Interrelationships between Length of the Day, Moon Distance, Phanerozoic Geodynamic Cycles, Tidal Dissipation and Earth’s Core: Review and Analysis

The rotation of the Earth and the related length of the day (LOD) are predominantly affected by tidal dissipation through the Moon and the growth of the Earth’s core. Due to the increased concentration of mass around the rotation axis of the spinning Earth during the growth of the core the rotation should have been accelerated. Controversially the tidal dissipation by the Moon, which is mainly dependent on the availability of open shallow seas and the kind of Moon escape from a nearby position, acts towards a deceleration of the rotating Earth. Measurements of LOD for Phanerozoic and Precambrian times open ways to solve questions concerning the geodynamical history of the Earth. These measurements encompass investigations of growth patterns in fossils and depositional patterns in sediments (Cyclostratigraphy, Tidalites, Stromatolites, Rhythmites). These patterns contain information on the LOD and on the changing distance between Earth and Moon and can be used as well for a discussion about the growth of the Earth’s core. By updating an older paper with its simple approach as well as incorporating newly published results provided by the geoscientific community, a moderate to fast growth of the core in a hot early Earth will be favored controversially to the assumption of a delayed development of the core in an originally cold Earth. Core development with acceleration of Earth’s rotation and the contemporaneous slowing down due to tidal dissipation during the filling of the ocean may significantly interrelate.

Seizure detection using earth movers' distance and SVM in intracranial EEG

Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.

中国大陆M_(S)≥6.0地震前地电阻率异常特征

我国地震地电阻率观测通过连续监测地下介质电阻率随时间的变化及其空间分布规律,服务于地震中短期预测。自1967年开展观测以来,已积累大量观测数据和中强地震前异常变化资料。通过文献调研和数据分析,系统梳理了在观测站网及附近区域发生的M_(S)≥6.0地震的地电阻率震前异常。分析发现:(1)M_(S)≥6.0地震前,具有地电阻率异常的观测站比率随震中距的增加而显著降低;(2)M_(S)6.0—6.9地震前,地电阻率异常持续时间随震级增加而略微增加,而M_(S)≥7.0地震前未出现异常持续时间随震级增加的现象;(3)M_(S)≥7.0地震前,地电阻率异常持续时间随震中距增加有所减小,但统计结果的离散度较大,而M_(S)6.0—6.9地震前则无此现象。整体而言,不同震中距范围内,地电阻率异常持续时间并未呈现较好的规律性。

Accurate Approximation of the Earth Mover's Distance in Linear Time

Color descriptors are one of the important features used in content-based in, age retrieval. The dominant color descriptor （DCD） represents a few perceptually dominant colors in an image through color quantization. For image retrieval based on DCD, the earth mover＇s distance （EMD） and the optimal color composition distance were proposed to measure the dissimilarity between two images. Although providing good retrieval results, both methods are too time-consuming to be used in a large image database. To solve the problem, we propose a new distance function that calculates an approximate earth mover＇s distance in linear time. To calculate the dissimilarity in linear time, the proposed approach employs the space-filling curve for multidimensional color space. To improve the accuracy, the proposed approach uses multiple curves and adjusts the color positions. As a result, our approach achieves order-of-magnitude time improvement but incurs small errors. We have performed extensive experiments to show the effectiveness and efficiency of the proposed approach. The results reveal that our approach achieves almost the same results with the EMD in linear time.

基于敏感属性值语义桶分组的t-closeness隐私模型

t-closeness模型是数据发布领域中用于抵御相似性攻击和偏斜攻击的一种有效方法,但其采用的EMD(earth mover's distance)距离没有考虑等价类与数据表间敏感属性分布的稳定性,不能全面地衡量分布间距离,在分布间稳定差异过大时会大大提高隐私泄露的风险.针对这种局限,提出了一种SABuk t-closeness模型,它在传统t-closeness模型的基础上,为更加准确地度量分布间距离,以EMD距离与KL散度(kullback-leibler divergence)结合构建距离度量标准.同时,根据敏感属性的层次树结构,对数据表进行语义相似性桶分组划分,然后采用贪心思想生成满足要求的最小等价类,并且运用k-近邻的思想来选取QI(quasi-identifiers)值相似的元组生成等价类.实验结果表明,SABuk t-closeness模型在牺牲少量时间的前提下减少了信息损失,能在有效地保护敏感信息不泄露的同时保持较高的数据效用.

