Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation

End-to-end speech translation, a hot topic in recent years, aims to translate a segment of audio into a specific language with an end-to-end model. Conventional approaches employ multi-task learning and pre-training methods for this task, but they suffer from the huge gap between pre-training and fine-tuning. To address these issues, we propose a Tandem Connectionist Encoding Network (TCEN) which bridges the gap by reusing all subnets in fine-tuning, keeping the roles of subnets consistent, and pre-training the attention module. Furthermore, we propose two simple but effective methods to guarantee the speech encoder outputs and the MT encoder inputs are consistent in terms of semantic representation and sequence length. Experimental results show that our model outperforms baselines 2.2 BLEU on a large benchmark dataset.Comment: AAAI202

Effects of Continuous Exercise on Physiological Indexes among Middle-aged and Elderly Chronic Patients in Northwest China

With the increase of aging population, accompanied by a series of aging problems, the study showed that the probability of chronic disease in the elderly population is 92.1%, and further research shows that the probability of having two or more chronic diseases is 70.0%. Therefore, understanding of the distribution and spatial-temporal variation trend of risk factors related to chronic diseases can provide scientific basis for the formulation of policies and intervention strategies for the prevention and treatment of chronic diseases. It is of urgent practical significance to improve the quality of life of the elderly and reduce the social medical burden. Analysis of the data indicated that after 1 year of continuous exercise intervention, the experimental group’s blood pressure was controlled at a normal level in nearly 2 months [90～140mmHg/100～160mmHg(SBP/DBP) ]. The results showed that moderate physical activity can reduce stress and help control blood pressure in patients with high blood pressure. After 1 year of targeted exercise intervention, the experimental group significantly improved fasting blood glucose (controlled under 7.2mmol/ liter) in nearly 2 months after the second questionnaire survey. After the exercise, the blood glucose was controlled within the normal range and gradually increased. After one year of exercise intervention, the blood lipid index of the experimental group was significantly different from that of the control group, indicating that physical exercise has a positive effect on the elderly with hyperlipemia

MuseCoco: Generating Symbolic Music from Text

Generating music from text descriptions is a user-friendly mode since the text is a relatively easy interface for user engagement. While some approaches utilize texts to control music audio generation, editing musical elements in generated audio is challenging for users. In contrast, symbolic music offers ease of editing, making it more accessible for users to manipulate specific musical elements. In this paper, we propose MuseCoco, which generates symbolic music from text descriptions with musical attributes as the bridge to break down the task into text-to-attribute understanding and attribute-to-music generation stages. MuseCoCo stands for Music Composition Copilot that empowers musicians to generate music directly from given text descriptions, offering a significant improvement in efficiency compared to creating music entirely from scratch. The system has two main advantages: Firstly, it is data efficient. In the attribute-to-music generation stage, the attributes can be directly extracted from music sequences, making the model training self-supervised. In the text-to-attribute understanding stage, the text is synthesized and refined by ChatGPT based on the defined attribute templates. Secondly, the system can achieve precise control with specific attributes in text descriptions and offers multiple control options through attribute-conditioned or text-conditioned approaches. MuseCoco outperforms baseline systems in terms of musicality, controllability, and overall score by at least 1.27, 1.08, and 1.32 respectively. Besides, there is a notable enhancement of about 20% in objective control accuracy. In addition, we have developed a robust large-scale model with 1.2 billion parameters, showcasing exceptional controllability and musicality

Moment-based analysis of pinning synchronization in complex networks with sign inner-coupling configurations

In this paper, pinning synchronization of complex networks with sign inner-coupling configurations is investigated from a moment-based analysis approach. First, two representative non-linear systems with varying dynamics parameters are presented to illustrate the bifurcation of the synchronized regions. The influence of sign inner-coupling configurations on network synchronizability is then studied in detail. It is found that adding negative parameters in the inner-coupling matrix can significantly enhance the network synchronizability. Furthermore, the eigenvalue distribution of the coupling and control matrix in the pinned network is estimated using the spectral moment analysis. Finally, numerical simulations are given for illustration