Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

4D Printing of Shape Memory Materials for Textiles: Mechanism,Mathematical Modeling,and Challenges

Shape memory materials (SMMs) in 3D printing (3DP) technology garnered much attention due to their ability to respond to external stimuli, which direct this technology toward an emerging area of research, “4D printing (4DP) technology.” In contrast to classical 3D printed objects, the fourth dimension, time, allows printed objects to undergo significant changes in shape, size, or color when subjected to external stimuli. Highly precise and calibrated 4D materials, which can perform together to achieve robust 4D objects, are in great demand in various fields such as military applications, space suits, robotic systems, apparel, healthcare, sports, etc. This review, for the first time, to the best of the authors’ knowledge, focuses on recent advances in SMMs (e.g., polymers, metals, etc.) based wearable smart textiles and fashion goods. This review integrates the basic overview of 3DP technology, fabrication methods, the transition of 3DP to 4DP, the chemistry behind the fundamental working principles of 4D printed objects, materials selection for smart textiles and fashion goods. The central part summarizes the effect of major external stimuli on 4D textile materials followed by the major applications. Lastly, prospects and challenges are discussed, so that future researchers can continue the progress of this technology.     

基于K-means和SOM的水下传感器数据采集方法

随着海洋资源勘探和海洋污染物监控工作的开展，水文数据的监测和采集等已经成为重要的研究方向。其中，水下无线传感器网络在水文数据采集过程中起着举足轻重的作用。本文研究的是水下无线传感器二维监测网络模型中，传感器节点数据采集的问题，其设计方法是通过自组织映射（Self-organizing mapping，SOM）对传感器节点进行路径最优化处理，结合优化的路径图形和K-means算法找到路径内部聚合点，利用聚合点和传感器的节点得到传感器通信半径内的数据采集点，最后通过SOM得到水下机器人（Autonomous underwater vehicle，AUV）到各个数据采集点采集数据的最优路径。经过实验验证，在水下1 200 m×1 750 m范围内布置52个传感器节点的情景下，数据采集点相比于传感器节点路径规划采用相同的采集顺序得到的路径优化了6.7%；对数据采集点重新进行自组织路径规划得到的路径比传感器结点路径的最优解提高了12.2%。增加传感器节点的数量，其结果也大致相同，因此采用该方法可以提高水下机器人采集数据的效率。     

Ferroelectric and electromechanical performance of diverse engineered states of Mn-doped 0.75Pb(Mg1/3Nb2/3)O3-0.25PbTiO3 ceramics

The materials processing history has a great influence on their properties and finally determines their application effect. In this paper, the ferroelectric, polarization-switching current, and strain properties of Mn-doped 0.75Pb(Mg_1/3Nb_2/3)O₃-0.25PbTiO₃ ceramics were studied in fresh state, aged state, and poled state, respectively. Compared with the symmetric polarization-electric-field (P-E) hysteresis loops, current-density-electric-field (J-E) curves, and bipolar electric-field-induced strain (S-E) curves in fresh state samples, asymmetric P-E loops, J-E curves, and bipolar S-E curves were obtained in poled state samples. Well-aged-state samples exhibit double hysteresis P-E loop, four peaks J-E curves, and symmetric S-E curves without negative strain. The symmetry-conforming short-range order (SC-SRO) principle of point defects and internal electric field E_i is employed to clarify the different phenomenon of three states. Results indicated that randomly oriented defect polarization P_D in aged samples can reverse the spontaneous polarization P_S back and result in the double hysteresis P-E loop and four peaks J-E curves. The oriented P_D and resulting E_i in poled-state samples will lead to the asymmetric loops and strain memory effect.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

