Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
Solute diffusion controlled solidification model was used to simulate the initial stage cellular to dendrite transition of Ti44Al alloys during directional solidification at different velocities. The simulation result...Solute diffusion controlled solidification model was used to simulate the initial stage cellular to dendrite transition of Ti44Al alloys during directional solidification at different velocities. The simulation results show that during this process, a mixed structure composed of cells and dendrites was observed, where secondary dendrites are absent at facing surface with parallel closely spaced dendrites, which agrees with the previous experimental observation. The dendrite spacings are larger than cellular spacings at a given rate, and the columnar grain spacing sharply increases to a maximum as solidification advance to coexistence zone. In addition, simulation also revealed that decreasing the numbers of the seed causes the trend of unstable dendrite transition to increase. Finally, the main influence factors affecting cell/dendrite transition were analyzed, which could be the change of growth rates resulting in slight fluctuations of liquid composition occurred at growth front. The simulation results are in reasonable agreement with the results of previous theoretical models and experimental observation at low cooling rates.展开更多
Aim The purpose of this study was to develop a mathe-matical model to quantitatively describe the passive trans-port of macromolecules within dental biofilms. Methodology Fluorescently labeled dextrans with different ...Aim The purpose of this study was to develop a mathe-matical model to quantitatively describe the passive trans-port of macromolecules within dental biofilms. Methodology Fluorescently labeled dextrans with different molecular mass （3 kD,10 kD,40 kD,70 kD,2 000 kD） were used as a series of diffusion probes. Streptococcus mutans,Streptococcus sanguinis,Actinomyces naeslundii and Fusobacterium nucleatum were used as inocula for biofilm formation. The diffusion processes of different probes through the in vitro biofilm were recorded with a confocal laser microscope. Results Mathematical function of biofilm penetration was constructed on the basis of the inverse problem method. Based on this function,not only the relationship between average concentration of steady-state and molecule weights can be analyzed,but also that between penetrative time and molecule weights. Conclusion This can be used to predict the effective concentration and the penetrative time of anti-biofilm medicines that can diffuse through oral biofilm. Further-more,an improved model for large molecule is proposed by considering the exchange time at the upper boundary of the dental biofilm.展开更多
Polyvinylpyrrolidone K-30(PVP) was introduced into the preparation of nanozero-valent iron(n ZVI) and the traditional liquid-phase reduction was improved. The introduction of PVP simplified the traditional method.The ...Polyvinylpyrrolidone K-30(PVP) was introduced into the preparation of nanozero-valent iron(n ZVI) and the traditional liquid-phase reduction was improved. The introduction of PVP simplified the traditional method.The n ZVI prepared with this new approach showed excellent surface characters and high performance on the removal of cadmium. TEM results showed that the aggregates of n ZVI can reach to several micrometers in length but less than 100 nm in diameter. The iron particles that were enclosed by a layer of oxide film that is less than10 nm, demonstrated that the n ZVI possesses a core–shell structure. BET results indicate that the specific surface area of the n ZVI was 20.3159 m^2g^(-1). A three factor and three level orthogonal experiment was employed to find out the dominant factor that affects the removal rate of cadmium by n ZVI. Based on the range values, the prominence order of each factor was: initial p H of the solution N initial concentration of cadmium N dosage of n ZVI, the range was 96.453, 3.294 and 1.747, respectively. A simulation was performed under the same condition and a same conclusion was derived, this consistence confirmed the validity of the conclusion that p H is the most significant factor that affects the adsorption efficiency.展开更多
In this study,the removal of monovalent and divalent cations,Nat,Kt,Mg2t,and Ca2t,in a diluted solution from Chott-El Jerid Lake,Tunisia,was investigated with the electrodialysis technique.The process was tested using...In this study,the removal of monovalent and divalent cations,Nat,Kt,Mg2t,and Ca2t,in a diluted solution from Chott-El Jerid Lake,Tunisia,was investigated with the electrodialysis technique.The process was tested using two cation-exchange membranes:sulfonated polyether sulfone cross-linked with 10%hexamethylenediamine(HEXCl)and sulfonated polyether sulfone grafted with octylamine(S-PESOS).The commercially available membrane Nafion®was used for comparison.The results showed that Nafion®and S-PESOS membranes had similar removal behaviors,and the investigated cations were ranked in the following descending order in terms of their demineralization rates:Nat>Ca2t>Mg2t>Kt.Divalent cations were more effectively removed by HEXCl than by monovalent cations.The plots based on the WebereMorris model showed a strong linearity.This reveals that intra-particle diffusion was not the removal rate-determining step,and the removal process was controlled by two or more concurrent mechanisms.The Boyd plots did not pass through their origin,and the sole controlling step was determined by film-diffusion resistance,especially after a long period of electrodialysis.Additionally,a semi-empirical model was established to simulate the temporal variation of the treatment process,and the physical significance and values of model parameters were compared for the three membranes.The findings of this study indicate that HEXCl and S-PESOS membranes can be efficiently utilized for water softening,especially when effluents are highly loaded with calcium and magnesium ions.展开更多
