检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Xu, Weiwei Jiang, Linhua Li, Ran Luo, Bin Brandt, W. Nielsen Zhang, Chaoli Erben, Thomas Peking University Beijing100871 China Chinese Academy of Sciences Beijing100101 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy School of Physics Peking University Beijing100871 China School of Astronomy and Space Science University of Chinese Academy of Science Beijing100049 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210093 China Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education Jiangsu Nanjing210093 China Department of Astronomy and Astrophysics The Pennsylvania State University 525 Davey Lab University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics The Pennsylvania State University 104 Davey Lab University ParkPA16802 United States College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China Universität Bonn Auf dem Hügel 71 Bonn53121 Germany

Context. To explain the well-known tension between cosmological parameter constraints obtained from the primary cosmic microwave background (CMB) and those drawn from galaxy cluster samples, we propose a possible explanation for the incompleteness of detected clusters are higher than estimated, i.e., certain types of galaxy groups or clusters have been overlooked in previous works. Aims. We aim to search for galaxy groups and clusters with particularly extended surface brightness distributions by creating a new X-ray-selected catalog of extended galaxy clusters from the XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS) data, based on a dedicated source detection and characterization algorithm that is optimized for extended sources. Methods. Our state-of-the-art algorithm is composed of wavelet filtering, source detection, and characterization. We make a visual inspection of the optical image, and spatial distribution of galaxies within the same redshift layer to confirm the existence of clusters and estimate the cluster redshift with the spectroscopic and photometric redshifts of galaxies. The growth curve analysis is used to characterize the detections. Results. We report a catalog of extended X-ray galaxy clusters detected from the XMM-SERVS data, named the XMMSERVS X-ray eXtended Galaxy Cluster (XVXGC) catalog. It includes 141 cluster candidates. Specifically, there are 52 clusters previously identified as clusters with the intra-cluster medium (ICM) emission (class 3), 37 ones previously known as optical or infrared clusters but detected as X-ray clusters for the first time (class 2), and 52 identified as clusters for the first time (class 1). Compared with the class 3 sample, the "class 1 + class 2" sample is systematically fainter, and exhibits a flatter surface brightness profile. Specifically, the median flux in [0.1-2.4] keV band for "class 1 + class 2" and class 3 sample is 2.336×10-14erg/s/cm2and 3.163×10-14erg/s/cm2, respectively. The me

关键词： Galaxies

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A High-Fidelity Virtual Liver Model Incorporating Biological Characteristics

SSRN

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SSRN 2022年

作者： Zhang, Xiaorui Zhang, Wenzheng Sun, Wei Song, Aiguo Xu, Tong Wuxi Research Institute Nanjing University of Information Science & Technology Wuxi214100 China Engineering Research Center of Digital Forensics Ministry of Education Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China School of Automation Nanjing University of Information Science & Technology Nanjing210044 China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and Control School of Instrument Science and Engineering Southeast University Nanjing210096 China University of Southern California Los AngelesCA United States

Flexible tissue modeling plays an important role in the field of telemedicine. It is related to whether the soft tissue deformation process can be accurately, real-time and vividly simulated during surgery. Most existing models lack the unique biological characteristics. To enhance visual perception ability, a high-fidelity virtual liver model incorporating biological characteristics, such as the viscoelastic, anisotropic and nonlinear biological characteristics, is proposed based on the finite element model with the fourth-order Runge-Kutta method. When simulating the resection of diseased livers, the model undergoes a plastic deformation process where the model loses elasticity due to excessive deformation, but does not reach the cutting fracture threshold. To improve the fidelity of the liver model, this study is the first to introduce the viscoplasticity of biological tissues to the liver model. The 3DMax2020 and OpenGL4.6 were used to build a liver surgery simulation platform, and the PHANTOM OMNI manual controller was used to sense the feedback force during the operation. The proposed model was verified from three aspects of accuracy, fidelity and real-time performance. The experimental results show that the proposed virtual liver model can achieve real visual reproduction, with natural deformation effect, accurate simulation and high fidelity. © 2022, The Authors. All rights reserved.

关键词： Simulation platform

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Sketchy Bounding-box Supervision for 3D Instance Segmentation

arXiv

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arXiv 2025年

作者： Deng, Qian Hui, Le Xie, Jin Yang, Jian PCA Lab College of Computer Science Nankai University Tianjin China School of Electronics and Information Northwestern Polytechnical University Xi’an China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

