Generating animatable 3D cartoon faces from single portraits

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.

Learning to represent 2D human face with mathematical model

How to represent a human face pattern?While it is presented in a continuous way in human visual system,computers often store and process it in a discrete manner with 2D arrays of pixels.The authors attempt to learn a continuous surface representation for face image with explicit function.First,an explicit model(EmFace)for human face representation is pro-posed in the form of a finite sum of mathematical terms,where each term is an analytic function element.Further,to estimate the unknown parameters of EmFace,a novel neural network,EmNet,is designed with an encoder-decoder structure and trained from massive face images,where the encoder is defined by a deep convolutional neural network and the decoder is an explicit mathematical expression of EmFace.The authors demonstrate that our EmFace represents face image more accurate than the comparison method,with an average mean square error of 0.000888,0.000936,0.000953 on LFW,IARPA Janus Benchmark-B,and IJB-C datasets.Visualisation results show that,EmFace has a higher representation performance on faces with various expressions,postures,and other factors.Furthermore,EmFace achieves reasonable performance on several face image processing tasks,including face image restoration,denoising,and transformation.

A Deep Transfer Learning Approach for Addressing Yaw Pose Variation to Improve Face Recognition Performance

Identifying faces in non-frontal poses presents a significant challenge for face recognition(FR)systems.In this study,we delved into the impact of yaw pose variations on these systems and devised a robust method for detecting faces across a wide range of angles from 0°to±90°.We initially selected the most suitable feature vector size by integrating the Dlib,FaceNet(Inception-v2),and“Support Vector Machines(SVM)”+“K-nearest neighbors(KNN)”algorithms.To train and evaluate this feature vector,we used two datasets:the“Labeled Faces in the Wild(LFW)”benchmark data and the“Robust Shape-Based FR System(RSBFRS)”real-time data,which contained face images with varying yaw poses.After selecting the best feature vector,we developed a real-time FR system to handle yaw poses.The proposed FaceNet architecture achieved recognition accuracies of 99.7%and 99.8%for the LFW and RSBFRS datasets,respectively,with 128 feature vector dimensions and minimum Euclidean distance thresholds of 0.06 and 0.12.The FaceNet+SVM and FaceNet+KNN classifiers achieved classification accuracies of 99.26%and 99.44%,respectively.The 128-dimensional embedding vector showed the highest recognition rate among all dimensions.These results demonstrate the effectiveness of our proposed approach in enhancing FR accuracy,particularly in real-world scenarios with varying yaw poses.

Aryl hydrocarbon receptor dynamics in esophageal squamous cell carcinoma:From immune modulation to therapeutic opportunities

Esophageal squamous cell carcinoma(ESCC)is a substantial global health burden.Immune escape mechanisms are important in ESCC progression,enabling cancer cells to escape the surveillance of the host immune system.One key player in this process is the Aryl Hydrocarbon Receptor(AhR),which influences multiple cellular processes,including proliferation,differentiation,metabolism,and immune regulation.Dysregulated AhR signaling participates in ESCC development by stimulating carcinogenesis,epithelial-mesenchymal transition,and immune escape.Targeting AhR signaling is a potential therapeutic approach for ESCC,with AhR ligands showing efficacy in preclinical studies.Additionally,modification of AhR ligands and combination therapies present new opportunities for therapeutic intervention.This review aims to address the knowledge gap related to the role of AhR signaling in ESCC pathogenesis and immune escape.

Correction:The escape mechanisms of the proto-atmosphere on terrestrial planets:“boil-off”escape,hydrodynamic escape and impact erosion

In the original publication of the article,the affiliation“College of Earth and Planetary Sciences,University of Chinese Academy of Sciences,Beijing,People’s Republic of China”for author Ziqi Wang was missing and included in this correction article.

Generalized Newton’s Theory of Universal Gravitation and Black Holes

The Newton’s theory of universal gravitation is generalized. Significantly strong at short distances central interaction of bodies and particles is established in comparison with Newtonian. A connection is found with Black Holes, with the horizon of events. Possibility of systematization of all Black Holes is shown. An illustration is given on the example of Black Hole SgrA*.

Advancing Wound Filling Extraction on 3D Faces:An Auto-Segmentation and Wound Face Regeneration Approach

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.

