This paper introduces a new approach dedicated to the Ontology Personalization. Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive relevance of the cat...This paper introduces a new approach dedicated to the Ontology Personalization. Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive relevance of the categorization process, that is the one which is really perceived by the end-user. Practically, this process consists in decorating the Specialization/Generalization links (i.e. the is-a links) of the hierarchy of concepts with 2 gradients. The goal of the first gradient, called Conceptual Prototypicality Gradient, is to capture the user-sensitive relevance of the categorization process, that is the one which is perceived by the end-user. As this gradient is defined according to the three aspects of the semiotic triangle (i.e. intentional, extensional and expressional dimension), we call it Semiotic based Prototypicality Gradient. The objective of the second gradient, called Lexical Prototypicality Gradient, is to capture the user-sensitive relevance of the lexicalization process, i.e. the definition of a set of terms used to denote a concept. These gradients enrich the initial formal semantics of an ontology by adding a pragmatics defined according to a context of use which depends on parameters like culture, educational background and/or emotional context of the end-user. This paper also introduces a new similarity measure also defined in the context of a semiotic-based approach. The first originality of this measure, called SEMIOSEM, is to consider the three semiotic dimensions of the conceptualization underlying an ontology. Thus, SEMIOSEM aims at aggregating and improving existing extensional-based and intentional-based measures. The second originality of this measure is to be context-sensitive, and in particular user-sensitive. This makes SEMIOSEM more flexible, more robust and more close to the end-user’s judgment than the other similarity measures which are usually only based on one aspect of a conceptualization and never take the end-user’s perceptions and purposes into account.展开更多
In recent years,there have been fewer missions to detect neutrons in low Earth orbits(LEO),and the data obtained have been extremely limited.Studying the distribution of the neutron energy spectrum in LEO satellites t...In recent years,there have been fewer missions to detect neutrons in low Earth orbits(LEO),and the data obtained have been extremely limited.Studying the distribution of the neutron energy spectrum in LEO satellites through detection can help solve three major scientific problems:the source of particles in the inner radiation belt,information on solar-accelerated particles,and the proportion of neutrons from different sources in near-Earth space.The detection efficiency and accuracy of neutrons are affected by charged and primary particles in the environment and secondary neutrons produced by the spacecraft itself,which has been a hot research topic.The neutron spectrometer developed in this study adopts two combinations of 15 silicon detectors in terms of detector type and arrangement,which are used for neutron detection via the nuclear reaction method and recoil proton method,respectively,in which a 27μm-thick^(6)LiF conversion layer is used for thermal neutron detection up to 0.4 eV and a 300μm-thick high-density polyethylene conversion layer is used for fast-neutron detection up to 14 MeV and below.The design of the detector set can also remove the influence of primary charged particles and secondary neutrons in the detection environment to a certain extent,thereby improving the accuracy of neutron detection.In this study,the neutron spectrometer hardware,firmware,software design,and basic performance of the front-end readout chip SKIROC2A were tested.The readout circuit of each channel baseline ADC code was less than 17;thus,the channel consistency was good.The RMS noise of the channel baseline was only 7.1 mV and exhibited good stability.The maximum number of events that could be processed per second is 75.The overall power consumption was 3 W,the weight was 792 g,and the volume was less than 1 dm^(3).Furthermore,the neutron spectrometer was tested for principle and detection efficiency using various neutron sources,such as ^(241)Am-Be neutron source,2.5 MeV neutron beam,and 14 MeV neutron beam,and the experiments were analyzed with corresponding simulations.The experimental data and simulation results were in good agreement and met the design requirements.The intrinsic detection efficiency of the probes used in the neutron spectrometer was 1.05%for 14 MeV fast neutrons.展开更多
无监督异常检测因只需要正常样本进行训练而被广泛应用于工业质检等领域。直接将现有的单类别异常检测方法应用到多类别异常检测中会导致性能显著下降,其中基于知识蒸馏的异常检测方法将预训练的教师模型关于正常样本的特征知识蒸馏到...无监督异常检测因只需要正常样本进行训练而被广泛应用于工业质检等领域。直接将现有的单类别异常检测方法应用到多类别异常检测中会导致性能显著下降,其中基于知识蒸馏的异常检测方法将预训练的教师模型关于正常样本的特征知识蒸馏到学生模型中,然而它们在多类别异常检测中存在无法保证学生模型只学习到正常样本知识的问题。文中提出一种基于反向知识蒸馏框架的无监督多类别异常检测方法(Prototype based Reverse Distillation,PRD),其通过Multi-class Normal Prototype模块和Sparse Prototype Recall训练策略来学习教师模型关于多类别正常样本特征的Prototype,并以此来过滤学生模型的输入特征,从而确保学生模型只学习到教师模型关于正常样本的特征知识。PRD在多种工业异常检测数据集上性能均超越了现有的SOTA方法,定性、定量和消融实验验证了PRD整体框架和内部模块的有效性。展开更多
Radio frequency(RF)cavities for advanced storage rings,also known as diffraction-limited storage rings,are under development.To this end,a competitive and promising approach involves normal-conducting continuous wave ...Radio frequency(RF)cavities for advanced storage rings,also known as diffraction-limited storage rings,are under development.To this end,a competitive and promising approach involves normal-conducting continuous wave technology.The design and preliminary test of a 499.654 MHz RF cavity for the Wuhan Advanced Light Source(WALS)based on specific beam parameters were conducted at the SSRF.Multi-objective evolutionary algorithms have been utilized to optimize RF properties,such as the power loss and power density,resulting in better performance in the continuous wave mode.Further improvements were made to suppress multipacting effects in the working area.To operate stably with the beam,higher-order mode dampers were applied to better address the coupling bunch instability than in previous designs,along with thermal analysis to achieve the desired RF performance.Comprehensive simulation studies demonstrated the stable operation of the RF cavity at the defined beam parameters in the WALS design.A prototype RF cavity was then developed,and the RF performance results in a low-power test showed good agreement with the design and simulation,exhibiting readiness for high-power experiments and operation.展开更多
