The phenomenon of fear memory generalization can be defined as the expansion of an individual's originally specific fear responses to a similar yet genuinely harmless stimulus or situation subsequent to the occurr...The phenomenon of fear memory generalization can be defined as the expansion of an individual's originally specific fear responses to a similar yet genuinely harmless stimulus or situation subsequent to the occurrence of a traumatic event[1].Fear generalization within the normal range represents an adaptive evolutionary mechanism to facilitate prompt reactions to potential threats and to enhance the likelihood of survival.展开更多
Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN model...Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.展开更多
Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the rea...Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.展开更多
The challenge of enhancing the generalization capacity of reinforcement learning(RL)agents remains a formidable obstacle.Existing RL methods,despite achieving superhuman performance on certain benchmarks,often struggl...The challenge of enhancing the generalization capacity of reinforcement learning(RL)agents remains a formidable obstacle.Existing RL methods,despite achieving superhuman performance on certain benchmarks,often struggle with this aspect.A potential reason is that the benchmarks used for training and evaluation may not adequately offer a diverse set of transferable tasks.Although recent studies have developed bench-marking environments to address this shortcoming,they typically fall short in providing tasks that both ensure a solid foundation for generalization and exhibit significant variability.To overcome these limitations,this work introduces the concept that‘objects are composed of more fundamental components’in environment design,as implemented in the proposed environment called summon the magic(StM).This environment generates tasks where objects are derived from extensible and shareable basic components,facilitating strategy reuse and enhancing generalization.Furthermore,two new metrics,adaptation sensitivity range(ASR)and parameter correlation coefficient(PCC),are proposed to better capture and evaluate the generalization process of RL agents.Experimental results show that increasing the number of basic components of the object reduces the proximal policy optimization(PPO)agent’s training-testing gap by 60.9%(in episode reward),significantly alleviating overfitting.Additionally,linear variations in other environmental factors,such as the training monster set proportion and the total number of basic components,uniformly decrease the gap by at least 32.1%.These results highlight StM’s effectiveness in benchmarking and probing the generalization capabilities of RL algorithms.展开更多
In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when fa...In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when faced with testing scenarios from unknown domains.To address this problem,this paper proposes a novel semi-supervised approach for cardiac magnetic resonance image segmentation,aiming to enhance predictive capabilities and domain generalization(DG).This paper establishes an MT-like model utilizing pseudo-labeling and consistency regularization from semi-supervised learning,and integrates uncertainty estimation to improve the accuracy of pseudo-labels.Additionally,to tackle the challenge of domain generalization,a data manipulation strategy is introduced,extracting spatial and content-related information from images across different domains,enriching the dataset with a multi-domain perspective.This papers method is meticulously evaluated on the publicly available cardiac magnetic resonance imaging dataset M&Ms,validating its effectiveness.Comparative analyses against various methods highlight the out-standing performance of this papers approach,demonstrating its capability to segment cardiac magnetic resonance images in previously unseen domains even with limited annotated data.展开更多
This paper analyzes the generalization of minimax regret optimization(MRO)under distribution shift.A new learning framework is proposed by injecting the measure of con-ditional value at risk(CVaR)into MRO,and its gene...This paper analyzes the generalization of minimax regret optimization(MRO)under distribution shift.A new learning framework is proposed by injecting the measure of con-ditional value at risk(CVaR)into MRO,and its generalization error bound is established through the lens of uniform convergence analysis.The CVaR-based MRO can achieve the polynomial decay rate on the excess risk,which extends the generalization analysis associated with the expected risk to the risk-averse case.展开更多
The interpretation of representations and generalization powers has been a long-standing challenge in the fields of machine learning(ML)and artificial intelligence.This study contributes to understanding the emergence...The interpretation of representations and generalization powers has been a long-standing challenge in the fields of machine learning(ML)and artificial intelligence.This study contributes to understanding the emergence of universal scaling laws in quantum-probabilistic ML.We consider the generative tensor network(GTN)in the form of a matrix-product state as an example and show that with an untrained GTN(such as a random TN state),the negative logarithmic likelihood(NLL)L generally increases linearly with the number of features M,that is,L≃kM+const.This is a consequence of the so-called“catastrophe of orthogonality,”which states that quantum many-body states tend to become exponentially orthogonal to each other as M increases.This study reveals that,while gaining information through training,the linear-scaling law is suppressed by a negative quadratic correction,leading to L≃βM−αM^(2)+const.The scaling coefficients exhibit logarithmic relationships with the number of training samples and quantum channelsχ.The emergence of a quadratic correction term in the NLL for the testing(training)set can be regarded as evidence of the generalization(representation)power of the GTN.Over-parameterization can be identified by the deviation in the values ofαbetween the training and testing sets while increasingχ.We further investigate how orthogonality in the quantum-feature map relates to the satisfaction of quantum-probabilistic interpretation and the representation and generalization powers of the GTN.Unveiling universal scaling laws in quantum-probabilistic ML would be a valuable step toward establishing a white-box ML scheme interpreted within the quantum-probabilistic framework.展开更多
Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take ca...Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.展开更多
This paper puts forword 11 cartographic generalization operator models and introduces their mathematical definitions,and thus a precise mathematical form and quantitative description has been given to these formerly l...This paper puts forword 11 cartographic generalization operator models and introduces their mathematical definitions,and thus a precise mathematical form and quantitative description has been given to these formerly limited qualitative concepts.The meaning of mathematical definition of operators for cartographic generalization and the application prospect in computer_aided cartography (CAC) is stated.ract The Jurassic strata in Jingyan of Sichuan containing the Mamenchinsaurus fauna are dealt with and divided in this paper. The Mamenchisaurus fossils contained there are compared in morphological features and stratigraphically with other types of the genus on by one. The comprehensive analysis show that the Mamenchisaurus fauna of Jingyan appeared in the early Late Jurassic and is primitive in morphology. The results of the morphological identification and stratigraphical study agree with each other. Their evolutionary processes in different apoches of the Late Jurassic also made clear. Key words Jingyan, Sichuan, Mamenchisaurus Fauna, stratigraphy, evolution展开更多
Generalization ability is a major problem encountered when using neural networks to find the structures in noisy data sets. Controlling the network complexity is a common method to solve this problem. In this paper, h...Generalization ability is a major problem encountered when using neural networks to find the structures in noisy data sets. Controlling the network complexity is a common method to solve this problem. In this paper, however, a novel additive penalty term which represents the features extracted by hidden units is introduced to eliminate the overtraining of multilayer feedfoward networks. Computer simulations demonstrate that by using this unsupervised fashion penalty term, the generalization ability is greatly improved.展开更多
This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that th...This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that the outputs of the output layer in the FNNs for classification correspond to the estimates of posteriori probability of the input pattern samples with desired outputs 1 or 0. The theorem for the generalized kernel function in the radial basis function networks (RBFN) is given. For an 2-layer perceptron network (2-LPN). an idea of using extended samples to improve generalization capability is proposed. Finally. the experimental results of radar target classification are given to verify the generaliztion capability of the RBFNs.展开更多
Numerous researches have been published on the application of landslide susceptibility assessment models;however,they were only applied in the same areas as the models were originated,the effect of applying the models...Numerous researches have been published on the application of landslide susceptibility assessment models;however,they were only applied in the same areas as the models were originated,the effect of applying the models to other areas than the origin of the models has not been explored.This study is purposed to develop an optimized random forest(RF)model with best ratios of positive-to-negative cells and 10-fold cross-validation for landslide susceptibility mapping(LSM),and then explore its generalization ability not only in the area where the model is originated but also in area other than the origin of the model.Two typical counties(Fengjie County and Wushan County)in the Three Gorges Reservoir area,China,which have the same terrain and geological conditions,were selected as an example.To begin with,landslide inventory was prepared based on field investigations,satellite images,and historical records,and 1522 landslides were then identified in Fengjie County.22 landslide-conditioning factors under the influence of topography,geology,environmental conditions,and human activities were prepared.Then,combined with 10-fold cross-validation,three typical ratios of positive-to-negative cells,i.e.,1:1,1:5,and 1:10,were adopted for comparative analyses.An optimized RF model(Fengjie-based model)with the best ratios of positive-to-negative cells and 10-fold cross-validation was constructed.Finally,the Fengjie-based model was applied to Fengjie County and Wushan County,and the confusion matrix and area under the receiver operating characteristic(ROC)curve value(AUC)were used to estimate the accuracy.The Fengjie-based model delivered high stability and predictive capability in Fengjie County,indicating a great generalization ability of the model to the area where the model is originated.The LSM in Wushan County generated by the Fengjie-based model had a reasonable reference value,indicating the Fengjiebased model had a great generalization ability in area other than the origin of the model.The Fengjiebased model in this study could be applied in other similar areas/countries with the same terrain and geological conditions,and a LSM may be generated without collecting landslide information for modeling,so as to reduce workload and improve efficiency in practice.展开更多
