Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

SRv6 Policy业务可靠性方案探讨

介绍了SRv6 Policy、SBFD技术及5G承载网络SRv6 Policy业务可靠性方案。针对SBFD误触发问题,提出了在被动端生成BSID、部署引流策略及设置不同检测周期等3种完善措施,这些措施不仅提高了业务的可靠性,还显著提升了网络服务能力.

Policy Gradient Adaptive Dynamic Programming for Model-Free Multi-Objective Optimal Control

Dear Editor,In this letter,the multi-objective optimal control problem of nonlinear discrete-time systems is investigated.A data-driven policy gradient algorithm is proposed in which the action-state value function is used to evaluate the policy.In the policy improvement process,the policy gradient based method is employed.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

Accelerating the establishment of a new science-policy panel to address the triple planetary crisis

he triple planetary crisis—climate change,biodiversity loss,and pollution—threatens planetary health.In response to these challenges,the Intergovernmental Panel on Climate Change(IPCC)was established in 1988,followed by the formation of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services(IPBES)in 2012.Ongoing global initiatives through IPCC and IPBES have significantly advanced scientific understanding,raised public awareness,and informed policy-making in relation to climate change and biodiversity loss.However,pollution remains a pressing concern in all three crises.

Research on Policy Diffusion Path and Reproduction of River Chief System

The River Chief System (RCS) has evolved from local innovative practices to a national water governance strategy to address the current challenges in China’s water environmental management. In contrast to existing research that focuses on the strengths, weaknesses, and improvements of RCS, this study uses literature study to reveal the dynamic evolution of RCS through three phases, with RCS spreading from developed coastal areas to central and western inland regions. RCS’s diffusion path involves vertical diffusion between central and local levels and horizontal diffusion among local governments. Moreover, RCS has also achieved conceptual spillover, gradually expanding into other governance domains, such as the Lake Chief System, the Field Chief System, the Forestry Chief System, and the integration of multiple chief roles. However, it is essential to scrutinize the phenomenon of applying similar governance mechanisms to different areas, as it may result in challenges such as overburdening local governments, insufficient public participation, oversimplification of differences in natural resource endowments, and limited applicability. This study also provides suggestions on how to address these challenges. The study contributes theoretical insights and policy implications, providing a foundation for practical policy innovation.

Inactivated H9N2 vaccines developed with early strains do not protect against recent H9N2 viruses:Call for a change in H9N2 control policy

H9N2 virus has been widely distributed in wild birds and poultry around the world since its first emergence in the United States of America in 1966(Gu et al.2017;Carnaccini and Perez 2020).The virus appeared in chickens in China in the early 1990s,and over the last two decades has gradually become the dominant epidemic subtype(Sun and Liu 2015;Bi et al.2020).Although H9N2 virus infection alone cannot cause severe disease or death in poultry,H9N2 virus-infected birds experience a degree of egg production drop and can be easily infected by other pathogens,thus causing economic losses for poultry industry.

Automatic Generation of Attribute-Based Access Control Policies from Natural Language Documents

In response to the challenges of generating Attribute-Based Access Control(ABAC)policies,this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents.This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model.The manual retrieval and analysis involved in this transition are inefficient,prone to errors,and costly.Most organizations have high-level specifications defined for security policies that include a set of access control policies,which often exist in the form of natural language documents.Utilizing this rich source of information,our method effectively identifies and extracts the necessary attributes and rules for access control from natural language documents,thereby constructing and optimizing access control policies.This work transforms the problem of policy automation generation into two tasks:extraction of access control statements andmining of access control attributes.First,the Chat General Language Model(ChatGLM)isemployed to extract access control-related statements from a wide range of natural language documents by constructing unique prompts and leveraging the model’s In-Context Learning to contextualize the statements.Then,the Iterated Dilated-Convolutions-Conditional Random Field(ID-CNN-CRF)model is used to annotate access control attributes within these extracted statements,including subject attributes,object attributes,and action attributes,thus reassembling new access control policies.Experimental results show that our method,compared to baseline methods,achieved the highest F1 score of 0.961,confirming the model’s effectiveness and accuracy.

A quantitative study of disruptive technology policy texts:An example of China’s artificial intelligence policy

Purpose:The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention.This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence(AI)disruptive technology policy,and to put forward suggestions for optimizing China’s AI disruptive technology policy.Design/methodology/approach:Develop a three-dimensional analytical framework for“policy tools-policy actors-policy themes”and apply policy tools,social network analysis,and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools,cooperative relationships among policy actors,and the trends in policy theme settings within China’s innovative AI technology policy.Findings:We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close.Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects,forming a“center-periphery”network structure.Policy tool usage is predominantly focused on supply and environmental types,with a severe inadequacy in demand-side policy tool utilization.Policy themes are diverse,encompassing topics such as“Intelligent Services”“Talent Cultivation”“Information Security”and“Technological Innovation”,which will remain focal points.Under the themes of“Intelligent Services”and“Intelligent Governance”,policy tool usage is relatively balanced,with close collaboration among policy entities.However,the theme of“AI Theoretical System”lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities.Research limitations:The data sources and experimental scope are subject to certain limitations,potentially introducing biases and imperfections into the research results,necessitating further validation and refinement.Practical implications:The study introduces a three-dimensional analysis framework for disruptive technology policy texts,which is significant for formulating and enhancing disruptive technology policies.Originality/value:This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts.It systematically evaluates China’s AI policies quantitatively,focusing on policy tools,policy actors,policy themes.The study uncovers the characteristics and deficiencies of current AI policies,offering recommendations for formulating and enhancing disruptive technology policies.