Based on the analysis of surface geological survey,exploratory well,gravity-magnetic-electric and seismic data,and through mapping the sedimentary basin and its peripheral orogenic belts together,this paper explores s...Based on the analysis of surface geological survey,exploratory well,gravity-magnetic-electric and seismic data,and through mapping the sedimentary basin and its peripheral orogenic belts together,this paper explores systematically the boundary,distribution,geological structure,and tectonic attributes of the Ordos prototype basin in the geological historical periods.The results show that the Ordos block is bounded to the west by the Engorwusu Fault Zone,to the east by the Taihangshan Mountain Piedmont Fault Zone,to the north by the Solonker-Xilamuron Suture Zone,and to the south by the Shangnan-Danfeng Suture Zone.The Ordos Basin boundary was the plate tectonic boundary during the Middle Proterozoic to Paleozoic,and the intra-continental deformation boundary in the Meso-Cenozoic.The basin survived as a marine cratonic basin covering the entire Ordos block during the Middle Proterozoic to Ordovician,a marine-continental transitional depression basin enclosed by an island arc uplift belt at the plate margin during the Carboniferous to Permian,a unified intra-continental lacustrine depression basin in the Triassic,and an intra-continental cratonic basin circled by a rift system in the Cenozoic.The basin scope has been decreasing till the present.The large,widespread prototype basin controlled the exploration area far beyond the present-day sedimentary basin boundary,with multiple target plays vertically.The Ordos Basin has the characteristics of a whole petroleum(or deposition)system.The Middle Proterozoic wide-rift system as a typical basin under the overlying Phanerozoic basin and the Cambrian-Ordovician passive margin basin and intra-cratonic depression in the deep-sited basin will be the important successions for oil and gas exploration in the coming years.展开更多
There are many traditional villages with well-preserved architectural types and images in the Jingmai Mountain,Yunnan Province.Through field investigations in traditional villages in the research area,this study appli...There are many traditional villages with well-preserved architectural types and images in the Jingmai Mountain,Yunnan Province.Through field investigations in traditional villages in the research area,this study applied the architectural typology,analyzed Nuogang Village of the Dai Nationality and Wengji Village of the Bulang Nationality from 3 perspectives of“point,line and surface”,explored the characteristics of village,architecture and landscape,and extracted the“prototypes”,tried to figure out the problems of the villages and then propose corresponding protection strategies,so as to support the preservation,renovation,improvement and utilization of traditional villages.展开更多
Few-shot image semantic segmentation aims to achieve pixel-level classification for novel classes using only a few labeled examples.The method first trains the segmentation model on base classes,and then adapts it to ...Few-shot image semantic segmentation aims to achieve pixel-level classification for novel classes using only a few labeled examples.The method first trains the segmentation model on base classes,and then adapts it to novel classes.Although existing methods have achieved remarkable performance in few-shot image semantic segmentation,they still face the following challenges.Traditional methods typically rely on mask average pooling to generate single-category prototype vectors and perform feature matching via metric learning,but they exhibit significant limitations in modeling inter-category relationships and addressing complex background interference.Inspired by the analogy-based transfer mechanisms in cognitive psychology,we propose a Generalized Prototype Network(GPNet)to enhance the model's generalization ability for unseen categories and improve robustness in feature matching.GPNet consists of two key modules.The first is a generalized prototype enhancement module,which explores potential inter-category relationships to construct more discriminative category prototype representations.The second is a multi-scale feature alignment module,which dynamically aligns support and query features across multiple scales using an attention mechanism,thus mitigating background interference in complex scenarios.Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches on several few-shot semantic segmentation benchmarks,validating its effectiveness and generalization capabilities.展开更多
Scientists have devoted considerable effort overs several decades to reduce automobile exhaust emissions,and one practical and important strategy is the catalytic conversion of nitric oxide(NO)[1].Previous studies hav...Scientists have devoted considerable effort overs several decades to reduce automobile exhaust emissions,and one practical and important strategy is the catalytic conversion of nitric oxide(NO)[1].Previous studies have shown that lanthanide(Ln)metals can catalytically reduce NO.Thus,the reactions of NO with Ln to form lanthanide-nitric oxide(LnNO)complexes have been designed and served as the simplest prototype molecules for studying NO chemisorption on metal surfaces[2].展开更多
