Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks

Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an ImageNet-trained convolutional neural network. However, the recent past has seen the emergence of countless backbones pretrained using various algorithms and datasets. While this abundance of choice has led to performance increases for a range of systems, it is difficult for practitioners to make informed decisions about which backbone to choose. Battle of the Backbones (BoB) makes this choice easier by benchmarking a diverse suite of pretrained models, including vision-language models, those trained via self-supervised learning, and the Stable Diffusion backbone, across a diverse set of computer vision tasks ranging from classification to object detection to OOD generalization and more. Furthermore, BoB sheds light on promising directions for the research community to advance computer vision by illuminating strengths and weakness of existing approaches through a comprehensive analysis conducted on more than 1500 training runs. While vision transformers (ViTs) and self-supervised learning (SSL) are increasingly popular, we find that convolutional neural networks pretrained in a supervised fashion on large training sets still perform best on most tasks among the models we consider. Moreover, in apples-to-apples comparisons on the same architectures and similarly sized pretraining datasets, we find that SSL backbones are highly competitive, indicating that future works should perform SSL pretraining with advanced architectures and larger pretraining datasets. We release the raw results of our experiments along with code that allows researchers to put their own backbones through the gauntlet here: https://github.com/hsouri/Battle-of-the-BackbonesComment: Accepted to NeurIPS 202

Observations of Radiation Belt Losses Due to Cyclotron Wave-Particle Interactions

Electron loss to the atmosphere plays a critical role in driving dynamics of the Earths Van Allen radiation belts and slot region. This is a review of atmospheric loss of radiation belt electrons caused by plasma wave scattering via Doppler-shifted cyclotron resonance. In particular, the focus is on observational signatures of electron loss, which include direct measurements of precipitating electrons, measured properties of waves that drive precipitation, and variations in the trapped population resulting from loss. We discuss wave and precipitation measurements from recent missions, including simultaneous multi-payload observations, which have provided new insight into the dynamic nature of the radiation belts

Disappearance of plasmaspheric hiss following interplanetary shock

Abstract Plasmaspheric hiss is one of the important plasma waves controlling radiation belt dynamics. Its spatiotemporal distribution and generation mechanism are presently the object of active research. We here give the first report on the shock-induced disappearance of plasmaspheric hiss observed by the Van Allen Probes on 8 October 2013. This special event exhibits the dramatic variability of plasmaspheric hiss and provides a good opportunity to test its generation mechanisms. The origination of plasmaspheric hiss from plasmatrough chorus is suggested to be an appropriate prerequisite to explain this event. The shock increased the suprathermal electron fluxes, and then the enhanced Landau damping promptly prevented chorus waves from entering the plasmasphere. Subsequently, the shrinking magnetopause removed the source electrons for chorus, contributing significantly to the several-hours-long disappearance of plasmaspheric hiss

Covariate selection for multilevel models with missing data

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135979/1/sta4133_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135979/2/sta4133.pd