Learning Dilation Factors for Semantic Segmentation of Street Scenes

Contextual information is crucial for semantic segmentation. However, finding the optimal trade-off between keeping desired fine details and at the same time providing sufficiently large receptive fields is non trivial. This is even more so, when objects or classes present in an image significantly vary in size. Dilated convolutions have proven valuable for semantic segmentation, because they allow to increase the size of the receptive field without sacrificing image resolution. However, in current state-of-the-art methods, dilation parameters are hand-tuned and fixed. In this paper, we present an approach for learning dilation parameters adaptively per channel, consistently improving semantic segmentation results on street-scene datasets like Cityscapes and Camvid.Comment: GCPR201

Structure propagation for zero-shot learning

The key of zero-shot learning (ZSL) is how to find the information transfer model for bridging the gap between images and semantic information (texts or attributes). Existing ZSL methods usually construct the compatibility function between images and class labels with the consideration of the relevance on the semantic classes (the manifold structure of semantic classes). However, the relationship of image classes (the manifold structure of image classes) is also very important for the compatibility model construction. It is difficult to capture the relationship among image classes due to unseen classes, so that the manifold structure of image classes often is ignored in ZSL. To complement each other between the manifold structure of image classes and that of semantic classes information, we propose structure propagation (SP) for improving the performance of ZSL for classification. SP can jointly consider the manifold structure of image classes and that of semantic classes for approximating to the intrinsic structure of object classes. Moreover, the SP can describe the constrain condition between the compatibility function and these manifold structures for balancing the influence of the structure propagation iteration. The SP solution provides not only unseen class labels but also the relationship of two manifold structures that encode the positive transfer in structure propagation. Experimental results demonstrate that SP can attain the promising results on the AwA, CUB, Dogs and SUN databases

Provenance analysis for instagram photos

As a feasible device fingerprint, sensor pattern noise (SPN) has been proven to be effective in the provenance analysis of digital images. However, with the rise of social media, millions of images are being uploaded to and shared through social media sites every day. An image downloaded from social networks may have gone through a series of unknown image manipulations. Consequently, the trustworthiness of SPN has been challenged in the provenance analysis of the images downloaded from social media platforms. In this paper, we intend to investigate the effects of the pre-defined Instagram images filters on the SPN-based image provenance analysis. We identify two groups of filters that affect the SPN in quite different ways, with Group I consisting of the filters that severely attenuate the SPN and Group II consisting of the filters that well preserve the SPN in the images. We further propose a CNN-based classifier to perform filter-oriented image categorization, aiming to exclude the images manipulated by the filters in Group I and thus improve the reliability of the SPN-based provenance analysis. The results on about 20, 000 images and 18 filters are very promising, with an accuracy higher than 96% in differentiating the filters in Group I and Group II

In vitro investigation of the hypoglycemic activity of yeasts using models of rat epididymal adipocyte and differentiated mouse 3T3-L1 adipocyte

The differentiated mouse 3T3-L1 adipocytes (3T3-L1 model) were used in studying glucose metabolisms without the need for feeding (Sprague-Dawley, SD model) the rat prior to hypoglycemic activity evaluation. Both models were adopted to evaluate the hypoglycemic activities of 58 yeast strains isolated from various sources (grape, vine yard soil, winery soil). Among the 58 tested yeast isolates, strain 54 (Saccharomyces pastorianus no. 54) which showed the highest hypoglycemic activity was chosen to be the test strain. The optimal insulin concentration used in these 2 models (SD and 3T3-L1) for measuring the hypoglycemic activity of hypoglycemic yeast extract (HGYE) was 10 nM. The range of linear relation in the dose-response curve was 0-1000 g/ml for SD model, and 0-250 g/ml for 3T3-L1 model. The linear coefficient was 0.8611. The radioactive labeled 2-[1-14C]-Deoxy-D-Glucose was also used to confirm cytoplasmic glucose uptake by 3T3-L1 adipocytes. Comparing both the results of insulin effect and dose response of HGYE by both models, it was concluded that the 3T3-L1 model can serve as a rapid and reliable assay model for in vitro evaluation of hypoglycemic activity of yeast.Key words: 3T3-L1 adipocytes, Sprague-Dawley (SD) rat, epididymal adipocytes, hypoglycemic activity, yeast

