A Representative example of organ surface data before and after editing. by Takashi Kimura (178058)

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)

“…<p><b>(a)</b> UMAP, t-SNE, PCA, and Diffmap were first generated using the Python libraries Scikit-learn, UMAP, and PyDiffmap within Jupyter to generate static graphs as a starting point. …”

Python code for hierarchical cluster analysis of detected R-strategies from rule-based NLP on 500 circular economy definitions by Zahir Barahmand (18008947)

“…The resulting dendrogram visually represents how different strategies are conceptually related based on their co-occurrence in CE definitions.…”

Python implementation of a wildfire propagation example using m:n-CAk over Z and R. by Pau Fonseca i Casas (9507338)

“…</p><p dir="ltr"><br></p><p dir="ltr">## Files in the Project</p><p dir="ltr"><br></p><p dir="ltr">### Python Scripts</p><p dir="ltr">- **Wildfire_on_m_n-CAk.py**: This file contains the main code for the fire cellular automaton. …”

(a) System dynamics: schematic representation of all the reactions in the Tree model, which are aging, reproduction, color cycle, and competition. by Fabricio Cravo (21387854)

System Hardware ID Generator Script: A Cross-Platform Hardware Identification Tool by Pavel Izosimov (20096259)

“…This tool provides <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">code obfuscation in Python</a> and <a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank">Python code encryption</a>, enabling developers to <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">protect Python code</a> effectively.…”

Catalogue of compact radio sources in Messier-82 from e-MERLIN observations by Sibongumusa Shungube (21197363)

“…The columns are analogous to Table 3.1, providing an independent set of measurements based on automated source detection and Gaussian fitting.…”

OLS regression results—Distances under 9 ft per image. by Matthew Martell (18536273)

Experimental results of data retrieval from two Atlas DataLab setups: next-to-data deployment in the CSIC-DDC Hub (bottom) and remote data access from a user’s workstation (top). by Ezequiel Cimadevilla (8412531)

Data for "A hollow fiber membrane permeance evaluation device demonstrating outside-in and inside-out performance differences" by Timothy Warner (20222838)

“…The study presents a new device that can measure the flux and permeance of individual or multiple hollow fiber membranes in both the outside-in and inside-out orientations, partnered with a python-based open-source permeance analysis software. …”

Estimating dopamine effects on humans. by Domenico Guarino (20428000)

Dentate granule cells have polarised responses to neuromodulators switch spiking behaviour under neuromodulation. by Domenico Guarino (20428000)

Smart Reaction Templating: A Graph-Based Method for Automated Molecular Dynamics Input Generation by Julian Konrad (9609845)

“…This work introduces a Python-based algorithm that automates the generation of reaction templates for the LAMMPS REACTION package, leveraging graph-theoretical principles and subgraph isomorphism techniques. …”

Procedure for aligning the NeuroRat head model underlying the FEM simulations with the BBP circuit model. by Joseph James Tharayil (21416715)

Differences in compartment weights explain differences between EEG and ECoG. by Joseph James Tharayil (21416715)

Simplified calculation methods produce inaccurate signals. by Joseph James Tharayil (21416715)

BlueRecording simulates whisker-flick EEG. by Joseph James Tharayil (21416715)

BlueRecording simulates hippocampal LFP. by Joseph James Tharayil (21416715)

End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting by Jamie Hathaway (10285367)

“…</p><h3>policy/</h3><p dir="ltr">This folder contains pickled trajectories, in the form of a Python list.</p><p dir="ltr">The list's elements are TrajWithRew dataclass objects from the Imitation Python library (https://imitation.readthedocs.io/en/latest/)</p><p dir="ltr">TrajWithRew contains 4 main fields</p><ul><li> obs - the (unnormalised) observations, in the form of a [WINDOW_LENGTH * NUM_CHANNELS] array</li><li> acts - the actions in the form of a [WINDOW_LENGTH - 1 * NUM_ACTS] array</li><li> infos - the info values at each timestep, as a [WINDOW_LENGTH - 1] array of dicts</li><li> terminals - boolean indicating if that trajectory segment is a terminal segment</li><li> rews - the rewards as a [WINDOW_LENGTH - 1] array</li></ul><p dir="ltr">Each TrajWithRew represents not a full episodic trajectory, as is usually the case with Imitiation - rather they represent segments of a full episodic trajectory, of length WINDOW_LENGTH. …”

Introduction to the iModulonMiner using public <i>B</i>. <i>subtilis</i> RNA-seq data as a case study. by Anand V. Sastry (6619310)