Remote Sensing Target Detection Inspired by Scene Information and Inter-Object Relations

Remote sensing target detection has been widely used in industries. In various application scenarios, complicated contexts may inhibit target identification and reduce detection accuracy, especially in multi-target detection tasks. In this paper, a new remote sensing target detection method based on structural reasoning is proposed to improve target detection performance by integrating inter-object relationships and scene information. Based on inter-object information, a relation structure graph is designed to reduce errors and missed targets. To establish contextual constraints, semantic is used as a prior information for Bayesian criterion based on scene information. Experiments conducted on HRRSD dataset show that the average accuracy of the proposed method is 10.7 % higher than the state-of-the-art algorithms. The experimental results confirm that the proposed algorithm can achieve significant improvements and adapt to complex scenes in remote sensing by mining contextual information at both feature and semantic levels

Scientific Large Language Models: A Survey on Biological & Chemical Domains

Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. This growing interest has led to the advent of scientific LLMs, a novel subclass specifically engineered for facilitating scientific discovery. As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration. However, a systematic and up-to-date survey introducing them is currently lacking. In this paper, we endeavor to methodically delineate the concept of scientific language , whilst providing a thorough review of the latest advancements in scientific LLMs. Given the expansive realm of scientific disciplines, our analysis adopts a focused lens, concentrating on the biological and chemical domains. This includes an in-depth examination of LLMs for textual knowledge, small molecules, macromolecular proteins, genomic sequences, and their combinations, analyzing them in terms of model architectures, capabilities, datasets, and evaluation. Finally, we critically examine the prevailing challenges and point out promising research directions along with the advances of LLMs. By offering a comprehensive overview of technical developments in this field, this survey aspires to be an invaluable resource for researchers navigating the intricate landscape of scientific LLMs

Simultaneous and Accurate Determination of One-Bond ¹⁵N−¹³C‘ and Two-Bond ¹H^N−¹³C‘ Dipolar Couplings

Using the echo−anti-echo manipulation, the 15N−HN cross-peaks split in the E.COSY spectrum by the 13CO couplings are separated into different, distinct regions in the HSQC spectrum. From this novel E.COSY 15N−1HN HSQC spectrum, the small one-bond 15N−13C‘ and two-bond 1HN−13C‘ residual dipolar couplings can be extracted easily and accurately. These dipolar couplings provide a set of important long-range constraints for protein structure determination

Protein Backbone ¹H^N−¹³C^α and ¹⁵N−¹³C^α Residual Dipolar and <i>J</i> Couplings: New Constraints for NMR Structure Determination

A simple, sensitivity-enhanced experiment was devised for accurate measurement of backbone 15N−13Cα and 1HN−13Cα couplings in proteins. The measured residual dipolar couplings 2DHCA, 1DNCA, 3DHCA, and 2DNCA for protein GB1 display very good agreement with the refined NMR structure (PDB code:  3GB1). A Karplus-type relationship between the one-bond 1JNCA couplings and the backbone dihedral ψ angles holds, and on the basis of the two-bond 2JNCA couplings a secondary structure index can be established

Chaetochromones A and B, Two New Polyketides from the Fungus Chaetomium indicum (CBS.860.68)

Chaetochromones A (1) and B (2), two novel polyketides, were isolated from the crude extract of fungus Chaetomium indicum (CBS.860.68) together with three known analogues PI-3(3), PI-4 (4) and SB236050 (5). The structures of these compounds were determined by HRESI-MS and NMR experiments. Chaetochromones A (1) and B (2) are a member of the polyketides family, which might originate from a similar biogenetic pathway as the known compounds PI-3 (3), PI-4 (4) and SB236050 (5). The biological activities of these secondary metabolites were evaluated against eight plant pathogens, including Alternaria alternata, Ilyonectria radicicola, Trichoderma viride pers, Aspergillus niger, Fusarium verticillioide, Irpex lacteus (Fr.), Poria placenta (Fr.) Cooke and Coriolus versicolor (L.) Quél. Compound 1 displayed moderate inhibitory rate (>60%) against the brown rot fungus Poria placenta (Fr.) Cooke, which causes significant wood decay. In addition, the cytotoxic activities against three cancer cell lines A549, MDA-MB-231, PANC-1 were also tested, without any inhibitory activities being detected

Chaetochromones A and B, Two New Polyketides from the Fungus Chaetomium indicum (CBS.860.68)

Chaetochromones A (1) and B (2), two novel polyketides, were isolated from the crude extract of fungus Chaetomium indicum (CBS.860.68) together with three known analogues PI-3(3), PI-4 (4) and SB236050 (5). The structures of these compounds were determined by HRESI-MS and NMR experiments. Chaetochromones A (1) and B (2) are a member of the polyketides family, which might originate from a similar biogenetic pathway as the known compounds PI-3 (3), PI-4 (4) and SB236050 (5). The biological activities of these secondary metabolites were evaluated against eight plant pathogens, including Alternaria alternata, Ilyonectria radicicola, Trichoderma viride pers, Aspergillus niger, Fusarium verticillioide, Irpex lacteus (Fr.), Poria placenta (Fr.) Cooke and Coriolus versicolor (L.) Quél. Compound 1 displayed moderate inhibitory rate (>60%) against the brown rot fungus Poria placenta (Fr.) Cooke, which causes significant wood decay. In addition, the cytotoxic activities against three cancer cell lines A549, MDA-MB-231, PANC-1 were also tested, without any inhibitory activities being detected

RDVI: A Retrieval–Detection Framework for Verbal Irony Detection

Verbal irony is a common form of expression used in daily communication, where the intended meaning is often opposite to the literal meaning. Accurately recognizing verbal irony is essential for any NLP application for which the understanding of the true user intentions is key to performing the underlying tasks. While existing research has made progress in this area, verbal irony often involves connotative knowledge that cannot be directly inferred from the text or its context, which limits the detection model’s ability to recognize and comprehend verbal irony. To address this issue, we propose a Retrieval–Detection method for Verbal Irony (RDVI). This approach improves the detection model’s ability to recognize and comprehend verbal irony by retrieving the connotative knowledge from the open domain and incorporating it into the model using prompt learning. The experimental results demonstrate that our proposed method outperforms state-of-the-art models