Urbanization and health in China, thinking at the national, local and individual levels

BACKGROUND: China has the biggest population in the world, and has been experiencing the largest migration in history, and its rapid urbanization has profound and lasting impacts on local and national public health. Under these conditions, a systems understanding on the correlation among urbanization, environmental change and public health and to devise solutions at national, local and individual levels are in urgent need. METHODS: In this paper, we provide a comprehensive review of recent studies which have examined the relationship between urbanization, urban environmental changes and human health in China. Based on the review, coupled with a systems understanding, we summarize the challenges and opportunities for promoting the health and wellbeing of the whole nation at national, local, and individual levels. RESULTS: Urbanization and urban expansion result in urban environmental changes, as well as residents’ lifestyle change, which can lead independently and synergistically to human health problems. China has undergone an epidemiological transition, shifting from infectious to chronic diseases in a much shorter time frame than many other countries. Environmental risk factors, particularly air and water pollution, are a major contributing source of morbidity and mortality in China. Furthermore, aging population, food support system, and disparity of public service between the migrant worker and local residents are important contributions to China’s urban health. CONCLUSIONS: At the national level, the central government could improve current environmental policies, food safety laws, and make adjustments to the health care system and to demographic policy. At the local level, local government could incorporate healthy life considerations in urban planning procedures, make improvements to the local food supply, and enforce environmental monitoring and management. At the individual level, urban residents can be exposed to education regarding health behaviour choices while being encouraged to take responsibility for their health and to participate in environmental monitoring and management.This work was supported by National Science Foundation of China (No. 41371540, No. 41201598, No. 41201155, No. 41101551), Chinese Academy of Sciences(No.KFJ-EW-STS-088), and Major Special Project-The China High-Resolution Earth Observation System

A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition

As a common way of emotion signaling via non-linguistic vocalizations, vocal burst (VB) plays an important role in daily social interaction. Understanding and modeling human vocal bursts are indispensable for developing robust and general artificial intelligence. Exploring computational approaches for understanding vocal bursts is attracting increasing research attention. In this work, we propose a hierarchical framework, based on chain regression models, for affective recognition from VBs, that explicitly considers multiple relationships: (i) between emotional states and diverse cultures; (ii) between low-dimensional (arousal & valence) and high-dimensional (10 emotion classes) emotion spaces; and (iii) between various emotion classes within the high-dimensional space. To address the challenge of data sparsity, we also use self-supervised learning (SSL) representations with layer-wise and temporal aggregation modules. The proposed systems participated in the ACII Affective Vocal Burst (A-VB) Challenge 2022 and ranked first in the "TWO'' and "CULTURE'' tasks. Experimental results based on the ACII Challenge 2022 dataset demonstrate the superior performance of the proposed system and the effectiveness of considering multiple relationships using hierarchical regression chain models.Comment: 5 pages, 3 figures, 5 table

ValueNet: A New Dataset for Human Value Driven Dialogue System

Building a socially intelligent agent involves many challenges, one of which is to teach the agent to speak guided by its value like a human. However, value-driven chatbots are still understudied in the area of dialogue systems. Most existing datasets focus on commonsense reasoning or social norm modeling. In this work, we present a new large-scale human value dataset called ValueNet, which contains human attitudes on 21,374 text scenarios. The dataset is organized in ten dimensions that conform to the basic human value theory in intercultural research. We further develop a Transformer-based value regression model on ValueNet to learn the utility distribution. Comprehensive empirical results show that the learned value model could benefit a wide range of dialogue tasks. For example, by teaching a generative agent with reinforcement learning and the rewards from the value model, our method attains state-of-the-art performance on the personalized dialog generation dataset: Persona-Chat. With values as additional features, existing emotion recognition models enable capturing rich human emotions in the context, which further improves the empathetic response generation performance in the EmpatheticDialogues dataset. To the best of our knowledge, ValueNet is the first large-scale text dataset for human value modeling, and we are the first one trying to incorporate a value model into emotionally intelligent dialogue systems. The dataset is available at https://liang-qiu.github.io/ValueNet/.Comment: Paper accepted by AAAI 202

Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection

With the global population aging rapidly, Alzheimer's disease (AD) is particularly prominent in older adults, which has an insidious onset and leads to a gradual, irreversible deterioration in cognitive domains (memory, communication, etc.). Speech-based AD detection opens up the possibility of widespread screening and timely disease intervention. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features. Based on these features, the paper also proposes a novel task-oriented approach by modeling the relationship between the participants' description and the cognitive task. Experiments are carried out on the ADReSS dataset in a binary classification setup, and models are evaluated on the unseen test set. Results and comparison with recent literature demonstrate the efficiency and superior performance of proposed acoustic, linguistic and task-oriented methods. The findings also show the importance of semantic and syntactic information, and feasibility of automation and generalization with the promising audio-only and task-oriented methods for the AD detection task.Comment: 5 pages, 3 figures, 3 table

Assessment of the resilience of a Tartary Buckwheat (Fagopyrum tataricum) cultivation system in Meigu, Southwest China

Recent socioeconomic development, increased transport and new agricultural technology are endangering the survival of traditional agriculture and the Yi people’s traditional knowledge of cultivating Tartary buckwheat. The cultural heritage of Tartary buckwheat cultivation among the Yi communities needs to be investigated and protected before its loss. The main objectives of this study are to document the Tartary buckwheat cultivation system, to analyze the agroecosystem networks that support the current system, and to measure the resilience of the ecological, agricultural and social systems using relevant indicators. The Tartary buckwheat cultivation system in Meigu County uses a rotation system, in which various crops are planted alternatively (e.g., Tartary buckwheat, green manure and potato/corn), utilizing bunch planting and furrow drilling technology. Tartary buckwheat has an important position in the major festival activities among the Yi people’s communities. Network analysis on the current agricultural system, ecosystem and social system indicated that the system was stable. The mean score of ecological, agricultural and social stability were 2.50, 2.85 and 2.53, respectively, indicating moderately stability. In contrast, socio-ecological production landscapes and seascapes (SEPLS) resilience indicators in Meigu performed only moderately, with a score of 2.63. The assessment of the resilience of the Tartary buckwheat cultivation system can provide some guidance for policy makers to strengthen biodiversity conservation, sustainable agricultural production and livelihood development (e.g., land use, responding to extreme environmental stresses and improving education levels)

AceGPT, Localizing Large Language Models in Arabic

This paper explores the imperative need and methodology for developing a localized Large Language Model (LLM) tailored for Arabic, a language with unique cultural characteristics that are not adequately addressed by current mainstream models like ChatGPT. Key concerns additionally arise when considering cultural sensitivity and local values. To this end, the paper outlines a packaged solution, including further pre-training with Arabic texts, supervised fine-tuning (SFT) using native Arabic instructions and GPT-4 responses in Arabic, and reinforcement learning with AI feedback (RLAIF) using a reward model that is sensitive to local culture and values. The objective is to train culturally aware and value-aligned Arabic LLMs that can serve the diverse application-specific needs of Arabic-speaking communities. Extensive evaluations demonstrated that the resulting LLM called `AceGPT' is the SOTA open Arabic LLM in various benchmarks, including instruction-following benchmark (i.e., Arabic Vicuna-80 and Arabic AlpacaEval), knowledge benchmark (i.e., Arabic MMLU and EXAMs), as well as the newly-proposed Arabic cultural \& value alignment benchmark. Notably, AceGPT outperforms ChatGPT in the popular Vicuna-80 benchmark when evaluated with GPT-4, despite the benchmark's limited scale. % Natural Language Understanding (NLU) benchmark (i.e., ALUE) Codes, data, and models are in https://github.com/FreedomIntelligence/AceGPT.Comment: https://github.com/FreedomIntelligence/AceGP