The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

S1 Data - by Haoqiang Liu (193675)

Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

Pseudo Code of RBMO. by Chenyi Zhu (9383370)

Microsoft excel file containing exhaustive list of Abbreviated-Injury Scale codes used to generate binary variables encoding the presence or absence of injury to various body regio... by Michael D. Cobler-Lichter (22492930)

Experimental setup and trajectory estimation methodology. by Nouman Javed (16624785)

Estimated coefficients from the Best Linear Projection (BLP) of on . by Vishalie Shah (7301909)

Estimated coefficients from the Best Linear Projection (BLP) of on . by Vishalie Shah (7301909)

Surround Suppression mechanism. by Elnaz Nemati (21402730)

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) by Daniel Pérez Palau (11097348)

“…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at: Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …”

Pre-processing of raw images for pose estimation using physical model-trained convolutional neural network model. by Aniket Ravan (3174171)

Modeling Pregnancy Outcomes Through Sequentially Nested Regression Models by Xuan Bi (3096897)

“…Our approach explicitly bridges the connections across nested outcomes through computationally easy algorithms and enjoys theoretical guarantee of estimation and variable selection. …”

Supplementary Material for: Automated Detection and Diameter Estimation for Mouse Mesenteric Artery Using Semantic Segmentation by Higaki A. (11035161)

“…We observed a strong positive correlation between the wall/lumen ratio before dissection and the lumen expansion ratio (<i>R</i> = 0.832, <i>p</i> < 0.01). Using multivariate binary logistic regression, 2 models estimating whether the vessel met the size criteria (lumen size of 160–240 μm) were generated with an area under the receiver operating characteristic curve of 0.761 for the upper limit and 0.747 for the lower limit. …”

Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)

tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

NuSeT efficiently addresses common segmentation challenges. by Linfeng Yang (516708)

Table_1_Identification of sources of DIF using covariates in patient-reported outcome measures: a simulation study comparing two approaches based on Rasch family models.DOCX by Yseulys Dubuy (16809567)

“…The performance of the algorithms was assessed using: (i) the rates of false and correct detection of DIF, (ii) the DIF size and form recovery, and (iii) the bias in the latent variable level estimation. …”

Comparison of penalized logistic regression models for rare event case by Hülya Olmuş (7494017)

“…<p>The occurrence rate of the event of interest might be quite small (rare) in some cases, although sample size is large enough for Binary Logistic Regression (LR) model. …”

Adaptive Inference for Change Points in High-Dimensional Data by Yangfan Zhang (6451946)

“…A simple combination of test statistics corresponding to several different <i>q</i>’s leads to a test with adaptive power property, that is, it can be powerful against both sparse and dense alternatives. On the estimation front, we obtain the convergence rate of the maximizer of our test statistic standardized by sample size when there is one change-point in mean and <i>q</i> = 2, and propose to combine our tests with a wild binary segmentation algorithm to estimate the change-point number and locations when there are multiple change-points. …”

Fortran & C++: design fractal-type optical diffractive element by I-Lin Ho (13768960)

“…</p> <p>(4) export geometry/optics raw data and figures for binary DOE devices.</p> <p><br></p> <p>[Wolfram Mathematica code "square_triangle_DOE.nb"]:</p> <p>read the optimized binary DOE document (after Fortran & C++ code) to calculate its diffractive fields for comparison.…”