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Optics Express 2025年第12期33卷 25198-25209页

作者： Syed Muhammad Kazim Franziska Strasser Mia Kvåle Løvmo Andrii Nehrych Simon Moser Michał Ziemczonok Wolfgang Heidrich Ivo Ihrke Monika Ritsch-Marte Zentrum für Sensorsysteme University of Siegen Paul-Bonatz-Str. 9-11 57076 Siegen Germany Institute of Biomedical Physics Medical University of Innsbruck Müllerstr. 44 6020 Innsbruck Austria Warsaw University of Technology Institute of Micromechanics and Photonics św. Andrzeja Boboli 8 02-525 Warsaw Poland Visual Computing Center King Abdullah University of Science and Technology Building 1 (Al Khwarizmi) West Level 2 Thuwal 23955-6900 Saudi Arabia

We demonstrate coded wavefront sensing (Coded WFS) for video-rate quantitative phase imaging and 3D refractive index (RI) tomography of biological specimens. To evaluate the accuracy, we implement an experimental setup that supports measurements of specimens with Coded WFS as well as with digital holographic microscopy (DHM) under identical conditions, enabling direct comparison. We image a static 3D phantom fabricated via additive manufacturing and a rotating HEK293 cell in an acoustofluidic chamber. Our results demonstrate good agreement between the two methods, with the advantage that, in contrast to DHM, Coded WFS enables simple integration with standard microscopes. Furthermore, we apply a standard tomographic reconstruction algorithm to the HEK293 cell data for comparison, which demonstrates the potential of Coded WFS in *** by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License . Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

关键词： Fourier transforms Optical tweezers Phase contrast Phase imaging Three dimensional imaging Wave front sensing

ESGSenticNet: A Neurosymbolic Knowledge Base for Corporate Sustainability Analysis

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arXiv 2025年

作者： Ong, Keane Mao, Rui Xing, Frank Satapathy, Ranjan Sulaeman, Johan Cambria, Erik Mengaldo, Gianmarco College of Design and Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore School of Computing National University of Singapore 13 Computing Drive 117417 Singapore Asian Institute of Digital Finance National University of Singapore Innovation 4.0 3 Research Link #04-03 117602 Singapore College of Computing and Data Science Nanyang Technological University 50 Nanyang Ave 639798 Singapore Institute of High Performance Computing Agency for Science Technology and Research Fusionopolis Way #16-16 Connexis 138632 Singapore Sustainable Green Finance Institute National University of Singapore Innovation 4.0 3 Research Link #02-02 117602 Singapore NUS Business School National University of Singapore Mochtar Riady Building 15 Kent Ridge Dr Mocthar Riady Building 119245 Singapore Imperial College London United Kingdom

Evaluating corporate sustainability performance is essential to drive sustainable business practices, amid the need for a more sustainable economy. However, this is hindered by the complexity and volume of corporate sustainability data (i.e. sustainability disclosures), not least by the effectiveness of the NLP tools used to analyse them. To this end, we identify three primary challenges – immateriality, complexity, and subjectivity, that exacerbate the difficulty of extracting insights from sustainability disclosures. To address these issues, we introduce ESGSenticNet, a publicly available knowledge base for sustainability analysis. ESGSenticNet is constructed from a neurosymbolic framework that integrates specialised concept parsing, GPT-4o inference, and semi-supervised label propagation, together with a hierarchical taxonomy. This approach culminates in a structured knowledge base of 44k knowledge triplets – (‘halve carbon emission’, supports, ‘emissions control’), for effective sustainability analysis. Experiments indicate that ESGSenticNet, when deployed as a lexical method, more effectively captures relevant and actionable sustainability information from sustainability disclosures compared to state of the art baselines. Besides capturing a high number of unique ESG topic terms, ESGSenticNet outperforms baselines on the ESG relatedness and ESG action orientation of these terms by 26% and 31% respectively. These metrics describe the extent to which topic terms are related to ESG, and depict an action toward ESG. Moreover, when deployed as a lexical method, ESGSenticNet does not require any training, possessing a key advantage in its simplicity for non-technical stakeholders. © 2025, CC BY.

