Two questions in the research of animal personality—whether there is a correlation between a personality trait and individual reproductive success,and what is the genetic basis underlying a personality trait—remain ...Two questions in the research of animal personality—whether there is a correlation between a personality trait and individual reproductive success,and what is the genetic basis underlying a personality trait—remain unresolved.We addressed these two questions in three shrub-nesting birds,the Azure-winged Magpie(Cyanopica cyanus,AM),White-collared Blackbird(Turdus albocinctus,WB),and Brown-cheeked Laughingthrush(Trochalopteron henrici,BL).The personality type of an individual was first identified according to its response to a territorial intruder.Then,we compared the fleeing distance,breeding parameters,and differential expressed genes(DEGs) in the brain transcriptome between bold and shy breeders.In the three species,bold breeders exhibited more aggressiveness towards an intruder of their territory than did shy breeders.The reproductive success of bold breeders was significantly higher than that of shy breeders in AM but not in WB and BL.The three species shared one DEG,crabp1,which was up-regulated in bold relative to in shy individuals.By regulating the expression of corticotropin-releasing hormone,higher crabp1 gene expression can decrease cellular response to retinoic acid.Therefore,bold individuals are insensitive to external stresses and able to exhibit more aggressiveness to intruders than their shier counterparts.Aggressiveness is beneficial to bold individuals in AM but not in WB and BL because the former could evoke neighbors to make the same response of defending against intruders but the latter could not.Although a personality trait may have the same genetic basis across species,its correlation with reproductive success depends largely on the life history style of a species.展开更多
The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have ...The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.展开更多
Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, f...Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.展开更多
Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-...Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.展开更多
Medical Internet of Things(IoT)devices are becoming more and more common in healthcare.This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of mu...Medical Internet of Things(IoT)devices are becoming more and more common in healthcare.This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way.Existing methods,while useful,have limitations in predictive accuracy,delay,personalization,and user interpretability,requiring a more comprehensive and efficient approach to harness modern medical IoT devices.MAIPFE is a multimodal approach integrating pre-emptive analysis,personalized feature selection,and explainable AI for real-time health monitoring and disease detection.By using AI for early disease detection,personalized health recommendations,and transparency,healthcare will be transformed.The Multimodal Approach Integrating Pre-emptive Analysis,Personalized Feature Selection,and Explainable AI(MAIPFE)framework,which combines Firefly Optimizer,Recurrent Neural Network(RNN),Fuzzy C Means(FCM),and Explainable AI,improves disease detection precision over existing methods.Comprehensive metrics show the model’s superiority in real-time health analysis.The proposed framework outperformed existing models by 8.3%in disease detection classification precision,8.5%in accuracy,5.5%in recall,2.9%in specificity,4.5%in AUC(Area Under the Curve),and 4.9%in delay reduction.Disease prediction precision increased by 4.5%,accuracy by 3.9%,recall by 2.5%,specificity by 3.5%,AUC by 1.9%,and delay levels decreased by 9.4%.MAIPFE can revolutionize healthcare with preemptive analysis,personalized health insights,and actionable recommendations.The research shows that this innovative approach improves patient outcomes and healthcare efficiency in the real world.展开更多
Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focu...Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focus on examining age groups differences. The study sample included 378,500 respondents derived from the seventh data wave of Survey of Health, Aging and Retirement in Europe (SHARE). The physical health status of older Europeans was estimated by constructing an index considering the combined effect of well-established health indicators such as the number of chronic diseases, mobility limitations, limitations with basic and instrumental activities of daily living, and self-perceived health. This index was used for an overall physical health assessment, for which the higher the score for an individual, the worst health level. Then, through a dichotomization process applied to the retrieved Principal Component Analysis scores, a two-group discrimination (good or bad health status) of SHARE participants was obtained as regards their physical health condition, allowing for further con-structing logistic regression models to assess the predictive significance of “Big Five” and their protective role for physical health. Results showed that neuroti-cism was the most significant predictor of physical health for all age groups un-der consideration, while extraversion, agreeableness and openness were not found to significantly affect the self-reported physical health levels of midlife adults aged 50 up to 64. Older adults aged 65 up to 79 were more prone to open-ness, whereas the oldest old individuals aged 80 up to 105 were mainly affected by openness and conscientiousness. .展开更多
Objective:To explore the value of receiving personalized comprehensive care for patients with severe pneumonia.Methods:73 patients with severe pneumonia who visited the clinic from February 2020 to February 2023 were ...Objective:To explore the value of receiving personalized comprehensive care for patients with severe pneumonia.Methods:73 patients with severe pneumonia who visited the clinic from February 2020 to February 2023 were included in this study.The patients were randomly grouped into Group A and Group B.Group A received personalized comprehensive care whereas Group B received conventional care.The value of care was compared.Results:The duration of mechanical ventilation time,the time taken for fever and dyspnea relief,and the hospitalization time of Group A were shorter than those in Group B(P<0.05).The blood gas indexes such as PaO_(2),PaCO_(2),and blood pH of Group A were better than those of Group B(P<0.05).The pulmonary function indexes such as peak expiratory flow(PEF),forced vital capacity(FVC),and forced expiratory volume in 1 second(FEV_(1))of Group A were better than those of Group B,P<0.05.Moreover,the patients in Group A were generally more satisfied with the care given compared to the patients in Group B(P<0.05).Conclusion:Personalized comprehensive care improves blood gas indexes,enhances lung function,accelerates the relief of symptoms,and also enhances patient satisfaction in severe pneumonia patients.展开更多
