Robust Influence Maximization

In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem --- the task of finding $k$ seed nodes in a social network to maximize the influence spread. We propose the problem of robust influence maximization, which maximizes the worst-case ratio between the influence spread of the chosen seed set and the optimal seed set, given the uncertainty of the parameter input. We design an algorithm that solves this problem with a solution-dependent bound. We further study uniform sampling and adaptive sampling methods to effectively reduce the uncertainty on parameters and improve the robustness of the influence maximization task. Our empirical results show that parameter uncertainty may greatly affect influence maximization performance and prior studies that learned influence probabilities could lead to poor performance in robust influence maximization due to relatively large uncertainty in parameter estimates, and information cascade based adaptive sampling method may be an effective way to improve the robustness of influence maximization.Comment: 12 pages, 4 figures, Technical Report, contains proofs for the paper appeared in KDD'201

Spatiotemporal modeling of dams and consequent impacts on the mekong river basin ecosystem

The hydro-dam can help increase adaptation to climate change and meet water, energy, and food needs as a widely adopted water infrastructure. However, it alters and fragments ecosystems, especially at places where hydro-dam constructions are gaining popularity for the sake of more socio-economic benefits. This dissertation examines and characterizes the process and outcomes of ecosystem changes owing to hydro-dams, using the Mekong River Basin as an example. The overarching research question is answered from four angles, including 1) finding new essential properties of dams, 2) determining dams' impact scope on land change, 3) estimating cascade consequences of dams on significant water bodies, and 4) analyzing dams' ripple effect on the atmosphere. The main body (Chapters 2-4) of this dissertation consists of three articles. In Chapter 2, I achieve the first two research goals by performing time-serial trajectory analyses on 67 working Mekong hydro-dams and the lands surrounding them using long-term geospatial imageries and statistical methods. In Chapter 3, I calculated and analyzed the open water surface area of the Tonle Sap Lake and the changes at a 16-day interval from 2001 to 2015 to assess how upstream hydro-dam proliferation has influenced the largest inland lake in the lower basin. In Chapter 4, the spatial variations of inundation areas in the Tonle Sap Lake floodplain and temporal changes of the greenhouse gas (such as carbon dioxide and nitrous oxide) emissions from the changing lands were modeled and quantified using geospatial datasets and a biogeochemical model to provide a solution to the fourth research question. In summary, this dissertation has successfully established a new remote sensing approach that enables hydro-dam characterization and set up a combined framework combining geospatial modeling and biogeochemical modeling. The three studies come to the conclusions that 1) hydro-dams' impact scale on land change is spatially anisotropic at the local level, 2) hydro-dams' cascade consequence on a large water body at a remote place is significant, and 3) hydro-dams' ripple effect on floodplain via water and lands can cause more greenhouse gas emissions into the atmosphere. This dissertation can enrich the current literature regarding human-nature interactions, focusing on hydro-dam's role in the ecosystem. It also broadens the knowledge of hydro-dams' impacts and attracts more relevant studies and environmental protection efforts. More importantly, this dissertation can assist future policy-making, especially for sustainable hydro-dam planning and transboundary water resource management.Thesis (Ph. D.)--Michigan State University. Geography, 2021Includes bibliographical reference

Self context-aware emotion perception on human-robot interaction

Emotion recognition plays a crucial role in various domains of human-robot interaction. In long-term interactions with humans, robots need to respond continuously and accurately, however, the mainstream emotion recognition methods mostly focus on short-term emotion recognition, disregarding the context in which emotions are perceived. Humans consider that contextual information and different contexts can lead to completely different emotional expressions. In this paper, we introduce self context-aware model (SCAM) that employs a two-dimensional emotion coordinate system for anchoring and re-labeling distinct emotions. Simultaneously, it incorporates its distinctive information retention structure and contextual loss. This approach has yielded significant improvements across audio, video, and multimodal. In the auditory modality, there has been a notable enhancement in accuracy, rising from 63.10% to 72.46%. Similarly, the visual modality has demonstrated improved accuracy, increasing from 77.03% to 80.82%. In the multimodal, accuracy has experienced an elevation from 77.48% to 78.93%. In the future, we will validate the reliability and usability of SCAM on robots through psychology experiments.Comment: Australasian Conference on Robotics and Automation (ACRA). 202

Agent Based Simulation of Group Emotions Evolution and Strategy Intervention in Extreme Events

