检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

来源：评论

学校读者我要写书评

暂无评论

Capacity-shared heterogeneous CMP cache

引用

Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2008年第5期45卷 877-885页

作者： Gao, Xiang Zhang, Longbing Hu, Weiwu Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 China

The characteristics of advanced integrated circuit technologies require architects to look for new ways to utilize large numbers of gates and mitigate the effects of high interconnect delays. Chip multiprocessors (CMPs) exploit increasing transistor counts by placing multiple processors on a single die. As the chip multiprocessors (CMPs) have become the trend of high performance microprocessors, the target workloads become more and more diversified. Due to the wire delay problem and diversity of applications, neither private nor shared caches can provide both large capacity and fast access in CMPs. A novel CMP cache design, the heterogeneous CMP cache (HCC) is presented, in which chips are constructed by tiles of two different categories. L2 caches of private tiles provide lowest hit latency and L2 cache of shared tiles increases the effective cache capacity for shared data. Incorporating indirect-index cache technology to share capacity between different hierarchies, HCC provide a both capacity-effective and access-fast on-chip memory subsystem. Detailed full-system simulations are used to analyze the HCC performance for various programs, including SPEC CPU2000, SPLASH2 and commercial workloads. The result shows that HCC improves performance by 16% for single-threaded benchmarks and 9% for multi-thread benchmarks. HCC is easy to implement and the design ideas will be used in the future multi-core processors of Godson series.

关键词： Microprocessor chips

来源：评论

学校读者我要写书评

暂无评论

Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

arXiv

引用

arXiv 2024年

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A 1 km Line-of-Sight Ultraviolet Communication system with a Data Rate of 26.6kbps

A 1 km Line-of-Sight Ultraviolet Communication System with a...

引用

IEEE International Conference on Electrical Engineering and Photonics (EExPolytech)

作者： Mingyang Wang Tian Cao Shihan Chen Changyong Pan Jian Song Department of Electronic Engineering Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing China State Key Laboratory of Integrated Service Networks School of Telecommunications Engineering Xidian University Xian China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen China

Ultraviolet communication (UVC) has emerged as a promising solution for close-range communication within the intricate electromagnetic setting. The atmosphere's attenuation, however, restricts the scope and rate of communication. We demonstrated a line-of-sight (LOS) UV communication system. In our system, we employed ultraviolet light-emitting diodes (LEDs) in our system to transmit the signal light with a broad radiation angle and use lenses to enhance the communication range. We propose a combined approach of theory and practice to analyze the feasibility of a LOS UVC system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

引用

IEEE Transactions on Intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

End-to-End AI-Based Point-of-Care Diagnosis system for Classifying Respiratory Illnesses and Early Detection of COVID-19

arXiv

引用

arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Aggregation capacity of wireless sensor networks: Extended network case

Aggregation capacity of wireless sensor networks: Extended n...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Cheng Wang Changjun Jiang Yunhao Liu Xiang-Yang Li Shaojie Tang Huadong Ma Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science University of Tongji Shanghai China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China TNLIST School of Software Tsinghua University China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781424499199

A critical function of wireless sensor networks (WSNs) is data gathering. While, one is often only interested in collecting a relevant function of the sensor measurements at a sink node, rather than downloading all the data from all the sensors. This paper studies the capacity of computing and transporting the specific functions of sensor measurements to the sink node, called aggregation capacity, for WSNs. It focuses on random WSNs that can be classified into two types: random extended WSN and random dense WSN. All existing results about aggregation capacity are studied for dense WSNs, including random cases and arbitrary cases, under the protocol model (ProM) or physical model (PhyM). In this paper, we propose the first aggregation capacity scaling laws for random extended WSNs. We point out that unlike random dense WSNs, for random extended WSNs, the assumption made in ProM and PhyM that each successful transmission can sustain a constant rate is over-optimistic and unpractical due to transmit power *** derive the first result on aggregation capacity for random extended WSNs under the generalized physical model. Particularly, we prove that, for the type-sensitive perfectly compressible functions and type-threshold perfectly compressible functions, the aggregation capacities for random extended WSNs with n nodes are of order Θ ((log n) -β/2-1 ) and Θ (((log n) -β/2 )/(log log n)), respectively, where β >; 2 denotes the power attenuation exponent in the generalized physical model.

