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Propagating subduction initiation of the Caroline Plate at the Mussau Trench

science China Earth sciences 2025年第1期68卷 253-269页

作者： Chun-Feng LI Yongkang DENG Tao WU Dongdong DONG Guoliang ZHANG Jiazheng ZHANG Yutao LIU Yaqing LI Fugui TANG Lulu ZHANG Department of Marine Sciences Zhejiang UniversityZhoushan316021China Laboratory of Marine Mineral Resources Laoshan LaboratoryQingdao266237China Key Lab of Marine Geology and Environment Institute of OceanologyChinese Academy of SciencesQingdao266071China Key Lab of Ocean and Marginal Sea Geology South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou510301China Science&Technology on Integrated Information System Lab Institute of SoftwareChinese Academy of SciencesBeijing100190China

New seismic imaging from the Mussau Trench confirms that,under horizontal compression on an oceanic plate boundary,it is within the young and weak oceanic plate that a trench will develop,and it is the young plate,not so long after its birth,that starts to subduct toward the old *** Mesozoic Pacific Plate cannot easily bend,under the joint horizontal compression and vertical gravity pull,even with a pre-existing weak zone in place and with a large age offset across the plate *** bending of the Caroline Plate near the northern end of the Mussau Trench started only after a long-time span of trench migration,and/or after a prolonged transition from initial compression to trench *** contrasting trench deformation and sedimentary accretion between two parallel seismic sections support an evident migration of underthrusting or significant longitudinal heterogeneity along the *** swift transition from underthrusting to non-thrusting can be explained by a counter-clockwise rotation of the Caroline *** miniature accretionary wedges develop even before the subduction initiates imply strong compression and uplifting before the underthrusting can *** of the Caroline Plate have been halted as the Mussau Trench is seismically rather quiet and the Lyra Trough to the east shows relaxational extension.

关键词： Mussau Trench Subduction initiation Seismic reflection Caroline Plate

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SlotFusion: Object-Centric Audiovisual Feature Fusion with Slot Attention for Remote Sensing Scene Recognition

SlotFusion: Object-Centric Audiovisual Feature Fusion with S...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Han, Fangzhou Yu, Tianyi Zhang, Lamei Si, Lingyu Zhang, Yiqi Dept. of Information Engineering Harbin Institute of Technology Harbin China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Despite significant advancements in remote sensing multimodal learning, particularly in image-image feature fusion, the exploration of audio-image feature fusion remains insufficient. Given the complexity and redundancy of ground objects in remote sensing images, accurately aligning audio features with image features during the fusion process is a critical challenge. In this paper, we introduce an object-centric feature fusion method named SlotFusion. By employing a slot attention-based feature decoupling module and a slot-based audiovisual feature fusion module, we transform modality features with complex semantic information into a set of slot features corresponding to object units and use gated activation units to adaptively implement object-centric feature fusion. Experiments on the Audio Visual Aerial Scene Recognition dataset (ADVANCE) demonstrate that the proposed SlotFusion significantly improves remote sensing scene recognition performance, with a 7.04% increase in overall accuracy compared to previous methods, achieving state-of-the-art results. © 2025 IEEE.

关键词： Audiovisual feature fusion Gated feature fusion Object-centric learning Slot attention

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Repairing Catastrophic-Neglect in Text-to-Image Diffusion Models via Attention-Guided Feature Enhancement

Repairing Catastrophic-Neglect in Text-to-Image Diffusion Mo...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chang, Zhiyuan Li, Mingyang Wang, Junjie Liu, Yi Wang, Qing Liu, Yang State Key Laboratory of Intelligent Game Beijing China Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences China Nanyang Technological University Singapore

