Artículos de revistas sobre el tema "Multi-scale pattern clustering"
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Araabi, Babak Nadjar y Nasser Kehtarnavaz. "Hough Array Processing via Fast Multi-Scale Clustering". Real-Time Imaging 6, n.º 2 (abril de 2000): 129–41. http://dx.doi.org/10.1006/rtim.1999.0181.
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Nakamura, Eiji y Nasser Kehtarnavaz. "Determining number of clusters and prototype locations via multi-scale clustering". Pattern Recognition Letters 19, n.º 14 (diciembre de 1998): 1265–83. http://dx.doi.org/10.1016/s0167-8655(98)00099-3.
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Wen, Junhao, Erdem Varol, Aristeidis Sotiras, Zhijian Yang, Ganesh B. Chand, Guray Erus, Haochang Shou et al. "Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes". Medical Image Analysis 75 (enero de 2022): 102304. http://dx.doi.org/10.1016/j.media.2021.102304.
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Yadav, Dhirendra Prasad, Kamal Kishore, Ashish Gaur, Ankit Kumar, Kamred Udham Singh, Teekam Singh y Chetan Swarup. "A Novel Multi-Scale Feature Fusion-Based 3SCNet for Building Crack Detection". Sustainability 14, n.º 23 (4 de diciembre de 2022): 16179. http://dx.doi.org/10.3390/su142316179.
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Crack detection at an early stage is necessary to save people’s lives and to prevent the collapse of building/bridge structures. Manual crack detection is time-consuming, especially when a building structure is too high. Image processing, machine learning, and deep learning-based methods can be used in such scenarios to build an automatic crack detection system. This study uses a novel deep convolutional neural network, 3SCNet (3ScaleNetwork), for crack detection. The SLIC (Simple Linear Iterative Clustering) segmentation method forms the cluster of similar pixels and the LBP (Local Binary Pattern) finds the texture pattern in the crack image. The SLIC, LBP, and grey images are fed to 3SCNet to form pool of feature vector. This multi-scale feature fusion (3SCNet+LBP+SLIC) method achieved the highest sensitivity, specificity, an accuracy of 99.47%, 99.75%, and 99.69%, respectively, on a public historical building crack dataset. It shows that using SLIC super pixel segmentation and LBP can improve the performance of the CNN (Convolution Neural Network). The achieved performance of the model can be used to develop a real-time crack detection system.
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Tan, Jingang, Lili Chen, Kangru Wang, Jiamao Li y Xiaolin Zhang. "SASO: Joint 3D semantic‐instance segmentation via multi‐scale semantic association and salient point clustering optimization". IET Computer Vision 15, n.º 5 (9 de abril de 2021): 366–79. http://dx.doi.org/10.1049/cvi2.12033.
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GABRIEL, E., D. J. WILSON, A. J. H. LEATHERBARROW, J. CHEESBROUGH, S. GEE, E. BOLTON, A. FOX, P. FEARNHEAD, C. A. HART y P. J. DIGGLE. "Spatio-temporal epidemiology of Campylobacter jejuni enteritis, in an area of Northwest England, 2000–2002". Epidemiology and Infection 138, n.º 10 (5 de marzo de 2010): 1384–90. http://dx.doi.org/10.1017/s0950268810000488.
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SUMMARYA total of 969 isolates of Campylobacter jejuni originating in the Preston, Lancashire postcode district over a 3-year period were characterized using multi-locus sequence typing. Recently developed statistical methods and a genetic model were used to investigate temporal, spatial, spatio-temporal and genetic variation in human C. jejuni infections. The analysis of the data showed statistically significant seasonal variation, spatial clustering, small-scale spatio-temporal clustering and spatio-temporal interaction in the overall pattern of incidence, and spatial segregation in cases classified according to their most likely species-of-origin.
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Guo, Yishan y Mandan Liu. "Spatial-temporal trajectory anomaly detection based on an improved spectral clustering algorithm". Intelligent Data Analysis 27, n.º 1 (30 de enero de 2023): 31–58. http://dx.doi.org/10.3233/ida-216185.
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With the development of wireless communication technology, when users use wireless networks to meet various needs, wireless networks also record a large number of users’ spatial-temporal trajectory data. In order to better pay attention to the healthy development of students and promote the information construction on campus, a spectral clustering algorithm based on the multi-scale threshold and density combined with shared nearest neighbors (MSTDSNN-SC) is proposed. Firstly, it improves the affinity distance function based on the shortest time dis-tance-shortest time distance sub-sequence (STD-STDSS) by adding location popularity and uses this model to construct the initial adjacency matrix. Then it introduces the covariance scale threshold and spatial scale threshold to perform 0–1 processing on the adjacency matrix to obtain more accurate sample similarity. Next, it constructs an eigenvector space by eigenvalue decom-position of the adjacency matrix. Finally, it uses DBSCAN clustering algorithm with shared nearest neighbors to avoid to manually determine the number of clusters. Taking Internet usage data on campus as an example, multiple clustering algorithms are used for anomaly detection and four evaluation metrics are applied to estimate the clustering results. MSTDSNN-SC algorithm reflects better clustering performance. Furthermore, the abnormal trajectories list is verified to be effective and credible.
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He, Yueshun, Wei Zhang, Ping Du y Qiaohe Yang. "A Novel Strategy for Retrieving Large Scale Scene Images Based on Emotional Feature Clustering". International Journal of Pattern Recognition and Artificial Intelligence 34, n.º 08 (14 de noviembre de 2019): 2054019. http://dx.doi.org/10.1142/s0218001420540191.
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Due to complicated data structure, image can present rich information, and so images are applied widely at different fields. Although the image can offer a lot of convenience, handling such data consume much time and multi-dimensional space. Especially when users need to retrieve some images from larger-scale image datasets, the disadvantage is more obvious. So, in order to retrieve larger-scale image data effectively, a scene images retrieval strategy based on the MapReduce parallel programming model is proposed. The proposed strategy first, investigates how to effectively store large-scale scene images under a Hadoop cluster parallel processing architecture. Second, a distributed feature clustering algorithm MeanShift is introduced to implement the clustering process of emotional feature of scene images. Finally, several experiments are conducted to verify the effectiveness and efficiency of the proposed strategy in terms of different aspects such as retrieval accuracy, speedup ratio and efficiency and data scalability.
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Bak, Ji Hyun, Min Hyeok Kim, Lei Liu y Changbong Hyeon. "A unified framework for inferring the multi-scale organization of chromatin domains from Hi-C". PLOS Computational Biology 17, n.º 3 (16 de marzo de 2021): e1008834. http://dx.doi.org/10.1371/journal.pcbi.1008834.
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Chromosomes are giant chain molecules organized into an ensemble of three-dimensional structures characterized with its genomic state and the corresponding biological functions. Despite the strong cell-to-cell heterogeneity, the cell-type specific pattern demonstrated in high-throughput chromosome conformation capture (Hi-C) data hints at a valuable link between structure and function, which makes inference of chromatin domains (CDs) from the pattern of Hi-C a central problem in genome research. Here we present a unified method for analyzing Hi-C data to determine spatial organization of CDs over multiple genomic scales. By applying statistical physics-based clustering analysis to a polymer physics model of the chromosome, our method identifies the CDs that best represent the global pattern of correlation manifested in Hi-C. The multi-scale intra-chromosomal structures compared across different cell types uncover the principles underlying the multi-scale organization of chromatin chain: (i) Sub-TADs, TADs, and meta-TADs constitute a robust hierarchical structure. (ii) The assemblies of compartments and TAD-based domains are governed by different organizational principles. (iii) Sub-TADs are the common building blocks of chromosome architecture. Our physically principled interpretation and analysis of Hi-C not only offer an accurate and quantitative view of multi-scale chromatin organization but also help decipher its connections with genome function.
