DEEP LEARNING IN CHEMISTRY AND COMPUTER-GO

Deep learning a research field in artificial intelligence and also a fast-growing technology in helping human in different directions. This thesis will focus on two of its usages: chemistry and computer go. In the two fields, deep learning achieves state of art accuracy in prediction and game playing ability

Extreme Weather Events and Climate Variability Provide a Lens to How Shallow Lakes May Respond to Climate Change

Shallow lakes, particularly those in low-lying areas of the subtropics, are highly vulnerable to changes in climate associated with global warming. Many of these lakes are in tropical cyclone strike zones and they experience high inter-seasonal and inter-annual variation in rainfall and runoff. Both of those factors strongly modulate sediment–water column interactions, which play a critical role in shallow lake nutrient cycling, water column irradiance characteristics and cyanobacterial harmful algal bloom (CyanoHAB) dynamics. We illustrate this with three examples, using long-term (15–25 years) datasets on water quality and plankton from three shallow lakes: Lakes Okeechobee and George (Florida, USA) and Lake Taihu (China). Okeechobee and Taihu have been impacted repeatedly by tropical cyclones that have resulted in large amounts of runoff and sediment resuspension, and resultant increases in dissolved nutrients in the water column. In both cases, when turbidity declined, major blooms of the toxic CyanoHAB Microcystis aeruginosa occurred over large areas of the lakes. In Lake George, periods of high rainfall resulted in high dissolved color, reduced irradiance, and increased water turnover rates which suppress blooms, whereas in dry periods with lower water color and water turnover rates there were dense cyanobacteria blooms. We identify a suite of factors which, from our experience, will determine how a particular shallow lake will respond to a future with global warming, flashier rainfall, prolonged droughts and stronger tropical cyclones

Robust estimation of lake metabolism by coupling high frequency dissolved oxygen and chlorophyll fluorescence data in a Bayesian framework

Gross primary production (GPP) and community respiration (R) are increasingly calculated from high-frequency measurements of dissolved oxygen (DO) by fitting dynamic metabolic models to the observed DO time series. Because different combinations of metabolic components result in nearly the same DO time series, theoretical problems burden this inverse modeling approach. Bayesian parameter inference could improve identification of processes by including independent knowledge in the estimation procedure. This method, however, requires model development because parameters of existing metabolic models are too abstract to achieve a significant improvement. Because algal biomass is a key determinant of GPP and R, and high-frequency data on phytoplankton biomass are increasingly available, coupling DO and biomass time series within a Bayesian framework has a high potential to support identification of individual metabolic components. We demonstrate this potential in 3 lakes. Phytoplankton data were simulated via a sequential Bayesian learning procedure coupled with an error model that accounted for systematic errors caused by structural deficiencies of the metabolic model. This method provided ecologically coherent, and therefore presumably robust, estimates for biomass-specific metabolic rates and contributes to a better understanding of metabolic responses to natural and anthropogenic disturbances