两晋南北朝的“女子也习射”

两晋南北朝是民族大融合大发展时期,原处西北和北方的少数民族陆续进入黄河流域,逐渐与当地汉族人融为一体,并带来了很多新的因素。本文将从"女子也习射"入手对南北朝时期"习射"的盛行作一详细探讨。

走近《礼记》

司徒修六礼以节民性,明七教以兴民德,齐八政以防淫,一道德以同俗,养耆老以致孝,恤孤独以逮不足,上贤以崇德,简不肖以绌恶。命乡简不帅教者以告。耆老皆朝于庠,元日习射上功,习乡上齿,大司徒帅国之俊士与执事焉。不变,命国之右乡简不帅教者移之左。

3D Ice Shape Description Method Based on BLSOM Neural Network

When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.

Tree shrew models:A chronic social defeat model of depression and a one-trial captive conditioning model of learning and memory

Recent genome studies indicate that tree shrew is in the order or a closest sister of primates,and thus may be one of the best animals to model human diseases.In this paper,we report on a social defeat model of depression in tree shrew（Tupaia belangeri chinensis）.Two male tree shrews were housed in a pair-cage consisting of two independent cages separated by a wire mesh partition with a door connecting the two cages.After one week adaptation,the connecting door was opened and a brief fighting occurs between the two male tree shrews and this social conflict session consisted of 1 h direct conflict（fighting） and 23 h indirect influence（e.g.smell,visual cues） per day for 21 days.The defeated tree shrew was considered the subordinate.Compared with na？ve animals,subordinate tree shrews at the final week of social conflict session showed alterations in body weight,locomotion,avoidance behavior and urinary cortisol levels.Remarkably,these alterations persisted for over two weeks.We also report on a novel captive conditioning model of learning and memory in tree shrew.An automatic trapping cage was placed in a small closed room with a freely-moving tree shrew.For the first four trials,the tree shrew was not trapped when it entered the cage and ate the bait apple,but it was trapped and kept in the cage for 1 h on the fifth trial.Latency was defined as the time between release of the tree shrew and when it entered the captive cage.Latencies during the five trials indicated adaptation.A test trial 24 h later was used to measure whether the one-trial trapping during the fifth trial could form captive memory.Tree shrews showed much longer trapping latencies in the test trial than the adaptation trials.The N-methyl-d-aspartate（NMDA） receptor antagonist MK-801（0.2 mg/kg,i.p.）,known to prevent the formation of memory,did not affect latencies in the adaptation trails,but did block captive memory as it led to much shorter trapping latencies compared to saline treatment in the test trial.These results demonstrate a chronic social defeat model of depression and a novel one-trial captive conditioning model for learning and memory in tree shrews,which are important for mechanism studies of depression,learning,memory,and preclinical evaluation for new antidepressants.

INVERSE KINEMATICS FOR A 6 DOF MANIPULATOR BASED ON NEURAL NETWORK

A methodology is presented whereby a neural network is used to learn the inverse kinematic relationships of the position and orientation of a six joint manipulator. The arm solution for the orientation of a manipulator using a self organizing neural net is studied in this paper. A new training model of the self organizing neural network is proposed by thoroughly studying Martinetz, Ritter and Schulten′s self organizing neural network based on Kohonen′s self organizing mapping algorithm using a Widrow Hoff type error correction rule and closely combining the characters of the inverse kinematic relationship for a robot arm. The computer simulation results for a PUMA 560 robot show that the proposed method has a significant improvement over other methods documented in the references in self organizing capability and precision by training process.

SELF-DEPENDENT LOCALITY PRESERVING PROJECTION WITH TRANSFORMED SPACE-ORIENTED NEIGHBORHOOD GRAPH

Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data. However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space. To address this issue,a novel DR algorithm,called the self-dependent LPP （sdLPP） is proposed. And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space. Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP. Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination. Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers

Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply artificial intelligence(AI)techniques to multiple clinical scenarios of ovarian cancer,especially in the field of medical imaging.AI-assisted imaging studies have involved computer tomography(CT),ultrasonography(US),and magnetic resonance imaging(MRI).In this review,we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer,and bring up the advances in terms of four clinical aspects,including medical diagnosis,pathological classification,targeted biopsy guidance,and prognosis prediction.Meanwhile,current status and existing issues of the researches on AI application in ovarian cancer are discussed.

