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SSRN 2024年

作者： Wu, Luohong Seibold, Matthias Cavalcanti, Nicola A. Hein, Jonas Gerth, Tatiana Lekar, Roni Hoch, Armando Vlachopoulos, Lazaros Grabner, Helmut Zingg, Patrick Farshad, Mazda Fürnstahl, Philipp Research in Orthopedic Computer Science Balgrist University Hospital University of Zurich Lengghalde 5 Zurich Zurich8008 Switzerland Department of Orthopaedics Balgrist University Hospital University of Zurich Forchstrasse 340 Zurich Zurich8008 Switzerland Computer Vision and Geometry Group ETH Zurich Ramistrasse 101 Zurich Zurich8092 Switzerland Institute of Computer Science School of Engineering Zurich University of Applied Sciences Obere Kirchgasse 2 Winterthur Zurich8401 Switzerland Institute of Data Analysis and Process Design School of Engineering Zurich University of Applied Sciences Technikumstrasse 81 Winterthur Zurich8400 Switzerland

Background: Total Hip Arthroplasty (THA) is a well-established and common orthopedic surgery. Due to the complexity involved in THA, orthopedic surgeons require rigorous training. However, the current gold standard, the tutor-guided and -evaluated apprenticeship model is time-consuming, costly, and poses risks to patients. There is a pressing need for additional training resources to enhance the efficiency and safety of the training process. In this work, we present a novel Augmented Reality (AR)-based simulator designed for THA that helps enable a new self-paced training and learning paradigm without the need for ***: The simulator reduces the need for instructors by integrating an AR guidance module and an automated performance evaluation module. Three types of AR guidance were developed: Overlay, Virtual Twin, and Sectional Views. A feasibility study was conducted with five resident surgeons and two senior surgeons to compare these guidance methods quantitatively and qualitatively. The automated performance evaluation module was assessed against manual performance evaluation using Bland-Altman analysis with limits of agreement (LoA) and Mann-Whitney U ***: The quantitative feasibility results indicate the efficacy of the developed AR guidance, characterized by mean transitional and rotational deviation errors below 3 mm and 3 degrees. Based on the qualitative results, we provide recommendations for efficient AR guidance designs. The Bland-Altman analysis results (0.22 ± 1.32 mm with LoA -2.37 to 2.81 mm for distance deviation, 0.94 ± 2.41 degrees with LoA -3.78 to 5.66 degrees for yaw deviation, -0.34 ± 1.30 degrees with LoA -2.90 to 2.22 degrees for pitch deviation) and p-values of Mann-Whitney U tests (0.64 for distance deviation, 0.12 for yaw deviation, 0.11 for pitch deviation) indicate no statistically significant differences between the automated and manual performance evaluation at a significance level of ***: This wor

关键词： Augmented reality

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An Optimizing Method for Performance and ResourceUtilization in Quantum Machine Learning Circuits

Research Square

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Research Square 2022年

作者： Salehi, Tahereh Zomorodi, Mariam Plawiak, Pawel Abbaszade, Mina Salari, Vahid Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Department of Physics Isfahan University of Technology Isfahan84156-83111 Iran Bilbao Spain Department of Physics and Astronomy Howard University WashingtonDC20059 United States

Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. It makes certain kinds of problems be solved easier compared to classical computers. This advantage of quantum computing can be used to implement many existing problems in different fields incredibly effectively. One important field that quantum computing has shown great results in machine learning. Until now, many different quantum algorithms have been presented to perform different machine learning approaches. In some special cases, the execution time of these quantum algorithms will be reduced exponentially compared to the classical ones. But at the same time, with increasing data volume and computation time, taking care of systems to prevent unwanted interactions with the environment can be a daunting task and since these algorithms work on machine learning problems, which usually includes big data, their implementation is very costly in terms of quantum resources. Here, in this paper, we have proposed an approach to reduce the cost of quantum circuits and to optimize quantum machine learning circuits in particular. To reduce the number of resources used, in this paper an approach including different optimization algorithms is considered. Our approach is used to optimize quantum machine learning algorithms for big data. In this case, the optimized circuits run quantum machine learning algorithms in less time than the original ones and by preserving the original functionality. Our approach improves the number of quantum gates by 10.7% and 14.9% in different circuits and the number of time steps is reduced by three and 15 units, respectively. This is the amount of reduction for one iteration of a given sub-circuit U in the main circuit. For cases where this sub-circuit is repeated more times in the main circuit, the optimization rate is increased. Therefore, by applying the proposed method to circuits with big data, both cost and performance are

