检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Bentert, Matthias Kellerhals, Leon Niedermeier, Rolf University of Bergen Department of Informatics Algorithms Norway Technische Universität Berlin Faculty IV Institute of Software Engineering and Theoretical Computer Science Algorithmics and Computational Complexity Germany

The computation of short paths in graphs with arc lengths is a pillar of graph algorithmics and network science. In a more diverse world, however, not every short path is equally valuable. For the setting where each vertex is assigned to a group (color), we provide a framework to model multiple natural fairness aspects. We seek to find short paths in which the number of occurrences of each color is within some given lower and upper bounds. Among other results, we prove the introduced problems to be computationally intractable (NP-hard and parameterized hard with respect to the number of colors) even in very restricted settings (such as each color should appear with exactly the same frequency), while also presenting an encouraging algorithmic result ("fixed-parameter tractability") related to the length of the sought solution path for the general problem. Copyright © 2022, The Authors. All rights reserved.

关键词： Color

来源：评论

学校读者我要写书评

暂无评论

Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications

arXiv

引用

arXiv 2023年

作者： Ning, Zhaolong Hu, Hao Wang, Xiaojie Wu, Qingqing Yuen, Chau Yu, F. Richard Zhang, Yan The School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai Shanghai200240 China The School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada The Department of Informatics University of Oslo Oslo0316 Norway

Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications are expected to alleviate the load of ground base stations in a cost-effective way. Existing studies mainly focus on the deployment and resource allocation of a single IRS instead of multiple IRSs, whereas it is extremely challenging for joint multi-IRS multi-user association in UAV communications with constrained reflecting resources and dynamic scenarios. To address the aforementioned challenges, we propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation to maximize system energy efficiency. We first propose an inverse soft-Q learning-based algorithm to optimize multi-IRS multi-user association. Then, SCA and Dinkelbach-based algorithm are leveraged to optimize UAV trajectory followed by the optimization of SIC decoding order scheduling and power allocation. Finally, theoretical analysis and performance results show significant advantages of the designed algorithm in convergence rate and energy efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Forty Years of Event Graphs in Research and Education

Forty Years of Event Graphs in Research and Education

引用

Simulation Winter Conference

作者： Murat M. Gunal Yahya Ismail Osais Lee Schruben Gerd Wagner Enver Yücesan Faculty of Engineering and Architecture Fenerbahce University Istanbul Türkiye Computer Engineering Department Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Industrial Engineering and Operations Research University of California Berkeley Berkeley CA USA Department of Informatics Brandenburg University of Technology Cottbus Germany Technology and Operations Management Area INSEAD Singapore

Forty years ago, in 1983, Lee Schruben proposed the Event Graph formalism and modeling language, subsequently defining the paradigm of Event-Based Simulation, in a precise way, which had been pioneered 20 years before by SIMSCRIPT. The purpose of this panel is for a group of Event Graph researchers both from Operations Research and computer Science, including the inventor of Event Graphs and one of his former PhD students who has made essential contributions to their theory, to discuss their views on the history and potential of Event Graph modeling and simulation. In particular, the adoption of Event Graphs as a discrete process modeling language in Discrete Event Simulation and in computer Science, and their potential as a foundation for the entire field of Discrete Event Simulation and for the fields of process modeling and AI in computer Science is debated.

关键词：

来源：评论

学校读者我要写书评

暂无评论

All-photonic GKP-qubit repeater using analog-information-assisted multiplexed entanglement ranking

All-photonic GKP-qubit repeater using analog-information-ass...

引用

CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Rozpedek, Filip Seshadreesan, Kaushik P. Polakos, Paul Jiang, Liang Guha, Saikat Pritzker School of Molecular Engineering University of Chicago 5640 S Ellis Ave ChicagoIL60637 United States College of Information and Computer Sciences University of Massachusetts Amherst 140 Governors Drive AmherstMA01003 United States College of Optical Sciences University of Arizona 1630 E University Blvd TucsonAZ85721 United States Department of Informatics and Networked Systems University of Pittsburgh 135 N Bellefield Ave PittsburghPA15260 United States Cisco Systems New YorkNY10119 United States

We propose a novel strategy of using the bosonic Gottesman-Kitaev-Preskill (GKP) code in a repeater architecture with multiplexing. We also quantify the number of GKP qubits needed for the implementation of our scheme... 详细信息

ISBN: (纸本)9781957171395

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

Color Models for Skin Lesion Classification from Dermatoscopic Images 8th

Color Models for Skin Lesion Classification from Dermatoscop...

