Fusing a low-resolution hyperspectral (HS) image with the corresponding high-resolution multispectral image to obtain a high-resolution HS image is an important technique for capturing comprehensive ...scene information in both the spatial and spectral domains. Existing approaches adopt sparsity promoting strategy and encode the spectral information of each pixel independently, which results in noisy sparse representation. We propose a novel HS image super-resolution method via a self-similarity constrained sparse representation. We explore the similar patch structures across the whole image and the pixels with close appearance in the local regions to create global-structure groups and local-spectral super-pixels. By forcing the similarity of the sparse representations for pixels belonging to the same group and super-pixel, we alleviate the effect of the outliers in the learned sparse coding. Experiment results on benchmark datasets validate that the proposed method outperforms the state-of-the-art methods in both the quantitative metrics and visual effect.
Multi‐resonance induced by boron and nitrogen atoms in opposite resonance positions endows a thermally activated delayed fluorescence (MR‐TADF) emitter with a strikingly small full width at half ...maximum of only 26 nm and excellent photoluminescence quantum yield of up to 97.48 %. The introduction of a carbazole unit in the para position of the B‐substituted phenyl‐ring can significantly boost up the resonance effect without compromising the color fidelity, subsequently enhancing the performances of the corresponding pure blue TADF‐OLED, with an outstanding external quantum efficiency (EQE) up to 32.1 % and low efficiency roll‐off, making it one of the best TADF‐OLEDs in the blue region to date. Furthermore, utilizing this material as host for a yellow phosphorescent emitter, the device also shows a significantly reduced turn‐on voltage of 3.2 V and an EQEmax of 22.2 %.
Strong enhancement of the multi‐resonance effect in thermally activated delayed fluorescence species by a peripheral carbazole unit substitution was applied for a material with a photoluminescent quantum yield of up to 97.48 %. The maximum luminance exceeded 16 000 cd m−2 and the highest external quantum efficiency was up to 32.1 %.
Circularly polarized organic light‐emitting diodes (CP‐OLEDs) are particularly favorable for the direct generation of CP light, and they demonstrate a promising application in 3D display. However, up ...to now, such CP devices have suffered from low brightness, insufficient efficiency, and serious efficiency roll‐off. In this study, a pair of octahydro‐binaphthol (OBN)‐based chiral emitting enantiomers, (R/S)‐OBN‐Cz, are developed by ingeniously merging a chiral source and a luminophore skeleton. These chirality–acceptor–donor (C–A–D)‐type and rod‐like compounds concurrently generate thermally activated delayed fluorescence with a small ΔEST of 0.037 eV, as well as a high photoluminescence quantum yield of 92% and intense circularly polarized photoluminescence with dissymmetry factors (|gPL|) of ≈2.0 × 10−3 in thin films. The CP‐OLEDs based on (R/S)‐OBN‐Cz enantiomers not only display obvious circularly polarized electroluminescence signals with a |gEL| of ≈2.0 × 10−3, but also exhibit superior efficiencies with maximum external quantum efficiency (EQEmax) up to 32.6% and extremely low efficiency roll‐off with an EQE of 30.6% at 5000 cd m−2, which are the best performances among the reported CP devices to date.
Octahydrobinaphthol‐compound‐based circularly polarized delayed fluorescence enantiomers, (R/S)‐OBN‐Cz are developed by merging a chiral source and a luminophore skeleton. The circularly polarized organic light‐emitting diodes based on (R/S)‐OBN‐Cz display intense CP‐electroluminescence signals with a |gEL| of ≈2.0 × 10−3, and achieve superior efficiencies with external quantum efficiency (EQE) up to 32.6% and extremely low efficiency roll‐off with an EQE of 30.6% at 5000 cd m−2.
Macrophages are known to play an important role in hepatocyte mediated liver regeneration by secreting inflammatory mediators. However, there is little information available on the role of resident ...macrophages in oval cell mediated liver regeneration. In the present study we aimed to investigate the role of macrophages in oval cell expansion induced by 2-acetylaminofluorene/partial hepatectomy (2-AAF/PH) in rats.
We depleted macrophages in the liver of 2-AAF/PH treated rats by injecting liposome encapsulated clodronate 48 hours before PH. Regeneration of remnant liver mass, as well as proliferation and differentiation of oval cells were measured. We found that macrophage-depleted rats suffered higher mortality and liver transaminase levels. We also showed that depletion of macrophages yielded a significant decrease of EPCAM and PCK positive oval cells in immunohistochemical stained liver sections 9 days after PH. Meanwhile, oval cell differentiation was also attenuated as a result of macrophage depletion, as large foci of small basophilic hepatocytes were observed by day 9 following hepatectomy in control rats whereas they were almost absent in macrophage depleted rats. Accordingly, real-time polymerase chain reaction analysis showed lower expression of albumin mRNA in macrophage depleted livers. Then we assessed whether macrophage depletion may affect hepatic production of stimulating cytokines for liver regeneration. We showed that macrophage-depletion significantly inhibited hepatic expression of tumor necrosis factor-α and interleukin-6, along with a lack of signal transducer and activator of transcription 3 phosphorylation during the early period following hepatectomy.