A Framework for Driver DrowsinessMonitoring Using a Convolutional Neural Network and the Internet of Things

One of the major causes of road accidents is sleepy drivers.Such accidents typically result in fatalities and financial losses and disadvantage other road users.Numerous studies have been conducted to identify the driver’s sleepiness and integrate it into a warning system.Most studies have examined how the mouth and eyelids move.However,this limits the system’s ability to identify drowsiness traits.Therefore,this study designed an Accident Detection Framework(RPK)that could be used to reduce road accidents due to sleepiness and detect the location of accidents.The drowsiness detectionmodel used three facial parameters:Yawning,closed eyes(blinking),and an upright head position.This model used a Convolutional Neural Network(CNN)consisting of two phases.The initial phase involves video processing and facial landmark coordinate detection.The second phase involves developing the extraction of frame-based features using normalization methods.All these phases used OpenCV and TensorFlow.The dataset contained 5017 images with 874 open eyes images,850 closed eyes images,723 open-mouth images,725 closed-mouth images,761 sleepy-head images,and 1084 non-sleepy head images.The dataset of 5017 images was divided into the training set with 4505 images and the testing set with 512 images,with a ratio of 90:10.The results showed that the RPK design could detect sleepiness by using deep learning techniques with high accuracy on all three parameters;namely 98%for eye blinking,96%for mouth yawning,and 97%for head movement.Overall,the test results have provided an overview of how the developed RPK prototype can accurately identify drowsy drivers.These findings will have a significant impact on the improvement of road users’safety and mobility.

Automated Video-Based Face Detection Using Harris Hawks Optimization with Deep Learning

Face recognition technology automatically identifies an individual from image or video sources.The detection process can be done by attaining facial characteristics from the image of a subject face.Recent developments in deep learning(DL)and computer vision(CV)techniques enable the design of automated face recognition and tracking methods.This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking(HHODL-AFDT)method.The proposed HHODL-AFDT model involves a Faster region based convolution neural network(RCNN)-based face detection model and HHO-based hyperparameter opti-mization process.The presented optimal Faster RCNN model precisely rec-ognizes the face and is passed into the face-tracking model using a regression network(REGN).The face tracking using the REGN model uses the fea-tures from neighboring frames and foresees the location of the target face in succeeding frames.The application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the work.The experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60%and 88.08%under PICS and VTB datasets,respectively.

Participatory Forest Management and Gender Inclusiveness within the Community Forest Management Groups of Bhutan

Community forest management groups (CFMGs) in Bhutan exhibit participatory forest management practices that recognize the importance of community’s collective participation in the management of natural forest resources. This approach involves the community in the stewardship of designated forest areas and resources to ensure sustainable livelihoods and realization of forest conservation objectives. The increase of CFMGs in the country has been successful. However, research on the extent of gender-inclusive participation in CFMGs is either insufficient or missing vis-à-vis the allocation of decision-making power. Therefore, this study analyzes the factors influencing gender participation in CFMGs and their integration into decision-making processes. Primary data were collected from 12 study sites spanning 4 regions, complemented by secondary data from the Forest Department. Regression models were used to identify factors significantly influencing CFMG member participation in decision-making. The empirical results of this study reveal that gender is a significant factor influencing participation in CFMG decision-making. The study concludes that there is insufficient participation of women members in decision-making processes. Therefore, consideration of gender should be included in the development phase of the CFMG policy in addition to promoting awareness of inequity between gender and the promotion of leadership roles for women in CFMGs.

Possible mechanisms associated with immune escape and apoptosis on anti-hepatocellular carcinoma effect of Mu Ji Fang granules

BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common digestive system cancers with high mortality rates worldwide.The main ingredients in Mu Ji Fang Granules(MJF)are alkaloids,flavonoids,and polysaccharides.MJF has been used in the clinical treatment of hepatitis,cirrhosis and HCC for more than 30 years.Few previous studies have focused on the mechanism of MJF on tumor immunology in the treatment of HCC.AIM To explore the mechanism of action of MJF on tumor immunology in the treatment of HCC.METHODS The absorbable ingredients of MJF were identified using Molecule Network related to High Performance Liquid Chromatography-Electron Spray Ionization-Time of Flight-Mass Spectrometry,and hub potential anti-HCC targets were screened using network pharmacology and pathway enrichment analysis.Forty male mice were randomly divided into the Blank,Model,and MJF groups(1.8,5.4,and 10.8 g/kg/d)following 7 d of oral administration.Average body weight gain,spleen and thymus indices were calculated,tumor tissues were stained with hematoxylin and eosin,and Interferon gamma(IFN-γ),Tumor necrosis factorα(TNF-α),Interleukin-2,aspartate aminotransferase,alanine aminotransferase,alpha-fetoprotein(AFP),Fas,and FasL were measured by Enzyme-linked Immunosorbent Assay.Relevant mRNA expression of Bax and Bcl2 was evaluated by Real Time Quantitative PCR(RTqPCR)and protein expression of Transforming growth factorβ1(TGF-β1)and Mothers against decapentaplegic homolog(SMAD)4 was assessed by Western blotting.The HepG2 cell line was treated with 10 mg/mL,20 mg/mL,30 mg/mL,40 mg/mL of MJF,and another 3 groups were treated with TGF-β1 inhibitor(LY364947)and different doses of MJF.Relevant mRNA expression of TNF-α,IFN-γ,Bax and Bcl2 was evaluated by RT-qPCR and protein expression of TGF-β1,SMAD2,p-SMAD2,SMAD4,and SMAD7 was assessed by Western blotting.RESULTS It was shown that MJF improved body weight gain and tumor inhibition rate in H22 tumorbearing mice,protected immune organs and liver function,reduced the HCC indicator AFP,affected immunity and apoptosis,and up-regulated the TGF-β1/SMAD signaling pathway,by increasing the relative expression of TGF-β1,SMAD2,p-SMAD2 and SMAD4 and decreasing SMAD7,reducing immune factors TNF-αand IFN-γ,decreasing apoptosis cytokines Fas,FasL and Bcl2/Bax,and inhibiting the effect of LY364947 in HepG2 cells.CONCLUSION MJF inhibits HCC by activating the TGF-β1/SMAD signaling pathway,and affecting immune and apoptotic cytokines,which may be due to MJF adjusting immune escape and apoptosis.

Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end security.Despite numerous security tech-niques,face recognition is considered a high-security control.Developers fuse and carry out face identification as an access authority into these applications.Still,face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user.In the existing spoofing detection algorithm,there was some loss in the recreation of images.This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame of the sequenced set of frames to overcome the above-said problems.This research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused structure.First,this pro-posed method is tested with the Cross-ethnicity Face Anti-spoofing(CASIA),Fetal alcohol spectrum disorders(FASD)dataset.This database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video attacks.The images are taken with three different quality cameras:low,average,and high-quality real and spoofed images.An extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms.

Secondary electron emission and photoemission from a negative electron affinity semiconductor with large mean escape depth of excited electrons

The formulae for parameters of a negative electron affinity semiconductor(NEAS)with large mean escape depth of secondary electrons A(NEASLD)are deduced.The methods for obtaining parameters such asλ,B,E_(pom)and the maximumδandδat 100.0 keV≥E_(po)≥1.0 keV of a NEASLD with the deduced formulae are presented(B is the probability that an internal secondary electron escapes into the vacuum upon reaching the emission surface of the emitter,δis the secondary electron yield,E_(po)is the incident energy of primary electrons and E_(pom)is the E_(po)corresponding to the maximumδ).The parameters obtained here are analyzed,and it can be concluded that several parameters of NEASLDs obtained by the methods presented here agree with those obtained by other authors.The relation between the secondary electron emission and photoemission from a NEAS with large mean escape depth of excited electrons is investigated,and it is concluded that the presented method of obtaining A is more accurate than that of obtaining the corresponding parameter for a NEAS with largeλ_(ph)(λ_(ph)being the mean escape depth of photoelectrons),and that the presented method of calculating B at E_(po)>10.0 keV is more widely applicable for obtaining the corresponding parameters for a NEAS with largeλ_(ph).

Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.

Optimizing Deep Neural Networks for Face Recognition to Increase Training Speed and Improve Model Accuracy

Convolutional neural networks continually evolve to enhance accuracy in addressing various problems,leading to an increase in computational cost and model size.This paper introduces a novel approach for pruning face recognition models based on convolutional neural networks.The proposed method identifies and removes inefficient filters based on the information volume in feature maps.In each layer,some feature maps lack useful information,and there exists a correlation between certain feature maps.Filters associated with these two types of feature maps impose additional computational costs on the model.By eliminating filters related to these categories of feature maps,the reduction of both computational cost and model size can be achieved.The approach employs a combination of correlation analysis and the summation of matrix elements within each feature map to detect and eliminate inefficient filters.The method was applied to two face recognition models utilizing the VGG16 and ResNet50V2 backbone architectures.In the proposed approach,the number of filters removed in each layer varies,and the removal process is independent of the adjacent layers.The convolutional layers of both backbone models were initialized with pre-trained weights from ImageNet.For training,the CASIA-WebFace dataset was utilized,and the Labeled Faces in the Wild(LFW)dataset was employed for benchmarking purposes.In the VGG16-based face recognition model,a 0.74%accuracy improvement was achieved while reducing the number of convolution parameters by 26.85%and decreasing Floating-point operations per second(FLOPs)by 47.96%.For the face recognition model based on the ResNet50V2 architecture,the ArcFace method was implemented.The removal of inactive filters in this model led to a slight decrease in accuracy by 0.11%.However,it resulted in enhanced training speed,a reduction of 59.38%in convolution parameters,and a 57.29%decrease in FLOPs.

