<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…<p dir="ltr"><b>Opti2Phase: Python Scripts for Two-Stage Focal Reducer Design</b></p><p dir="ltr">The folder <b>Opti2Phase</b> contains the Python scripts used to generate the results presented in the manuscript. …”

Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

Verification of the in-line PS function. by Raul Castañeda (13932971)

Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…Inertia was calculated using the scikit learn module in Python. B) Resulting cluster distance from hierarchical clustering to averaged functional connectivity from healthy subjects, with different numbers of clusters. …”

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finit... by Hassan Mehboob (8960273)

Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration. by Raul Castañeda (13932971)

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. …”

ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space by Koushik Naskar (7510592)

“…The “ADT” program can be efficiently used to (a) formulate analytic functional forms of differential equations for ADT angles and diabatic potential energy matrix and (b) solve the set of coupled differential equations numerically to evaluate ADT angles, residue due to singularity­(ies), ADT matrices, and finally, diabatic potential energy surfaces (PESs). …”

Schematics for gmxapi operations. by M. Eric Irrgang (4487278)

gmxapi usage examples. by M. Eric Irrgang (4487278)

Search-based testing (Genetic Algorithm) - Chapter 11 of the book "Software Testing Automation" by Saeed Parsa (13893726)

“…</p> <p><br></p> <p>3. Algorithm</p> <p>Below is the main body of the test data generator program:</p> <p>  </p> <p>the main body of a Python program to generate test data for Python functions.…”

Example of use of the slightly off-axis strategies. by Raul Castañeda (13932971)

Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures. by Kevin Sawade (16726527)

“…However, our algorithm was 1000 times faster than pyROSETTA (both algorithms have been parallelized on a per-structure basis using the Python package joblib [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010531#pcbi.1010531.ref069" target="_blank">69</a>]).…”

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

Example of the fully-compensated phase reconstruction package for off-axis DHM holograms recorded in telecentric configuration. by Raul Castañeda (13932971)

Data_Sheet_1_Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.PDF by Minhua Guo (18392646)

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”

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)

“…</p><h2>Model Architecture</h2><p dir="ltr">The model is based on <code>pysentimiento/robertuito-base-uncased</code> with the following modifications:</p><ul><li>A dense classification layer was added over the base model</li><li>Uses input IDs and attention masks as inputs</li><li>Generates a multi-class classification with 5 hate categories</li></ul><h2>Dataset</h2><p dir="ltr"><b>HATEMEDIA Dataset</b>: Custom hate speech dataset with categorization by type:</p><ul><li><b>Labels</b>: 5 hate type categories (0-4)</li><li><b>Preprocessing</b>:</li><li>Null values ​​removed from text and labels</li><li>Reindexing and relabeling (original labels are adjusted by subtracting 1)</li><li>Exclusion of category 2 during training</li><li>Conversion of category 5 to category 2</li></ul><h2>Training Process</h2><h3>Configuration</h3><ul><li><b>Batch size</b>: 128</li><li><b>Epoches</b>: 5</li><li><b>Learning rate</b>: 2e-5 with 10% warmup steps</li><li><b>Early stopping</b> with patience=2</li><li><b>Class weights</b>: Balanced to handle class imbalance</li></ul><h3>Custom Metrics</h3><ul><li>Recall for specific classes (focus on class 2)</li><li>Precision for specific classes (focus on class 3)</li><li>F1-score (weighted)</li><li>AUC-PR</li><li>Recall at precision=0.6 (class 3)</li><li>Precision at recall=0.6 (class 2)</li></ul><h2>Evaluation Metrics</h2><p dir="ltr">The model is evaluated using:</p><ul><li>Macro recall, precision, and F1-score</li><li>One-vs-Rest AUC</li><li>Accuracy</li><li>Per-class metrics</li><li>Confusion matrix</li><li>Full classification report</li></ul><h2>Technical Features</h2><h3>Data Preprocessing</h3><ul><li><b>Tokenization</b>: Maximum length of 128 tokens (truncation and padding)</li><li><b>Encoding of labels</b>: One-hot encoding for multi-class classification</li><li><b>Data split</b>: 80% training, 10% validation, 10% testing</li></ul><h3>Optimization</h3><ul><li><b>Optimizer</b>: Adam with linear warmup scheduling</li><li><b>Loss function</b>: Categorical Crossentropy (from_logits=True)</li><li><b>Imbalance handling</b>: Class weights computed automatically</li></ul><h2>Requirements</h2><p dir="ltr">The following Python packages are required:</p><ul><li>TensorFlow</li><li>Transformers</li><li>scikit-learn</li><li>pandas</li><li>datasets</li><li>matplotlib</li><li>seaborn</li><li>numpy</li></ul><h2>Usage</h2><ol><li><b>Data format</b>:</li></ol><ul><li>CSV file or Pandas DataFrame</li><li>Required column name: <code>text</code> (string type)</li><li>Required column name: Data type label (integer type, 0-4) - optional for evaluation</li></ul><ol><li><b>Text preprocessing</b>:</li></ol><ul><li>Automatic tokenization with a maximum length of 128 tokens</li><li>Long texts will be automatically truncated</li><li>Handling of special characters, URLs, and emojis included</li></ul><ol><li><b>Label encoding</b>:</li></ol><ul><li>The model classifies hate speech into 5 categories (0-4)</li><li><code>0</code>: Political hatred: Expressions directed against individuals or groups based on political orientation.…”

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective by Xiangyang Jia (10026932)

Principal component analysis (PCA) of sub-layer mean intensities. by Maëlle Vilbert (17101954)