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Impacts of spatially inconsistent permafrost degradation on streamflow in the Lena River Basin

Science China(Technological Sciences) 2024年第11期67卷 3559-3570页

作者： XUE ZeHuan WANG YiChu ZHAO Yi LI DongDeng BORTHWICK Alistair George Liam The Key Laboratory of Water and Sediment Sciences Ministry of EducationCollege of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China College of Water Sciences Beijing Normal UniversityBeijing 100875China State Key Laboratory of Hydraulics and Mountain River Engineering Sichuan UniversityChengdu 610065China State Environmental Protection Key Laboratory of All Materials Fluxes in River Ecosystems Peking UniversityBeijing 100871China School of Engineering The University of EdinburghEdinburgh EH93JLUnited Kingdom School of Engineering Computing and MathematicsUniversity of PlymouthPlymouth PL48AAUnited Kingdom

Although permafrost degradation contributes significantly to hydrological change in cold regions, gaps remain in our understanding of streamflow variation induced by degrading permafrost in different river basins. We therefore used a deep learning model to simulate the long-term(≥ 30 years) monthly streamflow at 60 hydrological stations along the Lena River, the third longest circum-Arctic river. Analyzing the effects of precipitation, temperature, and thaw depth on streamflow variation throughout the Lena River Basin, we identified two feedback patterns relating streamflow to warming permafrost, observed in areas of continuous and discontinuous permafrost. In northern plain regions with continuous permafrost, 94% of stations presented an increasing trend in annual streamflow from the 1900s to the 2010s due to permafrost degradation. The enhanced streamflow was mainly due to increased meltwater in the flood season. In southern regions covered by both continuous and discontinuous permafrost, approximately 38% of stations exhibited a declining trend in annual streamflow in response to permafrost degradation, with a high proportion(61%) located in mountain regions(elevation ≥ 500 m). The decline is attributed to the enhanced infiltration capacity of thawing frozen layers within discontinuous permafrost regions. Our study provides new insights into the mechanisms behind permafrost degradation-induced streamflow variation and highlights the importance of formulating tailored strategies for sustainable river management in cold regions experiencing climate change.

关键词： permafrost degradation streamflow variation deep learning model climate change large cold basin

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Structure-constrain Source Free Domain Adaptation with Regularized Knowledge Distillation of Source Model 13

Structure-constrain Source Free Domain Adaptation with Regul...

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Yang, Dan Peng, Xin Su, Cheng Tan, Dayu Chen, Chaoyang Zhong, Weimin Ministry of Education East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Anhui Hefei230601 China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798350361674

Transfer learning aims to leverage the knowledge of the source domain to help the learning of unlabeled target domain models. However, all conventional transfer learning methods assume that samples from source and target domains are always freely accessible. However, this assumption is rarely plausible in real-world due to data privacy. To combat this challenge, we introduce Structure-constrain Source Free Domain Adaptation with regularized Knowledge Distillation of source model (SSFDA-KD). Our method is based on the observations that a deterministic classification result can be obtained when the target data distribution aligns with the source data distribution, and a well-trained model not only yields a deterministic classification for a sample but also has clear category boundaries, which implies samples that are close together belong to the same category. From those, we first generate the source model with labeled source data, which is regarded as a well-trained model. Then, we identify target prototypes with lower self-entropy and assign the pseudo-labels to other samples according to the neighborhood structure. Finally, we refine the target model by minimizing prediction loss, constraining internal structure via triplet loss, and reducing knowledge distillation loss. Experiments of fault diagnosis based on WWTPs are conducted to validate its effectiveness. © 2024 IEEE.

关键词： Domain Knowledge

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Adaptive delay-energy balanced partial offloading strategy in Mobile Edge computing networks

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Digital Communications and Networks 2023年第6期9卷 1310-1318页

作者： Shumei Liu Yao Yu Lei Guo Phee Lep Yeoh Branka Vucetic Yonghui Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyang110819China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Electrical and Information Engineering University of SydneySydneyNSW2006Australia

Mobile Edge computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy *** this paper,we propose a flexible MECbased requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy *** address the dimensional differences between time and energy,we introduce two normalized parameters and then derive the computational overhead of processing *** from existing works,this paper considers practical variations in the user request patterns,and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay,task workload and power *** the resulting problem is non-convex,we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading *** experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.

