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A Convex and Global Solution for the PnP Problem in 2D Forward-Looking Sonar

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arXiv 2025年

作者： Su, Jiayi Qian, Jingyu Yang, Liuqing Yuan, Yufan Fu, Yanbing Wu, Jie Wei, Yan Qu, Fengzhong Guangzhou511458 China Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of Education Ocean College Zhejiang University Zhoushan316021 China Provincial Key Laboratory of Cutting-edge Scientific Instruments R&D and Application Ocean College Zhejiang University Zhoushan316021 China Hainan Institute of Zhejiang University Sanya572025 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong

The perspective-n-point (PnP) problem is important for robotic pose estimation. It is well studied for optical cameras, but research is lacking for 2D forward-looking sonar (FLS) in underwater scenarios due to the vastly different imaging principles. In this paper, we demonstrate that, despite the nonlinearity inherent in sonar image formation, the PnP problem for 2D FLS can still be effectively addressed within a point-to-line (PtL) 3D registration paradigm through orthographic approximation. The registration is then resolved by a duality-based optimal solver, ensuring the global optimality. For coplanar cases, a null space analysis is conducted to retrieve the solutions from the dual formulation, enabling the methods to be applied to more general cases. Extensive simulations have been conducted to systematically evaluate the performance under different settings. Compared to non-reprojection-optimized state-of-the-art (SOTA) methods, the proposed approach achieves significantly higher precision. When both methods are optimized, ours demonstrates comparable or slightly superior precision. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problem

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The Journal of Machine Learning Research

The Journal of Machine Learning research 2020年第1期21卷 9253-9290页

作者： Peipei Tang Chengjing Wang Defeng Sun Kim-Chuan Toh School of Computer and Computing Science Zhejiang University City College Hangzhou China School of Mathematics and National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for solving these problems. Our key idea for making the proposed PMM to be efficient is to develop a sparse semismooth Newton method to solve the corresponding subproblems. By using the Kurdyka-Łojasiewicz property exhibited in the underlining problems, we prove that the PMM algorithm converges to a d-stationary point. We also analyze the oracle property of the initial subproblem used in our algorithm. Extensive numerical experiments are presented to demonstrate the high efficiency of the proposed PMM algorithm.

关键词： nonconvex square-root regression problems proximal majorization-minimization semismooth Newton method

A Q-learning-based trust model in underwater acoustic sensor networks (UASNs)

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Ad Hoc Networks 2025年 178卷

作者： Mehdi Hosseinzadeh Amir Haider Amir Masoud Rahmani Khursheed Aurangzeb Zhe Liu Mohammad Sadegh Yousefpoor Efat Yousefpoor Sang-Woong Lee Parisa Khoshvaght Pattern Recognition and Machine Learning Lab School of Computing Gachon University Seongnam 13120 Republic of Korea Department of AI and Robotics Sejong University Seoul 05006 Republic of Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh 11543 Saudi Arabia College of Mathematics and Computer Xinyu University Xinyu 338004 China School of Computer Sciences University Sains Malaysia Penang 11800 Malaysia Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering and Technology Duy Tan University Da Nang Viet Nam Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

Underwater acoustic sensor networks (UASNs) play a pivotal role in various civil and military fields. However, due to their open nature, they are susceptible to multiple security threats. As such, developing robust and reliable security strategies is essential to ensure the normal operation of UASNs. This paper proposes a Q-learning-based trust model (QLTM) for UASNs. To detect hostile nodes, each underwater sensor node is required to collect trust evidence –namely energy trust evidence, data trust evidence, and communication trust evidence–through communication and interaction with its neighboring nodes. After gathering the trust evidence, QLTM presents a distributed Q-learning-based trust management model that adapts to dynamic underwater environments. It continuously updates the trust parameters based on ongoing interactions between the agent and the environment. The Q-learning-based trust management model includes a state set with three states: trust, distrust, and uncertain. Additionally, the reward function is calculated according to the gathered trust evidence, and the weight of each trust evidence is determined such that evidence with a lower value carries more weight, thus having a greater effect on the generated reward. Experimental results demonstrate the effectiveness of QLTM compared to other trust mechanisms, so that QLTM improves the detection accuracy rate by 5.04%. However, when the attack mode changes in the network, QLTM performs approximately 4.29% worse than TUMRL in detecting malicious nodes. On the other hand, QLTM reduces the false alarm rate by about 7.39% and increases energy efficiency by approximately 4.26%.

