Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinct...Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods.展开更多
Weakly supervised semantic segmentation(WSSS)is a tricky task,which only provides category information for segmentation prediction.Thus,the key stage of WSSS is to generate the pseudo labels.For convolutional neural n...Weakly supervised semantic segmentation(WSSS)is a tricky task,which only provides category information for segmentation prediction.Thus,the key stage of WSSS is to generate the pseudo labels.For convolutional neural network(CNN)based methods,in which class activation mapping(CAM)is proposed to obtain the pseudo labels,and only concentrates on the most discriminative parts.Recently,transformer-based methods utilize attention map from the multi-headed self-attention(MHSA)module to predict pseudo labels,which usually contain obvious background noise and incoherent object area.To solve the above problems,we use the Conformer as our backbone,which is a parallel network based on convolutional neural network(CNN)and Transformer.The two branches generate pseudo labels and refine them independently,and can effectively combine the advantages of CNN and Transformer.However,the parallel structure is not close enough in the information communication.Thus,parallel structure can result in poor details about pseudo labels,and the background noise still exists.To alleviate this problem,we propose enhancing convolution CAM(ECCAM)model,which have three improved modules based on enhancing convolution,including deeper stem(DStem),convolutional feed-forward network(CFFN)and feature coupling unit with convolution(FCUConv).The ECCAM could make Conformer have tighter interaction between CNN and Transformer branches.After experimental verification,the improved modules we propose can help the network perceive more local information from images,making the final segmentation results more refined.Compared with similar architecture,our modules greatly improve the semantic segmentation performance and achieve70.2%mean intersection over union(mIoU)on the PASCAL VOC 2012 dataset.展开更多
The potential of 2-amino-1-propanol(AP)as a novel depressant in selectively floating ilmenite from titanaugite under weakly acidic conditions was investigated.Micro-flotation results show that AP significantly reduces...The potential of 2-amino-1-propanol(AP)as a novel depressant in selectively floating ilmenite from titanaugite under weakly acidic conditions was investigated.Micro-flotation results show that AP significantly reduces the recovery of titanaugite while having no evident impact on ilmenite flotation.Subsequent bench-scale flotation tests further confirm a remarkable improvement in separation efficiency upon the introduction of AP.Contact angle and adsorption tests reveal a stronger affinity of AP towards the titanaugite surface in comparison to ilmenite.Zeta potential measurements and X-ray photoelectron spectroscopy(XPS)analyses exhibit favorable adsorption characteristics of AP on titanaugite,resulting from a synergy of electrostatic attraction and chemical interaction.In contrast,electrostatic repulsion hinders any significant interaction between AP and the ilmenite surface.These findings highlight the potential of AP as a highly efficient depressant for ilmenite flotation,paving the way for reduced reliance on sulfuric acid in the industry.展开更多
Weakly solvating electrolyte(WSE)demonstrates superior compatibility with lithium(Li)metal batteries(LMBs).However,its application in fast-charging high-voltage LMBs is challenging.Here,we propose a diluent modified W...Weakly solvating electrolyte(WSE)demonstrates superior compatibility with lithium(Li)metal batteries(LMBs).However,its application in fast-charging high-voltage LMBs is challenging.Here,we propose a diluent modified WSE for fast-charging high-voltage LMBs,which is formed by adding diluent of 1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropyl ether(TTE)into the tetrahydropyran(THP)based WSE.A relatively loose solvation structure is formed due to the formation of weak hydrogen bond between TTE and THP,which accelerates the de-solvation kinetics of Li~+.Besides,more anions are involved in solvation structure in the presence of TTE,yielding inorganic-rich interphases with improved stability.Li(30μm)||Li Ni_(0.5)Co_(0.2)Mn_(0.3)O_(2)(4.1 mAh/cm^(2))batteries with the TTE modified WSE retain over 64%capacity retention after 175 cycles under high rate of 3 C and high-voltage of 4.5 V,much better than that with pure THP based WSE.This work points out that the combination of diluent with weakly solvating solvent is a promising approach to develop high performance electrolytes for fast-charging high-voltage LMBs.展开更多
Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only f...Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only focus on the most discriminative regions of images,resulting in their poor coverage performance.We attribute this to the deficiency in the recognition ability of a single classifier and the negative impacts caused by magnitudes during the CAMs normalisation process.To address the aforementioned issues,we propose to construct selective multiple classifiers(SMC).During the training process,we extract multiple prototypes for each class and store them in the corresponding memory bank.These prototypes are divided into foreground and background prototypes,with the former used to identify foreground objects and the latter aimed at preventing the false activation of background pixels.As for the inference stage,multiple prototypes are adaptively selected from the memory bank for each image as SMC.Subsequently,CAMs are generated by measuring the angle between SMC and features.We enhance the recognition ability of classifiers by adaptively constructing multiple classifiers for each image,while only relying on angle measurement to generate CAMs can alleviate the suppression phenomenon caused by magnitudes.Furthermore,SMC can be integrated into other WSSS approaches to help generate better CAMs.Extensive experiments conducted on standard WSSS benchmarks such as PASCAL VOC 2012 and MS COCO 2014 demonstrate the superiority of our proposed method.展开更多
A new detector array with a large solid angle coverage for the coincidence measurement of charged fragments was developed to study the breakup reaction mechanisms of weakly bound nuclear systems at energies around the...A new detector array with a large solid angle coverage for the coincidence measurement of charged fragments was developed to study the breakup reaction mechanisms of weakly bound nuclear systems at energies around the Coulomb barrier.The array has been used to explore the breakup reaction mechanisms of^(6,7)Li+^(209)Bi systems at E_(beam)=30,40,47 MeV,showing good performance in particle identification and complete kinematic measurements.Based on this,different breakup modes and breakup components were clearly distinguished,and some new breakup modes were discovered,such as^(7)Li→α+t breakup mode in6Li+209Bi system and^(7)Li→^(6)He+p breakup mode in^(7)Li+^(209)Bi system.This array can also be used to explore other breakup reaction mechanisms induced by weakly bound nuclei.展开更多
Despite the growing interest in fast-cha rging solid-state lithium(Li)-metal batteries(SSLMBs),their practical implementation has yet to be achieved,primarily due to an incomplete understanding of the disparate and of...Despite the growing interest in fast-cha rging solid-state lithium(Li)-metal batteries(SSLMBs),their practical implementation has yet to be achieved,primarily due to an incomplete understanding of the disparate and often conflicting requirements of the bulk electrolyte and the electrode-electrolyte interphase.Here,we present a weakly coordinating cationic polymer electrolyte(WCPE)specifically designed to regulate the Li^(+)coordination structure,thereby enabling fast-charging SSLMBs.The WCPE comprises an imidazolium-based polycationic matrix combined with a succinonitrile(SN)-based highconcentration electrolyte.Unlike conventional neutral polymer matrices,the polycationic matrix in the WCPE competes with Li^(+)for interactions with SN,weakening the original coordination between SN and Li^(+).This modulation of SN-Li^(+)interaction improves both Li^(+)conductivity of the WCPE(σ_(Li^(+))=1.29mS cm^(-1))and redox kinetics at the electrode-electrolyte interphase.Consequently,SSLMB cells(comprising LiFePO_(4)cathodes and Li-metal anodes)with the WCPE achieve fast-charging capability(reaching over 80%state of charge within 10 min),outperforming those of previously reported polymer electrolytebased SSLMBs.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
Finely tuning spectral characteristics of the epsilon-negative(ε'<0,EN) response is full of challenges when its regulatory mechanism in metacomposites is not yet clear.Herein,we have meticulously designed Cu/C...Finely tuning spectral characteristics of the epsilon-negative(ε'<0,EN) response is full of challenges when its regulatory mechanism in metacomposites is not yet clear.Herein,we have meticulously designed Cu/CaCu_(3)Ti_(4)O_(12)(Cu/CCTO) percolative metacomposites,successfully achieved both epsilon-negative and ε'-near-zero(ENZ)responses in the radio-frequency band.Before percolation,a large number of electric dipoles in the metacomposites achieved resonance characteristics near the ENZ point under the excitation of radio frequency electromagnetic fields,and as the Cu content increased,the ENZ frequency varied from 942,858,862 to 632 MHz.展开更多
Computational modeling plays a vital role in advancing our understanding and application of soliton theory.It allows researchers to both simulate and analyze complex soliton phenomena and discover new types of soliton...Computational modeling plays a vital role in advancing our understanding and application of soliton theory.It allows researchers to both simulate and analyze complex soliton phenomena and discover new types of soliton solutions.In the present study,we computationally derive the bright and dark optical solitons for a Schrödinger equation that contains a specific type of nonlinearity.This nonlinearity in the model is the result of the combination of the parabolic law and the non-local law of self-phase modulation structures.The numerical simulation is accomplished through the application of an algorithm that integrates the classical Adomian method with the Laplace transform.The results obtained have not been previously reported for this type of nonlinearity.Additionally,for the purpose of comparison,the numerical examination has taken into account some scenarios with fixed parameter values.Notably,the numerical derivation of solitons without the assistance of an exact solution is an exceptional take-home lesson fromthis study.Furthermore,the proposed approach is demonstrated to possess optimal computational accuracy in the results presentation,which includes error tables and graphs.It is important tomention that themethodology employed in this study does not involve any form of linearization,discretization,or perturbation.Consequently,the physical nature of the problem to be solved remains unaltered,which is one of the main advantages.展开更多
