Learning Adaptable Risk-Sensitive Policies to Coordinate in Multi-Agent General-Sum Games

In general-sum games, the interaction of self-interested learning agents commonly leads to socially worse outcomes, such as defect-defect in the iterated stag hunt (ISH). Previous works address this challenge by sharing rewards or shaping their opponents' learning process, which require too strong assumptions. In this paper, we demonstrate that agents trained to optimize expected returns are more likely to choose a safe action that leads to guaranteed but lower rewards. However, there typically exists a risky action that leads to higher rewards in the long run only if agents cooperate, e.g., cooperate-cooperate in ISH. To overcome this, we propose using action value distribution to characterize the decision's risk and corresponding potential payoffs. Specifically, we present Adaptable Risk-Sensitive Policy (ARSP). ARSP learns the distributions over agent's return and estimates a dynamic risk-seeking bonus to discover risky coordination strategies. Furthermore, to avoid overfitting training opponents, ARSP learns an auxiliary opponent modeling task to infer opponents' types and dynamically alter corresponding strategies during execution. Empirically, agents trained via ARSP can achieve stable coordination during training without accessing opponent's rewards or learning process, and can adapt to non-cooperative opponents during execution. To the best of our knowledge, it is the first method to learn coordination strategies between agents both in iterated prisoner's dilemma (IPD) and iterated stag hunt (ISH) without shaping opponents or rewards, and can adapt to opponents with distinct strategies during execution. Furthermore, we show that ARSP can be scaled to high-dimensional settings.Comment: arXiv admin note: substantial text overlap with arXiv:2205.1585

Reduction of Uncertainties for Damage Identification of Bridge Based on Fuzzy Nearness and Modal Data

To avoid the false results of deterministic identification methods induced by uncertainties, a fuzzy nearness-based method is proposed for the damage identification of bridge. An improved index based on ratios of modal shape components is used as identification measurements. The knowledge base for damage identification is established through corresponding relationship between fuzzified measurements and damage severities. The damage condition of test samples can be assessed based on approaching principle through fuzzy nearness with rules in knowledge base. A numerical analysis on a multigirder bridge considering uncertainty is presented to demonstrate the effectiveness of the proposed method. The results indicate that the fuzzy nearness-based method can achieve an accurate identification with success rate up to 93.75%. Antinoise analysis and the ability for dealing with incomplete information reveal its robustness

How Does Targeted Poverty Alleviation Policy Influence Residents' Perceptions of Rural Living Conditions? A Study of 16 Villages in Gansu Province, Northwest China

Rural living conditions (RLCs) in China are influential on the overall development and stability of regions, particularly for populations in distant poverty-stricken villages. This paper takes 16 villages of Chedao town in Gansu province, Northwest China (NWC) as our case study. Using data from the Poverty Alleviation and Assistance (PAA) project launched by Lanzhou University in June 2017, and the perceptions of residents of Chedao, we pinpoint RLC changes in the targeted poverty alleviation (TPA) process. The three main results show that: (1) From the residents' perceptions, the impact of alleviation measures on RLC is mainly reflected in improved housing conditions, infrastructure, and public services. We find no significant effect on cultural conditions. However, eco-environmental conditions have obviously weakened. (2) Housing size, accessibility, distance to shops, and safe drinking water are the most significant factors in housing conditions, infrastructure, public services, and eco-environmental conditions, respectively. (3) Out of the different levels of rural poverty households (RPHs), severe rurality villages are more strongly aware of the positive changes in RLC than residents of mild rurality villages. Moreover, in residents' view, housing conditions are most improved in severe rurality villages, infrastructure is most improved in moderate rurality villages, and public services are most improved in mild rurality villages. Eco-environmental conditions worsen across all levels. Our findings shed light on the perceptions of residents on changes occurring in rural living conditions, and provide a basis for subsequent studies of RLC in Northwest China

Prediction of Gene Expression Patterns With Generalized Linear Regression Model

Cell reprogramming has played important roles in medical science, such as tissue repair, organ reconstruction, disease treatment, new drug development, and new species breeding. Oct4, a core pluripotency factor, has especially played a key role in somatic cell reprogramming through transcriptional control and affects the expression level of genes by its combination intensity. However, the quantitative relationship between Oct4 combination intensity and target gene expression is still not clear. Therefore, firstly, a generalized linear regression method was constructed to predict gene expression values in promoter regions affected by Oct4 combination intensity. Training data, including Oct4 combination intensity and target gene expression, were from promoter regions of genes with different cell development stages. Additionally, the quantitative relationship between gene expression and Oct4 combination intensity was analyzed with the proposed model. Then, the quantitative relationship between gene expression and Oct4 combination intensity at each stage of cell development was classified into high and low levels. Experimental analysis showed that the combination height of Oct4-inhibited gene expression decremented by a temporal exponential value, whereas the combination width of Oct4-promoted gene expression incremented by a temporal logarithmic value. Experimental results showed that the proposed method can achieve goodness of fit with high confidence

Magic ratios for connectivity-driven electrical conductance of graphene-like molecules

