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 Clauser Horne--Shimony-Holt-type noncontextuality inequality and the Svetliehny inequality are derived from the Alicki-van Ryn quantumness witness. Thus connections between quantumness and quantum contextuality, a...The Clauser Horne--Shimony-Holt-type noncontextuality inequality and the Svetliehny inequality are derived from the Alicki-van Ryn quantumness witness. Thus connections between quantumness and quantum contextuality, and between quantumness and genuine multipartite nonlocality are established.展开更多
The contradiction between classical and quantum physics can be identified through quantum contextuality, which does not need composite systems or spacelike separation. Contextuality is proven either by a logical contr...The contradiction between classical and quantum physics can be identified through quantum contextuality, which does not need composite systems or spacelike separation. Contextuality is proven either by a logical contradiction between the noncontextuality hidden variable predictions and those of quantum mechanics or by the violation of noncontextual inequality. We propose an experimental scheme of state-independent contextual inequality derived from the Mermin proof of the Kochen–Specker(KS) theorem in eight-dimensional Hilbert space, which could be observed either in an individual system or in a composite system. We also show how to resolve the compatibility problems. Our scheme can be implemented in optical systems with current experiment techniques.展开更多
Klyachko-Can-Binicioglu-Shumovsky （KCBS） inequality is a Bell-like inequality, the violation of which can be used to confirm the existence of quantum contextuality. However, the imperfection of detection efficiency ...Klyachko-Can-Binicioglu-Shumovsky （KCBS） inequality is a Bell-like inequality, the violation of which can be used to confirm the existence of quantum contextuality. However, the imperfection of detection efficiency may cause the so-called loophole in actual KCBS＇s experiments. We derive an alternative KCBS inequality to deal with the loophole in actual KCBS＇s experiments. We prove that if the experimental data violate this KCBS inequality, the loophole-free violation of the original KCBS inequality will occur. We show that the minimum detection efficiency needed for a loophole-free violation of the KCBS inequality is about 0.9738.展开更多
Quantum nonlocality and quantum contextuality are the most curious properties that change our understanding of nature, and were observed independently in recent decades. One important question is whether both properti...Quantum nonlocality and quantum contextuality are the most curious properties that change our understanding of nature, and were observed independently in recent decades. One important question is whether both properties can be observed simultaneously. In this paper, we show that in a qutrit-qutrit system we can observe quantum nonlocality and quantum contextuality at the same time. From the perspective of quantum information, our experiment proves in principle that the two resources, quantum nonlocality and quantum contextuality, can be utilized simultaneously.展开更多
With the recent increase in data volume and diversity,traditional text representation techniques are struggling to capture context,particularly in environments with sparse data.To address these challenges,this study p...With the recent increase in data volume and diversity,traditional text representation techniques are struggling to capture context,particularly in environments with sparse data.To address these challenges,this study proposes a new model,the Masked Joint Representation Model(MJRM).MJRM approximates the original hypothesis by leveraging multiple elements in a limited context.It dynamically adapts to changes in characteristics based on data distribution through three main components.First,masking-based representation learning,termed selective dynamic masking,integrates topic modeling and sentiment clustering to generate and train multiple instances across different data subsets,whose predictions are then aggregated with optimized weights.This design alleviates sparsity,suppresses noise,and preserves contextual structures.Second,regularization-based improvements are applied.Third,techniques for addressing sparse data are used to perform final inference.As a result,MJRM improves performance by up to 4%compared to existing AI techniques.In our experiments,we analyzed the contribution of each factor,demonstrating that masking,dynamic learning,and aggregating multiple instances complement each other to improve performance.This demonstrates that a masking-based multi-learning strategy is effective for context-aware sparse text classification,and can be useful even in challenging situations such as data shortage or data distribution variations.We expect that the approach can be extended to diverse fields such as sentiment analysis,spam filtering,and domain-specific document classification.展开更多