The inner surface modification process by plasma-based low-energy ion implantation（PBLEII）with an electron cyclotron resonance（ECR）microwave plasma source located at the central axis of a cylindrical tube is model...The inner surface modification process by plasma-based low-energy ion implantation（PBLEII）with an electron cyclotron resonance（ECR）microwave plasma source located at the central axis of a cylindrical tube is modeled to optimize the low-energy ion implantation parameters for industrial applications.In this paper,a magnetized plasma diffusion fluid model has been established to describe the plasma nonuniformity caused by plasma diffusion under an axial magnetic field during the pulse-off time of low pulsed negative bias.Using this plasma density distribution as the initial condition,a sheath collisional fluid model is built up to describe the sheath evolution and ion implantation during the pulse-on time.The plasma nonuniformity at the end of the pulse-off time is more apparent along the radial direction compared with that in the axial direction due to the geometry of the linear plasma source in the center and the difference between perpendicular and parallel plasma diffusion coefficients with respect to the magnetic field.The normalized nitrogen plasma densities on the inner and outer surfaces of the tube are observed to be about 0.39 and 0.24,respectively,of which the value is 1 at the central plasma source.After a 5μs pulse-on time,in the area less than 2 cm from the end of the tube,the nitrogen ion implantation energy decreases from 1.5 keV to 1.3 keV and the ion implantation angle increases from several degrees to more than 40°;both variations reduce the nitrogen ion implantation depth.However,the nitrogen ion implantation dose peaks of about 2×10^（10）-7×10^（10）ions/cm^2 in this area are 2-4 times higher than that of 1.18×10^（10）ions/cm^2 and 1.63×10^（10）ions/cm^2 on the inner and outer surfaces of the tube.The sufficient ion implantation dose ensures an acceptable modification effect near the end of the tube under the low energy and large angle conditions for nitrogen ion implantation,because the modification effect is mainly determined by the ion implantation dose,just as the mass transfer process in PBLEII is dominated by low-energy ion implantation and thermal diffusion.Therefore,a comparatively uniform surface modification by the low-energy nitrogen ion implantation is achieved along the cylindrical tube on both the inner and outer surfaces.展开更多
Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited ...Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model’s applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.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
Deep learning has achieved great progress in image recognition,segmentation,semantic recognition and game theory.In this study,a latest deep learning model,a conditional diffusion model was adopted as a surrogate mode...Deep learning has achieved great progress in image recognition,segmentation,semantic recognition and game theory.In this study,a latest deep learning model,a conditional diffusion model was adopted as a surrogate model to predict the heat transfer during the casting process instead of numerical simulation.The conditional diffusion model was established and trained with the geometry shapes,initial temperature fields and temperature fields at t_(i) as the condition and random noise sampled from standard normal distribution as the input.The output was the temperature field at t_(i+1).Therefore,the temperature field at t_(i+1)can be predicted as the temperature field at t_(i) is known,and the continuous temperature fields of all the time steps can be predicted based on the initial temperature field of an arbitrary 2D geometry.A training set with 3022D shapes and their simulated temperature fields at different time steps was established.The accuracy for the temperature field for a single time step reaches 97.7%,and that for continuous time steps reaches 69.1%with the main error actually existing in the sand mold.The effect of geometry shape and initial temperature field on the prediction accuracy was investigated,the former achieves better result than the latter because the former can identify casting,mold and chill by different colors in the input images.The diffusion model has proved the potential as a surrogate model for numerical simulation of the casting process.展开更多
Accurately identifying building distribution from remote sensing images with complex background information is challenging.The emergence of diffusion models has prompted the innovative idea of employing the reverse de...Accurately identifying building distribution from remote sensing images with complex background information is challenging.The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds.Building on this concept,we propose a novel framework,building extraction diffusion model(BEDiff),which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion.Our approach begins with the design of booster guidance,a mechanism that extracts structural and semantic features from remote sensing images to serve as priors,thereby providing targeted guidance for the diffusion process.Additionally,we introduce a cross-feature fusion module(CFM)that bridges the semantic gap between different types of features,facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively.Our proposed BEDiff marks the first application of diffusion models to the task of building extraction.Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff,affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.展开更多
Although existing style transfer techniques have made significant progress in the field of image generation,there are still some challenges in the field of exhibition hall design.The existing style transfer methods ma...Although existing style transfer techniques have made significant progress in the field of image generation,there are still some challenges in the field of exhibition hall design.The existing style transfer methods mainly focus on the transformation of single dimensional features,but ignore the deep integration of content and style features in exhibition hall design.In addition,existing methods are deficient in detail retention,especially in accurately capturing and reproducing local textures and details while preserving the content image structure.In addition,point-based attention mechanisms tend to ignore the complexity and diversity of image features in multi-dimensional space,resulting in alignment problems between features in different semantic areas,resulting in inconsistent stylistic features in content areas.In this context,this paper proposes a semantic-enhanced multimodal style transfer algorithm tailored for exhibition hall design.The proposed approach leverages