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How to Coadd Images.Ⅱ.Anti-aliasing and PSF Deconvolution

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Research in Astronomy and Astrophysics 2024年第4期24卷 103-113页

作者： Lei Wang Huanyuan Shan Lin Nie Dezi Liu Zhaojun Yan Guoliang Li Cheng Cheng Yushan Xie Han Qu Wenwen Zheng Xi Kang Purple Mountain Observatory Chinese Academy of SciencesNanjing 210023China National Basic Science Data Center Beijing 100190China Zhejiang University-Purple Mountain Observatory Joint Research Center for Astronomy Zhejiang UniversityHangzhou 310030China Shanghai Astronomical Observatory Shanghai 200030China Key Laboratory of Radio Astronomy and Technology Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Department of Information Engineering Wuhan Institute of CityWuhan 430083China South-Westem Institute for Astronomy Research Yunnan UniversityKunming 650500China The Shanghai Key Lab for Astrophysics.Shanghai Normal University Shanghai200234China Chinese Academy of Sciences South America Center for Astronomy National Astronomical ObservatoriesCASBeijing 100101China CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China

We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer *** approach not only allows for the anti-aliasing of the images but also enables Point-Spread Function(PSF)deconvolution,resulting in enhanced restoration of extended sources,the highest peak signal-to-noise ratio,and reduced ringing *** test our method,we conducted numerical simulations that replicated observation runs of the China Space Station Telescope/the VLT Survey Telescope(VST)and compared our results to those obtained using previous *** simulation showed that our method outperforms previous approaches in several ways,such as restoring the profile of extended sources and minimizing ringing ***,because our method relies on the inherent advantages of least squares fitting,it is more versatile and does not depend on the local uniformity hypothesis for the ***,the new method consumes much more computation than the other approaches.

关键词： methods:analytical techniques:image processing gravitational lensing:weak (ISM:)cosmic rays

MTNet: A multi-task neural network for on-field calibration of low-cost air monitoring sensors

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arXiv 2021年

作者： Yu, Haomin Geng, Yangli-Ao Zhang, Yingjun Li, Qingyong Zhou, Jiayu The Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Department of Computer Science and Engineering Michigan State University Michigan48824 United States

The advances of sensor technology enable people to monitor air quality through widely distributed low-cost sensors. However, measurements from these sensors usually encounter high biases and require a calibration step to reach an acceptable performance in down-streaming analytical tasks. Most existing calibration methods calibrate one type of sensor at a time, which we call single-task calibration. Despite the popularity of this single-task schema, it may neglect interactions among calibration tasks of different sensors, which encompass underlying information to promote calibration performance. In this paper, we propose a multi-task calibration network (MTNet) to calibrate multiple sensors (e.g., carbon monoxide and nitrogen oxide sensors) simultaneously, modeling the interactions among tasks. MTNet consists of a single shared module, and several task-specific modules. Specifically, in the shared module, we extend the multi-gate mixture-of-experts structure to harmonize the task conflicts and correlations among different tasks;in each task-specific module, we introduce a feature selection strategy to customize the input for the specific task. These improvements allow MTNet to learn interaction information shared across different tasks, and task-specific information for each calibration task as well. We evaluate MTNet on three real-world datasets and compare it with several established baselines. The experimental results demonstrate that MTNet achieves the state-of-the-art performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Calibration

Distilling the Unknown to Unveil Certainty

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arXiv 2023年

作者： Zhao, Zhilin Cao, Longbing Zhang, Yixuan Lin, Kun-Yu Zheng, Wei-Shi The Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia The DataX Research Centre Macquarie University SydneyNSW2109 Australia The China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing Hangzhou Dianzi University Zhejiang Hangzhou310005 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China The School of Computer Science and Engineering Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University Guangzhou510275 China