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Darcy-Forchheimer Hybrid Nano Fluid Flow with Mixed Convection Past an Inclined Cylinder

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Computers, Materials & Continua 2021年第2期66卷 2025-2039页

作者： M.Bilal Imran Khan Taza Gul Asifa Tassaddiq Wajdi Alghamdi Safyan Mukhtar Poom Kumam Department of Mathematics City University of Science and Information TechnologyPeshawar25000Pakistan Department of Basic Sciences and Humanities College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah80261Saudi Arabia Basic Sciences Department Deanship of Preparatory YearKing Faisal UniversityAl Ahsa31982Saudi Arabia Center of Excellence in Theoretical and Computational Science(TaCS-CoE) SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand

This article aims to investigate the Darcy Forchhemier mixed convection flow of the hybrid nanofluid through an inclined extending *** different nanoparticles such as carbon nanotubes(CNTs)and iron oxide Fe3O4 have been added to the base fluid in order to prepare a hybrid *** partial differential equations for momentum,energy and convective diffusion have been changed into dimensionless ordinary differential equations after using Von Karman *** analysis method(HAM),a powerful analytical approach has been used to find the solution to the given *** effects of the physical constraints on velocity,concentration and temperature profile have been drawn as well for discussion *** numerical outcomes have been carried out for the drag force,heat transfer rate and diffusion rate *** Biot number of heat and mass transfer affects the fluid temperature whereas the Forchhemier parameter and the inclination angle decrease the velocity of the fluid *** results show that hybrid nanofluid is the best source of enhancing heat transfer and can be used for cooling purposes as well.

关键词： Mixed convection similarity transformation HAM hybrid nanofluid CNTs Darcy Forchhemier inclined cylinder

Fault Detection and Classification of Machinery Bearing under Variable Operating Conditions Based on Wavelet Transform and CNN

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Fault Detection and Classification of Machinery Bearing unde...

2021 International Mobile, Intelligent, and Ubiquitous computing Conference, MIUCC 2021

作者： Eltotongy, Assem Awad, Mohammed I. Maged, Shady A. Onsy, Ahmed Ain Shams University Mechatronics Engineering Faculty of Engineering Cairo Egypt Faculty of Engineering Galala University Suez Egypt University of Central Lancashire Computing and Technology Building School of Engineering PrestonPR1 2HE United Kingdom

ISBN: (纸本)9781665412438

The rolling bearing, one of the most critical components of wind turbines, is subject to variable operating conditions because of the unsteadiness of environmental aspects. The development of an efficient technique for predicting and classifying rolling bearing faults is a critical task. Condition-based maintenance (CBM) techniques are used to plan the maintenance procedure depending on the actual state of the asset. The convolutional neural network (CNN) structure was built using a neural architecture search (NAS) approach based on reinforcement learning. The time-series data of vibration signals are preprocessed using the continuous wavelet transform (CWT) before delivering to the CNN. The results confirmed that the proposed approach would automatically learn and discover distinct features from vibration signals, as well as identify various rolling bearing health conditions. © 2021 IEEE.

关键词： Wind turbines

A Stable Decoupled Perfectly Matched Layer for the 3d Wave Equation Using the Nodal Discontinuous Galerkin Method

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SSRN

SSRN 2024年

作者： Feriani, Sophia Julia Cosnefroy, Matthias Engsig-Karup, Allan Peter Warburton, Tim Pind, Finnur Jeong, Cheol-Ho Acoustic Technology Department of Electrical and Photonics Engineering Technical University of Denmark Ørsteds Plads Building 352 Kongens Lyngby2800 Denmark Treble Technologies Hafnartorg Kalkofnsvegur 2 Reykjavík101 Iceland Scientific Computing Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads Building 324 Kongens Lyngby2800 Denmark Data & Decision Sciences Virginia Tech 727 Prices Fork Road BlacksburgVA24060 United States

