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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Antonio Pievatolo Alessandro Magrini Giovanni Meccariello Loredana Cristaldi Gabriele Patrizi Nedka D. Nikiforova National Research Council Institute for Applied Mathematics and Information Technologies “E. Magenes” Milan Italy Department of Statistics Computer Science Applications “G. Parenti” University of Florence Florence Italy National Research Council Institute of Sciences and Technologies for Energy and Sustainable Mobility Naples Italy Department of Electronics Information and Bioengineering Polytechnic of Milan Milan Italy Department of Information Engineering University of Florence Florence Italy

We discuss an innovative decision-making frame-work for accelerated degradation tests and predictive maintenance, when information about the state of the system, represented by prior knowledge and experimental data, is encapsulated in a degradation model. We consider dynamic programming and reinforcement learning as the framework for sequential decision making in these areas, also including the degradation model learning when necessary. The application of these methods to the design of life testing experiments and to the maintenance of lithium-ion batteries is proposed.

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Dose Requirements for Fused Multi-Modal Electron Tomography

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1968-1969页

作者： Manassa, Jason Schwartz, Jonathan Jiang, Yi Zheng, Huihuo Fessler, Jeffrey A. Wendy Di, Zichao Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann Arbor MI United States Advanced Photon Source Facility Argonne National Laboratory Lemont IL United States Argonne Leadership Computing Facility Argonne National Laboratory Lemont IL United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL United States Applied Physics Program University of Michigan Ann Arbor MI United States

A new approach for determining optimal placement of PM2.5air quality sensors: Case study for the contiguous United States

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arXiv 2022年

作者： Kelp, Makoto M. Lin, Samuel Kutz, J. Nathan Mickley, Loretta J. Department of Earth and Planetary Sciences Harvard University CambridgeMA02138 United States Department of Computer Science Harvard University CambridgeMA02138 United States Department of Applied Mathematics University of Washington SeattleWA98195 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA01238 United States

Considerable financial resources are allocated for measuring ambient air pollution in the United States, yet the locations for these monitoring sites may not be optimized to capture the full extent of current pollution variability. Prior research on best sensor placement for monitoring fine particulate matter (PM2.5) pollution is scarce: most studies do not span areas larger than a medium-sized city or examine timescales longer than one week. Here we present a pilot study using multiresolution modal decomposition (mrDMD) to identify the optimal placement of PM2.5sensors from 2000-2016 over the contiguous United States. This novel approach incorporates the variation of PM2.5on timescales ranging from one day to over a decade to capture air pollution variability. We find that the mrDMD algorithm identifies high-priority sensor locations in the western United States, but a significantly lower density of sensors than expected along the eastern coast, where a large number of EPA PM2.5monitors currently reside. Specifically, 53% of mrDMD optimized sensor locations are west of the 100th meridian, compared to only 32% in the current EPA network. The mrDMD sensor locations can capture PM2.5from wildfires and high pollution events, with particularly high skill in the West. These results suggest significant gaps in the current EPA monitoring network in the San Joaquin Valley in California, northern California, and in the Pacific Northwest (Idaho, and Eastern Washington and Oregon). Our framework diagnoses where to place air quality sensors so that they can best monitor smoke from wildfires. Our framework may also be applied to urban areas for equitable placement of PM2.5monitors. © 2022, CC BY.

关键词： Fires

The direct RBF partition of unity (D-RBF-PU) method for solving PDEs

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arXiv 2020年

作者： Mirzaei, Davoud Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan81746-73441 Iran

In this paper, a new localized radial basis function (RBF) method based on partition of unity (PU) is proposed for solving boundary and initial-boundary value problems. The new method is benefited from a direct discretization approach and is called the ‘direct RBF partition of unity (D-RBF-PU)’ method. Thanks to avoiding all derivatives of PU weight functions as well as all lower derivatives of local approximants, the new method is faster and simpler than the standard RBF-PU method. Besides, we are allowed to use discontinuous weight functions for developing other efficient numerical algorithms. Alternatively, the new method is an RBF-generated finite difference (RBF-FD) method in a PU setting which is much faster and in some situations more accurate than the original RBF-FD. The polyharmonic splines are used for local approximations, and the error and stability issues are considered. Some numerical experiments on irregular 2D and 3D domains, as well as cost comparison tests, are performed to support the theoretical analysis and to show the efficiency of the new *** Codes 65N06, 65N30, 65Dxx, 41Axx Copyright © 2020, The Authors. All rights reserved.

