4,387 research outputs found

    Methods of Hierarchical Clustering

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    We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm.Comment: 21 pages, 2 figures, 1 table, 69 reference

    Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players

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    We present the concept of fiber-flux density for locally quantifying white matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g., fractional anisotropy) with fiber-flux measurements, we define new local descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each descriptor throughout fiber bundles allows along-tract coupling of a specific diffusion measure with geometrical properties, such as fiber orientation and coherence. A key step in the proposed framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tract-profiles enable meaningful inter-subject comparisons and group-wise statistical analysis. We demonstrate our method using two different datasets of contact sports players. Along-tract pairwise comparison as well as group-wise analysis, with respect to non-player healthy controls, reveal significant and spatially-consistent FFDD anomalies. Comparing our method with along-tract FA analysis shows improved sensitivity to subtle structural anomalies in football players over standard FA measurements

    A CLUE for CLUster Ensembles

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    Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package clue provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data structures for representing partitions and hierarchies, and facilities for computing on these, including methods for measuring proximity and obtaining consensus and "secondary" clusterings.

    Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions

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    3D action recognition has broad applications in human-computer interaction and intelligent surveillance. However, recognizing similar actions remains challenging since previous literature fails to capture motion and shape cues effectively from noisy depth data. In this paper, we propose a novel two-layer Bag-of-Visual-Words (BoVW) model, which suppresses the noise disturbances and jointly encodes both motion and shape cues. First, background clutter is removed by a background modeling method that is designed for depth data. Then, motion and shape cues are jointly used to generate robust and distinctive spatial-temporal interest points (STIPs): motion-based STIPs and shape-based STIPs. In the first layer of our model, a multi-scale 3D local steering kernel (M3DLSK) descriptor is proposed to describe local appearances of cuboids around motion-based STIPs. In the second layer, a spatial-temporal vector (STV) descriptor is proposed to describe the spatial-temporal distributions of shape-based STIPs. Using the Bag-of-Visual-Words (BoVW) model, motion and shape cues are combined to form a fused action representation. Our model performs favorably compared with common STIP detection and description methods. Thorough experiments verify that our model is effective in distinguishing similar actions and robust to background clutter, partial occlusions and pepper noise

    GABAB receptor allosteric modulators exhibit pathway-dependent and species-selective activity.

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    Positive modulation of the GABAB receptor (GABABR) represents a potentially useful therapeutic approach for the treatment of nicotine addiction. The positive allosteric modulators (PAMs) of GABABR GS39783 and BHF177 enhance GABA-stimulated [35S]GTP γS-binding, and have shown efficacy in a rodent nicotine self-administration procedure reflecting aspects of nicotine dependence. Interestingly, the structural related analog, NVP998, had no effect on nicotine self-administration in rats despite demonstrating similar pharmacokinetic properties. Extensive in vitro characterization of GS39783, BHF177, and NVP998 activity on GABABR-regulated signaling events, including modulation of cAMP, intracellular calcium levels, and ERK activation, revealed that these structurally related molecules display distinct pathway-specific signaling activities that correlate with the dissimilarities observed in rodent models and may be predictive of in vivo efficacy. Furthermore, these GABABR allosteric modulators exhibit species-dependent activity. Collectively, these data will be useful in guiding the development of GABABR allosteric modulators that display optimal in vivo efficacy in a preclinical model of nicotine dependence, and will identify those that have the potential to lead to novel antismoking therapies

    Algorithms for Hierarchical Clustering: An Overview, II

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    We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm. This review adds to the earlier version, Murtagh and Contreras (2012)

    GRAPHOS – An open-source software for photogrammetric applications

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    19 p.This paper reports the latest developments for the photogrammetric open‐source tool called GRAPHOS (inteGRAted PHOtogrammetric Suite). GRAPHOS includes some recent innovations in the image‐based 3D reconstruction pipeline, from automatic feature detection/description and network orientation to dense image matching and quality control. GRAPHOS also has a strong educational component beyond its automated processing functions, reinforced with tutorials and didactic explanations about algorithms and performance. The paper highlights recent developments carried out at different levels: graphical user interface (GUI), didactic simulators for image processing, photogrammetric processing with weight parameters, dataset creation and system evaluationS
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