In the era of AI,especially large models,the importance of open source has become increasingly prominent.First,open source allows innovation to avoid starting from scratch.Through iterative innovation,it promotes tech...In the era of AI,especially large models,the importance of open source has become increasingly prominent.First,open source allows innovation to avoid starting from scratch.Through iterative innovation,it promotes technical exchanges and learning globally.Second,resources required for large model R&D are difficult for a single institution to obtain.The evaluation of general large models also requires the participation of experts from various industries.Third,without open source collaboration,it is difficult to form a unified upper-layer software ecosystem.Therefore,open source has become an important cooperation mechanism to promote the development of AI and large models.There are two cases to illustrate how open source and international standards interact with each other.展开更多
Lung cancer has one of the highest rates of incidence and mortality worldwide,mak-ing research on its mechanisms and treatments crucial.Animal models are essential in lung cancer research as they accurately replicate ...Lung cancer has one of the highest rates of incidence and mortality worldwide,mak-ing research on its mechanisms and treatments crucial.Animal models are essential in lung cancer research as they accurately replicate the biological characteristics and treatment outcomes seen in human diseases.Currently,various lung cancer models have been established,including chemical induction models,orthotopic transplan-tation models,ectopic transplantation models,metastasis models,and gene editing mouse models.Additionally,lung cancer grafts can be categorized into two types:tissue-based and cell-based grafts.This paper summarizes the phenotypes,advan-tages,and disadvantages of various induction methods based on their modeling tech-niques.The goal is to enhance the simulation of clinical lung cancer characteristics and to establish a solid foundation for future clinical research.展开更多
The application of visual-language large models in the field of medical health has gradually become a research focus.The models combine the capability for image understanding and natural language processing,and can si...The application of visual-language large models in the field of medical health has gradually become a research focus.The models combine the capability for image understanding and natural language processing,and can simultaneously process multi-modality data such as medical images and medical reports.These models can not only recognize images,but also understand the semantic relationship between images and texts,effectively realize the integration of medical information,and provide strong support for clinical decision-making and disease diagnosis.The visual-language large model has good performance for specific medical tasks,and also shows strong potential and high intelligence in the general task models.This paper provides a comprehensive review of the visual-language large model in the field of medical health.Specifically,this paper first introduces the basic theoretical basis and technical principles.Then,this paper introduces the specific application scenarios in the field of medical health,including modality fusion,semi-supervised learning,weakly supervised learning,unsupervised learning,cross-domain model and general models.Finally,the challenges including insufficient data,interpretability,and practical deployment are discussed.According to the existing challenges,four potential future development directions are given.展开更多
Large models,such as large language models(LLMs),vision-language models(VLMs),and multimodal agents,have become key elements in artificial intelli⁃gence(AI)systems.Their rapid development has greatly improved percepti...Large models,such as large language models(LLMs),vision-language models(VLMs),and multimodal agents,have become key elements in artificial intelli⁃gence(AI)systems.Their rapid development has greatly improved perception,generation,and decision-making in various fields.However,their vast scale and complexity bring about new security challenges.Issues such as backdoor vulnerabilities during training,jailbreaking in multimodal rea⁃soning,and data provenance and copyright auditing have made security a critical focus for both academia and industry.展开更多
The application of generative artificial intelligence(AI)is bringing about notable changes in anime creation.This paper surveys recent advancements and applications of diffusion and language models in anime generation...The application of generative artificial intelligence(AI)is bringing about notable changes in anime creation.This paper surveys recent advancements and applications of diffusion and language models in anime generation,focusing on their demonstrated potential to enhance production efficiency through automation and personalization.Despite these benefits,it is crucial to acknowledge the substantial initial computational investments required for training and deploying these models.We conduct an in-depth survey of cutting-edge generative AI technologies,encompassing models such as Stable Diffusion and GPT,and appraise pivotal large-scale datasets alongside quantifiable evaluation metrics.Review of the surveyed literature indicates the achievement of considerable maturity in the capacity of AI models to synthesize high-quality,aesthetically compelling anime visual images from textual prompts,alongside discernible progress in the generation of coherent narratives.However,achieving perfect long-form consistency,mitigating artifacts like flickering in video sequences,and enabling fine-grained artistic control remain critical ongoing challenges.Building upon these