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Abstract Dense and flat La $$_{2}$$ 2 NiFeO $$_{6}$$ 6 (LNFO) films were fabricated on the indium tin oxide-coated glass (ITO/glass) substrate by sol–gel method. The bipolar resistive switching behavior (BRS) could be maintained in 100 cycles and remained after 30 days, indicating that the LNFO-based RS device owned good memory stability. Surprisingly, the multilevel RS characteristics were firstly observed in the Au/LNFO/ITO/glass device. The high resistance states (HRSs) and low resistance state (LRS) with the maximum ratio of $$\sim$$ ∼ 500 could be remained stably in 900 s and 130 cycles, demonstrating the fine retention and endurance ability of this LNFO-based RS device. The BRS behavior of Au/LNFO/ITO/glass devices primarily obeyed the SCLC mechanism controlled by oxygen vacancies (OVs) dispersed in the LNFO layer. Under the external electric field, injected electrons were captured or discharged by OVs during trapping or detrapping process in the LNFO layer. Thus, the resistive state switched between HRS and LRS reversibly. Moreover, the modulation of Schottky-like barrier formed at the Au/LNFO interface was contributed to the resistive states switchover. It was related to the change in OVs located at the dissipative region near the Au/LNFO interface. The multilevel RS ability of LNFO-based devices in this work provides an opportunity for researching deeply on the high density RS memory in lead-free double perovskite oxides-based devices.

Previous scientific literature retrieval methods, which are based on mathematical expression, ignore the literature attributes and the association between the literature, and the retrieval accuracy was affected. In this study, literature retrieval model based on Graph Convolutional Network (GCN) is proposed. By extracting document attributes from a structured document dataset, an Attribute Relation Graph (ARG) is constructed. Using GCN to capture the dependencies among literature nodes and generate literature representations by information aggregation to realize graph-based literature modeling; Introducing the advantages of Hesitant Fuzzy Set (HFS) theory in multi-attribute decision-making to realize the similarity evaluation between mathematical query expressions and mathematical retrieval result expressions. Finally, the similarity between literature features and mathematical expressions is integrated to obtain the ordered output of scientific literature retrieval results. Experiments were conducted on the arXiv public dataset, and the average precision of the top 10 retrieval results was 0.892, and the average NDCG value of the top 10 rankings was 0.875.

Achieving scientific document retrieval by considering the wealth of mathematical expressions and the semantic text they contain has become an inescapable trend. Current scientific document matching models focus solely on the textual features of expressions and frequently encounter hurdles like proliferative parameters and sluggish reasoning speeds in the pursuit of improved performance. To solve this problem, this paper proposes a scientific document retrieval method founded upon hesitant fuzzy sets (HFS) and local semantic distillation (LSD). Concretely, in order to extract both spatial and semantic features for each symbol within a mathematical expression, this paper introduces an expression analysis module that leverages HFS to establish feature indices. Secondly, to enhance contextual semantic alignment, the method of knowledge distillation is employed to refine the pretrained language model and establish a twin network for semantic matching. Lastly, by amalgamating mathematical expressions with contextual semantic features, the retrieval results can be made more efficient and rational. Experiments were implemented on the NTCIR dataset and the expanded Chinese dataset. The average MAP for mathematical expression retrieval results was 83.0%, and the average nDCG for sorting scientific documents was 85.8%.

In a retrieval system for mathematical documents based on mathematical expressions, the input and matching of mathematical expressions are key steps that affect the system's usability, accessibility and efficiency because of their special attributes. Therefore, this paper mainly focuses on improving the input efficiency and matching accuracy of mathematical expressions. This paper proposes a method for retrieval and ranking of mathematical documents based on CA-YOLOv5 and HFS (hesitation fuzzy set) by utilizing the advantages of CA (coordinate attention) model and YOLOv5 in target detection and the superiority of HFS in multiattribute decision-making. By embedding the CA model into the YOLOv5 network, the mathematical expressions in layout images are extracted and recognized to form mathematical query expressions. These expressions are then analyzed to obtain similarity evaluation features and matched with the candidate mathematical expressions indexed with the same features in a library of mathematical documents by employing the HFS as the similarity evaluation measure. Experiments were performed based on the TFD-ICDAR2019v2 dataset and the NTCIR dataset. The F1-score of the mathematical expression detection result was 76.54%, the MAP (mean average precision) of the mathematical documents retrieval result was 71.73%, and the average nDCG of mathematical documents ranking was 80.89%.

Scientific documents contain a large number of mathematical expressions and texts containing mathematical semantics. Simply using mathematical expressions or text to retrieve scientific documents can hardly meet retrieval needs. The real difficulty in retrieving scientific documents is to effectively integrate mathematical expressions and related textual features. Therefore, this study proposes a multi-attribute scientific documents retrieval and ranking model based on GBDT (gradient boosting decision tree) and LR (logistic regression) by integrating the expressions and text contained in scientific documents. First, the similarities of the five attributes are calculated, including mathematical expression symbols, mathematical expression sub-forms, mathematical expression context, scientific document keywords and the frequency of mathematical expressions. Next, the GBDT model is used to discretize and reorganize the five attributes. Finally, the reorganized features are input into the LR model, and the final retrieval and ranking results of scientific documents are obtained. The experiment in this study was carried out on the NTCIR dataset. The average value of the final MAP@20 of the scientific document recall was 81.92%. The average value of the scientific document ranking nDCG@20 was 86.05%.

