Turbo Message Passing Based Burst Interference Cancellation for Data Detection in Massive MIMO-OFDM Systems

This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.

UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

Orthogonal time frequency space(OTFS)technique, which modulates data symbols in the delayDoppler(DD) domain, presents a potential solution for supporting reliable information transmission in highmobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM). The empirical state evolution(SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

Parametric message passing-based relative navigation in joint tactical information distribution system

Relative navigation is a key feature in the joint tactical information distribution system（JTIDS）.A parametric message passing algorithm based on factor graph is proposed to perform relative navigation in JTIDS.First of all,the joint posterior distribution of all the terminals＇ positions is represented by factor graph.Because of the nonlinearity between the positions and time-of-arrival（TOA） measurement,messages cannot be obtained in closed forms by directly using the sum-product algorithm on factor graph.To this end,the Euclidean norm is approximated by Taylor expansion.Then,all the messages on the factor graph can be derived in Gaussian forms,which enables the terminals to transmit means and covariances.Finally,the impact of major error sources on the navigation performance are evaluated by Monte Carlo simulations,e.g.,range measurement noise,priors of position uncertainty and velocity noise.Results show that the proposed algorithm outperforms the extended Kalman filter and cooperative extended Kalman filter in both static and mobile scenarios of the JTIDS.

Vector Approximate Message Passing with Sparse Bayesian Learning for Gaussian Mixture Prior

Compressed sensing(CS)aims for seeking appropriate algorithms to recover a sparse vector from noisy linear observations.Currently,various Bayesian-based algorithms such as sparse Bayesian learning(SBL)and approximate message passing(AMP)based algorithms have been proposed.For SBL,it has accurate performance with robustness while its computational complexity is high due to matrix inversion.For AMP,its performance is guaranteed by the severe restriction of the measurement matrix,which limits its application in solving CS problem.To overcome the drawbacks of the above algorithms,in this paper,we present a low complexity algorithm for the single linear model that incorporates the vector AMP(VAMP)into the SBL structure with expectation maximization(EM).Specifically,we apply the variance auto-tuning into the VAMP to implement the E step in SBL,which decrease the iterations that require to converge compared with VAMP-EM algorithm when using a Gaussian mixture(GM)prior.Simulation results show that the proposed algorithm has better performance with high robustness under various cases of difficult measurement matrices.

UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular networks.In this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler.We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed algorithm.To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)algorithm.Finally,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

Hybrid simulation of q=1 high-order harmonics driven by passing energetic particles in tokamak plasmas

High-order harmonics q(ψ_(s))=1 energetic particle modes(EPMs)have been observed in toroidal plasmas experiments with neutral beam injection.To investigate these phenomena,linear properties and nonlinear dynamics of these EPMs driven by passing energetic particles(EPs)are studied via the global hybrid kinetic-magnetohydrodynamic code M3D-K.Simulation results demonstrate that passing EPs'effects on high mode-number harmonics(q(ψ_(s))=m/n=2/2,3/3,4/4)instability are more obvious than the q(ψ_(s))=1/1 mode,especially when q-profile is sufficiently flat in the core region.Furthermore,the effects of the pitch angleΛ_0 and beam ion pressure P_(hot)/P_(total)on the features of high n components are also analyzed specifically.It is found that there exists only one resonant condition for these EPMs.In the nonlinear phase,these high mode-number harmonics can induce significant energetic ions redistribution and chirping up phenomena,which differs from the classical fishbone excited by passing EPs.These discoveries are conducive to better apprehend the underlying physical mechanisms of the highorder harmonics driven by passing EPs.

China Mourns Passing Of Jiang Zemin

A memorial for Jiang Zemin,who passed away on November 30,2022,at the age of 96,was held on December 6,2022,at the Great Hall of the People in Beijing.President Xi Jinping addressed the meeting.In a eulogy delivered at the memorial,Xi attributed the tremendous achievements of the Party and the nation between 1989 and 2002 to the late Chinese leader’s eminence and bold vision,the key role he played,and his superb political leadership.

Non-Stationary and Resonant Passage of a System: A High-Frequency Cutoff Noise

We study non-equilibrium behaviors of a particle subjected to a high-frequency cutoff noise in terms of generalized Langevin equation, where the spectrum of internal noise is considered to be of the generalized Debye form. A closed solution is impossible even if the equation is linear, because the Laplace transform of the memory kernel is a multi-value function. We use a numerical method to calculate the velocity correlation function of a force-free particle and the probability of a particle passing over the top of an inverse harmonic potential. We indicate the nonergodicity of the second type, i.e., the auto-correlation function of the velocity approaches to non-stationary at large times. Applied to the barrier passage problem, we find and analyse a resonant phenomenon that the dependence of the cutoff frequency is nonmonotonic when the initial directional velocity of the particle is less than the critical value, the latter is determined by the passing probability equal to 0.5.

