A W-Net deep learning model detects asteroids in TESS data independently of trajectory by rotating training image cubes and using adaptive normalization for data scaling.
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2026 3verdicts
UNVERDICTED 3representative citing papers
BCI-sift provides an open-source, scikit-learn-compatible toolbox for applying optimization-based feature selection to BCI datasets, validated on HD ECoG speech data from eight participants where it improved accuracy and yielded anatomically consistent selections.
Hadronic SED modeling of 103 blazar candidates predicts proton synchrotron peaks in the MeV band for 99 sources and maximum neutrino fluxes detectable by up to 62 sources with next-generation telescopes.
citing papers explorer
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Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning
A W-Net deep learning model detects asteroids in TESS data independently of trajectory by rotating training image cubes and using adaptive normalization for data scaling.
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BCI-sift: An automated feature selection toolbox for Brain Computer Interface applications
BCI-sift provides an open-source, scikit-learn-compatible toolbox for applying optimization-based feature selection to BCI datasets, validated on HD ECoG speech data from eight participants where it improved accuracy and yielded anatomically consistent selections.
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Chasing the neutrino blazar candidates II: SED modeling with hadronic model
Hadronic SED modeling of 103 blazar candidates predicts proton synchrotron peaks in the MeV band for 99 sources and maximum neutrino fluxes detectable by up to 62 sources with next-generation telescopes.