Comparison Between Gross Errors Detection Methods in Surveying Measurements

The least squares estimation method is commonly used to process measurements. In practice, redundant measurements are carried out to ensure quality control and to check for errors that could affect the results. Therefore, an insurance of the quality of these measurements is an important issue. Measurement errors of collected data have different levels of influence due to their number, measured accuracy and redundancy. The aim of this paper is to examine the detection of gross error capabilities in vertical control networks using three methods; Global Test, Data Snooping and Tau Test to compare the effectiveness of these three methods. With the least squares’ method, if there are gross errors in the observations, the sizes of the corresponding residuals may not always be larger than for other residuals that do not have gross errors. This makes it difficult to find (detect) it. Therefore, it is not certain that serious errors should be detected by just examining the magnitudes of the residuals alone. These methods are used in conjunction with developed programs to calculate critical values for the distributions (in real time) rather than look for these in statistical tables. The main conclusion reached is that the tau (τ) statistic is the most sensitive to the presence gross error detection; therefore, it is the one recommended to be used in gross error detection

Textually Relevant Spatial Skylines

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A scalable approach for content based image retrieval in cloud datacenter

The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate and access interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose a scalable image retrieval framework which can efficiently support content similarity search and semantic search in the distributed environment. Its key idea is to integrate image feature vectors into distributed hash tables (DHTs) by exploiting the property of locality sensitive hashing (LSH). Thus, images with similar content are most likely gathered into the same node without the knowledge of any global information. For searching semantically close images, the relevance feedback is adopted in our system to overcome the gap between low-level features and high-level features. We show that our approach yields high recall rate with good load balance and only requires a few number of hops

Gaussian Processes for Time Series with Lead-Lag Effects with applications to biology data

Investigating the relationship, particularly the lead-lag effect, between time series is a common question across various disciplines, especially when uncovering biological process. However, analyzing time series presents several challenges. Firstly, due to technical reasons, the time points at which observations are made are not at uniform inintervals. Secondly, some lead-lag effects are transient, necessitating time-lag estimation based on a limited number of time points. Thirdly, external factors also impact these time series, requiring a similarity metric to assess the lead-lag relationship. To counter these issues, we introduce a model grounded in the Gaussian process, affording the flexibility to estimate lead-lag effects for irregular time series. In addition, our method outputs dissimilarity scores, thereby broadening its applications to include tasks such as ranking or clustering multiple pair-wise time series when considering their strength of lead-lag effects with external factors. Crucially, we offer a series of theoretical proofs to substantiate the validity of our proposed kernels and the identifiability of kernel parameters. Our model demonstrates advances in various simulations and real-world applications, particularly in the study of dynamic chromatin interactions, compared to other leading methods

Towards an effective processing of XML keyword query

Ph.DDOCTOR OF PHILOSOPH

筑波大学計算科学研究センター 平成22年度 年次報告書

1 平成22年度 重点施策・改善目標　……　42 平成22年度 実績報告　……　73 各研究部門の報告　……　11Ⅰ.素粒子物理研究部門　……　11Ⅱ.宇宙・原子核物理研究部門　……　23 Ⅱ-1.宇宙分野　……　23 Ⅱ-2.原子核分野　……　41Ⅲ.量子物性研究部門　……　50Ⅳ.生命科学研究部門　……　76 Ⅳ-1.生命機能情報分野　……　76 Ⅳ-2.分子進化分野　……　83Ⅴ.地球環境研究部門　……　89Ⅵ.高性能計算システム研究部門　……　99Ⅶ.計算情報学研究部門　……　107 Ⅶ-1.データ基盤分野　……　107 Ⅶ-2.計算メディア分野　……　12