In outdoor acoustics, the calculations of sound propagating in air can be computationally heavy if the domain is chosen large enough for the sound waves to decay. By strategically truncating the computational domain with an efficient boundary treatment, the computational cost is lowered. One commonly used boundary treatment is the perfectly matched layer (PML), which dampens outgoing waves without polluting the computed solution in the inner *** purpose of this study is to propose and assess a new perfectly matched layer formulation for the 3D acoustic wave equation, using the nodal discontinuous Galerkin finite element method. The formulation is based on an efficient PML formulation that can be decoupled to further increase the computational efficiency and guarantee stability without sacrificing accuracy. This decoupled PML formulation is demonstrated to be long-time stable, and an optimization procedure for the damping functions is proposed to enhance the performance of the formulation. © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

Hyperfine-mediated spin relaxation in donor-atom qubits in silicon

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Physical Review Research 2023年第2期5卷 023043-023043页

作者： Yu-Ling Hsueh Ludwik Kranz Daniel Keith Serajum Monir Yousun Chung Samuel K. Gorman Rajib Rahman Michelle Y. Simmons School of Physics University of New South Wales Sydney NSW 2052 Australia Silicon Quantum Computing Pty Ltd. Level 2 Newton Building UNSW Sydney Kensington NSW 2052 Australia Centre for Quantum Computation and Communication Technology School of Physics University of New South Wales Sydney NSW 2052 Australia

Donor electron spin qubits hosted within nanoscale devices have demonstrated seconds-long relaxation times at magnetic fields suitable for the operation of spin qubits in silicon of B=1.5T. The relaxation rates of these qubits have been shown at milliKelvin temperatures to be mediated by spin-orbit coupling with a B5 dependency on magnetic field for B>3T with a transition to a B3 dependency at magnetic fields below (B≤3T). This deviation has been observed in many spin qubit systems but is particularly notable in multidonor quantum dot qubits. The reason for this has remained a mystery. In this paper we show that for these multidonor low noise, crystalline qubits this deviation at low magnetic fields can be explained by a hyperfine mediated relaxation mechanism of the electron spin through a quantitative model of the relaxation rates. This model identifies the importance of donor nuclear spin flips which are more apparent in multidonor systems in which the larger numbers of donor nuclei creates stronger confinement potentials and enhanced hyperfine couplings. We show theoretically that with atomic precision engineering of the locations of the donor nuclei in these multidonor quantum dot qubits, and/or nuclear spin control, we can minimize the hyperfine mediated relaxation allowing T1 to extend to ∼200 seconds (B=1.5T).

关键词： Fine & hyperfine structure Open quantum systems & decoherence Quantum information with solid state qubits Spin relaxation Scanning tunneling microscopy

Guest Editorial: When Multimedia Meets Food: Multimedia computing for Food Data Analysis and Applications

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IEEE Transactions on Multimedia 2025年 27卷 2708-2712页

作者： Weiqing Min Shuqiang Jiang Petia Radeva Vladimir Pavlovic Chong-Wah Ngo Kiyoharu Aizawa Wanqing Li Institute of Computing Technology Chinese Academy of Sciences Haidian Beijing China Universitat de Barcelona and Computer Vision Center Edifici O Campus UAB Bellaterra (Cerdanyola) Barcelona Spain Rutgers University New Jersey New Brunswick NJ USA Singapore Management University Singapore University of Tokyo Bunkyo-ku Tokyo Japan University of Wollongong Administration Building Northfields Avenue Wollongong NSW Australia

G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors

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arXiv 2024年

作者： Yang, Haoxin Xu, Xuemiao Xu, Cheng Zhang, Huaidong Qin, Jing Wang, Yi Heng, Pheng-Ann He, Shengfeng The School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology State Key Laboratory of Subtropical Building and Urban Science China The Centre for Smart Health The Hong Kong Polytechnic University Hong Kong The School of Future Technology South China University of Technology Guangzhou China The School of Computer Science and Network Security Dongguan University of Technology Dongguan China The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The School of Computing and Information Systems Singapore Management University Singapore

Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent manipulation in pre-trained GANs can lead to changes in ID-irrelevant attributes, adversely affecting data utility due to GAN inversion inaccuracies. This paper introduces G2Face, which leverages both generative and geometric priors to enhance identity manipulation, achieving high-quality reversible face anonymization without compromising data utility. We utilize a 3D face model to extract geometric information from the input face, integrating it with a pre-trained GAN-based decoder. This synergy of generative and geometric priors allows the decoder to produce realistic anonymized faces with consistent geometry. Moreover, multi-scale facial features are extracted from the original face and combined with the decoder using our novel identity-aware feature fusion blocks (IFF). This integration enables precise blending of the generated facial patterns with the original ID-irrelevant features, resulting in accurate identity manipulation. Extensive experiments demonstrate that our method outperforms existing state-of-the-art techniques in face anonymization and recovery, while preserving high data utility. Code is available at https://***/Harxis/G2Face. Copyright © 2024, The Authors. All rights reserved.