advancements,research efforts have increasingly pivoted towards the synthesis of higher-dimensional content,such as video and three-dimensional assets,with recent studies demonstrating significant progress in this burgeoning field.Nevertheless,formidable challenges endure amidst these advancements.Foremost among these are the substantial computational exigencies requisite for training and deploying these sophisticated models,particularly pronounced in the realm of high-dimensional generation such as video synthesis.Additional persistent hurdles include maintaining spatial-temporal consistency across complex scenes and mitigating ethical considerations surrounding bias and the preservation of human creative autonomy.This research underscores the transformative potential and inherent complexities of AI-driven synergy within the creative industries.We posit that future research should be dedicated to the synergistic fusion of diffusion and autoregressive models,the integration of multimodal inputs,and the balanced consideration of ethical implications,particularly regarding bias and the preservation of human creative autonomy,thereby establishing a robust foundation for the advancement of anime creation and the broader landscape of AI-driven content generation.展开更多
Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or...Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or indirect slurs.To address this gap,we propose a hybrid framework combining Term Frequency-Inverse Document Frequency(TF-IDF),word-to-vector(Word2Vec),and Bidirectional Encoder Representations from Transformers(BERT)based models for multi-class cyberbullying detection.Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships,fused with BERT’s contextual embeddings to capture syntactic and semantic complexities.We evaluate the framework on a publicly available dataset of 47,000 annotated social media posts across five cyberbullying categories:age,ethnicity,gender,religion,and indirect aggression.Among BERT variants tested,BERT Base Un-Cased achieved the highest performance with 93%accuracy(standard deviation across±1%5-fold cross-validation)and an average AUC of 0.96,outperforming standalone TF-IDF(78%)and Word2Vec(82%)models.Notably,it achieved near-perfect AUC scores(0.99)for age and ethnicity-based bullying.A comparative analysis with state-of-the-art benchmarks,including Generative Pre-trained Transformer 2(GPT-2)and Text-to-Text Transfer Transformer(T5)models highlights BERT’s superiority in handling ambiguous language.This work advances cyberbullying detection by demonstrating how hybrid feature extraction and transformer models improve multi-class classification,offering a scalable solution for moderating nuanced harmful content.展开更多
Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
AlphaPanda(AlphaFold2[1]inspired protein-specific antibody design in a diffusional manner)is an advanced algorithm for designing complementary determining regions(CDRs)of the antibody targeted the specific epitope,com...AlphaPanda(AlphaFold2[1]inspired protein-specific antibody design in a diffusional manner)is an advanced algorithm for designing complementary determining regions(CDRs)of the antibody targeted the specific epitope,combining transformer[2]models,3DCNN[3],and diffusion[4]generative models.展开更多
The unprecedented scale of large models,such as large language models(LLMs)and text-to-image diffusion models,has raised critical concerns about the unauthorized use of copyrighted data during model training.These con...The unprecedented scale of large models,such as large language models(LLMs)and text-to-image diffusion models,has raised critical concerns about the unauthorized use of copyrighted data during model training.These concerns have spurred a growing demand for dataset copyright auditing techniques,which aim to detect and verify potential infringements in the training data of commercial AI systems.This paper presents a survey of existing auditing solutions,categorizing them across key dimensions:data modality,model training stage,data overlap scenarios,and model access levels.We highlight major trends,including the prevalence of black-box auditing methods and the emphasis on fine-tuning rather than pre-training.Through an in-depth analysis of 12 representative works,we extract four key observations that reveal the limitations of current methods.Furthermore,we identify three open challenges and propose future directions for robust,multimodal,and scalable auditing solutions.Our findings underscore the urgent need to establish standardized benchmarks and develop auditing frameworks that are resilient to low watermark densities and applicable in diverse deployment settings.展开更多
WITHDRAWAL:Zhang,J.J.,Guo,Y.Q.,Qin,Z.Y.,Wei,C.T.,Hu,Q.H.,Vandeginste,V.,Miao,H.Y.,Yao,P.,and Zhang,P.F.,“Predicting Irreducible Water Saturation of Unconventional Reservoirs by Using NMR T2 Spectra:Methods of Morphol...WITHDRAWAL:Zhang,J.J.,Guo,Y.Q.,Qin,Z.Y.,Wei,C.T.,Hu,Q.H.,Vandeginste,V.,Miao,H.Y.,Yao,P.,and Zhang,P.F.,“Predicting Irreducible Water Saturation of Unconventional Reservoirs by Using NMR T2 Spectra:Methods of Morphological Division and Fractal Models”,Acta Geologica Sinica-English Edition(Accepted Article):https://doi.org/10.1111/1755-6724.15094.展开更多