基于改进DPGN的少样本图像分类算法研究

DPGN(distribution propagation graph network)是基于深度学习的少样本图像分类算法,在数据稀疏的条件下可以顺利完成图像分类,但其分类的准确率仍需进一步提升。以DPGN算法为研究对象,提出SFOD_DPGN(SinAM_FRN_layer_ODConv_DM&EMD_distribution propagation graph network)算法。在骨干神经网络Resnet12的残差块中融入注意力机制;将Resnet12网络中批量归一化与ReLu激活函数搭配使用的方式改为滤波器响应归一化与阈值线性单元激活函数搭配使用的方式;在分类器模块中选用全维动态卷积替换普通卷积;使用马氏距离和推土机距离替换L2距离度量函数。在CUB-200-2011数据集上的实验表明,在5way-1shot和5way-5shot分类任务下,SFOD_DPGN算法比DPGN算法的准确率提升约7.97%和2.66%。

结合改进SWT和EMD的高分遥感影像道路提取

针对利用传统笔画宽度变换(stroke width transform,SWT)提取遥感影像道路阈值无法自动确定以及精度不够高的问题,提出了一种结合改进SWT和融合多特征后的陆地移动距离(earth mover’s distance,EMD)进行高分遥感影像道路提取的方法。首先,结合连通分量分析,将笔画宽度变换改进成可自动确定提取阈值的方法,进行初步道路提取;其次,确定一个道路参考区域,利用连通区作为索引,分析道路参考区域与其他区域之间的特征相似度,通过提取纹理与光谱特征,计算加权融合后的特征EMD值,设定合理的阈值过滤大部分非道路区域,提取出其他道路区域;最后,进行形态学后处理得到最终提取结果。通过实验实现该方法并与现有道路提取方法进行对比。经过评估,证明该方法在提取不同场景的道路时均能达到理想效果且具有较高精度。

基于耦合空间模糊C均值聚类和推土机距离的变化检测

在遥感影像变化检测领域中,当遥感影像受椒盐、高斯和混合噪声污染时,变化检测精度往往无法得到保证。虽然基于空间模糊C均值聚类的有监督变化检测算法能有效实现抗噪声变化检测,但是其人工训练成本和时间成本过高,在实时场景中无法大规模应用。对此,文章将5种空间模糊C均值算法分别与推土机距离(earth mover’s distance, EMD)耦合,实现了5种具有较好抗噪声能力的无监督遥感变化检测算法,能够保证噪声污染下的实时变化检测精度。实验证明,与最近提出的KPCAMNet和GMCD无监督变化检测算法相比,所提出算法能更好地处理受椒盐、高斯和混合噪声污染的遥感影像,具有一定的应用价值。

基于SDN的动态负载均衡策略

针对SDN中静态网络结构不能适应动态流量变化所引起的控制器负载不均衡问题,提出一种阶段式动态负载均衡策略。阶段一以控制器负载均值化为目标,确定迁入控制器候选集,且综合考虑时延、负载,设计指标函数,选取待迁移交换机;阶段二考虑网络节点间的连通性,以最小迁移代价为目标,提出改进的EMD模型,并用线性逼近算法快速求解,实现了交换机的快速并行迁移。实验结果表明,与现有的负载均衡策略相比,其改善了迁入控制器和交换机的选取过程,优化了网络性能,控制器的负载均衡度提升了约31.4%。

Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services

The number of mobile application services is showing an explosive growth trend,which makes it difficult for users to determine which ones are of interest.Especially,the new mobile application services are emerge continuously,most of them have not be rated when they need to be recommended to users.This is the typical problem of cold start in the field of collaborative filtering recommendation.This problem may makes it difficult for users to locate and acquire the services that they actually want,and the accuracy and novelty of service recommendations are also difficult to satisfy users.To solve this problem,a hybrid recommendation method for mobile application services based on content feature extraction is proposed in this paper.First,the proposed method in this paper extracts service content features through Natural Language Processing technologies such as word segmentation,part-of-speech tagging,and dependency parsing.It improves the accuracy of describing service attributes and the rationality of the method of calculating service similarity.Then,a language representation model called Bidirectional Encoder Representation from Transformers(BERT)is used to vectorize the content feature text,and an improved weighted word mover’s distance algorithm based on Term Frequency-Inverse Document Frequency(TFIDF-WMD)is used to calculate the similarity of mobile application services.Finally,the recommendation process is completed by combining the item-based collaborative filtering recommendation algorithm.The experimental results show that by using the proposed hybrid recommendation method presented in this paper,the cold start problem is alleviated to a certain extent,and the accuracy of the recommendation result has been significantly improved.

