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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Justus Robertson Rogelio E. Cardona-Rivera R. Michael Young University of York York UK School of Computing and the Entertainment Arts and Engineering Program University of Utah UT USA

ISBN: (数字)9781728174631

ISBN: (纸本)9781728174648

A key part of managing a player's virtual reality experience is ensuring that the environment behaves consistently to the player's interaction. In some instances, however, it is important to change how the world behaves-i.e. the world's simulation rules or mechanics-because doing so preserves the virtual environment's intended quality. Mechanics changes must be done carefully; if too overt, they may be perceivable and potentially thwart a player's sense of presence or *** paper reports the result of a study, which demonstrates the widely-held but heretofore-untested belief that changing an environment's mechanics without considering what the player knows is visible to the player. The study's findings motivate the paper's second contribution: an automated method to perform invisible dynamic mechanics adjustment, which affords shifting a game's previously-established mechanics in a manner that is not perceivably inconsistent to players. This method depends on a knowledge-tracking strategy and two such strategies are presented: (1) a conservative one, relevant to a wide variety of virtual environments, and (2) a more nuanced one, relevant to environments that will be experienced via head-mounted virtual reality displays. The paper concludes with a variety of design-centered considerations for the use of this artificial intelligence system within virtual reality.

关键词： Wounds Games Oils Solid modeling Virtual environments Artificial intelligence Tutorials

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Glaucoma identification with retinal fundus images using deep learning: Systematic review

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Informatics in Medicine Unlocked 2025年 56卷

作者： Meedeniya, Dulani Shyamalee, Thisara Lim, Gilbert Yogarajah, Pratheepan Department of Computer Science and Engineering University of Moratuwa Moratuwa 10400 Sri Lanka First-Year Division Faculty of Computing Sri Lanka Institute of Information Technology Malabe 10115 Sri Lanka SingHealth AI Health Program SingHealth Group Singapore 168582 Singapore School of Computing Engineering and Intelligent System Ulster University Londonderry BT48 7JL United Kingdom

Glaucoma is a leading cause of blindness, affecting millions of people worldwide. It is a chronic eye condition that damages the optic nerve and, if left untreated, can lead to vision loss and a decreased quality of life. Therefore, there is a need to explore practical and reliable mechanisms for glaucoma identification. This study systematically reviews deep-learning approaches for glaucoma identification using retinal fundus images from 2018 to 2024. Compared to existing survey studies, we cover the latest research, including several public retinal fundus image datasets, and focus on segmentation, classification based on convolutional neural networks and vision transformers, and explainability. The findings of this study, including comparisons of existing methods and key insights, will assist researchers and developers in identifying the most suitable techniques for glaucoma detection. © 2025 The Authors

关键词： Artificial intelligence Classification Deep learning Explainability Glaucoma Segmentation Trustworthiness

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Parallelized Domain Decomposition for Multi-Dimensional Lagrangian Random Walk, Mass-Transfer Particle Tracking Schemes

SSRN

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SSRN 2022年

作者： Schauer, Lucas Schmidt, Michael J. Engdahl, Nicholas B. Pankavich, Stephen D. Benson, David A. Bolster, Diogo Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States Center for Computing Research Sandia National Laboratories AlbuquerqueNM87185 United States Department of Civil and Environmental Engineering Washington State University PullmanWA99164 United States Hydrologic Science and Engineering Program Department of Geology and Geological Engineering Colorado School of Mines GoldenCO80401 United States Department of Civil and Environmental Engineering and Earth Sciences University of Notre Dame Notre DameIN46556 United States