Bounding box supervision has gained considerable attention in weakly supervised 3D instance segmentation. While this approach alleviates the need for extensive point-level annotations, obtaining accurate bounding boxes in practical applications remains challenging. To this end, we explore the inaccurate bounding box, named sketchy bounding box, which is imitated through perturbing ground truth bounding box by adding scaling, translation, and rotation. In this paper, we propose Sketchy-3DIS, a novel weakly 3D instance segmentation framework, which jointly learns pseudo labeler and segmentator to improve the performance under the sketchy bounding-box supervisions. Specifically, we first propose an adaptive box-to-point pseudo labeler that adaptively learns to assign points located in the overlapped parts between two sketchy bounding boxes to the correct instance, resulting in compact and pure pseudo instance labels. Then, we present a coarse-to-fine instance segmentator that first predicts coarse instances from the entire point cloud and then learns fine instances based on the region of coarse instances. Finally, by using the pseudo instance labels to supervise the instance segmentator, we can gradually generate high-quality instances through joint training. Extensive experiments show that our method achieves state-of-the-art performance on both the ScanNetV2 and S3DIS benchmarks, and even outperforms several fully supervised methods using sketchy bounding boxes. Code is available at https://***/dengq7/Sketchy-3DIS. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

arXiv

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arXiv 2022年

作者： Ma, Qian Gao, Wei Xiao, Qiang Ding, Lingsong Gao, Tianyi Zhou, Yajun Gao, Xinxin Yan, Tao Liu, Che Gu, Ze Kong, Xianghong Abbasi, Qammer H. Li, Lianlin Qiu, Cheng-Wei Li, Yuanqing Cui, Tie Jun Institute of Electromagnetic Space Southeast University Nanjing210096 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China State Key Laboratory of Millimeter Wave Southeast University Nanjing210096 China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou510330 China Research Center for Brain-Computer Interface Pazhou Lab Guangzhou510330 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Peking University Beijing100871 China

Brain-computer interfaces (BCIs), invasive or non-invasive, have projected unparalleled vision and promise for assisting patients in need to better their interaction with the surroundings. Inspired by the BCI-based rehabilitation technologies for nerve-system impairments and amputation, we propose an electromagnetic brain-computer-metasurface (EBCM) paradigm, regulated by human's cognition by brain signals directly and non-invasively. We experimentally show that our EBCM platform can translate human's mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively, which can be further processed and transported by an information metasurface in automated and wireless fashions. Directly wireless communications of the human minds are performed between two EBCM operators with accurate text transmissions. Moreover, several other proof-of-concept mind-control schemes are presented using the same EBCM platform, exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning, wave modulations, and pattern encoding. © 2022, CC BY.

关键词： Electrophysiology

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Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction

arXiv

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arXiv 2021年

作者： Wang, Yikai Sun, Fuchun Huang, Wenbing He, Fengxiang Tao, Dacheng State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China JD Explore Academy *** Inc China

Multimodal fusion and multitask learning are two vital topics in machine learning. Despite the fruitful progress, existing methods for both problems are still brittle to the same challenge—it remains dilemmatic to integrate the common information across modalities (resp. tasks) meanwhile preserving the specific patterns of each modality (resp. task). Besides, while they are actually closely related to each other, multimodal fusion and multitask learning are rarely explored within the same methodological framework before. In this paper, we propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for multimodal and multitask dense image prediction. At its core, CEN adaptively exchanges channels between subnetworks of different modalities. Specifically, the channel exchanging process is self-guided by individual channel importance that is measured by the magnitude of Batch-Normalization (BN) scaling factor during training. For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning. Extensive experiments on semantic segmentation via RGB-D data and image translation through multi-domain input verify the effectiveness of CEN compared to state-of-the-art methods. Detailed ablation studies have also been carried out, which demonstrate the advantage of each component we propose. Our code is available at https://***/yikaiw/CEN. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Disjoint-Path and Golden-Pin Based Irregular PCB Routing with Complex Constraints 23

Disjoint-Path and Golden-Pin Based Irregular PCB Routing wit...

引用

Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Qinghai Liu Qinfei Tang Jiarui Chen Chuandong Chen Ziran Zhu Huan He Jianli Chen Yao-Wen Chang Center for Discrete Mathematics Fuzhou University Fuzhou China and Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China Center for Discrete Mathematics Fuzhou University Fuzhou China College of Computer and Date Science Fuzhou University Fuzhou China Department of Microelectronics Fuzhou University Fuzhou China Department of Microelectronics Southeast University Nanjing China Hangzhou Huawei Enterprises Telecommunication Technologies Co. Ltd Hangzhou China State Key Lab of ASIC & System Fudan University Shanghai China Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan and Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)9798350323481

PCB routing becomes time-consuming as the complexity of PCB design increases. Unlike traditional schemes that treat the two essential PCB routing processes separately, namely, escape and bus routing, we consider the continuity between them and present a golden-pin-based routing scheme to find the desired solution with angle and topology constraints. Further, conventional rip-up and reroute methods are often ineffective and inefficient for congestion alleviation and routability optimization. We construct a component graph by modeling components as vertices and applying the minimum weight vertex covering method to improve the routability. A self-adaptable ordering method is presented for escape routing to arrange the pin order on the component boundary, guaranteeing successful bus routing. In addition, escape routing is performed based on a disjoint path method. We construct a dynamic Hanan grid in bus routing and utilize a novel congestion adjustment technique to improve solution quality. Compared with FreeRouting and Allegro, the experiment results show that our algorithm achieves high routability and a significant 90% runtime reduction.

关键词： escape routing

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Enhancing Motor Imagery EEG Signal Classification with Simplified GoogLeNet

Enhancing Motor Imagery EEG Signal Classification with Simpl...

