Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the ...Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.展开更多
Students are considered one of the groups most affected by psychological pro-blems.Given the highly dangerous nature of mental illnesses and the increasing-ly serious state of global mental health,it is imperative for...Students are considered one of the groups most affected by psychological pro-blems.Given the highly dangerous nature of mental illnesses and the increasing-ly serious state of global mental health,it is imperative for us to explore new me-thods and approaches concerning the prevention and treatment of mental illne-sses.Large multimodal models(LMMs),as the most advanced artificial intelligen-ce models(i.e.ChatGPT-4),have brought new hope to the accurate prevention,diagnosis,and treatment of psychiatric disorders.The assistance of these models in the promotion of mental health is critical,as the latter necessitates a strong foundation of medical knowledge and professional skills,emotional support,stigma mitigation,the encouragement of more honest patient self-disclosure,reduced health care costs,improved medical efficiency,and greater mental health service coverage.However,these models must address challenges related to health,safety,hallucinations,and ethics simultaneously.In the future,we should address these challenges by developing relevant usage manuals,accountability rules,and legal regulations;implementing a human-centered approach;and intelligently upgrading LMMs through the deep optimization of such models,their algorithms,and other means.This effort will thus substantially contribute not only to the maintenance of students’health but also to the achievement of global sustainable development goals.展开更多
Generative Artificial Intelligence(GAI)refers to a class of AI systems capable of creating novel,coherent,and contextually relevant content—such as text,images,audio,and video—based on patterns learned from extensiv...Generative Artificial Intelligence(GAI)refers to a class of AI systems capable of creating novel,coherent,and contextually relevant content—such as text,images,audio,and video—based on patterns learned from extensive training datasets.The public release and rapid refinement of large language models(LLMs)like ChatGPT have accelerated the adoption of GAI across various medical specialties,offering new tools for education,clinical simulation,and research.Dermatology training,which heavily relies on visual pattern recognition and requires extensive exposure to diverse morphological presentations,faces persistent challenges such as uneven distribu-tion of educational resources,limited patient exposure for rare conditions,and variability in teaching quality.Exploring the integration of GAI into pedagogical frameworks offers innovative approaches to address these challenges,potentially enhancing the quality,standardization,scalability,and accessibility of dermatology ed-ucation.This comprehensive review examines the core concepts and technical foundations of GAI,highlights its specific applications within dermatology teaching and learning—including simulated case generation,per-sonalized learning pathways,and academic support—and discusses the current limitations,practical challenges,and ethical considerations surrounding its use.The aim is to provide a balanced perspective on the significant potential of GAI for transforming dermatology education and to offer evidence-based insights to guide future exploration,implementation,and policy development.展开更多
Active object detection(AOD)is a crucial task in the field of robotics.A key challenge in household environments for AOD is that the target object is often undetectable due to partial occlusion,which leads to the fail...Active object detection(AOD)is a crucial task in the field of robotics.A key challenge in household environments for AOD is that the target object is often undetectable due to partial occlusion,which leads to the failure of traditional methods.To address the occlusion problem,this paper first proposes a novel occlusion handling method based on the large multimodal model(LMM).The method utilises an LMM to detect and analyse input RGB images and generates adjustment actions to progressively eliminate occlusion.After the occlusion is handled,an improved AOD method based on a deep Q-learning network(DQN)is used to complete the task.We introduce an attention mechanism to process image features,enabling the model to focus on critical regions of the input images.Additionally,a new reward function is proposed that comprehensively considers the bounding box of the target object and the robot's distance to the object,along with the actions performed by the robot.Ex-periments on the dataset and in real-world scenarios validate the effectiveness of the proposed method in performing AOD tasks under partial occlusion.展开更多
Sarcasm detection in Natural Language Processing(NLP)has become increasingly important,partic-ularly with the rise of social media and non-textual emotional expressions,such as images.Existing methods often rely on se...Sarcasm detection in Natural Language Processing(NLP)has become increasingly important,partic-ularly with the rise of social media and non-textual emotional expressions,such as images.Existing methods often rely on separate image and text modalities,which may not fully utilize the information available from both sources.To address this limitation,we propose a novel multimodal large model,i.e.,the PKME-MLM(Prior Knowledge and Multi-label Emotion analysis based Multimodal Large Model for sarcasm detection).The PKME-MLM aims to enhance sarcasm detection by integrating prior knowledge to extract useful textual information from images,which is then combined with text data for deeper analysis.This method improves the integration of image and text data,addressing the limitation of previous