Local topological scales of hierarchical community organization in structural and functional brain graphs. by Arian Ashourvan (6685232)

Multi-scale detection of hierarchical community architecture in structural and functional brain networks by Arian Ashourvan (6685232)

“…<div><p>Community detection algorithms have been widely used to study the organization of complex networks like the brain. …”

Network propagation improves the correlation between mRNA and protein levels of ageing tissues (brain and liver) and PCa samples. by Konstantina Charmpi (9901053)

Application of the algorithm to unseen data. Presynaptic neurons labelled by rabies viral injection into layer 2/3 primary visual cortex in Penk-Cre mice. by Adam L. Tyson (10892822)

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

“…<p>We plot the mse per pattern obtained by the AMP algorithm, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e072" target="_blank">Eq (32)</a>, as a function of the effective noise Δ (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e032" target="_blank">9</a>), for random (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e044" target="_blank">15</a>) and informed (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e045" target="_blank">16</a>) initialisations. …”

Similarity between the allegiance matrices of the multiplex SC-FC graph, the FC graph alone, and the SC graph alone, as a function of the topological scale for the group. by Arian Ashourvan (6685232)

PCA visualization of LSTM hidden layer activity of an example 7-hidden-unit network (#7). by Quan Wan (1329666)

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BCI architecture. by Stefan K. Ehrlich (6475490)

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Functional PCA With Covariate-Dependent Mean and Covariance Structure by Fei Ding (134577)

“…<p>Incorporating covariates into functional principal component analysis (PCA) can substantially improve the representation efficiency of the principal components and predictive performance. …”

Loss functions between PCA-DNN model and PCA-LSTM model. by Cihun-Siyong Alex Gong (11874066)

Exploration of Bioinspired Algorithms in Multithreshold Image Segmentation for Medical Images by Fernando Ramírez sarmiento (18359679)

“…<p dir="ltr">The brain is essential for human functioning, but it is vulnerable to multiple pathologies, including strokes, brain tumors, and other disorders that compromise its structure and function. …”

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

Total number of cells in each region, per brain, projecting to layer 2/3 neurons in primary visual cortex. by Adam L. Tyson (10892822)

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Illustration of the cell detection process. by Adam L. Tyson (10892822)

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Performance of PCA and Joint Autoencoder (JAE) denoising algorithms. by Ege Altan (11768156)

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

Testing function diagram of QIMPN-PCA-DC. by Jun Zhao (59250)

Automatic cropping algorithm. by Thomas W Georgi (12427189)

Data_Sheet_1_Resting-State Functional Connectivity Estimated With Hierarchical Bayesian Diffuse Optical Tomography.docx by Takatsugu Aihara (4433524)

“…We recently proposed a hierarchical Bayesian (HB) DOT algorithm and verified its performance in terms of task-related brain responses. …”

The loss function of network structures, LPC-DNN, LPC-PCA-DNN, LPC-LSTM, LPC-PCA-LSTM, MFCC-DNN, MFCC-PCA-DNN, MFCC-LSTM and MFCC-PCA-LSTM, as the epoch increases. by Cihun-Siyong Alex Gong (11874066)