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arXiv 2022年

作者： Zhu, Heqin Sun, Xu Li, Yuexiang Ma, Kai Zhou, S. Kevin Zheng, Yefeng Key Lab of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China Tencent Jarvis Lab Shenzhen China University of Science and Technology of China Suzhou215123 China

Automated salient object detection (SOD) plays an increasingly crucial role in many computer vision applications. By reformulating the depth information as supervision rather than as input, depth-supervised convolutional neural networks (CNN) have achieved promising results on both RGB and RGB-D SOD scenarios with the merits of no requirements for extra depth networks and depth inputs in the inference stage. This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture. Specifically, we develop a Depth-supervised Fusion TRansformer (DFTR), to further improve the accuracy of both RGB and RGB-D SOD. The proposed DFTR involves three primary features: 1) DFTR, to the best of our knowledge, is the first pure Transformer-based model for depth-supervised SOD;2) A multi-scale feature aggregation (MFA) module is proposed to fully exploit the multi-scale features encoded by the Swin Transformer in a coarse-to-fine manner;3) To enable bidirectional information flow across different streams of features, a novel multi-stage feature fusion (MFF) module is further integrated into our DFTR with the emphasis on salient regions at different network learning stages. We extensively evaluate the proposed DFTR on ten benchmarking datasets. Experimental results show that our DFTR consistently outperforms the existing state-of-the-art methods for both RGB and RGB-D SOD tasks. The code and model will be made publicly available. © 2022, CC BY-NC-SA.

关键词： Object detection

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Evidence-Aware Multi-Modal Data Fusion and its Application to Total Knee Replacement Prediction

Evidence-Aware Multi-Modal Data Fusion and its Application t...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors Digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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Closed-Loop Safe Correction for Reinforcement Learning Policy 1

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4th International Conference on Ubiquitous Security, UbiSec 2024

作者： Yi, Zhi Lv, Qi Chen, Shuhong Liang, Ying Dai, Yinglong Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China Blockchain Innovation Lab Swinburne University of Technology MelbourneVIC3122 Australia Molecular Nutrition Branch National Engineering Research Center of Rice and By-product Deep Processing College of Food Science and Engineering Central South University of Forestry and Technology Hunan Changsha410004 China

ISBN: (数字)9789819648368

ISBN: (纸本)9789819648351

Trial and error learning is an approach with uncertain consequences. How to maintain policy security, stability, and efficiency under controlled circumstances, posing a significant academic challenge. Such as Reinforcement Learning (RL) leverages the neural network parameter space to identify the optimal solution for achieving the optimal mapping of the policy space. However, even a slight fluctuation in the parameter space can lead to policy collapse. To solve it, we propose a method called Closed-loop Safe Correction (CLSC). This method employs twin agents to establish a closed-loop framework for policy and sampling data distribution, gradually improving policy efficiency while ensuring policy safety. Base on theoretical and experimental analysis, CLSC elucidates the performance, influencing factors, and advantages of this approach in policy safety scenarios. These findings provide novel research insights and practical directions for advancing safe RL. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Deep Learning for Logo Detection: A Survey

arXiv

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arXiv 2022年

作者： Hou, Sujuan Li, Jiacheng Min, Weiqing Hou, Qiang Zhao, Yanna Zheng, Yuanjie Jiang, Shuqiang School of Information Science and Engineering Shandong Normal University Shandong250358 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology China Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, etc. have been employed. This paper reviews the advance in applying deep learning techniques to logo detection. Firstly, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and the strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection to complete this survey. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

arXiv

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arXiv 2025年

作者： Wang, Yueqing Mei, Yikun Gao, Zhen Wan, Ziwei Ning, Boyu Mi, De Muhaidat, Sami School of Information and Electronics Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Computing and Digital Technology Birmingham City University BirminghamB55JU United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi United Arab Emirates

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs’ activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. Simulation codes are provided to reproduce the results in this article: https://***/owenwang517/tvt-2025. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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Single-cell Long Non-coding RNA Landscape of T Cells in Human Cancer Immunity