Agent based simulation method has become a prominent approach in computational modeling and analysis of public emergency management in social science research. The group emotions evolution, information diffusion, and collective behavior selection make extreme incidents studies a complex system problem, which requires new methods for incidents management and strategy evaluation. This paper studies the group emotion evolution and intervention strategy effectiveness using agent based simulation method. By employing a computational experimentation methodology, we construct the group emotion evolution as a complex system and test the effects of three strategies. In addition, the events-chain model is proposed to model the accumulation influence of the temporal successive events. Each strategy is examined through three simulation experiments, including two make-up scenarios and a real case study. We show how various strategies could impact the group emotion evolution in terms of the complex emergence and emotion accumulation influence in extreme events. This paper also provides an effective method of how to use agent-based simulation for the study of complex collective behavior evolution problem in extreme incidents, emergency, and security study domains

Damage Effect of Terrorist Attack Explosion-induced Shock Wave in a Double-deck Island Platform Metro Station

The objective of this research was to reasonably assess the damage to people and station structures caused by terrorist attack explosion at metro stations, taking the Liyuan station of Wuhan metro which adopts double-deck island platform as an typical example. The TNT explosion process inside the metro station was calculated and analyzed using the dynamic finite element numerical simulation software LS-DYNA. First, the peak overpressure curve and the positive pressure time curve of the shock wave of explosive under the condition of confined space in the metro station were obtained. Then, through the comparison and analysis of the theoretical formulas of explosive shock wave propagation characteristics, the accuracy and reliability of numerical calculation methods and model parameters were verified. At last, combining with the overpressure criterion of shock wave in explosive air, the distribution characteristics of the casualties in the metro station under the explosion shock wave are analyzed, and the dynamic response and damage effect of the pillar structure of the metro station under the explosion shock wave are studied

Accurate Quantification of Escherichia coli O157 in Grape Juice Using Hydrogel-Based Digital Polymerase Chain Reaction

In this study, we developed a direct hydrogel digital polymerase chain reaction (Direct-hdPCR) technique for the detection of Escherichia coli O157 in grape juice. In this method, samples were directly mixed with a hydrogel PCR system, followed by release of bacterial DNA through thermal lysis, and in situ digital PCR amplification of target DNA was achieved by taking advantage of the three-dimensional network structure of the hydrogel material. The amplified products were visualized as fluorescent dots, whose number of dots corresponded to the number of DNA molecules, enabling precise quantitative analysis. PCR primers specific to E. coli O157 were designed, and the developed method was validated for feasibility, sensitivity and specificity and was applied to real samples. The results demonstrated that this method exhibited high sensitivity, with a detection limit as low as the single bacterium level, without any need for DNA extraction or purification. It also showed strong specificity to the target bacterium and excellent anti-inhibition effects, without any need for sample pretreatment. The total detection time, from sample preparation to result reporting, was reduced to less than 40 minutes. Using the Direct-hdPCR method, all contaminated grape juice samples were directly detected with a positive detection rate of 100%, which is consistent with the results obtained by the dilution plating method. The average recoveries ranged from 96.9% to 112.0%, with a relative standard deviation (RSD) of 3.6% to 12.4%. This method offers advantages such as rapidity, high sensitivity, no need for sample pretreatment, and simple operation, making it suitable for on-site testing

A Non-Volatile Surface Tension-Driven Electrochemical Liquid Metal Actuator

We present a surface-tension driven electrochemical liquid metal (LM) actuator without the gas-producing side-reaction and capable of fabrication/operation in ambient air for practical applications. A hybrid supercapacitor is introduced to inhibit the common counter electrode side reactions, and the use of quasi-solid-state ionic hydrogel instead of liquid electrolyte further enables non-volatile operations. A 2×4 LM droplet array is demonstrated to actuate by a low driving voltage of 3.5 V for a maximum force of ~8.5 mN and a displacement of 0.56 mm in only 1.75 s. With the favorable scaling law of surface tension, further miniaturization could provide new opportunities in applications such as micro-robots, microfluidics, soft robots, and so on

Parameter Optimization of Pure Electric Vehicle Power System Based on Genetic Algorithm

In this paper, the ADVISOR software was used to establish a complete vehicle model of an electric vehicle, and the model was verified by CYC_NEDC under European urban conditions to meet the requirements. The maximum power of the driving motor, the speed ratio of the transmission system and the capacity of the storage battery are taken as the optimization objectives to carry out multi-objective optimization. Connect the model built by genetic algorithm and ADVISOR, run the program to simulate the two together, and get the result of parameter optimization of dynamic system. Through the simulation analysis and comparison under CYC_NEDC cycle conditions, the maximum speed, maximum climb slope, acceleration time and other dynamic performance parameters of this electric vehicle are effectively improved after optimization