Principal component analysis is a versatile statistical method for reducing a cases-by-
variables data table to its essential features, called principal components. Principal …

Harmonic, quasi-harmonic, and anharmonic phonon properties of crystals are getting to be
better predicted using first-principles phonon calculations by virtue of the progress of the …

We report multiple lines of evidence for a stochastic signal that is correlated among 67
pulsars from the 15 yr pulsar timing data set collected by the North American Nanohertz …

Pulsar timing arrays aim to detect nanohertz-frequency gravitational waves (GWs). A
background of GWs modulates pulsar arrival times and manifests as a stochastic process …

This paper introduces two extensions to the popular PyTorch machine learning framework,
TorchDynamo and TorchInductor, which implement the torch. compile feature released in …

Large language models (LLMs) are shown to possess a wealth of actionable knowledge that
can be extracted for robot manipulation in the form of reasoning and planning. Despite the …

Natural language processing and 2D vision models have attained remarkable proficiency on
many tasks primarily by escalating the scale of training data. However, 3D vision tasks have …

We present a method to formulate algorithm discovery as program search, and apply it to
discover optimization algorithms for deep neural network training. We leverage efficient …

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …

Spike sorting is the computational process of extracting the firing times of single neurons
from recordings of local electrical fields. This is an important but hard problem in …