252 research outputs found
Quantum Noise of Kramers-Kronig Receiver
Abstrac--Kramers-Kronig (KK) receiver, which is equivalent to heterodyne
detection with one single photodetector, provides an efficient method to
reconstruct the complex-valued optical field by means of intensity detection
given a minimum-phase signal. In this paper, quantum noise of the KK receiver
is derived analytically and compared with that of the balanced heterodyne
detection. We show that the quantum noise of the KK receiver keeps the radical
fluctuation of the measured signal the same as that of the balanced heterodyne
detection, while compressing the tangential noise to 1/3 times the radical one
using the information provided by the Hilbert transform. In consequence, the KK
receiver has 3/2 times the signal-to-noise ratio of balanced heterodyne
detection while presenting an asymmetric distribution of fluctuations, which is
also different from that of the latter. More interestingly, the projected
in-phase and quadrature field operators of the retrieved signal after down
conversion have a time dependent quantum noise distribution depending on the
time-varying phase. This property provides a feasible scheme for controlling
the fluctuation distribution according to the requirements of measurement
accuracy in the specific direction. Under the condition of strong carrier wave,
the fluctuations of the component requiring to be measured more accurately can
be compressed to 1 / 6, which is even lower than 1/4 by measuring a coherent
state. Finally, we prove the analytic conclusions by simulation results
AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline
An open-source Mandarin speech corpus called AISHELL-1 is released. It is by
far the largest corpus which is suitable for conducting the speech recognition
research and building speech recognition systems for Mandarin. The recording
procedure, including audio capturing devices and environments are presented in
details. The preparation of the related resources, including transcriptions and
lexicon are described. The corpus is released with a Kaldi recipe. Experimental
results implies that the quality of audio recordings and transcriptions are
promising.Comment: Oriental COCOSDA 201
China's "discourse power" strategy: taking China's Twitter publicity as a case
To strengthen china's external publicity, Xi Jinping has mobilized massive resources to secure China's interests and reinforce its reputation both at home and abroad. This dissertation inquiries into why and how China's Ministry of Foreign Affairs during the outbreak of COVID-19 pandemic era has been actively engaged in conducting online publicity by means of disseminating good images of China on Twitter. The analysis part illuminates the features of linguistic tendency in publicizing, narrative style and Xi's idea of "discourse power" strategy for "telling China's stories well (jianghao zhongguo gushi, 讲好ä¸å›½æ•…事)". In response to the need to enhance China’s discourse power, various propaganda approaches have been adopted and the impacts of China have been increased on some levels. Two spokespersons of the Ministry of Foreign Affairs (Hua Chunying and Zhao Lijian) as well as two Chinese state-owned mainstream media (CGTN and China Daily) play major roles in the construction of Twitter publicity fronts which serves China's diplomatic purposes. Three systemic analysis methods, which are interpersonal systemic analysis, modality systemic analysis and evidentiality analysis, are adopted to deconstruct and reclassify the text content of Hua and Zhao’s Tweets, and to observe and deduce the characteristics of "discourse power" strategy. Though a number of exploratory approaches to influencing foreign targeted audiences have been taken, China failed to achieve the desired effects; the COVID-19 publicity has further exacerbated the mutual political misunderstanding between China and the United States
Very severe chronic ventricular tachycardia due to an aneurysm in the left main coronary artery and a giant aneurysm-related pseudoaneurysm
Enhancing High-dimensional Bayesian Optimization by Optimizing the Acquisition Function Maximizer Initialization
Bayesian optimization (BO) is widely used to optimize black-box functions. It
works by first building a surrogate for the objective and quantifying the
uncertainty in that surrogate. It then decides where to sample by maximizing an
acquisition function defined by the surrogate model. Prior approaches typically
use randomly generated raw samples to initialize the acquisition function
maximizer. However, this strategy is ill-suited for high-dimensional BO. Given
the large regions of high posterior uncertainty in high dimensions, a randomly
initialized acquisition function maximizer is likely to focus on areas with
high posterior uncertainty, leading to overly exploring areas that offer little
gain. This paper provides the first comprehensive empirical study to reveal the
importance of the initialization phase of acquisition function maximization. It
proposes a better initialization approach by employing multiple heuristic
optimizers to leverage the knowledge of already evaluated samples to generate
initial points to be explored by an acquisition function maximizer. We evaluate
our approach on widely used synthetic test functions and real-world
applications. Experimental results show that our techniques, while simple, can
significantly enhance the standard BO and outperforms state-of-the-art
high-dimensional BO techniques by a large margin in most test cases
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