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Growing demands for precise current measurements, such as atto-ampere-level measurement of cross-cellular biological current transduction, have spotlighted a pressing need for low-noise resistors with ultra-high resistance immune to voltage fluctuations. Traditional semi-insulating materials, however, struggle to provide consistent resistance across varying voltages. To bridge this gap, we introduce a design that integrates semi-insulating 4H-SiC with atomic-level metal oxide interlayers and electrodes. The strategic adjustment of surface states via atomic-scale metal oxide layers optimizes the work functions on 4H-SiC surfaces, validated through density functional theory simulations. This design transcends conventional limitations, establishing an ideal Ohmic behavior and maintains Peta-Ohm-level resistance, unaffected by voltage variations. These on-chip devices with fine-tuned resistance are compatible with integrated circuit manufacturing processes, making them ideally suited for applications in precision electronics.

In moir\'e crystals resulting from the stacking of twisted two-dimensional (2D) layered materials, a subtle adjustment in the twist angle surprisingly gives rise to a wide range of correlated optical and electrical properties. Herein, we report the synthesis of supertwisted WS2 spirals and the observation of giant second harmonic generation (SHG) in these spirals. Supertwisted WS2 spirals featuring different twist angles are synthesized on a Euclidean or step-edge particle-induced non-Euclidean surface using a carefully designed water-assisted chemical vapor deposition. We observed an oscillatory dependence of SHG intensity on layer number, attributed to atomically phase-matched nonlinear dipoles within layers of supertwisted spiral crystals where inversion symmetry is restored. Through an investigation into the twist angle evolution of SHG intensity, we discovered that the stacking model between layers plays a crucial role in determining the nonlinearity, and the SHG signals in supertwisted spirals exhibit enhancements by a factor of 2 to 136 when compared with the SHG of the single-layer structure. These findings provide an efficient method for the rational growth of 2D twisted structures and the implementation of twist angle adjustable endowing them great potential for exploring strong coupling correlation physics and applications in the field of twistronics., Comment: 26 pages, 4 figures

The success of transformers in computer vision has led to several attempts to adapt them for mobile devices, but their performance remains unsatisfactory in some real-world applications. To address this issue, we propose PP-MobileSeg, a semantic segmentation model that achieves state-of-the-art performance on mobile devices. PP-MobileSeg comprises three novel parts: the StrideFormer backbone, the Aggregated Attention Module (AAM), and the Valid Interpolate Module (VIM). The four-stage StrideFormer backbone is built with MV3 blocks and strided SEA attention, and it is able to extract rich semantic and detailed features with minimal parameter overhead. The AAM first filters the detailed features through semantic feature ensemble voting and then combines them with semantic features to enhance the semantic information. Furthermore, we proposed VIM to upsample the downsampled feature to the resolution of the input image. It significantly reduces model latency by only interpolating classes present in the final prediction, which is the most significant contributor to overall model latency. Extensive experiments show that PP-MobileSeg achieves a superior tradeoff between accuracy, model size, and latency compared to other methods. On the ADE20K dataset, PP-MobileSeg achieves 1.57% higher accuracy in mIoU than SeaFormer-Base with 32.9% fewer parameters and 42.3% faster acceleration on Qualcomm Snapdragon 855. Source codes are available at https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.8., Comment: 8 pages, 3 figures

Interactive image segmentation aims at segmenting a target region through a way of human-computer interaction. Recent works based on deep learning have achieved excellent performance, while most of them focus on improving the accuracy of the training set and ignore potential improvement on the test set. In the inference phase, they tend to have a good performance on similar domains to the training set, and lack adaptability to domain shift, so they require more user efforts to obtain satisfactory results. In this work, we propose RAIS, a robust and accurate architecture for interactive segmentation with continuous learning, where the model can learn from both train and test data sets. For efficient learning on the test set, we propose a novel optimization strategy to update global and local parameters with a basic segmentation module and adaptation module, respectively. Moreover, we perform extensive experiments on several benchmarks that show our method can handle data distribution shifts and achieves SOTA performance compared with recent interactive segmentation methods. Besides, our method also shows its robustness in the datasets of remote sensing and medical imaging where the data domains are completely different between training and testing., Comment: 8 pages

