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arXiv 2022年

作者： Wang, Haiyang Shi, Shaoshuai Yang, Ze Fang, Rongyao Qian, Qi Li, Hongsheng Schiele, Bernt Wang, Liwei Center for Data Science Peking University China Max Planck Institute for Informatics Germany University of Toronto Canada The Chinese University of Hong Kong Hong Kong Alibaba Group China Key Laboratory of Machine Perception MOE School of Artificial Intelligence Peking University China International Center for Machine Learning Research Peking University China

As a fundamental problem in computer vision, 3D object detection is experiencing rapid growth. To extract the point-wise features from the irregularly and sparsely distributed points, previous methods usually take a feature grouping module to aggregate the point features to an object candidate. However, these methods have not yet leveraged the surface geometry of foreground objects to enhance grouping and 3D box generation. In this paper, we propose the RBGNet framework, a voting-based 3D detector for accurate 3D object detection from point clouds. In order to learn better representations of object shape to enhance cluster features for predicting 3D boxes, we propose a ray-based feature grouping module, which aggregates the point-wise features on object surfaces using a group of determined rays uniformly emitted from cluster centers. Considering the fact that foreground points are more meaningful for box estimation, we design a novel foreground biased sampling strategy in downsample process to sample more points on object surfaces and further boost the detection performance. Our model achieves state-of-the-art 3D detection performance on ScanNet V2 and SUN RGB-D with remarkable performance gains. Code will be available at https://***/Haiyang-W/RBGNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Object recognition

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Is L2 physics-informed loss always suitable for training physics-informed neural network? 22

Is L2 physics-informed loss always suitable for training phy...

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

作者： Chuwei Wang Shanda Li Di He Liwei Wang School of Mathematical Sciences Peking University Machine Learning Department School of Computer Science Carnegie Mellon University and Zhejiang Lab National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University and Center for Data Science Peking University

ISBN: (纸本)9781713871088

The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The L2 Physics- Informed Loss is the de-facto standard in training Physics-Informed Neural Networks. In this paper, we challenge this common practice by investigating the relationship between the loss function and the approximation quality of the learned solution. In particular, we leverage the concept of stability in the literature of partial differential equation to study the asymptotic behavior of the learned solution as the loss approaches zero. With this concept, we study an important class of high-dimensional non-linear PDEs in optimal control, the Hamilton-Jacobi- Bellman (HJB) Equation, and prove that for general Lp Physics-Informed Loss, a wide class of HJB equation is stable only if p is sufficiently large. Therefore, the commonly used L2 loss is not suitable for training PINN on those equations, while L∞ loss is a better choice. Based on the theoretical insight, we develop a novel PINN training algorithm to minimize the L∞ loss for HJB equations which is in a similar spirit to adversarial training. The effectiveness of the proposed algorithm is empirically demonstrated through experiments.

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Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant

Medicine Plus

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Medicine Plus 2024年第2期1卷 102-124页

作者： Mingze Yuan Peng Bao Jiajia Yuan Yunhao Shen Zifan Chen Yi Xie Jie Zhao Quanzheng Li Yang Chen Li Zhang Lin Shen Bin Dong Center for Data Science Peking UniversityBeijing 100871China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and Institute Beijing 100142China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Center for Machine Learning Research Peking University Beijing 100871China National Biomedical Imaging Center Peking UniversityBeijing 100871China Peking University Changsha Institute for Computing and Digital Economy Changsha 410205China Massachusetts General Hospital Boston MA 02114-2696USA Harvard Medical School BostonMA 02115USA

With the rapid development of artificial intelligence,large language models(LLMs)have shown promising capabilities in mimicking human-level language comprehen-sion and *** has sparked significant interest in applying LLMs to enhance various aspects of healthcare,ranging from medical education to clinical decision ***,medicine involves multifaceted data modalities and nuanced reasoning skills,presenting challenges for integrating *** review introduces the fundamental applications of general-purpose and specialized LLMs,demon-strating their utilities in knowledge retrieval,research support,clinical workflow automation,and diagnostic *** the inherent multimodality of medicine,the review emphasizes the multimodal LLMs and discusses their ability to process diverse data types like medical imaging and electronic health records to augment diagnostic *** address LLMs'limitations regarding personalization and complex clinical reasoning,the review further explores the emerging develop-ment of LLM-powered autonomous agents for ***,it summarizes the evaluation methodologies for assessing LLMs'reliability and safety in medical *** have transformative potential in medicine;however,there is a pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice.

关键词： Large language models Artificial intelligence Medicine Healthcare assistant Prompt engineering In-context learning

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Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-aware Connection Classifier

arXiv

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arXiv 2023年

作者： Zhang, Zhixing Zhao, Ziwei Wang, Dong Zhao, Shishuang Liu, Yuhang Liu, Jia Wang, Liwei Center for Data Science Peking University Beijing China National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Yizhun Medical AI Co. Ltd Beijing China Peking University First Hospital Beijing China Center for Machine Learning Research Peking University Beijing China Pazhou Lab Guangzhou China

Automatic labeling of coronary arteries is an essential task in the practical diagnosis process of cardiovascular diseases. For experienced radiologists, the anatomically predetermined connections are important for labeling the artery segments accurately, while this prior knowledge is barely explored in previous studies. In this paper, we present a new framework called TopoLab which incorporates the anatomical connections into the network design explicitly. Specifically, the strategies of intra-segment feature aggregation and inter-segment feature interaction are introduced for hierarchical segment feature extraction. Moreover, we propose the anatomy-aware connection classifier to enable classification for each connected segment pair, which effectively exploits the prior topology among the arteries with different categories. To validate the effectiveness of our method, we contribute high-quality annotations of artery labeling to the public orCaScore dataset. The experimental results on both the orCaScore dataset and an in-house dataset show that our TopoLab has achieved state-of-the-art performance. © 2023, CC BY.

关键词： Topology

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A Novel ECG Signal Classification Algorithm Based on Common and Specific Components Separation 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Huang, Jianfeng Huang, Chao Yang, Lihua Zhang, Qian School of Financial Mathematics and Statistics Guangdong University of Finance Guangzhou China College of Mathematics and Statistics Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Guangdong Province Key Laboratory of Computational Science School of Mathematics Sun Yat-sen University Guangzhou China

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

Electrocardiography (ECG) signal classification is a challenging task since the characteristics of ECG signals vary significantly for different patients. In this paper, we propose a new method for ECG signal classification based on the separation of common and specific components of a signal. The common components are obtained via Canonical Correlation Analysis (CCA). After removing the common components from the signal, we map the specific components to a lower dimensional feature space for classification. We first establish a basic model in the binary classification setting and then extend it to a more general version. Numerical experiments results on the MIT-BIH Arrhythmia database are presented and discussed. © 2020, Springer Nature Switzerland AG.

关键词： Electrocardiography

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Your transformer may not be as powerful as you expect 22

Your transformer may not be as powerful as you expect

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

作者： Shengjie Luo Shanda Li Shuxin Zheng Tie-Yan Liu Liwei Wang Di He National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University and Zhejiang Lab Machine Learning Department School of Computer Science Carnegie Mellon University Microsoft Research National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University and Center for Data Science Peking University National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University

ISBN: (纸本)9781713871088

Relative Positional Encoding (RPE), which encodes the relative distance between any pair of tokens, is one of the most successful modifications to the original Transformer. As far as we know, theoretical understanding of the RPE-based Transformers is largely unexplored. In this work, we mathematically analyze the power of RPE-based Transformers regarding whether the model is capable of approximating any continuous sequence-to-sequence functions. One may naturally assume the answer is in the affirmative—RPE-based Transformers are universal function approximators. However, we present a negative result by showing there exist continuous sequence-to-sequence functions that RPE-based Transformers cannot approximate no matter how deep and wide the neural network is. One key reason lies in that most RPEs are placed in the softmax attention that always generates a right stochastic matrix. This restricts the network from capturing positional information in the RPEs and limits its capacity. To overcome the problem and make the model more powerful, we first present sufficient conditions for RPE-based Transformers to achieve universal function approximation. With the theoretical guidance, we develop a novel attention module, called Universal RPE-based (URPE) Attention, which satisfies the conditions. Therefore, the corresponding URPE-based Transformers become universal function approximators. Extensive experiments covering typical architectures and tasks demonstrate that our model is parameter-efficient and can achieve superior performance to strong baselines in a wide range of applications.

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NDMFCS: An automatic fruit counting system in modern apple orchard using abatement of abnormal fruit detection

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期211卷

作者： Wu, Zhenchao Sun, Xiaoming Jiang, Hanhui Mao, Wulan Li, Rui Andriyanov, Nikita Soloviev, Vladimir Fu, Longsheng Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligent Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Shenzhen Res Inst Shenzhen 518000 Guangdong Peoples R China Financial Univ Govt Russian Federat Dept Data Anal & Machine Learning Moscow 125167 Russia Xinjiang Acad Agr Sci Inst Agr Mechanizat Urumqi 830000 Peoples R China

Automatic fruit counting is an important task for growers to estimate yield and manage orchards. Although many deep-learning-based fruit detection algorithms have been developed to improve performance of automatic fruit counting systems, abnormal fruit detection has often been caused by these algorithms detecting non-target fruits that have similar growth characteristics to target fruits. For abnormal fruit detection, detected fruits in the back row of the tree were defined as DFBRT, while detected fruits on the ground were defined as DFG. Both of them would result in a higher number of fruits counting than the ground truth. This study proposes an automatic fruit counting system called NDMFCS (Normal Detection Matched Fruit Counting System) to solve this problem for improving fruit counting accuracy in modern apple orchard. NDMFCS consists of three sub-systems, i.e. object detection based on You Only Look Once Version 4-tiny (YOLOv4-tiny), abatement of abnormal fruit detection based on threshold, and fruit counting based on trunk tracking and identity document (ID) assignment. YOLOv4tiny was selected to implement detection of fruits and trunks, whose output is confidence and pixel coordinates of detected object. The DFBRT and DFG were abated by thresholds to improve detection performance of fruit. This meant that detected fruits were removed when their distance from camera is further than a distance threshold or the confidence of fruit detection is less than a confidence threshold. Finally, fruit counting was implemented by trunk tracking and ID assignment, where each fruit was assigned a unique tracking ID. Results on 10 sets of original videos indicated that average fruit detection precision was improved from 89.1% to 93.3% after abatement of abnormal fruit detection. Also, Multiple Object Tracking Accuracy and Multiple Object Tracking Precision were improved on average by 4.2% and 3.3%, respectively, while average ID Switch Rate was decreased on average by 1.1%.

