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Locality-based migration strategies are widely used in existing memory space management. Such type of strategies are consistently confronts with challenges in efficiently managing pages migration within constrained memory space, especially when new architecture such as hybrid of DRAM and NVM are emerging. Here we propose TransMigrator, an innovative predictive page migration model based on transformer architecture, which obtains a qualitative leap in the breadth and accuracy of prediction compared with traditional local-based methods. TransMigrator utilizes an end-to-end neural network to learn memory access behavior and page migration record in the long-term history and predict the most likely next page to fetch. Furthermore, a migration-management mechanism is designed to support the page-feeding from predictor, which in another way enhance the model robustness. The model achieves an average prediction accuracy better than 0.72, and saves an average of 0.24 access time overhead compared to strategies such as AC-CLOCK, THMigrator, and VC-HMM. 

Abstract Unravelling biosphere feedback mechanisms is crucial for predicting the impacts of global warming. Soil priming, an effect of fresh plant-derived carbon (C) on native soil organic carbon (SOC) decomposition, is a key feedback mechanism that could release large amounts of soil C into the atmosphere. However, the impacts of climate warming on soil priming remain elusive. Here, we show that experimental warming accelerates soil priming by 12.7% in a temperate grassland. Warming alters bacterial communities, with 38% of unique active phylotypes detected under warming. The functional genes essential for soil C decomposition are also stimulated, which could be linked to priming effects. We incorporate lab-derived information into an ecosystem model showing that model parameter uncertainty can be reduced by 32–37%. Model simulations from 2010 to 2016 indicate an increase in soil C decomposition under warming, with a 9.1% rise in priming-induced CO₂emissions. If our findings can be generalized to other ecosystems over an extended period of time, soil priming could play an important role in terrestrial C cycle feedbacks and climate change. 

This paper summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic materials. The MagNet Challenge has (1) advanced the stateof-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research community; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advancements in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community. 

Abstract Ammonia-oxidizing archaea (AOA) are among the most abundant and ubiquitous microorganisms in the ocean, exerting primary control on nitrification and nitrogen oxides emission. Although united by a common physiology of chemoautotrophic growth on ammonia, a corresponding high genomic and habitat variability suggests tremendous adaptive capacity. Here, we compared 44 diverse AOA genomes, 37 from species cultivated from samples collected across diverse geographic locations and seven assembled from metagenomic sequences from the mesopelagic to hadopelagic zones of the deep ocean. Comparative analysis identified seven major marine AOA genotypic groups having gene content correlated with their distinctive biogeographies. Phosphorus and ammonia availabilities as well as hydrostatic pressure were identified as selective forces driving marine AOA genotypic and gene content variability in different oceanic regions. Notably, AOA methylphosphonate biosynthetic genes span diverse oceanic provinces, reinforcing their importance for methane production in the ocean. Together, our combined comparative physiological, genomic, and metagenomic analyses provide a comprehensive view of the biogeography of globally abundant AOA and their adaptive radiation into a vast range of marine and terrestrial habitats.