inferring regulatory cellular dynamics
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Updated
Sep 24, 2025 - Python
inferring regulatory cellular dynamics
Perturbational analysis by causality-aware generative model for single-cell RNA-sequencing data
Python solver for the Brownian, Stochastic, or Noisy Differential Equations
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks
GRAph Parallel Environment
Traffic light and sign recognition are key for Autonomous Vehicles (AVs) because perception mistakes directly influence navigation and safety. In addition to digital adversarial attacks, models are vulnerable to existing perturbations (glare, rain, dirt, or graffiti), which could lead to dangerous misclassifications.
PEAR is a Python program that takes advantage of Petri nets and logical conditions to develop a screening tool aimed at studying the effect of perturbations in interconnected systems.
Extracted data from the PEMS-SF dataset. This GUI contains visuals of the 900+ sensor locations across San Francisco and their occupancy rates.
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