Novel similarity measures for face representation based on local binary pattern

The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter and propose two novel similarity measures for the local matching methods and the holistic matching methods respectively.One is Earth Mover's Distance with Hamming and Lp ground distance(EMD-HammingLp),which is a cross-bin dissimilarity measure for LBP histograms.The other is IMage Hamming Distance(IMHD),which is a dissimilarity measure for the whole LBP images.Experiments on FERET database show that the proposed two similarity measures outperform the state-of-the-art Chi-square similarity measure for extraction of LBP features.

Behavior Targeting Based on Hierarchical Taxonomy Aggregation for Heterogeneous Online Shopping Applications

Behavior targeting(BT)based on individual web-browsing history has become more valuable in precision marketing for many companies through capturing users’interest and preference.It is common in practice that the behavior data collected from different online shopping applications are inconsistent since they are labelled by different item taxonomy,where the same behavior could have different representations and therefore analysis confusion arises.To address this issue,we propose a semantic similarity based strategy to transform the heterogeneous behavior extracted from deep packet inspection(DPI)data of a telecommunication operator into a unique standard one.The Word Mover’s Distance algorithm is exploited to evaluate the semantic similarity of the distributed representations of two web-browsing histories.Moreover,the architecture of the behavior targeting platform on Hadoop is implemented,which is capable of processing data with size of PB level every day.

An Image Retrieval Method Based on Color and Texture Features

The technique of image retrieval is widely used in science experiment, military affairs, public security, advertisement, family entertainment, library and so on. The existing algorithms are mostly based on the characteristics of color, texture, shape and space relationship. This paper introduced an image retrieval algorithm, which is based on the matching of weighted EMD（Earth Mover’s Distance） distance and texture distance. EMD distance is the distance between the histograms of two images in HSV（Hue, Saturation, Value） color space, and texture distance is the L1 distance between the texture spectra of two images. The experimental results show that the retrieval rate can be increased obviously by using the proposed algorithm.

Statute Recommendation Based on Word Embedding

The statute recommendation problem is a sub problem of the automated decision system, which can help the legal staff to deal with the process of the case in an intelligent and automated way. In this paper, an improved common word similarity algorithm is proposed for normalization. Meanwhile, word mover’s distance (WMD) algorithm was applied to the similarity measurement and statute recommendation problem, and the problem scene which was originally used for classification was extended. Finally, a variety of recommendation strategies different from traditional collaborative filtering methods were proposed. The experimental results show that it achieves the best value of Fmeasure reaching 0.799. And the comparative experiment shows that WMD algorithm can achieve better results than TF-IDF and LDA algorithm.

基于EMD距离度量的小样本矿物图像分类

在复杂的地质勘探条件下准确完成矿物识别是一项重要的任务。基于数据驱动的深度学习模型能精确识别各类岩石矿物,但需要构建庞大且完备的数据集,在实际情况下难以应用。针对此问题,结合小样本学习、度量学习以及元学习训练策略,使用EMD距离度量计算图像之间的结构距离,构建一种适应于小样本矿物的图像分类模型。核心思想在于利用图块级别度量并引入交叉参考权重机制,有效减少同类差异大和背景杂乱带来的影响,优于图与图判定分类的模型。在mini-ImageNet数据集上,5-way 1-shot和5-way 5-shot设置的分类准确率分别提高至55.91%、67.58%;将算法应用于小样本黏土矿物数据集上,5-way 5-shot设置的分类准确率为92.65%。实验结果表明,利用度量学习方法的分类精度高于其他小样本学习方法。

Organization of Propaganda Forums (Such as COP21) by Some Heads of State Seriously Disturbs Scientific Debates on Real Causes of Climate Global Changes

