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Surface Reconstruction from Normals: A Robust DGP-based Discontinuity Preservation Approach

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Surface Reconstruction from Normals: A Robust DGP-based Disc...

IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Wuyuan Xie Miaohui Wang Mingqiang Wei Jianmin Jiang Jing Qin College of Computer and Software Engineering Shenzhen University (SZU) Guangdong Key Laboratory of Intelligent Information Processing College of Information Engineering School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Nursing Hong Kong Polytechnic University

ISBN: (纸本)9781728132945

In 3D surface reconstruction from normals, discontinuity preservation is an important but challenging task. However, existing studies fail to address the discontinuous normal maps by enforcing the surface integrability in the continuous domain. This paper introduces a robust approach to preserve the surface discontinuity in the discrete geometry way. Firstly, we design two representative normal incompatibility features and propose an efficient discontinuity detection scheme to determine the splitting pattern for a discrete mesh. Secondly, we model the discontinuity preservation problem as a light-weight energy optimization framework by jointly considering the discontinuity detection and the overall reconstruction error. Lastly, we further shrink the feasible solution space to reduce the complexity based on the prior knowledge. Experiments show that the proposed method achieves the best performance on an extensive 3D dataset compared with the state-of-the-arts in terms of mean angular error and computational complexity.

关键词： Surface reconstruction discontinuities angular misalignment Solution space CONSERVATION optimum performance Arts complexity classes

Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

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arXiv 2023年

作者： Cheng, Sibo Quilodrán-Casas, César Ouala, Said Farchi, Alban Liu, Che Tandeo, Pierre Fablet, Ronan Lucor, Didier Iooss, Bertrand Brajard, Julien Xiao, Dunhui Janjic, Tijana Ding, Weiping Guo, Yike Carrassi, Alberto Bocquet, Marc Arcucci, Rossella Data Science Institute Department of Computing Imperial College London LondonSW7 2AZ United Kingdom Department of Earth Science and Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Computer Science and Engineering Hong Kong University of Science and Technology 999077 Hong Kong IMT Atlantique Lab-STICC UMR CNRS 6285 France and Odyssey Inria/IMT France RIKEN Center for Computational Science Kobe Japan CEREA École des Ponts and EDF R&D île-de-France France The Laboratoire Interdisciplinaire des Sciences du Numérique CNRS Paris-Saclay University OrsayF-91403 France 78401 Chatou France Institut de Mathématiques de Toulouse Toulouse31062 France SINCLAIR AI Lab Saclay France Bergen Norway School of Mathematical Sciences Tongji University Shanghai200092 China Mathematical Institute for Machine Learning and Data Science KU Eichstätt-Ingolstadt Bavaria Germany School of Information Science and Technology Nantong University Nantong226019 China Department of Physics and Astronomy Augusto Righi University of Bologna Bologna40124 Italy

Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surrogate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of the state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review aims at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models, but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of DA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed. © 2023, CC BY-NC-SA.

关键词： Dynamical systems

Dynamic Processing while Transmitting for SDN-based Space-Terrestrial Integrated Networks

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Dynamic Processing while Transmitting for SDN-based Space-Te...

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Guangxiong Wu Feilong Tang Lijun Cao Ping Han Yanqin Yang Wenchao Xu Xingjun Zhang Shanghai Jiao Tong University Shanghai China Innovation Resource Center KEDACOM Co. Ltd China The Sci-Tech Information Office of Xuzhou Public Security Bureau Jiangsu Province China School of Computer Science and Software Engineering East China Normal University Shanghai China School of Computer Science and Technology Xi’an Jiaotong UniversityXian China

ISBN: (纸本)9781728176505

The space-terrestrial information network integrates the characteristics of satellite networks and terrestrial networks and can support diversified space network requirements, which has also become a new trend in future network development. In the process of coordinated transmitting between space and terrestrial, satellite may generate a data flow with a set of tasks to be processed while transmitting. Because of the dynamic changes in the links between nodes in the satellite network, how to effectively place the tasks will be huge challenges. In this paper, we considere the traffic changes brought about by task processing, the effectiveness of links, and combined with the operation laws of satellite network and have studied the placement of tasks in dynamic transmitting and processing scenarios. First, we formulate the Task Placement In Dynamic Transmitting-Processing (TPDTP) problem, and then divide the problem into three parts: flow path selection, task allocation between planes, and task placement within plane. For flow path selection, we proposed the Waiting-Time Reducing Path Select (WTRPS) algorithm. For the task allocation problem between planes, we proposed the Time-Constrained Task Placement (TCTP) algorithm and Waiting-Time Utilization Task Placement(WTUTP) algorithm. Finally, we propose Intra-plane Task Placement (IPTP) algorithm to solve the problem of task placement within plane which comprehensive consider the changes in traffic rate and the processing performance of nodes. We evaluate the proposed algorithms using simulations and present evaluation results to show the effectiveness of our design.