In this paper, we introduce and discuss the robustness of contextuality（Ro C） R_C（e） and the contextuality cost C（e） of an empirical model e. The following properties of them are proved.（i） An empirical model ...In this paper, we introduce and discuss the robustness of contextuality（Ro C） R_C（e） and the contextuality cost C（e） of an empirical model e. The following properties of them are proved.（i） An empirical model e is contextual if and only if R_C（e） ＆gt; 0;（ii） the Ro C function R_C is convex, lower semi-continuous and un-increasing under an affine mapping on the set E M of all empirical models;（iii） e is non-contextual if and only if C（e） = 0;（iv） e is contextual if and only if C（e） ＆gt; 0;（v） e is strongly contextual if and only if C（e） = 1. Also, a relationship between RC（e） and C（e） is obtained. Lastly, the Ro C of three empirical models is computed and compared. Especially, the Ro C of the PR boxes is obtained and the supremum 0.5 is found for the Ro C of all no-signaling type（2, 2, 2） empirical models.展开更多
Recently, the robustness of contextuality(RoC) of an empirical model was discussed in [Sci. China-Phys. Mech. Astron. 59,640303(2016)], many important properties of the RoC have been proved except for its boundedness ...Recently, the robustness of contextuality(RoC) of an empirical model was discussed in [Sci. China-Phys. Mech. Astron. 59,640303(2016)], many important properties of the RoC have been proved except for its boundedness and continuity. The aim of this paper is to find an upper bound for the RoC over all of empirical models and prove that the RoC is a continuous function on the set of all empirical models. Lastly, a relationship between the RoC and the extent of violating the noncontextual inequalities is established for an n-cycle contextual box. This relationship implies that the RoC can be used to quantify the contextuality of n-cycle boxes.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
Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells an...Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.展开更多
Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently...Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently,in music appreciation activities for senior kindergarten classes,there is a widespread phenomenon of homogenized teaching content and mechanized teaching methods,which results in insufficient enthusiasm for participation among young children and a superficial understanding of music.The situational teaching method,by constructing concrete and immersive learning scenarios,can effectively activate young children’s multi-dimensional sensory experiences.Its characteristics of intuitiveness and interactivity are highly consistent with the traits of young children’s concrete thinking,thus providing a new approach to resolving the current predicament.The research focuses on the practical pain points in music appreciation activities for senior kindergarten classes and proposes targeted solutions from four dimensions:content design,method innovation,resource integration,and teacher training,aiming to reconstruct a child-centered,in-depth music learning model.Practice has shown that the situational teaching method can not only enhance young children’s perceptual sensitivity to musical elements but also guide them to achieve emotional resonance through role-playing and life-related associations,laying a foundation for the sustainable development of young children’s musical literacy.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
This paper proposes Flex-QUIC,an AIempowered quick UDP Internet connections(QUIC)enhancement framework that addresses the challenge of degraded transmission efficiency caused by the static parameterization of acknowle...This paper proposes Flex-QUIC,an AIempowered quick UDP Internet connections(QUIC)enhancement framework that addresses the challenge of degraded transmission efficiency caused by the static parameterization of acknowledgment(ACK)mechanisms,loss detection,and forward error correction(FEC)in dynamic wireless networks.Unlike the standard QUIC protocol,Flex-QUIC systematically integrates machine learning across three critical modules to achieve high-efficiency operation.First,a contextual multi-armed bandit-based ACK adaptation mechanism optimizes the ACK ratio to reduce wireless channel contention.Second,the adaptive loss detection module utilizes a long short-term memory(LSTM)model to predict the reordering displacement for optimizing the packet reordering tolerance.Third,the FEC transmission scheme jointly adjusts the redundancy level based on the LSTM-predicted loss rate and congestion window state.Extensive evaluations across Wi-Fi,5G,and satellite network scenarios demonstrate that Flex-QUIC significantly improves throughput and latency reduction compared to the standard QUIC and other enhanced QUIC variants,highlighting its adaptability to diverse and dynamic network conditions.Finally,we further discuss open issues in deploying AI-native transport protocols.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
