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Radiology Science 2024年第1期 15-24页

作者： Yilin Li Jiaojiao Liu Yanyan Zhang Jingwei Wei Yang Luo Xue Yang Yanbin Shi Lingling Zhao Wanshui Yang Hongjun Li Jie Tian School of Public Health Anhui Medical UniversityHefeiAnhuiChina Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Radiology Beijing Youan HospitalCapital Medical UniversityBeijingChina Beijing Key Laboratory of Molecular Imaging Beijing 100190China School of Life Science and Technology Xidian UniversityXi’anShaanxi 710071China Department of Radiology The Sixth Peoples Hospital of ZhengzhouHenanChina Department of Nutrition School of Public HealthAnhui Medical UniversityHefeiAnhuiChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxi 710126China

In the domain of medical image analysis,there is a burgeoning recognition and adoption of large models distinguished by their extensive parameter count and intricate neural network architecture that is predominantly due to their outstanding *** review article seeks to concisely explore the historical evolution,specific applications,and training methodologies associated with these large models considering their current prominence in medical image ***,we delve into the prevailing challenges and prospective opportunities related to the utilization of large models in the context of medical image *** a comprehensive analysis of these substantial models,this study aspires to provide valuable insights and guidance to researchers in the field of radiology,fostering further advances and optimizations in their incorporation into medical image analysis practices,in accordance with the submission requirements.

关键词： Large scale model Neural network architecture Medical image analysis Artificial intelligence Radiomics

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Deep Reinforcement Learning with Transformers for Text Adventure Games

Deep Reinforcement Learning with Transformers for Text Adven...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Yunqiu Xu Ling Chen Meng Fang Yang Wang Chengqi Zhang Centre for Artificial Intelligence University of Technology Sydney Sydney Australia Tencent Robotics X Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology China

ISBN: (数字)9781728145334

ISBN: (纸本)9781728145341

In this paper, we study transformers for text-based games. As a promising replacement of recurrent modules in Natural Language Processing (NLP) tasks, the transformer architecture could be treated as a powerful state representation generator for reinforcement learning. However, the vanilla transformer is neither effective nor efficient to learn with a huge amount of weight parameters. Unlike existing research that encodes states using LSTMs or GRUs, we develop a novel lightweight transformer-based representation generator featured with reordered layer normalization, weight sharing and block-wise aggregation. The experimental results show that our proposed model not only solves single games with much fewer interactions, but also achieves better generalization on a set of unseen games. Furthermore, our model outperforms state-of-the-art agents in a variety of man-made games.

关键词： Games Generators Task analysis Reinforcement learning Logic gates Buildings Computer architecture

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Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 329-342页

作者： Hailin Li Weiyuan Huang Siwen Wang Priya SBalasubramanian Gang Wu Mengjie Fang Xuebin Xie Jie Zhang Di Dong Jie Tian Feng Chen Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Radiology Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Psychiatry Weill Cornell MedicineNew YorkNY 10065USA Department of Radiotherapy Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China Department of Radiology Kiang Wu HospitalSanto AntónioMacao 999078China Department of Radiology Zhuhai People’s Hospital(Zhuhai Hospital Affiliated With Jinan University)ZhuhaiGuangdong 519000China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxi 710126China Zhuhai Precision Medical Center Zhuhai People’s HospitalZhuhaiGuangdong 519000China.

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed

关键词： Dynamic contrast-enhanced magnetic resonance imaging Magnetic resonance imaging Radiomics Prognostic prediction

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Two-Stage Attention-Based Model for Code Search with Textual and Structural Features

Two-Stage Attention-Based Model for Code Search with Textual...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Ling Xu Huanhuan Yang Chao Liu Jianhang Shuai Meng Yan Yan Lei Zhou Xu Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China

