Adaptive Reduced Rank Regression

We study the low rank regression problem $\mathbf{y} = M\mathbf{x} + \epsilon$, where $\mathbf{x}$ and $\mathbf{y}$ are $d_1$ and $d_2$ dimensional vectors respectively. We consider the extreme high-dimensional setting where the number of observations $n$ is less than $d_1 + d_2$. Existing algorithms are designed for settings where $n$ is typically as large as $\mathrm{rank}(M)(d_1+d_2)$. This work provides an efficient algorithm which only involves two SVD, and establishes statistical guarantees on its performance. The algorithm decouples the problem by first estimating the precision matrix of the features, and then solving the matrix denoising problem. To complement the upper bound, we introduce new techniques for establishing lower bounds on the performance of any algorithm for this problem. Our preliminary experiments confirm that our algorithm often out-performs existing baselines, and is always at least competitive.Comment: 40 page

Experimental study on the generated pyroshock level under different amount of explosive

The purpose of this study is to evaluate the effect of the amount of explosive on generated pyroshock of a typical igniter. In this study, pyrotechnic experiments of the igniter are performed. The output pressure is measured with a pressure transducer while acceleration data is obtained using piezoelectric accelerometers. Finally, the effects of the amount of explosive on the generated pyroshock are discussed based on results in time and frequency domain. Results show that the relation between the amount of explosive and peak pressure of typical igniter shows good agreement with Nobel-Abel equation of state (EOS). Moreover, peak acceleration and SRS both show an approximate linear growth with increased amount of explosive

Phonetic-assisted Multi-Target Units Modeling for Improving Conformer-Transducer ASR system

Exploiting effective target modeling units is very important and has always been a concern in end-to-end automatic speech recognition (ASR). In this work, we propose a phonetic-assisted multi-target units (PMU) modeling approach, to enhance the Conformer-Transducer ASR system in a progressive representation learning manner. Specifically, PMU first uses the pronunciation-assisted subword modeling (PASM) and byte pair encoding (BPE) to produce phonetic-induced and text-induced target units separately; Then, three new frameworks are investigated to enhance the acoustic encoder, including a basic PMU, a paraCTC and a pcaCTC, they integrate the PASM and BPE units at different levels for CTC and transducer multi-task training. Experiments on both LibriSpeech and accented ASR tasks show that, the proposed PMU significantly outperforms the conventional BPE, it reduces the WER of LibriSpeech clean, other, and six accented ASR testsets by relative 12.7%, 6.0% and 7.7%, respectively.Comment: 5 pages, 1 figures, submitted to ICASSP 202