通信运营商及通信企业相继发布通信大模型,通信大模型作为提升网络运营智能化的关键基础设施是产业共识。通信网络中如何引入AI,AI引入的演进思路和实施方法需要进一步明确。“AI for NET”基于全栈AI推动网络智能化提升。探讨了中国联...通信运营商及通信企业相继发布通信大模型,通信大模型作为提升网络运营智能化的关键基础设施是产业共识。通信网络中如何引入AI,AI引入的演进思路和实施方法需要进一步明确。“AI for NET”基于全栈AI推动网络智能化提升。探讨了中国联通AI for NET理念、架构、演进思路和实施方法,为业界AI赋能网络提供参考。展开更多
The convergence of Software Defined Networking(SDN)in Internet of Vehicles(IoV)enables a flexible,programmable,and globally visible network control architecture across Road Side Units(RSUs),cloud servers,and automobil...The convergence of Software Defined Networking(SDN)in Internet of Vehicles(IoV)enables a flexible,programmable,and globally visible network control architecture across Road Side Units(RSUs),cloud servers,and automobiles.While this integration enhances scalability and safety,it also raises sophisticated cyberthreats,particularly Distributed Denial of Service(DDoS)attacks.Traditional rule-based anomaly detection methods often struggle to detectmodern low-and-slowDDoS patterns,thereby leading to higher false positives.To this end,this study proposes an explainable hybrid framework to detect DDoS attacks in SDN-enabled IoV(SDN-IoV).The hybrid framework utilizes a Residual Network(ResNet)to capture spatial correlations and a Bi-Long Short-Term Memory(BiLSTM)to capture both forward and backward temporal dependencies in high-dimensional input patterns.To ensure transparency and trustworthiness,themodel integrates the Explainable AI(XAI)technique,i.e.,SHapley Additive exPlanations(SHAP).SHAP highlights the contribution of each feature during the decision-making process,facilitating security analysts to understand the rationale behind the attack classification decision.The SDN-IoV environment is created in Mininet-WiFi and SUMO,and the hybrid model is trained on the CICDDoS2019 security dataset.The simulation results reveal the efficacy of the proposed model in terms of standard performance metrics compared to similar baseline methods.展开更多
Scar-related ventricular tachycardia(VT)is a malignant arrhythmia with high mortality rates in patients with cardiomyopathies such as ischemic and dilated cardiomyopathy.[1]While implantable cardioverter defibrillator...Scar-related ventricular tachycardia(VT)is a malignant arrhythmia with high mortality rates in patients with cardiomyopathies such as ischemic and dilated cardiomyopathy.[1]While implantable cardioverter defibrillators(ICD)effectively terminate VT episodes and prevent sudden cardiac death,recurrent ICD discharges may precipitate electrical storms and severely impair quality of life.Radiofrequency catheter ablation is another available treatment for VT but faces challenges in rapidly mapping the critical isthmus during hemodynamically unstable VT.Stereotactic arrhythmia radioablation(STAR)has emerged as a novel,non-invasive,and effective approach for refractory VT over the past decade.展开更多
一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家...一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。展开更多
AI for Science(AI4S)构成图书馆学发展转型的全新语境。在剖析科研范式演进及其图书馆学映射的基础上,分析AI4S对图书馆学发展的可能影响,展望图书馆学未来发展的可能路径。AI4S能够通过转移图书馆学关注重点、增强学科问题需求、提振...AI for Science(AI4S)构成图书馆学发展转型的全新语境。在剖析科研范式演进及其图书馆学映射的基础上,分析AI4S对图书馆学发展的可能影响,展望图书馆学未来发展的可能路径。AI4S能够通过转移图书馆学关注重点、增强学科问题需求、提振人才市场需求、推动学科研究范式转型等,创变图书馆学内涵、知识体系、人才培养和研究范式,为图书馆学发展提供新的战略牵引。图书馆学应当面向战略需求,主动布局,寻找学科知识增长点,主动融入信息资源管理和数据智能超学科生态,打造开放式协作学科创新平台,实现图书馆学的长效创新发展。展开更多
在全面推进“人工智能+”行动和加快实现高水平科技自立自强的背景下,AI for Science正成为引领科研范式变革、培育新质生产力和重塑全球科技竞争格局的关键力量。2025年下半年,美国、英国、欧盟和日本等主要经济体密集出台了多项AI for...在全面推进“人工智能+”行动和加快实现高水平科技自立自强的背景下,AI for Science正成为引领科研范式变革、培育新质生产力和重塑全球科技竞争格局的关键力量。2025年下半年,美国、英国、欧盟和日本等主要经济体密集出台了多项AI for Science政策文件。虽然各国因资源禀赋与制度差异选择不同发展路径,但普遍将AI for Science上升为国家级战略工程,呈现出科研流程实体自动化、科学数据资产化以及主权型科研基础设施建设等共同趋势。美国强调以国家动员体系巩固科技霸权,英国聚焦优势领域的敏捷突围策略,欧盟通过泛欧整合维护技术主权,日本则以理论驱动和标准引领构建竞争优势。国际经验对我国“十五五”时期推进AI for Science具有重要启示意义。应将AI for Science作为系统性战略任务整体布局,统筹算力、能源与科研网络建设,推动自动化实验平台与接口标准自主可控,完善科学数据资产化制度,通过有组织科研与复合型人才培养,为新质生产力发展提供持续动力。展开更多
Monge–Ampere equations(MAEs)are fully nonlinear second-order partial differential equations(PDEs),which are closely related to various fields including optimal transport(OT)theory,geometrical optics and affine geomet...Monge–Ampere equations(MAEs)are fully nonlinear second-order partial differential equations(PDEs),which are closely related to various fields including optimal transport(OT)theory,geometrical optics and affine geometry.Despite their significance,MAEs are extremely challenging to solve.Although some classical numerical approaches can solve MAEs,their computational efficiency deteriorates significantly on fine grids,with convergence often heavily dependent on the quality of initial estimate.Research on deep learning methods for solving MAEs is still in its early stages,which predominantly addresses simple formulations with basic Dirichlet boundary conditions.Here,we propose a deep learning method based on physicsdriven