检索结果-内蒙古大学图书馆

SAFER-predictor: Sparse adversarial training framework for robust traffic prediction under missing and noisy data

Communications in Transportation Research 2025年 5卷

作者： Yutian Liu Chengfeng Jia Soora Rasouli Jian Gong Tao Feng Melvin Wong Tianjin Huang School of Urban Planning and Design Peking University Shenzhen Graduate School Shenzhen 518055 China Centre for Advanced Robotics Technology Innovation Nanyang Technological University Singapore 639798 Singapore Department of Built Environment Eindhoven University of Technology Eindhoven 5612 AE the Netherlands College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing 210023 China Urban and Data Science Lab Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima 739-0046 Japan Department of Computer Science University of Exeter Exeter EX44QJ UK

Accurate traffic flow forecasting is essential for developing intelligent transportation systems (ITSs) to reduce congestion, optimize road management, and improve safety. While data-driven traffic prediction approaches have shown high accuracy, they rely heavily on precise measurements, making them vulnerable to perturbed environmental factors, like sensor malfunctions, data storage issues, and adverse weather conditions. To overcome the limitation, we propose SAFER-Predictor, a novel sparse adversarial training (Sparse AT) framework for enhancing the reliability of deep learning based spatiotemporal traffic prediction models. Sparse AT extends traditional adversarial training (AT) through a two-phase process: pre-training and fine-tuning. In the pre-training phase, the model is optimized to capture normal traffic patterns, enhancing predictive performance by understanding standard dynamics without external disruptions. In the fine-tuning phase, the focus shifts to strengthening robustness against corrupted inputs by employing an iterative min-max strategy during AT, optimizing performance for worst-case scenarios. Furthermore, we derive theoretical formulations that establish an upper bound on the model's prediction error following Sparse AT under certain noise levels. Experimental results indicate that incorporating Sparse AT into the representative traffic flow prediction models improves stability and ensures high accuracy under various perturbation scenarios.

关键词： Traffic prediction Adversarial training (AT) Graph neural network Deep learning Missing data Prediction robustness

来源：评论

学校读者我要写书评

暂无评论

Learning data-adaptive non-parametric kernels

引用

The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

来源：评论

学校读者我要写书评

暂无评论

Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach

arXiv

引用

arXiv 2020年

作者： Wu, Qitian Zhang, Hengrui Gao, Xiaofeng Yan, Junchi Zha, Hongyuan Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China School of Data Science Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen China

Recommendation models can effectively estimate underlying user interests and predict one's future behaviors by factorizing an observed user-item rating matrix into products of two sets of latent factors. However, the user-specific embedding factors can only be learned in a transductive way, making it difficult to handle new users on-the-fly. In this paper, we propose an inductive collaborative filtering framework that contains two representation models. The first model follows conventional matrix factorization which factorizes a group of key users' rating matrix to obtain meta latents. The second model resorts to attention-based structure learning that estimates hidden relations from query to key users and learns to leverage meta latents to inductively compute embeddings for query users via neural message passing. Our model enables inductive representation learning for users and meanwhile guarantees equivalent representation capacity as matrix factorization. Experiments demonstrate that our model achieves promising results for recommendation on few-shot users with limited training ratings and new unseen users which are commonly encountered in open-world recommender systems. The codes are available at https://***/qitianwu/IDCF. © 2020, CC BY.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Effect of Explicit Emotional Adaptation on Prosocial Behavior of Humans towards Robots depends on Prior Robot Experience 27

Effect of Explicit Emotional Adaptation on Prosocial Behavio...

引用

27th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2018

作者： Kühnlenz, Barbara Kühnlenz, Kolja Busse, Fabian Förtsch, Pascal Wolf, Maximilian Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and Arts Robotics Research Lab Germany

ISBN: (纸本)9781538679807

Emotional adaptation increases pro-social behavior of humans towards robotic interaction partners. Social cues are an important factor in this context. This work investigates, if emotional adaptation still works under absence of human-like facial Action Units. A human-robot dialog scenario is chosen using NAO pretending to work for a supermarket and involving humans providing object names to the robot for training purposes. In a user study, two conditions are implemented with or without explicit emotional adaptation of NAO to the human user in a between-subjects design. Evaluations of user experience and acceptance are conducted based on evaluated measures of human-robot interaction (HRI). The results of the user study reveal a significant increase of helpfulness (number of named objects), anthropomorphism, and empathy in the explicit emotional adaptation condition even without social cues of facial Action Units, but only in case of prior robot contact of the test persons. Otherwise, an opposite effect is found. These findings suggest, that reduction of these social cues can be overcome by robot experience prior to the interaction task, e.g. realizable by an additional bonding phase, confirming the importance of such from previous work. Additionally, an interaction with academic background of the participants is found. © 2018 IEEE.

