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, 讲好中国故事)". 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

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