Disentangling facial expressions from other disturbing facial attributes in face images is an essential topic for facial expression *** methods only care about facial expression disentanglement(FED)itself,ignoring the...
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Disentangling facial expressions from other disturbing facial attributes in face images is an essential topic for facial expression *** methods only care about facial expression disentanglement(FED)itself,ignoring the negative effects of other facial *** to the annotations on limited facial attributes,it is difficult for existing FED solutions to disentangle all disturbance from the input *** solve this issue,we propose an expression complementary disentanglement network(ECDNet).ECDNet proposes to finish the FED task during a face reconstruction process,so as to address all facial attributes during *** from traditional reconstruction models,ECDNet reconstructs face images by progressively generating and combining facial appearance and matching *** designs the expression incentive(EIE) and expression inhibition(EIN) mechanisms,inducing the model to characterize the disentangled expression and complementary parts *** geometry and appearance,generated in the reconstructed process,are dealt with to represent facial expressions and complementary parts,*** combination of distinctive reconstruction model,EIE,and EIN mechanisms ensures the completeness and exactness of the FED *** results on RAF-DB,AffectNet,and CAER-S datasets have proven the effectiveness and superiority of ECDNet.
Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitati...
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Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature *** view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding *** have constructed a data set which includes eight ***,circ2CBA encodes circRNA sequences using the one-hot ***,a two-layer convolutional neural network(CNN)is used to initially extract the *** CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the *** AUC value of circ2CBA reaches *** of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.
The theory of network science has attracted great interest of many researchers in the realm of biomathematics and public health,and numerous valuable epidemic models have been *** previous studies,it is common to set ...
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The theory of network science has attracted great interest of many researchers in the realm of biomathematics and public health,and numerous valuable epidemic models have been *** previous studies,it is common to set up a one-to-one correspondence between the nodes of a multi-layer network,ignoring the more complex situations in *** the present work,we explore this situation by setting up a partially coupled model of a two-layer network and investigating the impact of asymptomatic infected individuals on *** propose a self-discovery mechanism for asymptomatic infected individuals,taking into account situations such as nucleic acid testing in the community and individuals performing self-antigen testing during the *** these factors together,through the microscopic Markov chain approach(MMCA)and extensive Monte Carlo(MC)numerical simulations,we find that the greater the coupling between the networks,the more information dissemination is *** order to control the epidemics,more asymptomatic infected individuals should be made aware of their *** adoption of nucleic acid testing and individual adoption of antigenic self-testing can help to contain epidemic ***,the epidemic threshold of the proposed model is derived,and then miscellaneous factors affecting the epidemic threshold are also *** results are conducive to devising the prevention and control policies of pandemics.
Cloud workloads are highly dynamic and complex,making task scheduling in cloud computing a challenging *** several scheduling algorithms have been proposed in recent years,they are mainly designed to handle batch task...
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Cloud workloads are highly dynamic and complex,making task scheduling in cloud computing a challenging *** several scheduling algorithms have been proposed in recent years,they are mainly designed to handle batch tasks and not well-suited for real-time *** address this issue,researchers have started exploring the use of Deep Reinforcement Learning(DRL).However,the existing models are limited in handling independent tasks and cannot process workflows,which are prevalent in cloud computing and consist of related *** this paper,we propose SA-DQN,a scheduling approach specifically designed for real-time cloud *** approach seamlessly integrates the Simulated Annealing(SA)algorithm and Deep Q-Network(DQN)*** SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server,serving as a crucial feature of the task for the neural network to *** provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results.
This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a coll...
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This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging *** augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization *** of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point ***,there has been a lack of focus on making the most of the numerous existing augmentation *** this deficiency,this research investigates the possibility of combining two fundamental data augmentation *** paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named *** of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or *** innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or *** crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data *** results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation *** is achieved without compromising computational *** examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point *** data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the ro
Intelligent Space(IS)is widely regarded as a promising paradigm for improving quality of life through using service task *** the field matures,various state-of-the-art IS architectures have been *** of the IS architec...
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Intelligent Space(IS)is widely regarded as a promising paradigm for improving quality of life through using service task *** the field matures,various state-of-the-art IS architectures have been *** of the IS architectures designed for service robots face the problems of fixedfunction modules and low scalability when performing service *** this end,we propose a hybrid cloud service robot architecture based on a Service-Oriented Architecture(SOA).Specifically,we first use the distributed deployment of functional modules to solve the problem of high computing resource ***,the Socket communication interface layer is designed to improve the calling efficiency of the function ***,the private cloud service knowledge base and the dataset for the home environment are used to improve the robustness and success rate of the robot when performing ***,we design and deploy an interactive system based on Browser/Server(B/S)architecture,which aims to display the status of the robot in real-time as well as to expand and call the robot *** system is integrated into the private cloud framework,which provides a feasible solution for improving the quality of ***,it also fully reveals how to actively discover and provide the robot service mechanism of service tasks in the right *** results of extensive experiments show that our cloud system provides sufficient prior knowledge that can assist the robot in completing service *** is an efficient way to transmit data and reduce the computational burden on the *** using our cloud detection module,the robot system can save approximately 25% of the averageCPUusage and reduce the average detection time by 0.1 s compared to the locally deployed system,demonstrating the reliability and practicality of our proposed architecture.
Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical ***,understanding the research and application development of MKGs will be crucial ...
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Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical ***,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical *** this end,we offer an in-depth review of MKG in this *** research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG ***,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for *** addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major ***,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.
1 school of computerscience,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of computerscience and control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of sciences,Shenzhen 518055,Ch...
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1 school of computerscience,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of computerscience and control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of sciences,Shenzhen 518055,China 3 Shenzhen Key Laboratory of Intelligent Bioinformatics,Shenzhen Institute of Advanced Technology,Chinese Academy of science,Shenzhen 518055,China E-mail:xjlei@***;yalichen@***;***@*** Received December 9,2022;accepted July 29,*** Identifying microbes associated with diseases is important for understanding the pathogenesis of diseases as well as for the diagnosis and treatment of *** this article,we propose a method based on a multi-source association network to predict microbe-disease associations,named ***,a heterogeneous network of multimolecule associations is constructed based on associations between microbes,diseases,drugs,and ***,the graph embedding algorithm Laplacian eigenmaps is applied to the association network to learn the behavior features of microbe nodes and disease *** the same time,the denoising autoencoder(DAE)is used to learn the attribute features of microbe nodes and disease ***,attribute features and behavior features are combined to get the final embedding features of microbes and diseases,which are fed into the convolutional neural network(CNN)to predict the microbedisease *** results show that the proposed method is more effective than existing *** addition,case studies on bipolar disorder and schizophrenia demonstrate good predictive performance of the MMHN-MDA model,and further,the results suggest that gut microbes may influence host gene expression or compounds in the nervous system,such as neurotransmitters,or metabolites that alter the blood-brain barrier.
The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power *** complexity necessitates t...
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The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power *** complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly *** the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization *** proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs *** study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the *** PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence ***,the convergence efficiency of the PPA is significantly influenced by the parameter *** address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational *** computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch *** simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.
Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a ...
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