Weak measurement offers a powerful framework for probing nonclassical features of quantum mechanics,with anomalous weak values serving as operational signatures of contextuality.While the anomalous weak value verifica...Weak measurement offers a powerful framework for probing nonclassical features of quantum mechanics,with anomalous weak values serving as operational signatures of contextuality.While the anomalous weak value verification of quantum contextuality has been predominantly investigated in the single-photon regime and analyzed under approximation condition of infinitesimally small perturbation strength.This study releases the approximation condition and takes into account the impact of perturbation strength on the rigor of the verification.And the investigation on the verification of contextuality is extended to the multi-photon scenarios for observing the influence of the correlation between photons on the verification.Without the limitation of infinitesimally small probability of disturbance,anomalous weak values are identified as necessary for contextuality to emerge,thereby refining the criterion proposed by Pusey[Phys.Rev.Lett.113200401(2014)].In the multi-photon scenarios,the emergence of contextuality also depends strongly on both the photon number and the photon-number distribution state.In particular,contextuality is found to be maximized when the single-photon component dominates and the second-order correlation is lower.These results highlight the critical role of photon statistics in experimental tests of contextuality via anomalous weak values.展开更多
The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issu...The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.展开更多
A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely...A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.展开更多
This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabu...This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabulary teaching and learning. The authors find that in a short duration there is a significant difference between the effect of bilingual （English ＆ Chinese） explanation and that of monolingual （Chinese） explanation on the students＇ recognition of English new words.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
Addressing issues such as the disconnect between theory and practice and low student engagement in control system education,this paper uses the course“The Working Process of Open-Loop Control Systems”as a case study...Addressing issues such as the disconnect between theory and practice and low student engagement in control system education,this paper uses the course“The Working Process of Open-Loop Control Systems”as a case study to explore the integration of AI technology with the“project-guided and task-driven”teaching model.By constructing a four-dimensional teaching framework of“situation-task-activity-evaluation,”AI tools are embedded in project practices such as the construction of a mechanical timed flower watering device and the optimization of a digital timed flower watering device,achieving precision,interactivity,and personalization in the teaching process.Teaching practice demonstrates that this model significantly enhances students’technical awareness,materialization capabilities,and engineering thinking,providing a reference for the teaching reform of technical courses in high school education.展开更多
Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One f...Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication.展开更多
Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performa...Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performance is affected by a combination of factors such as the mobility of user devices,limited communication and computational resources,thus making the user scheduling problem crucial.To tackle this problem,we jointly consider the user mobility,communication and computational capacities,and develop a stochastic optimization problem to minimize the convergence time.Specifically,we first establish a convergence bound on the training performance based on the heterogeneity of users’data,and then leverage this bound to derive the participation rate for each user.After deriving the user-specific participation rate,we aim to minimize the training latency by optimizing user scheduling under the constraints of the energy consumption and participation rate.Afterward,we transform this optimization problem to the contextual multi-armed bandit framework based on the Lyapunov method and solve it with the submodular reward enhanced linear upper confidence bound(SR-linUCB)algorithm.Experimental results demonstrate the superiority of our proposed algorithm on the training performance and time consumption compared with stateof-the-art algorithms for both independent and identically distributed(IID)and non-IID settings.展开更多
The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gaine...The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gained significant attention for improving training efficiency.Most current algorithms rely on Convolutional Neural Networks(CNNs)for feature extraction.Although CNNs are proficient at capturing local features,they often struggle with global context,leading to incomplete and false Class Activation Mapping(CAM).To address these limitations,this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation(CPEWS)model,which improves feature extraction by utilizing the Vision Transformer(ViT).By incorporating its intermediate feature layers to preserve semantic information,this work introduces the Intermediate Supervised Module(ISM)to supervise the final layer’s output,reducing boundary ambiguity and mitigating issues related to incomplete activation.Additionally,the Contextual Prototype Module(CPM)generates class-specific prototypes,while the proposed Prototype Discrimination Loss and Superclass Suppression Loss guide the network’s training,(LPDL)(LSSL)effectively addressing false activation without the need for extra supervision.The CPEWS model proposed in this paper achieves state-of-the-art performance in end-to-end weakly supervised semantic segmentation without additional supervision.The validation set and test set Mean Intersection over Union(MIoU)of PASCAL VOC 2012 dataset achieved 69.8%and 72.6%,respectively.Compared with ToCo(pre trained weight ImageNet-1k),MIoU on the test set is 2.1%higher.In addition,MIoU reached 41.4%on the validation set of the MS COCO 2014 dataset.展开更多
基金Project supported by the National Natural Science Foun-dation of China(Grant Nos.62371199 and 62071186)the Natural Science Foundation of Guangdong Province,China(Grant No.2024A1515012427)+1 种基金the Quantum Science Strate-gic Initiative Project of Guangdong Province,China(Grant No.GDZX2305001)the Key Laboratory Project of Guangdong Province,China(Grant No.2020B1212060066).