关键词： Big data

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Strong magnon-magnon coupling in an ultralow damping all-magnetic-insulator heterostructure

arXiv

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arXiv 2023年

作者： Liu, Jiacheng Xiong, Yuzan Liang, Jingming Wu, Xuezhao Liu, Chen Cheung, Shun Kong Ren, Zheyu Liu, Ruizi Christy, Andrew Chen, Zehan Nugraha, Ferris Prima Zhang, Xi-Xiang Leung, Chi Wah Zhang, Wei Shao, Qiming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Physics and Astronomy The University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Applied Physics The Hong Kong Polytechnic University Hong Kong Thuwal23955-6900 Saudi Arabia IAS Center for Quantum Technologies The Hong Kong University of Science and Technology Hong Kong Department of Physics The Hong Kong University of Science and Technology Hong Kong

Magnetic insulators such as yttrium iron garnets (YIGs) are of paramount importance for spin-wave or magnonic devices as their ultralow damping enables ultralow power dissipation that is free of Joule heating, exotic magnon quantum state, and coherent coupling to other wave excitations. Magnetic insulator heterostructures bestow superior structural and magnetic properties and house immense design space thanks to the strong and engineerable exchange interaction between individual layers. To fully unleash their potential, realizing low damping and strong exchange coupling simultaneously is critical, which often requires high quality interface. Here, we show that such a demand is realized in an all-insulator thulium iron garnet (TmIG)/YIG bilayer system. The ultralow dissipation rates in both YIG and TmIG, along with their significant spin-spin interaction at the interface, enable strong and coherent magnon-magnon coupling with a benchmarking cooperativity value larger than the conventional ferromagnetic metal-based heterostructures. The coupling strength can be tuned by varying the magnetic insulator layer thickness and magnon modes, which is consistent with analytical calculations and micromagnetic simulations. Our results demonstrate TmIG/YIG as a novel platform for investigating hybrid magnonic phenomena and open opportunities in magnon devices comprising all-insulator heterostructures. © 2023, CC BY.

关键词： Damping

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PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems

arXiv

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arXiv 2023年

作者： Mao, Shunyuan Dong, Ruobing Lu, Lu Yi, Kwang Moo Wang, Sifan Perdikaris, Paris Department of Physics and Astronomy University of Victoria VictoriaBCV8P 5C2 Canada Department of Chemical and Biomolecular Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Computer Science University of British Columbia VancouverBCV6T 1Z4 Canada Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States

We develop a tool, which we name Protoplanetary Disk Operator Network (PPDONet), that can predict the solution of disk-planet interactions in protoplanetary disks in real-time. We base our tool on Deep Operator Networks (DeepONets), a class of neural networks capable of learning non-linear operators to represent deterministic and stochastic differential equations. With PPDONet we map three scalar parameters in a disk-planet system - the Shakura & Sunyaev viscosity α, the disk aspect ratio h0, and the planet-star mass ratio q - to steady-state solutions of the disk surface density, radial velocity, and azimuthal velocity. We demonstrate the accuracy of the PPDONet solutions using a comprehensive set of tests. Our tool is able to predict the outcome of disk-planet interaction for one system in less than a second on a laptop. A public implementation of PPDONet is available at https://***/smao-astro/PPDONet. Copyright © 2023, The Authors. All rights reserved.

关键词： Open source software

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Lattice Dynamics and Ab Initio Molecular Dynamics Study of Sio2-Tio2 and Sio2-Al2o3 Binary Slag Systems: Structural Physical and Electronic Properties Calculation by Density Functional Theory

SSRN

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SSRN 2023年

作者： Aamir, Muhammad Sajid, Muhammad Zubair, Muhammad Muhammad, Nazeer Souayeh, Basma Alyemeni, Abdullrahman Abdullah Mir, Waqas Alam Department of Basic Sciences Preparatory Year Deanship King Faisal University Al-Ahsa 31982 Saudi Arabia College of Materials Science and Engineering Chongqing University Chongqing400044 China Department of Mechanical and Nuclear Engineering University of Sharjah Sharjah27272 United Arab Emirates Department of Information Technology and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur22620 Pakistan Department of Physics College of Science King Faisal University Al-Ahsa 31982 Saudi Arabia College of Engineering King Faisal University Al-Ahsa 31982 Saudi Arabia

In current studies tmean square displacement, diffusion coefficient, radial distribution function, viscosity, electron density distribution and bond population of titanium-silicate and aluminum-silicate basedBFS systems. ab initio molecular dynamics (AIMD) and ab initio lattice dynamics (AILD) simulation techniques were used to calculate these parameters of SiO2-TiO2 and SiO2-Al2O3 Binary Slag *** effect of temperature variation on DCo and viscosity of molten slag was explore in the range from 1300 K to 2100 K for SiO2-TiO2, and SiO2-Al2O3 systems. The simulated result shows that the diffusion coefficient for ST system increases and it show decrease in SA system with the raise in temperature. Atomic and bond population analysis shows that the bond strength order of Si-O, Ti-O and Al-O is Si-O>Ti-O>Al-O. According to DOS analysis the O-atoms contribute more around Fermi energy level as compared to Si and Ti atoms. The EDD show that more electrons are accumulated around O-atoms. © 2023, The Authors. All rights reserved.