We develop a multi-dimensional, parallelized domain decomposition strategy (DDC) for mass-transfer particle tracking (MTPT) methods. These methods are a type of Lagrangian algorithm for simulating reactive transport and are able to be parallelized by employing large numbers of CPU cores to accelerate run times. In this work, we investigate different procedures for "tiling" the domain in two and three dimensions, (2-d and 3-d), as this type of formal DDC construction is currently limited to 1-d. An optimal tiling is prescribed based on physical problem parameters and the number of available CPU cores, as each tiling provides distinct results in both accuracy and run time. We further extend the most efficient technique to 3-d for comparison, leading to an analytical discussion of the effect of dimensionality on strategies for implementing DDC schemes. Increasing computational resources (cores) within the DDC method produces a trade-off between inter-node communication and on-node work. For an optimally subdivided diffusion problem, the 2-d parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run up to around 2700 cores, reducing a 5-hour simulation to 8 seconds, and the 3-d algorithm maintains appreciable speedup up to 1700 cores. © 2022, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Is Attention All You Need for EEG to Predict Neurological Outcomes in Cardiac Arrest?

Is Attention All You Need for EEG to Predict Neurological Ou...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Jefferson Dionisio Muhammad Rizwan Hidetaka Suzuki Kazi Mahmudul Hassan Ingon Chanpornpakdi Lian-Yu Lin Toshihisa Tanaka Che Lin Department of Smart Medicine and Health Informatics National Taiwan University Taipei Taiwan Artificial Intelligence of Things Taiwan International Graduate Program Academia Sinica Taipei Taiwan Tokyo University of Agriculture and Technology Tokyo Japan Department of Emergency and Critical Care Japanese Red Cross Musashino Hospital Tokyo Japan Department of Internal Medicine Division of Cardiology National Taiwan University College of Medicine and Hospital Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Sudden cardiac arrest (SCA) poses a significant health challenge, necessitating accurate predictions of neurological outcomes in comatose patients, where good outcomes are defined as the recovery of most cognitive functions. Electroencephalogram (EEG) serves as a valuable biomarker for monitoring neurological states due to its rich, time-dependent information. This study aims to predict neurological outcomes using early EEG data by employing the Transformer model, which leverages multi-headed attention to identify patterns in lengthy sequences such as hour-long EEG recordings. Unlike traditional methods that use subsampled EEG epochs, we utilize the entire EEG sequences, subdivided into time steps, allowing our model to capture detailed temporal patterns via the attention mechanism. Moreover, we trained our proposed model using each EEG recording as an individual data sample but evaluated our model through aggregated patient-wise predictions. This approach allows us to boost the data sample size. Our results demonstrate promising predictive performance, achieving an AUROC of 0.82 and AUPRC of 0.90 on the holdout test set and an AUROC of 0.73 and AUPRC of 0.93 on an external test set with patient-wise predictions. This study highlights the potential of utilizing attention mechanisms to capture important time series progressions across EEG sequences for improving SCA prognosis.

关键词： Attention mechanisms Biological system modeling Time series analysis Cardiac arrest Predictive models Brain modeling Transformers Electroencephalography Data models Recording

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Parallelized Domain Decomposition for Multi-Dimensional Lagrangian Random Walk, Mass-Transfer Particle Tracking Schemes

arXiv

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arXiv 2022年

关键词： Lagrange multipliers

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Machine Learning Approach to Predicting Tunnel Field-Effect Transistors

Machine Learning Approach to Predicting Tunnel Field-Effect ...

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International Symposium on VLSI Technology, Systems and Applications

作者： Chandni Akbar Narasimhulu Thoti Yiming Li Paralllel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu Taiwan EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Paralllel and Scientific Computing Laboratory EECS International Graduate Program Institue of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Institue of Communications Engineering National Chiao Tung University Hsinchu Taiwan

We for the first time investigate the possibility to replace the device simulation for tunnel field-effect transistors (TFETs) with a machine learning (ML) algorithm. By incorporating the experimentally validated device simulation, a keyML technique named random forest regression (RFR) model is advanced and applied to predict characteristics of TFETs. The results of this work may benefit the design and fabrication of TFETs based on the well-trained RFR model. Very fast and accurate drain current (ID) prediction in terms of the engineering acceptable root-mean-square (RMSE) error inaugurates TFET technology with ML with a potential application to significantly reduce the computational cost.

