GeoNER:Geological Named Entity Recognition with Enriched Domain Pre-Training Model and Adversarial Training

As important geological data,a geological report contains rich expert and geological knowledge,but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain knowledge.While generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents,their effectiveness is hampered by a dearth of domain-specific knowledge,which in turn leads to a pronounced decline in recognition accuracy.This study summarizes six types of typical geological entities,with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition(GNER).In addition,Geo Wo BERT-adv BGP(Geological Word-base BERTadversarial training Bi-directional Long Short-Term Memory Global Pointer)is proposed to address the issues of ambiguity,diversity and nested entities for the geological entities.The model first uses the fine-tuned word granularitybased pre-training model Geo Wo BERT(Geological Word-base BERT)and combines the text features that are extracted using the Bi LSTM(Bi-directional Long Short-Term Memory),followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference,the decoding finally being performed using a global association pointer algorithm.The experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.

A Modified CycleGAN for Multi-Organ Ultrasound Image Enhancement via Unpaired Pre-Training

Handheld ultrasound devices are known for their portability and affordability,making them widely utilized in underdeveloped areas and community healthcare for rapid diagnosis and early screening.However,the image quality of handheld ultrasound devices is not always satisfactory due to the limited equipment size,which hinders accurate diagnoses by doctors.At the same time,paired ultrasound images are difficult to obtain from the clinic because imaging process is complicated.Therefore,we propose a modified cycle generative adversarial network(cycleGAN) for ultrasound image enhancement from multiple organs via unpaired pre-training.We introduce an ultrasound image pre-training method that does not require paired images,alleviating the requirement for large-scale paired datasets.We also propose an enhanced block with different structures in the pre-training and fine-tuning phases,which can help achieve the goals of different training phases.To improve the robustness of the model,we add Gaussian noise to the training images as data augmentation.Our approach is effective in obtaining the best quantitative evaluation results using a small number of parameters and less training costs to improve the quality of handheld ultrasound devices.

Effective distributed convolutional neural network architecture for remote sensing images target classification with a pre-training approach

How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.

Pre-training Assessment Through the Web

Web-based training is growing quickly in popularit y for professionals in industrial organizations and large enterprises. The savings in cost and time are significant. The instructor-led trainings are bounded by time and place, not to mention the cost involved in traveling, accommodation and training venue. However, in the most online training courses, all trainees are given same training materials and teaching paradigms. The problem of differentia ting the trainees’ abilities is the main concern. We need a pre-training test t o identify and classify of the weaknesses and strengths of differentiate trainee s so as to devise an appropriate training programs for the trainees. Adaptation of a Web-based Computer adaptive Test (CAT) for the pre-training test make the web-based training more efficient. The advantages of CAT are self-pacing, eff iciency, time and cost saving, immediate scoring and feedback, accuracy and secu rity, etc (Rudner, 1998; UMN, 1999; Novell, 2000; Linacre, 2000; Windowsglore, 2 000). Moreover, Web-based CAT also gives greater flexibility and convenience. T his paper describes how this CAT tool is built, how it helps instructor identify the strengths and weaknesses of trainees, and how to assure quality on the CAT system.

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.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

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.

Adapter Based on Pre-Trained Language Models for Classification of Medical Text

We present an approach to classify medical text at a sentence level automatically.Given the inherent complexity of medical text classification,we employ adapters based on pre-trained language models to extract information from medical text,facilitating more accurate classification while minimizing the number of trainable parameters.Extensive experiments conducted on various datasets demonstrate the effectiveness of our approach.

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.

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.

从静态防御到动态安全:数据安全法律保护的范式变革

网络的代际更迭和风险特征的迁移引发数据安全制度体系的变迁和数据安全法律的范式变革。在数据安全法益对信息安全法益的逐步分离过程中,数据安全法律保护范式也完成了从静态防御范式到动态安全范式的转变。两者的技术特征、法律模式配置等均有显著不同,静态防御范式重视数据的要素保护,动态安全范式注重体系保护以及安全和发展的平衡。总体国家安全观指导下的数据安全观念与数据安全环境的情势变迁是其范式转换的逻辑基础。动态安全范式是多元多层的数据安全治理机制,包括政策、法律、标准共存的多维治理体系,数据全生命周期的合规体系,以及兼有协同共治、风险预防、市场激励的多元价值体系。应当在防御、监测、威慑三位一体的治理架构下,完善和扩充现有的数据安全法律制度。

