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arXiv 2024年

作者： Wang, Lei Shan, Huanyuan Nie, Lin Liu, Dezi Yan, Zhaojun Li, Guoliang Cheng, Cheng Xie, Yushan Qu, Han Zheng, Wenwen Kang, Xi Purple Mountain Observatory Chinese Academy of Sciences No. 10 Yuan Hua Road Nanjing210023 China Department of Information Engineering Wuhan Institute of City Hubei Wuhan430083 China Shanghai Astronomical Observatory Nandan Road 80 Shanghai200030 China National Basic Science Data Center Building No.2 4 Zhongguancun South 4th Street Haidian District Beijing190 China Zhejiang University Purple Mountain Observatory Joint Research Center for Astronomy Zhejiang University Hangzhou327 China South-Western Institute for Astronomy Research Yunnan University Kunming650500 China The Shanghai Key Lab for Astrophysics Shanghai Normal University Shanghai200234 China Chinese Academy of Sciences South America Center for Astronomy National Astronomical Observatories CAS Beijing100101 China CAS Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Key Laboratory of Radio Astronomy and Technology Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China

We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms. Our approach not only allows for the anti-aliasing of the images but also enables PSF deconvolution, resulting in enhanced restoration of extended sources, the highest PSNR, and reduced ringing artefacts. To test our method, we conducted numerical simulations that replicated observation runs of the CSST/VST telescope and compared our results to those obtained using previous algorithms. The simulation showed that our method outperforms previous approaches in several ways, such as restoring the profile of extended sources and minimizing ringing artefacts. Additionally, 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 PSF. However, the new method consumes much more computation than the other approaches. © 2024, CC BY.

关键词： Anti-aliasing

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Transmission Schemes for Backscatter Aided Wireless Communications on High Speed Rails

Transmission Schemes for Backscatter Aided Wireless Communic...

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IEEE Conference on Vehicular Technology (VTC)

作者： Zhongzhao Dou Yang Liu Wenjing Zhao Gongpu Wang Bo Ai Suili Feng School of Electronic and Information Engineering South China University of Technology China Yang Liu Beijing Institute of Aerospace Control Devices Beijing China Beijing Key Lab of Transportation Data Analysis and Mining Beijing Jiaotong University Beijing China

ISBN: (数字)9781728194844

ISBN: (纸本)9781728194851

Fast time-varying channel parameters and large penetration loss for signals passing through train carriages are two well-known challenges for wireless communications on high speed rails (HSRs). In this paper we introduce, for the first time, backscatter technology into wireless communication systems on HSRs which can address the two challenges and meanwhile have low complexity of signal processing and low cost of circuit implementation compared with traditional solutions such as relay or beamforming technology. Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and compare its performance with existing direct wireless transmission (DWT) scheme. We also propose the transceiver design for both BAWT and DWT, including joint carrier frequency offset (CFO) and channel estimator, and signal detector. We show that BAWT, instead of DWT, can obtain the channel statistical information in practical applications due to fixed train antennas and unchanged tracks, which can be utilized to facilitate channel estimation. Finally simulation results are provided to corroborate the proposed studies.

关键词： Wireless communication Rails Channel estimation Detectors Frequency estimation Discrete wavelet transforms Backscatter

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GraphNorm: A principled approach to accelerating graph neural network training

arXiv

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arXiv 2020年

作者： Cai, Tianle Luo, Shengjie Xu, keyulu He, Di Liu, Tie-Yan Wang, Liwei Department of Electrical and Computer Engineering Princeton University Zhongguancun Haihua Institute for Frontier Information Technology Key Laboratory of Machine Perception MOE School of EECS Peking University Center for Data Science Peking University Pazhou Lab Microsoft Research

Normalization is known to help the optimization of deep neural networks. Curiously, different architectures require specialized normalization methods. In this paper, we study what normalization is effective for Graph Neural Networks (GNNs). First, we adapt and evaluate the existing methods from other domains to GNNs. Faster convergence is achieved with InstanceNorm compared to BatchNorm and LayerNorm. We provide an explanation by showing that InstanceNorm serves as a preconditioner for GNNs, but such preconditioning effect is weaker with BatchNorm due to the heavy batch noise in graph datasets. Second, we show that the shift operation in InstanceNorm results in an expressiveness degradation of GNNs for highly regular graphs. We address this issue by proposing GraphNorm with a learnable shift. Empirically, GNNs with GraphNorm converge faster compared to GNNs using other normalization. GraphNorm also improves the generalization of GNNs, achieving better performance on graph classification benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Instance credibility inference for few-shot learning

arXiv

引用

arXiv 2020年

作者： Wang, Yikai Xu, Chengming Liu, Chen Zhang, Li Fu, Yanwei School of Data Science Fudan University China Department of Engineering Science University of Oxford United Kingdom MOE Frontiers Center for Brain Science Fudan University China Shanghai Key Lab of Intelligent Information Processing Fudan University China