Air quality estimation assesses the pollution level in the air,supports public health warnings,and is a valuable tool in environmental management.Although air sensors have proven helpful in this task,sensors are often...Air quality estimation assesses the pollution level in the air,supports public health warnings,and is a valuable tool in environmental management.Although air sensors have proven helpful in this task,sensors are often expensive and difficult to install,while cameras are becoming more popular and accessible,from which images can be collected as data for deep learning models to solve the above task.This leads to another problem:several labeled images are needed to achieve high accuracy when deep-learningmodels predict air quality.In this research,we have threemain contributions:(1)Collect and publish an air quality estimation dataset,namely PTIT_AQED,including environmental image data and air quality;(2)Propose a deep learning model to predict air quality with few data,called PTIT_FAQE(PTIT Few-shot air quality estimation).We build PTIT_FAQE based on EfficientNet-a CNN architecture that ensures high performance in deep learning applications and Few-shot Learning with Prototypical Networks.This helps the model use only a fewtraining data but still achieve high accuracy in air quality estimation.And(3)conduct experiments to prove the superiority of PTIT_FAQE compared to other studies on both PTIT_AQED and APIN datasets.The results show that our model achieves an accuracy of 0.9278 and an F1-Score of 0.9139 on the PTIT_AQED dataset and an accuracy of 0.9467 and an F1-Score of 0.9371 on the APIN dataset,which demonstrate a significant performance improvement compared to previous studies.We also conduct detailed experiments to evaluate the impact of each component on model performance.展开更多
Aircraft have received much attention because of their capability to adapt to various flight environments and complex missions.The nose cone is one of the key elements in optimising the aerodynamic shape of aircraft.A...Aircraft have received much attention because of their capability to adapt to various flight environments and complex missions.The nose cone is one of the key elements in optimising the aerodynamic shape of aircraft.A morphing nose cone(MNC)driven by a biomimetic 4-3R1U&3R sparallel mechanism is proposed in this study.Based on screw theory,the parallel mechanism’s configuration is determined,and the structure’s full-cycle degrees of freedom are concurrently confirmed.Examples in the paper demonstrate the viability of the structure by configuration synthesis,and diagrams also show the chains.This MNC is modelled after the structural design of the cicada’s abdomen and can be extended,contracted and bent.It can actively adjust its shape in response to change in the flight environments,thereby aerodynamic performance and enhancing the aircraft’s multi-mission capabilities.A scaled-down prototype is created to verify the deformation capacity of the MNC meeting the engineering requirements.Results show that the extension ratio is 36.7%,and the bending angle is 21.7°,which is better than expected.The relative error value is within a reasonable range and the extension process is incredibly stable.This research proposes new perspectives for the design of MNCs.展开更多
Since the introduction of vision Transformers into the computer vision field,many vision tasks such as semantic segmentation tasks,have undergone radical changes.Although Transformer enhances the correlation of each l...Since the introduction of vision Transformers into the computer vision field,many vision tasks such as semantic segmentation tasks,have undergone radical changes.Although Transformer enhances the correlation of each local feature of an image object in the hidden space through the attention mechanism,it is difficult for a segmentation head to accomplish the mask prediction for dense embedding of multi-category and multi-local features.We present patch prototype vision Transformer(PPFormer),a Transformer architecture for semantic segmentation based on knowledge-embedded patch prototypes.1)The hierarchical Transformer encoder can generate multi-scale and multi-layered patch features including seamless patch projection to obtain information of multiscale patches,and feature-clustered self-attention to enhance the interplay of multi-layered visual information with implicit position encodes.2)PPFormer utilizes a non-parametric prototype decoder to extract region observations which represent significant parts of the objects by unlearnable patch prototypes and then calculate similarity between patch prototypes and pixel embeddings.The proposed contrasting patch prototype alignment module,which uses new patch prototypes to update prototype bank,effectively maintains class boundaries for prototypes.For different application scenarios,we have launched PPFormer-S,PPFormer-M and PPFormer-L by expanding the scale.Experimental results demonstrate that PPFormer can outperform fully convolutional networks(FCN)-and attention-based semantic segmentation models on the PASCAL VOC 2012,ADE20k,and Cityscapes datasets.展开更多
A team of researchers from the University of Science and Technology of China(USTC)of the Chinese Academy of Sciences(CAS)and its partners have made significant advancements in random quantum circuit sampling with Zuch...A team of researchers from the University of Science and Technology of China(USTC)of the Chinese Academy of Sciences(CAS)and its partners have made significant advancements in random quantum circuit sampling with Zuchongzhi-3,a superconducting quantum computing prototype featuring 105 qubits and 182 couplers.展开更多
