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arXiv 2024年

作者： Yang, Xihong Wang, Yiqi Chen, Jin Fan, Wenqi Zhao, Xiangyu Zhu, En Liu, Xinwang Lian, Defu School of Computer National University of Defense Technology Changsha410073 China School of Business and Management Hong Kong University of Science and Technology Hong Kong The Hong Kong Polytechnic University Hong Kong Department of Data Science City University of Hong Kong Hong Kong Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China

Deep learning has been widely applied in recommender systems, which has recently achieved revolutionary progress. However, most existing learning-based methods assume that the user and item distributions remain unchanged between the training phase and the test phase. However, the distribution of user and item features can naturally shift in real-world scenarios, potentially resulting in a substantial decrease in recommendation performance. This phenomenon can be formulated as an Out-Of-Distribution (OOD) recommendation problem. To address this challenge, we propose a novel Dual Test-Time-Training framework for OOD Recommendation, termed DT3OR. In DT3OR, we incorporate a model adaptation mechanism during the test-time phase to carefully update the recommendation model, allowing the model to adapt specially to the shifting user and item features. To be specific, we propose a self-distillation task and a contrastive task to assist the model learning both the user’s invariant interest preferences and the variant user/item characteristics during the test-time phase, thus facilitating a smooth adaptation to the shifting features. Furthermore, we provide theoretical analysis to support the rationale behind our dual test-time training framework. To the best of our knowledge, this paper is the first work to address OOD recommendation via a test-time-training strategy. We conduct experiments on five datasets with various backbones. Comprehensive experimental results have demonstrated the effectiveness of DT3OR compared to other state-of-the-art baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised applications

arXiv

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arXiv 2023年

作者： Lin, Jiatai Han, Guoqiang Xu, Xuemiao Liang, Changhong Wong, Tien-Tsin Chen, C.L. Philip Liu, Zaiyi Han, Chu The School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Southern Medical University Guangzhou510080 China School of Computer Science and Engineering South China University of Technology State Key Laboratory of Subtropical Building Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Class activation mapping (CAM), a visualization technique for interpreting deep learning models, is now commonly used for weakly supervised semantic segmentation (WSSS) and object localization (WSOL). It is the weighted aggregation of the feature maps by activating the high class-relevance ones. Current CAM methods achieve it relying on the training outcomes, such as predicted scores (forward information), gradients (backward information), etc. However, when with small-scale data, unstable training may lead to less effective model outcomes and generate unreliable weights, finally resulting in incorrect activation and noisy CAM seeds. In this paper, we propose an outcome-agnostic CAM approach, called BroadCAM, for small-scale weakly supervised applications. Since broad learning system (BLS) is independent to the model learning, BroadCAM can avoid the weights being affected by the unreliable model outcomes when with small-scale data. By evaluating BroadCAM on VOC2012 (natural images) and BCSS-WSSS (medical images) for WSSS and OpenImages30k for WSOL, BroadCAM demonstrates superior performance than existing CAM methods with small-scale data (less than 5%) in different CNN architectures. It also achieves SOTA performance with large-scale training data. Extensive qualitative comparisons are conducted to demonstrate how BroadCAM activates the high class-relevance feature maps and generates reliable CAMs when with small-scale training data. © 2023, CC BY.

关键词： Learning systems

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引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoke Zhu Danyang Li Xiaopan Chen Fumin Qi Fan Zhang Xiao-Yuan Jing School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Department of System Operation National Supercomuting Center in Shenzhen Shenzhen China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.

关键词： Pattern matching Similarity learning

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The Newly Receiving Hood System for Tunnel Shield Machines: Design and Implementation

SSRN

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SSRN 2023年

作者： Lin, Yunhong Guo, Xiaodong Yao, Zhengyu Chen, Zhong He, Hongming Guangzhou Metro Design and Research Institute Co. Ltd Guangdong Guangzhou China School of Geographic Sciences East China Normal University Shanghai200241 China Key Laboratory of Geographic Information Science Ministry of Education East China Normal University Shanghai200241 China Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai200241 China Faculty of Land Resources Engineering Kunming University of Science and Technology Kunming630093 China

How to solve the current engineering risks and problems faced in the shield receiving process has become an important research hotspot in the industry. To this end, we have carried out research into newly receiving hood system for tunnel shield machines under unreinforced ground conditions. Based on the concept of active pressurization, a new type of sealing device is used, using an active pressurized sealing system for the whole process from the shield breaking gate to the construction of the ring beam of the gate. The results of the study show that the newly receiving hood system is suitable for all kinds of unreinforced soils and all sizes of shield machines, and is set up in three models of 20m, 40m and super 40m water pressure, using the technique of building pressure in the earth bin before turning the knife plate to grind the enclosure to achieve a good sealing effect. In addition, the station end settlement data shows that with the new initiation technique the works are carried out without negative impact on the surrounding environment. The study proposes a new receiving hood system that provides a good solution to the risks faced by shield operations. © 2023, The Authors. All rights reserved.