Isolation of 10 cyclosporine metabolites from human bile

Ten metabolites of cyclosporine were isolated from the ethyl ether extract of bile from four liver transplant patients receiving cyclosporine. Two of the metabolites were unique and previously unidentified. Liquid-liquid partitioning into diethyl ether with subsequent defatting with n-hexane was used for the initial extraction form bile. Separation of the individual metabolites (A-J) was performed using a Sephadex LH-20 column and a gradient high performance liquid chromatographic method. The molecular weights of the isolated metabolites were determined by fast atom bombardment/mass spectrometry. Gas chromatography with mass spectrometic amino acid analysis was also used to identify the amino acid composition and the hydroxylation position of metabolites A, B, C, D, and G. Proton nuclear magnetic resonance spectra were utilized to disinguish the chemical shifts of N-CH3 singlets and NH doublets of metabolites A, B, C, and D. Metabolites A, E, F, H, I, and J were reported previously in human urine and animal bile. Metabolites C and D are dihydroxylated compounds which cannot be clearly described as previously isolated compounds. Metabolites B and G are novel metabolites with a mass fragment which corresponded to a loss of 131 Da from the protonated molecular ion (MH+) in the fast atom bombardment/mass spectrometry, suggesting that the double bond in amino acid 1 has been modified. Metabolites B and G were primarily isolated from the bile of one of the liver transplant patients which contained abnormally high concentrations of these two metabolites. The method described is an efficient procedure for isolating milligram quantities of the major metabolites with greater than 95% purity

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

We focus on the challenging task of real-time semantic segmentation in this paper. It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel-wise label inference. We propose an image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address this challenge. We provide in-depth analysis of our framework and introduce the cascade feature fusion unit to quickly achieve high-quality segmentation. Our system yields real-time inference on a single GPU card with decent quality results evaluated on challenging datasets like Cityscapes, CamVid and COCO-Stuff.Comment: ECCV 201

Low-Symmetry Rhombohedral GeTe Thermoelectrics

High-symmetry thermoelectric materials usually have the advantage of very high band degeneracy, while low-symmetry thermoelectrics have the advantage of very low lattice thermal conductivity. If the symmetry breaking of band degeneracy is small, both effects may be realized simultaneously. Here we demonstrate this principle in rhombohedral GeTe alloys, having a slightly reduced symmetry from its cubic structure, to realize a record figure of merit (zT ∼ 2.4) at 600 K. This is enabled by the control of rhombohedral distortion in crystal structure for engineering the split low-symmetry bands to be converged and the resultant compositional complexity for simultaneously reducing the lattice thermal conductivity. Device ZT as high as 1.3 in the rhombohedral phase and 1.5 over the entire working temperature range of GeTe alloys make this material the most efficient thermoelectric to date. This work paves the way for exploring low-symmetry materials as efficient thermoelectrics. Thermoelectric materials enable a heat flow to be directly converted to a flow of charge carriers for generating electricity. The crystal structure symmetry is one of the most fundamental parameters determining the properties of a crystalline material including thermoelectrics. The common belief currently held is that high-symmetry materials are usually good for thermoelectrics, leading to great efforts having historically been focused on GeTe alloys in a high-symmetry cubic structure. Here we show a slight reduction of crystal structure symmetry of GeTe alloys from cubic to rhombohedral, enabling a rearrangement in electronic bands for more transporting channels of charge carriers and many imperfections for more blocking centers of heat-energy carriers (phonons). This leads to the discovery of rhombohedral GeTe alloys as the most efficient thermoelectric materials to date, opening new possibilities for low-symmetry thermoelectric materials. Cubic GeTe thermoelectrics have been historically focused on, while this work utilizes a slight symmetry-breaking strategy to converge the split valence bands, to reduce the lattice thermal conductivity and therefore realize a record thermoelectric performance, all enabled in GeTe in a rhombohedral structure. This not only promotes GeTe alloys as excellent materials for thermoelectric power generation below 800 K, but also expands low-symmetry materials as efficient thermoelectrics