a multimodal encoder architecture to integrate information from text,source images,and style images,using separate encoder modules for each modality to capture shallow,deep,and semantic features.A novel Style Transfer Convolution(STConv)convolutional kernel,based on the Visual Geometry Group(VGG)19 network,is introduced to improve feature extraction in style transfer.Additionally,an enhanced Transformer encoder is incorporated to capture contextual semantic information within images,while the CLIP model is employed for text data processing.A hybrid attention module is designed to precisely capture style features,achieving multimodal feature fusion via a diffusion model that generates exhibition hall design images aligned with stylistic requirements.Quantitative experiments show that compared with the most advanced algorithms,the proposed method has achieved significant performance improvement on both Fréchet Inception Distance(FID)and Kernel Inception Distance(KID)indexes.For example,on the ExpoArchive dataset,the proposed method has a FID value of 87.9 and a KID value of 1.98,which is significantly superior to other methods.展开更多
Fire detection has held stringent importance in computer vision for over half a century.The development of early fire detection strategies is pivotal to the realization of safe and smart cities,inhabitable in the futu...Fire detection has held stringent importance in computer vision for over half a century.The development of early fire detection strategies is pivotal to the realization of safe and smart cities,inhabitable in the future.However,the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets,lack of diversity,and class imbalance.In this work,we explore the possible ways forward to overcome these challenges posed by available datasets.We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art(SOTA)vision-based models and propose the use of generative models for data augmentation,as a future work direction.First,a comparative analysis of two prominent object detection architectures,You Only Look Once version 7(YOLOv7)and YOLOv8 has been carried out using a balanced dataset,where both models have been evaluated across various evaluation metrics including precision,recall,and mean Average Precision(mAP).The results are compared to other recent fire detection models,highlighting the superior performance and efficiency of the proposed YOLOv8 architecture as trained on our balanced dataset.Next,a fractal dimension analysis gives a deeper insight into the repetition of patterns in fire,and the effectiveness of the results has been demonstrated by a windowing-based inference approach.The proposed Slicing-Aided Hyper Inference(SAHI)improves the fire and smoke detection capability of YOLOv8 for real-life applications with a significantly improved mAP performance over a strict confidence threshold.YOLOv8 with SAHI inference gives a mAP:50-95 improvement of more than 25%compared to the base YOLOv8 model.The study also provides insights into future work direction by exploring the potential of generative models like deep convolutional generative adversarial network(DCGAN)and diffusion models like stable diffusion,for data augmentation.展开更多
Recently,generative artificial intelligence(GenAI)has developed into a new form of technology that can create copy,image,audio,and video content and adapt it to individual preferences on every channel and moment autom...Recently,generative artificial intelligence(GenAI)has developed into a new form of technology that can create copy,image,audio,and video content and adapt it to individual preferences on every channel and moment automatically.But most fail at proof-of-concept,as the pipelines needed to govern data,generate it controllably,deliver it,and do causal evaluation are absent or poorly aligned.This paper puts forward a practical end-to-end framework concerning personalized advertising driven by GenAI,which combines representation learning,constrained generation,and experimentation into a single operating cycle.First,we pick a modular architecture:profiles and contexts go into controllable large language and diffusion models that yield brand-safe assets under deterministic conditioning,which are chosen via a contextual bandit and vetted by policy and equality guardrails.Second,we give a measurement stack going from straightforward A/B/n tests to doubly-robust uplift modeling,making it possible to find out diverse treatment effects that are good to use in business metrics(incremental conversions and profit).Third,we operationalize latency budgets,humans in the loop,red teams,safety filters,and post-deployment monitoring with clear escalation paths.We focus throughout the paper on reproducibility,privacy(consent,privacy,differential privacy,on-device inference),and on GDPR/CCPA-like governance specifications.We end on our actionable blueprint,algorithmic choices,sample prompts,KPIs,and step-wise rollout to achieve trustworthy performance upgrades without putting creative quality,fairness,or compliance to the test.展开更多
The internal structures of cells as the basic units of life are a major wonder of the microscopic world.Cellular images provide an intriguing window to help explore and understand the composition and function of these...The internal structures of cells as the basic units of life are a major wonder of the microscopic world.Cellular images provide an intriguing window to help explore and understand the composition and function of these structures.Scientific imagery combined with artistic expression can further expand the potential of imaging in educational dissemination and interdisciplinary applications.展开更多
