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arXiv 2022年

作者： Mirikharaji, Zahra Abhishek, Kumar Bissoto, Alceu Barata, Catarina Avila, Sandra Valle, Eduardo Celebi, M. Emre Hamarneh, Ghassan Medical Image Analysis Lab School of Computing Science Simon Fraser University BurnabyV5A 1S6 Canada Institute for Systems and Robotics Instituto Superior Técnico Avenida Rovisco Pais Lisbon1049-001 Portugal RECOD.ai Lab Institute of Computing University of Campinas Av. Albert Einstein 1251 Campinas13083-852 Brazil RECOD.ai Lab School of Electrical and Computing Engineering University of Campinas Av. Albert Einstein 400 Campinas13083-952 Brazil Department of Computer Science and Engineering University of Central Arkansas 201 Donaghey Ave. ConwayAR72035 United States

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online. Copyright © 2022, The Authors. All rights reserved.

关键词： computer aided diagnosis

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Synthesis of Non-Integer Order Uncertain Thau-Luenberger Observer for Non-Integer Order Multiple Model with Unknown Inputs

Synthesis of Non-Integer Order Uncertain Thau-Luenberger Obs...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Abdelghani Djeddi Ahmad Taher Azar Arezki Fekik Nashwa Ahmad Kamal Chakib Ben Njima Faculty of Sciences and Technology Echahid Cheikh Larbi Tebessi University Tebessa Algeria College of Computer & Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems & Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Department of Electrical Engineering University Akli Mohand Oulhadj-Bouria Rue Drissi Yahia Bouira Algeria École Centrale Nantes CNRS LS2N UMR 6004 Nantes Université Nantes France Faculty of Engineering Cairo University Giza Egypt University of Sousse Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

The present work introduces a novel method for state estimation of physical systems represented by an uncertain Non-Integer Order Multiple Model (NIO-MM) model with unknown inputs. The key concept underlying this approach involves formulating a non-integer order Thau-Luenberger Observer (NIO-TLO) designed specifically for the NIO-MM. This observer is developed to handle situations where both known and unknown inputs are subject to uncertainties, and the model itself incorporates bounded unknown inputs. The analysis of the convergence of the error of the state estimation is presented through the formulation of linear matrix inequalities (LMIs). To validate the proposed approach, an academic example is examined.

关键词： Couplings Interpolation Uncertainty Simulation Computational modeling Observers Vectors

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TGGLinesPlus: A robust topological graph-guided computer vision algorithm for line detection from images

arXiv

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arXiv 2024年

作者： Yang, Liping Driscol, Joshua Gong, Ming Slack, Katie Zhang, Wenbin Wang, Shujie Potts, Catherine G. GeoAIR Lab Department of Geography and Environmental Studies University of New Mexico AlbuquerqueNM87131 United States University of New Mexico AlbuquerqueNM87131 United States Department of Computer Science University of New Mexico AlbuquerqueNM87106 United States Woodwell Climate Research Center FalmouthMA02540 United States Department of Electrical and Computer Engineering University of Dayton DaytonOH45469 United States Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL33199 United States Department of Geography The Pennsylvania State University University ParkPA16802 United States Earth and Environmental Systems Institute The Pennsylvania State University University ParkPA16802 United States D-Wave Quantum Inc. BurnabyBC Canada

Line detection is a classic and essential problem in image processing, computer vision and machine intelligence. Line detection has many important applications, including image vectorization (e.g., document recognition and art design), indoor mapping, and important societal challenges (e.g., sea ice fracture line extraction from satellite imagery). Many line detection algorithms and methods have been developed, but robust and intuitive methods are still lacking. In this paper, we proposed and implemented a topological graph-guided algorithm, named TGGLinesPlus, for line detection. Our experiments on images from a wide range of domains have demonstrated the flexibility of our TGGLinesPlus algorithm. We benchmarked our algorithm with five classic and state-of-the-art line detection methods and evaluated the benchmark results qualitatively and quantitatively, the results demonstrate the robustness of TGGLinesPlus. Copyright © 2024, The Authors. All rights reserved.