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Genomics, Proteomics & Bioinformatics 2021年第3期19卷 377-393页

作者： Haitao Luo Dechao Bu Lijuan Shao Yang Li Liang Sun Ce Wang Jing Wang Wei Yang Xiaofei Yang Jun Dong Yi Zhao Furong Li Translational Medicine Collaborative Innovation Center The Second Clinical Medical College(Shenzhen People’s Hospital)Jinan UniversityShenzhen 518020China Shenzhen Key Laboratory of Stem Cell Research and Clinical Transformation Shenzhen 518020China Integrated Chinese and Western Medicine Postdoctoral Research Station Jinan UniversityGuangzhou 510632China Bioinformatics Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research CenterInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Department of Gastrointestinal Surgery The Second Clinical Medical College(Shenzhen People’s Hospital)Jinan UniversityShenzhen 518020China

The development of new biomarkers or therapeutic targets for cancer immunotherapies requires deep understanding of *** date,the complete landscape and systematic characterization of long noncoding RNAs(lncRNAs)in T cells in cancer immunity are ***,by systematically analyzing full-length single-cell RNA sequencing(scRNA-seq)data of more than 20,000 libraries of T cells across three cancer types,we provided the first comprehensive catalog and the functional repertoires of lncRNAs in human T ***,we developed a custom pipeline for de novo transcriptome assembly and obtained a novel lncRNA catalog containing 9433 *** increased the number of current human lncRNA catalog by 16%and nearly doubled the number of lncRNAs expressed in T *** found that a portion of expressed genes in single T cells were lncRNAs which had been overlooked by the majority of previous *** on metacell maps constructed by the MetaCell algorithm that partitions scRNA-seq datasets into disjointed and homogenous groups of cells(metacells),154 signature lncRNA genes were *** were associated with effector,exhausted,and regulatory T cell ***,84 of them were functionally annotated based on the co-expression networks,indicating that lncRNAs might broadly participate in the regulation of T cell *** findings provide a new point of view and resource for investigating the mechanisms of T cell regulation in cancer immunity as well as for novel cancer-immune biomarker development and cancer immunotherapies.

关键词： Long non-coding RNA Transcriptome assembly Metacell Immune regulation Functional annotation

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Enhanced Second-Harmonic Generation Driven by Dual-Quasi Bound States in the Continuum

Enhanced Second-Harmonic Generation Driven by Dual-Quasi Bou...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yuhang Liu Xingang Ren Kaikun Niu Zhixiang Huang School of Electronic and Information Engineer Anhui University Hefei China Engineering Technology Research Center of Specific Power Devices with Novel Semiconductor Materials Chizhou Anhui Province China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

Bound state in the continuum (BIC) is a dark mode with infinitely high quality factors(Q-factors), it has recently attracted considerable research attention in photonics. Here, we present a metasurface that can support two BICs, which consists of AlGaAs. By breaking the structural symmetry, the two BICs are transformed into quasi-bic modes because they are not protected by the symmetry. Furthermore, it is found that the two quasi-BICs resonate near the fundamental and the second harmonic respectively, which can be used to enhance efficiency of second harmonic generation. By exploiting the high Q characteristics of this structure at both frequencies, we can obtain a second harmonic generation efficiency of nearly 10– 3 with 1GW/cm 2 of pump intensity. Our proposed metasurface will offer good prospects for nonlinear operation.

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Harmonic Frequency-Separable Transformer for Instrument-Agnostic Music Transcription

Harmonic Frequency-Separable Transformer for Instrument-Agno...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yulun Wu Weixing Wei Dichucheng Li Mengbo Li Yi Yu Yongwei Gao Wei Li School of Computer Science and Technology Fudan University Shanghai China Digital Content and Media Sciences Research Division National Institute of Informatics (NII) Tokyo Japan School of Statistics and Information Shanghai University of International Business and Economics Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Automatic Music Transcription (AMT) aims to convert music audio into symbolic representations. Recently, transformer-based methods have been successfully applied to instrument-agnostic music transcription. This allows transcription models can no longer focus on specific characteristics for an instrument class. However, these transformer-based methods designs for AMT were mainly motivated by other research fields and uses additional large-scale datasets, without considering the intrinsic features and patterns of the music signals. In this paper, we propose the Harmonic Frequency-Separable Transformer (HFSFormer), providing effective prior information based on music knowledge for instrument-agnostic transcription. The HFSFormer can capture the harmonic structure of music and separate time-frequency representations to decouple multiple pitches and different timbres, which can better explicitly model the note’s onset/offset and pitch. Experimental results show that our proposed method outperforms state-of-the-art peers on public datasets while having an order of magnitude fewer parameters.