Multi-scale problems in Computational Fluid Dynamics(CFD)often require numerous simulations across various design parameters.Using a fixed mesh for all cases may fail to capture critical physical features.Moving mesh ...Multi-scale problems in Computational Fluid Dynamics(CFD)often require numerous simulations across various design parameters.Using a fixed mesh for all cases may fail to capture critical physical features.Moving mesh adaptation provides an optimal resource allocation to obtain high-resolution flow-fields on low-resolution meshes.However,most existing methods require manual experience and the flow posteriori information poses great challenges to practical applications.In addition,generating adaptive meshes directly from design parameters is difficult due to highly nonlinear relationships.The diffusion model is currently the most popular model in generative tasks that integrates the diffusion principle into deep learning to capture the complex nonlinear correlations.A dual diffusion framework,Para2Mesh,is proposed to predict the adaptive meshes from design parameters by exploiting the robust data distribution learning ability of the diffusion model.Through iterative denoising,the proposed dual networks accurately reconstruct the flow-field to provide flow features as supervised information,and then achieve rapid and reliable mesh movement.Experiments in CFD scenarios demonstrate that Para2Mesh predicts similar meshes directly from design parameters with much higher efficiency than traditional method.It could become a real-time adaptation tool to assist engineering design and optimization,providing a promising solution for high-resolution flow-field analysis.展开更多
Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has prov...Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has proven highly successful in image generation,speech generation,time series modelling etc.and now opens a new avenue for traffic data imputation.In this paper,we propose a conditional diffusion model,called the implicit-explicit diffusion model,for traffic data imputation.This model exploits both the implicit and explicit feature of the data simultaneously.More specifically,we design two types of feature extraction modules,one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series.This approach not only inherits the advantages of the diffusion model for estimating missing data,but also takes into account the multiscale correlation inherent in traffic data.To illustrate the performance of the model,extensive experiments are conducted on three real-world time series datasets using different missing rates.The experimental results demonstrate that the model improves imputation accuracy and generalization capability.展开更多
Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(...Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(TPFs).Methods YOLOv8n-cls was used to construct a baseline model on the data of 3781 patients from the Orthopedic Trauma Center of Wuhan Union Hospital.Additionally,a segmentation-guided classification approach was proposed.To enhance the dataset,a diffusion model was further demonstrated for data augmentation.Results The novel method that integrated the segmentation-guided classification and diffusion model augmentation sig-nificantly improved the accuracy and robustness of fracture classification.The average accuracy of classification for TPFs rose from 0.844 to 0.896.The comprehensive performance of the dual-stream model was also significantly enhanced after many rounds of training,with both the macro-area under the curve(AUC)and the micro-AUC increasing from 0.94 to 0.97.By utilizing diffusion model augmentation and segmentation map integration,the model demonstrated superior efficacy in identifying SchatzkerⅠ,achieving an accuracy of 0.880.It yielded an accuracy of 0.898 for SchatzkerⅡandⅢand 0.913 for SchatzkerⅣ;for SchatzkerⅤandⅥ,the accuracy was 0.887;and for intercondylar ridge fracture,the accuracy was 0.923.Conclusion The dual-stream attention-based classification network,which has been verified by many experiments,exhibited great potential in predicting the classification of TPFs.This method facilitates automatic TPF assessment and may assist surgeons in the rapid formulation of surgical plans.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
In recent years,diffusion models have achieved remarkable progress in image generation.However,extending them to text-to-video(T2V)generation remains challenging,particularly in maintaining semantic consistency and vi...In recent years,diffusion models have achieved remarkable progress in image generation.However,extending them to text-to-video(T2V)generation remains challenging,particularly in maintaining semantic consistency and visual quality across frames.Existing approaches often overlook the synergy between high-level semantics and low-level texture information,resulting in blurry or temporally inconsistent outputs.To address these issues,we propose Dual Consistency Training(DCT),a novel framework designed to jointly optimize semantic and texture consistency in video generation.Specifically,we introduce a multi-scale spatial adapter to enhance spatial feature extraction,and leverage the complementary strengths of CLIP and VGG—where CLIP focuses on high-level semantics and VGG captures fine-grained texture and detail.During training,a stepwise strategy is adopted to impose semantic and texture losses,constraining discrepancies between generated and ground-truth frames.Furthermore,we propose CLWS,which dynamically adjusts the balance between semantic and texture losses to facilitate more stable and effective optimization.Remarkably,DCT achieves high-quality video generation using only a single training video on a single NVIDIA A6000 GPU.Extensive experiments demonstrate that our method significantly improves temporal coherence and visual fidelity across various video generation tasks,verifying its effectiveness and generalizability.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金National Natural Science Foundation of China (50434030)
文摘Solute diffusion controlled solidification model was used to simulate the initial stage cellular to dendrite transition of Ti44Al alloys during directional solidification at different velocities. The simulation results show that during this process, a mixed structure composed of cells and dendrites was observed, where secondary dendrites are absent at facing surface with parallel closely spaced dendrites, which agrees with the previous experimental observation. The dendrite spacings are larger than cellular spacings at a given rate, and the columnar grain spacing sharply increases to a maximum as solidification advance to coexistence zone. In addition, simulation also revealed that decreasing the numbers of the seed causes the trend of unstable dendrite transition to increase. Finally, the main influence factors affecting cell/dendrite transition were analyzed, which could be the change of growth rates resulting in slight fluctuations of liquid composition occurred at growth front. The simulation results are in reasonable agreement with the results of previous theoretical models and experimental observation at low cooling rates.
基金supported by a grant from the National Natural Science Foundation of China (NSFC) No. 81070826/30872886/30400497Sponsored by Shanghai Rising-Star Program No. 09QA1403700+1 种基金funded by Shanghai Leading Academic Discipline Project (Project Number: S30206)the Science and Technology Commission of Shanghai (08DZ2271100)
文摘Aim The purpose of this study was to develop a mathe-matical model to quantitatively describe the passive trans-port of macromolecules within dental biofilms. Methodology Fluorescently labeled dextrans with different molecular mass （3 kD,10 kD,40 kD,70 kD,2 000 kD） were used as a series of diffusion probes. Streptococcus mutans,Streptococcus sanguinis,Actinomyces naeslundii and Fusobacterium nucleatum were used as inocula for biofilm formation. The diffusion processes of different probes through the in vitro biofilm were recorded with a confocal laser microscope. Results Mathematical function of biofilm penetration was constructed on the basis of the inverse problem method. Based on this function,not only the relationship between average concentration of steady-state and molecule weights can be analyzed,but also that between penetrative time and molecule weights. Conclusion This can be used to predict the effective concentration and the penetrative time of anti-biofilm medicines that can diffuse through oral biofilm. Further-more,an improved model for large molecule is proposed by considering the exchange time at the upper boundary of the dental biofilm.
基金Supported by the National Natural Science Foundation of China(51278147)the Funds for Creative Research Groups of China(51121062)State Key Laboratory of Urban Water Resource and Environment(Harbin Institute of Technology)(2014TS02)
文摘Polyvinylpyrrolidone K-30(PVP) was introduced into the preparation of nanozero-valent iron(n ZVI) and the traditional liquid-phase reduction was improved. The introduction of PVP simplified the traditional method.The n ZVI prepared with this new approach showed excellent surface characters and high performance on the removal of cadmium. TEM results showed that the aggregates of n ZVI can reach to several micrometers in length but less than 100 nm in diameter. The iron particles that were enclosed by a layer of oxide film that is less than10 nm, demonstrated that the n ZVI possesses a core–shell structure. BET results indicate that the specific surface area of the n ZVI was 20.3159 m^2g^(-1). A three factor and three level orthogonal experiment was employed to find out the dominant factor that affects the removal rate of cadmium by n ZVI. Based on the range values, the prominence order of each factor was: initial p H of the solution N initial concentration of cadmium N dosage of n ZVI, the range was 96.453, 3.294 and 1.747, respectively. A simulation was performed under the same condition and a same conclusion was derived, this consistence confirmed the validity of the conclusion that p H is the most significant factor that affects the adsorption efficiency.
文摘In this study,the removal of monovalent and divalent cations,Nat,Kt,Mg2t,and Ca2t,in a diluted solution from Chott-El Jerid Lake,Tunisia,was investigated with the electrodialysis technique.The process was tested using two cation-exchange membranes:sulfonated polyether sulfone cross-linked with 10%hexamethylenediamine(HEXCl)and sulfonated polyether sulfone grafted with octylamine(S-PESOS).The commercially available membrane Nafion®was used for comparison.The results showed that Nafion®and S-PESOS membranes had similar removal behaviors,and the investigated cations were ranked in the following descending order in terms of their demineralization rates:Nat>Ca2t>Mg2t>Kt.Divalent cations were more effectively removed by HEXCl than by monovalent cations.The plots based on the WebereMorris model showed a strong linearity.This reveals that intra-particle diffusion was not the removal rate-determining step,and the removal process was controlled by two or more concurrent mechanisms.The Boyd plots did not pass through their origin,and the sole controlling step was determined by film-diffusion resistance,especially after a long period of electrodialysis.Additionally,a semi-empirical model was established to simulate the temporal variation of the treatment process,and the physical significance and values of model parameters were compared for the three membranes.The findings of this study indicate that HEXCl and S-PESOS membranes can be efficiently utilized for water softening,especially when effluents are highly loaded with calcium and magnesium ions.
