检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Jasour, Ashkan Han, Weiqiao Williams, Brian MIT Computer Science and Artificial Intelligence Laboratory United States

In this paper, we address the trajectory planning problem in uncertain nonconvex static and dynamic environments that contain obstacles with probabilistic location, size, and geometry. To address this problem, we provide a risk bounded trajectory planning method that looks for continuous-time trajectories with guaranteed bounded risk over the planning time horizon. Risk is defined as the probability of collision with uncertain obstacles. Existing approaches to address risk bounded trajectory planning problems either are limited to Gaussian uncertainties and convex obstacles or rely on sampling-based methods that need uncertainty samples and time discretization. To address the risk bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk bounded planning problem into a deterministic optimization problem. Risk contours are the set of all points in the uncertain environment with guaranteed bounded risk. The obtained deterministic optimization is, in general, nonlinear and nonconvex time-varying optimization. We provide convex methods based on sum-of-squares optimization to efficiently solve the obtained nonconvex time-varying optimization problem and obtain the continuous-time risk bounded trajectories without time discretization. The provided approach deals with arbitrary (and known) probabilistic uncertainties, nonconvex and nonlinear, static and dynamic obstacles, and is suitable for online trajectory planning problems. In addition, we provide convex methods based on sum-of-squares optimization to build the max-sized tube with respect to its parameterization along the trajectory so that any state inside the tube is guaranteed to have bounded risk. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

引用

computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

来源：评论

学校读者我要写书评

暂无评论

Lightweight Ethylene-Vinyl Acetate Copolymer/Low-density Polyethylene/Carbon Nanotube Foams via Supercritical Carbon Dioxide Foaming for Piezoresistive Sensors

引用

Chinese Journal of Polymer science 2025年

作者： Hao-Hao Hou Xin He Hui Ma Hong-Fu Zhou Bian-Ying Wen Xiang-Dong Wang Ya-Feng Deng Key Laboratory of Processing and Application of Polymeric Foams of China National Light Industry Council School of Light Industry Science and EngineeringBeijing Technology and Business University School of Computer and Artificial Intelligence Beijing Technology and Business University

Flexible polymer-based foam sensors have significant potential for application in wearable electronics and motion monitoring. However, these prospects are hindered by the complex and unenvironmentally friendly manufacturing processes. In this study, we employed melt blending and supercritical carbon dioxide foaming to fabricate an ethylene-vinyl acetate copolymer（EVA）/low-density polyethylene（LDPE）/carbon nanotube（CNT） piezoresistive foam sensor. The cross-linking agent bis（tert-butyldioxyisopropyl） benzene and the conductive filler CNT were incorporated into the EVA/LDPE composite, successfully achieving a chemically cross-linked and physically entangled composite structure that significantly enhanced the storage modulus and complex viscosity. Additionally, the compressive strength of EVA/LDPE/CNT foam with 10 parts per hundred rubber（phr） CNT reached 1.37 MPa at 50% compression, marking a 340% increase compared to the 0.31 MPa of the CNT-free sample. Furthermore, the EVA/LDPE/CNT composite foams, which incorporated 10 phr CNT, were prepared under specific foaming conditions, resulting in an ultra-low density of 0.11 g/cm3and a higher sensitivity, with a gauge factor of –2.3. The piezoresistive foam sensors developed in this work could accurately detect human motion, thereby expanding their applications in the field of piezoresistive foam sensors and providing an effective strategy for the advancement of high-performance piezoresistive foam sensors.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CPK-Adapter: Infusing Medical Knowledge into K-Adapter with Continuous Prompt 8

CPK-Adapter: Infusing Medical Knowledge into K-Adapter with ...

引用

8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Liu, Chen Zhang, Shaojie Li, Chuanfu Zhao, Haifeng Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China The First Affiliated Hospital of Anhui University of Chinese Medcine Institute of Artificial Intelligence Hefei Comprehensive National Science Ceter Hefei China

ISBN: (纸本)9798350302455

Recently, pre-trained language models (PLMs) have been significantly improved for downstream tasks by infusing knowledge. In the field of medical research, with the continuous updating and increasing of data, PLM often requires continuous learning of knowledge. Most existing methods jointly store new knowledge and historical knowledge in an entangled way by updating the overall parameters of PLM. However, when new knowledge infuses into PLM continuously, the historical knowledge is replaced by new knowledge. In this article, we propose CPK-Adapter, a method combines prompt learning and continual learning to balance the abilities of PLM to learn new knowledge and to memorize historical knowledge. We evaluated CPK-Adapter on a suit of BERT models, including BERT, BioBERT, ClinicalBERT, and BlueBERT. The experiments demonstrate that CPK-Adapter outperforms the comparative PLMs in medical tasks, and CPK-Adapter is significantly better than K-Adapter. Furthermore, the performance of CPK-Adapter approaches or exceeds DiseaseBERT which stores task knowledge by updating the overall parameters of PLM. © 2023 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

