FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion

Voice conversion (VC) can be achieved by first extracting source content information and target speaker information, and then reconstructing waveform with these information. However, current approaches normally either extract dirty content information with speaker information leaked in, or demand a large amount of annotated data for training. Besides, the quality of reconstructed waveform can be degraded by the mismatch between conversion model and vocoder. In this paper, we adopt the end-to-end framework of VITS for high-quality waveform reconstruction, and propose strategies for clean content information extraction without text annotation. We disentangle content information by imposing an information bottleneck to WavLM features, and propose the spectrogram-resize based data augmentation to improve the purity of extracted content information. Experimental results show that the proposed method outperforms the latest VC models trained with annotated data and has greater robustness

Investigating the Influence of Empowerment on Patients’ Satisfaction: How to Empower Patients in Online Health Consultation Platform

Online health consultation platform becomes a significant channel for health consumers to seek online support and health consultation. As the health consultation moves from offline to online, it significantly changes the communication circumstance between patients and physicians. It is crucial to understand the empowerment process embedded in online physician-patient interaction, in turn to improve patients’ satisfaction on line health services. This study examined how social-structural empowerment and psychological empowerment affect patients’ satisfaction, as an empowerment outcome, in the online health consultation platform using text mining techniques and econometric analysis. Our results indicate that informational and emotional support can extrinsically empower patients and thereby increase their satisfaction. Psychological empowerment is also found that has two roles in the empowerment process, a partial mediating effect and a moderating effect on the relationship between social-structural empowerment and patients’ satisfaction. This study enriches the empowerment theory from a text mining perspective and extends the empowerment theory in the organizational context to the context of digital health

COVID-19 spreading patterns in family clusters reveal gender roles in China

Unfolding different gender roles is preceding the efforts to reduce gender inequality. This paper analyzes COVID-19 family clusters outside Hubei Province in mainland China during the 2020 outbreak, revealing significant differences in spreading patterns across gender and family roles. Results show that men are more likely to be the imported cases of a family cluster, and women are more likely to be infected within the family. This finding provides new supportive evidence of the men as breadwinner and women as homemaker (MBWH) gender roles in China. Further analyses reveal that the MBWH pattern is stronger in eastern than in western China, stronger for younger than for elder people. This paper offers not only valuable references for formulating gender-differentiated epidemic prevention policies but also an exemplification for studying group differences in similar scenarios.Comment: 13 pages, 5 figures, 2 table

Spatiotemporal trends and factors influencing online attention for China’s tea industry

In the context of the “Internet plus” era, the study of tea industry online attention is a new perspective in research on the tea industry and an opportunity for the sustainable and high-quality development of this industry. Based on the Baidu index, this paper obtains web attention data from 2012 to 2021, analyzes the spatial and temporal evolution characteristics of online attention using the seasonal concentration index and geographic concentration index, and quantitatively discusses the influencing factors using correlation analysis and GeoDetector. The results show the following: The interannual change in China’s tea industry online attention shows “rapid growth, high level of stability, slow decline,” the monthly distribution has an intense concentration, mainly in March-April and October, and the interday distribution of attention peaks on weekdays. The spatial distribution shows an intense geographical concentration, with an overall trend of “light concentration first, then light dispersion.” The migration trajectory of the center of attention is tilted toward the southwest. Economic development status, residents’ income, the natural environment of tea growing, the leisure time of followers, and the price level of tea are the essential factors affecting the of the tea industry online attention. In contrast, the other factors we have chosen have a weaker impact on online attention compared to the few factors just mentioned

LiCamGait: Gait Recognition in the Wild by Using LiDAR and Camera Multi-modal Visual Sensors

LiDAR can capture accurate depth information in large-scale scenarios without the effect of light conditions, and the captured point cloud contains gait-related 3D geometric properties and dynamic motion characteristics. We make the first attempt to leverage LiDAR to remedy the limitation of view-dependent and light-sensitive camera for more robust and accurate gait recognition. In this paper, we propose a LiDAR-camera-based gait recognition method with an effective multi-modal feature fusion strategy, which fully exploits advantages of both point clouds and images. In particular, we propose a new in-the-wild gait dataset, LiCamGait, involving multi-modal visual data and diverse 2D/3D representations. Our method achieves state-of-the-art performance on the new dataset. Code and dataset will be released when this paper is published

Modification Strategies of Titanium Dioxide

Titanium dioxide (TiO2) is a standard white pigment. However, when TiO2 is exposed to ultraviolet light, it will catalyze the degradation of the surrounding organic matrix. Surface coating of TiO2 is an effective method for reducing the catalytic effect of TiO2. It can also improve the dispersion of TiO2 in an organic matrix. This review critically introduces recent results on the surface coating of TiO2. First, the main features of TiO2, including processes, structure, and final properties, are described briefly. Second, this chapter reports and discusses different surface coating methods for TiO2 with inorganic oxides and organic matter. Inorganic oxides, such as Al2O3, SiO2, and ZrO2, would form a continuous dense film and block the defects of the TiO2 lattice. They can give TiO2 excellent weather resistance. The organic matter available for surface treatment includes the surfactant, the coupling agent, and the macromolecule. They can improve the affinity of TiO2 with various organic matrices. Surfactant treatment is relatively simple. Coupling agents can give TiO2 more novel properties, such as thermal stability. Macromolecules can increase the volume of TiO2 particles through steric hindrance and improve the dispersion of TiO2 in an organic matrix. However, coating TiO2 in a single matter is challenging to meet the increasing performance requirements. Therefore, it is necessary to study further the effect of co-coating with different inorganic oxides and organic matter on the structure and properties of TiO2

