Nitrogen doping has significant effects on the photocatalytic performance of ceria(CeO_(2)),and the possible synergistic effect with the inevitably introduced abundant oxygen vacancies(OVs)is of great significance for...Nitrogen doping has significant effects on the photocatalytic performance of ceria(CeO_(2)),and the possible synergistic effect with the inevitably introduced abundant oxygen vacancies(OVs)is of great significance for further investigation,and the specifically exposed crystal faces of CeO_(2)may have an impact on the performance of nitrogen doped CeO_(2).Herein,nitrogen-doped CeO_(2)with different morphologies and exposed crystal faces was prepared,and its performances in the photocatalytic degradation of tetracycline(TC)or hydrogen production via water splitting were evaluated.Density functional theory(DFT)was used to simulate the band structures,density of states,and oxygen defect properties of different CeO_(2)structures.It was found that nitrogen doping and OVs synergistically promoted the catalytic activity of nitrogen-doped CeO_(2).In addition,the exposed crystal faces of CeO_(2)have significant effects on the introduction of nitrogen and the ease of OV generation,as well as the synergistic effect of nitrogen doping with OVs.Among them,the rod-like nitrogen-doped CeO_(2)with exposed(110)face(R-CeO_(2)-NH_(3))showed a photocatalytic degradation ratio of 73.59%for TC and hydrogen production of 156.89μmol/g,outperforming other prepared photocatalysts.展开更多
Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveill...Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.展开更多
Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantita...Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.展开更多
To address the seismic face stability challenges encountered in urban and subsea tunnel construction,an efficient probabilistic analysis framework for shield tunnel faces under seismic conditions is proposed.Based on ...To address the seismic face stability challenges encountered in urban and subsea tunnel construction,an efficient probabilistic analysis framework for shield tunnel faces under seismic conditions is proposed.Based on the upper-bound theory of limit analysis,an improved three-dimensional discrete deterministic mechanism,accounting for the heterogeneous nature of soil media,is formulated to evaluate seismic face stability.The metamodel of failure probabilistic assessments for seismic tunnel faces is constructed by integrating the sparse polynomial chaos expansion method(SPCE)with the modified pseudo-dynamic approach(MPD).The improved deterministic model is validated by comparing with published literature and numerical simulations results,and the SPCE-MPD metamodel is examined with the traditional MCS method.Based on the SPCE-MPD metamodels,the seismic effects on face failure probability and reliability index are presented and the global sensitivity analysis(GSA)is involved to reflect the influence order of seismic action parameters.Finally,the proposed approach is tested to be effective by a engineering case of the Chengdu outer ring tunnel.The results show that higher uncertainty of seismic response on face stability should be noticed in areas with intense earthquakes and variation of seismic wave velocity has the most profound influence on tunnel face stability.展开更多
Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D avatars.However,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the sim...Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D avatars.However,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being modeled.This study presents a novel framework for generating animatable 3D cartoon faces from a single portrait image.Methods First,we transferred an input real-world portrait to a stylized cartoon image using StyleGAN.We then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed texture.Our two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark supervision.Finally,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation transfer.Conclusions Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity criteria.Furthermore,we demonstrated the capability of the proposed 3D model for real-time facial animation.展开更多
Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications.In this paper,we propose an efficient approach for automating 3D facial wound seg...Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications.In this paper,we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network.Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions.To achieve accurate segmentation,we conducted thorough experiments and selected a high-performing model from the trainedmodels.The selectedmodel demonstrates exceptional segmentation performance for complex 3D facial wounds.Furthermore,based on the segmentation model,we propose an improved approach for extracting 3D facial wound fillers and compare it to the results of the previous study.Our method achieved a remarkable accuracy of 0.9999993% on the test suite,surpassing the performance of the previous method.From this result,we use 3D printing technology to illustrate the shape of the wound filling.The outcomes of this study have significant implications for physicians involved in preoperative planning and intervention design.By automating facial wound segmentation and improving the accuracy ofwound-filling extraction,our approach can assist in carefully assessing and optimizing interventions,leading to enhanced patient outcomes.Additionally,it contributes to advancing facial reconstruction techniques by utilizing machine learning and 3D bioprinting for printing skin tissue implants.Our source code is available at https://github.com/SIMOGroup/WoundFilling3D.展开更多
In the digital era,emojis have enriched the way people communicate and research on emojis explosively increased in recent years.However,few noticed their functions from the neurocognitive perspective,especially their ...In the digital era,emojis have enriched the way people communicate and research on emojis explosively increased in recent years.However,few noticed their functions from the neurocognitive perspective,especially their similarities and differences with facial expressions in traditional face-to-face communication.To fill this gap,we conducted a Meta-analysis with 25 independent effect sizes from previous experimental studies.The present study shows that emojis have slight advantages in processing efficiency,which might be attributed to their simplicity in design,namely the omission of complex facial features,but the difference between emoji and face processing is not significant.In addition,emotional valence and experimental methods do not have significant influences,which suggests that emojis are equally effective as human faces in emotional expression.The current research contributes to the knowledge in digital communication and the crucial role played by emojis therein.展开更多
Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN model...Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.展开更多
Objective Category-specific recognition and naming deficits have been observed in a variety of patient populations. However, the category-specific cortices for naming famous faces, animals and man-made objects remain ...Objective Category-specific recognition and naming deficits have been observed in a variety of patient populations. However, the category-specific cortices for naming famous faces, animals and man-made objects remain controversial. The present study aimed to study the specific areas involved in naming pictures of these 3 categories using functional magnetic resonance imaging. Methods Functional images were analyzed using statistical parametric mapping and the 3 different contrasts were evaluated using t statistics by comparing the naming tasks to their baselines.The contrast images were entered into a random-effects group level analysis.The results were reported in Montreal Neurological Institute co-ordinates,and anatomical regions were identified using an automated anatomical labeling method with XJview 8.Results Naming famous faces caused more activation in the bilateral head of the hippocampus and amygdala with significant left dominance. Bilateral activation of pars triangularis and pars opercularis in the naming of famous faces was also revealed. Naming animals evoked greater responses in the left supplementary motor area, while naming man-made objects evoked more in the left premotor area,left pars orbitalis and right supplementary motor area. The extent of bilateral fusiform gyri activation by naming man-made objects was much larger than that by naming of famous faces or animals.Even in the overlapping sites of activation,some differences among the categories were found for activation in the fusiform gyri.Conclusion The cortices involved in the naming process vary with the naming of famous faces,animals and man-made objects.This finding suggests that different categories of pictures should be used during intra-operative language mapping to generate a broader map of language function, in order to minimize the incidence of false-negative stimulation and permanent post-operative deficits.展开更多
巨噬细胞样细胞(macrophage-like cells, MLC)指起源、功能与巨噬细胞类似的免疫细胞,包括小胶质细胞、玻璃体细胞及巨噬细胞。将en face OCT显示层面设置在视网膜表明即可观测到视网膜表明的MLC(epiretinal MLC, eMLC),随后利用Image ...巨噬细胞样细胞(macrophage-like cells, MLC)指起源、功能与巨噬细胞类似的免疫细胞,包括小胶质细胞、玻璃体细胞及巨噬细胞。将en face OCT显示层面设置在视网膜表明即可观测到视网膜表明的MLC(epiretinal MLC, eMLC),随后利用Image J软件即可对细胞进行提取和量化。研究表明,eMLC在炎症情况下均可出现细胞募集及活化现象,但在不同眼底病中各具特点。在糖尿病视网膜病变、视网膜静脉阻塞等视网膜缺血缺氧性疾病中,eMLC密度越高,黄斑水肿可能越严重。此外,eMLC密度更高的视网膜静脉阻塞患者抗VEGF疗效更差,视力预后不佳,提示基于en face OCT的eMLC不仅可用于评估视网膜炎症情况,而且还能充当提示疾病疗效及预后的标志物。在葡萄膜炎等免疫炎症性疾病中,en face OCT亦可观测到eMLC密度、形态等改变。白塞病葡萄膜炎患者视网膜血管渗漏程度与eMLC密度相关性强,故eMLC密度可充当无创评估视网膜血管渗漏程度的新指标。然而,目前提取和量化eMLC的方法及标准不统一,降低了各研究间的可比性。因此,亟需制定统一的操作规范和评估标准。此外eMLC所代表的具体细胞类型及功能仍需进一步探究。未来,研究者可以利用en face OCT对眼底炎症地进行无创评估。基于en face OCT的eMLC还能作为基础研究与临床研究之间的桥梁,为揭示疾病的致病机制提供重要参考。展开更多
