Joint image reconstruction and segmentation of real-time cardiovascular magnetic resonance imaging in free-breathing using a model based on disentangled representation learning by Tobias Wech, Oliver Schad, Simon Sauer, Jonas Kleineisel, Nils Petri, Peter Nordbeck, Thorsten A. Bley, Bettina Baeßler, Bernhard Petritsch, Julius F. Heidenreich

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Accelerating machine learning at the edge with approximate computing on FPGAs by Luis Gerardo León-Vega, Eduardo Salazar-Villalobos, Jorge Castro-Godínez

“… Performing inference of complex machine learning (ML) algorithms at the edge is becoming important to unlink the system functionality from the cloud. …”
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Machine learning for discrimination of phase‐change chalcogenide glasses by Qundao Xu, Meng Xu, Siqi Tang, Shaojie Yuan, Ming Xu, Wei Zhang, Xian‐Bin Li, Zhongrui Wang, Xiangshui Miao, Chengliang Wang, Matthias Wuttig

“…This has led to a dilemma in materials design since an atomistic view of the arrangement in the amorphous state is the key to understanding and optimizing the functionality of these glasses. To tackle this challenge, we present a machine learning (ML) approach to separate electronic phase‐change materials (ePCMs) from other chalcogenides, based upon subtle differences in the short‐range order inside the glassy phase. …”
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Addressable and perceptible dynamic reprogram of ferromagnetic soft machines by Yuxuan Sun, Boxi Sun, Xiang Cui, Weihua Li, Yue Zhang, Li He, Shutong Nong, Zhengqing Zhu, Jiyang Wu, Dongxiao Li, Xingxiang Li, Shiwu Zhang, Xiangyang Li, Mujun Li

“…The machine body is composed of microbeads made from a low-melting-point alloy and NdFeB microparticles. …”
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The Evaluation Model of College English Diagnostic Exercises Based on Machine Learning by Qi Wang, Hao Wu

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Study of the work of modern logging machines in mountain conditions by Glushkov Sergey, Boyadzhiev Dmitryi, Chernykh Aleksander, Popikov Petr, Afonichev Dmitryi, Abramov Vitaliy, Polukarov Dmitryi, Savchenko Konstantin, Zhuzhukin Stanislav

“…This allows for a comprehensive assessment of the functioning of the Mounty 4000 machine simultaneously from economic and environmental points of view at the stage of technological design of logging operations.…”
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Analysis of variant interactions in families with autism points to genes involved in the development of the central nervous system. by Jacek Kaczyński, Marta Pasenkiewicz-Gierula

“…Analysis of the statistical interactions also points to genes whose biological functions are not yet known.…”
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Multi-point sensing organic light-emitting diode display based mobile cardiovascular monitor by Chul Kim, Kwangjin Lee, Jongin Kim, Dongwook Yang, Hyeonjun Lee, Gyeongub Moon, Yuna Kim, Dongrae Cho, Kwang Soo Bae, Gunhee Kim, Yongjo Kim, Changhee Lee

“…Therefore, we achieved: 1) multi-point concurrent photoplethysmography and high-resolution dynamic image sensing, and 2) user-interactive sensing within the large display area. …”
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Control and management in a complex biotechnical system of a dairy farm by V. V. Kirsanov, R. A. Baisheva

“…The research is aimed at profound study of the influence of the "machine" factor (M) in the "human-machineanimal" system ("H-M-A") with the detailed description of the functions performed by "M", taking into account the convey of "M" control and control functions from the subsystems "human-operator" (HO) and "animal" (A). …”
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Physics‐Guided Deep Learning for Modeling Single‐Point Wave Spectra Using Wind Inputs of Two Resolutions by Tianxiang Gao, Haoyu Jiang

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Performance Analysis of Diabetes Detection Using Machine Learning Classifiers by Hung Huynh, Liu Hui, Ngoc Han Nguyen, Ruixuan Qiao

“…Three types of machine learning classifiers are used: Tree-based, Function-based, and Rule-based. …”
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Fault Diagnosis of Axial Piston Pump Based on Extreme-Point Symmetric Mode Decomposition and Random Forests by Lei Yafei, Jiang Wanlu, Niu Hongjie, Shi Xiaodong, Yang Xukang

“…Aiming at fault diagnosis of axial piston pumps, a new fusion method based on the extreme-point symmetric mode decomposition method (ESMD) and random forests (RFs) was proposed. …”
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$$\alpha$$ -decay half-life predictions with support vector machine by Amir Jalili, Feng Pan, Jerry P. Draayer, Ai-Xi Chen, Zhongzhou Ren

“…Our analysis of 2232 nuclear data points demonstrates that the use of the radial basis function kernel yields predictive models with root mean square errors of 0.819 (for set1) and 0.352 (for set2), aligning with results obtained from comparable machine learning methodologies. …”
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Interpretable machine learning for predicting isolated basal septal hypertrophy. by Lei Gao, Boyan Tian, Qiqi Jia, Xingyu He, Guannan Zhao, Yueheng Wang

“…This is a common echocardiographic finding with a prevalence of approximately 7-20%, which may indicate early structural and functional remodeling of the left ventricle in certain pathologies. …”
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Recent advances in ultra-precision machining of lithium niobate crystals by Yebing TIAN, Chengwei WEI, Xiaomei SONG, Cheng QIAN

“…Given the fundamental challenges and technological implications, the ultra-precision machining of LiNbO3 crystals is expected to remain a focal point of research for the foreseeable future, warranting continued investigation and development in this field.…”
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Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – it Does Not Mean Total Automation, Human Is Still in the Centre: Part I – Manufacturing Singularity and a... by Goran D. PUTNIK, Vaibhav SHAH, Zlata PUTNIK, Luis FERREIRA

“…In many popular, as well scientific, discourses it is suggested that the "massive" use of Artificial Intelligence, including Machine Learning, and reaching the point of ‘singularity’ through so-called Artificial General Intelligence (AGI), and Artificial Super-Intelligence (ASI), will completely exclude humans from decision making, resulting in total dominance of machines over human race. …”
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A new method for reconstructing building model using machine learning by Shengjie Wu, Haibo Ye, Antao Li, Huawei Tu, Shenxin Xu, Dong Liang

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On Safety of Unary and Non-unary IFP-operators by Sergey Dudakov

“…In this paper, we investigate the safety of unary inflationary fixed point operators (IFPoperators). The safety is a computability in finitely many steps. …”
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Using Nearest-Neighbor Distributions to Quantify Machine Learning of Materials’ Microstructures by Jeffrey M. Rickman, Katayun Barmak, Matthew J. Patrick, Godfred Adomako Mensah

“…In particular, we assess the rate of microstructural learning in terms of the moments of the <i>k</i>-th nearest-neighbor pixel distributions and associated metrics, including a microstructural cross-entropy, that embody the spatial correlations among the pixels through a hierarchy of <i>n</i>-point correlation functions. From the moments of these distributions, we obtain so-called learning functions that highlight the rate at which the important topological features of a grain-boundary network appear. …”
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Utilising machine learning classification models for meteorological drought monitoring and analysis by Iqra Mumtaz, Rizwan Niaz, Zamama Sajid, Abdu Qaid Alameri, Zulfiqar Ali, Khaled A. Gepreel

“…Independent variables included average temperature, specific humidity, soil moisture, and dew point. To address model-specific challenges, ridge regression was applied to mitigate multicollinearity in Logistic Regression, while SVM incorporated the Radial Basis Function (RBF) kernel and isolation forest to manage non-linearity and outliers. …”
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