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Images or videos always contain multiple objects or actions. Multi-label recognition has been witnessed to achieve pretty performance attribute to the rapid development of deep learning technologies. Recently, graph convolution network (GCN) is leveraged to boost the performance of multi-label recognition. However, what is the best way for label correlation modeling and how feature learning can be improved with label system awareness are still unclear. In this paper, we propose a label graph superimposing framework to improve the conventional GCN+CNN framework developed for multi-label recognition in the following two aspects. Firstly, we model the label correlations by superimposing label graph built from statistical co-occurrence information into the graph constructed from knowledge priors of labels, and then multi-layer graph convolutions are applied on the final superimposed graph for label embedding abstraction. Secondly, we propose to leverage embedding of the whole label system for better representation learning. In detail, lateral connections between GCN and CNN are added at shallow, middle and deep layers to inject information of label system into backbone CNN for label-awareness in the feature learning process. Extensive experiments are carried out on MS-COCO and Charades datasets, showing that our proposed solution can greatly improve the recognition performance and achieves new state-of-the-art recognition performance.

features for developing high-performance X-ray imagers and ultraviolet sensors. [3] For imaging application, IGZO TFTs were mainly integrated with amorphous silicon and organic photodiodes, [3c,e,4] the spectral of which were usually limited to visible light detection. Large-area, active-matrix infrared sensing, therefore, remains a challenge.Organic-inorganic hybrid perovskites possess excellent optoelectronic properties with high absorption coefficient, high charge carrier mobility, long carrier diffusion lengths, and tunable bandgaps, [5] rendering it a promising material for across-the-board optoelectronic devices, [6] including photovoltaic cells, [7] light-emitting, lasing devices, [8] and high-performance photodetectors. [9] In particular, compared with predominant photodetectors made of inorganic semiconductors, solution-processable perovskite is more promising for lowcost, flexible, and large-area scenarios. [6d] Conventional lead-based perovskite photodiodes (PDs) provide a detection spectral range from 300 to 800 nm. [10] To further extend the spectral response to the near-infrared (NIR) range, there are normally two approaches. One is to combine perovskite with narrow bandgap polymers or quantum dots such as PDPP3T [11] and PbS quantum dots. [12] Another approach is to introduce an Sn-Pb binary perovskite PDs. [13] The smaller ionic radii of Sn 2+ than Pb 2+ (Sn 2+ :1.35 Å and Pb 2+ : 1.49 Å) [14] reduces the bandgap of perovskites due to the bowing effect, [15] so that Sn-Pb hybrid perovskite has lower bandgap to extend the light absorption to ≈1000 nm. Recently Xiaobao et al. reported MA 0.5 FA 0.5 Pb 0.5 Sn 0.5 I 3 perovskite photodetector, exhibiting a detectivity of over 10 12 Jones ranging from 800 to 970 nm. [13c] Wang et al. fabricated mixed Sn-Pb perovskite photodetectors with a broadband response from 300 to 1000 nm, responsivity (R) of over 0.4 A W −1 , and detectivity (D*) of over 10 12 Jones in the near-infrared region. [13b] Encouraging improvements in the Sn-Pb based perovskite stability have also been reported very recently, such as using GuaSCN passivation to achieve 1 µs carrier lifetime with long-term stability. [16] The above development of low bandgap organic-inorganic perovskite materials has brought new opportunity for developing advanced flat-panel Flat-panel imagers have wide applications in industrial and medical inspections. Nonetheless, large area infrared imaging remains a challenge due to the fact that the state-of-the-art infrared sensors are usually based on silicon or germanium technologies, which are limited by the wafer size. Recent advances in low bandgap Sn-Pb perovskite photodiodes (PDs) and indium gallium zinc oxide (IGZO) thin-film transistors (TFTs) matrix backplane bring new opportunity for developing the large area near-infrared image sensor. As a proof of concept, a 12 × 12 pixels array with each pixel independently controlled by the gate voltage of a TFT are constructed. Arrays of Sn-Pb based perovskite PDs are spin deposited onto the IGZO TF...

Controlling defects and energy‐band alignments are of paramount importance to the development of high‐performance perovskite‐based photodiodes. Yet, concurrent improvements in interfacial contacts and defect reduction simply by tailoring bottom contacts have not been investigated. An effective strategy is reported that can simultaneously improve energy‐band alignments and structural defects by introducing low‐dimensional contact (LDC) layers at the bottom interface. It is found that LDC‐based perovskites considerably suppress undesirable structural defects induced by microstrains, resulting in reduced nonradiative recombination centers and improved carrier lifetimes. Additionally, the resulting LDC‐based interface structures help block minority carrier injection from the electrodes by forming built‐in electric fields. As a consequence, LDC‐based perovskite photodiodes showed improved light detection capabilities. The result opens an avenue to yield highly efficient photodiodes.

Simultaneous catalytic elimination of nitrogen oxides (NO x , x = 1 and 2) and volatile organic compounds (VOCs) is of great importance for environmental preservation in China. In this work, the interactions of simultaneous removal of NO x and methylbenzene (PhCH3) were investigated on a CeO2–TiO2 mixed oxide catalyst, which demonstrated excellent bifunctional removal efficiencies for the two pollutants. The results indicated that NO x positively promotes PhCH3 oxidation, while NH3 negatively inhibits through competitive adsorption with PhCH3. The underlying mechanism is that a pseudo PhCH3-SCR reaction happened in this process is parallel to NH3-SCR. Combined with in situ diffuse reflectance infrared Fourier transform spectroscopy and quasi in situ X-ray photoelectron spectroscopy, the interaction mechanism between NO x and PhCH3 is proposed. Specifically, NO x is adsorbed on the catalyst surface to produce nitrate species, which reacts with the carboxylate generated during PhCH3 oxidation to form organic nitrogen intermediates that create N2 and CO2 in the following reactions. In the reaction process, the superoxide (O2 –) generated by O2 activation on the catalyst surface is an important species for the propelling of oxidation reaction. This work could provide guidelines for the design of state-of-the-art catalysts for simultaneous catalytic removal of NO x and VOCs.

This article presents the modeling and experimental validation of a piezoelectric stack energy harvester with a flexurefree convex force amplification frame to convert walking force into electricity. Compared to a stand-alone piezoelectric stack, experiments show an 8 times greater voltage output and a 112 times greater power output of such an energy harvester. A finite element method is used to provide a more accurate electromechanical model using Hamilton's principle and the piezoelectric constitutive equations. Simulation results from such a finite element method agree with the single-degreeof-freedom model. Experimental measurement shows the percentage errors of the output power are of 3.53% for the finite element method and 8.04% for the single-degree-of-freedom model of the piezoelectric stack energy harvester.

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A stable mesoporous Ni–Fe–O electrocatalyst with high OER efficiency is constructed using mesoporous fumed silica as a template.