Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables.Due to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal solutions.The proposed algorithm adopts a dual-population framework and an improved environmental selection method.It utilizes a convergence archive to help the first population improve the quality of solutions.The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population.The combination of these two strategies helps to effectively balance and enhance conver-gence and diversity performance.In addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed.The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

Regularized automatic frequency response function acquisition of a milling robot operating in a high-dimensional workspace

Because robotic milling has become an important means for machining significant large parts,obtaining the structural frequency response function(FRF)of a milling robot is an important basis for machining process optimization.However,because of its articulated serial structure,a milling robot has an enormous number of operating postures,and its dynamics are affected by the motion state.To accurately obtain the FRF in the operating state of a milling robot,this paper proposes a method based on the structural modification concept.Unlike the traditional excitation method,the proposed method uses robot joint motion excitation instead of hammering excitation to realize automation.To address the problem of the lack of information brought by motion excitation,which leads to inaccurate FRF amplitudes,this paper derives the milling robot regularization theory based on the sensitivity of structural modification,establishes the modal regularization factor,and calibrates the FRF amplitude.Compared to the commonly used manual hammering experiments,the proposed method has high accuracy and reliability when the milling robot is in different postures.Because the measurement can be performed directly and automatically in the operation state,and the problem of inaccurate amplitudes is solved,the proposed method provides a basis for optimizing the machining posture of a milling robot and improving machining efficiency.

Chip-Based High-Dimensional Optical Neural Network

Parallel multi-thread processing in advanced intelligent processors is the core to realize high-speed and high-capacity signal processing systems.Optical neural network(ONN)has the native advantages of high parallelization,large bandwidth,and low power consumption to meet the demand of big data.Here,we demonstrate the dual-layer ONN with Mach-Zehnder interferometer(MZI)network and nonlinear layer,while the nonlinear activation function is achieved by optical-electronic signal conversion.Two frequency components from the microcomb source carrying digit datasets are simultaneously imposed and intelligently recognized through the ONN.We successfully achieve the digit classification of different frequency components by demultiplexing the output signal and testing power distribution.Efficient parallelization feasibility with wavelength division multiplexing is demonstrated in our high-dimensional ONN.This work provides a high-performance architecture for future parallel high-capacity optical analog computing.

A Social Semiotic Approach to the Multimodal Discourse of an Advertisement of “Vivo”

Multimodal Discourse Analysis which is based on the Systematic Functional Linguistic Theory has been hotly discussed in the field of linguistics and social semiotics recently.Based on Kress and Van Leeuween s the Grammar of Visual Design,this paper analyzes the advertisement of Vivo from the representational meaning,interactive meaning and compositional meaning of the images.This reveals the expressional function of the images as a kind of social semiotics.

Brain Mechanisms of College Students’ Social Adjustment: Evidence from Multimodal Magnetic Resonance Imaging (MRI)

This study investigated the neural basis of social adjustment using multimodal brain imaging and social-adjustment measurements to analyze functional and structural brain features during social adjustment in college students. The results showed that, regarding brain function, some dimensions of social adjustment were associated with the insula, and some regions of the frontal and occipital lobes. Self-adjustment and satisfaction required activation of the middle frontal gyrus, while career adjustment and academic adjustment required inhibition of the inferior frontal gyrus and lingual gyrus, respectively. Decreased metabolic activity of the lingual gyrus was beneficial for obtaining satisfaction. Regarding brain structure, the total score and some dimensions of social adaptation were associated with the gray matter of portions of the temporal and parietal lobes. The superior temporal gyrus was associated with the total social adjustment and satisfaction score, the middle temporal gyrus with campus-life adjustment and satisfaction, and the post central gyrus and the inferior parietal lobule with emotional adjustment. The changes in the gray matter volume of these brain regions to a certain extent reflected socially adaptive behaviors. The results suggest that social adaptability is associated with various brain regions dispersed among both hemispheres of the brain, and requires synergistic inter-actions between multiple brain regions and both brain hemispheres.