To enable simultaneous transmit and receive(STAR)on the same frequency in a densely deployed space with multi-interference sources,this work proposes a digitally-assisted analog selfinterference cancellation method,wh...To enable simultaneous transmit and receive(STAR)on the same frequency in a densely deployed space with multi-interference sources,this work proposes a digitally-assisted analog selfinterference cancellation method,which can acquire reference signals through flexible wired/wireless switching access.Based on this method,the Minimum Mean Square Error algorithm with known channel state information is derived in detail,determining the upper limit of the cancellation performance,and the Adaptive Dithered Linear Search algorithm for real-time engineering cancellation is given.The correctness of theoretical analysis is verified by the practical self-interference channel measured by a vector network analyzer.Furthermore,we have designed and implemented the corresponding multiinterference cancellation prototype with the digitallyassisted structure,capable of handling multiple interferences(up to three)and supporting a large receive bandwidth of 100 MHz as well as a wide frequency coverage from 30 MHz to 3000 MHz.Prototype test results demonstrate that in the presence of three interferences,when the single interference bandwidth is 0.2/2/20 MHz(corresponding to the receive bandwidth of 2/20/100 MHz),the cancellation performance can reach 46/32/22 dB or more.展开更多
Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are st...Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data patterns.We propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and merging.On behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of seeds.Different from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of patterns.To get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes possible.Inspired by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data samples.Experimental results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.展开更多
Using the latest global datasets of hydrocarbon fields and reservoirs,this study systematically investigates the characteristics of differential hydrocarbon enrichment and its primary controlling factors in the southe...Using the latest global datasets of hydrocarbon fields and reservoirs,this study systematically investigates the characteristics of differential hydrocarbon enrichment and its primary controlling factors in the southern Tethys Domain within the context of Tethys tectonic evolution.The results indicate that although the southern Tethys Domain comprises only one-third of the Tethys Domain in areal extent,it hosts nearly 80%of its total hydrocarbon reserves,exhibiting a markedly uneven distribution pattern.Specifically,the Middle East sub-segment is identified as the core enrichment area,with the Arabian Basin serving as a typical example.Through tectonic subdivision,classification of sedimentary basins,analysis of source rock distribution and reservoir-seal assemblages,as well as an integrated investigation of the relationship between succeeding paleo-uplifts and hydrocarbon enrichment,the study demonstrates that the superimposition patterns of prototype basins,the scale and distribution of source rocks,the effectiveness of reservoir-seal assemblages,and the basement paleo-uplifts are the key factors governing hydrocarbon enrichment in the southern Tethys Domain.The findings of this study provide valuable references for deeper understanding of hydrocarbon accumulation patterns in the central and northern Tethys Domain and even other global regions with similar geological settings,and offer a scientific basis for selection of favorable play fairways in the southern Tethys Domain.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
Time series with missing values are ubiquitous in healthcare scenarios,presenting significant challenges for analysis.Despite existing methods addressing imputation,they predominantly focus on leveraging intra-series ...Time series with missing values are ubiquitous in healthcare scenarios,presenting significant challenges for analysis.Despite existing methods addressing imputation,they predominantly focus on leveraging intra-series information,neglecting the potential benefits that inter-series information could provide,such as reducing uncertainty and memorization effect.To bridge this gap,we propose PRIME,Prototype Recurrent Imputation ModEl,which integrates both intra-series and inter-series information for imputing missing values in irregularly sampled time series.PRIME comprises a prototype memory module for learning inter-series information,a bidirectional gated recurrent unit utilizing prototype information for imputation,and an attentive prototypical refinement module for adjusting imputations.We conduct extensive experiments on four datasets,and the results underscore PRIME’s superiority over the state-of-the-art models by up to 26%relative improvement in mean square error.Our code is available at https://jcst.ict.ac.cn/news/382.展开更多
Research on reconstructing imperfect faces is a challenging task.In this study,we explore a data-driven approach using a pre-trained MICA(MetrIC fAce)model combined with 3D printing to address this challenge.We propos...Research on reconstructing imperfect faces is a challenging task.In this study,we explore a data-driven approach using a pre-trained MICA(MetrIC fAce)model combined with 3D printing to address this challenge.We propose a training strategy that utilizes the pre-trained MICA model and self-supervised learning techniques to improve accuracy and reduce the time needed for 3D facial structure reconstruction.Our results demonstrate high accuracy,evaluated by the geometric loss function and various statistical measures.To showcase the effectiveness of the approach,we used 3D printing to create a model that covers facial wounds.The findings indicate that our method produces a model that fits well and achieves comprehensive 3D facial reconstruction.This technique has the potential to aid doctors in treating patients with facial injuries.展开更多
文摘This paper introduces a new approach dedicated to the Ontology Personalization. Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive relevance of the categorization process, that is the one which is really perceived by the end-user. Practically, this process consists in decorating the Specialization/Generalization links (i.e. the is-a links) of the hierarchy of concepts with 2 gradients. The goal of the first gradient, called Conceptual Prototypicality Gradient, is to capture the user-sensitive relevance of the categorization process, that is the one which is perceived by the end-user. As this gradient is defined according to the three aspects of the semiotic triangle (i.e. intentional, extensional and expressional dimension), we call it Semiotic based Prototypicality Gradient. The objective of the second gradient, called Lexical Prototypicality Gradient, is to capture the user-sensitive relevance of the lexicalization process, i.e. the definition of a set of terms used to denote a concept. These gradients enrich the initial formal semantics of an ontology by adding a pragmatics defined according to a context of use which depends on parameters like culture, educational background and/or emotional context of the end-user. This paper also introduces a new similarity measure also defined in the context of a semiotic-based approach. The first originality of this measure, called SEMIOSEM, is to consider the three semiotic dimensions of the conceptualization underlying an ontology. Thus, SEMIOSEM aims at aggregating and improving existing extensional-based and intentional-based measures. The second originality of this measure is to be context-sensitive, and in particular user-sensitive. This makes SEMIOSEM more flexible, more robust and more close to the end-user’s judgment than the other similarity measures which are usually only based on one aspect of a conceptualization and never take the end-user’s perceptions and purposes into account.
基金supported by the National Natural Science Foundation of China(NSFC)(Nos.42225405 and U2106202)。
文摘In recent years,there have been fewer missions to detect neutrons in low Earth orbits(LEO),and the data obtained have been extremely limited.Studying the distribution of the neutron energy spectrum in LEO satellites through detection can help solve three major scientific problems:the source of particles in the inner radiation belt,information on solar-accelerated particles,and the proportion of neutrons from different sources in near-Earth space.The detection efficiency and accuracy of neutrons are affected by charged and primary particles in the environment and secondary neutrons produced by the spacecraft itself,which has been a hot research topic.The neutron spectrometer developed in this study adopts two combinations of 15 silicon detectors in terms of detector type and arrangement,which are used for neutron detection via the nuclear reaction method and recoil proton method,respectively,in which a 27μm-thick^(6)LiF conversion layer is used for thermal neutron detection up to 0.4 eV and a 300μm-thick high-density polyethylene conversion layer is used for fast-neutron detection up to 14 MeV and below.The design of the detector set can also remove the influence of primary charged particles and secondary neutrons in the detection environment to a certain extent,thereby improving the accuracy of neutron detection.In this study,the neutron spectrometer hardware,firmware,software design,and basic performance of the front-end readout chip SKIROC2A were tested.The readout circuit of each channel baseline ADC code was less than 17;thus,the channel consistency was good.The RMS noise of the channel baseline was only 7.1 mV and exhibited good stability.The maximum number of events that could be processed per second is 75.The overall power consumption was 3 W,the weight was 792 g,and the volume was less than 1 dm^(3).Furthermore,the neutron spectrometer was tested for principle and detection efficiency using various neutron sources,such as ^(241)Am-Be neutron source,2.5 MeV neutron beam,and 14 MeV neutron beam,and the experiments were analyzed with corresponding simulations.The experimental data and simulation results were in good agreement and met the design requirements.The intrinsic detection efficiency of the probes used in the neutron spectrometer was 1.05%for 14 MeV fast neutrons.
文摘无监督异常检测因只需要正常样本进行训练而被广泛应用于工业质检等领域。直接将现有的单类别异常检测方法应用到多类别异常检测中会导致性能显著下降,其中基于知识蒸馏的异常检测方法将预训练的教师模型关于正常样本的特征知识蒸馏到学生模型中,然而它们在多类别异常检测中存在无法保证学生模型只学习到正常样本知识的问题。文中提出一种基于反向知识蒸馏框架的无监督多类别异常检测方法(Prototype based Reverse Distillation,PRD),其通过Multi-class Normal Prototype模块和Sparse Prototype Recall训练策略来学习教师模型关于多类别正常样本特征的Prototype,并以此来过滤学生模型的输入特征,从而确保学生模型只学习到教师模型关于正常样本的特征知识。PRD在多种工业异常检测数据集上性能均超越了现有的SOTA方法,定性、定量和消融实验验证了PRD整体框架和内部模块的有效性。
基金supported by National Natural Science Foundation of China(Nos.12222513,12105345,12175292,and No.12405178)。
文摘Radio frequency(RF)cavities for advanced storage rings,also known as diffraction-limited storage rings,are under development.To this end,a competitive and promising approach involves normal-conducting continuous wave technology.The design and preliminary test of a 499.654 MHz RF cavity for the Wuhan Advanced Light Source(WALS)based on specific beam parameters were conducted at the SSRF.Multi-objective evolutionary algorithms have been utilized to optimize RF properties,such as the power loss and power density,resulting in better performance in the continuous wave mode.Further improvements were made to suppress multipacting effects in the working area.To operate stably with the beam,higher-order mode dampers were applied to better address the coupling bunch instability than in previous designs,along with thermal analysis to achieve the desired RF performance.Comprehensive simulation studies demonstrated the stable operation of the RF cavity at the defined beam parameters in the WALS design.A prototype RF cavity was then developed,and the RF performance results in a low-power test showed good agreement with the design and simulation,exhibiting readiness for high-power experiments and operation.