In recent years,large vision-language models(VLMs)have achieved significant breakthroughs in cross-modal understanding and generation.However,the safety issues arising from their multimodal interactions become promine...In recent years,large vision-language models(VLMs)have achieved significant breakthroughs in cross-modal understanding and generation.However,the safety issues arising from their multimodal interactions become prominent.VLMs are vulnerable to jailbreak attacks,where attackers craft carefully designed prompts to bypass safety mechanisms,leading them to generate harmful content.To address this,we investigate the alignment between visual inputs and task execution,uncovering locality defects and attention biases in VLMs.Based on these findings,we propose VOTI,a novel jailbreak framework leveraging visual obfuscation and task induction.VOTI subtly embeds malicious keywords within neutral image layouts to evade detection,and breaks down harmful queries into a sequence of subtasks.This approach disperses malicious intent across modalities,exploiting VLMs’over-reliance on local visual cues and their fragility in multi-step reasoning to bypass global safety mechanisms.Implemented as an automated framework,VOTI integrates large language models as red-team assistants to generate and iteratively optimize jailbreak strategies.Extensive experiments across seven mainstream VLMs demonstrate VOTI’s effectiveness,achieving a 73.46%attack success rate on GPT-4o-mini.These results reveal critical vulnerabilities in VLMs,highlighting the urgent need for improving robust defenses and multimodal alignment.展开更多
We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of t...We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field.The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts,even if the model is not specifically trained in that language.Through experiments,the research questions explore the performance of transformer language models in dealing with complex syntax constructs,the difference in performance between models trained in English and German,and the impact of translating the source text to English before conducting the summarization.We conducted an evaluation of four PLMs(GPT-3,a translation-based approach also utilizing GPT-3,a German language Model,and a domain-specific bio-medical model approach).The evaluation considered the informativeness using 3 types of metrics based on Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and the quality of results which is manually evaluated considering 5 aspects.The results show that text summarization models could be used in the German healthcare domain and that domain-independent language models achieved the best results.The study proves that text summarization models can simplify the search for pre-existing German knowledge in various domains.展开更多
Extracting data from visually rich documents and charts using traditional methods that rely on OCR-based parsing poses multiple challenges,including layout complexity in unstructured formats,limitations in recognizing...Extracting data from visually rich documents and charts using traditional methods that rely on OCR-based parsing poses multiple challenges,including layout complexity in unstructured formats,limitations in recognizing visual elements,and the correlation between different parts of the documents,as well as domain-specific semantics.Simply extracting text is not sufficient;advanced reasoning capabilities are proving to be essential to analyze content and answer questions accurately.This paper aims to evaluate the ability of the Large Language Models(LLMs)to correctly answer questions about various types of charts,comparing their performance when using images as input versus directly parsing PDF files.To retrieve the images from the PDF,ColPali,a model leveraging state-of-the-art visual languagemodels,is used to identify the relevant page containing the appropriate chart for each question.Google’s Gemini multimodal models were used to answer a set of questions through two approaches:1)processing images derived from PDF documents and 2)directly utilizing the content of the same PDFs.Our findings underscore the limitations of traditional OCR-based approaches in visual document understanding(VrDU)and demonstrate the advantages of multimodal methods in both data extraction and reasoning tasks.Through structured benchmarking of chart question answering(CQA)across input formats,our work contributes to the advancement of chart understanding(CU)and the broader field of multimodal document analysis.Using two diverse and information-rich sources:the World Health Statistics 2024 report by theWorld Health Organisation and the Global Banking Annual Review 2024 by McKinsey&Company,we examine the performance ofmultimodal LLMs across different input modalities,comparing their effectiveness in processing charts as images versus parsing directly from PDF content.These documents were selected due to their multimodal nature,combining dense textual analysis with varied visual representations,thus presenting realistic challenges for vision-language models.This comparison is aimed at assessing how advanced models perform with different input formats and to determine if an image-based approach enhances chart comprehension in terms of accurate data extraction and reasoning capabilities.展开更多
Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across vari...Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across various domains.However,the deployment of such models in resource-constrained environments presents a unique set of challenges that require innovative solutions.Resource-constrained environments encompass scenarios where computing resources,memory,and energy availability are restricted.To empower sentiment analysis in resource-constrained environments,we address the crucial need by leveraging lightweight pre-trained models.These models,derived from popular architectures such as DistilBERT,MobileBERT,ALBERT,TinyBERT,ELECTRA,and SqueezeBERT,offer a promising solution to the resource limitations imposed by these environments.By distilling the knowledge from larger models into smaller ones and employing various optimization techniques,these lightweight models aim to strike a balance between performance and resource efficiency.This paper endeavors to explore the performance of multiple lightweight pre-trained models in sentiment analysis tasks specific to such environments and provide insights into their viability for practical deployment.展开更多