Dimensionality Reduction of High-Dimensional Highly Correlated Multivariate Grapevine Dataset

Viticulturists traditionally have a keen interest in studying the relationship between the biochemistry of grapevines’ leaves/petioles and their associated spectral reflectance in order to understand the fruit ripening rate, water status, nutrient levels, and disease risk. In this paper, we implement imaging spectroscopy (hyperspectral) reflectance data, for the reflective 330 - 2510 nm wavelength region (986 total spectral bands), to assess vineyard nutrient status;this constitutes a high dimensional dataset with a covariance matrix that is ill-conditioned. The identification of the variables (wavelength bands) that contribute useful information for nutrient assessment and prediction, plays a pivotal role in multivariate statistical modeling. In recent years, researchers have successfully developed many continuous, nearly unbiased, sparse and accurate variable selection methods to overcome this problem. This paper compares four regularized and one functional regression methods: Elastic Net, Multi-Step Adaptive Elastic Net, Minimax Concave Penalty, iterative Sure Independence Screening, and Functional Data Analysis for wavelength variable selection. Thereafter, the predictive performance of these regularized sparse models is enhanced using the stepwise regression. This comparative study of regression methods using a high-dimensional and highly correlated grapevine hyperspectral dataset revealed that the performance of Elastic Net for variable selection yields the best predictive ability.

Discourse Analysis of Taylor Swift’s“Look What You Made Me Do”—From the Perspective of Multimodal Metaphor and the Formal Level of SFL Basis

Multimodal discourse analysis is a relatively new area of study and has received great attention these years.Relative studies have also made development in recent years concerning advertisement,classroom education,political speech,and commer⁃cial events,but few have stepped into the music domain.This paper made use of Taylor Swift’s hit single“Look What You Made Me Do”to explore multimodal discourse analysis on music from the perspective of multimodal metaphor and the formal level of sys⁃temic functional linguistics(SFL),and to reveal the relationship between its lyric,tune,and music video,which shows how these multimodal discourse interrelated and coordinated to express the producer’s meaning,attitude,and ideas.

A Multimodal Discourse Analysis of A Movie Poster——“Tangled”

As is known to all, language takes a significant role in communication and understanding. Actually, according to Systemic-Function and visual grammar, a varitey of semiotic resources such as image, color, could be analysed and understood as a language. A movie poster is an example of decoding a complex intergration of a variety of semiotic resources. Through analysing a movie poster, it is clear to see how the realization of the three meta-functions based on Systemic-Function contributes to the overall meaning conveyed by movie posters.

多模态影像技术在儿童症状性癫痫中的应用进展

儿童是癫痫的好发人群,由于其中枢神经系统处于发育阶段,所以病因更加复杂、表现形式更加多样,早期诊断和及时、有效处理能减少儿童中枢神经损伤,最大程度地保护患儿的脑功能。根据有无明显抽搐可将癫痫分为惊厥性和非惊厥性,而非惊厥性因无明显临床症状,诊断相对比较困难,容易导致误诊而延误治疗,明确诊断需要依靠脑电图(癫痫样或节律性放电)。随着影像检查手段的不断发展,明确癫痫病因、定位癫痫病灶的方式更加多样化,定位更加准确,同时功能成像检查方式也使得早期评估癫痫的脑功能变化成为可能。本文就近年来症状性儿童癫痫的MRI及其与正电子发射计算机断层显像(positron emission tomography,PET)融合应用的研究进展及临床应用进行综述,旨在为今后的相关研究提供参考和借鉴。

基于结构功能交叉神经网络的多模态医学图像融合

针对多模态医学图像融合中存在纹理细节模糊和对比度低的问题,提出了一种结构功能交叉神经网络的多模态医学图像融合方法。首先,根据医学图像的结构信息和功能信息设计了结构功能交叉神经网络模型,不仅有效地提取解剖学和功能学医学图像的结构信息和功能信息,而且能够实现这两种信息之间的交互,从而很好地提取医学图像的纹理细节信息。其次,利用交叉网络通道和空间特征变化构造了一种新的注意力机制,通过不断调整结构信息和功能信息权重来融合图像,提高了融合图像的对比度和轮廓信息。最后,设计了一个从融合图像到源图像的分解过程,由于分解图像的质量直接取决于融合结果,因此分解过程可以使融合图像包含更多的细节信息。通过与近年来提出的7种高水平方法相比,本文方法的AG,EN,SF,MI,QAB/F和CC客观评价指标分别平均提高了22.87%,19.64%,23.02%,12.70%,6.79%,30.35%,说明本文方法能够获得纹理细节更清晰、对比度更好的融合结果,在主观视觉和客观指标上都优于其他对比算法。