引用

8th International Workshop on Combinations of Intelligent Methods and Applications, CIMA 2018 held in conjunction with the 30th IEEE International Conference on Tools with Artificial Intelligence, IEEE ICTAI 2018

作者： Mporas, Iosif Perikos, Isidoros Paraskevas, Michael Communications and Intelligent Systems Group School of Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom Computer Engineering and Informatics Department University of Patras Patras Greece Computer Technology Institute and Press ‘Diophantus’ Patras Greece Electrical and Computer Engineering Department University of Peloponnese Patras Greece

ISBN: (纸本)9789811519178

In this paper, we present an architecture for classification of pigmented skin lesions from dermatoscopic images. The architecture is using image preprocessing for natural hair removal and image segmentation for extraction of the skin lesion area followed by computation of statistical values of colors as features. The color-based features were extracted from several well-known and widely used color models. Several classification algorithms were evaluated with the best performing classification algorithm being the AdaBoost with random forest classifier with classification accuracy equal to 73.08% when using RGB-based features only and 74.26% when combining RGB, HSV, and YIQ color model-based features. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

ON SPECIFYING FOR TRUSTWORTHINESS

arXiv

引用

arXiv 2022年

作者： Abeywickrama, Dhaminda B. Bennaceur, Amel Chance, Greg Demiris, Yiannis Kordoni, Anastasia Levine, Mark Moffat, Luke Moreau, Luc Mousavi, Mohammad Reza Nuseibeh, Bashar Ramamoorthy, Subramanian Ringert, Jan Oliver Wilson, James Windsor, Shane Eder, Kerstin Department of Computer Science University of Bristol United Kingdom Department of Computing Open University United Kingdom Electrical and Electronic Engineering Imperial College London United Kingdom Department of Psychology Lancaster University United Kingdom Department of Sociology Lancaster University United Kingdom Department of Informatics King's College London United Kingdom School of Informatics University of Edinburgh United Kingdom Department of Computer Science Bauhaus University Weimar Germany Department of Engineering Mathematics University of Bristol United Kingdom Department of Aerospace Engineering University of Bristol United Kingdom

As autonomous systems (AS) increasingly become part of our daily lives, ensuring their trustworthiness is crucial. In order to demonstrate the trustworthiness of an AS, we first need to specify what is required for an AS to be considered trustworthy. This roadmap paper identifies key challenges for specifying for trustworthiness in AS, as identified during the "Specifying for Trustworthiness" workshop held as part of the UK Research and Innovation (UKRI) Trustworthy Autonomous Systems (TAS) programme. We look across a range of AS domains with consideration of the resilience, trust, functionality, verifiability, security, and governance and regulation of AS and identify some of the key specification challenges in these domains. We then highlight the intellectual challenges that are involved with specifying for trustworthiness in AS that cut across domains and are exacerbated by the inherent uncertainty involved with the environments in which AS need to operate. Copyright © 2022, The Authors. All rights reserved.

关键词： Specifications

来源：评论

学校读者我要写书评

暂无评论

Rethinking domain generalization in medical image segmentation: One image as one domain

arXiv

引用

arXiv 2025年

作者： Hong, Jin Liu, Bo Long, Guoli Li, Siyue Muhammad, Khan School of Information Engineering Nanchang University Nanchang330031 China School of Mathematics and Computer Science Nanchang University Nanchang330031 China Department of Radiological Sciences University of California Los Angeles Los AngelesCA United States Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Medical image segmentation faces persistent challenges from domain discrepancies, particularly when integrating data across institutions. Notably, variations within single centers—driven by differences in scanner configurations or imaging protocols—often create shifts as substantial as those observed between facilities. To overcome these limitations, our work introduces the One Image as One Domain (OIOD) hypothesis, treating each image as an independent domain to enable adaptable generalization. Building on this principle, we present UniDDG, a disentanglement-driven framework that eliminates dependency on domain labels while remaining adaptable to both single- and multi-source scenarios. The architecture maintains consistent complexity regardless of data sources, streamlining implementation across clinical environments. UniDDG operates by isolating anatomical features from device-specific characteristics through cross-batch recombination of content and style components during segmentation and reconstruction. This process preserves structural consistency across imaging conditions while discarding scanner-induced artifacts. To further bolster resilience, two complementary innovations are integrated: Expansion Mask Attention (EMA) prioritizes anatomical boundaries in low-contrast regions, while Style Augmentation (SA) emulates diverse acquisition parameters by generating synthetic style variations. Validation across optic disc/cup and prostate segmentation tasks demonstrates the framework’s efficacy. In multi-to-single-center generalization, UniDDG achieved Dice scores of 84.43% (optic disc and optic cup) and 86.96% (prostate), and achieved that of 88.91% and 88.56% respectively for single-center generalization—surpassing existing state-of-the-art methods. These results highlight its capacity to mitigate domain discrepancies without architectural modifications, offering a scalable solution for real-world clinical heterogeneity. Copyright © 2025, The Authors. All rights