models that process these modalities separately.Additionally,we incorporate multi-label sentiment analysis,refining sentiment labels to improve sarcasm recognition accuracy.This design overcomes the limitations of prior models that treated sentiment classification as a single-label problem,thereby improving sarcasm recognition by distinguishing subtle emotional cues from the text.Experimental results demonstrate that our approach achieves significant performance improvements in multimodal sarcasm detection tasks,with an accuracy(Acc.)of 94.35%,and Macro-Average Precision and Recall reaching 93.92%and 94.21%,respectively.These results highlight the potential of multimodal models in improving sarcasm detection and suggest that further integration of modalities could advance future research.This work also paves the way for incorporating multimodal sentiment analysis into sarcasm detection.展开更多
BACKGROUND Gastrointestinal diseases have complex etiologies and clinical presentations.An accurate diagnosis requires physicians to integrate diverse information,including medical history,laboratory test results,and ...BACKGROUND Gastrointestinal diseases have complex etiologies and clinical presentations.An accurate diagnosis requires physicians to integrate diverse information,including medical history,laboratory test results,and imaging findings.Existing artificial intelligence-assisted diagnostic tools are limited to single-modality information,resulting in recommendations that are often incomplete and may be associated with clinical or legal risks.AIM To develop and evaluate a collaborative multimodal large language model(LLM)framework for clinical decision-making in digestive diseases.METHODS In this observational study,DeepGut,a multimodal LLM collaborative diagnostic framework,was developed to integrate four distinct large models into a four-tiered structure.The framework sequentially accomplishes multimodal infor-mation extraction,logical“chain”construction,diagnostic and treatment suggestion generation,and risk analysis.The model was evaluated using objective metrics,which assess the reliability and comprehensiveness of model-generated results,and subjective expert opinions,which examine the effectiveness of the framework in assisting physicians.RESULTS The diagnostic and treatment recommendations generated by the DeepGut framework achieved exceptional performance,with a diagnostic accuracy of 97.8%,diagnostic completeness of 93.9%,treatment plan accuracy of 95.2%,and treatment plan completeness of 98.0%,significantly surpassing the capabilities of single-modal LLM-based diagnostic tools.Experts evaluating the framework commended the completeness,relevance,and logical coherence of its outputs.However,the collaborative multimodal LLM approach resulted in increased input and output token counts,leading to higher computational costs and extended diagnostic times.CONCLUSION The framework achieves successful integration of multimodal diagnostic data,demonstrating enhanced performance enabled by multimodal LLM collaboration,which opens new horizons for the clinical application of artificial intelligence-assisted technology.展开更多
This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large mode...This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology.展开更多
Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural ...Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.展开更多
The additive manufacturing(AM)landscape has significantly transformed in alignment with Industry 4.0 principles,primarily driven by the integration of artificial intelligence(AI)and digital twins(DT).However,current i...The additive manufacturing(AM)landscape has significantly transformed in alignment with Industry 4.0 principles,primarily driven by the integration of artificial intelligence(AI)and digital twins(DT).However,current intelligent AM(IAM)systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction.This paper reviews existing IAM solutions,emphasizing control,monitoring,process autonomy,and end-to-end integration,and identifies key limitations,such as the absence of a high-level controller for global decision-making.To address these gaps,we propose a transition from IAM to autonomous AM,featuring a hierarchical framework with four integrated layers:knowledge,generative solution,operational,and cognitive.In the cognitive layer,AI agents notably enable machines to independently observe,analyze,plan,and execute operations that traditionally require human intervention.These capabilities streamline production processes and expand the possibilities for innovation,particularly in sectors like in-space manufacturing.Additionally,this paper discusses the role of AI in self-optimization and lifelong learning,positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy,fostering a more adaptive,resilient manufacturing ecosystem.展开更多
The rapid advancement of artificial intelligence has significantly impacted education,with largescalefoundatiomn odels(LFMs)emerging as transformative tools.While LFMs have demonstrated exceptional performance across ...The rapid advancement of artificial intelligence has significantly impacted education,with largescalefoundatiomn odels(LFMs)emerging as transformative tools.While LFMs have demonstrated exceptional performance across diverse domains,their integration into K-12 education remains in its early stages,requiring alignment with pedagogical principles,cognitive development,and curriculum standards.This paper provides a comprehensive technological review of LFM applications in K-12 education,examining current workflows,challenges,and future opportunities.We explore how LFMs facilitate personalized learning,teacher-student collaboration,and automated assessment while highlighting critical issues such as motivation,engagement,and_age-appropriate instructional strategies.By analyzing global developments,this study offers valuable insights for educators seeking to optimize AI-driven teaching methods and for students leveraging AI for self-directed learning.Our findings aim to inform future research and drive innovation in educational Al,ensuring the effective and ethical integration of LFMs into the evolving K-12 educational landscape.展开更多