基金Supported by the National Natural Science Foundation of China(42330810)Major Science and Technology Project of PetroChina Changqing Oilfield Company(ZDZX2021-01).
文摘Based on the analysis of surface geological survey,exploratory well,gravity-magnetic-electric and seismic data,and through mapping the sedimentary basin and its peripheral orogenic belts together,this paper explores systematically the boundary,distribution,geological structure,and tectonic attributes of the Ordos prototype basin in the geological historical periods.The results show that the Ordos block is bounded to the west by the Engorwusu Fault Zone,to the east by the Taihangshan Mountain Piedmont Fault Zone,to the north by the Solonker-Xilamuron Suture Zone,and to the south by the Shangnan-Danfeng Suture Zone.The Ordos Basin boundary was the plate tectonic boundary during the Middle Proterozoic to Paleozoic,and the intra-continental deformation boundary in the Meso-Cenozoic.The basin survived as a marine cratonic basin covering the entire Ordos block during the Middle Proterozoic to Ordovician,a marine-continental transitional depression basin enclosed by an island arc uplift belt at the plate margin during the Carboniferous to Permian,a unified intra-continental lacustrine depression basin in the Triassic,and an intra-continental cratonic basin circled by a rift system in the Cenozoic.The basin scope has been decreasing till the present.The large,widespread prototype basin controlled the exploration area far beyond the present-day sedimentary basin boundary,with multiple target plays vertically.The Ordos Basin has the characteristics of a whole petroleum(or deposition)system.The Middle Proterozoic wide-rift system as a typical basin under the overlying Phanerozoic basin and the Cambrian-Ordovician passive margin basin and intra-cratonic depression in the deep-sited basin will be the important successions for oil and gas exploration in the coming years.
文摘There are many traditional villages with well-preserved architectural types and images in the Jingmai Mountain,Yunnan Province.Through field investigations in traditional villages in the research area,this study applied the architectural typology,analyzed Nuogang Village of the Dai Nationality and Wengji Village of the Bulang Nationality from 3 perspectives of“point,line and surface”,explored the characteristics of village,architecture and landscape,and extracted the“prototypes”,tried to figure out the problems of the villages and then propose corresponding protection strategies,so as to support the preservation,renovation,improvement and utilization of traditional villages.
文摘Few-shot image semantic segmentation aims to achieve pixel-level classification for novel classes using only a few labeled examples.The method first trains the segmentation model on base classes,and then adapts it to novel classes.Although existing methods have achieved remarkable performance in few-shot image semantic segmentation,they still face the following challenges.Traditional methods typically rely on mask average pooling to generate single-category prototype vectors and perform feature matching via metric learning,but they exhibit significant limitations in modeling inter-category relationships and addressing complex background interference.Inspired by the analogy-based transfer mechanisms in cognitive psychology,we propose a Generalized Prototype Network(GPNet)to enhance the model's generalization ability for unseen categories and improve robustness in feature matching.GPNet consists of two key modules.The first is a generalized prototype enhancement module,which explores potential inter-category relationships to construct more discriminative category prototype representations.The second is a multi-scale feature alignment module,which dynamically aligns support and query features across multiple scales using an attention mechanism,thus mitigating background interference in complex scenarios.Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches on several few-shot semantic segmentation benchmarks,validating its effectiveness and generalization capabilities.
基金the National Key Research and Development Program of China(No.2021YFB3501501)the National Natural Science Foundation of China(No.22276013)the Beijing Natural Science Foundation(No.2242009)for financial support,and thank Tianhe2-JK HPC for generous computer time.
文摘Scientists have devoted considerable effort overs several decades to reduce automobile exhaust emissions,and one practical and important strategy is the catalytic conversion of nitric oxide(NO)[1].Previous studies have shown that lanthanide(Ln)metals can catalytically reduce NO.Thus,the reactions of NO with Ln to form lanthanide-nitric oxide(LnNO)complexes have been designed and served as the simplest prototype molecules for studying NO chemisorption on metal surfaces[2].