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Bossennec, Claire, Matthis Frey, Lukas Seib, Kristian Bär y Ingo Sass. "Multiscale Characterisation of Fracture Patterns of a Crystalline Reservoir Analogue". Geosciences 11, n.º 9 (3 de septiembre de 2021): 371. http://dx.doi.org/10.3390/geosciences11090371.
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For an accurate multiscale property modelling of fractured crystalline geothermal reservoirs, an enhanced characterisation of the geometrical features and variability of the fracture network properties is an essential prerequisite. Combining regional digital elevation model analysis and local outcrop investigation, the study comprises the characterisation of the fracture pattern of a crystalline reservoir analogue in the Northern Odenwald, with LiDAR and GIS structural interpretation. This approach provides insights into the 3D architecture of the fault and fracture network, its clustering, and its connectivity. Mapped discontinuities show a homogeneous length distribution, which follows a power law with a −2.03 scaling factor. The connectivity of the fracture network is heterogenous, due to a fault control at the hectometric scale. Clustering is marked by long sub-vertical fractures at the outcrop scale, and strongly enhance heterogeneity around weathered fracture and fault corridors. The multi-variable dataset created within this study can be used as input data for accurate discrete fracture networks and fluid-flow modelling of reservoirs of similar type.
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Lin, Rongheng, Fangchun Yang, Mingyuan Gao, Budan Wu y Yingying Zhao. "AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales". Energies 12, n.º 16 (15 de agosto de 2019): 3144. http://dx.doi.org/10.3390/en12163144.
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With the rapid growth of Smart Grid, electricity load analysis has become the simplest and most effective way to divide user groups and understand user behavior. This paper proposes an AUD-MTS (Abnormal User Detection approach based on power load multi-step clustering with Multiple Time Scales). Firstly, we combine RBM (Restricted Boltzmann Machine) hidden feature learning with K-Means clustering to extract typical load patterns in the short-term. Secondly, time scale conversion is performed so that the analysis subject can be transformed from load pattern to user behavior. Finally, a two-step clustering in long-term is adopted to divide users from both coarse-grained and fine-grained dimensions so as to detect abnormal users referring to customized OutlierIndex. Experiments are conducted using annual 24-point power load data of American users in all states. The accuracy of clustering methods in AUD-MTS reaches 87.5% referring to the 16 commercial building types defined by the U.S. Department of Energy, which outperforms other common clustering algorithms on AMI (Advanced Metering Infrastructure). After that, the OutlierIndex score of AUD-MTS can be increased by 0.16 compared with other outlier detection algorithms, which shows that the proposed method can detect abnormal users precisely and efficiently. Furthermore, we summarized possible causes including federal holidays, climate zones and summertime that may lead to abnormal behavior changes and discussed countermeasures respectively, which accounts for 82.3% of anomalies. The rest may be potential electricity stealing users, which requires further investigation.
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Zhang, Hui, Tu Bao Ho, Mao-Song Lin y Wei Huang. "Combining the Global and Partial Information for Distance-Based Time Series Classification and Clustering". Journal of Advanced Computational Intelligence and Intelligent Informatics 10, n.º 1 (20 de enero de 2006): 69–76. http://dx.doi.org/10.20965/jaciii.2006.p0069.
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Many time series representation schemes for classification and clustering have been proposed. Most of the proposed representation focuses on the prominent series by considering the global information of the time series. The partial information of time series that indicates the local change of time series is often ignored. Recently, researches shown that the partial information is also important for time series mining. However, the combination of these two types of information has not been well studied in the literature. Moreover, most of the proposed time series representation requires predefined parameters. The classification and clustering results are considerably influenced by the parameter settings, and, users often have difficulty in determining the parameters. We attack above two problems by exploiting the multi-scale property of wavelet decomposition. The main contributions of this work are: (1) extracting features combining the global information and partial information of time series (2) automatically choosing appropriate features, namely, features in an appropriate wavelet decomposition scale according to the concentration of wavelet coefficients within this scale. Experiments performed on several benchmark time series datasets justify the usefulness of the proposed approach.
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Cesario, Eugenio, Paolo Lindia y Andrea Vinci. "Detecting Multi-Density Urban Hotspots in a Smart City: Approaches, Challenges and Applications". Big Data and Cognitive Computing 7, n.º 1 (8 de febrero de 2023): 29. http://dx.doi.org/10.3390/bdcc7010029.
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Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such an amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, crime forecasting, traffic flows, etc. In particular, the detection of city hotspots is de facto a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are a valuable support for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable for discovering hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. In fact, a proper threshold setting is very difficult when clusters in different regions have considerably different densities, or clusters with different density levels are nested. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate for discovering city hotspots. Indeed, such algorithms rely on multiple minimum threshold values and are able to detect multiple pattern distributions of different densities, aiming at distinguishing between several density regions, which may or may not be nested and are generally of a non-convex shape. This paper discusses the research issues and challenges for analyzing urban data, aimed at discovering multi-density hotspots in urban areas. In particular, the study compares the four approaches (DBSCAN, OPTICS-xi, HDBSCAN, and CHD) proposed in the literature for clustering urban data and analyzes their performance on both state-of-the-art and real-world datasets. Experimental results show that multi-density clustering algorithms generally achieve better results on urban data than classic density-based algorithms.
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Wang, Xueqi y Zhichong Zou. "Open Data Based Urban For-Profit Music Venues Spatial Layout Pattern Discovery". Sustainability 13, n.º 11 (1 de junio de 2021): 6226. http://dx.doi.org/10.3390/su13116226.
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The spatial pattern of music venues is one of the key decision-making factors for urban planning and development strategies. Understanding the current configurations and future demands of music venues is fundamental to scholars, planners, and designers. There is an urgent need to discover the spatial pattern of music venues nationwide with high precision. This paper aims at an open data solution to discover the hidden hierarchical structure of the for-profit music venues and their dynamic relationship with urban economies. Data collected from the largest two public ticketing websites are used for clustering-based ranking modeling and spatial pattern discovery of music venues in 28 cities as recorded. The model is based on a multi-stage hierarchical clustering algorithm to level those cities into four groups according to the website records which can be used to describe the total music industry scale and activity vitality of cities. Data collected from the 2018 China City Statistical Year Book, including the GDP per capita, disposable income per capita, the permanent population, and the number of patent applications, are used as socio-economic indicators for the city-level potential capability of music industry development ranking. The Spearman’s rank correlation coefficient and the Kendall rank correlation coefficient are applied to test the consistency of the above city-level rankings. The results are 0.782 and 0.744 respectively, which means there is a relatively significant correlation between the scale level of current music venue configuration and the potential to develop the music industry. Average nearest neighbor index (ANNI), quadrate analysis, and Moran’s I are used to identify the spatial patterns of music venues of individual cities. The results indicate that music venues in urban centers show more spatial aggregation, where the spatial accessibility of music activity services takes the lead significantly, while a certain amount of venues with high service capacity distribute in suburban areas. The findings can provide decision support for urban planners to formulate effective policies and rational site-selection schemes on urban cultural facilities, leading to smart city rational construction and sustainable economic benefit.