Traditional quantitative investment research is encountering diminishing returns alongside rising labor and time costs.To overcome these challenges,we introduce the large investment model(LIM),a novel research paradig...Traditional quantitative investment research is encountering diminishing returns alongside rising labor and time costs.To overcome these challenges,we introduce the large investment model(LIM),a novel research paradigm designed to enhance both performance and efficiency at scale.LIM employs end-to-end learning and universal modeling to create an upstream foundation model,which is capable of autonomously learning comprehensive signal patterns from diverse financial data spanning multiple exchanges,instruments,and frequencies.These“global patterns”are subsequently transferred to downstream strategy modeling,optimizing performance for specific tasks.We detail the system architecture design of LIM,address the technical challenges inherent in this approach,and outline potential directions for future research.展开更多
Video-text retrieval (VTR) is an essential task in multimodal learning, aiming to bridge the semantic gap between visual and textual data. Effective video frame sampling plays a crucial role in improving retrieval per...Video-text retrieval (VTR) is an essential task in multimodal learning, aiming to bridge the semantic gap between visual and textual data. Effective video frame sampling plays a crucial role in improving retrieval performance, as it determines the quality of the visual content representation. Traditional sampling methods, such as uniform sampling and optical flow-based techniques, often fail to capture the full semantic range of videos, leading to redundancy and inefficiencies. In this work, we propose CLIP4Video-Sampling: Global Semantics-Guided Multi-Granularity Frame Sampling for Video-Text Retrieval, a global semantics-guided multi-granularity frame sampling strategy designed to optimize both computational efficiency and retrieval accuracy. By integrating multi-scale global and local temporal sampling and leveraging the CLIP (Contrastive Language-Image Pre-training) model’s powerful feature extraction capabilities, our method significantly outperforms existing approaches in both zero-shot and fine-tuned video-text retrieval tasks on popular datasets. CLIP4Video-Sampling reduces redundancy, ensures keyframe coverage, and serves as an adaptable pre-processing module for multimodal models.展开更多
基金We acknowledge funding from NSFC Grant 62306283.
文摘Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.
文摘Students are considered one of the groups most affected by psychological pro-blems.Given the highly dangerous nature of mental illnesses and the increasing-ly serious state of global mental health,it is imperative for us to explore new me-thods and approaches concerning the prevention and treatment of mental illne-sses.Large multimodal models(LMMs),as the most advanced artificial intelligen-ce models(i.e.ChatGPT-4),have brought new hope to the accurate prevention,diagnosis,and treatment of psychiatric disorders.The assistance of these models in the promotion of mental health is critical,as the latter necessitates a strong foundation of medical knowledge and professional skills,emotional support,stigma mitigation,the encouragement of more honest patient self-disclosure,reduced health care costs,improved medical efficiency,and greater mental health service coverage.However,these models must address challenges related to health,safety,hallucinations,and ethics simultaneously.In the future,we should address these challenges by developing relevant usage manuals,accountability rules,and legal regulations;implementing a human-centered approach;and intelligently upgrading LMMs through the deep optimization of such models,their algorithms,and other means.This effort will thus substantially contribute not only to the maintenance of students’health but also to the achievement of global sustainable development goals.
文摘Generative Artificial Intelligence(GAI)refers to a class of AI systems capable of creating novel,coherent,and contextually relevant content—such as text,images,audio,and video—based on patterns learned from extensive training datasets.The public release and rapid refinement of large language models(LLMs)like ChatGPT have accelerated the adoption of GAI across various medical specialties,offering new tools for education,clinical simulation,and research.Dermatology training,which heavily relies on visual pattern recognition and requires extensive exposure to diverse morphological presentations,faces persistent challenges such as uneven distribu-tion of educational resources,limited patient exposure for rare conditions,and variability in teaching quality.Exploring the integration of GAI into pedagogical frameworks offers innovative approaches to address these challenges,potentially enhancing the quality,standardization,scalability,and accessibility of dermatology ed-ucation.This comprehensive review examines the core concepts and technical foundations of GAI,highlights its specific applications within dermatology teaching and learning—including simulated case generation,per-sonalized learning pathways,and academic support—and discusses the current limitations,practical challenges,and ethical considerations surrounding its use.The aim is to provide a balanced perspective on the significant potential of GAI for transforming dermatology education and to offer evidence-based insights to guide future exploration,implementation,and policy development.