There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods...There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.展开更多
Wireless communication involving unmanned aerial vehicles(UAVs)is expected to play an important role in future wireless networks.However,different from conventional terrestrial communication systems,UAVs typically hav...Wireless communication involving unmanned aerial vehicles(UAVs)is expected to play an important role in future wireless networks.However,different from conventional terrestrial communication systems,UAVs typically have rather limited onboard energy on one hand,and require additional flying energy consumption on the other hand.This renders energy-efficient UAV communication with smart energy expenditure of paramount importance.In this paper,via extensive flight experiments,we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs,and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging,if not impossible.Specifically,we first investigate how UAV power consumption varies with its flying speed for the simplest straight-and-level flight.With about 12,000 valid power-speed data points collected,we first apply the model-based curve fitting to obtain the modelling parameters based on the theoretical closed-form energy model in the existing literature.In addition,in order to exclude the potential bias caused by the theoretical energy model,the obtained measurement data is also trained using a model-free deep neural network.It is found that the obtained curve from both methods can match quite well with the theoretical energy model.Next,we further extend the study to arbitrary 2-dimensional(2-D)flight,where,to our best knowledge,no rigorous theoretical derivation is available for the closed-form energy model as a function of its flying speed,direction,and acceleration.To fill the gap,we first propose a heuristic energy model for these more complicated cases,and then provide experimental validation based on the measurement results for circular level flight.展开更多
Complicated changes occur inside the steel parts during quenching process. A three dimensional nonlinear mathematical model for quenching process has been established and the numerical simulation on temperature field,...Complicated changes occur inside the steel parts during quenching process. A three dimensional nonlinear mathematical model for quenching process has been established and the numerical simulation on temperature field, microstructure and stress field has been realized. The alternative technique for the formation of high-strength materials has been developed on the basis of intensification of heat transfer at phase transformations. The technology for the achievement of maximum compressive residual stresses on the hard surface is introduced. It has been shown that there is an optimal depth of hard layer providing the maximum compression stresses on the surface. It has also been established that in the surface hard layer additional strengthening (superstrengthening) of the material is observed. The generalized formula for the determination of the time of reaching maximum compressive stresses on the surface has been proposed.展开更多
With the construction of spatial data infi＇astructure, automated topographic map generalization becomes an indispensable component in the community of cartography and geographic information science. This paper descri...With the construction of spatial data infi＇astructure, automated topographic map generalization becomes an indispensable component in the community of cartography and geographic information science. This paper describes a topographic map generalization system recently developed by the authors. The system has the following characteristics： 1） taking advantage of three levels of automation, i.e. fully automated generalization, batch generalization, and interactive generalization, to undertake two types of processes, i.e. intelligent inference process and repetitive operation process in generalization; 2） making use of two kinds of sources for generalizing rule library, i.e. written specifications and cartographers＇ experiences, to define a six-element structure to describe the rules; 3） employing a hierarchical structure for map databases, logically and physically; 4） employing a grid indexing technique and undo/redo operation to improve database retrieval and object generalization efficiency. Two examples of topographic map generalization are given to demonstrate the system. It reveals that the system works well. In fact, this system has been used for a number of projects and it has been found that a great improvement in efficiency compared with traditional map general- ization process can be achieved.展开更多
This paper proposes an algorithm for road density analysis based on skeleton partitioning. Road density provides metric and statistical information about overall road distribution at the macro level. Existing measurem...This paper proposes an algorithm for road density analysis based on skeleton partitioning. Road density provides metric and statistical information about overall road distribution at the macro level. Existing measurements of road density based on grid method, fractal geometry and mesh density are reviewed, and a new method for computing road density based on skeleton partitioning is proposed. Experiments illustrate that road density based on skeleton partitioning may reveal the overall road distribution. The proposed measurement is further tested against road maps at 1:10k scale and their generalized version at 1:50k scale. By comparing the deletion percentage within different density interval, a road density threshold can be found, which indicate the need for further operations during generalization. Proposed road density may be used to examine the quality of road generalization, to explore the variation of road network through temporal and spatial changes, and it also has future usage in urban planning, transportation and estates evaluation practice.展开更多
