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Research Square 2021年

作者： Lansky, Jan Mohammadi, Mokhtar Mohammed, Adil Hussein Taher Karim, Sarkhel H. Rashidi, Shima Rahmani, Amir Masoud Hosseinzadeh, Mehdi Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Information Technology Lebanese French University Kurdistan Region Erbil Iraq Department of Communication and computer engineering Cihan university-Erbil Kurdistan Region Erbil Iraq Computer Department College of Science University of Halabja Halabja Iraq Sulaimani Polytechnic University Technical College of Informatics Computer Networks Department Sulaymaniyah Iraq Department of Computer Science University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of computer science Khazar University Baku Azerbaijan Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran

Mobile Edge Computing (MEC) is an interesting technology aimed at providing various processing and storage resources at the edge of the Internet of things (IoT) networks. However, MECs contain limited resources, and they should be managed effectively to improve resource utilization. Workflow scheduling is a process that tries to map the workflow tasks to the most proper set of computing resources regarding some objectives. For this purpose, this paper presents DBOA, a discrete version of the Butterfly Optimization Algorithm (BOA) that applies the Levy flight to improve its convergence speed and prevent the local optima problem. Then, DBOA is applied for DVFS-based data-intensive workflow scheduling and data placement in MEC environments. This scheme also employs the HEFT algorithm's task prioritization method to find the task execution order in the scientific workflows. For evaluating the performance of the proposed scheduling scheme, extensive simulations are conducted on various well-known scientific workflows with different sizes. The obtained experimental results indicate that this method can outperform other algorithms regarding energy consumption, data access overheads, etc. © 2021, CC BY.

关键词： Optimization

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Research on Image Super-resolution Reconstruction Based on Deep Learning

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Journal of Physics: Conference Series 2021年第4期1802卷

作者： Yaobin Zhang Zhiheng Zhang Tingle Li Computer science and engineering South China University of Technology Guangzhou Guangdong 510006 Department of Electrical Engineering Shandong University of Science and Technology Jinan Shandong 250031 College of computer science and technology Tianjin Polytechnic University Tianjin 300387

Image super-resolution processing technology refers to the technology of reconstructing high-resolution images by using low-resolution images with mutual displacement of multiple frames of the same scene. At present, image super-resolution processing technology is widely used in image reconstruction in various fields, such as: medical images, remote sensing images, robot vision, etc. In recent years, deep learning has also been widely used in various fields, especially in computer vision and speech processing. Since image super-resolution reconstruction the method of deep learning is introduced into the technology. Due to the good performance advantages of the deep learning method, it has become an important method for the research of image superresolution reconstruction technology. In this paper, the current status of domestic and foreign research on image super-resolution reconstruction is first simplified. Then, the theory of deep learning was explained, mainly the artificial neural network theory, the related theory of super-resolution reconstruction based on deep learning, and the method of image super-resolution reconstruction based on deep learning.

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Impact of land use and land cover changes on carbon storage in rubber dominated tropical Xishuangbanna,South West China

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Ecosystem Health and Sustainability 2021年第1期7卷 121-134页

作者： Chaya Sarathchandra Yirga Alemu Abebe Fiona Ruth Worthy Iresha Lakmali Wijerathne Huaixia Ma Bi Yingfeng Guo Jiayu Huafang Chen Qiaoshun Yan Yanfei Geng Dayani SWeragoda Li-Li Li Yang Fengchun Sriyani Wickramasinghe Jianchu Xu State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China CAS Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of SciencesKunming 650201YunnanChina University of Chinese Academy of Sciences Beijing 100049China Department of Biological Science Faculty of Applied SciencesRajarata University of Sri LankaMihintale Sri Lanka State Key Laboratory of Super Lattices and Microstructures Institute of SemiconductorsChinese Academy of SciencesBeijing China Department of Electrical and computer engineering Faculty of Electrical EngineeringInstitute of TechnologyHawassa UniversityHawassaEthiopia World Agroforestry Centre(ICRAF) East and Central Asia Regional OfficeKunming YunnanChina Ailaoshan Station for Subtropical Forest Ecosystem Studies Chinese Academy of SciencesJingdongYunnan College of Tea Science Guizhou UniversityGuiyang China Carbon Consulting Company Colombo Sri Lanka Center for Integrative Conservation Xishuangbanna Tropical Botanical GardenChinese Academy of SciencesMenglaYunnanChina Yibin Vocational and Technical College YibinSichuanChina School of Chinese Medicinal Material Resources Guangdong Pharmaceutical UniversityChina

