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"en": "1. Raw Data in Spss:\r\n\r\nIn the Spss file, you can find the scores of 68 students (12 years old; Female = 34, Male = 34 ). Student's emotional awareness (EA) and Internalizing problems (IP) score measured at Time1 and one month later at Time 2. For measuring EA, the level of emotional awareness scale (LEAS-C) and for measuring IP, the Strength and Difficulty Questionnaire (SDQ) is used. The internalizing problems score obtained by summing up the emotional and peer problem scales of SDQ.\r\n\r\n2. R-script for the Cross-Lagged Panel Model (CLPM) and two-factor latent model using Lavaan package (Rosseel, 2012)"
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"en": "ObjectiveTotal hip arthroplasty (THA) affects pelvic posture and spinal alignment. These postural changes may further predispose patients to mechanical complications (MCs) after THA. The aim of this study was to conduct a systematic review to investigate whether any high-quality studies have assessed the association between sagittal spinal alignment and MCs after primary THA.MethodsInclusion criteria for studies were adult patients (age ≥18 years), primary THA, pre- and postoperative spinopelvic standing sagittal radiographs acquired preoperatively and at a minimum of 6-month follow-up, measurements of spinopelvic parameters, and reporting of possible MCs after THA. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.ResultsSix articles met the inclusion criteria. Although several studies confirmed the importance of spinal alignment when planning THA, these mainly investigated pelvic mobility as a risk factor for THA dislocation. Radiological follow-up imaging practices varied, and studies focused on different individual spinopelvic parameters.ConclusionBased on our study findings, no conclusions can be drawn regarding the association between sagittal spinal alignment and MCs after primary THA. Further research is needed to improve our knowledge of the connection between MCs after THA and sagittal spinal alignment."
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"en": "Embedding nonlinear dynamical systems into artificial neural networks is a powerful new formalism for machine learning. By parameterizing ordinary differential equations (ODEs) as neural network layers, these Neural ODEs are memory-efficient to train, process time-series naturally and incorporate knowledge of physical systems into deep learning models. However, the practical applications of Neural ODEs are limited due to long inference times, because the outputs of the embedded ODE layers are computed numerically with differential equation solvers that can be computationally demanding. Here we show that mathematical model order reduction methods can be used for compressing and accelerating Neural ODEs by accurately simulating the continuous nonlinear dynamics in low-dimensional subspaces. We implement our novel compression method by developing Neural ODEs that integrate the necessary subspace-projection and interpolation operations as layers of the neural network. We validate our approach by comparing it to neuron pruning and SVD-based weight truncation methods from the literature in image and time-series classification tasks. The methods are evaluated by acceleration versus accuracy when adjusting the level of compression. On this spectrum, we achieve a favourable balance over existing methods by using model order reduction when compressing a convolutional Neural ODE. In compressing a recurrent Neural ODE, SVD-based weight truncation yields good performance. Based on our results, our integration of model order reduction with Neural ODEs can facilitate efficient, dynamical system-driven deep learning in resource-constrained applications."
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