关键词： Time-frequency analysis Instruments Multimedia systems Design methodology Music Transformers Harmonic analysis

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Adapter-Enhanced Semantic Prompting for Continual Learning

arXiv

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arXiv 2024年

作者： Yin, Baocai Zhao, Ji Jiang, Huajie Hou, Ningning Hu, Yongli Beheshti, Amin Yang, Ming-Hsuan Qi, Yuankai Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Electrical Engineering and Computer Science University of California at Merced MercedCA95343 United States School of Computing Macquarie University SydneyNSW2109 Australia

Continual learning (CL) is essential for enabling models to adapt to dynamic data streams, with the primary challenge being the mitigation of catastrophic forgetting of previously acquired knowledge. Recent advancements have highlighted the potential of prompt-based CL methodologies, which have shown promise in preserving the knowledge embedded within pre-trained models, thereby addressing catastrophic forgetting. However, these methodologies are constrained by their focus on learning visual prompts for individual tasks, resulting in suboptimal generalization to unseen categories. To enhance the model’s ability to retain knowledge from prior tasks while simultaneously improving generalization to new tasks, we introduce a novel, lightweight framework for CL termed Adapter-Enhanced Semantic Prompting (AESP). This framework leverages class semantic information to augment visual features, thereby establishing relational links among different categories. This not only fortifies the retention of prior knowledge but also facilitates adaptation to new tasks. To seamlessly integrate visual and semantic information, we have developed a lightweight Adapter-enhanced Vision Transformer (ViT) architecture specifically designed for feature adaptation. Furthermore, we have implemented a robust Query-key matching strategy to select the optimal task prompt pair, thereby enhancing the accuracy of final predictions. Comprehensive experiments conducted across three benchmark continual learning datasets demonstrate that our proposed framework outperforms several state-of-the-art approaches, showcasing its efficacy and robustness. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Modeling human travel and social contact with multi-layer networks for epidemic prediction 9

Modeling human travel and social contact with multi-layer ne...

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9th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2021

作者： Duan, Wei Wang, Tao Wang, Peng Ju, Rusheng Wang, Xiao Yang, Tian College of Systems Engineering National University of Defense Technology Changsha City China State Key Laboratry of Complex Systems Management and Control Institute of Automation Chinese Academy of Sciences Beijing City China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha City China

ISBN: (纸本)9780738132020

It is a key issue to reasonably represent human travel and social contact in epidemic models. Various measures were applied to develop the models of human mobility and contact in a long range or a short range, such as Brown movement, random walks, spatial networks, gravity models, contact networks. We proposed a method of representing human daily movement and social contact by using multi-layer networks with temporal edge weights. We combined bipartite networks with social networks to describe human daily trip and social contact, respectively. Temporal edge weights of multi-layer networks were employed to denote the propensity of individual movement and contact. We also verified our models and parameters by incorporating human daily travel and contact regularities, as well as comparing experimental results with human behavior statistical laws. At last, we applied a Chinese university campus as a case study to investigate students' daily travel and social contact, and studied the transmission and control strategies of COVID-19 virus. We found stricter control strategies are needed to mitigate the transmission of COVID-19 virus in a university. Once a patient case emerges in a university, it is better to close the campus and quarantine all students. Partial control strategies such as quarantining a part of students and buildings cannot achieve a great effect of mitigating the transmission of COVID-19 virus. Our works are beneficial for the practitioners in the field of computational epidemiology. © 2021 IEEE.

关键词： Viruses

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