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Sun, Yangjie, Zhongliang Fu y Liang Fan. "A Novel Hyperspectral Image Classification Pattern Using Random Patches Convolution and Local Covariance". Remote Sensing 11, n.º 16 (20 de agosto de 2019): 1954. http://dx.doi.org/10.3390/rs11161954.
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Today, more and more deep learning frameworks are being applied to hyperspectral image classification tasks and have achieved great results. However, such approaches are still hampered by long training times. Traditional spectral–spatial hyperspectral image classification only utilizes spectral features at the pixel level, without considering the correlation between local spectral signatures. Our article has tested a novel hyperspectral image classification pattern, using random-patches convolution and local covariance (RPCC). The RPCC is an effective two-branch method that, on the one hand, obtains a specified number of convolution kernels from the image space through a random strategy and, on the other hand, constructs a covariance matrix between different spectral bands by clustering local neighboring pixels. In our method, the spatial features come from multi-scale and multi-level convolutional layers. The spectral features represent the correlations between different bands. We use the support vector machine as well as spectral and spatial fusion matrices to obtain classification results. Through experiments, RPCC is tested with five excellent methods on three public data-sets. Quantitative and qualitative evaluation indicators indicate that the accuracy of our RPCC method can match or exceed the current state-of-the-art methods.
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Zhang, Tong, Jianlong Wang, Chenrong Cui, Yicong Li, Wei He, Yonghua Lu y Qinghua Qiao. "Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery". ISPRS International Journal of Geo-Information 8, n.º 10 (30 de septiembre de 2019): 434. http://dx.doi.org/10.3390/ijgi8100434.
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Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas.
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Qian, Bingxue y Ning Zhang. "Topology and Robustness of Weighted Air Transport Networks in Multi-Airport Region". Sustainability 14, n.º 11 (2 de junio de 2022): 6832. http://dx.doi.org/10.3390/su14116832.
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Topological analyses of multi-airport regional air transport networks are the basis for the sustainable development of multi-airport systems. In this study, we modeled the Yangtze River Delta (YRD) region’s airport infrastructure as a network and presented a weighted approach by which to analyze the network structure and robustness from the perspective of complex network theory. The analysis of the Yangtze River Delta Airport Network (YRDAN) indicates that it is a small-world network, and its cumulative degree has a power-law distribution, suggesting that it has scale-free properties. As its weighted clustering coefficient was found to be much smaller than the non-weighted counterpart, this demonstrates that most of the network traffic is focused on a hub-and-spoke pattern. Furthermore, the over-centrality of the YRDAN suggests weak accessibility of small cities and high dependence of air transport on the hub-and-spoke pattern. The assessment of the robustness of the YRDAN in the face of intentional attacks found that domestic networks are more robust than foreign aviation networks. However, the isolation of a small fraction of selected nodes can cause serious problems in the functioning of the YRDAN.
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Panigrahi, Lipismita, Kesari Verma y Bikesh Kumar Singh. "Hybrid segmentation method based on multi‐scale Gaussian kernel fuzzy clustering with spatial bias correction and region‐scalable fitting for breast US images". IET Computer Vision 12, n.º 8 (5 de noviembre de 2018): 1067–77. http://dx.doi.org/10.1049/iet-cvi.2018.5332.
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Wei, Wei, Xiwen Ren y Shiyi Guo. "Evaluation of Public Service Facilities in 19 Large Cities in China from the Perspective of Supply and Demand". Land 11, n.º 2 (18 de enero de 2022): 149. http://dx.doi.org/10.3390/land11020149.
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The equalization of urban public service facilities is important to the daily lives of urban residents. Spatial quantification of the supply and demand of public service facilities can reveal relationships between supply and demand agents and provide a foundation for the planning of urban public service facilities. This study proposed a comprehensive framework to assess the current state of supply (accessibility of facilities) and demand (population carrying pressure) of various public services in cities and determine patterns between different public service facilities. This framework contains three elements: (a) multi-scale spatial quantification of the matching of supply and demand, (b) spatial matching of supply and demand, and (c) spatial clustering analysis of the supply and demand balance. This study analyzed 19 major cities in China from a supply and demand perspective and examined implications for matching the supply and demand of public service facilities. The results indicated that education service facilities had the most appropriate supply and demand relationship. Areas where public service facilities had a good matching of supply and demand demonstrated a strong pattern of clustering. There were significant differences in the level of matching of the supply and demand of public service facilities among various regions in China. The limitations of the framework and future directions are discussed.
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Meng, Shuo, Jingan Wang, Ruru Pan, Weidong Gao, Jian Zhou y Wentao He. "Recognition of the layout of colored yarns in yarn-dyed fabrics". Textile Research Journal 91, n.º 1-2 (28 de junio de 2020): 100–114. http://dx.doi.org/10.1177/0040517520932830.
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The layout of colored yarns in yarn-dyed fabrics is a significant part of designing and production in the textile industry, which is still analyzed manually at present. Existing methods based on image processing have some limitations in accuracy and stability. Therefore, an automatic method is proposed to recognize the layout of colored yarns and some other basic fabric structure parameters: the fabric density and weave pattern. First, a large dataset with fabric structure parameters is constructed. The fabric images are captured by a wireless portable device. Then the yarns and floats are accurately located using a novel multi-task and multi-scale convolutional neural network. Finally, a density-based color clustering algorithm is proposed to recognize the layout of colored yarns. The results of extensive experiments show that the proposed method can automatically identify the basic structure parameters with high effectiveness and robustness.
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Wang, Lifang, Chaoyu Shi, Suzhen Lin, Pinle Qin y Yanli Wang. "Convolutional Sparse Representation and Local Density Peak Clustering for Medical Image Fusion". International Journal of Pattern Recognition and Artificial Intelligence 34, n.º 07 (22 de octubre de 2019): 2057003. http://dx.doi.org/10.1142/s0218001420570037.
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Aiming at the problem of insufficient detail retention in multimodal medical image fusion (MMIF) based on sparse representation (SR), an MMIF method based on density peak clustering and convolution sparse representation (CSR-DPC) is proposed. First, the base layer is obtained based on the registered input image by the averaging filter, and the original image minus the base layer to obtain the detail layer. Second, for retaining the details of the fused image, the detail layer image is fused by CSR to obtain the fused detail layer image, then the base layer image is segmented into several image blocks, and the blocks are clustered by using DPC to obtain some clusters, and each class cluster is trained to obtain a sub-dictionary, and all the sub-dictionaries are fused to obtain an adaptive dictionary. The sparse coefficient is fused through the learned adaptive dictionary, and the fused base layer image is obtained through reconstruction. Finally, fusing the detail layer and the base layer and reconstructing them forms the ultimate fused image. Experiments show that compared to the state-of-the-art two multi-scale transformation methods and five SR methods, the proposed method(CSR-DPC) outperforms the other methods in terms of the image details, the visual quality and the objective evaluation index, which can be helpful for clinical diagnosis and adjuvant treatment.
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Qin, Q., S. Xu, M. Du y S. Li. "URBAN FUNCTIONAL ZONE IDENTIFICATION BY CONSIDERING THE HETEROGENEOUS DISTRIBUTION OF POINTS OF INTERESTS". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2022 (18 de mayo de 2022): 83–90. http://dx.doi.org/10.5194/isprs-annals-v-4-2022-83-2022.
