The lower-bounded k-median problem plays a key role in many applications related to privacy protection, which requires that the amount of assigned client to each facility should not be less than the requirement. Unfor...
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The lower-bounded k-median problem plays a key role in many applications related to privacy protection, which requires that the amount of assigned client to each facility should not be less than the requirement. Unfortunately, the lower-bounded clustering problem remains elusive under the widely studied k-median objective. Within this paper, we convert this problem to the capacitated facility location problem and successfully give a(516+ε)-approximation for this problem.
Given a bipartite graph G = (T∪ B, E), the problem bipartite 1-sided vertex explosion is to decide whether there exists a planar 2-layer embedding of G after exploding at most k vertices of B. For this problem, which...
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During outbreaks of large infectious diseases like COVID-19, there is a strain on healthcare resources worldwide. To alleviate the burden on healthcare workers during the initial stages of the outbreak, there is an ur...
During outbreaks of large infectious diseases like COVID-19, there is a strain on healthcare resources worldwide. To alleviate the burden on healthcare workers during the initial stages of the outbreak, there is an urgent need for the development of automated tools. Federated learning offers a privacypreserving solution to the challenge that limited annotated data within single healthcare facilities. However, existing federated aggregation strategies cannot adapt to the real-world medical image data problem of mixed heterogeneous. This paper introduces Personalized Federated Adaptive Regularization (pFedAR), an adaptive framework for federated learning that effectively utilizes multi-site COVID-19 CT datasets with mixed heterogeneous of distribution discrepancies. To solve the mixed distribution of label skew and feature shift, we propose a two-stage adaptive regularization. In the client training stage, we use the balance loss term to balance the COVID-19 clients with missing label. In the aggregation stage, we correct the client gradient conflict. Our method is developed and evaluated using eight real-world COVID-19 diagnosis datasets composed of CT images. Extensive experiments demonstrate the consistent improvement achieved by our method across the datasets.
Business process management is the end-to-end business process modeling, analysis, and optimization to achieve business goals. Business Process Modeling Notation (BPMN) is usually used to represent business process mo...
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International Classification of Diseases (ICD) coding is the task of assigning ICD diagnosis codes to clinical notes. This can be challenging given the large quantity of labels (nearly 9,000) and lengthy texts (up to ...
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International Classification of Diseases (ICD) coding is the task of assigning ICD diagnosis codes to clinical notes. This can be challenging given the large quantity of labels (nearly 9,000) and lengthy texts (up to 8,000 tokens). However, unlike the single-pass reading process in previous works, humans tend to read the text and label definitions again to get more confident answers. Moreover, although pretrained language models have been used to address these problems, they suffer from huge memory usage. To address the above problems, we propose a simple but effective model called the Multi-Hop label-wise ATtention (MHLAT), in which multi-hop label-wise attention is deployed to get more precise and informative representations. Extensive experiments on three benchmark MIMIC datasets indicate that our method achieves significantly better or competitive performance on all seven metrics, with much fewer parameters to optimize.
International Classification of Diseases (ICD) coding is the task of assigning ICD diagnosis codes to clinical notes. This can be challenging given the large quantity of labels (nearly 9,000) and lengthy texts (up to ...
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Nowadays,enterprises need to continually adjust their business processes to adapt to the changes of business environments,especially when one business needs to be deployed in different application scenarios,which is c...
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Nowadays,enterprises need to continually adjust their business processes to adapt to the changes of business environments,especially when one business needs to be deployed in different application scenarios,which is called spatial variability in this *** the field of BPM(Business Process Management),configurable business process models have demonstrated their effectiveness in aspects of process modeling and model ***,we found that the existing techniques lead to complex configurable models,and are inadequate for model reuse especially for the spatial variability issue because they neglect the root impact of organizations on control flow.S-BPM(Subject-oriented Business Process Management)models provide a solid foundation for dealing with complex applications and help to bridge the gap between business and IT for process *** this paper,we propose an organization-driven business process configurable modeling approach for spatial variability by integrating both restriction and extension operations based on the S-BPM paradigm,in which business objects are also *** approach is validated with a general business process developed for the Real Estate Administration(REA)in a certain province of *** resulting configurable modeling framework can express the heterogeneous activity sequences for one business and has the potential to generate process models for uncertain environments in a new organization structure.
In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed by considering the hidden environm...
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In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed by considering the hidden environmental preference information in QoS and the common characteristics of multi-class QoS. QoS data was modeled as user-service bipartite graph at first, then, multi-component graph convolution neural network was used for feature extraction and mapping, and weighted fusion method was used for the same dimensional mapping of multi-class of QoS features. Subsequently, the attention factor decomposition machine was used to extract the first-order features, second-order interactive features and high-order interactive features of the mapped feature vector. Finally, the results of each part were combined to achieve the joint QoS prediction. The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE). IEEE
Protein-ligand interactions (PLIs) play important roles in cellular activities and drug discovery. Due to the technical difficulty and high cost of experimental methods, there is considerable interest in the developme...
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ISBN:
(数字)9781665468190
ISBN:
(纸本)9781665468206
Protein-ligand interactions (PLIs) play important roles in cellular activities and drug discovery. Due to the technical difficulty and high cost of experimental methods, there is considerable interest in the development of computational approaches, such as protein-ligand docking, to decipher PLI patterns. One of the most important and difficult aspects of protein-ligand docking is recognizing near-native conformations from a set of decoys, but unfortunately, traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications. We present a new deep learning-based scoring function for ranking protein-ligand docking models based on Vision Transformer(ViT), named ViTRMSE. To recognize near-native conformations from a set of decoys, ViTRMSE voxelizes the protein-ligand interactional pocket into a 3D grid labeled by the occupancy contribution of atoms in different physicochemical classes. Benefiting from the Vision Transformer architecture, ViTRMSE can effectively capture the subtle differences between spatially and energetically favorable near-native conformations and unfavorable non-native decoys without needing extra information. ViTRMSE is extensively evaluated on diverse test sets including PDBbind2019 and CASF2016, and obtains significant improvements over existing methods in terms of RMSE, R and docking power.
Human activity recognition based on millimeter-wave radar is dedicated to monitor people’s daily activities and detect specific dangerous actions. Although existing methods achieve some improvement, they rarely consi...
Human activity recognition based on millimeter-wave radar is dedicated to monitor people’s daily activities and detect specific dangerous actions. Although existing methods achieve some improvement, they rarely consider the challenges of domain difference, such as ages and environments. To address this challenge, we propose a source-free domain adaptation method for millimeter wave radar based human activity recognition, which achieves knowledge transfer from the source domain to the target domain. Firstly, we propose balanced clustering to obtain cluster centers of source domain as the prior-knowledge through the pre-trained model. Then, in order to perform domain adaptation, the model is fine-tuned by the integration of domain adaptation and self-supervision of the target domain. Experiment results on several transfer tasks show that our proposed method is effective in human activity recognition and outperforms some other advanced transfer learning methods.
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