In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fi...In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.展开更多
This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,an...This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.展开更多
We Built Railways on the Roof of the World Author:Wang Quanquan Paperback,155 pages Published by Foreign Languages Press The jade-green Fuxing(rejuvenation)bullet train,snow white hada(prayer scarves),and beaming face...We Built Railways on the Roof of the World Author:Wang Quanquan Paperback,155 pages Published by Foreign Languages Press The jade-green Fuxing(rejuvenation)bullet train,snow white hada(prayer scarves),and beaming faces keep flashing into the photographer’s lens.展开更多
From a distance,they look like vivid pieces of abstract art-but move a little closer and dozens of small and characterful portraits shine out of the work.The ambitious idea of the City of Portraits project,a decade in...From a distance,they look like vivid pieces of abstract art-but move a little closer and dozens of small and characterful portraits shine out of the work.The ambitious idea of the City of Portraits project,a decade in the making and nowhere near complete,is to record the faces of all 1,800 people who live in Britain's smallest city,St Davids in south-west Wales.展开更多
In the thousand-year-old mural of Mogao Grottoes in Dunhuang,a special symbol tells the story of exchanges among civilisations.Three Rabbits Sharing Three Ears depicts three rabbits chasing each other,with each two sh...In the thousand-year-old mural of Mogao Grottoes in Dunhuang,a special symbol tells the story of exchanges among civilisations.Three Rabbits Sharing Three Ears depicts three rabbits chasing each other,with each two sharing one ear.It is said that its earliest version appeared in Dunhuang in the 6th century,and it had travelled across the desert along the ancient Silk Road,over mountains and rivers,leaping onto British ceramic tiles,integrating into Egyptian pottery and jumping cross the clock faces of German churches.展开更多
Research on reconstructing imperfect faces is a challenging task.In this study,we explore a data-driven approach using a pre-trained MICA(MetrIC fAce)model combined with 3D printing to address this challenge.We propos...Research on reconstructing imperfect faces is a challenging task.In this study,we explore a data-driven approach using a pre-trained MICA(MetrIC fAce)model combined with 3D printing to address this challenge.We propose a training strategy that utilizes the pre-trained MICA model and self-supervised learning techniques to improve accuracy and reduce the time needed for 3D facial structure reconstruction.Our results demonstrate high accuracy,evaluated by the geometric loss function and various statistical measures.To showcase the effectiveness of the approach,we used 3D printing to create a model that covers facial wounds.The findings indicate that our method produces a model that fits well and achieves comprehensive 3D facial reconstruction.This technique has the potential to aid doctors in treating patients with facial injuries.展开更多
Face masks play a pivotal role in preventing infection transmission.However,the capture of infection-sourced particles in face masks poses challenges related to reuse,necessitating proper disposal.We developed a self-...Face masks play a pivotal role in preventing infection transmission.However,the capture of infection-sourced particles in face masks poses challenges related to reuse,necessitating proper disposal.We developed a self-sterilizable polypropylene-based membrane for face masks to address challenges associated with infection transmission prevention.The membrane,created using 3D printing,underwent functionalization with zinc oxide(ZnO)and polydopamine(PDA)-TEMPO to achieve broad-spectrum light absorption and facilitate self-sterilization through photocatalytic and photothermal effects upon light exposure.The hydrophobic surface(water contact angle:133±2°)minimized moisture accumulation,and the membrane exhibited robust mechanical properties,including shear strength(1.25±0.5kPa)and peel resistance strength(112.8±11.2kPa).The evaluation demonstrated stability in airflow(0-500cm^(3)/s)and excellent aerosol filtration efficiency(94.8±0.6%)for particles(PM 0.3,PM 2.5,PM 10),comparable to commercial masks.The membrane showed antibacterial efficacy over five uses in a simulated respiratory environment.Safety assessments confirmed biocompatibility through cytocompatibility and skin irritation assays.In conclusion,this membrane offers efficient filtration and photo-triggered sterilization,presenting a promising solution for next-generation face masks to address concerns related to reuse,disposal,and infection control.展开更多
