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We introduce DiffSteISR, a pioneering framework for reconstructing real-world stereo images. DiffSteISR utilizes the powerful prior knowledge embedded in pre-trained text-to-image model to efficiently recover the lost texture details in low-resolution stereo images. Specifically, DiffSteISR implements a time-aware stereo cross attention with temperature adapter (TASCATA) to guide the diffusion process, ensuring that the generated left and right views exhibit high texture consistency thereby reducing disparity error between the super-resolved images and the ground truth (GT) images. Additionally, a stereo omni attention control network (SOA ControlNet) is proposed to enhance the consistency of super-resolved images with GT images in the pixel, perceptual, and distribution space. Finally, DiffSteISR incorporates a stereo semantic extractor (SSE) to capture unique viewpoint soft semantic information and shared hard tag semantic information, thereby effectively improving the semantic accuracy and consistency of the generated left and right images. Extensive experimental results demonstrate that DiffSteISR accurately reconstructs natural and precise textures from low-resolution stereo images while maintaining a high consistency of semantic and texture between the left and right views.

Despite advances in the paradigm of pre-training then fine-tuning in low-level vision tasks, significant challenges persist particularly regarding the increased size of pre-trained models such as memory usage and training time. Another concern often encountered is the unsatisfying results yielded when directly applying pre-trained single-image models to multi-image domain. In this paper, we propose a efficient method for transferring a pre-trained single-image super-resolution (SISR) transformer network to the domain of stereo image super-resolution (SteISR) through a parameter-efficient fine-tuning (PEFT) method. Specifically, we introduce the concept of stereo adapters and spatial adapters which are incorporated into the pre-trained SISR transformer network. Subsequently, the pre-trained SISR model is frozen, enabling us to fine-tune the adapters using stereo datasets along. By adopting this training method, we enhance the ability of the SISR model to accurately infer stereo images by 0.79dB on the Flickr1024 dataset. This method allows us to train only 4.8% of the original model parameters, achieving state-of-the-art performance on four commonly used SteISR benchmarks. Compared to the more complicated full fine-tuning approach, our method reduces training time and memory consumption by 57% and 15%, respectively.

This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a diverse test set containing a variety of 4K images ranging from digital art to gaming and photography. The images are compressed using the modern AVIF codec, instead of JPEG. All the proposed methods improve PSNR fidelity over Lanczos interpolation, and process images under 10ms. Out of the 160 participants, 25 teams submitted their code and models. The solutions present novel designs tailored for memory-efficiency and runtime on edge devices. This survey describes the best solutions for real-time SR of compressed high-resolution images., Comment: CVPR 2024, AI for Streaming (AIS) Workshop

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field., Comment: NTIRE 2024 Challenge Report

Familial cerebral cavernous malformation (FCCM) is a hereditary disorder characterized by abnormal vascular structures within the central nervous system. The FCCM lesions are often numerous and intricate, making quantitative analysis of the lesions a labor-intensive task. Consequently, clinicians face challenges in quantitatively assessing the severity of lesions and determining whether lesions have progressed. To alleviate this problem, we propose a quantitative statistical framework for FCCM, comprising an efficient annotation module, an FCCM lesion segmentation module, and an FCCM lesion quantitative statistics module. Our framework demonstrates precise segmentation of the FCCM lesion based on efficient data annotation, achieving a Dice coefficient of 93.22\%. More importantly, we focus on quantitative statistics of lesions, which is combined with image registration to realize the quantitative comparison of lesions between different examinations of patients, and a visualization framework has been established for doctors to comprehensively compare and analyze lesions. The experimental results have demonstrated that our proposed framework not only obtains objective, accurate, and comprehensive quantitative statistical information, which provides a quantitative assessment method for disease progression and drug efficacy study, but also considerably reduces the manual measurement and statistical workload of lesions, assisting clinical decision-making for FCCM and accelerating progress in FCCM clinical research. This highlights the potential of practical application of the framework in FCCM clinical research and clinical decision-making. The codes are available at https://github.com/6zrg/Quantitative-Statistics-of-FCCM.

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

Charged lepton flavor violation (CLFV) represents a clear new physics (NP) signal beyond the standard model (SM). In this work, we investigate CLFV processes $l_j^-\rightarrow l_i^- \gamma$ utilizing mass insertion approximation(MIA) in the minimal supersymmetric extension of the SM with local B-L gauge symmetry (B-LSSM). The MIA method can provide a set of simple analytic formulae for the form factors and the associated effective vertices, so that the movement of the CLFV decays $l_j^-\rightarrow l_i^- \gamma$ with the sensitive parameters will be intuitively analyzed. Considering the SM-like Higgs boson mass and the muon anomalous dipole moment (MDM) within $4\sigma$, $3\sigma$ and $2\sigma$ regions, we discuss the corresponding constraints on the relevant parameter space of the model., Comment: to be published in Physical Review D

