Texas A&M University – Corpus Christi
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Dr. Clotilde Garcia with Barbara Canales
No full textPhotograph of Dr. Clotilde Garcia with Barbara Canales holding a bab
Photograph of a man whom Artie thinks is Uncle Gideon
No full textPhotograph of a man whom Artie thinks is Uncle Gideo
Primary Water Level Time Series - East Matagorda Old Gulf Cut
No full textEast Matagorda Old Gulf Cut primary water level measurements (PWL). Raw data as received at CBI in CSV format. Time stamp following ISO 8601. PWL in meters referenced to station datum. Time series span: 2000-08-18 11:00 GMT through 2005-11-15 14:54 GMT.US Army Corps of Engineers (USACE), Texas General Land Office (GLO), Texas Water Development Board (TWDB) and in collaboration and following the standards of the National Oceanic and Atmospheric Administration (NOAA)
Corals in crisis: How temperature and nutrient fluctuations affect physiological responses of corals and their microbiome in Kāne’ohe Bay, Hawai’i
No full textCoral reefs are the foundation to the social, cultural, and economic life in Hawai i; however, these reefs have not escaped the conditions that have ravaged coral reefs worldwide. Along the east coast of O ahu lies Kāne ohe Bay, which serves as a living laboratory with distinct difference in environmental gradients due to variation in circulation and residency times. Landward, there is a distinct gradient of cesspool presence and therefore a gradient of potential effluent intrusion and nutrient loading to these reefs. Together, these provide a unique opportunity to explore the impact of water quality and ongoing ocean warming on coral health, susceptibility and tolerance. This research investigates how temperature and nutrients influence the coral holobiont across a spatial and temporal environmental gradient. Pairs of known bleached/non-bleached corals were collected at two sites within Kāne ohe Bay which encompass this spatial gradient in temperature and nutrient influence. Corals were then subjected to experimental treatments (Control, Nutrient, Heated, Heated + Nutrient) for one month. Measurements of bleaching were collected at the beginning, middle, and end of the experiment and coral subsamples were collected at the beginning and end of the experiment for subsequent metagenomics analysis. I hypothesize that (A) coral subjected to a combined increase in temperature and nutrients will experience higher levels of bleaching and lower levels of survivorship, (B) historically non-bleached phenotypes will show higher levels of survivorship than their historically bleached counterparts, and (C) there will be an observed shift in microbial community composition across corals due to these stressors. If validated, these findings will support that coral bleaching susceptibility is manifested throughout the coral holobiont and the physiological response to stressors such as temperature and nutrient loading can be better understood and potentially mitigated, therefore supporting reef resiliency and restoration in the face of climate change
Hierarchical DDPG for manipulator motion planning in dynamic environments
No full textIn this paper, a hierarchical reinforcement learning (HRL) architecture, namely a “Hierar chical Deep Deterministic Policy Gradient (HDDPG)” has been proposed and studied. A HDDPG utilizes manager and worker formation similar to other HRL structures. However, unlike others, the HDDPG enables sharing an identical environment and state among workers and managers, while a unique reward system is required for each Deep Deterministic Policy Gradient (DDPG) agent. Therefore, the HDDPG allows easy structural expansion with probabilistic action selection of a worker by the manager. Due to its innate structural advantage, the HDDPG has a merit in building a general AI to deal with a complex time-horizon tasks with various conflicting sub-goals. The experimental results demonstrated its usefulness with a manipulator motion planning problem in a dynamic environment, where path planning and collision avoidance conflict each other. The proposed HDDPG is compared with an HAM and a single DDPG for performance evaluation. The result shows that the HDDPG demonstrated more than 40% of reward gain and more than two times the reward improvement rate. Another important feature of the proposed HDDPG is the biased manager training capability. By adding a preference factor to each worker, the manager can be trained to prefer a certain worker to achieve better success rate for a specific objective if needed.In this paper, a hierarchical reinforcement learning (HRL) architecture, namely a “Hierar chical Deep Deterministic Policy Gradient (HDDPG)” has been proposed and studied. A HDDPG utilizes manager and worker formation similar to other HRL structures. However, unlike others, the HDDPG enables sharing an identical environment and state among workers and managers, while a unique reward system is required for each Deep Deterministic Policy Gradient (DDPG) agent. Therefore, the HDDPG allows easy structural expansion with probabilistic action selection of a worker by the manager. Due to its innate structural advantage, the HDDPG has a merit in building a general AI to deal with a complex time-horizon tasks with various conflicting sub-goals. The experimental results demonstrated its usefulness with a manipulator motion planning problem in a dynamic environment, where path planning and collision avoidance conflict each other. The proposed HDDPG is compared with an HAM and a single DDPG for performance evaluation. The result shows that the HDDPG demonstrated more than 40% of reward gain and more than two times the reward improvement rate. Another important feature of the proposed HDDPG is the biased manager training capability. By adding a preference factor to each worker, the manager can be trained to prefer a certain worker to achieve better success rate for a specific objective if needed
Bluchers at a gathering
No full textL to R: Mary Julia, Dick, Nick Jr., Claudia, Carla, Carl, Claude, Conrad, Zula, and Georg