854 research outputs found
Unweighted estimation based on optimal sample under measurement constraints
To tackle massive data, subsampling is a practical approach to select the
more informative data points. However, when responses are expensive to measure,
developing efficient subsampling schemes is challenging, and an optimal
sampling approach under measurement constraints was developed to meet this
challenge. This method uses the inverses of optimal sampling probabilities to
reweight the objective function, which assigns smaller weights to the more
important data points. Thus the estimation efficiency of the resulting
estimator can be improved. In this paper, we propose an unweighted estimating
procedure based on optimal subsamples to obtain a more efficient estimator. We
obtain the unconditional asymptotic distribution of the estimator via
martingale techniques without conditioning on the pilot estimate, which has
been less investigated in the existing subsampling literature. Both asymptotic
results and numerical results show that the unweighted estimator is more
efficient in parameter estimation
SFD: Single Shot Scale-invariant Face Detector
This paper presents a real-time face detector, named Single Shot
Scale-invariant Face Detector (SFD), which performs superiorly on various
scales of faces with a single deep neural network, especially for small faces.
Specifically, we try to solve the common problem that anchor-based detectors
deteriorate dramatically as the objects become smaller. We make contributions
in the following three aspects: 1) proposing a scale-equitable face detection
framework to handle different scales of faces well. We tile anchors on a wide
range of layers to ensure that all scales of faces have enough features for
detection. Besides, we design anchor scales based on the effective receptive
field and a proposed equal proportion interval principle; 2) improving the
recall rate of small faces by a scale compensation anchor matching strategy; 3)
reducing the false positive rate of small faces via a max-out background label.
As a consequence, our method achieves state-of-the-art detection performance on
all the common face detection benchmarks, including the AFW, PASCAL face, FDDB
and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for
VGA-resolution images.Comment: Accepted by ICCV 2017 + its supplementary materials; Updated the
latest results on WIDER FAC
Separation of core-shell structured carbon black nanoparticles from waste tires by light pyrolysis
The separation of core-shell structured carbon black (CBlp) nanoparticles from waste tires was investigated by applying a reactive extrusion process. The polymeric shell consisting primarily of crosslinked rubber and loosely bound rubber could be selectively separated by varying the extrusion temperature to 260, 280 and 300 °C. The structure, chemical composition and structure of the separated CBlp were characterized using thermo-gravimetric analysis, X-ray photoelectron spectroscopy, scanning electron microscopy, transmission electron microscopy and dynamic light scattering. The crosslinked structure was persevered in the rubber shell of CBlp after extruding at 260 °C. A layer of loosely bound rubber was observed only in the rubber shell when extruded at 280 °C and 300 °C. The composition of the bound rubber layer is also dependent on the processing temperature
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