The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficu...The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration.展开更多
DeepSeek,a Chinese artificial intelligence(AI)startup,has released their V3 and R1 series models,which attracted global attention due to their low cost,high performance,and open-source advantages.This paper begins by ...DeepSeek,a Chinese artificial intelligence(AI)startup,has released their V3 and R1 series models,which attracted global attention due to their low cost,high performance,and open-source advantages.This paper begins by reviewing the evolution of large AI models focusing on paradigm shifts,the mainstream large language model(LLM)paradigm,and the DeepSeek paradigm.Subsequently,the paper highlights novel algorithms introduced by DeepSeek,including multi-head latent attention(MLA),mixture-of-experts(MoE),multi-token prediction(MTP),and group relative policy optimization(GRPO).The paper then explores DeepSeek's engineering breakthroughs in LLM scaling,training,inference,and system-level optimization architecture.Moreover,the impact of DeepSeek models on the competitive AI landscape is analyzed,comparing them to mainstream LLMs across various fields.Finally,the paper reflects on the insights gained from DeepSeek's innovations and discusses future trends in the technical and engineering development of large AI models,particularly in data,training,and reasoning.展开更多
This study explores the determinants of impact on ecology in Northern Tanzania.By examining key socio-economic,institutional,and structural factors influencing engagement the study provides insights in strengthening a...This study explores the determinants of impact on ecology in Northern Tanzania.By examining key socio-economic,institutional,and structural factors influencing engagement the study provides insights in strengthening agribusiness networks and improving livelihoods.Data was collected from 215 farmers and 320 traders through a multistage sampling procedure.Heckman AI sample selection model was used in data analysis whereby the findings showed key factors influencing farmers’decisions on ecology were gender and years of formal education at p<0.1,and access to finance and off-farm income at p<0.05.The degree of farmers participation in social groups was influenced by age,household size,off-farm income and business network at p<0.05,number of years in formal education and access to finance at p<0.01,and distance to the market at p<0.1.The decision of traders to impact on ecology was significantly influenced by age and trading experience at p<0.1.Meanwhile,the degree of their involvement in social groups was strongly affected by gender,formal education,and trust at p<0.01,as well as by access to finance and business networks at p<0.05.The study concluded that natural ecology is influenced by socio economic and structural factors but trust among group members determine the degree of participation.The study recommends that strategies to improve agribusiness networks should understand underlying causes of impact on ecology and strengthen available social groups to improve performance of farmers and traders.展开更多
With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medici...With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medicine and transportation.In this paper,we systematically expound on the intelligent decision-making technology and prospects driven by large AI models.Specifically,we first review the development of large AI models in recent years.Then,from the perspective of methods,we introduce important theories and technologies of large decision models,such as model architecture and model adaptation.Next,from the perspective of applications,we introduce the cutting-edge applications of large decision models in various fields,such as autonomous driving and knowledge decision-making.Finally,we discuss existing challenges,such as security issues,decision bias and hallucination phenomenon as well as future prospects,from both technology development and domain applications.We hope this review paper can help researchers understand the important progress of intelligent decision-making driven by large AI models.展开更多
We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We...We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We systematically analyze the technical pathways for deploying LLMs in disaster scenarios,emphasizing three breakthrough directions:(1)knowledge graph-driven dynamic risk modeling,(2)reinforcement learning-optimized emergency decision systems,and(3)secure local deployment architectures.The DeepSeek model demonstrates unique advantages through its hybrid reasoning mechanism combining semantic analysis with geospatial pattern recognition,enabling cost-effective processing of multi-source data spanning historical disaster records,real-time IoT sensor feeds,and socio-environmental parameters.A modular system architecture is designed to achieve three critical objectives:(a)automated construction of domain-specific knowledge graphs through unsupervised learning of disaster physics relationships,(b)scenario-adaptive resource allocation using risk simulations,and(c)preserving emergency coordination via federated learning across distributed response nodes.The proposed local deployment paradigm addresses critical data security concerns in cross-border disaster management while complying with the FAIR principles(Findable,Accessible,Interoperable,Reusable)for geoscientific data governance.This work establishes a methodological foundation for next-generation AI-earth science convergence in disaster mitigation.展开更多