Following the groundbreaking introduction of the Transformer architecture in 2017,the development of Large Language Models(LLMs)formally commenced.In May 2020,Chat GPT-3,with over one hundred billion parameters,entere...Following the groundbreaking introduction of the Transformer architecture in 2017,the development of Large Language Models(LLMs)formally commenced.In May 2020,Chat GPT-3,with over one hundred billion parameters,entered the public eye,marking a significant milestone in LLM advancement.展开更多
Anticancer drug resistance remains a major challenge in cancer treatment hindering the efficacy of chemotherapy and targeted therapies.Conventional two-dimensional(2D)cell cultures cannot replicate the complexity of t...Anticancer drug resistance remains a major challenge in cancer treatment hindering the efficacy of chemotherapy and targeted therapies.Conventional two-dimensional(2D)cell cultures cannot replicate the complexity of the in vivo tumor microenvironment(TME),limiting their utility for drug resistance research.Therefore,three-dimensional(3D)tumor models have proven to be a promising alternative for investigating chemoresistance mechanisms.In this review,various cancer 3D models,including spheroids,organoids,scaffold-based models,and bioprinted models,are comprehensively evaluated with a focus on their application in drug resistance studies.We discuss the materials,properties,and advantages of each model,highlighting their ability to better mimic the TME and represent complex mechanisms of drug resistance such as epithelial-mesenchymal transition(EMT),drug efflux,and tumor-stroma interactions.Furthermore,we investigate the limitations of these models,including scalability,reproducibility and technical challenges,as well as their potential therapeutic impact on personalized medicine.Through a thorough comparison of model performance,we provide insights into the strengths and weaknesses of each approach and offer guidance for model selection based on specific research needs.展开更多
Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and langua...Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether ...Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether Large Language Models(LLMs)can play a role in this process.Design/methodology/approach:This article assesses which ChatGPT inputs(full text without tables,figures,and references;title and abstract;title only)produce better quality score estimates,and the extent to which scores are affected by ChatGPT models and system prompts.Findings:The optimal input is the article title and abstract,with average ChatGPT scores based on these(30 iterations on a dataset of 51 papers)correlating at 0.67 with human scores,the highest ever reported.ChatGPT 4o is slightly better than 3.5-turbo(0.66),and 4o-mini(0.66).Research limitations:The data is a convenience sample of the work of a single author,it only includes one field,and the scores are self-evaluations.Practical implications:The results suggest that article full texts might confuse LLM research quality evaluations,even though complex system instructions for the task are more effective than simple ones.Thus,whilst abstracts contain insufficient information for a thorough assessment of rigour,they may contain strong pointers about originality and significance.Finally,linear regression can be used to convert the model scores into the human scale scores,which is 31%more accurate than guessing.Originality/value:This is the first systematic comparison of the impact of different prompts,parameters and inputs for ChatGPT research quality evaluations.展开更多
Depressive disorder is a chronic,recurring,and potentially life-endangering neuropsychiatric disease.According to a report by the World Health Organization,the global population suffering from depression is experienci...Depressive disorder is a chronic,recurring,and potentially life-endangering neuropsychiatric disease.According to a report by the World Health Organization,the global population suffering from depression is experiencing a significant annual increase.Despite its prevalence and considerable impact on people,little is known about its pathogenesis.One major reason is the scarcity of reliable animal models due to the absence of consensus on the pathology and etiology of depression.Furthermore,the neural circuit mechanism of depression induced by various factors is particularly complex.Considering the variability in depressive behavior patterns and neurobiological mechanisms among different animal models of depression,a comparison between the neural circuits of depression induced by various factors is essential for its treatment.In this review,we mainly summarize the most widely used behavioral animal models and neural circuits under different triggers of depression,aiming to provide a theoretical basis for depression prevention.展开更多
文摘In the era of AI,especially large models,the importance of open source has become increasingly prominent.First,open source allows innovation to avoid starting from scratch.Through iterative innovation,it promotes technical exchanges and learning globally.Second,resources required for large model R&D are difficult for a single institution to obtain.The evaluation of general large models also requires the participation of experts from various industries.Third,without open source collaboration,it is difficult to form a unified upper-layer software ecosystem.Therefore,open source has become an important cooperation mechanism to promote the development of AI and large models.There are two cases to illustrate how open source and international standards interact with each other.
