3,324 research outputs found

    Unsupervised Texture Transfer from Images to Model Collections

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    Large 3D model repositories of common objects are now ubiquitous and are increasingly being used in computer graphics and computer vision for both analysis and synthesis tasks. However, images of objects in the real world have a richness of appearance that these repositories do not capture, largely because most existing 3D models are untextured. In this work we develop an automated pipeline capable of transporting texture information from images of real objects to 3D models of similar objects. This is a challenging problem, as an object's texture as seen in a photograph is distorted by many factors, including pose, geometry, and illumination. These geometric and photometric distortions must be undone in order to transfer the pure underlying texture to a new object --- the 3D model. Instead of using problematic dense correspondences, we factorize the problem into the reconstruction of a set of base textures (materials) and an illumination model for the object in the image. By exploiting the geometry of the similar 3D model, we reconstruct certain reliable texture regions and correct for the illumination, from which a full texture map can be recovered and applied to the model. Our method allows for large-scale unsupervised production of richly textured 3D models directly from image data, providing high quality virtual objects for 3D scene design or photo editing applications, as well as a wealth of data for training machine learning algorithms for various inference tasks in graphics and vision

    Exercise-Induced Changes in Exhaled NO Differentiates Asthma With or Without Fixed Airway Obstruction From COPD With Dynamic Hyperinflation.

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    Asthmatic patients with fixed airway obstruction (FAO) and patients with chronic obstructive pulmonary disease (COPD) share similarities in terms of irreversible pulmonary function impairment. Exhaled nitric oxide (eNO) has been documented as a marker of airway inflammation in asthma, but not in COPD. To examine whether the basal eNO level and the change after exercise may differentiate asthmatics with FAO from COPD, 27 normal subjects, 60 stable asthmatics, and 62 stable COPD patients were studied. Asthmatics with FAO (n = 29) were defined as showing a postbronchodilator FEV(1)/forced vital capacity (FVC) ≤70% and FEV(1) less than 80% predicted after inhaled salbutamol (400 μg). COPD with dynamic hyperinflation (n = 31) was defined as a decrease in inspiratory capacity (ΔIC%) after a 6 minute walk test (6MWT). Basal levels of eNO were significantly higher in asthmatics and COPD patients compared to normal subjects. The changes in eNO after 6MWT were negatively correlated with the percent change in IC (r = −0.380, n = 29, P = 0.042) in asthmatics with FAO. Their levels of basal eNO correlated with the maximum mid-expiratory flow (MMEF % predicted) before and after 6MWT. In COPD patients with air-trapping, the percent change of eNO was positively correlated to ΔIC% (rs = 0.404, n = 31, P = 0.024). We conclude that asthma with FAO may represent residual inflammation in the airways, while dynamic hyperinflation in COPD may retain NO in the distal airspace. eNO changes after 6MWT may differentiate the subgroups of asthma or COPD patients and will help toward delivery of individualized therapy for airflow obstruction

    On generalized processor sharing and objective functions: analytical framework

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    Today, telecommunication networks host a wide range of heterogeneous services. Some demand strict delay minima, while others only need a best-effort kind of service. To achieve service differentiation, network traffic is partitioned in several classes which is then transmitted according to a flexible and fair scheduling mechanism. Telecommunication networks can, for instance, use an implementation of Generalized Processor Sharing (GPS) in its internal nodes to supply an adequate Quality of Service to each class. GPS is flexible and fair, but also notoriously hard to study analytically. As a result, one has to resort to simulation or approximation techniques to optimize GPS for some given objective function. In this paper, we set up an analytical framework for two-class discrete-time probabilistic GPS which allows to optimize the scheduling for a generic objective function in terms of the mean unfinished work of both classes without the need for exact results or estimations/approximations for these performance characteristics. This framework is based on results of strict priority scheduling, which can be regarded as a special case of GPS, and some specific unfinished-work properties in two-class GPS. We also apply our framework on a popular type of objective functions, i.e., convex combinations of functions of the mean unfinished work. Lastly, we incorporate the framework in an algorithm to yield a faster and less computation-intensive result for the optimum of an objective function

    On the force field optimisation of β -lactam cores using the force field Toolkit

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    When employing molecular dynamics (MD) simulations for computer-aided drug design, the quality of the used force fields is highly important. Here we present reparametrisations of the force fields for the core molecules from 9 different β-lactam classes, for which we utilized the force field Toolkit and Gaussian calculations. We focus on the parametrisation of the dihedral angles, with the goal of reproducing the optimised quantum geometry in MD simulations. Parameters taken from CGenFF turn out to be a good initial guess for the multiplicity of each dihedral angle, but the key to a successful parametrisation is found to lie in the phase shifts. Based on the optimised quantum geometry, we come up with a strategy for predicting the phase shifts prior to the dihedral potential fitting. This allows us to successfully parameterise 8 out of the 11 molecules studied here, while the remaining 3 molecules can also be parameterised with small adjustments. Our work highlights the importance of predicting the dihedral phase shifts in the ligand parametrisation protocol, and provides a simple yet valuable strategy for improving the process of parameterising force fields of drug-like molecules

