Lazy learner text categorization algorithm based on embedded feature selection

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.

Morphological features of allogenic nerve segment in rats after subcutaneous embedment

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.

Using video imagery to reconstruct the 3D intertidal terrain along a beach with multiple cusps

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.

Cooperative multi-target hunting by unmanned surface vehicles based on multi-agent reinforcement learning

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.

Heterogeneous data-driven aerodynamic modeling based on physical feature embedding

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.