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)

Membership function of output for tank 1. by Tamiru Demelash Kassie (20678515)

Membership function of output for tank 2. by Tamiru Demelash Kassie (20678515)

Table_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Presentation_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Table_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Image_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Presentation_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Presentation_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Table_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Characterization of the TD² dataset. by Benjamin Gallois (10131683)

Optimization of tracking parameters based on <i>P</i>_<i>swap</i>. by Benjamin Gallois (10131683)

Effect of confinment on <i>p</i>_<i>inc</i>. by Benjamin Gallois (10131683)

Automatic tracking parameters. by Benjamin Gallois (10131683)

DataSheet1_Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images.PDF by Niccolò Marini (11247936)

“…<p>Algorithms proposed in computational pathology can allow to automatically analyze digitized tissue samples of histopathological images to help diagnosing diseases. …”

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)

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses by Dominykas Lukauskis (14143149)

“…OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. …”

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses by Dominykas Lukauskis (14143149)

“…OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. …”

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses by Dominykas Lukauskis (14143149)

“…OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. …”