Overview of the main features of the mentioned software tools. “” represents a feature that has not been possible to evaluate. by Giuseppe Agapito (104634)

Graphs of the regularization terms. by Tomokaze Shiratori (9635271)

“…To solve this problem efficiently, we design a DC algorithm in which the graphical lasso algorithm is repeatedly executed to solve convex optimization subproblems. …”

The moving process of five UAVs in the second scenario is presented from a top-view perspective of 2D figures and axonometric projection of 3D figures. by Quoc Bao Diep (18452207)

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach by Soundous Touati (20282599)

“…Following this, XGBoost regression algorithms are employed to interrogate the data set, enabling predictions of volume (achieving an optimal accuracy of 98.41%, with a mean absolute error (MAE) of 2.395 ų and a root-mean-square error (RMSE) of 4.416 ų), formation energy (an optimal accuracy of 97.36%, with an MAE of 0.075 eV/atom and an RMSE of 0.132 eV/atom), and band gap energy (an optimal accuracy of 87.00%, an MAE of 0.391 eV, and an RMSE of 0.574 eV). …”

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach by Soundous Touati (20282599)

“…Following this, XGBoost regression algorithms are employed to interrogate the data set, enabling predictions of volume (achieving an optimal accuracy of 98.41%, with a mean absolute error (MAE) of 2.395 ų and a root-mean-square error (RMSE) of 4.416 ų), formation energy (an optimal accuracy of 97.36%, with an MAE of 0.075 eV/atom and an RMSE of 0.132 eV/atom), and band gap energy (an optimal accuracy of 87.00%, an MAE of 0.391 eV, and an RMSE of 0.574 eV). …”

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach by Soundous Touati (20282599)

“…Following this, XGBoost regression algorithms are employed to interrogate the data set, enabling predictions of volume (achieving an optimal accuracy of 98.41%, with a mean absolute error (MAE) of 2.395 ų and a root-mean-square error (RMSE) of 4.416 ų), formation energy (an optimal accuracy of 97.36%, with an MAE of 0.075 eV/atom and an RMSE of 0.132 eV/atom), and band gap energy (an optimal accuracy of 87.00%, an MAE of 0.391 eV, and an RMSE of 0.574 eV). …”

This figure shows a non-exhaustive representation of the main models reported in our discussion, for illustrative purpose only. by Giuseppe Agapito (104634)

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

“…To address batch effects introduced during the merging process, the Empirical Bayes algorithm from the sva package (via the ComBat function) was applied. …”

Hub node genes in the PPI network identified with a filtering node degree = 2. by Ravinder Sharma (13154079)

Suppression of COL8A1 expression significantly reduces the proliferation and migration of STAD cells. by Xiangxin Wu (22320058)

The “Bubblepole” (BUPO) Method for Linear-Scaling Coulomb Matrix Construction with or without Density Fitting by Frank Neese (736441)

“…For any target object (shell pair or auxiliary shell), one might envision that the bubbles “carve” out what might be referred to as a “far-field surface”. Using the default settings determined in this work, we demonstrate that the algorithm reaches submicro-Eh and even nano-Eh accuracy in the total Coulomb energy for systems as large as 700 atoms and 7000 basis functions. …”

Comparison to STM on specific datasets. by Nicholas A. Rondoni (21573261)

Frequency response analysis of the commonly used indicator GCaMP6s, <i>k</i>_<i>f</i> = 0.0514, <i>k</i>_<i>r</i> = 7.6, <i>L</i> = 30. by Nicholas A. Rondoni (21573261)

Comparison of method’s standard deviations. Across all datasets MPC has a smaller standard deviation than Oasis, but larger than that of STM. by Nicholas A. Rondoni (21573261)

Comparison of Victor-Purpura distance by Nicholas A. Rondoni (21573261)

Pearson correlation coefficient for this specific 10 second subset is 0.61, though the whole 12 min recording scores a 0.50. by Nicholas A. Rondoni (21573261)

Comparison of Victor-Purpura distance by Nicholas A. Rondoni (21573261)

Visualization of approximations by Nicholas A. Rondoni (21573261)

Violin plot of correlation score by Nicholas A. Rondoni (21573261)

Neuron by neuron comparison of correlation coefficient for the state-of-the-art methods STM and Oasis. by Nicholas A. Rondoni (21573261)