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SSRN 2024年

作者： Liu, Yangshuo Huang, Keke Meng, Yao Wang, Chubo Qiao, Liang Yan, Yaotian Zheng, Xiao Hang School of Materials Science and Engineering Harbin Institute of Technology Harbin150001 China Key Laboratory of Materials Design and Quantum Simulation College of Science Changchun University Changchun130022 China State Key Laboratory of Precision Welding and Joining of Materials and Structures Harbin Institute of Technology Harbin150001 China

Searching for highly efficient electrocatalysts for the hydrogen evolution reaction (HER) is principal to the development electrolytic water production industry. Experimental screening of highly active electrocatalysts is time-consuming and complicated. In this work, an Artificial Neural Network model is proposed to accelerate the screening for Ag(M) catalysts(M= Al, Si, Ti, V, Cr, and so on). The Ag(Ni) catalyst is identified as a potential electrocatalyst with the nearly ideal Gibbs free energy change of hydrogen (ΔGH), which affords the relatively low overpotentials of 159 mV for HER at 10 mA cm-2. The incorporation of Ni effectively optimizes the electronic environment of the materials, which results in the electrons accumulating around Ni atoms and drastically reduces the Gibbs free energy change of hydrogen (ΔGH). Our method is significantly more efficient, running 1184 times faster than the traditional DFT method. Our work paves an efficient way to design and develop potential HER catalysts. © 2024, The Authors. All rights reserved.

关键词： Electrocatalysts

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3S-TSE: Efficient Three-Stage Target Speaker Extraction for Real-Time and Low-Resource Applications

3S-TSE: Efficient Three-Stage Target Speaker Extraction for ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shulin He Jinjiang Liu Hao Li Yang Yang Fei Chen Xueliang Zhang College of Computer Science Inner Mongolia University China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources China

Target speaker extraction (TSE) aims to isolate a specific voice from multiple mixed speakers relying on a registerd sample. Since voiceprint features usually vary greatly, current end-to-end neural networks require large model parameters which are computational intensive and impractical for real-time applications, espetially on resource-constrained platforms. In this paper, we address the TSE task using microphone array and introduce a novel three-stage solution that systematically decouples the process: First, a neural network is trained to estimate the direction of the target speaker. Second, with the direction determined, the Generalized Sidelobe Canceller (GSC) is used to extract the target speech. Third, an Inplace Convolutional Recurrent Neural Network (ICRN) acts as a denoising post-processor, refining the GSC output to yield the final separated speech. Our approach delivers superior performance while drastically reducing computational load, setting a new standard for efficient real-time target speaker extraction.

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The 2023 report of the synergetic roadmap on carbon neutrality and clean air for China: Carbon reduction, pollution mitigation, greening, and growth

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Environmental science and Ecotechnology 2025年第1期23卷 1-18页

作者： Jicheng Gong Zhicong Yin Yu Lei Xi Lu Qiang Zhang Cilan Cai Qimin Chai Huopo Chen Renjie Chen Wenhui Chen Jing Cheng Xiyuan Chi Hancheng Dai Zhanfeng Dong Guannan Geng Jianlin Hu Shan Hu Cunrui Huang Tiantian Li Wei Li Xiaomei Li Yongsheng Lin Jun Liu Jinghui Ma Yue Qin Weiqi Tang Dan Tong Jiaxing Wang Lijuan Wang Qian Wang Xuhui Wang Xuying Wang Libo Wu Rui Wu Qingyang Xiao Yang Xie Xiaolong Xu Tao Xue Haipeng Yu Da Zhang Li Zhang Ning Zhang Shaohui Zhang Shaojun Zhang Xian Zhang Zengkai Zhang Hongyan Zhao Bo Zheng Yixuan Zheng Tong Zhu Huijun Wang Jinnan Wang Kebin He State Key Joint Laboratory for Environment Simulation and Pollution Control College of Environmental Sciences and Engineering and Center for Environment and HealthPeking UniversityBeijing100871China Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing210044China Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing100041China Center for Carbon Neutrality Chinese Academy of Environmental PlanningBeijing100041China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Institute for Carbon Neutrality Tsinghua UniversityBeijing100084China Ministry of Education Key Laboratory for Earth System Modelling Department of Earth System ScienceTsinghua UniversityBeijing100084China National Center for Climate Change Strategy and International CooperationBeijing100035China Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China School of Public Health Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of HealthFudan UniversityShanghai200032China School of Economics and Management Beijing University of Chemical TechnologyBeijing100029China Department of Earth System Science University of CaliforniaIrvineIrvineCA92697USA National Meteorological Center China Meteorological AdministrationBeijing100081China College of Environmental Sciences and Engineering Peking UniversityBeijing100871China Institute of Eco-Environmental Management and Policy Chinese Academy of Environmental PlanningBeijing100041China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Contr