ISBN: (纸本)9798891761681

Text-to-Image Diffusion Models (T2I DMs) have garnered significant attention for their ability to generate high-quality images from textual descriptions. However, these models often produce images that do not fully align with the input prompts, resulting in semantic inconsistencies. The most prominent issue among these semantic inconsistencies is catastrophic-neglect, where the images generated by T2I DMs miss key objects mentioned in the prompt. We first conduct an empirical study on this issue, exploring the prevalence of catastrophic-neglect, potential mitigation strategies with feature enhancement, and the insights gained. Guided by the empirical findings, we propose an automated repair approach named Patcher to address catastrophic-neglect in T2I DMs. Specifically, Patcher first determines whether there are any neglected objects in the prompt, and then applies attention-guided feature enhancement to these neglected objects, resulting in a repaired prompt. Experimental results on three versions of Stable Diffusion demonstrate that Patcher effectively repairs the issue of catastrophic-neglect, achieving 10.1%-16.3% higher Correct Rate in image generation compared to baselines. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Transformer-based Time Series Prediction Model with Contrastive Learning for Nuclear Power system State Monitoring 15

A Transformer-based Time Series Prediction Model with Contra...

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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Gou, Pengfei Zheng, Daiwei Tang, Lei Liu, Mengjuan School of Information and Software Engineering University of Electronic Science and Technology of China UESTC Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China NPIC Chengdu China

ISBN: (纸本)9798350354010

Prognostics and Health Management (PHM) is currently the mainstream solution for the condition monitoring and maintenance of industrial systems. In PHM, the system condition is monitored by monitoring the operational status parameters of the system to diagnose equipment faults, predict the remaining useful life, etc. Utilizing sequential operational status parameters to complete prediction and classification tasks is usually modeled as a time series processing task. Therefore, a high-performance time series prediction model is crucial for PHM. This paper focuses on the use of time series prediction models to achieve the prediction of key state parameters of the nuclear power system. However, there are still some challenges when directly applying existing time series prediction models to industrial systems. First, obtaining operational data on the nuclear power system is relatively difficult, resulting in a small amount of training data, which causes many models to face the risk of insufficient training or overfitting. Second, most of the existing time series prediction models are composed of high- and low-frequency decoupling modules, while the operational data of nuclear power systems often have fewer high-frequency components, making it difficult for many existing SOTA models to achieve good results. In order to address these challenges, we have developed a Transformer-based time series prediction model with contrastive learning for monitoring the status of nuclear power systems. We evaluated the new model on three nuclear power simulation datasets and four power transformer datasets. The experimental results show that the new model's performance is superior to baseline models. We also proved the effectiveness of our improvement through ablation experiments. © 2024 IEEE.

关键词： Prediction models

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A Valuable and Low-Budget Process Scheme of Equivalized 1 nm technology Node Based on 2D Materials

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Nano-Micro Letters 2025年第8期17卷 294-305页

作者： Yang Shen Zhejia Zhang Zhujun Yao Mengge Jin Jintian Gao Yuhan Zhao Wenzhong Bao Yabin Sun He Tian College of Integrated Circuit Science and Engineering Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghai 200241People’s Republic of China Institute of Microelectronics and Beijing National Research Center for Information Science and Technology(BNRist) Tsinghua UniversityBeijing 100084People’s Republic of China State Key Laboratory of ASIC and System School of MicroelectronicsFudan UniversityShanghai 200433People’s Republic of China Shaoxin Laboratory Shaoxing 312000People’s Republic of China

Emerging two-dimensional(2D)semiconductors are among the most promising materials for ultra-scaled transistors due to their intrinsic atomic-level *** the stacking process advances,the complexity and cost of nanosheet field-effect transistors(NSFETs)and complementary FET(CFET)continue to *** 1 nm technology node is going to be based on Si-CFET process according to international roadmap for devices and systems(IRDS)(2022,https://***/),but not publicly confirmed,indicating that more possibilities still *** miniaturization advantage of 2D semiconductors motivates us to explore their potential for reducing process costs while matching the performance of next-generation nodes in terms of area,power consumption and *** this study,a comprehensive framework is built.A set of MoS2 NSFETs were designed and fabricated to extract the key parameters and *** then for benchmarking,the sizes of 2D-NSFET are scaled to a extent that both of the Si-CFET and 2D-NSFET have the same average device *** these conditions,the frequency of ultra-scaled 2D-NSFET is found to improve by 36%at a fixed power *** work verifies the feasibility of replacing silicon-based CFETs of 1 nm node with 2D-NSFETs and proposes a 2D technology solution for 1 nm nodes,i.e.,“2D eq 1 nm”*** the same time,thanks to the lower characteristic length of 2D semiconductors,the miniaturized 2D-NSFET achieves a 28%frequency increase at a fixed power ***,developing a standard cell library,these devices obtain a similar trend in 16-bit RISC-V *** work quantifies and highlights the advantages of 2D semiconductors in advanced nodes,offering new possibilities for the application of 2D semiconductors in high-speed and low-power integrated circuits.

