基于密度峰值聚类的Tri-training算法

Tri-training利用无标签数据进行分类可有效提高分类器的泛化能力,但其易将无标签数据误标,从而形成训练噪声。提出一种基于密度峰值聚类的Tri-training(Tri-training with density peaks clustering,DPC-TT)算法。密度峰值聚类通过类簇中心和局部密度可选出数据空间结构表现较好的样本。DPC-TT算法采用密度峰值聚类算法获取训练数据的类簇中心和样本的局部密度,对类簇中心的截断距离范围内的样本认定为空间结构表现较好,标记为核心数据,使用核心数据更新分类器,可降低迭代过程中的训练噪声,进而提高分类器的性能。实验结果表明:相比于标准Tritraining算法及其改进算法,DPC-TT算法具有更好的分类性能。

基于Tri-training的社交媒体药物不良反应实体抽取

社交媒体因其数据的实时性,对其充分利用可以弥补传统医疗文献药物不良反应中实体抽取的迟滞性问题,但社交媒体文本面临标注数据成本高、数据噪声大等问题,使得模型难以发挥良好的效果。针对社交媒体大量未标注语料存在标注成本高的问题,采用Tri-training半监督的方法进行社交媒体药物不良反应实体抽取,通过三个学习器Transformer+CRF、BiLSTM+CRF和IDCNN+CRF对未标注数据进行标注,再利用一致性评价函数迭代地扩展训练集,最后通过加权投票整合模型输出标签。针对社交媒体的文本不正式性(口语化严重、错别字等)问题,通过融合字与词两个粒度的向量作为整个模型嵌入层的输入,来提取更丰富的语义信息。实验结果表明,提出的模型在“好大夫在线”网站获取的数据集上取得了良好表现。

Cloudless-Training:基于serverless的高效跨地域分布式ML训练框架

跨地域分布式机器学习(ML)训练能够联合多区域的云资源协作训练,可满足许多新兴ML场景(比如大型模型训练、联邦学习)的训练需求。但其训练效率仍受2方面挑战的制约。首先,多区域云资源缺乏有效的弹性调度,这会影响训练的资源利用率和性能;其次,模型跨地域同步需要在广域网(WAN)上高频通信,受WAN的低带宽和高波动的影响,会产生巨大通信开销。本文提出Cloudless-Training,从3个方面实现高效的跨地域分布式ML训练。首先,它基于serverless计算模式实现,使用控制层和训练执行层的2层架构,支持多云区域的弹性调度和通信。其次,它提供一种弹性调度策略,根据可用云资源的异构性和训练数据集的分布自适应地部署训练工作流。最后,它提供了2种高效的跨云同步策略,包括基于梯度累积的异步随机梯度下降(ASGD-GA)和跨云参数服务器(PS)间的模型平均(MA)。Cloudless-Training是基于OpenFaaS实现的,并被部署在腾讯云上评估,实验结果表明Cloudless-Training可显著地提高跨地域分布式ML训练的资源利用率(训练成本降低了9.2%~24.0%)和同步效率(训练速度最多比基线快1.7倍),并能保证模型的收敛精度。

The influence of resistance exercise training prescription variables on skeletal muscle mass,strength,and physical function in healthy adults:An umbrella review

Purpose:The aim of this umbrella review was to determine the impact of resistance training(RT)and individual RT prescription variables on muscle mass,strength,and physical function in healthy adults.Methods:Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,we systematically searched and screened eligible systematic reviews reporting the effects of differing RT prescription variables on muscle mass(or its proxies),strength,and/or physical function in healthy adults aged>18 years.Results:We identified 44 systematic reviews that met our inclusion criteria.The methodological quality of these reviews was assessed using A Measurement Tool to Assess Systematic Reviews;standardized effectiveness statements were generated.We found that RT was consistently a potent stimulus for increasing skeletal muscle mass(4/4 reviews provide some or sufficient evidence),strength(4/6 reviews provided some or sufficient evidence),and physical function(1/1 review provided some evidence).RT load(6/8 reviews provided some or sufficient evidence),weekly frequency(2/4 reviews provided some or sufficient evidence),volume(3/7 reviews provided some or sufficient evidence),and exercise order(1/1 review provided some evidence)impacted RT-induced increases in muscular strength.We discovered that 2/3 reviews provided some or sufficient evidence that RT volume and contraction velocity influenced skeletal muscle mass,while 4/7 reviews provided insufficient evidence in favor of RT load impacting skeletal muscle mass.There was insufficient evidence to conclude that time of day,periodization,inter-set rest,set configuration,set end point,contraction velocity/time under tension,or exercise order(only pertaining to hypertrophy)influenced skeletal muscle adaptations.A paucity of data limited insights into the impact of RT prescription variables on physical function.Conclusion:Overall,RT increased muscle mass,strength,and physical function compared to no exercise.RT intensity(load)and weekly frequency impacted RT-induced increases in muscular strength but not muscle hypertrophy.RT volume(number of sets)influenced muscular strength and hypertrophy.

