86,434 research outputs found
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Factors in human recognition of timbre lexicons generated by data clustering
Since the development of sound recording technologies, the palette of sound timbres available for music creation was extended way beyond traditional musical instruments. The organization and categorization of timbre has been a common endeavor. The availability of large databases of sound clips provides an opportunity for obtaining datadriven timbre categorizations via content-based clustering. In this article we describe an experiment aimed at understanding what factors influence the process of learning a given clustering of sound samples. We clustered a large database of short sound clips, and analyzed the success of participants in assigning sounds to the “correct” clusters after listening to a few examples of each. The results of the experiment suggest a number of relevant factors related both to the strategies followed by users and to the quality measures of the clustering solution, which can guide the design of creative applications based on audio clip clustering
An intuitive control space for material appearance
Many different techniques for measuring material appearance have been
proposed in the last few years. These have produced large public datasets,
which have been used for accurate, data-driven appearance modeling. However,
although these datasets have allowed us to reach an unprecedented level of
realism in visual appearance, editing the captured data remains a challenge. In
this paper, we present an intuitive control space for predictable editing of
captured BRDF data, which allows for artistic creation of plausible novel
material appearances, bypassing the difficulty of acquiring novel samples. We
first synthesize novel materials, extending the existing MERL dataset up to 400
mathematically valid BRDFs. We then design a large-scale experiment, gathering
56,000 subjective ratings on the high-level perceptual attributes that best
describe our extended dataset of materials. Using these ratings, we build and
train networks of radial basis functions to act as functionals mapping the
perceptual attributes to an underlying PCA-based representation of BRDFs. We
show that our functionals are excellent predictors of the perceived attributes
of appearance. Our control space enables many applications, including intuitive
material editing of a wide range of visual properties, guidance for gamut
mapping, analysis of the correlation between perceptual attributes, or novel
appearance similarity metrics. Moreover, our methodology can be used to derive
functionals applicable to classic analytic BRDF representations. We release our
code and dataset publicly, in order to support and encourage further research
in this direction
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Ewé: a web-based ethnobotanical database for storing and analysing data
Ethnobotanical databases serve as repositories of traditional knowledge (TK), either at international or local scales. By documenting plant species with traditional use, and most importantly, the applications and modes of use of such species, ethnobotanical databases play a role in the conservation of TK and also provide access to information that could improve hypothesis generation and testing in ethnobotanical studies. Brazil has a rich medicinal flora and a rich cultural landscape. Nevertheless, cultural change and ecological degradation can lead to loss of TK. Here, we present an online database developed with open-source tools with a capacity to include all medicinal flora of Brazil. We present test data for the Leguminosae comprising a total of 2078 records, referred to here as use reports, including data compiled from literature and herbarium sources. Unlike existing databases, Ewé provides tools for the visualization of large datasets, facilitating hypothesis generation and meta-analyses. The Ewé database is currently available at www.ewedb.com
Best Practice Statement for Screening, Assessment and Management of Vision Problems in the First 30 Days after an Acute Stroke
No abstract available
Understanding research dynamics
Rexplore leverages novel solutions in data mining, semantic technologies and visual analytics, and provides an innovative environment for exploring and making sense of scholarly data. Rexplore allows users: 1) to detect and make sense of important trends in research; 2) to identify a variety of interesting relations between researchers, beyond the standard co-authorship relations provided by most other systems; 3) to perform fine-grained expert search with respect to detailed multi-dimensional parameters; 4) to detect and characterize the dynamics of interesting communities of researchers, identified on the basis of shared research interests and scientific trajectories; 5) to analyse research performance at different levels of abstraction, including individual researchers, organizations, countries, and research communities
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