On Adversarial Robust Generalization of DNNs for Remote Sensing Image Classification by Wei Xue, Yonghao Wang, Shaoquan Zhang, Xiao Zheng, Ping Zhong

“…Specifically speaking, we impose a Euclidean regularization constraint on the gradient of the adversarial loss function in order to achieve a narrowing of the robust generalization gap by enhancing the gradient alignment between clean and adversarial examples. …”
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Modelling of wax deposition for waxy crude oil by ANSYS fluent by Salman Hadi Dahwal, Zarana Patel, Ashish Nagar

“…A Computational Fluid Dynamics (CFD) model was created to predict the wax gelation as a function of time, temperature and pipeline length for Indian crude oil using field data. …”
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Machine Learning Based Early Diagnosis of ADHD with SHAP Value Interpretation: A Retrospective Observational Study by Zhang X, Xiao X, Luo Y, Xiao W, Cao Y, Chang Y, Wu D, Xu H, Zhao J, Deng X, Jiang Y, Xie R, Liu Y

“…Clinical data, including complete blood count, liver and kidney function tests, blood glucose levels, serum electrolyte tests, and serum 25-dihydroxyvitamin D3 levels, were collected. …”
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Research on Abnormal Ship Brightness Temperature Detection Based on Infrared Image Edge-Enhanced Segmentation Network by Xiaobin Hong, Guanqiao Chen, Yuanming Chen, Ruimou Cai

“…Finally, a specialized loss function with edge weights supervises the fusion features. …”
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Insight into the avidity–affinity relationship of the bivalent, pH-dependent interaction between IgG and FcRn by Johannes Reusch, Jan Terje Andersen, Ulrich Rant, Tilman Schlothauer

“…Thus, the methodology provides reliable kinetic rate parameters for both binding modes and their direct relationship as a function of pH. Also, it deciphers the potential effect of the variable Fab arms on FcRn binding, in which SPR has limitations. …”
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Assessment of geogenic radon potential with activation of advective soil air flow by I. V. Yarmoshenko, G. P. Malinovsky, I. A. Yurkov, V. S. Izgagin

“…The results of measurements at 12 points on the experimental site yielded a number of values, including advective radon flux density as a function of pressure difference between the accumulation chamber and the atmosphere (in the range 4–20 Pa), radon concentration in soil air, and resistance to air flow in the soil-chamber system. …”
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Model-Based Electroencephalogram Instantaneous Frequency Tracking: Application in Automated Sleep–Wake Stage Classification by Masoud Nateghi, Mahdi Rahbar Alam, Hossein Amiri, Samaneh Nasiri, Reza Sameni

“…Evaluation on the extended Sleep-EDF dataset (Sleep-EDFx), with 153 EEG recordings from 78 subjects, demonstrated compelling results with classifiers including Logistic Regression, Support Vector Machines, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). …”
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A method for isolating small extracellular vesicles from parenchymal tissues by Yanan Sun, Junqing An, Jie Yuan, Chaoshan Han, Gangjian Qin

“…Our method may facilitate the isolation of sEV from solid tissues with better quality for further molecular characterization and functional studies.…”
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Bond Behavior and Interaction Mechanism Between Metal Ce-Fe by Kai ZHANG, Chang-shui WANG

“…Subsequently, under the influence of concentration gradient and thermal gradient diffusion, diffusion channels were formed on the surface of the substrate, and Ce atoms gradually diffused from the surface into the interior of the substrate. …”
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Peculiarities of cardiovascular remodeling in hypertensive patients at high additional risk with concomitant subclinical hypothyroidism by V. V. Syvolap, Ye. V. Novikov

“…Changes of structural, geometrical and functional indexes of the heart in patients with AH and SH were significant increase in the left atrial size by 3.5 % (P < 0.05), end diastolic size by 3.0 % (P < 0.05) and end systolic size of the left ventricle by 2.3 % (P < 0.05), myocardial mass index by 11.1 % (P < 0.05), index Е/Е´ by 13.0 % (P < 0.05), gradient of transpulmonary flow by 6.6 % (P < 0.05) in comparison to patients with AH without SH. …”
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Dynamic interpretation of hedgehog signaling in the Drosophila wing disc. by Marcos Nahmad, Angelike Stathopoulos

