The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

Optimization Performance Comparison of Metaheuristic Algorithms. by Xie Qiu (13243770)

Baseline characteristics of recipients’ comparison before and after data match by the unsupervised machine learning algorithm. by Mahmoudreza Moein (22127153)

Benchmark functions. by Qinghe Guo (21519322)

Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

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FFD control body frame diagram. by Yesong Wang (331723)

“…We propose two distinct spline function parameterization techniques: a global mapping model for Bezier surfaces and a local mapping model for Free-Form Deformation (FFD) control bodies. …”

Greedy optimization algorithm. by Alexis Bénichou (19832361)

Training accuracy/loss as a function of epochs for different datasets. by Hiam Alquran (17636906)

Supplementary Material for: A radiomics-based analysis of functional dopaminergic scintigraphic imaging for the diagnosis of dementia with Lewy bodies by figshare admin karger (2628495)

“…Introduction Radiomics features, a technique based on quantitative image analysis, can be used to capture tissue and lesion characteristics, such as heterogeneity and shape. Using functional dopaminergic scintigraphy, we aim to study the value of radiomics features in predicting the diagnosis of dementia with Lewy bodies (DLB). …”

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

Kinematics timeries and features of cluster 1 before and one year after surgery. by Xavier Gasparutto (8374578)

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Kinematics timeries and features of cluster 3 before and one year after surgery. by Xavier Gasparutto (8374578)

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Spatiotemporal parameters for patients clusters (before and one year after surgery) and control group. by Xavier Gasparutto (8374578)

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Characteristics and patients reported outcomes for patient clusters before and one year after surgery compared to the control group. by Xavier Gasparutto (8374578)

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Implant characteristics of the whole cohort, datasets and clusters. by Xavier Gasparutto (8374578)

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Kinematics timeseries and features for patients clusters, with between clusters comparison. by Xavier Gasparutto (8374578)

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Kinematic features for all patients before (PRE) and one year after surgery (P1Y) as well as all control group values. by Xavier Gasparutto (8374578)

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Kinematics timeries and features of cluster 2 before and one year after surgery. by Xavier Gasparutto (8374578)

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Clinical features and patient reported outcome measures of patients (median [IQR] or n patient & percentage), clusters and control group. by Xavier Gasparutto (8374578)

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Data Sheet 3_Continuous wavelet based transfer function analysis of cerebral autoregulation dynamics for neuromonitoring using near-infrared spectroscopy.pdf by Marcus Thudium (6441863)

“…The algorithm was validated in simulated signals and data of five healthy male subjects undergoing a protocol to produce large changes in hemodynamics by using lower body positive pressure (LBNP) with mild hypoxia and lower body negative pressure (LBNP). …”