Table_1_Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.DOCX by Shakiru A. Alaka (9302864)

“…We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment.…”

Data_Sheet_1_Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.PDF by Shakiru A. Alaka (9302864)

“…We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment.…”

The number of inactive nodes as a function of time steps in (a) a random network and (b) a scale-free network. by Waseem Al-Aqqad (14123917)

Variations of loss functions versus epoch number. by Xueliang Guo (4797057)

Scatter plots showing the relationship between inter-site |Δθ| in high-<i>q</i> (y-axis) and low-<i>q</i> (x-axis) states for representative frequencies (columns) and the five beha... by Melisa Menceloglu (7610213)

Distributions of |Δθ| for the stable low-|Δθ| (left columns), stable high-|Δθ| (middle columns), and shifting-|Δθ| (right columns) networks for different <i>q</i> ranges in 5-perce... by Melisa Menceloglu (7610213)

Variance of local fields versus mean of local fields for (a) , (b) . by Ali Talebi (7164203)

The number of inactive nodes as a function of time steps at higher values of <i>P</i> and <i>α</i> in (a) a random network and (b) a scale-free network. by Waseem Al-Aqqad (14123917)

Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures. by Kevin Sawade (16726527)

The Chemical Nature of Ti₄O₁₀^–: Vibrational Predissociation Spectroscopy Combined with Global Structure Optimization by Fabian Müller (1454203)

“…The gas-phase infrared spectrum of Ti₄O₁₀^– is studied in the spectral range from 400 cm^–1 to 1250 cm^–1 using cryogenic ion trap vibrational spectroscopy, in combination with density functional theory (DFT). …”

Low-Rank Covariance Function Estimation for Multidimensional Functional Data by Jiayi Wang (294312)

“…In this article, we propose a novel nonparametric covariance function estimation approach under the framework of reproducing kernel Hilbert spaces (RKHS) that can handle both sparse and dense functional data. …”

Summary: Dipole-spread-function engineering for simultaneously measuring the 3D orientations and 3D positions of fluorescent molecules by Tingting Wu (21440889)

“…However, spatial and angular information are mixed within the image of a fluorescent molecule–the microscope’s dipole-spread function (DSF). We demonstrate the pixOL algorithm to simultaneously optimize all pixels within a phase mask to produce an engineered Green’s tensor–the dipole extension of point-spread function engineering. …”

Flowchart of BWEMFO. by Jingjing Ma (419752)

“…The scalability of the algorithm is confirmed through benchmark functions. …”

Various MFOs from three mechanisms. by Jingjing Ma (419752)

“…The scalability of the algorithm is confirmed through benchmark functions. …”

The flowchart of MFO. by Jingjing Ma (419752)

“…The scalability of the algorithm is confirmed through benchmark functions. …”

Parameters. by Jingjing Ma (419752)

“…The scalability of the algorithm is confirmed through benchmark functions. …”

Reconstructing <i>sparse</i>, binary patterns using message passing algorithms and PCA. by Sebastian Goldt (14522594)

“…Lines depict the result of the state evolution, while crosses denote the performance of the AMP algorithm on an instance of the problem. While AMP performs the same starting from both initialisations for <i>ρ</i> = 0.1 and <i>ρ</i> = 0.3, there is a gap in performance for <i>ρ</i> = 0.05, which might hint at the existence of a hard phase (see main text). …”

Supporting Data for “All-temperature barocaloric effects at pressure-induced phase transitions” by Zhao Xueting (21796316)

“…The electronic band structures of the monoclinic I and rhombohedral phases of KPF6 were computed and displayed in Supplementary Fig. 9, showing the insulating states for both phases.…”

Data_Sheet_1_Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics.DOCX by Eric Maltbie (445150)

“…While the QPPs are slightly more likely to occur during specific SWC clusters, the SWC clustering does not vary during the 24s QPP sequences, the goal of this work is to improve both the practical and theoretical understanding of different resting-state dynamics methods, thereby enabling investigators to better conceptualize and implement these tools for characterizing functional brain networks.…”

Video_1_Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics.MP4 by Eric Maltbie (445150)

“…While the QPPs are slightly more likely to occur during specific SWC clusters, the SWC clustering does not vary during the 24s QPP sequences, the goal of this work is to improve both the practical and theoretical understanding of different resting-state dynamics methods, thereby enabling investigators to better conceptualize and implement these tools for characterizing functional brain networks.…”