Searching and reusing existing code from a large scale codebase can largely improve developers' programming efficiency. To support code reuse, early code search models leverage information retrieval (IR) techniques to index a large-scale code corpus and return relevant code according to developers' search query. However, IR-based models fail to capture the semantics in code and query. To tackle this issue, developers applied deep learning (DL) techniques to code search models. However, these models either are too complex to determine an effective method efficiently or learning for semantic correlation between code and query *** bridge the semantic gap between code and query effectively and efficiently, we propose a code search model TabCS (Two-stage Attention-Based model for Code Search) in this study. TabCS extracts code and query information from the code textual features (i.e., method name, API sequence, and tokens), the code structural feature (i.e., abstract syntax tree), and the query feature (i.e., tokens). TabCS performs a two-stage attention net-work structure. The first stage leverages attention mechanisms to extract semantics from code and query considering their semantic gap. The second stage leverages a co-attention mechanism to capture their semantic correlation and learn better code/query representation. We evaluate the performance of TabCS on two existing large-scale datasets with 485k and 542k code snippets, respectively. Experimental results show that TabCS achieves an MRR of 0.57 on Hu et al.'s dataset, outperforming three state-of-the-art models CARLCS-CNN, DeepCS, and UNIF by 18%, 70%, 12%, respectively. Meanwhile, TabCS gains an MRR of 0.54 on Husain et al.'s, outperforming CARLCS-CNN, DeepCS, and UNIF by 32%, 76%, 29%, respectively.

关键词： Deep learning Correlation Semantics Syntactics Programming Feature extraction Information retrieval

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Response of Soil Fertility to Soil Erosion on a Regional Scale: A Case Study of Northeast China

SSRN

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SSRN 2023年

作者： Xiong, Junfeng Wu, Hanyi Yi, Shaorong Lin, Chen Ma, Ronghua International Research Center of Big Data for Sustainable Development Goals Beijing100094 China Key Laboratory of Watershed Geographic Sciences Nanjing Institute of Geography and Limnology Chinese Academy of Sciences Nanjing210008 China State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing100875 China Key Laboratory of Environmental Change and Natural Disaster Ministry of Education Beijing Normal University Beijing100875 China School of Water Conservancy and Ecological Engineering Nanchang Institute of Technology Nanchang330099 China University of Chinese Academy of Sciences Nanjing211135 China

Climate change and human activities have increased the risk of soil erosion, resulting in widespread soil degradation in many regions worldwide. In this study, our purpose was to estimate the spatial and temporal changes in the soil fertility index of Northeast China from the 1990s to the 2010s from a regional perspective. The soil erosion modulus, calculated using the Revised Universal Soil Loss Equation, was combined with soil nutrient data for four nutrients in two time stages, and the response of soil fertility to soil erosion was analysed under nine land-use patterns. The results indicated that soil fertility in Northeast China had decreased by 4.46% from the 1990s to the 2010s, with the fertility of cultivated land showing the smallest decline at 1.69%, followed by grassland at 4.81%, and forest land at 6.21%, in that order. Compared with unchanged land use, soil fertility for changed land use decreased in a more obvious manner. The IFI was negatively correlated with the soil erosion modulus, especially when the average soil erosion modulus exceeded 2 t/(ha/yr). Furthermore, IFI was mostly negative, indicating obvious soil degradation. The contribution rate of nutrient loss in the 1990s averaged over 70%, which was significantly higher than that in the 2010s, indicating that the soil and water conservation measures implemented by most city governments after 2010 had been effective. In most cases, the contribution rates of the 1990s_TK and the 1990s_SOC were significantly higher than those of the other indicators, with the highest contribution rate of SOC loss being observed for cultivated land and the highest contribution rate of TK loss being observed for forest- and grass-lands. Forestland is conducive to maintaining soil fertility, and when other types of land are converted into forestland the contribution rate of nutrient loss becomes lower. © 2023, The Authors. All rights reserved.

关键词： Land use

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Denoi-SpEx+: A Speaker Extraction Network based Speech Dialogue System

Denoi-SpEx+: A Speaker Extraction Network based Speech Dialo...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Yun Hao Xiangkang Huang Huichou Huang Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou China City University of Hong Kong Hong Kong China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

The speech dialogue system has gradually been widely used in daily life. Users can consult and communicate with the system through natural language. However, in practical applications, third-person background sounds and background noise interference in real dialogue scenes will be encountered. The uncertainty and complexity of these background sounds will have a bad impact on the recognition of the system. A good speech enhancement module can help us to separate the target speaker from the original speech. Recently, a solution called SpEx+ was proposed from the time domain, but SpEx+ needs a reference speech to assist in training. This reference speech may have noise in actual applications that will affect performance. Therefore, we propose a Denoi-SpEx+ model. Before the reference speech is input to the network, a speech denoising network is added, so that the quality of speech separation in practical applications can be guaranteed. Experiments show that our model can significantly improve the performance of speech separation model of noisy reference speech.