The response to climate change and air pollution control demonstrates strong synergy across scientific mechanisms,targets,strategies,and governance *** report,based on a monitoring indicator system for coordinated governance of air pollution and climate change,employs an interdisciplinary approach combining natural and social *** establishes 20 indicators across five key areas:air pollution and climate change,governance systems and practices,structural transformation and technologies,atmospheric components and emission reduction pathways,and health impacts and *** report tries to provide actionable insights into the interconnectedness of air pollution and climate *** highlights key policy gaps,presents updated indicators,and offers a refined monitoring framework to track progress toward China's dual goals of reducing emissions and improving air *** to previous editions,this year's report has updated four key indicators:meteorological impacts on air quality,climate change and its effects,governance policies,and low-carbon building energy *** aim is to further refine the monitoring framework,track progress,and establish a comprehensive theory for collaborative governance while identifying challenges and proposing solutions for China's pathway to carbon neutrality and clean *** report comprises six *** executive summary chapter is followed by analyzing air pollution and climate change *** systems and practices are discussed in the third chapter,focusing on policy implementation and local *** fourth chapter addresses structural transformations and emission reduction technologies,including energy and industrial shifts,transportation,low-carbon buildings,carbon capture and storage,and power *** fifth chapter outlines atmospheric component dynamics and emission pathways,presenting insights into emission drivers and future *** sixth chapter assesses he

关键词： Synergetic roadmap Carbon neutrality Clean air

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Cross-Attention-Guided Wavenet for Mel Spectrogram Reconstruction in The ICASSP 2024 Auditory EEG Challenge

Cross-Attention-Guided Wavenet for Mel Spectrogram Reconstru...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Yuan Fang Hao Li Xueliang Zhang Fei Chen Guanglai Gao College of Computer Science Inner Mongolia University China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources China

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

This paper provides an overview of our submission to Task 2 of the Auditory EEG Challenge at ICASSP 2024 Signal Processing Grand Challenge (SPGC). We introduce a novel approach, employing a cross-attention-guided WaveNet with a coarse-to-fine generation strategy, aimed at enhancing the detailed reconstruction of Mel spectrograms from time-domain EEG. Specifically, the model utilizes WaveNet to sequentially reconstruct the envelope, 10-band Mel, 80-band Mel, and magnitude from coarse to fine granular levels. To bridge the gap between different modalities, we introduce a cross-attention mechanism, exploring correlations across modalities. A combined loss function is employed to refine the reconstruction performance. Notably, we achieved Pearson correlation values of 0.0651 ± 0.0153 for the validation set and 0.0413 ± 0.0169 for the heldout-subjects test set, securing the second position in the competition. We release the training code for our model online 1 .

关键词： Training Analytical models Correlation Brain modeling Electroencephalography Decoding Time-domain analysis

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FedECCR: Federated Learning Method with Encoding Comparison and Classification Rectification 19th

FedECCR: Federated Learning Method with Encoding Comparison...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yan Zheng, Hui Wang, Xin Zhang, Beibei Zhou, Mingyao Zhang, Jilin Ren, YongJian School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Zhejiang Lab Hangzhou311100 China Hangzhou Huawei Communication Technology Co. Ltd. Hangzhou310052 China

ISBN: (纸本)9783031545306

Federated learning is a distributed training method that integrates multi-party data information using privacy-preserving technologies through dispersed client data sets to jointly construct a global model under the coordination of a central server. However, in practical applications, there is a high degree of data distribution skewness among clients, which causes the optimization direction of the client models to diverge, resulting in model bias and reducing the accuracy of the global model. Existing methods require the calculation and transmission of much information to correct the optimization direction of the client models, or only roughly limit the deviation of the client models end-to-end, ignoring targeted processing of the internal structure of the model, resulting in unclear improvement effects. To address these problems, we propose a federated optimization algorithm FedECCR based on encoding contrast and classification correction. This algorithm divides the model into an encoder and a classifier. It utilizes prototype contrastive training of the model encoder and unbiased classification correction of the classifier. This approach notably improves the accuracy of the global model while maintaining low communication costs. We conducted experiments on multiple data sets to evaluate the validity of our method, and the quantified results showed that FedECCR can improve the global model classification accuracy by approximately 1% to 6% compared to FedAvg, FedProx, and MOON. © ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Classification (of information)

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Real-Time nonlinear predictive controller design for drive-by-wire vehicle lateral stability with dynamic boundary conditions

