To Achieve Security and High Spectrum Efficiency: A New Transmission System Based on Faster-than-Nyquist and Deep Learning

With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable. Faster-than-Nyquist (FTN) signaling, which was proposed in the 1970s, has been a promising paradigm to improve the spectrum utilization. In this paper, we try to apply FTN into secure communications and propose a secure and high-spectrum-efficiency transmission system based on FTN and deep learning (DL). In the proposed system, the hopping symbol packing ratio with random values makes it difficult for the eavesdropper to obtain the accurate symbol rate and inter-symbol interference (ISI). While the receiver can use the blind estimation to choose the true parameters with the aid of DL. The results show that without the accurate symbol packing ratio, the eavesdropper will suffer from severe performance degradation. As a result, the system can achieve a secure transmission with a higher spectrum efficiency. Also, we propose a simplified symbol packing ratio estimation which has bee employed in our proposed system. Results show that the proposed simplified estimation achieves nearly the same performance as the original structure while its complexity has been greatly reduced

Expressive paragraph text-to-speech synthesis with multi-step variational autoencoder

Neural networks have been able to generate high-quality single-sentence speech with substantial expressiveness. However, it remains a challenge concerning paragraph-level speech synthesis due to the need for coherent acoustic features while delivering fluctuating speech styles. Meanwhile, training these models directly on over-length speech leads to a deterioration in the quality of synthesis speech. To address these problems, we propose a high-quality and expressive paragraph speech synthesis system with a multi-step variational autoencoder. Specifically, we employ multi-step latent variables to capture speech information at different grammatical levels before utilizing these features in parallel to generate speech waveform. We also propose a three-step training method to improve the decoupling ability. Our model was trained on a single-speaker French audiobook corpus released at Blizzard Challenge 2023. Experimental results underscore the significant superiority of our system over baseline models.Comment: 5 pages, 1 figure, 2 table

A causational analysis of scholars' years of active academic careers vis-à-vis their academic productivity and academic influence

Taking the scholarly activities of 73 doctoral program mentors working at the Chinese Academy of Medical Sciences &amp; Peking Union Medical College (the CAMS &amp; PUMC) as a sample of our investigative survey, we tried using such statistical methods as the analysis of variance (ANOVA), factor analysis and correlation analysis to compare the different characteristics of scholarship assessment of Chinese medical scholars as exhibited in their published papers in domestic and foreign journals. Our research findings show that citations per paper and A-index are more suitable for assessing the highly accomplished senior Chinese medical professionals (e.g. academicians) for their domestic and international scholarship attainment. In contrast, the m-quotient is not deemed appropriate to assess their academic influence both at home and abroad. Upon our further analysis of 6 evaluative indicators, we noticed that these indicators might be applied in two different aspects: One is from the viewpoint of Chinese scholars' academic influence at home, which has been evaluated mainly from the perspective of &quot;total&quot; amount and &quot;average&quot; amount of both publications and citations. The other is from their academic impact embodied by the means of documents retrieved from the Web of Science, which is mainly assessed from the two viewpoints of publications and citations. It is suggested that the accumulated time-length of a given scholar's active engagement in professional practice in a specific subject area be taken into consideration while assessing a researcher's performance at home and abroad</p

Ultraviolet C (UVC) Standards and Best Practices for the Swine Industry

Ultraviolet C (UVC) light has been widely used for disinfection for a long time in many industries, including human medicine and food processing. The practical application of this technology in livestock production is a more recent development and is increasingly being used on swine farms as producers look for ways to improve biosecurity in response to endemic diseases and the threat of transboundary and foreign animal diseases, such as African swine fever virus (ASFV). However, many swine producers and veterinarians are unfamiliar with the physics/mechanisms of UVC, the doses required to inactivate swine pathogens, and practical conditions under which UVC can operate effectively and practically on swine farms. Safety and maintenance requirements regarding the application are also not widely known. The pork industry lacks standards and best practices to apply this technology effectively and safely. To address this need, subject matter experts were convened for a one-day workshop to define standards and best practices for the use of UVC in the swine industry. The members of the working group included practicing swine veterinarians as well as academics with expertise in epidemiology, infectious disease, biosecurity, chemistry, and engineering. This white paper is the outcome of the workshop. In addition, the content of the white paper may be used to develop fact sheets, brochures and/or tutorial videos for swine producers and veterinarians

Using protection motivation theory to explain the intention to initiate human papillomavirus vaccination among men who have sex with men in China

Human papillomavirus (HPV) infection and related diseases are common among men who have sex with men (MSM). The most effective prevention is HPV vaccination. In China, however, men are not included in the HPV vaccination plan. We investigated the intention to initiate HPV vaccination and associated factors among MSM in China. Methods We surveyed 563 unvaccinated MSM aged 18 or older from six cities in China. Participants completed an electronic questionnaire about demographics, knowledge of and attitude towards HPV and HPV vaccine, intention to initiate HPV vaccination, willingness to recommend HPV vaccine to peers, feeling about government policy about HPV vaccination. We used the structural equation modeling (SEM) to analyze factors associated with HPV vaccine intention. Results The knowledge of HPV and HPV vaccine among participants was low. The mean score of knowledge about HPV and HPV vaccine was only 1.59 (range 0–11). The intention to initiate HPV vaccination within 6 months among participants was moderate (43.3% in total, 18.1% for ‘very high' and 25.2% for ‘above average')

Local Temporal Correlation Common Spatial Patterns for Single Trial EEG Classification during Motor Imagery

Common spatial pattern (CSP) is one of the most popular and effective feature extraction methods for motor imagery-based braincomputer interface (BCI), but the inherent drawback of CSP is that the estimation of the covariance matrices is sensitive to noise. In this work, local temporal correlation (LTC) information was introduced to further improve the covariance matrices estimation (LTCCSP). Compared to the Euclidean distance used in a previous CSP variant named local temporal CSP (LTCSP), the correlation may be a more reasonable metric to measure the similarity of activated spatial patterns existing in motor imagery period. Numerical comparisons among CSP, LTCSP, and LTCCSP were quantitatively conducted on the simulated datasets by adding outliers to Dataset IVa of BCI Competition III and Dataset IIa of BCI Competition IV, respectively. Results showed that LTCCSP achieves the highest average classification accuracies in all the outliers occurrence frequencies. The application of the three methods to the EEG dataset recorded in our laboratory also demonstrated that LTCCSP achieves the highest average accuracy. The above results consistently indicate that LTCCSP would be a promising method for practical motor imagery BCI application