Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.     

视觉显著性对横幅广告注意及记忆效果的影响研究

目前对横幅广告视觉注意和记忆效果的研究出现了“独特性观”和“盲视观”两种矛盾的观点。本研究从任务驱动的视角出发,引入计算神经科学的视觉注意计算模型,借助眼动追踪技术,探讨不同任务驱动下横幅广告的视觉显著性对消费者的注意和记忆效果的影响。结果表明:在不同任务驱动下,横幅广告的视觉显著性对消费者的注意及记忆效果的影响具有显著差异。具体而言,在浏览任务中,视觉显著性高的横幅广告比视觉显著性低的横幅广告能带来更好的广告注意及记忆效果;在搜索任务中,不同视觉显著性水平的横幅广告对消费者的注意和记忆效果的影响没有显著差异。本研究丰富了计算神经科学在营销领域的运用,为企业选择合适的网络广告策略提供借鉴。     

基于模糊标签和深度学习的TOF-MRA影像脑动脉瘤自动检测

脑动脉瘤破裂造成的蛛网膜下腔出血致死致残率极高，借助深度学习网络辅助医生实现高效筛查具有重要意义．为提高基于时间飞跃法磁共振血管造影（Time of Flight-Magnetic Resonance Angiography，TOF-MRA）的脑动脉瘤自动检测的精度，本文基于模糊标签方式，提出一种基于变体3D U-Net和双分支通道注意力（Dual-branch Channel Attention，DCA）的深度神经网络DCAU-Net，DCA模块可以自适应地调整通道特征的响应，提高特征提取能力．首先对260例病例的TOF-MRA影像预处理，将数据集分为174例训练集、43例验证集和43例测试集，然后使用处理后的数据训练和验证DCAU-Net，测试集实验结果表明DCAU-Net可以达到90.69%的敏感度，0.83个/例的假阳性计数和0.52的阳性预测值，有望为动脉瘤筛查提供参考．     

基于言语工作记忆内容的视觉注意捕获

摘要：工作记忆内容对于解决视场中多个物体之间的注意资源竞争可能起着重要作用.本研究旨在考察保持在言语工作记忆中的特征值信息对于视觉选择性注意的自动引导作用.实验1要求被试在言语工作记忆保持阶段完成1个探测区分任务,结果发现有记忆匹配项条件下的探测反应时要显著慢于无匹配项条件下的反应时,而在实验2中没有记忆要求时却没有发现这种效应.实验结果表明,言语工作记忆中的特征值信息能够自动引导注意选择视场中与之匹配的物体特征.     

改进YOLO轻量化网络的行人检测算法

针对当前行人检测方法计算量大、检测精度低的问题,基于YOLOv4-tiny提出一种改进的行人检测算法.引入通道注意力和空间注意力模块(CBAM)至CSPDarknet53-tiny网络中,通过学习图像的位置信息和通道信息得到更加丰富的特征;在骨干网络CSPDarknet53-tiny之后引入空间金字塔池化模块,能够极大...     

Spiking threshold and overarousal effects in serial learning

Possible dependencies of serial learning data on physiological parameters such as spiking thresholds, arousal level, and decay rate of potentials are considered in a rigorous learning model. Influence of these parameters on the invertedU in learning, skewing of the bowed curve, primacy vs. recency, associational span, distribution of remote associations, and growth of associations is studied. A smooth variation of parameters leads from phenomena characteristic of normal subjects to abnormal phenomena, which can be interpreted in terms of increased response interference and consequent poor paying attention in the presence of overarousal. The study involves a type of biological many-body problem including dynamical time-reversals due to macroscopically nonlocal interactions.Supported in part by the A. P. Sloan Foundation (71609), the NSF (GP-13778), and the ONR (N00014-67-A-0204-00-0051).Supported in part by the ONR 4102 (02).     

Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions

The adaptation of deep learning models within safety-critical systems cannot rely only on good prediction performance but needs to provide interpretable and robust explanations for their decisions. When modeling complex sequences, attention mechanisms are regarded as the established approach to support deep neural networks with intrinsic interpretability. This paper focuses on the emerging trend of specifically designing diagnostic datasets for understanding the inner workings of attention mechanism based deep learning models for multivariate forecasting tasks. We design a novel benchmark of synthetically designed datasets with the transparent underlying generating process of multiple time series interactions with increasing complexity. The benchmark enables empirical evaluation of the performance of attention based deep neural networks in three different aspects: (i) prediction performance score, (ii) interpretability correctness, (iii) sensitivity analysis. Our analysis shows that although most models have satisfying and stable prediction performance results, they often fail to give correct interpretability. The only model with both a satisfying performance score and correct interpretability is IMV-LSTM, capturing both autocorrelations and crosscorrelations between multiple time series. Interestingly, while evaluating IMV-LSTM on simulated data from statistical and mechanistic models, the correctness of interpretability increases with more complex datasets.     

基于改进二阶段检测网络的长时跟踪重检测方法

为构建适用于长时跟踪的重检测模块,受改进二阶段检测网络的GlobalTrack方法的启发,提出了一种高效的对特定模板目标进行端到端重检测的深度网络：首先,为了在大尺度图像上更高效地融合模板特征,通过构造交叉信息增强模块改进深度互相关方法,利用交叉通道注意力信息编码搜索特征和模板特征;此外,采用动态实例交互模块替代传统二阶段网络的RPN(region proposal network)和RCNN(region-based convolutional neural networks)结构,根据模板信息指导检测网络的分类和回归阶段,构建了端到端的稀疏重检测结构。在LaSOT和OxUva长时跟踪数据集上进行对比实验,本文方法相较于原始方法性能提升3%,实时帧率提升173%。实验结果表明,改进后的方法可以在全图范围内更准确、快速地重新检测模板目标。     

气体中甲醛含量测定能力验证实验注意事项

为了促进用分光光度法测定气态甲醛样品中甲醛含量的能力验证实验,通过气态甲醛样品中甲醛含量的测定实验,对实验过程进行探讨。为了提高检测结果的准确性应注意如下事项:气路各个环节保持良好密封;连接气袋与气瓶或采样管的塑料软管尽量短;由气瓶往气袋放气时,控制气体流量和放气时间;采样时,气袋中的甲醛气体温度应与环境温度一致;合理选择采样流量和采样时间;确保采样前后采样器的流量一样;使用甲醛标准物质进行质量控制;标准系列的浓度范围与能力验证样品接近。     

S2A: Scale-Attention-Aware Networks for Video Super-Resolution

Convolutional Neural Networks (CNNs) have been widely used in video super-resolution (VSR). Most existing VSR methods focus on how to utilize the information of multiple frames, while neglecting the feature correlations of the intermediate features, thus limiting the feature expression of the models. To address this problem, we propose a novel SAA network, that is, Scale-and-Attention-Aware Networks, to apply different attention to different temporal-length streams, while further exploring both spatial and channel attention on separate streams with a newly proposed Criss-Cross Channel Attention Module (C3AM). Experiments on public VSR datasets demonstrate the superiority of our method over other state-of-the-art methods in terms of both quantitative and qualitative metrics.