基金Sichuan Provincial Administration of Traditional Chinese Medicine,Grant/Award Number:2023MS564National Natural Science Foundation of China,Grant/Award Number:82474436。
文摘Lung cancer has one of the highest rates of incidence and mortality worldwide,mak-ing research on its mechanisms and treatments crucial.Animal models are essential in lung cancer research as they accurately replicate the biological characteristics and treatment outcomes seen in human diseases.Currently,various lung cancer models have been established,including chemical induction models,orthotopic transplan-tation models,ectopic transplantation models,metastasis models,and gene editing mouse models.Additionally,lung cancer grafts can be categorized into two types:tissue-based and cell-based grafts.This paper summarizes the phenotypes,advan-tages,and disadvantages of various induction methods based on their modeling tech-niques.The goal is to enhance the simulation of clinical lung cancer characteristics and to establish a solid foundation for future clinical research.
基金The Natural Science Foundation of Hebei Province(F2024501044).
文摘The application of visual-language large models in the field of medical health has gradually become a research focus.The models combine the capability for image understanding and natural language processing,and can simultaneously process multi-modality data such as medical images and medical reports.These models can not only recognize images,but also understand the semantic relationship between images and texts,effectively realize the integration of medical information,and provide strong support for clinical decision-making and disease diagnosis.The visual-language large model has good performance for specific medical tasks,and also shows strong potential and high intelligence in the general task models.This paper provides a comprehensive review of the visual-language large model in the field of medical health.Specifically,this paper first introduces the basic theoretical basis and technical principles.Then,this paper introduces the specific application scenarios in the field of medical health,including modality fusion,semi-supervised learning,weakly supervised learning,unsupervised learning,cross-domain model and general models.Finally,the challenges including insufficient data,interpretability,and practical deployment are discussed.According to the existing challenges,four potential future development directions are given.
文摘Large models,such as large language models(LLMs),vision-language models(VLMs),and multimodal agents,have become key elements in artificial intelli⁃gence(AI)systems.Their rapid development has greatly improved perception,generation,and decision-making in various fields.However,their vast scale and complexity bring about new security challenges.Issues such as backdoor vulnerabilities during training,jailbreaking in multimodal rea⁃soning,and data provenance and copyright auditing have made security a critical focus for both academia and industry.
基金supported by the National Natural Science Foundation of China(Grant No.62202210).
