Piperidinium bis­(2-oxidobenzoato-κ2 O 1,O 2)borate

The asymmetric unit of the title compound, C5H12N+·C14H8BO6 − or [C5H12N][BO4(C7H4O)2], contains two piperidinium cations and two bis­(salicylato)borate anions. The coordination geometries around the B atoms are distorted tetra­hedral. In the two mol­ecules, the aromatic rings are oriented at dihedral angles of 76.27 (3) and 83.86 (3)°. The rings containing B atoms have twist-boat conformations, while the two cations adopt chair conformations. In the crystal, the component species are linked by N—H⋯O hydrogen bonds. In the crystal structure, intra- and inter­molecular N—H⋯O hydrogen bonds link the mol­ecules

Object-based attention mechanism for color calibration of UAV remote sensing images in precision agriculture.

Color calibration is a critical step for unmanned aerial vehicle (UAV) remote sensing, especially in precision agriculture, which relies mainly on correlating color changes to specific quality attributes, e.g. plant health, disease, and pest stresses. In UAV remote sensing, the exemplar-based color transfer is popularly used for color calibration, where the automatic search for the semantic correspondences is the key to ensuring the color transfer accuracy. However, the existing attention mechanisms encounter difficulties in building the precise semantic correspondences between the reference image and the target one, in which the normalized cross correlation is often computed for feature reassembling. As a result, the color transfer accuracy is inevitably decreased by the disturbance from the semantically unrelated pixels, leading to semantic mismatch due to the absence of semantic correspondences. In this article, we proposed an unsupervised object-based attention mechanism (OBAM) to suppress the disturbance of the semantically unrelated pixels, along with a further introduced weight-adjusted Adaptive Instance Normalization (AdaIN) (WAA) method to tackle the challenges caused by the absence of semantic correspondences. By embedding the proposed modules into a photorealistic style transfer method with progressive stylization, the color transfer accuracy can be improved while better preserving the structural details. We evaluated our approach on the UAV data of different crop types including rice, beans, and cotton. Extensive experiments demonstrate that our proposed method outperforms several state-of-the-art methods. As our approach requires no annotated labels, it can be easily embedded into the off-the-shelf color transfer approaches. Relevant codes and configurations will be available at https://github.com/huanghsheng/object-based-attention-mechanis

Comparison of solid and liquid fractions of pretreated Norway spruce as reductants in LPMO-supported saccharification of cellulose

The role of lignin in enzymatic saccharification of cellulose involving lytic polysaccharide monooxygenase (LPMO) was investigated in experiments with the solid and liquid fractions of pretreated Norway spruce from a biorefinery demonstration plant using hydrothermal pretreatment and impregnation with sulfur dioxide. Pretreated biomass before and after enzymatic saccharification was characterized using HPAEC, HPLC, Py-GC/MS, 2D-HSQC NMR, FTIR, and SEM. Chemical characterization indicated that relatively harsh pretreatment conditions resulted in that the solid phase contained no or very little hemicellulose but considerable amounts of pseudo-lignin, and that the liquid phase contained a relatively high concentration (∼5 g/L) of lignin-derived phenolics. As judged from reactions continuously supplied with either air or nitrogen gas, lignin and lignin fragments from both the solid and the liquid phases efficiently served as reductants in LPMO-supported saccharification. When air was used to promote LPMO activity, the enzymatic conversion of cellulose after 72 h was 25% higher in reactions with pretreated solids and buffer, and 14% higher in reactions with pretreatment liquid and microcrystalline cellulose. Research in this area is useful for designing efficient saccharification steps in biochemical conversion of lignocellulosic biomass

Citrus fruit detection based on an improved YOLOv5 under natural orchard conditions.

