Search for: All records

Over the past three decades, assessments of the contemporary global carbon budget consistently report a strong net land carbon sink. Here, we review evidence supporting this paradigm and quantify the differences in global and Northern Hemisphere estimates of the net land sink derived from atmospheric inversion and satellite-derived vegetation biomass time series. Our analysis, combined with additional synthesis, supports a hypothesis that the net land sink is substantially weaker than commonly reported. At a global scale, our estimate of the net land carbon sink is 0.8 ± 0.7 petagrams of carbon per year from 2000 through 2019, nearly a factor of two lower than the Global Carbon Project estimate. With concurrent adjustments to ocean (+8%) and fossil fuel (−6%) fluxes, we develop a budget that partially reconciles key constraints provided by vegetation carbon, the north-south CO₂gradient, and O₂trends. We further outline potential modifications to models to improve agreement with a weaker land sink and describe several approaches for testing the hypothesis. 

Privacy-preserving machine learning (PPML) enables multiple distrusting parties to jointly train ML models on their private data without revealing any information beyond the final trained models. In this work, we study the client-aided two-server setting where two non-colluding servers jointly train an ML model on the data held by a large number of clients. By involving the clients in the training process, we develop efficient protocols for training algorithms including linear regression, logistic regression, and neural networks. In particular, we introduce novel approaches to securely computing inner product, sign check, activation functions (e.g., ReLU, logistic function), and division on secret shared values, leveraging lightweight computation on the client side. We present constructions that are secure against semi-honest clients and further enhance them to achieve security against malicious clients. We believe these new client-aided techniques may be of independent interest. We implement our protocols and compare them with the two-server PPML protocols presented in SecureML (Mohassel and Zhang, S&P’17) across various settings and ABY2.0 (Patra et al., Usenix Security’21) theoretically. We demonstrate that with the assistance of untrusted clients in the training process, we can significantly improve both the communication and computational efficiency by orders of magnitude. Our protocols compare favorably in all the training algorithms on both LAN and WAN networks. 

Abstract Understanding the genetic basis of adaptive evolution following habitat expansion can have important implications for pest management. The pink rice borer (PRB),Sesamia inferens(Walker), is a destructive pest of rice that was historically restricted to regions south of 34° N latitude in China. However, with changes in global climate and farming practices, the distribution of this moth has progressively expanded, encompassing most regions in North China. Here, 3 highly differentiated subpopulations were discovered using high‐quality single‐nucleotide polymorphism and structural variant datasets across China, corresponding to northern, southern China regions, and the Yunnan‐Guizhou Plateau, with significant patterns of isolation by geographic and environmental distances. Our estimates of evolutionary history indicate asymmetric migration with varying population sizes across the 3 subpopulations. Selective sweep analyses estimated strong selection at insect cuticle glycine‐rich cuticular protein genes which are associated with enhanced desiccation adaptability in the northern group, and at the histone‐lysine‐N‐methyltransferase gene associated with range expansion and local adaptation in the Shandong population. Our findings have significant implications for the development of effective strategies to control this pest.