deep neural networks,enabling the solution of both simple and generalised MAEs with transport boundary conditions.In this method,we deal with two first-order sub-equations separated from MAE instead of solving the single MAE directly,which facilitates the imposition of transport boundary conditions and simplifies the training of neural networks.Moreover,we constrain the convexity of solution using the Lagrange multiplier method and maintain the optimisation process differentiable with bilinear interpolation.We provide three progressively complex examples ranging from a simple MAE with an analytical solution to a highly nonlinear variant arising in phase retrieval to validate the effectiveness of our method.For comparison,we benchmark against state-of-the-art deep learning approaches that have been systematically adapted to accommodate the specific requirements of each example.展开更多
Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning sy...Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning system for the early detection of Autism Spectrum Disorder(ASD)in children.Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning.For this,we combined several different models,including Random Forest,XGBoost,and Neural Networks,into a single,more powerful framework.We used two different types of datasets:(i)a standard behavioral dataset and(ii)a more complex multimodal dataset with images,audio,and physiological information.The datasets were carefully preprocessed for missing values,redundant features,and dataset imbalance to ensure fair learning.The results outperformed the state-of-the-art with a Regularized Neural Network,achieving 97.6%accuracy on behavioral data.Whereas,on the multimodal data,the accuracy is 98.2%.Other models also did well with accuracies consistently above 96%.We also used SHAP and LIME on a behavioral dataset for models’explainability.展开更多
With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE ...With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.展开更多
Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between...Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).展开更多
AI for Science(又称AI4S,即人工智能驱动的科学研究)是指利用人工智能技术和方法来加速科学研究和发现的过程。近年来,AI4S在蛋白质结构预测、重大疾病诊断、化学材料合成等方面实现了密集突破,正在推动实验科学、理论科学和计算科学...AI for Science(又称AI4S,即人工智能驱动的科学研究)是指利用人工智能技术和方法来加速科学研究和发现的过程。近年来,AI4S在蛋白质结构预测、重大疾病诊断、化学材料合成等方面实现了密集突破,正在推动实验科学、理论科学和计算科学等传统科学研究范式变革,已成为全球科技强国争相抢占的战略制高点。展开更多
The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent ...The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent cultivation of smart agriculture major in universities from"technology application"to"intelligent innovation".In response to the problems of insufficient AI integration,lack of contextualization,and insufficient collaboration between industry and education in the traditional"technology+"practical course system,this paper takes the smart agriculture major at Yulin Normal University as an example to construct a"AI+agriculture"practical course reconstruction framework and propose a four-dimensional transformation path of"goal-content-mode-evaluation".Through the practical exploration of modular curriculum design,scenario based practical design,integration of industry and education,and intelligent evaluation reform,a practical teaching system with local application-oriented university characteristics has been formed,providing a reference example for the cultivation of smart agriculture professionals under the background of new agricultural science.展开更多
Coordinate transformation models often fail to account for nonlinear and spatially dependent distortions,leading to significant residual errors in geospatial applications.Here,we propose a residual-based neural correc...Coordinate transformation models often fail to account for nonlinear and spatially dependent distortions,leading to significant residual errors in geospatial applications.Here,we propose a residual-based neural correction(RBNC)strategy,in which a neural network learns to model only the systematic distortions left by an initial geometric transformation.By focusing solely on residual patterns,RBNC reduces model complexity and improves performance,particularly in scenarios with sparse or structured control point configurations.We evaluate the method using both simulated datasets(with varying distortion intensities and sampling strategies)and real-world image georeferencing tasks.Compared with direct neural network coordinate converters and classical transformation models,RBNC delivers more accurate and stable results under challenging conditions,while maintaining comparable performance in ideal cases.These findings demonstrate the effectiveness of residual modelling as a light-weight and robust alternative for improving coordinate transformation accuracy.展开更多
