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Ultrastable centimeter-sized atomically flat 2D polymer thin films are produced by interfacial polymerization for flexible electronics.

Genomic Knowledgebase (GenomicKB) is a graph database for researchers to explore and investigate human genome, epigenome, transcriptome, and 4D nucleome with simple and efficient queries. The database uses a knowledge graph to consolidate genomic datasets and annotations from over 30 consortia and portals, including 347 million genomic entities, 1.36 billion relations, and 3.9 billion entity and relation properties. GenomicKB is equipped with a web-based query system (https://gkb.dcmb.med.umich.edu/) which allows users to query the knowledge graph with customized graph patterns and specific constraints on entities and relations. Compared with traditional tabular-structured data stored in separate data portals, GenomicKB emphasizes the relations among genomic entities, intuitively connects isolated data matrices, and supports efficient queries for scientific discoveries. GenomicKB transforms complicated analysis among multiple genomic entities and relations into coding-free queries, and facilitates data-driven genomic discoveries in the future.

In this paper, we propose an estimation of quantum resources necessary for recovering a key using Known Plain Text Attack (KPA) model for SPARKLE family of LWC authenticated block ciphers - SCHWAEMM. The procedure is based on a general attack using Grover's search algorithm with encryption oracle over key space in superposition. The paper explains step by step how to evaluate the cost of each operation type in encryption oracle in terms of various quantum and reversible gates. The result of this paper is an implementation of the simplified version of this cipher using quantum computer and summary table which shows the depth of quantum circuit, the size of quantum register and how many gates of NCT family are required for implementing the ciphers and attacks on them.

A series of effects caused by temperature change are the biggest problems faced by biological systems. These irregular environmental characteristics have brought new challenges to people and even animal groups. The immune function of human intestinal flora has a great protective effect on other organs and the body environment. It can help the human body carry out intestinal digestion, food absorption, nutritional metabolism, and so on. Based on the above situation, this paper uses the time series prediction model to study the factors affecting the imbalance of intestinal flora in the process of temperature change. Firstly, biological experiments are carried out with animals to simulate the human environment. Based on the sequence information of historical temperature change parameters, a temperature prediction device based on time series model is proposed. The effects of air factors and carbon dioxide content on the prediction results are evaluated by statistical analysis. Secondly, in order to ensure the accuracy of the experimental data, the neural network algorithm is used to optimize the model, and the white blood cell count is used to analyze the influence of temperature change on the intestinal flora structure of the two organisms. Finally, the experimental results are applied to the human environment to analyze the research results. The results showed that with the irregular change of temperature, the number of intestinal flora and internal colony structure also changed. The richness index in the normal temperature environment is relatively large, which can effectively explain the high richness of intestinal microflora in the experimental population. Further analyze the subjects and distinguish the animals according to sex. The number of Bacteroides in the intestinal flora of male animals was higher than that of female animals, but this phenomenon disappeared immediately after physical ligation. In addition, the intestinal flora abundance of female animals is higher than that of male animals, and the metabolic level is faster.

Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used. In recent years, deep learning methods have rapidly become widespread in the field of video object detection, achieving excellent results compared with those of traditional methods. However, the presence of duplicate information and abundant spatiotemporal information in video data poses a serious challenge to video object detection. Therefore, in recent years, many scholars have investigated deep learning detection algorithms in the context of video data and have achieved remarkable results. Considering the wide range of applications, a comprehensive review of the research related to video object detection is both a necessary and challenging task. This survey attempts to link and systematize the latest cutting-edge research on video object detection with the goal of classifying and analyzing video detection algorithms based on specific representative models. The differences and connections between video object detection and similar tasks are systematically demonstrated, and the evaluation metrics and video detection performance of nearly 40 models on two data sets are presented. Finally, the various applications and challenges facing video object detection are discussed.

