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arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

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A survey of facial expression recognition based on deep learning

A survey of facial expression recognition based on deep lear...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Heng Wei Zhi Zhang Collage of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

Facial expression recognition is the key research direction in many fields such as machine vision, pattern recognition and artificial intelligence. lt has become a research hotspot of many scholars and experts. This paper gives a comprehensive overview of deep expression recognition from image preprocessing, common expression recognition networks, and common expression databases. Then we review the current design and training strategies of deep neural networks based on static images and dynamic images, and discuss their advantages and limitations. Finally, the future challenges and corresponding opportunities in the field are analyzed, as well as the future direction of deep learning expression recognition systems.

关键词： Deep learning Training Image recognition databases Face recognition Neural networks Training data

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Automatic Generation Method of Ancient Poetry Based on LSTM

Automatic Generation Method of Ancient Poetry Based on LSTM

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Hanshuang Zhang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

This paper mainly focuses on the literary genre of ancient poetry with a certain rhythm and cadence, and proposes a novel automatic generation model of ancient poetry. The model uses about 300,000 Tang poems and Song poems as training data, uses One-hot encoding to process data, and uses long short-term memory networks (LSTMs) to learn the semantics of ancient poetry texts and conduct research across a single RNN structure. According to the user's requirements, the model can automatically calculate the most relevant coherent words in the context of the selected context, and generate common ancient poetry and specified words. The method of BLEU automatic evaluation and manual evaluation finally demonstrates the effectiveness of the experiment.

关键词： Industrial electronics Semantics Training data Manuals Rhythm Encoding Periodic structures

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Spent bleaching earth as a source of oil and catalyst for renewable hydrocarbon conversion

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Journal of Environmental Chemical engineering 2025年第3期13卷

作者： Ainnur Rusydina Ab Halim Zeni Rahmawati Mohamad Nasir Mohamad Ibrahim Chua Yong Shen Gnanasikamani Balaji Soh Kheang Loh Nuryono Nuryono Wan Nazwanie Wan Abdullah School of Chemical Sciences Universiti Sains Malaysia 11800 Penang Malaysia Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo Surabaya 60111 Indonesia Department of Mechanical Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu 603203 India Energy and Environment Unit Engineering and Processing Division Malaysian Palm Oil Board 6 Persiaran Institusi Bandar Baru Bangi Kajang Selangor 43000 Malaysia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Bulaksumur Yogyakarta 55281 Indonesia

Spent bleaching earth (SBE) is a solid waste generated by the vegetable oil refining industries. It contains entrapped crude oil, making its disposal challenging for industries. In this study, valorization of SBE for catalytic biodiesel conversion using alginate-SBE catalyst was explored. The alginate-SBE catalyst was synthesized using a simple impregnation method. It was characterized by Brunauer-Emmett-Teller (BET), X-ray diffraction (XRD), field emission scanning electron microscopy with energy dispersive X-ray (FESEM- EDX) and temperature programmed desorption of carbon dioxide (CO 2/ TPD) to assess its structural and morphological properties. The recovered bleached oil was optimally transesterified at temperature of 80 °C, oil-to-methanol ratio of 1:22, and catalyst loading of 9 wt% within 2 h production, achieving a maximum biodiesel yield of 93.35 %. Reusability tests demonstrated that the alginate-SBE catalyst can retain > 90 % of initial activity after five cycles, highlighting its potential for practical applications. Gas chromatography-mass spectrometry (GC-MS) results indicated potential conversion of biodiesel to renewable hydrocarbons (C8-C15). This study highlights the dual benefits of utilizing industrial waste for both feedstock and catalyst support, promoting a cleaner and economically viable approach for biofuel generation.

关键词： Alginate Biodiesel Hydrocarbon Spent bleaching earth (SBE) Waste oils

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Probing Globular Cluster with MeerKAT and FAST: A Pulsar Polarization Census

arXiv

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arXiv 2025年

作者： Zhang, Lei Abbate, Federico Li, Di Possenti, Andrea Bailes, Matthew Ridolfi, Alessandro Freire, Paulo C.C. Ransom, Scott M. Zhang, Yong-Kun Guo, Meng Ni, Meng-Meng Hu, Jia-Le Feng, Yi Wang, Pei Zhang, Jie Zhi, Qi-Jun National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Centre for Astrophysics and Supercomputing Swinburne University of Technology VIC3122 Australia INAF Osservatorio Astronomico di Cagliari Via della Scienza 5 SelargiusI-09047 Italy Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Astronomy Tsinghua University Beijing100190 China Swinburne University of Technology VIC3122 Australia Faculty of Physics University of Bielefeld Bielefeld33501 Germany National Radio Astronomy Observatory 520 Edgemont Road Charlottesville22903 United States National Supercomputing Center in Jinan Qilu University of Technology Jinan250103 China Jinan Institute of Supercomputing Technology 28666 East Jingshi Road Jinan250103 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Institute for Astronomy School of Physics Zhejiang University Hangzhou310027 China College of Physics and Electronic Engineering Qilu Normal University Jinan250200 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China

