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Mn-modulated Co-N-C oxygen electrocatalysts for robust and temperature-adaptative zinc-air batteries

Chinese Journal of Structural Chemistry 2024年第7期43卷 39-46页

作者： Shaojie Ding Henan Wang Xiaojing Dai Yuru Lv Xinxin Niu Ruilian Yin Fangfang Wu Wenhui Shi Wenxian Liu Xiehong Cao College of Materials Science and Engineering Zhejiang University of TechnologyHangzhou310014PR China College of Chemical Engineering Zhejiang University of TechnologyHangzhou310014PR China Center for Membrane and Water Science&Technology College of Chemical Engineering Zhejiang University of TechnologyHangzhou310014PR China College of Materials Science and Engineering South China University of TechnologyGuangzhou510640PR China

Flexible zinc-air batteries(FZABs)are featured with safety and high theoretical capacity and become one of the ideal energy supply devices for flexible ***,the lack of cost-effective electrocatalysts remains a major obstacle to their ***,we synthesized a porous dodecahedral nitrogen-doped carbon(NC)material with Co and Mn bimetallic co-embedding(CoxMni-x@NC)as a highly efficient oxygen reduction reaction(ORR)catalyst for *** incorporation of Mn effectively modulates the electronic structure of Co sites,which may lead to optimized energetics with oxygen-containing intermediates thereby significantly enhancing catalytic ***,the optimized Co4Mn1@NC catalyst exhibits superior E1/2(0.86 V)and jL(limiting current density,5.96 mA cm-2)compared to Pt/C and other recent ***,aqueous ZAB using Co4Mn1@NC as a cathodic catalyst demonstrates a high peak power density of 163.9 mW cm-2 and maintains stable charging and discharging for over 650 ***,FZAB based on Co4Mn1@NC can steadily operate within the temperature range of-10 to 40 ℃,demonstrating the potential for practical applications in complex climatic conditions.

关键词： Zinc-air batteries Oxygen reduction reaction Electrocatalysis Nitrogen-doped carbon Temperature adaptability

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IAST: Instance Association Relying on Spatio-Temporal Features for Video Instance Segmentation 48

IAST: Instance Association Relying on Spatio-Temporal Featur...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chen, Junhao Liu, Sheng Chen, Ruixiang Guo, Bingnan Zhang, Feng Zhejiang University of Technology College of Computer Science and Technology China

ISBN: (纸本)9781728163277

Most offline video instance segmentation (VIS) methods lack consideration for multi-scale spatio-temporal features, which leads to unstable instance association across frames. To address this problem, we propose IAST that builds Instance Association relying on Spatio-Temporal features for video instance segmentation. In detail, we design a novel Scale-to-Scale Attention Module in the encoder of IAST, which constructs stable cross-frame instance associations by completely leveraged multi-scale spatio-temporal features. In addition, we introduce a new data augmentation method called Sequential Copy-Paste, which effectively alleviates the overfitting problem caused by insufficient training data and enhances the robustness of the model. Empirically, IAST achieves the state-of-the-art VIS benchmarks with a ResNet-50 backbone: 47.4% AP, 41.6% AP on YouTube-VIS 2019 & 2021. Such achievements significantly outperform the previous state-of-the-art performance of 1.0% at the expense of fewer parameters. Code is available at https://***/clozureyez/IAST. © 2023 IEEE.

关键词： data augmentation instance association spatio-temporal features video instance segmentation

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E2Usd: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series 24

E2Usd: Efficient-yet-effective Unsupervised State Detection ...

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33rd ACM Web Conference, WWW 2024

作者： Lai, Zhichen Li, Huan Zhang, Dalin Zhao, Yan Qian, Weizhu Jensen, Christian S. Department of Computer Science Aalborg University Aalborg Denmark College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798400701719

Cyber-physical system sensors emit multivariate time series (MTS) that monitor physical system processes. Such time series generally capture unknown numbers of states, each with a different duration, that correspond to specific conditions, e.g., "walking"or "running"in human-activity monitoring. Unsupervised identification of such states facilitates storage and processing in subsequent data analyses, as well as enhances result interpretability. Existing state-detection proposals face three challenges. First, they introduce substantial computational overhead, rendering them impractical in resourceconstrained or streaming settings. Second, although state-of-the-art (SOTA) proposals employ contrastive learning for representation, insufficient attention to false negatives hampers model convergence and accuracy. Third, SOTA proposals predominantly only emphasize offline non-streaming deployment, we highlight an urgent need to optimize online streaming scenarios. We propose E2Usd that enables efficient-yet-accurate unsupervised MTS state detection. E2Usd exploits a Fast Fourier Transform-based Time Series Compressor (fftCompress) and a Decomposed Dual-view Embedding Module (ddEM) that together encode input MTSs at low computational overhead. Additionally, we propose a False Negative Cancellation Contrastive Learning method (fnccLearning) to counteract the effects of false negatives and to achieve more cluster-friendly embedding spaces. To reduce computational overhead further in streaming settings, we introduce Adaptive Threshold Detection (adaTD). Comprehensive experiments with six baselines and six datasets offer evidence that E2Usd is capable of SOTA accuracy at significantly reduced computational overhead. Our code is available at https://***/AI4CTS/E2Usd. © 2024 ACM.