Land use and land cover(LULC)play a significant role in carbon ***-China accounts for~65%of China’s carbon *** Xishuangbanna(South-China),rubber is expanding rapidly creating an urgent need to understand and monitor LULC change and how spatial variation affects carbon storage(CS).This is vital for the formation and implementation of better land use management *** studied LULC changes of 22-year period;addressing how these changes have affected the *** quantified LULC changes between1988 and 2010 using remote sensing methods and calculated CS changes using *** showed that between 1988 and 2010,the rate of deforestation accelerated to 203.2 km^(2)y^(−1)and~23%of forest were *** of natural forest to rubber was responsible for 78%of this *** expansion rate was 153.4 km^(2)y^(−1).Changes to LULC drove a temporal CS reduction 0.223 Tg C/km^(2).Local stakeholders have strong economic interest in converting land to more profitable *** efforts is required to control land conversion through new policies and incentives to retain natural *** of specific potential land use change will be required to avoid promoting the conversion of high carbon storage land uses to low carbon storage land uses.

关键词： Carbon storage deforestation economic plantations ecosystem services land cover changes land use

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Nonreciprocal transport in a bilayer of MnBi2Te4 and Pt

arXiv

引用

arXiv 2022年

作者： Ye, Chen Xie, Xiangnan Lv, Wenxing Huang, Ke Yang, Allen Jian Jiang, Sicong Liu, Xue Zhu, Dapeng Qiu, Xuepeng Tong, Mingyu Zhou, Tong Hsu, Chuang-Han Chang, Guoqing Lin, Hsin Li, Peisen Yang, Kesong Wang, Zhenyu Jiang, Tian Wang, Xiao Renshaw Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link 637371 Singapore State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Physics Laboratory Industrial Training Center Shenzhen Polytechnic Guangdong Shenzhen518055 China Department of NanoEngineering Program of Chemical Engineering University of California La Jolla San DiegoCA92093-0448 United States Program of Materials Science and Engineering University of California La Jolla San DiegoCA92093-0418 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao266000 China Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Insitute of Physics Academia Sinica Taipei115229 Taiwan College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology Academy of Military Sciences PLA China Beijing100010 China Beijing Academy of Quantum Information Sciences Beijing100084 China Beijing Institute for Advanced Study National University of Defense Technology Changsha410073 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Ave 639798 Singapore

MnBi2Te4 (MBT) is the first intrinsic magnetic topological insulator with the interaction of spin-momentum locked surface electrons and intrinsic magnetism, and it exhibits novel magnetic and topological phenomena. Recent studies suggested that the interaction of electrons and magnetism can be affected by the Mn-doped Bi2Te3 phase at the surface due to inevitable structural defects. Here we report an observation of nonreciprocal transport, i.e. current-direction-dependent resistance, in a bilayer composed of antiferromagnetic MBT and nonmagnetic Pt. The emergence of the nonreciprocal response below the Néel temperature confirms a correlation between nonreciprocity and intrinsic magnetism in the surface state of MBT. The angular dependence of the nonreciprocal transport indicates that nonreciprocal response originates from the asymmetry scattering of electrons at the surface of MBT mediated by magnon. Our work provides an insight into nonreciprocity arising from the correlation between magnetism and Dirac surface electrons in intrinsic magnetic topological insulators. © 2022, CC BY.

关键词： Magnetism

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GAMMA Challenge: Glaucoma grAding from Multi-Modality imAges

arXiv

引用

arXiv 2022年

作者： Wu, Junde Fang, Huihui Li, Fei Fu, Huazhu Lin, Fengbin Li, Jiongcheng Huang, Yue Yu, Qinji Song, Sifan Xu, Xinxing Xu, Yanyu Wang, Wensai Wang, Lingxiao Lu, Shuai Li, Huiqi Huang, Shihua Lu, Zhichao Ou, Chubin Wei, Xifei Liu, Bingyuan Kobbi, Riadh Tang, Xiaoying Lin, Li Zhou, Qiang Hu, Qiang Bogunović, Hrvoje Orlando, José Ignacio Zhang, Xiulan Xu, Yanwu State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China Singapore School of Informatics Xiamen University Xiamen China Shanghai Jiao Tong University Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Institute of High Performance Computing A*STAR Singapore Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin China School of Medical Technology Beijing Institute of Technology Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Weizhi Medical Technology Company Suzhou China École de Technologie Supérieure Montreal Montreal Canada DIAGNOS Inc. Quebec Canada Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Technology Co. Ltd. Shanghai China School of Information and Electronics Beijing Institute of Technology Beijing China Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology Medical University of Vienna Austria Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfill the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyze their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniqu

关键词： Grading

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Enhanced PEMFC parameter estimation using a hybrid gorilla troops optimizer and honey badger algorithm

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computers and Chemical engineering 2025年 201卷