关键词： Sea ice

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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On the design of quaternary arithmetic logic unit based on CNTFETs

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International Journal of Electronics Letters 2019年第1期7卷 1-13页

作者： Sharifi, Fazel Moaiyeri, Mohammad Hossein Sharifi, Hojjat Navi, Keivan Thapliyal, Himanshu Nanotechnology & Quantum Computing lab Shahid Beheshti University Tehran Iran Faculty of Electrical Engineering Shahid Beheshti University Tehran Iran Department of Computer Engineering Vali-e-Asr University of Rafsanjan Rafsanjan Iran Department of Electrical and Computer Engineering University of Kentucky Lexington United States

A new approach for designing a quaternary arithmetic logic unit (ALU) based on carbon nanotube field-effect transistors (CNTFETs) is presented in this paper. The proposed design performs basic arithmetic and logic functions and is extendable to perform additional functions (both arithmetic and logic functions). Efficient circuit-level optimisation techniques such as data gating are used to reduce the power consumption and enhance the performance of the proposed design. In addition, the unique properties of CNTFETs such as adjusting the threshold voltages of CNTFETs by tuning the CNTs diameters are utilised for designing a quaternary ALU. The proposed design is simulated and examined in different conditions and in the presence of major process variations using the Synopsys HSPICE simulator with Stanford 32nm CNTFET technology. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.

关键词： ALU carbon nanotube field effect transistor (CNTFET) multiple valued logic (MVL) Nanoelectronics quaternary

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Gate-tunable Josephson diode in proximitized InAs supercurrent interferometers

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Physical Review Research 2023年第3期5卷 033131-033131页

作者： Carlo Ciaccia Roy Haller Asbjørn C. C. Drachmann Tyler Lindemann Michael J. Manfra Constantin Schrade Christian Schönenberger Quantum- and Nanoelectronics Lab Department of Physics University of Basel 4056 Basel Switzerland Center for Quantum Devices Niels Bohr Institute University of Copenhagen 2100 Copenhagen Denmark NNF Quantum Computing Programme Niels Bohr Institute University of Copenhagen 2100 Copenhagen Denmark Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Birck Nanotechnology Center Purdue University West Lafayette Indiana 47907 USA Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA School of Materials Engineering Purdue University West Lafayette Indiana 47907 USA Swiss Nanoscience Institute University of Basel 4056 Basel Switzerland

The Josephson diode (JD) is a nonreciprocal circuit element that supports a larger critical current in one direction compared to the other. This effect has gained growing interest because of promising applications in superconducting electronic circuits with low power consumption. Some implementations of a JD rely on breaking the inversion symmetry in the material used to realize Josephson junctions (JJs), but recent theoretical proposals have suggested that the effect can also be engineered by combining two JJs hosting highly transmitting Andreev bound states in a Superconducting Quantum Interference Device (SQUID) at a small, but finite flux bias. We have realized a SQUID with two JJs fabricated in a proximitized InAs two-dimensional electron gas (2DEG). We demonstrate gate control of the diode efficiency from zero up to around 30% at specific flux bias values which comes close to the maximum of ∼40% predicated in Souto et al. [Phys. Rev. Lett. 129, 267702 (2022)]. The key ingredients to the JD effect in the SQUID arrangement is the presence of highly transmitting channels in the JJs, a flux bias, and an asymmetry between the two SQUID arms.

关键词： Non-reciprocal transmission Proximity effect Quantum transport Diodes III-V semiconductors Josephson junctions SQUID Superconducting devices Interferometry Microwave techniques

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Unsupervised partial point set registration via joint shape completion and registration

arXiv

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arXiv 2020年

作者： Li, Xiang Wang, Lingjing Fang, Yi NYU Multimedia and Visual Computing Lab NYU Tandon Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering NYU Tandon United States Department of Electrical and Computer Engineering NYU Abu Dhabi United Arab Emirates

We propose a self-supervised method for partial point set registration. While recent proposed learning-based methods have achieved impressive registration performance on the full shape observations, these methods mostly suffer from performance degradation when dealing with partial shapes. To bridge the performance gaps between partial point set registration with full point set registration, we proposed to incorporate a shape completion network to benefit the registration process. To achieve this, we design a latent code for each pair of shapes, which can be regarded as a geometric encoding of the target shape. By doing so, our model does need an explicit feature embedding network to learn the feature encodings. More importantly, both our shape completion network and the point set registration network take the shared latent codes as input, which are optimized along with the parameters of two decoder networks in the training process. Therefore, the point set registration process can thus benefit from the joint optimization process of latent codes, which are enforced to represent the information of full shape instead of partial ones. In the inference stage, we fix the network parameter and optimize the latent codes to get the optimal shape completion and registration results. Our proposed method is pure unsupervised and does not need any ground truth supervision. Experiments on the ModelNet40 dataset demonstrate the effectiveness of our model for partial point set registration. Copyright © 2020, The Authors. All rights reserved.