关键词： Time series

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Accelerating Exact K-Means++ Seeding Using Lower Bound Based Framework 28th

Accelerating Exact K-Means++ Seeding Using Lower Bound Based...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Zhang, Haowen Li, Jing College of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9783031306778

The k-means++ seeding is a widely used approach to obtain reasonable initial centers of k-means clustering, and it performs empirical well. Nevertheless, the time complexity of k-means++ seeding makes it suffer from being slow on large datasets. Therefore, it is necessary to improve the efficiency of k-means++ seeding to make it scale well for large datasets. Most of the previous work focused on approximating k-means++ seeding. They can achieve speedup effectively;however, they are inherently approximation methods and cannot get the same results as k-means++ seeding. In this paper, a lower bound based framework (LBF) is proposed to accelerate k-means++ seeding. LBF is an exact approach which can perform k-means++ seeding in less time while guaranteeing the same results. Two specific lower bound schemes based on progressive partial distance and piecewise aggregate approximation are embedded into the LBF, and we provide the theoretical analysis to show their lower bound property and computational efficiency. Experimental results on various synthetic and real-world datasets demonstrate the effectiveness of the proposals compared with the baseline k-means++ seeding and approximate method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Aggregates

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MLGNet: A Multi-Period Local and Global Temporal Dynamic Pattern Integration Network for Long-Term Forecasting

MLGNet: A Multi-Period Local and Global Temporal Dynamic Pat...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Hui Wang, Liping Zhao, Peipei Qiu, Qicang University of Technology College of Computer Science Technology College of Software Zhejiang Hangzhou China Computer Science & Technology Zhejiang Lab HanZhou China

ISBN: (纸本)9798350337020

Long-term forecasting is widely used in meteorology, hydrology, and finance. However, non-stationary time series make it hard to make accurate long-term predictions because of their complicated multi-period local-global temporal dynamic patterns. Currently, state-of-the-art methods use transformers or temporal convolutions to obtain global and local temporal dynamic patterns. Nevertheless, the former suffers from the computational complexity of self-attention mechanisms despite having a global temporal receptive field. Despite being able to catch local temporal patterns, the latter requires additional layers to capture global temporal patterns. Moreover, the present research disregards integrating multi-period patterns into longterm forecasting. In this paper, we propose MLGNet to tackle the mentioned challenges, which integrates local and global temporal dynamic patterns with multiple periods for longterm forecasting. In particular, we suggest using the maximal overlap discrete wavelet transform (MODWT) as a multi-period decoupling method to decompose non-stationary time series and apply it for the first time to long-term forecasting. In addition, we suggest a multi-scale encoder-decoder framework to capture and fuse local-global temporal dynamic patterns in each decomposed period. Inception dilated causal convolutions-based encoder and a lightweight MLP-based decoder in the framework capture local and global temporal dynamic patterns in series while avoiding the high computational complexity of self-attention mechanisms. Lastly, we suggest time-separable convolutions for aggregating information on temporal dynamic patterns among multiple periods. The above method helps MLGNet better balance the representation ability of time series in 1D and 2D space. Evaluation of five benchmark datasets shows that MLGNet outperforms traditional and state-of-the-art methods, with relative improvements of 13.8 % and 21.9% for multivariate and univariate long-term forecasting, resp

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CA-DBMNet: a channel attention based dual branch multi-scale network for depth map super-resolution

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Multimedia Tools and Applications 2025年第4期84卷 1577-1596页

作者： Miao, Yongwei Zhang, Xinjie Sun, Yuliang Wang, Jinrong School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China School of Information Science and Technology Zhejiang Shuren University Hangzhou310015 China