Fear memories are critical for survival.Nevertheless,over-generalization of these memories,depicted by a failure to distinguish threats from safe stimuli,is typical in stress-related disorders.Previous studies have su...Fear memories are critical for survival.Nevertheless,over-generalization of these memories,depicted by a failure to distinguish threats from safe stimuli,is typical in stress-related disorders.Previous studies have supported a protective role of ketamine against stress-induced depressive behavior.However,the effect of ketamine on fear generalization remains unclear.In this study,we investigated the effects of ketamine on fear generalization in a fear-generalized mouse model.The mice were given a single sub-anesthetic dose of ketamine(30 mg/kg,i.p.)1 h before,1 week before,immediately after,or 22 h after fear conditioning.The behavioral measure of fear(indicated by freezing level)and synaptic protein expression in the basolateral amygdala(BLA)and inferior-limbic pre-frontal cortex(IL-PFC)of mice were examined.We found that only ketamine administered 22 h after fear conditioning significantly decreased the fear generalization,and the effect was dose-dependent and lasted for at least 2 weeks.The fear-generalized mice showed a lower level of brainderived neurotrophic factor(BDNF)and a higher level of GluN2B protein in the BLA and IL-PFC,and this was reversed by a single administration of ketamine.Moreover,the GluN2B antagonist ifenprodil decreased the fear generalization when infused into the IL-PFC,but had no effect when infused into the BLA.Infusion of ANA-12(an antagonist of the BDNF receptor TrkB)into the BLA or ILPFC blocked the effect of ketamine on fear generalization.These findings support the conclusion that a single dose of ketamine administered 22 h after fear conditioning alleviates the fear memory generalization in mice and the GluN2B-related BDNF signaling pathway plays an important role in the alleviation of fear generalization.展开更多
The difficulty of build-up area extraction is due to complexity of remote sensing data in terms of heterogeneous appearance with large intra-class variations and lower inter-class variations. In order to extract the b...The difficulty of build-up area extraction is due to complexity of remote sensing data in terms of heterogeneous appearance with large intra-class variations and lower inter-class variations. In order to extract the built-up area from Landsat 8-OLI images provided by Google earth engine(GEE), we propose a convolutional neural networks(CNN) utilizing spatial and spectral information synchronously, which is built in Google drive using Colaboratory-Keras. To train a CNN model with good generalization ability, we choose Beijing, Lanzhou, Chongqing, Suzhou and Guangzhou of China as the training sites, which are very different in term of natural environments. The Arc GIS-Model Builder is employed to automatically select 99 332 samples from the 38-m global built-up production of the European Space Agency(ESA) in 2014. The validate accuracy of the five experimental sites is higher than 90%. We compare the results with other existing building data products. The classification results of CNN can be very good for the details of the built-up areas, and greatly reduce the classification error and leakage error. We applied the well-trained CNN model to extract built-up areas of Chengdu, Xi’an, Zhengzhou, Harbin, Hefei, Wuhan, Kunming and Fuzhou, for the sake of evaluating the generalization ability of the CNN. The fine classification results of the eight sites indicate that the generalization ability of the well-trained CNN is pretty good. However, the extraction results of Xi’an, Zhengzhou and Hefei are poor. As for the training data, only Lanzhou is located in the northwest region, so the trained CNN has poor image classification ability in the northwest region of China.展开更多
In order to avoid the discretization in the classical rough set theory, a generlization rough set theory is proposed. At first, the degree of general importance of an attribute and attribute subsets are presented. The...In order to avoid the discretization in the classical rough set theory, a generlization rough set theory is proposed. At first, the degree of general importance of an attribute and attribute subsets are presented. Then, depending on the degree of general importance of attribute, the space distance can be measured with weighted method. At last, a generalization rough set theory based on the general near neighborhood relation is proposed. The proposed theory partitions the universe into the tolerant modules, and forms lower approximation and upper approximation of the set under general near neighborhood relationship, which avoids the discretization in Pawlak's rough set theory.展开更多
基金supported by the Shandong Provincial Natural Science Foundation(ZR2022QH144).
文摘The phenomenon of fear memory generalization can be defined as the expansion of an individual's originally specific fear responses to a similar yet genuinely harmless stimulus or situation subsequent to the occurrence of a traumatic event[1].Fear generalization within the normal range represents an adaptive evolutionary mechanism to facilitate prompt reactions to potential threats and to enhance the likelihood of survival.
