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Remote sensing target detection technology in cloud and mist scenes is of great significance for applications such as marine safety monitoring and airport traffic management. However, the degradation and loss of features caused by the obstruction of cloud and mist elements still pose a challenging problem for this technology. To enhance object detection performance in adverse weather conditions, we propose a novel target detection method named CM-YOLO that integrates background suppression and semantic context mining, which can achieve accurate detection of targets under different cloud and mist conditions. Specifically, a component-decoupling-based background suppression (CDBS) module is proposed, which extracts cloud and mist components based on characteristic priors and effectively enhances the contrast between the target and the environmental background through a background subtraction strategy. Moreover, a local-global semantic joint mining (LGSJM) module is utilized, which combines convolutional neural networks (CNNs) and hierarchical selective attention to comprehensively mine global and local semantics, achieving target feature enhancement. Finally, the experimental results on multiple public datasets indicate that the proposed method realizes state-of-the-art performance compared to six advanced detectors, with mAP, precision, and recall indicators reaching 85.5%, 89.4%, and 77.9%, respectively.

Background Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Results Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. Conclusions The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.

In recent years, as the knowledge graph has attained significant achievements in many specific fields, which has become one of the core driving forces for the development of the internet and artificial intelligence. However, there is no mature knowledge graph in the field of agriculture, so it is a great significance study on the construction technology of agricultural knowledge graph. Named entity recognition and relation extraction are key steps in the construction of knowledge graph. In this paper, based on the joint extraction model LSTM-LSTM-Bias brought in BERT pre-training language model to proposed a agricultural entity relationship joint extraction model BERT-BILSTM-LSTM which is applied to the standard data set NYT and self-built agricultural data set AgriRelation. Experimental results showed that the model can effectively extracted the relationship between agricultural entities and entities.

The high dimensionality of text data is a challenging issue that requires efficient methods to reduce vector space and improve classification accuracy. Existing filter-based methods fail to address the redundancy issue, resulting in the selection of irrelevant and redundant features. Information theory-based methods effectively solve this problem but are not practical for large amounts of data due to their high time complexity. The proposed method, termed semantic similarity-aware feature selection and redundancy removal (SS-FSRR), employs joint mutual information between the pairs of semantically related terms and the class label to capture redundant features. It is predicated on the assumption that semantically related terms imply potentially redundant ones, which can significantly reduce execution time by avoiding sequential search strategies. In this work, we use Word2Vec’s CBOW model to obtain semantic similarity between terms. The efficiency of the SS-FSRR is compared to six state-of-the-art competitive selection methods for categorical data using two traditional classifiers (SVM and NB) and a robust deep learning model (LSTM) on seven datasets with 10-fold cross-validation, where experimental results show that the SS-FSRR outperforms the other methods on most tested datasets with high stability as measured by the Jaccard’s Index.

Relation extraction aims to identify semantic relations between entities in text. In recent years, this task has been extended to the joint extraction of entities and relations, which requires the simultaneous identification of entities and their relations from sentences. However, existing methods, limited by the existing tagging scheme, fail to identify more complex entities, which in turn limits the performance of the joint extraction task. This article presents a joint extraction model for entities and relations called MLRA-LSTM-CRF that uses multi-label tagging and relational alignment to transform this task into a multi-label tag recognition problem. The proposed model first tags the entities and their relations according to the multi-label tagging scheme and then uses a joint entity and relation extraction module with a multi-layer attention mechanism to extract the triplets in the sentence. Finally, the relational alignment module is used to align the predicted relation classification results. Experimental results on the New York Times and Wiki-KBP datasets indicate that MLRA-LSTM-CRF is significantly better than that of several state-of-the-art models and baseline.

This study investigated (1) the influence of verbal and conceptual processing on the retrieval and phenomenological evaluation of olfactory evoked memories, and (2) whether the experienced qualities of retrieved information are affected by olfactory exposure per se. Seventy-two older adults were randomized into one of three cue conditions (odor only, name only, or odor name) and asked to relate any autobiographical event for the given cue. The results indicated that semantic knowledge of an odor's name significantly affects the age distribution of memories such that the memory peak in childhood observed for odors only was attenuated. Also, experiential factors such as pleasantness and feelings of being brought back in time were lower when odors were presented with their respective names. Olfactory evoked memories were associated with a higher emotional arousal that could not be accounted for by the perceptual stimulation alone. Taken together, the overall pattern of findings suggests that retrieval of olfactory evoked information is sensitive to semantic and conceptual processing, and that odor-evoked representations are more emotional than memories triggered by verbal information.

