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SSRN 2022年

作者： Wu, Ke Xie, Yinao Lu, Lei Yin, Yongkai Xi, Jiangtao Faculty of Artificial Intelligence in Education Central China Normal University Wuhan430079 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Shandong Provincial Key Laboratory of Laser Technology and Application Department of Optics School of Information Science and Engineering Shandong University Jinan250100 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia

High quality 3D reconstruction of HDR object can be achieved by projecting extra fringe patterns with different intensity. However, it requires the object to be kept static. The object motion not only introduces phase shift among the fringe patterns, but also changes the position of the overexposure pixels. This paper proposes a new method to reconstruct the moving HDR object by utilizing the features of motion. For the specific point on the object, overexposure will not happen among all the fringe patterns as the overexposure position is changed with the motion. The unsaturation fringe patterns are selected from the captured fringe patterns and the correct phase information is retrieved based on unequal phase shift algorithm. The effectiveness of the proposed algorithm is verified by the experiments. © 2022, The Authors. All rights reserved.

关键词： Interferometry

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Quadratic sparse Gaussian graphical model estimation method for massive variables 29

Quadratic sparse Gaussian graphical model estimation method ...

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29th International Joint Conference on artificial intelligence, IJCAI 2020

作者： Zhang, Jiaqi Wang, Meng Li, Qinchi Wang, Sen Chang, Xiaojun Wang, Beilun College of Software Engineering Southeast University China School of Computer Science and Engineering Southeast University China School of Artificial Intelligence Southeast University China Electrical Engineering and Automation YOUPEI College Yancheng Institute of Technology China School of Information Technology and Electrical Engineering University of Queensland Australia Department of Data Science and AI Monash University Australia

ISBN: (纸本)9780999241165

We consider the problem of estimating a sparse Gaussian Graphical Model with a special graph topological structure and more than a million variables. Most previous scalable estimators still contain expensive calculation steps (e.g., matrix inversion or Hessian matrix calculation) and become infeasible in high-dimensional scenarios, where p (number of variables) is larger than n (number of samples). To overcome this challenge, we propose a novel method, called Fast and Scalable Inverse Covariance Estimator by Thresholding (FST). FST first obtains a graph structure by applying a generalized threshold to the sample covariance matrix. Then, it solves multiple block-wise subproblems via element-wise thresholding. By using matrix thresholding instead of matrix inversion as the computational bottleneck, FST reduces its computational complexity to a much lower order of magnitude (O(p2)). We show that FST obtains the same sharp convergence rate O(p(log max{p, n}/n) as other state-of-the-art methods. We validate the method empirically, on multiple simulated datasets and one real-world dataset, and show that FST is two times faster than the four baselines while achieving a lower error rate under both Frobenius-norm and max-norm. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Inverse problems

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Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

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Journal of Healthcare engineering 2022年 2022卷 4138666页

作者： Teoh, Yun Xin Lai, Khin Wee Usman, Juliana Goh, Siew Li Mohafez, Hamidreza Hasikin, Khairunnisa Qian, Pengjiang Jiang, Yizhang Zhang, Yuanpeng Dhanalakshmi, Samiappan Department of Biomedical Engineering Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Medicine Universiti Malaya Kuala Lumpur50603 Malaysia School of Artificial Intelligence and Computer Sciences Jiangnan University Wuxi214122 China School Nantong University Nantong226001 China Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur603203 India

Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren-Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks s

关键词： Grading

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Pareto Set Learning through Genetic Programming for Multi-Objective Dynamic Scheduling

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IEEE Transactions on Evolutionary Computation 2025年

作者： Xu, Meng Mei, Yi Zhang, Fangfang Ong, Yew Soon Zhang, Mengjie Agency for Science Technology and Research Singapore Institute of Manufacturing Technology Singapore Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Agency for Science Technology and Research College of Computing and Data Science Nanyang Technological University Centre for Frontier AI Research Institute of High Performance Computing Singapore Singapore

The multi-objective dynamic flexible job shop scheduling (MO-DFJSS) problem is crucial in modern manufacturing, impacting productivity and operational costs. Genetic Programming (GP) has emerged as a prominent method for MO-DFJSS due to its ability to evolve real-time responsible and effective scheduling heuristics. However, existing GP approaches often learn multiple heuristics for different regions of the Pareto front, making their management and selection complicated in real-world applications. This paper proposes a novel Pareto set learning GP (PSLGP) framework that addresses this limitation by learning a single, preference-conditioned heuristic that encompasses the entire Pareto front based on user preferences. This simplifies scheduling and allows for real-time adaptation to user-defined priorities. The framework employs a novel preference-conditioned heuristic representation that incorporates user preferences as additional inputs, enabling dynamic heuristic adjustments. To efficiently evaluate fitness without increasing training time, a surrogate model is used to estimate individual performance across different preferences, and three new fitness aggregation strategies are designed to ensure effective heuristic alignment across the Pareto front. Experimental results demonstrate that PSLGP significantly outperforms the state-of-the-art multi-objective GP approach, particularly in less busy MO-DFJSS environments, providing a more adaptable and efficient solution for dynamic scheduling challenges. Further analyses of preference influence, solution distribution, and heuristic structure provide evidence that the proposed PSLGP effectively learns preference-conditioned scheduling heuristics that align user preferences with various regions of the Pareto front. © 1997-2012 IEEE.