Many workers experienced income reduction during the coronavirus disease 2019 (COVID-19) pandemic, which may link to adverse mental health.This study aimed to examine the association of current income and reduction in income during COVID-19 with anxiety and depression levels among non-healthcare workers.This is a multi-city cross-sectional study. We used standardized questionnaires to collect information. We regrouped the current income and income reduction during COVID-19 according to the tertile and median value of each specific city. Depression, Anxiety and Stress Scales-21 item short version (DASS-21) was used to assess anxiety and depression levels. We performed multinomial logistic regression to examine the association of current and reduced income with anxiety and depression. Path models were developed to outline the potential modification/indirect effect of subsidies from government.Large income reduction and low current income were significantly associated with more anxiety/depression symptoms. Path analysis showed that government subsidies could not significantly alleviate the impact of reduced income on anxiety/depression.Our findings showed that large income reduction and low current income were independently associated with anxiety/depression, while these symptoms may not be ameliorated by one-off government funds. This study suggests the need for long-term policies (e.g. developing sustained economic growth policies) to mitigate negative impacts of the COVID-19.

With the development of the imaging technology of various sensors, multisource image classification has become a key challenge in the field of image interpretation. In this article, a novel classification method, called the deep multiview union learning network (DMULN), is proposed to classify multisensor data. First, an associated feature extractor is designed to process the multisource data by canonical correlation analysis (CCA) in the head of the network. Second, an improved deep learning architecture with two branches is presented to extract high-level view features from the associated features. Third, a novel pooling, called view union pooling, is proposed to fuse the multiview feature from the deep model. Finally, the fused feature is fed into the classifier. The proposed framework is easy to optimize since it is an end-to-end network. Extensive experiments and analysis on the datasets IEEE_grss_dfc_2017 and IEEE_grss_dfc_2018 show that the proposed method achieves comparable results. Our results demonstrate that abundant multisource information can improve the classification performance.

A conductive MOF, named Bi-based catecholate BiHHTP, exhibited excellent catalytic performance towards CO2 electroreduction.

Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems. The accurate classification of the modulation format plays a vital role in the subsequent decoding of the transmitted data. End-to-end deep learning methods have been recently applied to AMC, outperforming traditional feature engineering techniques. However, AMC still has limitations in low signal-to-noise ratio (SNR) environments. To address the drawback, we propose a novel AMC-Net that improves recognition by denoising the input signal in the frequency domain while performing multi-scale and effective feature extraction. Experiments on two representative datasets demonstrate that our model performs better in efficiency and effectiveness than the most current methods., Comment: Accepted to ICASSP 2023 (5 pages, 2 figures, 3 table)

The fusion of hyperspectral image (HSI) and multispectral image (MSI) refers to enhance the spatial resolution of HSI with the help of a corresponding MSI that has a high spatial resolution to finally obtain an HSI with high resolution in both spatial and spectral domains. In this letter, we propose a variational tensor subspace decomposition-based fusion method to fully explore the differences and correlations among three modes of the HSI tensor. Experimental results on two HSI datasets show that the proposed method can achieve superior performance compared with existing state-of-the-art fusion methods with high computational efficiency.

The feature representation learning process greatly determines the performance of networks in classification tasks. By combining multiscale geometric tools and networks, better representation and learning can be achieved. However, relatively fixed geometric features and multiscale structures are always used. In this article, we propose a more flexible framework called the multiscale dynamic curvelet scattering network (MSDCCN). This data-driven dynamic network is based on multiscale geometric prior knowledge. First, multiresolution scattering and multiscale curvelet features are efficiently aggregated in different levels. Then, these features can be reused in networks flexibly and dynamically, depending on the multiscale intervention flag. The initial value of this flag is based on the complexity assessment, and it is updated according to feature sparsity statistics on the pretrained model. With the multiscale dynamic reuse structure, the feature representation learning process can be improved in the following training process. Also, multistage fine-tuning can be performed to further improve the classification accuracy. Furthermore, a novel multiscale dynamic curvelet scattering module, which is more flexible, is developed to be further embedded into other networks. Extensive experimental results show that better classification accuracies can be achieved by MSDCCN. In addition, necessary evaluation experiments have been performed, including convergence analysis, insight analysis, and adaptability analysis.