The Facebook Algorithm’s Active Role in Climate Advertisement Delivery

arXiv

引用

arXiv 2023年

作者： Sankaranarayanan, Aruna Hemberg, Erik O’Reilly, Una-May Computer Science and Artificial Intelligence Laboratory MIT United States

Communication strongly influences attitudes on climate change. Within sponsored communication, high spend and high reach advertising dominates. In the advertising ecosystem we can distinguish actors with adversarial stances: organizations with contrarian or advocacy communication goals, who direct the advertisement delivery algorithm to launch ads in different destinations by specifying targets and campaign objectives. We present an observational (N=275,632) and a controlled (N=650) study which collectively indicate that the advertising delivery algorithm could itself be an actor, asserting statistically significant influence over advertisement destinations, characterized by U.S. state, gender type, or age range. This algorithmic behaviour may not entirely be understood by the advertising platform (and its creators). These findings have implications for climate communications and misinformation research, revealing that targeting intentions are not always fulfilled as requested and that delivery itself could be manipulated. © 2023, CC BY-NC-ND.

关键词： Climate change

来源：评论

学校读者我要写书评

暂无评论

A Novel Adaptive 360° Livestreaming with Graph Representation Learning based FoV Prediction

引用

IEEE Transactions on Emerging Topics in Computing 2024年 1-14页

作者： Chen, Xingyan Du, Huaming Wang, Mu Zhao, Yu Shu, Xiaoyang Xu, Changqiao Muntean, Gabriel-Miro Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province Institute of Digital Economy and Interdisciplinary Science Innovation School of Computer and Artificial Intelligence Southwestern University of Finance and Economics Chengdu P. R. China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing P. R. China School of Electronic Engineering Dublin City University Glasnevin Dublin 9 Ireland

The exceptionally high bandwidth requirements associated with the delivery of live 360° video content pose significant challenges in the current network context. An avenue for addressing this bandwidth challenge is to use the limited network resources for sending the user's Field-of-View (FoV) tiles at a high resolution, instead of transmitting all frame components at high quality. However, precisely forecasting the FoV for 360° live video content distribution remains a complex endeavor due to the lack of pre-knowledge on user viewing behaviors. In this paper, we present GL360, a novel 360° transmission framework, which employs Graph Representation Learning for FoV prediction. First, we analyze the interaction between users and tiles in panoramic videos utilizing a dynamic heterogeneous Relational Graph Convolutional Network (RGCN), which facilitates efficient user and tile embedding representation learning. Secondly, we propose an online dynamic heterogeneous graph learning (DHGL)-based algorithm to dynamically capture the time-varying features of the user's viewing behaviors with limited prior knowledge. Further, we design a FoV-aware content delivery algorithm that allows the edge servers to determine the video tiles' resolution for each accessed user. Experimental results based on real traces demonstrate how our solution outperforms four other solutions in terms of FoV prediction and network performance IEEE

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

PCRTAM-Net:A Novel Pre-Activated Convolution Residual and Triple Attention Mechanism Network for Retinal Vessel Segmentation

引用

Journal of computer science & Technology 2023年第3期38卷 567-581页

作者：汪华登李紫正保罗黎兵兵潘细朋刘振丙蓝如师罗笑南 Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin 541004China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin 541004China Department of Pathology Ganzhou Municipal HospitalGanzhou 341000China

Retinal images play an essential role in the early diagnosis of ophthalmic *** segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast *** the same time,automated models struggle to capture representative and discriminative retinal vascular *** fully utilize the structural information of the retinal blood vessels,we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network(PCRTAM-Net).PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the *** addition,the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow.A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature *** evaluate the proposed PCRTAM-Net on four publicly available datasets,DRIVE,CHASE_DB1,STARE,and *** model achieves state-of-the-art performance of 97.10%,97.70%,97.68%,and 97.14%for ACC and 83.05%,82.26%,84.64%,and 81.16%for F1,respectively.