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Dynamical multiple polynomial-based neural networks classifier realized with the aid of dropfilter and dual statistical selection

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engineering applications of Artificial Intelligence 2025年 157卷

作者： Zhen Wang Sung-Kwun Oh Zunwei Fu Seok-Beom Roh Witold Pedrycz Research Center for Big Data and Artificial Intelligence Linyi University Linyi 276005 China School of Automation and Electrical Engineering Linyi University Linyi 276005 Shandong China School of Electrical & Electronic Engineering The University of Suwon 17 Wauan-gil Bongdam-eup Hwaseong-si Gyeonggi-do 18323 South Korea Department of Electronic Engineering Seokyeong University Seoul 02713 South Korea Department of Computer Science The University of Suwon 17 Wauan-gil Bongdam-eup Hwaseong-si Gyeonggi-do 18323 South Korea Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences 00-901 Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer Istanbul Turkiye

Polynomial neural networks (PNN) have emerged as an effective regression modeling methodology in computational intelligence, relying on its interpretable polynomial nodes to fit complex nonlinear data relationships and the adaptive nature of self-organizing networks. To break the bottleneck of PNN structure in the field of multi-classification, this study designs a dynamical multiple polynomial-based neural networks (DMPNN) classifier, focusing on developing a flexible polynomial network classification methodology that enhances predictive capabilities without sacrificing the advantages of PNN structures. Our approach effectively addresses the challenges of multi-class classification with uncertain class boundaries and reduces computational complexity, which is achieved through the synergy of several proposed techniques. Three key issues underpin the proposed DMPNN: (a) The integration of PNN regression models using the one-against-all strategy can provide effective and scalable solutions to multi-class classification problems, especially for uncertain class boundary issues. (b) The dual statistical selection (DSS) approach aims to eliminate redundant inputs during data processing, reduce the computational burden, and increase the variety of neural network nodes in the model neuron selection stage. (c) The synergy of regularization methods including the ℓ2 norm-based method (ℓ2-LSM) and the DropFilter, is exploited to mitigate potential overfitting in coefficient estimation and enhance the generalization capabilities of the proposed classifier. A series of ablation experiments and parameter analysis were conducted to demonstrate the stability and reliability of the proposed model. Then, we applied DMPNN to 17 publicly available datasets and two engineering applications: Recycling of black plastic wastes and phased resolved partial discharge. The performance results show that the DMPNN model outperforms five classical classifiers and four state-of-the-art (SOTA) class

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Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison

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Scientific Reports 2025年第1期15卷 1-9页

作者： Altalbawy, Farag M. A. Alkhayyat, Ahmad Jhala, Ramdevsinh Yadav, Anupam Ramachandran, T. Shankhyan, Aman Karthikeyan, A. Thatoi, Dhirendra Nath Sinitsin, Vladimir Vladimirovich Department of Chemistry University College of Duba University of Tabuk Tabuk Saudi Arabia Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Babylon Babylon Iraq Marwadi University Research Center Department of Mechanical Engineering Faculty of Engineering & Technology Marwadi University Gujarat Rajkot 360003 India Department of Computer Engineering and Application GLA University Mathura 281406 India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Karnataka Bangalore India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Mechanical Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Mechanical Engineering Siksha ’O’ Anusandhan (Deemed to be University) Odisha Bhubaneswar 751030 India Research Laboratory of Technical Self-Diagnostics and Self-Control of Devices and Systems Department of Scientific and Innovative Activities Research Institute of Mechanical Engineering South Ural State University Lenin prospect 76 Chelyabinsk 454080 Russian Federation

This paper introduces an approach to temperature prediction by employing three distinct machine learning models: K-Nearest Neighbors (KNN), Gaussian Process Regression (GPR), and Multi-layer Perceptron (MLP) which are integrated into Computational Fluid Dynamics (CFD). The dataset consists of inputs, represented by the features x and y, and the corresponding output, which is the temperature. The case study is fluid flow of nanofluid through a pipe and the nanofluid contains CuO particles. To enhance model efficacy, the Political Optimizer (PO) algorithm was utilized for fine-tuning purposes. The findings substantiate the capability of the optimized frameworks in delivering precise temperature estimations. GPR yielded a notable R² value of 0.998, reflecting a strong concordance between the estimated and actual measurements. In a comparable manner, the KNN approach exhibited outstanding predictive performance, also attaining an R² value of 0.998. MLP, although slightly lower in performance compared to the other models, still proves to be a reliable predictor of temperature values, with an R-squared score of 0.984. Overall, the combination of the PO algorithm and the machine learning models showcases promising results in temperature prediction. The study’s findings offer valuable insights for decision-making processes in a variety of domains that rely on accurate temperature forecasting. © The Author(s) 2025.