关键词： Social behavior

来源：评论

学校读者我要写书评

暂无评论

Correction to: A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

引用

Complex & intelligent systems 2023年第2期9卷 2205页

作者： Tanzeela Shakeel Shaista Habib Wadii Boulila Anis Koubaa Abdul Rehman Javed Muhammad Rizwan Thippa Reddy Gadekallu Mahmood Sufiyan School of System and Technology University of Management and Technology Lahore Pakistan. Robotics and Internet of Things Lab Prince Sultan University Riyadh 12435 Saudi Arabia. Department of Cyber Security PAF Complex E-9 Air University Islamabad Pakistan. Department of Computer Science Kinnaird College for Women Lahore Pakistan. School of Information Technology and Engineering Vellore Institute of Technology Vellore India.

[This corrects the article DOI: 10.1007/s40747-022-00767-w.].

关键词：

来源：评论

学校读者我要写书评

暂无评论

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

arXiv

引用

arXiv 2022年

作者： Nwoye, Chinedu Innocent Alapatt, Deepak Yu, Tong Vardazaryan, Armine Xia, Fangfang Zhao, Zixuan Xia, Tong Jia, Fucang Yang, Yuxuan Wang, Hao Yu, Derong Zheng, Guoyan Duan, Xiaotian Getty, Neil Sanchez-Matilla, Ricardo Robu, Maria Zhang, Li Chen, Huabin Wang, Jiacheng Wang, Liansheng Zhang, Bokai Gerats, Beerend Raviteja, Sista Sathish, Rachana Tao, Rong Kondo, Satoshi Pang, Winnie Ren, Hongliang Abbing, Julian Ronald Sarhan, Mohammad Hasan Bodenstedt, Sebastian Bhasker, Nithya Oliveira, Bruno Torres, Helena R. Ling, Li Gaida, Finn Czempiel, Tobias Vilaça, João L. Morais, Pedro Fonseca, Jaime Egging, Ruby Mae Wijma, Inge Nicole Qian, Chen Bian, Guibin Li, Zhen Balasubramanian, Velmurugan Sheet, Debdoot Luengo, Imanol Zhu, Yuanbo Ding, Shuai Aschenbrenner, Jakob-Anton van der Kar, Nicolas Elini Xu, Mengya Islam, Mobarakol Seenivasan, Lalithkumar Jenke, Alexander Stoyanov, Danail Mutter, Didier Mascagni, Pietro Seeliger, Barbara Gonzalez, Cristians Padoy, Nicolas ICube University of Strasbourg CNRS France IHU Strasbourg France Department of Computer Science University of Chicago United States Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Management Hefei University of Technology Hefei China Institue of Medical Robotics Shanghai Jiao Tong University Shanghai China Argonne National Laboratory 9700 S Cass Ave LemontIL60439 United States Digital Surgery A Medtronic Company London United Kingdom Institute of Automation Chinese Academy of Sciences China Department of Computer Science School of Informatics Xiamen University Xiamen China Johnson & Johnson Meander Medical Centre Netherlands Indian Institute of Technology Kharagpur India Muroran Institute of Technology Japan Department of Biomedical Engineering National University of Singapore Singapore Department for Translational Surgical Oncology National Center for Tumor Diseases Partner Site Dresden Germany 2Ai School of Technology IPCA Barcelos Portugal School of Medicine University of Minho Braga Portugal Algoritimi Center School of Engineering University of Minho Guimeraes Portugal Technical University of Munich Germany Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Wellcome EPSRC Center for Interventional and Surgical Science University College London United Kingdom Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy University Hospital of Strasbourg France

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity;yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery. © 2022, CC BY-NC-SA.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Data-driven geometric system identification for shape-underactuated dissipative systems

arXiv

引用

arXiv 2020年

作者： Bittner, Brian Hatton, Ross L. Revzen, Shai Robotics Institute University of Michigan Ann Arbor United States Johns Hopkins University Applied Physics Lab LaurelMD United States Institute School of Mechanical Industrial and Manufacturing Engineering Oregon State University Corvallis United States Electrical Engineering and Computer Science Department University of Michigan Ann Arbor United States