This paper deals with Mckean-Vlasov backward stochastic differential equations with weak monotonicity coefficients.We first establish the existence and uniqueness of solutions to Mckean-Vlasov backward stochastic diff...This paper deals with Mckean-Vlasov backward stochastic differential equations with weak monotonicity coefficients.We first establish the existence and uniqueness of solutions to Mckean-Vlasov backward stochastic differential equations.Then we obtain a comparison theorem in one-dimensional situation.展开更多
In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can...In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can provide a more reliable approach in these situations.Current popular approaches mainly adopt the classification-based class activation maps(CAM)as initial pseudo labels to solve the task.展开更多
In this paper,we introduce the notion of G_(C)-X-injective modules,where X denotes a class of left S-modules and C represents a faithfully semidualizing bimodule.Under the condition that X satisfies certain hypotheses...In this paper,we introduce the notion of G_(C)-X-injective modules,where X denotes a class of left S-modules and C represents a faithfully semidualizing bimodule.Under the condition that X satisfies certain hypotheses,some properties and some equivalent characterizations of G_(C)-X-injective modules are investigated,and we also show that the triple(■,cores■,■)is a weak co-AB-context.As an application,two complete cotorsion pairs and a new model structure in Mod S are given.展开更多
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 weak and broken roof,explosive control and other problems seriously restrict the promotion of non coal pillar self-forming roadway technology.In order to solve such problems,a new method of non coal pillar self-fo...The weak and broken roof,explosive control and other problems seriously restrict the promotion of non coal pillar self-forming roadway technology.In order to solve such problems,a new method of non coal pillar self-forming roadway through non-blasting roof cutting and pressure relief was proposed in this study.A systematic research system of"theoretical analysis-physical experiment-engineering verification"was constructed with the 9103 working face of Longmenta Coal Mine as the research object.Firstly,the theoretical analysis was conducted to reveal the roof cutting mechanics mechanism of rock mass weakened by dense drilling,establish the design criteria for key drilling parameters,and obtain the key design parameters of dense drilling in the test working face.Secondly,the physical model test was conducted to make clear that the dense drilling method can directionally cut off the goaf roof along the set position,reducing the stress and deformation of the roadway surrounding rock.Finally,the field engineering tests were conducted,and monitoring results showed that the pressure relief effect of the dense drilling method was comparable to that of the directional blasting method,achieving non coal pillar self-forming roadway mining under non blasting conditions.展开更多
This study proposes a class of augmented subspace schemes for the weak Galerkin(WG)finite element method used to solve eigenvalue problems.The augmented subspace is built with the conforming linear finite element spac...This study proposes a class of augmented subspace schemes for the weak Galerkin(WG)finite element method used to solve eigenvalue problems.The augmented subspace is built with the conforming linear finite element space defined on the coarse mesh and the eigen-function approximations in the WG finite element space defined on the fine mesh.Based on this augmented subspace,solving the eigenvalue problem in the fine WG finite element space can be reduced to the solution of the linear boundary value problem in the same WG finite element space and a low dimensional eigenvalue problem in the augmented sub-space.The proposed augmented subspace techniques have the second order convergence rate with respect to the coarse mesh size,as demonstrated by the accompanying error esti-mates.Finally,a few numerical examples are provided to validate the proposed numerical techniques.展开更多
Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks(UUSNs),significantly limiting their service ability in collaborative missions such as military reconnaissance and env...Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks(UUSNs),significantly limiting their service ability in collaborative missions such as military reconnaissance and environmental monitoring.Existing failure models primarily focus on power grids and traffic systems,and don't address the unique challenges of weak-communication UUSNs.In UUSNs,cascading failure present a complex and dynamic process driven by the coupling of unstable acoustic channels,passive node drift,adversarial attacks,and network heterogeneity.To address these challenges,a directed weighted graph model of UUSNs is first developed,in which node positions are updated according to ocean-current-driven drift and link weights reflect the probability of successful acoustic transmission.Building on this UUSNs graph model,a cascading failure model is proposed that integrates a normal-failure-recovery state-cycle mechanism,multiple attack strategies,and routingbased load redistribution.Finally,under a five-level connectivity UUSNs scheme,simulations are conducted to analyze how dynamic topology,network load,node recovery delay,and attack modes jointly affect network survivability.The main findings are:(1)moderate node drift can improve survivability by activating weak links;(2)based-energy routing(BER)outperform based-depth routing(BDR)in harsh conditions;(3)node self-recovery time is critical to network survivability;(4)traditional degree-based critical node metrics are inadequate for weak-communication UUSNs.These results provide a theoretical foundation for designing robust survivability mechanisms in weak-communication UUSNs.展开更多
A power flow analysis method for weakly looped distribution systems with PV buses is proposed in this paper. The proposed method is computationally more efficient and more robust compared with the conventional compens...A power flow analysis method for weakly looped distribution systems with PV buses is proposed in this paper. The proposed method is computationally more efficient and more robust compared with the conventional compensation methods. The robustness is achieved by embedding the boundary conditions of loops and PV buses into the Jacobian matrix. The computational efficiency is achieved by the carefully designed factorization of Jacobian matrix. Test results on a 33 bus system are presented.展开更多
文摘Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods.
文摘Weakly supervised semantic segmentation(WSSS)is a tricky task,which only provides category information for segmentation prediction.Thus,the key stage of WSSS is to generate the pseudo labels.For convolutional neural network(CNN)based methods,in which class activation mapping(CAM)is proposed to obtain the pseudo labels,and only concentrates on the most discriminative parts.Recently,transformer-based methods utilize attention map from the multi-headed self-attention(MHSA)module to predict pseudo labels,which usually contain obvious background noise and incoherent object area.To solve the above problems,we use the Conformer as our backbone,which is a parallel network based on convolutional neural network(CNN)and Transformer.The two branches generate pseudo labels and refine them independently,and can effectively combine the advantages of CNN and Transformer.However,the parallel structure is not close enough in the information communication.Thus,parallel structure can result in poor details about pseudo labels,and the background noise still exists.To alleviate this problem,we propose enhancing convolution CAM(ECCAM)model,which have three improved modules based on enhancing convolution,including deeper stem(DStem),convolutional feed-forward network(CFFN)and feature coupling unit with convolution(FCUConv).The ECCAM could make Conformer have tighter interaction between CNN and Transformer branches.After experimental verification,the improved modules we propose can help the network perceive more local information from images,making the final segmentation results more refined.Compared with similar architecture,our modules greatly improve the semantic segmentation performance and achieve70.2%mean intersection over union(mIoU)on the PASCAL VOC 2012 dataset.
基金supported by the National Key Research and Development Program of China(No.2019YFC1803501)the National Natural Science Foundation of China(No.52074357)+2 种基金the Natural Science Foundation of Hunan Province,China(No.2022JJ30713)the Vanadium Titanium Union Foundationthe Project of Technology Innovation Center for Comprehensive Utilization of Strategic Mineral Resources,Ministry of Natural Resources,China。
文摘The potential of 2-amino-1-propanol(AP)as a novel depressant in selectively floating ilmenite from titanaugite under weakly acidic conditions was investigated.Micro-flotation results show that AP significantly reduces the recovery of titanaugite while having no evident impact on ilmenite flotation.Subsequent bench-scale flotation tests further confirm a remarkable improvement in separation efficiency upon the introduction of AP.Contact angle and adsorption tests reveal a stronger affinity of AP towards the titanaugite surface in comparison to ilmenite.Zeta potential measurements and X-ray photoelectron spectroscopy(XPS)analyses exhibit favorable adsorption characteristics of AP on titanaugite,resulting from a synergy of electrostatic attraction and chemical interaction.In contrast,electrostatic repulsion hinders any significant interaction between AP and the ilmenite surface.These findings highlight the potential of AP as a highly efficient depressant for ilmenite flotation,paving the way for reduced reliance on sulfuric acid in the industry.