关键词： Tunneling machines

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The Newly Launching Hood System for Tunnel Shield Machines: Design and On-Site Operation

SSRN

引用

SSRN 2023年

作者： Lin, Yunhong Chang, Ruicheng Yao, Zhengyu Chen, Zhong He, Hongming Guangzhou Metro Design and Research Institute Co. Ltd Guangdong Guangzhou China Faculty of Land Resources Engineering Kunming University of Science and Technology Kunming630093 China School of Geographic Sciences East China Normal University Shanghai200241 China Key Laboratory of Geographic Information Science Ministry of Education East China Normal University Shanghai200241 China Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai200241 China

The resolution of engineering risks and problems during the shield machine launching process has become a crucial research topic in the industry. To address this issue, we conducted a study on a newly launching hood system for unconsolidated soil conditions. Based on the concept of active pressurization and using a new sealing device, we applied an active pressurized sealing system throughout the construction process from the shield breakthrough door to the ring beam of the door. The research results demonstrated that the newly launching hood system is suitable for various unconsolidated soil layers and shield machine sizes, with three models of 20m, 40m, and above 40m water pressure. By using the technology of pressurizing the soil before turning the cutterhead to grind the surrounding structure, a good sealing effect was achieved. Moreover, station end settlement data showed that the use of the newly launching hood system does not have a negative impact on the surrounding environment during construction. The launching hood system for tunnel shield provides an excellent solution for the risks faced in shield machine operations. © 2023, The Authors. All rights reserved.

关键词： Grinding (machining)

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Real-time chaotic video encryption based on multithreaded parallel confusion and diffusion

arXiv

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arXiv 2023年

作者： Jiang, Dong Yuan, Zhen Li, Wen-Xin Lu, Liang-Liang School of Internet Anhui University Hefei230039 China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui University Hefei230601 China Key Laboratory of Optoelectronic Technology of Jiangsu Province Nanjing Normal University Nanjing210023 China School of Physical Science and Technology Nanjing Normal University Nanjing210023 China National Laboratory of Solid State Microstructures Nanjing University Nanjing210093 China

MSC Codes 68P25 (Primary) 68W10 (Secondary)Due to the strong correlation between adjacent pixels, most image encryption schemes perform multiple rounds of confusion and diffusion to protect the image against attacks. Such operations, however, are time-consuming, cannot meet the real-time requirements of video encryption. Existing works, therefore, realize video encryption by simplifying the encryption process or encrypting specific parts of video frames, which results in lower security compared to image encryption. To solve the problem, this paper proposes a real-time chaotic video encryption strategy based on multithreaded parallel confusion and diffusion. It takes a video as the input, splits the frame into subframes, creates a set of threads to simultaneously perform five rounds of confusion and diffusion operations on corresponding subframes, and efficiently outputs the encrypted frames. The encryption speed evaluation shows that our method significantly improves the confusion and diffusion speed, realizes real-time 480 × 480, 576 × 576, and 768 × 768 24FPS video encryption using Intel Core i5-1135G7, Intel Core i7-8700, and Intel Xeon Gold 6226R, respectively. The statistical and security analysis prove that the deployed cryptosystems have outstanding statistical properties, can resist attacks, channel noise, and data loss. Compared with previous works, to the best of our knowledge, the proposed strategy achieves the fastest encryption speed, and realizes the first real-time chaotic video encryption based on multi-round confusion-diffusion architecture, thus, providing a more secure and feasible solution for practical applications and related research. © 2023, CC BY.