Person search mainly consists of two submissions,namely Person Detection and Person Re-identification(reID).Existing approaches are primarily based on Faster R-CNN and Convolutional Neural Network(CNN)(e.g.,ResNet).Wh...Person search mainly consists of two submissions,namely Person Detection and Person Re-identification(reID).Existing approaches are primarily based on Faster R-CNN and Convolutional Neural Network(CNN)(e.g.,ResNet).While these structures may detect high-quality bounding boxes,they seem to degrade the performance of re-ID.To address this issue,this paper proposes a Dual-Transformer Head Network(DTHN)for end-to-end person search,which contains two independent Transformer heads,a box head for detecting the bounding box and extracting efficient bounding box feature,and a re-ID head for capturing high-quality re-ID features for the re-ID task.Specifically,after the image goes through the ResNet backbone network to extract features,the Region Proposal Network(RPN)proposes possible bounding boxes.The box head then extracts more efficient features within these bounding boxes for detection.Following this,the re-ID head computes the occluded attention of the features in these bounding boxes and distinguishes them from other persons or backgrounds.Extensive experiments on two widely used benchmark datasets,CUHK-SYSU and PRW,achieve state-of-the-art performance levels,94.9 mAP and 95.3 top-1 scores on the CUHK-SYSU dataset,and 51.6 mAP and 87.6 top-1 scores on the PRW dataset,which demonstrates the advantages of this paper’s approach.The efficiency comparison also shows our method is highly efficient in both time and space.展开更多
Person Search is a task involving pedestrian detection and person re-identification,aiming to retrieve person images matching a given objective attribute from a large-scale image library.The Person Search models need ...Person Search is a task involving pedestrian detection and person re-identification,aiming to retrieve person images matching a given objective attribute from a large-scale image library.The Person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively.The current popular Person Search models,whether end-to-end or two-step,are based on anchor boxes.However,due to the limitations of the anchor itself,the model inevitably has some disadvantages,such as unbalance of positive and negative samples and redundant calculation,which will affect the performance of models.To address the problem of fine-grained understanding of target pedestrians in complex scenes and small sizes,this paper proposes a Deformable-Attention-based Anchor-free Person Search model(DAAPS).Fully Convolutional One-Stage(FCOS),as a classic Anchor-free detector,is chosen as the model’s infrastructure.The DAAPS model is the first to combine the Anchor-free Person Search model with Deformable Attention Mechanism,applied to guide the model adaptively adjust the perceptual.The Deformable Attention Mechanism is used to help the model focus on the critical information and effectively improve the poor accuracy caused by the absence of anchor boxes.The experiment proves the adaptability of the Attention mechanism to the Anchor-free model.Besides,with an improved ResNeXt+network frame,the DAAPS model selects the Triplet-based Online Instance Matching(TOIM)Loss function to achieve a more precise end-to-end Person Search task.Simulation experiments demonstrate that the proposed model has higher accuracy and better robustness than most Person Search models,reaching 95.0%of mean Average Precision(mAP)and 95.6%of Top-1 on the CUHK-SYSU dataset,48.6%of mAP and 84.7%of Top-1 on the Person Re-identification in the Wild(PRW)dataset,respectively.展开更多
Psychosis has increasingly become a social problem,emphasizing the need to understand the relationship between mental disorders and personality.This study aimed to investigate the relationship between mental disorders...Psychosis has increasingly become a social problem,emphasizing the need to understand the relationship between mental disorders and personality.This study aimed to investigate the relationship between mental disorders and personality among psychiatric outpatients based on real-world data.Symptom Checklist 90(SCL-90)and Eysenck Personality Questionnaire(EPQ)were used to evaluate the personality and psychopathological symptoms of patients(n=8409)in the Psychiatric Outpatient Department at Nanjing Drum Tower Hospital.t-test was used to compare scores between patients and national norms.Pearson’s correlation coefficient and path analysis were used to explore the relationship between mental health status and personality.The correlation coefficient between the neuroticism(N)score and each factor score of the SCL-90 test,as well as the correlation between psychoticism(P)and hostility and paranoia,exceeded 0.4.Path analysis revealed that the standardized path coefficients of N score and SCL-90 were all higher than 0.4.In addition,the standardized path coefficient of hostility and paranoia on P score were 0.313 and 0.280,respectively.Interpersonal sensitivity,depression and obsessive-compulsive symptoms were affected by extraversion(E)score,with standardized path coefficients of-0.149,-0.138,and-0.105,respectively.The path analysis also showed the direct and indirect effects of age,gender,education,and marital status on SCL-90.Patients characterized as melancholic had higher scores in all factors of SCL-90.In conclusion,mental health was related to personality traits of neuroticism,psychoticism and introversion.展开更多
The formation of personality comes from people’s choices and pursuit of self-realization,which is influenced by objective factors but not determined by them,so personality does not belong to the domain of objectivity...The formation of personality comes from people’s choices and pursuit of self-realization,which is influenced by objective factors but not determined by them,so personality does not belong to the domain of objectivity.The concept of general personality rights in the German Constitution was initially premised on the objective determinability in the field of personality,but in constitutional jurisprudence,it gradually shifted to something with individual autonomy as the core and personal self-realization as the goal,and the scope of relevant rights expanded accordingly,so that they could not be clearly distinguished from general freedom of action and thus became the general principle of constitutional rights.The protection of constitutional personality rights in the United States and Japan can also confirm this process,providing evidence for the constitutional nature of personality rights.Deeper research shows that constitutional personality rights actually manifest the highest value of modern constitutions—human dignity.In contrast,the theoretical justification of personality rights in civil law just lies in the objectivity and defensive nature of personality elements.展开更多