关键词： Training Uncertainty Conferences Noise reduction Natural languages Interference Speech enhancement

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A second-scale periodicity in an active repeating fast radio burst source

arXiv

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arXiv 2025年

作者： Du, Chen Huang, Yong-Feng Geng, Jin-Jun Gao, Hao-Xuan Zhang, Li Deng, Chen Cui, Lang Liao, Jie Jiang, Peng-Fei Zhang, Liang Wang, Pei Hu, Chen-Ran Dong, Xiao-Fei Xu, Fan Li, Liang Zou, Ze-Cheng Kurban, Abdusattar School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education Jiangsu Nanjing210023 China Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China College of Big Data and Information Engineering Guizhou University Guizhou Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang550025 China Xinjiang Astronomical Observatory Chinese Academy of Sciences Xinjiang Urumqi830011 China Key Laboratory of Radio Astronomy and Technology Chinese Academy of Sciences Beijing100101 China Xinjiang Key Laboratory of Radio Astrophysics Xinjiang Urumqi830011 China College of Astronomy and Space Science University of Chinese Academy of Sciences Beijing101408 China Guizhou Vocational College of Economics and Business Guizhou Duyun558022 China CAS Key Laboratory of FAST NAOC Chinese Academy of Sciences Beijing100101 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Physics Anhui Normal University Anhui Wuhu241002 China Institute of Fundamental Physics and Quantum Technology Ningbo University Zhejiang Ningbo315211 China Department of Physics School of Physical Science and Technology Ningbo University Zhejiang Ningbo315211 China INAF-Osservatorio Astronomico d’Abruzzo Teramo64100 Italy

Fast radio bursts (FRBs) are fierce radio flashes from the deep sky. Abundant observations have indicated that highly magnetized neutron stars might be involved in these energetic bursts, but the underlying trigger mechanism is still enigmatic. Especially, the widely expected periodicity connected to the spin of the central engine has never been discovered, which leads to further debates on the nature of FRBs. Here we report the first discovery of a ∼ 1.7 s period in the repeating source of FRB 20201124A. This is an active repeater, from which more than 2800 FRBs have been observed on a total of 49 days. The phase-folding method is adopted to analyze the bursts on each day separately. While no periodical signal is found in all other datasets, a clear periodicity does appear on two specific days, i.e. a period of 1.706015(2) s on MJD 59310, and a slightly larger period of 1.707972(1) s on MJD 59347. A period derivative of 6.14 × 10−10 s s−1 can be derived from these two periods, which further implies a surface magnetic field strength of 1.04 × 1015 G and a spin-down age of 44 years for the central engine. It is thus concluded that FRB 20201124A should be associated with a young magnetar. © 2025, CC BY.

关键词： Magnetoplasma

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Graph Edit Distance Reward: Learning to Edit Scene Graph 16th

Graph Edit Distance Reward: Learning to Edit Scene Graph

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16th European Conference on Computer Vision, ECCV 2020

作者： Chen, Lichang Lin, Guosheng Wang, Shijie Wu, Qingyao Nanyang Technological University Singapore Singapore Zhejiang University Hangzhou China Huazhong University of Science and Technology Wuhan China School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Beijing China

ISBN: (纸本)9783030585280

Scene Graph, as a vital tool to bridge the gap between language domain and image domain, has been widely adopted in the cross-modality task like VQA. In this paper, we propose a new method to edit the scene graph according to the user instructions, which has never been explored. To be specific, in order to learn editing scene graphs as the semantics given by texts, we propose a Graph Edit Distance Reward, which is based on the Policy Gradient and Graph Matching algorithm, to optimize neural symbolic model. In the context of text-editing image retrieval, we validate the effectiveness of our method in CSS and CRIR dataset. Besides, CRIR is a new synthetic dataset generated by us, which we will publish soon for future use. © 2020, Springer Nature Switzerland AG.