Two-stage instance segmentation method outperforms the one-stage counterpart on complex occasions. However, we found that the RoIAlign operation identifies the feature map to smaller size, and the convolution or up-sampling causes the loss of detailed information. All these make it difficult to achieve precise segmentation. To circumvent the issue, we propose a simple and efficient anchor-free model for instance segmentation. We name it as CSAS because it combines the detection-based and segmentation-based idea. The CSAS adopts the two-stage paradigm, which mainly includes detection and segmentation. The box head not only considers the location accuracy into confidence score but calculates the IoU loss of regression, which leads to a gain of 1.5%. The mask head adopts the multi-task learning to accomplish precise segmentation, and it grows 1.7 points. Using the ResNet-50-FPN, a single CSAS obtains 1.6% improvement over the Mask R-CNN. Our result demonstrates that CSAS is capable of gaining the complete mask of instance. We conclude that the detailed feature information is essential for precise segmentation, the idea is available for other segmentation tasks.

Abstract High-density Cs2AgBiBr6 films with uniform grains were prepared by a simple one-step and low-temperature sol–gel method on indium tin oxide (ITO) substrates. An explicit tristate bipolar resistance switching behavior was observed in the Pt/Cs2 AgBiBr6/ITO/glass devices under irradiation of 10 mW/cm2 (445 nm). This behavior was stable over 1200 s. The maximum ratio of the high and low resistance states was about 500. Based on the analysis of electric properties, valence variation and absorption spectra, the resistive switching characteristics were attributed to the trap-controlled space charge-limited current mechanism due to the bromine vacancies in the Cs2AgBiBr6 layer. On the other hand, it is suggested that the ordering of the Schottky-like barrier located at Pt/Cs2AgBiBr6 affects the three-state resistance switching behavior under light irradiation. The ability to adjust the photoelectrical properties of Cs2AgBiBr6-based resistive switching memory devices is a promising strategy to develop high-density memory. Graphical Abstract

When retrieving scientific documents with mathematical expressions as the main content, both mathematical expressions and their contextual text features require consideration. However, mathematical expressions are different from texts in terms of grammar and semantics. Thus, integrating the above features and realizing scientific document retrieval is difficult. In this study, a retrieval method of scientific documents based on HFS (Hesitation Fuzzy Sets) and BERT (Bidirectional Encoder Representations from Transformer) is proposed. This method is realized through utilizing the advantages of HFS in multi-attribute decision making and BERT in context-dependent similarity calculation. By analyzing mathematical expressions and calculating the membership degree of symbolic multi-attributes, the similarity of mathematical expressions can be obtained, which can improve the accuracy of mathematical expression recall. With the extraction of the text of the expression context, BERT is used to calculate the context similarity. Then, the recalled technical documents are sorted according to the similarity of context, and the final retrieval result can be obtained. Experiments were carried out on 10,372 Chinese and 11,770 English scientific documents in the NTCIR extended data set. The average value of MAP_ $k (k=10)$ for the recall results of scientific documents was 74.13%. The average $n$ DCG ( $n=10$ ) for the ranking of scientific documents was 86.04%.

To address the low segmentation accuracy caused by the rich glyph styles of ancient Chinese characters and the complex layout of ancient Chinese books, which affects the retrieval and recognition results, an algorithm for the layout image analysis of ancient Chinese books and Chinese character image segmentation is proposed. The initial segmentation results were obtained through the projection method of the layout of ancient Chinese books, and the connected component analysis of the above results was carried out to determine the rough divided blocks of under-segmentation and over-segmentation. Considering under-segmentation of adhesive Chinese characters, the improved K-means clustering method was used to segment adhesive blocks to obtain single-character images. To address the over-segmentation of character components separation, a method based on interval-valued hesitant fuzzy set is proposed. This method analyzed the features of the connected component in the block, characterized the over-segmentation connected component. The hesitant fuzzy distances between other connected components and the standard merge evaluation interval number were calculated in sequence. The connected component with the smallest distance was preferentially merged with the over-segmentation connected component until no over-segmentation connected component remained in the block. The experimental segmentation accuracy was 89.94%.

Traditional mathematical search models retrieve scientific documents only by mathematical expressions and their contexts and do not consider the ontological attributes of scientific documents, which result in gaps between the queries and the retrieval results. To solve this problem, a retrieval and ranking model is constructed that synthesizes the information of mathematical expressions with related texts, and the ontology attributes of scientific documents are extracted to further sort the retrieval results. First, the hesitant fuzzy set of mathematical expressions is constructed by using the characteristics of the hesitant fuzzy set to address the multi-attribute problem of mathematical expression matching; then, the similarity of the mathematical expression context sentence is calculated by using the BiLSTM two-way coding feature, and the retrieval result is obtained by synthesizing the similarity between the mathematical expression and the sentence; finally, considering the ontological attributes of scientific documents, the retrieval results are ranked to obtain the final search results. The MAP_10 value of the mathematical expression retrieval results on the Ntcir-Mathir-Wikipedia-Corpus dataset is 0.815, and the average value of the NDCG@10 of the scientific document ranking results is 0.9; these results prove the effectiveness of the scientific document retrieval and ranking method.

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We present distinct antiblockade features of strongly interacting 64 D _5/2 Rydberg atoms employing a two-color excitation scheme. The first color (pulse A) is set to resonantly excite a few seed Rydberg atoms, each of which establishes a blockade region due to the long-range multipole interactions. The second color (pulse B) is blue detuned so that the multipole-interaction-induced shifts of certain atoms are well compensated to result in the antiblockade effect. We find in particular that a few seed atoms can lead to a remarkable difference of the Rydberg excitation in the presence of pulse B for a wide range of blue detuning. Relevant dynamics of this antiblockade excitation is also investigated by varying the pulse-B duration for a fixed blue detuning, further confirming the facilitation process of Rydberg excitation accompanied by a saturation effect. These experimental results can be well recovered by theoretical simulations based on a multilevel two-body model.