82,868 research outputs found
Comparing the writing style of real and artificial papers
Recent years have witnessed the increase of competition in science. While
promoting the quality of research in many cases, an intense competition among
scientists can also trigger unethical scientific behaviors. To increase the
total number of published papers, some authors even resort to software tools
that are able to produce grammatical, but meaningless scientific manuscripts.
Because automatically generated papers can be misunderstood as real papers, it
becomes of paramount importance to develop means to identify these scientific
frauds. In this paper, I devise a methodology to distinguish real manuscripts
from those generated with SCIGen, an automatic paper generator. Upon modeling
texts as complex networks (CN), it was possible to discriminate real from fake
papers with at least 89\% of accuracy. A systematic analysis of features
relevance revealed that the accessibility and betweenness were useful in
particular cases, even though the relevance depended upon the dataset. The
successful application of the methods described here show, as a proof of
principle, that network features can be used to identify scientific gibberish
papers. In addition, the CN-based approach can be combined in a straightforward
fashion with traditional statistical language processing methods to improve the
performance in identifying artificially generated papers.Comment: To appear in Scientometrics (2015
The Fire and Smoke Model Evaluation Experiment—A Plan for Integrated, Large Fire–Atmosphere Field Campaigns
The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry. To address these requirements the FASMEE campaign design includes a study plan to guide the suite of required measurements in forested sites representative of many prescribed burning programs in the southeastern United States and increasingly common high-intensity fires in the western United States. Here we provide an overview of the proposed experiment and recommendations for key measurements. The FASMEE study provides a template for additional large-scale experimental campaigns to advance fire science and operational fire and smoke models
FEATURE SELECTION APPLIED TO THE TIME-FREQUENCY REPRESENTATION OF MUSCLE NEAR-INFRARED SPECTROSCOPY (NIRS) SIGNALS: CHARACTERIZATION OF DIABETIC OXYGENATION PATTERNS
Diabetic patients might present peripheral microcirculation impairment and might benefit from physical training. Thirty-nine diabetic patients underwent the monitoring of the tibialis anterior muscle oxygenation during a series of voluntary ankle flexo-extensions by near-infrared spectroscopy (NIRS). NIRS signals were acquired before and after training protocols. Sixteen control subjects were tested with the same protocol. Time-frequency distributions of the Cohen's class were used to process the NIRS signals relative to the concentration changes of oxygenated and reduced hemoglobin. A total of 24 variables were measured for each subject and the most discriminative were selected by using four feature selection algorithms: QuickReduct, Genetic Rough-Set Attribute Reduction, Ant Rough-Set Attribute Reduction, and traditional ANOVA. Artificial neural networks were used to validate the discriminative power of the selected features. Results showed that different algorithms extracted different sets of variables, but all the combinations were discriminative. The best classification accuracy was about 70%. The oxygenation variables were selected when comparing controls to diabetic patients or diabetic patients before and after training. This preliminary study showed the importance of feature selection techniques in NIRS assessment of diabetic peripheral vascular impairmen
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
Reconstruction of freeform surfaces for metrology
The application of freeform surfaces has increased since their complex shapes closely express a product's functional specifications and their machining is obtained with higher accuracy. In particular, optical surfaces exhibit enhanced performance especially when they take aspheric forms or more complex forms with multi-undulations. This study is mainly focused on the reconstruction of complex shapes such as freeform optical surfaces, and on the characterization of their form. The computer graphics community has proposed various algorithms for constructing a mesh based on the cloud of sample points. The mesh is a piecewise linear approximation of the surface and an interpolation of the point set. The mesh can further be processed for fitting parametric surfaces (Polyworks® or Geomagic®). The metrology community investigates direct fitting approaches. If the surface mathematical model is given, fitting is a straight forward task. Nonetheless, if the surface model is unknown, fitting is only possible through the association of polynomial Spline parametric surfaces. In this paper, a comparative study carried out on methods proposed by the computer graphics community will be presented to elucidate the advantages of these approaches. We stress the importance of the pre-processing phase as well as the significance of initial conditions. We further emphasize the importance of the meshing phase by stating that a proper mesh has two major advantages. First, it organizes the initially unstructured point set and it provides an insight of orientation, neighbourhood and curvature, and infers information on both its geometry and topology. Second, it conveys a better segmentation of the space, leading to a correct patching and association of parametric surfaces.EMR
You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems
Visual query systems (VQSs) empower users to interactively search for line
charts with desired visual patterns, typically specified using intuitive
sketch-based interfaces. Despite decades of past work on VQSs, these efforts
have not translated to adoption in practice, possibly because VQSs are largely
evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we
collaborated with experts from three diverse domains---astronomy, genetics, and
material science---via a year-long user-centered design process to develop a
VQS that supports their workflow and analytical needs, and evaluate how VQSs
can be used in practice. Our study results reveal that ad-hoc sketch-only
querying is not as commonly used as prior work suggests, since analysts are
often unable to precisely express their patterns of interest. In addition, we
characterize three essential sensemaking processes supported by our enhanced
VQS. We discover that participants employ all three processes, but in different
proportions, depending on the analytical needs in each domain. Our findings
suggest that all three sensemaking processes must be integrated in order to
make future VQSs useful for a wide range of analytical inquiries.Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25
in Vancouver, Canada. Paper will also be published in a special issue of IEEE
Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS
(InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing,
Visualization, Visualization design and evaluation method
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