文摘The application of generative artificial intelligence(AI)is bringing about notable changes in anime creation.This paper surveys recent advancements and applications of diffusion and language models in anime generation,focusing on their demonstrated potential to enhance production efficiency through automation and personalization.Despite these benefits,it is crucial to acknowledge the substantial initial computational investments required for training and deploying these models.We conduct an in-depth survey of cutting-edge generative AI technologies,encompassing models such as Stable Diffusion and GPT,and appraise pivotal large-scale datasets alongside quantifiable evaluation metrics.Review of the surveyed literature indicates the achievement of considerable maturity in the capacity of AI models to synthesize high-quality,aesthetically compelling anime visual images from textual prompts,alongside discernible progress in the generation of coherent narratives.However,achieving perfect long-form consistency,mitigating artifacts like flickering in video sequences,and enabling fine-grained artistic control remain critical ongoing challenges.Building upon these advancements,research efforts have increasingly pivoted towards the synthesis of higher-dimensional content,such as video and three-dimensional assets,with recent studies demonstrating significant progress in this burgeoning field.Nevertheless,formidable challenges endure amidst these advancements.Foremost among these are the substantial computational exigencies requisite for training and deploying these sophisticated models,particularly pronounced in the realm of high-dimensional generation such as video synthesis.Additional persistent hurdles include maintaining spatial-temporal consistency across complex scenes and mitigating ethical considerations surrounding bias and the preservation of human creative autonomy.This research underscores the transformative potential and inherent complexities of AI-driven synergy within the creative industries.We posit that future research should be dedicated to the synergistic fusion of diffusion and autoregressive models,the integration of multimodal inputs,and the balanced consideration of ethical implications,particularly regarding bias and the preservation of human creative autonomy,thereby establishing a robust foundation for the advancement of anime creation and the broader landscape of AI-driven content generation.
基金funded by Scientific Research Deanship at University of Hail-Saudi Arabia through Project Number RG-23092.
文摘Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or indirect slurs.To address this gap,we propose a hybrid framework combining Term Frequency-Inverse Document Frequency(TF-IDF),word-to-vector(Word2Vec),and Bidirectional Encoder Representations from Transformers(BERT)based models for multi-class cyberbullying detection.Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships,fused with BERT’s contextual embeddings to capture syntactic and semantic complexities.We evaluate the framework on a publicly available dataset of 47,000 annotated social media posts across five cyberbullying categories:age,ethnicity,gender,religion,and indirect aggression.Among BERT variants tested,BERT Base Un-Cased achieved the highest performance with 93%accuracy(standard deviation across±1%5-fold cross-validation)and an average AUC of 0.96,outperforming standalone TF-IDF(78%)and Word2Vec(82%)models.Notably,it achieved near-perfect AUC scores(0.99)for age and ethnicity-based bullying.A comparative analysis with state-of-the-art benchmarks,including Generative Pre-trained Transformer 2(GPT-2)and Text-to-Text Transfer Transformer(T5)models highlights BERT’s superiority in handling ambiguous language.This work advances cyberbullying detection by demonstrating how hybrid feature extraction and transformer models improve multi-class classification,offering a scalable solution for moderating nuanced harmful content.
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
基金supported by the Key Project of International Cooperation of Qilu University of Technology(Grant No.:QLUTGJHZ2018008)Shandong Provincial Natural Science Foundation Committee,China(Grant No.:ZR2016HB54)Shandong Provincial Key Laboratory of Microbial Engineering(SME).
文摘AlphaPanda(AlphaFold2[1]inspired protein-specific antibody design in a diffusional manner)is an advanced algorithm for designing complementary determining regions(CDRs)of the antibody targeted the specific epitope,combining transformer[2]models,3DCNN[3],and diffusion[4]generative models.
基金supported in part by NSFC under Grant Nos.62402379,U22A2029 and U24A20237.
文摘The unprecedented scale of large models,such as large language models(LLMs)and text-to-image diffusion models,has raised critical concerns about the unauthorized use of copyrighted data during model training.These concerns have spurred a growing demand for dataset copyright auditing techniques,which aim to detect and verify potential infringements in the training data of commercial AI systems.This paper presents a survey of existing auditing solutions,categorizing them across key dimensions:data modality,model training stage,data overlap scenarios,and model access levels.We highlight major trends,including the prevalence of black-box auditing methods and the emphasis on fine-tuning rather than pre-training.Through an in-depth analysis of 12 representative works,we extract four key observations that reveal the limitations of current methods.Furthermore,we identify three open challenges and propose future directions for robust,multimodal,and scalable auditing solutions.Our findings underscore the urgent need to establish standardized benchmarks and develop auditing frameworks that are resilient to low watermark densities and applicable in diverse deployment settings.
文摘WITHDRAWAL:Zhang,J.J.,Guo,Y.Q.,Qin,Z.Y.,Wei,C.T.,Hu,Q.H.,Vandeginste,V.,Miao,H.Y.,Yao,P.,and Zhang,P.F.,“Predicting Irreducible Water Saturation of Unconventional Reservoirs by Using NMR T2 Spectra:Methods of Morphological Division and Fractal Models”,Acta Geologica Sinica-English Edition(Accepted Article):https://doi.org/10.1111/1755-6724.15094.