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Abstract. Urban Functional Zone (UFZ) identification facilitates the understanding of urban systems, which are complex and huge, and helps promote sustainable urban development. Existing studies on UFZ identification with Points of Interests (POIs) have focused much on more accurately extracting functional semantics, but ignored the fine delineation of UFZs in the spatial domain. The fine delineation of the spatial units of UFZs is also a key issue in UFZ identification. Since the sizes of UFZs can be different in practice, it is difficult to delineate spatially heterogeneous UFZs on a fixed scale. To solve the issue, a novel multi-scale spatial segmentation method was proposed in this study. Through taking the homogeneous socio-economic attributes of UFZs into account, we firstly generated a number of multi-scale spatial units by computing the mixed degree of POIs types, which reflects the mixed functions of each UFZs, using information entropy. Subsequently, we constructed the urban functional corpus of each spatial unit by measuring the spatial distribution pattern of POIs. The Word2Vec model was employed to obtain the semantic embedding vectors of UFZs, following which we adopted cosine distance-based K-means clustering method to group similar UFZs into one cluster. Finally, the enrichment factor was used to help annotate each functional cluster with a specific label. The UFZ identification results were compared with the Baidu e-maps and Baidu street view images for evaluation, and an accuracy of 82.7% was obtained. This study considering the heterogeneous distribution of POIs supports the fine-grained identification of UFZs, providing reference for urban planning.
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Zhang, Hong y Jie Li. "Dynamical Topology Analysis Of Vanet Based On Complex Networks Theory". Cybernetics and Information Technologies 14, n.º 5 (1 de diciembre de 2014): 172–86. http://dx.doi.org/10.2478/cait-2014-0053.
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Abstract A Vehicular Ad hoc NETwork (VANET) is a special subset of multi-hop Mobile Ad hoc Networks, in which the vehicles wireless interfaces can communicate with each other, as well as with fixed equipments alongside city roads or highways. Vehicular mobility dynamic characteristics, including high speed, predictable, restricted mobility pattern significantly affect the performance of routing protocols in a real VANET. Based on the existing studies, here we propose a testing network according to the preferential attachment on the degree of nodes and analyze VANET model characteristics for finding out the dynamic topology from the instantaneous degree distribution, instantaneous clustering coefficient and average path length. Analysis and simulation results demonstrate that VANET has a small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The dynamic topology analysis indicates a possible mechanism of VANET, which might be helpful in the traffic congestion, safety and management.
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Fan, Zhiyu, Qingming Zhan, Chen Yang, Huimin Liu y Meng Zhan. "How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level". International Journal of Environmental Research and Public Health 17, n.º 17 (28 de agosto de 2020): 6274. http://dx.doi.org/10.3390/ijerph17176274.
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Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM2.5 and PM10 during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM2.5 and PM10 concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R2 of 0.711 and 0.732 for PM2.5 and PM10, respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM2.5 in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM10 in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM10 drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future.
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McCloskey, Terrence Allen y Kam-biu Liu. "A sedimentary-based history of hurricane strikes on the southern Caribbean coast of Nicaragua". Quaternary Research 78, n.º 3 (24 de agosto de 2012): 454–64. http://dx.doi.org/10.1016/j.yqres.2012.07.003.
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AbstractMulti-millennial hurricane landfall records from the western North Atlantic indicate that landfall frequency has varied dramatically over time, punctuated by multi-centennial to millennial scale periods of hyperactivity. We extend the record geographically by presenting a paleostrike record inferred from a four-core transect from a marsh on the Caribbean coast of Nicaragua. Fossil pollen indicates that the site was a highly organic wetland from ~ 5400–4900 cal yr BP, at which time it became a shallow marine lagoon until ~ 2800 cal yr BP when it transitioned back into swamp/marsh, freshening over time, with the present fresh-to-brackish Typha marsh developing over the very recent past. Hurricane Joan, 1988, is recorded as a distinctive light-colored sand–silt–clay layer across the top of the transect, identifiable by abrupt shifts in color from the dark marsh deposits, increased grain size, and two upward-fining sequences, which are interpreted as representing the storm's traction and suspension loads. The six layers identified as hurricane-generated display temporal clustering, featuring a marked increase in landfall frequency ~ 800 cal yr BP. This pattern is anti-phase with the activity pattern previously identified from the northern Caribbean and the Atlantic coast of North America, thereby opposing the view that hyperactivity occurs simultaneously across the entire basin.
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Jayashree, B., Manindra S. Hanspal, Rajgopal Srinivasan, R. Vigneshwaran, Rajeev K. Varshney, N. Spurthi, K. Eshwar, N. Ramesh, S. Chandra y David A. Hoisington. "An Integrated Pipeline of Open Source Software Adapted for Multi-CPU Architectures: Use in the Large-Scale Identification of Single Nucleotide Polymorphisms". Comparative and Functional Genomics 2007 (2007): 1–7. http://dx.doi.org/10.1155/2007/35604.
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The large amounts of EST sequence data available from a single species of an organism as well as for several species within a genus provide an easy source of identification of intra- and interspecies single nucleotide polymorphisms (SNPs). In the case of model organisms, the data available are numerous, given the degree of redundancy in the deposited EST data. There are several available bioinformatics tools that can be used to mine this data; however, using them requires a certain level of expertise: the tools have to be used sequentially with accompanying format conversion and steps like clustering and assembly of sequences become time-intensive jobs even for moderately sized datasets. We report here a pipeline of open source software extended to run on multiple CPU architectures that can be used to mine large EST datasets for SNPs and identify restriction sites for assaying the SNPs so that cost-effective CAPS assays can be developed for SNP genotyping in genetics and breeding applications. At the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), the pipeline has been implemented to run on a Paracel high-performance system consisting of four dual AMD Opteron processors running Linux with MPICH. The pipeline can be accessed through user-friendly web interfaces at http://hpc.icrisat.cgiar.org/PBSWeb and is available on request for academic use. We have validated the developed pipeline by mining chickpea ESTs for interspecies SNPs, development of CAPS assays for SNP genotyping, and confirmation of restriction digestion pattern at the sequence level.
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Gharibbafghi, Zeinab, Jiaojiao Tian y Peter Reinartz. "Modified Superpixel Segmentation for Digital Surface Model Refinement and Building Extraction from Satellite Stereo Imagery". Remote Sensing 10, n.º 11 (17 de noviembre de 2018): 1824. http://dx.doi.org/10.3390/rs10111824.
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Superpixels, as a state-of-the-art segmentation paradigm, have recently been widely used in computer vision and pattern recognition. Despite the effectiveness of these algorithms, there are still many limitations and challenges dealing with Very High-Resolution (VHR) satellite images especially in complex urban scenes. In this paper, we develop a superpixel algorithm as a modified edge-based version of Simple Linear Iterative Clustering (SLIC), which is here called ESLIC, compatible with VHR satellite images. Then, based on the modified properties of generated superpixels, a heuristic multi-scale approach for building extraction is proposed, based on the stereo satellite imagery along with the corresponding Digital Surface Model (DSM). First, to generate the modified superpixels, an edge-preserving term is applied to retain the main building boundaries and edges. The resulting superpixels are then used to initially refine the stereo-extracted DSM. After shadow and vegetation removal, a rough building mask is obtained from the normalized DSM, which highlights the appropriate regions in the image, to be used as the input of a multi-scale superpixel segmentation of the proper areas to determine the superpixels inside the building. Finally, these building superpixels with different scales are integrated and the output is a unified building mask. We have tested our methods on building samples from a WorldView-2 dataset. The results are promising, and the experiments show that superpixels generated with the proposed ESLIC algorithm are more adherent to the building boundaries, and the resulting building mask retains urban object shape better than those generated with the original SLIC algorithm.
