Search results for "Hierarchical"
showing 10 items of 260 documents
Cellular automata and urban development simulation : a transition rules creation process based on statistical analysis
2015
National audience; Nowadays land use evolution study has become a major stake in urban planning. The main focus is to understand the way in which land use evolves across time and to understand processes that take place. This understanding would allow to plan urban developments based on a knowledge as complete as possible covering as many fields as possible (i.e. urban planning, politics, sociology, etc.). Simulation tools can be used to merge and display different points of view and stakes from different stakeholders (Parrott & Meyer, 2012).
Structural clustering of millions of molecular graphs
2014
We propose an algorithm for clustering very large molecular graph databases according to scaffolds (i.e., large structural overlaps) that are common between cluster members. Our approach first partitions the original dataset into several smaller datasets using a greedy clustering approach named APreClus based on dynamic seed clustering. APreClus is an online and instance incremental clustering algorithm delaying the final cluster assignment of an instance until one of the so-called pending clusters the instance belongs to has reached significant size and is converted to a fixed cluster. Once a cluster is fixed, APreClus recalculates the cluster centers, which are used as representatives for…
The Argument Dependency Model
2015
This chapter summarizes the architecture of the extended Argument Dependency Model (eADM), a model of language comprehension that aspires toward neurobiological plausibility. It combines design principles from neurobiology with insights on cross-linguistic diversity. Like other current models, the eADM posits that auditory language processing proceeds along two distinct streams in the brain emanating from auditory cortex: the antero-ventral and postero-dorsal streams. Both streams are organized hierarchically and information processing takes place in a cascaded fashion. Each stream has functionally unified computational properties congruent with its role in primate audition. While the dorsa…
A Greedy Algorithm for Hierarchical Complete Linkage Clustering
2014
We are interested in the greedy method to compute an hierarchical complete linkage clustering. There are two known methods for this problem, one having a running time of \({\mathcal O}(n^3)\) with a space requirement of \({\mathcal O}(n)\) and one having a running time of \({\mathcal O}(n^2 \log n)\) with a space requirement of Θ(n 2), where n is the number of points to be clustered. Both methods are not capable to handle large point sets. In this paper, we give an algorithm with a space requirement of \({\mathcal O}(n)\) which is able to cluster one million points in a day on current commodity hardware.
V11. Functional hierarchy within an overall network for visual motion processing and ocular-motor control at rest
2015
Introduction Visual motion processing on one hand and ocular motor functions on the other are rarely studied together in vivo in humans. The interrelation of these functional networks is rather unclear, even though their functional dependence seems obvious. In several fMRI studies the essential nodes of both networks could be localized using voluntary optokinetic ('look') nystagmus (OKN) in the horizontal plane incorporating visual motion tracking (Dieterich et al., 2009). Here, functional connectivity (FC) between these nodes representing both networks was studies using resting-state FC. Methods Resting-state fMRI data of 200 healthy adults (age 44.1±17.9; 79 male) were included in the cro…
Space-Frequency Quantization using Directionlets
2007
In our previous work we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments (DVMs) imposed in the corresponding basis functions along different directions, called directionlets. Here, we combine the directionlets with the space-frequency quantization (SFQ) image compression method, originally based on the standard two-dimensional (2-D) wavelet transform (WT). We show that our new compression method outperforms the standard SFQ as well as the state-of-the-art compression methods, like SPIHT and JPEG-2000, in terms of the quality of compressed images, especially in a low-rate compression regime. We also show that the order of comp…
Cluster-based RF fingerprint positioning using LTE and WLAN signal strengths
2017
Wireless Local Area Network (WLAN) positioning has become a popular localization system due to its low-cost installation and widespread availability of WLAN access points. Traditional grid-based radio frequency (RF) fingerprinting (GRFF) suffers from two drawbacks. First it requires costly and non-efficient data collection and updating procedure; secondly the method goes through time-consuming data pre-processing before it outputs user position. This paper proposes Cluster-based RF Fingerprinting (CRFF) to overcome these limitations by using modified Minimization of Drive Tests data which can be autonomously collected by cellular operators from their subscribers. The effect of environmental…
Understanding the use of mobile phones in the agricultural sector in rural Indonesia: using the capability approach as lens
2012
This study aims to investigate the use of mobile phones in the agricultural sector and its possible contributions to development of developing countries, with special reference to Indonesia. In our interpretive work, the capability approach is used as a lens, and the study is informed by data from interviews with various actors and field observations. The paper unearths the pattern of use of mobile phones among farmers in their professional network and shows that ownership and use of mobile phones varies, depending on the farmers' location in the network hierarchy. The study further unveils that the conversion factors play a significant role in order to realise achievement (functionings) fr…
Efficient and Accurate OTU Clustering with GPU-Based Sequence Alignment and Dynamic Dendrogram Cutting.
2015
De novo clustering is a popular technique to perform taxonomic profiling of a microbial community by grouping 16S rRNA amplicon reads into operational taxonomic units (OTUs). In this work, we introduce a new dendrogram-based OTU clustering pipeline called CRiSPy. The key idea used in CRiSPy to improve clustering accuracy is the application of an anomaly detection technique to obtain a dynamic distance cutoff instead of using the de facto value of 97 percent sequence similarity as in most existing OTU clustering pipelines. This technique works by detecting an abrupt change in the merging heights of a dendrogram. To produce the output dendrograms, CRiSPy employs the OTU hierarchical clusterin…
A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis.
2020
Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior …