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arXiv 2019年

作者： Cen, Keting Shen, Huawei Gao, Jinhua Cao, Qi Xu, Bingbing Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology

GCN is a recent effective algorithm which effectively learns a function incorporate both graph structure and node features for semisupervised graph based node classification. Although GCN exceeds other state-of-the-art methods, the number of parameters that need to be learned are still significantly more than the number of used samples. Because only labeled nodes and their 2-hop neighbors are used for learning parameters in GCN. As a consequence the accuracy of node classification declines sharply when decreasing the size of train data. In order to reducing parameters and making better use of unlabeled nodes, we proposes to use unsupervised representation algorithm such as autoencoder to pretrain a embedding for each node, which captures effective information from input features and reduce parameters in graph convolution layers. Experiments show that our model has increased by 0.87%, 1.96%, 1.87% compared to baseline on Cora, Citeseer and Pubmed data respectively. The performance of our model declines slower than GCN when decreasing the train samples. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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ICTNET at TREC 2019 Complex Answer Retrieval Track 28

ICTNET at TREC 2019 Complex Answer Retrieval Track

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28th Text REtrieval Conference, TREC 2019

作者： Ren, Hongfei Xiong, Ruibin Zeng, Yutao Chen, Jiangui Cai, Yinqiong Jiang, Haoquan University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology China

We participate in the Complex Answer Retrieval(CAR) track at TREC 2019. We applied several useful models in this work. In the rough ranking, we applied doc2query model to predict queries and retrieve using BM25. In the re-ranking, we submitted 5 different runs which use 5 different models, include BM25, DRMMTKS, Bert-DRMMTKS, Bert-ConvKNRM and Bert-ConvKNRM-50, to try to get the best result. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

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Influence of key Ecological Function Area Policy on the Components of Water Balance-A case Study in Yangtze River Basin

SSRN

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SSRN 2025年

作者： You, Guangyong Xu, Yan Zhang, Minxia Gao, Yuanyun He, Honglin Zhang, Mengyu Liu, Bo Zhu, Minxin You, Juan Nanjing Institute of Environmental Sciences Ministry of Ecology and Environment Jiangsu Nanjing210042 China Jiangsu Frontier Electric Power Technology Company Limited Jiangsu Nanjing211102 China Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China National Ecosystem Science Data Center Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100190 China School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Hebei Engineering Consulting Institute Hebei Shijiazhuang050057 China

The National key Ecological Function Area (KEFA) policy of China, the world's largest ecological compensation program, played an important role in improving the ecological functions. To reveal the effects of KEFA on land surface water balance components, this study analyzed the difference between KEFA area and the rest of the area (NKEF) in Yangtze River Basin (YRB). The results showed that, 1) the KEFA had higher interception loss (Ei) but lower actual evaporation (Ea) and transpiration (Et) than NKEF. The proportions of Ea, Et, Ei to precipitation (Pre) were higher in KEFA than NKEF, suggesting the KEFA had higher water consumption possibly resulting from the vegetation growth and dynamics. 2) Over the past decades, Ea, Et, and Ei, as well as their proportions to Precipitation (PRE), were significantly increased in YRB. A significant change point around the year 2000 coincided with the climate cycles periods and implementations of national ecological restoration programs i.e. National Grain for Green Program. 3) The long-term trends of Ea/Pre and the Ei/Pre were higher in KEFA than NKEF, especially for the post-2000 period. Compared with pre-2000 period, The Ea/Pre, Et/Pre of the NKEF in the post-2000 period displayed sharp decrease, suggesting the decreasing vegetation cover and water consumption. This study revealed the hydrological effects of KEFA policy, the findings are meaningful in evaluating and optimizing the KEFA policy. © 2025, The Authors. All rights reserved.

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Physics and geometry-augmented neural implicit surfaces for rigid bodies

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Computer Aided Geometric Design 2025年 119卷

作者： Yuanmu Xu Guanli Hou Jiangbei Hu Tenglong Ren Xiaokun Wang Yalan Zhang Xiaojuan Ban Chen Qian Fei Hou Ying He School of Intelligence Science and Technology University of Science and Technology Beijing China Shunde Innovation School University of Science and Technology Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Software Dalian University of Technology China College of Humanities Arts and Social Sciences Nanyang Technological University Singapore Sensetime China Key Laboratory of System Software (CAS) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

This paper tackles the challenges of physics-based simulation of rigid bodies in neural rendering, with a focus on 3D model representation and collision handling. We propose Physics and Geometry-Augmented Neural Implicit Surfaces (PGA-NeuS), a novel approach that combines neural implicit surfaces with a differentiable physics solver. In the pre-processing stage, PGA-NeuS reconstructs static scene and object geometry from multi-view images using signed distance fields (SDFs). For dynamic scenes captured in monocular videos, these SDFs, along with the initial position and orientation of moving rigid bodies, are fed into a differentiable rigid body solver to optimize physical parameters, such as initial velocity and friction coefficients. Subsequently, PGA-NeuS leverages color loss, physics loss, and object mask supervision to iteratively refine the neural implicit surface, ensuring the target object's alignment with the predicted motion sequence. We evaluate PGA-NeuS on five real-world scenes, demonstrating its ability to accurately reconstruct realistic motion sequences and estimate physical parameters such as position and velocity. dataset and source code are available at https://***/Raining00/PGA-NeuS .

