To avoid the curse of dimensionality, text categorization （TC） algorithms based on machine learning （ML） have to use an feature selection （FS） method to reduce the dimensionality of feature space. Although havin...To avoid the curse of dimensionality, text categorization （TC） algorithms based on machine learning （ML） have to use an feature selection （FS） method to reduce the dimensionality of feature space. Although having been widely used, FS process will generally cause information losing and then have much side-effect on the whole performance of TC algorithms. On the basis of the sparsity characteristic of text vectors, a new TC algorithm based on lazy feature selection （LFS） is presented. As a new type of embedded feature selection approach, the LFS method can greatly reduce the dimension of features without any information losing, which can improve both efficiency and performance of algorithms greatly. The experiments show the new algorithm can simultaneously achieve much higher both performance and efficiency than some of other classical TC algorithms.展开更多
BACKGROUND ： Some studies demonstrate that allogenic peripheral nerve segment embedded subcutaneously significantly reduce the infiltration of lymphocyte and decrease immunological reaction.OBJECTIVE ： To observe th...BACKGROUND ： Some studies demonstrate that allogenic peripheral nerve segment embedded subcutaneously significantly reduce the infiltration of lymphocyte and decrease immunological reaction.OBJECTIVE ： To observe the gross shape, optical and electron microscope results of allogenic nerve segment in rats 2 weeks after subcutaneous embedment, and compare with subcutaneous emdedment of autologous nerve segment. DESIGN ： A randomized and controlled experiment.SETTING ： Department of Orthopaedics of Fifth People＇s Hospital of Zhengzhou; Department of Orthopaedics,First Hospital Affiliated to Chongqing Medical University.MATERIALS ： Totally 30 adult healthy Wistar male rats, with body mass of （200±20） g, were enrolled. Ten rats were chosen as the donors of allogenic nerve transplantation. The other 20 rats were randomly divided into 2 groups： allogenic nerve embedment group and autologous nerve embedment group, with 10 rats in each one. JEM-1220 transmission electron microscope （Japan） and Olympus BX50 optical microscope （Japan） were used. METHODS ： This experiment was carried out at the laboratory of Orthopaedic Department, Chongqing Medical University from October 2000 to April 2002. ① Sciatic nerve of donor rats for allogenic nerve transplantation was cut off at 5 mm distant from pelvic strait.15 mm sciatic nerve segment was chosen from lateral part as graft, allogenic nerve embedment group： 15 mm sciatic nerve form the donor rats was embedded in the posterior part of right legs. Autologous nerve embedment group： 15 mm sciatic nerve segment of autologous left side was embedded in the posterior side of right legs. ② Nerve segment embedded subcutaneously was taken out at postoperative 2 weeks and performed gross observation; then 5 samples chosen randomly respectively from 2 groups and given haematoxylin-eosin staining and observation under optical microscope （×400）;The other 5 samples were made into ultrathin sections （0.5μm）and observed under transmission electron microscope（×17 000）. MAIN OUTCOME MEASURES ： Gross shape, optical and electron microscope results of nerve segments of rats between two groups at 2 weeks after subcutaneous embedment. RESULTS ： ① Results of gross observation： Appearance of nerve segment was similar between 2 groups. ② Results of optical observation： medullary sheath denaturation, axonotmesis, vascular engorgement, desmoplasia of adventitia and infiltration of inflammatory cells were all found in both 2 groups. Inflammatory reaction was a little more severe in the allogenic nerve embedment group than in the autologous nerve embedment groups.③Results of electron microscope ： Similar cataplasia and denaturation of medullary sheath and cataplasia of Schwann cell were all found in the 2 groups. CONCLUSION： Some inflammatory reaction occurs after allogenic nerve embedment, but the activity of Schwann cell is similar to that of peripheral nerve after autologous nerve embedment.展开更多
Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full ...Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full use of both integrated and distributed loads,a modeling paradigm,called the heterogeneous data-driven aerodynamic modeling,is presented.The essential concept is to incorporate the physical information of distributed loads as additional constraints within the end-to-end aerodynamic modeling.Towards heterogenous data,a novel and easily applicable physical feature embedding modeling framework is designed.This framework extracts lowdimensional physical features from pressure distribution and then effectively enhances the modeling of the integrated loads via feature embedding.The proposed framework can be coupled with multiple feature extraction methods,and the well-performed generalization capabilities over different airfoils are verified through a transonic case.Compared with traditional direct modeling,the proposed framework can reduce testing errors by almost 50%.Given the same prediction accuracy,it can save more than half of the training samples.Furthermore,the visualization analysis has revealed a significant correlation between the discovered low-dimensional physical features and the heterogeneous aerodynamic loads,which shows the interpretability and credibility of the superior performance offered by the proposed deep learning framework.展开更多
To solve the problem of multi-target hunting by an unmanned surface vehicle(USV)fleet,a hunting algorithm based on multi-agent reinforcement learning is proposed.Firstly,the hunting environment and kinematic model wit...To solve the problem of multi-target hunting by an unmanned surface vehicle(USV)fleet,a hunting algorithm based on multi-agent reinforcement learning is proposed.Firstly,the hunting environment and kinematic model without boundary constraints are built,and the criteria for successful target capture are given.Then,the cooperative hunting problem of a USV fleet is modeled as a decentralized partially observable Markov decision process(Dec-POMDP),and a distributed partially observable multitarget hunting Proximal Policy Optimization(DPOMH-PPO)algorithm applicable to USVs is proposed.In addition,an observation model,a reward function and the action space applicable to multi-target hunting tasks are designed.To deal with the dynamic change of observational feature dimension input by partially observable systems,a feature embedding block is proposed.By combining the two feature compression methods of column-wise max pooling(CMP)and column-wise average-pooling(CAP),observational feature encoding is established.Finally,the centralized training and decentralized execution framework is adopted to complete the training of hunting strategy.Each USV in the fleet shares the same policy and perform actions independently.Simulation experiments have verified the effectiveness of the DPOMH-PPO algorithm in the test scenarios with different numbers of USVs.Moreover,the advantages of the proposed model are comprehensively analyzed from the aspects of algorithm performance,migration effect in task scenarios and self-organization capability after being damaged,the potential deployment and application of DPOMH-PPO in the real environment is verified.展开更多
A high-frequency,high-resolution shore-based video monitoring system(VMS)was installed on a macrotidal(tidal amplitude>4 m)beach with multiple cusps along the Quanzhou coast,China.Herein,we propose a video imagery-...A high-frequency,high-resolution shore-based video monitoring system(VMS)was installed on a macrotidal(tidal amplitude>4 m)beach with multiple cusps along the Quanzhou coast,China.Herein,we propose a video imagery-based method that is coupled with waterline and water level observations to reconstruct the terrain of the intertidal zone over one tidal cycle.Furthermore,the beach cusp system(BCS)was precisely processed and embedded into the digital elevation model(DEM)to more effectively express the microrelief and detailed characteristics of the intertidal zone.During a field experiment conducted in January 2022,the reconstructed DEM was deemed satisfactory.The DEM was verified by RTK-GPS and had an average vertical root mean square error along corresponding RTK-GPS-derived intertidal profiles and corresponding BCS points of 0.134 m and 0.065 m,respectively.The results suggest that VMSs are an effective tool for investigating coastal geomorphic processes.展开更多
