Rapid Identification of Vegetable Oil Species Using Low-Field Nuclear Magnetic Resonance

The relaxation signals of rapeseed oil, soybean oil, peanut oil, sunflower oil and corn oil were investigated using low-field nuclear magnetic resonance (LF-NMR), and the correlation between the composition of vegetable oils and their NMR relaxation characteristics was analyzed. Furthermore, a classification model for vegetable oils was established based on the echo attenuation information of LF-NMR using principal component analysis-linear discriminant analysis (PCA-LDA), and the effects of discriminant functions and the number of principal components (PC) on the model’s performance were studied. The experimental results showed that the decreasing order of the attenuation rates of the echo curves was peanut oil > rapeseed oil > corn oil > soybean oil > sunflower oil. The types of vegetable oils had significant effects on relaxation properties including T2W, T23, S23 and Stotal (P < 0.05). There existed extremely significant correlations between T2W, T22, T23, S23 and Stotal and the contents of C18:1, C18:2, C20:0, monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) (P < 0.01). The classification precision of the PCA-LDA model developed using linear discriminant function and 10 PCs for the training and prediction sets were 100.0% and 88.2%, respectively. It can be seen that it is feasible to identify vegetable oil species using LF-NMR. This study can provide a theoretical basis and technical support for quality and safety detection of different edible vegetable oils

QueryForm: A Simple Zero-shot Form Entity Query Framework

Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities. We present a novel query-based framework, QueryForm, that extracts entity values from form-like documents in a zero-shot fashion. QueryForm contains a dual prompting mechanism that composes both the document schema and a specific entity type into a query, which is used to prompt a Transformer model to perform a single entity extraction task. Furthermore, we propose to leverage large-scale query-entity pairs generated from form-like webpages with weak HTML annotations to pre-train QueryForm. By unifying pre-training and fine-tuning into the same query-based framework, QueryForm enables models to learn from structured documents containing various entities and layouts, leading to better generalization to target document types without the need for target-specific training data. QueryForm sets new state-of-the-art average F1 score on both the XFUND (+4.6%~10.1%) and the Payment (+3.2%~9.5%) zero-shot benchmark, with a smaller model size and no additional image input.Comment: Accepted to Findings of ACL 202

Design and Analysis of Self-Adapted Task Scheduling Strategies in Wireless Sensor Networks

In a wireless sensor network (WSN), the usage of resources is usually highly related to the execution of tasks which consume a certain amount of computing and communication bandwidth. Parallel processing among sensors is a promising solution to provide the demanded computation capacity in WSNs. Task allocation and scheduling is a typical problem in the area of high performance computing. Although task allocation and scheduling in wired processor networks has been well studied in the past, their counterparts for WSNs remain largely unexplored. Existing traditional high performance computing solutions cannot be directly implemented in WSNs due to the limitations of WSNs such as limited resource availability and the shared communication medium. In this paper, a self-adapted task scheduling strategy for WSNs is presented. First, a multi-agent-based architecture for WSNs is proposed and a mathematical model of dynamic alliance is constructed for the task allocation problem. Then an effective discrete particle swarm optimization (PSO) algorithm for the dynamic alliance (DPSO-DA) with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm’s ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results show that the proposed solution can achieve significant better performance than other algorithms

The Relative Molecular Mass, Monosaccharide Composition and Antioxidant Activity of Ziziphus jujuba FleshPolysaccharide at Different Stages

For the purpose of exploring the polysaccharide content, relative molecular mass, monosaccharide composition, as well as antioxidant activity in Ziziphus jujuba flesh polysaccharide (ZJFP) at distinct growth stages. The content, relative molecular weight, monosaccharide composition and free radical scavenging ability of ZJFP in wild-type and plant-type at different growth stages were studied by phenol-sulfuric acid assay, high performance gel chromatography, high performance ion chromatography, and DPPH free radical scavenging experiment. Moreover, the results determined that the polysaccharide content of ZJFP in both wild and plant types demonstrated a trend of first decreasing and consequently rising. Furthermore, the relative molecular mass of the two types of ZJFP gradually decreased with the development of the Ziziphus jujuba. During the initial stage of fruit development, the ZJFP exhibited the highest content of rhamnose, reaching 46.14 mg/g, followed by galactose up to 33.10 mg/g. Whereas, in the later stage of fruit development, the highest monosaccharide content in the two types of ZJFP was arabinose up to 60.30 mg/g, preceded by galacturonic acid up to 45.02 mg/g. Additionally, the trend of DPPH free radical scavenging capacity of ZJFP of the two types was identical to the trend of polysaccharide content in Ziziphus jujuba flesh. Plant-type and wild-type Ziziphus jujuba illustrated the identical trend regarding polysaccharide content, the relative molecular weight of polysaccharides, monosaccharide composition, as well as free radical scavenging ability, and both were of the type of fruit that reduces sugar accumulation. The research serve as a valuable reference for the future development and utilization of Ziziphus jujuba flesh resources

FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction

The recent advent of self-supervised pre-training techniques has led to a surge in the use of multimodal learning in form document understanding. However, existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data. In FormNetV2, we introduce a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss. The graph contrastive objective maximizes the agreement of multimodal representations, providing a natural interplay for all modalities without special customization. In addition, we extract image features within the bounding box that joins a pair of tokens connected by a graph edge, capturing more targeted visual cues without loading a sophisticated and separately pre-trained image embedder. FormNetV2 establishes new state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size.Comment: Accepted to ACL 202