文摘Air quality estimation assesses the pollution level in the air,supports public health warnings,and is a valuable tool in environmental management.Although air sensors have proven helpful in this task,sensors are often expensive and difficult to install,while cameras are becoming more popular and accessible,from which images can be collected as data for deep learning models to solve the above task.This leads to another problem:several labeled images are needed to achieve high accuracy when deep-learningmodels predict air quality.In this research,we have threemain contributions:(1)Collect and publish an air quality estimation dataset,namely PTIT_AQED,including environmental image data and air quality;(2)Propose a deep learning model to predict air quality with few data,called PTIT_FAQE(PTIT Few-shot air quality estimation).We build PTIT_FAQE based on EfficientNet-a CNN architecture that ensures high performance in deep learning applications and Few-shot Learning with Prototypical Networks.This helps the model use only a fewtraining data but still achieve high accuracy in air quality estimation.And(3)conduct experiments to prove the superiority of PTIT_FAQE compared to other studies on both PTIT_AQED and APIN datasets.The results show that our model achieves an accuracy of 0.9278 and an F1-Score of 0.9139 on the PTIT_AQED dataset and an accuracy of 0.9467 and an F1-Score of 0.9371 on the APIN dataset,which demonstrate a significant performance improvement compared to previous studies.We also conduct detailed experiments to evaluate the impact of each component on model performance.
基金Supported by Hebei Provincial Natural Science Foundation(Grant Nos.E2024203052,E2024203105)National Natural Science Foundation of China(Grant No.52375028)Science and Technology Project of Hebei Education Department(Grant No.QN2023206).
文摘Aircraft have received much attention because of their capability to adapt to various flight environments and complex missions.The nose cone is one of the key elements in optimising the aerodynamic shape of aircraft.A morphing nose cone(MNC)driven by a biomimetic 4-3R1U&3R sparallel mechanism is proposed in this study.Based on screw theory,the parallel mechanism’s configuration is determined,and the structure’s full-cycle degrees of freedom are concurrently confirmed.Examples in the paper demonstrate the viability of the structure by configuration synthesis,and diagrams also show the chains.This MNC is modelled after the structural design of the cicada’s abdomen and can be extended,contracted and bent.It can actively adjust its shape in response to change in the flight environments,thereby aerodynamic performance and enhancing the aircraft’s multi-mission capabilities.A scaled-down prototype is created to verify the deformation capacity of the MNC meeting the engineering requirements.Results show that the extension ratio is 36.7%,and the bending angle is 21.7°,which is better than expected.The relative error value is within a reasonable range and the extension process is incredibly stable.This research proposes new perspectives for the design of MNCs.
基金supported in part by the Gansu Haizhi Characteristic Demonstration Project(No.GSHZTS2022-2).
文摘Since the introduction of vision Transformers into the computer vision field,many vision tasks such as semantic segmentation tasks,have undergone radical changes.Although Transformer enhances the correlation of each local feature of an image object in the hidden space through the attention mechanism,it is difficult for a segmentation head to accomplish the mask prediction for dense embedding of multi-category and multi-local features.We present patch prototype vision Transformer(PPFormer),a Transformer architecture for semantic segmentation based on knowledge-embedded patch prototypes.1)The hierarchical Transformer encoder can generate multi-scale and multi-layered patch features including seamless patch projection to obtain information of multiscale patches,and feature-clustered self-attention to enhance the interplay of multi-layered visual information with implicit position encodes.2)PPFormer utilizes a non-parametric prototype decoder to extract region observations which represent significant parts of the objects by unlearnable patch prototypes and then calculate similarity between patch prototypes and pixel embeddings.The proposed contrasting patch prototype alignment module,which uses new patch prototypes to update prototype bank,effectively maintains class boundaries for prototypes.For different application scenarios,we have launched PPFormer-S,PPFormer-M and PPFormer-L by expanding the scale.Experimental results demonstrate that PPFormer can outperform fully convolutional networks(FCN)-and attention-based semantic segmentation models on the PASCAL VOC 2012,ADE20k,and Cityscapes datasets.
文摘A team of researchers from the University of Science and Technology of China(USTC)of the Chinese Academy of Sciences(CAS)and its partners have made significant advancements in random quantum circuit sampling with Zuchongzhi-3,a superconducting quantum computing prototype featuring 105 qubits and 182 couplers.
基金supported in part by the National Natural Science Foundation of China under Grant 62071094in part by the National Key Laboratory of Wireless Communications Foundation under Grant IFN202402in part by the Postdoctoral Fellowship Program(Grade C)of China Postdoctoral Science Foundation under Grant GZC20240217.