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Yue, Xiaoli, Yang Wang, Yabo Zhao y Hongou Zhang. "Estimation of Urban Housing Vacancy Based on Daytime Housing Exterior Images—A Case Study of Guangzhou in China". ISPRS International Journal of Geo-Information 11, n.º 6 (14 de junio de 2022): 349. http://dx.doi.org/10.3390/ijgi11060349.
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The traditional methods of estimating housing vacancies rarely use daytime housing exterior images to estimate housing vacancy rates (HVR). In view of this, this study proposed the idea and method of estimating urban housing vacancies based on daytime housing exterior images, taking Guangzhou, China as a case study. Considering residential quarters as the basic evaluation unit, the spatial pattern and its influencing factors were studied by using average nearest neighbor analysis, kernel density estimation, spatial autocorrelation analysis, and geodetector. The results show that: (1) The urban housing vacancy rate can be estimated by the method of daytime housing exterior images, which has the advantage of smaller research scale, simple and easy operation, short time consumption, and less difficulty in data acquisition. (2) Overall, the housing vacancy rate in Guangzhou is low in the core area and urban district, followed by suburban and higher in the outer suburb, showing a spatial pattern of increasing core area–urban district–suburban–outer suburb. Additionally, it has obvious spatial agglomeration characteristics, with low–low value clustered in the inner circle and high–high value clustered in the outer suburb. (3) The residential quarters with low vacancy rates (<5%) are distributed in the core area, showing a “dual-core” pattern, while residential quarters with high vacancy rates (>50%) are distributed in the outer suburb in a multi-core point pattern, both of which have clustering characteristics. (4) The results of the factor detector show that all seven influencing factors have an impact on the housing vacancy rate, but the degree of impact is different; the distance from CBD (Central Business District) has the strongest influence, while subway accessibility has the weakest influence. This study provides new ideas and methods for current research on urban housing vacancies, which can not only provide a reference for residents to purchase houses rationally, but also provide a decision-making basis for housing planning and policy formulation in megacities.
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Majumdar, Abhishek, Yueze Liu, Yaoqin Lu, Shaofeng Wu y Lijun Cheng. "kESVR: An Ensemble Model for Drug Response Prediction in Precision Medicine Using Cancer Cell Lines Gene Expression". Genes 12, n.º 6 (30 de mayo de 2021): 844. http://dx.doi.org/10.3390/genes12060844.
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Background: Cancer cell lines are frequently used in research as in-vitro tumor models. Genomic data and large-scale drug screening have accelerated the right drug selection for cancer patients. Accuracy in drug response prediction is crucial for success. Due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data to predict drug response in precision medicine. Method: A novelty k-means Ensemble Support Vector Regression (kESVR) is developed to predict each drug response values for single patient based on cell-line gene expression data. The kESVR is a blend of supervised and unsupervised learning methods and is entirely data driven. It utilizes embedded clustering (Principal Component Analysis and k-means clustering) and local regression (Support Vector Regression) to predict drug response and obtain the global pattern while overcoming missing data and outliers’ noise. Results: We compared the efficiency and accuracy of kESVR to 4 standard machine learning regression models: (1) simple linear regression, (2) support vector regression (3) random forest (quantile regression forest) and (4) back propagation neural network. Our results, which based on drug response across 610 cancer cells from Cancer Cell Line Encyclopedia (CCLE) and Cancer Therapeutics Response Portal (CTRP v2), proved to have the highest accuracy (smallest mean squared error (MSE) measure). We next compared kESVR with existing 17 drug response prediction models based a varied range of methods such as regression, Bayesian inference, matrix factorization and deep learning. After ranking the 18 models based on their accuracy of prediction, kESVR ranks first (best performing) in majority (74%) of the time. As for the remaining (26%) cases, kESVR still ranked in the top five performing models. Conclusion: In this paper we introduce a novel model (kESVR) for drug response prediction using high dimensional cell-line gene expression data. This model outperforms current existing prediction models in terms of prediction accuracy and speed and overcomes overfitting. This can be used in future to develop a robust drug response prediction system for cancer patients using the cancer cell-lines guidance and multi-omics data.
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Zhang, Wei, Chunrong Pu, Fang Li y Zilin Fan. "Temporal and spatial evolution characteristics and driving mechanism of urban-rural inversion of population aging in China". International Journal of Population Studies 7, n.º 1 (30 de marzo de 2021): 77. http://dx.doi.org/10.18063/ijps.v7i1.1360.
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The phenomenon of urban-rural inversion population aging is a severe issue faced by China in building a well-off society in an all-round way. Using GIS spatial clustering and multiple stepwise regression models, this paper analyzes the temporal and spatial evolution characteristics and driving mechanism of urban-rural inversion of population aging in China on a provincial scale. The results show that: 1) In terms of time series evolution, with the continuous higher level of China’s population aging, the phenomenon of urban-rural inversion is becoming more and more obvious. 2) In terms of spatial pattern evolution, from 1995 to 2018, the spatial agglomeration intensity of urban-rural inversion showed an inverted U-shaped change trend of “low—high—low”. It first appeared in the Eastern coastal areas, then gradually expanded to the Central and Western regions, and finally evolved into a national common phenomenon. 3) In terms of driving mechanism, there is a complex multi-dimensional and nonlinear interaction mechanism behind the phenomenon of population aging and urban-rural inversion. Among them, population and economic factors are the main driving factors of this phenomenon. For the western provinces with underdeveloped economy, serious population outflow and high level of rural aging, the phenomenon of “old and poor” in rural areas has become a key challenge in the implementation of strategies such as rural revitalization and targeted poverty alleviation.
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Sanjeev, Rama Krishna, Prashanth Nuggehalli Srinivas, Bindu Krishnan, Yogish Channa Basappa, Akshay S. Dinesh y Sabu K. Ulahannan. "Does cereal, protein and micronutrient availability hold the key to the malnutrition conundrum? An exploratory analysis of cereal cultivation and wasting patterns of India". Wellcome Open Research 5 (2 de junio de 2020): 118. http://dx.doi.org/10.12688/wellcomeopenres.15934.1.
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Background: High prevalence of maternal malnutrition, low birth-weight and child malnutrition in India contribute substantially to the global malnutrition burden. Rural India has disproportionately higher levels of child malnutrition. Stunting and wasting are the primary determinants of malnutrition and their district-level distribution shows clustering in different geographies and regions. Methods: The last round of National Family Health Survey (NFHS4) has disaggregated data by district, enabling a more nuanced understanding of the prevalence of markers of malnutrition. We used data from NFHS4 and agricultural statistics datasets to analyse relationship of cereal cultivation with the prevalence of child malnutrition. We studied the current science on growth-related nutrient-sensing pathways to explain this pattern. Results: Stunting and wasting patterns across districts show a distinct geographical and age distribution; districts with higher wasting showed early prevalence of 40% at six months of age. Wasting was associated with higher cultivation of millets, with a stronger association seen for jowar and other millets. Low maternal BMI in districts with higher wasting could be linked to the consumption of millets as staple. We conceptualised a hypothetical schematic pathway linking early origin of wasting in children with millet-based diet, driven by inhibition of critical intra-cellular pathways controlling growth covering pre-natal, post-natal and early childhood. The analysis was limited by lack of fine-scale data on prevalence of low birth-weight and type of cereal consumed. Conclusions: Multi-site observational studies of long-term effects of type of cereals consumed could help explain the ecogeographic distribution of malnutrition in India. Cereals, particularly millets constitute the bulk of protein intake among the poor, especially in rural areas in India where wasting persists. Policies and programs targeting malnutrition need to address type of cereal consumed in order to impact childhood malnutrition in parts of India where subsistence cultivation of millets for staple consumption is prevalent.