基金funded by Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydney.Moreover,Ongoing Research Funding Program(ORF-2025-14)King Saud University,Riyadh,Saudi Arabia,under Project ORF-2025-。
文摘Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.
基金supported by the National Natural Science Foundation of China(62101575)the Research Project of NUDT(ZK22-57)the Self-directed Project of State Key Laboratory of High Performance Computing(202101-16).
文摘Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.
基金Supported by the National Key R&D Program of China(No.2023YFB4502200)the National Natural Science Foundation of China(No.U22A2028,61925208,62222214,62341411,62102398,62102399,U20A20227,62302478,62302482,62302483,62302480,62302481)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB0660300,XDB0660301,XDB0660302)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(No.YSBR-029)the Youth Innovation Promotion Association of Chinese Academy of Sciences and Xplore Prize.
文摘The challenge of enhancing the generalization capacity of reinforcement learning(RL)agents remains a formidable obstacle.Existing RL methods,despite achieving superhuman performance on certain benchmarks,often struggle with this aspect.A potential reason is that the benchmarks used for training and evaluation may not adequately offer a diverse set of transferable tasks.Although recent studies have developed bench-marking environments to address this shortcoming,they typically fall short in providing tasks that both ensure a solid foundation for generalization and exhibit significant variability.To overcome these limitations,this work introduces the concept that‘objects are composed of more fundamental components’in environment design,as implemented in the proposed environment called summon the magic(StM).This environment generates tasks where objects are derived from extensible and shareable basic components,facilitating strategy reuse and enhancing generalization.Furthermore,two new metrics,adaptation sensitivity range(ASR)and parameter correlation coefficient(PCC),are proposed to better capture and evaluate the generalization process of RL agents.Experimental results show that increasing the number of basic components of the object reduces the proximal policy optimization(PPO)agent’s training-testing gap by 60.9%(in episode reward),significantly alleviating overfitting.Additionally,linear variations in other environmental factors,such as the training monster set proportion and the total number of basic components,uniformly decrease the gap by at least 32.1%.These results highlight StM’s effectiveness in benchmarking and probing the generalization capabilities of RL algorithms.
基金Supported by the National Natural Science Foundation of China(No.62001313)the Key Project of Liaoning Provincial Department of Science and Technology(No.2021JH2/10300134,2022JH1/10500004)。
文摘In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when faced with testing scenarios from unknown domains.To address this problem,this paper proposes a novel semi-supervised approach for cardiac magnetic resonance image segmentation,aiming to enhance predictive capabilities and domain generalization(DG).This paper establishes an MT-like model utilizing pseudo-labeling and consistency regularization from semi-supervised learning,and integrates uncertainty estimation to improve the accuracy of pseudo-labels.Additionally,to tackle the challenge of domain generalization,a data manipulation strategy is introduced,extracting spatial and content-related information from images across different domains,enriching the dataset with a multi-domain perspective.This papers method is meticulously evaluated on the publicly available cardiac magnetic resonance imaging dataset M&Ms,validating its effectiveness.Comparative analyses against various methods highlight the out-standing performance of this papers approach,demonstrating its capability to segment cardiac magnetic resonance images in previously unseen domains even with limited annotated data.
基金Supported by Education Science Planning Project of Hubei Province(2020GB198)Natural Science Foundation of Hubei Province(2023AFB523).
文摘This paper analyzes the generalization of minimax regret optimization(MRO)under distribution shift.A new learning framework is proposed by injecting the measure of con-ditional value at risk(CVaR)into MRO,and its generalization error bound is established through the lens of uniform convergence analysis.The CVaR-based MRO can achieve the polynomial decay rate on the excess risk,which extends the generalization analysis associated with the expected risk to the risk-averse case.
基金supported in part by the Beijing Natural Science Foundation (Grant No. 1232025)the Ministry of Education Key Laboratory of Quantum Physics and Photonic Quantum Information (Grant No. ZYGX2024K020)Academy for Multidisciplinary Studies, Capital Normal University.
