Face analysis using curve edge maps

This paper proposes an automatic and real-time system for face analysis, usable in visual communication applications. In this approach, faces are represented with Curve Edge Maps, which are collections of polynomial segments with a convex region. The segments are extracted from edge pixels using an adaptive incremental linear-time fitting algorithm, which is based on constructive polynomial fitting. The face analysis system considers face tracking, face recognition and facial feature detection, using Curve Edge Maps driven by histograms of intensities and histograms of relative positions. When applied to different face databases and video sequences, the average face recognition rate is 95.51%, the average facial feature detection rate is 91.92% and the accuracy in location of the facial features is 2.18% in terms of the size of the face, which is comparable with or better than the results in literature. However, our method has the advantages of simplicity, real-time performance and extensibility to the different aspects of face analysis, such as recognition of facial expressions and talking

Coarse-to-fine autoencoder networks (CFAN) for real-time face alignment

Abstract. Accurate face alignment is a vital prerequisite step for most face perception tasks such as face recognition, facial expression analysis and non-realistic face re-rendering. It can be formulated as the nonlinear inference of the facial landmarks from the detected face region. Deep network seems a good choice to model the nonlinearity, but it is nontrivial to apply it directly. In this paper, instead of a straightforward application of deep network, we propose a Coarse-to-Fine Auto-encoder Networks (CFAN) approach, which cascades a few successive Stacked Auto-encoder Networks (SANs). Specifically, the first SAN predicts the landmarks quickly but accurately enough as a preliminary, by taking as input a low-resolution version of the detected face holistically. The following SANs then progressively refine the landmark by taking as input the local features extracted around the current landmarks (output of the previous SAN) with higher and higher resolution. Extensive experiments conducted on three challenging datasets demonstrate that our CFAN outperforms the state-of-the-art methods and performs in real-time(40+fps excluding face detection on a desktop)

Pulse-shape discrimination with PbWO4_4 crystal scintillators

The light output, $\alpha/\beta$ ratio, and pulse shape have been investigated at $-25^\circ$ C with PbWO$_4$ crystal scintillators undoped, and doped by F, Eu, Mo, Gd and S. The fast $0.01-0.06 \mu$s and middle $0.1-0.5 \mu$s components of scintillation decay were observed for all the samples. Slow components of scintillation signal with the decay times $1-3 \mu$s and $13-28 \mu$s with the total intensity up to $\approx50$ have been recognized for several samples doped by Molybdenum. We found some indications of a pulse-shape discrimination between $\alpha$ particles and $\gamma$ quanta with PbWO$_4$ (Mo doped) crystal scintillators.Comment: 12 pages, 5 figures, submitted to NIM

Deletion of Sirt3 does not affect atherosclerosis but accelerates weight gain and impairs rapid metabolic adaptation in LDL receptor knockout mice: implications for cardiovascular risk factor development.

Sirt3 is a mitochondrial NAD(+)-dependent deacetylase that governs mitochondrial metabolism and reactive oxygen species homeostasis. Sirt3 deficiency has been reported to accelerate the development of the metabolic syndrome. However, the role of Sirt3 in atherosclerosis remains enigmatic. We aimed to investigate whether Sirt3 deficiency affects atherosclerosis, plaque vulnerability, and metabolic homeostasis. Low-density lipoprotein receptor knockout (LDLR(-/-)) and LDLR/Sirt3 double-knockout (Sirt3(-/-)LDLR(-/-)) mice were fed a high-cholesterol diet (1.25 % w/w) for 12 weeks. Atherosclerosis was assessed en face in thoraco-abdominal aortae and in cross sections of aortic roots. Sirt3 deletion led to hepatic mitochondrial protein hyperacetylation. Unexpectedly, though plasma malondialdehyde levels were elevated in Sirt3-deficient mice, Sirt3 deletion affected neither plaque burden nor features of plaque vulnerability (i.e., fibrous cap thickness and necrotic core diameter). Likewise, plaque macrophage and T cell infiltration as well as endothelial activation remained unaltered. Electron microscopy of aortic walls revealed no difference in mitochondrial microarchitecture between both groups. Interestingly, loss of Sirt3 was associated with accelerated weight gain and an impaired capacity to cope with rapid changes in nutrient supply as assessed by indirect calorimetry. Serum lipid levels and glucose tolerance were unaffected by Sirt3 deletion in LDLR(-/-) mice. Sirt3 deficiency does not affect atherosclerosis in LDLR(-/-) mice. However, Sirt3 controls systemic levels of oxidative stress, limits expedited weight gain, and allows rapid metabolic adaptation. Thus, Sirt3 may contribute to postponing cardiovascular risk factor development

GS-2: A novel Broad-Spectrum agent for environmental microbial control

The environmental control of microbial pathogens currently relies on compounds that do not exert long-lasting activity on surfaces, are impaired by soil, and contribute to the growing problem of antimicrobial resistance. This study presents the scientific development and characterization of GS-2, a novel, water-soluble ammonium carboxylate salt of capric acid and L-arginine that demonstrates activity against a range of bacteria (particularly Gram-negative bacteria), fungi, and viruses. In real-world surface testing, GS-2 was more effective than a benzalkonium chloride disinfectant at reducing the bacterial load on common touch-point surfaces in a high-traffic building (average 1.6 vs. 32.6 CFUs recovered from surfaces 90 min after application, respectively). Toxicology testing in rats confirmed GS-2 ingredients were rapidly cleared and posed no toxicities to humans or animals. To enhance the time-kill against Gram-positive bacteria, GS-2 was compounded at a specific ratio with a naturally occurring monoterpenoid, thymol, to produce a water-based antimicrobial solution. This GS-2 with thymol formulation could generate a bactericidal effect after five minutes of exposure and a viricidal effect after 10 min of exposure. Further testing of the GS-2 and thymol combination on glass slides demonstrated that the compound retained bactericidal activity for up to 60 days. Based on these results, GS-2 and GS-2 with thymol represent a novel antimicrobial solution that may have significant utility in the long-term reduction of environmental microbial pathogens in a variety of settings

Automatic Multi-organ Segmentation Using Learning-Based Segmentation and Level Set Optimization

Abstract. We present a novel generic segmentation system for the fully automatic multi-organ segmentation from CT medical images. Thereby we combine the advantages of learning-based approaches on point cloud-based shape representation, such a speed, robustness, point correspon-dences, with those of PDE-optimization-based level set approaches, such as high accuracy and the straightforward prevention of segment overlaps. In a benchmark on 10–100 annotated datasets for the liver, the lungs, and the kidneys we show that the proposed system yields segmentation accuracies of 1.17–2.89mm average surface errors. Thereby the level set segmentation (which is initialized by the learning-based segmentations) contributes with an 20%-40 % increase in accuracy.