Microstructure Characterization Techniques for Shale Reservoirs : A Review

Funding This work was funded by the National Natural Science Foundation of China (Grant nos. U19B6003-03-01 and 42030804).Peer reviewedPublisher PD

Multi-Agent Game Abstraction via Graph Attention Neural Network

In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms.Comment: Accepted by AAAI202

From Few to More: Large-scale Dynamic Multiagent Curriculum Learning

A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale multiagent settings due to the complex dynamics between the environment and agents and the explosion of state-action space. In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents. We propose three transfer mechanisms across curricula to accelerate the learning process. Moreover, due to the fact that the state dimension varies across curricula,, and existing network structures cannot be applied in such a transfer setting since their network input sizes are fixed. Therefore, we design a novel network structure called Dynamic Agent-number Network (DyAN) to handle the dynamic size of the network input. Experimental results show that DyMA-CL using DyAN greatly improves the performance of large-scale multiagent learning compared with state-of-the-art deep reinforcement learning approaches. We also investigate the influence of three transfer mechanisms across curricula through extensive simulations.Comment: Accepted by AAAI202

Rethink Baseline of Integrated Gradients from the Perspective of Shapley Value

Numerous approaches have attempted to interpret deep neural networks (DNNs) by attributing the prediction of DNN to its input features. One of the well-studied attribution methods is Integrated Gradients (IG). Specifically, the choice of baselines for IG is a critical consideration for generating meaningful and unbiased explanations for model predictions in different scenarios. However, current practice of exploiting a single baseline fails to fulfill this ambition, thus demanding multiple baselines. Fortunately, the inherent connection between IG and Aumann-Shapley Value forms a unique perspective to rethink the design of baselines. Under certain hypothesis, we theoretically analyse that a set of baseline aligns with the coalitions in Shapley Value. Thus, we propose a novel baseline construction method called Shapley Integrated Gradients (SIG) that searches for a set of baselines by proportional sampling to partly simulate the computation path of Shapley Value. Simulations on GridWorld show that SIG approximates the proportion of Shapley Values. Furthermore, experiments conducted on various image tasks demonstrate that compared to IG using other baseline methods, SIG exhibits an improved estimation of feature's contribution, offers more consistent explanations across diverse applications, and is generic to distinct data types or instances with insignificant computational overhead.Comment: 12 page

Risk prediction of placenta previa based on the distance from the lower edge of the gestational sac to the internal cervical os in early pregnancy

Objectives: To explore the relationship between the distance from the lower edge of the gestational sac to the internal cervical os in early pregnancy and placenta previa. Material and methods: A prospective cohort study of women who underwent pregnancy examination in Weifang People's Hospital or Sunshine Union Hospital from January 2020 to June 2021. The distance from the lower edge of the gestational sac to the internal cervical os was measured at 5–6 weeks’ gestation. There were 86 women with distance < 2.5 cm, and 105 women with distance ≥ 2.5 cm were randomly selected. There were 92 cases of scarred uterus and 99 cases of non-scarred uterus among the 191 women. They were divided into six groups according to the distance: (1) < 1.0 cm; (2) 1.0 cm to < 1.5 cm; (3) 1.5 cm to < 2.0cm; (4) 2.0 cm to < 2.5 cm; (5) 2.5 cm to < 3.0 cm; (6) ≥ 3.0 cm. All included women were followed-up during pregnancy and pregnancy outcome, and the likelihood ratio of different distances in early pregnancy was calculated and risk stratification was performed, and ROC curve was constructed. Results: There were 15 women in the included studies who were lost to follow-up, 47 had a scarred uterus with placenta previa and 29 had a non-scarred uterus with placenta previa after delivery at 28 weeks or later. The distance from the lower edge of the gestational sac to the internal cervical os in early pregnancy of the scarred uterus < 1.5 cm, and the likelihood ratio was ∞; and the distance ≥ 3.0 cm, the likelihood ratio was 0. The distance from the lower edge of the non-scarred gestational sac to the internal cervical os < 1.0 cm, and the likelihood ratio was ∞; and the distance ≥ 3.0 cm, the likelihood ratio was 0. The ROC curve showed that when the area AUC under the curve was 87%, the optimal diagnostic cut-off value was 2.4 cm. Conclusions: When the distance from the lower edge of the gestational sac to the internal cervical os was < 1.5 cm and the distance between the non-scarred uterus was < 1.0 cm, it eventually developed into placenta previa; the distance from the lower edge of the gestational sac to the internal cervical os in the first trimester of pregnancy between the scarred uterus and the non-scarred uterus was ≥ 3.0 cm, and it would hardly develop into placenta previa. When the distance from the lower edge of the gestational sac to the internal cervical os in early pregnancy was ≤ 2.4 cm, it could be used as a predictor of placenta previa

