Circularly polarized thermally activated delayed fluorescence (CP‐TADF) and multiple‐resonance thermally activated delayed fluorescence (MR‐TADF), which exhibit novel circularly polarized ...luminescence and excellent color fidelity, respectively, have gained immense popularity. In this study, integrated CP‐TADF and MR‐TADF (CPMR‐TADF) are prepared by strategic design and synthesis of asymmetrical peripherally locked enantiomers, which are separated and denoted as (P,P″,P″)‐/(M,M″,M″)‐BN4 and (P,P″,P″)‐/(M,M″,M″)‐BN5 and exhibit TADF and circularly polarized light (CPL) properties. As the entire molecular frame participates in the frontier molecular orbitals, the resulting helical chirality of (+)/(−)‐BN4‐ and (+)/(−)‐BN5‐based solution‐processed organic light‐emitting diodes (OLEDs) helps in achieving a narrow full width at half maximum (FWHM) of 49/49 and 48/48 nm and a high maximum external quantum efficiency (EQE) of 20.6%/19.0% and 22.0%/26.5%, respectively. Importantly, unambiguous circularly polarized electroluminescence signals with dissymmetry factors (gEL) of +3.7 × 10−3/−3.1 × 10−3 (BN4) and +1.9 × 10−3/−1.6 × 10−3 (BN5) are obtained. The results indicate successful exploitation of CPMR‐TADF‐emitter‐based OLEDs to exhibit three characteristics: high efficiency, color purity, and circularly polarized light.
Circularly polarized thermally activated delayed fluorescence (CP‐TADF) and multiple‐resonance thermally activated delayed fluorescence (MR‐TADF) properties are integrated into a new advanced material, a CPMR‐TADF material. OLEDs based on these CPMR‐TADF emitters show excellent performance, attaining a three‐in‐one advantage: high efficiency, color purity, and circular polarized light simultaneously.
The optical technology presents non-invasive, non-destructive, and non-ionizing features and has the ability to display various chemical components in tissues to provide useful information for ...various biomedical applications. Regarding selection of light wavelengths, second near-infrared (NIR-II, 900-1700 nm) light is a much better choice compared to both visible (380-780 nm) and traditional near-infrared (780-900 nm) light, because of its advantages including deeper penetration into biological tissues, less tissue scattering or absorption, and decreased interference by fluorescent proteins. Thus, using optical nano-agents that absorb or emit light in the NIR-II window can achieve deeper tissue optical imaging with higher signal-to-background ratios and better spatial resolution for diagnosis. What's more, some of these nano-agents can be further applied for imaging guided surgical removal, real-time monitoring of drug delivery, labeling lymphatic metastasis, biosensing, and imaging guided phototherapy. In this review, we attempt to summarize the recent advances of various NIR-II nano-agents (including single-walled carbon nanotubes, quantum dots, rare-earth doped nanoparticles, other inorganic nanomaterials, small organic molecule-based nanoparticles, and semiconducting polymer nanoparticles) in both bioimaging and therapeutic applications, and discuss the challenges and perspectives of these nano-agents for clinical practice in the near future.
This review summarizes the recent advances of optical nano-agents for various biomedical applications in the NIR-II window.
Exposure correction is one of the fundamental tasks in image processing and computational photography. While various methods have been proposed, they either fail to produce visually pleasing results, ...or only work well for limited types of image (e.g., underexposed images). In this paper, we present a novel automatic exposure correction method, which is able to robustly produce high‐quality results for images of various exposure conditions (e.g., underexposed, overexposed, and partially under‐ and over‐exposed). At the core of our approach is the proposed dual illumination estimation, where we separately cast the under‐and over‐exposure correction as trivial illumination estimation of the input image and the inverted input image. By performing dual illumination estimation, we obtain two intermediate exposure correction results for the input image, with one fixes the underexposed regions and the other one restores the overexposed regions. A multi‐exposure image fusion technique is then employed to adaptively blend the visually best exposed parts in the two intermediate exposure correction images and the input image into a globally well‐exposed image. Experiments on a number of challenging images demonstrate the effectiveness of the proposed approach and its superiority over the state‐of‐the‐art methods and popular automatic exposure correction tools.