文摘The interpretation of representations and generalization powers has been a long-standing challenge in the fields of machine learning(ML)and artificial intelligence.This study contributes to understanding the emergence of universal scaling laws in quantum-probabilistic ML.We consider the generative tensor network(GTN)in the form of a matrix-product state as an example and show that with an untrained GTN(such as a random TN state),the negative logarithmic likelihood(NLL)L generally increases linearly with the number of features M,that is,L≃kM+const.This is a consequence of the so-called“catastrophe of orthogonality,”which states that quantum many-body states tend to become exponentially orthogonal to each other as M increases.This study reveals that,while gaining information through training,the linear-scaling law is suppressed by a negative quadratic correction,leading to L≃βM−αM^(2)+const.The scaling coefficients exhibit logarithmic relationships with the number of training samples and quantum channelsχ.The emergence of a quadratic correction term in the NLL for the testing(training)set can be regarded as evidence of the generalization(representation)power of the GTN.Over-parameterization can be identified by the deviation in the values ofαbetween the training and testing sets while increasingχ.We further investigate how orthogonality in the quantum-feature map relates to the satisfaction of quantum-probabilistic interpretation and the representation and generalization powers of the GTN.Unveiling universal scaling laws in quantum-probabilistic ML would be a valuable step toward establishing a white-box ML scheme interpreted within the quantum-probabilistic framework.
文摘Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.
文摘This paper puts forword 11 cartographic generalization operator models and introduces their mathematical definitions,and thus a precise mathematical form and quantitative description has been given to these formerly limited qualitative concepts.The meaning of mathematical definition of operators for cartographic generalization and the application prospect in computer_aided cartography (CAC) is stated.ract The Jurassic strata in Jingyan of Sichuan containing the Mamenchinsaurus fauna are dealt with and divided in this paper. The Mamenchisaurus fossils contained there are compared in morphological features and stratigraphically with other types of the genus on by one. The comprehensive analysis show that the Mamenchisaurus fauna of Jingyan appeared in the early Late Jurassic and is primitive in morphology. The results of the morphological identification and stratigraphical study agree with each other. Their evolutionary processes in different apoches of the Late Jurassic also made clear. Key words Jingyan, Sichuan, Mamenchisaurus Fauna, stratigraphy, evolution
文摘Generalization ability is a major problem encountered when using neural networks to find the structures in noisy data sets. Controlling the network complexity is a common method to solve this problem. In this paper, however, a novel additive penalty term which represents the features extracted by hidden units is introduced to eliminate the overtraining of multilayer feedfoward networks. Computer simulations demonstrate that by using this unsupervised fashion penalty term, the generalization ability is greatly improved.
文摘This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that the outputs of the output layer in the FNNs for classification correspond to the estimates of posteriori probability of the input pattern samples with desired outputs 1 or 0. The theorem for the generalized kernel function in the radial basis function networks (RBFN) is given. For an 2-layer perceptron network (2-LPN). an idea of using extended samples to improve generalization capability is proposed. Finally. the experimental results of radar target classification are given to verify the generaliztion capability of the RBFNs.
基金the National Natural Science Foundation of China(No.41807498)the National Key Research and Development Program of China(No.2018YFC1505501)the Humanities and Social Sciences Foundation of the Ministry of Education of China(No.20XJAZH002)。
文摘Numerous researches have been published on the application of landslide susceptibility assessment models;however,they were only applied in the same areas as the models were originated,the effect of applying the models to other areas than the origin of the models has not been explored.This study is purposed to develop an optimized random forest(RF)model with best ratios of positive-to-negative cells and 10-fold cross-validation for landslide susceptibility mapping(LSM),and then explore its generalization ability not only in the area where the model is originated but also in area other than the origin of the model.Two typical counties(Fengjie County and Wushan County)in the Three Gorges Reservoir area,China,which have the same terrain and geological conditions,were selected as an example.To begin with,landslide inventory was prepared based on field investigations,satellite images,and historical records,and 1522 landslides were then identified in Fengjie County.22 landslide-conditioning factors under the influence of topography,geology,environmental conditions,and human activities were prepared.Then,combined with 10-fold cross-validation,three typical ratios of positive-to-negative cells,i.e.,1:1,1:5,and 1:10,were adopted for comparative analyses.An optimized RF model(Fengjie-based model)with the best ratios of positive-to-negative cells and 10-fold cross-validation was constructed.Finally,the Fengjie-based model was applied to Fengjie County and Wushan County,and the confusion matrix and area under the receiver operating characteristic(ROC)curve value(AUC)were used to estimate the accuracy.The Fengjie-based model delivered high stability and predictive capability in Fengjie County,indicating a great generalization ability of the model to the area where the model is originated.The LSM in Wushan County generated by the Fengjie-based model had a reasonable reference value,indicating the Fengjiebased model had a great generalization ability in area other than the origin of the model.The Fengjiebased model in this study could be applied in other similar areas/countries with the same terrain and geological conditions,and a LSM may be generated without collecting landslide information for modeling,so as to reduce workload and improve efficiency in practice.
