Graph representation learning aims to represent vertices as low-dimensional and real-valued vectors to facilitate subsequent downstream tasks, i.e., node classification, link predictions. Recently, some novel graph re...
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ISBN:
(数字)9781728160344
ISBN:
(纸本)9781728160351
Graph representation learning aims to represent vertices as low-dimensional and real-valued vectors to facilitate subsequent downstream tasks, i.e., node classification, link predictions. Recently, some novel graph representation learning frameworks, which try to approximate the underlying true connectivity distribution of the vertices, show their superiority. These methods characterize the distance between the true connectivity distribution and generated connectivity distribution by Kullback-Leibler or Jensen-Shannon divergence. However, since these divergences are not continuous with respect to the generator's parameters, such methods easily lead to unstable training and poor convergence. In contrast, Wasserstein distance is continuous and differentiable almost everywhere, which means it can produce more reliable gradient, allowing the training more stable and more convergent. In this paper, we utilize Wasserstein distance to characterize the distance between the underlying true connectivity distribution and generated distribution in graph representation learning. Experimental results show that the accuracy of our method exceeds existing baselines in tasks of both node classification and link prediction.
Machine reading comprehension (MRC) has become a core component in a variety of natural language processing (NLP) applications such as question answering and dialogue systems. It becomes a practical challenge that an ...
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The abductive natural language inference task (αNLI) is proposed to evaluate the abductive reasoning ability of a learning system. In the αNLI task, two observations are given and the most plausible hypothesis is as...
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The present study explores the application of Landsat-9 OLI and Sentinel-1 SAR data for effective lineament extraction and structural mapping in the Proterozoic North Singhbhum Mobile Belt, Eastern India, an area char...
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Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing. In this paper, we focus on Chinese medical ...
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In this paper, we generalize the concept of strong quantum nonlocality from two aspects. Firstly in Cd ⊗ Cd ⊗ Cd quantum system, we present a construction of strongly nonlocal quantum states containing 6(d−1)2 orthogo...
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Graph convolutional networks gain remarkable success in semi-supervised learning on graph-structured data. The key to graph-based semi-supervised learning is capturing the smoothness of labels or features over nodes e...
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Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e.g., the short-term thun...
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High-quality long-distance entanglement is essential for both quantum communication and scalable quantum networks. Entanglement purification is to distill high-quality entanglement from low-quality entanglement in a n...
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High-quality long-distance entanglement is essential for both quantum communication and scalable quantum networks. Entanglement purification is to distill high-quality entanglement from low-quality entanglement in a noisy environment and it plays a key role in quantum repeaters. The previous significant entanglement purification experiments require two pairs of low-quality entangled states and were demonstrated in tabletop. Here we propose and report a high-efficiency and long-distance entanglement purification using only one pair of hyperentangled state. We also demonstrate its practical application in entanglement-based quantum key distribution (QKD). One pair of polarization spatial-mode hyperentanglement was distributed over 11 km multicore fiber (noisy channel). After purification, the fidelity of polarization entanglement arises from 0.771 to 0.887 and the effective key rate in entanglement-based QKD increases from 0 to 0.332. The values of Clauser-Horne-Shimony-Holt inequality of polarization entanglement arises from 1.829 to 2.128. Moreover, by using one pair of hyperentanglement and deterministic controlled-NOT gates, the total purification efficiency can be estimated as 6.6×103 times than the experiment using two pairs of entangled states with spontaneous parametric down-conversion sources. Our results offer the potential to be implemented as part of a full quantum repeater and large-scale quantum network.
Schizophrenia is a devastating mental disorder affecting 20 million people *** diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the ...
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Schizophrenia is a devastating mental disorder affecting 20 million people *** diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the *** on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.
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