糖尿病周围神经病变多模态MRI研究进展

糖尿病周围神经病变(diabetes peripheral neuropathy,DPN)是较常见的糖尿病慢性并发症,严重影响患者生活质量。DPN发病机制比较复杂,目前研究表明与神经病理性改变关系密切。近些年多模态MRI获得长足进展,MRI以软组织分辨率高、无创、无辐射等优点著称,成为探索DPN中枢神经及周围神经发病机制的重要方法。本文应用基于体素的形态学测量(voxel-based morphometry,VBM)、弥散张量成像(diffusion tensor imaging,DTI)、功能MRI(functional MRI,fMRI)等技术,对研究DPN脑结构、脑功能改变以及周围神经结构异常的文献进行综述,为临床早期诊断和制订精准治疗方案提供依据。

基于虚拟点云的二阶段多模态融合网络

针对点云的稀疏性和无序性对目标检测准确率的影响,本文提出了一种基于虚拟点云的二阶段多模态融合网络VPC-VoxelNet。首先,利用图像检测目标信息构造虚拟点云,增加点云的密集程度,从而提高目标特征的表现;其次,增加点云特征维度以区分真实和虚拟点云,并使用含置信度编码的体素,增强点云的相关性;最后,采用虚拟点云的比例系数设计损失函数,增加图像检测有监督训练,提高二阶段网络训练效率,避免二阶段端到端网络模型存在的模型误差累计问题。该目标检测网络VPC-VoxelNet在KITTI数据集上进行了测试,检测精度优于经典三维点云检测网络和某些多传感器信息融合网络,车辆检测精度达到了86.9%。

昼夜节律在脑的多模态MRI中的研究进展

昼夜节律的存在使大脑结构、功能发生昼夜波动。目前大多数神经影像学研究没有控制一天中的时间这个生物变量,使结果的可重复性降低以及解释复杂化。近年来,随着多模态MRI快速发展,基于体素的形态学测量(voxel-based morphometry,VBM)、扩散MRI (diffusion MRI,dMRI)、功能MRI (functional MRI,fMRI)、磁共振波谱(magnetic resonance spectroscopy,MRS)及动脉自旋标记(arterial spin labeling,ASL)等方法被用于研究昼夜节律对大脑结构及功能的影响。大多数研究发现时间对大脑结构和功能存在显著的影响,但报告的结果存在不一致。目前的研究仅关注了白天的时间点和一天中2~3个时间点,缺少对夜间时间点和一天中多个时间点的研究。笔者对近年来昼夜节律在脑的MRI中的研究进展进行综述,以提高大家对时间作为影响神经影像学研究的生物变量的认识。

积极心理学联合多模态运动干预在乳腺癌化疗患者中的应用

目的探究积极心理学联合多模态运动干预在乳腺癌化疗患者中的应用价值。方法将住院行化疗的360例乳腺癌患者,按病区随机分为对照组和观察组各180例;对照组行常规化疗护理,观察组在对照组的基础上实施积极心理学干预联合多模态运动干预。两组均化疗3个疗程,连续干预12周后评价效果。结果干预后观察组希望水平得分及应对方式的面对维度得分显著高于对照组,屈服、回避维度,癌因性疲乏得分显著低于对照组(均P<0.05);CD 3+、CD^(4+)含量、CD^(4+)/CD^(8+)显著优于对照组(均P<0.05)。结论积极心理学联合多模态运动干预可改善患者心理与应对方式,缓解疲乏和改善免疫功能。

不同功能磁共振成像技术在乳腺癌中的应用进展

乳腺癌成为全球第一大癌症,充分了解不同功能MRI技术在乳腺癌中的应用有利于推进乳腺癌诊疗的发展。本文通过介绍不同功能MRI技术在乳腺癌早期诊断和晚期预后评估中展现出的临床科研价值,说明基于灌注、代谢、扩散、合成相关MRI,使肿瘤组织微血管渗透、分布、血流动力学状态,肿瘤代谢物含量、间质成分变化及组织固有属性等特征可视化,旨在总结各成像序列在乳腺癌应用中的优势和前景,以期为后续乳腺癌影像科学研究提供新方向,从而帮助影像医师更加全面地了解乳腺癌相关MRI技术手段。