A convolutional neural artistic stylization algorithm for suppressing image distortion

Aiming at the problems of image semantic content distortion and blurred foreground and background boundaries during the transfer process of convolutional neural image stylization,we propose a convolutional neural artistic stylization algorithm for suppressing image distortion.Firstly,the VGG-19 network model is used to extract the feature map from the input content image and style image and to reconstruct the content and style.Then the transfer of the input content image and style image to the output image is constrained in the local affine transformation of the color space.And the Laplacian matting matrix is constructed by combining the local affine of the input image RGB channel.For each output blocks,affine transformation maps the RGB value of the input image to the corresponding output and position,which realizes the constraint of semantic content and the control of spatial layout.Finally,the synthesized image is superimposed on the white noise image and updated iteratively with the back propagation algorithm to minimize the loss function to complete the image stylization.Experimental results show that the method can generate images with obvious foreground and background edges,clear texture,restrained semantic content-distortion,realized spatial constraint and color mapping of the transfer images,and made the stylized images visually satisfactory.

EFFICIENT GRADIENT DESCENT METHOD OFRBF NEURAL ENTWORKS WITHADAPTIVE LEARNING RATE

A new algorithm to exploit the learning rates of gradient descent method is presented, based on the second-order Taylor expansion of the error energy function with respect to learning rate, at some values decided by "award-punish" strategy. Detailed deduction of the algorithm applied to RBF networks is given. Simulation studies show that this algorithm can increase the rate of convergence and improve the performance of the gradient descent method.

Teaching College English Grammar in a Cognitive Task-Based Approach

Teaching College English in a cognitive task-based approach is actually a way to incorporate the grammar teaching within the well-sequenced activities. In this approach, language teachers draw learners＇ attention to the grammatical structures and try to be explicit about the relation between the form and the function either to confirm or to correct learners＇ hypotheses and develop their cognitive form-meaning mapping. The tasks should be designed to allow the structure to happen incidentally rather than give an overt explanation of rules. By the individualized mapping of meaning to the forms in the real meaningful context, the learners＇ initiatives in learning grammar would be greatly triggered.

A NEW APPROACH FOR UNSUPERVISED RESTORING IMAGES BASED ON WAVELET-DOMAIN PROJECTION PURSUIT LEARNING NETWORK

The Wavelet-Domain Projection Pursuit Learning Network (WDPPLN) is proposedfor restoring degraded image. The new network combines the advantages of both projectionpursuit and wavelet shrinkage. Restoring image is very difficult when little is known about apriori knowledge for multisource degraded factors. WDPPLN successfully resolves this problemby separately processing wavelet coefficients and scale coefficients. Parameters in WDPPLN,which are used to simulate degraded factors, are estimated via WDPPLN training, using scalecoefficients. Also, WDPPLN uses soft-threshold of wavelet shrinkage technique to suppress noisein three high frequency subbands. The new method is compared with the traditional methodsand the Projection Pursuit Learning Network (PPLN) method. Experimental results demonstratethat it is an effective method for unsupervised restoring degraded image.

Configurable ontology mapping based on multi-feature

A configurable ontology mapping approach based on different kinds of concept feature information is introduced in this paper. In this approach, ontology concept feature information is classified as five kinds, which respectively corresponds to five kinds of concept similarity computation methods. Many existing ontology mapping approaches have adopted the multi-feature reasoning, whereas not all feature information can be com- puted in the real ontology mapping and only fractional feature information needs to be selected in the mapping computation. Consequently a eonfigurable ontology mapping model is introduced, which is composed of CMT model, SMT model and related transformation model. Through the configurable model, users can conveniently select the most suitable features and configure the suitable weights. Simultaneously, a related 3-step ontology mapping approach is also introduced. Associated with the traditional name and instance learner-based ontology mapping approach, this approach is evaluated by an ontology mapping application example.

Radon Gas Measurements through Geiger and Sodium Iodide Scintillator——Study Efficiency Comparison

With the use ofa Geiger proportional counter with sensor tube of Russian and Chinese origin, a comparison was made in this work between measurements of environmental ionizing radiation with these detectors and a sodium iodide scintillator activated with TI （Thallium NaI）. Through measurements carried out in a room located inside a tower 25 meters high on the ITA （Technological Institute of Aeronautics） campus, it was possible to study the efficiency of the three instruments for the environmental measurement of ionizing radiations at that location. Between March 7th and June 2nd of 2017, in that region, nine intense and weak rains were observed with 12 cold fronts coming from southern Brazil. Radiation measurements and the local meteorology involved are analyzed in this work to verify possible correlations.