Surgical intervention combined with weight-bearing walking training promotes recovery in patients with chronic spinal cord injury:a randomized controlled study

For patients with chronic spinal cord injury,the co nventional treatment is rehabilitation and treatment of spinal cord injury complications such as urinary tract infection,pressure sores,osteoporosis,and deep vein thrombosis.Surgery is rarely perfo rmed on spinal co rd injury in the chronic phase,and few treatments have been proven effective in chronic spinal cord injury patients.Development of effective therapies fo r chronic spinal co rd injury patients is needed.We conducted a randomized controlled clinical trial in patients with chronic complete thoracic spinal co rd injury to compare intensive rehabilitation(weight-bearing walking training)alone with surgical intervention plus intensive rehabilitation.This clinical trial was registered at ClinicalTrials.gov(NCT02663310).The goal of surgical intervention was spinal cord detethering,restoration of cerebrospinal fluid flow,and elimination of residual spinal cord compression.We found that surgical intervention plus weight-bearing walking training was associated with a higher incidence of American Spinal Injury Association Impairment Scale improvement,reduced spasticity,and more rapid bowel and bladder functional recovery than weight-bearing walking training alone.Overall,the surgical procedures and intensive rehabilitation were safe.American Spinal Injury Association Impairment Scale improvement was more common in T7-T11 injuries than in T2-T6 injuries.Surgery combined with rehabilitation appears to have a role in treatment of chronic spinal cord injury patients.

Transurethral resection of bladder tumor: A systematic review of simulator-based training courses and curricula

Objective:Transurethral resection of bladder tumor is one of the most common everyday urological procedures.This kind of surgery demands a set of skills that need training and experience.In this review,we aimed to investigate the current literature to find out if simulators,phantoms,and other training models could be used as a tool for teaching urologists.Methods:A systematic review was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses statement and the recommendations of the European Association of Urology guidelines for conducting systematic reviews.Fifteen out of 932 studies met our inclusion criteria and are presented in the current review.Results:The UroTrainer(Karl Storz GmbH,Tuttlingen,Germany),a virtual reality training simulator,achieved positive feedback and an excellent face and construct validity by the participants.The inspection of bladder mucosa,blood loss,tumor resection,and procedural time was improved after the training,especially for inexperienced urologists and medical students.The construct validity of UroSim®(VirtaMed,Zurich,Switzerland)was established.SIMBLA simulator(Samed GmbH,Dresden,Germany)was found to be a realistic and useful tool by experts and urologists with intermediate experience.The test objective competency model based on SIMBLA simulator could be used for evaluating urologists.The porcine model of the Asian Urological Surgery Training and Education Group also received positive feedback by the participants that tried it.The Simulation and Technology Enhanced Learning Initiative Project had an extraordinary face and content validity,and 60%of participants would like to use the simulators in the future.The 5-day multimodal training curriculum“Boot Camp”in the United Kingdom achieved an increase of the level of confidence of the participants that lasted months after the project.Conclusion:Simulators and courses or curricula based on a simulator training could be a valuable learning tool for any surgeon,and there is no doubt that they should be a part of every urologist's technical education.