The simulation of lubrication on rough surfaces is essential for the design and optimization of tribological performance.Although the application of physics-informed neural networks(PINNs)in analyzing hydrodynamic lub...The simulation of lubrication on rough surfaces is essential for the design and optimization of tribological performance.Although the application of physics-informed neural networks(PINNs)in analyzing hydrodynamic lubrication has been increasing,their implementation has predominantly been restricted to smooth surfaces.This limitation arises from the inherent spectral bias of conventional PINN methodologies,which tend to prioritize the learning of low-frequency features,thereby hindering their ability to analyze rough surfaces characterized by high-frequency signals effectively.To date,there have been no reported instances of PINN methodologies being applied to rough surface lubrication.In response to these challenges,this paper presents an innovative multiscale lubrication neural network(MLNN)architecture that incorporates a trainable Fourier feature embedding.By integrating learnable feature embedding frequencies,this architecture is capable of automatically adjusting to various frequency components,thus improving the analysis of rough surface characteristics.The proposed method has been evaluated across a range of surface topographies,with results compared to those derived from the finite element method(FEM).The comparative analysis indicates a high degree of consistency between the MLNN results and those obtained through FEM.Moreover,this novel architecture demonstrates superior performance in terms of both accuracy and computational efficiency when compared to traditional Fourier feature networks that utilize fixed feature embedding frequencies.As a result,the MLNN model represents a more effective tool for the analysis of lubrication on rough surfaces.展开更多
Binary analysis, as an important foundational technology, provides support for numerous applications in the fields of software engineering and security research. With the continuous expansion of software scale and the...Binary analysis, as an important foundational technology, provides support for numerous applications in the fields of software engineering and security research. With the continuous expansion of software scale and the complex evolution of software architecture, binary analysis technology is facing new challenges. To break through existing bottlenecks, researchers have applied artificial intelligence (AI) technology to the understanding and analysis of binary code. The core lies in characterizing binary code, i.e., how to use intelligent methods to generate representation vectors containing semantic information for binary code, and apply them to multiple downstream tasks of binary analysis. In this paper, we provide a comprehensive survey of recent advances in binary code representation technology, and introduce the workflow of existing research in two parts, i.e., binary code feature selection methods and binary code feature embedding methods. The feature selection section includes mainly two parts: definition and classification of features, and feature construction. First, the abstract definition and classification of features are systematically explained, and second, the process of constructing specific representations of features is introduced in detail. In the feature embedding section, based on the different intelligent semantic understanding models used, the embedding methods are classified into four categories based on the usage of text-embedding models and graph-embedding models. Finally, we summarize the overall development of existing research and provide prospects for some potential research directions related to binary code representation technology.展开更多
文摘To avoid the curse of dimensionality, text categorization （TC） algorithms based on machine learning （ML） have to use an feature selection （FS） method to reduce the dimensionality of feature space. Although having been widely used, FS process will generally cause information losing and then have much side-effect on the whole performance of TC algorithms. On the basis of the sparsity characteristic of text vectors, a new TC algorithm based on lazy feature selection （LFS） is presented. As a new type of embedded feature selection approach, the LFS method can greatly reduce the dimension of features without any information losing, which can improve both efficiency and performance of algorithms greatly. The experiments show the new algorithm can simultaneously achieve much higher both performance and efficiency than some of other classical TC algorithms.