Variations of deep soil moisture under different vegetation types and influencing factors in a watershed of the Loess Plateau, China

Soil moisture in deep soil layers is a relatively stable water resource for vegetation growth in the semi-arid Loess Plateau of China. Characterizing the variations in deep soil moisture and its influencing factors at a moderate watershed scale is important to ensure the sustainability of vegetation restoration efforts. In this study, we focus on analyzing the variations and factors that influence the deep soil moisture (DSM) in 80–500 cm soil layers based on a soil moisture survey of the Ansai watershed in Yan'an in Shanxi Province. Our results can be divided into four main findings. (1) At the watershed scale, higher variations in the DSM occurred at 120–140 and 480–500 cm in the vertical direction. At the comparable depths, the variation in the DSM under native vegetation was much lower than that in human-managed vegetation and introduced vegetation. (2) The DSM in native vegetation and human-managed vegetation was significantly higher than that in introduced vegetation, and different degrees of soil desiccation occurred under all the introduced vegetation types. Caragana korshinskii and black locust caused the most serious desiccation. (3) Taking the DSM conditions of native vegetation as a reference, the DSM in this watershed could be divided into three layers: (i) a rainfall transpiration layer (80–220 cm); (ii) a transition layer (220–400 cm); and (iii) a stable layer (400–500 cm). (4) The factors influencing DSM at the watershed scale varied with vegetation types. The main local controls of the DSM variations were the soil particle composition and mean annual rainfall; human agricultural management measures can alter the soil bulk density, which contributes to higher DSM in farmland and apple orchards. The plant growth conditions, planting density, and litter water holding capacity of introduced vegetation showed significant relationships with the DSM. The results of this study are of practical significance for vegetation restoration strategies, especially for the choice of vegetation types, planting zones, and proper human management measures

Porcine reproductive and respiratory syndrome virus infection triggers HMGB1 release to promote inflammatory cytokine production

AbstractThe high mobility group box 1 (HMGB1) protein is an endogenous damage-associated molecular pattern (DAMP) molecule involved in the pathogenesis of various infectious agents. Based on meta-analysis of all publicly available microarray datasets, HMGB1 has recently been proposed as the most significant immune modulator during the porcine response to porcine reproductive and respiratory syndrome virus (PRRSV) infection. However, the function of HMGB1 in PRRSV pathogenesis is unclear. In this study, we found that PRRSV infection triggers the translocation of HMGB1 from the nucleus to the extracellular milieu in MARC-145 cells and porcine alveolar macrophages. Although HMGB1 has no effect on PRRSV replication, HMGB1 promotes PRRSV-induced NF-κB activation and subsequent expression of inflammatory cytokines through receptors RAGE, TLR2 and TLR4. Our findings show that HMGB1 release, triggered by PRRSV infection, enhances the efficiency of virus-induced inflammatory responses, thereby providing new insights into the pathogenesis of PRRSV infection

ACETest: Automated Constraint Extraction for Testing Deep Learning Operators

Deep learning (DL) applications are prevalent nowadays as they can help with multiple tasks. DL libraries are essential for building DL applications. Furthermore, DL operators are the important building blocks of the DL libraries, that compute the multi-dimensional data (tensors). Therefore, bugs in DL operators can have great impacts. Testing is a practical approach for detecting bugs in DL operators. In order to test DL operators effectively, it is essential that the test cases pass the input validity check and are able to reach the core function logic of the operators. Hence, extracting the input validation constraints is required for generating high-quality test cases. Existing techniques rely on either human effort or documentation of DL library APIs to extract the constraints. They cannot extract complex constraints and the extracted constraints may differ from the actual code implementation. To address the challenge, we propose ACETest, a technique to automatically extract input validation constraints from the code to build valid yet diverse test cases which can effectively unveil bugs in the core function logic of DL operators. For this purpose, ACETest can automatically identify the input validation code in DL operators, extract the related constraints and generate test cases according to the constraints. The experimental results on popular DL libraries, TensorFlow and PyTorch, demonstrate that ACETest can extract constraints with higher quality than state-of-the-art (SOTA) techniques. Moreover, ACETest is capable of extracting 96.4% more constraints and detecting 1.95 to 55 times more bugs than SOTA techniques. In total, we have used ACETest to detect 108 previously unknown bugs on TensorFlow and PyTorch, with 87 of them confirmed by the developers. Lastly, five of the bugs were assigned with CVE IDs due to their security impacts.Comment: Accepted by ISSTA 202