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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Song Wei, Xiaoyu Chen, Xinyue Ren, Yazhou He, Jing Pu, Xiaorong School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Guangdong Shenzhen China Faculty of Medicine & Biomedical Sciences The University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9798400706868

Semi-supervised medical image segmentation has gained increasing attention due to its potential to alleviate the manual annotation burden. Mainstream methods typically involve two subnets, and conduct a consistency objective to ensure them producing consistent predictions for unlabeled data. However, they often ignore that the complementarity of model predictions is equally crucial. To realize the potential of the multi-subnet architecture, we propose a novel cross-view mutual learning method with a two-branch co-training framework. Specifically, we first introduce a novel conflict-based feature learning (CFL) that encourages the two subnets to learn distinct features from the same input. These distinct features are then decoded into complementary model predictions, allowing both subnets to understand the input from different views. More importantly, we propose a cross-view mutual learning (CML) to maximize the effectiveness of CFL. This approach requires only modifications to the model inputs and supervisory signals, and implements a heterogeneous consistency objective to fully explore the complementarity of model predictions. Consequently, the aggregated predictions can effectively capture both consistency and complementarity across two subnets. Experimental results on three public datasets demonstrate the superiority of CML over previous SoTA methods. Code is available at https://***/SongwuJob/CML. © 2024 ACM.

关键词： Self-supervised learning

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3D Object Detection Based on Spatially Adaptive Sparse Convolution

3D Object Detection Based on Spatially Adaptive Sparse Convo...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Hu, Jing Jing Fan, Yu Ling Zhang, Ping Duan, Chang Zhang, Chao Chen, Si Yu Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Information Southwest Petroleum University Chengdu China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province Luzhou China School of Computer Science And Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350391213

Autonomous driving has gradually gained attention in recent years due to its potential to reduce driver burden and improve safety, in which 3D object detection is the basis of the autonomous driving perception system, aiming at predicting the location, size, and class of 3D objects in the vicinity of an autonomous vehicle, which is an important guide for subsequent path planning, motion prediction, and so on. In particular, 3D object detection has difficulties in input point cloud data processing and multimodal fusion. Unlike the image distribution where pixels are regularly distributed in the image plane, the point cloud is a sparse and irregular 3D representation, so it is difficult to apply the traditional convolution directly on the point cloud for detection. In this paper, we carry out a method for 3D object detection for point clouds, using deformable convolution and sparse convolution as a tool to explore the feature extraction method of sparse convolution for irregular point clouds and propose a spatially adaptive sparse convolution-based method. © 2024 IEEE.

关键词： Motion planning

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Impact-aware Maneuver Decision with Enhanced Perception for Autonomous Vehicle 39

Impact-aware Maneuver Decision with Enhanced Perception for ...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Liu, Shuncheng Xia, Yuyang Chen, Xu Xie, Jiandong Su, Han Zheng, Kai University of Electronic Science and Technology of China School of Computer Science and Engineering China Huawei Cloud Database Innovation Lab China China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study China

ISBN: (纸本)9798350322279

Autonomous driving is an emerging technology that has developed rapidly over the last decade. There have been numerous interdisciplinary challenges imposed on the current transportation system by autonomous vehicles. In this paper, we conduct an algorithmic study on the autonomous vehicle decision-making process, which is a fundamental problem in the vehicle automation field and the root cause of most traffic congestion. We propose a perception-and-decision framework, called HEAD, which consists of an enHanced pErception module and a mAneuver Decision module. HEAD aims to enable the autonomous vehicle to perform safe, efficient, and comfortable maneuvers with minimal impact on other vehicles. In the enhanced perception module, a graph-based state prediction model with a strategy of phantom vehicle construction is proposed to predict the one-step future states for multiple surrounding vehicles in parallel, which deals with sensor limitations such as limited detection range and poor detection accuracy under occlusions. Then in the maneuver decision module, a deep reinforcement learning-based model is designed to learn a policy for the autonomous vehicle to perform maneuvers in continuous action space w.r.t. a parameterized action Markov decision process. A hybrid reward function takes into account aspects of safety, efficiency, comfort, and impact to guide the autonomous vehicle to make optimal maneuver decisions. Extensive experiments offer evidence that HEAD can advance the state of the art in terms of both macroscopic and microscopic effectiveness. © 2023 IEEE.

