BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs ...

For over a decade, scientists have debated the risks and benefits of so-called “gain of function” research. They’ve long tinkered with viruses and bacteria to endow them with new functions ...

Existing optimization methods rely on iterative minimization operations that can result in a rapid increase in the runtime when the number of variables and terms of the Boolean functions increase. We ...

The PyGSP is a Python package to ease Signal ... Despite all the pre-defined models, you can easily use a custom graph by defining its adjacency matrix, and a custom filter bank by defining a set of ...

Robust variance-regularized risk minimization ... create -n bdd-mv python=3.9 jupyter matplotlib pip pytables scikit-learn scipy unzip $ conda activate bdd-mv Having made (and activated) this new ...

Because the fractional atomic coordinates for these calculations are known a priori, this upper bound energy can be quickly and accurately predicted with a scale-invariant graph neural ... structures ...

python_graphs also allows for Alternative Composite Program Graphs, which lets the user select the desired nodes and edges to construct the graph. Inter-procedural Control-flow Graphs let you create ...