Experiments using a mechanically-controlled break junction and calculations based on density functional theory demonstrate a new magic ratio rule (MRR),which captures the contribution of connectivity to the electrical conductance of graphene-like aromatic molecules. When one electrode is connected to a site i and the other is connected to a site i' of a particular molecule, we assign the molecule a magic integer Mii'. Two molecules with the same aromatic core, but different pairs of electrode connection sites (i,i' and j,j' respectively) possess different magic integers Mii' and Mjj'. Based on connectivity alone, we predict that when the coupling to electrodes is weak and the Fermi energy of the electrodes lies close to the centre of the HOMO-LUMO gap, the ratio of their conductances is equal to (Mii' /Mjj')2. The MRR is exact for a tight binding representation of a molecule and a qualitative guide for real molecules

Retrieval of Multiple Atmospheric Environmental Parameters From Images With Deep Learning

Retrieving atmospheric environmental parameters such as atmospheric horizontal visibility and mass concentration of aerosol particles with a diameter of 2.5 or 10 μm or less (PM 2.5 , PM 10 , respectively) from digital images provides new tools for horizontal environmental monitoring. In this study, we propose a new end-to-end convolutional neural network (CNN) for the retrieval of multiple atmospheric environmental parameters (RMEPs) from images. In contrast to other retrieval models, RMEP can retrieve a suite of atmospheric environmental parameters including atmospheric horizontal visibility, relative humidity (RH), ambient temperature, PM 2.5 , and PM 10 simultaneously from a single image. Experimental results demonstrate that: 1) it is possible to simultaneously retrieve multiple atmospheric environmental parameters; 2) spatial and spectral resolutions of images are not the key factors for the retrieval on the horizontal scale; and 3) RMEP achieves the best overall retrieval performance compared with several classic CNNs such as AlexNet, ResNet-50, and DenseNet-121, and the results are based on experiments on images extracted from webcams located in different continents (test R2 values are 0.63, 0.72, and 0.82 for atmospheric horizontal visibility, RH, and ambient temperature, respectively). Experimental results show the potential of utilizing webcams to help monitor the environment. Code and more results are available at https://github.com/cvvsu/RMEP .Peer reviewe

Influence of photochemical loss of volatile organic compounds on understanding ozone formation mechanism

Volatile organic compounds (VOCs) tend to be consumed by atmospheric oxidants, resulting in substantial photochemical loss during transport. An observation-based model was used to evaluate the influence of photochemical loss of VOCs on the sensitivity regime and mechanisms of ozone formation. Our results showed that a VOC-limited regime based on observed VOC concentrations shifted to a transition regime with a photochemical initial concentration of VOCs (PIC-VOCs) in the morning. The net ozone formation rate was underestimated by 3 ppbh(-1) (similar to 36 ppb d(-1)) based on the measured VOCs when compared with the PIC-VOCs. The relative contribution of the RO2 path to ozone production based on the PIC-VOCs accordingly increased by 13.4 %; in particular, the contribution of alkene-derived RO(2 )increased by approximately 10.2 %. In addition, the OH-HO2 radical cycle was obviously accelerated by highly reactive alkenes after accounting for photochemical loss of VOCs. The contribution of local photochemistry might be underestimated for both local and regional ozone pollution if consumed VOCs are not accounted for, and policymaking on ozone pollution prevention should focus on VOCs with a high reactivity.Peer reviewe

Numerical Investigation of Storage Behaviors of A Liquid CO2 Tank

The dynamic behavior of heat transfer induced by flow of the storage tank during the storage process was investigated using the computational fluid dynamics (CFD) approach, with the target of the liquid CO2 storage tank in a CO2 injection station in an oilfield. The flow field distribution outside the tank was simulated, exhibiting the patterns of air flow near the tank wall. The effect of progressive cooling leakage in the tank under various conditions was determined through simulation of the dynamic of flow heat transfer under various storage settings, with the result indicating that tank pressure has a beneficial effect on cooling capacity. The medium level, on the other hand, had a negative impact on cooling capacity. Finally, the impact of environmental variables on fluid loss was evaluated. This finding supports the safety and cost-benefit analysis of liquid CO2 storage systems

Intelligent and Scalable Air Quality Monitoring with 5G Edge

Air pollution introduces a major challenge for societies, where it leads to the premature deaths of millions of people each year globally. Massive deployment of air quality sensing devices and data analysis for the resultant data will pave the way for the development of real-time intelligent applications and services, e.g., minimization of exposure to poor air quality either on an individual or city scale. 5G and edge computing supports dense deployments of sensors at high resolution with ubiquitous connectivity, high bandwidth, high-speed gigabit connections, and ultralow latency analysis. This article conceptualizes AI-powered scalable air quality monitoring and presents two systems of calibrating low-cost air quality sensors and the image processing of pictures captured by hyperspectral cameras to better detect air quality. We develop and deploy different AI algorithms in these two systems on a 5G edge testbed and perform a detailed analytics regarding to 1) the performance of AI algorithms and 2) the required communication and computation resources.Peer reviewe