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SSRN 2023年

作者： Guo, Qian Guo, Yi Zhao, Jin Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200436 China Shanghai Engineering Research Center of Big Data & Internet Audience Shanghai200072 China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai200433 China

Knowledge representation is becoming an effective way of information extraction. However, considerable studies ignored its application scenarios in the zero-shot setting. In this paper, we propose a novel framework for prompt language models based on external ontology knowledge called Knowledge-Based Prompt Tuning for the Zero-shot Relation Triplet Extraction (KBPT), which encourages further investigation in low-resource regimes to address the data scarcity problem in Relation Triplet Extraction (RTE). The major task of relation triplet extraction in zero-shot learning is to extract multiple triplets that are consisted of head entities, tail entities, and relation labels from an input sentence where the extracted relation labels do not exist in the training set. The fundamental idea of prompt tuning is to construct a prompt template, then append it behind the input text as the pre-trained language models (PLMs) input, thus, transforming the classification task into a masked language model prediction. However, our proposed model does not involve the mask language model prediction but a well-designed prompt template in a structured text format to generate synthetic training data containing the unseen relation category. Concretely, we utilize the relation labels and incorporate virtual tokens sources from the relation semantics to construct a structured prompt template for generating synthetic training instances. Moreover, to further enrich and supplement prior knowledge, we draw on ontology schema based on external knowledge bases to enhance the capability of semantic representation in the prompt template. To address the problem of knowledge heterogeneity, we synergistically optimize these embedding representations by way of collective training. In addition, we carefully design a Multiple Triplets Decoding (MTD) algorithm to break through the limitation of extracting multiple relation triplets in a sentence, and our proposed model is model-agnostic and can be orthogon

关键词： Ontology

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Primary cilia support cartilage regeneration after injury

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International Journal of Oral science 2023年第2期15卷 291-303页

作者： Dike Tao Lei Zhang Yunpeng Ding Na Tang Xiaoqiao Xu Gongchen Li Pingping Niu Rui Yue Xiaogang Wang Yidong Shen Yao Sun Department of Implantology School&Hospital of StomatologyTongji UniversityShanghaiChina Shanghai Engineering Research Center of Tooth Restoration and Regeneration ShanghaiChina State Key Laboratory of Cell Biology Shanghai Institute of Biochemistry and Cell BiologyCenter for Excellence in Molecular Cell ScienceChinese Academy of Sciences University of Chinese Academy of Sciences ShanghaiChina Department of Oral and Maxillofacial Surgery School&Hospital of StomatologyTongji UniversityShanghaiChina Institute for Regenerative Medicine Shanghai East HospitalFrontier Science Center for Stem Cell ResearchShanghai Key Laboratory of Signaling and Disease ResearchSchool of Life Sciences and TechnologyTongji UniversityShanghaiChina Shanghai Institute of Stem Cell Research and Clinical Translation ShanghaiChin Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingChina

In growing children,growth plate cartilage has limited self-repair ability upon fracture injury always leading to limb growth ***,one type of fracture injuries within the growth plate achieve amazing self-healing,however,the mechanism is *** this type of fracture mouse model,we discovered the activation of Hedgehog(Hh)signaling in the injured growth plate,which could activate chondrocytes in growth plate and promote cartilage repair.

关键词： fracture plate injuries

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SELF-ATTENTION RECURRENT SUMMARIZATION NETWORK WITH REINFORCEMENT LEARNING FOR VIDEO SUMMARIZATION TASK

SELF-ATTENTION RECURRENT SUMMARIZATION NETWORK WITH REINFORC...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Phaphuangwittayakul, Aniwat Guo, Yi Ying, Fangli Xu, Wentian Zheng, Zheng Department of Computer Science and Engineering East China University of Science and Technology China National Engineering Laboratory for Big Data Distribution and Exchange Technologies China Shanghai Engineering Research Center of Big Data and Internet Audience China

ISBN: (纸本)9781665438643

With the exponential growth of video data, video summarization techniques are urgently needed for reducing people's efforts in the videos' content exploration by generating succinct but informative summaries from original lengthy videos. Though supervised video summarization approaches have demonstrated the state-of-the-art performance, unsupervised methods are still highly demanded due to resourcefully expensive human annotations and the subjectiveness of video summarization tasks. In this paper, a novel unsupervised-based Deep Self-attention Recurrent summarization network with Reinforcement Learning (DSR-RL) for video summarization is proposed. The model can learn the input video sequence and suggest the key-shot summary without additional human annotations by integrating self-attention, BRNN, and reinforcement learning mechanisms. The DSR-RL improves not only importance score through the attention map vector of self-attention network but also the diversity of summaries via the reward function of reinforcement learning. Our method outperforms the state-of-the-art unsupervised video summarization methods on both SumMe and TVSum datasets. The source code is available at https://***/phaphuang/DSR-RL. © 2021 IEEE

关键词： Reinforcement learning

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Image Denoising Method Based on Channel Compression and Split-Attention Mechanism

Image Denoising Method Based on Channel Compression and Spli...