基金National Natural Science Foundation of China(Grant No.61573233)Guangdong Provincial Natural Science Foundation of China(Grant No.2018A0303130188)+1 种基金Guangdong Provincial Science and Technology Special Funds Project of China(Grant No.190805145540361)Special Projects in Key Fields of Colleges and Universities in Guangdong Province of China(Grant No.2020ZDZX2005).
文摘There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.
基金This work was supported in part by the Program for Innovative Talents and Entrepreneur in Jiangsu Province under Grant 1104000402in part by the Research Fund by Nanjing Government under Grant 1104000396+4 种基金in part by the National Science Foundation of China under Grants 62001109&61921004in part by the China Postdoctoral Science Foundation under Grants BX20200083&2020M681456in part by the Fundamental Research Funds for the Central Universities of China under Grants 3204002004A2&2242020R20011in part by the open research fund of the National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology under Grant No.KFJJ20180205in part by the NUPTSF Grants No.NY218113&No.NY219077.
文摘Wireless communication involving unmanned aerial vehicles(UAVs)is expected to play an important role in future wireless networks.However,different from conventional terrestrial communication systems,UAVs typically have rather limited onboard energy on one hand,and require additional flying energy consumption on the other hand.This renders energy-efficient UAV communication with smart energy expenditure of paramount importance.In this paper,via extensive flight experiments,we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs,and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging,if not impossible.Specifically,we first investigate how UAV power consumption varies with its flying speed for the simplest straight-and-level flight.With about 12,000 valid power-speed data points collected,we first apply the model-based curve fitting to obtain the modelling parameters based on the theoretical closed-form energy model in the existing literature.In addition,in order to exclude the potential bias caused by the theoretical energy model,the obtained measurement data is also trained using a model-free deep neural network.It is found that the obtained curve from both methods can match quite well with the theoretical energy model.Next,we further extend the study to arbitrary 2-dimensional(2-D)flight,where,to our best knowledge,no rigorous theoretical derivation is available for the closed-form energy model as a function of its flying speed,direction,and acceleration.To fill the gap,we first propose a heuristic energy model for these more complicated cases,and then provide experimental validation based on the measurement results for circular level flight.
文摘Complicated changes occur inside the steel parts during quenching process. A three dimensional nonlinear mathematical model for quenching process has been established and the numerical simulation on temperature field, microstructure and stress field has been realized. The alternative technique for the formation of high-strength materials has been developed on the basis of intensification of heat transfer at phase transformations. The technology for the achievement of maximum compressive residual stresses on the hard surface is introduced. It has been shown that there is an optimal depth of hard layer providing the maximum compression stresses on the surface. It has also been established that in the surface hard layer additional strengthening (superstrengthening) of the material is observed. The generalized formula for the determination of the time of reaching maximum compressive stresses on the surface has been proposed.
基金Under the auspices of the National Natural Science Foundation of China (No. 40301037), and a PolyU Project(G-T873)
文摘With the construction of spatial data infi＇astructure, automated topographic map generalization becomes an indispensable component in the community of cartography and geographic information science. This paper describes a topographic map generalization system recently developed by the authors. The system has the following characteristics： 1） taking advantage of three levels of automation, i.e. fully automated generalization, batch generalization, and interactive generalization, to undertake two types of processes, i.e. intelligent inference process and repetitive operation process in generalization; 2） making use of two kinds of sources for generalizing rule library, i.e. written specifications and cartographers＇ experiences, to define a six-element structure to describe the rules; 3） employing a hierarchical structure for map databases, logically and physically; 4） employing a grid indexing technique and undo/redo operation to improve database retrieval and object generalization efficiency. Two examples of topographic map generalization are given to demonstrate the system. It reveals that the system works well. In fact, this system has been used for a number of projects and it has been found that a great improvement in efficiency compared with traditional map general- ization process can be achieved.
