Coincidence and reproducibility in the EHT black hole experiment

This paper discusses some philosophical aspects related to the recent publication of the experimental results of the 2017 black hole experiment, namely the first image of the supermassive black hole at the center of galaxy M87. In this paper I present a philosophical analysis of the 2017 Event Horizon Telescope (EHT) black hole experiment. I first present Hacking's philosophy of experimentation. Hacking gives his taxonomy of elements of laboratory science and distinguishes a list of elements. I show that the EHT experiment conforms to major elements from Hacking's list. I then describe with the help of Galison's Philosophy of the Shadow how the EHT Collaboration created the famous black hole image. Galison outlines three stages for the reconstruction of the black hole image: Socio-Epistemology, Mechanical Objectivity, after which there is an additional Socio-Epistemology stage. I subsequently present my own interpretation of the reconstruction of the black hole image and I discuss model fitting to data. I suggest that the main method used by the EHT Collaboration to assure trust in the results of the EHT experiment is what philosophers call the Argument from Coincidence. I show that using this method for the above purpose is problematic. I present two versions of the Argument from Coincidence: Hacking's Coincidence and Cartwright's Reproducibility by which I analyse the EHT experiment. The same estimation of the mass of the black hole is reproduced in four different procedures. The EHT Collaboration concludes: the value we have converged upon is robust. I analyse the mass measurements of the black hole with the help of Cartwright's notion of robustness. I show that the EHT Collaboration construe Coincidence/Reproducibility as Technological Agnosticism and I contrast this interpretation with van Fraassen's scientific agnosticism.     

Non-invasive genetic study and population monitoring of the brown bear (Ursus arctos) (Mammalia: Ursidae) in Kastoria region – Greece

The brown bear (Ursus arctos) in Greece is considered endangered but little is known about the genetic status and the exact size of local populations. Non-invasive genetic sampling was used in this study to investigate the genetic diversity and genetic structure of the brown bear population in the Kastoria region (northwest Macedonia, Greece) and to estimate its population size. Estimation of demographic parameters was based on innovative, well-evaluated methods that can provide estimates from a single sampling session. DNA was extracted from hairs, scat and blood samples and subsequent amplification of 10 microsatellite loci allowed the identification of a minimum number of 75 living bears in the study area while the mark–recapture-based analysis resulted in a point estimation of 219 individuals. Relatively high diversity values, lack of heterozygosity deficiency as well as estimated effective population size, support the Kastoria bear population having good conservation status.     

基于Kriging模型的多点加点准则和并行代理优化算法

针对并行仿真环境下复杂工程系统的优化设计问题,提出一种基于Kriging模型、多目标策略和聚类方法的并行代理优化算法.该算法的多点加点准则,以同时优化期望改进准则和可行性概率准则为目标,首先生成兼具目标响应改进和可行域边界刻画功能的备选试验点集;再利用聚类方法从备选点集中选取多个有代表性的新试验点.通过两个数值算例和一个工程算例,将所提并行优化算法与已有算法做比较,结果表明所提算法具有更高的优化精度、效率和稳健性.     

鲸鱼优化相关向量机的网络控制系统变采样周期调度

针对资源受限的网络控制系统,提出一种基于鲸鱼优化相关向量机的变采样周期调度算法。通过网络监测模块获取网络带宽与数据传输时间数据,建立鲸鱼优化相关向量机的预测模型,实现对网络带宽及数据传输时间的预测。采用模糊推理计算系统各回路通信带宽的分配权重,进而结合通信带宽及数据传输时间的预测值对各闭环回路的采样周期进行计算,完成采样周期的实时调节。仿真结果表明,在资源受限条件下,所提算法保证了系统的稳定性与控制精度。     

Compressive sensing based multiuser detector for massive MBM MIMO uplink

Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.     

多精英采样与个体差分学习的分布估计算法

提出了基于多精英采样和差分搜索的分布估计算法EDA-M/D (Estimation distribution algorithm based on multiple elites sampling and individuals differential search)。EDA-M/D利用多精英个体独立采样生成子代来提升算法全局搜索能力,利用精英群体分布的σ²约束采样半径,实现种群从全局搜索逐步过度到局部搜索。当精英群体停滞时,劣势个体借助精英群体的μ和种群历史最优解进行差分搜索,帮助种群跳出局部最优解。通过多精英采样与差分搜索的自适应协同实现种群宏观信息与个体微观信息的有机融合。实验结果表明EDA-M/D在稳定性和搜索能力方面均表现出明显的优势。     

双参数优先级递推式变采样周期调度算法

针对资源受限的网络控制系统,提出了一种基于双参数优先级的递推式变采样周期动态调度算法。计算控制回路的网络需求度和网络紧急度双参数,并以控制回路误差的绝对值建立二次平方根映射函数来确定双参数权重,实现对传感器优先级的调度。同时,综合分析控制回路性能递推动态调节各控制回路的采样周期,在保证控制系统的稳定的前提下完成调度调节。仿真表明该调度算法提高了网络控制系统的控制质量。     

Dynamic Latent Class Model Averaging for Online Prediction

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state‐space model for the parameters of each of the candidate models of the system with a Markov chain model for the best model. We propose a polychotomous regression model for the transition weights to assume that the probability of a change in time depends on the past through the values of the most recent time periods and spatial correlation among the regions. The evolution of the parameters in each submodel is defined by exponential forgetting. This structure allows the ‘correct’ model to vary over both time and regions. In contrast to existing methods, the proposed model naturally incorporates clustering and prediction analysis in a single unified framework. We develop an efficient Gibbs algorithm for computation, and we demonstrate the value of our framework on simulated experiments and on a real‐world problem: forecasting IBM's corporate revenue. Copyright © 2014 John Wiley & Sons, Ltd.     

采样频率与数据长度对中心动脉波形重建的影响

传递函数法重建CAP(central aortic pressure)多是基于自回归各态历经(auto regressive eXogenous, ARX)模型或傅里叶变换,未考虑采样频率、数据长度.为了研究采样频率和数据长度对重建CAP的影响,基于ARX模型和傅里叶变换,重建CAP并分析误差.结果表明,采样频率100Hz,数据长度大于3s时,基于ARX模型重建CAP效果较好(均方根误差(306.6±80.0)Pa,波形匹配度89%);基于傅里叶变换的算法对采样频率不敏感,数据长度为6s时效果较好(均方根误差(493.3±320.0)Pa,波形匹配度84%).     

基于AUV的航迹追踪自适应UKF算法

无迹卡尔曼滤波算法(UKF,unscented kalman filter)是一种常见的(AUV,autonomous underwater vehicle)加权统计线性回归航迹追踪算法,其算法冗余度低于(EKF,extended kalman filter)、(PF,particle filter)及(PSO,particle swarm optimization)等数值优化算法,且算法效率较高。然而,UKF控制算法中的系统采样时间间隔通常会被设置为常数,由此可能会产生预测值的误差累积,从而影响导航预测结果的精度。因此,笔者提出了基于AUV的航迹追踪自适应无迹卡尔曼滤波算法(AUKF,adaptive unscented kalamn filter algorithm),以期降低预测算法的累积误差。该预测方法依据标准UKF算法的原理,通过构造相应的约束、判断与反馈机制,调整系统状态方程中每一步的采样间隔t,从而提升算法的航迹追踪精度并减少过程噪声及传感器噪声对预测过程的影响。最后,通过仿真实验与结果对比,近一步验证了之前所提出的设想。