关键词： Training TFETs Machine learning algorithms Computational modeling Vegetation Predictive models Data models

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Learning interface conditions in domain decomposition solvers 22

Learning interface conditions in domain decomposition solver...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Ali Taghibakhshi Nicolas Nytko Tareq Zaman Scott MacLachlan Luke Olson Matthew West Mechanical Science and Engineering University of Illinois Urbana-Champaign Urbana IL Computer Science University of Illinois Urbana-Champaign Urbana IL Scientific Computing Program Memorial University of Newfoundland and Labrador St. John's NL Canada Mathematics and Statistics Memorial University of Newfoundland and Labrador St. John's NL Canada

ISBN: (纸本)9781713871088

Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations. Yet the optimal construction of these methods requires tedious analysis and is often available only in simplified, structured-grid settings, limiting their use for more complex problems. In this work, we generalize optimized Schwarz domain decomposition methods to unstructured-grid problems, using Graph Convolutional Neural Networks (GCNNs) and unsupervised learning to learn optimal modifications at subdomain interfaces. A key ingredient in our approach is an improved loss function, enabling effective training on relatively small problems, but robust performance on arbitrarily large problems, with computational cost linear in problem size. The performance of the learned linear solvers is compared with both classical and optimized domain decomposition algorithms, for both structured- and unstructured-grid problems.

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The Arquive of Tatuoca Magnetic Observatory Brazil: from paper to intelligent bytes

The Arquive of Tatuoca Magnetic Observatory Brazil: from pap...

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IEEE International Conference on Big Data

作者： Cristian Berrío-Zapata Ester Ferreira da Silva Mayara Costa Pinheiro Vinicius Augusto Carvalho de Abreu Cristiano Mendel Martins Mario Augusto Góngora Kelso Dunman Faculty of Archival Science Information Science Post Graduation Program (PPGCI) Universidade Federal do Pará (UFPA) Belém Brazil Archives and Libraries in the Digital Environment Universidad Carlos III (UC3M) Madrid Spain Post Graduation Program (PPGCI) Universidade Federal do Pará (UFPA) Belém Brazil Faculty of Computer Science Information Science Post graduation Program (PPGCI) Universidade Federal do Pará (UFPA) Belém Brazil Faculty of Geology Geoscience Post graduation Program (PPGGF) Universidade Federal do Pará (UFPA) Belém Brazil Faculty of Computing Engineering and Media De Montfort University Leicester UK School of Information MAS/LIS program University of British Columbia Canada

ISBN: (纸本)9781665480468

The Magnetic Observatory of Tatuoca (TTB) was installed by Observatório Nacional (ON) in 1957, near Belém city in the state of Pará, Brazilian Amazon. Its history goes back to 1933, when a Danish mission used this location to collect data, due to its privileged position near the terrestrial equator. Between 1957 and 2007, TTB produced 18,000 magnetograms on paper using photographic variometers, and other associated documents like absolute value forms and yearbooks. Data was obtained manually from these graphs with rulers and grids, taking 24 average readings per day, that is, one per hour. In 2017, the Federal University of Pará (UFPA in the Portuguese acronym) and ON collaborated to rescue this physical archive. In 2022 UFPA took a step forward and proposed not only digitizing the documents but also developing an intelligent agent capable of reading and extracting the information of the curves with a resolution better than an hour, being this the central goal of the project. If the project succeeds, it will rescue 50 years of data imprisoned in paper, increasing measurement sensitivity far beyond what these sources used to give. This will also open the possibility of applying the same AI to similar documents in other observatories or disciplines like seismography. This article recaps the project, and the complex challenges faced in articulating Archival Science principles with AI and Geoscience.

