Hist-8000 sequence similarity scores are closer to the ground truth than the corresponding Sum-of-learnt-Trigrams scores. (NW, Hist-8000) and (NW, Hist-SDM12) are both higher than... by Frixos Papadopoulos (22001664)

Example of use of the slightly off-axis strategies. by Raul Castañeda (13932971)

Example of the fully-compensated phase reconstruction package for off-axis DHM holograms recorded in telecentric configuration. by Raul Castañeda (13932971)

Table_1_Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals.XLSX by Nadia Youssef (11586382)

“…Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. …”

Table_1_Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals.XLSX by Nadia Youssef (11586382)

“…Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. …”

Comparison of edge detector and CHT in detecting droplets. by Manibarathi Vaithiyanathan (6655274)

Glioblastoma multiforme (GBM) pathway modules identified by the netboxr package from cancer genomics alteration data without the use of pre-defined gene sets. by Eric Minwei Liu (9592176)

Supporting data for "Personalized Electromyography Based Control Algorithm Development and Training System for Hand Prosthesis" for HKU Libraries by Xiaojun Wang (9067289)

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Graphical summary and pseudo code of our gait signatures framework and algorithm. by Taniel S. Winner (17296029)

Data_Sheet_1_Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals.docx by Nadia Youssef (11586382)

“…Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. …”

Data_Sheet_1_Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals.docx by Nadia Youssef (11586382)

“…Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. …”

Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions by Anne Richelle (6589178)

“…Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. …”

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

Modeling Molecularly Imprinted Nanoparticles with LNKD: A Resource Efficient Algorithm for Polymer Cross-Linking by Emma Stevens (22257358)

“…Here, we propose LNKD (Linking Nodes in KD-trees), a resource-efficient algorithm for predicting pairs of reactive atoms in pre-cross-linked 3D structures of monomers that applies not only to the modeling of MIPs, but also chemical cross-linking in other materials. …”

Applied workflow to generate word2vec embeddings. by Halima Alachram (11570633)

Subjects:

Python code, input data, and outputs of Fourier analysis and Dynamic Time Warping from McGlasson et al. by Riley McGlasson (13876874)

“…</p> <h4><strong>Python Function Files:</strong></h4> <p>radarfuncs.py: Holds three functions used for FFT and DTW analysis.…”

Real time spike sorting with latencies in the range of synaptic transmission. by Tjitse van der Molen (20391106)

ClaritySpectra: Raman spectra analysis tool by Aaron Celestian (9395696)

“…</li></ul><h3>PEAK FITTING </h3><ul><li>Automated background subtraction using asymmetric least squares fitting</li><li>A new suggested background feature that lets you preview the background that you like best</li><li>Interactive background fitting lets you further tune the background to perfection</li><li>Four choice of peaks: Gaussian, Lorentzian, Pseudo-Voigt, and the new Asymmetric Voigt functions</li><li>Overlapping view of how well the peaks fit with quality metrics</li><li>No need to define regions, the algorithm is smart enough to what a peak looks like.…”

ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)