Protein surface serves as an important representation of protein structure,revealing how protein interacts with other biomolecules to perform its *** forms the basis for pharmaceutical and fundamental biological resea...
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Protein surface serves as an important representation of protein structure,revealing how protein interacts with other biomolecules to perform its *** forms the basis for pharmaceutical and fundamental biological research[1].Datadriven deep learning methods in protein surface representation face challenges of label scarcity,since labeled data are typically obtained through wet lab experiments.
The embedded system with energy harvest equipment collects the energy required for system operation from its working environment and releases it from the battery. However, the equipment can only provide intermittent p...
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Target speaker extraction (TSE) models are expected to extract the target speech from a cocktail party mixture signal. When only trained with present target speaker samples (PT), these models output noise in the absen...
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To improve the accuracy and robustness of existing speech denoising methods, this study proposes an adaptive speech noise reduction method based on noise classification. First, to adapt to different time-varying chara...
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作者：
Du, AnJia, JieChen, JianWang, XingweiHuang, MingNortheastern University
School of Computer Science and Engineering Engineering Research Center of Security Technology of Complex Network System Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University
School of Computer Science and Engineering Shenyang110819 China
Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm of...
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The data encountered by Session-based Recommendation System(SBRS) is typically highly sparse, which also serves as one of the bottlenecks limiting the accuracy of recommendations. So Contrastive Learning(CL) is applie...
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1 Introduction Deep neural networks have exhibited excellent performance in supervised tasks on point clouds,such as classification,segmentation[1]and registration[2].In supervised learning schemes,manual labeling of ...
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1 Introduction Deep neural networks have exhibited excellent performance in supervised tasks on point clouds,such as classification,segmentation[1]and registration[2].In supervised learning schemes,manual labeling of massive point clouds is needed for model ***,point clouds captured from different scenarios exist inevitable distribution discrepancy,and model trained from one domain always generalize badly in another *** reduce the doamin distribution discrepancy,many studies[3–6]have emerged for point cloud unsupervised domain adaptation(UDA)by learning domain-invariant features,where[5]proposed using adaptive nodes to align the local features between the source and the target domains[3,4],and[6]proposed utilizing self-supervised tasks to help capture highly transferable feature representations.
Existing deep learning-based models can achieve a prompt diagnosis of operational anomalies by analyzing the audios emitted from power transformers. However, the practical abnormal data are insufficient for model trai...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Existing deep learning-based models can achieve a prompt diagnosis of operational anomalies by analyzing the audios emitted from power transformers. However, the practical abnormal data are insufficient for model training, resulting in limited diagnostic performance. To address this problem, we propose an abnormal audio generation method based on the improved cycle generative adversarial networks (ImCycleGAN) to augment the limited training dataset. In the ImCycleGAN, the generator and discriminator are redesigned and adversarially trained to generate the realistic-like audios of six abnormal statuses. Moreover, we combine adversarial, cycle consistency and identity mapping losses to optimize the training process of ImCycleGAN and enhance its ability to capture nonlinear features of audios. Finally, the generated data is evaluated in terms of similarity and fault classification. Experimental results show that our method can generate abnormal audios with high similarity to the real ones, and significantly improve the classification accuracy of existing fault diagnosis models.
Entity Alignment (EA) is a critical task in Knowledge Graph (KG) integration, aimed at identifying and matching equivalent entities that represent the same real-world objects. While EA methods based on knowledge repre...
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The rapid development of microarray technology has generated a large amount of microarray data, and the classification of these data is meaningful for cancer diagnosis, treatment and prognosis. The classification of h...
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