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1,063 results on '"Li, Junyu"'

2. Deriving beliefs about children's moral responsibility from capacity beliefs

Author

Li, Junyu, Hardecker, Susanne, Haun, Daniel Benjamin Moritz, and Bohn, Manuel

Abstract

Adults have rich beliefs about children’s development timelines, and they interpret and react to children’s behaviors across ages, holding children responsible to some degree. While children’s mental capacity and potential could motivate moral agency attribution, a question remains whether a consistent relation exists between the empirical beliefs about children’s various capacities and the responsibility attribution to their behaviors that manifest the corresponding capacities. Here, we tested 361 adults (UK, US) on their folk psychology and moral beliefs about different ages with vignettes that reflect agential control in various domains (motor control, inhibitory control, theory of mind, planning, moral evaluation) combined with several variants of scenarios. We characterized the relation between adults’ expectations and responsibility attribution with mixed models. We found that this moral reasoning varies for targets of different ages and the amount of responsibility is mostly determined by age. We suggest an alternative mechanism between capacity- and moral beliefs.

Published
2024

4. MUVF-YOLOX: A Multi-modal Ultrasound Video Fusion Network for Renal Tumor Diagnosis

Author

Li, Junyu, Huang, Han, Ni, Dong, Xue, Wufeng, Zhu, Dongmei, and Cheng, Jun

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Early diagnosis of renal cancer can greatly improve the survival rate of patients. Contrast-enhanced ultrasound (CEUS) is a cost-effective and non-invasive imaging technique and has become more and more frequently used for renal tumor diagnosis. However, the classification of benign and malignant renal tumors can still be very challenging due to the highly heterogeneous appearance of cancer and imaging artifacts. Our aim is to detect and classify renal tumors by integrating B-mode and CEUS-mode ultrasound videos. To this end, we propose a novel multi-modal ultrasound video fusion network that can effectively perform multi-modal feature fusion and video classification for renal tumor diagnosis. The attention-based multi-modal fusion module uses cross-attention and self-attention to extract modality-invariant features and modality-specific features in parallel. In addition, we design an object-level temporal aggregation (OTA) module that can automatically filter low-quality features and efficiently integrate temporal information from multiple frames to improve the accuracy of tumor diagnosis. Experimental results on a multicenter dataset show that the proposed framework outperforms the single-modal models and the competing methods. Furthermore, our OTA module achieves higher classification accuracy than the frame-level predictions. Our code is available at \url{https://github.com/JeunyuLi/MUAF}., Comment: MICCAI 2023

Published
2023

5. Multi-IMU with Online Self-Consistency for Freehand 3D Ultrasound Reconstruction

Author

Luo, Mingyuan, Yang, Xin, Yan, Zhongnuo, Li, Junyu, Zhang, Yuanji, Chen, Jiongquan, Hu, Xindi, Qian, Jikuan, Cheng, Jun, and Ni, Dong

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Ultrasound (US) imaging is a popular tool in clinical diagnosis, offering safety, repeatability, and real-time capabilities. Freehand 3D US is a technique that provides a deeper understanding of scanned regions without increasing complexity. However, estimating elevation displacement and accumulation error remains challenging, making it difficult to infer the relative position using images alone. The addition of external lightweight sensors has been proposed to enhance reconstruction performance without adding complexity, which has been shown to be beneficial. We propose a novel online self-consistency network (OSCNet) using multiple inertial measurement units (IMUs) to improve reconstruction performance. OSCNet utilizes a modal-level self-supervised strategy to fuse multiple IMU information and reduce differences between reconstruction results obtained from each IMU data. Additionally, a sequence-level self-consistency strategy is proposed to improve the hierarchical consistency of prediction results among the scanning sequence and its sub-sequences. Experiments on large-scale arm and carotid datasets with multiple scanning tactics demonstrate that our OSCNet outperforms previous methods, achieving state-of-the-art reconstruction performance., Comment: Accepted by MICCAI-2023

Published
2023

6. Dynamic Adjustment Neural Network–Based Cooperative Control for Vehicle Platoons with State Constraints

Author

Wang Ping, Gao Min, Li Junyu, and Zhang Anguo

Subjects
vehicle platoon, dynamic adjustment neural network (dann), cooperative control, state constraint, Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

This paper addresses the challenge of managing state constraints in vehicle platoons, including maintaining safe distances and aligning velocities, which are key factors that contribute to performance degradation in platoon control. Traditional platoon control strategies, which rely on a constant time-headway policy, often lead to deteriorated performance and even instability, primarily during dynamic traffic conditions involving vehicle acceleration and deceleration. The underlying issue is the inadequacy of these methods to adapt to variable time-delays and to accurately modulate the spacing and speed among vehicles. To address these challenges, we propose a dynamic adjustment neural network (DANN) based cooperative control scheme. The proposed strategy employs neural networks to continuously learn and adjust to time varying conditions, thus enabling precise control of each vehicle’s state within the platoon. By integrating a DANN into the platoon control system, we ensure that both velocity and inter-vehicular spacing adapt in response to real-time traffic dynamics. The efficacy of our proposed control approach is validated using both Lyapunov stability theory and numeric simulation, which confirms substantial gains in stability and velocity tracking of the vehicle platoon.

Published
2024
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7. Uniform tensor clustering by jointly exploring sample affinities of various orders

Author

Cai, Hongmin, Qi, Fei, Li, Junyu, Hu, Yu, Zhang, Yue, Cheung, Yiu-ming, and Hu, Bin

Subjects
Computer Science - Machine Learning
Abstract

Conventional clustering methods based on pairwise affinity usually suffer from the concentration effect while processing huge dimensional features yet low sample sizes data, resulting in inaccuracy to encode the sample proximity and suboptimal performance in clustering. To address this issue, we propose a unified tensor clustering method (UTC) that characterizes sample proximity using multiple samples' affinity, thereby supplementing rich spatial sample distributions to boost clustering. Specifically, we find that the triadic tensor affinity can be constructed via the Khari-Rao product of two affinity matrices. Furthermore, our early work shows that the fourth-order tensor affinity is defined by the Kronecker product. Therefore, we utilize arithmetical products, Khatri-Rao and Kronecker products, to mathematically integrate different orders of affinity into a unified tensor clustering framework. Thus, the UTC jointly learns a joint low-dimensional embedding to combine various orders. Finally, a numerical scheme is designed to solve the problem. Experiments on synthetic datasets and real-world datasets demonstrate that 1) the usage of high-order tensor affinity could provide a supplementary characterization of sample proximity to the popular affinity matrix; 2) the proposed method of UTC is affirmed to enhance clustering by exploiting different order affinities when processing high-dimensional data.

Published
2023

30. Multi-energy Coordinated Dispatch of Integrated Transportation Hub During Peak-Period Transactions

Author

Sun, Zhiyun, Wei, Qizhen, Chen, Hewei, Tang, Yuancheng, Li, Junyu, Lu, Xiaolong, Fang, Conglin, Luo, Qiang, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Yang, Hongming, editor, Fei, Jiang, editor, and Qiang, Tang, editor

Published
2023
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45. Over 60 h of Stable Water‐Operation for N‐Type Organic Electrochemical Transistors with Fast Response and Ambipolarity

Author

Pan, Tao, primary, Jiang, Xinnian, additional, van Doremaele, Eveline R. W., additional, Li, Junyu, additional, van der Pol, Tom P. A., additional, Yan, Chenshuai, additional, Ye, Gang, additional, Liu, Jian, additional, Hong, Wenjing, additional, Chiechi, Ryan C., additional, de Burgt, Yoeri van, additional, and Zhang, Yanxi, additional

Published
2024
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