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
Both linguistics and language teaching take language as their subject. Linguistics can provide language teaching with theories and language teaching can further prove the validity of linguistics. To some extent, lingu...Both linguistics and language teaching take language as their subject. Linguistics can provide language teaching with theories and language teaching can further prove the validity of linguistics. To some extent, linguistics is more theoretical while language teaching is more practical. Since linguistics is defined as the scientific study of language, it seems obvious that such a study would have direct relations to language teaching. This paper attempts to make an analysis of difference between Contextual theory of meaning and the teaching of EFL reading.展开更多
三期答案1.Explain the contextual meaning of the following words and expressions(highlighted in blue)in English.(1)insouciance(title)insouciance:Insouciance is lack of concern shown by someone about something which the...三期答案1.Explain the contextual meaning of the following words and expressions(highlighted in blue)in English.(1)insouciance(title)insouciance:Insouciance is lack of concern shown by someone about something which they might be expected to take more seriously.(2)peep,bridge(Para.2)a)peep:If you peep,or peep at something,you have a quick look at it,often secretly and quietly.展开更多
A core issue in foreign language teaching is to foster actual competence of language learners to practically utilize language, which is called pragmatic competence. But compared with traditional linguistic competence,...A core issue in foreign language teaching is to foster actual competence of language learners to practically utilize language, which is called pragmatic competence. But compared with traditional linguistic competence, the levels of their pragmatic competence need to be further understood. Thus it is essential to make an investigation into this. From the investigation results it is expected to reveal from which aspects teachers and students should carry on in order to improve pragmatic competence and meet foreign language learning need in new age.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.
基金Supported by the National Basic Research Program of China under Grant No 2012CB921900the National Natural Science Foundation of China under Grant Nos 11175089 and 11475089
文摘The Clauser Horne--Shimony-Holt-type noncontextuality inequality and the Svetliehny inequality are derived from the Alicki-van Ryn quantumness witness. Thus connections between quantumness and quantum contextuality, and between quantumness and genuine multipartite nonlocality are established.
基金Project supported by the National Natural Science Foundation of China (Grant No. U1930402)support from the Project Funded by China Postdoctoral Science Foundation (Grant Nos. 2020M680006 and 2021T140045)+1 种基金support from the National Natural Science Foundation of China (Grant No. 12004184)the Natural Science Foundation of Jiangsu Province, China (Grants No. BK20190428)。
文摘The contradiction between classical and quantum physics can be identified through quantum contextuality, which does not need composite systems or spacelike separation. Contextuality is proven either by a logical contradiction between the noncontextuality hidden variable predictions and those of quantum mechanics or by the violation of noncontextual inequality. We propose an experimental scheme of state-independent contextual inequality derived from the Mermin proof of the Kochen–Specker(KS) theorem in eight-dimensional Hilbert space, which could be observed either in an individual system or in a composite system. We also show how to resolve the compatibility problems. Our scheme can be implemented in optical systems with current experiment techniques.
基金Project supported by the National Natural Science Foundation of China(Grant No.11005031)the Natural Science Foundation of Zhejiang Province,China(Grant No.Y6110314)
文摘Klyachko-Can-Binicioglu-Shumovsky （KCBS） inequality is a Bell-like inequality, the violation of which can be used to confirm the existence of quantum contextuality. However, the imperfection of detection efficiency may cause the so-called loophole in actual KCBS＇s experiments. We derive an alternative KCBS inequality to deal with the loophole in actual KCBS＇s experiments. We prove that if the experimental data violate this KCBS inequality, the loophole-free violation of the original KCBS inequality will occur. We show that the minimum detection efficiency needed for a loophole-free violation of the KCBS inequality is about 0.9738.