关键词： Two-dimensional semiconductors 1 nm technology node Nanosheet field-effect transistors Complementary field-effect transistors Horizontal scaling

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Intelligent recognition and information extraction of radar complex jamming based on time-frequency features

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Journal of systems Engineering and Electronics 2024年第5期35卷 1148-1166页

作者： PENG Ruihui WU Xingrui WANG Guohong SUN Dianxing YANG Zhong LI Hongwen College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China Qingdao Innovation and Development Center Harbin Engineering UniversityQingdao 266000China Information Fusion Research Institute Naval Aeronautical UniversityYantai 264001China National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval University of EngineeringWuhan 430033China

In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more *** is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)*** this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging ***,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter ***,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other ***,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.

关键词： complex jamming recognition time frequency feature convolutional neural network(CNN) parameter estimation

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Bi-Directional and Triangular Circulation Fusion Neural Networks for Small Object Detection

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IEEE Transactions on Circuits and systems for Video technology 2024年第6期35卷 5140-5152页

作者： Li, Fangyu Duan, Junzhu Zhang, Qiyu Shan, Caifeng Han, Honggui the School of Information Science and Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Artificial Intelligence Institute Beijing University of Technology Beijing100124 China the State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China the School of Intelligence Science and Technology Nanjing University Suzhou215163 China

Deep learning-driven object detection models are capable of accurately identifying and localizing objects. However, small objects contain limited information relative to global features, resulting in the fact that detection models often do not learn small object features adequately. To enhance the precision in detecting small objects, we propose a bi-directional and triangular circulation fusion neural network (BTFN). First, to selectively strengthen the position features of small objects, we propose a feature circulation extraction module composed of a bi-directional triangular densely nested convolutional network (BTF), thus achieving repetitive multi-layer feature fusion. Second, to fill up the semantic gaps between different scales of features, we design a mixed dual attention module (MDA) in the bi-directional triangular densely nested network. Third, to mitigate the lost information in the neural networks with deep layers as well as improve the inference time, we design a re-parameterization bi-directional composite feature fusion module (Rep-BFM) that fuses the features of multiple scales. The proposed model is evaluated extensively on the MS COCO, Tsinghua-Tencent 100k, and Haier dismantled parts of used home appliances datasets. The experiment results show that the proposed model improves the AP on MS COCO by 4%, especially the APS of small objects is improved by 7.7% compared with SOTA models. © 2024 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Domestic appliances

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Research on the Remaining Useful Life Prediction Model of Complex Equipment 4

Research on the Remaining Useful Life Prediction Model of Co...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Tang, Lei Tang, Yifei Zhang, Junhao Liu, Mengjuan Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Glasgow College University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331541729

With the rapid advancement of artificial intelligence technology, the development of intelligent manufacturing has become an inevitable trend. Utilizing AI technology to ensure the safe operation of factories is a crucial aspect of smart factories. In particular, research focused on fault diagnosis and remaining useful life (RUL) prediction models based on machine learning has garnered significant attention from both academia and industry. This paper explores RUL prediction models for complex equipment, introduces four representative models, and analyzes their performance using two benchmark datasets, C-MAPSS and N-CMAPSS. Finally, the paper discusses the future research and application trends of artificial intelligence technology in complex equipment operation and maintenance. © 2024 IEEE.