关键词： Anonymity

Empirical Measurements of AI Training Power Demand on a GPU-Accelerated Node

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arXiv 2024年

作者： Latif, Imran Newkirk, Alex C. Carbone, Matthew R. Munir, Arslan Lin, Yuewei Koomey, Jonathan Yu, Xi Dong, Zhihua Computing and Data Sciences Directorate Brookhaven National Laboratory UptonNY United States Building Technology and Urban Systems Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL United States Koomey Analytics San FranciscoCA United States

The expansion of artificial intelligence (AI) applications has driven substantial investment in computational infrastructure, especially by cloud computing providers. Quantifying the energy footprint of this infrastructure requires models parameterized by the power demand of AI hardware during training. We empirically measured the instantaneous power draw of an 8-GPU NVIDIA H100 HGX node during the training of open-source image classifier (ResNet) and large-language models (Llama2-13b). The maximum observed power draw was approximately 8.4 kW, 18% lower than the manufacturer-rated 10.2 kW, even with GPUs near full utilization. Holding model architecture constant, increasing batch size from 512 to 4096 images for ResNet reduced total training energy consumption by a factor of 4. These findings can inform capacity planning for data center operators and energy use estimates by researchers. Future work will investigate the impact of cooling technology and carbon-aware scheduling on AI workload energy consumption. © 2024, CC BY.

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Enabling Spatial Digital Twins: Technologies, Challenges, and Future Research Directions

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arXiv 2023年

作者： Ali, Mohammed Eunus Cheema, Muhammad Aamir Hashem, Tanzima Ulhaq, Anwaar Babar, Muhammad Ali Department of Computer Science and Engineering Bangladesh University of Engineering and Technology ECE Building Dhaka1000 Bangladesh Faculty of Information Technology Monash University 20 Exhibition Walk ClaytonVIC3164 Australia School of Computing Charles Sturt University Port MacquarieNSW2444 Australia School of Computer and Mathematical Sciences The University of Adelaide AdelaideSA5005 Australia

A Digital Twin (DT) is a virtual replica of a physical object or system, created to monitor, analyze, and optimize its behavior and characteristics. A Spatial Digital Twin (SDT) is a specific type of digital twin that emphasizes the geospatial aspects of the physical entity, incorporating precise location and dimensional attributes for a comprehensive understanding within its spatial environment. With the recent advancement in spatial technologies and breakthroughs in other computing technologies such as AI/ML, the SDTs market is expected to rise to $25 billions covering a wide range of applications. The majority of existing research focuses on DTs and often fails to address the necessary spatial technologies essential for constructing SDTs. The current body of research on SDTs primarily concentrates on analyzing their potential impact and opportunities within various application domains. As building an SDT is a complex process and requires a variety of spatial computing technologies, it is not straightforward for practitioners and researchers of this multi-disciplinary domain to grasp the underlying details of enabling technologies of the SDT. In this paper, we are the first to systematically analyze different spatial technologies relevant to building an SDT in layered approach (starting from data acquisition to visualization). More specifically, we present the key components of SDTs into four layers of technologies: (i) data acquisition;(ii) spatial database management & big data analytics systems;(iii) GIS middleware software, maps & APIs;and (iv) key functional components such as visualizing, querying, mining, simulation and prediction. Moreover, we discuss how modern technologies such as AI/ML, blockchains, and cloud computing can be effectively utilized in enabling and enhancing SDTs. Finally, we identify a number of research challenges and opportunities in SDTs. This work serves as an important resource for SDT researchers and practitioners as it explicitly d

关键词： Data acquisition