In recent years, the rapid development of deep learning has brought great advancements to image and video segmentation methods based on neural networks. However, to unleash the full potential of such models, large numbers of high-quality annotated images are necessary for model training. Currently, many widely used open-source image segmentation software relies heavily on manual annotation which is tedious and time-consuming. In this work, we introduce EISeg, an Efficient Interactive SEGmentation annotation tool that can drastically improve image segmentation annotation efficiency, generating highly accurate segmentation masks with only a few clicks. We also provide various domain-specific models for remote sensing, medical imaging, industrial quality inspections, human segmentation, and temporal aware models for video segmentation. The source code for our algorithm and user interface are available at: https://github.com/PaddlePaddle/PaddleSeg., Comment: 8 pages

Natural image matting is a fundamental and challenging computer vision task. It has many applications in image editing and composition. Recently, deep learning-based approaches have achieved great improvements in image matting. However, most of them require a user-supplied trimap as an auxiliary input, which limits the matting applications in the real world. Although some trimap-free approaches have been proposed, the matting quality is still unsatisfactory compared to trimap-based ones. Without the trimap guidance, the matting models suffer from foreground-background ambiguity easily, and also generate blurry details in the transition area. In this work, we propose PP-Matting, a trimap-free architecture that can achieve high-accuracy natural image matting. Our method applies a high-resolution detail branch (HRDB) that extracts fine-grained details of the foreground with keeping feature resolution unchanged. Also, we propose a semantic context branch (SCB) that adopts a semantic segmentation subtask. It prevents the detail prediction from local ambiguity caused by semantic context missing. In addition, we conduct extensive experiments on two well-known benchmarks: Composition-1k and Distinctions-646. The results demonstrate the superiority of PP-Matting over previous methods. Furthermore, we provide a qualitative evaluation of our method on human matting which shows its outstanding performance in the practical application. The code and pre-trained models will be available at PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.

Real-world applications have high demands for semantic segmentation methods. Although semantic segmentation has made remarkable leap-forwards with deep learning, the performance of real-time methods is not satisfactory. In this work, we propose PP-LiteSeg, a novel lightweight model for the real-time semantic segmentation task. Specifically, we present a Flexible and Lightweight Decoder (FLD) to reduce computation overhead of previous decoder. To strengthen feature representations, we propose a Unified Attention Fusion Module (UAFM), which takes advantage of spatial and channel attention to produce a weight and then fuses the input features with the weight. Moreover, a Simple Pyramid Pooling Module (SPPM) is proposed to aggregate global context with low computation cost. Extensive evaluations demonstrate that PP-LiteSeg achieves a superior trade-off between accuracy and speed compared to other methods. On the Cityscapes test set, PP-LiteSeg achieves 72.0% mIoU/273.6 FPS and 77.5% mIoU/102.6 FPS on NVIDIA GTX 1080Ti. Source code and models are available at PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.

Extensive studies have focused on degradation of perovskite at low charge carrier density (<10^16 cm^-3), but few have surveyed the degradation mechanism at high charge carrier density (~10^18 cm^-3). Here, we investigate the degradation mechanisms of perovskite under high charge carrier conditions. Unlike the observations in previous works, we find that MAPbI3 degradation starts at surface defects and progressing from the surface defects towards neighboring regions under high charge carrier density condition. By using PbI2 passivation, the defect-initiated degradation is significantly suppressed and the nanoplatelet degrades in a layer-by-layer way, enabling the MAPbI3 laser sustain for 4500 s (2.7*10^7 pulses), which is almost 3 times longer than that of the nanoplatelet laser without passivation. Meanwhile, the PbI2 passivated MAPbI3 nanoplatelet laser with the nanoplatelet cavity displaying a maximum quality factor up to ~7800, the highest reported for all MAPbI3 nanoplatelet cavities. Furthermore, a high stability MAPbI3 nanoplatelet laser that can last for 8500 s (5.1*10^7 pulses) is demonstrated based on a dual passivation strategy, by retarding the defect-initiated degradation and surface-initiated degradation, simultaneously. This work provides in-depth insights for understanding the degradation of perovskite at high charge carrier density., Comment: 37 pages,19 figures