Modeling system dynamics becomes challenging when the properties of individual system components cannot be directly measured, and often requires identification of properties from observed motion. In this paper, we show that systems whose movement is highly dissipative have features which provide an opportunity to more easily identify models and more quickly optimize motions than would be possible with general techniques. Geometric mechanics provides means for reduction of the dynamics by environmental homogeneity, while the dissipative nature minimizes the role of second order (inertial) features in the dynamics. Here we extend the tools of geometric system identification to "Shape-Underactuated Dissipative Systems (SUDS)" – systems whose motions are more dissipative than inertial, but whose actuation is restricted to a subset of the body shape coordinates. Many animal motions are SUDS, including micro-swimmers such as nematodes and flagellated bacteria, and granular locomotors such as snakes and lizards. Many soft robots are also SUDS, particularly robots that incorporate highly damped series elastic actuators to reduce the rigidity of their interactions with their environments during locomotion and manipulation. We motivate the use of SUDS models, and validate their ability to predict motion of a variety of simulated viscous swimming platforms. For a large class of SUDS, we show how the shape velocity actuation inputs can be directly converted into torque inputs, suggesting that systems with soft pneumatic or dielectric elastomer actuators can be modeled with the tools presented. Based on fundamental assumptions in the physics, we show how our model complexity scales linearly with the number of passive shape coordinates. This scaling offers a large reduction on the number of trials needed to identify the system model from experimental data, and may reduce overfitting. The sample efficiency of our method suggests its use in modeling, control, and optimization in ro

关键词： Energy dissipation

来源：评论

学校读者我要写书评

暂无评论

A Multimodal Human-Robot Interaction Manager for Assistive Robots

A Multimodal Human-Robot Interaction Manager for Assistive R...

引用

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Bahareh Abbasi Natawut Monaikul Zhanibek Rysbek Barbara Di Eugenio Miloš Žefran The Robotics Lab Electrical and Computer Engineering Department University of Illinois at Chicago Chicago IL USA The Natural Language Processing Lab Computer Science Department University of Illinois at Chicago Chicago IL USA

With rapid advances in social robotics, humanoids and autonomy, robot assistants appear to be within reach. However, robots are still unable to effectively interact with humans in activities of daily living. One of the challenges is in the frequent use of multiple communication modalities when humans engage in collaborative activities. In this paper, we propose a Multimodal Interaction Manager, a framework for an assistive robot that maintains an active multimodal interaction with a human partner while performing physical collaborative tasks. The heart of our framework is a Hierarchical Bipartite Action-Transition Network (HBATN), which allows the robot to infer the state of the task and the dialogue given spoken utterances and observed pointing gestures from a human partner, and to plan its next actions. Finally, we implemented this framework on a robot to provide preliminary evidence that the robot can successfully participate in a task-oriented multimodal interaction.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PointAtrousGraph: Deep hierarchical encoder-decoder with point atrous convolution for unorganized 3D points

arXiv

引用

arXiv 2019年

作者： Pan, Liang Chew, Chee-Meng Lee, Gim Hee Advanced Robotics Centre National University of Singapore Department of Mechanical Engineering National University of Singapore Lab Department of Computer Science National University of Singapore

Motivated by the success of encoding multi-scale contextual information for image analysis, we propose our PointAtrousGraph (PAG)-a deep permutation-invariant hierarchical encoder-decoder for efficiently exploiting multi-scale edge features in point clouds. Our PAG is constructed by several novel modules, such as Point Atrous Convolution (PAC), Edgepreserved Pooling (EP) and Edge-preserved Unpooling (EU). Similar with atrous convolution, our PAC can effectively enlarge receptive fields of filters and thus densely learn multi-scale point features. Following the idea of non-overlapping maxpooling operations, we propose our EP to preserve critical edge features during subsampling. Correspondingly, our EU modules gradually recover spatial information for edge features. In addition, we introduce chained skip subsampling/upsampling modules that directly propagate edge features to the final stage. Particularly, our proposed auxiliary loss functions can further improve our performance. Experimental results show that our PAG outperform previous state-of-the-art methods on various 3D semantic perception applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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：