Environmental personality interests based on human rights reflect the multiple values of ecological order,ecological justice,and ecological freedom,and are closely linked to the protection of the right to life and the...Environmental personality interests based on human rights reflect the multiple values of ecological order,ecological justice,and ecological freedom,and are closely linked to the protection of the right to life and the right to health.They are also related to human dignity and the personal freedom of civil subjects and conform to formal and essential standards of personality rights,which should be included in the scope of personality rights for protection.The construction and application of environmental personality rights faces bottlenecks such as the partiality of subjects,limitation of objects,and hysteresis of responsibilities in the protection of environmental personality rights.Environmental personality rights are supposed to reflect the needs of the development of modern human rights.We should expand the scope of its connotative power and function based on the Green Principle of the Civil Code,and follow a networked,typified,and systematic path of protection,so as to manifest the people-centered philosophy of the Civil Code and the Environmental Protection Law.展开更多
The model for protection of personal information dis-closed according to the law has changed from indirect protection to direct protection.The indirect protection model for traditional repu-tation rights and privacy r...The model for protection of personal information dis-closed according to the law has changed from indirect protection to direct protection.The indirect protection model for traditional repu-tation rights and privacy rights was not enough to meet the practical needs of governance.However;due to the ambiguity in the application of the“reasonable”processing requirements,the direct protection model centered on Article 27 of the Personal Information Protection Law also is not enough to effectively respond to practical disputes.The essence of the problem is to resolve the tension between informa-tion circulation and risk control and reshape the legal order for the protection of personal information disclosed according to the law.The determination of“reasonable”should be centered on the scenario theory and holism interpretation and carried out by using the interpre-tation technique of the dynamic system under Article 998 of the Civil Code.With the support of scenario-based discussions and comparative propositions,the crawling and tag extraction of personal information.disclosed according to the law should be considered as reasonable processing;profiling and automated decision-making should not be covered in the scope of reasonable processing,in principle;for behav-iors such as correlation analysis,elements like information subject,identifiability and sensitivity should be comprehensively considered to draw open and inclusive conclusions in individual cases.展开更多
With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce ...With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce casualties and maintain military combat effectiveness,is widely developed.This paper systematically reviewed various PPE based on individual combat through literature research and comprehensive discussion,and introduced in detail the latest application progress of PPE in terms of material and technology from three aspects:individual integrated protection system,traditional protection equipment,and intelligent protection equipment,respectively,and discussed in depth the functional improvement and optimization status brought by advanced technology for PPE,focusing on the achievements of individual equipment technology application.Finally,the problems and technical bottlenecks in the development of PPE were analyzed and summarized,and the development trend of PPE were pointed out.The results of the review will provide a forward-looking reference for the current development of individual PPE,and are important guidance for the design and technological innovation of advanced equipment based on the future technological battlefield.展开更多
This paper proposes a personalized headrelated transfer function(HRTF)prediction method based on Light GBM using anthropometric data.Considering the overfitting problems of the current training-based prediction method...This paper proposes a personalized headrelated transfer function(HRTF)prediction method based on Light GBM using anthropometric data.Considering the overfitting problems of the current training-based prediction methods,we use Light GBM and a specific network structure to prevent over-fitting and enhance the prediction performance.By decomposing and combining the data to be predicted,we set up 90 Light GBM models to separately predict the 90instants of HRTF in log domain.At the same time,the method of 10-fold cross-validation is used to score the accuracy of the model.For models with scores below 80 points,Bayesian optimization is used to adjust model hyperparameters to obtain a better model structure.The results obtained by Light GBM are evaluated with spectral distortion(SD)which can show the fitting error between the prediction and the original data.The mean SD values of both ears on the whole test set are 2.32 d B and 2.28 d B respectively.Compared with the non-linear regression method and the latest method,SD value of Light GBM-based method relatively decreases by 83.8%and 48.5%.展开更多
Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies a...Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies and quantitative analysis of evacuation panic, such as panic behaviors, panic evolution, and the stress responses of pedestrians with different personality traits to panic emotion are still rare. Here, combined with the theories of OCEAN(openness, conscientiousness,extroversion, agreeableness, neuroticism) model and SIS(susceptible, infected, susceptible) model, an extended cellular automata model is established by the floor field method in order to investigate the dynamics of panic emotion in the crowd and dynamics of pedestrians affected by emotion. In the model, pedestrians are divided into stable pedestrians and sensitive pedestrians according to their different personality traits in response to emotion, and their emotional state can be normal or panic. Besides, emotion contagion, emotion decay, and the influence of emotion on pedestrian movement decision-making are also considered. The simulation results show that evacuation efficiency will be reduced, for panic pedestrians may act maladaptive behaviors, thereby making the crowd more chaotic. The results further suggest that improving pedestrian psychological ability and raising the standard of management can effectively increase evacuation efficiency. And it is necessary to reduce the panic level of group as soon as possible at the beginning of evacuation. We hope this research could provide a new method to analyze crowd evacuation in panic situations.展开更多
