Egocentric interaction recognition aims to recognize the camera wearer’s interactions with the interactor who faces the camera wearer in egocentric videos. In such a human-human interaction analysis problem, it is cr...
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BACKGROUND:Ageing is a complex and multi-dimensional process that manifests heterogeneities across different organs/systems, individuals and countries. We aimed to delineate the life-course percentile curves and estab...
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BACKGROUND:Ageing is a complex and multi-dimensional process that manifests heterogeneities across different organs/systems, individuals and countries. We aimed to delineate the life-course percentile curves and establish the normative values of multi-systemic (e.g., muscle-skeletal, brain, cardiovascular and pulmonary) ageing metrics for people under distinct sociodemographic contexts (i.e., sex, income and education).
METHODS:Three national datasets, the UKB (the United Kingdom), the NHANES (the United States) and the CHARLS (China) were utilized for the analyses. We selected 14 ageing metrics (e.g., body mass index, grip strength, fat-free mass index, bone mineral content [BMC], bone mineral density [BMD], diastolic blood pressure, cognitive function and frailty index_Lab) that represent the functions of different organs/systems and plotted their sex-, educational- and income-specific percentile curves utilizing the GMALSS model. We also estimated the age-specific normative values for each ageing metric in distinct sociodemographic contexts.
RESULTS:The functions of all metrics, except for cognitive function, manifested a progressive decline or maintained stability after adulthood (20s), especially after middle age (40s-50s). The cognitive function showed an evident decline in old age (70s-75s) (e.g., in the CHARLS: the median [IQR] cognitive function scores were 11.6 [9.1, 13.8], 10.3 [7.5, 12.9], 8.3 [5.5, 11.0] at the ages of 60, 70 and 80 for males, respectively). In the stratified analyses, males and females manifested disparities in percentile curves of ageing metrics involving the muscle-skeletal and cardiovascular systems. For instance, BMC and BMD manifested an evident decline after middle age in females, whereas they showed a slow decline after adulthood in males. Notably, we observed substantial income and educational disparities in percentile curves of several ageing metrics within Chinese participants: the 'low-income' and 'low-education' subgroups m
In the field of computer vision, network architectures are critical to the performance of tasks. Vision Graph Neural Network (ViG) has shown remarkable results in handling various vision tasks with their unique charac...
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To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using aux...
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The problem of network function computation over a directed acyclic network is investigated in this paper. In such a network, a sink node desires to compute with zero error a target function, of which the inputs are g...
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Recently, patent data analysis has attracted a lot of attention, and patent keyword extraction is a hot problem. Most existing methods for patent keyword extraction are based on the frequency of words without semantic...
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Recently, patent data analysis has attracted a lot of attention, and patent keyword extraction is a hot problem. Most existing methods for patent keyword extraction are based on the frequency of words without semantic information. In this paper, we propose an Unsupervised keyword Extraction Method (UKEM) based on Chinese patent clustering. More specifically, we use a Skip-gram model to train word embeddings based on a Chinese patent corpus. Then each patent is represented as a vector called patent vector. These patent vectors are clustered to obtain several cluster centroids. Next, the distance between each word vector in patent abstract and cluster centroid is computed to indicate the semantic importance of this word. The experimental results on several Chinese patent datasets show that the performance of our proposed method is better than several competitive methods.
Large-scale cooperation underpins the evolution of ecosystems and the human society, and the collective behaviors by self-organization of multi-agent systems are the key for understanding. As artificial intelligence (...
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The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the tra...
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Today, bigdata brings benefits to many areas of scientific research. However, processing these large amounts of data often requires extensive computing time and a large storage space. Global feature analysis is consi...
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Since different ontologies are mostly developed independently, establishing meaningful links between their entities, so-called ontology matching, is critical to ensure their communication. Due to the complexity of the...
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ISBN:
(数字)9781728160924
ISBN:
(纸本)9781728160931
Since different ontologies are mostly developed independently, establishing meaningful links between their entities, so-called ontology matching, is critical to ensure their communication. Due to the complexity of the ontology matching problem, Evolutionary Algorithm (EA) can present a good methodology for determining ontology alignments. However, since none of the similarity measure can distinguish all the identical ontology entities in any context, ontology alignments generated by the automatic matching tools should be validated by the users to ensure their qualities. To improve the quality of the ontology alignment, in this work, a similarity measure is first proposed to calculate the similarity value of two ontology entities; then, an optimal model for ontology matching problem is constructed; after that, an EA-based automatic matcher is presented to solve the ontology matching problem, which can also adaptively determine the timing of getting user involved; and finally, the ontology concept hierarchy graph based reasoning approaches are proposed to tradeoff the user's workload and his work's effect. The experiment is conducted on the Interactive track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of our proposal.
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