Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.     

Discrimination of Patients with Varying Degrees of Coronary Artery Stenosis by ECG and PCG Signals Based on Entropy

Coronary heart disease (CHD) is the leading cause of cardiovascular death. This study aimed to propose an effective method for mining cardiac mechano-electric coupling information and to evaluate its ability to distinguish patients with varying degrees of coronary artery stenosis (VDCAS). Five minutes of electrocardiogram and phonocardiogram signals was collected synchronously from 191 VDCAS patients to construct heartbeat interval (RRI)–systolic time interval (STI), RRI–diastolic time interval (DTI), HR-corrected QT interval (QTcI)–STI, QTcI–DTI, Tpeak–Tend interval (TpeI)–STI, TpeI–DTI, Tpe/QT interval (Tpe/QTI)–STI, and Tpe/QTI–DTI series. Then, the cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), joint distribution entropy (JDistEn), magnitude-squared coherence function, cross power spectral density, and mutual information were applied to evaluate the coupling of the series. Subsequently, support vector machine recursive feature elimination and XGBoost were utilized for feature selection and classification, respectively. Results showed that the joint analysis of XSampEn, XFuzzyEn, and JDistEn had the best ability to distinguish patients with VDCAS. The classification accuracy of severe CHD—mild-to-moderate CHD group, severe CHD—chest pain and normal coronary angiography (CPNCA) group, and mild-to-moderate CHD—CPNCA group were 0.8043, 0.7659, and 0.7500, respectively. The study indicates that the joint analysis of XSampEn, XFuzzyEn, and JDistEn can effectively capture the cardiac mechano-electric coupling information of patients with VDCAS, which can provide valuable information for clinicians to diagnose CHD.     

电子束长程传输中离子通道的振荡特性研究

离子通道可以有效抑制电子束在等离子体环境内传输过程中的径向扩散,已有工作研究了离子通道对电子束的影响,但离子通道建立过程和暂态特性研究则更有助于理解和利用离子通道在电子束长程传输中的作用。本文利用PIC方法对离子通道的时空分布进行二维模拟,并基于单粒子理论推导出描述离子通道振荡的解析模型,对上述两种模型的结果相互校验。上述模型的计算结果表明,在长程传输过程中,相对论电子束在等离子体内部建立的离子通道是持续周期振荡的,电子束密度、电子束初始半径以及环境等离子体密度都会对离子通道的振荡规律产生影响,针对不同的等离子体环境选择合适的电子束参数可以有效提高离子通道的稳定性,进而提升传输过程中电子束的束流质量。     

减反膜对单晶硅太阳能电池性能的影响分析

在太阳能电池效率的评价中,电池材料、掺杂浓度、扩散长度等都是比较重要的参数,合理地改变相关参数可以优化太阳能电池的性能,提高电池效率。此外,在太阳能电池表面镀一层具有减反作用的光学薄膜（简称减反膜）也是提高电池效率的重要手段。以提高电池效率为目标,对单晶硅太阳能电池的掺杂浓度和扩散长度等微观参数进行计算优化,分析了掺杂浓度和扩散长度变化对电池效率的影响。并在此基础上分析了不同类型的减反膜对于电池效率的影响,给出了最佳减反膜材料及其膜系厚度,并且结合镀膜后电池量子效率的变化验证了其准确性。结果表明,在优化电池掺杂浓度和扩散长度的基础上,选择合适的减反膜,电池效率最高可达20.35%,相比于优化前提高了8.25%。     

新体制引信干扰机理分析

为应对新体制无线电引信带来的威胁,从引信对抗角度以超宽带无线电引信为研究对象,研究干扰作用下其失效机理。揭示了超宽带无线电引信敏感干扰波形响应机理,理论推导了周期调制干扰信号作用下引信响应特性,仿真计算了在射频噪声、正弦波调幅、正弦波调频以及扫频正弦波调幅干扰信号作用下超宽带引信接收机相关器输出响应特性。以干信比增益为表征参量,通过理论计算、仿真及试验验证得出结论：周期类调制干扰中调幅类干扰对超宽带引信干扰效果最好。     

