Immigrants’ health education and economic behaviours: saving rates, social medical insurance and house purchase

Healthy China is a crucial policy for advancing global health, addressing inequality between rural and urban health education, and helping the domestic markets recover after the COVID-19 outbreak. This study combines life cycle mechanisms and safety beliefs to evaluate the long-lasting values of health education. We employed data from the China Migration Dynamic Surveys to examine the economic behaviours of 720,900 immigrants using a robust empirical approach combining an Extended Regression Model (E.R.M.), Average Treatment Effects (A.T.E.), and heterogeneous treatment effects. We find that health education increases participation in social medical insurance and the likelihood of purchasing a house. In contrast, the relationship between health education and saving rates is non-linear effects. Empirically robust heterogeneous treatment effects account for heterogeneity in the previous and the younger generations, as well as urban and rural citizens’ long-run effects of health education. This study’s findings suggest that health education stimulates immigrants’ consumption behaviours; however, extra health education is not desirable. Rural-urban citizenship acquisition bias is found to significantly affect health education

Complex Image Generation SwinTransformer Network for Audio Denoising

Achieving high-performance audio denoising is still a challenging task in real-world applications. Existing time-frequency methods often ignore the quality of generated frequency domain images. This paper converts the audio denoising problem into an image generation task. We first develop a complex image generation SwinTransformer network to capture more information from the complex Fourier domain. We then impose structure similarity and detailed loss functions to generate high-quality images and develop an SDR loss to minimize the difference between denoised and clean audios. Extensive experiments on two benchmark datasets demonstrate that our proposed model is better than state-of-the-art methods

Re-Discussion on Defining Standards of Chinese Noun-Quantity Compound Word

Chinese noun-quantity compound word is a special structural style in Chinese compound words. Based on the previous research, this paper attempts to define the six standards of Chinese noun-quantity compound word, which is more comprehensive and perfect

DCHT: Deep Complex Hybrid Transformer for Speech Enhancement

Most of the current deep learning-based approaches for speech enhancement only operate in the spectrogram or waveform domain. Although a cross-domain transformer combining waveform- and spectrogram-domain inputs has been proposed, its performance can be further improved. In this paper, we present a novel deep complex hybrid transformer that integrates both spectrogram and waveform domains approaches to improve the performance of speech enhancement. The proposed model consists of two parts: a complex Swin-Unet in the spectrogram domain and a dual-path transformer network (DPTnet) in the waveform domain. We first construct a complex Swin-Unet network in the spectrogram domain and perform speech enhancement in the complex audio spectrum. We then introduce improved DPT by adding memory-compressed attention. Our model is capable of learning multi-domain features to reduce existing noise on different domains in a complementary way. The experimental results on the BirdSoundsDenoising dataset and the VCTK+DEMAND dataset indicate that our method can achieve better performance compared to state-of-the-art methods.Comment: IEEE DDP conferenc

SAI: Solving AI Tasks with Systematic Artificial Intelligence in Communication Network

In the rapid development of artificial intelligence, solving complex AI tasks is a crucial technology in intelligent mobile networks. Despite the good performance of specialized AI models in intelligent mobile networks, they are unable to handle complicated AI tasks. To address this challenge, we propose Systematic Artificial Intelligence (SAI), which is a framework designed to solve AI tasks by leveraging Large Language Models (LLMs) and JSON-format intent-based input to connect self-designed model library and database. Specifically, we first design a multi-input component, which simultaneously integrates Large Language Models (LLMs) and JSON-format intent-based inputs to fulfill the diverse intent requirements of different users. In addition, we introduce a model library module based on model cards which employ model cards to pairwise match between different modules for model composition. Model cards contain the corresponding model's name and the required performance metrics. Then when receiving user network requirements, we execute each subtask for multiple selected model combinations and provide output based on the execution results and LLM feedback. By leveraging the language capabilities of LLMs and the abundant AI models in the model library, SAI can complete numerous complex AI tasks in the communication network, achieving impressive results in network optimization, resource allocation, and other challenging tasks

Succinct Explanations With Cascading Decision Trees

The decision tree is one of the most popular and classical machine learning models from the 1980s. However, in many practical applications, decision trees tend to generate decision paths with excessive depth. Long decision paths often cause overfitting problems, and make models difficult to interpret. With longer decision paths, inference is also more likely to fail when the data contain missing values. In this work, we propose a new tree model called Cascading Decision Trees to alleviate this problem. The key insight of Cascading Decision Trees is to separate the decision path and the explanation path. Our experiments show that on average, Cascading Decision Trees generate 63.38% shorter explanation paths, avoiding overfitting and thus achieve higher test accuracy. We also empirically demonstrate that Cascading Decision Trees have advantages in the robustness against missing values

Analysis of Communication Strategies and Approaches of Social Smart Elderly Caring Service Platform

With the development of Internet technology and the intensification of population aging, whether to provide effective smart old-age service security for the elderly has become a social public issue of concern. Through convenient Internet information technology, build an Internet communication platform for smart elderly caring services, and provide comprehensive care and convenience for the elderly with the help of elderly care information dissemination and community mutual assistance in the platform, in order to improve the quality of life of the elderly and the level of social elderly care services, and promote the development of community elderly care services and the elderly silver industry chain. Therefore, aiming at the possible problems in the information communication process of the social smart elderly caring service platform, this paper explores the effective communication strategies and approaches of the social smart elderly caring service platform, which has practical social significance and value

DPATD: Dual-Phase Audio Transformer for Denoising

Recent high-performance transformer-based speech enhancement models demonstrate that time domain methods could achieve similar performance as time-frequency domain methods. However, time-domain speech enhancement systems typically receive input audio sequences consisting of a large number of time steps, making it challenging to model extremely long sequences and train models to perform adequately. In this paper, we utilize smaller audio chunks as input to achieve efficient utilization of audio information to address the above challenges. We propose a dual-phase audio transformer for denoising (DPATD), a novel model to organize transformer layers in a deep structure to learn clean audio sequences for denoising. DPATD splits the audio input into smaller chunks, where the input length can be proportional to the square root of the original sequence length. Our memory-compressed explainable attention is efficient and converges faster compared to the frequently used self-attention module. Extensive experiments demonstrate that our model outperforms state-of-the-art methods.Comment: IEEE DD