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Fang, Feng, Thomas Potter, Thinh Nguyen y Yingchun Zhang. "Dynamic Reorganization of the Cortical Functional Brain Network in Affective Processing and Cognitive Reappraisal". International Journal of Neural Systems 30, n.º 10 (19 de agosto de 2020): 2050051. http://dx.doi.org/10.1142/s0129065720500513.
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Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a ‘top-down’ pattern that occurs across four brain network states that arise at different time instants (0–170[Formula: see text]ms, 170–370[Formula: see text]ms, 380–620[Formula: see text]ms, and 620–1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.
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Liao, Kaiyang, Fan Zhao, Yuanlin Zheng, Congjun Cao y Mingzhu Zhang. "Parallel N-Path Quantification Hierarchical K-Means Clustering Algorithm for Video Retrieval". International Journal of Pattern Recognition and Artificial Intelligence 31, n.º 09 (9 de febrero de 2017): 1750029. http://dx.doi.org/10.1142/s021800141750029x.
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Using clustering method to detect useful patterns in large datasets has attracted considerable interest recently. The HKM clustering algorithm (Hierarchical K-means) is very efficient in large-scale data analysis. It has been widely used to build visual vocabulary for large scale video/image retrieval system. However, the speed and even the accuracy of hierarchical K-means clustering algorithm still have room to be improved. In this paper, we propose a Parallel N-path quantification hierarchical K-means clustering algorithm which improves on the hierarchical K-means clustering algorithm in the following ways. Firstly, we replace the Euclidean kernel with the Hellinger kernel to improve the accuracy. Secondly, the Greedy N-best Paths Labeling method is adopted to improve the clustering accuracy. Thirdly, the multi-core processors-based parallel clustering algorithm is proposed. Our results confirm that the proposed clustering algorithm is much faster and more effective.
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Quaranta, Giuseppe, Giovanni Formica, J. Tenreiro Machado, Walter Lacarbonara y Sami F. Masri. "Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy". Nonlinear Dynamics 101, n.º 3 (agosto de 2020): 1583–619. http://dx.doi.org/10.1007/s11071-020-05902-1.
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Abstract The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of a SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilarities of the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way toward a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.
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Thompson, Amy E., John P. Walden, Adrian S. Z. Chase, Scott R. Hutson, Damien B. Marken, Bernadette Cap, Eric C. Fries et al. "Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale". PLOS ONE 17, n.º 11 (2 de noviembre de 2022): e0275916. http://dx.doi.org/10.1371/journal.pone.0275916.
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Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization.
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Gui, Zhipeng, Dehua Peng, Huayi Wu y Xi Long. "MSGC: Multi-scale grid clustering by fusing analytical granularity and visual cognition for detecting hierarchical spatial patterns". Future Generation Computer Systems 112 (noviembre de 2020): 1038–56. http://dx.doi.org/10.1016/j.future.2020.06.053.
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GUALTIERI, P., F. PICANO y C. M. CASCIOLA. "Anisotropic clustering of inertial particles in homogeneous shear flow". Journal of Fluid Mechanics 629 (15 de junio de 2009): 25–39. http://dx.doi.org/10.1017/s002211200900648x.
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Recently, clustering of inertial particles in turbulence has been thoroughly analysed for statistically homogeneous isotropic flows. Phenomenologically, spatial homogeneity of particle configurations is broken by the advection of a range of eddies determined by the Stokes relaxation time of the particles. This in turn results in a multi-scale distribution of local particle concentration and voids. Much less is known concerning anisotropic flows. Here, by addressing direct numerical simulations (DNS) of a statistically steady particle-laden homogeneous shear flow, we provide evidence that the mean shear preferentially orients particle patterns. By imprinting anisotropy on large-scale velocity fluctuations, the shear indirectly affects the geometry of the clusters. Quantitative evaluation is provided by a purposely designed tool, the angular distribution function (ADF) of particle pairs, which allows to address the anisotropy content of particle aggregates on a scale-by-scale basis. The data provide evidence that, depending on the Stokes relaxation time of the particles, anisotropic clustering may occur even in the range of scales in which the carrier phase velocity field is already recovering isotropy. The strength of the singularity in the anisotropic component of the ADF quantifies the level of fine-scale anisotropy, which may even reach values of more than 30% direction-dependent variation in the probability to find two closeby particles at viscous-scale separation.
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Sekh, Arif Ahmed, Debi Prosad Dogra, Samarjit Kar y Partha Pratim Roy. "Video trajectory analysis using unsupervised clustering and multi-criteria ranking". Soft Computing 24, n.º 21 (13 de mayo de 2020): 16643–54. http://dx.doi.org/10.1007/s00500-020-04967-9.
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Abstract Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead.
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Peerbasha, S. y M. Mohamed Surputheen. "Study and Analysis of Data Mining Algorithms for Identifying the Students’ for Psychology Motivation". Asian Journal of Computer Science and Technology 8, S2 (5 de marzo de 2019): 83–87. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2018.
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The development of many educational institutions is based on the performance of students learning and understanding capabilities. Here, we analyzed their academic profile with their grades and various cumulative attributes. The academic performance in learning their subjects could be improved by motivational approach. The analysis of student performance is carried out through knowledge-based data mining process. But, the problem is arrived by a probability of information prediction accuracy from student data set which is not accurate. Here, we propose a novel machine learning algorithm based on subspace clustering and multi-perspective classification techniques to identify psychological motivation required students. Also, the extraction of relational patterns to form enhanced clustering classes is done. This discovers the innovative relations between students and their educational performance in the various attributes using surf scale nested clustering approach based on an intelligent predicting system from soft computing processing tasks. This improves the data prediction rate by considering the time factor analysis and complexity to design and develop an efficient clustering algorithm which maximizes the clustering and classification accuracy for improving academic performance.
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Kumar, Vipin, Simon Leclerc y Yuichi Taniguchi. "BHi-Cect: a top-down algorithm for identifying the multi-scale hierarchical structure of chromosomes". Nucleic Acids Research 48, n.º 5 (3 de febrero de 2020): e26-e26. http://dx.doi.org/10.1093/nar/gkaa004.
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Abstract High-throughput chromosome conformation capture (Hi-C) technology enables the investigation of genome-wide interactions among chromosome loci. Current algorithms focus on topologically associating domains (TADs), that are contiguous clusters along the genome coordinate, to describe the hierarchical structure of chromosomes. However, high resolution Hi-C displays a variety of interaction patterns beyond what current TAD detection methods can capture. Here, we present BHi-Cect, a novel top-down algorithm that finds clusters by considering every locus with no assumption of genomic contiguity using spectral clustering. Our results reveal that the hierarchical structure of chromosome is organized as ‘enclaves’, which are complex interwoven clusters at both local and global scales. We show that the nesting of local clusters within global clusters characterizing enclaves, is associated with the epigenomic activity found on the underlying DNA. Furthermore, we show that the hierarchical nesting that links different enclaves integrates their respective function. BHi-Cect provides means to uncover the general principles guiding chromatin architecture.
