Motion velocity, acceleration, and energy expenditure estimations are important in quantitative assessments for physical recovery and exercise-based interventions for post-stroke patients with partial ...

The recently proposed Graph Matching Network models (GMNs) effectively improve the inference accuracy of graph similarity analysis tasks. GMNs often take graph pairs as input, embed nodes features, ...

Now, a team of researchers from the German research facility Deutsches Elektronen-Synchrotron (DESY) have made significant progress in realizing the laser-plasma acceleration technology.

Binomial Expression and Approximation, Functions & Graphs, Logarithms, Differentiation and Integration Frame of Reference, Motion under Gravity (Free Fall Motion), Graph of 1D Motion, Variable ...

The Drude-Lorentz model examined in the previous issue lends to the simulation of electron motion in the Mathematica computing environment.

The Wolfpack will go with a rare uniform combo against NIU and started off "spooky season" with a stop motion video for the release.

Learn the key differences between centripetal and centrifugal forces, their real-world applications in physics, and how they shape our understanding of circular motion.

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Hi, I have read the "examples\\NPU compilation tutorial.ipynb" about graph mode and eager mode, which helped me a lot. I was wondering if I could use graph mode in LLM inference to reduce the weight ...

Graph Neural Network (GNN)–based motion planning has emerged as a promising approach in robotic systems for its efficiency in pathfinding and navigation tasks. This approach leverages GNNs to learn ...

This is the implementation of the Graph Attention Structure-from-Motion (GASFM) architecture, presented in our CVPR 2024 paper Learning Structure-from-Motion with Graph Attention Networks. The ...