基金Project(52164025)supported by the National Natural Science Foundation of ChinaProject([2020]1Y219)supported by the Basic Research Program from the Science&Technology Department of Guizhou Province,China+2 种基金Project([2019]30)supported by the Training Project from Guizhou University,ChinaProject([2023]04)supported by the Guizhou University Innovation Talent Team Project,ChinaProject([2022]041)supported by the Natural Science Research Project of Guizhou Provincial Department of Education,China。
文摘Nitrogen doping has significant effects on the photocatalytic performance of ceria(CeO_(2)),and the possible synergistic effect with the inevitably introduced abundant oxygen vacancies(OVs)is of great significance for further investigation,and the specifically exposed crystal faces of CeO_(2)may have an impact on the performance of nitrogen doped CeO_(2).Herein,nitrogen-doped CeO_(2)with different morphologies and exposed crystal faces was prepared,and its performances in the photocatalytic degradation of tetracycline(TC)or hydrogen production via water splitting were evaluated.Density functional theory(DFT)was used to simulate the band structures,density of states,and oxygen defect properties of different CeO_(2)structures.It was found that nitrogen doping and OVs synergistically promoted the catalytic activity of nitrogen-doped CeO_(2).In addition,the exposed crystal faces of CeO_(2)have significant effects on the introduction of nitrogen and the ease of OV generation,as well as the synergistic effect of nitrogen doping with OVs.Among them,the rod-like nitrogen-doped CeO_(2)with exposed(110)face(R-CeO_(2)-NH_(3))showed a photocatalytic degradation ratio of 73.59%for TC and hydrogen production of 156.89μmol/g,outperforming other prepared photocatalysts.
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project grant number(BFP/RGP/ICT/22/490).
文摘Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.
基金supported by the National Natural Science Foundation of China(Nos.52279107 and 52379106)the Qingdao Guoxin Jiaozhou Bay Second Submarine Tunnel Co.,Ltd.,the Academician and Expert Workstation of Yunnan Province(No.202205AF150015)the Science and Technology Innovation Project of YCIC Group Co.,Ltd.(No.YCIC-YF-2022-15)。
文摘Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.
基金Project([2018]3010)supported by the Guizhou Provincial Science and Technology Major Project,China。
文摘To address the seismic face stability challenges encountered in urban and subsea tunnel construction,an efficient probabilistic analysis framework for shield tunnel faces under seismic conditions is proposed.Based on the upper-bound theory of limit analysis,an improved three-dimensional discrete deterministic mechanism,accounting for the heterogeneous nature of soil media,is formulated to evaluate seismic face stability.The metamodel of failure probabilistic assessments for seismic tunnel faces is constructed by integrating the sparse polynomial chaos expansion method(SPCE)with the modified pseudo-dynamic approach(MPD).The improved deterministic model is validated by comparing with published literature and numerical simulations results,and the SPCE-MPD metamodel is examined with the traditional MCS method.Based on the SPCE-MPD metamodels,the seismic effects on face failure probability and reliability index are presented and the global sensitivity analysis(GSA)is involved to reflect the influence order of seismic action parameters.Finally,the proposed approach is tested to be effective by a engineering case of the Chengdu outer ring tunnel.The results show that higher uncertainty of seismic response on face stability should be noticed in areas with intense earthquakes and variation of seismic wave velocity has the most profound influence on tunnel face stability.
文摘Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D avatars.However,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being modeled.This study presents a novel framework for generating animatable 3D cartoon faces from a single portrait image.Methods First,we transferred an input real-world portrait to a stylized cartoon image using StyleGAN.We then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed texture.Our two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark supervision.Finally,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation transfer.Conclusions Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity criteria.Furthermore,we demonstrated the capability of the proposed 3D model for real-time facial animation.
