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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Integrated Multi-Omics Analysis Identities a Machine Learning-Derived Signature for Predicting Prognosis and Therapeutic Vulnerability in Clear Cell Renal Cell Carcinoma

SSRN

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SSRN 2024年

作者： Chi, Shengqiang Ma, Jing Ding, Yiming Lu, Zeyi Zhou, Zhenwei Wang, Mingchao Li, Gonghui Chen, Yuanlei Research Center for Data Hub and Security Zhejiang Laboratory Hangzhou311121 China Department of Urology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou310016 China The Engineering Research Center of EMR and Intelligent Expert System Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China

Background: Clear cell renal cell carcinoma (ccRCC) shows considerable variation within and between tumors, presents varying treatment responses among patients, possibly due to molecular distinctions. Methods: To address this complexity, we constructed an integrative multi-omics analysis using 10 clustering algorithms on ccRCC patient data. Afterwards, we applied bootstrapping in univariate Cox regression and the Boruta algorithm to pinpoint clinically relevant genes. Based on this, we developed a robust Prognosis and therapeutic vulnerability signature (PTVS) using seven machine learning algorithms, capable of predicting patient prognosis and drug ***: Our analysis revealed two distinct ccRCC subtypes with differential prognostic implications, notably identifying subtype 2 with poorer outcomes. Patients in the low PTVS group exhibited superior prognosis statistics and an augmented sensitivity to immunotherapy, features consistent with a 'hot tumor' phenotype. Conversely, individuals within the high PTVS group exhibited diminished prognosis statistic and restricted advantages from immunotherapy. Importantly, the PTVS holds future potential as a notable biomarker for guiding personalized treatment strategies, with four prospective targets (CTSK, XDH, PKMYT1, and EGLN2) indicating therapeutic promise in patients scoring high on PTVS. Conclusions: The integrative analysis of multi-omics data profoundly enhances the molecular stratification of ccRCC, underscoring far-reaching impact of such comprehensive profiling on its therapeutic strategies. © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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III-V/Si hybrid external-cavity laser utilizing TM mode in the passive photonic circuit 27

III-V/Si hybrid external-cavity laser utilizing TM mode in t...

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27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022

作者： Guo, Yuyao Li, Xinhang Jin, Minhui Lu, Liangjun Xie, Jingya Chen, Jianping Zhou, Linjie Shanghai Institute for Advanced Communication and Data Science State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation Center University of Shanghai for Science and Technology Shanghai Key Lab of Modern Optical System Shanghai200093 China

We present a III-V/Si hybrid laser using the silicon waveguide TM mode to reduce the propagation loss, backscattering, and two-photon absorption. The TM-mode laser has superior performance than the TE-mode laser. ... 详细信息

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Photons

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FuXi Weather: A data-to-forecast machine learning system for global weather

arXiv

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arXiv 2024年

作者： Sun, Xiuyu Zhong, Xiaohui Xu, Xiaoze Huang, Yuanqing Li, Hao Neelin, J. David Chen, Deliang Feng, Jie Han, Wei Wu, Libo Qi, Yuan Shanghai Academy of Artificial Intelligence for Science Shanghai200232 China Artificial Intelligence Innovation and Incubation Institute Fudan University Shanghai200433 China School of Atmospheric Physics Nanjing University of Information Science and Technology Nanjing210044 China Earth System Modeling and Prediction Centre China Meteorological Administration Beijing100081 China Department of Atmospheric and Oceanic Sciences University of California Los Angeles90095 United States Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing100084 China University of Gothenburg Gothenburg Sweden Department of Atmospheric and Oceanic Sciences Institute of Atmospheric Sciences Fudan University Shanghai200433 China School of Data Science Fudan University Shanghai200433 China Institute for Big Data Fudan University Shanghai200433 China MOE Laboratory for National Development and Intelligent Governance Fudan University Shanghai200433 China

Weather forecasting traditionally relies on numerical weather prediction (NWP) systems that integrates global observational systems, data assimilation (DA), and forecasting models. Despite steady improvements in forecast accuracy over recent decades, further advances are increasingly constrained by high computational costs, the underutilization of vast observational datasets, and the challenges of obtaining finer resolution. These limitations, alongside the uneven distribution of observational networks, result in global disparities in forecast accuracy, leaving some regions vulnerable to extreme weather. Recent advances in machine learning present a promising alternative, providing more efficient and accurate forecasts using the same initial conditions as NWP. However, current machine learning models still depend on the initial conditions generated by NWP systems, which require extensive computational resources and expertise. Here we introduce FuXi Weather, a machine learning weather forecasting system that assimilates data from multiple satellites. Operating on a 6-hourly DA and forecast cycle, FuXi Weather generates reliable and accurate 10-day global weather forecasts at a spatial resolution of 0.25◦. FuXi Weather is the first system to achieve all-grid, all-surface, all-channel, and all-sky DA and forecasting, extending skillful forecast lead times beyond those of the European Centre for Medium-range Weather Forecasts (ECMWF) high-resolution forecasts (HRES) while using significantly fewer observations. FuXi Weather consistently outperforms ECMWF HRES in observation-sparse regions, such as central Africa, demonstrating its potential to improve forecasts where observational infrastructure is limited. © 2024, CC BY.

