The most beautiful recognizable feature of the toco toucan is its large and colorful bill. Despite its importance, such as in feeding and acting as a thermal regulator, few systematic approaches have been proposed for...
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The most beautiful recognizable feature of the toco toucan is its large and colorful bill. Despite its importance, such as in feeding and acting as a thermal regulator, few systematic approaches have been proposed for characterizing its structural organization. A framework for identifying and characterizing the trabecular bone tissue located inside the bill is reported in this work. An image processing pipeline for segmenting the trabecular bones is proposed, followed by the derivation of a 3D coordinate system defined by the shape of the bill. These coordinates are then used for further characterization of the mass distribution inside the bill.
Autism Spectrum Disorders are associated with atypical movements, of which stereotypical motor movements (SMMs) interfere with learning and social interaction. The automatic SMM detection using inertial measurement un...
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We present the first analysis of the Euclid Early Release Observations (ERO) program that targets fields around two lensing clusters, Abell 2390 and Abell 2764. We use VIS and NISP imaging to produce photometric catal...
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This volume presents selected, peer-reviewed, short papers that were accepted for presentation in the 5th International Conference on Variable Neighborhood Search (ICVNS'17) which was held in Ouro Preto, Brazil, d...
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This volume presents selected, peer-reviewed, short papers that were accepted for presentation in the 5th International Conference on Variable Neighborhood Search (ICVNS'17) which was held in Ouro Preto, Brazil, during October 2–4, 2017.
The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity ...
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This paper presents a framework for selecting a combination of existing systems to satisfy new, emerging requirements while reusing existing and proven capabilities to ensure mission success. Decision attributes will ...
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This paper presents a framework for selecting a combination of existing systems to satisfy new, emerging requirements while reusing existing and proven capabilities to ensure mission success. Decision attributes will be considered during the selection process and will be used to measure the networked computer system's effectiveness to accomplish the mission. This approach will enable system stakeholders to make critical, well-informed decisions to address the continuing evolution of missions, threats, budget and technology.
Speech separation is the task of separating target speech from background interference. Traditionally, speech separation is studied as a signal processing problem. A more recent approach formulates speech separation a...
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Identification of module or community structures is important for characterizing and understanding complex systems. While designed with different objectives, i.e., stochastic models for regeneration and modularity max...
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Identification of module or community structures is important for characterizing and understanding complex systems. While designed with different objectives, i.e., stochastic models for regeneration and modularity maximization models for discrimination, both these two types of model look for low-rank embedding to best represent and reconstruct network topology. However, the mapping through such embedding is linear, whereas real networks have various nonlinear features, making these models less effective in practice. Inspired by the strong representation power of deep neural networks, we propose a novel nonlinear reconstruction method by adopting deep neural networks for representation. We then extend the method to a semi-supervised community detection algorithm by incorporating pairwise constraints among graph nodes. Extensive experimental results on synthetic and real networks show that the new methods are effective, outperforming most state-of-the-art methods for community detection.
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