文摘Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications.In this paper,we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network.Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions.To achieve accurate segmentation,we conducted thorough experiments and selected a high-performing model from the trainedmodels.The selectedmodel demonstrates exceptional segmentation performance for complex 3D facial wounds.Furthermore,based on the segmentation model,we propose an improved approach for extracting 3D facial wound fillers and compare it to the results of the previous study.Our method achieved a remarkable accuracy of 0.9999993% on the test suite,surpassing the performance of the previous method.From this result,we use 3D printing technology to illustrate the shape of the wound filling.The outcomes of this study have significant implications for physicians involved in preoperative planning and intervention design.By automating facial wound segmentation and improving the accuracy ofwound-filling extraction,our approach can assist in carefully assessing and optimizing interventions,leading to enhanced patient outcomes.Additionally,it contributes to advancing facial reconstruction techniques by utilizing machine learning and 3D bioprinting for printing skin tissue implants.Our source code is available at https://github.com/SIMOGroup/WoundFilling3D.
基金supported by Science Foundation of China University of Petroleum,Beijing(No.2462023YXZZ006)Undergraduate Key Teaching Reform Project(30GK2312).
文摘In the digital era,emojis have enriched the way people communicate and research on emojis explosively increased in recent years.However,few noticed their functions from the neurocognitive perspective,especially their similarities and differences with facial expressions in traditional face-to-face communication.To fill this gap,we conducted a Meta-analysis with 25 independent effect sizes from previous experimental studies.The present study shows that emojis have slight advantages in processing efficiency,which might be attributed to their simplicity in design,namely the omission of complex facial features,but the difference between emoji and face processing is not significant.In addition,emotional valence and experimental methods do not have significant influences,which suggests that emojis are equally effective as human faces in emotional expression.The current research contributes to the knowledge in digital communication and the crucial role played by emojis therein.
基金funded by Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydney.Moreover,Ongoing Research Funding Program(ORF-2025-14)King Saud University,Riyadh,Saudi Arabia,under Project ORF-2025-。
文摘Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.
基金supported bythe Foundation of Science and Technology Program of Guangdong Province,China(No.2008A030201021)the Natural Science Foundation of Guangdong Province,China(No.10151001002000010)
文摘Objective Category-specific recognition and naming deficits have been observed in a variety of patient populations. However, the category-specific cortices for naming famous faces, animals and man-made objects remain controversial. The present study aimed to study the specific areas involved in naming pictures of these 3 categories using functional magnetic resonance imaging. Methods Functional images were analyzed using statistical parametric mapping and the 3 different contrasts were evaluated using t statistics by comparing the naming tasks to their baselines.The contrast images were entered into a random-effects group level analysis.The results were reported in Montreal Neurological Institute co-ordinates,and anatomical regions were identified using an automated anatomical labeling method with XJview 8.Results Naming famous faces caused more activation in the bilateral head of the hippocampus and amygdala with significant left dominance. Bilateral activation of pars triangularis and pars opercularis in the naming of famous faces was also revealed. Naming animals evoked greater responses in the left supplementary motor area, while naming man-made objects evoked more in the left premotor area,left pars orbitalis and right supplementary motor area. The extent of bilateral fusiform gyri activation by naming man-made objects was much larger than that by naming of famous faces or animals.Even in the overlapping sites of activation,some differences among the categories were found for activation in the fusiform gyri.Conclusion The cortices involved in the naming process vary with the naming of famous faces,animals and man-made objects.This finding suggests that different categories of pictures should be used during intra-operative language mapping to generate a broader map of language function, in order to minimize the incidence of false-negative stimulation and permanent post-operative deficits.
文摘巨噬细胞样细胞(macrophage-like cells, MLC)指起源、功能与巨噬细胞类似的免疫细胞,包括小胶质细胞、玻璃体细胞及巨噬细胞。将en face OCT显示层面设置在视网膜表明即可观测到视网膜表明的MLC(epiretinal MLC, eMLC),随后利用Image J软件即可对细胞进行提取和量化。研究表明,eMLC在炎症情况下均可出现细胞募集及活化现象,但在不同眼底病中各具特点。在糖尿病视网膜病变、视网膜静脉阻塞等视网膜缺血缺氧性疾病中,eMLC密度越高,黄斑水肿可能越严重。此外,eMLC密度更高的视网膜静脉阻塞患者抗VEGF疗效更差,视力预后不佳,提示基于en face OCT的eMLC不仅可用于评估视网膜炎症情况,而且还能充当提示疾病疗效及预后的标志物。在葡萄膜炎等免疫炎症性疾病中,en face OCT亦可观测到eMLC密度、形态等改变。白塞病葡萄膜炎患者视网膜血管渗漏程度与eMLC密度相关性强,故eMLC密度可充当无创评估视网膜血管渗漏程度的新指标。然而,目前提取和量化eMLC的方法及标准不统一,降低了各研究间的可比性。因此,亟需制定统一的操作规范和评估标准。此外eMLC所代表的具体细胞类型及功能仍需进一步探究。未来,研究者可以利用en face OCT对眼底炎症地进行无创评估。基于en face OCT的eMLC还能作为基础研究与临床研究之间的桥梁,为揭示疾病的致病机制提供重要参考。
文摘In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.
