RFI mitigation with phase-only adaptive beamforming

Connected radio interferometers are sometimes used in the tied-array mode: signals from antenna elements are coherently added and the sum signal applied to a VLBI backend or pulsar processing machine. Usually there is no computer-controlled amplitude weighting in the existing radio interferometer facilities. Radio frequency interference (RFI) mitigation with phase-only adaptive beamforming is proposed for this mode of observation. Small phase perturbations are introduced in each of the antenna's signal. The values of these perturbations are optimized in such a way that the signal from a radio source of interest is preserved and RFI signals suppressed. An evolutionary programming algorithm is used for this task. Computer simulations, made for both one-dimensional and two-dimensional array set-ups, show considerable suppression of RFI and acceptable changes to the main array beam in the radio source direction.Comment: 7 pages, 11 figure

Memory-Efficient Adaptive Optimization

Adaptive gradient-based optimizers such as Adagrad and Adam are crucial for achieving state-of-the-art performance in machine translation and language modeling. However, these methods maintain second-order statistics for each parameter, thus introducing significant memory overheads that restrict the size of the model being used as well as the number of examples in a mini-batch. We describe an effective and flexible adaptive optimization method with greatly reduced memory overhead. Our method retains the benefits of per-parameter adaptivity while allowing significantly larger models and batch sizes. We give convergence guarantees for our method, and demonstrate its effectiveness in training very large translation and language models with up to 2-fold speedups compared to the state-of-the-art