Mapping Climate Services for Disaster Risk Management: A Systematic Review and Research Gaps from a Policy Process Perspective

Climate services (CS) are crucial for mitigating and managing the impacts and risks associated with climate-induced disasters. While evidence over the past decade underscores their effectiveness across various domains, particularly agriculture, to maximize their potential, it is crucial to identify emerging priority areas and existing research gaps for future research agendas. As a contribution to this effort, this paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the state-of-the-art in the field of climate services for disaster risk management. A comprehensive search across five literature databases combined with a snowball search method using ResearchRabbit was conducted and yielded 242 peer-reviewed articles, book sections, and reports over 2013-2023 after the screening process. The analysis revealed flood, drought, and food insecurity as major climate-related disasters addressed in the reviewed literature. Major climate services addressed included early warning systems, (sub)seasonal forecasts and impact-based warnings. Grounded in the policy processes’ theoretical perspective, the main focus identified and discussed three prevailing policy-oriented priority areas: 1) development of climate services, 2) use-adoption-uptake, and 3) evaluation of climate services. In response to the limitations of the prevalent supply-driven and top-down approach to climate services promotion, co-production emerges as a cross-cutting critical aspect of the identified priority areas. Despite the extensive research in the field, more attention is needed, particularly pronounced in the science-policy interface perspective, which in practice bridges scientific knowledge and policy decisions for effective policy processes. This perspective offers a valuable analytical lens as an entry point for further investigation. Hence, future research agendas would generate insightful evidence by scrutinizing this critical aspect given its importance to institutions and climate services capacity, to better understand intricate facets of the development and the integration of climate services into disaster risk management.

Provincial-level analysis of electrification feasibility and climate policy interactions

Improving electrification feasibility is essential for reducing emissions from non-electric energy sources,thereby enhancing air quality and public health.Concurrently,climate mitigation actions,such as carbon pricing policies,have significant potential to alleviate increasing carbon dioxide(CO_(2))and other co-emitted air pollutants.However,the interactions between climate policy and the improvement of electrification feasibility at the provincial level remain unclear,collectively impacting the net-zero transition of energy-intensive sectors.Here we combine a technologically rich economic-energy-environment model with air quality modeling across China to examine the health,climate,and economic implications of large-scale upgrades in electrification feasibility and climate policies from 2017 to 2030.The results indicate that advancing electrification feasibility,coupled with adopting carbon pricing policies,is likely to facilitate a transition towards electricity-dominant energy systems.Improved electrification feasibility is projected to yield a 7-25%increase in nationwide climate benefits and a 5-14%increase in health benefits by 2030.These incremental benefits,coupled with reduced economic costs,result in a 22-68%increase in net benefits.However,regionally,improvements in electrification feasibility will lead to heightened power demand and unintended emissions from electric energy production in certain provinces(e.g.,Nei Mongol)due to the coal-dominated power system.Additionally,in major coal-producing provinces like Shanxi and Shaanxi,enhanced electrification feasibility exacerbates the negative economic impacts of climate policies.This study provides quantitative insights into how improving electrification feasibility reshapes energy evolution and the benefit-cost profile of climate policy at the provincial level.The findings underscore the necessity of a well-designed compensation scheme between affected and unaffected provinces and coordinated emission mitigation across the power and other end-use sectors.

Geographical Distribution of Atmospheric Nitrogen Deposition in China and Its Response to Emission Control Policy

Atmospheric nitrogen(N)deposition has experienced significant change because of anthropogenic emissions,thereby exert-ing a pronounced impact on global ecosystem services.With the rapid development of industry and agriculture and the swift expansion of urban areas in China since the 1980s,reactive nitrogen(Nr)emissions and N deposition have substantially increased.In pursuit of im-proving air quality,China has implemented a series of environmental protection policies and undertaken diverse measures to reduce pol-lutant emissions.This paper is a review of multivariate data sources of atmospheric N deposition based on the results of literature from 1980 to 2023,and the original data from 1980 to 2020 are summarized,counted and calculated.The main findings are as follows:1)the annual average atmospheric N deposition ranged from approximately 20-40 kg/(ha·yr),with the variability primarily linked to different assessment methods;2)regional disparities were evident in the spatial distribution of N deposition,with elevated values concentrated in areas with intense Nr emissions;3)atmospheric N deposition significantly declined after 2010,particularly the deposition of oxidized N,while reduced N deposition remained stable.These results reflect the effects of China's serious control policies on nitrogen oxide(NO.)emissions and strengthen the importance of agricultural NH3 emission mitigation.This study contributes to a comprehensive understand-ing of the N dynamics in the emission-deposition process,and provides a scientific foundation for the research of environmental protec-tion,climate change,and sustainable development.

