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arXiv 2023年

作者： Chen, Ying Liu, Xi Chai, Zhenhua Shi, Baochang School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China

In this work, a Cole-Hopf transformation based fourth-order multiple-relaxation-time lattice Boltzmann (MRT-LB) model for d-dimensional coupled Burgers’ equations is developed. We first adopt the Cole-Hopf transformation where an intermediate variable θ is introduced to eliminate the nonlinear convection terms in the Burgers’ equations on the velocity u = (u1, u2, . . ., ud). In this case, a diffusion equation on the variable θ can be obtained, and particularly, the velocity u in the coupled Burgers’ equations is determined by the variable θ and its gradient term ∇θ. Then we develop the MRT-LB model with the natural moments for the d-dimensional transformed diffusion equation and present the corresponding macroscopic finite-difference scheme. At the diffusive scaling, the fourth-order modified equation of the developed MRT-LB model is derived through the Maxwell iteration method. With the aid of the free parameters in the MRT-LB model, we find that not only the consistent fourth-order modified equation can be obtained, but also the gradient term ∇θ with a fourth-order accuracy can be determined by the non-equilibrium distribution function, this indicates that theoretically, the MRT-LB model for d-dimensional coupled Burgers’ equations can achieve the fourth-order accuracy in space. Finally, some simulations are conducted to test the MRT-LB model, and the numerical results show that the proposed MRT-LB model has a fourth-order convergence rate, which is consistent with our theoretical analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Distribution functions

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A general fourth-order mesoscopic multiple-relaxation-time lattice Boltzmann model and equivalent macroscopic finite-difference scheme for two-dimensional diffusion equations

arXiv

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arXiv 2023年

作者： Chen, Ying Chai, Zhenhua Shi, Baochang School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China

In this work, we first develop a general mesoscopic multiple-relaxation-time lattice Boltzmann (MRT-LB) model for the two-dimensional diffusion equation with the constant diffusion coefficient and source term, where the D2Q5 (five discrete velocities in two-dimensional space) lattice structure is considered. Then we exactly derive the equivalent macroscopic finite-difference scheme of the MRT-LB model. Additionally, we also propose a proper MRT-LB model for the diffusion equation with a linear source term, and obtain an equivalent macroscopic six-level finite-difference scheme. After that, we conduct the accuracy and stability analysis of the finite-difference scheme and the mesoscopic MRT-LB model. It is found that at the diffusive scaling, both of them can achieve a fourth-order accuracy in space based on the Taylor expansion. The stability analysis also shows that they are both unconditionally stable. Finally, some numerical experiments are conducted, and the numerical results are also consistent with our theoretical analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Finite difference method

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Comparative Analysis of Differential Privacy Implementations on Synthetic Data

Comparative Analysis of Differential Privacy Implementations...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Huwida E. Said Qusay H. Mahmoud Mandeep Goyal Faiza Hashim Department of Computing and Applied Technolgy College of Technological Innovation Zayed University Dubai UAE Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Computer Science and Software Engineering College of Information Technology UAE University AI-Ain UAE

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments.

关键词： Differential privacy Costs Accuracy Conferences Noise Machine learning Software Synthetic data

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Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

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Intelligent Automation & Soft computing 2023年第2期35卷 1643-1655页

作者： Ashit Kumar Dutta Yasser Albagory Majed Alsanea Hamdan I.Almohammed Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Basic Medical Sciences College of Medicine.Almaarefa UniversityAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Eye state classiﬁcation acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so *** modiﬁcations in the cognitive levels can be reﬂected via transforming the electro-encephalogram(EEG)*** deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-siﬁcation model in the recognition *** paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classiﬁ-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing ***,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG *** addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classiﬁ***,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classiﬁcation results to a maximum *** extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.

关键词： EEG eye state data classiﬁcation deep learning medical data analysis chimp optimization algorithm

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Breast Cancer Diagnosing Empowered with Transfer Learning

Breast Cancer Diagnosing Empowered with Transfer Learning

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International Conference on Computer and Communications (ICCC)

作者： Ayesha Ashfaq Yi Wenhui Si Jinhai Muhammad Umar Nasir School of Electronics Science and Information Technology Xi'an Jiaotong University Xian China School of Computing and Applied Sciences Riphah International University Lahore Pakistan

Breast cancer is the most common type of cancer worldwide in the last five years. Breast cancer is the second most common cause of death in women, after skin cancer. Breast cancer detection at an early stage has the potential to reduce morbidity and mortality. There are many technologies and diagnostic tests available to diagnose breast cancer. Artificial neural networks can automatically extract multiple features and make prediction about breast cancer. Multiple labeled images are required to train neural networks that are an unconventional method for some types of data images, such as breast magnetic resonance imaging (MRI). The most effective solution is to fine-tuning the neural network. In this paper, the proposed transfer learning model uses AlexNet in a convolutional neural network to extract range features from breast cancer MRI images in order to train the model. The proposed model is more efficient and significant because it achieves higher accuracy during the training and testing processes, with 99.65% and 90.57% of training and testing, respectively. As a result, this study demonstrates that fine-tuning in the neural network can detect breast cancer using magnetic resonance imaging and that training a neural network classifier by feature extraction using the proposed model is faster and more efficient.

关键词： Training Sensitivity Magnetic resonance imaging Transfer learning Predictive models Feature extraction Breast cancer

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Designing a Smartphone Exergame for Children with Cerebral Palsy in the Home Environment 22

Designing a Smartphone Exergame for Children with Cerebral P...

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9th ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play, CHI PLAY 2022

作者： Alberts, Max De Ridder, Ellen A.M. Lodewijks, Joris A.J. Pinos Cisneros, Tamara V. Schoorl, Kayleigh Salah, Albert Ali Schouten, Ben A.M. Industrial Design Eindhoven University Of Technology Netherlands Department Of Interaction Design University Of Twente Enschede Netherlands Digital Life And Civil Interaction Design Amsterdam University Of Applied Sciences Amsterdam Netherlands Department Of Information And Computing Sciences Utrecht University Netherlands Department Of Computer Engineering Bogazici University Istanbul Turkey

ISBN: (纸本)9781450392112

Children with cerebral palsy must perform daily exercise which is a tedious and energy consuming task. Exergames can make this routine more engaging, which can increase the compliance of the patient. This research explores the feasibility of an exergame device called the Squid Monster. The device is the result of a research through design process, and it is designed to be played on smartphones in the home environment. It operates on the smartphone's integrated sensors and two external squeeze sensors, making it accessible and cost-effective. We conceptualize how the design can be supported using a machine learning adaptive difficulty system, aiming to increase flow and therapeutic adherence of the device. Ultimately, guidelines are provided to designers for future work in this field. © 2022 Owner/Author.

关键词： Smartphones

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ZX Graphical Calculus for Continuous-Variable Quantum Processes

arXiv

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arXiv 2024年

作者： Nagayoshi, Hironari Asavanant, Warit Ide, Ryuhoh Fukui, Kosuke Sakaguchi, Atsushi Yoshikawa, Jun-Ichi Menicucci, Nicolas C. Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Centre for Quantum Computation & Communication Technology School of Science RMIT University MelbourneVIC3000 Australia

Continuous-variable (CV) quantum information processing is a promising candidate for large-scale fault-tolerant quantum computation. However, analysis of CV quantum process relies mostly on direct computation of the evolution of operators in the Heisenberg picture, and the features of CV space has yet to be thoroughly investigated in an intuitive manner. One key ingredient for further exploration of CV quantum computing is the construction of a computational model that brings visual intuition and new tools for analysis. In this paper, we delve into a graphical computational model, inspired by a similar model for qubit-based systems called the ZX calculus, that enables the representation of arbitrary CV quantum process as a simple directed graph. We demonstrate the utility of our model as a graphical tool to comprehend CV processes intuitively by showing how equivalences between two distinct quantum processes can be proven as a sequence of diagrammatic transformations in certain cases. We also examine possible applications of our model, such as measurement-based quantum computing, characterization of Gaussian and non-Gaussian processes, and circuit optimization. Copyright © 2024, The Authors. All rights reserved.

关键词： Directed graphs

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RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning

arXiv

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arXiv 2023年

作者： Lan, Zixun Zeng, Zuo Hong, Binjie Liu, Zhenfu Ma, Fei Department of Applied Mathematics Xi’an Jiaotong-Liverpool University Suzhou China Hours Technology Co. Ltd. Suzhou China Department of Information and Computing Science Xi’an Jiaotong-Liverpool University Suzhou China

The reaction center consists of atoms in the product whose local properties are not identical to the corresponding atoms in the reactants. Prior studies on reaction center identification are mainly on semi-templated retrosynthesis methods. Moreover, they are limited to single reaction center identification. However, many reaction centers are comprised of multiple bonds or atoms in reality. We refer to it as the multiple reaction center. This paper presents RCsearcher, a unified framework for single and multiple reaction center identification that combines the advantages of the graph neural network and deep reinforcement learning. The critical insight in this framework is that the single or multiple reaction center must be a node-induced subgraph of the molecular product graph. At each step, it considers choosing one node in the molecular product graph and adding it to the explored node-induced subgraph as an action. Comprehensive experiments demonstrate that RCsearcher consistently outperforms other baselines and can extrapolate the reaction center patterns that have not appeared in the training set. Ablation experiments verify the effectiveness of individual components, including the beam search and one-hop constraint of action space. Copyright © 2023, The Authors. All rights reserved.

关键词： Atoms

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An Automated Optical Inspection (Aoi) System for Three-Dimensional (3d) Defects Detection on Glass Micro Optical Components (Gmoc)

SSRN

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SSRN 2023年

作者： Du, Yinchao Chen, Jiangpeng Zhou, Han Yang, Xiaoling Wang, Zhongqi Zhang, Jie Shi, Yuechun Chen, Xiangfei Zheng, Xuezhe National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced Microstructure College of Engineering and Applied Sciences Micro-Wave-Photonics Technology Laboratory Nanjing University Nanjing210093 China Intelligent Computing Technology Suzhou CAS Suzhou215000 China Beijing Institute of Technology Beijing100081 China Institute of Computing Technology Chinese Academy of Sciences Beijing100090 China No.8 Xiasheng Road Suzhou Industrial Park Suzhou215000 China

As wavelength division multiplexing (WDM) being widely deployed, large number of glass micro optical components (GMOC) are utilized in optical transceivers. Visual inspection for these GMOCs is a necessary and critical manufacturing step for quality and reliability control. However, due to the facts that the glass components are usually transparent and defects are typically small and located randomly in 3D, manual inspection is often labor intensive and time consuming while the automated optical inspection (AOI) hasn’t been able to provide desirable accuracy and efficiency. In this paper, an AOI system incorporating 3D video acquisition and a novel machine-learning algorithm based on a two-stage neural network was developed successfully for 3D defects detection on GMOCs. It consists of a robotic arm for moving the parts in 3D, a camera with an illumination module for video acquisition of a part moving in 3D, and a video streaming processing unit empowered by a machine vision algorithm to detect the defects on GMOCs in real time on a production line. The robotic arm enables the fixed camera capture multi-perspective video of a test sample without having to refocus. The two-stage machine learning network is based on a modified YOLOv4 architecture with addition of color channel separation (CCS) convolution, an image quality evaluation (IQE) module, and frame fusion module to integrate the single frame detection results. It is capable of processing the multi-perspective video stream in a coarse-to-fine manner in real time for defects detection. Trained with 30 samples, the AOI system achieved very promising performances with a recall rate of 1, a detection accuracy of 97%, and an inspection time of 48s per part. © 2023, The Authors. All rights reserved.

关键词： Glass

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Visual Object Tracking Using Machine Learning 1st

Visual Object Tracking Using Machine Learning

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Science, Engineering Management and Information technology First International Conference, SEMIT 2022

作者： Odeh, Ammar Keshta, Ismail Al-Fayoumi, Mustafa Department of Computer Science King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Amman Jordan Department of Computer Science College of Applied Sciences AlMaarefa University Riyadh Riyadh Saudi Arabia

ISBN: (纸本)9783031403972

Visual object tracking has become a very active research area in recent years. Each year, a growing number of tracking algorithms are proposed. Object detection and tracking is a critical and challenging task in many critical computer vision applications, including automated video surveillance, traffic monitoring, autonomous robot navigation, and intelligent environments. Object tracking is segmenting an object of interest and tracking its velocity, orientation, and occlusion in a video scene to extract useful information. Over the last two decades, several object tracking approaches have been developed to design a robust object tracker that covers all practical obstacles in real-world operations. This paper reviews recent trends and advances in tracking and assesses the reliability of various trackers based on feature extraction techniques. In video processing, visual tracking has a wide range of applications. When a target is identified in one video frame, it is frequently advantageous to track that object in subsequent frames. Every successful frame in which the target is tracked yields more information about the target’s identity and activity. Because tracking is more straightforward than detection, tracking algorithms can require fewer computational resources than object detectors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object recognition

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