关键词： Radial basis function networks

Assessment of the Impact of Fractal Dimension of the Fracture Surface of Cement Mortar on its Strength

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Assessment of the Impact of Fractal Dimension of the Fractur...

Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Mykola Kotov Volodymyr Volchuk Oleksandr Konoplianyk Yevhen Plakhtii Olena Rabich Irina Meshcheriakova Department of Reinforced Concrete and Masonry Structures Prydniprovska State Academy of Civil Engineering and Architecture Dnipro Ukraine Department of Materials Science and Materials Processing Prydniprovska State Academy of Civil Engineering and Architecture Dnipro Ukraine Department of Computer Sciences Information Technologies and Applied Mathematics Prydniprovska State Academy of Civil Engineering and Architecture Dnipro Ukraine Department of Life Safety Prydniprovska State Academy of Civil Engineering and Architecture Dnipro Ukraine

The study of the relationship between the structure and properties of cement mortars that make up concrete is complicated by the complex geometric structure of structural elements. The fractal approach was used to assess adequately the fracture surface of cement mortar with a plasticizer. The fractal dimension of the identified elements of mortar macrostructure is calculated: dark areas with cement stone predominance, light areas with sand predominance and pores. Linear models of strength prediction based on the influence of dark areas of the structure (the pair correlation coefficient is 0.77), light areas $(R^{2}=0.76)$ and pores $(R^{2}=0.72)$ are obtained, which indicates the sensitivity of physical and mechanical properties to changes in the fractal dimension of structural elements. An increase in the strength indicators of the mortar was recorded from 8 to 27 MPa with an increase in the fractal dimension of cement stone from 1.787 to 1.959 (by 8.9 %). Strength indicators decrease with an increase in the fractal dimension of light sections of the structure from 1.534 to 1.891 (by 19.9 %) and the fractal dimension of pores from 1.632 to 1.807 (by 9.7 %). A multiparametric fractal model of the influence of fractal dimensions of the identified structural components is constructed, which has a relatively high correlation coefficient of 0.89. The proposed approach can be used in industrial conditions as an additional method for operational forecasting of flexural strength indicators of cement mortar based on the fractal dimension of its macrostructure elements.

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Enhancement and anisotropy of electron Landé factor due to spin-orbit interaction in semiconductor nanowires

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arXiv 2023年

作者： Czarnecki, Julian Bertoni, Andrea Goldoni, Guido Wójcik, Pawel AGH University of Krakow Faculty of Physics and Applied Computer Science al. A. Mickiewicza 30 Krakow30-059 Poland CNR-NANO S3 Istituto Nanoscienze Via Campi 213/a Modena41125 Italy Department of Physics Informatics and Mathematics University of Modena and Reggio Emilia via Campi 213/a Modena41125 Italy

We investigate the effective Landé factor in semiconductor nanowires with strong Rashba spin-orbit coupling. Using the k · p theory and the envelope function approach we derive a conduction band Hamiltonian where g∗ is explicitly related to the spin-orbit coupling contants αR. Our model includes orbital effects from the Rashba spin-orbit term, leading to a significant enhancement of the effective Landé factor which is naturally anisotropic. For nanowires based on the low-gap, high spin-orbit coupled material InSb, we investigate the anisotropy of the effective Landé factor with respect to the magnetic field direction, exposing a twofold symmetry for the bottom gate architecture. The anisotropy results from the competition between the localization of the envelope function and the spin polarization of the electronic state, both determined by the magnetic field direction. Copyright © 2023, The Authors. All rights reserved.

关键词： Anisotropy

Domain-Invariant Feature Learning for Domain Adaptation

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International Journal of Pattern Recognition and Artificial Intelligence 2023年第3期37卷 2351002-2351002页

作者： Tu, Ching-Ting Lin, Hsiau-Wen Lin, Hwei Jen Tokuyama, Yoshimasa Chu, Chia-Hung Department of Applied Mathematics National Chung Hsing University Taichung401 Taiwan Department of Information Management Chihlee University of Technology Banqiao Dist. New Taipei City220305 Taiwan Department of Computer Science and Information Engineering Tamkang University Tamsui Dist. New Taipei City251 Taiwan Department of Media and Image Technology Faculty of Engineering Tokyo Polytechnic University Tokyo164-8678 Japan

Unsupervised domain adaptation (UDA) mainly explores how to learn domain-invariant features from the source domain when the target domain label is unknown. To learn domain-invariant features requires aligning the distribution of samples from two domains in the feature space, which can be achieved by minimizing the maximum mean discrepancy (MMD) of samples from the two domains. However, there is still no effective way to find the best parameter values of MMD. Such a problem is addressed in the MMD with deep kernels (MMD-D), whose optimal parameters can be obtained through training. This study proposes a method of domain-invariant feature learning for UDA, whose architecture, named MMDDCDA, comprises an MMD-D module and a cross domain adaptation (CDA) module. MMDDCDA performs alternating training similar to adversarial training to alternately boost the power of the two modules. To our knowledge, this is the first UDA method that performs such alternating training on a UDA architecture using MMD with deep kernels. Experimental validation showed that the proposed method yields state-of-the-art results among UDA methods using other MMD variants and some UDA benchmarks. © 2023 World Scientific Publishing Company.

关键词： Generative adversarial networks

AstroMLab 1: Who Wins Astronomy Jeopardy!?

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arXiv 2024年

作者： Ting, Yuan-Sen Nguyen, Tuan Dung Ghosal, Tirthankar Pan, Rui Arora, Hardik Sun, Zechang de Haan, Tijmen Ramachandra, Nesar Wells, Azton Madireddy, Sandeep Accomazzi, Alberto Research School of Astronomy & Astrophysics Australian National University Cotter Rd. WestonACT2611 Australia School of Computing Australian National University ActonACT2601 Australia Department of Astronomy The Ohio State University ColumbusOH43210 United States The Ohio State University ColumbusOH43210 United States Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA19104 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science and Engineering Hong Kong University of Science and Technology Kowloon Hong Kong Indian Institute of Technology Patna Bihar Bihta801106 India Department of Astronomy MongManWai Building Tsinghua University Beijing100084 China Institute of Particle and Nuclear Studies High Energy Accelerator Research Organization Ibaraki Tsukuba305-0801 Japan Ibaraki Tsukuba305-0801 Japan Computational Science Division Argonne National Laboratory LemontIL60439 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States

We present a comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset. This dataset comprises 4,425 multiple-choice questions curated from the Annual Review of Astronomy and Astrophysics, covering a broad range of astrophysical topics.a) Our analysis examines model performance across various astronomical subfields and assesses response calibration, crucial for potential deployment in research environments. Claude-3.5-Sonnet outperforms competitors by up to 4.6 percentage points, achieving 85.0% accuracy. For proprietary models, we observed a universal reduction in cost every 3-to-12 months to achieve similar score in this particular astronomy benchmark. open-weights models have rapidly improved, with LLaMA-3-70b (80.6%) and Qwen-2-72b (77.7%) now competing with some of the best proprietary models. We identify performance variations across topics, with non-English-focused models generally struggling more in exoplanet-related fields, stellar astrophysics, and instrumentation related questions. These challenges likely stem from less abundant training data, limited historical context, and rapid recent developments in these areas. This pattern is observed across both open-weights and proprietary models, with regional dependencies evident, highlighting the impact of training data diversity on model performance in specialized scientific domains. Top-performing models demonstrate well-calibrated confidence, with correlations above 0.9 between confidence and correctness, though they tend to be slightly underconfident. The development for fast, low-cost inference of open-weights models presents new opportunities for affordable deployment in astronomy. The rapid progress observed suggests that LLM-driven research in astronomy may become feasible in the near future. © 2024, CC BY.

