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6. Older People Online Shopping Acceptance

Author

Zhao, Kexin, Appolloni, Andrea, Series Editor, Caracciolo, Francesco, Series Editor, Ding, Zhuoqi, Series Editor, Gogas, Periklis, Series Editor, Huang, Gordon, Series Editor, Nartea, Gilbert, Series Editor, Ngo, Thanh, Series Editor, Striełkowski, Wadim, Series Editor, Tehseen, Shehnaz, editor, Ahmad, Mohd Naseem Niaz, editor, and Afroz, Rafia, editor

Published
2024
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14. Research Progress on the Correlation between Different Dietary Patterns and Hyperuricemia Mediated by Intestinal Flora

Author

LU Han, ZHAO Kexin, XUE Yuling, MA Xinying, WANG Shijie

Subjects
hyperuricemia, gout, intestinal flora, dietary patterns, Food processing and manufacture, TP368-456
Abstract

Hyperuricemia (HUA) is a purine metabolism disorder caused by the imbalance between uric acid production and excretion, which, when severe, can lead to gout and renal function damage. At present, the prevalence of HUA/gout is increasing yearly in China and the HUA/gout patients are becoming younger and younger. Recent studies have shown that the intestinal flora of patients with HUA/gout is imbalanced when compared with that of healthy people, and different dietary patterns can affect the level of uric acid by adjusting the intestinal flora. This paper elaborates on the characteristics of the intestinal flora in patients with HUA/gout and the effect of the intestinal flora on uric acid metabolism, which will lay a theoretical foundation for the prevention, early diagnosis and auxiliary treatment of HUA/gout. This paper also summarizes recent progress in understanding the impact of different dietary patterns on uric acid levels in order to guide patients to effectively prevent and delay the occurrence of HUA/gout by adjusting their dietary patterns.

Published
2024
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32. DiffWave: A Versatile Diffusion Model for Audio Synthesis

Author

Kong, Zhifeng, Ping, Wei, Huang, Jiaji, Zhao, Kexin, and Catanzaro, Bryan

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Statistics - Machine Learning
Abstract

In this work, we propose DiffWave, a versatile diffusion probabilistic model for conditional and unconditional waveform generation. The model is non-autoregressive, and converts the white noise signal into structured waveform through a Markov chain with a constant number of steps at synthesis. It is efficiently trained by optimizing a variant of variational bound on the data likelihood. DiffWave produces high-fidelity audios in different waveform generation tasks, including neural vocoding conditioned on mel spectrogram, class-conditional generation, and unconditional generation. We demonstrate that DiffWave matches a strong WaveNet vocoder in terms of speech quality (MOS: 4.44 versus 4.43), while synthesizing orders of magnitude faster. In particular, it significantly outperforms autoregressive and GAN-based waveform models in the challenging unconditional generation task in terms of audio quality and sample diversity from various automatic and human evaluations., Comment: ICLR 2021 (oral)

Published
2020

33. A property in vector-valued function spaces

Author

Zhao, Kexin and Tan, Dongni

Subjects
Mathematics - Functional Analysis, 46B20
Abstract

This paper deals with a property which is equivalent to generalised-lushness for separable spaces. It thus may be seemed as a geometrical property of a Banach space which ensures the space to have the Mazur-Ulam property. We prove that if a Banach space $X$ enjoys this property if and only if $C(K,X)$ enjoys this property. We also show the same result holds for $L_\infty(\mu,X)$ and $L_1(\mu,X)$., Comment: 15 pages,0 figures

Published
2020

38. Periodic Flooding Characteristics of Annular Flow in Narrow Rectangular Duct

Author

JIN Guangyuan;ZHAO Kexin;WANG Rui;BAI Jinghu;DU Lipeng;ZHANG Wenchao

Subjects
narrow rectangular duct, annular flow, flooding, multiscale permutation entropy, Nuclear engineering. Atomic power, TK9001-9401, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