关键词： Autonomous vehicles

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An Obstacle Detection Technique for Marine Intelligent Transportation System based on Maximum likelihood Method

An Obstacle Detection Technique for Marine Intelligent Trans...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Ma, Xin-Ming Song, Hai-Yan Yang, Chang-Yi Song, Xiao-Jun Gao, Dong-Xu Heilongjiang Institute of Technology School of Electrical and Information Engineering Harbin China National Penghu University of Science and Technology Department of Computer Science and Information Engineering Magong Taiwan Petro China Tarim Oil Field Company Korla China

ISBN: (纸本)9798350324174

Obstacle detection is a key technology for marine intelligent transportation system. In this paper, an obstacle detection technique for marine intelligent transportation system based on Maximum Likelihood (ML) processor is proposed. Different from most of the former source localization algorithms, our proposed algorithm explicitly makes use of both the horizontal L-Shaped array and the underwater acoustic propagation model to achieve a relatively high spatial resolution. Numerical simulations are presented to demonstrate the superior performance of our proposed algorithm. © 2023 IEEE.

关键词： Maximum likelihood

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A Joint DOA and Range Estimation Technique for Underwater Transportation System based on Compressive Sensing

A Joint DOA and Range Estimation Technique for Underwater Tr...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Song, Hai-Yan Yang, Chang-Yi Zhang, Yu-Jie Tang, Jia-Xin Gao, Jia- Qi Heilongjiang Institute of Technology School of Electrical and Information Engineering Harbin China National Penghu University of Science and Technology Department of Computer Science and Information Engineering Magong Taiwan Petro China Tarim Oil Field Company Korla China

ISBN: (纸本)9798350324174

Obstacle detection can help human to reduce risk on underwater transportation activities. In our work, we use compressive sensing technique to estimate the Direction of Arrival (DOA) and range for spatial targets especially in underwater transportation system. According to underwater acoustic propagation ray theory, the signal model for horizontal L-Shaped array is first constructed. Then, the joint DOA and range estimation is converted into an optimizing procedure, which can be finally solved by an efficient toolbox for Disciplined Convex Programming (CVX). The performance of our proposed method is discussed in the computer simulation results. © 2023 IEEE.

关键词： Compressed sensing

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DNA Archives: Revolutionizing Data Storage 3

DNA Archives: Revolutionizing Data Storage

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Patel, Radhika Garg, Dweepna Shah, Milind Dharmajwala, Safeya Jindal, Kush Nayak, Amit Department of Information Technology Gujarat Changa388421 India Department of Computer Engineering Gujarat Changa388421 India Department of Computer Science & Engineering Gujarat Vadodara India Strawberry Field High School Chandigarh India

ISBN: (纸本)9798350343632

Every day, the internet generates an average of 2.5 quintillion bytes of data. As a result, while the need for data storage is increasing exponentially, the capacity of existing storage media is not. The field of data storage requires a revolution right now. The unique technique for storing digital data in DNA sequences is examined in this research. The goal is to mimic nature's method of storing data. The process uses, and challenges related to this hypothetical idea of data storage are detailed in this study. DNA is an appealing option for archiving data since it is highly dense, with a raw limit of 1 Exabyte. This study examines nearly every major milestone in the science of DNA storage, from the benefits and drawbacks to how and why everything is feasible, as well as everything that impedes the advancement of such technology. This study also focuses on how much data storage can be achieved now. © 2023 IEEE.