It’s no secret that China has an aging population.Statistics from the Seventh National Population Census in 2020 showed that the country had 190.64 million people aged 65 or above,accounting for13.5 percent of its to...It’s no secret that China has an aging population.Statistics from the Seventh National Population Census in 2020 showed that the country had 190.64 million people aged 65 or above,accounting for13.5 percent of its total population.This proportion is now gradually approaching the internationally recognized threshold of 14 percent for a deeply aging society.China’s rapidly aging and mobility-limited population faces a severe shortage of millions of senior care workers.展开更多
At Beijing Tongren Hospital,an AI-powered retinal screening system can screen for 10 chronic illnesses from just two photos in two minutes.Using one fundus image from each eye,it scans for early signs of diabetic reti...At Beijing Tongren Hospital,an AI-powered retinal screening system can screen for 10 chronic illnesses from just two photos in two minutes.Using one fundus image from each eye,it scans for early signs of diabetic retinopathy,hypertension,atherosclerosis and other conditions,with a reported accuracy of about 90 percent.展开更多
Recent years have witnessed transformative changes brought about by artificial intelligence(AI)techniques with billions of parameters for the realization of high accuracy,proposing high demand for the advanced and AI ...Recent years have witnessed transformative changes brought about by artificial intelligence(AI)techniques with billions of parameters for the realization of high accuracy,proposing high demand for the advanced and AI chip to solve these AI tasks efficiently and powerfully.Rapid progress has been made in the field of advanced chips recently,such as the development of photonic computing,the advancement of the quantum processors,the boost of the biomimetic chips,and so on.Designs tactics of the advanced chips can be conducted with elaborated consideration of materials,algorithms,models,architectures,and so on.Though a few reviews present the development of the chips from their unique aspects,reviews in the view of the latest design for advanced and AI chips are few.Here,the newest development is systematically reviewed in the field of advanced chips.First,background and mechanisms are summarized,and subsequently most important considerations for co-design of the software and hardware are illustrated.Next,strategies are summed up to obtain advanced and AI chips with high excellent performance by taking the important information processing steps into consideration,after which the design thought for the advanced chips in the future is proposed.Finally,some perspectives are put forward.展开更多
Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classificati...Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.展开更多
Diabetic retinopathy(DR)is a leading cause of vision loss among working-age populations,with early screening significantly reducing the risk of blindness.However,resource-limited regions often face challenges in DR sc...Diabetic retinopathy(DR)is a leading cause of vision loss among working-age populations,with early screening significantly reducing the risk of blindness.However,resource-limited regions often face challenges in DR screening due to a shortage of ophthalmologists.This study reports the implementation and outcomes of the Chinese local standard DB52/T 1726-2023,Regulations for the application of diabetic retinopathy screening artificial intelligence,in Cambodian healthcare institutions.A pilot DR screening program with independent operational capability is established by providing a non-mydriatic fundus camera and deploying a localized diabetic retinopathy artificial intelligence(DR-AI)screening platform at the Cambodia-Kingdom Friendship Hospital in Phnom Penh,along with comprehensive training.From January to August 2025,a total of 565 patients with type 2 diabetes were screened,yielding a DR detection rate of 26.0%(147 cases).Research findings demonstrate that applying mature Chinese DR-AI screening standards and technological solutions through international collaboration in regions with a scarcity of ophthalmic professionals is both feasible and effective.This project serves as a reference for promoting DR-AI in resource-constrained countries and regions,highlighting its significant potential to leverage AI in addressing the global burden of chronic diseases and advancing the modernization of health systems.展开更多
文摘通信运营商及通信企业相继发布通信大模型,通信大模型作为提升网络运营智能化的关键基础设施是产业共识。通信网络中如何引入AI,AI引入的演进思路和实施方法需要进一步明确。“AI for NET”基于全栈AI推动网络智能化提升。探讨了中国联通AI for NET理念、架构、演进思路和实施方法,为业界AI赋能网络提供参考。
基金extend their appreciation to the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R760)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors also extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through small group research under grant number RGP2/714/46.
