Inference of interactions between players based on asynchronously updated evolutionary game data

The interactions between players of the prisoner's dilemma game are inferred using observed game data.All participants play the game with their counterparts and gain corresponding rewards during each round of the game.The strategies of each player are updated asynchronously during the game.Two inference methods of the interactions between players are derived with naive mean-field(n MF)approximation and maximum log-likelihood estimation(MLE),respectively.Two methods are tested numerically also for fully connected asymmetric Sherrington-Kirkpatrick models,varying the data length,asymmetric degree,payoff,and system noise(coupling strength).We find that the mean square error of reconstruction for the MLE method is inversely proportional to the data length and typically half(benefit from the extra information of update times)of that by n MF.Both methods are robust to the asymmetric degree but work better for large payoffs.Compared with MLE,n MF is more sensitive to the strength of couplings and prefers weak couplings.

System Reliability Analysis for Truss Structures Based on Automatic Updated Model

Multiple failure modes tend to be identified in the reliability analysis of a redundant truss structure.This identification process involves updating the model for identifying the next potential failure members.Herein we intend to update the finite element model automatically in the identification process of failure modes and further perform the system reliability analysis efficiently.This study presents a framework that is implemented through the joint simulation of MATLAB and APDL and consists of three parts:reliability index of a single member,identification of dominant failure modes,and system-level reliability analysis for system reliability analysis of truss structures.Firstly,RSM(response surface method)combines with a constrained optimization model to calculate the reliability indices ofmembers.Then theβ-unzipping method is adopted to identify the dominant failuremodes,and the system function in MATLAB,as well as the EKILL command in APDL,is used to facilitate the automatic update of the finite element model and realize load-redistribution.Besides,the differential equivalence recursion algorithmis performed to approximate the reliability indices of failuremodes efficiently and accurately.Eventually,the PNET(probabilistic network evaluation technique)is used to calculate the joint failure probability as well as the system reliability index.Two illustrative examples demonstrate the accuracy and efficiency of the proposed system reliability analysis framework through comparison with corresponding references.

Dynamic Update Scheme of Spectrum Information Based on Spectrum Opportunity Incentive in the Database-Assisted Dynamic Spectrum Management

To solve the problem of delayed update of spectrum information(SI) in the database assisted dynamic spectrum management(DB-DSM), this paper studies a novel dynamic update scheme of SI in DB-DSM. Firstly, a dynamic update mechanism of SI based on spectrum opportunity incentive is established, in which spectrum users are encouraged to actively assist the database to update SI in real time. Secondly, the information update contribution(IUC) of spectrum opportunity is defined to describe the cost of accessing spectrum opportunity for heterogeneous spectrum users, and the profit of SI update obtained by the database from spectrum allocation. The process that the database determines the IUC of spectrum opportunity and spectrum user selects spectrum opportunity is mapped to a Hotelling model. Thirdly, the process of determining the IUC of spectrum opportunities is further modelled as a Stackelberg game by establishing multiple virtual spectrum resource providers(VSRPs) in the database. It is proved that there is a Nash Equilibrium in the game of determining the IUC of spectrum opportunities by VSRPs. Finally, an algorithm of determining the IUC based on a genetic algorithm is designed to achieve the optimal IUC. The-oretical analysis and simulation results show that the proposed method can quickly find the optimal solution of the IUC, and ensure that the spectrum resource provider can obtain the optimal profit of SI update.

Finite element model updating for structural damage detection using transmissibility data

This paper presents a new finite element model updating method for estimating structural parameters and detecting structural damage location and severity based on the structural responses(output-only data).The method uses the sensitivity relation of transmissibility data through a least-squares algorithm and appropriate normalization of the extracted equations.The proposed transmissibility-based sensitivity equation produces a more significant number of equations than the sensitivity equations based on the frequency response function(FRF),which can estimate the structural parameters with higher accuracy.The abilities of the proposed method are assessed by using numerical data of a two-story two-bay frame model and a plate structure model.In evaluating different damage cases,the number,location,and stiffness reduction of the damaged elements and the severity of the simulated damage have been accurately identified.The reliability and stability of the presented method against measurement and modeling errors are examined using error-contaminated data.The parameter estimation results prove the method’s capabilities as an accurate model updating algorithm.

Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations

In evolutionary games,most studies on finite populations have focused on a single updating mechanism.However,given the differences in individual cognition,individuals may change their strategies according to different updating mechanisms.For this reason,we consider two different aspiration-driven updating mechanisms in structured populations:satisfied-stay unsatisfied shift(SSUS)and satisfied-cooperate unsatisfied defect(SCUD).To simulate the game player’s learning process,this paper improves the particle swarm optimization algorithm,which will be used to simulate the game player’s strategy selection,i.e.,population particle swarm optimization(PPSO)algorithms.We find that in the prisoner’s dilemma,the conditions that SSUS facilitates the evolution of cooperation do not enable cooperation to emerge.In contrast,SCUD conditions that promote the evolution of cooperation enable cooperation to emerge.In addition,the invasion of SCUD individuals helps promote cooperation among SSUS individuals.Simulated by the PPSO algorithm,the theoretical approximation results are found to be consistent with the trend of change in the simulation results.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

An improved interval model updating method via adaptive Kriging models

Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.

Cancer statistics: updated cancer burden in China

Cancer is a major public health issue in most of countries, including China. Accurate and valid information on cancer incidence, mortality, survival and relevant factors is irreplaceable for cancer prevention and control. Since the national program of cancer registry was launched by the Ministry of Health of China in 2008, the National Central Cancer Registry （NCCR） has been releasing the cancer incidence and mortality based on the data collected from cancer registries supported by the program. The cancer statistics provide current data from registered areas and aims to accurately reflect the cancer burden and epidemic in China. In 2014, the NCCR collected data for calendar year 2011 from 234 registries. After comprehensive quality＇ evaluation, data from 177 registries have been selected as sources of the reports reflecting cancer incidence and mortaliD, in the registration areas in 2011. These reports are the updated cancer statistics so far, covering much more registries and a hig population.

Updated Kimura-Takemoto classification of atrophic gastritis

BACKGROUND The Updated Sydney system for visual evaluation of gastric mucosal atrophy viaendoscopic observation is subject to sampling error and interobserver variability.The Kimura-Takemoto classification system was developed to overcome theselimitations.AIMTo compare the morphological classification of atrophic gastritis between theKimura-Takemoto system and the Updated Sydney system.METHODSA total of 169 patients with atrophic gastritis were selected according to diagnosisby the visual endoscopic Kimura-Takemoto method. Following the UpdatedKimura-Takemoto classification system, one antrum biopsy and five gastriccorpus biopsies were taken according to the visual stages of the Kimura-Takemoto system. The Updated Kimura-Takemoto classification system was thenapplied to each and showed 165 to have histological mucosal atrophy;theremaining 4 patients had no histological evidence of atrophy in any biopsy. The Updated Kimura-Takemoto classification was verified as a referencemorphological method and applied for the diagnosis of atrophic gastritis. Addingone more biopsy from the antrum to the six biopsies according to the Updated Kimura-Takemoto classification, constitutes the updated combined Kimura-Takemoto classification and Sydney system.RESULTSThe sensitivity for degree of mucosal atrophy assessed by the Updated Sydneysystem was 25% for mild, 36% for moderate, and 42% for severe, when comparedwith the Updated Kimura-Takemoto classification of atrophic gastritis formorphological diagnosis. Four types of multifocal atrophic gastritis wereidentified: sequential uniform (type 1;in 28%), sequential non-uniform (type 2;in7%), diffuse uniform (type 3;in 23%), diffuse non-uniform (type 4;in 24%), and"alternating atrophic – non-atrophic" (type 5;in 18%). The pattern of the spread ofatrophy, sequentially from the antrum to the cardiac segment of the stomach,which was described by the Updated Kimura-Takemoto system, washistologically confirmed in 82% of cases evaluated.CONCLUSIONThe Updated Sydney system is significantly inferior to the Updated Kimura-Takemoto classification for morphological verification of atrophic gastritis.

Scoping review update on somatic symptom disorder that includes additional Chinese data

Somatic symptom disorder(SSD)is a new diagnosis introduced into the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition(DSM-5),which is expected to solve the diagnostic difficulties of patients with medically unexplained symptoms.Based on the previous work,this review aims to comprehensively synthesise updated evidence related to SSD from recent years in English publications and,more extensively,from data published in Chinese language journals.The scoping review update was based on an earlier scoping review and included Chinese language publication data from China National Knowledge Internet(CNKI),WANFANG and WEIPU between January 2013 and May 2022 and data from PubMed,PsyclNFO,and Cochrane Library between June 2020 and May 2022.Initially,2984 articles were identified,of which 63 full texts were included for analysis.In China,SSD is mainly applied in scientific research,but it also shows good predictive validity and clinical application potential.The mean frequency of SSD was 4.5%in the general population,25.2%in the primary care population and 33.5%in diverse specialised care settings.Biological factors,such as brain region changes and heart rate variability,are associated with the onset of sSD.Psychological impairment related to somatic symptoms is the best predictor of prognosis.While adolescent SSD is significantly associated with family function,SSD overall is associated with an increased dysfunction of cognition and emotion,decreased quality of life,and high comorbidity with anxiety and depressive disorders.Further research is needed on suicide risk and cultural and gender-related issues.Updating the data of Chinese language studies,our research enriches the evidence-based findings related to the topics addressed in the text sections of the SSD chapter of DSM-5.However,research gaps remain about SSD reliability,population-based prevalence,suicide risk,and cultural and gender-related issues.

