An Ex Post Evaluation of the Conservation Reserve, Federal Crop Insurance, and Other Government Programs: Program Participation and Soil Erosion

Recent research has questioned the extent to which government policies, including conservation and risk management programs, have influenced environmental indicators. The impacts of income-supporting and risk management programs on soil erosion are considered. An econometric model of the determinants of soil erosion, program participation, conservation effort, and input usage is estimated. While the Conservation Reserve Program has reduced erosion an average of 1.02 tons per acre from 1982 to 1992, approximately half of this reduction has been offset by increased erosion resulting from government programs other than federally subsidized crop insurance.Conservation Reserve Program, farm policy, soil erosion, Agricultural and Food Policy,

Harvest-Time Protein Shocks and Price Adjustment in U.S. Wheat Markets

Dynamic relationships between three classes of wheat are investigated using threshold VAR models incorporating the effects of protein availability. Changes in the stock of protein are found to generate significant impulse responses in the price of hard spring red wheat and hard red winter wheat but not soft red wheat. These impulse responses to identical changes in protein stocks are larger when the absolute deviations of protein stocks from normal levels are large. Shocks to the prices of individual classes of wheat result in complex impulse responses in the prices of the other wheats. Notably, however, a shock to the price of hard red winter wheat appears to result in little or no impluse response in the price of hard spring wheat, though, importantly, the opposite is not true.Demand and Price Analysis,

Domain Adaptive Neural Networks for Object Recognition

We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From experiments, we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set. We also show that our proposed model, preceded by the denoising auto-encoder pretraining, achieves better performance than recent benchmark models on the same data sets. This work represents the first study of MMD measure in the context of neural networks

Harvest-Time Protein Shocks and Price Adjustment in U.S. Wheat Markets

Dynamic relationships among three classes of wheat are investigated using threshold VAR models that incorporate the effects of protein availability. Changes in the stock of protein are found to generate significant responses in the prices of hard red spring wheat and hard red winter wheat, but not soft red wheat. The responses to identical changes in protein stocks are larger when the magnitudes of deviations of protein stocks from normal levels are large. Shocks to the prices of individual classes of wheat result in complex responses in the prices of the other wheat classes. Notably, however, a shock to the price of hard red winter wheat appears to result in little or no response in the price of hard spring wheat, though importantly, the opposite is not true.protein, thresholds, vector autoregressions, wheat prices, Crop Production/Industries,

A Learning-Based Approach to Caching in Heterogenous Small Cell Networks

A heterogenous network with base stations (BSs), small base stations (SBSs) and users distributed according to independent Poisson point processes is considered. SBS nodes are assumed to possess high storage capacity and to form a distributed caching network. Popular files are stored in local caches of SBSs, so that a user can download the desired files from one of the SBSs in its vicinity. The offloading-loss is captured via a cost function that depends on the random caching strategy proposed here. The popularity profile of cached content is unknown and estimated using instantaneous demands from users within a specified time interval. An estimate of the cost function is obtained from which an optimal random caching strategy is devised. The training time to achieve an $\epsilon>0$ difference between the achieved and optimal costs is finite provided the user density is greater than a predefined threshold, and scales as $N^2$, where $N$ is the support of the popularity profile. A transfer learning-based approach to improve this estimate is proposed. The training time is reduced when the popularity profile is modeled using a parametric family of distributions; the delay is independent of $N$ and scales linearly with the dimension of the distribution parameter.Comment: 12 pages, 5 figures, published in IEEE Transactions on Communications, 2016. arXiv admin note: text overlap with arXiv:1504.0363

A non-dispersive Raman D-band activated by well-ordered interlayer interactions in rotationally stacked bi-layer Graphene

Raman measurements on monolayer graphene folded back upon itself as an ordered but skew-stacked bilayer (i.e. with interlayer rotation) presents new mechanism for Raman scattering in sp2 carbons that arises in systems that lack coherent AB interlayer stacking. Although the parent monolayer does not exhibit a D-band, the interior of the skewed bilayer produces a strong two-peak Raman feature near 1350 cm-1; one of these peaks is non-dispersive, unlike all previously observed D-band features in sp2 carbons. Within a double-resonant model of Raman scattering, these unusual features are consistent with a skewed bilayer coupling, wherein one layer imposes a weak but well-ordered perturbation on the other. The discrete Fourier structure of the rotated interlayer interaction potential explains the unusual non-dispersive peak near 1350 cm-1

Non-line-of-sight Node Localization based on Semi-Definite Programming in Wireless Sensor Networks

An unknown-position sensor can be localized if there are three or more anchors making time-of-arrival (TOA) measurements of a signal from it. However, the location errors can be very large due to the fact that some of the measurements are from non-line-of-sight (NLOS) paths. In this paper, we propose a semi-definite programming (SDP) based node localization algorithm in NLOS environment for ultra-wideband (UWB) wireless sensor networks. The positions of sensors can be estimated using the distance estimates from location-aware anchors as well as other sensors. However, in the absence of LOS paths, e.g., in indoor networks, the NLOS range estimates can be significantly biased. As a result, the NLOS error can remarkably decrease the location accuracy. And it is not easy to efficiently distinguish LOS from NLOS measurements. In this paper, an algorithm is proposed that achieves high location accuracy without the need of identifying NLOS and LOS measurement.Comment: submitted to IEEE ICC'1

Optical modeling of agricultural fields and rough-textured rock and mineral surfaces

Review was made of past models for describing the reflectance and/or emittance properties of agricultural/forestry and geological targets in an effort to select the best theoretical models. An extension of the six parameter Allen-Gayle-Richardson model was chosen as the agricultural plant canopy model. The model is used to predict the bidirectional reflectance of a field crop from known laboratory spectra of crop components and approximate plant geometry. The selected geological model is based on Mie theory and radiative transfer equations, and will assess the effect of textural variations of the spectral emittance of natural rock surfaces