Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in *** general graphs,edge closeness centrality indicates the importance of edges by the shorte...
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Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in *** general graphs,edge closeness centrality indicates the importance of edges by the shortest distances from the edge to all the other *** betweenness centrality ranks which edges are significant based on the fraction of all-pairs shortest paths that pass through the ***,extensive research efforts go into centrality computation over general graphs that omit time ***,numerous real-world networks are modeled as temporal graphs,where the nodes are related to each other at different time *** temporal property is important and should not be neglected because it guides the flow of information in the *** state of affairs motivates the paper’s study of edge centrality computation methods on temporal *** introduce the concepts of the label,and label dominance relation,and then propose multi-thread parallel labeling-based methods on OpenMP to efficiently compute edge closeness and betweenness centralities *** types of optimal temporal *** edge closeness centrality computation,a time segmentation strategy and two observations are presented to aggregate some related temporal edges for uniform *** edge betweenness centrality computation,to improve efficiency,temporal edge dependency formulas,a labeling-based forward-backward scanning strategy,and a compression-based optimization method are further proposed to iteratively accumulate centrality *** experiments using 13 real temporal graphs are conducted to provide detailed insights into the efficiency and effectiveness of the proposed *** with state-ofthe-art methods,labeling-based methods are capable of up to two orders of magnitude speedup.
In recent years, multimodal sentiment analysis (MSA) has gained prominence with the proliferation of social media. However, prior studies have often disregarded the possibility of spurious correlations between multimo...
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Optical Coherence Tomography (OCT) is a high-resolution, non-invasive imaging technology increasingly used for biometric data collection from fingertips. OCT captures volume data up to 3mm below the skin surface in th...
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Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a t...
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Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a typical virtual reality game that entails multi-user *** a user approaches and interacts with a target user in the VE,the user is expected to approach and interact with the target user in the corresponding PE as *** methods of multi-user RDW mainly focus on obstacle avoidance,which does not account for the relative positional relationship between the users in both VE and *** To enhance the user experience and facilitate potential interaction,this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking(DA-RDW)in a shared PE where the target user and other users are *** algorithm adopts improved artificial potential fields,where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic *** the best alignment,this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal ***,this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent *** balance obstacle avoidance and alignment,this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force *** The efficacy of the proposed method was evaluated using a series of simulations and live-user *** experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.
Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework f...
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Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.
Coverage-guided fuzzing is prevalent in detecting DBMS (Database Management System) bugs. However, current coverage-guided DBMS fuzzers suffer from two limitations that prevent fuzzers from discovering bugs efficientl...
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Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy ...
Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy *** learning(FL) enhances RS's privacy by enabling model training on decentralized data [2]. Although integrating KG and FL can address both data sparsity and privacy issues in RSs [3], several challenges persist. CH1,Each client's local model relies on a consistent global model from the server, limiting personalized deployment to endusers.
Aqueous sodium-ion batteries(ASIBs)have garnered significant attention as promising candidates for large-scale energy storage *** interest is primarily due to their abundant resource availability,environmental friendl...
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Aqueous sodium-ion batteries(ASIBs)have garnered significant attention as promising candidates for large-scale energy storage *** interest is primarily due to their abundant resource availability,environmental friendliness,cost-effectiveness,and high ***,their electrochemical performance is limited by the thermodynamic properties of water molecules,resulting in inadequate cycling stability and insufficient specific energy *** address these challenges,this study developed a hydrogen-bond enhanced urea-glycerol eutectic electrolyte(UGE)to expand the electrochemical stability window(ESW)of the electrolyte and suppress corresponding side *** eutectic component disrupts the original hydrogen bonding network in water,creating a new,enhanced network that reduces the activity of free water and forms a uniform,dense passivation layer on the *** a result,the optimized composition of UGE exhibits a broad ESW of up to 3 V(-1.44 to 1.6 V ***/AgCl).The Prussian blue(PB)/UGE/NaTi_(2)(PO_(4))_(3)@C full cell exhibits an exceptionally long lifespan of 10,000 cycles at 10 *** study introduces a low-cost,ultra-long-life ASIB system,utilizing a green and economical eutectic electrolyte,which expands the use of eutectic electrolytes in aqueous batteries and opens a new research horizon for constructing efficient electrochemical energy storage and conversion.
The proliferation of deep learning(DL)has amplified the demand for processing large and complex datasets for tasks such as modeling,classification,and ***,traditional DL methods compromise client privacy by collecting...
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The proliferation of deep learning(DL)has amplified the demand for processing large and complex datasets for tasks such as modeling,classification,and ***,traditional DL methods compromise client privacy by collecting sensitive data,underscoring the necessity for privacy-preserving solutions like Federated Learning(FL).FL effectively addresses escalating privacy concerns by facilitating collaborative model training without necessitating the sharing of raw *** that FL clients autonomously manage training data,encouraging client engagement is pivotal for successful model *** overcome challenges like unreliable communication and budget constraints,we present ENTIRE,a contract-based dynamic participation incentive mechanism for *** ensures impartial model training by tailoring participation levels and payments to accommodate diverse client *** approach involves several key ***,we examine how random client participation impacts FL convergence in non-convex scenarios,establishing the correlation between client participation levels and model ***,we reframe model performance optimization as an optimal contract design challenge to guide the distribution of rewards among clients with varying participation *** balancing budget considerations with model effectiveness,we craft optimal contracts for different budgetary constraints,prompting clients to disclose their participation preferences and select suitable contracts for contributing to model ***,we conduct a comprehensive experimental evaluation of ENTIRE using three real *** results demonstrate a significant 12.9%enhancement in model performance,validating its adherence to anticipated economic properties.
In the product conceptual design, designers utilize multiple design representations to ideate, externalize, and refine concepts iteratively. Mixed representations, defined as the simultaneous presentation of multiple ...
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