Expression of CD80 and CD86 costimulatory molecules are potential markers for better survival in nasopharyngeal carcinoma

<p>Abstract</p> <p>Background</p> <p>B7 Costimulatory signal is essential to trigger T-cell activation upon the recognition of tumor antigens. This study examined the expression of B7-1 (CD80) and B7-2 (CD86) costimulatory molecules along with HLA-DR and the presence of infiltrating lymphocytes and dendritic cells to assess their significance in patients with nasopharyngeal carcinoma (NPC).</p> <p>Methods</p> <p>Expression of CD80, CD86, HLA-DR, S-100 protein and the presence of infiltrating lymphocytes and follicular dendritic reticulum cells were immunohistochemically examined on the paraffin-embedded tissue blocks from newly diagnosed NPC patients (n = 50). The results were correlated with clinical outcome of patients.</p> <p>Results</p> <p>CD80 and CD86 were each expressed in 10 of 50 cases in which they co-expressed in 9 cases. Univariate analysis revealed that patients with CD80/CD86 expression had significantly better overall survival than those without it (P = 0.017), but after adjustment for stage, nodal status, and treatment, the expression of CD80/CD86 did not significantly correlate with overall survival. Expression of HLA-DR and the presence of infiltrating lymphocytes and dendritic cells did not appear to have impact on the survival of patients.</p> <p>Conclusion</p> <p>Expression of CD80 and CD86 costimulatory molecules appears to be a marker of better survival in patient with NPC.</p

Structural Evolution During Formation and Filling of Self-patterned Nanoholes on GaAs (100) Surfaces

Nanohole formation on an AlAs/GaAs superlattice gives insight to both the “drilling” effect of Ga droplets on AlAs as compared to GaAs and the hole-filling process. The shape and depth of the nanoholes formed on GaAs (100) substrates has been studied by the cross-section transmission electron microscopy. The Ga droplets “drill” through the AlAs layer at a much slower rate than through GaAs due to differences in activation energy. Refill of the nanohole results in elongated GaAs mounds along the [01−1] direction. As a result of capillarity-induced diffusion, GaAs favors growth inside the nanoholes, which provides the possibility to fabricate GaAs and AlAs nanostructures

Diurnal seismicity cycle linked to subsurface melting on an ice shelf

ABSTRACTSeismograms acquired on the McMurdo Ice Shelf, Antarctica, during an Austral summer melt season (November 2016–January 2017) reveal a diurnal cycle of seismicity, consisting of hundreds of thousands of small ice quakes limited to a 6–12 hour period during the evening, in an area where there is substantial subsurface melting. This cycle is explained by thermally induced bending and fracture of a frozen surface superimposed on a subsurface slush/water layer that is supported by solar radiation penetration and absorption. A simple, one-dimensional model of heat transfer driven by observed surface air temperature and shortwave absorption reproduces the presence and absence (as daily weather dictated) of the observed diurnal seismicity cycle. Seismic event statistics comparing event occurrence with amplitude suggest that the events are generated in a fractured medium featuring relatively low stresses, as is consistent with a frozen surface superimposed on subsurface slush. Waveforms of the icequakes are consistent with hydroacoustic phases at frequency ${\bf \gt} \bf 75\,{\bf Hz}$ and flexural-gravity waves at frequency $\bf {\bf \lt}25\,{\bf Hz}$. Our results suggest that seismic observation may prove useful in monitoring subsurface melting in a manner that complements other ground-based methods as well as remote sensing.</jats:p