190 research outputs found
Load Index Metrics for an Optimized Management of Web Services: A Systematic Evaluation
The lack of precision to predict service performance through load indices may lead to wrong decisions regarding the use of web services, compromising service performance and raising platform cost unnecessarily. This paper presents experimental studies to qualify the behaviour of load indices in the web service context. The experiments consider three services that generate controlled and significant server demands, four levels of workload for each service and six distinct execution scenarios. The evaluation considers three relevant perspectives: the capability for representing recent workloads, the capability for predicting near-future performance and finally stability. Eight different load indices were analysed, including the JMX Average Time index (proposed in this paper) specifically designed to address the limitations of the other indices. A systematic approach is applied to evaluate the different load indices, considering a multiple linear regression model based on the stepwise-AIC method. The results show that the load indices studied represent the workload to some extent; however, in contrast to expectations, most of them do not exhibit a coherent correlation with service performance and this can result in stability problems. The JMX Average Time index is an exception, showing a stable behaviour which is tightly-coupled to the service runtime for all executions. Load indices are used to predict the service runtime and therefore their inappropriate use can lead to decisions that will impact negatively on both service performance and execution cost
Image Denoising using Attention-Residual Convolutional Neural Networks
During the image acquisition process, noise is usually added to the data
mainly due to physical limitations of the acquisition sensor, and also
regarding imprecisions during the data transmission and manipulation. In that
sense, the resultant image needs to be processed to attenuate its noise without
losing details. Non-learning-based strategies such as filter-based and noise
prior modeling have been adopted to solve the image denoising problem.
Nowadays, learning-based denoising techniques showed to be much more effective
and flexible approaches, such as Residual Convolutional Neural Networks. Here,
we propose a new learning-based non-blind denoising technique named Attention
Residual Convolutional Neural Network (ARCNN), and its extension to blind
denoising named Flexible Attention Residual Convolutional Neural Network
(FARCNN). The proposed methods try to learn the underlying noise expectation
using an Attention-Residual mechanism. Experiments on public datasets corrupted
by different levels of Gaussian and Poisson noise support the effectiveness of
the proposed approaches against some state-of-the-art image denoising methods.
ARCNN achieved an overall average PSNR results of around 0.44dB and 0.96dB for
Gaussian and Poisson denoising, respectively FARCNN presented very consistent
results, even with slightly worsen performance compared to ARCNN.Comment: Published in: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and
Images (SIBGRAPI
Nanopigmented Acrylic Resin Cured Indistinctively by Water Bath or Microwave Energy for Dentures
The highlight of this study was the synthesis of nanopigmented poly(methyl methacrylate) nanoparticles that were further processed using a water bath and/or microwave energy for dentures. The experimental acrylic resins were physicochemically characterized, and the adherence of Candida albicans and biocompatibility were assessed. A nanopigmented acrylic resin cured by a water bath or by microwave energy was obtained. The acrylic specimens possess similar properties to commercial acrylic resins, but the transverse strength and porosity were slightly improved. The acrylic resins cured with microwave energy exhibited reduced C. albicans adherence. These results demonstrate an improved noncytotoxic material for the manufacturing of denture bases in dentistry
The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment
The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in
operation since July 2014. This paper describes the second data release from
this phase, and the fourteenth from SDSS overall (making this, Data Release
Fourteen or DR14). This release makes public data taken by SDSS-IV in its first
two years of operation (July 2014-2016). Like all previous SDSS releases, DR14
is cumulative, including the most recent reductions and calibrations of all
data taken by SDSS since the first phase began operations in 2000. New in DR14
is the first public release of data from the extended Baryon Oscillation
Spectroscopic Survey (eBOSS); the first data from the second phase of the
Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2),
including stellar parameter estimates from an innovative data driven machine
learning algorithm known as "The Cannon"; and almost twice as many data cubes
from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous
release (N = 2812 in total). This paper describes the location and format of
the publicly available data from SDSS-IV surveys. We provide references to the
important technical papers describing how these data have been taken (both
targeting and observation details) and processed for scientific use. The SDSS
website (www.sdss.org) has been updated for this release, and provides links to
data downloads, as well as tutorials and examples of data use. SDSS-IV is
planning to continue to collect astronomical data until 2020, and will be
followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14
happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov
2017 (this is the "post-print" and "post-proofs" version; minor corrections
only from v1, and most of errors found in proofs corrected
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