Artificial intelligence(AI)models are promising to improve the accuracy of wireless positioning systems,particularly in indoor environments where unpredictable radio propagation channel is a great challenge.Although g...Artificial intelligence(AI)models are promising to improve the accuracy of wireless positioning systems,particularly in indoor environments where unpredictable radio propagation channel is a great challenge.Although great efforts have been made to explore the effectiveness of different AI models,it is still an open problem whether these models,trained with the data collected from all base stations(BSs),could work when some BSs are unavailable.In this paper,we make the first effort to enhance the generalization ability of AI wireless positioning model to adapt to the scenario where only partial BSs work.Particularly,a Siamese Network based Wireless Positioning Model(SNWPM)is proposed to predict the location of mobile user equipment from channel state information(CSI)collected from 5G BSs.Furthermore,a Feature Aware Attention Module(FAAM)is introduced to reinforce the capability of feature extraction from CSI data.Experiments are conducted on the 2022 Wireless Communication AI Competition(WAIC)dataset.The proposed SNWPM achieves decimeter-level positioning accuracy even if the data of partial BSs are unavailable.Compared with other AI models,the proposed SNWPM can reduce the positioning error by nearly 50%to more than 60%while using less parameters and lower computation resources.展开更多
In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community ca...In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.展开更多
By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help s...By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.展开更多
From globally popular video game Black Myth:Wukong,which has garnered a dedicated player base around the world,to DeepSeek,an artificial intelligence(AI)model developed at an impressively low cost that rivals U.S.comp...From globally popular video game Black Myth:Wukong,which has garnered a dedicated player base around the world,to DeepSeek,an artificial intelligence(AI)model developed at an impressively low cost that rivals U.S.company OpenAI’s ChatGPT,and the perfectly synchronized robotic ensemble performing with precision at this year’s China Central Television Spring Festival Gala,a Chinese New Year’s Eve extravaganza that aired on January 28-these big tech breakthroughs have risen to prominence one after another,generating massive buzz.展开更多
Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap fr...Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models.This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance.Therefore,algorithm-hardware co-design has emerged as the primary methodology,which ana-lyzes algorithm behaviors on hardware to identify common computational properties.These properties can motivate algo-rithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency.We then reviewed recent works on robotic and embodied AI algorithms and computing hard-ware to demonstrate this algorithm-hardware co-design methodology.In the end,we discuss future research opportunities by answering two questions:(1)how to adapt the computing platforms to the rapid evolution of embodied AI algorithms,and(2)how to transform the potential of emerging hardware innovations into end-to-end inference improvements.展开更多
In recent years,the rapid advancement of artificial intelligence(AI)has fostered deep integration between large AI models and robotic technology.Robots such as robotic dogs capable of carrying heavy loads on mountaino...In recent years,the rapid advancement of artificial intelligence(AI)has fostered deep integration between large AI models and robotic technology.Robots such as robotic dogs capable of carrying heavy loads on mountainous terrain or performing waste disposal tasks and humanoid robots that can execute high-precision component installations have gradually reached the public eye,raising expectations for embodied intelligent robots.展开更多
Time series anomaly detection is critical in domains such as manufacturing,finance,and cybersecurity.Recent generative AI models,particularly Transformer-and Autoencoder-based architectures,show strong accuracy but th...Time series anomaly detection is critical in domains such as manufacturing,finance,and cybersecurity.Recent generative AI models,particularly Transformer-and Autoencoder-based architectures,show strong accuracy but their robustness under noisy conditions is less understood.This study evaluates three representative models—AnomalyTransformer,TranAD,and USAD—on the Server Machine Dataset(SMD)and cross-domain benchmarks including the SoilMoisture Active Passive(SMAP)dataset,theMars Science Laboratory(MSL)dataset,and the Secure Water Treatment(SWaT)testbed.Seven noise settings(five canonical,two mixed)at multiple intensities are tested under fixed clean-data training,with variations in window,stride,and thresholding.Results reveal distinct robustness profiles:AnomalyTransformermaintains recall but loses precision under abrupt noise,TranAD balances sensitivity yet is vulnerable to structured anomalies,and USAD resists Gaussian perturbations but collapses under block anomalies.Quantitatively,F1 drops 60%–70%on noisy SMD,with severe collapse in SWaT(F1≤0.10,Drop up to 84%)but relative stability on SMAP/MSL(Drop within±10%).Overall,generative models exhibit complementary robustness patterns,highlighting noise-type dependent vulnerabilities and providing practical guidance for robust deployment.展开更多