Charged lepton flavor violation (CLFV) represents a clear new physics (NP) signal beyond the standard model (SM). In this work, we investigate the CLFV decays of vector mesons $V\rightarrow l_i^{\pm}l_j^{\mp}$ with $V\in\{\phi, J/\Psi, \Upsilon(1S), \Upsilon(2S),\Upsilon(3S)\}$ in the $\mathrm{U}(1)_{X} \mathrm{SSM}$. Considering the SM-like Higgs boson mass within $3\sigma$ region, we discuss the corresponding constraints on the relevant parameter space of the model, which indicate this model can produce significant contributions to such CLFV decays. From the numerical analyses, the main sensitive parameters and CLFV sources originate from the non-diagonal elements correspond to the initial and final generations of the leptons. And the branching ratios to these CLFV processes can easily reach the present experimental upper bounds. Therefore, searching for CLFV processes of vector mesons may be an effective channel to study new physics.

Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generate inter-domain samples with limited variety. These previous methods make limited contributions to describing the decision boundaries for binary classification. In this paper, we propose a distance guided GAN (DisGAN) which controls the variation degrees of generated samples in the hyperplane space. Specifically, we instantiate the idea of DisGAN by combining two ways. The first way is vertical distance GAN (VerDisGAN) where the inter-domain generation is conditioned on the vertical distances. The second way is horizontal distance GAN (HorDisGAN) where the intra-domain generation is conditioned on the horizontal distances. Furthermore, VerDisGAN can produce the class-specific regions by mapping the source images to the hyperplane. Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification. The proposed method can apply to different classification architectures and has potential to extend to multi-class classification., Comment: 12 pages, 8 figures. This work has been submitted to the IEEE TNNLS for possible publication. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media

Real-world stereo image super-resolution has a significant influence on enhancing the performance of computer vision systems. Although existing methods for single-image super-resolution can be applied to improve stereo images, these methods often introduce notable modifications to the inherent disparity, resulting in a loss in the consistency of disparity between the original and the enhanced stereo images. To overcome this limitation, this paper proposes a novel approach that integrates a implicit stereo information discriminator and a hybrid degradation model. This combination ensures effective enhancement while preserving disparity consistency. The proposed method bridges the gap between the complex degradations in real-world stereo domain and the simpler degradations in real-world single-image super-resolution domain. Our results demonstrate impressive performance on synthetic and real datasets, enhancing visual perception while maintaining disparity consistency. The complete code is available at the following \href{https://github.com/fzuzyb/SCGLANet}{link}.

Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets. In this paper, we propose a parameterized GAN (ParaGAN) that effectively controls the changes of synthetic samples among domains and highlights the attention regions for downstream classification. Specifically, ParaGAN incorporates projection distance parameters in cyclic projection and projects the source images to the decision boundary to obtain the class-difference maps. Our experiments show that ParaGAN can consistently outperform the existing augmentation methods with explainable classification on two small-scale medical datasets., Comment: 5 pages, 4 figures. This work has been submitted to the IEEE ICASSP for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

In recent years, the field of metal-halide perovskite emitters has rapidly emerged as a new community in solid-state lighting. Their exceptional optoelectronic properties have contributed to the rapid rise in external quantum efficiencies (EQEs) in perovskite light-emitting diodes (PeLEDs) from <1% (in 2014) to approaching 30% (in 2023) across a wide range of wavelengths. However, several challenges still hinder their commercialization, including the relatively low EQEs of blue/white devices, limited EQEs in large-area devices, poor device stability, as well as the toxicity of the easily accessible lead components and the solvents used in the synthesis and processing of PeLEDs. This roadmap addresses the current and future challenges in PeLEDs across fundamental and applied research areas, by sharing the community's perspectives. This work will provide the field with practical guidelines to advance PeLED development and facilitate more rapid commercialization., Comment: 103 pages, 29 figures. This is the version of the article before peer review or editing, as submitted by an author to Journal of Physics: Photonics. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it

Supervised learning algorithms based on Convolutional Neural Networks have become the benchmark for medical image segmentation tasks, but their effectiveness heavily relies on a large amount of labeled data. However, annotating medical image datasets is a laborious and time-consuming process. Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation. We propose a novel pseudo-label utilization scheme, which combines labeled and unlabeled data to augment the dataset effectively. Additionally, we enforce the consistency between different scales in the decoder module of the segmentation network and propose a loss function suitable for evaluating the consistency. Moreover, we incorporate a sharpening operation on the predicted results, further enhancing the accuracy of the segmentation. Extensive experiments on three publicly available datasets demonstrate that the PLGDF framework can largely improve performance by incorporating the unlabeled data. Meanwhile, our framework yields superior performance compared to six state-of-the-art semi-supervised learning methods. The codes of this study are available at https://github.com/ortonwang/PLGDF.

Although the COVID pandemic dramatically expanded K-12 remote learning in its first years, little is known about the lasting effects on virtual schooling policies and practices. Drawing on evolutionary theories of change and qualitative data from 2019 to 2022, we explore this topic in Oregon, a state with a long history of virtual schools. We find that the pandemic contributed to limited change in state virtual schooling policy and some shifts in local organizational practices. Our data also raise questions about the durability of any changes and the implications for marginalized students. Ultimately, this study provides much-needed insights on the impact of the pandemic on policy and practice, and the utility of multi-disciplinary, equity-oriented theory to understand change processes in K-12 education. [This is the online first version of an article published in "Educational Evaluation and Policy Analysis."]