基金supported by the National Key Research and Development Program of China(2017YFA0304100)National Natural Science Foundation of China(11374288,11774335,61327901,11474268,11325419 and 11504253)+2 种基金the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(QYZDY-SSW-SLH003)the Fundamental Research Funds for the Central UniversitiesAnhui Initiative in Quantum Information Technologies(AHY020100 and AHY060300)
文摘Quantum nonlocality and quantum contextuality are the most curious properties that change our understanding of nature, and were observed independently in recent decades. One important question is whether both properties can be observed simultaneously. In this paper, we show that in a qutrit-qutrit system we can observe quantum nonlocality and quantum contextuality at the same time. From the perspective of quantum information, our experiment proves in principle that the two resources, quantum nonlocality and quantum contextuality, can be utilized simultaneously.
基金supported by the SungKyunKwan University and the BK21 FOUR(Graduate School Innovation)funded by the Ministry of Education(MOE,Korea)and National Research Foundation of Korea(NRF).
文摘With the recent increase in data volume and diversity,traditional text representation techniques are struggling to capture context,particularly in environments with sparse data.To address these challenges,this study proposes a new model,the Masked Joint Representation Model(MJRM).MJRM approximates the original hypothesis by leveraging multiple elements in a limited context.It dynamically adapts to changes in characteristics based on data distribution through three main components.First,masking-based representation learning,termed selective dynamic masking,integrates topic modeling and sentiment clustering to generate and train multiple instances across different data subsets,whose predictions are then aggregated with optimized weights.This design alleviates sparsity,suppresses noise,and preserves contextual structures.Second,regularization-based improvements are applied.Third,techniques for addressing sparse data are used to perform final inference.As a result,MJRM improves performance by up to 4%compared to existing AI techniques.In our experiments,we analyzed the contribution of each factor,demonstrating that masking,dynamic learning,and aggregating multiple instances complement each other to improve performance.This demonstrates that a masking-based multi-learning strategy is effective for context-aware sparse text classification,and can be useful even in challenging situations such as data shortage or data distribution variations.We expect that the approach can be extended to diverse fields such as sentiment analysis,spam filtering,and domain-specific document classification.
基金supported by the National Natural Science Foundation of China(Grant Nos.1137101211401359+1 种基金11471200 and 11571213)the Fundamental Research Funds for the Central Universities(Grant No.GK201301007)
文摘In this paper, we introduce and discuss the robustness of contextuality（Ro C） R_C（e） and the contextuality cost C（e） of an empirical model e. The following properties of them are proved.（i） An empirical model e is contextual if and only if R_C（e） ＆gt; 0;（ii） the Ro C function R_C is convex, lower semi-continuous and un-increasing under an affine mapping on the set E M of all empirical models;（iii） e is non-contextual if and only if C（e） = 0;（iv） e is contextual if and only if C（e） ＆gt; 0;（v） e is strongly contextual if and only if C（e） = 1. Also, a relationship between RC（e） and C（e） is obtained. Lastly, the Ro C of three empirical models is computed and compared. Especially, the Ro C of the PR boxes is obtained and the supremum 0.5 is found for the Ro C of all no-signaling type（2, 2, 2） empirical models.
基金supported by the National Natural Science Foundation of China(Grant Nos.11371012,11401359,11471200,11571211 and11571213)the Fundamental Research Funds for the Central Universities(Grant No.GK201604001)the Innovation Fund Project for Graduate Program of Shaanxi Normal University(Grant No.2016CBY005)
文摘Recently, the robustness of contextuality(RoC) of an empirical model was discussed in [Sci. China-Phys. Mech. Astron. 59,640303(2016)], many important properties of the RoC have been proved except for its boundedness and continuity. The aim of this paper is to find an upper bound for the RoC over all of empirical models and prove that the RoC is a continuous function on the set of all empirical models. Lastly, a relationship between the RoC and the extent of violating the noncontextual inequalities is established for an n-cycle contextual box. This relationship implies that the RoC can be used to quantify the contextuality of n-cycle boxes.
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.