Distribution laws of abutment pressure around fully mechanized top-coal caving face by in-situ measurement

In order to obtain the distribution rules of abutment pressure around the 1151 （3） fully mechanized top-coal caving （FMTC） face of Xieqiao Colliery, the KSE-II-1 type bore-hole stress gauges were installed in the tailentry and headentry to measure the mining-induced stress. The distribution rules of the front and side abutment pressure were demonstrated. The results show that distribution rules of stress are obviously different in the vicinity of the face and entries. The peak value of abutment pressure in the protective coal pillar and face are located commonly in front of the working face along the strike, and they are located at the stress-decreased zone near the face. There is no stress peak value in the lateral coal mass beside the headentry in front of the face on the strike, and the peak value of abutment pressure appears at the rear area of the face. There are stress peak values both in the protective coal pillar and in the lateral coal mass beside the headentry to the dip.

Probabilistic analysis of tunnel face seismic stability in layered rock masses using Polynomial Chaos Kriging metamodel

Face stability is an essential issue in tunnel design and construction.Layered rock masses are typical and ubiquitous;uncertainties in rock properties always exist.In view of this,a comprehensive method,which combines the Upper bound Limit analysis of Tunnel face stability,the Polynomial Chaos Kriging,the Monte-Carlo Simulation and Analysis of Covariance method(ULT-PCK-MA),is proposed to investigate the seismic stability of tunnel faces.A two-dimensional analytical model of ULT is developed to evaluate the virtual support force based on the upper bound limit analysis.An efficient probabilistic analysis method PCK-MA based on the adaptive Polynomial Chaos Kriging metamodel is then implemented to investigate the parameter uncertainty effects.Ten input parameters,including geological strength indices,uniaxial compressive strengths and constants for three rock formations,and the horizontal seismic coefficients,are treated as random variables.The effects of these parameter uncertainties on the failure probability and sensitivity indices are discussed.In addition,the effects of weak layer position,the middle layer thickness and quality,the tunnel diameter,the parameters correlation,and the seismic loadings are investigated,respectively.The results show that the layer distributions significantly influence the tunnel face probabilistic stability,particularly when the weak rock is present in the bottom layer.The efficiency of the proposed ULT-PCK-MA is validated,which is expected to facilitate the engineering design and construction.

Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

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.

Stability analysis of tunnel face reinforced with face bolts

Face bolting has been widely utilized to enhance the stability of tunnel face,particularly in soft soil tunnels.However,the influence of bolt reinforcement and its layout on tunnel face stability has not been systematically studied.Based on the theory of linear elastic mechanics,this study delved into the specific mechanisms of bolt reinforcement on the tunnel face in both horizontal and vertical dimensions.It also identified the primary failure types of bolts.Additionally,a design approach for tunnel face bolts that incorporates spatial layout was established using the limit equilibrium method to enhance the conventional wedge-prism model.The proposed model was subsequently validated through various means,and the specific influence of relevant bolt design parameters on tunnel face stability was analyzed.Furthermore,design principles for tunnel face bolts under different geological conditions were presented.The findings indicate that bolt failure can be categorized into three stages:tensile failure,pullout failure,and comprehensive failure.Increasing cohesion,internal friction angle,bolt density,and overlap length can effectively enhance tunnel face stability.Due to significant variations in stratum conditions,tailored design approaches based on specific failure stages are necessary for bolt design.

MAVEN observation of magnetosonic waves in the Martian magnetotail region

Magnetosonic waves are an important medium for energy transfer in collisionless space plasma.Magnetosonic waves have been widely investigated in the upstream of the bow shock at Mars.These waves are believed to originate from pickup ions or reflected particles.By utilizing MAVEN spacecraft data,we have observed the occurrence of quasi-perpendicularly propagating magnetosonic emissions near the proton gyrofrequency in the Martian magnetotail region.These plasma waves are associated with a significant enhancement of proton and oxygen flux.The excited magnetosonic waves could possibly heat the protons through resonance and facilitate the ionospheric plasma escape.Our results could be helpful to better understand the Mars’magnetospheric dynamics and offer insights into possible energy redistribution between waves and plasma in the Martian nightside magnetosphere.