While supervised learning has achieved remarkable success, obtaining large-scale labeled datasets in biomedical imaging is often impractical due to high costs and the time-consuming annotations required from radiologists. Semi-supervised learning emerges as an effective strategy to overcome this limitation by leveraging useful information from unlabeled datasets. In this paper, we present a novel semi-supervised learning method, Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation (DCPA), for medical image segmentation. We devise a consistency regularization to promote consistent representations during the training process. Specifically, we use distinct decoders for student and teacher networks while maintain the same encoder. Moreover, to learn from unlabeled data, we create pseudo-labels generated by the teacher networks and augment the training data with the pseudo-labels. Both techniques contribute to enhancing the performance of the proposed method. The method is evaluated on three representative medical image segmentation datasets. Comprehensive comparisons with state-of-the-art semi-supervised medical image segmentation methods were conducted under typical scenarios, utilizing 10% and 20% labeled data, as well as in the extreme scenario of only 5% labeled data. The experimental results consistently demonstrate the superior performance of our method compared to other methods across the three semi-supervised settings. The source code is publicly available at https://github.com/BinYCn/DCPA.git.

In recent years, deep learning has become a breakthrough technique in assisting medical image diagnosis. Supervised learning using convolutional neural networks (CNN) provides state-of-the-art performance and has served as a benchmark for various medical image segmentation and classification. However, supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and even impractical to acquire in medical imaging applications. Active Learning (AL) methods have been widely applied in natural image classification tasks to reduce annotation costs by selecting more valuable examples from the unlabeled data pool. However, their application in medical image segmentation tasks is limited, and there is currently no effective and universal AL-based method specifically designed for 3D medical image segmentation. To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks. We extensively validated our proposed active learning method on three publicly available and challenging medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our PCDAL can achieve significantly improved performance with fewer annotations in 2D classification and segmentation and 3D segmentation tasks. The codes of this study are available at https://github.com/ortonwang/PCDAL.

$U(1)_X$SSM is a non-universal Abelian extension of the Minimal Supersymmetric Standard Model (MSSM) and its local gauge group is extended to $SU(3)_C\times SU(2)_L \times U(1)_Y\times U(1)_X$. Based on the latest data of neutral meson mixing and experimental limitations, we investigate the process of $B^{0}-\bar{B^{0}}$ mixing in $U(1)_X$SSM. Using the effective Hamiltonian method, the Wilson coefficients and mass difference $\triangle m_{B}$ are derived. The abundant numerical results verify that $~v_S,~M^2_D,~\lambda_C,~{\mu},~M_2,~\tan{\beta},~g_{YX},~M_1$ and $~\lambda_H$ are sensitive parameters to the process of $B^{0}-\bar{B^{0}}$ mixing. With further measurement in the experiment, the parameter space of the $U(1)_X$SSM will be further constrained during the mixing process of $B^{0}-\bar{B^{0}}$.

We study lepton flavor violating (LFV) decays $B^0\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$ ($B^0\rightarrow e{\mu}$, $B^0\rightarrow e{\tau}$ and $B^0\rightarrow {\mu}{\tau}$) in the $U(1)_X$SSM (SSM is acronym for the supersymmetric standard model), which is the $U(1)_X$ extension of the minimal supersymmetric standard model (MSSM). The local gauge group of $U(1)_X$SSM is $SU(3)_C\times SU(2)_L \times U(1)_Y \times U(1)_X$. These processes ($B^0\rightarrow e{\mu}$, $B^0\rightarrow e{\tau}$ and $B^0\rightarrow {\mu}{\tau}$) are strictly forbidden in the standard model (SM), but these LFV decays are a signal of new physics (NP). We use the Mass Insertion Approximation (MIA) to find sensitive parameters that directly influence the result of the branching ratio of LFV decay $B_d\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$. Combined with the latest experimental results, we analyze the relationship between different sensitive parameters and the branching ratios of the three processes. According to the numerical analysis, these elements are very restricted from the non observation of CLFV.

In this paper, we study the lepton flavor violating decays of the $l_j\rightarrow l_i \gamma\gamma$ (j=2, 3; i=1, 2) processes under the $U(1)_X$SSM. The $U(1)_X$SSM is the addition of three singlet new Higgs superfields and right-handed neutrinos to the minimal supersymmetric standard model (MSSM). Based on the latest experimental constraints of $l_j\rightarrow l_i \gamma\gamma$, we analyze the effects of different sensitive parameters on the results and made reasonable predictions for future experimental development. Numerical analysis shows that many parameters have a greater or lesser effect on lepton flavor violation(LFV), but the main sensitive parameters and sources leading to LFV are the non-diagonal elements involving the initial and final leptons. This work could provide a basis for the discovery of the existence of new physics (NP).