关键词： Apple orchard Abatement of abnormal fruit detection Object detection Fruit counting ID assignment

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Analysis of the Research Status of Information Technology Education Literature in China and Abroad

Analysis of the Research Status of Information Technology Ed...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yulin Zhao Kai Liu Junke Li School of Computer and Information Qiannan Normal University for Nationalities Duyun China Key Laboratory of Machine learning and Unstructured Data Processing of Qiannan Duyun China School of Information Engineering Suqian University Suqian Jiangsu China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu China

Information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad.

关键词： Statistical analysis databases Education Bibliometrics Educational technology Market research Internet

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Morphological estimation of primary branch length of individual apple trees during the deciduous period in modern orchard based on PointNet plus

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 220卷

作者： Sun, Xiaoming He, Leilei Jiang, Hanhui Li, Rui Mao, Wulan Zhang, Dong Majeed, Yaqoob Andriyanov, Nikita Soloviev, Vladimir Fu, Longsheng Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligen Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Shenzhen Res Inst Shenzhen 518000 Guangdong Peoples R China Xinjiang Acad Agr Sci Inst Agr Mechanizat Urumqi 830000 Peoples R China Northwest A&F Univ Coll Hort Yangling 712100 Shaanxi Peoples R China Univ Agr Faisalabad Fac Agr Engn & Technol Faisalabad 38000 Pakistan Financial Univ Govt Russian Federat Dept Data Anal & Machine Learning Moscow 125167 Russia

Primary branch length is an important morphological trait of individual apple tree phenotypes. This study presents a novel method for estimating the primary branch lengths of individual apple trees during the deciduous period by distinguishing their instances, i.e., merging those belonging to the same primary branch based on part segmentation outputs of PointNet++. Firstly, colored and colorless 3D-datasets were prepared for training PointNet++ models. The model with higher overall accuracy (OA), class average accuracy (CAA), and mean intersection-over-union (mIoU) was employed to segment the point cloud of a tree into primary branches (PB), trunk (TK), and end-points of primary branches (EPB) of individual apple trees. Skeletonization was applied to the outputs of the three parts of individual apple trees. Subsequently, each primary branch instance was distinguished by determining its corresponding path and retaining the longest path of the same primary branch only. Finally, the primary branch length was estimated by calculating the sum of Euclidean distances between adjacent points on the corresponding path. Results indicated that adding color to point clouds did not improve segmentation accuracy of PointNet++ on segmenting PB, TK, and EPB with similar color features. The PointNet++ model that was trained without color achieved an OA, CAA, and mIoU of 0.84, 0.83, and 0.70, respectively. The proportion of estimated and ground-truth values of the number of primary branches was 93.64 %. The mean absolute percentage error of estimating primary branch lengths was 12.00 %. These findings demonstrate that the proposed method is promising for high-throughput phenotyping of apple trees.

关键词： Morphological trait Primary branch instance Point cloud Deep learning Part segmentation

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Weakly-Supervised Depression Detection in Speech Through Self-learning Based Label Correction

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 748-758页

作者： Yanfei Sun Yuanyuan Zhou Xinzhou Xu Jin Qi Feiyi Xu Zhao Ren Björn W. Schuller School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Wuxi University Wuxi China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria Key Laboratory of Modern Acoustics MOE Nanjing University Nanjing China Cognitive Systems Lab University of Bremen Bremen Germany Chair of Health Informatics Technische Universität München (TUM) München Germany Munich Data Science Institute Munich Germany Munich Center for Machine Learning Munich Germany GLAM – the Group on Language Audio & Music Imperial College London London U.K.

Automated Depression Detection (ADD) in speech aims to automatically estimate one's depressive attributes through artificial intelligence tools towards spoken signals. Nevertheless, existing speech-based ADD works fail to sufficiently consider weakly-supervised cases with inaccurate labels, which may typically appear in intelligent mental health. In this regard, we propose the Self-learning-based Label Correction (SLLC) approach for weakly-supervised depression detection in speech. The proposed approach employs a self-learning manner connecting a label correction module and a depression detection module. Within the approach, the label correction module fuses likelihood-ratio-based and prototype-based label correction strategies in order to effectively correct the inaccurate labels, while the depression detection module aims at detecting depressed samples through a 1D convolutional recurrent neural network with multiple types of losses. The experimental results on two depression detection corpora show that our proposed SLLC approach performs better compared with existing state-of-the-art speech-based depression detection approaches, in the case of weak supervision with inaccurate labels for depression detection in speech.

关键词： Depression Feature extraction Training Speech processing Prototypes Noise measurement Telecommunications Sun Internet of Things Weak supervision

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