The periodic flooding characteristic of annular flow in a narrow rectangular duct is one of the phenomena of air and liquid flowing in reverse through rod bundle structure space of the reactor core, and the research on the characteristics of flooding behaviors, occurrence conditions, and flooding mechanism can provide data and theoretical support for the accident treatment of nuclear power plant. In this study, the periodic flooding characteristics of annular flow in a narrow rectangular duct were investigated, the high-speed photographic observation shows that each cycle of the flooding process consists of four flow regions, i.e., the onset flooding region, the full flooding region, the liquid membrane reversal region and the liquid phase downstream region. When the liquid phase flow rate is low, the annular flow flooding period increases slightly with the increase of the gas phase velocity, and when the liquid phase flow rate is high, the flooding period decreases with the increase of the gas phase velocity. The time proportion of the full flooding region gradually increases with the increase of the gas phase velocity, and the time proportion of the liquid phase downstream region gradually decreases until the annular flow reaches the complete rising pattern. In the area of low liquid phase velocity, the time of the onset flooding region increases with the increase of the gas phase velocity, while the time of the liquid membrane reversal region remains the same. In the area of high liquid phase velocity, the time of the onset flooding region and the liquid membrane reversal region decreases correspondingly. According to the experimental results, the mechanism of the periodic flooding behavior analysis was obtained. The thickness of the liquid film in annular flow increases because of the space bondage in the narrow rectangular duct, which makes the periodic flooding time increasing and the flow range of downward liquid phase increasing at the same time. The fundamental reason for the periodic flooding of annular flow in the narrow rectangular duct is the “protection” effect of the channel structure on the top layer of the liquid phase. The multiscale permutation entropy of the annular flow is disordered when the gas phase velocity is small, while the entropy of the annular flow gradually decreases with the increase of the gas phase velocity when the gas phase velocity is high, so the judgment conditions of the liquid phase declining and complete rising can be determined by the conversion point of “unordered” to “ordered” of the multiscale permutation entropy.

Published
2023
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41. Safety and immunogenicity of the COVID-19 mRNA vaccine CS-2034: A randomized, double-blind, dose-exploration, placebo-controlled multicenter Phase I clinical trial in healthy Chinese adults

Author

Jin, Zhili, Wu, Jingxuan, Wang, Ying, Huang, Tao, Zhao, Kexin, Liu, Jian, Wang, Haomeng, Zhu, Tao, Gou, Jinbo, Huang, Haitao, Wu, Xiaofang, Yin, Hang, Song, Jian, Li, Ran, Zhang, Jianxiong, Li, Lijun, Chen, Jingcheng, Li, Xiao, Zhang, Meijuan, Li, JiangShuo, Hou, Mengyu, Song, Yuqin, Wang, Bingyan, Gao, Qiannan, Wu, Le, Kong, Yanhong, and Dong, Ruihua

Published
2023
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43. WaveFlow: A Compact Flow-based Model for Raw Audio

Author

Ping, Wei, Peng, Kainan, Zhao, Kexin, and Song, Zhao

Subjects
Computer Science - Sound, Computer Science - Computation and Language, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

In this work, we propose WaveFlow, a small-footprint generative flow for raw audio, which is directly trained with maximum likelihood. It handles the long-range structure of 1-D waveform with a dilated 2-D convolutional architecture, while modeling the local variations using expressive autoregressive functions. WaveFlow provides a unified view of likelihood-based models for 1-D data, including WaveNet and WaveGlow as special cases. It generates high-fidelity speech as WaveNet, while synthesizing several orders of magnitude faster as it only requires a few sequential steps to generate very long waveforms with hundreds of thousands of time-steps. Furthermore, it can significantly reduce the likelihood gap that has existed between autoregressive models and flow-based models for efficient synthesis. Finally, our small-footprint WaveFlow has only 5.91M parameters, which is 15$\times$ smaller than WaveGlow. It can generate 22.05 kHz high-fidelity audio 42.6$\times$ faster than real-time (at a rate of 939.3 kHz) on a V100 GPU without engineered inference kernels., Comment: Published at ICML 2020. Code and pre-trained models: https://github.com/PaddlePaddle/Parakeet

Published
2019

44. Multi-Speaker End-to-End Speech Synthesis

Author

Park, Jihyun, Zhao, Kexin, Peng, Kainan, and Ping, Wei

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

In this work, we extend ClariNet (Ping et al., 2019), a fully end-to-end speech synthesis model (i.e., text-to-wave), to generate high-fidelity speech from multiple speakers. To model the unique characteristic of different voices, low dimensional trainable speaker embeddings are shared across each component of ClariNet and trained together with the rest of the model. We demonstrate that the multi-speaker ClariNet outperforms state-of-the-art systems in terms of naturalness, because the whole model is jointly optimized in an end-to-end manner.

Published
2019

45. Non-Autoregressive Neural Text-to-Speech

Author

Peng, Kainan, Ping, Wei, Song, Zhao, and Zhao, Kexin

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

In this work, we propose ParaNet, a non-autoregressive seq2seq model that converts text to spectrogram. It is fully convolutional and brings 46.7 times speed-up over the lightweight Deep Voice 3 at synthesis, while obtaining reasonably good speech quality. ParaNet also produces stable alignment between text and speech on the challenging test sentences by iteratively improving the attention in a layer-by-layer manner. Furthermore, we build the parallel text-to-speech system and test various parallel neural vocoders, which can synthesize speech from text through a single feed-forward pass. We also explore a novel VAE-based approach to train the inverse autoregressive flow (IAF) based parallel vocoder from scratch, which avoids the need for distillation from a separately trained WaveNet as previous work., Comment: Published at ICML 2020. (v3 changed paper title)

Published
2019

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