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

Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN sampler

Bayesian inference with deep generative prior has received considerable interest for solving imaging inverse problems in many scientific and engineering fields. The selection of the prior distribution is learned from, and therefore an important representation learning of, available prior measurements. The SA-Roundtrip, a novel deep generative prior, is introduced to enable controlled sampling generation and identify the data's intrinsic dimension. This prior incorporates a self-attention structure within a bidirectional generative adversarial network. Subsequently, Bayesian inference is applied to the posterior distribution in the low-dimensional latent space using the Hamiltonian Monte Carlo with preconditioned Crank-Nicolson (HMC-pCN) algorithm, which is proven to be ergodic under specific conditions. Experiments conducted on computed tomography (CT) reconstruction with the MNIST and TomoPhantom datasets reveal that the proposed method outperforms state-of-the-art comparisons, consistently yielding a robust and superior point estimator along with precise uncertainty quantification

Prediction of Electric Vehicle Energy Consumption in an Intelligent and Connected Environment

Accurate energy consumption prediction is essential for improving the driving experience. In the urban road scenario, we discussed the influencing factors of energy consumption and divided the modes from various perspectives. The differences in energy consumption characteristics and distribution laws for electric vehicles using the IDM and CACC car-following models under different traffic flows are compared. An energy consumption prediction framework based on the LightGBM model is proposed. According to the study, driving range, acceleration, accelerating time, decelerating time and cruising time all significantly impact the overall energy consumption of electric vehicles. There are apparent differences in energy consumption characteristics and distribution laws under different traffic flows: average energy consumption is lower under low flow and increased under high flow. The CACC-electric vehicles consume more energy in low flow than IDM-electric vehicles. Under high flow, the opposite is true. The results show that the proposed framework has a high accuracy: the MAPE based on IDM datasets is 3.45% and the RMSE is 0.039 kWh; the MAPE based on CACC datasets is 5.57% and the RMSE is 0.042 kWh. The MAPE and RMSE are reduced by 33.7% and 50.6% (maximum extent) compared to the best comparison algorithm

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

FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models

Large Language models (LLMs) usually rely on extensive training datasets. In the financial domain, creating numerical reasoning datasets that include a mix of tables and long text often involves substantial manual annotation expenses. To address the limited data resources and reduce the annotation cost, we introduce FinLLMs, a method for generating financial question-answering data based on common financial formulas using Large Language Models. First, we compile a list of common financial formulas and construct a graph based on the variables these formulas employ. We then augment the formula set by combining those that share identical variables as new elements. Specifically, we explore formulas obtained by manual annotation and merge those formulas with shared variables by traversing the constructed graph. Finally, utilizing GPT-3.5, we generate financial question-answering data that encompasses both tabular information and long textual content, building on the collected formula set. Our experiments demonstrate that synthetic data generated by FinLLMs effectively enhances the performance of several large-scale numerical reasoning models in the financial domain, outperforming two established benchmark financial question-answering datasets.Comment: Under submission of IEEE Transaction

<em>Fusarium graminearum</em> Species Complex and Trichothecene Genotype

The fungal phytopathogen in Fusarium species can cause Fusarium head blight of wheat, barley, oats, and other small cereal grain crops worldwide. Most importantly, these fungi can produce different kinds of mycoxins, and they are harmful to humans and animal health. FAO reported that approximately 25% of the world’s grains were contaminated by mycotoxins annually. This chapter will focus on several topics as below: (1) composition of Fusarium graminearum species complex; (2) genotype determination of Fusarium graminearum species complex strains from different hosts and their population structure changes; (3) genetic approaches to genotype determination in type B-trichothecene producing Fusaria fungi; and (4) some newly identified trichothecene mycotoxins, their toxicity, and distribution of the producers

Correlation between circuital current, Cu(II) reduction and cellular electron transfer in EAB isolated from Cu(II)-reduced biocathodes of microbial fuel cells

The performance of four indigenous electrochemically active bacteria (EAB) (Stenotrophomonas maltophilia JY1, Citrobacter sp. JY3, Pseudomonas aeruginosa JY5 and Stenotrophomonas sp. JY6) was evaluated for Cu(II) reduction on the cathodes of microbial fuel cells (MFCs). These EAB were isolated from well adapted mixed cultures on the MFC cathodes operated for Cu(II) reduction. The relationship between circuital current, Cu(II) reduction rate, and cellular electron transfer processes was investigated from a mechanistic point of view using X-ray photoelectron spectroscopy, scanning electronic microscopy coupled with energy dispersive X-ray spectrometry, linear sweep voltammetry and cyclic voltammetry. JY1 and JY5 exhibited a weak correlation between circuital current and Cu(II) reduction. A much stronger correlation was observed for JY3 followed by JY6, demonstrating the relationship between circuital current and Cu(II) reduction for these species. In the presence of electron transfer inhibitors (2,4-dinitrophenol or rotenone), significant inhibition on JY6 activity and a weak effect on JY1, JY3 and JY5 was observed, confirming a strong correlation between cellular electron transfer processes and either Cu(II) reduction or circuital current. This study provides evidence of the diverse functions played by these EAB, and adds to a deeper understanding of the capabilities exerted by diverse EAB associated with Cu(II) reduction

Age-related sensitivity and pathological differences in infections by 2009 pandemic influenza A (H1N1) virus

<p>Abstract</p> <p>Background</p> <p>The highly pandemic 2009 influenza A H1N1 virus infection showed distinguished skewed age distribution with majority of infection and death in children and young adults. Although previous exposure to related antigen has been proposed as an explanation, the mechanism of age protection is still unknown.</p> <p>Methods</p> <p>In this study, murine model of different ages were inoculated intranasally with H1N1 (A/Beijing/501/09) virus and the susceptibility and pathological response to 2009 H1N1 infection were investigated.</p> <p>Results</p> <p>Our results showed that the younger mice had higher mortality rate when infected with the same dose of virus and the lethal dose increased with age. Immunohistochemical staining of H1N1 antigens in mice lung indicated infection was in the lower respiratory tract. Most bronchial and bronchiolar epithelial cells in 4-week mice were infected while only a minor percentage of those cells in 6-month and 1-year old mice did. The young mice developed much more severe lung lesions and had higher virus load in lung than the two older groups of mice while older mice formed more inducible bronchus-associated lymphoid tissue in their lungs and more severe damage in spleen.</p> <p>Conclusions</p> <p>These results suggest that young individuals are more sensitive to H1N1 infection and have less protective immune responses than older adults. The age factor should be considered when studying the pathogenesis and transmission of influenza virus and formulating strategies on vaccination and treatment.</p