Background and purpose The low-field MRI is a promising tool to accurately diagnose strokes.We here report our study on the accuracy of a 0.23-Tesla(0.23-T)MRI using the haematoma enhanced inversion recovery(HEIR)sequ...Background and purpose The low-field MRI is a promising tool to accurately diagnose strokes.We here report our study on the accuracy of a 0.23-Tesla(0.23-T)MRI using the haematoma enhanced inversion recovery(HEIR)sequence to detect acute ischaemic stroke(AIS)and intracerebral haemorrhage(ICH)within 24 hours of symptom onset.Methods A novel HEIR sequence based on fluid-attenuated inversion recovery T1-weighted,with a scanning time of 1 min and 17 s,was developed using an ICH and AIS pig model on a 0.23-T MRI.Images of the pig model were obtained hourly for 24 hours in order to monitor value changes on T1/T2 and verify the differential diagnosis of AIS and ICH.Then,30 patients with AIS and 30 patients with ICH with confirmed diagnoses by 3T-MRI/CT were included.Diagnostic criteria on a 0.23-T MRI for ICH was the hyperintensity signal on both the diffusion-weighted imaging(DWI)and HEIR sequence,while for AIS was the hyperintensity on DWI and isointensity on the HEIR sequence.Two blinded raters independently assessed the images obtained by the 0.23-T MRI for the presence of ICH/AIS.Results In the pig model,setting the inversion time to 800 ms enabled clear differentiation of ICH from brain parenchymal tissue and AIS.In real patients,a correct 0.23-T MRI diagnosis of either an AIS or ICH was made in all 60 patients within 24 hours of symptom onset(100%overall accuracy).No adverse events occurred.Conclusions The 0.23-T MRI may have the potential to differentiate cerebral haemorrhage from cerebral infarction with both speed and accuracy,making brain MRI scans easier,faster and cheaper.It might be possible to improve the screening imaging process for strokes in the emergency room.Further multicentre studies are needed to validate our findings.展开更多
As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become ...As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and industry.However,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial attacks.Adversarial examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong results.Therefore,the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications.In this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial principle.Next we present more detailed adversarial indicators to evaluate attack and defense behavior.Finally,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.展开更多
Generating carbon credits in rural and wetland lagoon environments is important for the economic and social survival of the same.There are many methodologies to study and certificate the Carbon Sink such as the ISO 14...Generating carbon credits in rural and wetland lagoon environments is important for the economic and social survival of the same.There are many methodologies to study and certificate the Carbon Sink such as the ISO 14064,VCS VERRA,UNI-BNEUTRAL,GOLD STANDARD and others.Many methods done before 2018 are obsolete since research has developed greatly in recent years.The methods are all different,but they share a continuous and real monitoring of the environment to ensure a true CCS(Carbon Capture and Storage)action.In the case of absence of monitoring,the method uses a system of provision of carbon credits called“buffer”.This system allows maintaining a credit-generating activity even in the presence of important anomalies due to adverse weather events.This research shows the complex analytic web of the different sensors in a continuous environmental monitoring system via GSM(Global System for Mobile)Communication and IoT(Internet of Things).By 2011,a monitoring network was installed in the wetland environments of Northern Italy Venetian Lagoon(UNESCO heritage)and used to understand and validate,the CCS action.Thingspeak cloud platform is used to collect data and is used to send alert to the user if the biological sink is reversed to emission.The obtained large dataset was used to prepare a AI(Artificial Intelligence)model“CCS wetland forecast”by Google COLAB.This model can fit the trend to avoid the direct and spot chemical field analysis and demonstrate the real efficacy of the model chosen.This network is now implemented by the Italian national method UNI PdR 99:2021 BNeutral generation of carbon credits.展开更多
Prompt engineering, the art of crafting effective prompts for artificial intelligence models, has emerged as a pivotal factor in determining the quality and usefulness of AI (Artificial Intelligence)-generated outputs...Prompt engineering, the art of crafting effective prompts for artificial intelligence models, has emerged as a pivotal factor in determining the quality and usefulness of AI (Artificial Intelligence)-generated outputs. This practice involves strategically designing and structuring prompts to guide AI models toward desired outcomes, ensuring that they generate relevant, informative, and accurate responses. The significance of prompt engineering cannot be overstated. Well-crafted prompts can significantly enhance the capabilities of AI models, enabling them to perform tasks that were once thought to be exclusively human domain. By providing clear and concise instructions, prompts can guide AI models to generate creative text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Moreover, prompt engineering can help mitigate biases and ensure that AI models produce outputs that are fair, equitable, and inclusive. However, prompt engineering is not without its challenges. Crafting effective prompts requires a deep understanding of both the AI model’s capabilities and the specific task at hand. Additionally, the quality of the prompts can be influenced by factors such as the model’s training data [1] and the complexity of the task. As AI models continue to evolve, prompt engineering will likely become even more critical in unlocking their full potential.展开更多