Only one globular cluster (GC), 47 Tuc, has been found to containintracluster medium, with an electron density 100 times higher than that of theISM in its vicinity. The characteristics of this intracluster medium areclosely related to GC evolution and the compact objects within. However,significant knowledge gaps remain regarding the ionized gas content of GCs,particularly in Galactic halo clusters. We carried out a polarization census ofGC pulsars using MeerKAT and FAST. This first combined effort of observationsfrom these two major radio telescopes resulted in high signal-to-noise ratio,full polarization pulse profiles for 43 pulsars in 8 GCs, doubling the numberof rotation measures (RMs) known in these clusters. The accuracy of dispersionmeasures (DMs) was improved by a factor of 8 compared to previous *** intracluster medium was found, and at least two halo GCs showed morestringent upper limits on electron density than that detected in 47 Tuc. Thesurprising barrenness of GCs suggests effective gas removal mechanisms, such asstrong winds from millisecond pulsars and/or ionizing radiation from post-AGBstars and young white dwarfs. Copyright © 2025, The Authors. All rights reserved.

关键词： White dwarfs

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3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping

arXiv

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arXiv 2022年

作者： Chen, Zhifeng Liao, Congyu Cao, Xiaozhi Poser, Benedikt A. Xu, Zhongbiao Lo, Wei-Ching Wen, Manyi Cho, Jaejin Tian, Qiyuan Wang, Yaohui Feng, Yanqiu Xia, Ling Chen, Wufan Liu, Feng Bilgic, Berkin School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou China Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School CharlestownMA United States Department of Data Science and AI Faculty of IT Monash University ClaytonVIC Australia Department of Radiology Stanford University Stanford CA United States Maastricht Brain Imaging Center Faculty of Psychology and Neuroscience University of Maastricht Netherlands Department of Radiotherapy Cancer Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Science Guangzhou China Siemens Medical Solutions BostonMA United States Department of Chemical Pathology The Chinese University of Hong Kong Hong Kong Division of Superconducting Magnet Science and Technology Institute of Electrical Engineering Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence Key Laboratory of Mental Health of the Ministry of Education Southern Medical University Guangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD Australia Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States

Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T2* mapping. Methods: 3D-Blip-Up and -Down Acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D GRE-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permits T2* mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction. Results: Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance the image quality. For T2* mapping, T2* values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis. Conclusions: The proposed technique enables rapid 3D distortion-free high-resolution imaging and T2* mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3 T and 9 s on a 7 T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brain T2* mapping in 47 s at 1.1×1.1×1.0 mm3 resolution. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Material image segmentation with the machine learning method and complex network method

Material image segmentation with the machine learning method...

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MRS Advances

作者： Lai, Chuanbin Song, Leilei Han, Yuexing Li, Qian Gu, Hui Wang, Bing Qian, Quan Chen, Wei School of Computer Engineering and Science Shanghai University 99 Shangda Road Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University 99 Shangda Road Shanghai200444 China Material Genome Institute and School of Materials Science and Engineering Shanghai University 99 Shangda Road Shanghai200444 China Key Laboratory of Power Beam Processing AVIC Manufacturing Technology Institute Beijing100024 China

The study of the relationship among the manufacturing process, the structure and the property of materials can help to develop the new materials. The material images contain the microstructures of materials, therefore, the quantitative analysis for the material images is the important means to study the characteristics of material structures. Generally, the quantitative analysis for the material microstructures is based on the exact segmentation of the materials images. However, most material microstructures are shown with various shapes and complex textures in images, and they seriously hinder the exact segmentation of the component elements. In this research, machine learning method and complex networks method are adopted to the challenge of automatic material image segmentation. Two segmentation tasks are completed: on the one hand, the images of the titanium alloy are segmented based on the pixel-level classification through feature extraction and machine learning algorithm;on the other hand, the ceramic images are segmented with the complex networks theory. In the first task, texture and shape features near each pixel in titanium alloy image are calculated, such as Gabor filters, Hu moments and GLCM (Gray-Level Co-occurrence Matrix) etc. The feature vector for the pixel can be obtained by arraying these features. Then, classification is performed with the random forest model. Once each pixel is classified, the image segmentation is completed. In the second task, a complex network structure is built for the ceramic image. Then, a clustering algorithm of complex network is used to obtain network connection area. Finally, the clustered network structure is mapped back to the image and getting the contours among the component elements. The experimental results demonstrate that these methods can accurately segment material images. Copyright © Materials Research Society 2019.