遮蔽、澄明与“双减”:创新人才涌现的哲学基础与关键

自然科学基础学科中的拔尖创新人才何以涌现是当下中国的核心论题,其哲学基础和关键能否得以揭示关系到教育观念变革和教育实践定向。此论题同时关涉“双减”这一教育政策概念尚未被把握的本质。在《科学革命的结构》以及库恩一系列论述中,范式与教-学间的联系是常规科学时期科学教育的本质所涉,不适当的教-学会在学生头脑中形成范式陷阱。范式陷阱作为隐喻与海德格尔的“遮蔽”隐喻虽有不同但高度类似,彼此唤醒。教-学中的教科书以“真”的文字和语言形成了作为掩埋的遮蔽而类似“陷阱”,扭曲/失真的遮蔽使反常现象难以被“看见”和发现,改信的困难凸显范式陷阱作为“陷阱”的特征。在海德格尔处,真理的本质揭示自身为自由,即不被旧范式遮蔽的自由或不陷入范式陷阱的自由。Doublelighten作为英译方使“双减”概念中隐藏不露的微光与澄明得以显露。与遮蔽相对的隐喻是澄明,减少本科学习量和“双减”的哲学本质在澄明。澄明和自由是自然科学领域真理发现和创新人才涌现的本质和关键,全民族始终要将学生在(旧)范式中、在不断增长的知识和技术中所保有的澄明和自由视为珍宝。本文是勾连库恩和海德格尔的一次尝试。

Establishment of Optimized Eco-productive Paradigm in the Farming-Pastoral Zone of Northern China

The farming-pastoral zone of northern China plays a dual role in ecological conditions and production and occupies an important position in the national economy. In this paper, the methodology of system engineering is introduced to construct and optimize an eco-productive paradigm system for the typical areas of the farming-pastoral zone. The system was constructed in the following steps: (1) design the framework of the paradigm system based on the data of physical site characteristics, biocommunities, production and economy, social culture and historical changes; (2) analyze the vegetation patterns, the interactions between vegetation and environmental factors (natural, social, economic, etc.) and the contributions of vegetation to the area, including the synthesis of the existing researches; and (3) provide the spatial arrangements of ecosystems and planning of each area, raise a comprehensive indicators of evaluation, evaluate the feasibility and soundness, and determine the optimum eco-productive paradigms for policy-makers which were land-use patterns within the threshold of ecological conservation. Three primitive paradigms of Maowusu (Mu Us) Ssandy Land, Loess Plateau and Huailai Basin in the farming-pastoral zone of northern China were proposed according to this paradigm system.

人工智能赋能人文社会科学研究方法变革

以AlphaFold和ChatGPT为代表的人工智能,正在改变整个人类社会,驱动科学研究也驱动人文社会科学研究新范式的形成。“人工智能驱动的人文社会科学研究(AI4SS)”又称人文社科研究第五范式。与分别基于实验、基于理论、基于仿真、基于数据驱动的科学研究第一、二、三、四范式不同,人文社科研究第五范式具有AI全学科全过程赋能人文社会科学研究、机器涌现的智能成为人文社会科学研究的组成部分、利用AI概率统计模型解决社会科学研究中的组合爆炸问题、AI为人文社会科学研究构筑多节点的复杂网络、AI虚拟现实促进人文社会科学理论研究与实践结合以及以大模型为特征的科研大平台成为人文社会科学研究的必需等六大特点。AI4SS引发人文社会科学领域的革命,在人文社会科学研究中全面驱动选题与学术史梳理、假设生成及假设论证的方法变革。为加快推进AI赋能人文社会科学研究方法变革,加快面向其研究的开源开放大模型建设是必需的。

LCZ696开启射血分数减低心力衰竭患者治疗的新时代:来自PARADIGM-HF的证据

心力衰竭(心衰)是由于任何心脏结构或功能异常导致心室充盈或射血能力受损的一组复杂临床综合征,其发病率、病死率高。肾素-血管紧张素-醛固酮系统(RAAS)的长期激活在心衰发生发展的病理生理学机制中发挥着重要作用。血管紧张素转换酶抑制剂(ACEI)、血管紧张素受体拮抗剂(ARB)以及醛固酮受体拮抗剂(MRA)已经被大规模临床试验证实对心衰具有良好的治疗效果,在心衰药物治疗策略中处于重要地位。2014年8月30日在欧洲心脏病学会年会首次公布PARADIM-HF的研究结果显示,在患者已经使用了β受体阻滞剂和MRA基础之上,LCZ696使心血管病死亡风险降低了20%,慢性心衰患者因心衰而住院的次数减少了21%。LCZ696以其独特的双相作用模式,再次证实调节神经内分泌是治疗心衰的现代观念。LCZ696将带来传统心衰药物治疗的革新。心衰治疗将大跨步进入一个新时代。