关键词： Data Storage DNA DNA sequences Emerging Technologies

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Adaptive Multi-Modality Prompt Learning 24

Adaptive Multi-Modality Prompt Learning

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Zongqian Liu, Yujing Zhan, Mengmeng Hu, Ping Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Guangxi Key Lab of Multisource Information Mining and Security Guangxi Normal University Guilin China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (纸本)9798400706868

Although current prompt learning methods have successfully been designed to effectively reuse the large pre-trained models without fine-tuning their large number of parameters, they still have limitations to be addressed, i.e., without considering the adverse impact of meaningless patches in every image and without simultaneously considering in-sample generalization and out-of-sample generalization. In this paper, we propose an adaptive multi-modality prompt learning to address the above issues. To do this, we employ previous text prompt learning and propose a new image prompt learning. The image prompt learning achieves in-sample and out-of-sample generalization, by first masking meaningless patches and then padding them with the learnable parameters and the information from texts. Moreover, each of the prompts provides auxiliary information to each other, further strengthening these two kinds of generalization. Experimental results on real datasets demonstrate that our method outperforms SOTA methods, in terms of different downstream tasks. © 2024 ACM.

关键词： Contrastive Learning

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The connection between meridians and physiological functions:A quantum principle

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Nano Research 2023年第11期16卷 12817-12820页

作者： Xiang-Yu Kong Min Xiang Jiancheng Fang Dong Han Bo Song Lei Jiang CAS Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing 100190China Science and Technology Center for Quantum Biology National Institute of Extremely-Weak Magnetic Field InfrastructureHangzhou 310051China CAS Center for Excellence in Nanoscience National Center for Nanoscience and TechnologyBeijing 100190China School of Optical-Electrical Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Future Technology University of Chinese Academy of SciencesBeijing 100049China

In the long history of traditional Chinese medicine(TCM),meridians play essential roles as the critical network to regulate the normal physiological functions of the human *** are regarded to be the channels connecting the internal organs with the body surface and various parts of the *** there are many studies and doctrines trying to reveal the nature of meridians for their validation in TCM,the mechanism underlying the meridians remains ***,based on our macroscopic quantum state concept of ion channels(i.e.,sub-nanometer scale channels),we propose a quantum principle of *** acupoints and organ symptom are in a macroscopic coherence state of the ion channels in *** applying TCM treatments(e.g.,TCM massage,acupuncture,moxibustion,and electroacupuncture)on the acupoint,the corresponding organ symptom could be well regulated with help of quantum meridian state.

关键词： traditional Chinese medicine meridians macroscopic quantum state external stimulation

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Effects of different scattering components in speckle autocorrelation imaging

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Optics Letters 2025年第3期50卷 880-883页

作者： Xie, Gengchen Zhang, Xuyu Zhang, Dawei Han, Shensheng Liu, Honglin School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Hangzhou Institute for Advanced Study Hangzhou310024 China

Various methods of imaging through scattering media suffer a rapid degradation in image quality as medium thickness increases. To address this issue, based on speckle autocorrelation imaging we designed a double-layer diffuser experiment to study the effects of different scattering components on imaging. The results show that both singly scattered light and doubly scattered light can transmit information. However, the difference in memory effect range leads to diverse abilities in information extraction, thereby affecting image recovery. This study may provide insights into the mechanisms of information transmission within thick scattering media and methods to extract information from multiply scattered light to reconstruct images. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Speckle

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通过深度神经网络与皮层网络的多级整合增强自然感知的神经编码模型

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science Bulletin 2024年第11期69卷 1738-1747页

作者：李远宁杨虎铮顾实 School of Biomedical Engineering&State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech UniversityShanghai 201210China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Computer and Information Science University of PennsylvaniaPhiladelphiaPA 19104USA Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China

Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks(DNNs) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks, leading to suboptimal brain-wide correspondence during cognitive tasks. To address this challenge, this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks. Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration, resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception. Furthermore, this multi-level integration framework enables a deeper understanding of the brain's neural representation mechanism, accurately predicting the neural response to complex visual concepts. We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks.

关键词： Neural encoding Visual perception Artificial neural networks Functional neuroimaging Naturalistic stimuli

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