Few-shot learning (FSL) aims to recognize new objects with extremely limited training data for each category. Previous efforts are made by either leveraging meta-learning paradigm or novel principles in data augmentation to alleviate this extremely data-scarce problem. In contrast, this paper presents a simple statistical approach, dubbed Instance Credibility Inference (ICI) to exploit the distribution support of unlabeled instances for few-shot learning. Specifically, we first train a linear classifier with the labeled few-shot examples and use it to infer the pseudo-labels for the unlabeled data. To measure the credibility of each pseudo-labeled instance, we then propose to solve another linear regression hypothesis by increasing the sparsity of the incidental parameters and rank the pseudo-labeled instances with their sparsity degree. We select the most trustworthy pseudo-labeled instances alongside the labeled examples to re-train the linear classifier. This process is iterated until all the unlabeled samples are included in the expanded training set, i.e. the pseudo-label is converged for unlabeled data pool. Extensive experiments under two few-shot settings show that our simple approach can establish new state-of-the-arts on four widely used few-shot learning benchmark datasets including miniImageNet, tieredImageNet, CIFAR-FS, and CUB. Our code is available at: https://***/Yikai-Wang/ICI-FSL Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Exploratory simulation drilling environment design based on interactive data generation method 5

Exploratory simulation drilling environment design based on ...

引用

5th International Conference on Systems, Control and Communications, ICSCC 2019

作者： Zhu, Dandan Wang, Fei Zhu, Liping Liu, Hao Chen, Dong Gao, Peng Beijing Key Lab of Petroleum Data Mining China University of Petroleum-Beijing Beijing102249 China College of Petroleum Engineering China University of Petroleum-Beijing Beijing1022490 China PetroChina Huabei Oilfield Company Renqiu062550 China

ISBN: (纸本)9781450372640

As one of the cutting-edge drilling technologies developed in recent years, intelligent guided drilling technology addresses the problems of drilling complex structural wells and ultra-deep wells under complex geological conditions. In order to provide a simulation environment for intelligent drilling algorithms to meet the actual drilling work, reasonable and reliable 3D geological modeling and visualization work is essential. However, in current geological modeling and visualization work, there are various problems such as insufficient modeling data, poor interaction with agents and lack of exploratory. In view of the above problems, this paper designs an intelligent guided drilling algorithm interactive system that can dynamically generate simulation data based on a small number of geological data samples and construct a three-dimensional geological environment in real time. We propose an interactive data generation mechanism, which can quickly generate simulated LWD (log while drilling) data within the detectable range according to the real-time position of drill bit, and provides a basis for real-time decision making of the intelligent guided drill model. At the same time, the system refers to the uncertainty of the actual formation distribution and adds some randomness in the process of simulating stratigraphic sections. Therefore, based on cross-sectional stacking method combined with the characteristics of intelligent guided drilling algorithms, the 3D geological model of drilling environment can be dynamically constructed during the real-time guiding process. Moreover, exploratory environment modeling method can provide a low-cost, versatile simulation test platform for intelligent guided drilling algorithms. © 2019 ACM International Conference Proceeding Series. All rights reserved.

关键词： Visualization

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Meta Mask Correction for Nuclei Segmentation in Histopathological Image

Meta Mask Correction for Nuclei Segmentation in Histopatholo...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiangbo Shi Chang Jia Zeyu Gao Tieliang Gong Chunbao Wang Chen Li National Engineering Lab for Big Data Analytics Xi’an Jiao tong University Xi’an Shaanxi China School of Computer Science and Technology Xi’an Jiao tong University Xi’an Shaanxi China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi’an Jiao tong University Xi’an Shaanxi China the First Affiliated Hospital of Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (纸本)9781665429825

Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data. Then the corrected masks can be used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model in an end-to-end way. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. It even achieves comparable performance with the model training on supervised data in some noisy settings.

关键词： Training Learning systems Image segmentation Pathology Annotations Biological system modeling Optimization methods

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Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

arXiv

引用

arXiv 2021年

作者： Ahmad, Muhammad Shabbir, Sidrah Roy, Swalpa Kumar Hong, Danfeng Wu, Xin Yao, Jing Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Chanussot, Jocelyn Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Pakistan The Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Institute of Data Science and Artificial Intelligence Innopolis University Innopolis 420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines. © 2021, CC BY.

关键词： Surveys

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

引用

23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict

arXiv

引用

arXiv 2021年

作者： Liu, Yiyi Wang, Yequan Sun, Aixin Meng, Xuying Li, Jing Guo, Jiafeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing Academy of Artificial Intelligence Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute of Computing Technology Chinese Academy of Sciences Beijing China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

Sarcasm employs ambivalence, where one says something positive but actually means negative, and vice versa. The essence of sarcasm, which is also a sufficient and necessary condition, is the conflict between literal and implied sentiments expressed in one sentence. However, it is difficult to recognize such sentiment conflict because the sentiments are mixed or even implicit. As a result, the recognition of sophisticated and obscure sentiment brings in a great challenge to sarcasm detection. In this paper, we propose a Dual-Channel Framework by modeling both literal and implied sentiments separately. Based on this dual-channel framework, we design the Dual-Channel Network (DC-Net) to recognize sentiment conflict. Experiments on political debates (i.e., IAC-V1 and IAC-V2) and Twitter datasets show that our proposed DC-Net achieves state-of-the-art performance on sarcasm recognition. Our code is released to support research https://***/yiyi-ict/dual-channel-for-sarcasm. © 2021, CC BY.

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