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Timescales and contribution of heating and helicity effect in helicity-dependent all-optical switching

Rare Metals 2023年第1期42卷 234-243页

作者： Guan-Qi Li Xiang-Yu Zheng Jun-Lin Wang Xian-Yang Lu Jing Wu Jian-Wang Cai Hao Meng Bo Liu Thomas A.Ostler Yong-Bing Xu School of Integrated Circuits Guangdong University of TechnologyGuangzhou510006China York-Nanjing International Joint Center in Spintronics School of Electronic Science and EngineeringNanjing UniversityNanjing210093China Spintronics and Nanodevice Laboratory Department of Electronics EngineeringUniversity of YorkYorkYO105DDUK Institute of Physics Chinese Academy of SciencesBeijing100190China Key Laboratory of Spintronics Materials Devices and System of Zhejiang ProvinceHangzhou311305China College of Business Technology and EngineeringSheffield Hallam UniversitySheffieldS11WBUK

The heating and helicity effects induced by circularly polarized laser excitation are entangled in the helicity-dependent all-optical switching(HD-AOS),which hinders understanding the magnetization dynamics ***,applying a dual-pump laser excitation,first with a linearly polarized(LP) laser pulse followed by a circularly polarized(CP) laser pulse,the timescales and contribution from heating and helicity effects in HD-AOS were identified with a Pt/Co/Pt *** the LP laser pulses preheat the sample to a nearly fully demagnetized state,the CP laser pulses with a power reduced by 80% switch the sample’s *** varying the time delay between the two pump pulses,the results show that the helicity effect,which gives rise to the deterministic helicity-induced switching,arises almost instantly within 200 fs close to the pulse width upon laser *** results reveal that the transient magnetization state upon which CP laser pulses impinge is the key factor for achieving HD-AOS,and importantly,the tunability between heating and helicity effects with the unique dualpump laser excitation approach will enable HD-AOS in a wide range of magnetic material systems having wideranging implications for potential ultrafast spintronics applications.

关键词： Helicity-dependent all-optical switching Ultrafast laser Ultrafast magnetization Magneto-optical interaction

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Concurrent Pipeline Rendering Scheme based on GPU multi-queue and partitioning images

Concurrent Pipeline Rendering Scheme based on GPU multi-queu...

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2023 International Conference on Optics and Machine Vision, ICOMV 2023

作者： Qiu, Zixia Ma, Junchao Zhang, Haitang Sibahee, Mustafa A.Al. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Applied Technology Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61001 Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518063 China Faculty of Biological & Physical Sciences Tom Mboya University Homabay40300 Kenya

ISBN: (纸本)9781510664777

Compared with rasterization rendering, ray tracing rendering can improve the image’s visual effect and make the image look more realistic. Real-time ray tracing requires very high computing power of graphics processing unit (GPU). When the number of GPUs is limited and the performance of a single GPU cannot be fully utilized, high rendering latency will occur. In this paper, we propose Multi-Queue Concurrent Pipeline Rendering (MQCPR), a novel ray-tracing parallel rendering scheme based on GPU multi-queue. This scheme divides the image area into multiple parts and uses multi-queue of GPU to enable the computation and transmission tasks in the rendering process to be executed simultaneously, which can maximize the performance of a single GPU and improve the graphics rendering speed. MQCPR may keep the GPU busy to make full use of the GPU resources. Experiments illustrate that in the case of a single GPU, compared with the single queue serial rendering scheme, the number of frames per second (FPS) is increased by 1.5 times after using MQCPR. © 2023 SPIE. All rights reserved.

关键词： Graphics processing unit

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Exploring Cutting-Edge Machine Learning and data Mining Techniques for Enhancing Big data Management with Advanced Algorithmic Strategies for Optimal data Processing and Analysis 2

Exploring Cutting-Edge Machine Learning and Data Mining Tech...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Anitha, M. Kumari, V. Sheeja Pillai, N. Muthuvairavan Jayarin, P. Jesu David, D. Beulah Department of Artificial Intelligence & Data Science CARE College of Engineering Tamil Nadu Trichy India Department of Computational Intelligence Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Thandalam Chennai India Department of Computer Science and O. Business Systems R.M.D Engineering College Tamil Nadu Thiruvallur India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Thandalam Chennai India

ISBN: (纸本)9798350383867

This work focuses on diverse machine learning methods for big data analytics, which works to leverage predictive performance. The techniques like data preprocessing, dimensionality reduction, feature selection, model selection, hyperparameter tuning, and model evaluation are a focus. A number of algorithms including Random Forests, SVM (Support Vector Machines) Gradient Boosting Machines (GBM), Neural Networks, and ensemble methods (Bagging and Boosting) have their performance evaluated. Hyperparameter tuning applies the Grid Search and Random Search algorithms while model evaluation is done by the K-fold Cross-validation. Fleiss' kappa provide differing levels of accuracy, precision, recall, and F1-score for different models and techniques. Ensemble methods actually show how they have all the necessary qualities to win and perform better than individual models. repeatedly testing improve the models' reliability and effectiveness for practical use. The study highlights the crucial significance of choices on the methods and algorithms that suit the peculiarities of the dataset and problem domain. Capitalizing on the machine learning techniques and in-depth evaluation methods, organizations can get the actionable insights from the big data that will ensure the headquarters' decision-making process and accomplish the meaningful outcomes. Future research could instead be exploring new algorithms and techniques to improve the prediction and scalability in big data analytics setting. © 2024 IEEE.

关键词： Adversarial machine learning

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Music Recommendation based on Facial Expressions using data Augmentation

Music Recommendation based on Facial Expressions using Data ...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Tanisha Kapoor Arnaja Ganguly D Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

Emotion recognition from facial expressions plays a pivotal role in human-computer interaction, psychology, marketing, and various other domains. This research paper presents a comprehensive study and implementation of an automated emotion recognition system based on deep learning techniques. The project addresses the challenge of accurately classifying a wide range of emotions from facial images in real-time. Leveraging Convolutional Neural Networks (CNNs) and modern deep learning approaches, the system demonstrates high accuracy in identifying emotions, including happiness, sadness, anger, and more. This project used the image dataset FER-2013 for the mood prediction portion of the project, and Spotify music data to predict emotions for music recommendation. Both the datasets utilized are freely available on Kaggle. The proposed system can achieve an accuracy of 91.23% in predicting the users’ emotion based on their facial expressions.

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An Extensive Study on Pretrained Models for Natural Language Processing Based on Transformers

An Extensive Study on Pretrained Models for Natural Language...

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2022 International Conference on Electronics and Renewable systems, ICEARS 2022

作者： Ramprasath, M. Dhanasekaran, K. Karthick, T. Velumani, R. Sudhakaran, P. Srm Institute of Science and Technology Department of Data Science and Business Systems School of Computing TamilNadu Chennai India Department of Computer Science and Engineering Andhra Pradesh Madhrawada India Srm Trp Enginerring College Department of Computer Science and Engineering Tamil Nadu Trichy India

ISBN: (纸本)9781665484251

In recent years, Pretraining Language Models based on Transformers -Natural Language Processing (PLMT-NLP) have been highly successful in nearly every NLP task. In the beginning, Generative Pre-trained model-based Transformer, BERT- Bidirectional Encoder model Representations using Transformers was used to develop these models. Models constructed on transformers, Self-supervise knowledge acquiring, and transfer learning establish the foundation of these designs. Transformed-based pre-trained models acquire common linguistic illustrations from vast amounts of textual information through self-supervised model and apply this information to downstream tasks. To eliminate the need to retrain downstream models, these models provide a solid foundation of knowledge. In this paper, the enhanced learning on PLMT-NLP has been discussed. Initially, a quick introduction to self-supervised learning is presented, then diverse core concepts used in PLMT-NLP are explained. Furthermore, a list of relevant libraries for working with PLMT-NLP has been provided. Lastly, the paper discusses about the upcoming research directions that will further improve these models. Because of its thoroughness and relevance to current PLMT-NLP developments, this survey study will positively serve as a valuable resource for those seeking to understand both basic ideas and new developments better. © 2022 IEEE.

关键词： Natural language processing systems

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Cursive Hand writing Recognition Using Neural Networks with VGG-16

Cursive Hand writing Recognition Using Neural Networks with ...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Anushka Anil Rangari Swagat Das Rajeswari D Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India

Recognition of cursive handwritten text from photographs is a method for finding handwritten text. Identification is difficult because every author has a distinctive writing style. The proposed work uses cutting-edge technologies including RNN, KNN, MLP, and CRNN twith VGG16 to push the boundaries of character and handwriting recognition. Additionally, it compares neural networks to CNNwith VGG16 and finds that neural networks are more accurate. In the first stage, image acquisition, the scanned image is acquired along with its normalization, feature extraction, and segmentation. Additionally, it combines with the most effective feature extraction technique to improve the accuracy of our model. It also compares the accuracy with six uniquepretreat. approach, which are image segmentation image data generator data augmentation closed loop pre-processing, sigmoid Stretching and geometric *** provides the highest accuracy (98.36%) when using data augmentation. CNN has the highest accuracy (95.25%) when using picture segmentation. The third experiment's results for the image data generator show that CNN produced the best results (92.47%), while the highest model, RNN, gave closed loop processing accuracy of 92.20%. a change in geometry KNN has the highest accuracy at 95.39%.

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A Machine Learning Model for Foreign Exchange Prediction 2nd

A Machine Learning Model for Foreign Exchange Prediction

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： San, Tan Jun Ajibade, Samuel-Soma M. Ajibade, Temiloluwa Iyanuoluwa Jasser, Muhammed Basheer Ayodele, Olamide Emmanuel Oyewopo, Abdulgafar Olorunleke Adediran, Anthonia Oluwatosin Majeed, Anwar P. P. Abdul Luo, Yang Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Petaling Jaya Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Department of Business Management Scholar School System Birmingham United Kingdom School of Engineering and Technology Sunway University Selangor Darul Ehsan Petaling Jaya47500 Malaysia Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Administration Kwara State University Malete Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The rise of deep learning algorithms has sparked interest in the financial domain. The foreign exchange market (FOREX) is the globally leading financial market with millions of active stakeholders. Research in deep learning for forecasting price movements has shown its efficacy in financial markets. Notably, LSTM have empirically proven the best performance among other machine learning techniques. However, studies have shown the lack of deep learning hybrid comparisons to fortify empirical claims, especially in the FX market characterized for its high volatility and variance among currency pairs. This study develops a generalized LSTM model to forecast the major pairs and conducts a comparative analysis with a secondary hybrid deep learning model to assess and solidify the robustness of deep learning methods in forex forecasting. The empirical scenario simulates a classification problem employing cross-validation techniques to optimize the models’ generalization rigorously. Evaluation of the model utilized the accuracy, precision, recall, and f1-score metrics. Results indicated the performance of the models from best to worst were LSTM, SRNN and followed by CNN indicating the importance of testing deep learning approaches in different empirical setups. Conclusively, this study highlights the need challenges in forex forecasting and raises the need for more robust assessments on machine learning models forecasting capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Colour Correction and Detail Enhancement in Underwater Images using Hybrid Real-ESRGAN

Colour Correction and Detail Enhancement in Underwater Image...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Vijay Chandar Vishnu G Nair D Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

The motive of underwater image enhancement is to improve and enhance the quality of photographs taken underwater. Various traditonal image processing techniques and deep learning models have been developed to work on this task. But most often the deep learning models and conventional methods of image processing work in silos, leading to subpar results. This research proposes a strategy that utilizes a combination of both approaches to solve this issue. Initially, traditional image processing techniques like colour correction and image stretching are used to enhance the colour of the images. The enhanced images are then fed into a custom Real-ESRGAN model that has been fine-tuned on an underwater image dataset to optimize performance in this context. This hybrid approach has the potential to outperform the models utilizing either traditional or deep learning methods in isolation by leveraging the strengths of both. In addition, batch processing is used in the pipeline to efficiently handle multiple images at the same time. This effectively addresses the scalability problem that arises frequently in image enhancement tasks. With eye-catching visuals, the proposed methodology achieves a Peak Signal-to-Noise Ratio (PSNR) of 23.66 and a Mean Squared Error (MSE) of 0.28, suggesting excellent image restoration and noise reduction. The qualitative assessments show that the improved image graphs have better colour, contrast, and clarity than the originals.

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TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Xinquan Xie, Jinheng Li, Xuechen Li, Xuguang Shen, Linlin Deng, Yongqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China National University of Singapore Show Lab Singapore Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen University General Hospital Department of Stomatology Shenzhen China

ISBN: (纸本)9798350337488

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods. © 2023 IEEE.

关键词： Dental Implant Multi-module Detection Multi-slice Learning Text Guided Detection Transformer

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AIGC-Driven Real-Time Interactive 4D Traffic Scene Generation in Vehicular Networks

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IEEE Network 2025年

作者： Li, Xiaolong Deng, Ruiting Wei, Jianhao Wu, Xin Chen, Jiayuan Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin Hunan University of Technology and Business School of Computer Science China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Concordia University Department of Electrical and Computer Engineering Montreal Canada Nanyang Technological University College of Computing and Data Science Singapore University of Waterloo Department of Electrical and Computer Engineering Canada

Real-time, interactive 4D traffic scene generation enables rapid digital twinning of traffic scenarios, improving management and decision-making in intelligent transportation systems. However, current text-to-video models, such as Sora, struggle to maintain the temporal coherence of traffic elements and interact with dynamic environments and users when generating 4D scenes. This article introduces a novel cloud-edge-terminal collaborative framework that leverages Artificial Intelligence-Generated Content (AIGC) in vehicular networks to tackle these challenges, ensuring long-term coherence and improved interactivity. The framework presents a comprehensive architecture for real-time interactive 4D scene generation, encompassing data collection, management, model pre-training, fine-tuning, and inference. We examine key design requirements and challenges, demonstrating that our microservice-based framework enables the system to generate and update 4D traffic scenes in real time, effectively responding to traffic data and user inputs. To the best of our knowledge, this is the first successful implementation of real-time, interactive 4D traffic scene generation. Performance evaluations show the superiority of our framework, powered by microservice-based code fine-tuning, over traditional frameworks. Finally, we discuss future research directions to enhance AIGC-driven 4D traffic scene generation. © 1986-2012 IEEE.

关键词： Decision making

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