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles,susceptibility to traffic flow bottlenecks,and imperfect data in perceiving environmental information,rendering them a vital issue in the practical application of autonomous driving.To address the traffic challenges,this work focused on complex roundabouts with multi-lane and proposed a Perception EnhancedDeepDeterministic Policy Gradient(PE-DDPG)for AutonomousDriving in the Roundabouts.Specifically,themodel incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework,enhancing the vehicle’s capability to comprehend complex roundabout environments and make decisions.Furthermore,the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios,effectively mitigating traffic bottlenecks and augmenting throughput efficiency.Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO,and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process,the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles(AVs).Generally,the proposed PE-DDPGmodel could be employed for autonomous driving in complex scenarios with imperfect data.

Tracking direct and indirect impact on technology and policy of transformative research via ego citation network

Purpose:The disseminating of academic knowledge to nonacademic audiences partly relies on the transition of subsequent citing papers.This study aims to investigate direct and indirect impact on technology and policy originating from transformative research based on ego citation network.Design/methodology/approach:Key Nobel Prize-winning publications(NPs)in fields of gene engineering and astrophysics are regarded as a proxy for transformative research.In this contribution,we introduce a network-structural indicator of citing patents to measure technological impact of a target article and use policy citations as a preliminary tool for policy impact.Findings:The results show that the impact on technology and policy of NPs are higher than that of their subsequent citation generations in gene engineering but not in astrophysics.Research limitations:The selection of Nobel Prizes is not balanced and the database used in this study,Dimensions,suffers from incompleteness and inaccuracy of citation links.Practical implications:Our findings provide useful clues to better understand the characteristics of transformative research in technological and policy impact.Originality/value:This study proposes a new framework to explore the direct and indirect impact on technology and policy originating from transformative research.

China’s Proactive Fiscal Policy

In contrast to the traditional Western approach to macro-fiscal management,China’s proactive fiscal policy is founded on a people-centered development philosophy and,with distinctive Chinese characteristics,is a significant policy innovation of macroeconomic management in the Chinese modernization.Although there are notable distinctions between the Western“Keynesian”and the“nonKeynesian”schools of thought,both of these approaches’core policy goals and methodological roots are the same,composing the traditional Western macro-fiscal approach.This approach faces increasing real dilemmas.China’s proactive fiscal policy,however,places greater emphasis on future potential growth rates in addition to equilibrium between supply and demand,achieving a fiscal policy transformation with a new approach.In this paper we argue that with such a new approach,China should reconsider the nature and reasonable level of the fiscal deficit,the function and risk assessment criteria of government debt,the scope and effects of reductions in taxes and fees,its approach and focus of demand management,and the costs and resulting efficiencies of policies in order to develop a new fiscal policy paradigm that is more in line with its stated goals.

CAN INCREMENTAL POLICIES SOLVE EMPLOYMENT PROBLEMS?

On October 8,the Information Office of the State Council of the People’s Republic of China held a press conference to outline the steps of“systematically implementing a package of incremental policies to solidly promote economic growth,structural optimization and sustained momentum of development”in the wake of the Third Plenary Session of the 20th Central Committee of the Communist Party of China.The series of policy measures mentioned at the press conference all demonstrated strong continuity,stability,and sustainability.The package of incremental policies reflects three“more attentions,”namely:more attention on improving the quality of economic development,more attention on supporting the healthy development of the real economy and business entities,and more attention on coordinating high-quality development and high-level security.One central goal is to promote high-quality full employment.

Review of China's Energy Policy in 2023

In 2023,China's energy policy primarily focused on deepening systemic and institutional reforms,enhancing energy security capabilities,strengthening energy conservation and carbon reduction,and improving the standard system.It has laid the foundation for China's policy direction,which is predicated on ensuring energy security,centered on economic construction,and aimed at achieving the carbon peak and carbon neutrality goals on schedule.In the current key tasks,China has accelerated the construction of a big unified electricity market,vigorously promoted upgrading industries for low-carbon,high-end,and intelligent development,and established carbon markets and standard systems aligned with international practices,achieving substantial progress.

HELLO ASEAN Visa-free policy sparks surge of Chinese tourists to Southeast Asia

As of March 1,Malaysia,Singapore,and Thailand have all implemented visa-free entry policy for Chinese citizens,allowing stays of up to 30 days.The three Southeast Asian countries have been popular destinations for Chinese outbound tourists since the late 1980s and early 1990s.The implementation of the visa-free entry policy is expected to attract more Chinese tourists,especially young people,to travel to Southeast Asia and boost the recovery of the tourism industry in the region.

COMRADES FOREVER--To Lam’s visit demonstrates that China remains Vietnam’s top foreign policy priority

On August 20,2024,General Secretary of the Communist Party of Vietnam(CPV)Central Committee and Vietnamese President To Lam concluded his state visit to China.China was the destination for Lam's first overseas visit after taking office as general secretary of the CPV Central Committee,which clearly demonstrates that the heads of both countries attach importance to developing the bilateral relations and that Vietnam regards China as the strategic choice and top priority for its foreign policy.