引用

International Winter Workshop on Brain-computer Interface (BCI)

作者： Lu Wang Junkongshuai Wang Bo Wen Wei Mu Lusheng Liu Jiaguan Han Lihua Zhang Jie Jia Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical NeurobiologyAcademy for Engineering & Technology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of AI & Robotics Institute of Meta-MedicalFudan University Shanghai China Ji Hua Laboratory Guangdong Province Foshan China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

The brain-computer interface is a novel tool for interaction between the brain and external devices, and is an important tool at the forefront of international brain science research. Motor imagery does not require external stimulation and is one of the most widely used BCI paradigms, and is now widely used to address the treatment of neurological impairments such as paralysis, stroke, and acromegaly. Electrophysiological source imaging is a technique based on the observation of electromagnetic signals from the cerebral cortex, reflecting the electrophysiological activity of brain nerve cells on the scalp surface of the brain. The wavelet transform, on the other hand, is commonly used for the processing of non-smooth signals and aims to extract excellent signal features. This paper combines these two algorithms as feature extraction methods. In recent years, deep learning has been increasingly combined with MI-EEG-based BCI. GoogLeNet is one of the most classical convolutional neural networks and has made a splash in ImageNet image recognition. However, using GoogLeNet to train the BCI public dataset BCI IV IIa and dataset HGD has shown problems such as long training time, overfitting, and gradient disappearance. In response, this paper proposes an improved and streamlined version of GoogLeNet for training based on this dataset to achieve an overall improvement in accuracy, training time, number of parameters, and other indicators. It is useful for the realistic application of MI-based BCI systems.

关键词： Training Wavelet transforms Surface waves Scalp Pattern classification Electrophysiology Streaming media

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Offboard Occupancy Refinement with Hybrid Propagation for Autonomous Driving

arXiv

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arXiv 2024年

作者： Shi, Hao Wang, Song Zhang, Jiaming Yin, Xiaoting Wang, Zhongdao Wang, Guangming Zhu, Jianke Yang, Kailun Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany Huawei Noah’s Ark Lab. Canada The Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC), presents a significant challenge in computer vision. Previous methods, confined to onboard processing, struggle with simultaneous geometric and semantic estimation, continuity across varying viewpoints, and single-view occlusion. Our paper introduces OccFiner, a novel offboard framework designed to enhance the accuracy of vision-based occupancy predictions. OccFiner operates in two hybrid phases: 1) a multi-to-multi local propagation network that implicitly aligns and processes multiple local frames for correcting onboard model errors and consistently enhancing occupancy accuracy across all distances. 2) the region-centric global propagation, focuses on refining labels using explicit multi-view geometry and integrating sensor bias, especially to increase the accuracy of distant occupied voxels. Extensive experiments demonstrate that OccFiner improves both geometric and semantic accuracy across various types of coarse occupancy, setting a new state-of-the-art performance on the SemanticKITTI dataset. Notably, OccFiner elevates vision-based SSC models to a level even surpassing that of LiDAR-based onboard SSC models. Furthermore, OccFiner is the first to achieve automatic annotation of SSC in a purely vision-based approach. Quantitative experiments prove that OccFiner successfully facilitates occupancy data loop-closure in autonomous driving. Additionally, we quantitatively and qualitatively validate the superiority of the offboard approach on city-level SSC static maps. The source code will be made publicly available at https://***/MasterHow/OccFiner. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval

arXiv

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arXiv 2022年

作者： Dong, Jianfeng Wang, Yabing Chen, Xianke Qu, Xiaoye Li, Xirong He, Yuan Wang, Xun The College of Computer and Information Engineering Zhejiang Gongshang University Hangzhou310035 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Electronic Information and Communications Huazhong University of Science and Technology Hubei430074 China The Key Lab of Data Engineering and Knowledge Engineering Renmin University of China The AIMC Lab. School of Information Renmin University of China Beijing100872 China The Alibaba Group Beijing100102 China

This paper aims for the task of text-to-video retrieval, where given a query in the form of a natural-language sentence, it is asked to retrieve videos which are semantically relevant to the given query, from a great number of unlabeled videos. The success of this task depends on cross-modal representation learning that projects both videos and sentences into common spaces for semantic similarity computation. In this work, we concentrate on video representation learning, an essential component for text-to-video retrieval. Inspired by the reading strategy of humans, we propose a Reading-strategy Inspired Visual Representation Learning (RIVRL) to represent videos, which consists of two branches: a previewing branch and an intensive-reading branch. The previewing branch is designed to briefly capture the overview information of videos, while the intensive-reading branch is designed to obtain more in-depth information. Moreover, the intensive-reading branch is aware of the video overview captured by the previewing branch. Such holistic information is found to be useful for the intensive-reading branch to extract more fine-grained features. Extensive experiments on three datasets are conducted, where our model RIVRL achieves a new state-of-the-art on TGIF and VATEX. Moreover, on MSR-VTT, our model using two video features shows comparable performance to the state-of-the-art using seven video features and even outperforms models pre-trained on the large-scale HowTo100M dataset. Code is available at https://***/LiJiaBei-7/rivrl. © 2022, CC BY.

关键词： Video recording

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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