Shape restriction of the multi-dimensional Bernstein prior for density functions

Random Bernstein polynomials induces a probability measure on the space of multivariate density functions on a unit cube. For density estimation, it is important that the Bernstein prior can be restricted to an admissible class of densities with certain geometric properties of the target density. In this article, we study the shape properties such as monotonicity, convexity, and symmetry of the Bernstein prior.     

Action semantics: A unifying conceptual framework for the selective use of multimodal and modality-specific object knowledge

Our capacity to use tools and objects is often considered one of the hallmarks of the human species. Many objects greatly extend our bodily capabilities to act in the physical world, such as when using a hammer or a saw. In addition, humans have the remarkable capability to use objects in a flexible fashion and to combine multiple objects in complex actions. We prepare coffee, cook dinner and drive our car. In this review we propose that humans have developed declarative and procedural knowledge, i.e. action semantics that enables us to use objects in a meaningful way. A state-of-the-art review of research on object use is provided, involving behavioral, developmental, neuropsychological and neuroimaging studies. We show that research in each of these domains is characterized by similar discussions regarding (1) the role of object affordances, (2) the relation between goals and means in object use and (3) the functional and neural organization of action semantics. We propose a novel conceptual framework of action semantics to address these issues and to integrate the previous findings. We argue that action semantics entails both multimodal object representations and modality-specific sub-systems, involving manipulation knowledge, functional knowledge and representations of the sensory and proprioceptive consequences of object use. Furthermore, we argue that action semantics are hierarchically organized and selectively activated and used depending on the action intention of the actor and the current task context. Our framework presents an integrative account of multiple findings and perspectives on object use that may guide future studies in this interdisciplinary domain.     

一种快速暗通道去雾算法

基于暗通道的去雾算法计算初始透射率需要进行大量的数据比较,优化透射率时需要计算融合矩阵,这两个过程耗时巨大,使其难以投入实际应用。针对这一问题,提出了一种快速暗通道去雾方法。首先利用分区最小表的数据结构,提高初始透射率的计算速度;接着,在优化透射率时,采用基于形态学梯度的彩色图像边缘检测算法提取图像边缘信息,减少优化范围,再利用边缘信息与像素的空间信息优化透射率,避免计算复杂矩阵,从而加快了优化速度;最后,运用最小可觉差模型补偿前两个步骤导致的图像画质下降,提高图像清晰度。实验证明,快速暗通道去雾算法在保持恢复图像效果基本不变的基础上,很大程度提高了算法速度。     

基于脏脸博弈模型的人车路口穿越行为研究

行人与车辆在穿越无信号路口时的行为冲突尤其具有危险性, 行人和机动车不仅具有运动特征, 更具有行为特征, 因此需要从社会学层面对人车路口穿越问题进行建模. 通过对无信号路口处人车干扰机理的深入剖析, 提出基于脏脸博弈模型研究人车“理性人”的行为特征以及交互行为背后的微观机理, 分析了人车穿越路口时共同知识的形成过程、人车双方优势策略的产生机理和人车脏脸博弈的动力学过程. 理论推导了人、车收益的数学期望和发生人车冲突的概率. 分析结果表明: 行人穿越的理论时间和机动车穿越的理论时间越接近, 人车越容易发生冲突. 同时, 进一步分析了不同车型、不同行人等待时间和个体认知能力异质性对人车冲突概率的影响.     

Bayesian Variable Selection for Median Regression

When the data has heavy tail feature or contains outliers, conventional variable selection methods based on penalized least squares or likelihood functions perform poorly. Based on Bayesian inference method, we study the Bayesian variable selection problem for median linear models. The Bayesian estimation method is proposed by using Bayesian model selection theory and Bayesian estimation method through selecting the Spike and Slab prior for regression coefficients, and the effective posterior Gibbs sampling procedure is also given. Extensive numerical simulations and Boston house price data analysis are used to illustrate the effectiveness of the proposed method.     

