A Bio-Conjugated Fullerene as a Subcellular-Targeted and Multifaceted Phototheranostic Agent

Fullerenes are candidates for theranostic applications because of their high photodynamic activity and intrinsic multimodal imaging contrast. However, fullerenes suffer from low solubility in aqueous media, poor biocompatibility, cell toxicity, and a tendency to aggregate. C₇₀@lysozyme is introduced herein as a novel bioconjugate that is harmless to a cellular environment, yet is also photoactive and has excellent optical and optoacoustic contrast for tracking cellular uptake and intracellular localization. The formation, water-solubility, photoactivity, and unperturbed structure of C₇₀@lysozyme are confirmed using UV-visible and 2D ¹H, ¹⁵N NMR spectroscopy. The excellent imaging contrast of C₇₀@lysozyme in optoacoustic and third harmonic generation microscopy is exploited to monitor its uptake in HeLa cells and lysosomal trafficking. Last, the photoactivity of C₇₀@lysozyme and its ability to initiate cell death by means of singlet oxygen (¹O₂) production upon exposure to low levels of white light irradiation is demonstrated. This study introduces C₇₀@lysozyme and other fullerene-protein conjugates as potential candidates for theranostic applications.     

Multisensory exploration and object individuation in infancy.

Recent research indicates that by 4.5 months, infants use shape and size information as the basis for individuating objects but that it is not until 11.5 months that they use color information for this purpose. The present experiments investigated the extent to which infants' sensitivity to color information could be increased through select experiences. Five experiments were conducted with 10.5- and 9.5-month-olds. The results revealed that multimodal (visual and tactile), but not unimodal (visual only), exploration of the objects prior to the individuation task increased 10.5-month-olds' sensitivity to color differences. These results suggest that multisensory experience with objects facilitates infants' use of color information when individuating objects. In contrast, 9.5-month-olds did not benefit from the multisensory procedure; possible explanations for this finding are explored. Together, these results reveal how an everyday experience--combined visual and tactile exploration of objects--can promote infants' use of color information as the basis for individuating objects. More broadly, these results shed light on the nature of infants' object representations and the cognitive mechanisms that support infants' changing sensitivity to color differences. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

多模智能 PID 控制及其在液压电梯速度控制系统中的应用

本文基于普通ＰＩＤ控制器和人工智能的理论，针对液压电梯速度控制系统非线性和建模困难等特点。采用了一种多模式智能ＰＩＤ控制算法，它结合了普通ＰＩＤ控制器的优点。应用实例显示了这种控制方法的有效性。     

Challenges and opportunities of multimodal data in human learning: The computer science students' perspective

Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non-technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student-facing MLA innovations.     

基于CCA的人耳和侧面人脸特征融合的身份识别*

鉴于人耳和人脸特殊的生理位置关系,从非打扰识别的角度出发,提出仅采集侧面人脸图像,利用典型相关分析的思想提取人耳和侧面人脸的关联特征,进行人耳和侧面人脸在特征层的融合.实验结果表明,此方法与单一的人耳或侧面人脸特征识别比较,识别率得到提高.     

Multimodal Pre-training Method for Vision-language Understanding and Generation

Most existing vision-language pre-training methods focus on understanding tasks and use BERT-like loss functions (masked language modeling and image-text matching) during pre-training. Despite their good performance in the understanding of downstream tasks, such as visual question answering, image-text retrieval, and visual entailment, these methods cannot generate information. To tackle this problem, this study proposes Unified multimodal pre-training for Vision-Language understanding and generation (UniVL). The proposed UniVL is capable of handling both understanding tasks and generation tasks. It expands existing pre-training paradigms and uses random masks and causal masks simultaneously, where causal masks are triangular masks that mask future tokens, and such pre-trained models can have autoregressive generation abilities. Moreover, several vision-language understanding tasks are turned into text generation tasks according to specifications, and the prompt-based method is employed for fine-tuning of different downstream tasks. The experiments show that there is a trade-off between understanding tasks and generation tasks when the same model is used, and a feasible way to improve both tasks is to use more data. The proposed UniVL framework attains comparable performance to recent vision-language pre-training methods in both understanding tasks and generation tasks. Moreover, the prompt-based generation method is more effective and even outperforms discriminative methods in few-shot scenarios.     

从社会符号学视角解读上海世博会吉祥物

当今社会已经步入了一个数字化和信息化的时代,随着人们的交流方式逐渐由文字向图像化转变,话语分析的研究范围也随之延伸到语言文字之外的图片、声音、颜色等社会符号。文章运用社会符号学框架下的视觉语法理论分析了2010年上海世博会吉祥物-海宝这个多模态语篇,说明除语言文字以外的其他社会符号也是意义的源泉。     

多式联运中运输方式与运输路径集成优化模型研究*

运输方式和运输路径选择问题是影响多式联运时间和费用的关键问题,直接影响承运人和客户的利益。依据运输方式选择和运输路径优化的关系特点,采用主从混合智能启发式方法,构建了运输方式选择和运输路径优化集成模型,给出了粒子群—蚁群双层优化算法求解方案,解决了运输网络多节点、多方式、多路径的集成优化问题。实验结果表明,该方案优于蚁群算法和遗传算法。     

自适应多模态特征融合胶质瘤分级网络

目的 胶质瘤的准确分级是辅助制定个性化治疗方案的主要手段,但现有研究大多数集中在基于肿瘤区域的分级预测上,需要事先勾画感兴趣区域,无法满足临床智能辅助诊断的实时性需求。因此,本文提出一种自适应多模态特征融合网络（adaptive multi-modal fusion net,AMMFNet）,在不需要勾画肿瘤区域的情况下,实现原始采集图像到胶质瘤级别的端到端准确预测。方法 AMMFNet方法采用4个同构异义网络分支提取不同模态的多尺度图像特征;利用自适应多模态特征融合模块和降维模块进行特征融合;结合交叉熵分类损失和特征嵌入损失提高胶质瘤的分类精度。为了验证模型性能,本文采用MICCAI （Medical Image Computing and Computer Assisted Intervention Society）2018公开数据集进行训练和测试,与前沿深度学习模型和最新的胶质瘤分类模型进行对比,并采用精度以及受试者曲线下面积（area under curve,AUC）等指标进行定量分析。结果 在无需勾画肿瘤区域的情况下,本文模型预测胶质瘤分级的AUC为0.965;在使用肿瘤区域时,其AUC高达0.997,精度为0.982,比目前最好的胶质瘤分类模型——多任务卷积神经网络同比提高1.2%。结论 本文提出的自适应多模态特征融合网络,通过结合多模态、多语义级别特征,可以在未勾画肿瘤区域的前提下,准确地实现胶质瘤分级预测。     

基于多任务学习的时序多模态情感分析模型

针对时序多模态情感分析中存在的单模态特征表示和跨模态特征融合问题,结合多头注意力机制,提出一种基于多任务学习的情感分析模型。首先,使用卷积神经网络（CNN）、双向门控循环神经网络（BiGRU）和多头自注意力（MHSA）实现了对时序单模态的特征表示;然后,利用多头注意力实现跨模态的双向信息融合;最后,基于多任务学习思想,添加额外的情感极性分类和情感强度回归任务作为辅助,从而提升情感评分回归主任务的综合性能。实验结果表明,相较于多模态分解模型,所提模型的二分类准确度指标在CMU-MOSEI和CMU-MOSI多模态数据集上分别提高了7.8个百分点和3.1个百分点。该模型适用于多模态场景下的情感分析问题,能够为商品推荐、股市预测、舆情监控等应用提供决策支持。