文摘The convergence of Software Defined Networking(SDN)in Internet of Vehicles(IoV)enables a flexible,programmable,and globally visible network control architecture across Road Side Units(RSUs),cloud servers,and automobiles.While this integration enhances scalability and safety,it also raises sophisticated cyberthreats,particularly Distributed Denial of Service(DDoS)attacks.Traditional rule-based anomaly detection methods often struggle to detectmodern low-and-slowDDoS patterns,thereby leading to higher false positives.To this end,this study proposes an explainable hybrid framework to detect DDoS attacks in SDN-enabled IoV(SDN-IoV).The hybrid framework utilizes a Residual Network(ResNet)to capture spatial correlations and a Bi-Long Short-Term Memory(BiLSTM)to capture both forward and backward temporal dependencies in high-dimensional input patterns.To ensure transparency and trustworthiness,themodel integrates the Explainable AI(XAI)technique,i.e.,SHapley Additive exPlanations(SHAP).SHAP highlights the contribution of each feature during the decision-making process,facilitating security analysts to understand the rationale behind the attack classification decision.The SDN-IoV environment is created in Mininet-WiFi and SUMO,and the hybrid model is trained on the CICDDoS2019 security dataset.The simulation results reveal the efficacy of the proposed model in terms of standard performance metrics compared to similar baseline methods.
文摘Scar-related ventricular tachycardia(VT)is a malignant arrhythmia with high mortality rates in patients with cardiomyopathies such as ischemic and dilated cardiomyopathy.[1]While implantable cardioverter defibrillators(ICD)effectively terminate VT episodes and prevent sudden cardiac death,recurrent ICD discharges may precipitate electrical storms and severely impair quality of life.Radiofrequency catheter ablation is another available treatment for VT but faces challenges in rapidly mapping the critical isthmus during hemodynamically unstable VT.Stereotactic arrhythmia radioablation(STAR)has emerged as a novel,non-invasive,and effective approach for refractory VT over the past decade.
文摘一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。
文摘AI for Science(AI4S)构成图书馆学发展转型的全新语境。在剖析科研范式演进及其图书馆学映射的基础上,分析AI4S对图书馆学发展的可能影响,展望图书馆学未来发展的可能路径。AI4S能够通过转移图书馆学关注重点、增强学科问题需求、提振人才市场需求、推动学科研究范式转型等,创变图书馆学内涵、知识体系、人才培养和研究范式,为图书馆学发展提供新的战略牵引。图书馆学应当面向战略需求,主动布局,寻找学科知识增长点,主动融入信息资源管理和数据智能超学科生态,打造开放式协作学科创新平台,实现图书馆学的长效创新发展。
文摘在全面推进“人工智能+”行动和加快实现高水平科技自立自强的背景下,AI for Science正成为引领科研范式变革、培育新质生产力和重塑全球科技竞争格局的关键力量。2025年下半年,美国、英国、欧盟和日本等主要经济体密集出台了多项AI for Science政策文件。虽然各国因资源禀赋与制度差异选择不同发展路径,但普遍将AI for Science上升为国家级战略工程,呈现出科研流程实体自动化、科学数据资产化以及主权型科研基础设施建设等共同趋势。美国强调以国家动员体系巩固科技霸权,英国聚焦优势领域的敏捷突围策略,欧盟通过泛欧整合维护技术主权,日本则以理论驱动和标准引领构建竞争优势。国际经验对我国“十五五”时期推进AI for Science具有重要启示意义。应将AI for Science作为系统性战略任务整体布局,统筹算力、能源与科研网络建设,推动自动化实验平台与接口标准自主可控,完善科学数据资产化制度,通过有组织科研与复合型人才培养,为新质生产力发展提供持续动力。
基金supported by CAAI-Huawei MindSpore Open Fund(CAAIXSJLJJ-2022-010A).
