Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling

Improving and optimizing oceanographic sampling is a crucial task for marine science and maritime resource management. Faced with limited resources in understanding processes in the water-column, the combination of statistics and autonomous systems provide new opportunities for experimental design. In this work we develop efficient spatial sampling methods for characterizing regions defined by simultaneous exceedances above prescribed thresholds of several responses, with an application focus on mapping coastal ocean phenomena based on temperature and salinity measurements. Specifically, we define a design criterion based on uncertainty in the excursions of vector-valued Gaussian random fields, and derive tractable expressions for the expected integrated Bernoulli variance reduction in such a framework. We demonstrate how this criterion can be used to prioritize sampling efforts at locations that are ambiguous, making exploration more effective. We use simulations to study and compare properties of the considered approaches, followed by results from field deployments with an autonomous underwater vehicle as part of a study mapping the boundary of a river plume. The results demonstrate the potential of combining statistical methods and robotic platforms to effectively inform and execute data-driven environmental sampling

I\u27m guilty and I need to talk about it

We investigated how trait moral emotions (guilt-proneness, shame-proneness) predict how people speak about their past wrongs. Past research suggests that guilt and shame may have \u27approach\u27 and \u27avoidance\u27 qualities (respectively), so we predicted that guilt-prone individuals would disclose longer narratives to a researcher than shame-prone individuals. In a secondary data analysis from two large, online samples (n = 400 and 497), we found a significant correlation between guilt-proneness and word count across samples and measures of guilt (r values = .28 to .30, all p \u3c .001). Specifically, repair tendencies significantly predicted length of disclosure (standardized B = 0.22, 0.23, p \u3c .001), controlling for shame. Surprisingly, shame did not significantly predict word count

ChickScope: An Interactive MRI Classroom Curriculum Innovation for K-12

Researchers from several departments of the University of Illinois at Urbana-Champaign initiated ChickScope, a 21-day chick embryonic development project, to demonstrate the remote control of a magnetic resonance imaging (MRI) instrument through the World Wide Web. For 21 days, students and teachers from ten kindergarten to high school classrooms participated in this innovative project using an interactive Web lab book. From classroom computers with access to the Internet, students were able to login to the computers at the university that controlled the MRI system, manipulate experimental conditions through a simple on-line form to generate their own data, and then view resulting images of the chick embryo in real-time. Researchers answered students' questions about their MR images and other related issues. ChickScope made extraordinary hardware, software, and human resources available to the classrooms. However, it left to teachers the tasks of integrating these resources into the classroom and adapting them to the needs and abilities of the students. Thus, the implementation was teacher-based, and its meaning was realized in different ways in each setting. This paper describes the planning, implementation, and the impact of ChickScope in classrooms for facilitating learning and teaching. We provide examples from various grade levels?primary to high school. We conclude with lessons learned and the implications of advanced technologies for K-12 outreach.Biomedical Magnetic Resonance Laboratory, Beckman Institute Visualization Facility, National Center for Supercomputing Applications, and several other units of the University of Illinois at Urbana-Champaign, Champaign County Extension Unitpublished or submitted for publicationis peer reviewe

Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling

Improving and optimizing oceanographic sampling is a crucial task for marine science and maritime resource management. Faced with limited resources in understanding processes in the water column, the combination of statistics and autonomous systems provides new opportunities for experimental design. In this work we develop efficient spatial sampling methods for characterizing regions, defined by simultaneous exceedances above prescribed thresholds of several responses, with an application focus on mapping coastal ocean phenomena based on temperature and salinity measurements. Specifically, we define a design criterion based on uncertainty in the excursions of vector-valued Gaussian random fields and derive tractable expressions for the expected integrated Bernoulli variance reduction in such a framework. We demonstrate how this criterion can be used to prioritize sampling efforts at locations that are ambiguous, making exploration more effective. We use simulations to study and compare properties of the considered approaches, followed by results from field deployments with an autonomous underwater vehicle as part of a study mapping the boundary of a river plume. The results demonstrate the potential of combining statistical methods and robotic platforms to effectively inform and execute data-driven environmental sampling