关键词： Gaussian process regression Heat transfer K-Nearest neighbors Multi-layer perceptron Nanofluid

Roadmap on Deep Learning for Microscopy

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography

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arXiv 2023年

作者： Zhang, Linglin Brown-Mulry, Beatrice Nalla, Vineela Hwang, InChan Gichoya, Judy Wawira Gastounioti, aimilia Banerjee, Imon Seyyed-Kalantari, Laleh Woo, MinJae Trivedi, Hari School of Data Science and Analytics Kennesaw State University 3391 Town Point Dr NW KennesawGA30144 United States Department of Information Technology Kennesaw State University 1100 South Marietta Pkwy MariettaGA30060 United States Department of Radiology and Imaging Sciences Emory University 1364 E Clifton Rd NE AtlantaGA30322 United States Computational Imaging Research Center Washington University in St. Louis School of Medicine 4525 Scott Avenue St. LouisMO63110 United States Department of Radiology Mayo Clinic Arizona 13400 E Shea Blvd ScottsdaleAZ85259 United States School of Computing and Augmented Intelligence Arizona State University 699 S Mill Ave TempeAZ85281 United States Department of Electrical Engineering and Computer Science York University 4700 Keele St TorontoONM3J 1P3 Canada

Although deep learning models for abnormality classification can perform well in screening mammography, the demographic, imaging, and clinical characteristics associated with increased risk of model failure remain unclear. This retrospective study uses the Emory BrEast Imaging dataset (EMBED) containing mammograms from 115,931 patients imaged at Emory Healthcare between 2013-2020, with Breast Imaging Reporting and data System (BI-RADS) assessment, region of interest coordinates for abnormalities, imaging features, pathologic outcomes, and patient demographics. Multiple deep learning models were trained to distinguish between abnormal tissue patches and randomly selected normal tissue patches from screening mammograms. We assessed model performance by subgroups defined by age, race, pathologic outcome, tissue density, and imaging characteristics and investigated their associations with false negatives and false positives. We also performed multivariate logistic regression to control for confounding between subgroups. The top-performing model, ResNet152V2, achieved accuracy of 92.6% (95%CI=92.0-93.2%), and area under the receiver operating characteristics curve 0.975 (95%CI=0.972-0.978). Before controlling for confounding, nearly all subgroups showed statistically significant differences in model performance. However, after controlling for confounding, we found lower false negative risk associates with Other race (RR=0.828;p= .050), biopsy-proven benign lesions (RR=0.927;p= .011), and mass (RR=0.921;p= .010) or asymmetry (RR=0.854;p= .040);higher false negative risk associates with architectural distortion (RR=1.037;p Copyright © 2023, The Authors. All rights reserved.

关键词： Mammography

Efficient product formulas for commutators and applications to quantum simulation

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arXiv 2021年

作者： Chen, Yu-An Childs, Andrew M. Hafezi, Mohammad Jiang, Zhang Kim, Hwanmun Xu, Yijia Google Quantum AI 340 Main Street VeniceCA90291 United States Department of Physics Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Condensed Matter Theory Center University of Maryland College ParkMD20472 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States Department of Computer Science University of Maryland College ParkMD20742 United States Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States

We construct product formulas for exponentials of commutators and explore their applications. First, we directly construct a third-order product formula with six exponentials by solving polynomial equations obtained using the operator differential method. We then derive higher-order product formulas recursively from the third-order formula. We improve over previous recursive constructions, reducing the number of gates required to achieve the same accuracy. In addition, we demonstrate that the constituent linear terms in the commutator can be included at no extra cost. As an application, we show how to use the product formulas in a digital protocol for counterdiabatic driving, which increases the fidelity for quantum state preparation. We also discuss applications to quantum simulation of one-dimensional fermion chains with nearest- and next-nearest-neighbor hopping terms, and two-dimensional fractional quantum Hall phases. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum chemistry

ICD2Vec: Mathematical representation of diseases

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Research Square

research Square 2021年

作者： Lee, Yeong Chan Jung, Sang-Hyuk Kumar, Aman Shim, Injeong Song, Minku Kim, Min Seo Kim, Kyunga Myung, Woojae Park, Woong-Yang Won, Hong-Hee Sungkyunkwan University Samsung Medical Center Seoul06355 Korea Republic of Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia19104 United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal 721302 India Statistics and Data Center Research Institute for Future Medicine Samsung Medical Center Seoul06355 Korea Republic of Department of Neuropsychiatry Seoul National University Bundang Hospital Seongnam13620 Korea Republic of Samsung Genome Institute Samsung Medical Center Seoul06351 Korea Republic of

The International Classification of Diseases (ICD) codes represent the global standard for reporting disease conditions. The current ICD codes are hierarchically structured and only connote partial relationships among diseases. Therefore, it is important to represent the ICD codes as mathematical vectors to indicate the complex relationships across diseases. Here, we proposed a framework denoted "ICD2Vec" for providing mathematical representations of diseases by encoding corresponding information. First, we presented the arithmetic and semantic relationships between diseases by mapping composite vectors for symptoms or diseases to the most similar ICD codes. Second, we confirmed the validity of ICD2Vec by comparing the biological relationships and cosine similarities among the vectorized ICD codes. Third, we proposed a new risk score derived from ICD2Vec, and demonstrated its potential clinical utility for coronary artery disease, type 2 diabetes, dementia, and liver cancer, based on a large prospective cohort from the UK and large electronic medical records from a medical centre in South Korea. In summary, ICD2Vec is applicable for diverse quantitative analyses using ICD codes in biomedical research. © 2021, CC BY.

关键词： Diseases