Removing rain streaks from a single image has been drawing considerable attention as rain streaks can severely degrade the image quality and affect the performance of existing outdoor vision tasks. While recent CNN-based derainers have reported promising performances, deraining remains an open problem for two reasons. First, existing synthesized rain datasets have only limited realism, in terms of modeling real rain characteristics such as rain shape, direction and intensity. Second, there are no public benchmarks for quantitative comparisons on real rain images, which makes the current evaluation less objective. The core challenge is that real world rain/clean image pairs cannot be captured at the same time. In this paper, we address the single image rain removal problem in two ways. First, we propose a semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images. Using this method, we construct a large-scale dataset of ∼29.5K rain/rain-free image pairs that covers a wide range of natural rain scenes. Second, to better cover the stochastic distribution of real rain streaks, we propose a novel SPatial Attentive Network (SPANet) to remove rain streaks in a local-to-global manner. Extensive experiments demonstrate that our network performs favorably against the state-of-the-art deraining methods.

Quantum walks are powerful kernels in quantum computing protocols that possess strong capabilities in speeding up various simulation and optimisation tasks. One striking example is given by quantum walkers evolving on glued trees for their faster hitting performances than in the case of classical random walks. However, its experimental implementation is challenging as it involves highly complex arrangements of exponentially increasing number of nodes. Here we propose an alternative structure with a polynomially increasing number of nodes. We successfully map such graphs on quantum photonic chips using femtosecond laser direct writing techniques in a geometrically scalable fashion. We experimentally demonstrate quantum fast hitting by implementing two-dimensional quantum walks on these graphs with up to 160 nodes and a depth of 8 layers, achieving a linear relationship between the optimal hitting time and the network depth. Our results open up a scalable way towards quantum speed-up in complex problems classically intractable.Adapting well-known classical mathematical models in a way to include quantum mechanical laws has shown the emergence of new interesting behaviors. In some cases, the modified protocols have revealed an advantage with respect to the original ones in solving specific problems. This has clearly triggered the interest of the scientific community in the quest for a better understanding and exploitation of these new tools 1 . A striking example is given by quantum walks, the adaptation of the classical random walk to the world of quantum mechanics 2 . Quantum walks have already found applications in several scenarios, including spatial search problems 3,4 , the element distinctness problem 5 , testing matrix identities 6 , evaluating Boolean formulas 7 , judging graph isomorphism 8,9 , which all theoretically promise quantum speed-up and may inspire the breakthrough in real-life applications.One feature of quantum walks on complex graphs that is key in quantum algorithms is their ability to propagate from a node to a distant one in an efficient way. This is often denoted as fast hitting. In particular, fast hitting on a structure known as glued tree is extremely charming due to its exponential speed-up over its classical counterpart 10,11 . A glued tree is obtained by connecting the "final leaves" of two binary tree graphs 12 of the same depth, as shown in Fig.1(a). The process assumes a particle starting in the left-most vertex (called the Entry site), evolving through the graph, and finally hitting the right-most vertex (called the Exit site). It has been shown that, in a scenario where the central connections are randomly chosen, any algorithm exploiting a classical walker (i.e., a particle following the laws of classical mechanics) would require on average a time scaling exponentially with the graph depth to reach the Exit. On the other hand, a quantum walker will require a time that scales only linearly 11,13,14 . Due to the close relation between binary trees and decision trees in computer s...

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The prevalence of ERM in elderly urban Chinese was similar to that in Caucasians. Risk factors for idiopathic ERM were older age, female gender, myopia and hyperlipaemia.

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Metabolism is the collection of biochemical reactions enabled by chemically diverse metabolites, which facilitate different physiological processes to exchange substances and synthesize energy in diverse living organisms. Metabolomics has emerged as a cutting‐edge method to qualify and quantify the metabolites in different biological matrixes, and it has the extraordinary capacity to interrogate the biological significance that underlies metabolic modification and modulation. Liquid chromatography combined with mass spectrometry (LC/MS), as a robust platform for metabolomics analysis, has increased in popularity over the past 10 years due to its excellent sensitivity, throughput, and versatility. However, metabolomics investigation currently provides us with only phenotype data without revealing the biochemical functions and associated mechanisms. This limitation indeed weakens the core value of metabolomics data in a broad spectrum of the life sciences. In recent years, the scientific community has actively explored the functional features of metabolomics and translated this cutting‐edge approach to be used to solve key multifaceted questions, such as disease pathogenesis, the therapeutic discovery of drugs, nutritional issues, agricultural problems, environmental toxicology, and microbial evolution. Here, we are the first to briefly review the history and applicable progression of LC/MS‐based metabolomics, with an emphasis on the applications of metabolic phenotyping. Furthermore, we specifically highlight the next era of LC/MS‐based metabolomics to target functional metabolomes, through which we can answer phenotype‐related questions to elucidate biochemical functions and associated mechanisms implicated in dysregulated metabolism. Finally, we propose many strategies to enhance the research capacity of functional metabolomics by enabling the combination of contemporary omics technologies and cutting‐edge biochemical techniques. The main purpose of this review is to improve the understanding of LC/MS‐based metabolomics, extending beyond the conventional metabolic phenotype toward biochemical functions and associated mechanisms, to enhance research capability and to enlarge the applicable scope of functional metabolomics in small‐molecule metabolism in different living organisms.

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