基金supported by National Natural Science Foundation of China(Nos.50725519,51271048,51321004)
文摘The inner surface modification process by plasma-based low-energy ion implantation（PBLEII）with an electron cyclotron resonance（ECR）microwave plasma source located at the central axis of a cylindrical tube is modeled to optimize the low-energy ion implantation parameters for industrial applications.In this paper,a magnetized plasma diffusion fluid model has been established to describe the plasma nonuniformity caused by plasma diffusion under an axial magnetic field during the pulse-off time of low pulsed negative bias.Using this plasma density distribution as the initial condition,a sheath collisional fluid model is built up to describe the sheath evolution and ion implantation during the pulse-on time.The plasma nonuniformity at the end of the pulse-off time is more apparent along the radial direction compared with that in the axial direction due to the geometry of the linear plasma source in the center and the difference between perpendicular and parallel plasma diffusion coefficients with respect to the magnetic field.The normalized nitrogen plasma densities on the inner and outer surfaces of the tube are observed to be about 0.39 and 0.24,respectively,of which the value is 1 at the central plasma source.After a 5μs pulse-on time,in the area less than 2 cm from the end of the tube,the nitrogen ion implantation energy decreases from 1.5 keV to 1.3 keV and the ion implantation angle increases from several degrees to more than 40°;both variations reduce the nitrogen ion implantation depth.However,the nitrogen ion implantation dose peaks of about 2×10^（10）-7×10^（10）ions/cm^2 in this area are 2-4 times higher than that of 1.18×10^（10）ions/cm^2 and 1.63×10^（10）ions/cm^2 on the inner and outer surfaces of the tube.The sufficient ion implantation dose ensures an acceptable modification effect near the end of the tube under the low energy and large angle conditions for nitrogen ion implantation,because the modification effect is mainly determined by the ion implantation dose,just as the mass transfer process in PBLEII is dominated by low-energy ion implantation and thermal diffusion.Therefore,a comparatively uniform surface modification by the low-energy nitrogen ion implantation is achieved along the cylindrical tube on both the inner and outer surfaces.
基金supported by the Yonsei University graduate school Department of Integrative Biotechnology.
文摘Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model’s applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.
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sponsored by Tsinghua-Toyota Joint Research Fund
文摘Deep learning has achieved great progress in image recognition,segmentation,semantic recognition and game theory.In this study,a latest deep learning model,a conditional diffusion model was adopted as a surrogate model to predict the heat transfer during the casting process instead of numerical simulation.The conditional diffusion model was established and trained with the geometry shapes,initial temperature fields and temperature fields at t_(i) as the condition and random noise sampled from standard normal distribution as the input.The output was the temperature field at t_(i+1).Therefore,the temperature field at t_(i+1)can be predicted as the temperature field at t_(i) is known,and the continuous temperature fields of all the time steps can be predicted based on the initial temperature field of an arbitrary 2D geometry.A training set with 3022D shapes and their simulated temperature fields at different time steps was established.The accuracy for the temperature field for a single time step reaches 97.7%,and that for continuous time steps reaches 69.1%with the main error actually existing in the sand mold.The effect of geometry shape and initial temperature field on the prediction accuracy was investigated,the former achieves better result than the latter because the former can identify casting,mold and chill by different colors in the input images.The diffusion model has proved the potential as a surrogate model for numerical simulation of the casting process.
基金supported by the National Natural Science Foundation of China(Nos.61906168,62202429 and 62272267)the Zhejiang Provincial Natural Science Foundation of China(No.LY23F020023)the Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects(No.2022SDSJ01)。
文摘Accurately identifying building distribution from remote sensing images with complex background information is challenging.The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds.Building on this concept,we propose a novel framework,building extraction diffusion model(BEDiff),which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion.Our approach begins with the design of booster guidance,a mechanism that extracts structural and semantic features from remote sensing images to serve as priors,thereby providing targeted guidance for the diffusion process.Additionally,we introduce a cross-feature fusion module(CFM)that bridges the semantic gap between different types of features,facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively.Our proposed BEDiff marks the first application of diffusion models to the task of building extraction.Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff,affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.