关键词： Astrophysics

Modeling Crowdedness of Emergency departments Leveraging Crowdsensing Mobility Data

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Modeling Crowdedness of Emergency Departments Leveraging Cro...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Tieqi Shou Zhiyuan Wang Shang Shi Dingqi Yang Binbin Zhou Cheng Wang Longbiao Chen spAtial Sensing and Computing Lab Xiamen University Xiamen China School of Engineering and Applied Science University of Virginia Charlottesville USA Department of Mathematics Imperial College London London United Kingdom State Key Laboratory of Internet of Things for Smart City University of Macau Macao China Department of Computer Science and Computing Zhejiang University City College Hangzhou China

Hospital emergency departments (EDs) are crucial medical facilities providing emergency healthcare. Understanding and measuring the patient flow in EDs plays a key role in maximizing the utility of scarce hospital resources. Existing approaches either require manual reporting by staff or collect sensitive data about patients via camera or hospital registration system, causing potential privacy breaches. Against this background, we propose a data-driven modeling strategy of ED crowdedness leveraging multi-source urban crowdsensing data, which automatically provides fine-grained and timely information about the crowdedness of EDs. Specifically, our model can not only accurately extract the emergency visit demand from noisy human mobility data with minimum expert knowledge using active learning and co-training techniques, but also estimate ED crowdedness by modeling the emergency service process by integrating three queueing models for general practice, internal medicine, and surgical clinics, respectively. We evaluate our method leveraging large-scale taxi and hire vehicle trajectory datasets and hospital information system datasets from the government open data portal. Results show that our approach effectively extracts the emergency visit demand and models the emergency service process to assess the crowdedness of EDs. We have deployed the system in Xiamen City by collaborating with the municipal government to provide services for citizens and health providers.

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Wrinkling instability of 3D auxetic bilayers in tension

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arXiv 2024年

作者： Venkata, Sairam Pamulaparthi Balbi, Valentina Destrade, Michel Fu, Yuxin Danesh, Hooman Fu, Yibin School of Mathematical and Statistical Sciences University of Galway GalwayH91 TK33 Ireland Department of Mechanics Tianjin University Tianjin300072 China Institute of Applied Mechanics RWTH Aachen University Mies-van-der-Rohe-Str.1 Aachen52074 Germany School of Computer Science and Mathematics Keele University Keele StaffordshireST5 5BG United Kingdom

Bilayers (soft substrates coated with stiff films) are commonly found in nature with examples including skin tissue, vesicles, or organ membranes. They exhibit various types of instabilities when subjected to compression, depending on the contrast in material properties between the two components. In this letter, we unravel the mechanisms behind wrinkling instabilities in auxetic bilayer systems under uni-axial tension. We find that in tension, a soft bilayer can experience large lateral contraction. With an adequate contrast in the Poisson ratios, compressive stresses may develop and generate wrinkles aligned with the tensile direction. We model analytically the onset of wrinkles and develop a new numerical method to validate our predictions with advanced Finite Element simulations in ABAQUS. Our findings reveal that wrinkles are observed when the Poisson ratio of the substrate is greater than that of the film. As the two Poisson ratios converge to a common value, the critical stretch of instability shoots up rapidly, and the wrinkling disappears. We also confirm these results by asymptotic analysis. Our proposed numerical method is versatile and can be used to predict wrinkling in bilayers where auxeticity is associated with microstructural patterns. Using inverse analysis, we design the film microstructures to achieve desired effective Poisson's ratios and further validate the effective properties of these microstructures with the Finite Element code, FEAP. We show that the critical stretch ratio for buckling in auxetic structures with microstructural patterns is in strong agreement with the predictions of homogenized models. The new method proposed in this letter has a significant potential for controlling surface patterns of auxetic skin grafts and hydrogel organ patches under mechanical loads. Moreover, the asymptotic expressions in this work can be used under finite strain for buckling-based metrology applications. © 2024, CC BY.

关键词： Poisson ratio