Estimation of effect of voids on frequency response of mountain tunnel lining based on microtremor method

Nowadays, aged tunnels keep continuously increasing all over the world, which require effective inspection methods to assess their health conditions. In this study, both in situ acceleration wave measurements and numerical simulations were carried out to study the microtremor characteristics of mountain tunnel lining. Power spectrum density (PSD) of signals was calculated and peak frequencies were identified using the peak-picking method. Discontinuous contacts between rock masses and lining concrete were simplified as weak interfaces with low stiffness, which play the role of elastic supporting during dynamic calculation. Influences of voids, rock type and concrete type on their peak frequencies were evaluated. The results of the numerical analysis show that the normal stiffness of rock-concrete interface has strong correlation with the magnitude of peak frequency. The frequency response of tunnel lining is affected by the presence of voids located around tunnel circumference behind lining. The first peak frequency is dependent on the void size and location. The larger the void size and location angle, the greater the decrease of the first peak frequency. The peak frequency also decreases as the strength of concrete decreases, and is not affected by the change of properties of unweathered rock masses. Additional frequency modes can be identified when voids are not located on the central axis of tunnel. The first peak frequency variation can be considered to be intimately linked with the stress state of the tunnel lining influenced by the existence of voids

Evidence for increasing global wheat yield potential

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.Fil: Guarin, Jose Rafael. National Aeronautics and Space Administration; Estados Unidos. Columbia University; Estados Unidos. Florida State University; Estados UnidosFil: Martre, Pierre. Institut Agro Montpellier SupAgro; FranciaFil: Ewert, Frank. Universitat Bonn; Alemania. Leibniz Centre for Agricultural Landscape Research; AlemaniaFil: Webber, Heidi. Universitat Bonn; Alemania. Leibniz Centre for Agricultural Landscape Research; AlemaniaFil: Dueri, Sibylle. Institut Agro Montpellier SupAgro; FranciaFil: Calderini, Daniel Fernando. Universidad Austral de Chile; ChileFil: Reynolds, Matthew. International Maize and Wheat Improvement Center ; MéxicoFil: Molero, Gemma. KWS; FranciaFil: Miralles, Daniel Julio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Garcia, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Slafer, Gustavo Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universitat de Lleida; España. Institució Catalana de Recerca i Estudis Avancats; EspañaFil: Giunta, Francesco. Consiglio Nazionale Delle Ricerche. Istituto Di Scienze Dell Atmosfera E del Clima.; ItaliaFil: Pequeno, Diego N.L.. International Maize and Wheat Improvement Center; MéxicoFil: Stella, Tommaso. Universitat Bonn; Alemania. Leibniz Centre for Agricultural Landscape Research; AlemaniaFil: Ahmed, Mukhtar. University Of Pakistan; PakistánFil: Alderman, Phillip D.. Oklahoma State University; Estados UnidosFil: Basso, Bruno. Michigan State University; Estados UnidosFil: Berger, Andres G.. Instituto Nacional de Investigacion Agropecuaria;Fil: Bindi, Marco. Università degli Studi di Firenze; ItaliaFil: Bracho-Mujica, Gennady. Universität Göttingen; AlemaniaFil: Cammarano, Davide. Purdue University; Estados UnidosFil: Chen, Yi. Chinese Academy of Sciences; República de ChinaFil: Dumont, Benjamin. Université de Liège; BélgicaFil: Rezaei, Ehsan Eyshi. Leibniz Institute Of Plant Genetics And Crop Plant Research.; AlemaniaFil: Fereres, Elias. Universidad de Córdoba; EspañaFil: Ferrise, Roberto. Michigan State University; Estados UnidosFil: Gaiser, Thomas. Universitat Bonn; AlemaniaFil: Gao, Yujing. Florida State University; Estados UnidosFil: Garcia Vila, Margarita. Universidad de Córdoba; EspañaFil: Gayler, Sebastian. Universidad de Hohenheim; Alemani