基金National Natural Science Foundation of China,Grant No.62273203National Key R&D Program of China,Grant No.2018YFB1307101Taishan Scholars Program of Shandong Province,Grant No.ts201511005.
文摘Active object detection(AOD)is a crucial task in the field of robotics.A key challenge in household environments for AOD is that the target object is often undetectable due to partial occlusion,which leads to the failure of traditional methods.To address the occlusion problem,this paper first proposes a novel occlusion handling method based on the large multimodal model(LMM).The method utilises an LMM to detect and analyse input RGB images and generates adjustment actions to progressively eliminate occlusion.After the occlusion is handled,an improved AOD method based on a deep Q-learning network(DQN)is used to complete the task.We introduce an attention mechanism to process image features,enabling the model to focus on critical regions of the input images.Additionally,a new reward function is proposed that comprehensively considers the bounding box of the target object and the robot's distance to the object,along with the actions performed by the robot.Ex-periments on the dataset and in real-world scenarios validate the effectiveness of the proposed method in performing AOD tasks under partial occlusion.
基金funding partly by the National Natural Science Foundation of China under grant number 61701179.
文摘Sarcasm detection in Natural Language Processing(NLP)has become increasingly important,partic-ularly with the rise of social media and non-textual emotional expressions,such as images.Existing methods often rely on separate image and text modalities,which may not fully utilize the information available from both sources.To address this limitation,we propose a novel multimodal large model,i.e.,the PKME-MLM(Prior Knowledge and Multi-label Emotion analysis based Multimodal Large Model for sarcasm detection).The PKME-MLM aims to enhance sarcasm detection by integrating prior knowledge to extract useful textual information from images,which is then combined with text data for deeper analysis.This method improves the integration of image and text data,addressing the limitation of previous models that process these modalities separately.Additionally,we incorporate multi-label sentiment analysis,refining sentiment labels to improve sarcasm recognition accuracy.This design overcomes the limitations of prior models that treated sentiment classification as a single-label problem,thereby improving sarcasm recognition by distinguishing subtle emotional cues from the text.Experimental results demonstrate that our approach achieves significant performance improvements in multimodal sarcasm detection tasks,with an accuracy(Acc.)of 94.35%,and Macro-Average Precision and Recall reaching 93.92%and 94.21%,respectively.These results highlight the potential of multimodal models in improving sarcasm detection and suggest that further integration of modalities could advance future research.This work also paves the way for incorporating multimodal sentiment analysis into sarcasm detection.
基金Supported by China Health Promotion Foundation Young Doctors’Research Foundation for Inflammatory Bowel DiseaseTaishan Scholars Program of Shandong Province,China,NO.tsqn202306343National Natural Science Foundation of China,No.82270580,No.82070552,No.82270578,and No.82300599.
文摘BACKGROUND Gastrointestinal diseases have complex etiologies and clinical presentations.An accurate diagnosis requires physicians to integrate diverse information,including medical history,laboratory test results,and imaging findings.Existing artificial intelligence-assisted diagnostic tools are limited to single-modality information,resulting in recommendations that are often incomplete and may be associated with clinical or legal risks.AIM To develop and evaluate a collaborative multimodal large language model(LLM)framework for clinical decision-making in digestive diseases.METHODS In this observational study,DeepGut,a multimodal LLM collaborative diagnostic framework,was developed to integrate four distinct large models into a four-tiered structure.The framework sequentially accomplishes multimodal infor-mation extraction,logical“chain”construction,diagnostic and treatment suggestion generation,and risk analysis.The model was evaluated using objective metrics,which assess the reliability and comprehensiveness of model-generated results,and subjective expert opinions,which examine the effectiveness of the framework in assisting physicians.RESULTS The diagnostic and treatment recommendations generated by the DeepGut framework achieved exceptional performance,with a diagnostic accuracy of 97.8%,diagnostic completeness of 93.9%,treatment plan accuracy of 95.2%,and treatment plan completeness of 98.0%,significantly surpassing the capabilities of single-modal LLM-based diagnostic tools.Experts evaluating the framework commended the completeness,relevance,and logical coherence of its outputs.However,the collaborative multimodal LLM approach resulted in increased input and output token counts,leading to higher computational costs and extended diagnostic times.CONCLUSION The framework achieves successful integration of multimodal diagnostic data,demonstrating enhanced performance enabled by multimodal LLM collaboration,which opens new horizons for the clinical application of artificial intelligence-assisted technology.
