Differentially Private ERM Based on Data Perturbation

In this paper, after observing that different training data instances affect the machine learning model to different extents, we attempt to improve the performance of differentially private empirical risk minimization (DP-ERM) from a new perspective. Specifically, we measure the contributions of various training data instances on the final machine learning model, and select some of them to add random noise. Considering that the key of our method is to measure each data instance separately, we propose a new `Data perturbation' based (DB) paradigm for DP-ERM: adding random noise to the original training data and achieving ($\epsilon,\delta$)-differential privacy on the final machine learning model, along with the preservation on the original data. By introducing the Influence Function (IF), we quantitatively measure the impact of the training data on the final model. Theoretical and experimental results show that our proposed DBDP-ERM paradigm enhances the model performance significantly

Exogenous glutathione improves high root-zone temperature tolerance by modulating photosynthesis, antioxidant and osmolytes systems in cucumber seedlings

To investigate the physiological responses of plants to high root-zone temperature (HT, 35 °C) stress mitigated by exogenous glutathione (GSH), cucumber (Cucumis sativus L.) seedlings were exposed to HT with or without GSH treatment for 4 days and following with 4 days of recovery. Plant physiological variables, growth, and gene expression related to antioxidant enzymes and Calvin cycle were quantified. The results showed that HT significantly decreased GSH content, the ratio of reduced to oxidized glutathione (GSH/GSSG), chlorophyll content, photosynthesis and related gene expression, shoot height, stem diameter, as well as dry weight. The exogenous GSH treatment clearly lessened the HT stress by increasing the above variables. Meanwhile, HT significantly increased soluble protein content, proline and malondialdehyde (MDA) content as well as O2•− production rate, the gene expression and activities of antioxidant enzymes. The GSH treatment remarkably improved soluble protein content, proline content, antioxidant enzymes activities, and antioxidant enzymes related gene expression, and reduced the MDA content and O2•− production rate compared to no GSH treatment in the HT condition. Our results suggest that exogenous GSH enhances cucumber seedling tolerance of HT stress by modulating the photosynthesis, antioxidant and osmolytes systems to improve physiological adaptation

Model Simulation of Cucumber Yield and Microclimate Analysis in a Semi-closed Greenhouse in China

Adequate greenhouse environmental management is very important for improving resource use efficiency and increasing vegetable yield. The objective of this study was to explore suitable climate and cultivation management for cucumber to achieve high yield and build optimal yield models in semi-closed greenhouses. A fruit cucumber cultivar Deltastar was grown over 4 years in greenhouse and weekly data of yields (mean, highest and lowest) and environmental variables, including total radiation, air temperature, relative humidity, and carbon dioxide (CO2) concentration were collected. Regression analyses were applied to develop the relationships and build best regression models of yields with environmental variables using the first 2 years of data. Data collected in years 3 and 4 were used for model validation. Results showed that total radiation, nutrient, temperature, CO2 concentration, and average nighttime relative humidity had significant correlations with cucumber yields. The best regression models fit the mean, lowest, and highest yields very well with R2 values of 0.67, 0.66, and 0.64, respectively. Total radiation and air temperature had the most significant contributions to the variations of the yields. Our results of this study provide useful information for improving greenhouse climate management and yield forecast in semi-closed greenhouses