基于线-超图神经网络的阿尔兹海默症分类

在阿尔兹海默症分类问题中,超图神经网络可以从被试间的超图关系中提取特征,在表示学习复杂图结构方面具有很好的优势,但大多数模型都直接或间接地将超图所表示的被试间的高阶复杂关系分解,转化为简单的二元关系进行特征学习,没有有效利用超边的高阶信息,因此提出了基于线-超图神经网络(line-hypergraph neural network, L-HGNN)的阿尔兹海默症分类模型,该模型利用稀疏线性回归表征被试间多元相关性,借助超图和线图的转换在神经网络模型中实现节点的高阶邻域信息传递和超边整体结构特征学习,同时,结合注意力机制生成更具区分性的节点嵌入,进而用于阿尔兹海默症的辅助诊断.在ADNI数据上与常用的两种方法比较,实验结果表明,该方法能有效提高分类准确率,在阿尔兹海默症早期诊断上具有重要的应用价值.     

Development,integration, and field evaluation of an autonomous citrus-harvesting robot

Citrus harvesting is a labor-intensive and time-intensive task. As the global population continues to age, labor costs are increasing dramatically. Therefore, the citrus-harvesting robot has attracted considerable attention from the business and academic communities. However, robotic harvesting in unstructured and natural citrus orchards remains a challenge. This study aims to address some challenges faced in commercializing citrus-harvesting robots. We present a fully integrated, autonomous, and innovative solution for citrus-harvesting robots to overcome the harvesting difficulties derived from the natural growth characteristics of citrus. This solution uses a fused simultaneous localization and mapping algorithm based on multiple sensors to perform high-precision localization and navigation for the robot in the field orchard. Besides, a novel visual method for estimating fruit poses is proposed to cope with the randomization of citrus growth orientations. Further, a new end-effector is designed to improve the success and conformity rate of citrus stem cutting. Finally, a fully autonomous harvesting robot system has been developed and integrated. Field evaluations showed that the robot could harvest citrus continuously with an overall success rate of 87.2% and an average picking time of 10.9 s/fruit. These efforts provide a solid foundation for the future commercialization of citrus-harvesting robots.     

AccFed:物联网中基于模型分割的联邦学习加速

随着物联网(IoT)的快速发展,人工智能(AI)与边缘计算(EC)的深度融合形成了边缘智能(Edge AI)。但由于IoT设备计算与通信资源有限,并且这些设备通常具有隐私保护的需求,那么在保护隐私的同时,如何加速Edge AI仍然是一个挑战。联邦学习(FL)作为一种新兴的分布式学习范式,在隐私保护和提升模型性能等方面,具有巨大的潜力,但是通信及本地训练效率低。为了解决上述难题,该文提出一种FL加速框架AccFed。首先,根据网络状态的不同,提出一种基于模型分割的端边云协同训练算法,加速FL本地训练;然后,设计一种多轮迭代再聚合的模型聚合算法,加速FL聚合;最后实验结果表明,AccFed在训练精度、收敛速度、训练时间等方面均优于对照组。     

Vapor Phase Growth of Centimeter-Sized Band Gap Engineered Cesium Lead Halide Perovskite Single-Crystal Thin Films with Color-tunable Stimulated Emission

Single crystal metal halide perovskites thin films are considered to be a promising optical, optoelectronic materials with extraordinary performance due to their low defect densities. However, it is still difficult to achieve large-scale perovskite single-crystal thin films (SCTFs) with tunable bandgap by vapor-phase deposition method. Herein, the synthesis of CsPbCl_3(1–x)Br_3x SCTFs with centimeter size (1 cm × 1 cm) via vapor-phase deposition is reported. The Br composition of CsPbCl_3(1–x)Br_3x SCTFs can be gradually tuned from x = 0 to x = 1, leading the corresponding bandgap to change from 2.29 to 2.91 eV. Additionally, an low-threshold (≈23.9 µJ cm⁻²) amplified spontaneous emission is achieved based on CsPbCl_3(1–x)Br_3x SCTFs at room temperature, and the wavelength is tuned from 432 to 547 nm by varying the Cl/Br ratio. Importantly, the high-quality CsPbCl_3(1–x)Br_3x SCTFs are ideal optical gain medium with high gain up to 1369.8 ± 101.2 cm⁻¹. This study not only provides a versatile method to fabricate high quality CsPbCl_3(1–x)Br_3x SCTFs with different Cl/Br ratio, but also paves the way for further research of color-tunable perovskite lasing.     

四旋翼无人机姿态系统复合连续快速非奇异终端滑模控制EI北大核心CSCD

本文针对受多源干扰影响的四旋翼无人机姿态系统,基于复合连续快速非奇异终端滑模算法,研究了姿态指令变化率未知情况下的连续有限时间姿态跟踪控制问题.首先,基于四旋翼无人机姿态回路动力学模型,通过引入虚拟控制量实现姿态跟踪误差动态的三通道解耦;其次,分别针对各通道跟踪误差动态设计高阶滑模观测器,实现跟踪误差变化率和集总干扰的有限时间估计;最后,结合姿态跟踪误差变化率的估计信息,构建动态快速非奇异终端滑模面,并在控制设计中用指数幂函数代替符号函数以保证控制量连续.并且基于Lyapunov分析方法给出了姿态跟踪误差有限时间收敛的严格证明,仿真结果验证了所提方法的有效性.     

