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The Pelona–Orocopia–Rand (POR) schists were emplaced during the Farallon flat subduction in the early Cenozoic and now occupy the root of major strike-slip faults of the San Andreas Fault system. The POR schists are considered frictionally stable at lower temperatures than other basement rocks, limiting the maximum depth of seismicity in Southern California. However, experimental constraints on the composition and frictional properties of POR schists are still missing. Here, we study the frictional behavior of synthetic gouge derived from Pelona, Portal, and Rand Mountain schist wall rocks under hydrothermal, triaxial conditions. We conduct velocity-step experiments from 0.04 to 1 μm/s from room temperature to 500ºC under 200 MPa effective normal stress, including a 30 MPa porefluid pressure. The frictional stability of POR schists in the lower crust is caused by a thermally activated transition from slip-rate- and state-dependent friction to inherently stable, rate-dependent creep between 300ºC and 500ºC, depending on sample composition and slip-rate. The mineralogy of POR schists shows much variability caused by different protoliths and metamorphic grades, featuring various amounts of phyllosilicates, quartz, feldspar, and amphibole. Pelona and Portal schists exhibit a velocity-weakening regime enabling the nucleation and propagation of earthquakes when exhumed in the middle crust, as in the Mojave section of the San Andreas Fault. The contrasted frictional properties of POR schists exemplify the lithological control of seismic processes and associated hazards. 

Online reinforcement learning (RL) enhances policies through direct interactions with the environment, but faces challenges related to sample efficiency. In contrast, offline RL leverages extensive pre-collected data to learn policies, but often produces suboptimal results due to limited data coverage. Recent efforts integrate offline and online RL in order to harness the advantages of both approaches. However, effectively combining online and offline RL remains challenging due to issues that include catastrophic forgetting, lack of robustness to data quality and limited sample efficiency in data utilization. In an effort to address these challenges, we introduce A3RL, which incorporates a novel confidence aware Active Advantage Aligned (A3) sampling strategy that dynamically prioritizes data aligned with the policy's evolving needs from both online and offline sources, optimizing policy improvement. Moreover, we provide theoretical insights into the effectiveness of our active sampling strategy and conduct diverse empirical experiments and ablation studies, demonstrating that our method outperforms competing online RL techniques that leverage offline data. Our code will be publicly available at:this https URL. 

Primary forests play a crucial role in providing essential ecosystem services and supporting biodiversity compared to secondary forests. With increasing threats from extreme climate events and human activities, monitoring primary forest loss is critical for understanding the impact of these threats on ecosystems and biodiversity. Dense time series data from remotely sensed satellite imagery allow us to track historical disturbances, making it an effective source for mapping primary forests over time. However, distinguishing between primary and secondary forests based on spectral-temporal information remains challenging as primary forests can show high resilience to certain natural disturbances (e.g., drought), and secondary forests may not have experienced any disturbance during the satellite observation period. In this context, this study aims to map primary forests on the Caribbean island of Hispaniola using the time series approach and resilience metrics given that primary forests tend to be more resilient than secondary forests. To achieve this, we used spectral-temporal features from COntinuous monitoring of Land Disturbance (COLD) algorithm based on all available Landsat data between 1984 and 2023. Additionally, a resilience map is generated from deseasonalized and detrended spectral observations using the lag-1 autocorrelation method. Then, a Random Forest model was employed to generate an annual primary forest map.