential to revolutionize healthcare practices. However, the success of AI models and their impacts hinge on the synergy between AI and medical specialists, with clinicians assuming a dominant role. Unfortunately, the intricate dynamics and interactions between AI and clinicians remain undiscovered and thus hinder AI from being translated into medical practice. To address this gap, we have curated a groundbreaking database called ***. This database is the first of its kind for studying the interactions between AI and clinicians. It derives from 7,500 collaborative diagnosis records on a life-threatening medical emergency – Sepsis – from 14 medical centers across China. For the patient cohorts well-chosen from MIMIC databases, the AI-related information comprises the model property, feature input, diagnosis decision, and inferred probabilities of sepsis onset presently and within next three hours. The clinician-related information includes the viewed examination data and sequence, viewed time, preliminary and final diagnosis decisions with or without AI assistance, and recommended treatment. © 2024, CC BY-NC-ND.

关键词： Database systems

来源：评论

学校读者我要写书评

暂无评论

An acupoint-originated human interstitial fluid circulatory network

引用

Chinese Medical Journal 2021年第19期134卷 2365-2369页

作者： Hong-Yi Li Fang Wang Min Chen Zhi-Jian Zheng Ya-Jun Yin Jun Hu Hua Li Andreas Sammer Georg Feigl Norbert Maurer Chao Ma Fu-Sui Ji Department of Cardiology Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Radiology Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Accupuncture Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Engineering Mechanics Tsinghua UniversityBeijing 10084China Key Lab of Interfacial Physics and Technology Shanghai Institute of Applied PhysicsChinese Academy of SciencesShanghai 201210China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201210China Key Lab of Intelligent Information Processing Institute of Computing Technology and University of Chinese Academy of SciencesBeijing 100190China Ordination Dr.Sammer Dr.med.univ.Andreas SammerGrazAustria Institute of Anatomy and Clinical Morphology University of Witten/HerdeckeWittenGermany University Clinic for Physical Medicine Rehabilitation and Occupational MedicineMedical University of ViennaViennaAustria Institute of Basic Medical Sciences Chinese Academy of Medical SciencesDepartment of Human AnatomyHistology and EmbryologySchool of Basic MedicinePeking Union Medical CollegeBeijing 100005China Chinese Institute for Brain Research Beijing 100005China

To the Editor:Since 1027,when the Tian Sheng Bronze Statue showing acupoints and meridians was first introduced for medical practitioners,it has evolved to the currently used acupoints and meridians atlas in traditional Chinese medicine(TCM).[1]To date,the acupoints represent defined areas on the body surface relative to certain *** acupoints in the extremity endings would connect anatomically with diverse associated visceral organs or tissues,forming a Meridian and Collateral *** on the atlas,each main meridian is usually a virtual line connecting a group of adjacent acupoints that correlate with certain visceral *** modern medical science,the anatomical structures of main meridians remain largely unclarified.

关键词： organs originated connecting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：