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computer engineering and Intelligent Control (ICCEIC) International Conference on

作者： Hao Sun Xiaoyan Qiao Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Shandong

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In the image restoration task, how to make full use of spatial and channel feature information to improve the reconstruction quality of the model without significantly increasing the computational complexity is an important research issue. To address this challenge, this paper proposes a CS module (Channel Compression and Split-Attention) specifically for optimizing image denoising tasks. The module first compresses the features through a preliminary convolution operation, then introduces the split attention convolution mechanism to split the features by channel and dynamically weight them, thereby enhancing the model's adaptive attention to different feature channels and improving feature expression capabilities. Through experiments on multiple image denoising datasets, the results show that the CS module significantly improves the image reconstruction quality, especially in terms of peak signal-to-noise ratio (PSNR). At the same time, the introduction of the CS module does not lead to a significant increase in computational complexity, maintaining a high computational efficiency, which verifies the practicality and superiority of the module in image denoising tasks.

关键词： Image coding PSNR Convolution Computational modeling Refining Image restoration Computational efficiency Computational complexity Image reconstruction Image denoising

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Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model

SSRN

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SSRN 2025年

作者： Xiong, Yida Li, Kun Chen, Jiameng Zhang, Hongzhi Lin, Di Che, Yan Hu, Wenbin School of Computer Science Wuhan University Wuhan China Engineering Research Center for Big Data Application in Private Health Medicine of Fujian Universities Putian University Putian China Wuhan University Shenzhen Research Institute Shenzhen China

Molecular optimization (MO) is a crucial stage in drug discovery in which task-oriented generated molecules are optimized to meet practical industrial requirements. Existing mainstream MO approaches primarily utilize external property predictors to guide iterative property optimization. However, learning all molecular samples in the vast chemical space is unrealistic for predictors. As a result, errors and noise are inevitably introduced during property prediction due to the nature of approximation. This leads to discrepancy accumulation, generalization reduction and suboptimal molecular candidates. In this paper, we propose a text-guided multi-property molecular optimization method utilizing transformer-based diffusion language model (TransDLM). TransDLM leverages standardized chemical nomenclature as semantic representations of molecules and implicitly embeds property requirements into textual descriptions, thereby mitigating error propagation during diffusion process. By fusing physically and chemically detailed textual semantics with specialized molecular representations, TransDLM effectively integrates diverse information sources to guide precise optimization, which enhances the model's ability to balance structural retention and property enhancement. Additionally, the success of a case study further demonstrates TransDLM's ability to solve practical problems. Experimentally, our approach surpasses state-of-the-art methods in maintaining molecular structural similarity and enhancing chemical properties on the benchmark dataset. © 2025, The Authors. All rights reserved.

关键词： Cyclization

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Joint Scheduling and Beamforming Design in Traffic-Aware RIS Aided MEC Network

Joint Scheduling and Beamforming Design in Traffic-Aware RIS...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Li, Aichen Liu, Yang Wu, Qingqing Shi, Qingjiang Zhao, Jun Dalian University of Technology School of Information and Communication Engineering China University of Macau Department of Electrical and Computer Engineering China Tongji Univ. Shanghai Shenzhen Research Institute of Big Data School of Software Eng. Shenzhen China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9781665459754

This paper considers exploiting reconfigurable intelligent surface (RIS) in a multi-user multi-access mobile computing (MEC) system. To precisely model the traffic behaviors of the tasks, we consider queuing procedures at both mobile users and the cloud server. This consideration leads to concatenation of queues and makes our problem much more difficult than existing literature since analytic expression of the objective is impossible. Our goal is to jointly optimize the task scheduling, RIS configuration and computation resource allocation to improve the MEC network's overall computation rate in the long run while maintaining the stability of the queues. To tackle this highly challenging stochastic problem, we develop a Lyapunov guided deep reinforcement learning (DRL) solution, which borrows the Lyapunov optimization theory to stabilize queues and at the same time can effectively optimize the intractable objective in an online manner. Numerical results will be presented to verify the effectiveness of our solution and the performance boost due to the deployment of RIS. © 2022 IEEE.

关键词： Edge computing

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Proximity-induced magnetic order in topological insulator on ferromagnetic semiconductor

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science China(Information sciences) 2023年第12期66卷 267-274页

作者： Hangtian WANG Koichi MURATA Weiran XIE Jing LI Jie ZHANG Kang L.WANG Weisheng ZHAO Tianxiao NIE School of Integrated Circuit Science and Engineering and Advanced Innovation Center for Big Data and Brain Computing Beihang University Beihang-Goertek Joint Microelectronics Institute Qingdao Research InstituteBeihang University Department of Electrical Engineering University of California

Introducing magnetic order into topological insulator（TI） to break the time-reversal symmetry can yield numerous fascinating physical phenomena,which brings new hope for the emerging spintronic *** proximity effect is regarded as a promising strategy that could advance the step for realistic application by choosing a suitable ferromagnetic layer with the merits of high Curie temperature and high compatibility with mainstream semiconductor ***,we prepare a Bi2Se3thin film on Sicompatible ferromagnetic semiconductor（FMS） of MnxGe1-xby molecular beam *** integration,the nonmagnetic Bi2Se3exhibits an anomalous Hall signal and a clear weak localization cusp in magnetoresistance until 150 K,confirming that a high-temperature magnetism can be induced by the proximity *** investigation of the magnetoconductance quantitatively indicates that the Bi2Se3conductance suffers a transition from weak antilocalization to weak localization behavior after integrating with MnxGe1-x,and an 80 meV bandgap is predicted to be opened in the surface states in Bi2Se3layer due to the proximityinduced *** results prove that the proximity effect could be an important method to achieve topological magnetism at high temperatures,and reveals its potential for the manipulation of the topological surface states.

