Width of stripes as a function of time. حسب Pratik Mullick (12851011)

Results of human and algorithmic categorization performance. حسب John K. Tsotsos (7435094)

"…<p>A,B: Box plots of human categorization performance plotted as a function of the percentage of the target within the parafovea. …"

Multidomain, Automated Photopatterning of DNA-functionalized Hydrogels (MAPDH). حسب Moshe Rubanov (7289156)

"…The hydrogel location, size, shape, and composition are specified, after which an automated script is run, and the output is a chamber with photopatterned hydrogels of different sizes, shapes, and compositions. <b>B)</b> Pseudocode for MAPDH in Python. The algorithm takes as input the vials that will be flowed through the patterning chamber. …"

Simulation results by genetic algorithms (GAs). حسب Yuji Omachi (15307241)

Application of the smooth-index to bandwidth-limited matrices (A,B) and to block-diagonal matrices (C,D). حسب Alexander Borst (32619)

Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement - Fig 4 حسب Antoine Falisse (6061601)

Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement - Fig 3 حسب Antoine Falisse (6061601)

Datasheet1_Gait analysis comparison between manual marking, 2D pose estimation algorithms, and 3D marker-based system.pdf حسب Dimitrios Menychtas (16935831)

Attack and defense behaviors with the sensory–motor algorithm between multiple predators and prey. حسب Hiroyuki Ichijo (13891153)

Fraction of selective neurons as a function of the imaging signal-to-noise ratio. حسب Ziqiang Wei (5953964)

Unsupervised Machine Learning Organization of the Functional Dark Proteome of Gram-Negative “Superbugs”: Six Protein Clusters Amenable for Distinct Scientific Applications حسب Carlos Sicilia (14228509)

الموضوعات:

Spatial properties of RFs generated by thresholding algorithms and in macaques’ V1 and V2. حسب Ilias Rentzeperis (10215602)

Screening and diagnosis pipeline for the Challenge. حسب Matthew A. Reyna (16965038)

الموضوعات:

Sparkler plot representation of functional impact of ARAF variants. حسب Alexis M. Thornton (11068403)

Confusion matrix <i>N</i> for clinical outcome detection with two classes: Clinical outcome abnormal and clinical outcome normal. حسب Matthew A. Reyna (16965038)

الموضوعات:

Confusion matrix <i>M</i> for murmur detection with three classes: Murmur present, murmur unknown, and murmur absent. حسب Matthew A. Reyna (16965038)

الموضوعات:

Functions in nhppp. حسب Thomas A. Trikalinos (5785877)

"…We developed it to facilitate the sampling of event times in discrete event and statistical simulations. The package’s functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function; the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme. …"

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit حسب Gonzalo Colmenarejo (650249)

"…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …"

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) حسب 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.…"

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... حسب Hector Torres (11708207)