Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution

Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low light, intense glow, light scattering, and the presence of multicolored light sources. Existing nighttime dehazing methods often struggle with handling glow or low-light conditions, resulting in either excessively dark visuals or unsuppressed glow outputs. In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions. To handle glow effects, our framework learns from the rendered glow pairs. Specifically, a light source aware network is proposed to detect light sources of night images, followed by the APSF (Angular Point Spread Function)-guided glow rendering. Our framework is then trained on the rendered images, resulting in glow suppression. Moreover, we utilize gradient-adaptive convolution, to capture edges and textures in hazy scenes. By leveraging extracted edges and textures, we enhance the contrast of the scene without losing important structural details. To boost low-light intensity, our network learns an attention map, then adjusted by gamma correction. This attention has high values on low-light regions and low values on haze and glow regions. Extensive evaluation on real nighttime haze images, demonstrates the effectiveness of our method. Our experiments demonstrate that our method achieves a PSNR of 30.38dB, outperforming state-of-the-art methods by 13$\%$ on GTA5 nighttime haze dataset. Our data and code is available at: \url{https://github.com/jinyeying/nighttime_dehaze}.Comment: Accepted to ACM'MM2023, https://github.com/jinyeying/nighttime_dehaz

The role of health system governance in strengthening the rural health insurance system in China.

BACKGROUND: Systems of governance play a key role in the operation and performance of health systems. In the past six decades, China has made great advances in strengthening its health system, most notably in establishing a health insurance system that enables residents of rural areas to achieve access to essential services. Although there have been several studies of rural health insurance schemes, these have focused on coverage and service utilization, while much less attention has been given to the role of governance in designing and implementing these schemes. METHODS: Information from publications and policy documents relevant to the development of two rural health insurance policies in China was obtained, analysed, and synthesise. 92 documents on CMS (Cooperative Medical Scheme) or NCMS (New Rural Cooperative Medical Scheme) from four databases searched were included. Data extraction and synthesis of the information were guided by a framework that drew on that developed by the WHO to describe health system governance and leadership. RESULTS: We identified a series of governance practices that were supportive of progress, including the prioritisation by the central government of health system development and certain health policies within overall national development; strong government commitment combined with a hierarchal administrative system; clear policy goals coupled with the ability for local government to adopt policy measures that take account of local conditions; and the accumulation and use of the evidence generated from local practices. However these good practices were not seen in all governance domains. For example, poor collaboration between different government departments was shown to be a considerable challenge that undermined the operation of the insurance schemes. CONCLUSIONS: China's success in achieving scale up of CMS and NCMS has attracted considerable interest in many low and middle income countries (LMICs), especially with regard to the schemes' designs, coverage, and funding mechanisms. However, this study demonstrates that health systems governance may be critical to enable the development and operation of such schemes. Given that many LMICs are expanding health financing system to cover populations in rural areas or the informal sectors, we argue that strengthening specific practices in each governance domain could inform the adaptation of these schemes to other settings

Distinct MicroRNA Subcellular Size and Expression Patterns in Human Cancer Cells

Introduction. Small noncoding RNAs have important regulatory functions in different cell pathways. It is believed that most of them mainly play role in gene post-transcriptional regulation in the cytoplasm. Recent evidence suggests miRNA and siRNA activity in the nucleus. Here, we show distinct genome-wide sub-cellular localization distribution profiles of small noncoding RNAs in human breast cancer cells. Methods. We separated breast cancer cell nuclei from cytoplasm, and identified small RNA sequences using a high-throughput sequencing platform. To determine the relationship between miRNA sub-cellular distribution and cancer progression, we used microarray analysis to examine the miRNA expression levels in nucleus and cytoplasm of three human cell lines, one normal breast cell line and two breast cancer cell lines. Logistic regression and SVM were used for further analysis. Results. The sub-cellular distribution of small noncoding RNAs shows that numerous miRNAs and their isoforms (isomiR) not only locate to the cytoplasm but also appeare in the nucleus. Subsequent microarray analyses indicated that the miRNA nuclear-cytoplasmic-ratio is a significant characteristic of different cancer cell lines. Conclusions. Our results indicate that the sub-cellular distribution is important for miRNA function, and that the characterization of the small RNAs sub-cellular localizome may contribute to cancer research and diagnosis

Escaping from pollution: the effect of air quality on inter-city population mobility in China

China faces severe air pollution issues due to the rapid growth of the economy, causing concerns for human physical and mental health as well as behavioral changes. Such adverse impacts can be mediated by individual avoidance behaviors such as traveling from polluted cities to cleaner ones. This study utilizes smartphone-based location data and instrumental variable regression to try and find out how air quality affects population mobility. Our results confirm that air quality does affect the population outflows of cities. An increase of 100 points in the air quality index will cause a 49.60% increase in population outflow, and a rise of 1 μg m−3 in PM2.5 may cause a 0.47% rise in population outflow. Air pollution incidents can drive people to leave their cities 3 days or a week later by railway or road. The effect is heterogeneous among workdays, weekends and holidays. Our results imply that air quality management can be critical for urban tourism and environmental competitiveness

Extending access to essential services against constraints: the three-tier health service delivery system in rural China (1949-1980).