关键词： Semantics

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Single-cell technologies:From research to application

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The Innovation 2022年第6期3卷 92-108页

作者： Lu Wen Guoqiang Li Tao Huang Wei Geng Hao Pei Jialiang Yang Miao Zhu Pengfei Zhang Rui Hou Geng Tian Wentao Su Jian Chen Dake Zhang Pingan Zhu Wei Zhang Xiuxin Zhang Ning Zhang Yunlong Zhao Xin Cao Guangdun Peng Xianwen Ren Nan Jiang Caihuan Tian Zi-Jiang Chen Biomedical Pioneering Innovation Centre(BIOPIC) Peking UniversityBeijing 100871China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghai 200031China School of Chemical Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China Mozhuo Biotech(Zhejiang)Co. Ltd.TongxiangJiaxing 314500China Geneis(Beijing)Co. Ltd.Beijing 100102China Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhou 510530China Department of Medical Oncology Zhongshan HospitalFudan UniversityShanghai 200032China School of Food Science and Technology Dalian Polytechnic UniversityDalian 116034China State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationBeijing Advanced Innovation Center for Biomedical EngineeringSchool of Engineering MedicineBeihang UniversityBeijing 100083China Department of Mechanical Engineering City University of Hong KongHong Kong 999077China Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China Center of Peony Institute of Vegetables and FlowersChinese Academy of Agricultural SciencesKey Laboratory of Biology and Genetic Improvement of Flower Crops(North China)Key Laboratory of Biology and Genetic Improvement of Horticultural CropsMinistry of Agriculture and Rural AffairsBeijing 100081China Department of Hepatic Surgery Fudan University Shanghai Cancer CenterShanghai 200032China Advanced Technology Institute University of SurreyGuildfordSurreyGU27XHUK National Physical Laboratory TeddingtonMiddlesex TW110LWUK Institute of Clinical Science Zhongshan HospitalFudan UniversityShanghai 200032China West China School of Basic Medical Sciences&Forensic Medicine Sichuan UniversityChengdu 610041China Jinfeng Laboratory Chongqing 401329China Center for Reproduc

In recent years,more and more single-cell technologies have been developed.A vast amount of single-cell omics data has been generated by large projects,such as the Human Cell Atlas,the Mouse Cell Atlas,the Mouse RNA Atlas,the Mouse ATAC Atlas,and the Plant Cell *** on these single-cell big data,thousands of bioinformatics algorithms for quality control,clustering,cell-type annotation,developmental inference,cell-cell transition,cell-cell interaction,and spatial analysis are *** powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence,the molecular landscape at the single-cell level can be revealed.

关键词： Atlas landscape application

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Using stochastic gradient descent and deep learning to defect detection for medicinal hollow capsule

Using stochastic gradient descent and deep learning to defec...

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Chinese Automation Congress (CAC)

作者： Jing Yang Yifan Wang Zheng Wang Ji Xu Xingxing Zhang Shihao Tang State Key Laboratory of Public Big Data Guizhou University GuiYang China School of Mechanical Engineering Guizhou University GuiYang China Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University GuiYang China BeijingDaheng Image Vision Co. Ltd. Beijing China

ISBN: (纸本)9781665426480

To detect capsule depression defects in the process of filling pharmaceutical granules with medicinal hollow capsules, we consider using stochastic gradient descent and deep learning to defect detection for the medicinal hollow capsule. Firstly, A background-weakening method based on image filtering is introduced for the surface defects of medicinal capsules under complex background. Then, combined with the acquisition and feature extraction of capsule images in the actual production process, we propose a YOLO-OL object detection method based on image contour extraction method. The proposed method is validated on an online detection platform under a complex background to verity their accuracy and practicability. Experimental results show that the proposed method is more accurate than the YOLOv3 and traditional edge extraction algorithms, and the detection method has good robustness.

关键词： Deep learning Image edge detection Production Object detection Feature extraction Robustness Image filtering

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