These data indicate that macrophages play an important role in oval cell mediated liver regeneration in the 2-AAF/PH model.
Two subsets X and Y of a permutation group G acting on Ω are cross-intersecting if for every x∈X and every y∈Y there exists some point α∈Ω such that αx=αy. Based on several observations made on the ...cross-independent version of Hoffman’s theorem, we characterize in this paper the cross-intersecting families of certain permutation groups. Our proof uses a Cayley graph on a permutation subgroup with respect to the derangement. By carefully analyzing the cross-independent version of Hoffman’s theorem, we obtain a useful theorem to consider cross-intersecting subsets of certain kinds of permutation subgroups, such as PGL(2,q), PSL(2,q) and Sn.
The efficient global optimization method (EGO) based on kriging surrogate model and expected improvement (EI) has received much attention for optimization of high-fidelity, expensive functions. ...However, when the standard EI method is directly applied to a variable-fidelity optimization (VFO) introducing assistance from cheap, low-fidelity functions via hierarchical kriging (HK) or cokriging, only high-fidelity samples can be chosen to update the variable-fidelity surrogate model. The theory of infilling low-fidelity samples towards the improvement of high-fidelity function is still a blank area. This article proposes a variable-fidelity EI (VF-EI) method that can adaptively select new samples of both low and high fidelity. Based on the theory of HK model, the EI of the high-fidelity function associated with adding low- and high-fidelity sample points are analytically derived, and the resulting VF-EI is a function of both the design variables
x
and the fidelity level
l
. Through maximizing the VF-EI, both the sample location and fidelity level of next numerical evaluation are determined, which in turn drives the optimization converging to the global optimum of high-fidelity function. The proposed VF-EI is verified by six analytical test cases and demonstrated by two engineering problems, including aerodynamic shape optimizations of RAE 2822 airfoil and ONERA M6 wing. The results show that it can remarkably improve the optimization efficiency and compares favorably to the existing methods.
In order to exploit the abundant potential information of the unlabeled data and contribute to analyzing the correlation among heterogeneous data, we propose the semi-supervised model named adaptive ...semi-supervised feature selection for cross-modal retrieval. First, we utilize the semantic regression to strengthen the neighboring relationship between the data with the same semantic. And the correlation between heterogeneous data can be optimized via keeping the pairwise closeness when learning the common latent space. Second, we adopt the graph-based constraint to predict accurate labels for unlabeled data, and it can also keep the geometric structure consistency between the label space and the feature space of heterogeneous data in the common latent space. Finally, an efficient joint optimization algorithm is proposed to update the mapping matrices and the label matrix for unlabeled data simultaneously and iteratively. It makes samples from different classes to be far apart, while the samples from same class lie as close as possible. Meanwhile, the <inline-formula><tex-math notation="LaTeX">{l_{2,1}}</tex-math></inline-formula>-norm constraint is used for feature selection and outlier reduction when the mapping matrices are learned. In addition, we propose learning different mapping matrices corresponding to different sub-tasks to emphasize the semantic and structural information of query data. Experiment results on three datasets demonstrate that our method performs better than the state-of-the-art methods.
The complexity of deep learning models affects the real-time performance of gesture recognition, therebylimiting the application of gesture recognition algorithms in actual scenarios. Hence, a ...residual learning neuralnetwork based on a deep convolutional neural network is proposed. First, small convolution kernels are usedto extract the local details of gesture images. Subsequently, a shallow residual structure is built to share weights,thereby avoiding gradient disappearance or gradient explosion as the network layer deepens; consequently, thedifficulty of model optimisation is simplified. Additional convolutional neural networks are used to acceleratethe refinement of deep abstract features based on the spatial importance of the gesture feature distribution.
Finally, a fully connected cascade softmax classifier is used to complete the gesture recognition. Comparedwith the dense connection multiplexing feature information network, the proposed algorithm is optimised infeature multiplexing to avoid performance fluctuations caused by feature redundancy. Experimental resultsfrom the ISOGD gesture dataset and Gesture dataset prove that the proposed algorithm affords a fastconvergence speed and high accuracy. KCI Citation Count: 1
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