    Developing an EEG-based on-line closed-loop lapse detection and mitigation system

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    © 2014 Wang, Huang, Wei, Huang, Ko, Lin, Cheng and Jung. In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments

    Stable isotopic and biomarker evidence of terrigenous organic matter export to the deep sea during tropical storms

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    The global export of organic carbon (OC) is intimately linked to the total flux of terrestrial sediment to the ocean, with the continental margins receiving similar to 90% of the sediment generated by erosion on land. Recent studies suggest that a substantial amount of particulate OC (POC) might escape from the shelf and be exported to the continental slope-deep sea sector, although the mechanisms and magnitude of such deep sea POC transfer remain unknown. Here we investigate hyperpycnal flow-associated total suspended matter (TSM) collected from water depths of similar to 3000 m, near the bottom of sea floor, in the Gaoping Submarine Canyon (GSC) off southwestern Taiwan. Elemental (C, N), isotopic (delta C-13, delta N-15) and biomarker compositions of TSM were investigated to understand its biogeochemical characteristics. A two end-member delta C-13 mixing model indicates that deep sea TSM contains similar to 90% terrigenous OC, while a similar mixing model using delta N-15 reveals a lower proportion (similar to 58%). Organic biomarkers of TSM suggest contributions from a mixture of resuspended, continental-margin derived marine organic matter (OMMAR) and terrigenous sources, revealing that terrestrial OC likely mixes with nitrogen-rich marine material during rapid transport. This study documents that rapid transfer of terrigenous organic matter (OMTERR) into the deeper regions of GSC occurred within a week of typhoon Morakot, likely through hyperpycnal injection of sediment-laden, warm freshwater from southern Taiwan. Evidence from this typhoon Morakot-induced hyperpycnal plume event in Taiwan demonstrates that extreme storm events provide an efficient way to export terrigenous OC without oxidation to hitherto unknown water depths of deep sea in the Oceania region. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND licens

    A model-based approach to recovering the structure of a plant from images

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    We present a method for recovering the structure of a plant directly from a small set of widely-spaced images. Structure recovery is more complex than shape estimation, but the resulting structure estimate is more closely related to phenotype than is a 3D geometric model. The method we propose is applicable to a wide variety of plants, but is demonstrated on wheat. Wheat is made up of thin elements with few identifiable features, making it difficult to analyse using standard feature matching techniques. Our method instead analyses the structure of plants using only their silhouettes. We employ a generate-and-test method, using a database of manually modelled leaves and a model for their composition to synthesise plausible plant structures which are evaluated against the images. The method is capable of efficiently recovering accurate estimates of plant structure in a wide variety of imaging scenarios, with no manual intervention

    Structural basis for oligomerization and glycosaminoglycan binding of CCL5 and CCL3.

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    CC chemokine ligand 5 (CCL5) and CCL3 are critical for immune surveillance and inflammation. Consequently, they are linked to the pathogenesis of many inflammatory conditions and are therapeutic targets. Oligomerization and glycosaminoglycan (GAG) binding of CCL5 and CCL3 are vital for the functions of these chemokines. Our structural and biophysical analyses of human CCL5 reveal that CCL5 oligomerization is a polymerization process in which CCL5 forms rod-shaped, double-helical oligomers. This CCL5 structure explains mutational data and offers a unified mechanism for CCL3, CCL4, and CCL5 assembly into high-molecular-weight, polydisperse oligomers. A conserved, positively charged BBXB motif is key for the binding of CC chemokines to GAG. However, this motif is partially buried when CCL3, CCL4, and CCL5 are oligomerized; thus, the mechanism by which GAG binds these chemokine oligomers has been elusive. Our structures of GAG-bound CCL5 and CCL3 oligomers reveal that these chemokine oligomers have distinct GAG-binding mechanisms. The CCL5 oligomer uses another positively charged and fully exposed motif, KKWVR, in GAG binding. However, residues from two partially buried BBXB motifs along with other residues combine to form a GAG-binding groove in the CCL3 oligomer. The N termini of CC chemokines are shown to be involved in receptor binding and oligomerization. We also report an alternative CCL3 oligomer structure that reveals how conformational changes in CCL3 N termini profoundly alter its surface properties and dimer-dimer interactions to affect GAG binding and oligomerization. Such complexity in oligomerization and GAG binding enables intricate, physiologically relevant regulation of CC chemokine functions
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