As the COVID-19 pandemic rampages across the world, the demands of video conferencing surge. To this end, real-time portrait segmentation becomes a popular feature to replace backgrounds of conferencing participants. While feature-rich datasets, models and algorithms have been offered for segmentation that extract body postures from life scenes, portrait segmentation has yet not been well covered in a video conferencing context. To facilitate the progress in this field, we introduce an open-source solution named PP-HumanSeg. This work is the first to construct a large-scale video portrait dataset that contains 291 videos from 23 conference scenes with 14K fine-labeled frames and extensions to multi-camera teleconferencing. Furthermore, we propose a novel Semantic Connectivity-aware Learning (SCL) for semantic segmentation, which introduces a semantic connectivity-aware loss to improve the quality of segmentation results from the perspective of connectivity. And we propose an ultra-lightweight model with SCL for practical portrait segmentation, which achieves the best trade-off between IoU and the speed of inference. Extensive evaluations on our dataset demonstrate the superiority of SCL and our model. The source code is available at https://github.com/PaddlePaddle/PaddleSeg., Comment: Accepted by WACV workshop

Organic photodetectors (OPDs) possess bright prospects in applications of medical imaging and wearable electronics due to the advantages such as low cost, good biocompatibility, and good flexibility. Photomultiplication OPDs (PM-OPDs) enabled by the trap-assisted carrier tunneling injection effect exhibit external quantum efficiencies far greater than unity, thus the acquired responsivities are extremely high. However, the reported PM-OPDs with high responsivity performances are all accompanied by high dark currents due to the introduction of carrier traps, which inevitably results in inferior detectivities. In this work, we modify a P3HT:PCBM donor-rich PM-OPD by introducing an atomically thin Al2O3 interfacial layer through the ALD technique, obtaining a high responsivity of 8294 A/W and high detectivity of 6.76*10^14 Jones, simultaneously, both of which are among the highest reported for bulk heterojunction PM-OPDs. Ascribed to the introduction of the atomically thin Al2O3 layer, the metal-insulator-semiconductor (MIS) tunneling junction is formed, which brings forward a suppressed dark current along with an increased amounts of holes tunneling under forward bias. Meanwhile, the weak light detection limit of the modified PM-OPD within the linear response range reaches the level of nW/cm2. Based on the proposed PM-OPD, a proof-of-concept image sensor with 26*26 pixels is demonstrated, which can respond to both ultraviolet light and visible light. The PM-OPD based sensor arrays can find broad applications for medical imaging, wearable electronics, etc., Comment: 25 pages, 12 figures, 1 table

High-quality training data play a key role in image segmentation tasks. Usually, pixel-level annotations are expensive, laborious and time-consuming for the large volume of training data. To reduce labelling cost and improve segmentation quality, interactive segmentation methods have been proposed, which provide the result with just a few clicks. However, their performance does not meet the requirements of practical segmentation tasks in terms of speed and accuracy. In this work, we propose EdgeFlow, a novel architecture that fully utilizes interactive information of user clicks with edge-guided flow. Our method achieves state-of-the-art performance without any post-processing or iterative optimization scheme. Comprehensive experiments on benchmarks also demonstrate the superiority of our method. In addition, with the proposed method, we develop an efficient interactive segmentation tool for practical data annotation tasks. The source code and tool is avaliable at https://github.com/PaddlePaddle/PaddleSeg., Comment: accepted by ICCV Workshop

Image Segmentation plays an essential role in computer vision and image processing with various applications from medical diagnosis to autonomous car driving. A lot of segmentation algorithms have been proposed for addressing specific problems. In recent years, the success of deep learning techniques has tremendously influenced a wide range of computer vision areas, and the modern approaches of image segmentation based on deep learning are becoming prevalent. In this article, we introduce a high-efficient development toolkit for image segmentation, named PaddleSeg. The toolkit aims to help both developers and researchers in the whole process of designing segmentation models, training models, optimizing performance and inference speed, and deploying models. Currently, PaddleSeg supports around 20 popular segmentation models and more than 50 pre-trained models from real-time and high-accuracy levels. With modular components and backbone networks, users can easily build over one hundred models for different requirements. Furthermore, we provide comprehensive benchmarks and evaluations to show that these segmentation algorithms trained on our toolkit have more competitive accuracy. Also, we provide various real industrial applications and practical cases based on PaddleSeg. All codes and examples of PaddleSeg are available at https://github.com/PaddlePaddle/PaddleSeg.