基金Supported by the National Natural Science Foundation of China(72088101,42372175)PetroChina Science and Technology Innovation Fund Program(2021DQ02-0904)。
文摘This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology.
基金supported by the National Key Research and Development Program of China(2023YFF0906502)the Postgraduate Research and Innovation Project of Hunan Province under Grant(CX20240473).
文摘Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.
基金funded by the MUREP High Volume project(80NSSC22M0132)through the U.S.NASA Office of STEM Engagementthe SMART IAC Project(DE-EE0009726)through the U.S.Department of Energy Office of Manufacturing and Energy Supply Chainssupport of San Diego Supercomputer Center(SDSC)National Research Platform(NRP)Nautilus sponsored by the U.S.NSF(2100237,2120019)。
文摘The additive manufacturing(AM)landscape has significantly transformed in alignment with Industry 4.0 principles,primarily driven by the integration of artificial intelligence(AI)and digital twins(DT).However,current intelligent AM(IAM)systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction.This paper reviews existing IAM solutions,emphasizing control,monitoring,process autonomy,and end-to-end integration,and identifies key limitations,such as the absence of a high-level controller for global decision-making.To address these gaps,we propose a transition from IAM to autonomous AM,featuring a hierarchical framework with four integrated layers:knowledge,generative solution,operational,and cognitive.In the cognitive layer,AI agents notably enable machines to independently observe,analyze,plan,and execute operations that traditionally require human intervention.These capabilities streamline production processes and expand the possibilities for innovation,particularly in sectors like in-space manufacturing.Additionally,this paper discusses the role of AI in self-optimization and lifelong learning,positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy,fostering a more adaptive,resilient manufacturing ecosystem.
文摘The rapid advancement of artificial intelligence has significantly impacted education,with largescalefoundatiomn odels(LFMs)emerging as transformative tools.While LFMs have demonstrated exceptional performance across diverse domains,their integration into K-12 education remains in its early stages,requiring alignment with pedagogical principles,cognitive development,and curriculum standards.This paper provides a comprehensive technological review of LFM applications in K-12 education,examining current workflows,challenges,and future opportunities.We explore how LFMs facilitate personalized learning,teacher-student collaboration,and automated assessment while highlighting critical issues such as motivation,engagement,and_age-appropriate instructional strategies.By analyzing global developments,this study offers valuable insights for educators seeking to optimize AI-driven teaching methods and for students leveraging AI for self-directed learning.Our findings aim to inform future research and drive innovation in educational Al,ensuring the effective and ethical integration of LFMs into the evolving K-12 educational landscape.
文摘Traditional quantitative investment research is encountering diminishing returns alongside rising labor and time costs.To overcome these challenges,we introduce the large investment model(LIM),a novel research paradigm designed to enhance both performance and efficiency at scale.LIM employs end-to-end learning and universal modeling to create an upstream foundation model,which is capable of autonomously learning comprehensive signal patterns from diverse financial data spanning multiple exchanges,instruments,and frequencies.These“global patterns”are subsequently transferred to downstream strategy modeling,optimizing performance for specific tasks.We detail the system architecture design of LIM,address the technical challenges inherent in this approach,and outline potential directions for future research.
文摘Video-text retrieval (VTR) is an essential task in multimodal learning, aiming to bridge the semantic gap between visual and textual data. Effective video frame sampling plays a crucial role in improving retrieval performance, as it determines the quality of the visual content representation. Traditional sampling methods, such as uniform sampling and optical flow-based techniques, often fail to capture the full semantic range of videos, leading to redundancy and inefficiencies. In this work, we propose CLIP4Video-Sampling: Global Semantics-Guided Multi-Granularity Frame Sampling for Video-Text Retrieval, a global semantics-guided multi-granularity frame sampling strategy designed to optimize both computational efficiency and retrieval accuracy. By integrating multi-scale global and local temporal sampling and leveraging the CLIP (Contrastive Language-Image Pre-training) model’s powerful feature extraction capabilities, our method significantly outperforms existing approaches in both zero-shot and fine-tuned video-text retrieval tasks on popular datasets. CLIP4Video-Sampling reduces redundancy, ensures keyframe coverage, and serves as an adaptable pre-processing module for multimodal models.
