Preserving Differential Privacy in Deep Learning Based on Feature Relevance Region Segmentation

In the era of big data, deep learning techniques provide intelligent solutions for various problems in real-life scenarios. However, deep neural networks depend on large-scale datasets including sensitive data, which causes the potential risk of privacy leakage. In addition, various constantly evolving attack methods are also threatening the data security in deep learning models. Protecting data privacy effectively at a lower cost has become an urgent challenge. This paper proposes an Adaptive Feature Relevance Region Segmentation (AFRRS) mechanism to provide differential privacy preservation. The core idea is to divide the input features into different regions with different relevance according to the relevance between input features and the model output. Less noise is intentionally injected into the region with stronger relevance, and more noise is injected into the regions with weaker relevance. Furthermore, we perturb loss functions by injecting noise into the polynomial coefficients of the expansion of the objective function to protect the privacy of data labels. Theoretical analysis and experiments have shown that the proposed AFRRS mechanism can not only provide strong privacy preservation for the deep learning model, but also maintain the good utility of the model under a given moderate privacy budget compared with existing methods

Tripling of western US particulate pollution from wildfires in a warming climate

International audienceSignificance Record-setting fires in the western United States over the last decade caused severe air pollution, loss of human life, and property damage. Enhanced drought and increased biomass in a warmer climate may fuel larger and more frequent wildfires in the coming decades. Applying an empirical statistical model to fires projected by Earth System Models including climate–ecosystem–socioeconomic interactions, we show that fine particulate pollution over the US Pacific Northwest could double to triple during late summer to fall by the late 21st century under intermediate- and low-mitigation scenarios. The historic fires and resulting pollution extremes of 2017–2020 could occur every 3 to 5 y under 21st-century climate change, posing challenges for air quality management and threatening public health

Poplar Wood Torrefaction: Kinetics, Thermochemistry and Implications

The kinetic and thermochemical models for poplar wood torrefaction were developed in the present work. The torrefaction kinetic model satisfactorily fitted the experimental thermogravimetric analysis (TGA) data of poplar wood torrefaction and provided a coherent description of the evolution of torrefaction volatile and solid products in terms of a set of identifiable chemical components and elemental compositions. The torrefaction thermochemical model described the thermochemical performance of poplar wood torrefaction processes. The results from the kinetic and thermochemical models for poplar wood torrefaction showed that (1) high temperature increases the evolution rate of torrefaction products, and favors the formation of torrefaction volatiles; (2) the heating rate has a slight effect on evolution for torrefaction process; (3) the mass and energy yields of torrefaction products are significantly influenced by both torrefaction temperature and residence time; (4) the heat of torrefaction reaction is mostly endothermic with a relatively small amount (less than 10% of the raw material energy content); (5) for the overall torrefaction processes, the sensible and latent energy of torrefaction products accounts for 5–18% of the total energy input and the remaining energy input transfers into the energy contents of torrefaction products. This work provides a theoretical guidance for future evaluation and optimization of woody biomass torrefaction systems/processes, and thereafter for the industrial application of woody biomass thermochemical conversion

Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer

Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Here, the authors developed and validated a radiomic signature (RS) for prediction of DM within a multicenter dataset, and suggest that it may help with stratification of patients who might benefit from adjuvant chemotherapy for DM