关键词： Diffusion

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A CNN-based Stock Price Trend Prediction with Futures and Historical Price 1

A CNN-based Stock Price Trend Prediction with Futures and Hi...

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1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020

作者： Wu, Jimmy Ming-Tai Li, Zhongcui Srivastava, Gautam Frnda, Jaroslav Diaz, Vicente Garcia Lin, Jerry Chun-Wei Shandong University of Science and Technology School of Computer Science and Engineering China Brandon University Department of Mathematics and Computer Science Brandon Canada University of Zilina Department of Quantitative Methods and Economic Informatics Zilina Slovakia University of Oviedo School of Computer Engineering Oviedo Spain Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9781665404839

In this paper, focusing on the task of feature extraction using financial time series as well as trend prediction for prices, a new stock sequence array convolutional neural network model is presented. This model shows promising results in improving the accuracy in stock trading forecasts. The implemented model collects data from historical sources and futures (leading indicators) of stocks. The model then uses arrays as the input map for a Convolutional Neural Network (CNN) framework. Through in-depth experimental results using the Taiwanese stock market, we are able to show that the designed approach achieves strong results. Furthermore, where compared with state-of-the-art similar models, our results show promising performance among all compared approaches. © 2020 IEEE.

关键词： Forecasting

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Distribution based image classification and its application in person re-identification 12

Distribution based image classification and its application ...

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12th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2021

作者： Ding, Guangtai Chen, Fuhua Zhang, Xuemao School of Computer Engineering and Science Shanghai University Shanghai 200444 China Department of Physical Sciences and Math West Liberty University West Liberty WV26074 United States Department of Mathematics East Stroudsburg University East Stroudsburg PA18301 United States

ISBN: (纸本)9781950492497

Image classification is widely used in many fields. Traditional metric learning based classification methods always maximize inter-class distances and minimize intra-class distances based on features calculated from each individual. Different from traditional methods, this paper takes each class as a distribution and try to maximize the distances among different distributions using information geometry. In order to minimize the distance among individuals inside a class, this paper assume that each class follows a joint Gaussian distribution and take an exploratory study on the relation between intra-class distance and the determinant of the covariance matrix of the distribution. It is found that under some assumptions, the average intra-class distance among the same class is proportional to the standard deviation or product of standard deviations of each feature. We therefore use the determinant of the covariance matrix to substitute the intra-class distance in the metric learning. The proposed method therefore saves a lot of computational cost. The method is then applied to person re-identification. To our surprise, the proposed method is very competitive than many state-of-the-art methods while saving the computational cost in the learning process. Experimental results demonstrate the effectiveness of the proposed method. © IMCIC 2021 - 12th International Multi-Conference on Complexity, Informatics and Cybernetics, Proceedings. All rights reserved.

关键词： Gaussian distribution

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Nanomaterial-based biosorbents:Adsorbent for efficient removal of selected organic pollutants from industrial wastewater

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Emerging Contaminants 2022年第1期8卷 46-58页

作者： Hussein.K.Okoro Sadanand Pandey Clement O.Ogunkunle Catherine J.Ngila C.Zvinowanda Ismaila Jimoh Isiaka A.Lawal Muyiwa M.Orosun Adewale George Adeniyi Analytical-Environmental Research Group Department of Industrial ChemistryFaculty of Physical SciencesP.M.B.1515University of IlorinPostal Code 240003IlorinNigeria Department of Chemistry College of Natural ScienceYeungnam University280 Daehak-RoGyeongsanGyeongbuk38541Republic of Korea Environmental Botany Unit Department of Plant BiologyUniversity of IlorinIlorinNigeria Analytical-Environmental and Membrane Nanotechnology Research Group Department of Chemical SciencesFaculty of ScienceUniversity of JohannesburgP.O.Box 17011Doornfontein2028JohannesburgSouth Africa Psychometrics Department JAMBNigeria Chemistry Department Faculty of Applied and Computer ScienceVaal University of Technology1900VanderbijlparkSouth Africa Health and Environmental Physics Research Group Physics DepartmentUniversity of IlorinIlorinNigeria The African Academy of Sciences P.O.Box 24916-00502 NairobiKenya Physical Address:8 Miotoni LaneKarenNairobiKenya Department of Chemical Engineering Faculty of EngineeringUniversity of IlorinPMB1515IlorinNigeria