Visible-infrared Cross-modality Person Re-identification(VI-ReID)is a critical technology in smart public facilities such as cities,campuses and libraries.It aims to match pedestrians in visible light and infrared ima...Visible-infrared Cross-modality Person Re-identification(VI-ReID)is a critical technology in smart public facilities such as cities,campuses and libraries.It aims to match pedestrians in visible light and infrared images for video surveillance,which poses a challenge in exploring cross-modal shared information accurately and efficiently.Therefore,multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes.However,existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks,the fusion module.This paper introduces a novel network called the Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network(ADMPFF-Net),incorporating the Multi-Granularity Pose-Aware Feature Fusion(MPFF)module to generate discriminative representations.MPFF efficiently explores and learns global and local features with multi-level semantic information by inserting disentangling and duplicating blocks into the fusion module of the backbone network.ADMPFF-Net also provides a new perspective for designing multi-granularity learning networks.By incorporating the multi-granularity feature disentanglement(mGFD)and posture information segmentation(pIS)strategies,it extracts more representative features concerning body structure information.The Local Information Enhancement(LIE)module augments high-performance features in VI-ReID,and the multi-granularity joint loss supervises model training for objective feature learning.Experimental results on two public datasets show that ADMPFF-Net efficiently constructs pedestrian feature representations and enhances the accuracy of VI-ReID.展开更多
Ensuring food safety is paramount worldwide.Developing effective detection methods to ensure food safety can be challenging owing to trace hazards,long detection time,and resource-poor sites,in addition to the matrix ...Ensuring food safety is paramount worldwide.Developing effective detection methods to ensure food safety can be challenging owing to trace hazards,long detection time,and resource-poor sites,in addition to the matrix effects of food.Personal glucose meter(PGM),a classic point-of-care testing device,possesses unique application advantages,demonstrating promise in food safety.Currently,many studies have used PGM-based biosensors and signal amplification technologies to achieve sensitive and specific detection of food hazards.Signal amplification technologies have the potential to greatly improve the analytical performance and integration of PGMs with biosensors,which is crucial for solving the challenges associated with the use of PGMs for food safety analysis.This review introduces the basic detection principle of a PGM-based sensing strategy,which consists of three key factors:target recognition,signal transduction,and signal output.Representative studies of existing PGM-based sensing strategies combined with various signal amplification technologies(nanomaterial-loaded multienzyme labeling,nucleic acid reaction,DNAzyme catalysis,responsive nanomaterial encapsulation,and others)in the field of food safety detection are reviewed.Future perspectives and potential opportunities and challenges associated with PGMs in the field of food safety are discussed.Despite the need for complex sample preparation and the lack of standardization in the field,using PGMs in combination with signal amplification technology shows promise as a rapid and cost-effective method for food safety hazard analysis.展开更多
Person re-identification is a prevalent technology deployed on intelligent surveillance.There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a...Person re-identification is a prevalent technology deployed on intelligent surveillance.There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution,yet such models are not applicable to the open world.In real world,the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera inconsistent.When low-resolution(LR)images in the query set are matched with high-resolution(HR)images in the gallery set,it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR images.To address the above issues,we present a dualstream coupling network with wavelet transform(DSCWT)for the cross-resolution person re-identification task.Firstly,we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images,which is applied to restore the lost detail information of LR images.Then,we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various resolutions.Extensive qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.展开更多
基金provided by the National Natural Science Foundation of China (Grant 32071491, 31772465, 31672299, 31572271, and 32260128)the Natural Sciences Foundation of the Tibetan (XZ202101ZR0051G)。
文摘Two questions in the research of animal personality—whether there is a correlation between a personality trait and individual reproductive success,and what is the genetic basis underlying a personality trait—remain unresolved.We addressed these two questions in three shrub-nesting birds,the Azure-winged Magpie(Cyanopica cyanus,AM),White-collared Blackbird(Turdus albocinctus,WB),and Brown-cheeked Laughingthrush(Trochalopteron henrici,BL).The personality type of an individual was first identified according to its response to a territorial intruder.Then,we compared the fleeing distance,breeding parameters,and differential expressed genes(DEGs) in the brain transcriptome between bold and shy breeders.In the three species,bold breeders exhibited more aggressiveness towards an intruder of their territory than did shy breeders.The reproductive success of bold breeders was significantly higher than that of shy breeders in AM but not in WB and BL.The three species shared one DEG,crabp1,which was up-regulated in bold relative to in shy individuals.By regulating the expression of corticotropin-releasing hormone,higher crabp1 gene expression can decrease cellular response to retinoic acid.Therefore,bold individuals are insensitive to external stresses and able to exhibit more aggressiveness to intruders than their shier counterparts.Aggressiveness is beneficial to bold individuals in AM but not in WB and BL because the former could evoke neighbors to make the same response of defending against intruders but the latter could not.Although a personality trait may have the same genetic basis across species,its correlation with reproductive success depends largely on the life history style of a species.
