Oregon State University

About: Oregon State University is a education organization based out in Corvallis, Oregon, United States. It is known for research contribution in the topics: Population & Gene. The organization has 28192 authors who have published 64044 publications receiving 2634108 citations. The organization is also known as: Oregon Agricultural College & OSU.

Principles of Explanatory Debugging to Personalize Interactive Machine Learning

Abstract: How can end users efficiently influence the predictions that machine learning systems make on their behalf? This paper presents Explanatory Debugging, an approach in which the system explains to users how it made each of its predictions, and the user then explains any necessary corrections back to the learning system. We present the principles underlying this approach and a prototype instantiating it. An empirical evaluation shows that Explanatory Debugging increased participants' understanding of the learning system by 52% and allowed participants to correct its mistakes up to twice as efficiently as participants using a traditional learning system.

Inter‐biome comparison of factors controlling stream metabolism

Abstract: 'SUMMARY 1. We studied whole-ecosystem metabolism in eight streams from several biomes in North America to identify controls on the rate of stream metabolism over a large geographic range. The streams studied had climates ranging from tropical to cool-temperate and from humid to arid and were all relatively uninfluenced by human disturbances. 2. Rates of gross primary production (GPP), ecosystem respiration (R) and net ecosystem production (NEP) were determined using the open-system, two-station diurnal oxygen change method. 3. Three general patterns in metabolism were evident among streams: (1) relatively high GPP with positive NEP (i.e. net oxygen production) in early afternoon, (2) moderate primary production with a distinct peak in GPP during daylight but negative NEP at all times and (3) little or no evidence of GPP during daylight and a relatively constant and negative NEP over the entire day. ', 4. Gross primary production was most strongly correlated with photosynthetically active radiation (PAR). A multiple regression model that included log PAR and stream water soluble reactive phosphorus (SRP) concentration explained 90% of the variation in log GPP. 5. Ecosystem respiration was significantly correlated with SRP concentration and size of the transient storage zone and, together, these factors,explained 73% of the variation in R. The rate of R was poorly correlated with the rate of GPP. 6. Net ecosystem production was significantly correlated only with PAR, with 53% of the variation in log NEP explained by log PAR. Only Sycamore Creek, a desert stream in Arizona, had positive NEP (GPP: R > I), supporting the idea that streams are generally net sinks rather than net sources of organic matter.

Time Series Models to Simulate and Forecast Wind Speed and Wind Power

Abstract: A general approach for modeling wind speed and wind power is described. Because wind power is a function of wind speed, the methodology is based on the development of a model of wind speed. Values of wind power are estimated by applying the appropriate transformations to values of wind speed. The wind speed modeling approach takes into account several basic features of wind speed data, including autocorrelation, non-Gaussian distribution, and diurnal nonstationarity. The positive correlation between consecutive wind speed observations is taken into account by fitting an autoregressive process to wind speed data transformed to make their distribution approximately Gaussian and standardized to remove diurnal nonstationarity. As an example, the modeling approach is applied to a small set of hourly wind speed data from the Pacific Northwest. Use of the methodology for simulating and forecasting wind speed and wind power is discussed and an illustration of each of these types of applications is presen...