文摘In recent years,large vision-language models(VLMs)have achieved significant breakthroughs in cross-modal understanding and generation.However,the safety issues arising from their multimodal interactions become prominent.VLMs are vulnerable to jailbreak attacks,where attackers craft carefully designed prompts to bypass safety mechanisms,leading them to generate harmful content.To address this,we investigate the alignment between visual inputs and task execution,uncovering locality defects and attention biases in VLMs.Based on these findings,we propose VOTI,a novel jailbreak framework leveraging visual obfuscation and task induction.VOTI subtly embeds malicious keywords within neutral image layouts to evade detection,and breaks down harmful queries into a sequence of subtasks.This approach disperses malicious intent across modalities,exploiting VLMs’over-reliance on local visual cues and their fragility in multi-step reasoning to bypass global safety mechanisms.Implemented as an automated framework,VOTI integrates large language models as red-team assistants to generate and iteratively optimize jailbreak strategies.Extensive experiments across seven mainstream VLMs demonstrate VOTI’s effectiveness,achieving a 73.46%attack success rate on GPT-4o-mini.These results reveal critical vulnerabilities in VLMs,highlighting the urgent need for improving robust defenses and multimodal alignment.
文摘We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field.The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts,even if the model is not specifically trained in that language.Through experiments,the research questions explore the performance of transformer language models in dealing with complex syntax constructs,the difference in performance between models trained in English and German,and the impact of translating the source text to English before conducting the summarization.We conducted an evaluation of four PLMs(GPT-3,a translation-based approach also utilizing GPT-3,a German language Model,and a domain-specific bio-medical model approach).The evaluation considered the informativeness using 3 types of metrics based on Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and the quality of results which is manually evaluated considering 5 aspects.The results show that text summarization models could be used in the German healthcare domain and that domain-independent language models achieved the best results.The study proves that text summarization models can simplify the search for pre-existing German knowledge in various domains.
基金supported by a grant from the Ministry of Research,Innovation and Digitization,CNCS/CCCDI-UEFISCDI,project number COFUND-CETP-SMART-LEM-1,within PNCDI Ⅳ.
文摘Extracting data from visually rich documents and charts using traditional methods that rely on OCR-based parsing poses multiple challenges,including layout complexity in unstructured formats,limitations in recognizing visual elements,and the correlation between different parts of the documents,as well as domain-specific semantics.Simply extracting text is not sufficient;advanced reasoning capabilities are proving to be essential to analyze content and answer questions accurately.This paper aims to evaluate the ability of the Large Language Models(LLMs)to correctly answer questions about various types of charts,comparing their performance when using images as input versus directly parsing PDF files.To retrieve the images from the PDF,ColPali,a model leveraging state-of-the-art visual languagemodels,is used to identify the relevant page containing the appropriate chart for each question.Google’s Gemini multimodal models were used to answer a set of questions through two approaches:1)processing images derived from PDF documents and 2)directly utilizing the content of the same PDFs.Our findings underscore the limitations of traditional OCR-based approaches in visual document understanding(VrDU)and demonstrate the advantages of multimodal methods in both data extraction and reasoning tasks.Through structured benchmarking of chart question answering(CQA)across input formats,our work contributes to the advancement of chart understanding(CU)and the broader field of multimodal document analysis.Using two diverse and information-rich sources:the World Health Statistics 2024 report by theWorld Health Organisation and the Global Banking Annual Review 2024 by McKinsey&Company,we examine the performance ofmultimodal LLMs across different input modalities,comparing their effectiveness in processing charts as images versus parsing directly from PDF content.These documents were selected due to their multimodal nature,combining dense textual analysis with varied visual representations,thus presenting realistic challenges for vision-language models.This comparison is aimed at assessing how advanced models perform with different input formats and to determine if an image-based approach enhances chart comprehension in terms of accurate data extraction and reasoning capabilities.