文摘BACKGROUND ： Some studies demonstrate that allogenic peripheral nerve segment embedded subcutaneously significantly reduce the infiltration of lymphocyte and decrease immunological reaction.OBJECTIVE ： To observe the gross shape, optical and electron microscope results of allogenic nerve segment in rats 2 weeks after subcutaneous embedment, and compare with subcutaneous emdedment of autologous nerve segment. DESIGN ： A randomized and controlled experiment.SETTING ： Department of Orthopaedics of Fifth People＇s Hospital of Zhengzhou; Department of Orthopaedics,First Hospital Affiliated to Chongqing Medical University.MATERIALS ： Totally 30 adult healthy Wistar male rats, with body mass of （200±20） g, were enrolled. Ten rats were chosen as the donors of allogenic nerve transplantation. The other 20 rats were randomly divided into 2 groups： allogenic nerve embedment group and autologous nerve embedment group, with 10 rats in each one. JEM-1220 transmission electron microscope （Japan） and Olympus BX50 optical microscope （Japan） were used. METHODS ： This experiment was carried out at the laboratory of Orthopaedic Department, Chongqing Medical University from October 2000 to April 2002. ① Sciatic nerve of donor rats for allogenic nerve transplantation was cut off at 5 mm distant from pelvic strait.15 mm sciatic nerve segment was chosen from lateral part as graft, allogenic nerve embedment group： 15 mm sciatic nerve form the donor rats was embedded in the posterior part of right legs. Autologous nerve embedment group： 15 mm sciatic nerve segment of autologous left side was embedded in the posterior side of right legs. ② Nerve segment embedded subcutaneously was taken out at postoperative 2 weeks and performed gross observation; then 5 samples chosen randomly respectively from 2 groups and given haematoxylin-eosin staining and observation under optical microscope （×400）;The other 5 samples were made into ultrathin sections （0.5μm）and observed under transmission electron microscope（×17 000）. MAIN OUTCOME MEASURES ： Gross shape, optical and electron microscope results of nerve segments of rats between two groups at 2 weeks after subcutaneous embedment. RESULTS ： ① Results of gross observation： Appearance of nerve segment was similar between 2 groups. ② Results of optical observation： medullary sheath denaturation, axonotmesis, vascular engorgement, desmoplasia of adventitia and infiltration of inflammatory cells were all found in both 2 groups. Inflammatory reaction was a little more severe in the allogenic nerve embedment group than in the autologous nerve embedment groups.③Results of electron microscope ： Similar cataplasia and denaturation of medullary sheath and cataplasia of Schwann cell were all found in the 2 groups. CONCLUSION： Some inflammatory reaction occurs after allogenic nerve embedment, but the activity of Schwann cell is similar to that of peripheral nerve after autologous nerve embedment.
基金supported by the National Natural Science Foundation of China(Nos.92152301,12072282)。
文摘Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full use of both integrated and distributed loads,a modeling paradigm,called the heterogeneous data-driven aerodynamic modeling,is presented.The essential concept is to incorporate the physical information of distributed loads as additional constraints within the end-to-end aerodynamic modeling.Towards heterogenous data,a novel and easily applicable physical feature embedding modeling framework is designed.This framework extracts lowdimensional physical features from pressure distribution and then effectively enhances the modeling of the integrated loads via feature embedding.The proposed framework can be coupled with multiple feature extraction methods,and the well-performed generalization capabilities over different airfoils are verified through a transonic case.Compared with traditional direct modeling,the proposed framework can reduce testing errors by almost 50%.Given the same prediction accuracy,it can save more than half of the training samples.Furthermore,the visualization analysis has revealed a significant correlation between the discovered low-dimensional physical features and the heterogeneous aerodynamic loads,which shows the interpretability and credibility of the superior performance offered by the proposed deep learning framework.
基金financial support from National Natural Science Foundation of China(Grant No.61601491)Natural Science Foundation of Hubei Province,China(Grant No.2018CFC865)Military Research Project of China(-Grant No.YJ2020B117)。
文摘To solve the problem of multi-target hunting by an unmanned surface vehicle(USV)fleet,a hunting algorithm based on multi-agent reinforcement learning is proposed.Firstly,the hunting environment and kinematic model without boundary constraints are built,and the criteria for successful target capture are given.Then,the cooperative hunting problem of a USV fleet is modeled as a decentralized partially observable Markov decision process(Dec-POMDP),and a distributed partially observable multitarget hunting Proximal Policy Optimization(DPOMH-PPO)algorithm applicable to USVs is proposed.In addition,an observation model,a reward function and the action space applicable to multi-target hunting tasks are designed.To deal with the dynamic change of observational feature dimension input by partially observable systems,a feature embedding block is proposed.By combining the two feature compression methods of column-wise max pooling(CMP)and column-wise average-pooling(CAP),observational feature encoding is established.Finally,the centralized training and decentralized execution framework is adopted to complete the training of hunting strategy.Each USV in the fleet shares the same policy and perform actions independently.Simulation experiments have verified the effectiveness of the DPOMH-PPO algorithm in the test scenarios with different numbers of USVs.Moreover,the advantages of the proposed model are comprehensively analyzed from the aspects of algorithm performance,migration effect in task scenarios and self-organization capability after being damaged,the potential deployment and application of DPOMH-PPO in the real environment is verified.