Community discovery in complex networks is the problem of detecting, for each node of the network, its membership to one of more groups of nodes, the communities , that are densely connected, or highly interactive, or, more in general, similar , according to a similarity function. So far, the problem has been widely studied in monodimensional networks, i.e. networks where only one connection between two entities may exist. However, real networks are often multidimensional, i.e., multiple connections between any two nodes may exist, either reflecting different kinds of relationships, or representing different values of the same type of tie. In this context, the problem of community discovery has to be redefined, taking into account multidimensional structure of the graph. We define a new concept of community that groups together nodes sharing memberships to the same monodimensional communities in the different single dimensions. As we show, such communities are meaningful and able to group nodes even if they might not be connected in any of the monodimensional networks. We devise frequent pAttern mining-BAsed Community discoverer in mUltidimensional networkS (ABACUS), an algorithm that is able to extract multidimensional communities based on the extraction of frequent closed itemsets from monodimensional community memberships. Experiments on two different real multidimensional networks confirm the meaningfulness of the introduced concepts, and open the way for a new class of algorithms for community discovery that do not rely on the dense connections among nodes.

Aspect sentiment triplet extraction is an important research in the field of sentiment analysis, aiming at extracting aspect, opinion expression, and aspect-based sentiment at once. Most of existing end-to-end methods are tagging-based, which neglect the interaction between aspect term and opinion term while predicting the sentiment polarity. Besides, these methods cannot accurately resolve the complex correspondence between aspect terms and opinion terms. To address above concerns, we propose a span-level framework to extract aspect sentiment triplets, which is enhanced by introducing syntactic dependency relation and part-of-speech combination features through graph convolutional networks to deal with the complex relationships between aspect terms and opinion terms. Moreover, the proposed model explicitly considers the interaction between aspect and opinion spans generated by dual-channel pruning strategy when predicting their sentiment polarity. Experimental results demonstrate that our framework achieves strong performance over the baselines, especially on triplets including multi-word entities and complex corresponding relationships.

Background/Objectives: Older patients with fractures often present with a complex interplay of factors associated with frailty and functional decline. The emerging concept of Orthogeriatric Fracture Syndrome (OFS) aims to characterize these distinct relationships of pathologies and outcomes. Despite increasing recognition of OFS in clinical practice, due to the distributed nature of fragility factors across medical disciplines, it remains poorly defined in the literature. Methods: We used large-scale text mining of 26 million PubMed abstracts to quantify the occurrence and interrelationship of OFS-related concepts across all disciplines in biomedical research. Results: OFS terms were more prevalent in fragility fractures than in other fracture types, particularly osteoporosis (0.52 vs. 0.09, p < 0.05). In pairwise keyword correlation (Pearson φ), the correlations presented between OFS keywords are comparable to the ones in the more established metabolic syndrome (e.g., φ = 0.07 between stroke and hypertension, p < 0.05). For OFS, osteoporosis emerged as the central node linking OFS outcomes and pathologies, correlating with fragility fracture (φ = 0.176, p < 0.05) and sarcopenia (φ = 0.03, p < 0.05). Sarcopenia in turn correlated with gait (φ = 0.04, p < 0.05), malnutrition (φ = 0.05, p < 0.05), and frailty (φ = 0.032, p < 0.05). Old age keywords showed substantially higher association with OFS keywords (e.g., φ = 0.06 for elderl* and hip fracture, p < 0.05) than with metabolic syndrome terms (elderl* and insulin resistance, p > 0.05). Conclusions: Overall, the analysis showed statistically significant associations between keywords representing OFS outcomes, pathologies and old age. The combined occurrence of osteoporosis, sarcopenia, frailty and risk of falls may help conceptually identify older adults at risk and inform preventive measures. This large-scale bibliometric analysis supports OFS as a conceptually coherent, proposed theoretical framework for cross-disciplinary awareness and coordinated care, with a literature-level organizational pattern comparable to metabolic syndrome, however, pending prospective clinical validation. This study reframes fragility fractures as the endpoint of a broader, potentially modifiable risk constellation and underscores the need for further clinical and epidemiological validation.

Background Most Chinese joint entity and relation extraction tasks in medicine involve numerous nested entities, overlapping relations, and other challenging extraction issues. In response to these problems, some traditional methods decompose the joint extraction task into multiple steps or multiple modules, resulting in local dependency in the meantime. Methods To alleviate this issue, we propose a joint extraction model of Chinese medical entities and relations based on RoBERTa and single-module global pointer, namely RSGP, which formulates joint extraction as a global pointer linking problem. Considering the uniqueness of Chinese language structure, we introduce the RoBERTa-wwm pre-trained language model at the encoding layer to obtain a better embedding representation. Then, we represent the input sentence as a third-order tensor and score each position in the tensor to prepare for the subsequent process of decoding the triples. In the end, we design a novel single-module global pointer decoding approach to alleviate the generation of redundant information. Specifically, we analyze the decoding process of single character entities individually, improving the time and space performance of RSGP to some extent. Results In order to verify the effectiveness of our model in extracting Chinese medical entities and relations, we carry out the experiments on the public dataset, CMeIE. Experimental results show that RSGP performs significantly better on the joint extraction of Chinese medical entities and relations, and achieves state-of-the-art results compared with baseline models. Conclusion The proposed RSGP can effectively extract entities and relations from Chinese medical texts and help to realize the structure of Chinese medical texts, so as to provide high-quality data support for the construction of Chinese medical knowledge graphs.