文摘To enable simultaneous transmit and receive(STAR)on the same frequency in a densely deployed space with multi-interference sources,this work proposes a digitally-assisted analog selfinterference cancellation method,which can acquire reference signals through flexible wired/wireless switching access.Based on this method,the Minimum Mean Square Error algorithm with known channel state information is derived in detail,determining the upper limit of the cancellation performance,and the Adaptive Dithered Linear Search algorithm for real-time engineering cancellation is given.The correctness of theoretical analysis is verified by the practical self-interference channel measured by a vector network analyzer.Furthermore,we have designed and implemented the corresponding multiinterference cancellation prototype with the digitallyassisted structure,capable of handling multiple interferences(up to three)and supporting a large receive bandwidth of 100 MHz as well as a wide frequency coverage from 30 MHz to 3000 MHz.Prototype test results demonstrate that in the presence of three interferences,when the single interference bandwidth is 0.2/2/20 MHz(corresponding to the receive bandwidth of 2/20/100 MHz),the cancellation performance can reach 46/32/22 dB or more.
基金supported by the National Natural Science Foundation of China under Grant No.62162009the Key Technologies R&D Program of He’nan Province under Grant No.242102211065+1 种基金the Scientific Research Innovation Team of Xuchang University under GrantNo.2022CXTD003Postgraduate Education Reform and Quality Improvement Project of Henan Province under Grant No.YJS2024JD38.
文摘Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data patterns.We propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and merging.On behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of seeds.Different from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of patterns.To get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes possible.Inspired by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data samples.Experimental results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.
基金Supported by National Natural Science Foundation of China(92255302,91755104)Fundamental Research Cooperation Project under the Strategic Cooperation Plan between PetroChina and Peaking University(23-2-1).
文摘Using the latest global datasets of hydrocarbon fields and reservoirs,this study systematically investigates the characteristics of differential hydrocarbon enrichment and its primary controlling factors in the southern Tethys Domain within the context of Tethys tectonic evolution.The results indicate that although the southern Tethys Domain comprises only one-third of the Tethys Domain in areal extent,it hosts nearly 80%of its total hydrocarbon reserves,exhibiting a markedly uneven distribution pattern.Specifically,the Middle East sub-segment is identified as the core enrichment area,with the Arabian Basin serving as a typical example.Through tectonic subdivision,classification of sedimentary basins,analysis of source rock distribution and reservoir-seal assemblages,as well as an integrated investigation of the relationship between succeeding paleo-uplifts and hydrocarbon enrichment,the study demonstrates that the superimposition patterns of prototype basins,the scale and distribution of source rocks,the effectiveness of reservoir-seal assemblages,and the basement paleo-uplifts are the key factors governing hydrocarbon enrichment in the southern Tethys Domain.The findings of this study provide valuable references for deeper understanding of hydrocarbon accumulation patterns in the central and northern Tethys Domain and even other global regions with similar geological settings,and offer a scientific basis for selection of favorable play fairways in the southern Tethys Domain.
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 under Grant Nos.62402017 and U23A20468the Beijing Natural Science Foundation under Grant Nos.L244063 and L244025+2 种基金the Peking University Clinical Medicine Plus X(Young Scholars Project-the Fundamental Research Funds for the Central Universities of China under Grant No.PKU2025PKULCXQ024the Pilot Program-Key Technologies Project under Grant No.2024YXXLHGG007)the Xuzhou Scientific Technological Projects under Grant No.KC23143.
文摘Time series with missing values are ubiquitous in healthcare scenarios,presenting significant challenges for analysis.Despite existing methods addressing imputation,they predominantly focus on leveraging intra-series information,neglecting the potential benefits that inter-series information could provide,such as reducing uncertainty and memorization effect.To bridge this gap,we propose PRIME,Prototype Recurrent Imputation ModEl,which integrates both intra-series and inter-series information for imputing missing values in irregularly sampled time series.PRIME comprises a prototype memory module for learning inter-series information,a bidirectional gated recurrent unit utilizing prototype information for imputation,and an attentive prototypical refinement module for adjusting imputations.We conduct extensive experiments on four datasets,and the results underscore PRIME’s superiority over the state-of-the-art models by up to 26%relative improvement in mean square error.Our code is available at https://jcst.ict.ac.cn/news/382.
文摘Research on reconstructing imperfect faces is a challenging task.In this study,we explore a data-driven approach using a pre-trained MICA(MetrIC fAce)model combined with 3D printing to address this challenge.We propose a training strategy that utilizes the pre-trained MICA model and self-supervised learning techniques to improve accuracy and reduce the time needed for 3D facial structure reconstruction.Our results demonstrate high accuracy,evaluated by the geometric loss function and various statistical measures.To showcase the effectiveness of the approach,we used 3D printing to create a model that covers facial wounds.The findings indicate that our method produces a model that fits well and achieves comprehensive 3D facial reconstruction.This technique has the potential to aid doctors in treating patients with facial injuries.