关键词： Cryptography

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A review of big data technology and its application in cancer care

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Computers in Biology and Medicine 2024年 176卷 108577-108577页

作者： Xiao, Tianyun Kong, Shanshan Zhang, Zichen Hua, Dianbo Liu, Fengchun Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing101149 China

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future. © 2024 Elsevier Ltd

关键词： Big data

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CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and analysis

arXiv

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arXiv 2023年

作者： Guo, Di Li, Sijin Liu, Jun Tu, Zhangren Qiu, Tianyu Xu, Jingjing Feng, Liubin Lin, Donghai Hong, Qing Lin, Meijin Lin, Yanqin Qu, Xiaobo School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China The Key Laboratory for Chemical Biology of Fujian Province MOE Key Laboratory of Spectrochemical Analysis & Instrumentation College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China Department of Applied Marine Physics and Engineering College of Ocean and Earth Sciences Xiamen University Xiamen361102 China The China Mobile Group Xiamen361005 China

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://***/***. © 2023, CC BY.

关键词： Deep learning

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Pioneering case: Robot-assisted remote radical distal gastrectomy for gastric cancer based on 5G communication technology

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Intelligent Surgery 2024年第1期7卷 22-26页

作者： Honghai Guo Yuan Tian Jia Shi Peigang Yang Jiaxuan Yang Pingan Ding Xuefeng Zhao Zhidong Zhang Qun Zhao The Third Department of Surgery The Fourth Hospital of Hebei Medical University Shijiazhuang Hebei 050011 China Hebei Provincial Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer Shijiazhuang Hebei 050011 China Big Data Analysis and Mining Application for Accurate Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research Center Shijiazhuang Hebei 050011 China Department of Academic Research The First Affiliated Hospital of Shandong First Medical University Jinan Shandong 250014 China

Background The aim of this study is to describe technical considerations and short-term outcomes of the first officially published robot-assisted remote radical distal gastrectomy for gastric cancer with 5G technology. Methods A 51-year-old gastric cancer patient with clinical stage T2N0M0 underwent 5G-enabled robot-assisted remote radical distal gastrectomy on September 11, 2023 at the Third Department of Surgery, the Fourth Hospital of Hebei Medical University. Results The surgery procedure was successfully performed with an average total intraoperative delay of 225 ms, average round-trip delay of 30 ms and no packet loss rate. The operative time was 140 min and intraoperative blood loss was 20 ml. The patient was discharged on postoperative day 6 without any postoperative complications. Conclusion This case has preliminarily verified the feasibility and safety of 5G-enabled robot-assisted remote radical distal gastrectomy for patients with gastric cancer. The initial study fills in the gaps in related fields and lays a solid foundation for subsequent research. To verify its superior operative outcomes, further clinical trials are needed.

关键词： 5G technology Robotic surgery Radical gastrectomy Remote surgery

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Internal drivers of the global pandemic of the Omicron variants of SARS-CoV-2

Medicine Advances

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Medicine Advances 2024年第3期2卷 262-273页

作者： Yilin Niu Wuxiu Quan Yunyi Li Ziying Chen Yunfei Gao School of Public Health Southern Medical University Guangzhou Guangdong China Medical Big Data Center Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou Guangdong China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou Guangdong China School of Software Engineering South China University of Technology Guangzhou Guangdong China

Background The high transmissibility of the Omicron variants of severe acute respiratory syndrome coronavirus 2 continues to impose a significant burden on public health systems worldwide. Continuous mutations in the virus challenge the efficacy of vaccines and prior immunity from other variants, posing a severe threat to human health. Therefore, there is an urgent need to predict mutations of the Omicron variants and identify key factors influencing their spread. This study investigated critical amino acid mutations of the Omicron variants using epidemiological data and the mechanisms underlying their transmission. The aim was to understand the key factors driving the spread of the Omicron variants and provide insights for effective control and prevention strategies. Methods A total of 488,646 Omicron cases recorded between December 2021 and February 2023 were analyzed using a sliding time window and the Epi Score model, which has high accuracy for the identification of mutation sites. MutPred2, PolyPhen2 and VarSite tools were used to predict future mutations and identify factors driving the prevalence of the Omicron variants. Results Epi Scores showed fluctuating patterns at mutation sites, highlighting N969K, Y505H, N764K, T478K, and S371F mutations on the spike protein as significant for future prevention efforts. The spread of the Omicron variants was linked to changes in the viral entry pathway and improved angiotensin-converting enzyme 2 (ACE2) binding and immune evasion tactics. Conclusions The findings of this study reveal trends in the evolution of the Omicron variants, including altered cell entry, increased affinity for ACE2, and evasion of the immune system. These factors are critical for understanding the global spread of the Omicron variants and developing effective control strategies.

关键词： amino acids coronavirus immune evasion mutation public health SARS-CoV-2 spike glycoprotein

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