GitHub

lm_realdata_analysis_colab.py

1. Load a physics jet dataset from a Python package. 2. Construct genuine four-vectors (E, px, py, pz). 3. Fit classical, MCD, and Tyler Lorentz--Mahalanobis rest-frame models. 4. Rank and profile ...
IEEE

Windowing-Based Factor Graph Optimization With Anomaly Detection Using Mahalanobis Distance for Underwater INS/DVL/USBL Integration

Abstract: Factor graph optimization (FGO) provides a new means for asynchronous data fusion of integrated underwater vehicle navigation in a plug-and-play unified framework. However, in complex ...
IEEE

Advancing GAN Evaluation: The Advanced Mahalanobis Distance Learning Metric for Realistic Car Damage Image Assessment

Abstract: Generative Adversarial Networks (GANs) have demonstrated remarkable capability in synthesizing high-quality images from limited data, addressing challenges of data scarcity and diversity in ...
BBC

Thinking, braking and stopping distances

Stopping vehicles as quick as possible in an emergency is important but many factors affect this. The driver's reactions and the road and vehicle conditions play a part, as well as mass and speed. The ...
BBC

How to calculate speed, distance and time

Units of speed are always a unit of length per unit of time. Different units of length and time may be chosen depending on the situation. This makes it easier to read the measurements. For example, ...
GitHub

lm_realdata_analysis_clean.py

for example raw Tyler LM score magnitudes, whose absolute scale is arbitrary. Main goals: 1. Load a real/physics jet dataset through EnergyFlow. 2. Construct genuine four-vectors (E, px, py, pz). 3.