作者： Aljaidi, Mohammad Agrawal, Sunilkumar P. Agrawal, Toshika R. Pandya, Sundaram B. Parmar, Anil Jangir, Pradeep Arpita Gulothungan, G. Alshammari, Muteb Jangid, Reena Department of Computer Science Faculty of Information Technology Zarqa University Zarqa 13110 Jordan Department of Electrical Engineering Government Engineering College Gujarat Gandhinagar 382028 India Department of Civil Engineering Government Polytechnic for Girls Ahmedabad 380009 India Department of Electrical Engineering Shri K.J. Polytechnic Bharuch 392 001 India Department of Electrical and Electronics Engineering J.J. College of Engineering and Technology Tamilnadu Tiruchirappalli India Innovation Center for Artificial Intelligence Applications Yuan Ze University Taoyuan 320315 Taiwan Applied Science Research Center Applied Science Private University Amman 11937 Jordan Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602 105 India Jadara University Research Center Jadara University Irbid Jordan Department of Electronics and Communication Engineering SRM Institute of Science and Technology SRM Nagar Kattankulathur Tamilnadu Chengalpattu 603203 India Department of Information Technology Faculty of Computing and Information Technology Northern Border University Arar Saudi Arabia Department of CSE Graphic Era Hill University Dehradun 248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun 248002 India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The optimization of proton exchange membrane fuel cell (PEMFC) parameters is crucial for improving performance, durability, and efficiency across various applications. However, existing metaheuristic algorithms face limitations such as slow convergence, susceptibility to local optima, and high computational costs. To address these issues, this study proposes a hybrid Artificial Gorilla Troops Optimizer and Honey Badger Algorithm (GTOHBA) to enhance PEMFC parameter estimation. The proposed algorithm integrates the efficient global search of GTO with the adaptive foraging behavior of HBA, ensuring better exploration-exploitation balance. The performance of GTOHBA was evaluated on six different PEMFC models: BCS 500 W, SR-12, Horizon H-12, Nedstack 600 W PS6, STD 250 W and Ballard Mark V. The results demonstrate that GTOHBA achieved the lowest sum of squared errors (SSEmin = 0.025493) with an exceptionally low standard deviation (SSESD = 8.35×10⁻⁶), outperforming other state-of-the-art optimization methods. Moreover, GTOHBA exhibited superior computational efficiency with the shortest runtime (0.179909 s), significantly reducing the computational burden compared to PSO and MFO. The theoretical analysis confirms that GTOHBA effectively mitigates issues of premature convergence and enhances parameter extraction accuracy through its hybrid structure. Practically, the algorithm enables precise modeling of PEMFC behavior, improving power output predictions with minimal absolute error (AE = 0.012913) and percentage relative error ( %RE = 0.061363 %). The key benefit for readers is the demonstration of a robust, fast, and highly accurate optimization framework for PEMFC parameter estimation. However, further research is needed to assess GTOHBA's adaptability under dynamic real-time conditions and large-scale fuel cell stacks.

关键词： Artificial gorilla troops optimizer (GTO) Honey badger algorithm (HBA) Metaheuristic optimization Parameter estimation Proton exchange membrane fuel cell (PEMFC) Sum of squared errors (SSE)

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Phonons and low thermal expansion in sodalite zinc borate Zn4B6O12X(X=O,S,Se)

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Physical Review B 2021年第17期104卷 174310-174310页

作者： Zhongsheng Wei Xingxing Jiang Anthony E. Phillips Zheshuai Lin Martin T. Dove School of Physical and Chemical Sciences Queen Mary University of London Mile End Road London E1 4NS United Kingdom Key Laboratory of Functional Crystals and Laser Technology of the Chinese Academy of Sciences Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing 100190 China College of Computer Science Sichuan University Chengdu 610065 China Department of Physics School of Sciences Wuhan University of Technology 205 Luoshi Road Hongshan district Wuhan Hubei 430070 China School of Mechanical Engineering Dongguan University of Technology 1st Daxue Road Songshan Lake Dongguan Guangdong 523000 China

We examine isotropic near-zero thermal expansion in the cubic materials Zn4B6O12X(X=O,S,Se) using density functional theory calculations of the lattice dynamics within the Grüneisen approximation. The calculated thermal expansion is in excellent agreement with experiment. The near-zero thermal expansion is shown to arise from a balance between two specific groups of vibrations that favor positive and negative thermal expansion, respectively. This gives a clear general indicator of how crystal structures can be engineered to give near-zero thermal expansion.

关键词： Crystal structure Lattice dynamics Thermal expansion Density functional calculations

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224-Gb/s 16QAM SV-DD Transmission Using Pilot-Assisted Polarization Recovery with Integrated Receiver

224-Gb/s 16QAM SV-DD Transmission Using Pilot-Assisted Polar...