文摘Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across various domains.However,the deployment of such models in resource-constrained environments presents a unique set of challenges that require innovative solutions.Resource-constrained environments encompass scenarios where computing resources,memory,and energy availability are restricted.To empower sentiment analysis in resource-constrained environments,we address the crucial need by leveraging lightweight pre-trained models.These models,derived from popular architectures such as DistilBERT,MobileBERT,ALBERT,TinyBERT,ELECTRA,and SqueezeBERT,offer a promising solution to the resource limitations imposed by these environments.By distilling the knowledge from larger models into smaller ones and employing various optimization techniques,these lightweight models aim to strike a balance between performance and resource efficiency.This paper endeavors to explore the performance of multiple lightweight pre-trained models in sentiment analysis tasks specific to such environments and provide insights into their viability for practical deployment.
文摘Following the groundbreaking introduction of the Transformer architecture in 2017,the development of Large Language Models(LLMs)formally commenced.In May 2020,Chat GPT-3,with over one hundred billion parameters,entered the public eye,marking a significant milestone in LLM advancement.
基金funded by the Ministry of Science,Technological Development and Innovation of the Republic of Serbia(grant numbers 451-03-136/2025-03/200007 and 451-03-136/2025-03/200042).
文摘Anticancer drug resistance remains a major challenge in cancer treatment hindering the efficacy of chemotherapy and targeted therapies.Conventional two-dimensional(2D)cell cultures cannot replicate the complexity of the in vivo tumor microenvironment(TME),limiting their utility for drug resistance research.Therefore,three-dimensional(3D)tumor models have proven to be a promising alternative for investigating chemoresistance mechanisms.In this review,various cancer 3D models,including spheroids,organoids,scaffold-based models,and bioprinted models,are comprehensively evaluated with a focus on their application in drug resistance studies.We discuss the materials,properties,and advantages of each model,highlighting their ability to better mimic the TME and represent complex mechanisms of drug resistance such as epithelial-mesenchymal transition(EMT),drug efflux,and tumor-stroma interactions.Furthermore,we investigate the limitations of these models,including scalability,reproducibility and technical challenges,as well as their potential therapeutic impact on personalized medicine.Through a thorough comparison of model performance,we provide insights into the strengths and weaknesses of each approach and offer guidance for model selection based on specific research needs.
基金supported by National Natural Science Foundation of China(62376219 and 62006194)Foundational Research Project in Specialized Discipline(Grant No.G2024WD0146)Faculty Construction Project(Grant No.24GH0201148).
文摘Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
文摘Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether Large Language Models(LLMs)can play a role in this process.Design/methodology/approach:This article assesses which ChatGPT inputs(full text without tables,figures,and references;title and abstract;title only)produce better quality score estimates,and the extent to which scores are affected by ChatGPT models and system prompts.Findings:The optimal input is the article title and abstract,with average ChatGPT scores based on these(30 iterations on a dataset of 51 papers)correlating at 0.67 with human scores,the highest ever reported.ChatGPT 4o is slightly better than 3.5-turbo(0.66),and 4o-mini(0.66).Research limitations:The data is a convenience sample of the work of a single author,it only includes one field,and the scores are self-evaluations.Practical implications:The results suggest that article full texts might confuse LLM research quality evaluations,even though complex system instructions for the task are more effective than simple ones.Thus,whilst abstracts contain insufficient information for a thorough assessment of rigour,they may contain strong pointers about originality and significance.Finally,linear regression can be used to convert the model scores into the human scale scores,which is 31%more accurate than guessing.Originality/value:This is the first systematic comparison of the impact of different prompts,parameters and inputs for ChatGPT research quality evaluations.
基金supported by the Brain&Behavior Research Foundation(30233).
文摘Depressive disorder is a chronic,recurring,and potentially life-endangering neuropsychiatric disease.According to a report by the World Health Organization,the global population suffering from depression is experiencing a significant annual increase.Despite its prevalence and considerable impact on people,little is known about its pathogenesis.One major reason is the scarcity of reliable animal models due to the absence of consensus on the pathology and etiology of depression.Furthermore,the neural circuit mechanism of depression induced by various factors is particularly complex.Considering the variability in depressive behavior patterns and neurobiological mechanisms among different animal models of depression,a comparison between the neural circuits of depression induced by various factors is essential for its treatment.In this review,we mainly summarize the most widely used behavioral animal models and neural circuits under different triggers of depression,aiming to provide a theoretical basis for depression prevention.