基金supported in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B186 and No.2022D01B05)。
文摘The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.
基金the Competitive Research Fund of the University of Aizu,Japan.
文摘Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.
基金This work has been partially supported by FEDER and the State Research Agency(AEI)of the Spanish Ministry of Economy and Competition under Grant SAFER:PID2019-104735RB-C42(AEI/FEDER,UE)the General Subdirection for Gambling Regulation of the Spanish ConsumptionMinistry under the Grant Detec-EMO:SUBV23/00010the Project PLEC2021-007681 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR.
文摘Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.
文摘Medical Internet of Things(IoT)devices are becoming more and more common in healthcare.This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way.Existing methods,while useful,have limitations in predictive accuracy,delay,personalization,and user interpretability,requiring a more comprehensive and efficient approach to harness modern medical IoT devices.MAIPFE is a multimodal approach integrating pre-emptive analysis,personalized feature selection,and explainable AI for real-time health monitoring and disease detection.By using AI for early disease detection,personalized health recommendations,and transparency,healthcare will be transformed.The Multimodal Approach Integrating Pre-emptive Analysis,Personalized Feature Selection,and Explainable AI(MAIPFE)framework,which combines Firefly Optimizer,Recurrent Neural Network(RNN),Fuzzy C Means(FCM),and Explainable AI,improves disease detection precision over existing methods.Comprehensive metrics show the model’s superiority in real-time health analysis.The proposed framework outperformed existing models by 8.3%in disease detection classification precision,8.5%in accuracy,5.5%in recall,2.9%in specificity,4.5%in AUC(Area Under the Curve),and 4.9%in delay reduction.Disease prediction precision increased by 4.5%,accuracy by 3.9%,recall by 2.5%,specificity by 3.5%,AUC by 1.9%,and delay levels decreased by 9.4%.MAIPFE can revolutionize healthcare with preemptive analysis,personalized health insights,and actionable recommendations.The research shows that this innovative approach improves patient outcomes and healthcare efficiency in the real world.
文摘Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focus on examining age groups differences. The study sample included 378,500 respondents derived from the seventh data wave of Survey of Health, Aging and Retirement in Europe (SHARE). The physical health status of older Europeans was estimated by constructing an index considering the combined effect of well-established health indicators such as the number of chronic diseases, mobility limitations, limitations with basic and instrumental activities of daily living, and self-perceived health. This index was used for an overall physical health assessment, for which the higher the score for an individual, the worst health level. Then, through a dichotomization process applied to the retrieved Principal Component Analysis scores, a two-group discrimination (good or bad health status) of SHARE participants was obtained as regards their physical health condition, allowing for further con-structing logistic regression models to assess the predictive significance of “Big Five” and their protective role for physical health. Results showed that neuroti-cism was the most significant predictor of physical health for all age groups un-der consideration, while extraversion, agreeableness and openness were not found to significantly affect the self-reported physical health levels of midlife adults aged 50 up to 64. Older adults aged 65 up to 79 were more prone to open-ness, whereas the oldest old individuals aged 80 up to 105 were mainly affected by openness and conscientiousness. .
文摘Objective:To explore the value of receiving personalized comprehensive care for patients with severe pneumonia.Methods:73 patients with severe pneumonia who visited the clinic from February 2020 to February 2023 were included in this study.The patients were randomly grouped into Group A and Group B.Group A received personalized comprehensive care whereas Group B received conventional care.The value of care was compared.Results:The duration of mechanical ventilation time,the time taken for fever and dyspnea relief,and the hospitalization time of Group A were shorter than those in Group B(P<0.05).The blood gas indexes such as PaO_(2),PaCO_(2),and blood pH of Group A were better than those of Group B(P<0.05).The pulmonary function indexes such as peak expiratory flow(PEF),forced vital capacity(FVC),and forced expiratory volume in 1 second(FEV_(1))of Group A were better than those of Group B,P<0.05.Moreover,the patients in Group A were generally more satisfied with the care given compared to the patients in Group B(P<0.05).Conclusion:Personalized comprehensive care improves blood gas indexes,enhances lung function,accelerates the relief of symptoms,and also enhances patient satisfaction in severe pneumonia patients.