基金supported by Hengyang City,Hunan Province Science and Technology Innovation Project(No.202250045319)the National Natural Science Foundation of China(Nos.11375084,21808125)the Scientific Research Planning Project of Jilin Provincial Education Department(No.JJKH20241249KJ)。
文摘Weakly solvating electrolyte(WSE)demonstrates superior compatibility with lithium(Li)metal batteries(LMBs).However,its application in fast-charging high-voltage LMBs is challenging.Here,we propose a diluent modified WSE for fast-charging high-voltage LMBs,which is formed by adding diluent of 1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropyl ether(TTE)into the tetrahydropyran(THP)based WSE.A relatively loose solvation structure is formed due to the formation of weak hydrogen bond between TTE and THP,which accelerates the de-solvation kinetics of Li~+.Besides,more anions are involved in solvation structure in the presence of TTE,yielding inorganic-rich interphases with improved stability.Li(30μm)||Li Ni_(0.5)Co_(0.2)Mn_(0.3)O_(2)(4.1 mAh/cm^(2))batteries with the TTE modified WSE retain over 64%capacity retention after 175 cycles under high rate of 3 C and high-voltage of 4.5 V,much better than that with pure THP based WSE.This work points out that the combination of diluent with weakly solvating solvent is a promising approach to develop high performance electrolytes for fast-charging high-voltage LMBs.
基金supported by the National Natural Science Foundation of China(Grants 62176097,61433007)Fundamental Research Funds for the Central Universities(Grant 2019kfyXKJC024)the 111 Project on Computational Intelligence and Intelligent Control(Grant B18024).
文摘Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only focus on the most discriminative regions of images,resulting in their poor coverage performance.We attribute this to the deficiency in the recognition ability of a single classifier and the negative impacts caused by magnitudes during the CAMs normalisation process.To address the aforementioned issues,we propose to construct selective multiple classifiers(SMC).During the training process,we extract multiple prototypes for each class and store them in the corresponding memory bank.These prototypes are divided into foreground and background prototypes,with the former used to identify foreground objects and the latter aimed at preventing the false activation of background pixels.As for the inference stage,multiple prototypes are adaptively selected from the memory bank for each image as SMC.Subsequently,CAMs are generated by measuring the angle between SMC and features.We enhance the recognition ability of classifiers by adaptively constructing multiple classifiers for each image,while only relying on angle measurement to generate CAMs can alleviate the suppression phenomenon caused by magnitudes.Furthermore,SMC can be integrated into other WSSS approaches to help generate better CAMs.Extensive experiments conducted on standard WSSS benchmarks such as PASCAL VOC 2012 and MS COCO 2014 demonstrate the superiority of our proposed method.
基金supported by the National Key R&D Program of China(Nos.2022YFA1602302 and 2023YFA1606402)the National Natural Science Foundation of China(Nos.U2167204,12175314,12235020 and 12275360)+2 种基金the Continuous-Support Basic Scientific Research Projectthe"111 Center"the China Scholarship Council(CSC)。
文摘A new detector array with a large solid angle coverage for the coincidence measurement of charged fragments was developed to study the breakup reaction mechanisms of weakly bound nuclear systems at energies around the Coulomb barrier.The array has been used to explore the breakup reaction mechanisms of^(6,7)Li+^(209)Bi systems at E_(beam)=30,40,47 MeV,showing good performance in particle identification and complete kinematic measurements.Based on this,different breakup modes and breakup components were clearly distinguished,and some new breakup modes were discovered,such as^(7)Li→α+t breakup mode in6Li+209Bi system and^(7)Li→^(6)He+p breakup mode in^(7)Li+^(209)Bi system.This array can also be used to explore other breakup reaction mechanisms induced by weakly bound nuclei.
