Clayton T. Morrison

Computer science can use more science

Software developers should use empirical methods to analyze their designs to predict how working systems will behave.

Factoring in canopy cover heterogeneity on evapotranspiration partitioning: Beyond big-leaf surface homogeneity assumptions

The vast majority of water on Earths terrestrial surface is lost through evapotranspiration (ET; vaporization processes that include evaporation [E] of intercepted water, E from free-water surfaces, and transpiration [T] from vegetation [Savenije 2004]) (Jasechko et al. 2013). Management and conservation of water resources require explicit understanding of ET, particularly due to the potential for global change to alter water fluxes. Although mostly considered by its hydrological nature, ET is the result of a suite of both physical and biological processes interacting at multiple spatial and temporal scales (Jarvis 1995) and constitutes a key driver of ecosystem function via the effects of T on ecosystem water and energy balance, impacting productivity (Jackson et al. 2001). During the twentieth century, important empirical and theoretical models that described ET based on its physical drivers—particularly relevant to agriculture and water resource management—as well as sophisticated measurement techniques relevant to local scales were developed (Shuttleworth 2007). Although vegetation is acknowledged to strongly influence ET, theories that explicitly considered vegetation applied generally to two extreme cases: bare or fully vegetated soil (Shuttleworth 2007; Caylor et al. 2005). Widely used empirical models for ET, mostly derived from the Penman-Montheith equation (Montheith 1965), use a simplifying assumption…

Intrinsically motivated information foraging

We treat information gathering as a POMDP in which the goal is to maximize an accumulated intrinsic reward at each time step based on the negative entropy of the agents beliefs about the world state. We show that such information foraging agents can discover intelligent exploration policies that take into account the long-term effects of sensor and motor actions, and can automatically adapt to variations in sensor noise, different amounts of prior information, and limited memory conditions.

Challenges to decoding the intention behind natural instruction

Currently, most systems for human-robot teaching allow only one mode of teacher-student interaction (e.g., teaching by demonstration or feedback), and teaching episodes have to be carefully set-up by an expert. To understand how we might integrate multiple, interleaved forms of human instruction into a robot learner, we performed a behavioral study in which 44 untrained humans were allowed to freely mix interaction modes to teach a simulated robot (secretly controlled by a human) a complex task. Analysis of transcripts showed that human teachers often give instructions that are nontrivial to interpret and not easily translated into a form useable by machine learning algorithms. In particular, humans often use implicit instructions, fail to clearly indicate the boundaries of procedures, and tightly interleave testing, feedback, and new instruction. In this paper, we detail these teaching patterns and discuss the challenges they pose to automatic teaching interpretation as well as the machine-learning algorithms that must ultimately process these instructions. We highlight the challenges by demonstrating the difficulties of an initial automatic teacher interpretation system.

Human Computation as an Educational Opportunity

“Citizen science” refers to the emerging practice in which individuals in the community, often en masse, partner with researchers to assist with data collection, analysis or interpretation. Such partnerships benefit researchers through access to data at a scale not possible for individuals or small teams. To date, the benefits to the citizen scientists have been less apparent, although some have argued that participation increases critical thinking and appreciation for science methodologies. The present chapter reports a case study in which 12-year-old citizen scientists contributed to a major research investigation of evapotranspiration and, in turn, deepened their own understanding of the water cycle.

Large-scale automated machine reading discovers new cancer-driving mechanisms

Abstract PubMed, a repository and search engine for biomedical literature, now indexes >1 million articles each year. This exceeds the processing capacity of human domain experts, limiting our ability to truly understand many diseases. We present Reach, a system for automated, large-scale machine reading of biomedical papers that can extract mechanistic descriptions of biological processes with relatively high precision at high throughput. We demonstrate that combining the extracted pathway fragments with existing biological data analysis algorithms that rely on curated models helps identify and explain a large number of previously unidentified mutually exclusive altered signaling pathways in seven different cancer types. This work shows that combining human-curated ‘big mechanisms’ with extracted ‘big data’ can lead to a causal, predictive understanding of cellular processes and unlock important downstream applications.