关键词： Decision making

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A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning

arXiv

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arXiv 2023年

作者： Masuyama, Naoki Takebayashi, Takanori Nojima, Yusuke Loo, Chu Kiong Ishibuchi, Hisao Wermter, Stefan Graduate School of Informatics Department of Core Informatics Osaka Metropolitan University 1-1 Gakuen-cho Naka-ku Sakai-Shi Osaka599-8531 Japan Department of Artificial Intelligence Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Informatics Faculty of Mathematics Computer Science and Natural Sciences University of Hamburg Vogt-Koelln-Str. 30 Hamburg22527 Germany

In general, a similarity threshold (i.e., a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on clustering performance. In addition, an edge deletion threshold in a topological clustering algorithm plays an important role in adaptively generating well-separated clusters during a self-organizing process. In this paper, we propose a new parameter-free ART-based topological clustering algorithm capable of continual learning by introducing parameter estimation methods. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to the state-of-the-art clustering algorithms without any parameter pre-specifications. © 2023, CC BY-NC-ND.

关键词： Clustering algorithms

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Asynchronous control for Markov jump systems subject to actuator saturation

Asynchronous control for Markov jump systems subject to actu...

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第33届中国控制与决策会议

作者： Chao Wang Lei Liu Wei Wang Xiaobing Luo Huijin Fan the National Key Laboratory of Science and Technology of Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology the School of Automation Science and Electrical Engineering and the Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Technology and Software Engineering Wuhan Polytechnic

In this paper,the asynchronous controller problem is considered for Markov jump systems(MJSs) subject to actuator *** asynchronous phenomenon between the controller and the plant is addressed by introducing a hidden Markov model and the closed-loop hidden MJSs are *** on the closed-loop hidden MJSs and using convex hull technique and linear matrix inequality technique,a controller design algorithm is derived to ensure that the closed-loop hidden MJSs satisfy the stochastic ***,a numerical example is provided to demonstrate the effectiveness of the proposed asynchronous controller design algorithm.

关键词： Asynchronous control actuator saturation Markov jump systems

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Seamless Decision-Making in the Big data Era: A Modular Approach to Integrating IoT, Cloud Computing, and data Lakes

Seamless Decision-Making in the Big Data Era: A Modular Appr...

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International Conference on Electrical, Communication, and Computer engineering (ICECCE)

作者： Mohamed Zemmouri Fatma Zohra Laalam Okba Kazar Yassine Himeur Adel Oulefki Chahrazad Toumi Wathiq Mansoor Shadi Attala Artificial Intelligence and Information Technologies Laboratory Faculty of New Information and Communication Technologies Kasdi Merbah Ouargla University Ouargla Algeria Department of Computer Science - LINFI Laboratory Mohamed Khider University Biskra Algeria Department of Information Systems and Security - College of Information Technology United Arab Emirate University Abu Dhabi United Arab Emirates College of Engineering and Information Technology University of Dubai Dubai UAE Department of Computer Science University of Sharjah Sharjah United Arab Emirates

The integration of big data with cutting-edge technologies like IoT and Cloud Computing has profoundly influenced various aspects of modern life, including everyday service processes. To facilitate data-driven decision-making, big data analytics—focused on identifying patterns, trends, and correlations in large data sets—is indispensable. While traditional statistical techniques are useful, new tools and infrastructures such as Hadoop, Spark, and NoSQL are essential to tackle big data challenges. However, modifying the existing environment can be impractical, especially in production settings, due to the need for significant investment and specialized expertise. This article presents a novel computational paradigm that adds a decision-making layer atop existing systems for data analysis, eliminating the need to alter the environment. The approach treats the current information system as a data lake and introduces a new data recovery layer through web services, drawing inspiration from big data technologies like MapReduce. This system offers the advantage of being modular, reusable, and universally compatible, making it an independent decisional framework that can work with any information system or data source.

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From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering

arXiv

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arXiv 2025年

作者： Li, Wenran Cadet, Xavier F. Medina-Ortiz, David Davari, Mehdi D. Sowdhamini, Ramanathan Damour, Cedric Li, Yu Miranville, Alain Cadet, Frederic University of Paris City University of Reunion INSERM BIGR DSIMB ParisF-75015 France Department of Computing Imperial College London United Kingdom Department of Computer Engineering University of Magallanes Punta Arenas Chile Centre for Biotechnology and Bioengineering Universidad de Chile Santiago Chile Department of Bioorganic Chemistry Leibniz Institute of Plant Biochemistry Halle06120 Germany National Centre for Biological Science TIFR Bangalore India Molecular Biophysics Unit Indian Institute of Science Bangalore India Institute of Bioinformatics and Applied Biotechnology Bangalore India EnergyLab EA 4079 Faculty of Sciences and Technology University of Reunion France School of Information Science and Technology Beijing Institute of Artificial Intelligence China Laboratoire de Mathématiques Appliquées University Le Havre Normandie le Havre France PEACCEL Artificial Intelligence Department AI for Bi-ologics Paris75013 France

Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generative capabilities, offering unique advantages in certain applications such as protein design. In this review, we first give the definition and characteristics of diffusion models and then focus on two strategies: Denoising Diffusion Probabilistic Models (DDPM) and Score-based Generative Models (SGM), where DDPM is the discrete form of SGM. Furthermore, we discuss their applications in protein design, peptide generation, drug discovery, and protein-ligand interaction. Finally, we outline the future perspectives of diffusion models to advance autonomous protein design and engineering. The E(3) group consists of all rotations, reflections, and translations in three-dimensions. The equivariance in the E(3) group can maintain the physical stability of the N − Cα − C frame of each amino acid as much as possible, and we reflect on how to keep the diffusion model E(3) equivariant for protein generation. © 2025, CC BY.

关键词： Biomolecules

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Social-enhanced recommendation using graph-based contrastive learning

Social-enhanced recommendation using graph-based contrastive...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Peng Xue Qian Gao Jun Fan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models.

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with artificial intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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