SOYBEAN OUTLOOK/DISCUSSION PRESENTED FOR THE 2004 MIDWEST/GREAT PLAINS & WESTERN OUTLOOK CONFERENCE, KANSAS CITY, MO, AUGUST 16-17, 2004

We should expect 2004-05 soybean prices to be much lower than 2003-04, but near the historically average. On the other hand, while the numbers shown are my best analysis, I feel there is a lot of price risk in both directions as we go from now through the 2004-05 marketing year. While looking good now, the crop was planted late, and some disease problems are showing up. On the demand side, exports will be the key.Crop Production/Industries,

New Classes of Off-Diagonal Cosmological Solutions in Einstein Gravity

In this work, we apply the anholonomic deformation method for constructing new classes of anisotropic cosmological solutions in Einstein gravity and/or generalizations with nonholonomic variables. There are analyzed four types of, in general, inhomogeneous metrics, defined with respect to anholonomic frames and their main geometric properties. Such spacetimes contain as particular cases certain conformal and/or frame transforms of the well known Friedman-Robertson-Walker, Bianchi, Kasner and Godel universes and define a great variety of cosmological models with generic off-diagonal metrics, local anisotropy and inhomogeneity. It is shown that certain nonholonomic gravitational configurations may mimic de Sitter like inflation scenaria and different anisotropic modifications without satisfying any classical false-vacuum equation of state. Finally, we speculate on perspectives when such off-diagonal solutions can be related to dark energy and dark matter problems in modern cosmology.Comment: latex2e, 11pt, 33 pages with table of content, a variant accepted to IJT

Towards a Universal Theory of Artificial Intelligence based on Algorithmic Probability and Sequential Decision Theory

Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown distribution. We unify both theories and give strong arguments that the resulting universal AIXI model behaves optimal in any computable environment. The major drawback of the AIXI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIXI^tl, which is still superior to any other time t and space l bounded agent. The computation time of AIXI^tl is of the order t x 2^l.Comment: 8 two-column pages, latex2e, 1 figure, submitted to ijca

Rethinking the scale, structure & scope of U.S. energy institutions

This essay notes some of the key institutions created in the twentieth century for the purpose of delivering energy in North America. Those institutions are being challenged by a combination of stresses in three interconnected areas: reliability, economics, and environmental sustainability. The essay argues that these three stresses create an “energy trilemma” requiring institutional reform. We suggest that new and modi½ed institutions can best be understood if we evaluate them along three dimensions: institutional scale, structure, and scope. We consider real-world examples of recent institutions in light of each of these dimensions and note both successes and concerns that those factors illuminate. We conclude by noting that some institutional changes will be organic and unplanned; but many others, including responses to climate change, will bene½t from conscious attention to scale, structure, and scope by those engaged in designing and building the energy institutions needed in the twenty-½rst century

Some Remarks about the Complexity of Epidemics Management

Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that the assumptions underlying the established theory of epidemics management are too idealistic. For an improvement of procedures and organizations involved in fighting epidemics, extended models of epidemics management are required. The necessary extensions consist in a representation of the management loop and the potential frictions influencing the loop. The effects of the non-deterministic frictions can be taken into account by including the measures of robustness and risk in the assessment of management options. Thus, besides of the increased structural complexity resulting from the model extensions, the computational complexity of the task of epidemics management - interpreted as an optimization problem - is increased as well. This is a serious obstacle for analyzing the model and may require an additional pre-processing enabling a simplification of the analysis process. The paper closes with an outlook discussing some forthcoming problems

The Impact of Big Data Utilization on Quality Improvement in Inpatient Facilities

Introduction: Poor quality in healthcare has resulted in avoidable patient complications, including readmission rates. Big data in healthcare can be analyzed and built into a tools, with machine learning, to aid in reduced readmission rates and overall positive patient outcomes. Purpose of the Study: The intention of this study was to evaluate the ways that big data can be analyzed to improve healthcare, specifically readmissions, patient outcomes, and show cost savings. This study examined different ways that big data could be used in concordance with machine learning, including predictive analysis, to make these improvements. Methodology: The hypothesis was the use of big data has improved quality categories in inpatient facilities led to improved patient outcomes, decreased readmission rates, and reduced costs. 27 publications were included and limited to the English language and were published between the year(s) of 2010 and 2023. Results: The study displayed many solutions to different concerns within the scope of readmissions and patient outcomes through machine learning tools alongside big data. Examined were solutions for reducing readmission rates, and patient outcomes which further included addressing appointment no-shows, disease risks, and outcome outlook. Discussion: The hypothesis of this study was partially conclusive, as improvement to readmission rates and patient outcomes were examined, but financial data was unavailable to confirm cost savings potential. The study limitations included the inability to obtain financial statistics and the potential for machine learning training data to not be clean. An interview with an expert in quality was also conducted and subject opinions were displayed alongside its corresponding subcategory. Conclusion: The research provided descriptive and valuable data on the abilities of big data and machine learning techniques. Given the study limitations, there has been shown a need for research and data on the common use of these techniques within inpatient facilities

Glassy Phase of Optimal Quantum Control

We study the problem of preparing a quantum many-body system from an initial to a target state by optimizing the fidelity over the family of bang-bang protocols. We present compelling numerical evidence for a universal spin-glass-like transition controlled by the protocol time duration. The glassy critical point is marked by a proliferation of protocols with close-to-optimal fidelity and with a true optimum that appears exponentially difficult to locate. Using a machine learning (ML) inspired framework based on the manifold learning algorithm t-SNE, we are able to visualize the geometry of the high-dimensional control landscape in an effective low-dimensional representation. Across the transition, the control landscape features an exponential number of clusters separated by extensive barriers, which bears a strong resemblance with replica symmetry breaking in spin glasses and random satisfiability problems. We further show that the quantum control landscape maps onto a disorder-free classical Ising model with frustrated nonlocal, multibody interactions. Our work highlights an intricate but unexpected connection between optimal quantum control and spin glass physics, and shows how tools from ML can be used to visualize and understand glassy optimization landscapes.Comment: Modified figures in appendix and main text (color schemes). Corrected references. Added figures in SI and pseudo-cod

TFP Growth in Old and New Europe

Using Solow-Tornqvist residuals as well as two alternative measurements, we present estimates of total factor productivity (TFP) growth in a sample of 30 European economies for the period 1994-2005. In most of Western Europe, we find a deceleration of TFP growth since 2000. However, the economies of New Europe exhibit a higher level of TFP growth overall and have slowed less than those of Old Europe. In the new market economies of Central and Eastern Europe, we nd both high TFP growth as well as acceleration in the second half of the sample. Regression evidence from Western Europe suggests that product market regulation may adversely aect TFP growth and may thus impair convergence.Total factor productivity growth, Solow residual, product and labor market regulation