虚拟人一种导航方法研究

基于Gibson的可供性理论，提出了虚拟人一种导航方法．某个可供性是环境的不变量，是关于空间、时间和行动之间的一种联系．虚拟人能够直接感知到可供性，可以在虚拟环境中的特定区域设定语义信息，从而建立导航区域的概念．场景的八叉树用来模拟虚拟人对空间的知觉，并引入注意机制来增强感知的真实性．最后，在微机上实现了一个基于本文模型的虚拟人行为动画系统．     

机构调研行为的示范效应研究——基于深证“互动易”平台的实验证据

基于深交所“互动易”平台的机构调研信息和投资者问答数据,以投资者关注为聚焦点,通过剖析机构调研行为-投资者关注-股票流动性的传导路径,检验中国股票市场机构调研行为的示范效应。结论表明,机构调研行为具有强烈的示范效应,能够通过引起其他投资者的关注,促进中短期股票流动性的提高,且机构调研行为越集中,这种示范效应越强。此外,对于信息披露质量较低和机构持股比例较低的公司,机构调研行为更容易引起投资者的关注,示范效应更强。研究结论验证了机构投资者的信息优势性和行为影响力,支持了投资者的信息解读能力,为机构投资者的市场引导作用提供解释依据。     

Modern Science: A Case of Collective Intelligence? On the Role of Thought Economy and Gratifying Attention in Knowledge Production

Your attention please: Phenomenal conciousness, that is, how something feels, does not exist for an observer. As science relies on observations, it is not aware of the nature of subjectivity and thus science is not often defined as a collective intelligence. In this Essay, the roles of intelligence and attention are discussed, as well as an analysis of scientific communication and citation, in order to evaluate whether science is a case of collective intelligence.     

通道注意网络和模糊划分熵图割的图像去雾

针对雾图成像时变化的场景光及去雾过程中不同雾相关信息在处理上的差异性,提出了通道注意网络和模糊划分熵图割的单幅图像去雾算法。以考虑变化场景光的大气散射物理成像模型为基础,首先使用通道注意的编码解码网络来估计透射率,并在编码器最后及解码器起始处添加通道注意模块,以便为编码器提取的不同雾相关特征图分配不同的权重,准确地计算透射率;然后利用所提出的模糊划分熵图割算法将透射率划分为不同场景光覆盖下的近景、中景、远景,此分割策略将考虑空间相关性的图割算法与模糊划分熵的阈值分割算法相结合,解决了单一阈值分割算法产生的区域误分问题;最后估计场景光和大气光,得到去雾图像。实验结果表明,算法在合成雾图及真实雾图上均有较好的去雾效果。与已有的去雾算法相比,本文算法在峰值信噪比及结构相似性上均有提升,单张图像的平均处理时间为3.9 s。     

基于视觉注意的彩色图像检索方法

基于视觉注意模型提取的特征能够反映图像高层语义的新特征,将视觉注意机制引入到图像分析领域能有效地减小语义鸿沟,获得高效的图像检索性能.根据视觉感知的特点,对Itti视觉注意模型进行了改进.采用主分量图表示亮度图,将纹理粗糙度信息融入视觉注意模型,进而提出了一种基于视觉注意空间分布特征的图像检索算法.首先由改进视觉注意模型将图像分解得到38个视觉特征图,然后采用网格平分法提取视觉特征图的空间分布信息,组成特征矢量来多层次地对图像特征进行描述,用于图像检索.实验结果表明,该算法利用基于改进注意力模型方法来提取图像空间分布特征进行图像检索,能获得较高的检索率.     

一种快速的近岸红外目标检测

近岸红外目标检测由于背景复杂和实时性的要求,是当前研究的一个难点。基于上下文的视觉注意机制是解决这一难题的有效途径。首先根据近岸红外目标存在于海面这一先验知识,利用灰度、纹理特征及位置关系,分割出海面;其次利用基于相位谱的视觉注意机制定位显著区域;最后综合海面和显著区域位置,便可检测出近岸红外目标。实验证明,这种算法高效、准确,能够满足目标检测阶段的实时性要求。     

Young children's recognition of quantitative relations in mathematically unspecified settings

Children have been found to be able to reason about quantitative relations, such as non-symbolic proportions, already by the age of 5 years. However, these studies utilize settings in which children were explicitly guided to notice the mathematical nature of the tasks. This study investigates children's spontaneous recognition of quantitative relations on mathematically unspecified settings. Participants were 86 Finnish-speaking children, ages 5–8. Two video-recorded tasks, in which participants were not guided to notice the mathematical aspects, were used. The tasks could be completed in a number of ways, including by matching quantitative relations, numerosity, or other aspects. Participants’ matching strategies were analyzed with regard to the most mathematically advanced level utilized. There were substantial differences in participants’ use of quantitative relations, numerosity and other aspects in their matching strategies. The results of this novel experimental setting show that investigating children's spontaneous recognition of quantitative relations provides novel insight into children's mathematical thinking and furthers the understanding of how children recognize and utilize mathematical aspects when not explicitly guided to do so.     

Learning Numerosity Representations with Transformers: Number Generation Tasks and Out-of-Distribution Generalization

One of the most rapidly advancing areas of deep learning research aims at creating models that learn to disentangle the latent factors of variation from a data distribution. However, modeling joint probability mass functions is usually prohibitive, which motivates the use of conditional models assuming that some information is given as input. In the domain of numerical cognition, deep learning architectures have successfully demonstrated that approximate numerosity representations can emerge in multi-layer networks that build latent representations of a set of images with a varying number of items. However, existing models have focused on tasks requiring to conditionally estimate numerosity information from a given image. Here, we focus on a set of much more challenging tasks, which require to conditionally generate synthetic images containing a given number of items. We show that attention-based architectures operating at the pixel level can learn to produce well-formed images approximately containing a specific number of items, even when the target numerosity was not present in the training distribution.     

基于视觉显著图的数字水印算法

为了更加有效地解决数字水印不可见性的问题,提出了一种基于视觉显著图的水印方法。对Itti视觉计算注意模进行改进,提取图像视觉显著图;利用二级哈尔小波变换方法,根据图像视觉显著图,将水印信息嵌入到图像的低频域内;采用多组SIFT特征点匹配进行精确几何校正处理,提取水印信息。实验结果表明,含水印图像的PSNR达到80dB以上,提取水印NC值都在0.8以上。因此,数字水印方法具有极好的不可感知性,可以有效抵抗噪声、滤波和JPEG压缩等常规图像处理攻击,尤其是对任意角度的旋转攻击、缩放、平移等几何攻击具有极强的鲁棒性。