Popularity Ratio Maximization: Surpassing Competitors through Influence Propagation

In this paper, we present an algorithmic study on how to surpass competitors in popularity by strategic promotions in social networks. We first propose a novel model, in which we integrate the Preferential Attachment (PA) model for popularity growth with the Independent Cascade (IC) model for influence propagation in social networks called PA-IC model. In PA-IC, a popular item and a novice item grab shares of popularity from the natural popularity growth via the PA model, while the novice item tries to gain extra popularity via influence cascade in a social network. The {\em popularity ratio} is defined as the ratio of the popularity measure between the novice item and the popular item. We formulate {\em Popularity Ratio Maximization (PRM)} as the problem of selecting seeds in multiple rounds to maximize the popularity ratio in the end. We analyze the popularity ratio and show that it is monotone but not submodular. To provide an effective solution, we devise a surrogate objective function and show that empirically it is very close to the original objective function while theoretically, it is monotone and submodular. We design two efficient algorithms, one for the overlapping influence and non-overlapping seeds (across rounds) setting and the other for the non-overlapping influence and overlapping seed setting, and further discuss how to deal with other models and problem variants. Our empirical evaluation further demonstrates that the proposed PRM-IMM method consistently achieves the best popularity promotion compared to other methods. Our theoretical and empirical analyses shed light on the interplay between influence maximization and preferential attachment in social networks.Comment: 22 pages, 8 figures, to be appear SIGMOD 202

Environment Diversification with Multi-head Neural Network for Invariant Learning

Neural networks are often trained with empirical risk minimization; however, it has been shown that a shift between training and testing distributions can cause unpredictable performance degradation. On this issue, a research direction, invariant learning, has been proposed to extract invariant features insensitive to the distributional changes. This work proposes EDNIL, an invariant learning framework containing a multi-head neural network to absorb data biases. We show that this framework does not require prior knowledge about environments or strong assumptions about the pre-trained model. We also reveal that the proposed algorithm has theoretical connections to recent studies discussing properties of variant and invariant features. Finally, we demonstrate that models trained with EDNIL are empirically more robust against distributional shifts.Comment: In Proceedings of 36th Conference on Neural Information Processing Systems (NeurIPS 2022

Influence of a repump laser on a nearly degenerate four-wave-mixing spectrum in atomic vapors

The influence of a repump laser on a nearly degenerate four-wave-mixing (NDFWM) spectrum was investigated. We found the amplitude and line shape of the NDFWM depended strongly on the detuning of the repump field. A five-peak structure was observed. And at some certain repump detuning a dip appeared at the central peak. A rough analysis was proposed to explain this effect

A Multi-Hazard Safety Evaluation Framework for a Submerged Bridge using Machine Learning Model

This study proposes a submerged bridge safety evaluation process against seismic and flood hazards. Due to the uncertainties in the scours, seismic hazard, and structural performance for a given seismic excitation are inevitable, reliability analysis is adopted. A machine-learning based scour risk curve, which is established by the multivariate adaptive regression splines (MARS) incorporated with firefly algorithm (FA), is built to reflect the flood hazard. The seismic hazard is measured using a code-based probabilistic seismic hazard curve. A series of nonlinear time-history analyses are performed to determine the structural performance under different peak-ground-acceleration values. Displacement ductility is used to measure the bridge performance under attacks of both hazards. The influence of the immersed water depth on a bridges performance is investigated. A case study, in which the nonlinear behaviors in concrete (including core and cover areas), steel bar and soil are included in a bridge model, is conducted to illustrate the proposed methodology and the structural performances with added mass are investigated to show the submerged water effect. According to the results obtained, highly variability of seismic performances is observed and it is important to include the immersed water depth to capture the seismic capacity of a bridge if the submerged bridge depth is great