文摘Although existing style transfer techniques have made significant progress in the field of image generation,there are still some challenges in the field of exhibition hall design.The existing style transfer methods mainly focus on the transformation of single dimensional features,but ignore the deep integration of content and style features in exhibition hall design.In addition,existing methods are deficient in detail retention,especially in accurately capturing and reproducing local textures and details while preserving the content image structure.In addition,point-based attention mechanisms tend to ignore the complexity and diversity of image features in multi-dimensional space,resulting in alignment problems between features in different semantic areas,resulting in inconsistent stylistic features in content areas.In this context,this paper proposes a semantic-enhanced multimodal style transfer algorithm tailored for exhibition hall design.The proposed approach leverages a multimodal encoder architecture to integrate information from text,source images,and style images,using separate encoder modules for each modality to capture shallow,deep,and semantic features.A novel Style Transfer Convolution(STConv)convolutional kernel,based on the Visual Geometry Group(VGG)19 network,is introduced to improve feature extraction in style transfer.Additionally,an enhanced Transformer encoder is incorporated to capture contextual semantic information within images,while the CLIP model is employed for text data processing.A hybrid attention module is designed to precisely capture style features,achieving multimodal feature fusion via a diffusion model that generates exhibition hall design images aligned with stylistic requirements.Quantitative experiments show that compared with the most advanced algorithms,the proposed method has achieved significant performance improvement on both Fréchet Inception Distance(FID)and Kernel Inception Distance(KID)indexes.For example,on the ExpoArchive dataset,the proposed method has a FID value of 87.9 and a KID value of 1.98,which is significantly superior to other methods.
基金supported by a grant from R&D Program Development of Rail-Specific Digital Resource Technology Based on an AI-Enabled Rail Support Platform,grant number PK2401C1,of the Korea Railroad Research Institute.
文摘Fire detection has held stringent importance in computer vision for over half a century.The development of early fire detection strategies is pivotal to the realization of safe and smart cities,inhabitable in the future.However,the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets,lack of diversity,and class imbalance.In this work,we explore the possible ways forward to overcome these challenges posed by available datasets.We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art(SOTA)vision-based models and propose the use of generative models for data augmentation,as a future work direction.First,a comparative analysis of two prominent object detection architectures,You Only Look Once version 7(YOLOv7)and YOLOv8 has been carried out using a balanced dataset,where both models have been evaluated across various evaluation metrics including precision,recall,and mean Average Precision(mAP).The results are compared to other recent fire detection models,highlighting the superior performance and efficiency of the proposed YOLOv8 architecture as trained on our balanced dataset.Next,a fractal dimension analysis gives a deeper insight into the repetition of patterns in fire,and the effectiveness of the results has been demonstrated by a windowing-based inference approach.The proposed Slicing-Aided Hyper Inference(SAHI)improves the fire and smoke detection capability of YOLOv8 for real-life applications with a significantly improved mAP performance over a strict confidence threshold.YOLOv8 with SAHI inference gives a mAP:50-95 improvement of more than 25%compared to the base YOLOv8 model.The study also provides insights into future work direction by exploring the potential of generative models like deep convolutional generative adversarial network(DCGAN)and diffusion models like stable diffusion,for data augmentation.
文摘Recently,generative artificial intelligence(GenAI)has developed into a new form of technology that can create copy,image,audio,and video content and adapt it to individual preferences on every channel and moment automatically.But most fail at proof-of-concept,as the pipelines needed to govern data,generate it controllably,deliver it,and do causal evaluation are absent or poorly aligned.This paper puts forward a practical end-to-end framework concerning personalized advertising driven by GenAI,which combines representation learning,constrained generation,and experimentation into a single operating cycle.First,we pick a modular architecture:profiles and contexts go into controllable large language and diffusion models that yield brand-safe assets under deterministic conditioning,which are chosen via a contextual bandit and vetted by policy and equality guardrails.Second,we give a measurement stack going from straightforward A/B/n tests to doubly-robust uplift modeling,making it possible to find out diverse treatment effects that are good to use in business metrics(incremental conversions and profit).Third,we operationalize latency budgets,humans in the loop,red teams,safety filters,and post-deployment monitoring with clear escalation paths.We focus throughout the paper on reproducibility,privacy(consent,privacy,differential privacy,on-device inference),and on GDPR/CCPA-like governance specifications.We end on our actionable blueprint,algorithmic choices,sample prompts,KPIs,and step-wise rollout to achieve trustworthy performance upgrades without putting creative quality,fairness,or compliance to the test.
基金supported by the Fundamental Research Funds for the Central Universities(No.226-2024-00038),China.
文摘The internal structures of cells as the basic units of life are a major wonder of the microscopic world.Cellular images provide an intriguing window to help explore and understand the composition and function of these structures.Scientific imagery combined with artistic expression can further expand the potential of imaging in educational dissemination and interdisciplinary applications.