关键词： Satellites Heuristic algorithms Computational modeling High performance computing Low earth orbit satellites Focusing Market research

General linear group action on tensors: A candidate for post-quantum cryptography

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arXiv 2019年

作者： Ji, Zhengfeng Qiao, Youming Song, Fang Yun, Aaram Centre for Quantum Software and Information School of Software Faculty of Engineering and Information Technology University of Technology SydneyNSW Australia Department of Computer Science and Engineering Texas A&M University Texas United States Department of Cyber Security Division of Software Science and Engineering Ewha Womans University Seoul Korea Republic of

Starting from the one-way group action framework of Brassard and Yung (Crypto'90), we revisit building cryptography based on group actions. Several previous candidates for one-way group actions no longer stand, due to progress both on classical algorithms (e.g., graph isomorphism) and quantum algorithms (e.g., discrete logarithm). We propose the general linear group action on tensors as a new candidate to build cryptography based on group actions. Recent works (Futorny-Grochow-Sergeichuk Lin. Alg. Appl., 2019) suggest that the underlying algorithmic problem, the tensor isomorphism problem, is the hardest one among several isomorphism testing problems arising from areas including coding theory, computational group theory, and multivariate cryptography. We present evidence to justify the viability of this proposal from comprehensive study of the state-of-art heuristic algorithms, theoretical algorithms and hardness results, as well as quantum algorithms. We then introduce a new notion called pseudorandom group actions to further develop group-action based cryptography. Briefly speaking, given a group G acting on a set S, we assume that it is hard to distinguish two distributions of (s, t) either uniformly chosen from S × S, or where s is randomly chosen from S and t is the result of applying a random group action of g ∈ G on s. This subsumes the classical decisional Diffie-Hellman assumption when specialized to a particular group action. We carefully analyze various attack strategies that support the general linear group action on tensors as a candidate for this assumption. Finally, we establish the quantum security of several cryptographic primitives based on the one-way group action assumption and the pseudorandom group action assumption. Copyright © 2019, The Authors. All rights reserved.

关键词： Tensors

On efficient and robust metrics for RANSAC hypotheses and 3D rigid registration

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arXiv 2020年

作者： Yang, Jiaqi Huang, Zhiqiang Quan, Siwen Zhang, Qian Zhang, Yanning Cao, Zhiguo National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University Xi’an710129 China School of Electronic and Control Engineering Chang’an University Xi’an710064 China School of Resources and Environment Hubei University Wuhan430062 China National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration. Estimating six-degree-of-freedom (6-DoF) pose from feature correspondences remains a popular approach to 3D rigid registration, where random sample consensus (RANSAC) is a de-facto choice to this problem. However, existing metrics for RANSAC hypotheses are either time-consuming or sensitive to common nuisances, parameter variations, and different application scenarios, resulting in performance deterioration in overall registration accuracy and speed. We alleviate this problem by first analyzing the contributions of inliers and outliers, and then proposing several efficient and robust metrics with different designing motivations for RANSAC hypotheses. Comparative experiments on four standard datasets with different nuisances and application scenarios verify that the proposed metrics can significantly improve the registration performance and are more robust than several state-of-the-art competitors, making them good gifts to practical applications. This work also draws an interesting conclusion, i.e., not all inliers are equal while all outliers should be equal, which may shed new light on this research problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Deterioration

Identity-based public key cryptographic primitive with delegated equality test against insider attack in cloud computing

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International Journal of Network Security 2020年第5期22卷 743-751页

作者： Alornyo, Seth Mensah, Acheampong Edward Abbam, Abraham Opanfo School of Information and Software Engineering University of Electronic Science and Technology of China 4 E 2nd Sec. 1st Ring Rd Jianshe Road Chenghua Chengdu Sichuan China Computer Science Department Koforidua Technical University Koforidua Ghana

The notion of attacks perpetuated by an insider (cloud server) is paramount in this era of cloud computing and data analytics. When a cloud server is delegated with certain responsibilities, it is possible for a cloud server to peddle with users' encrypted data for profit gains. The cloud server takes advantage of it's authorization to launch what we referred to as insider attack. We put forward a new improved scheme on identity-based public key cryptographic primitive which integrate delegated equality test to resist insider attack in cloud computing. Our scheme resist the insider attack perpetuated by the cloud server (insider). We refer to our new scheme as identity-based public key cryptographic primitive with delegated equality test against insider attack in cloud computing (IB-PKC-DETIA). We construct our scheme using a witness based cryptographic primitive with an added pairing operation. Our scheme achieves weak indistinquishable identity chosen ciphertext (W-IND-ID-CCA) security using the random oracle model. © Femto Technique Co., Ltd.

