Data-Driven Energy Levels Calculation of Neutral Ytterbium (ZZ = 70)

In view of the difficulty in calculating the atomic structure parameters of high-$Z$ elements, the HFR (Hartree-Fock with relativistic corrections) theory in combination with the ridge regression (RR) algorithm rather than the Cowan code's least squares fitting (LSF) method is proposed and applied. By analyzing the energy level structure parameters of the HFR theory and using the fitting experimental energy level extrapolation method, some excited state energy levels of the {Yb~I} ($Z=70$) atom including the $4f$ open shell are calculated. The advantages of the ridge regression algorithm are demonstrated by comparing it with Cowan's least squares results. In addition, the results obtained by the new method are compared with the experimental results and other theoretical results to demonstrate the reliability and accuracy of our approach

GRASS: Unified Generation Model for Speech Semantic Understanding

This paper explores the instruction fine-tuning technique for speech semantic understanding by introducing a unified end-to-end (E2E) framework that generates semantic labels conditioned on a task-related prompt for audio data. We pre-train the model using large and diverse data, where instruction-speech pairs are constructed via a text-to-speech (TTS) system. Extensive experiments demonstrate that our proposed model significantly outperforms state-of-the-art (SOTA) models after fine-tuning downstream tasks. Furthermore, the proposed model achieves competitive performance in zero-shot and few-shot scenarios. To facilitate future work on instruction fine-tuning for speech-to-semantic tasks, we release our instruction dataset and code

Association Between VDR FokI Polymorphism and Intervertebral Disk Degeneration

AbstractIntervertebral disk degeneration (IDD) is strongly associated with genetic predisposition and environmental susceptibility. Several studies been conducted to investigate the potential association between IDD and FokI polymorphism located in the gene encoding the vitamin D receptor (VDR), and inconsistent conclusions had been reached among different ethnic populations. In order to assess the association between the FokI polymorphism and the risk of IDD, we performed a comprehensive and systematic meta-analysis. Candidate articles were retrieved from PubMed, EMBASE, China National Knowledge Infrastructure (CNKI), and China Biology Medical (CBM) with strict inclusion criteria in January 2015. Among the 54 articles that were retrieved, only eight studies met the inclusion criteria. The pooled data analysis based on allele contrast, homozygote, heterozygote, dominant, and recessive models revealed no significant correlation between the FokI polymorphism and the risk of IDD. However, when stratified by ethnicity, significant associations were detected for Hispanics based on allele contrast (OR=1.395, 95% CI=1.059–1.836, P=0.018), homozygote (OR=1.849, 95% CI=1.001–3.416, P=0.049), heterozygote (OR=1.254, 95% CI=1.049–1.498, P=0.013), and dominant (OR=1.742, 95% CI=1.174–2.583, P=0.006) models, and for Asians using the dominant model (OR=1.293, 95% CI=1.025–1.632, P=0.030), whereas there is no significant association detected for Caucasians. In conclusion, FokI polymorphism is not generally associated with IDD, but there is increased risk for IDD in Hispanics and Asians carrying FokI allele T