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Fundamental Research 2022年第1期2卷 131-143页

作者： Xiyue Zhang Ping Wang Jiamei Lin Hong Chen Jinlong Hong Lin Zhang State Key Laboratory of Automotive Simulation and Control Jilin University Changchun 130025China The Department of Control Science and Engineering School of Communication EngineeringJilin UniversityChangchun 130025China College of Electronics and Information Engineering Tongji UniversityShanghai201804China The Postdoctoral Station of Mechanical Engineering School of Automotive StudiesTongji UniversityShanghai201804China

Due to flexible drive-by-wire technology,vehicle stability control can improve handling and lateral stability under extreme ***,this technology can also increase the probability of random transmission *** paper proposes a nonlinear model predictive control(NMPC)strategy to improve vehicle stability and compensate for the random time ***,by combining the nonlinear dynamic characteristics and driver behavior,we obtain a stable region of the yaw rate and the sideslip angle under complex driving ***,an NMPC controller is designed to track the reference values in the identified stable region to improve the handling and lateral ***,the actuator receives the optimized control sequence and compensates for the random time delay of the transmission ***/Simulink simulation and hardware-in-the-loop experiment results show that the proposed controller with dynamic boundary conditions can better track the expected value of the yaw rate and suppress the sideslip angle under low adhesion road conditions.

关键词： Nonlinear model predictive control Stable region Random time delay Delay compensator Vehicle stability control

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The 2022 report of synergetic roadmap on carbon neutrality and clean air for China:Accelerating transition in key sectors

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Environmental science and Ecotechnology 2024年第3期19卷 4-18页

作者： Yu Lei Zhicong Yin Xi Lu Qiang Zhang Jicheng Gong Bofeng Cai Cilan Cai Qimin Chai Huopo Chen Renjie Chen Shi Chen Wenhui Chen Jing Cheng Xiyuan Chi Hancheng Dai Xiangzhao Feng Guannan Geng Jianlin Hu Shan Hu Cunrui Huang Tiantian Li Wei Li Xiaomei Li Jun Liu Xin Liu Zhu Liu Jinghui Ma Yue Qin Dan Tong Xuhui Wang Xuying Wang Rui Wu Qingyang Xiao Yang Xie Xiaolong Xu Tao Xue Haipeng Yu Da Zhang Ning Zhang Shaohui Zhang Shaojun Zhang Xian Zhang Xin Zhang Zengkai Zhang Bo Zheng Yixuan Zheng Jian Zhou Tong Zhu Jinnan Wang Kebin He Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing100041China Center for Carbon Neutrality Chinese Academy of Environmental PlanningBeijing100041China Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing210044China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Institute for Carbon Neutrality Tsinghua UniversityBeijing100084China Ministry of Education Key Laboratory for Earth System Modelling Department of Earth System ScienceTsinghua UniversityBeijing100084China State Key Joint Laboratory for Environment Simulation and Pollution Control College of Environmental Sciences and Engineering and Center for Environment and HealthPeking UniversityBeijing100871China National Center for Climate Change Strategy and International CooperationBeijing100035China Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China School of Public Health Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of HealthFudan UniversityShanghai200032China School of Economics and Management Beijing University of Chemical TechnologyBeijing100029China Department of Earth System Science University of CaliforniaIrvineCA92697USA National Meteorological Center China Meteorological AdministrationBeijing100081China College of Environmental Sciences and Engineering Peking UniversityBeijing100871China Policy Research Center for Environment and Economy Ministry of Ecology and Environment of the People's Republic of ChinaBeijing100029China Jiangsu Key Laboratory of Atmospheric Environment Monitoring

China is now confronting the intertwined challenges of air pollution and climate *** the high synergies between air pollution abatement and climate change mitigation,the Chinese government is actively promoting synergetic control of these two *** Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key *** Synergetic Roadmap 2022 report is the first annual update,featuring 20 indicators across five aspects:synergetic governance system and practices,progress in structural transition,air pollution and associated weather-climate interactions,sources,sinks,and mitigation pathway of atmospheric composition,and health impacts and benefits of coordinated *** to the comprehensive review presented in the 2021 report,the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant *** milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time,a decline in the production of crude steel and cement after years of growth,and the surging penetration of electric ***,in 2022,China issued the first national policy that synergizes abatements of pollution and carbon emissions,marking a new era for China's pollution-carbon *** changes highlight China's efforts to reshape its energy,economic,and transportation structures to meet the demand for synergetic control and sustainable ***,the country has witnessed a slowdown in carbon emission growth,improved air quality,and increased health benefits in recent years.

