Arkansas Soybean Performance Tests 2018

Soybean variety and strain performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture’s Arkansas Crop Variety Improvement Program. The tests provide information to companies developing varieties and/or marketing seed within the State, and aid the Arkansas Cooperative Extension Service in formulating variety recommendations for soybean producers

Arkansas Corn and Grain Sorghum Peformance Tests 2017

Corn and grain sorghum performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture. The tests provide information to companies marketing seed within the state, and aid the Arkansas Cooperative Extension Service in formulating recommendations for producers

Arkansas Corn and Grain Sorghum Performance Tests 2014

Corn and grain sorghum performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture. The tests provide information to companies marketing seed within the state, and aid the Arkansas Cooperative Extension Service in formulating recommendations for producers

Arkansas Soybean Performance Tests 2015

Soybean variety and strain performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture’s Arkansas Crop Variety Improvement Program. The tests provide information to companies developing varieties and/or marketing seed within the State, and aid the Arkansas Cooperative Extension Service in formulating variety recommendations for soybean producers

Information theoretic approach to interactive learning

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.Comment: 6 page

Effects of Particle Shape on Growth Dynamics at Edges of Evaporating Colloidal Drops

We study the influence of particle shape on growth processes at the edges of evaporating drops. Aqueous suspensions of colloidal particles evaporate on glass slides, and convective flows during evaporation carry particles from drop center to drop edge, where they accumulate. The resulting particle deposits grow inhomogeneously from the edge in two-dimensions, and the deposition front, or growth line, varies spatio-temporally. Measurements of the fluctuations of the deposition front during evaporation enable us to identify distinct growth processes that depend strongly on particle shape. Sphere deposition exhibits a classic Poisson like growth process; deposition of slightly anisotropic particles, however, belongs to the Kardar-Parisi-Zhang (KPZ) universality class, and deposition of highly anisotropic ellipsoids appears to belong to a third universality class, characterized by KPZ fluctuations in the presence of quenched disorder

Arkansas Soybean Performance Tests 2011

Soybean variety and strain performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture Arkansas Crop Variety Improvement Program. The tests provide information to companies developing varieties and/or marketing seed within the state, and aid the Arkansas Cooperative Extension Service in formulating variety recommendations for soybean producers

Arkansas Soybean Performance Tests 2012

Soybean variety and strain performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture Arkansas Crop Variety Improvement Program. The tests provide information to companies developing varieties and/or marketing seed within the state, and aid the Arkansas Cooperative Extension Service in formulating variety recommendations for soybean producers