QS-TTS: Towards Semi-Supervised Text-to-Speech Synthesis via Vector-Quantized Self-Supervised Speech Representation Learning

This paper proposes a novel semi-supervised TTS framework, QS-TTS, to improve TTS quality with lower supervised data requirements via Vector-Quantized Self-Supervised Speech Representation Learning (VQ-S3RL) utilizing more unlabeled speech audio. This framework comprises two VQ-S3R learners: first, the principal learner aims to provide a generative Multi-Stage Multi-Codebook (MSMC) VQ-S3R via the MSMC-VQ-GAN combined with the contrastive S3RL, while decoding it back to the high-quality audio; then, the associate learner further abstracts the MSMC representation into a highly-compact VQ representation through a VQ-VAE. These two generative VQ-S3R learners provide profitable speech representations and pre-trained models for TTS, significantly improving synthesis quality with the lower requirement for supervised data. QS-TTS is evaluated comprehensively under various scenarios via subjective and objective tests in experiments. The results powerfully demonstrate the superior performance of QS-TTS, winning the highest MOS over supervised or semi-supervised baseline TTS approaches, especially in low-resource scenarios. Moreover, comparing various speech representations and transfer learning methods in TTS further validates the notable improvement of the proposed VQ-S3RL to TTS, showing the best audio quality and intelligibility metrics. The trend of slower decay in the synthesis quality of QS-TTS with decreasing supervised data further highlights its lower requirements for supervised data, indicating its great potential in low-resource scenarios

Effect of continuous glucose monitoring compared with self-monitoring of blood glucose in gestational diabetes patients with HbA1c<6%: a randomized controlled trial

ObjectiveThis study evaluated the effect of continuous glucose monitoring (CGM) versus self-monitored blood glucose (SMGB) in gestational diabetes mellitus (GDM) with hemoglobin A1c (HbA1c) &lt;6%.MethodsFrom January 2019 to February 2021, 154 GDM patients with HbA1c&lt;6% at 24–28 gestational weeks were recruited and assigned randomly to either SMBG only or CGM in addition to SMBG, with 77 participants in each group. CGM was used in combination with fingertip blood glucose monitoring every four weeks until antepartum in the CGM group, while in the SMBG group, fingertip blood glucose monitoring was applied. The CGM metrics were evaluated after 8 weeks, HbA1c levels before delivery, gestational weight gain (GWG), adverse pregnancy outcomes and CGM medical costs were compared between the two groups.ResultsCompared with patients in the SMBG group, the CGM group patients had similar times in range (TIRs) after 8 weeks (100.00% (93.75-100.00%) versus 99.14% (90.97-100.00%), p=0.183) and HbA1c levels before delivery (5.31 ± 0.06% versus 5.35 ± 0.06%, p=0.599). The proportion with GWG within recommendations was higher in the CGM group (59.7% versus 40.3%, p=0.046), and the newborn birth weight was lower (3123.79 ± 369.58 g versus 3291.56 ± 386.59 g, p=0.015). There were no significant differences in prenatal or obstetric outcomes, e.g., cesarean delivery rate, hypertensive disorders, preterm births, macrosomia, hyperbilirubinemia, neonatal hypoglycemia, respiratory distress, and neonatal intensive care unit admission &gt;24 h, between the two groups. Considering glucose monitoring, SMBG group patients showed a lower cost than CGM group patients.ConclusionsFor GDM patients with HbA1c&lt;6%, regular SMBG is a more economical blood glucose monitoring method and can achieve a similar performance in glycemic control as CGM, while CGM is beneficial for ideal GWG

Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features

The misalignment of the drive system of the DFIG (Doubly Fed Induction Generator) wind turbine is one of the important factors that cause damage to the gears, bearings of the high-speed gearbox and the generator bearings. How to use the limited information to accurately determine the type of failure has become a difficult study for the scholars. In this paper, the time-domain indexes and frequency-domain indexes are extracted by using the vibration signals of various misaligned simulation conditions of the wind turbine drive system, and the time-frequency domain features—energy entropy are also extracted by the IEMD (Improved Empirical Mode Decomposition). A mixed-domain feature set is constructed by them. Then, SVM (Support Vector Machine) is used as the classifier, the mixed-domain features are used as the inputs of SVM, and PSO (Particle Swarm Optimization) is used to optimize the parameters of SVM. The fault types of misalignment are classified successfully. Compared with other methods, the accuracy of the given fault isolation model is improved