关键词： Synergetic roadmap Carbon neutrality Clean air

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MP: Endowing Large Language Models with Lateral Thinking 39

MP: Endowing Large Language Models with Lateral Thinking

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Bai, Tian Cao, Yongwang Ge, Yan Yu, Haitao College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Graduate School of Comprehensive Human Sciences University of Tsukuba Japan Institute of Library Information and Media Science University of Tsukuba Japan

ISBN: (纸本)157735897X

The recent studies show that Large Language Models (LLMs) often fall short in tasks demanding creative, lateral thinking due to lacking a clear awareness of their own reasoning processes. To cope with this issue, we propose a novel metacognitive prompting method (titled as MP) by mimicking human metacognition. Through integrating metacognitive principles, MP endows LLMs with lateral thinking ability, thereby enhancing their abilities to strategize, monitor, and reflect on their responses when dealing with creative tasks. The experimental results with five base LLMs across three lateral thinking datasets demonstrate that: All LLMs armed with MP consistently outperform the representative baseline methods. For example, MP demonstrates superior performance over CoT prompting across Sentence Puzzle (+5.00%), Word Puzzle (+10.07%), BiRdQA (+6.48%), and RiddleSense (+2.65%) with GPT-3.5-turbo model. In particular, the deployment of MP with GPT-4 achieves significant performance improvements that even surpass human performance on BRAINTEASER benchmark, demonstrating the transformative potential of MP in enhancing the creative problem-solving abilities of LLMs. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Piezoelectric Enhancement and Mechanism in Defect Dipole-Engineered Sr2Nb2O7 Crystals

SSRN

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SSRN 2024年

作者： Wang, Xiaohan Li, Xiaohe Liu, Runru Liu, Fujun Sun, Enwei Tang, Wenting Wang, Xinwei Li, Xinyang Tian, Fubo Li, Fangfei Zhou, Qiang Li, Liang Changchun130000 China Key Laboratory of Material Design and Quantum Simulation College of Materials Science and Engineering Changchun University Changchun130022 China Nanophotonics and Biophotonics Key Laboratory of Jilin Province School of Physics Changchun University of Science and Technology Changchun130022 China Condensed Matter Science and Technology Institute School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin150080 China

The eco-friendly nature, high Curie temperature (Tc) and high-performance piezoelectricity are beneficial for sensors and actuators in extreme environments. In this study, lead-free piezoelectric single crystal Sr2Nb2O7 with Tc up to 1573 K were grown using optical floating zone (OFZ) method. A defect dipole formation strategy was proposed based on defect engineering, achieving a piezoelectric coefficient (d33) up to 24.7 pC/N and a remanent polarization (Pr) of 10 μC/cm2 under 15 kV/cm electric field, which goes well beyond the performance boundary of d33 vs. Tc. Local polar vector analysis, lattice vibration studies, and density functional theory calculations revealed that this high performance is due to the combined effect of defect dipoles and local structural inhomogeneity. Furthermore, the mechanism of temperature induced ferroelectric phase transition was provided in this work. These findings provide critical insights for developing high-temperature lead-free piezoelectric materials. © 2024, The Authors. All rights reserved.

关键词： Single crystals

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RFR-WWANet: Weighted Window Attention-Based Recovery Feature Resolution Network for Unsupervised Image Registration

arXiv

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arXiv 2023年

作者： Ma, Mingrui Wang, Tao Wang, Weijie Song, Lei Liu, Guixia College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy

The Swin transformer has recently attracted attention in medical image analysis due to its computational efficiency and long-range modeling capability. Owing to these properties, the Swin Transformer is suitable for establishing more distant relationships between corresponding voxels in different positions in complex abdominal image registration tasks. However, the registration models based on transformers combine multiple voxels into a single semantic token. This merging process limits the transformers to model and generate coarse-grained spatial information. To address this issue, we propose Recovery Feature Resolution Network (RFRNet), which allows the transformer to contribute fine-grained spatial information and rich semantic correspondences to higher resolution levels. Furthermore, shifted window partitioning operations are inflexible, indicating that they cannot perceive the semantic information over uncertain distances and automatically bridge the global connections between windows. Therefore, we present a Weighted Window Attention (WWA) to build global interactions between windows automatically. It is implemented after the regular and cyclic shift window partitioning operations within the Swin transformer block. The proposed unsupervised deformable image registration model, named RFR-WWANet, detects the long-range correlations, and facilitates meaningful semantic relevance of anatomical structures. Qualitative and quantitative results show that RFR-WWANet achieves significant improvements over the current state-of-the-art methods. Ablation experiments demonstrate the effectiveness of the RFRNet and WWA designs. Our code is available at https://***/MingR-Ma/RFR-WWANet. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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