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Fan, Lilin, Jiahu Zhang, Wentao Mao y Fukang Cao. "Unsupervised Anomaly Detection for Intermittent Sequences Based on Multi-Granularity Abnormal Pattern Mining". Entropy 25, n.º 1 (7 de enero de 2023): 123. http://dx.doi.org/10.3390/e25010123.
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In the actual maintenance of manufacturing enterprises, abnormal changes in after-sale parts demand data often make the inventory strategies unreasonable. Due to the intermittent and small-scale characteristics of demand sequences, it is difficult to accurately identify the anomalies in such sequences using current anomaly detection algorithms. To solve this problem, this paper proposes an unsupervised anomaly detection method for intermittent time series. First, a new abnormal fluctuation similarity matrix is built by calculating the squared coefficient of variation and the maximum information coefficient from the macroscopic granularity. The abnormal fluctuation sequence can then be adaptively screened by using agglomerative hierarchical clustering. Second, the demand change feature and interval feature of the abnormal sequence are constructed and fed into the support vector data description model to perform hypersphere training. Then, the unsupervised abnormal point location detection is realized at the micro-granularity level from the abnormal sequence. Comparative experiments are carried out on the actual demand data of after-sale parts of two large manufacturing enterprises. The results show that, compared with the current representative anomaly detection methods, the proposed approach can effectively identify the abnormal fluctuation position in the intermittent sequence of small samples, and also obtain better detection results.
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Li, Jian, Jingwen He, Ying Liu, Daojie Wang, Loretta Rafay, Can Chen, Tao Hong, Hailan Fan y Yongming Lin. "Spatial Autocorrelation Analysis of Multi-Scale Damaged Vegetation in the Wenchuan Earthquake-Affected Area, Southwest China". Forests 10, n.º 2 (21 de febrero de 2019): 195. http://dx.doi.org/10.3390/f10020195.
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Major earthquakes can cause serious vegetation destruction in affected areas. However, little is known about the spatial patterns of damaged vegetation and its influencing factors. Elucidating the main influencing factors and finding out the key vegetation type to reflect spatial patterns of damaged vegetation are of great interest in order to improve the assessment of vegetation loss and the prediction of the spatial distribution of damaged vegetation caused by earthquakes. In this study, we used Moran’s I correlograms to study the spatial autocorrelation of damaged vegetation and its potential driving factors in the nine worst-hit Wenchuan earthquake-affected cities and counties. Both dependent and independent variables showed a positive spatial autocorrelation but with great differences at four aggregation levels (625 × 625 m, 1250 × 1250 m, 2500 × 2500 m, and 5000 × 5000 m). Shrubs can represent the characteristics of all damaged vegetation due to the significant linear relationship between their Moran’s I at the four aggregation levels. Clustering of similar high coverage of damaged vegetation occurred in the study area. The residuals of the standard linear regression model also show a significantly positive autocorrelation, indicating that the standard linear regression model cannot explain all the spatial patterns in damaged vegetation. Spatial autoregressive models without spatially autocorrelated residuals had the better goodness-of-fit to deal with damaged vegetation. The aggregation level 8 × 8 is a scale threshold for spatial autocorrelation. There are other environmental factors affecting vegetation destruction. Our study provides useful information for the countermeasures of vegetation protection and conservation, as well as the prediction of the spatial distribution of damaged vegetation, to improve vegetation restoration in earthquake-affected areas.
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Alhameli, Falah, Ali Ahmadian y Ali Elkamel. "Multiscale Decision-Making for Enterprise-Wide Operations Incorporating Clustering of High-Dimensional Attributes and Big Data Analytics: Applications to Energy Hub". Energies 14, n.º 20 (15 de octubre de 2021): 6682. http://dx.doi.org/10.3390/en14206682.
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In modern systems, there is a tendency to model issues more accurately with low computational cost and considering multiscale decision-making which increases the complexity of the optimization. Therefore, it is necessary to develop tools to cope with these new challenges. Supply chain management of enterprise-wide operations usually involves three decision levels: strategic, tactical, and operational. These decision levels depend on each other involving different time scales. Accordingly, their integration usually leads to multiscale models that are computationally intractable. In this work, the aim is to develop novel clustering methods with multiple attributes to tackle the integrated problem. As a result, a clustering structure is proposed in the form of a mixed integer non-linear program (MINLP) later converted into a mixed integer linear program (MILP) for clustering shape-based time series data with multiple attributes through a multi-objective optimization approach (since different attributes have different scales or units) and minimize the computational complexity of multiscale decision problems. The results show that normal clustering is closer to the optimal case (full-scale model) compared with sequence clustering. Additionally, it provides improved solution quality due to flexibility in terms of sequence restrictions. The developed clustering algorithms can work with any two-dimensional datasets and simultaneous demand patterns. The most suitable applications of the clustering algorithms are long-term planning and integrated scheduling and planning problems. To show the performance of the proposed method, it is investigated on an energy hub as a case study, the results show a significant reduction in computational cost with accuracies ranging from 95.8% to 98.3%.
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Zarzar, Christopher y Jamie Dyer. "The Influence of Synoptic-Scale Air Mass Conditions on Seasonal Precipitation Patterns over North Carolina". Atmosphere 10, n.º 10 (16 de octubre de 2019): 624. http://dx.doi.org/10.3390/atmos10100624.
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This paper characterizes the influence of synoptic-scale air mass conditions on the spatial and temporal patterns of precipitation in North Carolina over a 16-year period (2003–2018). National Center for Environmental Prediction Stage IV multi-sensor precipitation estimates were used to describe seasonal variations in precipitation in the context of prevailing air mass conditions classified using the spatial synoptic classification system. Spatial analyses identified significant clustering of high daily precipitation amounts distributed along the east side of the Appalachian Mountains and along the Coastal Plains. Significant and heterogeneous clustering was prevalent in summer months and tended to coincide with land cover boundaries and complex terrain. The summer months were dominated by maritime tropical air mass conditions, whereas dry moderate air mass conditions prevailed in the winter, spring, and fall. Between the three geographic regions of North Carolina, the highest precipitation amounts were received in western North Carolina during the winter and spring, and in eastern North Carolina in the summer and fall. Central North Carolina received the least amount of precipitation; however, there was substantial variability between regions due to prevailing air mass conditions. There was an observed shift toward warmer and more humid air mass conditions in the winter, spring, and fall months throughout the study period (2003–2018), indicating a shift toward air mass conditions conducive to higher daily average rain rates in North Carolina.
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Zhao, Tongtiegang, Wei Zhang, Yongyong Zhang, Zhiyong Liu y Xiaohong Chen. "Significant spatial patterns from the GCM seasonal forecasts of global precipitation". Hydrology and Earth System Sciences 24, n.º 1 (3 de enero de 2020): 1–16. http://dx.doi.org/10.5194/hess-24-1-2020.
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Abstract. Fully coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations to derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the 10 sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlations at neighbouring grid cells with one another. The global Moran's I associates anomaly correlation at the global scale and indicates that anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells. The local Moran's I links anomaly correlation at one grid cell with its spatial lag and reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate centre exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80 % of grid cells across the globe fall into the cluster of high anomaly correlation under at least 1 of the 10 sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.