A novel catalyst based on electrospun silver-doped silica fibers with ribbon morphology

Mesoporézní silikonová nanovlákna a kompozitní nanovlákna s morfologií pásky obohacená stříbrem byla vyrobena kombinací elektrostatického zvlákňování a sol-gel metody. Komponenty solu pro výrobu stříbrem dopovaných hybridních silikonových stužek byly tetraetyl ortosilikát, polyvinylpyrolidon (PVP), triblokový polyetylenoxid-b-polypropylenoxid-b-polyetylenoxid, kopolymer Pluronic P123 a dusičnan stříbrný (AgNO3). Odvod tepla u P123 v hybridních vláknech při vysokých teplotách měl za následek vznik mesoporézní morfologie, a dále degradace PVP způsobila přeměnu z AgNO3 na stříbro ve formě nanočástic. Velikost a obsah částic v hybridních páscích byl ovlivněn koncentrací AgNO3 a tepelnými podmínkami reakce. Pro charakterizaci kompozitní pásky byly použity skenovací elektronová mikroskopie, absorpční?desorpční isoterma dusíku, transmisní elektronová mikroskopie, rentgenová difrakce a UV-Vis spektroskopie. Katalytická aktivita stuhy byla hodnocena na methylenové modři se zjištěním, že je lepší než v publikovaných předchozích studiích.Mesoporous silica nanofibers and Ag-doped composite nanoribbons were synthesized by a facile combination of an electrospinning technique and the sol-gel method. Tetraethyl orthosilicate, polyvinylpyrrolidone (PVP), triblock poly(ethylene oxide)-b-poly(propylene oxide)-b-poly(ethylene oxide), copolymer Pluronic P123, and silver nitrate (AgNO3) were the components of sol for the production of Ag-doped hybrid silica ribbons. Heat removal of structure-directing agent P123 in the hybrid fibers at high temperatures resulted in a mesoporous morphology, and the degradation of PVP caused AgNO3 to convert into silver in the form of nanoparticles. The size and content of the particles in the hybrid ribbons could be controlled by the concentration of AgNO3 and thermal treatment conditions. Scanning electron microscopy, N2 adsorption?desorption isotherm, transmission electron microscopy, X-ray diffraction, and UV-Vis spectroscopy were used to characterize the composite ribbons. The catalytic activity of the ribbons was evaluated by reduction of methylene blue dye and found to be better than in previous studies

Power supply for on-line monitoring device of power lines based on double-half ring core

In order to solve the problem of insufficient energy supply of the on-line monitoring device of the transmission line, a sensor energy acquisition scheme directly installed on the transmission line is proposed. In the current power supply, induction power supply is a kind of energy extraction method with better practical applicability. Since the monitoring devices and electronic equipment on the transmission line are often in a strong magnetic field and high voltage environment, designing a stable and reliable power supply is the guarantee for the stable operation of the monitoring devices and electronic equipment. This paper presents a structure of energy-absorbing iron core with two semi-circular magnetic cores, and studies the magnetic saturation problem in the inductive power supply and the design of the DC output circuit

A Novel Hybrid Model for Short-Term Traffic Flow Prediction Based on Extreme Learning Machine and Improved Kernel Density Estimation

