Federated Learning (FL) recently emerges as a paradigm to train a global machine learning model across distributed clients without sharing raw data. Knowledge Graph (KG) embedding represents KGs in a continuous vector...
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Using first-principles calculations and crystal structure search methods, we found that many covalently bonded molecules such as H2, N2, CO2, NH3, H2O and CH4may react with NaC l, a prototype ionic solid,and form stab...
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Using first-principles calculations and crystal structure search methods, we found that many covalently bonded molecules such as H2, N2, CO2, NH3, H2O and CH4may react with NaC l, a prototype ionic solid,and form stable compounds under pressure while retaining their molecular structure. These molecules,despite whether they are homonuclear or heteronuclear, polar or non-polar, small or large, do not show strong chemical interactions with surrounding Na and Cl ions. In contrast, the most stable molecule among all examples, N2, is found to transform into cyclo-N5-anions while reacting with NaC l under high pressures. It provides a new route to synthesize pentazolates, which are promising green energy materials with high energy density. Our work demonstrates a unique and universal hybridization propensity of covalently bonded molecules and solid compounds under pressure. This surprising miscibility suggests possible mixing regions between the molecular and rock layers in the interiors of large planets.
In this paper we propose a first empirical mapping between the RST-DT and the PDTB 3.0. We provide an original algorithm which allows the mapping of 6,510 (80.0%) explicit and implicit discourse relations between the ...
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SHA-256 plays an important role in widely used applications, such as data security, data integrity, digital signatures, and cryptocurrencies. However, most of the current optimized implementations of SHA-256 are based...
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In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiol...
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In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.
Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains. In this study, we introduce a novel mixed-sample data augmentation method called Ra...
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GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for in...
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For the high-performance computing in a WAN environment,the geographical locations of national supercomputing centers are scattered and the network topology is complex,so it is difficult to form a unified view of *** ...
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For the high-performance computing in a WAN environment,the geographical locations of national supercomputing centers are scattered and the network topology is complex,so it is difficult to form a unified view of *** aggregate the widely dispersed storage resources of national supercomputing centers in China,we have previously proposed a global virtual data space named GVDS in the project of“High Performance Computing Virtual Data Space”,a part of the National Key Research and Development Program of *** GVDS enables large-scale applications of the high-performance computing to run efficiently across ***,the applications running on the GVDS are often data-intensive,requiring large amounts of data from multiple supercomputing centers across *** this regard,the GVDS suffers from performance bottlenecks in data migration and access across *** solve the above-mentioned problem,this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation *** a WAN environment,the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources,guaranteeing lower average migration latency when multiple data migration tasks are running in *** addition,it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests(the IO proxy is a module of GVDS),so as to improve the application’s performance across *** experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.
Executing computer programs described in natural language has long been a pursuit of computer science. With the advent of enhanced natural language understanding capabilities exhibited by large language models (LLMs),...
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To better characterize the properties of surface-initiated polymers, simultaneous bulk-and surface-initiated polymerizations are usually carried out by assuming that the properties of the surface-initiated polymers re...
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To better characterize the properties of surface-initiated polymers, simultaneous bulk-and surface-initiated polymerizations are usually carried out by assuming that the properties of the surface-initiated polymers resemble those of the bulk-initiated polymers. Through a Monte Carlo simulation using a heterogeneous stochastic reaction model, it was discovered that the bulk-initiated polymers exhibit a higher molecular weight and a lower dispersity than the corresponding surface-initiated polymers, which indicates that the equivalent assumption is invalid. Furthermore, the molecular weight distributions of the two types of polymers are also different, suggesting different polymerization mechanisms. The results can be simply explained by the heterogeneous distributions of reactants in the system. This study is helpful to better understand surface-initiated polymerization.
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