正反重强子对构成的强子分子态能谱

近二十年来,世界上众多高能物理实验发现了大量超出传统夸克模型的共振结构。为了理解 这些性质奇特的共振结构,科学家们提出了很多方法和模型。其中,因为实验上发现的这些奇特态 大多处在一对强子阈值附近,强子分子态的观点得到了很多关注。本文我们将以粲{反粲强子对为例 来探究哪些系统可以形成分子态,进而给出粲{反粲强子分子态的能谱。我们考虑了所有由 S 波粲 介子、粲重子以及窄的 P 波粲介子构成的粲{反粲强子对。我们假定它们之间的相互作用由介子交 换主导,在最低阶近似下可简化为常数。通过求解常数相互作用的 Bethe---Salpeter 方程,我们可以 找到振幅的极点进而确定该系统分子态的质量。最终我们发现,那些通过轻矢量介子交换在阈值附 近产生吸引力的系统,会存在一个靠近阈值的极点。不同的系统,由于其相互作用强度不同,极点 可能会处于能量复平面的第一黎曼面或者第二黎曼面,分别对应于束缚态或者虚态。我们总共发现 了 229 个强子分子态。很多实验上发现的那些位于粲---反粲强子对阈值附近的共振结构可以很好地 与我们的结果吻合。另外,我们需要强调所预言的一个 ΛcΛc  分子态,这个态可以很好地解释北京正负电子谱仪 (BESIII) 上测量的数据。     

SensorAware: visual analysis of both static and mobile sensor information

Journal of Visualization - Sensor networks composed of static and mobile sensors are applicable for situation monitoring. In this paper, we propose SensorAware, an interactive system for...     

Analysis of Electromagnetic Information Leakage Based on Cryptographic Integrated Circuits

Cryptographic algorithm is the most commonly used method of information security protection for many devices. The secret key of cryptographic algorithm is usually stored in these devices’ registers. In this paper, we propose an electromagnetic information leakage model to investigate the relationship between the electromagnetic leakage signal and the secret key. The registers are considered as electric dipole models to illustrate the source of the electromagnetic leakage. The equivalent circuit of the magnetic field probe is developed to bridge the output voltage and the electromagnetic leakage signal. Combining them, the electromagnetic information leakage model’s function relationship can be established. Besides, an electromagnetic leakage model based on multiple linear regression is proposed to recover the secret key and the model’s effectiveness is evaluated by guess entropy. Near field tests are conducted in an unshielded ordinary indoor environment to investigate the electromagnetic side-channel information leakage. The experiment result shows the correctness of the proposed electromagnetic leakage model and it can be used to recover the secret key of the cryptographic algorithm.     

单载流子传输的双异质结光敏晶体管探测器的研究

基于器件仿真器Atlas, 建立了InP/InGaAsP单向载流子传输的双异质结光敏晶体管(UTC-DHPT)的二维模型, 分析讨论了器件性能与外延结构参数的关系. 设计出同时具有高响应度(≥17.93 A/W)和高特征频率(≥121.68 GHz)的UTC-DHPT, 缓解了传统的异质结光敏晶体管光电探测器中探测效率和工作速度的矛盾. 关键词： 单向载流子 光敏晶体管 电流放大倍数 响应度     

Nanohybridization of Low-Dimensional Nanomaterials: Synthesis,Classification, and Application

Nanomaterials have attracted much attention from academic to industrial research. General methodologies are needed to impose architectural order in low-dimensional nanomaterials composed of nanoobjects of various shapes and sizes, such as spherical particles, rods, wires, combs, horns, and other non specified geometrical architectures. These nanomaterials are the building blocks for nanohybrid materials, whose applications have improved and will continuously enhance the quality of the daily life of mankind. In this article, we present a comprehensive review on the synthesis, dimension, properties, and present and potential future applications of nanomaterials and nanohybrids. Due to the large number of review articles on specific dimension, morphology, or application of nanomaterials, we will focus on different forms of nanomaterials, such as, linear, particulate, and miscellaneous forms. We believe that almost all the nanomaterials and nanohybrids will come under these three categories. Every form or dimension or morphology has its own significant properties and advantages. These low-dimensional nanomaterials can be integrated to create novel nano-composite material applications for next-generation devices needed to address the current energy crisis, environmental sustainability, and better performance requirements. We discuss the synthesis, properties, and morphology of different forms of nanomaterials (building blocks). Moreover, we elaborate on the synthesis, modification, and application of nanohybrids. The applications of these nanomaterials and nanohybrids in sensors, solar cells, lithium batteries, electronic, catalysis, photocatalysis, electrocatalysis, and bio-based applications will be detailed. The time is now ripe to explore new nanohybrids that use individual nanomaterial components as basic building blocks, potentially affording additionally novel behavior and leading to new, useful applications. In this regard, the combination or integration of linear nanorods/nanowires and spherical nanoparticles to produce mixed-dimensionality, higher-level nanocomposites of greater complexity is an interesting theme, which we explore in this review article.