关键词： topological insulator ferromagnetic semiconductor proximity effect quantum interference

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Correction: Prompt4LJP: prompt learning for legal judgment prediction

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The Journal of Supercomputing 2025年第5期81卷 1-2页

作者： Huang, Qiongyan Xia, Yuhan Long, Yunfei Fang, Hui Liang, Ruiwei Guan, Yin Xu, Ge College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou China School of Computer Science and Electronic Engineering University of Essex Colchester UK School of Computer and Big Data Minjiang University Fuzhou China Fujian Mental Health Human-Computer Interaction Technology Research Center Minjiang University Fuzhou China Fuzhou Institute of Oceanography Fuzhou China

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Listening and Seeing Again: Generative Error Correction for Audio-Visual Speech Recognition

arXiv

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arXiv 2025年

作者： Liu, Rui Yuan, Hongyu Li, Haizhou Inner Mongolia University China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore

Unlike traditional Automatic Speech Recognition (ASR), Audio-Visual Speech Recognition (AVSR) takes audio and visual signals simultaneously to infer the transcription. Recent studies have shown that Large Language Models (LLMs) can be effectively used for Generative Error Correction (GER) in ASR by predicting the best transcription from ASR-generated N-best hypotheses. However, these LLMs lack the ability to simultaneously understand audio and visual, making the GER approach challenging to apply in AVSR. In this work, we propose a novel GER paradigm for AVSR, termed AVGER, that follows the concept of "listening and seeing again". Specifically, we first use the powerful AVSR system to read the audio and visual signals to get the N-Best hypotheses, and then use the Q-former-based Multimodal Synchronous Encoder to read the audio and visual information again and convert them into an audio and video compression representation respectively that can be understood by LLM. Afterward, the audio-visual compression representation and the N-Best hypothesis together constitute a Cross-modal Prompt to guide the LLM in producing the best transcription. In addition, we also proposed a Multi-Level Consistency Constraint training criterion, including logits-level, utterance-level and representations-level, to improve the correction accuracy while enhancing the interpretability of audio and visual compression representations. The experimental results on the LRS3 dataset show that our method outperforms current mainstream AVSR systems. The proposed AVGER can reduce the Word Error Rate (WER) by 24% compared to them. Code and models can be found at: https://***/CircleRedRain/AVGER. Copyright © 2025, The Authors. All rights reserved.

关键词： Speech recognition

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Automated Mapping of Global 30-m Tidal Flats Using Time-Series Landsat Imagery:Algorithm and Products

Journal of Remote Sensing

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Journal of Remote Sensing 2023年第1期3卷 362-377页

作者： Xiao Zhang Liangyun Liu Jinqing Wang Tingting Zhao Wendi Liu Xidong Chen International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China College of Geomatics Xi’an University of Science and TechnologyXi’an 710054China North China University of Water Resources and Electric Power Zhengzhou 450046China

Tidal flats are an important part of coastal ecosystems and play an important role in shoreline protection and biodiversity *** many efforts have been made in tidal flat mapping,an accurate global tidal flat product covering all coasts globally is still lacking and urgently *** this study,a novel method is proposed for the automated mapping of global tidal flats at 30 m(GTF30)in 2020 based on the Google Earth Engine,which is also the first global tidal flat dataset covering the high latitudes(>60°N).Specifically,we first propose a new spectral index named the LTideI index through a sensitivity analysis,which is robust and can accurately capture low-tide ***,globally distributed training samples are automatically generated by combining multisource datasets and the spatiotemporal refinement ***,the global coasts are divided into 5885°×5°geographical tiles,and the local adaptive classification strategy is used to map tidal flats in each 5°×5°region by using multisourced training features and the derived globally distributed training *** statistical results show that the total global area of tidal flats is about 140,922.5 km2,with more than 75% distributed on 3 continents in the Northern Hemisphere,especially in Asia(approximately 43.1% of the total).Finally,the GTF30 tidal flat dataset is quantitatively assessed using 13,994 samples,yielding a good overall accuracy of 90.34%.Meanwhile,the intercomparisons with several existing tidal flat datasets indicate that the GTF30 products can greatly improve the mapping accuracy of tidal ***,the novel method can support the automated mapping of tidal flats,and the GTF30 dataset can provide scientific guidance and data support for protecting coastal ecosystems and supporting coastal economic and social *** GTF30 tidal flat dataset in 2020 is freely accessible via https://***/10.5281/zenodo.7936721.

关键词： tidal coastal Tidal

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