基金Supported by the National 863 Program of China(No2007AA12Z225)the Natural Science Foundation of China(No40771168)
文摘This paper proposes an algorithm for road density analysis based on skeleton partitioning. Road density provides metric and statistical information about overall road distribution at the macro level. Existing measurements of road density based on grid method, fractal geometry and mesh density are reviewed, and a new method for computing road density based on skeleton partitioning is proposed. Experiments illustrate that road density based on skeleton partitioning may reveal the overall road distribution. The proposed measurement is further tested against road maps at 1:10k scale and their generalized version at 1:50k scale. By comparing the deletion percentage within different density interval, a road density threshold can be found, which indicate the need for further operations during generalization. Proposed road density may be used to examine the quality of road generalization, to explore the variation of road network through temporal and spatial changes, and it also has future usage in urban planning, transportation and estates evaluation practice.
基金supported by grants from the National Natural Science Foundation of China(81530061 and 81471829)the Pearl River Nova Program of Guangzhou(201610010154)the Natural Science Foundation of Guangdong Province China(2017A030313095).
文摘Fear memories are critical for survival.Nevertheless,over-generalization of these memories,depicted by a failure to distinguish threats from safe stimuli,is typical in stress-related disorders.Previous studies have supported a protective role of ketamine against stress-induced depressive behavior.However,the effect of ketamine on fear generalization remains unclear.In this study,we investigated the effects of ketamine on fear generalization in a fear-generalized mouse model.The mice were given a single sub-anesthetic dose of ketamine(30 mg/kg,i.p.)1 h before,1 week before,immediately after,or 22 h after fear conditioning.The behavioral measure of fear(indicated by freezing level)and synaptic protein expression in the basolateral amygdala(BLA)and inferior-limbic pre-frontal cortex(IL-PFC)of mice were examined.We found that only ketamine administered 22 h after fear conditioning significantly decreased the fear generalization,and the effect was dose-dependent and lasted for at least 2 weeks.The fear-generalized mice showed a lower level of brainderived neurotrophic factor(BDNF)and a higher level of GluN2B protein in the BLA and IL-PFC,and this was reversed by a single administration of ketamine.Moreover,the GluN2B antagonist ifenprodil decreased the fear generalization when infused into the IL-PFC,but had no effect when infused into the BLA.Infusion of ANA-12(an antagonist of the BDNF receptor TrkB)into the BLA or ILPFC blocked the effect of ketamine on fear generalization.These findings support the conclusion that a single dose of ketamine administered 22 h after fear conditioning alleviates the fear memory generalization in mice and the GluN2B-related BDNF signaling pathway plays an important role in the alleviation of fear generalization.
基金supported by the National Key R&D Program of China(No.2017YFB0504104)the National Natural Science Foundation of China(No.41571334)。
文摘The difficulty of build-up area extraction is due to complexity of remote sensing data in terms of heterogeneous appearance with large intra-class variations and lower inter-class variations. In order to extract the built-up area from Landsat 8-OLI images provided by Google earth engine(GEE), we propose a convolutional neural networks(CNN) utilizing spatial and spectral information synchronously, which is built in Google drive using Colaboratory-Keras. To train a CNN model with good generalization ability, we choose Beijing, Lanzhou, Chongqing, Suzhou and Guangzhou of China as the training sites, which are very different in term of natural environments. The Arc GIS-Model Builder is employed to automatically select 99 332 samples from the 38-m global built-up production of the European Space Agency(ESA) in 2014. The validate accuracy of the five experimental sites is higher than 90%. We compare the results with other existing building data products. The classification results of CNN can be very good for the details of the built-up areas, and greatly reduce the classification error and leakage error. We applied the well-trained CNN model to extract built-up areas of Chengdu, Xi’an, Zhengzhou, Harbin, Hefei, Wuhan, Kunming and Fuzhou, for the sake of evaluating the generalization ability of the CNN. The fine classification results of the eight sites indicate that the generalization ability of the well-trained CNN is pretty good. However, the extraction results of Xi’an, Zhengzhou and Hefei are poor. As for the training data, only Lanzhou is located in the northwest region, so the trained CNN has poor image classification ability in the northwest region of China.
基金Natural Science Foundation of Jiangsu Province of China ( No.BK2006176)High-Tech Key Laboratory of Jiangsu,China (No.BM2007201)
文摘In order to avoid the discretization in the classical rough set theory, a generlization rough set theory is proposed. At first, the degree of general importance of an attribute and attribute subsets are presented. Then, depending on the degree of general importance of attribute, the space distance can be measured with weighted method. At last, a generalization rough set theory based on the general near neighborhood relation is proposed. The proposed theory partitions the universe into the tolerant modules, and forms lower approximation and upper approximation of the set under general near neighborhood relationship, which avoids the discretization in Pawlak's rough set theory.