基金supported by the Natural Science Foundation of Shanghai under Grant 21ZR1426500the National Natural Science Foundation of China under Grant 61873160.
文摘Person search mainly consists of two submissions,namely Person Detection and Person Re-identification(reID).Existing approaches are primarily based on Faster R-CNN and Convolutional Neural Network(CNN)(e.g.,ResNet).While these structures may detect high-quality bounding boxes,they seem to degrade the performance of re-ID.To address this issue,this paper proposes a Dual-Transformer Head Network(DTHN)for end-to-end person search,which contains two independent Transformer heads,a box head for detecting the bounding box and extracting efficient bounding box feature,and a re-ID head for capturing high-quality re-ID features for the re-ID task.Specifically,after the image goes through the ResNet backbone network to extract features,the Region Proposal Network(RPN)proposes possible bounding boxes.The box head then extracts more efficient features within these bounding boxes for detection.Following this,the re-ID head computes the occluded attention of the features in these bounding boxes and distinguishes them from other persons or backgrounds.Extensive experiments on two widely used benchmark datasets,CUHK-SYSU and PRW,achieve state-of-the-art performance levels,94.9 mAP and 95.3 top-1 scores on the CUHK-SYSU dataset,and 51.6 mAP and 87.6 top-1 scores on the PRW dataset,which demonstrates the advantages of this paper’s approach.The efficiency comparison also shows our method is highly efficient in both time and space.
基金to the Natural Science Foundation of Shanghai under Grant 21ZR1426500,and the Top-Notch Innovative Talent Training Program for Graduate Students of Shanghai Maritime University under Grant 2021YBR008for their generous support and funding through the project funding program.This funding has played a pivotal role in the successful completion of our research.We are deeply appreciative of their invaluable contribution to our research efforts.
文摘Person Search is a task involving pedestrian detection and person re-identification,aiming to retrieve person images matching a given objective attribute from a large-scale image library.The Person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively.The current popular Person Search models,whether end-to-end or two-step,are based on anchor boxes.However,due to the limitations of the anchor itself,the model inevitably has some disadvantages,such as unbalance of positive and negative samples and redundant calculation,which will affect the performance of models.To address the problem of fine-grained understanding of target pedestrians in complex scenes and small sizes,this paper proposes a Deformable-Attention-based Anchor-free Person Search model(DAAPS).Fully Convolutional One-Stage(FCOS),as a classic Anchor-free detector,is chosen as the model’s infrastructure.The DAAPS model is the first to combine the Anchor-free Person Search model with Deformable Attention Mechanism,applied to guide the model adaptively adjust the perceptual.The Deformable Attention Mechanism is used to help the model focus on the critical information and effectively improve the poor accuracy caused by the absence of anchor boxes.The experiment proves the adaptability of the Attention mechanism to the Anchor-free model.Besides,with an improved ResNeXt+network frame,the DAAPS model selects the Triplet-based Online Instance Matching(TOIM)Loss function to achieve a more precise end-to-end Person Search task.Simulation experiments demonstrate that the proposed model has higher accuracy and better robustness than most Person Search models,reaching 95.0%of mean Average Precision(mAP)and 95.6%of Top-1 on the CUHK-SYSU dataset,48.6%of mAP and 84.7%of Top-1 on the Person Re-identification in the Wild(PRW)dataset,respectively.
基金supported by a grant from the Health Commission of Nanjing(Grant Number:ZKX22019),China.
文摘Psychosis has increasingly become a social problem,emphasizing the need to understand the relationship between mental disorders and personality.This study aimed to investigate the relationship between mental disorders and personality among psychiatric outpatients based on real-world data.Symptom Checklist 90(SCL-90)and Eysenck Personality Questionnaire(EPQ)were used to evaluate the personality and psychopathological symptoms of patients(n=8409)in the Psychiatric Outpatient Department at Nanjing Drum Tower Hospital.t-test was used to compare scores between patients and national norms.Pearson’s correlation coefficient and path analysis were used to explore the relationship between mental health status and personality.The correlation coefficient between the neuroticism(N)score and each factor score of the SCL-90 test,as well as the correlation between psychoticism(P)and hostility and paranoia,exceeded 0.4.Path analysis revealed that the standardized path coefficients of N score and SCL-90 were all higher than 0.4.In addition,the standardized path coefficient of hostility and paranoia on P score were 0.313 and 0.280,respectively.Interpersonal sensitivity,depression and obsessive-compulsive symptoms were affected by extraversion(E)score,with standardized path coefficients of-0.149,-0.138,and-0.105,respectively.The path analysis also showed the direct and indirect effects of age,gender,education,and marital status on SCL-90.Patients characterized as melancholic had higher scores in all factors of SCL-90.In conclusion,mental health was related to personality traits of neuroticism,psychoticism and introversion.