关键词： Neural implicit surfaces Neural rendering Physics-based simulation Rigid bodies

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Augmentation-Aware Self-Supervision for data-Efficient GAN Training 37

Augmentation-Aware Self-Supervision for Data-Efficient GAN T...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Hou, Liang Cao, Qi Yuan, Yige Zhao, Songtao Ma, Chongyang Pan, Siyuan Wan, Pengfei Wang, Zhongyuan Shen, Huawei Cheng, Xueqi CAS Key Laboratory of AI Safety and Security Institute of Computing Technology Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Kuaishou Technology China

ISBN: (纸本)9781713899921

Training generative adversarial networks (GANs) with limited data is challenging because the discriminator is prone to overfitting. Previously proposed differentiable augmentation demonstrates improved data efficiency of training GANs. However, the augmentation implicitly introduces undesired invariance to augmentation for the discriminator since it ignores the change of semantics in the label space caused by data transformation, which may limit the representation learning ability of the discriminator and ultimately affect the generative modeling performance of the generator. To mitigate the negative impact of invariance while inheriting the benefits of data augmentation, we propose a novel augmentation-aware self-supervised discriminator that predicts the augmentation parameter of the augmented data. Particularly, the prediction targets of real data and generated data are required to be distinguished since they are different during training. We further encourage the generator to adversarially learn from the self-supervised discriminator by generating augmentation-predictable real and not fake data. This formulation connects the learning objective of the generator and the arithmetic - harmonic mean divergence under certain assumptions. We compare our method with state-of-the-art (SOTA) methods using the class-conditional BigGAN and unconditional StyleGAN2 architectures on data-limited CIFAR-10, CIFAR-100, FFHQ, LSUN-Cat, and five low-shot datasets. Experimental results demonstrate significant improvements of our method over SOTA methods in training data-efficient GANs. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Generative adversarial networks

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Learning knowledge representation across knowledge graphs 31

Learning knowledge representation across knowledge graphs

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31st AAAI Conference on Artificial Intelligence, AAAI 2017

作者： Cai, Pengshan Li, Wei Feng, Yansong Wang, Yuanzhuo Jia, Yantao CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Science Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing China Institute of Computer Science and Technology Peking University Beijing China

ISBN: (纸本)9781577357865

Distributed knowledge representation learning (KRL) methods encode both entities and relations in knowledge graphs (KG) in a lower-dimensional semantic space, which model relatively dense knowledge graphs well and greatly improve the performance of knowledge graph completion and knowledge reasoning. However, existing KRL methods including Trans(E, H, R, D and Sparse) hardly obtain comparative performances on sparse KGs where most of entities and relations have very low frequencies. Furthermore, all existing methods target at KRL on one knowledge graph independently. The embeddings of different KGs are independent with each other. In this paper, we propose a novel cross-knowledge- graph (cross-KG) KRL method which learns embeddings for two different KGs simultaneously. Through projecting semantic related entities and relations in two KGs to a uniform semantic space, our method could learn better embeddings for sparse KGs by incorporating information from another relatively larger and denser KG. The learned embeddings are also helpful for downstream cross-KGs or cross-linguals tasks like ontology alignment. The experiment results show that our method could significandy outperform corresponding baseline methods on knowledge graph completion on single KG and cross-KG entity prediction and mapping tasks. © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge graph

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A Linked List Encryption Scheme for Image Steganography without Embedding

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Computer Modeling in Engineering & sciences 2024年第10期141卷 331-352页

作者： Pengbiao Zhao Qi Zhong Jingxue Chen Xiaopei Wang Zhen Qin Erqiang Zhou Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Faculty of Data Science City University of MacaoMacao999078China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information *** enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover *** this paper,a novel encrypted communication scheme for image SWE is *** reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer *** employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be *** receiver can restore the secretmessage fromthe received image using only the list header position *** scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover *** boasts high concealment and security,along with a complete message restoration rate,making it resistant to ***,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of *** validate the proposed framework,practical tests and comparisons are conducted using multiple *** results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

