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.

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.

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%.

The Research on Cross-Cultural Critical Thinking in English Teaching: A Pedagogical Paradigm Within the Ideological and Political Course Framework

The conflation of linguistic competencies with ideological and political educational paradigms presents unique opportunities and challenges for English language teaching(ELT)in our increasingly interconnected world.This research seeks to address the role of cross-cultural critical thinking within the landscape of English pedagogy,framed by the imperatives of ideological and political education.Employing a multi-method approach,including thematic content analysis and action research,the study proposes a pedagogical model designed to integrate transcultural discourse and ideologically reflective practices into the conventional ELT curriculum.This paper argues for a reimagined approach to teaching English that encourages students to engage with diverse cultural perspectives and develop a nuanced understanding of global issues.

From Control to Empowerment:A Paradigm Shift in the Discourse of Educational Supervision

As educational reforms intensify and societal emphasis shifts towards empowerment,the traditional discourse paradigm of management and control in educational supervision faces growing challenges.This paper explores the transformation of this discourse paradigm through the lens of empowerment,analyzing its distinct characteristics,potential pathways,and effective strategies.This paper begins by reviewing the concept of empowerment and examining the current research landscape surrounding the discourse paradigm in educational supervision.Subsequently,we conduct a comparative analysis of the“control”and“empowerment”paradigms,highlighting their essential differences.This analysis illuminates the key characteristics of an empowerment-oriented approach to educational supervision,particularly its emphasis on dialogue,collaboration,participation,and,crucially,empowerment itself.Ultimately,this research advocates for a shift in educational supervision towards an empowerment-oriented discourse system.This entails a multi-pronged approach:transforming ingrained beliefs,embracing renewed pedagogical concepts,fostering methodological innovation,and optimizing existing mechanisms and strategies within educational supervision.These changes are proposed to facilitate the more effective alignment of educational supervision with the pursuit of high-quality education.

Innovative Scientific Discoveries:The Role of Intelligent Computing in the Fifth Paradigm Shift

This article explores the key role of intelligent computing in driving the paradigm shift of scientific discovery.The article first outlines the five paradigms of scientific discovery,from empirical observation to theoretical models,then to computational simulation and data intensive science,and finally introduces intelligent computing as the core of the fifth paradigm.Intelligent computing enhances the ability to understand,predict,and automate scientific discoveries of complex systems through technologies such as deep learning and machine learning.The article further analyzes the applications of intelligent computing in fields such as bioinformatics,astronomy,climate science,materials science,and medical image analysis,demonstrating its practical utility in solving scientific problems and promoting knowledge development.Finally,the article predicts that intelligent computing will play a more critical role in future scientific research,promoting interdisciplinary integration,open science,and collaboration,providing new solutions for solving complex problems.

Construction of a Multi-Level Strategic System for Cultivating Cultural Industry Management Talents in Colleges and Universities

Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities.First of all,based on SWOT analysis,it is found that colleges and universities have rich educational resources and policy support,but they face challenges such as insufficient practical teaching and intensified international competition.External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion,while threats come from global market competition and talent flow.Secondly,PEST analysis reveals the key factors in the macro-environment:at the political level,the state vigorously supports the cultivation of cultural industry management talents;at the economic level,the market demand for cultural industries is strong;at the social level,the public cultural consumption is upgraded;at the technological level,digital transformation promotes industry innovation.On this basis,this paper puts forward a multi-level strategic system covering theoretical education,practical skill improvement,interdisciplinary integration,and international vision training.The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge,practical skills,and global vision,so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.

An In-depth Interpretation of the Universal Declaration of Human Rights With the Common Values of Humanity As the Research Paradigm

Interpreting the Universal Declaration of Human Rights from political,juridical and philosophical perspectives is es-sential for promoting the guiding principles of the Declaration,build-ing consensus on human rights,and advancing human rights practice in the new historical context.To conduct an academic,systematic in-terpretation of the Declaration that conforms to the trends of the times and answers the fundamental questions of the world,it is necessary to find a new research paradigm.The common values of humanity,namely peace,development,equity,justice,democracy and freedom,put forward by Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,provide the most explanatory and penetrating scientific paradigm for reaching the issue.This paper an-alyzes and reflects on the views,value foundation and principled(con-tractual)consensus of human rights in the Declaration,and narrates and foresees the far-reaching significance of the three global initia-tives(namely,the Global Development Initiative,the Global Security Initiative,and the Global Civilization Initiative)with the common val-ues of humanity as the soul in advancing the modernization of global human rights governance and building a new form of human rights civilization.

DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

From AlphaGo to ChatGPT,the field of AI has launched a series of remarkable achievements in recent years.Analyzing,comparing,and summarizing these achievements at the paradigm level is important for future AI innovation,but has not received sufficient attention.In this paper,we give an overview and perspective on machine learning paradigms.First,we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge perspective.Accordingly,we give an overview on three basic and twelve extended learning paradigms,such as Ensemble Learning,Transfer Learning,etc.,with figures in unified style.We further analyze three advanced paradigms,i.e.,AlphaGo,AlphaFold and ChatGPT.Second,to enable more efficient and effective scientific discovery,we propose to build a new ecosystem that drives AI paradigm shifts through the decentralized science(DeSci)movement based on decentralized autonomous organization(DAO).To this end,we design the Hanoi framework,which integrates human factors,parallel intelligence based on a combination of artificial systems and the natural world,and the DAO to inspire AI innovations.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

FPGA based hardware platform for trapped-ion-based multi-level quantum systems

We report a design and implementation of a field-programmable-gate-arrays(FPGA)based hardware platform,which is used to realize control and signal readout of trapped-ion-based multi-level quantum systems.This platform integrates a four-channel 2.8 Gsps@14 bits arbitrary waveform generator,a 16-channel 1 Gsps@14 bits direct-digital-synthesisbased radio-frequency generator,a 16-channel 8 ns resolution pulse generator,a 10-channel 16 bits digital-to-analogconverter module,and a 2-channel proportion integration differentiation controller.The hardware platform can be applied in the trapped-ion-based multi-level quantum systems,enabling quantum control of multi-level quantum system and highdimensional quantum simulation.The platform is scalable and more channels for control and signal readout can be implemented by utilizing more parallel duplications of the hardware.The hardware platform also has a bright future to be applied in scaled trapped-ion-based quantum systems.

William Morris Davis: Father of Geomorphology or Father of Geology’s Unrecognized Paradigm Problem

An often unrecognized problem is the geology and glacial history paradigm’s inability to explain topographic map drainage system and erosional landform evidence, which means geology research studies rarely address that type of topographic map evidence. The problem originated in the late 19th century with William Morris Davis who is sometimes called the father of geomorphology and was one of the first geologists to interpret what in the late 19th century were newly available topographic maps. An 1889 Davis paper describes selected drainage system evidence observed on an advance copy of the 1890 Doylestown (Pennsylvania) topographic map and an 1892 Ward paper written after discussions with Davis describes additional selected drainage system evidence seen on the same map. Both papers fail to mention the majority of the Doylestown map’s drainage system features including most barbed tributaries, asymmetric drainage divides, and through (dry) valleys crossing major drainage divides. Had Davis used all of the map’s drainage system and erosional landform evidence he should have recognized the map evidence shows headward erosion of an east-oriented Neshaminy Creek valley captured southwest-oriented streams which headward erosion of the south-oriented Delaware River valley and its east-oriented tributary Tohickon Creek valley had beheaded. Consciously or unconsciously, Davis chose not to alert future investigators that Doylestown topographic map evidence did not support his yet-to-be-published Pennsylvania and New Jersey erosion history interpretations and instead Davis proceeded to develop and promote erosion history interpretations which the map evidence did not support.

法治现代化的“共同特征”和“中国特色”

“中国式法治现代化”这一概念提出的理论和实践依据在于:中国式法治现代化是中国式现代化的内在组成部分;法治现代化是现代化的核心要素和重要标志;中国式法治现代化是对中国式法治现代化实践的理论表达和学理表征。世界各国法治现代化的“共同特征”包括宪法至上、尊重和保障人权、监督和制约公权、维护公平正义、构建社会秩序、促进人类和平、立足本国国情、法治文明互鉴。中国式法治现代化的特色在于坚持中国共产党领导、以人民为中心、全面依法治国、大国法治、赓续中华法治文明、面向全球化。共同特征和中国特色的完美结合构成了法治现代化的中国范式。

Locomotion Paradigms of Topological Space in Shakespeare’s Double Falsehood

In the 16th and 17th century,Britain was constantly strained by sporadic plagues,famines,fires and political conflicts.As a response in literature,Shakespeare has constructed an annular locomotion paradigm in Fletcher and his collaboration work Cardenio.Be more specific,the locomotion routes can be exemplified in Möbius strip,a rhizome system,a reciprocating juxtaposition between the foreground and the background,either of which finally runs to an annular schema.The annular in narration corresponds with The Globe theater and the round world.Shakespeare may express his expectation and prospect for the uprising bourgeoise and the newly found world by the resurrection theme in literature.