BACKGROUND: China has made remarkable progress in scaling up essential services during the last six decades, making health care increasingly available in rural areas. This was partly achieved through the building of a three-tier health system in the 1950s, established as a linked network with health service facilities at county, township and village level, to extend services to the whole population. METHODS: We developed a Theory of Change to chart the policy context, contents and mechanisms that may have facilitated the establishment of the three-tier health service delivery system in rural China. We systematically synthesized the best available evidence on how China achieved universal access to essential services in resource-scarce rural settings, with a particular emphasis on the experiences learned before the 1980s, when the country suffered a particularly acute lack of resources. RESULTS: The search identified only three peered-reviewed articles that fit our criteria for scientific rigor. We therefore drew extensively on government policy documents, and triangulated them with other publications and key informant interviews. We found that China's three-tier health service delivery system was established in response to acute health challenges, including high fertility and mortality rates. Health system resources were extremely low in view of the needs and insufficient to extend access to even basic care. With strong political commitment to rural health and a "health-for-all" policy vision underlying implementation, a three-tier health service delivery model connecting villages, townships and counties was quickly established. We identified several factors that contributed to the success of the three-tier system in China: a realistic health human resource development strategy, use of mass campaigns as a vehicle to increase demand, an innovative financing mechanisms, public-private partnership models in the early stages of scale up, and an integrated approach to service delivery. An implementation process involving gradual adaptation and incorporation of the lessons learnt was also essential. CONCLUSIONS: China's 60 year experience in establishing a de-professionalized, community-based, health service delivery model that is economically feasible, institutionally and culturally appropriate mechanism can be useful to other low- and middle-income countries (LMICs) seeking to extend essential services. Lessons can be drawn from both reform content and from its implementation pathway, identifying the political, institutional and contextual factors shaping the three-tier delivery model over time

Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets often lack practical values for large-scale real-world applications. In this paper, we describe Tenrec, a novel and publicly available data collection for RS that records various user feedback from four different recommendation scenarios. To be specific, Tenrec has the following five characteristics: (1) it is large-scale, containing around 5 million users and 140 million interactions; (2) it has not only positive user feedback, but also true negative feedback (vs. one-class recommendation); (3) it contains overlapped users and items across four different scenarios; (4) it contains various types of user positive feedback, in forms of clicks, likes, shares, and follows, etc; (5) it contains additional features beyond the user IDs and item IDs. We verify Tenrec on ten diverse recommendation tasks by running several classical baseline models per task. Tenrec has the potential to become a useful benchmark dataset for a majority of popular recommendation tasks

Strengthening public health services to achieve universal health coverage in China.

Better integration of public health and medical services and greater focus on quality of services are needed to make further progress on health outcomes, say Beibei Yuan and colleague

TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

Learning large-scale pre-trained models on broad-ranging data and then transfer to a wide range of target tasks has become the de facto paradigm in many machine learning (ML) communities. Such big models are not only strong performers in practice but also offer a promising way to break out of the task-specific modeling restrictions, thereby enabling task-agnostic and unified ML systems. However, such a popular paradigm is mainly unexplored by the recommender systems (RS) community. A critical issue is that standard recommendation models are primarily built on categorical identity features. That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms. To pursue the transferable recommendations, we propose studying pre-trained RS models in a novel scenario where a user's interaction feedback involves a mixture-of-modality (MoM) items, e.g., text and images. We then present TransRec, a very simple modification made on the popular ID-based RS framework. TransRec learns directly from the raw features of the MoM items in an end-to-end training manner and thus enables effective transfer learning under various scenarios without relying on overlapped users or items. We empirically study the transferring ability of TransRec across four different real-world recommendation settings. Besides, we look at its effects by scaling source and target data size. Our results suggest that learning neural recommendation models from MoM feedback provides a promising way to realize universal RS

A token-based dynamic scheduled MAC protocol for health monitoring

Developments of wireless body area networks (WBANs) facilitate the pervasive health monitoring with mHealth applications. WBANs can support continuous health monitoring for the human body in convenience and high efficiency without any intervention. The monitoring data in health care have the characteristics of various data flows and heterogeneous data arrival rates, the transmission of which must be in timeliness and reliability, especially the burst data. Moreover, the energy-constraint nodes should be provident in energy consumption. Designing MAC protocols with high reliability and energy efficiency for WBANs is the prime consideration. In this paper, we propose a token-based two-round reservation MAC (TTR MAC) protocol based on IEEE 802.15.6 with considering the data features of health monitoring. With analyzing the characteristics of monitoring data, one-round reservation is conducted for periodic data and two-round reservation is generated adaptively for burst data to save energy. Besides, TTR MAC protocol assigns appropriate number of allocation slots to nodes in heterogeneous data arrival rates. Furthermore, a token is introduced on the basis of user priority and health severity index to indicate the transmission order of nodes with burst data, which highly decreases the average delay. In addition, a bit sequence scheduled algorithm is proposed for m-periodic (m>1) monitoring data for network capacity expansion. The simulation results show that TTR MAC protocol achieves higher energy efficiency and longer lifetime compared with IEEE 802.15.6 and other one-round reservation MAC (OR MAC) protocols for both 1-periodic and m-periodic data.info:eu-repo/semantics/publishedVersio