关键词： Steganography encryption steganography without embedding coverless steganography

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Lactic acid in tumor microenvironments causes dysfunction of NKT cells by interfering with mTOR signaling

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science China(Life sciences) 2016年第12期59卷 1290-1296页

作者： Di Xie Shasha Zhu Li Bai CAS Key Laboratory of lnnate lmmunity and Chronic Disease CAS Center for Excellence in Molecular Cell Science School of Life Sciences and Medical Center University of Science and Technology of China Hefei 230027 China Innovation Center for Cell Signaling Network Hefei National Laboratory for Physical Sciences at Microscale Hefei 230027 China

Cellular metabolism has been shown to regulate differentiation and function of immune cells. Tumor associated immune cells undergo phenotypic and functional alterations due to the change of cellular metabolism in tumor microenvironments. NKT cells are good candidates for immunotherapies against tumors and have been used in several clinical trials. However, the influences of tumor microenvironments on NKT cell functions remain unclear. In our studies, lactic acid in tumor microenvironments inhibited IFN？ and IL4 productions from NKT cells, and more profound influence on IFN？ was observed. By adjusting the pH of culture medium we fiu-ther showed that, dysfunction of NKT cells could simply be induced by low extracellular pH. Moreover, low extracellular pH inhibited NKT cell functions by inhibiting mammalian target of rapamycin （roTOR） signaling and nuclear translocation of promyelocytic leukemia zinc-finger （PLZF）. Together, our results suggest that tumor acidic microenvironments could interfere with NKT cell functions through metabolic controls.

关键词： lactic acid NKT cell IFNT mTOR PLZF

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NKT cells in liver diseases

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Frontiers of Medicine 2018年第3期12卷 249-261页

作者： Shasha Zhu Huimin Zhang Li Bai CAS Key Laboratory of Innate Immunity and Chronic Disease CAS Center for Excellence in Molecular Cell Science School of Life Sciences and Medical Center University of Science and Technology of China Hefei 230027 China Innovation Center for Cell Signaling Network Hefei National Laboratory for Physical Sciences at Microscale Hefei 230027 China

Natural killer T cells are innate-like and tissue-resident lymphocytes, which recognize lipid antigens and are enriched in the liver. Natural killer T cells play important roles in infections, tumors, autoimmune diseases, and metabolic diseases. In this study, we summarize recent findings on biology of natural killer T cells and their roles in hepatitis B virus and hepatitis C virus infection, autoimmune liver diseases, alcoholic liver disease, nonalcoholic fatty liver disease, and hepatocellular carcinoma. Controversial results from previous studies are discussed, and indicate the dynamic alteration in the role of natural killer T cells during the progression of liver diseases, which might be caused by changes in natural killer T subsets, factors skewing cytokine responses, and intercellular crosstalk between natural killer T cells and CDld-expressing cells or bystander cells.

关键词： natural killer T cells hepatitis B virus and hepatitis C virus infection autoimmune liver diseases alcoholic liverdisease nonalcoholic fatty liver disease hepatocellular carcinoma

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Heterogeneous Fault-Tolerant Aggregate Signcryption with Equality Test for Vehicular Sensor networks

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Computer Modeling in Engineering & sciences 2023年第10期137卷 555-575页

作者： Yang Zhao Jingmin An Hao Li Saru Kumari Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Department of Mathematics Chaudhary Charan Singh UniversityMeerut250004India

The vehicular sensor network (VSN) is an important part of intelligent transportation, which is used for real-timedetection and operation control of vehicles and real-time transmission of data and information. In the environmentofVSN, massive private data generated by vehicles are transmitted in open channels and used by other vehicle users,so it is crucial to maintain high transmission efficiency and high confidentiality of data. To deal with this problem, inthis paper, we propose a heterogeneous fault-tolerant aggregate signcryption scheme with an equality test (HFTASET).The scheme combines fault-tolerant and aggregate signcryption,whichnot onlymakes up for the deficiency oflow security of aggregate signature, but alsomakes up for the deficiency that aggregate signcryption cannot tolerateinvalid signature. The scheme supports one verification pass when all signcryptions are valid, and it supportsunbounded aggregation when the total number of signcryptions grows dynamically. In addition, this schemesupports heterogeneous equality test, and realizes the access control of private data in different cryptographicenvironments, so as to achieve flexibility in the application of our scheme and realize the function of quick searchof plaintext or ciphertext. Then, the security of HFTAS-ET is demonstrated by strict theoretical analysis. Finally, weconduct strict and standardized experimental operation and performance evaluation, which shows that the schemehas better performance.

关键词： Aggregate signcryption fault-tolerant heterogeneous equality test vehicular sensor network

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