Organic photodetectors (OPDs) based on trap-assisted photomultiplication (PM) effect with external quantum efficiency (EQE) far exceeding 100% are quite appealing for achieving highly sensitive photodetection. A classic structure of PM-type OPDs is based on the active layer of P3HT:PC70BM with the weight ratio of 100:1, in which PC70BM forms islands and supply bulk electron traps for inducing strong interfacial band bending and thus hole tunneling from the Al electrode. In this paper, aiming for optimizing the PM effect, we study the photogenerated carrier distribution by tuning thickness of the active layer (P3HT:PC70BM). The combination effect of both the exciton generation and exciton dissociation processes affects the photogenerated carrier distribution, which ultimately determines the PM performances of OPDs. On the one hand, simulation reveals that the thinner the active layer, the stronger exciton generation near the Al electrode. On the other hand, the photoluminescence and surface morphology studies reflect that the active layer with thickness of 230 nm has the smooth surface and produces the highest exciton dissociation rate. The two effects result in the OPD with a 205 nm thick active layer has the champion EQE (105569%) and photoresponsivity (344 A/W), corresponding to an enhancement of 330% with respect to the OPD with a 325 nm thick active layer. Ascribed to the trade off of EQE and dark current, the detectivity performs differently at different wavelength ranges. At short wavelength range, the detectivity reaches the highest when the active layer is 205 nm thick, while the highest detectivity at long wavelength range is produced by the thickest active layer. Our work contributes to developing low cost organic photodetectors for detecting weak light signals.

The trapped rainbow effect has been mostly found on tapered anisotropic metamaterials (MMs) made of low loss noble metals, such as gold, silver, etc. In this work, we demonstrate that an anisotropic MM waveguide made of high loss metal tungsten can also support the trapped rainbow effect similar to the noble metal based structure. We show theoretically that an array of tungsten/germanium anisotropic nano-cones placed on top of a reflective substrate can absorb light at the wavelength range from 0.3 micrometer to 9 micrometer with an average absorption efficiency approaching 98%. It is found that the excitation of multiple orders of slow-light resonant modes is responsible for the efficient absorption at wavelengths longer than 2 micrometer, and the anti-reflection effect of tapered lossy material gives rise to the near perfect absorption at shorter wavelengths. The absorption spectrum suffers a small dip at around 4.2 micrometer where the first order and second order slow-light modes get overlapped, but we can get rid of this dip if the absorption band edge at long wavelength range is reduced down to 5 micrometer. The parametrical study reflects that the absorption bandwidth is mainly determined by the filling ratio of tungsten as well as the bottom diameter of the nano-cones and the interaction between neighboring nano-cones is quite weak. Our proposal has some potential applications in the areas of solar energy harvesting and thermal emitters., Comment: 20 pages, 10 figures

Plasmonic beam collimation effect has been thoroughly investigated based on the well-known nanometer-scale bull's eye structure formed by complex and high-cost fabrication processes. In this work, we report our effort for attaining beam collimation using an anisotropic metamaterial (AMM) slab that consists of a stack of alternating metal/dielectric layers and an integrated top metal grating. The results show that AMM slab allows creating the beam collimation effect similar to that of the bull's eye structure, an enabling technology for practical application due to its simple architecture and cost benefits. The excitation of surface plasmons at the AMM/air interface is derived. The structure of the AMM slab and its impact on beaming performance were analyzed using the effective medium theory and Finite Element Method., Comment: 17 pages, 4 figures

The performance of organic solar cells (OSCs) can be greatly improved by incorporating silica-coated gold nanorods (Au@SiO2 NRs) at the interface between the hole transporting layer and the active layer due to the plasmonic effect. The silica shell impedes the aggregation effect of the Au NRs in ethanol solution as well as the server charge recombination on the surface of the Au NRs otherwise they would bring forward serious reduction in open circuit voltage when incorporating the Au NRs at the positions in contact with the active materials. As a result, while the high open circuit voltage being maintained, the optimized plasmonic OSCs possess an increased short circuit current, and correspondingly an elevated power conversion efficiency with the enhancement factor of ~11%. The origin of performance improvement in OSCs with the Au@SiO2 NRs was analyzed systematically using morphological, electrical, optical characterizations along with theoretical simulation. It is found that the broadband enhancement in absorption, which yields the broadband enhancement in exciton generation in the active layer, is the major factor contributing to the increase in the short circuit current density. Simulation results suggest that the excitation of the transverse and longitudinal surface plasmon resonances of individual NRs as well as their mutual coupling can generate strong electric field near the vicinity of the NRs, thereby an improved exciton generation profile in the active layer. The incorporation of Au@SiO2 NRs at the interface between the hole transporting layer and the active layer also improves hole extraction in the OSCs., Comment: 8 pages, 8 figures