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Image Captioning with Reinforcement Learning

Image Captioning with Reinforcement Learning

引用

computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Anand Verma Saurabh Agarwal K.V. Arya Ivan Petrlik Roberto Esparza Ciro Rodriguez Department of Information Technology ABV-IIITM Gwalior Madhya Pradesh India Department of Computer Science and Engineering Multimedia and Information Security Research Group ABV-Indian Institute of Information Technology and Management Gwalior India Faculty of Industrial and Systems Engineering National University Federico Villarreal Lima Peru Faculty of Systems Engineering and Informatics National University Mayor de San Marcos

Image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Platoon-Based Distributed Control for Automated Material Handling Systems

Platoon-Based Distributed Control for Automated Material Han...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Alessandro Bozzi Jose-Fernando Jimenez Camilo Hernandez-Rodriguez Eliana-Maria Gonzalez-Neira Damien Trentesaux Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Italy Laboratory System and Materials for Mechatronics (SYMME) Université Savoie Mont Blanc Annecy France Industrial Engineering Department Pontificia Universidad Javeriana Bogota Columbia Laboratory of Industrial and Human Automation Control Mechanical Engineering and Computer Science (LAMIH) Université Polytechnique Hauts-de-France Valenciennes France

Smart manufacturing systems combine collaborative and autonomous control systems to provide optimal performance and respond to volatile demands. A key component of smart manufacturing is material management, as raw materials, work-in-progress, and finished products must be transported to the right location at the right time. However, during the production phase, it is imperative to establish an optimal traffic control scheme in order to avoid congestion and optimize material flow. This paper proposes a platooning-based control procedure implemented in a virtual agent-based manufacturing environment to increase the material flow efficiency on a manufacturing shop floor. An analysis of the production makespan and an indicator of the level of congestion in the material handling system was conducted in order to determine productivity improvements. An agent-based simulation is used to test the effectiveness of the novel platooning approach versus a traditional basic approach. A multivariate analysis of variance (MANOVA) reveals that the inclusion of a platoon-based procedure improves production performance and reduces congestion risks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Scribble2label: Scribble-supervised cell segmentation via self-generating pseudo-labels with consistency 23rd

Scribble2label: Scribble-supervised cell segmentation via se...

引用

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

作者： Lee, Hyeonsoo Jeong, Won-Ki School of Electrical and Computer Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of College of Informatics Department of Computer Science and Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9783030597092

Segmentation is a fundamental process in microscopic cell image analysis. With the advent of recent advances in deep learning, more accurate and high-throughput cell segmentation has become feasible. However, most existing deep learning-based cell segmentation algorithms require fully annotated ground-truth cell labels, which are time-consuming and labor-intensive to generate. In this paper, we introduce Scribble2Label, a novel weakly-supervised cell segmentation framework that exploits only a handful of scribble annotations without full segmentation labels. The core idea is to combine pseudo-labeling and label filtering to generate reliable labels from weak supervision. For this, we leverage the consistency of predictions by iteratively averaging the predictions to improve pseudo labels. We demonstrate the performance of Scribble2Label by comparing it to several state-of-the-art cell segmentation methods with various cell image modalities, including bright-field, fluorescence, and electron microscopy. We also show that our method performs robustly across different levels of scribble details, which confirms that only a few scribble annotations are required in real-use cases. © Springer Nature Switzerland AG 2020.

关键词： Deep 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：