文摘The formation of personality comes from people’s choices and pursuit of self-realization,which is influenced by objective factors but not determined by them,so personality does not belong to the domain of objectivity.The concept of general personality rights in the German Constitution was initially premised on the objective determinability in the field of personality,but in constitutional jurisprudence,it gradually shifted to something with individual autonomy as the core and personal self-realization as the goal,and the scope of relevant rights expanded accordingly,so that they could not be clearly distinguished from general freedom of action and thus became the general principle of constitutional rights.The protection of constitutional personality rights in the United States and Japan can also confirm this process,providing evidence for the constitutional nature of personality rights.Deeper research shows that constitutional personality rights actually manifest the highest value of modern constitutions—human dignity.In contrast,the theoretical justification of personality rights in civil law just lies in the objectivity and defensive nature of personality elements.
基金staged research result of the major project of the MOE Humanities and Social Sciences Project Base“Research on the Human Rights Value Connotation and Legal Guarantee in Xi Jinping’s Thought on Ecological Civilization”(Project Approval No.21JJD820007)+1 种基金the staged research result of the major project of the National Social Science Fund of China“Research on Establishing and Improving the Property Rights System for Natural Resource Assets”(Project Approval No.22ZDA109),aiming to study and interpret the spirit of the Sixth Plenary Session of the 19th CPC Central Committee。
文摘Environmental personality interests based on human rights reflect the multiple values of ecological order,ecological justice,and ecological freedom,and are closely linked to the protection of the right to life and the right to health.They are also related to human dignity and the personal freedom of civil subjects and conform to formal and essential standards of personality rights,which should be included in the scope of personality rights for protection.The construction and application of environmental personality rights faces bottlenecks such as the partiality of subjects,limitation of objects,and hysteresis of responsibilities in the protection of environmental personality rights.Environmental personality rights are supposed to reflect the needs of the development of modern human rights.We should expand the scope of its connotative power and function based on the Green Principle of the Civil Code,and follow a networked,typified,and systematic path of protection,so as to manifest the people-centered philosophy of the Civil Code and the Environmental Protection Law.
文摘The model for protection of personal information dis-closed according to the law has changed from indirect protection to direct protection.The indirect protection model for traditional repu-tation rights and privacy rights was not enough to meet the practical needs of governance.However;due to the ambiguity in the application of the“reasonable”processing requirements,the direct protection model centered on Article 27 of the Personal Information Protection Law also is not enough to effectively respond to practical disputes.The essence of the problem is to resolve the tension between informa-tion circulation and risk control and reshape the legal order for the protection of personal information disclosed according to the law.The determination of“reasonable”should be centered on the scenario theory and holism interpretation and carried out by using the interpre-tation technique of the dynamic system under Article 998 of the Civil Code.With the support of scenario-based discussions and comparative propositions,the crawling and tag extraction of personal information.disclosed according to the law should be considered as reasonable processing;profiling and automated decision-making should not be covered in the scope of reasonable processing,in principle;for behav-iors such as correlation analysis,elements like information subject,identifiability and sensitivity should be comprehensively considered to draw open and inclusive conclusions in individual cases.
基金supported by the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(Projects No.52202012)the National Natural Science Foundation of China(Projects No.51834007)。
文摘With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce casualties and maintain military combat effectiveness,is widely developed.This paper systematically reviewed various PPE based on individual combat through literature research and comprehensive discussion,and introduced in detail the latest application progress of PPE in terms of material and technology from three aspects:individual integrated protection system,traditional protection equipment,and intelligent protection equipment,respectively,and discussed in depth the functional improvement and optimization status brought by advanced technology for PPE,focusing on the achievements of individual equipment technology application.Finally,the problems and technical bottlenecks in the development of PPE were analyzed and summarized,and the development trend of PPE were pointed out.The results of the review will provide a forward-looking reference for the current development of individual PPE,and are important guidance for the design and technological innovation of advanced equipment based on the future technological battlefield.
基金supported by the cooperation between BIT and Ericssonpartially supported by the National Natural Science Foundation of China under Grants No.62071039。
文摘This paper proposes a personalized headrelated transfer function(HRTF)prediction method based on Light GBM using anthropometric data.Considering the overfitting problems of the current training-based prediction methods,we use Light GBM and a specific network structure to prevent over-fitting and enhance the prediction performance.By decomposing and combining the data to be predicted,we set up 90 Light GBM models to separately predict the 90instants of HRTF in log domain.At the same time,the method of 10-fold cross-validation is used to score the accuracy of the model.For models with scores below 80 points,Bayesian optimization is used to adjust model hyperparameters to obtain a better model structure.The results obtained by Light GBM are evaluated with spectral distortion(SD)which can show the fitting error between the prediction and the original data.The mean SD values of both ears on the whole test set are 2.32 d B and 2.28 d B respectively.Compared with the non-linear regression method and the latest method,SD value of Light GBM-based method relatively decreases by 83.8%and 48.5%.