Quality education is one of the primary objectives of any nation-build-ing strategy and is one of the seventeen Sustainable Development Goals(SDGs)by the United Nations.To provide quality education,delivering top-qual...Quality education is one of the primary objectives of any nation-build-ing strategy and is one of the seventeen Sustainable Development Goals(SDGs)by the United Nations.To provide quality education,delivering top-quality con-tent is not enough.However,understanding the learners’emotions during the learning process is equally important.However,most of this research work uses general data accessed from Twitter or other publicly available databases.These databases are generally not an ideal representation of the actual learning process and the learners’sentiments about the learning process.This research has col-lected real data from the learners,mainly undergraduate university students of dif-ferent regions and cultures.By analyzing the emotions of the students,appropriate steps can be suggested to improve the quality of education they receive.In order to understand the learning emotions,the XLNet technique is used.It investigated the transfer learning method to adopt an efficient model for learners’sentiment detection and classification based on real data.An experiment on the collected data shows that the proposed approach outperforms aspect enhanced sentiment analysis and topic sentiment analysis in the online learning community.展开更多
The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machin...The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising frompotentially fraudulent activities.However,a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations.While sampling techniques can significantly reduce computational time,the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed.Such datasets often lack true representativeness of realworld data,potentially introducing secondary issues that affect the precision of the results.For instance,undersampling may result in the loss of critical information,while over-sampling can lead to overfitting machine learning models.In this paper,we proposed a classification study of credit card fraud using fundamental machine learning models without the application of any sampling techniques on all the features present in the original dataset.The results indicate that Support Vector Machine(SVM)consistently achieves classification performance exceeding 90%across various evaluation metrics.This discovery serves as a valuable reference for future research,encouraging comparative studies on original dataset without the reliance on sampling techniques.Furthermore,we explore hybrid machine learning techniques,such as ensemble learning constructed based on SVM,K-Nearest Neighbor(KNN)and decision tree,highlighting their potential advancements in the field.The study demonstrates that the proposed machine learning models yield promising results,suggesting that pre-processing the dataset with sampling algorithm or additional machine learning technique may not always be necessary.This research contributes to the field of credit card fraud detection by emphasizing the potential of employing machine learning models directly on original datasets,thereby simplifying the workflow and potentially improving the accuracy and efficiency of fraud detection systems.展开更多
Background:Retinal ganglion cell(RGC)death caused by acute ocular hypertension is an important characteristic of acute glaucoma.Receptor-interacting protein kinase 3(RIPK3)that mediates necroptosis is a potential ther...Background:Retinal ganglion cell(RGC)death caused by acute ocular hypertension is an important characteristic of acute glaucoma.Receptor-interacting protein kinase 3(RIPK3)that mediates necroptosis is a potential therapeutic target for RGC death.However,the current understanding of the targeting agents and mechanisms of RIPK3 in the treatment of glaucoma remains limited.Notably,artificial intelligence(AI)technologies have significantly advanced drug discovery.This study aimed to discover RIPK3 inhibitor with AI assistance.Methods:An acute ocular hypertension model was used to simulate pathological ocular hypertension in vivo.We employed a series of AI methods,including large language and graph neural network models,to identify the target compounds of RIPK3.Subsequently,these target candidates were validated using molecular simulations(molecular docking,absorption,distribution,metabolism,excretion,and toxicity[ADMET]prediction,and molecular dynamics simulations)and biological experiments(Western blotting and fluorescence staining)in vitro and in vivo.Results:AI-driven drug screening techniques have the potential to greatly accelerate drug development.A compound called HG9-91-01,identified using AI methods,exerted neuroprotective effects in acute glaucoma.Our research indicates that all five candidates recommended by AI were able to protect the morphological integrity of RGC cells when exposed to hypoxia and glucose deficiency,and HG9-91-01 showed a higher cell survival rate compared to the other candidates.Furthermore,HG9-91-01 was found to protect the retinal structure and reduce the loss of retinal layers in an acute glaucoma model.It was also observed that the neuroprotective effects of HG9-91-01 were highly correlated with the inhibition of PANoptosis(apoptosis,pyroptosis,and necroptosis).Finally,we found that HG9-91-01 can regulate key proteins related to PANoptosis,indicating that this compound exerts neuroprotective effects in the retina by inhibiting the expression of proteins related to apoptosis,pyroptosis,and necroptosis.Conclusion:AI-enabled drug discovery revealed that HG9-91-01 could serve as a potential treatment for acute glaucoma.展开更多
文摘The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration.