基金funded by the China Chongqing Municipal Science and Technology Bureau,grant numbers 2024TIAD-CYKJCXX0121,2024NSCQ-LZX0135Chongqing Municipal Commission of Housing and Urban-Rural Development,grant number CKZ2024-87+3 种基金the Chongqing University of Technology graduate education high-quality development project,grant number gzlsz202401the Chongqing University of Technology-Chongqing LINGLUE Technology Co.,Ltd.,Electronic Information(Artificial Intelligence)graduate joint training basethe Postgraduate Education and Teaching Reform Research Project in Chongqing,grant number yjg213116the Chongqing University of Technology-CISDI Chongqing Information Technology Co.,Ltd.,Computer Technology graduate joint training base.
文摘Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.
文摘Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently,in music appreciation activities for senior kindergarten classes,there is a widespread phenomenon of homogenized teaching content and mechanized teaching methods,which results in insufficient enthusiasm for participation among young children and a superficial understanding of music.The situational teaching method,by constructing concrete and immersive learning scenarios,can effectively activate young children’s multi-dimensional sensory experiences.Its characteristics of intuitiveness and interactivity are highly consistent with the traits of young children’s concrete thinking,thus providing a new approach to resolving the current predicament.The research focuses on the practical pain points in music appreciation activities for senior kindergarten classes and proposes targeted solutions from four dimensions:content design,method innovation,resource integration,and teacher training,aiming to reconstruct a child-centered,in-depth music learning model.Practice has shown that the situational teaching method can not only enhance young children’s perceptual sensitivity to musical elements but also guide them to achieve emotional resonance through role-playing and life-related associations,laying a foundation for the sustainable development of young children’s musical literacy.
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supported in part by the National Key R&D Program of China with Grant number 2019YFB1803400.
文摘This paper proposes Flex-QUIC,an AIempowered quick UDP Internet connections(QUIC)enhancement framework that addresses the challenge of degraded transmission efficiency caused by the static parameterization of acknowledgment(ACK)mechanisms,loss detection,and forward error correction(FEC)in dynamic wireless networks.Unlike the standard QUIC protocol,Flex-QUIC systematically integrates machine learning across three critical modules to achieve high-efficiency operation.First,a contextual multi-armed bandit-based ACK adaptation mechanism optimizes the ACK ratio to reduce wireless channel contention.Second,the adaptive loss detection module utilizes a long short-term memory(LSTM)model to predict the reordering displacement for optimizing the packet reordering tolerance.Third,the FEC transmission scheme jointly adjusts the redundancy level based on the LSTM-predicted loss rate and congestion window state.Extensive evaluations across Wi-Fi,5G,and satellite network scenarios demonstrate that Flex-QUIC significantly improves throughput and latency reduction compared to the standard QUIC and other enhanced QUIC variants,highlighting its adaptability to diverse and dynamic network conditions.Finally,we further discuss open issues in deploying AI-native transport protocols.
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.
文摘Both linguistics and language teaching take language as their subject. Linguistics can provide language teaching with theories and language teaching can further prove the validity of linguistics. To some extent, linguistics is more theoretical while language teaching is more practical. Since linguistics is defined as the scientific study of language, it seems obvious that such a study would have direct relations to language teaching. This paper attempts to make an analysis of difference between Contextual theory of meaning and the teaching of EFL reading.
文摘三期答案1.Explain the contextual meaning of the following words and expressions(highlighted in blue)in English.(1)insouciance(title)insouciance:Insouciance is lack of concern shown by someone about something which they might be expected to take more seriously.(2)peep,bridge(Para.2)a)peep:If you peep,or peep at something,you have a quick look at it,often secretly and quietly.
文摘A core issue in foreign language teaching is to foster actual competence of language learners to practically utilize language, which is called pragmatic competence. But compared with traditional linguistic competence, the levels of their pragmatic competence need to be further understood. Thus it is essential to make an investigation into this. From the investigation results it is expected to reveal from which aspects teachers and students should carry on in order to improve pragmatic competence and meet foreign language learning need in new age.
