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To shed some light on the John--Nirenberg space, the authors in this article introduce the John--Nirenberg-$Q$ space via congruent cubes, $JNQ^\alpha_{p,q}(\mathbb{R}^n)$, which when $p=\infty$ and $q=2$ coincides with the space $Q_\alpha(\mathbb{R}^n)$ introduced by Ess\'en et al. in [Indiana Univ. Math. J. 49 (2000), 575-615]. Moreover, the authors show that, for some particular indices, $JNQ^\alpha_{p,q}(\mathbb{R}^n)$ coincides with the congruent John--Nirenberg space, or that the (fractional) Sobolev space is continuously embedded into $JNQ^\alpha_{p,q}(\mathbb{R}^n)$. Furthermore, the authors characterize $JNQ^\alpha_{p,q}(\mathbb{R}^n)$ via mean oscillations, and then use this characterization to study the dyadic counterparts. Also, the authors obtain some properties of composition operators on such spaces. The main novelties of this article are twofold: establishing a general equivalence principle for a kind of `almost increasing' set function introduced in this article, and using the fine geometrical properties of dyadic cubes to properly classify any collection of cubes with pairwise disjoint interiors and equal edge length., Comment: 38 pages, submitted

In this article, the authors establish a general (two-weight) boundedness criterion for a pair of functions, $(F,f)$, on $\mathbb{R}^n$ in the scale of weighted Lebesgue spaces, weighted Lorentz spaces, (Lorentz--)Morrey spaces, and variable Lebesgue spaces. As applications, the authors give a unified approach to prove the (two-weight) boundedness of Calder\'on--Zygmund operators, Littlewood--Paley $g$-functions, Lusin area functions, Littlewood--Paley $g^\ast_\lambda$-functions, and fractional integral operators, in the aforementioned function spaces. Moreover, via applying the above (two-weight) boundedness criterion, the authors further obtain the (two-weight) boundedness of Riesz transforms, Littlewood--Paley $g$-functions, and fractional integral operators associated with second-order divergence elliptic operators with complex bounded measurable coefficients on $\mathbb{R}^n$ in the aforementioned function spaces., Comment: 46 pages. Comments are welcome!

The scalability of optical addressing is one of the key challenges for the large-scale trapped-ion quantum computing (QC). Metasurfaces is a promising solution due to excellent optical performance, integration, and miniaturization. Here, we propose and numerically demonstrate a design of integrated scalable metalens array (ISMA) working in the UV 350nm range for optical addressing. These ISMAs can focus the collimated individual addressing beams array into diffraction-limited spots array, featuring crosstalk below 0.82%, focusing efficiency as high as 36.90%. For various applications, the x-polarized, polarization-insensitive and right-handed circularly polarized ISMA are designed. The design proposed in this paper can also find applications in optical trapping., Comment: 7 pages,3 figures

To develop a deep-learning model that integrates radiomics analysis for enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using chest X-ray image, two deep-learning models were trained based on a pre-trained VGG-16 architecture: in the 1st model, X-ray image was the sole input; in the 2nd model, X-ray image and 2 radiomic feature maps (RFM) selected by the saliency map analysis of the 1st model were stacked as the input. Both models were developed using 812 chest X-ray images with 262/288/262 COVID-19/Non-COVID-19 pneumonia/healthy cases, and 649/163 cases were assigned as training-validation/independent test sets. In 1st model using X-ray as the sole input, the 1) sensitivity, 2) specificity, 3) accuracy, and 4) ROC Area-Under-the-Curve of COVID-19 vs Non-COVID-19 pneumonia detection were 1) 0.90$\pm$0.07 vs 0.78$\pm$0.09, 2) 0.94$\pm$0.04 vs 0.94$\pm$0.04, 3) 0.93$\pm$0.03 vs 0.89$\pm$0.03, and 4) 0.96$\pm$0.02 vs 0.92$\pm$0.04. In the 2nd model, two RFMs, Entropy and Short-Run-Emphasize, were selected with their highest cross-correlations with the saliency maps of the 1st model. The corresponding results demonstrated significant improvements (p<0.05) of COVID-19 vs Non-COVID-19 pneumonia detection: 1) 0.95$\pm$0.04 vs 0.85$\pm$0.04, 2) 0.97$\pm$0.02 vs 0.96$\pm$0.02, 3) 0.97$\pm$0.02 vs 0.93$\pm$0.02, and 4) 0.99$\pm$0.01 vs 0.97$\pm$0.02. The reduced variations suggested a superior robustness of 2nd model design.

