Clerodane diterpenes: sources, structures, and biological activities

The clerodane diterpenoids are a widespread class of secondary metabolites and have been found in several hundreds of plant species from various families and in organisms from other taxonomic groups

Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition

Incremental Named Entity Recognition (INER) involves the sequential learning of new entity types without accessing the training data of previously learned types. However, INER faces the challenge of catastrophic forgetting specific for incremental learning, further aggravated by background shift (i.e., old and future entity types are labeled as the non-entity type in the current task). To address these challenges, we propose a method called task Relation Distillation and Prototypical pseudo label (RDP) for INER. Specifically, to tackle catastrophic forgetting, we introduce a task relation distillation scheme that serves two purposes: 1) ensuring inter-task semantic consistency across different incremental learning tasks by minimizing inter-task relation distillation loss, and 2) enhancing the model's prediction confidence by minimizing intra-task self-entropy loss. Simultaneously, to mitigate background shift, we develop a prototypical pseudo label strategy that distinguishes old entity types from the current non-entity type using the old model. This strategy generates high-quality pseudo labels by measuring the distances between token embeddings and type-wise prototypes. We conducted extensive experiments on ten INER settings of three benchmark datasets (i.e., CoNLL2003, I2B2, and OntoNotes5). The results demonstrate that our method achieves significant improvements over the previous state-of-the-art methods, with an average increase of 6.08% in Micro F1 score and 7.71% in Macro F1 score.Comment: Accepted by CIKM2023 as a long paper with an oral presentatio

Local Feature Matching Using Deep Learning: A Survey

Local feature matching enjoys wide-ranging applications in the realm of computer vision, encompassing domains such as image retrieval, 3D reconstruction, and object recognition. However, challenges persist in improving the accuracy and robustness of matching due to factors like viewpoint and lighting variations. In recent years, the introduction of deep learning models has sparked widespread exploration into local feature matching techniques. The objective of this endeavor is to furnish a comprehensive overview of local feature matching methods. These methods are categorized into two key segments based on the presence of detectors. The Detector-based category encompasses models inclusive of Detect-then-Describe, Joint Detection and Description, Describe-then-Detect, as well as Graph Based techniques. In contrast, the Detector-free category comprises CNN Based, Transformer Based, and Patch Based methods. Our study extends beyond methodological analysis, incorporating evaluations of prevalent datasets and metrics to facilitate a quantitative comparison of state-of-the-art techniques. The paper also explores the practical application of local feature matching in diverse domains such as Structure from Motion, Remote Sensing Image Registration, and Medical Image Registration, underscoring its versatility and significance across various fields. Ultimately, we endeavor to outline the current challenges faced in this domain and furnish future research directions, thereby serving as a reference for researchers involved in local feature matching and its interconnected domains. A comprehensive list of studies in this survey is available at https://github.com/vignywang/Awesome-Local-Feature-Matching .Comment: Accepted by Information Fusion 2024. Project page: https://github.com/vignywang/Awesome-Local-Feature-Matchin

Comparison of doxycycline and benzathine penicillin G for the treatment of early syphilis

Doxycycline is the preferred recommended second-line treatment for the treatment of early syphilis. Recent reports showed a declining efficacy trend of doxycycline in treatment of early syphilis. The aim of our study was to assess the serological response to the treatment for early syphilis with doxycycline compared with benzathine penicillin G and evaluate whether doxycycline is still an effective agent for the treatment of early syphilis. A record-based retrospective study was conducted. Patients were diagnosed with early syphilis in an sexually transmitted disease (STD) clinic from January 1, 2008 to December 31, 2014. They were treated with a single dose of benzathine penicillin G 2.4MU or oral doxycycline 100 mg twice daily for 14 days. Pearson’s chi-squared test was used for data analysis. 601 cases were included in the final study sample: 105 (17.5%) patients received a 14-day course of doxycycline (doxycycline group), and 496 (82.5%) patients received single-dose benzathine penicillin G (BPG group). The serological responses at 6 months and 12 months after treatment were compared. No statistically significant differences were found between the two groups at 6 months (69.52% vs. 75.00%, P=0.245), and at 12 months (92.38% vs. 96.17%, P=0.115). Doxycycline is still an effective agent for the treatment of early syphilis. </p

The Development of LLMs for Embodied Navigation

In recent years, the rapid advancement of Large Language Models (LLMs) such as the Generative Pre-trained Transformer (GPT) has attracted increasing attention due to their potential in a variety of practical applications. The application of LLMs with Embodied Intelligence has emerged as a significant area of focus. Among the myriad applications of LLMs, navigation tasks are particularly noteworthy because they demand a deep understanding of the environment and quick, accurate decision-making. LLMs can augment embodied intelligence systems with sophisticated environmental perception and decision-making support, leveraging their robust language and image-processing capabilities. This article offers an exhaustive summary of the symbiosis between LLMs and embodied intelligence with a focus on navigation. It reviews state-of-the-art models, research methodologies, and assesses the advantages and disadvantages of existing embodied navigation models and datasets. Finally, the article elucidates the role of LLMs in embodied intelligence, based on current research, and forecasts future directions in the field. A comprehensive list of studies in this survey is available at https://github.com/Rongtao-Xu/Awesome-LLM-E

Topography, structural and exhumation history of the Admiralty Mountains region, northern Victoria Land, Antarctica

International audience; The Admiralty Mountains region forms the northern termination of the northern Victoria Land, Antarctica. Few quantitative data are available to reconstruct the Cenozoic morpho-tectonic evolution of this sector of the Antarctic plate, where the Admiralty Mountains region forms the northern termination of the western shoulder of the Mesozoic-Cenozoic West Antarctica Rift System. In this study we combine new low-temperature thermochronological data (apatite fission-track and (U-Th-Sm)/He analyses) with structural and topography analysis. The regional pattern of the fission-track ages shows a general tendency to older ages (80–60 Ma) associated with shortened mean track-lengths in the interior, and younger fission-track ages clustering at 38–26 Ma with long mean track-lengths in the coastal region. Differently from other regions of Victoria Land, the younger ages are found as far as 50–70 km inland. Single grain apatite (U-Th-Sm)/He ages cluster at 50–30 Ma with younger ages in the coastal domain. Topography analysis reveals that the Admiralty Mountains has high local relief, with an area close to the coast, 180 km long and 70 km large, having the highest local relief of >2500 m. This coincides with the location of the youngest fission-track ages. The shape of the area with highest local relief matches the shape of a recently detected low velocity zone beneath the northern TAM, indicating that high topography of the Admiralty Mountains region is likely sustained by a mantle thermal anomaly. We used the obtained constraints on the amount of removed crustal section to reconstruct back-eroded profiles and calculate the erosional load in order to test flexural uplift models. We found that our back-eroded profiles are better reproduced by a constant elastic thickness of intermediate values (Te = 20–30 km). This suggests that, beneath the Admiralty Mountains, the elastic properties of the lithosphere are different with respect to other TAM sectors, likely due to a stationary Cenozoic upper mantle thermal anomaly in the region