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

A transcriptomic map of EGFR-induced epithelial-to-mesenchymal transition identifies prognostic and therapeutic targets for head and neck cancer

Background Epidermal growth factor receptor (EGFR) is both a driver oncogene and a therapeutic target in advanced head and neck squamous cell carcinoma (HNSCC). However, response to EGFR treatment is inconsistent and lacks markers for treatment prediction. This study investigated EGFR-induced epithelial-to-mesenchymal transition (EMT) as a central parameter in tumor progression and identified novel prognostic and therapeutic targets, and a candidate predictive marker for EGFR therapy response. Methods Transcriptomic profiles were analyzed by RNA sequencing (RNA-seq) following EGFR-mediated EMT in responsive human HNSCC cell lines. Exclusive genes were extracted via differentially expressed genes (DEGs) and a risk score was determined through forward feature selection and Cox regression models in HNSCC cohorts. Functional characterization of selected prognostic genes was conducted in 2D and 3D cellular models, and findings were validated by immunohistochemistry in primary HNSCC. Results An EGFR-mediated EMT gene signature composed of n = 171 genes was identified in responsive cell lines and transferred to the TCGA-HNSCC cohort. A 5-gene risk score comprising DDIT4, FADD, ITGB4, NCEH1, and TIMP1 prognosticated overall survival (OS) in TCGA and was confirmed in independent HNSCC cohorts. The EGFR-mediated EMT signature was distinct from EMT hallmark and partial EMT (pEMT) meta-programs with a differing enrichment pattern in single malignant cells. Molecular characterization showed that ITGB4 was upregulated in primary tumors and metastases compared to normal mucosa and correlated with EGFR/MAPK activity in tumor bulk and single malignant cells. Preferential localization of ITGB4 together with its ligand laminin 5 at tumor-stroma interfaces correlated with increased tumor budding in primary HNSCC tissue sections. In vitro, ITGB4 knock-down reduced EGFR-mediated migration and invasion and ITGB4-antagonizing antibody ASC8 impaired 2D and 3D invasion. Furthermore, a logistic regression model defined ITGB4 as a predictive marker of progression-free survival in response to Cetuximab in recurrent metastatic HNSCC patients. Conclusions EGFR-mediated EMT conveyed through MAPK activation contributes to HNSCC progression upon induction of migration and invasion. A 5-gene risk score based on a novel EGFR-mediated EMT signature prognosticated survival of HNSCC patients and determined ITGB4 as potential therapeutic and predictive target in patients with strong EGFR-mediated EMT

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