“…Although the prevailing view is that Hh functions in the Drosophila wing disc as a classical morphogen, a direct correspondence between the borders of these patterns and Hh concentration thresholds has not been demonstrated. …”
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Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study by Heonyi Lee, Yi-Jun Kim, Jin-Hong Kim, Soo-Kyung Kim, Tae-Dong Jeong

“…We evaluated the performance of 4 ML models: gradient boosting machine, random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGB). …”
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Deep Reinforcement Learning-Based Enhancement of Robotic Arm Target-Reaching Performance by Ldet Honelign, Yoseph Abebe, Abera Tullu, Sunghun Jung

“…This work investigates the implementation of the Deep Deterministic Policy Gradient (DDPG) algorithm to enhance the target-reaching capability of the seven degree-of-freedom (7-DoF) Franka Pandarobotic arm. …”
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Peristaltic Transport of Non-Newtonian Fluid under Effects of Magnetic Force and Heat Transfer in a Symmetric Channel through a Porous Medium by Amaal Mohi Nassief, Ahmed M. Ahmed M.

“…The Adomain decomposition technique is utilized for explaining expressions for axial velocity, stream function, temperature, Nusselt number, pressure gradient, magnetic pressure, and magnetic force. …”
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Nonlinear Elasto-Mammography for Characterization of Breast Tissue Properties by Z. G. Wang, Y. Liu, G. Wang, L. Z. Sun

“…An adjoint method is derived to calculate the gradient of the objective function. Simulations are conducted on a 3D breast phantom consisting of the fatty tissue, glandular tissue, and cancerous tumor, whose mechanical responses are hyperelastic in nature. …”
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First principles study of dielectric properties of ferroelectric perovskite oxides with extended Hubbard interactions by Min Chul Choi, Wooil Yang, Young-Woo Son, Se Young Park

“…We compute band structures, ferroelectric distortions, polarization, Born effective charges, and switching barriers, compared with local density approximation, generalized gradient approximation (GGA), meta-GGA, and hybrid (HSE06) functionals. …”
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Accelerated unconstrained latent factorization of tensor model for Web service QoS estimation by Mingwei LIN, Wenqiang LI, Xiuqin XU, Jian LIU

“…Aiming at the problem that the Web service quality of service (QoS) estimation methods based on the non-negative latent factorization of tensor model (NLFT) depend heavily on non-negative initial random data and specially designed non-negative training schemes, which lead to low compatibility and scalability, an accelerated unconstrained latent factorization of tensor (AULFT) model was proposed.The proposed model consisted of three main parts.The non-negative constraints from decision parameters were transferred to output latent factors and they were connected through the single-element-dependent mapping function.A momentum-incorporated stochastic gradient descent (MSGD) algorithm was used to effectively improve the convergence rate and estimation accuracy of the proposed AULFT model.The detailed algorithm and result analysis of the proposed AULFT model were presented.The empirical study on two dynamic QoS datasets in real industrial applications demonstrates that the proposed AULFT model has higher computational efficiency and estimation accuracy than the state-of-the-art QoS estimation models.…”
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Probing the local thermal expansion coefficient of single liquid Sn nanoparticles using EELS in STEM by A. Kryshtal, O. Khshanovska

“…The surface and volume plasmon energies of the liquid Sn nanoparticle were measured as a function of temperature using spatially resolved valence electron energy loss spectroscopy in a scanning transmission electron microscope. …”
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Autobiographical Memory in Normal Ageing and Dementia by Harvey J. Sagar, Edith V. Sullivan, Suzanne Corkin

“…Patients and control subjects drew most memories from the recent 20 years: episode age related to anterograde memory function but not subject age or dementia. Subjects also related a secondary peak of memories from early adulthood; episode age related to subject age and severity of dementia. …”
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Variational approach to photonic quantum circuits via the parameter shift rule by Francesco Hoch, Giovanni Rodari, Taira Giordani, Paul Perret, Nicolò Spagnolo, Gonzalo Carvacho, Ciro Pentangelo, Simone Piacentini, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame, Fabio Sciarrino

“…Among various techniques to train variational circuits, the parameter shift rule enables the exact calculation of cost function derivatives efficiently, facilitating gradient descent-based optimization. …”
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