Entity and relation extraction is a critical task in information *** approaches have emphasized obtaining improved span ***,existing work suffers from two major ***,there is an overabundance of low-quality candidate s...
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Entity and relation extraction is a critical task in information *** approaches have emphasized obtaining improved span ***,existing work suffers from two major ***,there is an overabundance of low-quality candidate spans,which hinders the effective extraction of information from high-quality candidate ***,the information encoded by existing marker strategies is often too simple to fully capture the nuances of the span,resulting in the loss of potentially valuable *** address these issues,we propose an enhancing entity and relation extraction with high-quality spans and enhanced marker(HSEM)strategies,it assigns adaptive weights to different spans in order to make the model more focused on high quality ***,the HSEM model enriches marker representation to incorporate more span information and enhance entity ***,we design a span scoring framework that assesses span quality based on the fusion of internal information and focuses the model on training high-quality samples to improve *** results on six benchmark datasets demonstrate that our model achieves state-of-the-art results after discriminating span quality.
Disordered flexible linkers(DFLs)are the functional disordered regions in proteins,which are the sub-regions of intrinsically disordered regions(IDRs)and play important roles in connecting domains and maintaining inte...
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Disordered flexible linkers(DFLs)are the functional disordered regions in proteins,which are the sub-regions of intrinsically disordered regions(IDRs)and play important roles in connecting domains and maintaining inter-domain *** with the limited available DFLs,the existing DFL predictors based on the machine learning techniques tend to predict the ordered residues as DFLs,leading to a high false positive rate(FPR)and low prediction *** studies have shown that DFLs are extremely flexible disordered regions,which are usually predicted as disordered residues with high confidence[P(D)>0.9]by an IDR ***,transferring an IDR predictor to an accurate DFL predictor is of great significance for understanding the functions of *** this study,we proposed a new predictor called TransDFL for identifying DFLs by transferring the RFPR-IDP predictor for IDR identification to the DFL *** RFPR-IDP was pre-trained with IDR sequences to learn the general features between IDRs and DFLs,which is helpful to reduce the false positives in the ordered ***-IDP was fine-tuned with the DFL sequences to capture the specific features of DFLs so as to be transferred into the *** results of two application scenarios(prediction of DFLs only in IDRs or prediction of DFLs in entire proteins)showed that TransDFL consistently outperformed other existing DFL predictors with higher accuracy.
Satellite imagery is found to be beneficial in many disciplines such as land use analysis, urban planning, and environmental monitoring. However, accurate segmentation of land features from satellite images requires m...
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Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumpt...
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Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumption of computing/communication resources and usually require reliable communications with bounded ***,these protocols may be unsuitable for Internet of Things(IoT)networks because the IoT devices are usually lightweight,battery-operated,and deployed in an unreliable wireless ***,this paper studies an efficient consensus protocol for blockchain in IoT networks via reinforcement ***,the consensus protocol in this work is designed on the basis of the Proof-of-Communication(PoC)scheme directly in a single-hop wireless network with unreliable communications.A distributed MultiAgent Reinforcement Learning(MARL)algorithm is proposed to improve the efficiency and fairness of consensus for miners in the blockchain *** this algorithm,each agent uses a matrix to depict the efficiency and fairness of the recent consensus and tunes its actions and rewards carefully in an actor-critic framework to seek effective *** results from the simulation show that the fairness of consensus in the proposed algorithm is guaranteed,and the efficiency nearly reaches a centralized optimal solution.
Circular RNA(circRNA)is a covalently closed RNA molecule formed by back *** role of circRNAs in posttranscriptional gene regulation provides new insights into several types of cancer and neurological *** are associate...
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Circular RNA(circRNA)is a covalently closed RNA molecule formed by back *** role of circRNAs in posttranscriptional gene regulation provides new insights into several types of cancer and neurological *** are associated with multiple diseases and are emerging biomarkers in cancer diagnosis and *** associations prediction is one of the current research hotspots in the field of *** research on circRNAs has made great progress,the traditional biological method of verifying circRNA-disease associations is still a great challenge because it is a difficult task and requires much ***,advances in computational methods have made considerable progress in circRNA *** review comprehensively discussed the functions and databases related to circRNA,and then focused on summarizing the calculation model of related predictions,detailed the mainstream algorithm into 4 categories,and analyzed the advantages and limitations of the 4 *** not only helps researchers to have overall understanding of circRNA,but also helps researchers have a detailed understanding of the past algorithms,guide new research directions and research purposes to solve the shortcomings of previous research.
Transactional stream processing engines (TSPEs) are central to modern stream applications handling shared mutable states. However, their full potential, particularly in adaptive scheduling, remains largely unexplored....
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Corn, Rice, and Wheat serve as primary staple foods globally, playing a pivotal role in the economies of numerous countries. Despite their paramount importance, these cereal crops face susceptibility to various diseas...
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The growing dependence on deep learning models for medical diagnosis underscores the critical need for robust interpretability and transparency to instill trust and ensure responsible usage. This study investigates th...
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This study focuses on the application of Explainable Artificial Intelligence (XAI) in Stroke, heart attack and cancer prediction, highlighting the importance of XAI. This technology helps users to clearly understand t...
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Pre-trained language models have significantly advanced text summarization by leveraging extensive pre-training data to enhance performance. Many cutting-edge models undergo an initial pre-training phase on a large co...
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