Out-of-distribution (OOD) detection is essential in identifying test samples that deviate from the in-distribution (ID) data upon which a standard network is trained, ensuring network robustness and reliability. This paper introduces OOD knowledge distillation, a pioneering learning framework applicable whether or not training ID data is available, given a standard network. This framework harnesses unknown OOD-sensitive knowledge from the standard network to craft a certain binary classifier adept at distinguishing between ID and OOD samples. To accomplish this, we introduce Confidence Amendment (CA), an innovative methodology that transforms an OOD sample into an ID one while progressively amending prediction confidence derived from the standard network. This approach enables the simultaneous synthesis of both ID and OOD samples, each accompanied by an adjusted prediction confidence, thereby facilitating the training of a binary classifier sensitive to OOD. Theoretical analysis provides bounds on the generalization error of the binary classifier, demonstrating the pivotal role of confidence amendment in enhancing OOD sensitivity. Extensive experiments spanning various datasets and network architectures confirm the efficacy of the proposed method in detecting OOD samples. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

Simultaneous Gait Generation and Path Following Control of Snake Robot Using MPC

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Simultaneous Gait Generation and Path Following Control of S...

IEEE International Conference on Robotics and Biomimetics

作者： Yichen Liang Chao Ren Yongchen Tang Xiaohan Li Shugen Ma School of Electrical and Information Engineering Tianjin University Tianjin China Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan Ritsumeikan University Shiga Japan

ISBN: (纸本)9781665405362

The path following control with given forward velocity for snake robot is important in real applications. Most of traditional model based snake robot control methods rely on predefined gaits. In this paper, the simultaneous gait generation and path following control with given forward velocity are investigated under the MPC scheme. Firstly, two cost functions are respectively studied for the snake robot, to realize path convergence and to maximize the forward velocity. Then a MPC scheme is proposed by combining the two cost functions, to achieve better path following control performance and higher forward velocity of the snake robot. Moreover, the forward velocity control of snake robot is realized by viewing the desired velocity as one constraint. Simulation results verify the effectiveness of the proposed control scheme.

关键词： Simulation Conferences Biomimetics Snake robots Velocity control Cost function Convergence

Unified Dual-view Cognitive Model for Interpretable Claim Verification

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arXiv 2021年

作者： Wu, Lianwei Rao, Yuan Lan, Yuqian Sun, Ling Qi, Zhaoyin Xi’an Key Lab. of Social Intelligence and Complexity Data Processing School of Software Engineering Xi’an Jiaotong University China China Research Institute of Xi’an Jiaotong University Shenzhen China

Recent studies constructing direct interactions between the claim and each single user response to capture evidence have shown remarkable success in interpretable claim verification. Owing to different single responses convey different cognition of individual users, the captured evidence belongs to the perspective of individual cognition. However, individuals’ cognition of social things is not always able to truly reflect the objective. There may be one-sided or biased semantics in their opinions on a claim. The captured evidence correspondingly contains some unobjective and biased information. In this paper, we propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) for interpretable claim verification. For collective cognition, we not only capture the word-level semantics based on individual users, but also focus on sentence-level semantics (i.e., the overall responses) among all users to generate global evidence. For individual cognition, we select the top-k articles with high degree of difference and interact with the claim to explore the local key evidence fragments. To weaken the bias of individual cognition-view evidence, we devise an inconsistent loss to suppress the divergence between global and local evidence for strengthening the consistent shared evidence between the both. Experiments on three benchmark datasets confirm the effectiveness of CICD. © 2021, CC BY.

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Fast Quaternion Product Units for Learning Disentangled Representations in SO(3)