基金supported by the National Natural Science Foundation of China(62233005,62293502,U2441245,62176185,U23B2057,62306112)the STCSM Science and Technology Innovation Action Plan Computational Biology Program(24JS2830400)+2 种基金the State Key Laboratory of Industrial Control Technology,China(ICT2024A22)the Shanghai Sailing Program(23YF1409400)the National Science and Technology Major Project(2024ZD0532403).
文摘DeepSeek,a Chinese artificial intelligence(AI)startup,has released their V3 and R1 series models,which attracted global attention due to their low cost,high performance,and open-source advantages.This paper begins by reviewing the evolution of large AI models focusing on paradigm shifts,the mainstream large language model(LLM)paradigm,and the DeepSeek paradigm.Subsequently,the paper highlights novel algorithms introduced by DeepSeek,including multi-head latent attention(MLA),mixture-of-experts(MoE),multi-token prediction(MTP),and group relative policy optimization(GRPO).The paper then explores DeepSeek's engineering breakthroughs in LLM scaling,training,inference,and system-level optimization architecture.Moreover,the impact of DeepSeek models on the competitive AI landscape is analyzed,comparing them to mainstream LLMs across various fields.Finally,the paper reflects on the insights gained from DeepSeek's innovations and discusses future trends in the technical and engineering development of large AI models,particularly in data,training,and reasoning.
基金financed as part of the project“Development of a methodology for instrumental base formation for analysis and modeling of the spatial socio-economic development of systems based on internal reserves in the context of digitalization”(FSEG-2023-0008).
文摘This study explores the determinants of impact on ecology in Northern Tanzania.By examining key socio-economic,institutional,and structural factors influencing engagement the study provides insights in strengthening agribusiness networks and improving livelihoods.Data was collected from 215 farmers and 320 traders through a multistage sampling procedure.Heckman AI sample selection model was used in data analysis whereby the findings showed key factors influencing farmers’decisions on ecology were gender and years of formal education at p<0.1,and access to finance and off-farm income at p<0.05.The degree of farmers participation in social groups was influenced by age,household size,off-farm income and business network at p<0.05,number of years in formal education and access to finance at p<0.01,and distance to the market at p<0.1.The decision of traders to impact on ecology was significantly influenced by age and trading experience at p<0.1.Meanwhile,the degree of their involvement in social groups was strongly affected by gender,formal education,and trust at p<0.01,as well as by access to finance and business networks at p<0.05.The study concluded that natural ecology is influenced by socio economic and structural factors but trust among group members determine the degree of participation.The study recommends that strategies to improve agribusiness networks should understand underlying causes of impact on ecology and strengthen available social groups to improve performance of farmers and traders.
基金supported by the National Natural Science Foundation of China(Grant 62293545)Shenzhen Science and Technology Program(Grant ZDSYS20220323112000001).
文摘With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medicine and transportation.In this paper,we systematically expound on the intelligent decision-making technology and prospects driven by large AI models.Specifically,we first review the development of large AI models in recent years.Then,from the perspective of methods,we introduce important theories and technologies of large decision models,such as model architecture and model adaptation.Next,from the perspective of applications,we introduce the cutting-edge applications of large decision models in various fields,such as autonomous driving and knowledge decision-making.Finally,we discuss existing challenges,such as security issues,decision bias and hallucination phenomenon as well as future prospects,from both technology development and domain applications.We hope this review paper can help researchers understand the important progress of intelligent decision-making driven by large AI models.
基金funded by the Chongqing Water Resources Bureau,China(Project No.CQS24C00836).
文摘We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We systematically analyze the technical pathways for deploying LLMs in disaster scenarios,emphasizing three breakthrough directions:(1)knowledge graph-driven dynamic risk modeling,(2)reinforcement learning-optimized emergency decision systems,and(3)secure local deployment architectures.The DeepSeek model demonstrates unique advantages through its hybrid reasoning mechanism combining semantic analysis with geospatial pattern recognition,enabling cost-effective processing of multi-source data spanning historical disaster records,real-time IoT sensor feeds,and socio-environmental parameters.A modular system architecture is designed to achieve three critical objectives:(a)automated construction of domain-specific knowledge graphs through unsupervised learning of disaster physics relationships,(b)scenario-adaptive resource allocation using risk simulations,and(c)preserving emergency coordination via federated learning across distributed response nodes.The proposed local deployment paradigm addresses critical data security concerns in cross-border disaster management while complying with the FAIR principles(Findable,Accessible,Interoperable,Reusable)for geoscientific data governance.This work establishes a methodological foundation for next-generation AI-earth science convergence in disaster mitigation.
