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Abstract The Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) Project has a new very long baseline interferometry (VLBI) Outrigger at the Green Bank Observatory (GBO), which forms a 3300 km baseline with CHIME operating at 400–800 MHz. Using 100 ms long full-array baseband “snapshots” collected commensally during FRB and pulsar triggers, we perform a shallow, wide-area VLBI survey covering a significant fraction of the northern sky targeted at the positions of compact sources from the Radio Fundamental Catalog. In addition, our survey contains calibrators detected from two 1 s long trial baseband snapshots for a deeper survey with CHIME and GBO. In this paper, we present the largest catalogue of compact calibrators suitable for 30 mas scale VLBI observations at subgigahertz frequencies to date. Our catalogue consists of 200 total calibrators in the Northern Hemisphere that are compact on 30 mas scales with fluxes above 100 mJy. This calibrator grid will enable the precise localization of hundreds of FRBs a year with CHIME/FRB Outriggers. 

We propose a simple architecture for deep reinforcement learning by embedding inputs into a learned Fourier basis and show that it improves the sample efficiency of both state-based and image-based RL. We perform an infinite-width analysis of our architecture using the Neural Tangent Kernel and theoretically show that tuning the initial variance of the Fourier basis is equivalent to functional regularization of the learned deep network. That is, these learned Fourier features allow for adjusting the degree to which networks underfit or overfit different frequencies in the training data, and hence provide a controlled mechanism to improve the stability and performance of RL optimization. Empirically, this allows us to prioritize learning low-frequency functions and speed up learning by reducing networks' susceptibility to noise in the optimization process, such as during Bellman updates. Experiments on standard state-based and image-based RL benchmarks show clear benefits of our architecture over the baselines. Code available at https://github.com/alexlioralexli/learned-fourier-features 

Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regression adjustment for randomly censored regression quantile models. Regression adjustment is based on a new estimating equation that adapts to censoring and leads to quantile score whenever the data do not exhibit censoring. The proposed procedure named censored quantile regression forest, allows us to estimate quantiles of time-to-event without any parametric modeling assumption. We establish its consistency under mild model specifications. Numerical studies showcase a clear advantage of the proposed procedure.