193 research outputs found
DySuse: Susceptibility Estimation in Dynamic Social Networks
Influence estimation aims to predict the total influence spread in social
networks and has received surged attention in recent years. Most current
studies focus on estimating the total number of influenced users in a social
network, and neglect susceptibility estimation that aims to predict the
probability of each user being influenced from the individual perspective. As a
more fine-grained estimation task, susceptibility estimation is full of
attractiveness and practical value. Based on the significance of susceptibility
estimation and dynamic properties of social networks, we propose a task, called
susceptibility estimation in dynamic social networks, which is even more
realistic and valuable in real-world applications. Susceptibility estimation in
dynamic networks has yet to be explored so far and is computationally
intractable to naively adopt Monte Carlo simulation to obtain the results. To
this end, we propose a novel end-to-end framework DySuse based on dynamic graph
embedding technology. Specifically, we leverage a structural feature module to
independently capture the structural information of influence diffusion on each
single graph snapshot. Besides, {we propose the progressive mechanism according
to the property of influence diffusion,} to couple the structural and temporal
information during diffusion tightly. Moreover, a self-attention block {is
designed to} further capture temporal dependency by flexibly weighting
historical timestamps. Experimental results show that our framework is superior
to the existing dynamic graph embedding models and has satisfactory prediction
performance in multiple influence diffusion models.Comment: This paper has been published in Expert Systems With Application
Viewing garden scenes: Interaction between gaze behavior and physiological responses
Previous research has shown that exposure to Japanese gardens reduces physiological measures of stress, e.g. heart rate, in both healthy subjects and dementia patients. However, the correlation between subjects’ physiological responses and their visual behavior while viewing the garden has not yet been investigated. To address this, we developed a system to collect simultaneous measurements of eye gaze and three physiological indicators of autonomic nervous system activity: electrocardiogram, blood volume pulse, and galvanic skin response. We recorded healthy subjects’ physiological/behavioral responses when they viewed two environments (an empty courtyard and a Japanese garden) in two ways (directly or as a projected 2D photograph). Similar to past work, we found that differences in subject’s physiological responses to the two environments when viewed directly, but not as a photograph. We also found differences in their behavioral responses. We quantified subject’s behavioral responses using several gaze metrics commonly considered to be measures of engagement of focus: average fixation duration, saccade amplitude, spatial entropy and gaze transition entropy. We found decrease in gaze transition entropy, the only metric that accounts for both the spatial and temporal properties of gaze, to have a weak positive correlation with decrease in heart rate. This suggests a relationship between engagement/focus and relaxation. Finally, we found gender differences: females’ gaze patterns were more spatially distributed and had higher transition entropy than males
HGCN-GJS: Hierarchical Graph Convolutional Network with Groupwise Joint Sampling for Trajectory Prediction
Accurate pedestrian trajectory prediction is of great importance for
downstream tasks such as autonomous driving and mobile robot navigation. Fully
investigating the social interactions within the crowd is crucial for accurate
pedestrian trajectory prediction. However, most existing methods do not capture
group level interactions well, focusing only on pairwise interactions and
neglecting group-wise interactions. In this work, we propose a hierarchical
graph convolutional network, HGCN-GJS, for trajectory prediction which well
leverages group level interactions within the crowd. Furthermore, we introduce
a novel joint sampling scheme for modeling the joint distribution of multiple
pedestrians in the future trajectories. Based on the group information, this
scheme associates the trajectory of one person with the trajectory of other
people in the group, but maintains the independence of the trajectories of
outsiders. We demonstrate the performance of our network on several trajectory
prediction datasets, achieving state-of-the-art results on all datasets
considered.Comment: 8 pages, 5 figures, in submission to conferenc
Dramatic Increases of Soil Microbial Functional Gene Diversity at the Treeline Ecotone of Changbai Mountain.
The elevational and latitudinal diversity patterns of microbial taxa have attracted great attention in the past decade. Recently, the distribution of functional attributes has been in the spotlight. Here, we report a study profiling soil microbial communities along an elevation gradient (500-2200 m) on Changbai Mountain. Using a comprehensive functional gene microarray (GeoChip 5.0), we found that microbial functional gene richness exhibited a dramatic increase at the treeline ecotone, but the bacterial taxonomic and phylogenetic diversity based on 16S rRNA gene sequencing did not exhibit such a similar trend. However, the β-diversity (compositional dissimilarity among sites) pattern for both bacterial taxa and functional genes was similar, showing significant elevational distance-decay patterns which presented increased dissimilarity with elevation. The bacterial taxonomic diversity/structure was strongly influenced by soil pH, while the functional gene diversity/structure was significantly correlated with soil dissolved organic carbon (DOC). This finding highlights that soil DOC may be a good predictor in determining the elevational distribution of microbial functional genes. The finding of significant shifts in functional gene diversity at the treeline ecotone could also provide valuable information for predicting the responses of microbial functions to climate change
Fairness-aware Competitive Bidding Influence Maximization in Social Networks
Competitive Influence Maximization (CIM) has been studied for years due to
its wide application in many domains. Most current studies primarily focus on
the micro-level optimization by designing policies for one competitor to defeat
its opponents. Furthermore, current studies ignore the fact that many
influential nodes have their own starting prices, which may lead to inefficient
budget allocation. In this paper, we propose a novel Competitive Bidding
Influence Maximization (CBIM) problem, where the competitors allocate budgets
to bid for the seeds attributed to the platform during multiple bidding rounds.
To solve the CBIM problem, we propose a Fairness-aware Multi-agent Competitive
Bidding Influence Maximization (FMCBIM) framework. In this framework, we
present a Multi-agent Bidding Particle Environment (MBE) to model the
competitors' interactions, and design a starting price adjustment mechanism to
model the dynamic bidding environment. Moreover, we put forward a novel
Multi-agent Competitive Bidding Influence Maximization (MCBIM) algorithm to
optimize competitors' bidding policies. Extensive experiments on five datasets
show that our work has good efficiency and effectiveness.Comment: IEEE Transactions on Computational Social Systems (TCSS), 2023, early
acces
System-level coupled modeling of piezoelectric vibration energy harvesting systems by joint finite element and circuit analysis
A practical piezoelectric vibration energy harvesting (PVEH) system is usually composed of two coupled parts: a harvesting structure and an interface circuit. Thus, it is much necessary to build system-level coupled models for analyzing PVEH systems, so that the whole PVEH system can be optimized to obtain a high overall efficiency. In this paper, two classes of coupled models are proposed by joint finite element and circuit analysis. The first one is to integrate the equivalent circuit model of the harvesting structure with the interface circuit and the second one is to integrate the equivalent electrical impedance of the interface circuit into the finite element model of the harvesting structure. Then equivalent circuit model parameters of the harvesting structure are estimated by finite element analysis and the equivalent electrical impedance of the interface circuit is derived by circuit analysis. In the end, simulations are done to validate and compare the proposed two classes of system-level coupled models. The results demonstrate that harvested powers from the two classes of coupled models approximate to theoretic values. Thus, the proposed coupled models can be used for system-level optimizations in engineering applications
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