基金supported by National Natural Science Foundation of China (No. 62076251)sponsored by IMT-2020(5G) Promotion Group 5G+AI Work Group+3 种基金jointly sponsored by China Academy of Information and Communications TechnologyGuangdong OPPO Mobile Telecommunications Corp., Ltdvivo Mobile Communication Co., LtdHuawei Technologies Co., Ltd
文摘Artificial intelligence(AI)models are promising to improve the accuracy of wireless positioning systems,particularly in indoor environments where unpredictable radio propagation channel is a great challenge.Although great efforts have been made to explore the effectiveness of different AI models,it is still an open problem whether these models,trained with the data collected from all base stations(BSs),could work when some BSs are unavailable.In this paper,we make the first effort to enhance the generalization ability of AI wireless positioning model to adapt to the scenario where only partial BSs work.Particularly,a Siamese Network based Wireless Positioning Model(SNWPM)is proposed to predict the location of mobile user equipment from channel state information(CSI)collected from 5G BSs.Furthermore,a Feature Aware Attention Module(FAAM)is introduced to reinforce the capability of feature extraction from CSI data.Experiments are conducted on the 2022 Wireless Communication AI Competition(WAIC)dataset.The proposed SNWPM achieves decimeter-level positioning accuracy even if the data of partial BSs are unavailable.Compared with other AI models,the proposed SNWPM can reduce the positioning error by nearly 50%to more than 60%while using less parameters and lower computation resources.
文摘In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.
文摘By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.
文摘From globally popular video game Black Myth:Wukong,which has garnered a dedicated player base around the world,to DeepSeek,an artificial intelligence(AI)model developed at an impressively low cost that rivals U.S.company OpenAI’s ChatGPT,and the perfectly synchronized robotic ensemble performing with precision at this year’s China Central Television Spring Festival Gala,a Chinese New Year’s Eve extravaganza that aired on January 28-these big tech breakthroughs have risen to prominence one after another,generating massive buzz.
基金supported in part by NSFC under Grant 62422407in part by RGC under Grant 26204424in part by ACCESS–AI Chip Center for Emerging Smart Systems, sponsored by the Inno HK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government
文摘Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models.This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance.Therefore,algorithm-hardware co-design has emerged as the primary methodology,which ana-lyzes algorithm behaviors on hardware to identify common computational properties.These properties can motivate algo-rithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency.We then reviewed recent works on robotic and embodied AI algorithms and computing hard-ware to demonstrate this algorithm-hardware co-design methodology.In the end,we discuss future research opportunities by answering two questions:(1)how to adapt the computing platforms to the rapid evolution of embodied AI algorithms,and(2)how to transform the potential of emerging hardware innovations into end-to-end inference improvements.
文摘In recent years,the rapid advancement of artificial intelligence(AI)has fostered deep integration between large AI models and robotic technology.Robots such as robotic dogs capable of carrying heavy loads on mountainous terrain or performing waste disposal tasks and humanoid robots that can execute high-precision component installations have gradually reached the public eye,raising expectations for embodied intelligent robots.
基金supported by the“Regional Innovation System&Education(RISE)”through the Seoul RISE Center,funded by the Ministry of Education(MOE)the Seoul Metropolitan Government(2025-RISE-01-018-04)supported by the Korea Digital Forensic Center.
文摘Time series anomaly detection is critical in domains such as manufacturing,finance,and cybersecurity.Recent generative AI models,particularly Transformer-and Autoencoder-based architectures,show strong accuracy but their robustness under noisy conditions is less understood.This study evaluates three representative models—AnomalyTransformer,TranAD,and USAD—on the Server Machine Dataset(SMD)and cross-domain benchmarks including the SoilMoisture Active Passive(SMAP)dataset,theMars Science Laboratory(MSL)dataset,and the Secure Water Treatment(SWaT)testbed.Seven noise settings(five canonical,two mixed)at multiple intensities are tested under fixed clean-data training,with variations in window,stride,and thresholding.Results reveal distinct robustness profiles:AnomalyTransformermaintains recall but loses precision under abrupt noise,TranAD balances sensitivity yet is vulnerable to structured anomalies,and USAD resists Gaussian perturbations but collapses under block anomalies.Quantitatively,F1 drops 60%–70%on noisy SMD,with severe collapse in SWaT(F1≤0.10,Drop up to 84%)but relative stability on SMAP/MSL(Drop within±10%).Overall,generative models exhibit complementary robustness patterns,highlighting noise-type dependent vulnerabilities and providing practical guidance for robust deployment.