基于供需理论的生成式人工智能赋能情报工作范式模型构建与应用研究

[目的/意义]当前生成式人工智能的发展对社会产生了强烈冲击。为响应国家创新发展的迫切需求,如何利用生成式人工智能更好地完成情报工作,以满足用户的情报需求,已成为当前情报学领域关注的热点问题。[方法/过程]基于供需理论提出从数据供给侧(Supply)、智慧情报分析中台(Analysis)、情报需求侧(Demand)三方构建生成式人工智能赋能情报工作范式模型——SAD范式模型,深入分析生成式人工智能赋能情报工作机理,并结合国际前沿项目案例探讨生成式人工智能与情报工作的前瞻性融合发展。[结果/结论]生成式人工智能赋能情报工作范式模型SAD,可以更好地促进生成式人工智能赋能情报工作的各个环节,提高情报工作效率,为情报工作范式研究提供了新思路。

Neighborhood Effects and Political Trust: A Multi-level Analysis of Chinese Rural-to-Urban Migrants’ Trust in County Government

Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in the countryside (mean 4.92 and 6.34 out of 10, respectively, p < 0.001). This article explores why migrants have a certain level of political trust in their county-level government. Using data of rural-to-urban migrants from the China Family Panel Survey, this study performs a hierarchical linear modeling (HLM) to unpack the multi-level explanatory factors of rural-to-urban migrants’ political trust. Findings show that the individual-level socio-economic characteristics and perceptions of government performance (Level-1), the neighborhood-level characteristics-the physical and social status and environment of neighborhoods (Level-2), and the objective macroeconomic performance of county-level government (Level-3), work together to explain migrants’ trust levels. These results suggest that considering the effects of neighborhood-level factors on rural-to-urban migrants’ political trust merits policy and public management attention in rapidly urbanizing countries.

数字化转型背景下课堂教学范式重构

课堂教学的数字化转型,是将课堂中的各种教学要素信息化,教学信息资源化,进而促进师生高效利用信息化资源,实现课堂教学育人方式的转变与教学质量的提升。数字化转型将推动课堂教学范式重构。传统课堂是“信息呈现”教学范式,用压抑学生学情信息来保障教师教学信息的质量。学情信息是课堂教学数字化转型的生长点,充分发挥其价值,需利用“任务设计”“结构变革”“学习地图”等举措,建构以发现和利用学情为旨趣的“信息引导”教学范式。在数字化的技术赋能下,“信息引导”教学范式将走向“精准教学”,塑造人才培养的全新课堂样态。

“双碳”目标下的经济社会系统性变革:特征、要求与路径

实现碳达峰碳中和是一场广泛而深刻的经济社会系统性变革,强调单一目标或有限目标调控的研究范式,对碳中和目标下净零碳转型复杂性的理解存在局限性,难以有效反映与应对多重目标、多重约束下社会经济系统性、全局性深刻变革的影响。理解与研判“双碳”目标下经济社会系统性变革的主要特征、具体要求与实现路径,能够提升对“双碳”目标的认识,更好地释放系统性变革带来的红利以及规避所伴随的风险,推动实现“双碳”目标下经济社会的高质量发展,并为全球碳中和理论构建与实践探索作出贡献。

领域法学视野下的数字法问题

数字法律现象呈现出客体复合性、主体多样性及法律关系多元性特征,决定了传统研究范式转型升级成为数字法学的必然选择。领域法学作为调整交叉领域法现象的研究范式,与数字法问题相吻合。领域法学具体化、立体化以及复合化思维,可与部门法学协同互补;其立体性、自足性和因应性特征可对传统法学研究方法进行吸收和补充。运用领域法学研究范式,可立体化提炼数字法现象,多维度整合数字交叉学科,以达到全方位解决数字法问题的效果。在宏观上,归纳“数字赋权-代码规制-数字正义”的数字治理逻辑;在微观上,演绎数据权利分类确定、数据交易分层规范、算法治理多元共治、权益救济多方协同的数字治理机制,构建完整的数字法学知识体系。

管理学范式视角下理论融合的内在逻辑与理论建构

理论融合属于基于范式的一般理论创新,是推动中层理论发展的重要基础。从管理学范式的视角对理论融合的内在逻辑和理论建构进行分析,利用多中心性思想辨析不同范式间的复杂关联,并构建新的范式研究体系来调和观点纷争以实现“求同存异”。不同的范式会依据兼容性与一致性的逻辑规则来推动理论融合,围绕边界分别形成范式分离、范式连接、范式互补和范式叠加的关联形式。这决定了源于不同范式产生的单一理论在融合过程中的结构关系,有助于完成理论建构并产生新的视角来增强对管理活动的解释力度。