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TechRxiv

TechRxiv 2022年

作者： Qin, Shaofei Zhang, Xuan Xu, Hongteng Xu, Yi MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Department of Computer Science & Engineering Texas A&M University College StationTX77843 United States Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Real-world 3D structured data like point clouds and skeletons often can be represented as data in a 3D rotation group (denoted as SO(3)). However, most existing neural networks are tailored for the data in the Euclidean space, which makes the 3D rotation data not closed under their algebraic operations and leads to sub-optimal performance in 3D-related learning tasks. To resolve the issues caused by the above mismatching between data and model, we propose a novel non-real neuron model called quaternion product unit (QPU) to represent data on 3D rotation groups. The proposed QPU leverages quaternion algebra and the law of the 3D rotation group, representing 3D rotation data as quaternions and merging them via a weighted chain of Hamilton products. We demonstrate that the QPU mathematically maintains the SO(3) structure of the 3D rotation data during the inference process and disentangles the 3D representations into "rotation-invariant" features and "rotation-equivariant" features, respectively. Moreover, we design a fast QPU to accelerate the computation of QPU. The fast QPU applies a tree-structured data indexing process, and accordingly, leverages the power of parallel computing, which reduces the computational complexity of QPU in a single thread from O(N) to O(log N). Taking the fast QPU as a basic module, we develop a series of quaternion neural networks (QNNs), including quaternion multi-layer perceptron (QMLP), quaternion message passing (QMP), and so on. In addition, we make the QNNs compatible with conventional real-valued neural networks and applicable for both skeletons and point clouds. Experiments on synthetic and real-world 3D tasks show that the QNNs based on our fast QPUs are superior to state-of-the-art real-valued models, especially in the scenarios requiring the robustness to random rotations. © 2022, CC BY.

关键词： 3D modeling

An enhanced MinHash encryption scheme for encrypted deduplication

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An enhanced MinHash encryption scheme for encrypted deduplic...

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Qiaowen Jia Guanxiong Ha Hanwei Wang Haoyu Ma Hang Chen State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Zhejiang Lab Hangzhou China The School of Cyber Engineering Xidian University Xi’an China

The encrypted deduplication can provide both storage savings and data confidentiality for cloud storage systems. Convergent encryption (CE) is a well-known solution for encrypted deduplication, but it brings huge computation and storage overheads for key management. MinHash encryption is an effective solution to this issue. It reduces the number of keys by grouping multiple consecutive chunks into segments and generating one key for each segment. However, MinHash encryption does not take full advantage of the segment similarity, which can be used to further reduce the overhead for key management. To this end, we augment MinHash encryption with Bloom filter and Locality Sensitivity Hash (LSH) to design an enhanced MinHash encryption scheme. Firstly, our scheme generates a sketch for each segment based on the Bloom filter, and projects sketches to the points in a hash table through LSH functions. Secondly, we detect the segment similarity by the distance between points, and similar segments are grouped into super-segments. Finally, we combine MinHash encryption and server-aided message-locked encryption to encrypt the super-segments for achieving encrypted deduplication. We conduct trace-driven experiments using a realworld dataset. Compared with MinHash encryption, our scheme has higher storage efficiency and better encryption performance.

关键词： Privacy Maximum likelihood estimation data privacy Cloud computing Sensitivity Encryption

Universal deep network for steganalysis of color image based on channel representation

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arXiv 2021年

作者： Wei, Kangkang Luo, Weiqi Tan, Shunquan Huang, Jiwu The Guangdong Key Lab of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design. Copyright © 2021, The Authors. All rights reserved.

关键词： Steganography

A Survey on Applications of Reinforcement Learning in Spatial Resource Allocation

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arXiv 2024年

作者： Zhang, Di Wang, Moyang Mango, Joseph Li, Xiang Xu, Xianrui Key Laboratory of Geographic Information Science Ministry of Education School of Geographic Sciences East China Normal University Shanghai200241 China Department of Transportation and Geotechnical Engineering University of Dar es Salaam Dar es salaam Tanzania United Republic of Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration East China Normal University Shanghai200241 China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources East China Normal University Shanghai200241 China School of Economics and Management Shanghai University of Sport Shanghai200438 China

The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Therefore, this paper aims to summarize and review recent theoretical methods and applied research utilizing reinforcement learning to address spatial resource allocation problems. It provides a summary and comprehensive overview of its fundamental principles, related methodologies, and applied research. Additionally, it highlights several unresolved issues that urgently require attention in this direction for the future. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning