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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： O'Hara, Seamus Costello, Joe Tian, Qi Wu, Qile West, Ken Pfeiffer, Loren Sherwin, Mark Wu, Liang Department of Physics Institute of Terahertz Science and Technology University of California Santa Barbara Santa BarbaraCA93106 United States Intelligence Community Post-Doctoral Research Fellowship Program PhiladelphiaPA19104 United States Department of Physics University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08542 United States

ISBN: (纸本)9781957171395

In this proceeding, we present polarization data of sidebands emitted from strongly driven quasiparticles, demonstrate the dependence of these data on various parameters of the external driving field, and describe the data using an analytical expression, dependent on driving field parameters and the effective Hamiltonian of the system. We propose using this method on spin-momentum locked systems, such as altermagnets, to observe and reconstruct sidebands which are influenced by the topology of individual bands. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Topology

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Parameter Estimation for Partially Observed McKean-Vlasov Diffusions

arXiv

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arXiv 2024年

作者： Jasra, Ajay Maama, Mohamed Tempone, Raul School of Data Science The Chinese University of Hong Kong CN Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Chair of Mathematics for Uncertainty Quantification RWTH Aachen University Aachen52062 Germany

In this article we consider likelihood-based estimation of static parameters for a class of partially observed McKean-Vlasov (POMV) diffusion process with discrete-time observations over a fixed time interval. In particular, using the framework of [5] we develop a new randomized multilevel Monte Carlo method for estimating the parameters, based upon Markovian stochastic approximation methodology. New Markov chain Monte Carlo algorithms for the POMV model are introduced facilitating the application of [5]. We prove, under assumptions, that the expectation of our estimator is biased, but with expected small and controllable bias. Our approach is implemented on several examples. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Comparative Analysis of Real-Time Multi-Person Pose Detection in Electrical Industrial Safety Scenarios Using YOLOv8-Pose and OpenPose

Comparative Analysis of Real-Time Multi-Person Pose Detectio...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Shang-Te Tsai Zong-Rong Wu Pin-Hsuan Lin Chin-Hsiung Chen Wei Chien Yu-Cheng Chang School of Art and Design Sanming University Fujian China Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Business Administration National Central University Taoyuan Taiwan Department of Electrical Engineering New Taipei Municipal Tamsui Commercial Industrial Vocational Senior High School New Taipei Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy.

关键词： Training Accuracy Microcontrollers Neural networks Real-time systems Safety Internet of Things

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Post-Covid-19 Digital Nomadism: Beyond Work from (Almost) Anywhere

Post-Covid-19 Digital Nomadism: Beyond Work from (Almost) An...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： De Almeida, Marcos Antonio De Souza, Jano Moreira Correia, António Schneider, Daniel Rio de Janeiro Brazil Inesc Tec University of Tras-os-Montes e Alto Douro Vila Real Portugal College of Information Science & Technology University of Nebraska Omaha United States Postgraduate Program in Informatics Ppgi Ufrj Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

ISBN: (纸本)9798350337020

In this paper, we continue our investigations on digital nomadism and the impact of COVID-19 pandemic on the work-related aspects and lifestyle of digital nomads (DN). The findings presented in this empirical study reflect the analysis of the impact of COVID-19 outbreak (and its waves) on the market economy and work-life boundaries of DNs as perceived from posts and comments gathered from a Reddit community during the period of early March 2020 until the end of 2022. From this point, our results indicate that the massification of remote work among formal workers in response to COVID-19 pandemic has impacted both the formal labor market and the DN ecosystem. As a consequence, we argue that digital nomadism tends to play a critical role beyond work from (almost) anywhere (WFA) in a post-COVID-19 era taking into account the novel facets of nomadic work-lifestyle. © 2023 IEEE.

关键词： COVID-19 digital nomadism lifestyle Reddit slowmads wannabes work from almost anywhere

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IEEE VR 2023 Workshops: Workshop: 3D Reconstruction, Digital Twinning, and Simulation for Virtual Experiences (ReDigiTS 2023)

IEEE VR 2023 Workshops: Workshop: 3D Reconstruction, Digital...

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2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023

作者： Cannavò, Alberto Kapralos, Bill Seinfeld, Sofia Pratticò, Filippo Gabriele Zhang, Congyi Politecnico di Torino Turin Italy Game Development and Interactive Media Program and MaxSIMhealth Lab Ontario Tech University Oshawa Canada Image Processing and Multimedia Technology Center Universitat Politècnica de Catalunya-Barcelona Tech Terrassa Spain Politecnico di Torino Turin Italy Department of Computer Science The University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798350348392

Today, Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) technologies are becoming fundamental across a wide variety of application domains. All these technologies fall under the Extended Reality (XR) spectrum. Areas in which XR technologies and, in particular, immersive experiences have been successfully introduced are expanding daily, from traditional areas, such as entertainment and video game production, to novel scenarios, such as industry, healthcare, smart cities, and autonomous vehicles, to mention a few. The spread of XR and VR is also driven by technological advancements that are making new ways of operating within virtual experiences easier, and sometimes even possible. In particular, the availability of new hardware and software tools make the use of '3D reconstructions', 'digital twins' and 'simulations' commonplace in many XR and VR applications. For example, the possibility to leverage networks with higher bandwidth than never before and lower latency makes it possible to transmit the amount of data needed for massive, real-time digital twins, whereas high-performance hardware enables the reconstruction of high-fidelity digital objects and environments, while lowering the time needed for high-demanding simulations. The aim of the 3D Reconstruction, Digital Twinning, and Simulation for Virtual Experiences (ReDigiTS) workshop is to bring together researchers, practitioners, educators, and students to share ideas and promote further work in this growing area of 3D reconstruction, digital twinning, and simulation in VR and related technologies. © 2023 IEEE.

关键词： Augmented reality

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Bambang Sulistio Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%.

关键词： Deep learning Sentiment analysis Analytical models Social networking (online) Blogs Decision making Text categorization Bidirectional control Transformers Encoding

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Interferometry Among Strongly Driven Quasiparticles with Distinct Dynamical and Geometrical Phases

Interferometry Among Strongly Driven Quasiparticles with Dis...

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Hamiltonians

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Parallel Approximations of the Tukey g-and-h Likelihoods and Predictions for Non-Gaussian Geostatistics

Parallel Approximations of the Tukey g-and-h Likelihoods and...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Sagnik Mondal Sameh Abdulah Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Kingdom of Saudi Arabia Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology

Maximum likelihood estimation is an essential tool in the procedure to impute missing data in climate/weather applications. By defining a particular statistical model, the maximum likelihood estimation can be used to understand the underlying structure of given geospatial data. The Gaussian random field has been widely used to describe geospatial data, as one of the most popular models under the hood of maximum likelihood estimation. Computation of Gaussian log-likelihood demands operations on a dense symmetric positive definite matrix, often parameterized by the Matérn correlation function. This computation of the log-likelihood requires $\mathcal{O}(n^{2})$ storage and $\mathcal{O}(n^{3})$ operations, which can be a huge task considering that the number of geographical locations, $n$ , now commonly reaches into the millions. However, despite its appealing theoretical properties, the assumptions of Gaussianity may be unrealistic since real data often show signs of skewness or have some extreme values. Herein, we consider the Tukey ${g-}$ and $-h$ (TGH) random field as an example of a non-Gaussian random field that shows more robustness in modeling geospatial data by including two more parameters to incorporate skewness and heavy tail features in the model. This work provides the first HPC implementation of the TGH random field's inference on parallel hardware architectures. Using task-based programming models associated with dynamic runtime systems, our implementation leverages the high concurrency of current parallel systems. This permits to run the exact log-likelihood evaluation of the Tukey g-and-h (TGH) random fields for a decent number of geospatial locations. To tackle large-scale problems, we provide additionally an implementation of the given model using two different low-rank approximations. We compress the aforementioned positive-definite symmetric matrix for computing the log-likelihood and rely on the Tile Low-Rank (TLR) and the Hierarchical Off-Diagon

关键词： Maximum likelihood estimation Symmetric matrices Runtime Computational modeling Linear algebra Tail Predictive models

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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Rissa Rahmania Yaya Heryadi Lukas Lukas Wayan Suparta Ilvico Sonata Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Group (CEG) Faculty of Engineering Universitas Katolik Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products.

关键词： Surveys computer vision Accuracy Image recognition Transformers Feature extraction Data augmentation Artificial intelligence Image classification

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Dynamic Model for Determining Disaster Evacuation Locations with Game Theory 1

Dynamic Model for Determining Disaster Evacuation Locations ...

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1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Safii, M. Efendi, Syahril Zarlis, Muhammad Mawengkang, Herman Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

Disaster management can be grouped into three stages with several activities that can be carried out starting from pre-disaster, during emergency response and post-disaster. In the pre-disasterstage one of which is mitigation, which is a series of efforts to reduce disaster risk, both through physical development and awareness raising and capacity to face the threat of disaster. In mitigation one important factor is about evacuation. Evacuation locations in disaster transportation management are urgently needed for an effective evacuation process. Evacuation location determination is a benchmark in handling evacuation that can minimize the impact of losses from the disaster. To determine the evacuation location dynamically using game theory can solve the problem by providing several alternative locations by showing the dominant factor in the variables in the selection of the evacuation site. Game theory will use pure and mixed strategies to provide the most optimum choice of criteria. With this method will produce a decision support system to determine the optimal evacuation location or safe area in disaster mitigation © 2020 Published under licence by IOP Publishing Ltd.

关键词： Site selection

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