关键词： Cryptography

Fair and autonomous sharing of federate learning models in mobile Internet of Things

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arXiv 2020年

作者： Hao, Xiaohan Ren, Wei Xiong, Ruoting Zheng, Xianghan Zhu, Tianqing Xiong, Neal N. School of Computer Science China University of Geoscience Wuhan China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Mingbyte Technology QingDao China School of Software University of Technology Sydney UltimoNSW2007 Australia Dept. of Mathematics and Computer Science Northeastern State University United States

Federate learning can conduct machine learning as well as protect the privacy of self-owned training data on corresponding ends, instead of having to upload to a central trusted data aggregation server. In mobile scenarios, a centralized trusted server may not be existing, and even though it exists, the delay will not be manageable, e.g., smart driving cars. Thus, mobile federate learning at the edge with privacy-awareness is attracted more and more attentions. It then imposes a problem - after data are trained on a mobile terminal to obtain a learned model, how to share the model parameters among others to create more accurate and robust accumulative final model. This kind of model sharing confronts several challenges, e.g., the sharing must be conducted without a third trusted party (autonomously), and the sharing must be fair as model training (by training data)is valuable. To tackle the above challenges, we propose a smart contract and IPFS (Inter-Planetary File System) based model sharing protocol and algorithms to address the challenges. The proposed protocol does not rely on a trusted third party, where individual-learned models are shared/stored in corresponding ends. Conducted through extensive experiments, three main steps of the proposed protocol are evaluated. The average executive time of the three steps are 0.059s, 0.060s and 0.032s, demonstrating its efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

GNDD: A Graph Neural Network-Based Method for Drug-Disease Association Prediction

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GNDD: A Graph Neural Network-Based Method for Drug-Disease A...

IEEE International Conference on Bioinformatics and Biomedicine

作者： Bei Wang Xiaoqing Lyu Jingwei Qu Haowen Sun Zehua Pan Zhi Tang Wangxuan Institute of Computer Technology Peking University School of Software Beihang University College of Electronic and Information Engineering Beijing Jiaotong University

ISBN: (纸本)9781728118680

Potential drug-disease association prediction is important to facilitate drug discovery. However, most of existing drug-disease association prediction approaches rely on assembling multiple drug (disease)-related biological information, which is usually not comprehensively available, and they always fail to explore the latent information in drug-disease network. To tackle these challenges, we propose a graph neural network-based method for drug-disease association prediction, dubbed GNDD, with capturing the complex information between drugs and diseases dispense with any side information. Specifically, GNDD introduces the idea of collaborative filtering in recommendation system to avoid the dependency on multi-data. Furthermore, an embedding propagation strategy is exploited to model the high-order relationships in drug-disease network. We conduct experiments on the Comparative Toxicogenomics Database, demonstrating the effectiveness of our method in drug-disease association prediction.

关键词： drug-disease associations graph neural network embedding propagation Association (Psychology) Recommender systems collaborative filtering Line graph Drug Discovery Pharmaceutical Preparations Association toxicogenomics associations

Integrating Dense LiDAR-Camera Road Detection Maps by a Multi-Modal CRF Model

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IEEE Transactions on Vehicular technology 2019年第12期68卷 11635-11645页

作者： Gu, Shuo Zhang, Yigong Tang, Jinhui Yang, Jian Alvarez, Jose M. Kong, Hui PCA Lab Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security Sch. of Comp. Sci. and Engineering Nanjing Univ. of Sci. and Technol. Nanjing 210094 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China

Road detection is an important task in autonomous navigation systems. In this paper, we propose a road detection method via a LiDAR-camera fusion strategy to exploit both the range and color information. The whole system consists of three parts. In the LiDAR based part, we transform the discrete 3D LiDAR point clouds to continuous 2D LiDAR range images and propose a distance-aware height-difference based scanning approach to get the road estimations quickly. In the camera based part, we apply a light-weight transfer learning based road segmentation network. In the LiDAR-camera fusion part, we transform the detection results from LiDAR and camera to dense and binary ones to solve the data imbalance problem and fuse them in a multi-modal conditional random field (MM-CRF) framework. Experiments show that the proposed MM-CRF fusion method can operate in real-time and achieve competitive performance compared with the state-of-the-art road detection algorithms on the KITTI-Road benchmark. © 1967-2012 IEEE.

关键词： KITTI LiDAR-camera fusion road detection