关键词： Deep learning Observatories Magnetic resonance imaging Urban areas Collaboration Big Data Data mining

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Self-supervised approach for facial movement based optical flow

arXiv

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arXiv 2021年

作者： Alkaddour, Muhannad Tariq, Usman Dhall, Abhinav Graduate Program in Mechatronics Engineering American University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering American University of Sharjah Sharjah United Arab Emirates Department of Human Centred Computing Monash University Melbourne Australia Department of Computer Science and Engineering Indian Institute of Technology Ropar Rupnagar India

computing optical flow is a fundamental problem in computer vision. However, deep learning-based optical flow techniques do not perform well for non-rigid movements such as those found in faces, primarily due to lack of the training data representing the fine facial motion. We hypothesize that learning optical flow on face motion data will improve the quality of predicted flow on faces. The aim of this work is threefold: (1) exploring self-supervised techniques to generate optical flow ground truth for face images;(2) computing baseline results on the effects of using face data to train Convolutional Neural Networks (CNN) for predicting optical flow;and (3) using the learned optical flow in micro-expression recognition to demonstrate its effectiveness. We generate optical flow ground truth using facial key-points in the BP4D-Spontaneous dataset. The generated optical flow is used to train the FlowNetS architecture to test its performance on the generated dataset. The performance of FlowNetS trained on face images surpassed that of other optical flow CNN architectures, demonstrating its usefulness. Our optical flow features are further compared with other methods using the STSTNet micro-expression classifier, and the results indicate that the optical flow obtained using this work has promising applications in facial expression analysis. © 2021, CC BY.

关键词： Optical flows

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Machine learning moment tensor potential for modeling dislocation and fracture in L10−TiAl and D019−Ti3Al alloys

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Physical Review Materials 2023年第10期7卷 103602-103602页

作者： Ji Qi Z. H. Aitken Qingxiang Pei Anne Marie Z. Tan Yunxing Zuo M. H. Jhon S. S. Quek T. Wen Zhaoxuan Wu Shyue Ping Ong Materials Science and Engineering Program University of California San Diego 9500 Gilman Dr Mail Code 0448 La Jolla California 92093-0448 USA Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way Connexis #16-16 Singapore 138632 Republic of Singapore Department of NanoEngineering University of California San Diego 9500 Gilman Dr Mail Code 0448 La Jolla California 92093-0448 USA Department of Mechanical Engineering The University of Hong Kong Hong Kong SAR China Department of Materials Science and Engineering City University of Hong Kong Hong Kong SAR China Hong Kong Institute for Advanced Study City University of Hong Kong Hong Kong SAR China

Dual-phase γ-TiAl and α2−Ti3Al alloys exhibit high strength and creep resistance at high temperatures. However, they suffer from low tensile ductility and fracture toughness at room temperature. Experimental studies show unusual plastic behavior associated with ordinary and superdislocations, making it necessary to gain a detailed understanding on their core properties in individual phases and at the two-phase interfaces. Unfortunately, extended superdislocation cores are widely dissociated beyond the length scales practical for routine first-principles density-functional theory (DFT) calculations, while extant interatomic potentials are not quantitatively accurate to reveal mechanistic origins of the unusual core-related behavior in either phases. Here, we develop a highly accurate moment tensor potential (MTP) for the binary Ti-Al alloy system using a DFT dataset covering a broad range of intermetallic and solid solution structures. The optimized MTP is rigorously benchmarked against both previous and new DFT calculations, and unlike existing potentials, is shown to possess outstanding accuracy in nearly all tested mechanical properties, including lattice parameters, elastic constants, surface energies, and generalized stacking fault energies (GSFE) in both phases. The utility of the MTP is further demonstrated by producing dislocation core structures largely consistent with expectations from DFT-GSFE and experimental observations. The new MTP opens the path to realistic modeling and simulations of bulk lattice and defect properties relevant to the plastic deformation and fracture processes in γ-TiAl and α2−Ti3Al dual-phase alloys.

关键词： Interatomic & molecular potentials Alloys Machine learning

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