关键词： retinal image segmentation triple attention mechanism atrous convolution residual network

来源：评论

学校读者我要写书评

暂无评论

Parallel Graph Learning with Temporal Stamp Encoding for Fraudulent Transactions Detections

引用

IEEE Transactions on Big Data 2024年

作者： Ma, Jiacheng Xiang, Sheng Li, Qiang Yuan, Liangyu Cheng, Dawei Jiang, Changjun University of Technology Sydney Australian Artificial Intelligence Institute Sydney Australia Tongji University Department of Computer Science and Technology Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

Financial transaction systems have become the critical backbone of modern society, and the sharp increase in fraudulent transactions has become an unavoidable significant topic. Their presence poses a severe threat to financial markets, impacting the health of the economic and social welfare systems of various countries. However, most existing fraud detection methods are limited to detecting individual fraudulent entities within static transaction networks, which are neither suitable for continuously changing dynamic transaction networks nor capable of detecting the increasingly prevalent organized fraud crimes. This paper introduces a novel approach, Parallel Graph Learning with Temporal Stamp Encoding (PGLTSE). On the one hand, it designs a history information module to perform temporal dimension feature learning to adapt to the continuous changes in transaction information in Continuous-Time Dynamic Graphs (CTDG). On the other hand, it designs a gang-aware risk propagation algorithm to infer the risk of organized fraudulent activities in the global transaction relation graph. By simultaneously conducting parallel graph representation learning in both homogeneous global transaction relation graphs and heterogeneous local entity interaction graphs, it aggregates local interaction and global association information for end-to-end training. Extensive experiments on diverse real-world datasets substantiate the superior performance of PGLTSE over existing methods, demonstrating its practical efficacy in detecting complex and evolving fraudulent behaviors in financial networks. © 2015 IEEE.

关键词： Health risks

来源：评论

学校读者我要写书评

暂无评论

Sparse Color Fourier Ptychographic Microscopy With Implicit Neural Representations

Sparse Color Fourier Ptychographic Microscopy With Implicit ...

引用

Computational Optical Sensing and Imaging, COSI 2024 - Part of Optica Imaging Congress

作者： Chan, Matthew A. Zhou, Haowen Feng, Brandon Y. Metzler, Christopher A. Department of Computer Science University of Maryland College ParkMD20742 United States Department of Electrical Engineering California Institute of Technology PasadenaCA91125 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

We apply implicit neural representations—which naturally capture spectral regularity—to reconstruct color Fourier ptychographic microscopy images from spectrally-sparse measurements. We conduct experiments on real-world specimens and demonstrate reconstruction quality comparable with fully sampled methods. © 2024 The Author(s).

关键词：

来源：评论

学校读者我要写书评

暂无评论

SHMT: self-supervised hierarchical makeup transfer via latent diffusion models 24

SHMT: self-supervised hierarchical makeup transfer via laten...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhaoyang Sun Shengwu Xiong Yaxiong Chen Fei Du Weihua Chen Fan Wang Yi Rong School of Computer Science and Artificial Intelligence Wuhan University of Technology and DAMO Academy Alibaba Group School of Computer Science and Artificial Intelligence Wuhan University of Technology and Sanya Science and Education Innovation Park Wuhan University of Technology and Shanghai AI Laboratory School of Computer Science and Artificial Intelligence Wuhan University of Technology DAMO Academy Alibaba Group and Hupan Laboratory School of Computer Science and Artificial Intelligence Wuhan University of Technology and Sanya Science and Education Innovation Park Wuhan University of Technology

ISBN: (纸本)9798331314385

This paper studies the challenging task of makeup transfer, which aims to apply diverse makeup styles precisely and naturally to a given facial image. Due to the absence of paired data, current methods typically synthesize sub-optimal pseudo ground truths to guide the model training, resulting in low makeup fidelity. Additionally, different makeup styles generally have varying effects on the person face, but existing methods struggle to deal with this diversity. To address these issues, we propose a novel Self-supervised Hierarchical Makeup Transfer (SHMT) method via latent diffusion models. Following a "decoupling-and-reconstruction" paradigm, SHMT works in a self-supervised manner, freeing itself from the misguidance of imprecise pseudo-paired data. Furthermore, to accommodate a variety of makeup styles, hierarchical texture details are decomposed via a Laplacian pyramid and selectively introduced to the content representation. Finally, we design a novel Iterative Dual Alignment (IDA) module that dynamically adjusts the injection condition of the diffusion model, allowing the alignment errors caused by the domain gap between content and makeup representations to be corrected. Extensive quantitative and qualitative analyses demonstrate the effectiveness of our method. Our code is available at https://***/Snowfallingplum/SHMT.

关键词：

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：