A Power Allocation Scheme Using Updated SLNR Value Based on Perturbation Theory

The performance of downlink multiple-input multiple-output (MIMO) cellular networks is limited by co-channel interference (CCI). In this paper, we propose a linear precoding scheme based on signal-to-leakage-and-noise ratio (SLNR) criteria which can reduce the CCI significantly. Since each user’s SLNR value is corresponding to the largest eigenvalue of the generalized matrix which indicates the channel quality that we propose a scheme to do a dynamic power allocation as an auxiliary way to improve SLNR precoding scheme. We use the perturbation theory to update each user’s SLNR value each time step in time-varying channels rather than directly decompose the channel matrix so as to reduce the amount of calculation. The simulation results show that the proposed scheme offers about 0.3 bps/Hz additional capacity gain and 0.5 dB BER gain over conventional SLNR precoding method with lower computational complexity. And it also obtains about 0.5 bps/Hz additional capacity gain and 1 dB BER gain compared to the scheme only update the preceding vectors.

Definition of hyperparasitemia in severe falciparum malaria should be updated

Hyperparasitemia is one criterion of severe falciparum malariaby World Health Organization(WHO)for more than two decades[1].Although there is a correlation between density of parasittemiaand severity of malaria,some individuals with high parasite countsmay not be severely ill,whereas others with low parasitemia mayhave ultimately fatal infections.Hyperparasitemia(more than 5%

A co-rotational updated Lagrangian formulation for a 2D beam element with consideration of the deformed curvature

A tensor-based updated Lagrangian (UL) formulation for the geometrically nonlinear analysis of 2D beam-column structures is developed by using curvilinear coordinates, which has considered the effects of the deformed curvature. Between the known configuration C1 and the desired configuration C2, a configuration C2* derived by rigid-body motion of C1 is introduced to eliminate the element-end transverse displacements between C2* and C2. A stiffness matrix is obtained in C2*; and then by a transformation defined by the element-end displacements, the stiffness matrix in C2* is transformed into that in C1. Comparing the stiffness matrix with that in the conventional UL formulation for a 2D beam element, the initial displacement stiffness matrix emerges, which results from the deformed curvature within the element. Numerical examples have verified the accuracy and efficiency of the present formulation, and the results show that the deformed curvatures have significant effects when deformations are large.

Updated Digital Map of Mafic Dyke Swarms and Large Igneous Provinces in Western Australia

Since 1894,the Geological Survey of Western Australia(GSWA)has released 14 versions of the‘Geological Map of Western Australia’.The latest in this series,published in December 2015,is the first bedrock geology map

Updates in the Diagnosis of Gestational Trophoblast Disease

GTD (Gestational Trophoblastic Disease) is a pathology that encompasses benign and malignant clinical forms, affects women of childbearing age, has a variable incidence and is more frequent in developing or underdeveloped countries, colliding with the economic barrier. The frequent absence of clear protocols and guidelines for the correct diagnosis of the pathology results in inadequate classification, imprecise treatment and failed post-therapeutic observation, increasing the risk of relapses, morbidity and mortality. The present study aims to point out updated national and international practice protocols of diagnosis of GTD, through an integrative review. Seven articles were selected and it was observed that the main international reference centers are agreed with the management suggested by the IFGO (International Federation of Gynecology and Obstetrics), being the conduct in the Hydatidiform Mole (HM): evacuation by suction and curettage under ultrasound guidance, followed by hCG monitoring every 1 - 2 weeks until normalized (usually one month for Partial Hydatidiform Mole six months for Complete Hydatidiform Mole and one year for Gestational Trophoblastic Neoplasia). Unfortunately, regarding the diagnosis of MH, the guidelines of some countries show the absence or difficulty of access to the karyotype test and ploid p57 or pelvic ultrasound accompanying the uterine curettage, contrary to what is proposed by the IFGO guideline. Establishing and complying with consistent guidelines can improve patient care, with early diagnosis of the pathology and its complications, reducing the rate of recurrence, morbidity and mortality, especially in less developed countries.