Information-driven robotic sampling in the coastal ocean

Efficient sampling of coastal ocean processes, especially mechanisms such as upwelling and internal waves and their influence on primary production, is critical for understanding our changing oceans. Coupling robotic sampling with ocean models provides an effective approach to adaptively sample such features. We present methods that capitalize on information from ocean models and in situ measurements, using Gaussian process modeling and objective functions, allowing sampling efforts to be concentrated to regions with high scientific interest. We demonstrate how to combine and correlate marine data from autonomous underwater vehicles, model forecasts, remote sensing satellite, buoy, and ship‐based measurements, as a means to cross‐validate and improve ocean model accuracy, in addition to resolving upper water‐column interactions. Our work is focused on the west coast of Mid‐Norway where significant influx of Atlantic Water produces a rich and complex physical–biological coupling, which is hard to measure and characterize due to the harsh environmental conditions. Results from both simulation and full‐scale sea trials are presented.Nansen Legacy Program, Grant/AwardNumber:27272; Senter for Autonome Marine Operasjoner og Systemer,Grant/Award Number: 223254; Norges Forskningsråd,Grant/Award Number: 255303/E40; European Union's Seventh Framework Programme(FP7/2007–2013), Grant/Award Number: 270180publishedVersio

Toward adaptive robotic sampling of phytoplankton in the coastal ocean

Currents, wind, bathymetry, and freshwater runoff are some of the factors that make coastal waters heterogeneous, patchy, and scientifically interesting—where it is challenging to resolve the spatiotemporal variation within the water column. We present methods and results from field experiments using an autonomous underwater vehicle (AUV) with embedded algorithms that focus sampling on features in three dimensions. This was achieved by combining Gaussian process (GP) modeling with onboard robotic autonomy, allowing volumetric measurements to be made at fine scales. Special focus was given to the patchiness of phytoplankton biomass, measured as chlorophyll a (Chla), an important factor for understanding biogeochemical processes, such as primary productivity, in the coastal ocean. During multiple field tests in Runde, Norway, the method was successfully used to identify, map, and track the subsurface chlorophyll a maxima (SCM). Results show that the algorithm was able to estimate the SCM volumetrically, enabling the AUV to track the maximum concentration depth within the volume. These data were subsequently verified and supplemented with remote sensing, time series from a buoy and ship-based measurements from a fast repetition rate fluorometer (FRRf), particle imaging systems, as well as discrete water samples, covering both the large and small scales of the microbial community shaped by coastal dynamics. By bringing together diverse methods from statistics, autonomous control, imaging, and oceanography, the work offers an interdisciplinary perspective in robotic observation of our changing oceans.publishedVersio

Compact models for adaptive sampling in marine robotics

Finding high-value locations for in situ data collection is of substantial importance in ocean science, where diverse bio-physical processes interact to create dynamically evolving phenomena. These cover a variable spatial extent, and are sparse and difficult to predict. Autonomous robotic platforms can sustain themselves in harsh conditions with persistent presence, but require deployment at the correct place and time. To that end, we consider the use of remote sensing data for building compact models that can improve skill in predicting sub-mesoscale features and inform onboard sampling. The model enables prediction of regional patterns based on sparse in situ data, a capability that is essential in regions where use of satellite remote sensing in real time is often limited by cloud cover. Our model is based on classification of sea-surface temperature (SST) images, but the technique is general across any remotely sensed parameter. Images having similar magnitude and spatial patterns are grouped into a compact set of conditional means representing the dominant states. The classification is unsupervised and uses a combination of dictionary learning and hierarchical clustering. The method is demonstrated using SST images from Monterey Bay, California. The consistency of the classification result is verified and compared with oceanographic forcing using historical wind measurements. The established model is then shown to work in a real application using measurements from an autonomous surface vehicle (ASV), together with forecast and sampling strategies. Finally an analysis of the model prediction error is presented and compared across different paths and survey duration