基金co-supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002 and 2019ZA052011)。
文摘Multi-scale problems in Computational Fluid Dynamics(CFD)often require numerous simulations across various design parameters.Using a fixed mesh for all cases may fail to capture critical physical features.Moving mesh adaptation provides an optimal resource allocation to obtain high-resolution flow-fields on low-resolution meshes.However,most existing methods require manual experience and the flow posteriori information poses great challenges to practical applications.In addition,generating adaptive meshes directly from design parameters is difficult due to highly nonlinear relationships.The diffusion model is currently the most popular model in generative tasks that integrates the diffusion principle into deep learning to capture the complex nonlinear correlations.A dual diffusion framework,Para2Mesh,is proposed to predict the adaptive meshes from design parameters by exploiting the robust data distribution learning ability of the diffusion model.Through iterative denoising,the proposed dual networks accurately reconstruct the flow-field to provide flow features as supervised information,and then achieve rapid and reliable mesh movement.Experiments in CFD scenarios demonstrate that Para2Mesh predicts similar meshes directly from design parameters with much higher efficiency than traditional method.It could become a real-time adaptation tool to assist engineering design and optimization,providing a promising solution for high-resolution flow-field analysis.
基金partially supported by the National Natural Science Foundation of China(62271485)the SDHS Science and Technology Project(HS2023B044)
文摘Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has proven highly successful in image generation,speech generation,time series modelling etc.and now opens a new avenue for traffic data imputation.In this paper,we propose a conditional diffusion model,called the implicit-explicit diffusion model,for traffic data imputation.This model exploits both the implicit and explicit feature of the data simultaneously.More specifically,we design two types of feature extraction modules,one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series.This approach not only inherits the advantages of the diffusion model for estimating missing data,but also takes into account the multiscale correlation inherent in traffic data.To illustrate the performance of the model,extensive experiments are conducted on three real-world time series datasets using different missing rates.The experimental results demonstrate that the model improves imputation accuracy and generalization capability.
基金supported by the National Natural Science Foundation of China(Nos.81974355 and 82172524)Key Research and Development Program of Hubei Province(No.2021BEA161)+2 种基金National Innovation Platform Development Program(No.2020021105012440)Open Project Funding of the Hubei Key Laboratory of Big Data Intelligent Analysis and Application,Hubei University(No.2024BDIAA03)Free Innovation Preliminary Research Fund of Wuhan Union Hospital(No.2024XHYN047).
文摘Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(TPFs).Methods YOLOv8n-cls was used to construct a baseline model on the data of 3781 patients from the Orthopedic Trauma Center of Wuhan Union Hospital.Additionally,a segmentation-guided classification approach was proposed.To enhance the dataset,a diffusion model was further demonstrated for data augmentation.Results The novel method that integrated the segmentation-guided classification and diffusion model augmentation sig-nificantly improved the accuracy and robustness of fracture classification.The average accuracy of classification for TPFs rose from 0.844 to 0.896.The comprehensive performance of the dual-stream model was also significantly enhanced after many rounds of training,with both the macro-area under the curve(AUC)and the micro-AUC increasing from 0.94 to 0.97.By utilizing diffusion model augmentation and segmentation map integration,the model demonstrated superior efficacy in identifying SchatzkerⅠ,achieving an accuracy of 0.880.It yielded an accuracy of 0.898 for SchatzkerⅡandⅢand 0.913 for SchatzkerⅣ;for SchatzkerⅤandⅥ,the accuracy was 0.887;and for intercondylar ridge fracture,the accuracy was 0.923.Conclusion The dual-stream attention-based classification network,which has been verified by many experiments,exhibited great potential in predicting the classification of TPFs.This method facilitates automatic TPF assessment and may assist surgeons in the rapid formulation of surgical plans.
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.
基金supported in part by the National Natural Science Foundation of China[Grant number 62471075]the Major Science and Technology Project Grant of the Chongqing Municipal Education Commission[Grant number KJZD-M202301901]Graduate Innovation Project Funding of Chongqing University of Technology[Grant number gzlcx20253249].
文摘In recent years,diffusion models have achieved remarkable progress in image generation.However,extending them to text-to-video(T2V)generation remains challenging,particularly in maintaining semantic consistency and visual quality across frames.Existing approaches often overlook the synergy between high-level semantics and low-level texture information,resulting in blurry or temporally inconsistent outputs.To address these issues,we propose Dual Consistency Training(DCT),a novel framework designed to jointly optimize semantic and texture consistency in video generation.Specifically,we introduce a multi-scale spatial adapter to enhance spatial feature extraction,and leverage the complementary strengths of CLIP and VGG—where CLIP focuses on high-level semantics and VGG captures fine-grained texture and detail.During training,a stepwise strategy is adopted to impose semantic and texture losses,constraining discrepancies between generated and ground-truth frames.Furthermore,we propose CLWS,which dynamically adjusts the balance between semantic and texture losses to facilitate more stable and effective optimization.Remarkably,DCT achieves high-quality video generation using only a single training video on a single NVIDIA A6000 GPU.Extensive experiments demonstrate that our method significantly improves temporal coherence and visual fidelity across various video generation tasks,verifying its effectiveness and generalizability.