In this paper, we present a multimodal emotion recognition framework called EmotionMeter that combines brain waves and eye movements. To increase the feasibility and wearability of EmotionMeter in ...real-world applications, we design a six-electrode placement above the ears to collect electroencephalography (EEG) signals. We combine EEG and eye movements for integrating the internal cognitive states and external subconscious behaviors of users to improve the recognition accuracy of EmotionMeter. The experimental results demonstrate that modality fusion with multimodal deep neural networks can significantly enhance the performance compared with a single modality, and the best mean accuracy of 85.11% is achieved for four emotions (happy, sad, fear, and neutral). We explore the complementary characteristics of EEG and eye movements for their representational capacities and identify that EEG has the advantage of classifying happy emotion, whereas eye movements outperform EEG in recognizing fear emotion. To investigate the stability of EmotionMeter over time, each subject performs the experiments three times on different days. EmotionMeter obtains a mean recognition accuracy of 72.39% across sessions with the six-electrode EEG and eye movement features. These experimental results demonstrate the effectiveness of EmotionMeter within and between sessions.
As the largest developing country, with fast-paced economic growth, China's development has been characterized by a high degree of energy consumption, high level of heavy industry, international ...trade and urbanization progress. In this study, we extend the current literature by incorporating urbanization, energy consumption and international trade into a production function using a panel data set model over the period from 2001 to 2012. The results show that urbanization and capital are the major contributors to China's economic growth. Meanwhile, there exists a “U-shaped” relationship between urbanization and economic growth; that heavy industry exerts a significant negative effect on economic growth using system generalized methods of moments (GMM-sys) estimation methods; and the relationship between international trade and economic growth is mixed and no consistent results support the conclusion that the international trade promotes economic growth. Adjusting the industry and trade structure in economic growth is the priority for the policy makers.
•Bring the energy consumption, trade, and urbanization factors into the same framework;•Heavy industry has a negative effect on China’s economic growth;•Static and dynamic panel model are used together;•The relationship between international trade and economic growth is mixed
Neural networks have dominated the research of hyperspectral image classification, attributing to the feature learning capacity of convolution operations. However, the fixed geometric structure of ...convolution kernels hinders long-range interaction between features from distant locations. In this article, we propose a novel spectral-spatial transformer network (SSTN), which consists of spatial attention and spectral association modules, to overcome the constraints of convolution kernels. Also, we design a factorized architecture search (FAS) framework that involves two independent subprocedures to determine the layer-level operation choices and block-level orders of SSTN. Unlike conventional neural architecture search (NAS) that requires a bilevel optimization of both network parameters and architecture settings, the FAS focuses only on finding out optimal architecture settings to enable a stable and fast architecture search. Extensive experiments conducted on five popular HSI benchmarks demonstrate the versatility of SSTNs over other state-of-the-art (SOTA) methods and justify the FAS strategy. On the University of Houston dataset, SSTN obtains comparable overall accuracy to SOTA methods with a small fraction (1.2%) of multiply-and-accumulate operations compared to a strong baseline spectral-spatial residual network (SSRN). Most importantly, SSTNs outperform other SOTA networks using only 1.2% or fewer MACs of SSRNs on the Indian Pines, the Kennedy Space Center, the University of Pavia, and the Pavia Center datasets.
Activation of stretch-sensitive baroreceptor neurons exerts acute control over heart rate and blood pressure. Although this homeostatic baroreflex has been described for more than 80 years, the ...molecular identity of baroreceptor mechanosensitivity remains unknown. We discovered that mechanically activated ion channels PIEZO1 and PIEZO2 are together required for baroreception. Genetic ablation of both
and
in the nodose and petrosal sensory ganglia of mice abolished drug-induced baroreflex and aortic depressor nerve activity. Awake, behaving animals that lack
had labile hypertension and increased blood pressure variability, consistent with phenotypes in baroreceptor-denervated animals and humans with baroreflex failure. Optogenetic activation of
-positive sensory afferents was sufficient to initiate baroreflex in mice. These findings suggest that PIEZO1 and PIEZO2 are the long-sought baroreceptor mechanosensors critical for acute blood pressure control.
The challenge of person re-identification (re-id) is to match individual images of the same person captured by different nonoverlapping camera views against significant and unknown cross-view feature ...distortion. While a large number of distance metric/ subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coRrelation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.
Solving the problem of matching people across non-overlapping multi-camera views, known as person re-identification (re-id), has received increasing interests in computer vision. In a real-world ...application scenario, a watch-list (gallery set) of a handful of known target people are provided with very few (in many cases only a single) image(s) (shots) per target. Existing re-id methods are largely unsuitable to address this open-world re-id challenge because they are designed for (1) a closed-world scenario where the gallery and probe sets are assumed to contain exactly the same people, (2) person-wise identification whereby the model attempts to verify exhaustively against each individual in the gallery set, and (3) learning a matching model using multi-shots. In this paper, a novel transfer local relative distance comparison (t-LRDC) model is formulated to address the open-world person re-identification problem by one-shot group-based verification. The model is designed to mine and transfer useful information from a labelled open-world non-target dataset. Extensive experiments demonstrate that the proposed approach outperforms both non-transfer learning and existing transfer learning based re-id methods.
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