866 research outputs found
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
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
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
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
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