S1 Fig - by Aniket Ravan (3174171)

“…(h) The sum of the squared pixel values of the three difference images is used as the cost function during optimization. (i-h) The optimization is run until convergence for each frame in parallel to obtain an ensemble of larval poses.…”

Uncertainty and Novelty in Machine Learning by Derek Scott Prijatelj (20364288)

“…Through the computation of the indicator function, model identifiability and sample complexity are defined and their properties are described for different data-generating processes, ranging from deterministic to ergodic stationary stochastic processes. …”

BrainPepPass: all scripts by Ewerton de Oliveira (16033631)

“…</p> <p><br></p> <p>Obs: Among the compressed files, there is the sLE_functions.py script. This script is responsible for providing the necessary functions of the sLE algorithms to run BrainPepPass framework.…”

Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100) by peng xin (21382394)

“…Bias correction was conducted using a 25-year baseline (1990–2014), with adjustments made monthly to correct for seasonal biases. The corrected bias functions were then applied to adjust the years (2020–2100) of daily rainfall data using the "ibicus" package, an open-source Python tool for bias adjustment and climate model evaluation. …”

CSPP instance by peixiang wang (19499344)

“…</b></p><p dir="ltr">Its primary function is to create structured datasets that simulate container terminal operations, which can then be used for developing, testing, and benchmarking optimization algorithms (e.g., for yard stacking strategies, vessel stowage planning).…”

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption by Samrendra K. Singh (6824585)

“…To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H₂ storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H₂ uptake. …”

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption by Samrendra K. Singh (6824585)

“…To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H₂ storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H₂ uptake. …”

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption by Samrendra K. Singh (6824585)

“…To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H₂ storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H₂ uptake. …”

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption by Samrendra K. Singh (6824585)

“…To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H₂ storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H₂ uptake. …”

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption by Samrendra K. Singh (6824585)

“…To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H₂ storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H₂ uptake. …”

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption by Samrendra K. Singh (6824585)

“…To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H₂ storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H₂ uptake. …”

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption by Samrendra K. Singh (6824585)

“…To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H₂ storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H₂ uptake. …”

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption by Samrendra K. Singh (6824585)

“…To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H₂ storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H₂ uptake. …”

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption by Samrendra K. Singh (6824585)

“…To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H₂ storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H₂ uptake. …”

DataSheet_1_PTCH1 and CTNNB1 emerge as pivotal predictors of resistance to neoadjuvant chemotherapy in ER+/HER2- breast cancer.docx by Gulnihal Ozcan (9673713)

“…For global health equity, robust predictors that can be cost-effectively incorporated into routine clinical management are essential.…”

Table_2_Obtaining SF-6D utilities from FACT-H&N in thyroid carcinoma patients: development and results from a mapping study.xls by Qing Yang (67856)

“…The mean SF-6D and cumulative distribution functions simulated from the recommended mapping algorithm generally matched the observed ones.…”

Table_1_Obtaining SF-6D utilities from FACT-H&N in thyroid carcinoma patients: development and results from a mapping study.xls by Qing Yang (67856)

“…The mean SF-6D and cumulative distribution functions simulated from the recommended mapping algorithm generally matched the observed ones.…”

Label-Noise Robust Deep Generative Model for Semi-Supervised Learning by Heegeon Yoon (12556386)

“…Empirical results on benchmark datasets demonstrate that the proposed model improves the classification performance over that of the baseline algorithms. We also present a case study on semiconductor manufacturing. …”

SParse EXact (SPEX) LU and Cholesky Factorization Library by Erick Moreno-Centeno (19460626)

“…., in computing radial basis functions for scattered data interpolation), and engineering (e.g., in studies of anharmonic oscillations in semiconductors). …”

Matlab source codes by Kyung-Chan Kim (7469375)

“…The package consists of six MATLAB source files, each with the following functions:</p><p dir="ltr"><b>1.</b><b> </b><b>tracer_main.m</b><br>The main program. …”