文摘Monge–Ampere equations(MAEs)are fully nonlinear second-order partial differential equations(PDEs),which are closely related to various fields including optimal transport(OT)theory,geometrical optics and affine geometry.Despite their significance,MAEs are extremely challenging to solve.Although some classical numerical approaches can solve MAEs,their computational efficiency deteriorates significantly on fine grids,with convergence often heavily dependent on the quality of initial estimate.Research on deep learning methods for solving MAEs is still in its early stages,which predominantly addresses simple formulations with basic Dirichlet boundary conditions.Here,we propose a deep learning method based on physicsdriven deep neural networks,enabling the solution of both simple and generalised MAEs with transport boundary conditions.In this method,we deal with two first-order sub-equations separated from MAE instead of solving the single MAE directly,which facilitates the imposition of transport boundary conditions and simplifies the training of neural networks.Moreover,we constrain the convexity of solution using the Lagrange multiplier method and maintain the optimisation process differentiable with bilinear interpolation.We provide three progressively complex examples ranging from a simple MAE with an analytical solution to a highly nonlinear variant arising in phase retrieval to validate the effectiveness of our method.For comparison,we benchmark against state-of-the-art deep learning approaches that have been systematically adapted to accommodate the specific requirements of each example.
基金the King Salman center for Disability Research for funding this work through Research Group No.KSRG-2024-050.
文摘Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning system for the early detection of Autism Spectrum Disorder(ASD)in children.Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning.For this,we combined several different models,including Random Forest,XGBoost,and Neural Networks,into a single,more powerful framework.We used two different types of datasets:(i)a standard behavioral dataset and(ii)a more complex multimodal dataset with images,audio,and physiological information.The datasets were carefully preprocessed for missing values,redundant features,and dataset imbalance to ensure fair learning.The results outperformed the state-of-the-art with a Regularized Neural Network,achieving 97.6%accuracy on behavioral data.Whereas,on the multimodal data,the accuracy is 98.2%.Other models also did well with accuracies consistently above 96%.We also used SHAP and LIME on a behavioral dataset for models’explainability.
基金supported by the Shanghai Municipal Education Research Project“Exploring the Practical Application of Generative Artificial Intelligence in Cultivating Innovative Thinking and Capabilities of Interdisciplinary Application Technology Talents‘Practice Path’”(C2025299)the university-level postgraduate course project“Software Process Management”(PX-2025251502)of Shanghai Sanda Universitythe key course project at the university level of Shanghai Sanda University,“Introduction to Software Engineering”(PX-5241216).
文摘With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.
基金J.YANG was supported by funding from the National Natural Science Foundation of China(Grant Nos.42475022,42261144671)the National Key R&D Program of China(Project No.2024YFC3013100)+2 种基金the Fundamental Research Funds for the Central UniversitiesM.LU was supported by the Otto Poon Centre of Climate Resilience and Sustainability at HKUST and the Hong Kong Research Grant Committee(Project No.16300424)Data processing and storage were supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).
文摘AI for Science(又称AI4S,即人工智能驱动的科学研究)是指利用人工智能技术和方法来加速科学研究和发现的过程。近年来,AI4S在蛋白质结构预测、重大疾病诊断、化学材料合成等方面实现了密集突破,正在推动实验科学、理论科学和计算科学等传统科学研究范式变革,已成为全球科技强国争相抢占的战略制高点。
基金Supported by the Autonomous Region-level Research and Practice Projects for New Engineering,New Medicine,New Agriculture,and New Humanities of Guangxi Department of Education(XNK202409)the Undergraduate Teaching Reform Project of Guangxi Higher Education(2024JGB332+1 种基金2024JGA304)the Guangxi Degree and Graduate Education Reform Project(JGY2025382).
文摘The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent cultivation of smart agriculture major in universities from"technology application"to"intelligent innovation".In response to the problems of insufficient AI integration,lack of contextualization,and insufficient collaboration between industry and education in the traditional"technology+"practical course system,this paper takes the smart agriculture major at Yulin Normal University as an example to construct a"AI+agriculture"practical course reconstruction framework and propose a four-dimensional transformation path of"goal-content-mode-evaluation".Through the practical exploration of modular curriculum design,scenario based practical design,integration of industry and education,and intelligent evaluation reform,a practical teaching system with local application-oriented university characteristics has been formed,providing a reference example for the cultivation of smart agriculture professionals under the background of new agricultural science.