基金supported by the Basic Science Research Program(RS-2024-00344021,RS-2023-00261543,and RS-202300257666)through the National Research Foundation of Korea(NRF),the National Research Council of Science(000)Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(RS-2024-00420590,HRD Program for Industrial Innovation)The computational resources were provided by KITSI(KSC-2024-CRE-0143)。
文摘Despite the growing interest in fast-cha rging solid-state lithium(Li)-metal batteries(SSLMBs),their practical implementation has yet to be achieved,primarily due to an incomplete understanding of the disparate and often conflicting requirements of the bulk electrolyte and the electrode-electrolyte interphase.Here,we present a weakly coordinating cationic polymer electrolyte(WCPE)specifically designed to regulate the Li^(+)coordination structure,thereby enabling fast-charging SSLMBs.The WCPE comprises an imidazolium-based polycationic matrix combined with a succinonitrile(SN)-based highconcentration electrolyte.Unlike conventional neutral polymer matrices,the polycationic matrix in the WCPE competes with Li^(+)for interactions with SN,weakening the original coordination between SN and Li^(+).This modulation of SN-Li^(+)interaction improves both Li^(+)conductivity of the WCPE(σ_(Li^(+))=1.29mS cm^(-1))and redox kinetics at the electrode-electrolyte interphase.Consequently,SSLMB cells(comprising LiFePO_(4)cathodes and Li-metal anodes)with the WCPE achieve fast-charging capability(reaching over 80%state of charge within 10 min),outperforming those of previously reported polymer electrolytebased SSLMBs.
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.
基金supported by the National Natural Science Foundation of China(No.52461002)
文摘Finely tuning spectral characteristics of the epsilon-negative(ε'<0,EN) response is full of challenges when its regulatory mechanism in metacomposites is not yet clear.Herein,we have meticulously designed Cu/CaCu_(3)Ti_(4)O_(12)(Cu/CCTO) percolative metacomposites,successfully achieved both epsilon-negative and ε'-near-zero(ENZ)responses in the radio-frequency band.Before percolation,a large number of electric dipoles in the metacomposites achieved resonance characteristics near the ENZ point under the excitation of radio frequency electromagnetic fields,and as the Cu content increased,the ENZ frequency varied from 942,858,862 to 632 MHz.
文摘Computational modeling plays a vital role in advancing our understanding and application of soliton theory.It allows researchers to both simulate and analyze complex soliton phenomena and discover new types of soliton solutions.In the present study,we computationally derive the bright and dark optical solitons for a Schrödinger equation that contains a specific type of nonlinearity.This nonlinearity in the model is the result of the combination of the parabolic law and the non-local law of self-phase modulation structures.The numerical simulation is accomplished through the application of an algorithm that integrates the classical Adomian method with the Laplace transform.The results obtained have not been previously reported for this type of nonlinearity.Additionally,for the purpose of comparison,the numerical examination has taken into account some scenarios with fixed parameter values.Notably,the numerical derivation of solitons without the assistance of an exact solution is an exceptional take-home lesson fromthis study.Furthermore,the proposed approach is demonstrated to possess optimal computational accuracy in the results presentation,which includes error tables and graphs.It is important tomention that themethodology employed in this study does not involve any form of linearization,discretization,or perturbation.Consequently,the physical nature of the problem to be solved remains unaltered,which is one of the main advantages.
基金Supported by the National Natural Science Foundation of China(12001074)the Research Innovation Program of Graduate Students in Hunan Province(CX20220258)+1 种基金the Research Innovation Program of Graduate Students of Central South University(1053320214147)the Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110025)。
文摘This paper deals with Mckean-Vlasov backward stochastic differential equations with weak monotonicity coefficients.We first establish the existence and uniqueness of solutions to Mckean-Vlasov backward stochastic differential equations.Then we obtain a comparison theorem in one-dimensional situation.
文摘In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can provide a more reliable approach in these situations.Current popular approaches mainly adopt the classification-based class activation maps(CAM)as initial pseudo labels to solve the task.
文摘In this paper,we introduce the notion of G_(C)-X-injective modules,where X denotes a class of left S-modules and C represents a faithfully semidualizing bimodule.Under the condition that X satisfies certain hypotheses,some properties and some equivalent characterizations of G_(C)-X-injective modules are investigated,and we also show that the triple(■,cores■,■)is a weak co-AB-context.As an application,two complete cotorsion pairs and a new model structure in Mod S are given.