关键词： Wireless sensor networks Signal to noise ratio Interference Throughput Lattices Routing PROM

来源：评论

学校读者我要写书评

暂无评论

LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation

arXiv

引用

arXiv 2023年

作者： Huang, Kai Zhao, Junqiao Zhu, Zhongyang Ye, Chen Feng, Tiantian The School of Surveying and Geo-Informatics Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Local geometric information, i.e., normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for data association, which further determines the direction of optimization and ultimately affects the accuracy of localization. However, estimating normal and distribution of points are time-consuming tasks even with the assistance of kdtree or volumetric maps. To achieve fast normal estimation, we look into the structure of LiDAR scan and propose a ring-based fast approximate least squares (Ring FALS) method. With the Ring structural information, estimating the normal requires only the range information of the points when a new scan arrives. To efficiently estimate the distribution of points, we extend the ikd-tree to manage the map in voxels and update the distribution of points in each voxel incrementally while maintaining its consistency with the normal estimation. We further fix the distribution after its convergence to balance the time consumption and the correctness of representation. Based on the extracted and maintained local geometric information, we devise a robust and accurate hierarchical data association scheme where point-to-surfel association is prioritized over point-to-plane. Extensive experiments on diverse public datasets demonstrate the advantages of our system compared to other state-of-the-art methods. Our open source implementation is available at https://***/tiev-tongji/LOG-LIO. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical radar

来源：评论

学校读者我要写书评

暂无评论

LIMOT: A Tightly-Coupled system for LiDAR-Inertial Odometry and Multi-Object Tracking

arXiv

引用

arXiv 2023年

作者： Zhu, Zhongyang Zhao, Junqiao Huang, Kai Tian, Xuebo Lin, Jiaye Ye, Chen Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China The School of Surveying and Geo-Informatics Tongji University Shanghai China

Simultaneous localization and mapping (SLAM) is critical to the implementation of autonomous driving. Most LiDAR-inertial SLAM algorithms assume a static environment, leading to unreliable localization in dynamic environments. Moreover, the accurate tracking of moving objects is of great significance for the control and planning of autonomous vehicles. This study proposes LIMOT, a tightly-coupled multi-object tracking and LiDAR-inertial odometry system that is capable of accurately estimating the poses of both ego-vehicle and objects. We propose a trajectory-based dynamic feature filtering method, which filters out features belonging to moving objects by leveraging tracking results before scan-matching. Factor graph-based optimization is then conducted to optimize the bias of the IMU and the poses of both the ego-vehicle and surrounding objects in a sliding window. Experiments conducted on the KITTI tracking dataset and self-collected dataset show that our method achieves better pose and tracking accuracy than our previous work DL-SLOT and other baseline methods. Our open-source implementation is available at https://***/tiev-tongji/LIMOT. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Joint Data Retransmission and Client Selection Optimization for Error-Tolerant Federated Learning in UAV Networks

引用

IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Dong, Chao Wu, Feiyu Qu, Yuben Akerkar, Rajendra Wu, Tao Zhou, Fuhui Wu, Qihui Guo, Song Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing210023 China Western Norway Research Institute Sogndal6856 Norway Hong Kong Polytechnic University Hong Kong Hong Kong University of Science and Technology 999077 Hong Kong The National University of Defense Technology Department of Computing Anhui Hefei230009 China

The flexibility and mobility of Unmanned Aerial Vehicle (UAV) swarms enable them to integrate with federated learning (FL), an emerging distributed machine learning framework. UAV-FL creates an edge intelligence system for UAV swarms. However, unreliable UAV networks lead to a large amount of retransmissions for the dropped data, making it difficult for FL in UAV swarms to achieve the expected accuracy within a short period. Fortunately, benefiting from the dilution effect of convolutional computations, FL can tolerate a limited amount of model parameter errors. Thus motivated, this paper proposes a novel FL framework called FedET, to jointly optimize data retransmission and client selection to achieve error-tolerant FL in UAV networks. Specifically, FedET utilizes error tolerance of FL to reduce the number of retransmissions, which navigates the trade-off between training time and model accuracy. Meanwhile, the impact of retransmission on client selection is also analyzed. We formulate the training utility maximization problem for FL via jointly optimizing data retransmission and client selection. To solve this problem, we propose an alternating optimization-based algorithm to reach the local optimal solution. We implement and evaluate FedET on widely used real-world UAV embedded devices (i.e., NVIDIA Jetson devices). Compared to existing FL algorithms, when faced with unreliable UAV networks, FedET on average reduces the total training time by ∼45.8%, and transmission latency by ∼67.9%, respectively. © 2015 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：