顾及多模态数据特征关联的城市功能区识别方法——以武汉市为例

城市功能区在城市化建设当中扮演着重要的角色,城市功能区建模与分析对于规划城市土地布局和优化土地利用模式有着重要意义。目前在城市功能区建模与分析的研究中,仍存在多模态数据利用率低、关联特征损失大的问题,这导致难以精确捕捉城市空间结构信息。因此,本研究以武汉市中心区域为例,基于街景影像数据和兴趣点数据构建城市功能区识别方法。首先,将研究区域划分为500 m×500 m的格网单元,运用自然语言模型、深度学习、空间插值等方法来提取多维度的城市功能特征;其次,利用核典型相关分析方法提取多模态数据的关联特征,分析不同共享空间维度和相关性的关系;最后,基于建成环境特征、社会经济语义特征、拼接特征及关联特征的多维度特征构建随机森林功能区识别模型,并分别与两种常用数据模态进行对比评价。结果表明,本方法相较于没有顾及关联特征的模型准确度提高了3.4%。研究成果可为政府决策规划、城市发展评估提供帮助,推动城市化建设。

自我决定理论视角下多模态运动训练对脑梗死后偏瘫患者肢体功能恢复的影响

目的探讨自我决定理论视角下多模态运动训练对脑梗死后偏瘫患者肢体功能恢复的影响。方法选取2020年1月至2021年11月收治的108例脑梗死后偏瘫患者,按随机数字表法分为对照组(采取常规护理)和观察组(采取自我决定理论视角下多模态运动训练),每组54例。比较2组间干预前后患者心理状态(状态-特质焦虑问卷),肢体功能(肢体运动功能评分量表),自我效能(慢性病自我效能量表)和生活自理能力(扩展Barthel指数量表)。结果干预后,2组心理状态评分均降低(P<0.05),2组肢体功能评分,自我效能评分和生活自理能力评分均升高(P<0.05),且观察组和对照组上述评分比较,差异均有统计学意义(P<0.05)。结论自我决定理论视角下多模态运动训练能有效改善脑梗死后偏瘫患者心理状态,增强肢体功能,提高患者自我效能和生活自理能力。

多模态MRI在终末期肾病合并认知障碍中的研究进展

认知障碍在终末期肾病(end-stage renal disease, ESRD)患者中非常普遍,这种障碍涉及一系列认知领域,包括注意力、记忆力和执行力等,严重降低了患者的生活质量。然而这种神经认知缺陷的确切机制尚不清楚,因此在早期阶段检测和管理这种认知障碍具有很大的挑战性。近年来多模态磁共振成像在这一领域取得了诸多成果,并有望选择出指导早期筛查及诊断慢性肾脏疾病(chronic kidney disease, CKD)患者认知缺陷的影像学生物标志物。本文将简要阐述多种MRI新兴技术及人工智能在ESRD合并认知障碍患者中的研究进展,尝试揭示ESRD患者脑结构、功能、灌注、代谢及神经血管耦合与认知功能障碍之间的联系,帮助临床了解ESRD患者大脑缺陷的潜在神经元机制,有利于合并认知障碍ESRD患者的早期诊断、治疗及随访。

脑震荡后综合征的磁共振成像研究进展

脑震荡后综合征(postconcussional syndrome,PCS)是脑外伤后最常见的慢性后遗症。PCS的损伤机制涉及多种神经病理生理过程,目前尚不明确。越来越多的磁共振成像技术,例如,扩散张量成像(diffusion tensor imaging,DTI)、灌注加权成像(perfusion weighted imaging,PWI)、氢质子磁共振波谱成像(hydrogen proton magnetic resonance spectroscopy,1H-MRS)等被用于探究PCS从急性期到慢性期的神经病理生理变化与临床症状的关系。本文根据患者受伤的不同时期及其疾病进展进行纳入,对多种高级MRI手段在PCS中的应用进行综述,从不同角度了解其潜在神经病理机制,更好地服务于疾病的诊疗及预后。