面向软件工程的情感分析技术研究

情感分析在软件工程领域具有广泛的应用场景,例如,从代码提交信息中检测开发者的情绪、从程序员问答论坛中识别开发者的观点等.但是,现有的“开箱即用”的情感分析工具无法在软件工程相关的任务中取得可靠的结果.已有研究表明,导致不可靠结果的最主要原因是,这些工具无法理解一些单词和短语在软件工程领域中的特定含义.此后,研究者们开始为软件工程领域定制监督学习和远程监督学习方法.为了验证这些方法的效果,研究者们使用软件工程相关的标注数据集来对它们进行数据集内验证,即,将同一数据集划分为训练集和测试集,分别用于方法的训练和测试.但是,对软件工程领域的某些情感分析任务来说,尚无标注数据集,且人工标注数据集耗时耗力.在此情况下,一种可选的方法就是使用为了相似任务从同一目标平台上提取的数据集或者使用从其他软件工程平台上提取的数据集.为了验证这两种做法的可行性,需要进一步以平台内设置和跨平台设置来验证现有情感分析方法.平台内设置指的是使用提取自同一平台的不同数据集作为训练集和测试集;跨平台设置指的是使用提取自不同平台的数据集作为训练集和测试集.目标旨在数据集内设置、平台内设置、跨平台设置这3种设置下,综合验证现...     

多智能体系统协同控制与优化专刊序言

<正>0引言近年来,随着机器人技术、人工智能技术、无线通信和计算机技术的快速发展,控制系统的规模变得更大,控制任务也变得更加复杂,由此推动了多智能体系统的形成和发展.多智能体系统是由多个具有简单功能的智能体相互关联形成的网络系统,其中每个智能体具备一定的计算、通信、感知、学习和执行能力.这些智能体之间可通过信息交互协同合作完成特定的任务,也可协调解决智能体之间的矛盾和冲突.     

Exploring the tidal effect of urban business district with large-scale human mobility data

Business districts are urban areas that have various functions for gathering people, such as work, consumption, leisure and entertainment. Due to the dynamic nature of business activities, there exists significant tidal effect on the boundary and functionality of business districts. Indeed, effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a city. However, with the implicit and complex nature of business district evolution, it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business districts. To this end, we propose a data-driven and multi-dimensional framework for dynamic business district analysis. Specifically, we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time periods. Then, we detect and forecast the functional changes in business districts. Experimental results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business districts. Moreover, the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business districts. For example, the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts.     

IXT: Improved searchable encryption for multi-word queries based on PSI

Oblivious Cross-Tags (OXT) [1] is the first efficient searchable encryption (SE) protocol for conjunctive queries in a single-writer single-reader framework. However, it also has a trade-off between security and efficiency by leaking partial database information to the server. Recent attacks on these SE schemes show that the leakages from these SE schemes can be used to recover the content of queried keywords. To solve this problem, Lai et al. [2] propose Hidden Cross-Tags (HXT), which reduces the access pattern leakage from Keyword Pair Result Pattern (KPRP) to Whole Result Pattern (WRP). However, the WRP leakage can also be used to recover some additional contents of queried keywords. This paper proposes Improved Cross-Tags (IXT), an efficient searchable encryption protocol that achieves access and searches pattern hiding based on the labeled private set intersection. We also prove the proposed labeled private set intersection (PSI) protocol is secure against semi-honest adversaries, and IXT is L^{\prime }-semi-honest secure (L^{\prime } is leakage function). Finally, we do experiments to compare IXT with HXT. The experimental results show that the storage overhead and computation overhead of the search phase at the client-side in IXT is much lower than those in HXT. Meanwhile, the experimental results also show that IXT is scalable and can be applied to various sizes of datasets.     

计及Crowbar状态改进识别的双馈风电场等值建模方法

故障过程中双馈风机Crowbar投入与不投入的暂态响应特性差异明显,因而在风电场等值中场内机组Crowbar状态是一个良好的机群划分指标。然而,双馈风电场与单台机组间故障特性的差异性,使得现有采用单机模型来识别场内机组Crowbar状态的方法存在识别不准确的问题,从而降低风电场等值建模精度。为此,该文提出一种计及Crowbar状态改进识别的双馈风电场等值建模方法。通过分析风电场故障特性,构建Crowbar状态特征向量;收集风电场各工况下的样本数据,建立基于支持向量机(support vector machine,SVM)的识别模型。在新工况下,依次以Crowbar状态识别结果和输入风速为分群指标对场内机组进行机群划分,从而建立风电场等值模型。仿真算例结果表明,该文提出的基于SVM的Crowbar状态识别方法在各个故障场景下相较于传统方法均有较好的识别效果,所建立的等值模型与详细模型故障暂态特性十分吻合,等值方法合理有效。