Efficient Topology-Optimized Couplers for On-Chip Single-Photon Sources by Omer Yesilyurt (11513040)

“…First, a library of designs for the different positions of the hBN flake containing an SPS with respect to a Si₃N₄ waveguide is generated, demonstrating an average coupling efficiency of 78%. …”

Overview of analysis scenarios. by Stephen R. Piccolo (8367780)

Summary of datasets used. by Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression predictors. by Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using clinical predictors. by Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression and clinical predictors. by Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing feature selection. by Stephen R. Piccolo (8367780)

Relative predictive performance when training on gene-expression predictors alone vs. using clinical predictors alone or gene-expression predictors in combination with clinical pre... by Stephen R. Piccolo (8367780)

Comparison based on hard instances from [79]. by Xiang Tian (4369285)

Hierarchical Optimization on large models. by Nisha Ann Viswan (20371285)

ROC curves for the test set of four models. by Meng Cao (105914)

“…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …”

Process of multi angle facial feature point detection algorithm (The character in the Figure is the first author, with her own consent). by Shuangyan Li (502833)

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML by Ayush Garg (21090944)

“…We have employed random forest (RF) and support vector machine (SVM) as representatives of ML classifier and AutoGluon-Tabular (version 0.6.1) and H2O AutoML (version 3.40.0.4) as representatives of AutoML tools. The important findings of our studies are as follows: (i) there is no effect of threshold optimization on ranking metrics such as AUC and AUPR, but AUC and AUPR get affected by class-weighting and SMOTTomek; (ii) for ML methods RF and SVM, significant percentage improvement up to 375, 33.33, and 450 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy, which are suitable for performance evaluation of imbalanced data sets; (iii) for AutoML libraries AutoGluon-Tabular and H2O AutoML, significant percentage improvement up to 383.33, 37.25, and 533.33 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy; (iv) the general pattern of percentage improvement in balanced accuracy is that the percentage improvement increases when the class ratio is systematically decreased from 0.5 to 0.1; in the case of F1 score and MCC, maximum improvement is achieved at the class ratio of 0.3; (v) for both ML and AutoML with balancing, it is observed that any individual class-balancing technique does not outperform all other methods on a significantly higher number of data sets based on F1 score; (vi) the three external balancing techniques combined outperformed the internal balancing methods of the ML and AutoML; (vii) AutoML tools perform as good as the ML models and in some cases perform even better for handling imbalanced classification when applied with imbalance handling techniques. …”

Presentation_1_Calibrating Seed-Based Heuristics to Map Short Reads With Sesame.zip by Guillaume J. Filion (9027317)

Closed search (±5ppm) comparison against Crux and MSFragger using real-world data from PRIDE repository PXD000612 (top), PXD009861 (middle), and PXD001468 (bottom). by Muhammad Usman Tariq (11022141)

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Training parameters for SpeCollate. by Muhammad Usman Tariq (11022141)

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Peptides. by Muhammad Usman Tariq (11022141)

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Fig 9 - by Muhammad Usman Tariq (11022141)

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UMAP. by Muhammad Usman Tariq (11022141)

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Process of generating MS/MS spectra from a protein mixture using mass-spectrometry analysis. by Muhammad Usman Tariq (11022141)

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