关键词： Smart manufacturing

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Make LLM a Testing Expert: Bringing Human-Like Interaction to Mobile GUI Testing via Functionality-Aware Decisions 24

Make LLM a Testing Expert: Bringing Human-Like Interaction t...

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44th ACM/IEEE International Conference on software Engineering, ICSE 2024

作者： Liu, Zhe Chen, Chunyang Wang, Junjie Chen, Mengzhuo Wu, Boyu Che, Xing Wang, Dandan Wang, Qing State Key Laboratory of Intelligent Game Beijing China Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Technical University of Munich Munich Germany DiDi Global Inc Science & Technology on Integrated Information System Laboratory China

ISBN: (纸本)9798400702174

Automated Graphical User Interface (GUI) testing plays a crucial role in ensuring app quality, especially as mobile applications have become an integral part of our daily lives. Despite the growing popularity of learning-based techniques in automated GUI testing due to their ability to generate human-like interactions, they still suffer from several limitations, such as low testing coverage, inadequate generalization capabilities, and heavy reliance on training data. Inspired by the success of Large Language Models (LLMs) like ChatGPT in natural language understanding and question answering, we formulate the mobile GUI testing problem as a Q&A task. We propose GPTDroid, asking LLM to chat with the mobile apps by passing the GUI page information to LLM to elicit testing scripts, and executing them to keep passing the app feedback to LLM, iterating the whole process. Within this framework, we have also introduced a functionality-aware memory prompting mechanism that equips the LLM with the ability to retain testing knowledge of the whole process and conduct long-term, functionality-based reasoning to guide exploration. We evaluate it on 93 apps from Google Play and demonstrate that it outperforms the best baseline by 32% in activity coverage, and detects 31% more bugs at a faster rate. Moreover, GPTDroid identifies 53 new bugs on Google Play, of which 35 have been confirmed and fixed. © 2024 ACM.

关键词： Graphical user interfaces

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A Valuable and Low-Budget Process Scheme of Equivalized 1 nm technology Node Based on 2D Materials

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Nano-Micro Letters 2025年第08期 302-313页

作者： Yang Shen Zhejia Zhang Zhujun Yao Mengge Jin Jintian Gao Yuhan Zhao Wenzhong Bao Yabin Sun He Tian College of Integrated Circuit Science and Engineering Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal University State Key Laboratory of ASIC and System School of MicroelectronicsFudan University Institute of Microelectronics and Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Shaoxin Laboratory

Emerging two-dimensional（2D） semiconductors are among the most promising materials for ultra-scaled transistors due to their intrinsic atomic-level thickness. As the stacking process advances, the complexity and cost of nanosheet field-effect transistors（NSFETs） and complementary FET（CFET） continue to rise. The 1 nm technology node is going to be based on Si-CFET process according to international roadmap for devices and systems（IRDS）(2022, https://irds. ieee. org/), but not publicly confirmed, indicating that more possibilities still exist. The miniaturization advantage of 2D semiconductors motivates us to explore their potential for reducing process costs while matching the performance of next-generation nodes in terms of area, power consumption and speed. In this study, a comprehensive framework is built. A set of MoS2NSFETs were designed and fabricated to extract the key parameters and performances. And then for benchmarking, the sizes of 2D-NSFET are scaled to a extent that both of the Si-CFET and 2D-NSFET have the same average device footprint. Under these conditions, the frequency of ultra-scaled 2D-NSFET is found to improve by 36% at a fixed power consumption. This work verifies the feasibility of replacing silicon-based CFETs of 1 nm node with 2D-NSFETs and proposes a 2D technology solution for 1 nm nodes, i.e., “2D eq 1 nm” nodes. At the same time, thanks to the lower characteristic length of 2D semiconductors, the miniaturized 2D-NSFET achieves a 28% frequency increase at a fixed power consumption. Further, developing a standard cell library, these devices obtain a similar trend in 16-bit RISC-V CPUs. This work quantifies and highlights the advantages of 2D semiconductors in advanced nodes, offering new possibilities for the application of 2D semiconductors in high-speed and low-power integrated circuits.

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