关键词： Weather forecasting

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

arXiv

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arXiv 2024年

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Brain Tumor Detection in MRI Images Using Image Processing Techniques 4

Brain Tumor Detection in MRI Images Using Image Processing T...

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4th International Symposium on Agents, Multi-Agents systems and Robotics, ISAMSR 2021

作者： Hashan, Antor Mahamudul Agbozo, Ebenezer Al-Saeedi, Adnan Adhab K. Saha, Srishti Haidari, Abdullah Rabi, Mohammad Naser Fazel Ural Federal University Department of Intelligent Information Technologies Yekaterinburg Russia Ural Federal University Department of Big Data Analysis and Methods of Video Analysis Yekaterinburg Russia National Research University MPEI Department of Applied Mathematics and Artificial Intelligence Moscow Russia National University of Science and Technology MISiS Department of IT and Automated Control System Moscow Russia

ISBN: (纸本)9781728166544

Biomedical technology plays an important role in the diagnosis and treatment of life-threatening diseases. Recently, one of the most common deadly diseases is brain tumors. The process of treating brain tumors depends on the experience and knowledge of physicians. For this reason, an automated tumor detection system is extremely important for radiologists and doctors to assist in the detection of brain tumors. Accurate analysis of magnetic resonance imaging (MRI) scans is needed to detect brain tumors and this paper proposes an effective method to predicting brain tumors by using convolution neural networks (CNNs). Methods include data augmentation and image pre-processing techniques that cut out the dark edges from images by finding extreme contours. The images are then normalized for a suitable scale. An Adaptive Moment Estimation (Adam) Optimizer has been added to expedite the training process in our network. MRI image dataset (own created Kaggle dataset) is used to train and evaluate the model to get maximized accuracy and the F1 score is calculated to evaluate the performance of the maximized model. © 2021 IEEE.

关键词： Magnetic resonance imaging

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A Multi-Stage Deep Learning Pipeline for Crack Detection and Segmentation in High-Speed Rail Brake Discs

A Multi-Stage Deep Learning Pipeline for Crack Detection and...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Ching-Heng Cheng Yu-Fan Lin Chia-Ming Lee Chih-Chung Hsu Yu-Lin Shih Ting Chin Department of Statistics Institute of Data Science National Cheng Kung Univ. Tainan Taiwan Department of Intelligent Technology Systems Institute of Miin Wu School of Computing National Cheng Kung Univ Tainan Taiwan Institute of Intelligent Systems College of Artificial Intelligence National Yang Ming Chiao Tung Univ. Hsinchu Taiwan Innovation Logistics System Technology Dept. Smart Logistics & Supply Chain Integration Service Division Service Systems Technology Center Industrial Technology Research Institute Hsinchu Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

The braking system of high-speed rail (HSR) is critical for passenger safety, but prolonged use of brake discs can lead to cracks, posing significant risks. Traditional manual inspection methods are time-consuming and labor-intensive. To address these challenges, we propose a multi-stage crack detection and segmentation system, a cascade mode to progressively identify cracks, leveraging deep learning techniques. The system includes automated circle detection, region cropping, and crack detection and segmentation to accurately identify cracks. Using a real-world dataset collected from HSR brake discs, our approach significantly improves data preprocessing efficiency and model performance. Furthermore, we evaluate the performance on un-seen data and compare our crack detection framework with other deep learning models. The experimental results demonstrate the system's effectiveness which is superior to other deep learning models, highlighting its potential for real-world deployment in HSR maintenance.

关键词： Deep learning Image segmentation Motion segmentation Computational modeling Pipelines Rail transportation data models Maintenance Safety Brakes

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

arXiv

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arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the Software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

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Attention-effective multiple instance learning on weakly stem cell colony segmentation

arXiv

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arXiv 2022年

作者： Yudistira, Novanto Kavitha, Muthu Subash Rajan, Jeny Kurita, Takio Intelligent System Lab Faculty of Computer Science Brawijaya University Indonesia School of Information and Data Sciences Nagasaki University Nagasaki Japan Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan

The detection of induced pluripotent stem cell (iPSC) colonies often needs the precise extraction of the colony features. However, existing computerized systems relied on segmentation of contours by preprocessing for classifying the colony conditions were task-extensive. To maximize the efficiency in categorizing colony conditions, we propose a multiple instance learning (MIL) in weakly supervised settings. It is designed in a single model to produce weak segmentation and classification of colonies without using finely labeled samples. As a single model, we employ a U-net-like convolution neural network (CNN) to train on binary image-level labels for MIL colonies classification. Furthermore, to specify the object of interest we used a simple post-processing method. The proposed approach is compared over conventional methods using five-fold cross-validation and receiver operating characteristic (ROC) curve. The maximum accuracy of the MIL-net is 95%, which is 15% higher than the conventional methods. Furthermore, the ability to interpret the location of the iPSC colonies based on the image level label without using a pixel-wise ground truth image is more appealing and cost-effective in colony condition recognition. © 2022, CC BY.

关键词： Learning systems

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