Faced with many problems (practically insoluble) that call them politicians lie (to save their careers), claiming that they are resolutely interested to earth’s environment protection. Unnecessary (for some) and outrageously expensive (for others) organization of COP21 is part of this logic of distracting the “international community” by (for example) making believe that the reduction of CO2 emissions is something reachable for humans and that this reduction can weaken the harmful effects of global warming such as: floods, endless droughts, melting glaciers, upsurge of hurricanes or tornadoes, etc. Can the “binding” commitments taken during COP21 be effective? Our objective and immediate answer is: No. Because those who have made commitments, ignore completely how the CO2 (main object of their attentions) or sulfur dioxide (SO2) behave with respect to some predominant elements of the atmosphere such as clouds. Moreover, a significant number of the propaganda forums’ stakeholders ignores that earth’s atmosphere is considerably affected by the radiation from the sun and that the intensity of solar radiation received at the surface of the earth mainly depends on the Earth-Sun distance and the precession angle of the earth relative to its orbit. Earth-Sun distance and precession angle of the Earth’s orbit vary in space and time: this Physical law must be constantly present in our minds. Teaching the fundamentals of Atmospheric Physics allows solar radiation and its derivative forms (e.g.: winds, snow or rains) to cease to be a mystery in the mind of the international community.

基于故障可诊断性定量评价的测试优化选择

目前,对于测试优化选择问题的研究大多局限于故障是否可以被诊断的定性研究,没有进一步考虑故障诊断的难度。针对以上问题,提出了一种基于故障可诊断性定量评价的测试优化选择方法。首先,基于推土机距离(EMD)对故障可诊断性进行定量评价;然后,以评价结果为依据,综合考虑测试的数量、可靠性和成本等因素,构建多目标测试优化选择问题,并提出了二进制金枪鱼群优化算法来求解满足测试性指标和故障可诊断性水平的最优测试集合;最后,以某装备的开关电源为实验案例,验证了所提方法的有效性。仿真结果表明:所提方法能够实现以提高系统故障可诊断性为目标的测试优化选择,从根本上提高系统的故障诊断能力。

基于EMD的故障可诊断性评价模型设计

构建可诊断性模型是进行可诊断性设计的前提,通过可诊断性模型能够分析系统内故障的可诊断性。目前的研究成果中大都集中于定性研究,即对故障可诊断性的评价集中于“故障是否能够被诊断”的定性评价,而没有进一步考虑量化故障被检测或被隔离的难易程度。针对以上问题,本文提出了一种基于推土机距离(EMD)的可诊断性定量评价模型,将故障诊断难度定量评价问题转化为测试数据多元分布之间的距离度量问题。首先建立了适用于可诊断性分析的多信号流图定性模型,用来描述系统的功能组成结构、故障-测试-信号的相关性关系;然后在定性模型的基础上,结合数据驱动框架,建立了基于EMD的可诊断性定量评价模型,除了能够定性地描述故障与测试之间相关性关系之外,还能够定量地衡量诊断故障的难易程度。最后以某型装备的开关电源为实验案例,验证了本文方法的有效性,为可诊断性建模提供了一条可行的思路。

一种基于部件空间分布的三维模型检索方法

三维形状分析是三维模型检索的关键问题.提出一种基于三维模型部件空间分布的形状特征描述方法.此方法的主要思想是依据认知心理学的理论,在描述对象形状时强调它的结构属性.首先将三维模型分割为若干个组成部件,每个部件用一个曲面片表示,然后采用曲面片的质心位置、面积占总面积的百分比的组合作为部件特征,最后将满足指定条件的部件特征的集合作为三维模型的形状特征.基于这一特征表示,给出了一种三维模型检索方法.该方法具有受模型精度和连通性影响较小、相似性度量的计算速度较快的优点.实验结果验证了该检索方法的有效性.

一种基于EMD的文档语义相似性度量

针对基于EMD(Earth Mover's Distance)的文档语义相似性算法不满足度量公理因而难以在信息检索与数据挖掘中推广应用的问题,该文提出了一种新的基于EMD的文档语义相似性度量——..Mdss_EMD(Metric for document semantic similarity based EMD)。首先在分析EMD及现有改进方法缺陷的基础上,给出了文档宽度、虚拟项的概念;随后通过增加虚拟项来对齐文档矢量的总权值,使所有度量公理得到满足;最后,为提高该度量的适应能力及处理速度,还实现了虚拟项相似距离的弹性设计并对EMD算法进行了简化。该方法把EMD扩展到度量空间中来,很大程度上提高了EMD的索引能力与精度,初步实验表明,Mdss_EMD的整体性能优于原EMD及现有其它类似方法。