基金Supported by the Fundamental Research Funds for the Central Universities(2024300443)the Natural Science Foundation of Jiangsu Province(BK20241224).
文摘This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.
文摘We Built Railways on the Roof of the World Author:Wang Quanquan Paperback,155 pages Published by Foreign Languages Press The jade-green Fuxing(rejuvenation)bullet train,snow white hada(prayer scarves),and beaming faces keep flashing into the photographer’s lens.
文摘From a distance,they look like vivid pieces of abstract art-but move a little closer and dozens of small and characterful portraits shine out of the work.The ambitious idea of the City of Portraits project,a decade in the making and nowhere near complete,is to record the faces of all 1,800 people who live in Britain's smallest city,St Davids in south-west Wales.
文摘In the thousand-year-old mural of Mogao Grottoes in Dunhuang,a special symbol tells the story of exchanges among civilisations.Three Rabbits Sharing Three Ears depicts three rabbits chasing each other,with each two sharing one ear.It is said that its earliest version appeared in Dunhuang in the 6th century,and it had travelled across the desert along the ancient Silk Road,over mountains and rivers,leaping onto British ceramic tiles,integrating into Egyptian pottery and jumping cross the clock faces of German churches.
文摘Research on reconstructing imperfect faces is a challenging task.In this study,we explore a data-driven approach using a pre-trained MICA(MetrIC fAce)model combined with 3D printing to address this challenge.We propose a training strategy that utilizes the pre-trained MICA model and self-supervised learning techniques to improve accuracy and reduce the time needed for 3D facial structure reconstruction.Our results demonstrate high accuracy,evaluated by the geometric loss function and various statistical measures.To showcase the effectiveness of the approach,we used 3D printing to create a model that covers facial wounds.The findings indicate that our method produces a model that fits well and achieves comprehensive 3D facial reconstruction.This technique has the potential to aid doctors in treating patients with facial injuries.
基金supported by Key Scientific Research Projects of Colleges and Universities in Henan Province(No.23ZX016)University Innovation Research and Training Program(No.202110467004)the Distinguished Professor Program of Institutions of Higher Learning in Henan Province,National Key R&D Program of China(No.2019YFE0101200).
文摘Face masks play a pivotal role in preventing infection transmission.However,the capture of infection-sourced particles in face masks poses challenges related to reuse,necessitating proper disposal.We developed a self-sterilizable polypropylene-based membrane for face masks to address challenges associated with infection transmission prevention.The membrane,created using 3D printing,underwent functionalization with zinc oxide(ZnO)and polydopamine(PDA)-TEMPO to achieve broad-spectrum light absorption and facilitate self-sterilization through photocatalytic and photothermal effects upon light exposure.The hydrophobic surface(water contact angle:133±2°)minimized moisture accumulation,and the membrane exhibited robust mechanical properties,including shear strength(1.25±0.5kPa)and peel resistance strength(112.8±11.2kPa).The evaluation demonstrated stability in airflow(0-500cm^(3)/s)and excellent aerosol filtration efficiency(94.8±0.6%)for particles(PM 0.3,PM 2.5,PM 10),comparable to commercial masks.The membrane showed antibacterial efficacy over five uses in a simulated respiratory environment.Safety assessments confirmed biocompatibility through cytocompatibility and skin irritation assays.In conclusion,this membrane offers efficient filtration and photo-triggered sterilization,presenting a promising solution for next-generation face masks to address concerns related to reuse,disposal,and infection control.