The contamination of the environment by organic pollutants is a major risk factor,particularly for developing *** organic pollutants(SOPs)like the phenolic compounds,polyaromatic hydrocarbons(PAHs),pesticides,and herbicides pose serious environmental and health issues owing to their toxic characteristics and poor *** from their potential mutagenicity,carcinogenicity,tetragenicity and high body accumulation,these pollutants have become an increase concern *** is a promising alternative strategy for removing organic pollutants during water purification *** have several advantages such as simplicity of operation,good sorption capacity,high recoverability and *** a result,the focus and novelty of this review is on recent trends in the use of biosorbents,with a particular emphasis on the removal of SOPs from *** also cover use of bacteria biosorbents,fungal,algae and chitosan/chitin *** from that,we have also reviewed various classes of SOPs,their levels in the environment,classification and available characteristics techniques suitable for the adsorption experiments of these nanocomposites *** addition,we have provided comprehensive explanations and conclusions on possible future application of biosorbents and the mechanism of adsorption of these materials for removal of these SOPs from wastewater during water purification processes.

关键词： Biosorbents Adsorption Organic pollutants Biosorption Wastewater

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Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study 7

Automated Detection of Brain Abnormality using Deep-Learning...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Nam, Yunyoung Rauf, Hafiz Tayyab Rajinikanth, Venkatesan Lawal, Isah A. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Soonchunhyang University Department of Computer Science and Engineering Asan-si Korea Republic of Staffordshire University Centre for Smart Systems Ai and Cybersecurity Stoke-on-Trent United Kingdom St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India

ISBN: (纸本)9781665441261

Brain is the vital organ in human physiology;which is conscientious for sensory signal handling and judgment making. The irregularity in brain severely influence entire decision making procedure and the unrecognized and untreated defect will lead to various harsh conditions. This research aims to implement pre-trained Deep-Learning-Scheme (DLS) to classify the brain MRI slices using a multi-class classifier. In this research, the brain MRI slices with classes;normal, stroke, Low-Grade-Glioma (LGG) and High-Grade-Glioma (HGG) are considered for the experimental study. In this work every test picture is resized into 224x224x3 pixels and these imagery are then considered to validate the performance of DLS, such as VGG16, VGG19 and ResNet50 using different classifiers. The attained classification accuracy of every DLS with classifiers, SoftMax, SVM-RBF and SVM-Cubic are presented and discussed. © 2021 IEEE.

关键词： Magnetic resonance imaging

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Sludge Age Prediction Using Machine Learning (Deep Neural Network vs Deep Bi-LSTM Model)

Sludge Age Prediction Using Machine Learning (Deep Neural Ne...

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International Workshop on Research and Education in Mechatronics (REM)

作者： Djeddou Messaoud Hellal Aouatef Loukam Imed Al Dallal Jehad Bachiri Aida A. Hameed Ibrahim Research Laboratory in Subterranean and Surface Hydraulics (LAHE) Faculty of Sciences and Technology Mohamed Khider University of Biskra Biskra Algeria Hydraulic Department Faculty of sciences and applied science Mohamed Larbi Ben M'Hidi University of Oum el-Bouaghi Algeria Research Laboratory in Subterranean and Surface Hydraulics (LARHYSS) Faculty of Sciences and Technology Mohamed Khider University of Biskra Biskra Algeria Civil Engineering Department Faculty of sciences and Technology Mohamed University of Souk Ahras Algeria Department of Computer Science Gulf University for Science and Technology Kuwait Department of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology (NTNU) Ålesund Norway

ISBN: (数字)9798331505974

ISBN: (纸本)9798331505981

The Sludge Age (SAGE) is a significant operational parameter in activated sludge processes. However, precisely estimating it can be difficult due to non-linear data and changeable operating conditions. The objective of this project is to create a bidirectional long short-term memory (Bi-LSTM) network to forecast sludge age (SAGE) using intricate time-series data obtained from a Wastewater Treatment Plant (WWTP). The Bi-LSTM architecture is recognized for efficiently handling time series and non-uniform data. The datasets used for training and evaluating the Bi-LSTM model was gathered from the Nine Springs Wastewater Treatment Plant located near Madison, Wisconsin, United States. Following the utilization of historical data for training, the suggested Bi-LSTM model outperform the DNN model and successfully forecasted SAGE, offering potential advantages to WWTP managers in enhancing operational performance, system management, and process stability.

关键词： Training Accuracy Computational modeling Time series analysis computer architecture Predictive models Data models Wastewater treatment Long short term memory Testing

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