Mapping relationship analysis of welding assembly properties for thin-walled parts with finite element and machine learning algorithm

The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.

Cultivating Students＇ Reflective Learning Ability in College English Teaching

This paper aims to explore whether reflective learning ability can be fostered and nurtured with special instruction and whether it can enhance students＇ motivation of learning English. The experiment lasted four months, taking two parallel classes of the sophomore students at English major in Ningbo Dahongying University as the subjects. The results show that students can raise their awareness of reflective thinking and acquire reflective learning ability with teachers＇ instruction, and the two reflective activities （writing reflective journals and peer-evaluation） are very effective to increase students＇ motivation of learning English and improve their academic performances in English study.

A Method of Identifying Electromagnetic Radiation Sources by Using Support Vector Machines

Electromagnetic Radiation Source Identification(ERSI) is a key technology that is widely used in military and radiation management and in electromagnetic interference diagnostics.The discriminative capability of machine learning methods has recently been used for facilitating ERSI.This paper presents a new approach to improve ERSI by adopting support vector machines,which are proven to be effective tools in pattern classification and regression,on the basis of the spatial distribution of electromagnetic radiation sources.Spatial information is converted from 3D cubes to 1D vectors with subscripts as inputs in order to simplify the model.The model is trained with 187 500 data sets in order to enable it to identify the types of radiation source types with an accuracy of up to 99.9%.The influence of parameters(e.g.,penalty parameter,reflection and noise from the ambient environment,and the scaling method for the input data) are discussed.The proposed method has good performance in noisy and reverberant environment.It has an identification accuracy of 82.15% when the signal-to-noise ratio is 20 dB.The proposed method has better accuracy in a noisy environment than artificial neural networks.Given that each Electromagnetic(EM) source has unique spatial characteristics,this method can be used for EM source identification and EM interference diagnostics.

Sub-pixel mapping method based on BP neural network

A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel.The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information.Then the sub-pixel scaled target could be predicted by the trained model.In order to improve the performance of BP network,BP learning algorithm with momentum was employed.The experiments were conducted both on synthetic images and on hyperspectral imagery(HSI).The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.

Duodenal pseudolymphoma: A case report and review of literature

We report a rare case of duodenal pseudolymphoma without any symptoms. The lesion located in front of the head of the pancreas was found accidentally during a medical examination. The findings of computed tomography and positron emission tomography-computed tomography suggested a stromal tumor or malignant lymphoma. Surgical resection was performed. The lesions were patho- logically diagnosed as duodenal pseudolymphoma.

Therapeutic Effect of Massotherapy for Prospermia

Clinical DataAll the 60 cases of prospermia treated in thisseries were outpatients of our hospital.Their ageranged from 24 to 46 years,averaging 36.4 years.The prospermia caused by chronic prostatitis wasfound in 26 cases,urethritis in 8 cases,andcolliculitis in 2 cases,and the rest were functionalprospermia.The patients were divided into twogroups according to the patient's condition,age,andother factors.There were 30 cases in themassotherapy group,including 13 cases

Study of Preservation Status and Dietary Reconstruction in the Human Remains Recovered from Roopkund Lake through Chemical Analysis of Faunal Remains

Present study is carried out in the bone samples collected from Roopkund Lake in district Chamoli Garhwal, Uttarakhand which is located at 5,029 meters from main sea level in between Nanda Ghunghti and Trishuli peak. This historical site belongs to 9th century A.D. All the samples selected for the study were dried in room temperature as well as hot air oven at 32 ~C. Cleaning, pretreatment and digestion process of faunal remains was followed through established scientific methods. Chemical analysis i.e. concentration of different elements such as calcium, strontium, barium, magnesium and zinc as well as isotopic ratios of Carbon and Nitrogen was estimated with the help of ICP （inductively coupled plasma spectroscopy） and AAS （atomic absorption spectrophotometer）. The results obtained from the chemical analysis are significant. On the basis of concentration of different elements and ratios of Nitrogen and Carbon isotopes, the dietary habits of the peoples buried in the Roopkund Lake are identified, which is different from sample to sample person to person. Besides this, the results are also significantly helpful for knowing the preservation status of faunal remains in Roopkund Lake. Finally this study also indicated the potentiality of chemical analysis for reconstructing the palaeodiet behaviour and preservation status of bone remains.