基于改进UpdateNet的行人视觉跟踪算法

目前,基于孪生网络的跟踪器主要是把跟踪过程看作是目标模板分支和待检测区域分支的一种互相关计算,它能够在速度和精度方面达到一个良好的均衡。然而,在跟踪过程中当前帧模板是对前一帧累积模板的线性结合,目标遮挡的问题一直很难被解决。针对该问题,本文采用改进SiamRPN跟踪器并融合UpdateNet网络的方案实现对行人单目标的跟踪。首先利用改进SiamRPN网络模块生成线性模板,然后在此基础上融合UpdateNet网络生成更新模板并进行多阶段训练,最后根据数据集的评价指标选取最优参数模型完成行人跟踪任务。本文在基准数据集OTB2015和其子集上进行实验,实验结果表明,本文所采用的方法取得的效果相比原来方法有明显提升,精度和成功率分别提高2.1和1.6个百分点,而且保持了实时跟踪帧率,同时在解决遮挡问题方面超过了DaSiamRPN、SiamDW等先进方法.

Health Data Availability Protection:Delta-XOR-Relay Data Update in Erasure-Coded Cloud Storage Systems

To achieve the high availability of health data in erasure-coded cloud storage systems,the data update performance in erasure coding should be continuously optimized.However,the data update performance is often bottlenecked by the constrained cross-rack bandwidth.Various techniques have been proposed in the literature to improve network bandwidth efficiency,including delta transmission,relay,and batch update.These techniques were largely proposed individually previously,and in this work,we seek to use them jointly.To mitigate the cross-rack update traffic,we propose DXR-DU which builds on four valuable techniques:(i)delta transmission,(ii)XOR-based data update,(iii)relay,and(iv)batch update.Meanwhile,we offer two selective update approaches:1)data-deltabased update,and 2)parity-delta-based update.The proposed DXR-DU is evaluated via trace-driven local testbed experiments.Comprehensive experiments show that DXR-DU can significantly improve data update throughput while mitigating the cross-rack update traffic.

Fast Verification of Network Configuration Updates

With the expansion of network services,large-scale networks have progressively become common.The network status changes rapidly in response to customer needs and configuration changes,so network configuration changes are also very frequent.However,no matter what changes,the network must ensure the correct conditions,such as isolating tenants from each other or guaranteeing essential services.Once changes occur,it is necessary to verify the after-changed network.Whereas,for the verification of large-scale network configuration changes,many current verifiers show poor efficiency.In order to solve the problem ofmultiple global verifications caused by frequent updates of local configurations in large networks,we present a fast configuration updates verification tool,FastCUV,for distributed control planes.FastCUV aims to enhance the efficiency of distributed control plane verification for medium and large networks while ensuring correctness.This paper presents a method to determine the network range affected by the configuration change.We present a flow model and graph structure to facilitate the design of verification algorithms and speed up verification.Our scheme verifies the network area affected by obtaining the change of the Forwarding Information Base(FIB)before and after.FastCUV supports rich network attributes,meanwhile,has high efficiency and correctness performance.After experimental verification and result analysis,our method outperforms the state-of-the-art method to a certain extent.

PLDMLT:Multi-Task Learning of Diabetic Retinopathy Using the Pixel-Level Labeled Fundus Images

In the field of medical images,pixel-level labels are time-consuming and expensive to acquire,while image-level labels are relatively easier to obtain.Therefore,it makes sense to learn more information(knowledge)from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training costs.In this paper,using Pixel-Level Labeled Images forMulti-Task Learning(PLDMLT),we focus on grading the severity of fundus images for Diabetic Retinopathy(DR).This is because,for the segmentation task,there is a finely labeled mask,while the severity grading task is without classification labels.To this end,we propose a two-stage multi-label learning weakly supervised algorithm,which generates initial classification pseudo labels in the first stage and visualizes heat maps at all levels of severity using Grad-Cam to further provide medical interpretability for the classification task.A multitask model framework with U-net as the baseline is proposed in the second stage.A label update network is designed to alleviate the gradient balance between the classification and segmentation tasks.Extensive experimental results show that our PLDMLTmethod significantly outperforms other stateof-the-art methods in DR segmentation on two public datasets,achieving up to 98.897%segmentation accuracy.In addition,our method achieves comparable competitiveness with single-task fully supervised learning in the DR severity grading task.