Effect of resistance training volume on body adiposity,metabolic risk,and inflammation in postmenopausal and older females:Systematic review and meta-analysis of randomized controlled trials

Purpose:This meta-analytical study aimed to explore the effects of resistance training(RT) volume on body adiposity,metabolic risk,and inflammation in postmenopausal and older females.Methods:A systematic search was performed for randomized controlled trials in PubMed,Scopus,Web of Science,and SciELO.Randomized controlled trials with postmenopausal and older females that compared RT effects on body adiposity,metabolic risk,and inflammation with a control group(CG) were included.Independent reviewers selected the studies,extracted the data,and performed the risk of bias and certainty of the evidence(Grading of Recommendations,Assessment,Development,and Evaluation(GRADE)) evaluations.Total body and abdominal adiposity,blood lipids,glucose,and C-reactive protein were included for meta-analysis.A random-effects model,standardized mean difference(Hedges’ g),and 95% confidence interval(95%CI) were used for meta-analysis.Results:Twenty randomized controlled trials(overall risk of bias:some concerns;GRADE:low to very low) with overweight/obese postmenopausal and older females were included.RT groups were divided into low-volume RT(LVRT,~44 sets/week) and high-volume RT(HVRT,~77 sets/week).Both RT groups presented improved body adiposity,metabolic risk,and inflammation when compared to CG.However,HVRT demonstrated higher effect sizes than LVRT for glucose(HVRT=-1.19;95%CI:-1.63 to-0.74;LVRT=-0.78;95%CI:-1.15 to-0.41) and C-reactive protein(HVRT=-1.00;95%CI:-1.32 to-0.67;LVRT=-0.34;95%CI,-0.63 to-0.04)) when compared to CG.Conclusion:Compared to CG,HVRT protocols elicit greater improvements in metabolic risk and inflammation outcomes than LVRT in overweight/obese postmenopausal and older females.

Attack-Resilient Distributed Cooperative Control of Virtually Coupled High-Speed Trains via Topology Reconfiguration

Dear Editor,This letter addresses the resilient distributed cooperative control problem of a virtually coupled train convoy under stochastic disturbances and cyber attacks.The main purpose is to achieve distributed coordination of virtually coupled high-speed trains with the prescribed inter-train distance and same cruise velocity.

Beam Training and Tracking in mmWave Communication:A Survey

Communicating on millimeter wave(mmWave)bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission.However,mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles.To overcome these challenges,highly directional beams are exploited to achieve robust links by hybrid beamforming.Accurately aligning the transmitter and receiver beams,i.e.beam training,is vitally important to high data rate transmission.However,it may cause huge overhead which has negative effects on initial access,handover,and tracking.Besides,the mobility patterns of users are complicated and dynamic,which may cause tracking error and large tracking latency.An efficient beam tracking method has a positive effect on sustaining robust links.This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation(6G)mobile network.Especially,some open research problems are proposed to realize fast,accurate,and robust beam training and tracking.We hope that this survey provides guidelines for the researchers in the area of mmWave communications.

Optimized operation scheme of flash-memory-based neural network online training with ultra-high endurance

With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.

Dynamic analysis of axle box bearings on the high-speed train caused by wheel-rail excitation

To explore the impact of wheel-rail excitation on the dynamic performance of axle box bearings,a dynamic model of the high-speed train including axle box bearings is developed.Subsequently,the dynamic response characteristics of the axle box bearing are examined.The investigation focuses on the acceleration characteristics of bearing vibration under excitation of track irregularities and wheel flats.In addition,experiments on both normal and faulty bearings are conducted separately,and the correctness of the model and some conclusions are verified.According to the research,track irregularity is unfavorable for bearing fault detection based on resonance demodulation.Under the same speed conditions,the acceleration peak of bearing is inversely proportional to the length of the wheel flat and directly proportional to its depth.The paper will contribute to a deeper understanding of the dynamic performance of axle box bearings.