基金National Council for Scientific and Technological Development,Grant No.421278/2023-4,No.309248/2025-6。
文摘Coordinate transformation models often fail to account for nonlinear and spatially dependent distortions,leading to significant residual errors in geospatial applications.Here,we propose a residual-based neural correction(RBNC)strategy,in which a neural network learns to model only the systematic distortions left by an initial geometric transformation.By focusing solely on residual patterns,RBNC reduces model complexity and improves performance,particularly in scenarios with sparse or structured control point configurations.We evaluate the method using both simulated datasets(with varying distortion intensities and sampling strategies)and real-world image georeferencing tasks.Compared with direct neural network coordinate converters and classical transformation models,RBNC delivers more accurate and stable results under challenging conditions,while maintaining comparable performance in ideal cases.These findings demonstrate the effectiveness of residual modelling as a light-weight and robust alternative for improving coordinate transformation accuracy.
文摘It’s no secret that China has an aging population.Statistics from the Seventh National Population Census in 2020 showed that the country had 190.64 million people aged 65 or above,accounting for13.5 percent of its total population.This proportion is now gradually approaching the internationally recognized threshold of 14 percent for a deeply aging society.China’s rapidly aging and mobility-limited population faces a severe shortage of millions of senior care workers.
文摘At Beijing Tongren Hospital,an AI-powered retinal screening system can screen for 10 chronic illnesses from just two photos in two minutes.Using one fundus image from each eye,it scans for early signs of diabetic retinopathy,hypertension,atherosclerosis and other conditions,with a reported accuracy of about 90 percent.
基金supported by the Hong Kong Polytechnic University(1-WZ1Y,1-W34U,4-YWER).
文摘Recent years have witnessed transformative changes brought about by artificial intelligence(AI)techniques with billions of parameters for the realization of high accuracy,proposing high demand for the advanced and AI chip to solve these AI tasks efficiently and powerfully.Rapid progress has been made in the field of advanced chips recently,such as the development of photonic computing,the advancement of the quantum processors,the boost of the biomimetic chips,and so on.Designs tactics of the advanced chips can be conducted with elaborated consideration of materials,algorithms,models,architectures,and so on.Though a few reviews present the development of the chips from their unique aspects,reviews in the view of the latest design for advanced and AI chips are few.Here,the newest development is systematically reviewed in the field of advanced chips.First,background and mechanisms are summarized,and subsequently most important considerations for co-design of the software and hardware are illustrated.Next,strategies are summed up to obtain advanced and AI chips with high excellent performance by taking the important information processing steps into consideration,after which the design thought for the advanced chips in the future is proposed.Finally,some perspectives are put forward.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(RS-2023-00249743).
文摘Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.
基金funded by the Chronic Disease Management Research Project of National Health Commission Capacity Building and Continuing Education Center 2025(GWJJMB202510024146)the Post-Subsidy Project for Standard Development of Guizhou Provincial Market Supervision and Administration Bureau 2025(DB52/T1726-2023)the Guizhou Provincial Health Commission Science and Technology Fund Project(gzwkj2024-076,gzwkj2026-146).
文摘Diabetic retinopathy(DR)is a leading cause of vision loss among working-age populations,with early screening significantly reducing the risk of blindness.However,resource-limited regions often face challenges in DR screening due to a shortage of ophthalmologists.This study reports the implementation and outcomes of the Chinese local standard DB52/T 1726-2023,Regulations for the application of diabetic retinopathy screening artificial intelligence,in Cambodian healthcare institutions.A pilot DR screening program with independent operational capability is established by providing a non-mydriatic fundus camera and deploying a localized diabetic retinopathy artificial intelligence(DR-AI)screening platform at the Cambodia-Kingdom Friendship Hospital in Phnom Penh,along with comprehensive training.From January to August 2025,a total of 565 patients with type 2 diabetes were screened,yielding a DR detection rate of 26.0%(147 cases).Research findings demonstrate that applying mature Chinese DR-AI screening standards and technological solutions through international collaboration in regions with a scarcity of ophthalmic professionals is both feasible and effective.This project serves as a reference for promoting DR-AI in resource-constrained countries and regions,highlighting its significant potential to leverage AI in addressing the global burden of chronic diseases and advancing the modernization of health systems.