基金The National Key Research and Development Program of China under contract No.2022YFC3106100the Key Program of National Natural Science Foundation of China under contract No.41930538.
文摘A high-frequency,high-resolution shore-based video monitoring system(VMS)was installed on a macrotidal(tidal amplitude>4 m)beach with multiple cusps along the Quanzhou coast,China.Herein,we propose a video imagery-based method that is coupled with waterline and water level observations to reconstruct the terrain of the intertidal zone over one tidal cycle.Furthermore,the beach cusp system(BCS)was precisely processed and embedded into the digital elevation model(DEM)to more effectively express the microrelief and detailed characteristics of the intertidal zone.During a field experiment conducted in January 2022,the reconstructed DEM was deemed satisfactory.The DEM was verified by RTK-GPS and had an average vertical root mean square error along corresponding RTK-GPS-derived intertidal profiles and corresponding BCS points of 0.134 m and 0.065 m,respectively.The results suggest that VMSs are an effective tool for investigating coastal geomorphic processes.
基金supported by the National Natural Science Foundation of China(Grant No.52130502)the National Key Laboratory of Marine Engine Science and Technology(Grant No.LAB2023-06-WD)。
文摘The simulation of lubrication on rough surfaces is essential for the design and optimization of tribological performance.Although the application of physics-informed neural networks(PINNs)in analyzing hydrodynamic lubrication has been increasing,their implementation has predominantly been restricted to smooth surfaces.This limitation arises from the inherent spectral bias of conventional PINN methodologies,which tend to prioritize the learning of low-frequency features,thereby hindering their ability to analyze rough surfaces characterized by high-frequency signals effectively.To date,there have been no reported instances of PINN methodologies being applied to rough surface lubrication.In response to these challenges,this paper presents an innovative multiscale lubrication neural network(MLNN)architecture that incorporates a trainable Fourier feature embedding.By integrating learnable feature embedding frequencies,this architecture is capable of automatically adjusting to various frequency components,thus improving the analysis of rough surface characteristics.The proposed method has been evaluated across a range of surface topographies,with results compared to those derived from the finite element method(FEM).The comparative analysis indicates a high degree of consistency between the MLNN results and those obtained through FEM.Moreover,this novel architecture demonstrates superior performance in terms of both accuracy and computational efficiency when compared to traditional Fourier feature networks that utilize fixed feature embedding frequencies.As a result,the MLNN model represents a more effective tool for the analysis of lubrication on rough surfaces.
文摘Binary analysis, as an important foundational technology, provides support for numerous applications in the fields of software engineering and security research. With the continuous expansion of software scale and the complex evolution of software architecture, binary analysis technology is facing new challenges. To break through existing bottlenecks, researchers have applied artificial intelligence (AI) technology to the understanding and analysis of binary code. The core lies in characterizing binary code, i.e., how to use intelligent methods to generate representation vectors containing semantic information for binary code, and apply them to multiple downstream tasks of binary analysis. In this paper, we provide a comprehensive survey of recent advances in binary code representation technology, and introduce the workflow of existing research in two parts, i.e., binary code feature selection methods and binary code feature embedding methods. The feature selection section includes mainly two parts: definition and classification of features, and feature construction. First, the abstract definition and classification of features are systematically explained, and second, the process of constructing specific representations of features is introduced in detail. In the feature embedding section, based on the different intelligent semantic understanding models used, the embedding methods are classified into four categories based on the usage of text-embedding models and graph-embedding models. Finally, we summarize the overall development of existing research and provide prospects for some potential research directions related to binary code representation technology.