基金National Natural Science Foundation of China(grant no.82271329grant no.82471356).
文摘Background and purpose The low-field MRI is a promising tool to accurately diagnose strokes.We here report our study on the accuracy of a 0.23-Tesla(0.23-T)MRI using the haematoma enhanced inversion recovery(HEIR)sequence to detect acute ischaemic stroke(AIS)and intracerebral haemorrhage(ICH)within 24 hours of symptom onset.Methods A novel HEIR sequence based on fluid-attenuated inversion recovery T1-weighted,with a scanning time of 1 min and 17 s,was developed using an ICH and AIS pig model on a 0.23-T MRI.Images of the pig model were obtained hourly for 24 hours in order to monitor value changes on T1/T2 and verify the differential diagnosis of AIS and ICH.Then,30 patients with AIS and 30 patients with ICH with confirmed diagnoses by 3T-MRI/CT were included.Diagnostic criteria on a 0.23-T MRI for ICH was the hyperintensity signal on both the diffusion-weighted imaging(DWI)and HEIR sequence,while for AIS was the hyperintensity on DWI and isointensity on the HEIR sequence.Two blinded raters independently assessed the images obtained by the 0.23-T MRI for the presence of ICH/AIS.Results In the pig model,setting the inversion time to 800 ms enabled clear differentiation of ICH from brain parenchymal tissue and AIS.In real patients,a correct 0.23-T MRI diagnosis of either an AIS or ICH was made in all 60 patients within 24 hours of symptom onset(100%overall accuracy).No adverse events occurred.Conclusions The 0.23-T MRI may have the potential to differentiate cerebral haemorrhage from cerebral infarction with both speed and accuracy,making brain MRI scans easier,faster and cheaper.It might be possible to improve the screening imaging process for strokes in the emergency room.Further multicentre studies are needed to validate our findings.
基金supported by the National Natural Science Foundation of China(61771154)the Fundamental Research Funds for the Central Universities(3072022CF0601)supported by Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology,Harbin Engineering University,Harbin,China.
文摘As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and industry.However,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial attacks.Adversarial examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong results.Therefore,the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications.In this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial principle.Next we present more detailed adversarial indicators to evaluate attack and defense behavior.Finally,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.
文摘Generating carbon credits in rural and wetland lagoon environments is important for the economic and social survival of the same.There are many methodologies to study and certificate the Carbon Sink such as the ISO 14064,VCS VERRA,UNI-BNEUTRAL,GOLD STANDARD and others.Many methods done before 2018 are obsolete since research has developed greatly in recent years.The methods are all different,but they share a continuous and real monitoring of the environment to ensure a true CCS(Carbon Capture and Storage)action.In the case of absence of monitoring,the method uses a system of provision of carbon credits called“buffer”.This system allows maintaining a credit-generating activity even in the presence of important anomalies due to adverse weather events.This research shows the complex analytic web of the different sensors in a continuous environmental monitoring system via GSM(Global System for Mobile)Communication and IoT(Internet of Things).By 2011,a monitoring network was installed in the wetland environments of Northern Italy Venetian Lagoon(UNESCO heritage)and used to understand and validate,the CCS action.Thingspeak cloud platform is used to collect data and is used to send alert to the user if the biological sink is reversed to emission.The obtained large dataset was used to prepare a AI(Artificial Intelligence)model“CCS wetland forecast”by Google COLAB.This model can fit the trend to avoid the direct and spot chemical field analysis and demonstrate the real efficacy of the model chosen.This network is now implemented by the Italian national method UNI PdR 99:2021 BNeutral generation of carbon credits.
文摘Prompt engineering, the art of crafting effective prompts for artificial intelligence models, has emerged as a pivotal factor in determining the quality and usefulness of AI (Artificial Intelligence)-generated outputs. This practice involves strategically designing and structuring prompts to guide AI models toward desired outcomes, ensuring that they generate relevant, informative, and accurate responses. The significance of prompt engineering cannot be overstated. Well-crafted prompts can significantly enhance the capabilities of AI models, enabling them to perform tasks that were once thought to be exclusively human domain. By providing clear and concise instructions, prompts can guide AI models to generate creative text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Moreover, prompt engineering can help mitigate biases and ensure that AI models produce outputs that are fair, equitable, and inclusive. However, prompt engineering is not without its challenges. Crafting effective prompts requires a deep understanding of both the AI model’s capabilities and the specific task at hand. Additionally, the quality of the prompts can be influenced by factors such as the model’s training data [1] and the complexity of the task. As AI models continue to evolve, prompt engineering will likely become even more critical in unlocking their full potential.
