Research on Palmprint Identification Method Based on Quantum Algorithms

Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT) is used to extract pattern features by only one operation due to quantum parallelism. The proposed algorithm exhibits an exponential speed-up compared with discrete Fourier transform in the feature extraction. Finally, quantum set operations and Grover algorithm are used in palmprint matching. According to the experimental results, quantum algorithm only needs to apply square of N operations to find out the target palmprint, but the traditional method needs N times of calculation. At the same time, the matching accuracy of quantum algorithm is almost 100%

TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents

Recently, automatically extracting information from visually rich documents (e.g., tickets and resumes) has become a hot and vital research topic due to its widespread commercial value. Most existing methods divide this task into two subparts: the text reading part for obtaining the plain text from the original document images and the information extraction part for extracting key contents. These methods mainly focus on improving the second, while neglecting that the two parts are highly correlated. This paper proposes a unified end-to-end information extraction framework from visually rich documents, where text reading and information extraction can reinforce each other via a well-designed multi-modal context block. Specifically, the text reading part provides multi-modal features like visual, textual and layout features. The multi-modal context block is developed to fuse the generated multi-modal features and even the prior knowledge from the pre-trained language model for better semantic representation. The information extraction part is responsible for generating key contents with the fused context features. The framework can be trained in an end-to-end trainable manner, achieving global optimization. What is more, we define and group visually rich documents into four categories across two dimensions, the layout and text type. For each document category, we provide or recommend the corresponding benchmarks, experimental settings and strong baselines for remedying the problem that this research area lacks the uniform evaluation standard. Extensive experiments on four kinds of benchmarks (from fixed layout to variable layout, from full-structured text to semi-unstructured text) are reported, demonstrating the proposed method's effectiveness. Data, source code and models are available

MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective

NER model has achieved promising performance on standard NER benchmarks. However, recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary (OOV) entity recognition. In this work, we propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective. The proposed approach contains two mutual information-based training objectives: i) generalizing information maximization, which enhances representation via deep understanding of context and entity surface forms; ii) superfluous information minimization, which discourages representation from rote memorizing entity names or exploiting biased cues in data. Experiments on various settings and datasets demonstrate that it achieves better performance in predicting OOV entities

Metabolic perturbation of Streptomyces albulus by introducing NADP-dependent glyceraldehyde 3-phosphate dehydrogenase

The available resources of Streptomyces represent a valuable repository of bioactive natural products that warrant exploration. Streptomyces albulus is primarily utilized in the industrial synthesis of ε-poly-L-lysine (ε-PL). In this study, the NADP-dependent glyceraldehyde 3-phosphate dehydrogenase (GapN) from Streptococcus mutans was heterologously expressed in S. albulus CICC11022, leading to elevated intracellular NADPH levels and reduced NADH and ATP concentrations. The resulting perturbation of S. albulus metabolism was comprehensively analyzed using transcriptomic and metabolomic methodologies. A decrease in production of ε-PL was observed. The expression of gapN significantly impacted on 23 gene clusters responsible for the biosynthesis of secondary metabolites. A comprehensive analysis revealed a total of 21 metabolites exhibiting elevated levels both intracellularly and extracellularly in the gapN expressing strain compared to those in the control strain. These findings underscore the potential of S. albulus to generate diverse bioactive natural products, thus offering valuable insights for the utilization of known Streptomyces resources through genetic manipulation

A retrospective study on the physical growth of twins in the first year after birth

ObjectivesThis study analyzed the physical growth of small for gestational age (SGA) and appropriate for gestational age (AGA) twins up to one year after birth.MethodsWeight, length, and head circumference data of 0–1 year-old twins were collected from the Child Health Care System from 2010 to 2019. Physical data were presented as Z-scores. Five parameters – growth level of weight, body length, head circumference, growth velocity, and body proportion (weight for length) were compared in twins.ResultsA total of 3,909 cases were collected (22.61% SGA, 77.39% AGA). 1. In both groups, WAZ (Weight for age z-score), HCZ (Head circumference for age z-score), and LAZ (Length for age z-score) increased more rapidly in the first 6 months. By one year of age, WAZ, HCZ, and LAZ had reached the normal range, but none had reached the average level of normal singleton children. 2. The mean values of WAZ, HCZ, and LAZ in the AGA group were between −1 and 0, and between −2 and − 1 in the SGA group, in the first year after birth. The SGA group lagged significantly behind the AGA group. The LAZ score of SGA and AGA was lower than the WAZ and HCZ scores. 3. The proportion of preterm AGA was the largest in twins, and the growth rate of preterm AGA was the fastest. Preterm twins had greater growth potential than term twins. However, the growth level of preterm SGA was always low. 4. The WFLZ (Weight for length z-score) in each group was approximately close to 0. The WFLZ of SGA was smaller than that of AGA twins at most time points. After 4 months of age, the WFLZ of twins had a downward trend. The WFLZ of preterm SGA approached −1 at approximately 1 year old.ConclusionThe physical growth of SGA and AGA in twins in the first year can reach the normal range but cannot reach the average level of normal singleton children. More attention should be paid to SGA in twins, especially preterm SGA. We should give proper nutritional guidance after 4 months of age to ensure the appropriate body proportion (weight for length) of SGA in twins.Clinical trial registrationwww.chictr.org.cn, CTR2000034761

Coating of a Novel Antimicrobial Nanoparticle with a Macrophage Membrane for the Selective Entry into Infected Macrophages and Killing of Intracellular Staphylococci

Internalization of Staphylococcus aureus by macrophages can inactivate bacterial killing mechanisms, allowing intracellular residence and dissemination of infection. Concurrently, these staphylococci can evade antibiotics that are frequently unable to pass mammalian cell membranes. A binary, amphiphilic conjugate composed of triclosan and ciprofloxacin is synthesized that self-assemble through micelle formation into antimicrobial nanoparticles (ANPs). These novel ANPs are stabilized through encapsulation in macrophage membranes, providing membrane-encapsulated, antimicrobial-conjugated NPs (Me-ANPs) with similar protein activity, Toll-like receptor expression and negative surface charge as their precursor murine macrophage/human monocyte cell lines. The combination of Toll-like receptors and negative surface charge allows uptake of Me-ANPs by infected macrophages/monocytes through positively charged, lysozyme-rich membrane scars created during staphylococcal engulfment. Me-ANPs are not engulfed by more negatively charged sterile cells possessing less lysozyme at their surface. The Me-ANPs kill staphylococci internalized in macrophages in vitro. Me-ANPs likewise kill staphylococci more effectively than ANPs without membrane-encapsulation or clinically used ciprofloxacin in a mouse peritoneal infection model. Similarly, organ infections in mice created by dissemination of infected macrophages through circulation in the blood are better eradicated by Me-ANPs than by ciprofloxacin. These unique antimicrobial properties of macrophage-monocyte Me-ANPs provide a promising direction for human clinical application to combat persistent infections