文摘Weak measurement offers a powerful framework for probing nonclassical features of quantum mechanics,with anomalous weak values serving as operational signatures of contextuality.While the anomalous weak value verification of quantum contextuality has been predominantly investigated in the single-photon regime and analyzed under approximation condition of infinitesimally small perturbation strength.This study releases the approximation condition and takes into account the impact of perturbation strength on the rigor of the verification.And the investigation on the verification of contextuality is extended to the multi-photon scenarios for observing the influence of the correlation between photons on the verification.Without the limitation of infinitesimally small probability of disturbance,anomalous weak values are identified as necessary for contextuality to emerge,thereby refining the criterion proposed by Pusey[Phys.Rev.Lett.113200401(2014)].In the multi-photon scenarios,the emergence of contextuality also depends strongly on both the photon number and the photon-number distribution state.In particular,contextuality is found to be maximized when the single-photon component dominates and the second-order correlation is lower.These results highlight the critical role of photon statistics in experimental tests of contextuality via anomalous weak values.
文摘The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.
基金Supported by the National Natural Science Foundation of China(60772066)
文摘A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.
文摘This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabulary teaching and learning. The authors find that in a short duration there is a significant difference between the effect of bilingual （English ＆ Chinese） explanation and that of monolingual （Chinese） explanation on the students＇ recognition of English new words.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
文摘Addressing issues such as the disconnect between theory and practice and low student engagement in control system education,this paper uses the course“The Working Process of Open-Loop Control Systems”as a case study to explore the integration of AI technology with the“project-guided and task-driven”teaching model.By constructing a four-dimensional teaching framework of“situation-task-activity-evaluation,”AI tools are embedded in project practices such as the construction of a mechanical timed flower watering device and the optimization of a digital timed flower watering device,achieving precision,interactivity,and personalization in the teaching process.Teaching practice demonstrates that this model significantly enhances students’technical awareness,materialization capabilities,and engineering thinking,providing a reference for the teaching reform of technical courses in high school education.
文摘Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication.
基金supported in part by the Key Technologies R&D Program of Jiangsu under Grants BE2023022 and BE2023022-2National Natural Science Foundation of China under Grants 62471204, 62531015+2 种基金Major Natural Science Foundation of the Higher Education Institutions of Jiangsu Province under Grant 24KJA510003Shanghai Kewei 24DP1500500the Fundamental Research Funds for the Central Universities under Grant 2242025K30025
文摘Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performance is affected by a combination of factors such as the mobility of user devices,limited communication and computational resources,thus making the user scheduling problem crucial.To tackle this problem,we jointly consider the user mobility,communication and computational capacities,and develop a stochastic optimization problem to minimize the convergence time.Specifically,we first establish a convergence bound on the training performance based on the heterogeneity of users’data,and then leverage this bound to derive the participation rate for each user.After deriving the user-specific participation rate,we aim to minimize the training latency by optimizing user scheduling under the constraints of the energy consumption and participation rate.Afterward,we transform this optimization problem to the contextual multi-armed bandit framework based on the Lyapunov method and solve it with the submodular reward enhanced linear upper confidence bound(SR-linUCB)algorithm.Experimental results demonstrate the superiority of our proposed algorithm on the training performance and time consumption compared with stateof-the-art algorithms for both independent and identically distributed(IID)and non-IID settings.
基金funding from the following sources:National Natural Science Foundation of China(U1904119)Research Programs of Henan Science and Technology Department(232102210054)+3 种基金Chongqing Natural Science Foundation(CSTB2023NSCQ-MSX0070)Henan Province Key Research and Development Project(231111212000)Aviation Science Foundation(20230001055002)supported by Henan Center for Outstanding Overseas Scientists(GZS2022011).
文摘The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gained significant attention for improving training efficiency.Most current algorithms rely on Convolutional Neural Networks(CNNs)for feature extraction.Although CNNs are proficient at capturing local features,they often struggle with global context,leading to incomplete and false Class Activation Mapping(CAM).To address these limitations,this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation(CPEWS)model,which improves feature extraction by utilizing the Vision Transformer(ViT).By incorporating its intermediate feature layers to preserve semantic information,this work introduces the Intermediate Supervised Module(ISM)to supervise the final layer’s output,reducing boundary ambiguity and mitigating issues related to incomplete activation.Additionally,the Contextual Prototype Module(CPM)generates class-specific prototypes,while the proposed Prototype Discrimination Loss and Superclass Suppression Loss guide the network’s training,(LPDL)(LSSL)effectively addressing false activation without the need for extra supervision.The CPEWS model proposed in this paper achieves state-of-the-art performance in end-to-end weakly supervised semantic segmentation without additional supervision.The validation set and test set Mean Intersection over Union(MIoU)of PASCAL VOC 2012 dataset achieved 69.8%and 72.6%,respectively.Compared with ToCo(pre trained weight ImageNet-1k),MIoU on the test set is 2.1%higher.In addition,MIoU reached 41.4%on the validation set of the MS COCO 2014 dataset.