引用

Optical Fiber Communication (OFC) Conference

作者： Sen Zhang Liwang Lu Linsheng Fan Bin Chen Tingting Zhang Tianjian Zuo Lei Liu Jin Tang Li Zeng Pengxin Chen Jiahao Huo Xian Zhou Alan Pak Tao Lau Chao Lu Liu Liu Changjian Guo Network Technology Laboratory Huawei Technologies Co. Ltd. Shenzhen China Guangdong Provincial Key Laboratory of Optical Information Materials and Technology South China Academy of Advanced Optoelectronics South China Normal University Guangzhou China State Key Laboratory for Modern Optical Instrumentation College of Optical Science and Engineering Centre for Optical & Electromagnetic Research International Research Center for Advanced Photonics Zhejiang University Hangzhou China B&P Laboratory Huawei Technologies Co. Ltd. Shenzhen China School of Computer & Communication Engineering Beijing University of Science and Technology Beijing China The Hong Kong Polytechnic University Hong Kong China The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen

We propose and experimentally demonstrate a low-complexity pilot-assisted analytical method for polarization demultiplexing in SV-DD systems, achieving a 224-Gb/s 16QAM transmission over 40-km SSMF and can tolerate up... 详细信息

ISBN: (纸本)9781943580866

关键词： Demultiplexing Receivers Optical fiber communication

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Enhancing Collaborative Inference on Heterogeneous Edge Devices via Adaptive Ensemble Knowledge Distillation

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IEEE Journal on Selected Areas in Communications 2025年

作者： Wu, Shangrui Li, Yupeng Wang, Wenhua Guo, Jianxiong Fan, Wentao Liu, Qin Jia, Weijia Yu, Shui Cao, Jiannong Wang, Tian Beijing Normal-Hong Kong Baptist University Guangdong Provincial/Zhuhai Key Laboratory of IRADS Department of Computer Science Zhuhai China Hong Kong Baptist University Hong Kong Hong Kong Hong Kong Baptist University Department of Interactive Media Hong Kong Hong Kong Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi'an China Hunan University College of Computer Science and Electronic Engineering Changsha China University of Technology Sydney School of Computer Science Sydney Australia The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

The integration of edge computing with deep neural networks (DNNs) is crucial for intelligent industrial cyber-physical systems. Typically, deploying DNNs on heterogeneous edge devices relies on methods like model compression and partitioning. However, these approaches often result in homogeneous models across devices. This homogeneity limits the collective capability of edge computing systems, particularly in terms of generalization to diverse data distributions and adaptation to dynamic industrial environments. In this work, we propose to treat each DNN on an edge device as an independent model, aggregating their capabilities via ensemble learning to enhance generalization and dynamic adaptability. To realize this, we introduce the Adaptive Ensemble Knowledge Distillation Framework (AEKDF), combining cloud-based model training with edge computing based collaborative inference. In the cloud, AEKDF develops an enhanced Born Again Network that generates diverse, lightweight models tailored to specific edge devices through knowledge distillation. This process ensures model diversity which is critical to effective ensemble learning. On the edge, AEKDF employs an adaptive ensemble technique that aggregates prediction logits across devices, enabling rapid adaptation to changing environments and maintaining inference efficiency. Our extensive evaluations conducted on a realistic prototype demonstrate the substantial boost in predictive performance achieved by our AEKDF, showcasing a 4% to 10% accuracy improvement on the CIFAR-100 compared to conventional single-model approaches, while maintaining low latency. © 1983-2012 IEEE.

关键词： Deep neural networks

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Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion

arXiv

引用

arXiv 2020年

作者： Chen, Cheng Dou, Qi Jin, Yueming Chen, Hao Qin, Jing Heng, Pheng-Ann Dept. of Computer Science and Engineering Chinese University of Hong Kong Department of Computing Imperial College Longdon Imsight Medical Technology Co. Ltd. China Centre for Smart Health School of Nursing Hong Kong Polytechnic University Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Chinese Academy of Sciences SIAT China

Accurate medical image segmentation commonly requires effective learning of the complementary information from multimodal data. However, in clinical practice, we often encounter the problem of missing imaging modalities. We tackle this challenge and propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities. Our network uses feature disentanglement to decompose the input modalities into the modality-specific appearance code, which uniquely sticks to each modality, and the modality-invariant content code, which absorbs multimodal information for the segmentation task. With enhanced modality-invariance, the disentangled content code from each modality is fused into a shared representation which gains robustness to missing data. The fusion is achieved via a learning-based strategy to gate the contribution of different modalities at different locations. We validate our method on the important yet challenging multimodal brain tumor segmentation task with the BRATS challenge dataset. With competitive performance to the state-of-the-art approaches for full modality, our method achieves outstanding robustness under various missing modality(ies) situations, significantly exceeding the state-of-the-art method by over 16% in average for Dice on whole tumor segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Tumors

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