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Grigorieva, Maria y Dmitry Grin. "Clustering error messages produced by distributed computing infrastructure during the processing of high energy physics data". International Journal of Modern Physics A 36, n.º 10 (10 de abril de 2021): 2150070. http://dx.doi.org/10.1142/s0217751x21500706.
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Large-scale distributed computing infrastructures ensure the operation and maintenance of scientific experiments at the LHC: more than 160 computing centers all over the world execute tens of millions of computing jobs per day. ATLAS — the largest experiment at the LHC — creates an enormous flow of data which has to be recorded and analyzed by a complex heterogeneous and distributed computing environment. Statistically, about 10–12% of computing jobs end with a failure: network faults, service failures, authorization failures, and other error conditions trigger error messages which provide detailed information about the issue, which can be used for diagnosis and proactive fault handling. However, this analysis is complicated by the sheer scale of textual log data, and often exacerbated by the lack of a well-defined structure: human experts have to interpret the detected messages and create parsing rules manually, which is time-consuming and does not allow identifying previously unknown error conditions without further human intervention. This paper is dedicated to the description of a pipeline of methods for the unsupervised clustering of multi-source error messages. The pipeline is data-driven, based on machine learning algorithms, and executed fully automatically, allowing categorizing error messages according to textual patterns and meaning.
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Cai, Zhicheng, Yueying He, Sirui Liu, Yue Xue, Hui Quan, Ling Zhang y Yi Qin Gao. "Hierarchical dinucleotide distribution in genome along evolution and its effect on chromatin packing". Life Science Alliance 4, n.º 8 (24 de junio de 2021): e202101028. http://dx.doi.org/10.26508/lsa.202101028.
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Dinucleotide densities and their distribution patterns vary significantly among species. Previous studies revealed that CpG is susceptible to methylation, enriched at topologically associating domain boundaries and its distribution along the genome correlates with chromatin compartmentalization. However, the multi-scale organizations of CpG in the linear genome, their role in chromatin organization, and how they change along the evolution are only partially understood. By comparing the CpG distribution at different genomic length scales, we quantify the difference between the CpG distributions of different species and evaluate how the hierarchical uneven CpG distribution appears in evolution. The clustering of species based on the CpG distribution is consistent with the phylogenetic tree. Interestingly, we found the CpG distribution and chromatin structure to be correlated in many different length scales, especially for mammals and avians, consistent with the mosaic CpG distribution in the genomes of these species.
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Zhou, Dawei, Si Zhang, Mehmet Yigit Yildirim, Scott Alcorn, Hanghang Tong, Hasan Davulcu y Jingrui He. "High-Order Structure Exploration on Massive Graphs". ACM Transactions on Knowledge Discovery from Data 15, n.º 2 (abril de 2021): 1–26. http://dx.doi.org/10.1145/3425637.
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Modeling and exploring high-order connectivity patterns, also called network motifs, are essential for understanding the fundamental structures that control and mediate the behavior of many complex systems. For example, in social networks, triangles have been proven to play the fundamental role in understanding social network communities; in online transaction networks, detecting directed looped transactions helps identify money laundering activities; in personally identifiable information networks, the star-shaped structures may correspond to a set of synthetic identities. Despite the ubiquity of such high-order structures, many existing graph clustering methods are either not designed for the high-order connectivity patterns, or suffer from the prohibitive computational cost when modeling high-order structures in the large-scale networks. This article generalizes the challenges in multiple dimensions. First ( Model ), we introduce the notion of high-order conductance, and define the high-order diffusion core, which is based on a high-order random walk induced by the user-specified high-order network structure. Second ( Algorithm ), we propose a novel high-order structure-preserving graph clustering framework named HOSGRAP , which partitions the graph into structure-rich clusters in polylogarithmic time with respect to the number of edges in the graph. Third ( Generalization ), we generalize our proposed algorithm to solve the real-world problems on various types of graphs, such as signed graphs, bipartite graphs, and multi-partite graphs. Experimental results on both synthetic and real graphs demonstrate the effectiveness and efficiency of the proposed algorithms.
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Di Giallonardo, Francesca, Jen Kok, Marian Fernandez, Ian Carter, Jemma Geoghegan, Dominic Dwyer, Edward Holmes y John-Sebastian Eden. "Evolution of Human Respiratory Syncytial Virus (RSV) over Multiple Seasons in New South Wales, Australia". Viruses 10, n.º 9 (6 de septiembre de 2018): 476. http://dx.doi.org/10.3390/v10090476.
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There is an ongoing global pandemic of human respiratory syncytial virus (RSV) infection that results in substantial annual morbidity and mortality. In Australia, RSV is a major cause of acute lower respiratory tract infections (ALRI). Nevertheless, little is known about the extent and origins of the genetic diversity of RSV in Australia, nor the factors that shape this diversity. We have conducted a genome-scale analysis of RSV infections in New South Wales (NSW). RSV genomes were successfully sequenced for 144 specimens collected between 2010–2016. Of these, 64 belonged to the RSVA and 80 to the RSVB subtype. Phylogenetic analysis revealed a wide diversity of RSV lineages within NSW and that both subtypes evolved rapidly in a strongly clock-like manner, with mean rates of approximately 6–8 × 10−4 nucleotide substitutions per site per year. There was only weak evidence for geographic clustering of sequences, indicative of fluid patterns of transmission within the infected population and no evidence of any clustering by patient age such that viruses in the same lineages circulate through the entire host population. Importantly, we show that both subtypes circulated concurrently in NSW with multiple introductions into the Australian population in each year and only limited evidence for multi-year persistence.
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Wang, Kun, Lijun Zhang, Meng Cai, Lingbo Liu, Hao Wu y Zhenghong Peng. "Measuring Urban Poverty Spatial by Remote Sensing and Social Sensing Data: A Fine-Scale Empirical Study from Zhengzhou". Remote Sensing 15, n.º 2 (8 de enero de 2023): 381. http://dx.doi.org/10.3390/rs15020381.
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Urban poverty is a major obstacle to the healthy development of urbanization. Identifying and mapping urban poverty is of great significance to sustainable urban development. Traditional data and methods cannot measure urban poverty at a fine scale. Besides, existing studies often ignore the impact of the built environment and fail to consider the equal importance of poverty indicators. The emerging multi-source big data provide new opportunities for accurately measuring and monitoring urban poverty. This study aims to map urban poverty spatial at a fine scale by using multi-source big data, including social sensing and remote sensing data. The urban core of Zhengzhou is selected as the study area. The characteristics of the community’s living environment are quantified by accessibility, block vitality, per unit rent, public service infrastructure, and socio-economic factors. The urban poverty spatial index (SI) model is constructed by using the multiplier index of the factors. The SOM clustering method is employed to identify urban poverty space based on the developed SI. The performance of the proposed SI model is evaluated at the neighborhood scale. The results show that the urban poverty spatial measurement method based on multi-source big data can capture spatial patterns of typical urban poverty with relatively high accuracy. Compared with the urban poverty space measured based on remote sensing data, it considers the built environment and socio-economic factors in the identification of the inner city poverty space, and avoids being affected by the texture information of the physical surface of the residential area and the external structure of the buildings. Overall, this study can provide a comprehensive, cost-effective, and efficient method for the refined management of urban poverty space and the improvement of built environment quality.