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Project(2024YFC2909500)supported by the National Key Research and Development Program of ChinaProjects(42377148,52204164)supported by the National Natural Science Foundation of ChinaProject(2022XJSB03)supported by the Fundamental Research Funds for the Central Universities,China。
文摘The weak and broken roof,explosive control and other problems seriously restrict the promotion of non coal pillar self-forming roadway technology.In order to solve such problems,a new method of non coal pillar self-forming roadway through non-blasting roof cutting and pressure relief was proposed in this study.A systematic research system of"theoretical analysis-physical experiment-engineering verification"was constructed with the 9103 working face of Longmenta Coal Mine as the research object.Firstly,the theoretical analysis was conducted to reveal the roof cutting mechanics mechanism of rock mass weakened by dense drilling,establish the design criteria for key drilling parameters,and obtain the key design parameters of dense drilling in the test working face.Secondly,the physical model test was conducted to make clear that the dense drilling method can directionally cut off the goaf roof along the set position,reducing the stress and deformation of the roadway surrounding rock.Finally,the field engineering tests were conducted,and monitoring results showed that the pressure relief effect of the dense drilling method was comparable to that of the directional blasting method,achieving non coal pillar self-forming roadway mining under non blasting conditions.
基金partly supported by the Beijing Natural Science Foundation(Grant No.Z200003)by the National Natural Science Foundation of China(Grant Nos.12331015,12301475,12301465)+1 种基金by the National Center for Mathematics and Interdisciplinary Science,Chinese Academy of Sciencesby the Research Foundation for the Beijing University of Technology New Faculty(Grant No.006000514122516).
文摘This study proposes a class of augmented subspace schemes for the weak Galerkin(WG)finite element method used to solve eigenvalue problems.The augmented subspace is built with the conforming linear finite element space defined on the coarse mesh and the eigen-function approximations in the WG finite element space defined on the fine mesh.Based on this augmented subspace,solving the eigenvalue problem in the fine WG finite element space can be reduced to the solution of the linear boundary value problem in the same WG finite element space and a low dimensional eigenvalue problem in the augmented sub-space.The proposed augmented subspace techniques have the second order convergence rate with respect to the coarse mesh size,as demonstrated by the accompanying error esti-mates.Finally,a few numerical examples are provided to validate the proposed numerical techniques.
基金supported in part by the National Natural Science Foundation of China(Key Program)under Grant No.62031021。
文摘Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks(UUSNs),significantly limiting their service ability in collaborative missions such as military reconnaissance and environmental monitoring.Existing failure models primarily focus on power grids and traffic systems,and don't address the unique challenges of weak-communication UUSNs.In UUSNs,cascading failure present a complex and dynamic process driven by the coupling of unstable acoustic channels,passive node drift,adversarial attacks,and network heterogeneity.To address these challenges,a directed weighted graph model of UUSNs is first developed,in which node positions are updated according to ocean-current-driven drift and link weights reflect the probability of successful acoustic transmission.Building on this UUSNs graph model,a cascading failure model is proposed that integrates a normal-failure-recovery state-cycle mechanism,multiple attack strategies,and routingbased load redistribution.Finally,under a five-level connectivity UUSNs scheme,simulations are conducted to analyze how dynamic topology,network load,node recovery delay,and attack modes jointly affect network survivability.The main findings are:(1)moderate node drift can improve survivability by activating weak links;(2)based-energy routing(BER)outperform based-depth routing(BDR)in harsh conditions;(3)node self-recovery time is critical to network survivability;(4)traditional degree-based critical node metrics are inadequate for weak-communication UUSNs.These results provide a theoretical foundation for designing robust survivability mechanisms in weak-communication UUSNs.
文摘A power flow analysis method for weakly looped distribution systems with PV buses is proposed in this paper. The proposed method is computationally more efficient and more robust compared with the conventional compensation methods. The robustness is achieved by embedding the boundary conditions of loops and PV buses into the Jacobian matrix. The computational efficiency is achieved by the carefully designed factorization of Jacobian matrix. Test results on a 33 bus system are presented.