How well prepared are the teachers of tomorrow? An examination of prospective mathematics teachers' pedagogical content knowledge

There is a growing emphasis in the teaching profession on pedagogical content knowledge (PCK) as an important knowledge component. The study reported in this article investigates Turkish prospective mathematics teachers’ mathematics teaching knowledge in the numbers content domain. A series of 10 open-ended scenario-type questions were adopted to challenge 83 prospective mathematics teachers’ knowledge of the learner and presentation of content in the context of PCK. The participants’ responses were analysed by means of rubrics and scoring guides developed by the researchers. The results showed that many of the future teachers performed well in determining what misconceptions students might express in the given scenarios. However, a majority of the participants performed poorly on presentation of content in terms of instructional strategies. In line with these results, the authors offer some suggestions for teacher training programmes.     

贝叶斯频率估计中频率的先验分布对有色噪声作用的影响

在贝叶斯参数估计理论框架下,研究了被测参数的先验分布对有色噪声的抑制作用.选择一个受1/f~α型谱密度有色噪声影响的自旋1/2量子比特作为量子探测系统来估计一个磁场强度的大小,利用贝叶斯代价函数的动力学演化来评判估计的精度,重点研究先验概率分布对噪声非高斯性的限制作用.研究发现:当先验概率的不确定度比较大时,有色噪声的非高斯性对频率估计精度的影响比较小;当先验概率的不确定度比较小时,有色噪声的非高斯性对频率估计精度的影响比较大.     

Reliable Semantic Communication System Enabled by Knowledge Graph

Semantic communication is a promising technology used to overcome the challenges of large bandwidth and power requirements caused by the data explosion. Semantic representation is an important issue in semantic communication. The knowledge graph, powered by deep learning, can improve the accuracy of semantic representation while removing semantic ambiguity. Therefore, we propose a semantic communication system based on the knowledge graph. Specifically, in our system, the transmitted sentences are converted into triplets by using the knowledge graph. Triplets can be viewed as basic semantic symbols for semantic extraction and restoration and can be sorted based on semantic importance. Moreover, the proposed communication system adaptively adjusts the transmitted contents according to channel quality and allocates more transmission resources to important triplets to enhance communication reliability. Simulation results show that the proposed system significantly enhances the reliability of the communication in the low signal-to-noise regime compared to the traditional schemes.     

Cancer and Traditional Plant Knowledge,an Interesting Field to Explore: Data from the Catalan Linguistic Area

Cancer is the second cause of death in the world and is foreseen to be responsible for about 16 million deaths in 2040. Approximately, 60% of the drugs used to treat cancer are of natural origin. Besides the extensive use of some of these drugs in therapies, such as those derived from the genus Taxus, a significant number of plants have revealed themselves as useful against cancer in recent years. The field of ethnobotany focuses on documenting traditional knowledge associated with plants, constituting a starting point to uncover the potential of new plant-based drugs to treat or prevent, in this case, tumour diseases and side effects of chemotherapy and radiotherapy. From a series of extensive ethnobotanical prospections across the Catalan linguistic area (CLA), we have recorded uses for 41 taxa with antitumour effects. The two most quoted botanical families are Asteraceae and Ranunculaceae, and the most frequently reported species is Ranunculus parnassifolius, a high-mountain species, which is widely collected for this purpose. The reported species have been used to treat an important number of cancer types, focusing on preventive, palliative, and curative uses, as well as to deal with the side effects of conventional treatments. Comparing our results in CLA with previous data available in the most comprehensive databases of pharmacology and a review of cytotoxicity assays revealed that for the several species reported here, there was no previous evidence of traditional uses against cancer. Despite the need for further analyses to experimentally validate the information presented here, combining traditional uses and phylogenetically-informed strategies to phytochemical and pharmacological research would represent new avenues to establish more integrative approaches, hence improving the ability to select new candidate taxa in cancer research.     

基于多层次模糊关联算法的通用航空产业知识挖掘研究

为挖掘通用航空产业领域知识的类型与发展规律,明确产业规划、推动产业健康发展,利用采集的3869份通用航空产业主题相关的网页资料,根据主题信息的关键词对通用航空产业领域知识进行分类,并基于多层次模糊关联算法进行知识挖掘分析.结果表明,通用航空产业领域知识中的通用航空产业主体与通用航空产业产品存在着紧密的联系和规则,而且采...