Accurate detection of citrus can be easily affected by adjacent branches and overlapped fruits in natural orchard conditions, where some specific information of citrus might be lost due to the resultant complex occlusion. Traditional deep learning models might result in lower detection accuracy and detection speed when facing occluded targets. To solve this problem, an improved deep learning algorithm based on YOLOv5, named IYOLOv5, was proposed for accurate detection of citrus fruits. An innovative Res-CSPDarknet network was firstly employed to both enhance feature extraction performance and minimize feature loss within the backbone network, which aims to reduce the miss detection rate. Subsequently, the BiFPN module was adopted as the new neck net to enhance the function for extracting deep semantic features. A coordinate attention mechanism module was then introduced into the network's detection layer. The performance of the proposed model was evaluated on a home-made citrus dataset containing 2000 optical images. The results show that the proposed IYOLOv5 achieved the highest mean average precision (93.5%) and F1-score (95.6%), compared to the traditional deep learning models including Faster R-CNN, CenterNet, YOLOv3, YOLOv5, and YOLOv7. In particular, the proposed IYOLOv5 obtained a decrease of missed detection rate (at least 13.1%) on the specific task of detecting heavily occluded citrus, compared to other models. Therefore, the proposed method could be potentially used as part of the vision system of a picking robot to identify the citrus fruits accurately

Endothelial CCRL2 induced by disturbed flow promotes atherosclerosis via chemerin-dependent β2 integrin activation in monocytes.

AIMS: Chemoattractants and their cognate receptors are essential for leucocyte recruitment during atherogenesis, and atherosclerotic plaques preferentially occur at predilection sites of the arterial wall with disturbed flow (d-flow). In profiling the endothelial expression of atypical chemoattractant receptors (ACKRs), we found that Ackr5 (CCRL2) was up-regulated in an endothelial subpopulation by atherosclerotic stimulation. We therefore investigated the role of CCRL2 and its ligand chemerin in atherosclerosis and the underlying mechanism. METHODS AND RESULTS: By analysing scRNA-seq data of the left carotid artery under d-flow and scRNA-seq datasets GSE131776 of ApoE-/- mice from the Gene Expression Omnibus database, we found that CCRL2 was up-regulated in one subpopulation of endothelial cells in response to d-flow stimulation and atherosclerosis. Using CCRL2-/-ApoE-/- mice, we showed that CCRL2 deficiency protected against plaque formation primarily in the d-flow areas of the aortic arch in ApoE-/- mice fed high-fat diet. Disturbed flow induced the expression of vascular endothelial CCRL2, recruiting chemerin, which caused leucocyte adhesion to the endothelium. Surprisingly, instead of binding to monocytic CMKLR1, chemerin was found to activate β2 integrin, enhancing ERK1/2 phosphorylation and monocyte adhesion. Moreover, chemerin was found to have protein disulfide isomerase-like enzymatic activity, which was responsible for the interaction of chemerin with β2 integrin, as identified by a Di-E-GSSG assay and a proximity ligation assay. For clinical relevance, relatively high serum levels of chemerin were found in patients with acute atherothrombotic stroke compared to healthy individuals. CONCLUSIONS: Our findings indicate that d-flow-induced CCRL2 promotes atherosclerotic plaque formation via a novel CCRL2-chemerin-β2 integrin axis, providing potential targets for the prevention or therapeutic intervention of atherosclerosis

Detection and localization of citrus fruit based on improved You Only Look Once v5s and binocular vision in the orchard

Intelligent detection and localization of mature citrus fruits is a critical challenge in developing an automatic harvesting robot. Variable illumination conditions and different occlusion states are some of the essential issues that must be addressed for the accurate detection and localization of citrus in the orchard environment. In this paper, a novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed. First, a new loss function (polarity binary cross-entropy with logit loss) for YOLO v5s is designed to calculate the loss value of class probability and objectness score, so that a large penalty for false and missing detection is applied during the training process. Second, to recover the missing depth information caused by randomly overlapping background participants, Cr-Cb chromatic mapping, the Otsu thresholding algorithm, and morphological processing are successively used to extract the complete shape of the citrus, and the kriging method is applied to obtain the best linear unbiased estimator for the missing depth value. Finally, the citrus spatial position and posture information are obtained according to the camera imaging model and the geometric features of the citrus. The experimental results show that the recall rates of citrus detection under non-uniform illumination conditions, weak illumination, and well illumination are 99.55%, 98.47%, and 98.48%, respectively, approximately 2–9% higher than those of the original YOLO v5s network. The average error of the distance between the citrus fruit and the camera is 3.98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization

Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on seed examples or existing datasets to construct instruction tuning data, GLAN exclusively utilizes a pre-curated taxonomy of human knowledge and capabilities as input and generates large-scale synthetic instruction data across all disciplines. Specifically, inspired by the systematic structure in human education system, we build the taxonomy by decomposing human knowledge and capabilities to various fields, sub-fields and ultimately, distinct disciplines semi-automatically, facilitated by LLMs. Subsequently, we generate a comprehensive list of subjects for every discipline and proceed to design a syllabus tailored to each subject, again utilizing LLMs. With the fine-grained key concepts detailed in every class session of the syllabus, we are able to generate diverse instructions with a broad coverage across the entire spectrum of human knowledge and skills. Extensive experiments on large language models (e.g., Mistral) demonstrate that GLAN excels in multiple dimensions from mathematical reasoning, coding, academic exams, logical reasoning to general instruction following without using task-specific training data of these tasks. In addition, GLAN allows for easy customization and new fields or skills can be added by simply incorporating a new node into our taxonomy.Comment: Work in progres

Tool Learning with Foundation Models

Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans. This paradigm, i.e., tool learning with foundation models, combines the strengths of specialized tools and foundation models to achieve enhanced accuracy, efficiency, and automation in problem-solving. Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field. To this end, we present a systematic investigation of tool learning in this paper. We first introduce the background of tool learning, including its cognitive origins, the paradigm shift of foundation models, and the complementary roles of tools and models. Then we recapitulate existing tool learning research into tool-augmented and tool-oriented learning. We formulate a general tool learning framework: starting from understanding the user instruction, models should learn to decompose a complex task into several subtasks, dynamically adjust their plan through reasoning, and effectively conquer each sub-task by selecting appropriate tools. We also discuss how to train models for improved tool-use capabilities and facilitate the generalization in tool learning. Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools. Finally, we discuss several open problems that require further investigation for tool learning. In general, we hope this paper could inspire future research in integrating tools with foundation models

Intégration des diatopismes du français dans la lexicographie bilingue français-chinois : conception d’un dictionnaire bilingue intégrant les mots de la francophonie

The aim of this study is to introduce to the Chinese public systematic knowledge about the geographic variation of French by designing a French-Chinese dictionary focused on French regionalisms, considering the dynamic growth of exchanges between China and the French-speaking world in recent decades. On the one hand, a bilingual dictionary is an essential tool in learning a foreign language; on the other hand, the inclusion of regionalisms in the French-Chinese lexicography is deficient. Elaborating such a dictionary implies two main steps: for the nomenclature, we carried out a comparative study between different representative general French dictionaries of their inclusion of regionalisms and finally chose the Petit and Grand Robert as references, since they included more emblematic regionalisms than others. For the microstructure, we analyzed the ones found in the French-Chinese, the differential and the French-speaking world dictionaries, and designed, on this basis, a rich microstructure of eleven types of information. With the website editor WordPress, we created and launched an electronic dictionary by leveraging the strengths of model lexicographic on-line tools (BDLP, Usito), so that it can be accessible to a wider Chinese audience (dicfrancophonie.com) in a particularly userfriendly interface. Through this dictionary, we wish to improve and promote international communication between China and the entire French-speaking world.L’objectif de cette étude est de mettre à la disposition du public sinophone des connaissances systématiques sur la variation diatopique du français, vu la croissance dynamique des échanges entre la Chine et le monde francophone ces dernières décennies, en concevant un dictionnaire français-chinois spécialement consacré aux mots de la francophonie. D’une part, un dictionnaire bilingue est un outil essentiel dans l’apprentissage d’une langue étrangère ; d’autre part, l’inclusion des diatopismes dans la lexicographie français-chinois présente de sérieuses lacunes. L’élaboration d’un tel dictionnaire implique deux grandes étapes : pour la nomenclature, nous avons effectué une étude comparative entre différents dictionnaires généraux du français, représentatifs quant à leur inclusion des diatopismes, et avons fait porter notre choix sur le Petit et le Grand Robert comme références, en raison du grand nombre de diatopismes emblématiques qu’ils hébergent. Pour la microstructure, nous avons analysé celles des dictionnaires différentiels, panfrancophones et français-chinois, et avons conçu, sur cette base, une microstructure riche de onze types d’information. Avec l’éditeur de site WordPress, nous avons créé et lancé un dictionnaire électronique en tirant parti des atouts des outils lexicographiques en ligne modèles (BDLP, Usito), afin qu’elle soit accessible à un plus large public chinois (dicfrancophonie.com), et ce sous une forme particulièrement conviviale. À travers ce dictionnaire, nous souhaitons améliorer et promouvoir la communication internationale entre la Chine et toute la francophonie