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

S3^3FD: Single Shot Scale-invariant Face Detector

This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S$^3$FD), 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