关键词： Mobile edge computing(MEC) Delay Energy consumption Dynamic balance Partial computation offloading

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Diverse nucleotide substitutions in rice base editing mediated by novel TadA variants

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Plant Communications 2024年第8期5卷 104-112页

作者： Man Yu Yongjie Kuang Chenyang Wang Xuemei Wu Shaofang Li Dawei Zhang Wenxian Sun Xueping Zhou Bin Ren Huanbin Zhou State Key Laboratory for Biology of Plant Diseases and Insect Pests Institute of Plant ProtectionChinese Academy of Agricultural SciencesBeijing 100193China Department of Plant Pathology China Agricultural UniversityBeijing 100193China Scientific Observing and Experimental Station of Crop Pests in Guilin Ministry of Agriculture and Rural AffairsGuilin 541399China Ministry of Education Key Laboratory for Bio-Resource and Eco-Environment College of Life ScienceState Key Laboratory of Hydraulics and Mountain River EngineeringSichuan UniversityChengdu 610064China State Key Laboratory of Vegetable Biobreeding National Engineering Research Center for VegetablesBeijing Vegetable Research CenterBeijing Academy of Agriculture and Forestry ScienceBeijing 100097China State Key Laboratory of Rice Biology Institute of BiotechnologyZhejiang UniversityHangzhou 310058China Key Laboratory of Gene Editing Technologies(Hainan) Ministry of Agricultural and Rural AffairsSanya 572024China

CRISPR-mediated base editors have been widely used to correct defective alleles and create novel alleles by artificial evolution for the rapid genetic improvement of *** editing capabilities of base editors strictly rely on the performance of various nucleotide modification *** with the welldeveloped adenine base editors(ABEs),cytosine base editors(CBEs)and dual base editors suffer from unstable editing efficiency and patterns at different genomic loci in rice,significantly limiting their ***,we comprehensively examined the base editing activities of multiple evolved TadA8e variants in *** found that both TadA-CDd and TadA-E27R/N46L achieved more robust C-to-T editing than previously reported hyperactive hAID*D,and TadA-CDd outperformed TadA-E27R/N46L.A C-to-G base editor(CGBE)engineered with TadA-CDd and OsUNG performed highly efficient C-to-G editing in rice compared with that of *** addition,a dual base editor constructed with a single protein,TadDE,enabled simultaneous,highly efficient C-to-T and A-to-G editing in ***,our results demonstrate that TadA8e derivatives improve both CBEs and dual base editors in rice,providing a powerful way to induce diverse nucleotide substitutions for plant genome editing.

关键词： CRISPR TadA variants cytosine base editing dual base editor rice

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MECNet: Multi-Scale Exposure-Consistency Learning via Fourier Transform for Exposure Correction

MECNet: Multi-Scale Exposure-Consistency Learning via Fourie...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Qiao, Gaofei Zhang, Zhibin He, Liqiang Inner Mongolia University Key Laboratory of Wireless Networks and Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education Hohhot010021 China Geomechanica Inc Canada

ISBN: (纸本)9781665410205

In the real world, due to various challenging lighting conditions such as low light, underexposure, and overexposure, captured images often exhibit undesirable appearances. Given that images with different exposure levels require different correction processes, a single neural network struggles to produce satisfactory results. We propose a coarse-to-fine exposure correction model for learning exposure consistency representation to address underexposure and overexposure issues. Building upon the bilateral activation mechanism, we introduce the Fourier transform to capture global information and fuse it with locally extracted information through convolution to achieve superior feature representation. Additionally, we employ Laplacian pyramids to decompose the source image into different spatial frequency bands, then the image details are enhanced by denoising high-frequency layers. Experimental results on the MSEC and SICE datasets demonstrate the superiority of our proposed method over current state-of-the-art approaches. Our code will be made available on GitHub. © 2024 IEEE.

关键词： Image enhancement

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An ultra-wideband high-isolation dual-port MIMO antenna design

An ultra-wideband high-isolation dual-port MIMO antenna desi...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Cao, Shuangqing Li, Minquan Zi, Zhenfa Li, Zhonghui Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China Hefei Normal University School of Physics and Materials Engineering Hefei230601 China

ISBN: (纸本)9798350355581

In this paper, an antenna for ultra-wideband multiple-input and multiple-output (UWB-MIMO) is designed. The design comprises two hexagonal monopole antennas. In order to achieve ultra-wideband characteristics and mitigate the coupling, a stepped microstrip line structure for impedance matching and T-shaped defective ground structure are employed, which optimizes the broadband characteristics and achieves high isolation characteristics. The antenna achieves a wide impedance bandwidth of 3.5 GHz to 13.9 GHz (relative bandwidth of 119%). Furthermore, the isolation between the two feed ports of the antenna exceeds 18 dB, the envelope correlation coefficient (ECC) is less than 0.012, and it exhibits favorable radiation characteristics. © 2024 IEEE.

关键词： Bandwidth

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A Dual-band Low-profile Quadrifilar-Helix Antenna for Satellite Navigation System

A Dual-band Low-profile Quadrifilar-Helix Antenna for Satell...

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Wang, Jiahong Qi, Junwei Li, Yingsong Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350336740

A new dual-band low-profile quadrifilar-helical antenna (QHA) was presented. The spiral radiation arm of QHA is printed to three circular dielectric plates to reduce the antenna height. In this case, the radiation arm of the lower layer is connected to the feed, and the L-shaped branch is connected to the ground through a 50 ohm resistance. The radiation arm in the middle layer adds branches to realize dual frequency. The upper radiation arm rotates inward and folds, further realizing miniaturization. As a result, the height of QHA is only 20mm. Through CST simulation and optimization, The operating frequency bands of QHA are 1GHz - 1.45GHz and 1.54GHz - 1.74GHz. The beam width of QHA is more than 120°, which is to provide service for satellite navigating systems. The AR for the operation bands are less than 3dB. © 2023 IEEE.

关键词： Satellites

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DAEA: Enhancing Entity Alignment in Real-World Knowledge Graphs Through Multi-Source Domain Adaptation 31

DAEA: Enhancing Entity Alignment in Real-World Knowledge Gra...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Yang, Linyan Zhou, Shiqiao Cheng, Jingwei Zhang, Fu Wan, Jizheng Wang, Shou Lee, Mark School of Computer Science and Engineering Northeastern University China School of Computer Science University of Birmingham United Kingdom Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University China

ISBN: (纸本)9798891761964

Entity Alignment (EA) is a critical task in Knowledge Graph (KG) integration, aimed at identifying and matching equivalent entities that represent the same real-world objects. While EA methods based on knowledge representation learning have shown strong performance on synthetic benchmark datasets such as DBP15K, their effectiveness significantly decline in real-world scenarios which often involve data that is highly heterogeneous, incomplete, and domain-specific, as seen in datasets like DOREMUS and AGROLD. Addressing this challenge, we propose DAEA, a novel EA approach with Domain Adaptation that leverages the data characteristics of synthetic benchmarks for improved performance in real-world datasets. DAEA introduces a multi-source KGs selection mechanism and a specialized domain adaptive entity alignment loss function to bridge the gap between real-world data and optimal benchmark data, mitigating the challenges posed by aligning entities across highly heterogeneous KGs. Experimental results demonstrate that DAEA outperforms state-of-the-art models on real-world datasets, achieving a 29.94% improvement in Hits@1 on DOREMUS and a 5.64% improvement on AGROLD. Code is available at https://***/yangxiaoxiaoly/DAEA. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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Adaptive Step-Size Sparse Millimeter Wave Imaging Algorithm Based on Approximate Observations 11

Adaptive Step-Size Sparse Millimeter Wave Imaging Algorithm ...

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11th IEEE International Conference on Computer Science and Network Technology, ICCSNT 2023

作者： Wang, Dianhao Huang, Xinqi Yu, Canping Mao, Qi Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350311594

The currently constructed millimeter wave imaging system has the problems of long sampling time and more sampling points of antenna units, and the use of compressed perception algorithm can improve the imaging quality when the number of sampling points are much smaller than the Nyquist distance sampling. The traditional compressed perception algorithm can achieve better sparse recovery than the matched filter imaging algorithm, but there is a large dimension of the measurement matrix and high computational complexity. For the problems of difficult data processing and large dimensions of measurement matrix, a sparse imaging regularization model is constructed based on approximate observation, and an improved soft threshold iterative algorithm is used with adaptive step size, which improves the convergence performance, reduces the computational complexity of the sparse recovery dramatically and achieves a better quality of sparse imaging than the traditional compressed perception algorithm. © 2023 IEEE.

关键词： Computational complexity

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Improving Adversarial Transferability with Ghost Samples 26

Improving Adversarial Transferability with Ghost Samples

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Zhao, Yi Mou, Ningping Ge, Yunjie Wang, Qian The Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Hubei Wuhan430072 China

ISBN: (纸本)9781643684369

Adversarial transferability presents an intriguing phenomenon, where adversarial examples designed for one model can effectively deceive other models. By exploiting this property, various transfer-based methods are proposed to conduct adversarial attacks without knowledge of target models, posing significant threats to practical black-box applications. However, these methods either have limited transferability or require high resource consumption. To bridge the gap, we investigate adversarial transferability from the optimization perspective and propose the ghost sample attack (GSA). GSA improves adversarial transferability by alleviating the overfitting issue of adversarial examples on the surrogate model. Based on the insight that a slight shift of the adversarial example is similar to a minor change in the decision boundary, we aggregate gradients of perturbed adversarial copies (named ghost samples) to efficiently achieve a similar effect to calculating gradients of multiple ensemble surrogate models. Extensive experiments demonstrate that GSA achieves state-of-the-art adversarial transferability with restricted resources. On average, GSA improves the attack success rate by 4.8% on normally trained models compared to state-of-the-art attacks. Additionally, GSA reduces the computational cost by 62% compared with TAIG-R. When combined with other methods, GSA further improves transferability to 96.9% on normally trained models and 82.7% on robust models. © 2023 The Authors.

关键词： Machine learning

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