Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

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Hyperspectral super-resolution, which aims at enhancing the spatial resolution of hyperspectral images (HSIs), has recently attracted considerable attention. A common way of hyperspectral super-resolution is to fuse the HSI with a higher spatial resolution multispectral image (MSI). Various approaches have been proposed to solve this problem by establishing the degradation model of low spatial resolution HSIs and MSIs based on matrix factorization methods, e.g., unmixing and sparse representation. However, this category of approaches cannot well construct the relationship between the high spatial resolution HSI and MSI. In fact, since the HSI and the MSI capture the same scene, these two image sources must have common factors. In this paper, we propose a nonlocal tensor decomposition model for HSI-MSI fusion. Firstly, the nonlocal similar patch tensors of the HSI are constructed according to the MSI, for the purpose of calculating the smooth order of all the patches for clustering. Then, the relationship between the high spatial resolution HSI and the MSI is explored through a coupled tensor canonical polyadic (CP) decomposition. The fundamental idea of the proposed model is that the factor matrices in CP decomposition of the high spatial resolution HSI's nonlocal tensor can be shared with the matrices factorized by the MSI's nonlocal tensor. Alternating direction method of multipliers is used to solve the proposed model. Through this method, the spatial structure of the MSI can be successfully transferred to the HSI. Experimental results on three synthetic datasets and one real dataset suggest that the proposed method substantially outperforms existing state-of-the-art HSI-MSI fusion methods. Index Terms-e Hyperspectral images, multispectral images, data fusion, nonlocal tensor, coupled CP decomposition.

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We propose and demonstrate that optical analog computing of spatial differentiation and edge detection can be realized with a single layer of dielectric metasurface. The optical transfer function for second-order derivation is obtained by engineering the spatial dispersion of electric dipole resonance supported by the silicon nanodisks in the metasurface. Benefiting from this unique mechanism of electric dipole resonance, spatial differentiation can be performed for two dimensions and arbitrary polarization with a large spatial bandwidth and high efficiency at the visible wavelength. Explicitly, we have numerically validated the application with one-dimensional spatial functions as well as an image, and the results show excellent performance. Our study can facilitate the research of optical computing with artificial nanostructures.

With the rapid development of compressed sensing theories and applications, sparse signal processing has been widely used in synthetic aperture radar (SAR) imaging during the recent years. As an efficient tool for sparse reconstruction, 1 optimization induces sparsity the most effectively, and the 1 -norm penalty is usually combined with the total variation norm (TV-norm) penalty to construct a compound regularizer in order to enhance the pointbased features as well as the region-based features. However, as a convex optimizer, the analytic solution of 1 regularization-based sparse signal reconstruction is usually a biased estimation. Aiming at this issue, in this article, we quantitatively analyzed the variation of reconstruction bias with respect to the complex reflectivity of targets, the undersampling ratio and the noise power. In order to reduce the bias effect and improve the reconstruction accuracy, we adopted the nonconvex regularization-based sparse SAR imaging method with a nonconvex penalty family. Furthermore, we linearly combined the nonconvex penalty and the TV-norm penalty to form a compound regularizer in the imaging model, which can improve the reconstruction accuracy of distributed targets and maintain the continuity of the backscattering coefficient. Simulation results showed that compared with 1 regularization, nonconvex regularization can reduce the average relative bias from 10.88% to 0.25%; compared with the matched filtering method and 1 and TV regularization, nonconvex & TV regularization can reduce the variance of the uniformly distributed targets by 80% without losing of reconstruction accuracy. Experiments on Gaofen-3 SAR data are also exploited to verify the effectiveness of the proposed method.

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