Short-term traffic flow prediction is the basis of and ensures intelligent traffic control. However, the conventional models cannot make accurate predictions due to the strong nonlinearity and randomness in short-term traffic flow data. To this end, the authors of this paper developed a novel hybrid model based on extreme learning machine (ELM), adaptive kernel density estimation (AKDE), and conditional kernel density estimation (CKDE). Specifically, the ELM model was employed for nonlinear prediction. Then, AKDE was established to estimate the bandwidth of CKDE (i.e., AKDE-CKDE), which predicted the training residuals obtained by ELM. Finally, the predicted results of the two models were superimposed to derive the final prediction of the hybrid model. Two case studies based on measured data were conducted to evaluate the performance of the proposed method. The experimental results indicate that the proposed method can realize a significant improvement in terms of forecasting accuracy in comparison with the other concerned models. For instance, it performed better than the single ELM model, with an improvement in the evaluation criterion of a mean relative percentage error of 7.46%

A Prognosis Classifier for Breast Cancer Based on Conserved Gene Regulation between Mammary Gland Development and Tumorigenesis: A Multiscale Statistical Model

National Basic Research Program of China [2010CB945004]; National Natural Science Foundation of China [30772546]Identification of novel cancer genes for molecular therapy and diagnosis is a current focus of breast cancer research. Although a few small gene sets were identified as prognosis classifiers, more powerful models are still needed for the definition of effective gene sets for the diagnosis and treatment guidance in breast cancer. In the present study, we have developed a novel statistical approach for systematic analysis of intrinsic correlations of gene expression between development and tumorigenesis in mammary gland. Based on this analysis, we constructed a predictive model for prognosis in breast cancer that may be useful for therapy decisions. We first defined developmentally associated genes from a mouse mammary gland epithelial gene expression database. Then, we found that the cancer modulated genes were enriched in this developmentally associated genes list. Furthermore, the developmentally associated genes had a specific expression profile, which associated with the molecular characteristics and histological grade of the tumor. These result suggested that the processes of mammary gland development and tumorigenesis share gene regulatory mechanisms. Then, the list of regulatory genes both on the developmental and tumorigenesis process was defined an 835-member prognosis classifier, which showed an exciting ability to predict clinical outcome of three groups of breast cancer patients (the predictive accuracy 64 similar to 72%) with a robust prognosis prediction (hazard ratio 3.3 similar to 3.8, higher than that of other clinical risk factors (around 2.0-2.8)). In conclusion, our results identified the conserved molecular mechanisms between mammary gland development and neoplasia, and provided a unique potential model for mining unknown cancer genes and predicting the clinical status of breast tumors. These findings also suggested that developmental roles of genes may be important criteria for selecting genes for prognosis prediction in breast cancer

A Prognosis Classifier for Breast Cancer Based on Conserved Gene Regulation between Mammary Gland Development and Tumorigenesis: A Multiscale Statistical Model

<div><p>Identification of novel cancer genes for molecular therapy and diagnosis is a current focus of breast cancer research. Although a few small gene sets were identified as prognosis classifiers, more powerful models are still needed for the definition of effective gene sets for the diagnosis and treatment guidance in breast cancer. In the present study, we have developed a novel statistical approach for systematic analysis of intrinsic correlations of gene expression between development and tumorigenesis in mammary gland. Based on this analysis, we constructed a predictive model for prognosis in breast cancer that may be useful for therapy decisions. We first defined developmentally associated genes from a mouse mammary gland epithelial gene expression database. Then, we found that the cancer modulated genes were enriched in this developmentally associated genes list. Furthermore, the developmentally associated genes had a specific expression profile, which associated with the molecular characteristics and histological grade of the tumor. These result suggested that the processes of mammary gland development and tumorigenesis share gene regulatory mechanisms. Then, the list of regulatory genes both on the developmental and tumorigenesis process was defined an 835-member prognosis classifier, which showed an exciting ability to predict clinical outcome of three groups of breast cancer patients (the predictive accuracy 64∼72%) with a robust prognosis prediction (hazard ratio 3.3∼3.8, higher than that of other clinical risk factors (around 2.0–2.8)). In conclusion, our results identified the conserved molecular mechanisms between mammary gland development and neoplasia, and provided a unique potential model for mining unknown cancer genes and predicting the clinical status of breast tumors. These findings also suggested that developmental roles of genes may be important criteria for selecting genes for prognosis prediction in breast cancer.</p> </div