基金The authors extend their appreciation to the Deanship of Scientific Research at Saudi Electronic University for funding this research work through project number(8141).
文摘Quality education is one of the primary objectives of any nation-build-ing strategy and is one of the seventeen Sustainable Development Goals(SDGs)by the United Nations.To provide quality education,delivering top-quality con-tent is not enough.However,understanding the learners’emotions during the learning process is equally important.However,most of this research work uses general data accessed from Twitter or other publicly available databases.These databases are generally not an ideal representation of the actual learning process and the learners’sentiments about the learning process.This research has col-lected real data from the learners,mainly undergraduate university students of dif-ferent regions and cultures.By analyzing the emotions of the students,appropriate steps can be suggested to improve the quality of education they receive.In order to understand the learning emotions,the XLNet technique is used.It investigated the transfer learning method to adopt an efficient model for learners’sentiment detection and classification based on real data.An experiment on the collected data shows that the proposed approach outperforms aspect enhanced sentiment analysis and topic sentiment analysis in the online learning community.
文摘The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising frompotentially fraudulent activities.However,a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations.While sampling techniques can significantly reduce computational time,the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed.Such datasets often lack true representativeness of realworld data,potentially introducing secondary issues that affect the precision of the results.For instance,undersampling may result in the loss of critical information,while over-sampling can lead to overfitting machine learning models.In this paper,we proposed a classification study of credit card fraud using fundamental machine learning models without the application of any sampling techniques on all the features present in the original dataset.The results indicate that Support Vector Machine(SVM)consistently achieves classification performance exceeding 90%across various evaluation metrics.This discovery serves as a valuable reference for future research,encouraging comparative studies on original dataset without the reliance on sampling techniques.Furthermore,we explore hybrid machine learning techniques,such as ensemble learning constructed based on SVM,K-Nearest Neighbor(KNN)and decision tree,highlighting their potential advancements in the field.The study demonstrates that the proposed machine learning models yield promising results,suggesting that pre-processing the dataset with sampling algorithm or additional machine learning technique may not always be necessary.This research contributes to the field of credit card fraud detection by emphasizing the potential of employing machine learning models directly on original datasets,thereby simplifying the workflow and potentially improving the accuracy and efficiency of fraud detection systems.
基金supported by grants from the Guangzhou National Laboratory(No.YW-SLJC0201)Macao Young Scholar Program(No.AM2023024).
文摘Background:Retinal ganglion cell(RGC)death caused by acute ocular hypertension is an important characteristic of acute glaucoma.Receptor-interacting protein kinase 3(RIPK3)that mediates necroptosis is a potential therapeutic target for RGC death.However,the current understanding of the targeting agents and mechanisms of RIPK3 in the treatment of glaucoma remains limited.Notably,artificial intelligence(AI)technologies have significantly advanced drug discovery.This study aimed to discover RIPK3 inhibitor with AI assistance.Methods:An acute ocular hypertension model was used to simulate pathological ocular hypertension in vivo.We employed a series of AI methods,including large language and graph neural network models,to identify the target compounds of RIPK3.Subsequently,these target candidates were validated using molecular simulations(molecular docking,absorption,distribution,metabolism,excretion,and toxicity[ADMET]prediction,and molecular dynamics simulations)and biological experiments(Western blotting and fluorescence staining)in vitro and in vivo.Results:AI-driven drug screening techniques have the potential to greatly accelerate drug development.A compound called HG9-91-01,identified using AI methods,exerted neuroprotective effects in acute glaucoma.Our research indicates that all five candidates recommended by AI were able to protect the morphological integrity of RGC cells when exposed to hypoxia and glucose deficiency,and HG9-91-01 showed a higher cell survival rate compared to the other candidates.Furthermore,HG9-91-01 was found to protect the retinal structure and reduce the loss of retinal layers in an acute glaucoma model.It was also observed that the neuroprotective effects of HG9-91-01 were highly correlated with the inhibition of PANoptosis(apoptosis,pyroptosis,and necroptosis).Finally,we found that HG9-91-01 can regulate key proteins related to PANoptosis,indicating that this compound exerts neuroprotective effects in the retina by inhibiting the expression of proteins related to apoptosis,pyroptosis,and necroptosis.Conclusion:AI-enabled drug discovery revealed that HG9-91-01 could serve as a potential treatment for acute glaucoma.