Torque effect on vibration behavior of high-speed train gearbox under internal and external excitations

The high-speed train transmission system,experiencing both the internal excitation originating from gear meshing and the external excitation originating from the wheel-rail interaction,exhibits complex dynamic behavior in the actual service environment.This paper focuses on the gearbox in the high-speed train to carry out the bench test,in which various operat-ing conditions(torques and rotation speeds)were set up and the excitation condition covering both internal and external was created.Acceleration responses on multiple positions of the gearbox were acquired in the test and the vibration behavior of the gearbox was studied.Meanwhile,a stochastic excitation modal test was also carried out on the test bench under different torques,and the modal parameter of the gearbox was identified.Finally,the sweep frequency response of the gearbox under gear meshing excitation was analyzed through dynamic modeling.The results showed that the torque has an attenuating effect on the amplitude of gear meshing frequency on the gearbox,and the effect of external excitation on the gearbox vibration cannot be ignored,especially under the rated operating condition.It was also found that the torque affects the modal param-eter of the gearbox significantly.The torque has a great effect on both the gear meshing stiffness and the bearing stiffness in the transmission system,which is the inherent reason for the changed modal characteristics observed in the modal test and affects the vibration behavior of the gearbox consequently.

Influences of the Fresh Air Volume on the Removal of Cough-Released Droplets in a Passenger Car of a High-Speed Train Using CFD

The spread and removal of pollution sources,namely,cough-released droplets in three different areas(front,middle,and rear areas)of a fully-loaded passenger car in a high-speed train under different fresh air flow volume were studied using computational fluid dynamics(CFD)method.In addition,the structure of indoor flow fields was also analysed.The results show that the large eddies are more stable and flow faster in the air supply under Mode 2(fresh air volume:2200m3/h)compared to Mode 1(fresh air volume:1100m3/h).By analysing the spreading process of droplets sprayed at different locations in the passenger car and with different particle sizes,the removal trends for droplets are found to be similar under the two air supply modes.However,when increasing the fresh air flow volume,the droplets in the middle and front areas of the passenger car are removed faster.When the droplets had dispersed for 60s,Mode 2 exhibited a removal rate approximately 1%–3%higher than Mode 1 for small and medium-sized droplets with diameters of 10 and 50μm.While those in the rear area,the situation is reversed,with Mode 1 slightly surpassing Mode 2 by 1%–3%.For large droplets with a diameter of 100μm,both modes achieved a removal rate of over 96%in all three regions at the 60 s.The results can provide guidance for air supply modes of passenger cars of high-speed trains,thus suppressing the spread of virus-carrying droplets and reducing the risk of viral infection among passengers.

Effect of cognitive training on brain dynamics

The human brain is highly plastic.Cognitive training is usually used to modify functional connectivity of brain networks.Moreover,the structures of brain networks may determine its dynamic behavior which is related to human cognitive abilities.To study the effect of functional connectivity on the brain dynamics,the dynamic model based on functional connections of the brain and the Hindmarsh–Rose model is utilized in this work.The resting-state fMRI data from the experimental group undergoing abacus-based mental calculation(AMC)training and from the control group are used to construct the functional brain networks.The dynamic behavior of brain at the resting and task states for the AMC group and the control group are simulated with the above-mentioned dynamic model.In the resting state,there are the differences of brain activation between the AMC group and the control group,and more brain regions are inspired in the AMC group.A stimulus with sinusoidal signals to brain networks is introduced to simulate the brain dynamics in the task states.The dynamic characteristics are extracted by the excitation rates,the response intensities and the state distributions.The change in the functional connectivity of brain networks with the AMC training would in turn improve the brain response to external stimulus,and make the brain more efficient in processing tasks.

Aerodynamic Features of High-Speed Maglev Trains with Different Marshaling Lengths Running on a Viaduct under Crosswinds

The safety and stability of high-speed maglev trains traveling on viaducts in crosswinds critically depend on their aerodynamic characteristics.Therefore,this paper uses an improved delayed detached eddy simulation(IDDES)method to investigate the aerodynamic features of high-speed maglev trains with different marshaling lengths under crosswinds.The effects of marshaling lengths(varying from 3-car to 8-car groups)on the train’s aerodynamic performance,surface pressure,and the flow field surrounding the train were investigated using the three-dimensional unsteady compressible Navier-Stokes(N-S)equations.The results showed that the marshaling lengths had minimal influence on the aerodynamic performance of the head and middle cars.Conversely,the marshaling lengths are negatively correlated with the time-average side force coefficient(CS)and time-average lift force coefficient(Cl)of the tail car.Compared to the tail car of the 3-car groups,the CS and Cl fell by 27.77%and 18.29%,respectively,for the tail car of the 8-car groups.It is essential to pay more attention to the operational safety of the head car,as it exhibits the highest time average CS.Additionally,the mean pressure difference between the two sides of the tail car body increased with the marshaling lengths,and the side force direction on the tail car was opposite to that of the head and middle cars.Furthermore,the turbulent kinetic energy of the wake structure on the windward side quickly decreased as marshaling lengths increased.

