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 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 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

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

The X-ray Properties of M101 ULX-1 = CXOKM101 J140332.74+542102

We report our analysis of X-ray data on M101 ULX-1, concentrating on high state Chandra and XMM-Newton observations. We find that the high state of M101 ULX-1 may have a preferred recurrence timescale. If so, the underlying clock may have periods around 160 or 190 days, or possibly around 45 days. Its short-term variations resemble those of X-ray binaries at high accretion rate. If this analogy is correct, we infer that the accretor is a 20-40 Msun object. This is consistent with our spectral analysis of the high state spectra of M101 ULX-1, from which we find no evidence for an extreme (> 10^40 ergs/s) luminosity. We present our interpretation in the framework of a high mass X-ray binary system consisting of a B supergiant mass donor and a large stellar-mass black hole.Comment: 23 pages, 7 figures, accepted for publication in the Astrophysical Journa

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