基金the National Natural Science Foundation of China (Grant Nos. 71790613 and 72091512)the Science and Technology Innovation Program of Hunan Province, China (Grant No. 2020SK2004)。
文摘Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies and quantitative analysis of evacuation panic, such as panic behaviors, panic evolution, and the stress responses of pedestrians with different personality traits to panic emotion are still rare. Here, combined with the theories of OCEAN(openness, conscientiousness,extroversion, agreeableness, neuroticism) model and SIS(susceptible, infected, susceptible) model, an extended cellular automata model is established by the floor field method in order to investigate the dynamics of panic emotion in the crowd and dynamics of pedestrians affected by emotion. In the model, pedestrians are divided into stable pedestrians and sensitive pedestrians according to their different personality traits in response to emotion, and their emotional state can be normal or panic. Besides, emotion contagion, emotion decay, and the influence of emotion on pedestrian movement decision-making are also considered. The simulation results show that evacuation efficiency will be reduced, for panic pedestrians may act maladaptive behaviors, thereby making the crowd more chaotic. The results further suggest that improving pedestrian psychological ability and raising the standard of management can effectively increase evacuation efficiency. And it is necessary to reduce the panic level of group as soon as possible at the beginning of evacuation. We hope this research could provide a new method to analyze crowd evacuation in panic situations.
基金supported in part by the National Natural Science Foundation of China under Grant 62177029,62307025in part by the Startup Foundation for Introducing Talent of Nanjing University of Posts and Communications under Grant NY221041in part by the General Project of The Natural Science Foundation of Jiangsu Higher Education Institution of China 22KJB520025,23KJD580.
文摘Visible-infrared Cross-modality Person Re-identification(VI-ReID)is a critical technology in smart public facilities such as cities,campuses and libraries.It aims to match pedestrians in visible light and infrared images for video surveillance,which poses a challenge in exploring cross-modal shared information accurately and efficiently.Therefore,multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes.However,existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks,the fusion module.This paper introduces a novel network called the Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network(ADMPFF-Net),incorporating the Multi-Granularity Pose-Aware Feature Fusion(MPFF)module to generate discriminative representations.MPFF efficiently explores and learns global and local features with multi-level semantic information by inserting disentangling and duplicating blocks into the fusion module of the backbone network.ADMPFF-Net also provides a new perspective for designing multi-granularity learning networks.By incorporating the multi-granularity feature disentanglement(mGFD)and posture information segmentation(pIS)strategies,it extracts more representative features concerning body structure information.The Local Information Enhancement(LIE)module augments high-performance features in VI-ReID,and the multi-granularity joint loss supervises model training for objective feature learning.Experimental results on two public datasets show that ADMPFF-Net efficiently constructs pedestrian feature representations and enhances the accuracy of VI-ReID.
基金supported by the Natural Science Foundation of Shandong Province(Grant No.:ZR2020QC250)China Agriculture Research System(Grant No.:CARS-38)+1 种基金Modern Agricultural Technology Industry System of Shandong Province(Grant No.:SDAIT10-10)Key Technology Research and Development Program of Shandong(Grant Nos.:2021CXGC010809 and 2021TZXD012).
文摘Ensuring food safety is paramount worldwide.Developing effective detection methods to ensure food safety can be challenging owing to trace hazards,long detection time,and resource-poor sites,in addition to the matrix effects of food.Personal glucose meter(PGM),a classic point-of-care testing device,possesses unique application advantages,demonstrating promise in food safety.Currently,many studies have used PGM-based biosensors and signal amplification technologies to achieve sensitive and specific detection of food hazards.Signal amplification technologies have the potential to greatly improve the analytical performance and integration of PGMs with biosensors,which is crucial for solving the challenges associated with the use of PGMs for food safety analysis.This review introduces the basic detection principle of a PGM-based sensing strategy,which consists of three key factors:target recognition,signal transduction,and signal output.Representative studies of existing PGM-based sensing strategies combined with various signal amplification technologies(nanomaterial-loaded multienzyme labeling,nucleic acid reaction,DNAzyme catalysis,responsive nanomaterial encapsulation,and others)in the field of food safety detection are reviewed.Future perspectives and potential opportunities and challenges associated with PGMs in the field of food safety are discussed.Despite the need for complex sample preparation and the lack of standardization in the field,using PGMs in combination with signal amplification technology shows promise as a rapid and cost-effective method for food safety hazard analysis.
基金supported by the National Natural Science Foundation of China(61471154,61876057)the Key Research and Development Program of Anhui Province-Special Project of Strengthening Science and Technology Police(202004D07020012).
文摘Person re-identification is a prevalent technology deployed on intelligent surveillance.There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution,yet such models are not applicable to the open world.In real world,the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera inconsistent.When low-resolution(LR)images in the query set are matched with high-resolution(HR)images in the gallery set,it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR images.To address the above issues,we present a dualstream coupling network with wavelet transform(DSCWT)for the cross-resolution person re-identification task.Firstly,we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images,which is applied to restore the lost detail information of LR images.Then,we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various resolutions.Extensive qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.