While nanoscale color generations have been studied for years, the high performance transmission structural color, simultaneously equipped with large gamut, high resolution, and optical multiplexing abilities, still remains as a hanging issue. Here, a silicon metasurface embedded Fabry-P\'erot cavity is demonstrated to address this problem. By changing the planar geometries of metasurface units, the cavities provide transmission colors with 194% sRGB gamut coverage and 141111 DPI resolution, along with more than 300% enhanced angular tolerance. Such high density allows two-dimensional color mixing at diffraction limit scale. Beneficial from the polarization manipulation capacity of the metasurface, arbitrary color arrangements between cyan and red for two orthogonal linear polarizations are also realized. Our proposed cavities can be used in filters, printings, optical storages and many other applications in need of high quality and density colors., Comment: Keywords: metasurfaces; structural colors; optical storage; multiplexing. 5 pages, 4 figures

This paper propose to combine pretrained language models with the modular dialogue paradigm for open-domain dialogue modeling. Our method, semantic-enhanced finetuning, instantiates conversation understanding, planning, and response generation as a language model finetuning task. At inference, we disentangle semantic and token variations by specifying sampling methods and constraints for each module separately. For training and evaluation, we present X-Weibo, a Chinese multi-turn open-domain dialogue dataset with automatic annotation for emotions, DAs, and topical words. Experiments show that semantic-enhanced finetuning outperforms strong baselines on non-semantic and semantic metrics, improves the human-evaluated relevance, coherence, and informativeness, and exhibits considerable controllability over semantic variables., Comment: Under review

Purpose: To develop a radiomics filtering technique for characterizing spatial-encoded regional pulmonary ventilation information on lung CT. Methods: The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was implemented across the lung volume to capture the spatial-encoded image information. Fifty-three radiomic features were extracted within the kernel, resulting in a 4th order tensor object. As such, each voxel coordinate of the original lung was represented as a 53-dimensional feature vector, such that radiomic features could be viewed as feature maps within the lungs. To test the technique as a potential pulmonary ventilation biomarker, the radiomic feature maps were compared to paired functional images (Galligas PET or DTPA-SPECT) based on Spearman correlation (r) analysis. Results: The radiomic feature map GLRLM-based Run-Length Non-Uniformity and GLCOM-based Sum Average are found to be highly correlated with the functional imaging. The achieved r (median [range]) for the two features are 0.46 [0.05, 0.67] and 0.45 [0.21, 0.65] across 46 patients and 2 functional imaging modalities, respectively. Conclusions: The results provide evidence that local regions of sparsely encoded heterogeneous lung parenchyma on CT are associated with diminished radiotracer uptake and measured lung ventilation defects on PET/SPECT imaging. These findings demonstrate the potential of radiomics to serve as a complementary tool to the current lung quantification techniques and provide hypothesis-generating data for future studies., Comment: 21 pages, 3 figures, 3 tables

In the application of neural networks, we need to select a suitable model based on the problem complexity and the dataset scale. To analyze the network's capacity, quantifying the information learned by the network is necessary. This paper proves that the distance between the neural network weights in different training stages can be used to estimate the information accumulated by the network in the training process directly. The experiment results verify the utility of this method. An application of this method related to the label corruption is shown at the end., Comment: 10 pages, 13 figure