关键词： Encoding (symbols)

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PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

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Heliyon 2024年第19期10卷 e38596页

作者： Shamrat, F M Javed Mehedi Idna Idris, Mohd Yamani Zhou, Xujuan Khalid, Majdi Sharmin, Sharmin Sharmin, Zeseya Ahmed, Kawsar Moni, Mohammad Ali Department of Computer System and Technology Universiti Malaya Kuala Lumpur 50603 Malaysia School of Business University of Southern Queensland Toowoomba Australia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh AI & Digital Health Technology Artificial Intelligence & Cyber Future Institute Charles Stuart University Bathurst 2795 NSW Australia AI & Digital Health Technology Rural Health Research Institute Charles Stuart University Orange 2800 NSW Australia

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research. © 2024 The Authors

关键词： Deep learning Explainable AI Image processing Pollen grain classification PollenNet model

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EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) 2.0: A MANIFESTO OF OPEN CHALLENGES AND INTERDISCIPLINARY RESEARCH DIRECTIONS

arXiv

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arXiv 2023年

作者： Longo, Luca Brcic, Mario Cabitza, Federico Choi, Jaesik Confalonieri, Roberto Ser, Javier Del Guidotti, Riccardo Hayashi, Yoichi Herrera, Francisco Holzinger, Andreas Jiang, Richard Khosravi, Hassan Lecue, Freddy Malgieri, Gianclaudio Páez, Andrés Samek, Wojciech Schneider, Johannes Speith, Timo Stumpf, Simone The Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Ireland University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi Sant’Ambrogio Milan Italy Kim Jaechul Graduate School of AI Korea Advanced Institute of Science & Technology Korea Republic of INEEJI Corporation Korea Republic of Department of Mathematics University of Padua Italy Derio Spain Bilbao Spain University of Pisa Pisa Italy Department of Computer Science Meiji University Tokyo Japan Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science & Computational Intelligence University of Granada Granada Spain Human-Centered AI Lab University of Natural Resources and Life Sciences Vienna Austria School of Computing and Communications Lancaster University United Kingdom The University of Queensland Brisbane Australia Sophia Antipolis France eLaw Center for Law and Digital Technologies Leiden University Netherlands Department of Philosophy Universidad de los Andes Bogotá Colombia Center for Research & Formation in Artificial Intelligence Universidad de los Andes Bogotá Colombia Technical University of Berlin Berlin Germany Fraunhofer Heinrich Hertz Institute Berlin Germany Berlin Germany Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Liechtenstein Department of Philosophy University of Bayreuth Bayreuth Germany Center for Perspicuous Computing Saarland University Saarbrücken Germany School of Computing Science University of Glasgow United Kingdom

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. © 2023, CC BY-SA.

关键词： Ethical technology

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Robust Internal Model Control Boosts PMSM Performance with Fractional Order Vector Control

Robust Internal Model Control Boosts PMSM Performance with F...

引用

International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Tassadit Chekari Arezki Fekik Ahmad Taher Azar Saim Ahmed Abdelghani Djeddi Nashwa Ahmad Kamal Chakib Ben Njima L2CSP Laboratory Mouloud MAMMERI University of Tizi-Ouzou Algeria Mhamed Bougara University of Boumerdes Algeria Department of Electrical Engineering University Akli Mohand Oulhadj-Bouria Algeria Nantes Université École Centrale Nantes CNRS LS2N Nantes France College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Sciences and Technology Echahid Cheikh Larbi Tebessi University Tebessa Algeria Faculty of Engineering Cairo University Giza Egypt University of Sousse Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

In this paper, a combination of fractional order control and a classical vector control scheme is proposed to drive the PMSM speed. The new proposed control scheme with fractional order property is based on IMC structure which is known for its robustness to modelling error and Bode’s ideal transfer function chosen as the reference model which exhibits iso-damping property. Simulation results show that the proposed control scheme tracks the PMSM speed at specified values and compensates the effect of load torque. Moreover, the time constant and fractional order of the Bode’s ideal transfer function can be used as tuning parameters to improve the PMSM speed and current responses.

关键词： Resistance Torque Simulation Transfer functions Stators Robustness Inverters

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