关键词： Complex networks

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Corrigendum to “GLRM: Logical pattern mining in the case of inconsistent data distribution based on multigranulation strategy” [Int. J. Approx. Reason. 143 (2022) 78–101]

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International Journal of Approximate Reasoning 2022年 151卷 271-271页

作者： Qian Guo Yuhua Qian Xinyan Liang Institute of Big Data Science and Industry Shanxi University Taiyuan 030006 Shanxi China Engineering Research Center for Machine Vision and Data Mining of Shanxi Province Taiyuan 030006 Shanxi China School of Computer and Information Technology Shanxi University Taiyuan 030006 Shanxi China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan 030006 Shanxi China

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Deploying Cationic Cellulose Nanofiber Confinement to Enable High Iodine Loadings Towards High Energy and High-Temperature Zn-I2Battery

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Angewandte Chemie 2023年第5期136卷

作者： Zhenglin Li Wenwen Cao Tao Hu Yichan Hu Rong Zhang Huilin Cui Funian Mo Chaozheng Liu Chunyi Zhi Guojin Liang Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen 518055 China Co-Innovation Center of Efficient Processing and Utilization of Forest Resources College of Materials Science and Engineering Nanjing Forestry University Nanjing 210000 China Contribution: Data curation (lead) Investigation (lead) Methodology (lead) Writing - original draft (lead) School of Chemistry and Chemical Engineering University of Jinan Jinan 250022 China Contribution: Conceptualization (equal) Data curation (lead) Investigation (lead) Methodology (lead) School of Materials Science and Engineering Key Laboratory of Structure and Functional Regulation of Hybrid Materials Ministry of Education Anhui University Hefei 230601 China Contribution: Data curation (equal) Formal analysis (equal) Investigation (supporting) Methodology (supporting) Contribution: Conceptualization (equal) Data curation (supporting) Formal analysis (equal) Investigation (equal) Department of Materials Science and Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong 999077 Hong Kong Contribution: Data curation (equal) Investigation (equal) Methodology (equal) Contribution: Conceptualization (supporting) Data curation (supporting) Formal analysis (equal) Investigation (equal) Shenzhen Key Laboratory of Flexible Printed Electronics Technology Center Harbin Institute of Technology Shenzhen Guangdong 518055 China Contribution: Conceptualization (lead) Data curation (lead) Resources (lead) Supervision (equal) Contribution: Conceptualization (equal) Data curation (lead) Investigation (lead) Supervision (equal) Contribution: Supervision (lead) Visualization (lead) Writing - review & editing (lead) Contribution: Conceptualization (lead) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead)

High iodine loading and high-temperature adaptability of the iodine cathode are prerequisites to achieving high energy density at full battery level and promoting the practical application for the zinc-iodine (Zn-I 2 ) battery. However, it would aggravate the polyiodide shuttle effect when employing high iodine loading and working temperature. Here, a sustainable cationic cellulose nanofiber (cCNF) was employed to confine the active iodine species through strong physiochemical adsorption to enlarge the iodine loading and stabilize it even at high temperatures. The cCNF could accommodate dual-functionality by enlarging the iodine loading and suppressing the polyiodide shuttle effect, owing to the unique framework structure with abundant surface positive charges. As a result, the iodine cathode based on the cCNF could deliver high iodine mass loading of 14.1 mg cm −2 with a specific capacity of 182.7 mAh g −1 , high areal capacity of 2.6 mAh cm −2 , and stable cycling over 3000 cycles at 2 A g −1 , thus enabling a high energy density of 34.8 Wh kg −1 and the maximum power density of 521.2 W kg −1 at a full Zn-I 2 battery level. In addition, even at a high temperature of 60 °C, the Zn-I 2 battery could still deliver a stable cycling.

关键词： Cationic Cellulose High- Temperature Battery Iodine Shuttle-Free Zn-I2 Battery

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