The aim of multispectral (MS) and panchromatic (PAN) image fusion is to obtain an MS image that has high resolution in both spectral and spatial domains. During the fusion process, there are two important issues, i.e., spectral information preservation and spatial information enhancement. In this article, we propose a dual-collaborative fusion model that considers not only the spectral correlation collaboration but also the spatial-spectral collaboration. First, the features of PAN and MS images are extracted by a shared feature embedding network. Then, in order to enhance the spatial details, the PAN features are decomposed into four subbands, and the collaborative relationships among subbands are fully explored to refine the features. After the refinement of the subbands, the high-frequency components are directly taken as the inputs of the reconstruction network, while the low-frequency components are transformed by the guidance generation network to accomplish the spatial-spectral collaboration and also make preparations for the spectral adjustment. To explore the spectral correlation collaboration, a novel graph convolutional network is designed for the modulation of intraspectral relationships. Finally, the adjusted MS features are combined with the high-frequency components of PAN features to reconstruct the high-resolution MS image. Experimental results show that the proposed method outperforms traditional state-of-the-art pan-sharpening methods as well as the available deep learning-based ones.

Scatter targets of complex land covers in polarimetric synthetic aperture radar (PolSAR) images are often randomly oriented and cause randomly fluctuating echoes, which brings a challenge to PolSAR image classification. Therefore, many existing methods have alleviated this problem through orientation compensation. However, there are still two obstacles that limit the improvement of classification accuracy. On the one hand, generally, these methods process PolSAR images with fixed polarization rotation angles, which is experience-dependent and inflexible. On the other hand, for the different land covers of a PolSAR image, the existing methods do not consider these rotation angles separately. For the first obstacle, we design a group of convolution kernels called polarization rotation kernels (PRKs) and utilize them to build the polarimetric convolutional neural network (CNN) (PolCNN). The PolCNN is the base network of our final model, and it can learn polarization rotation angles adaptively. For the second obstacle, we extend the PolCNN into a multipath structure, the final model polarimetric multipath CNN (PolMPCNN). The polarization rotation angles of different land covers are directly related to the networks of different paths within the PolMPCNN. Furthermore, we also put forward the two-scale sampling and the stagewise training algorithm in order that our PolMPCNN can fit different scales of PolSAR targets and pays more attention to difficult training samples. Experiments on real PolSAR images show that the proposed model achieves the best classification results with an extremely low sampling rate of 0.1%.

In this paper, a novel unsupervised change detection method called adaptive Contourlet fusion clustering based on adaptive Contourlet fusion and fast non-local clustering is proposed for multi-temporal synthetic aperture radar (SAR) images. A binary image indicating changed regions is generated by a novel fuzzy clustering algorithm from a Contourlet fused difference image. Contourlet fusion uses complementary information from different types of difference images. For unchanged regions, the details should be restrained while highlighted for changed regions. Different fusion rules are designed for low frequency band and high frequency directional bands of Contourlet coefficients. Then a fast non-local clustering algorithm (FNLC) is proposed to classify the fused image to generate changed and unchanged regions. In order to reduce the impact of noise while preserve details of changed regions, not only local but also non-local information are incorporated into the FNLC in a fuzzy way. Experiments on both small and large scale datasets demonstrate the state-of-the-art performance of the proposed method in real applications.

Realizing both high-performance and air-stability is key to advancing singlet-diradical-based semiconductors to practical applications and realizing their material potential associated with their open-shell nature. Here a concise synthetic route toward two stable dibenzooctazethrene isomers, DBOZ1 and DBOZ2, was demonstrated. In the crystalline phase, DBOZ2 exhibits two-dimensional brick wall packing with a high degree of intermolecular electronic coupling, leading to a record-breaking hole mobility of 3.5 cm