Scene depth information plays a fundamental role and can be beneficial to various computer vision or visual robotics applications. The scene color image acquired by consumer depth sensors usually has a high resolution, whilst its depth map counterpart often performs low resolution or man-made artifacts. Due to its strong similarity in terms of scene structures between RGB-D pairs, taking the color image as prior information, this paper proposes a Dual Branch Multi-scale Network (CA-DBMNet) based on the channel attention mechanism which can effectively guide the task of depth map super-resolution (SR). The network consists of two branches–color image feature extraction branch and depth map super-resolution branch. The first branch adopts the feature pyramid structure to extract the color image features, capturing image features and structures at different scales. The second branch is composed of three modules: 1) A dense residual feature fusion (DRFF) module to integrate the extracted features from two branches with dense connection and residual learning;2) A channel multi-scale (CMS) module to exploit multi-scale features from depth feature maps;3) A channel attention (CA) module to effectively enhance the channel proportion of high-frequency components in the depth feature maps. Extensive experiments demonstrate that CA-DBMNet can effectively reconstruct the high-resolution depth map with complete scene structures and sharp edges. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Color

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SQA: Strong Guidance Query with Self-Selected Attention for Human-Object Interaction Detection 48

SQA: Strong Guidance Query with Self-Selected Attention for ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Feng Sheng, Liu Guo, Bingnan Chen, Ruixiang Chen, Junhao Zhejiang University of Technology College of Computer Science and Technology China

ISBN: (纸本)9781728163277

The attention mechanism in Transformer-based HOI models plays important role in the comprehension of human and object interaction. However, most previous Transformer-based models ignore the guidance on the query and attention, which leads to a poor understanding of interaction behaviour. In this paper, we propose a strong guidance query model with self-selected attention called SQA. The model includes two novel modules, query feature extraction (QFE) and attention mask construction (AMC). QFE builds strong guidance query by concatenating guidance features. The strong guidance query effectively improves the ability to capture both human and object relationships. Meanwhile, AMC establishes distinctive attention masks for each query. The masks allow each query to contact self-selected particular attention regions. It facilitates directing query to obtain more accurate information during cross-attention even in the rare-sample case. We evaluate our SQA model on the mainstream HICO-DET and V-COCO datasets and it achieves a state-of-the-art result. The codes are available at https://***/nmbzdwss/SQA. © 2023 IEEE.

关键词： attention mask guidance feature human-object interaction transformer-based

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An overview of visually meaningful ciphertext image encryption

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Multimedia Tools and Applications 2025年第14期84卷 13617-13652页

作者： Shiwei, Jing Jianjun, Li School of Computer Science and Technology Hangzhou Dianzi University University Zone Zhejiang Hangzhou310018 China School of Information Science and Technology Hangzhou Normal University YuHang District Zhejiang Hangzhou311121 China

Image encryption algorithm has become one of the hotspots of cryptography research because of its characteristics of large amount of information and strong visualization. However, the noise-like ciphertext of traditional image encryption is easy to discover by attackers, so it is necessary to study meaningful ciphertext image encryption. Most of the existing image encryption reviews only focus on noise-like ciphertext encryption, or take meaningful ciphertext encryption as a part of it, and have not carefully sorted out and summarized the image encryption of meaningful ciphertext. To this end, this paper provides a comprehensive summary of image encryption algorithms for meaningful ciphertexts to help researchers identify research problems or select appropriate methods for their desired applications. This paper first introduces and classifies the meaningful ciphertext image encryption algorithms from 2015 to now, and then describes several different types of embedding methods in detail, and compares them with experiments. In addition to the conventional experiments, this paper also introduces steganographic analysis into meaningful ciphertext image encryption for the first time to judge the security of embedding algorithms. Finally, we discussed various outstanding problems and the direction of future development. The implementation is accessible at https://***/jsw1995/MCE-overview. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Subducting passive continental margins with crustal (ultra)mafic intrusions: An underappreciated mechanism for recycling water back into the mantle

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Earth and Planetary Physics 2023年第5期7卷 576-581页

作者： CaiCai Pan XiaoBo He Marine Science&Technology College Zhejiang Ocean UniversityZhoushan Zhejiang 316022China

It is well known that outer rise bending-assisted oceanic plate hydration is an important mechanism for transporting substantial amounts of water into the mantle.A key question is:Are there other equally or more important water transport mechanisms?Here we propose,for the first time,that subducting passive continental margins,particularly those with crustal(ultra)mafic intrusions,play a critical role in recycling water back into the *** for this mechanism is the exceptionally high outer rise seismicity observed in a subducting passive continental margin(i.e.,the northeastern South China Sea continental margin)near the northern Manila trench,characterized by a high-velocity lower crust that has been attributed to(ultra)mafic *** interpretation of this correlation between high outer rise seismicity and lower crust(ultra)mafic intrusions is that(ultra)mafic intrusions alter the crustal rheology and increase brittle deformation in the lower crust in this region,thereby promoting lithospheric fracturing and plate hydration,which is evidenced by increased outer rise seismicity.

关键词： (ultra)mafic intrusion passive margin lithospheric fracturing outer rise plate hydration

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Intelligent integrated sensing and communication:a survey

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science China(Information sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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