Paradigm:语言通约之基石

本文对语言通约性作了哲学界定,论述了Paradigm的作用原理,并且在对立统一规律的基础上认为不同的Paradigms之间可以找到一个共同的基础,语言的不可通约性之中存在着可通约性,Paradigms之间的转移是一个带有创新意义的、缓慢的诠释和接受的过程。

人工智能赋能高校科学研究范式创新:价值、风险与进路

高校是科学研究的主阵地,是基础研究与重大科技突破的主力军,肩负着加快形成新质生产力的重要使命。在新一轮科技革命浪潮下,人工智能技术在科学研究中的广泛应用与深度融合,不仅重塑了科学研究的基本过程,更引领着科学研究范式向智能科学研究范式转型。从逻辑实证主义视角出发,探讨人工智能赋能高校科学研究范式创新的多重价值,包括增强科学研究中的数据采集、加速科学假设的生成与验证、实现实验模拟与自动化,以及激发科学洞察与创意涌现,有助于理解AI赋能科学研究范式的价值和深远影响。基于此,审视当前科研范式转型过程中遭遇的诸多挑战与风险,包括人的边缘化风险(“人在旁路”现象)、数据偏见与误导性主张的双重困境(“数据陷阱”问题)、算法透明度的缺失(“算法黑箱”问题)以及由此引发的信任危机等,高校在科学研究范式转型中应紧密依托新一轮科技变革,植根中国科学研究实践土壤,立足已有范式寻求创变,探索一条符合我国国情且具有鲜明中国特色的高校科学研究道路。具体来讲,应加强跨学科合作与协同创新、规范数据使用标准、推动可解释性AI的发展,建立健全科研伦理审查机制,为科研范式创新提供坚实的理论支撑与行动指南。

元宇宙视域下的图书馆多模态知识流通范式

随着元宇宙图书馆发展,知识不断迭代、转化和重构,多模态知识被纳入知识服务过程。为避免知识冗余、信息不完备、信息价值缺失等知识发展弊端出现,文章梳理元宇宙图书馆和多模态知识流通发展现状,总结图书馆内数据流通问题,归纳元宇宙知识流通范式建设原则,从知识关联、知识组织到知识服务三个角度构建元宇宙视域下图书馆多模态知识流通范式(AOS),试图改善元宇宙环境下知识服务运行机制,为元宇宙图书馆各实践模型提供底层知识流动范式,为数字技术赋能以图书馆为代表的知识情报领域工作提供借鉴。

新时代西部大开发推进民族地区加快构建新发展格局的作用机理与实践进路

新时代西部大开发契合了中国全面构建现代化产业体系的战略布局要求,是推动民族地区与西部地区同步走向社会主义现代化的民族工作部署,是推动民族地区加快构建新发展格局的新动力。文章首先辨析了新时代西部大开发与民族地区加快构建新发展格局的历史背景,并从自然资源、地缘区位及国家政策三个方面分析了民族地区加快构建新发展格局的主要优势。研究发现,新时代推进西部大开发提升了民族地区科技创新能力、厚植了民族地区绿色发展能力、扩大了民族地区内需能力、深化了民族地区重点领域安全保障能力,进而加快了民族地区新发展格局生成。鉴于此,可以从四个方面探索新时代西部大开发推动民族地区加快构建新发展格局的实践进路:一是贯彻科技创新战略,建设具有民族地区特色的现代产业体系;二是坚持绿色发展底色,推进民族地区绿色低碳发展;三是创新人才队伍建设,统筹民族地区教育科技人才深度融合发展;四是推进基础设施互联互通建设,扩大高水平对外开放和畅通国内国际双循环。

可通约性——范式视域下中西医科学哲学分析

根据美国哲学家托马斯·库恩有关范式和科学革命的理论,中医与西医之间内蕴的符号系统与价值标准完全不同,属于相互竞争的两个不同“范式”,库恩认为不同范式之间存在着“不可通约性”,在科学革命的过程中新出现的理论会推翻和取代旧理论。文章从真理一元性和多样性的理论观点出发,认为对于同一研究对象的真理性认识只有一个,但真理的表现形式具有多样性。中西医是对于人类健康和疾病认识的不同呈现,研究对象的同一性奠定了中西医可通约的理论基础。另一方面,我国在探索中西医结合课程设置和专业人才培养的过程中取得的丰硕成果,也从实践中证实了中西医之间存在显著的可通约性。