基于RFieldbus的Token-Passing协议实时性能建模

RFieldbus 无线现场总线的 MAC 协议 Token Passing 可看成是一个多台多队列排队系统。在分析了 TTR(令牌目的循环时间)对 RFieldbus 中 3 个优先级队列的服务时间的动态限制后,多台单队列排队模型 RF-P 被提出。对从站的轮巡交换数据任务被转化为RF-P 中的令牌内走步时间,原有的高、低级别两个队列则化成了两个具有不同 Poisson 到达率的 M/G/1/∞服务台。利用排队论中的准守恒 PCL 定律,在将服务时间限制转换为数量限制后,求出了等待响应时间的数学解析式。与试验结果的比较表明,在负载不大于 60%时其偏差度约在 10-20%间。

An MPI parallel DEM-IMB-LBM framework for simulating fluid-solid interaction problems

The high-resolution DEM-IMB-LBM model can accurately describe pore-scale fluid-solid interactions,but its potential for use in geotechnical engineering analysis has not been fully unleashed due to its prohibitive computational costs.To overcome this limitation,a message passing interface(MPI)parallel DEM-IMB-LBM framework is proposed aimed at enhancing computation efficiency.This framework utilises a static domain decomposition scheme,with the entire computation domain being decomposed into multiple subdomains according to predefined processors.A detailed parallel strategy is employed for both contact detection and hydrodynamic force calculation.In particular,a particle ID re-numbering scheme is proposed to handle particle transitions across sub-domain interfaces.Two benchmarks are conducted to validate the accuracy and overall performance of the proposed framework.Subsequently,the framework is applied to simulate scenarios involving multi-particle sedimentation and submarine landslides.The numerical examples effectively demonstrate the robustness and applicability of the MPI parallel DEM-IMB-LBM framework.

权威与专制教养同低年级小学生认知过程的交叉滞后分析

为考察父母权威与专制教养方式同低年级小学生计划—注意—同时性加工—继时性加工(PASS)认知过程之间的双向关系,对二、三年级共113名小学生的认知过程及家长的教养方式进行为期一年两次的追踪测查,结果表明:权威教养与低年级小学生PASS认知过程不存在双向预测关系,专制教养与PASS认知过程之间存在负向的双向预测关系。专制教养会使儿童认知发展受到负面影响,儿童认知发展较差又会导致父母采用专制教养方式。基于此,父母应采用温暖、积极的养育行为,为儿童的认知发展提供家庭支持。

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

Glia-to-neuron reprogramming to the rescue?

Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be changed.An exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.

Train-induced aerodynamic characteristics of vertical sound barriers influenced by several factors

Investigations into the aerodynamic properties of vertical sound barriers exposed to high-speed operations employ computational fluid dynamics.The primary focus of this research is to evaluate the influence of train speed and the distance(D)from the track centerline under various operating conditions.The findings elucidate a marked elevation in the aerodynamic effect amplitude on sound barriers as train speeds increase.In single-train passages,the aerodynamic effect amplitude manifests a direct relationship with the square of the train speed.When two trains pass each other,the aerodynamic amplitude intensifies due to an additional aerodynamic increment on the sound barrier.This increment exhibits an approximate quadratic correlation with the retrograde train speed.Notably,the impact of high-speed trains on sound barrier aerodynamics surpasses that of low-speed trains,and this discrepancy amplifies with larger speed differentials between trains.Moreover,the train-induced aerodynamic effect diminishes significantly with greater distance(D),with occurrences of pressure coefficient(CP)exceeding the standard thresholds during dual-train passages.This study culminates in the formulation of universal equations for quantifying the influence of train speed and distance(D)on sound barrier aerodynamic characteristics across various operational scenarios.

Preface

This focused issue of the Communications on Applied Mathematics and Computation is dedicated to the memory of Professor Ching-Shan Chou,who passed away in November 2021.With her passing,our community of applied mathematicians lost not only a brilliant researcher but also a cherished friend and colleague.

An End-To-End Hyperbolic Deep Graph Convolutional Neural Network Framework

Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial distortions.Moreover,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures.To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic space.In HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic space.Additionally,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing framework.In addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention coefficients.Through the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical graphs.Experimental results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding representations.Furthermore,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhancements.

Efficient signal detector design for OTFS with index modulation

In this paper,efficient signal detectors are designed for Orthogonal Time Frequency Space(OTFS)modulation with Index Modulation(IM)systems.Firstly,the Minimum Mean Squared Error(MMSE)based linear equalizer and its corresponding soft-aided decision are studied for OTFS-IM.To further improve the performance,a Vectorby-Vector-aided Message Passing(VV-MP)detector and its associated soft-decision are proposed,where each IM symbol is considered an entire vector utilized for message calculation and passing.Simulation results are shown that the OTFS-IM system relying on the proposed detectors is capable of providing considerable Bit Error Rate(BER)performance gains over the OTFS and Orthogonal Frequency Division Multiplex(OFDM)with IM systems.

Apolipoprotein A-I binding protein-mediated neuroprotection in glaucomatous neuroinflammation and neurodegeneration

Glaucoma is a multifactorial eye disorder that can cause vision loss and irreversible blindness,affecting individ uals aged 40 to 80 yea rs worldwide.Due to the aging population,it is expected that the number of people affected by glaucoma will surpass 111 million by 2040 as the disease becomes more prevalent.Glaucoma primarily contributes to optic nerve axon loss and the progressive degeneration of retinal ganglion cells (RGCs),subsequently leading to vision impairment.

Evaluation of the Impact of Refined Quality Control Management Model on the Qualified Rate of Disinfection and Sterilization of Surgical Instruments in the Sterilization Supply Center

Objective:To evaluate the application value of a refined quality control management model for a sterilization supply center.Methods:A retrospective analysis was conducted on the work situation of the sterilization supply center from January 2021 to January 2023.The work situation before January 31,2022,was classified as the control group;a routine quality control management model was implemented,and the work situation after January 31,2022,was classified as the observation group.The quality of medical device management and department satisfaction between the two groups were compared.Results:The timely recovery and supply rate,classification and cleaning pass rate,disinfection pass rate,packaging pass rate,sterilization pass rate,and department satisfaction score in the observation group were all higher than those of the control group(P<0.05).Conclusion:Implementing a refined quality control management model in the sterilization supply center can improve the quality management level of medical devices and department satisfaction and is worthy of promotion.

Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.