WHY DO FARMERS FORWARD CONTRACT IN FACTOR MARKETS?

This study investigated farmers' incentives to forward purchase inputs. A model of farmer decision making was used to derive an optimal forward contracting rule. Explicit in the model was the tradeoff between the quantity of input to be purchased in advance, and the remaining portion to be purchased later on the spot market. Results indicated that the primary reasons farmers contract inputs are to reduce risk and to speculate on favorable price moves. A numerical example of fertilizer used in corn production indicated that the size of the price discount was the dominant factor in forward contracting decisions.Farm Management,

Creation, Coordination, and Activation of Resources in Physics and Mathematics Learning

This project seeks to study introductory college level courses in physics, mechanics, and mathematics. The research questions involve the processes by which students become able to use resources across contexts (such as between mathematics and physics), how ideas in math and physics form a resource network, and what mechanisms trigger individual resources or coordinated networks. The researcher will conduct clinical interviews, small group interviews, and statistical analysis of survey questions as well as videos from classroom and help sessions. The data being collected would be analyzed for purpose of describing the development of students as they refine skills in mathematics and physical reasoning. A small group of students (15) at the University of Maine will be the subject of the study.The outcome of this project is expected to be a better model of student reasoning and learning . The reviewers were particularly interested in the possibly useful observations about the connections between mathematics and physics learning. Papers would be prepared for all education research associations, including physics

Kernel Metric Learning for Clustering Mixed-type Data

Distance-based clustering and classification are widely used in various fields to group mixed numeric and categorical data. A predefined distance measurement is used to cluster data points based on their dissimilarity. While there exist numerous distance-based measures for data with pure numerical attributes and several ordered and unordered categorical metrics, an optimal distance for mixed-type data is an open problem. Many metrics convert numerical attributes to categorical ones or vice versa. They handle the data points as a single attribute type or calculate a distance between each attribute separately and add them up. We propose a metric that uses mixed kernels to measure dissimilarity, with cross-validated optimal kernel bandwidths. Our approach improves clustering accuracy when utilized for existing distance-based clustering algorithms on simulated and real-world datasets containing pure continuous, categorical, and mixed-type data.Comment: 23 pages, 5 tables, 2 figure

Cosmological Constraints on a Dynamical Electron Mass

Motivated by recent astrophysical observations of quasar absorption systems, we formulate a simple theory where the electron to proton mass ratio $\mu =m_{e}/m_{p}$ is allowed to vary in space-time. In such a minimal theory only the electron mass varies, with $\alpha$ and $m_{p}$ kept constant. We find that changes in $\mu$ will be driven by the electronic energy density after the electron mass threshold is crossed. Particle production in this scenario is negligible. The cosmological constraints imposed by recent astronomical observations are very weak, due to the low mass density in electrons. Unlike in similar theories for spacetime variation of the fine structure constant, the observational constraints on variations in $\mu$ imposed by the weak equivalence principle are much more stringent constraints than those from quasar spectra. Any time-variation in the electron-proton mass ratio must be less than one part in $10^{9}$since redshifts $z\approx 1.$This is more than one thousand times smaller than current spectroscopic sensitivities can achieve. Astronomically observable variations in the electron-proton must therefore arise directly from effects induced by varying fine structure 'constant' or by processes associated with internal proton structure. We also place a new upper bound of $2\times 10^{-8}$ on any large-scale spatial variation of $\mu$ that is compatible with the isotropy of the microwave background radiation.Comment: New bounds from weak equivalence principle experiments added, conclusions modifie

Narrow-line Laser Cooling by Adiabatic Transfer

We propose and demonstrate a novel laser cooling mechanism applicable to particles with narrow-linewidth optical transitions. By sweeping the frequency of counter-propagating laser beams in a sawtooth manner, we cause adiabatic transfer back and forth between the ground state and a long-lived optically excited state. The time-ordering of these adiabatic transfers is determined by Doppler shifts, which ensures that the associated photon recoils are in the opposite direction to the particle's motion. This ultimately leads to a robust cooling mechanism capable of exerting large forces via a weak transition and with reduced reliance on spontaneous emission. We present a simple intuitive model for the resulting frictional force, and directly demonstrate its efficacy for increasing the total phase-space density of an atomic ensemble. We rely on both simulation and experimental studies using the 7.5~kHz linewidth $^1$S$_0$ to $^3$P$_1$ transition in $^{88}$Sr. The reduced reliance on spontaneous emission may allow this adiabatic sweep method to be a useful tool for cooling particles that lack closed cycling transitions, such as molecules.Comment: 5 pages, 4 figure

Surveying structural complexity in quantum many-body systems

Quantum many-body systems exhibit a rich and diverse range of exotic behaviours, owing to their underlying non-classical structure. These systems present a deep structure beyond those that can be captured by measures of correlation and entanglement alone. Using tools from complexity science, we characterise such structure. We investigate the structural complexities that can be found within the patterns that manifest from the observational data of these systems. In particular, using two prototypical quantum many-body systems as test cases - the one-dimensional quantum Ising and Bose-Hubbard models - we explore how different information-theoretic measures of complexity are able to identify different features of such patterns. This work furthers the understanding of fully-quantum notions of structure and complexity in quantum systems and dynamics.Comment: 9 pages, 5 figure

Assembly and use of new task rules in fronto-parietal cortex

Severe capacity limits, closely associated with fluid intelligence, arise in learning and use of new task rules. We used fMRI to investigate these limits in a series of multirule tasks involving different stimuli, rules, and response keys. Data were analyzed both during presentation of instructions and during later task execution. Between tasks, we manipulated the number of rules specified in task instructions, and within tasks, we manipulated the number of rules operative in each trial block. Replicating previous results, rule failures were strongly predicted by fluid intelligence and increased with the number of operative rules. In fMRI data, analyses of the instruction period showed that the bilateral inferior frontal sulcus, intraparietal sulcus, and presupplementary motor area were phasically active with presentation of each new rule. In a broader range of frontal and parietal regions, baseline activity gradually increased as successive rules were instructed. During task performance, we observed contrasting fronto-parietal patterns of sustained (block-related) and transient (trial-related) activity. Block, but not trial, activity showed effects of task complexity. We suggest that, as a new task is learned, a fronto-parietal representation of relevant rules and facts is assembled for future control of behavior. Capacity limits in learning and executing new rules, and their association with fluid intelligence, may be mediated by this load-sensitive fronto-parietal network