PyNoetic’s channel selection module, which supports various channel selection criteria including CSP, correlation, Mutual Information, and Chi-squared. by Gursimran Singh (575288)

Subjects:

Tunable parameters in stimuli generation module. ^<i>a</i>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></su... by Gursimran Singh (575288)

Subjects:

The results of ICA performed using PyNoetic. by Gursimran Singh (575288)

Subjects:

PyNoetic’s pre-processing module, which supports filtering and artifact removal, including ICA. by Gursimran Singh (575288)

Subjects:

Performance of various deep learning models from PyNoetic’s classification module on the Motor Imagery decoding task. by Gursimran Singh (575288)

Subjects:

Illustration of recording paradigm with PyNoetic’s Stimuli generation and recording module. by Gursimran Singh (575288)

Subjects:

A typical BCI system depicting the flow and processing of EEG data. by Gursimran Singh (575288)

Subjects:

Data_Sheet_1_Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.PDF by Minhua Guo (18392646)

Step by step workflow for PRLF model training and data scoring. by John Prindle (10774286)

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…”

Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways. by Alexis M. Thornton (11068403)

Results of the replicated simulations. by Susana Conde (5495675)

“…We propose a <i>K</i>-means-type algorithm in which each cluster is defined by a function-on-function regression model, which, inter alia, allows for multiple functional explanatory variables. …”

LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery by M RN (9866504)

Search-based testing (Genetic Algorithm) - Chapter 11 of the book "Software Testing Automation" by Saeed Parsa (13893726)

“…</p> <p><br></p> <p>3. Algorithm</p> <p>Below is the main body of the test data generator program:</p> <p>  </p> <p>the main body of a Python program to generate test data for Python functions.…”

Cooperativity and nonlinear amplification of synaptic input in cell type specific models. by Daniele Linaro (227790)

Who has ears, listen: Citizen Listening Program for disease prevention. by Ramiro García Pereira (2836310)

Firing patterns of a-thorny cells encode more information about the statistics of presynaptic inputs. by Daniele Linaro (227790)

Distinct dendritic properties of cell type specific models. by Daniele Linaro (227790)

Example of a typical workflow to construct a network using NeKo. by Marco Ruscone (11747264)

Integral-Direct and Parallel Implementation of the CCSD(T) Method: Algorithmic Developments and Large-Scale Applications by László Gyevi-Nagy (2631796)

“…By fully exploiting the permutational symmetry, the presented algorithm is highly operation count and memory-efficient. …”