High-Throughput Screening for Phase-Change Memory Materials

Phase change memory (PCM) is an emerging non-volatile data storage technology concerned by the semiconductor industry. To improve the performances, previous efforts have mainly focused on partially replacing or doping elements in the flagship Ge-Sb-Te (GST) alloy based on experimental “trial-and-error” methods. Here, the current largest scale PCM materials searching is reported, starting with 124 515 candidate materials, using a rational high-throughput screening strategy consisting of criteria related to PCM characteristics. In the results, there are 158 candidates screened for PCM materials, of which ≈68% are not employed. By further analyses, including cohesive energy, bond angle analyses, and Born effective charge, there are 52 materials with properties similar to the GST system, including Ge₂Bi₂Te₅, GeAs₄Te₇, GeAs₂Te₄, so on and other candidates that have not been reported, such as TlBiTe₂, TlSbTe₂, CdPb₃Se₄, etc. Compared with GST, materials with close cohesive energy include AgBiTe₂, TlSbTe₂, As₂Te₃, TlBiTe₂, etc., indicating possible low power consumption. Through further melt-quenching molecular dynamic calculation and structural/electronic analyses, Ge₂Bi₂Te₅, CdPb₃Se₄, MnBi₂Te₄, and TlBiTe₂ are found suitable for optical/electrical PCM applications, which further verifies the effectiveness of this strategy. The present study will accelerate the exploration and development of advanced PCM materials for current and future big-data applications.     

Distributed two‐stage state estimation with event‐triggered strategy for multirate sensor networks

This paper investigates the state estimation issue for a class of wireless sensor networks (WSNs) with the consideration of limited energy resources. First, a multirate estimation model is established, and then, a new event‐triggered two‐stage information fusion algorithm is developed based on the optimal fusion criterion weighted by matrices. Compared with the existing methods, the presented fusion algorithm can significantly reduce the communication cost in WSNs and save energy resources of sensors efficiently. Furthermore, by presetting a desired containment probability over the interval [0,1] with the developed event‐triggered mechanism, one can obtain a suitable compromise between the communication cost and the estimation accuracy. Finally, a numerical simulation for the WSN tracking system is given to demonstrate the effectiveness of the proposed method.     

Bayesian Inference for ARFIMA Models

This article develops practical methods for Bayesian inference in the autoregressive fractionally integrated moving average (ARFIMA) model using the exact likelihood function, any proper prior distribution, and time series that may have thousands of observations. These methods utilize sequentially adaptive Bayesian learning, a sequential Monte Carlo algorithm that can exploit massively parallel desktop computing with graphics processing units (GPUs). The article identifies and solves several problems in the computation of the likelihood function that apparently have not been addressed in the literature. Four applications illustrate the utility of the approach. The most ambitious is an ARFIMA(2,d,2) model for the Campito tree ring time series (length 5405), for which the methods developed in the article provide an essentially uncorrelated sample of size 16,384 from the exact posterior distribution in under four hours. Less ambitious applications take as little as 4 minutes without exploiting GPUs.     

A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

基于QoE感知的星地一体化网络公平资源分配策略

星地一体化网络是卫星网络和地面网络互联，实现卫星网络和地面网络优势互补的一体化网络。随着无线通信的快速发展，卫星网络和地面网络的同频复用技术可有效提升资源利用效率，满足日益增长的无线数据业务需求。但由于无线数据业务类型的多样化和频谱资源的稀缺性，如何实现用户数据服务的服务体验质量（Quality of experience， QoE）和公平性提升也成为星地一体化网络亟待解决的关键问题。本文综合考虑信道时变性、服务体验多样性和服务公平性，提出了一种基于QoE感知的星地一体化网络资源分配策略。为此，将目标问题描述为基于时间平均的服务体验质量的网络公平效用最大化，并利用李雅普诺夫优化理论，将复杂的动态原始优化问题进一步转化并分解为3个瞬时优化子问题，每个子问题可以在每个时隙独立求解。同时，由于无线资源分配为典型的非凹混合组合优化问题，推导了一个低复杂度的两步算法来求解第三个资源分配子问题。仿真结果揭示了在不同的公平性参数下用户的长时QoE与公平性之间的折衷情况。