Boosting Adversarial Training with Learnable Distribution

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural networks.Adversarial training is one of the most potent methods to defend against adversarial attacks.However,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training.This paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture model.The distribution centroid is built to classify samples and constrain the distribution of the sample features.The natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the model.The proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial training.This algorithm gradually increases the accuracy and robustness of the model by scaling perturbation.Finally,the proposed method outputs the predicted labels and the distance between the sample and the distribution centroid.The distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model defense.The effectiveness of the proposed method is demonstrated through comprehensive experiments.

Simplified prediction models for acoustic installation effects of train-mounted equipment

Acoustic models of railway vehicles in standstill and pass-by conditions can be used as part of a virtual certification process for new trains.For each piece of auxiliary equipment,the sound power measured on a test bench is combined with meas-ured or predicted transfer functions.It is important,however,to allow for installation effects due to shielding by fairings or the train body.In the current work,fast-running analytical models are developed to determine these installation effects.The model for roof-mounted sources takes account of diffraction at the corner of the train body or fairing,using a barrier model.For equipment mounted under the train,the acoustic propagation from the sides of the source is based on free-field Green’s functions.The bottom surfaces are assumed to radiate initially into a cavity under the train,which is modelled with a simple diffuse field approach.The sound emitted from the gaps at the side of the cavity is then assumed to propagate to the receivers according to free-field Green’s functions.Results show good agreement with a 2.5D boundary element model and with measurements.Modelling uncertainty and parametric uncertainty are evaluated.The largest variability occurs due to the height and impedance of the ground,especially for a low receiver.This leads to standard deviations of up to 4 dB at low frequencies.For the roof-mounted sources,uncertainty over the location of the corner used in the equivalent barrier model can also lead to large standard deviations.

Numerical analysis of moving train induced vibrations on tunnel,surrounding ground and structure

This study is focused on the effect of vibration induced by moving trains in tunnels on the surrounding ground and structures.A three-dimensional finite element model is established for a one-track railway tunnel and an adjacent twelve-storey building frame by using commercial software Midas GTS-NX(2019)and Midas Gen.This study considered the moving load effect of a complete train,which varies with space as well as with time.The effect of factors such as train speed,overburden pressure on the tunnel and variation in soil properties are studied in the time domain.As a result,the variations in horizontal and vertical acceleration for two different sites,i.e.,the free ground surface(without structure)and the area containing the structure,are compared.Also,the displacement pattern of the raft foundation is plotted for different train velocities.At lower speeds,the heaving phenomenon is negligible,but as the speed increases,both the heaving and differential settlement increase in the foundation.This study demonstrates that the effect of moving train vibrations should be considered in the design of new nearby structures and proper ground improvement should be considered for existing structures.

Robust adaptive radar beamforming based on iterative training sample selection using kurtosis of generalized inner product statistics

In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.

Adaptive H_(∞)Filtering Algorithm for Train Positioning Based on Prior Combination Constraints

To solve the problem of data fusion for prior information such as track information and train status in train positioning,an adaptive H∞filtering algorithm with combination constraint is proposed,which fuses prior information with other sensor information in the form of constraints.Firstly,the train precise track constraint method of the train is proposed,and the plane position constraint and train motion state constraints are analysed.A model for combining prior information with constraints is established.Then an adaptive H∞filter with combination constraints is derived based on the adaptive adjustment method of the robustness factor.Finally,the positioning effect of the proposed algorithm is simulated and analysed under the conditions of a straight track and a curved track.The results show that the positioning accuracy of the algorithm with constrained filtering is significantly better than that of the algorithm without constrained filtering and that the algorithm with constrained filtering can achieve better performance when combined with track and condition information,which can significantly reduce the train positioning error.The effectiveness of the proposed algorithm is verified.