CHANGING SEASONAL PATTERNS IN THE POULTRY MARKET

The role of seasonality in modeling agricultural markets is well recognized. However, traditional approaches to account for seasonality assume that seasonal pattern is constant, even though some evidence of changing seasonal pattern exists in the literature. This paper seeks to explore the impact of incorporating changing seasonal pattern into poultry market modeling. Keywords: seasonality, trigonometric variable, seasonal frequency.seasonality, trigonometric variable, seasonal frequency., Livestock Production/Industries, Marketing,

A mechanism of e22h\frac{e^2}{2h} conductance plateau without 1D chiral Majorana fermions

We address the question about the origin of the $\frac12 \frac{e^2}{h}$ conductance plateau observed in a recent experiment on an integer quantum Hall (IQH) film covered by a superconducting (SC) film. Since 1-dimensional (1D) chiral Majorana fermions on the edge of the above device can give rise to the half quantized plateau, such a plateau was regarded as a smoking-gun evidence for the chiral Majorana fermions. However, in this paper we give another mechanism for the $\frac12 \frac{e^2}{h}$ conductance plateau. We find the $\frac12 \frac{e^2}{h}$ conductance plateau to be a general feature of a good electric contact between the IQH film and SC film, and cannot distinguish the existence or the non-existence of 1D chiral Majorana fermions. We also find that the contact conductance between SC and an IQH edge channel has a non-Ohmic form $\sigma_\text{SC-Hall} \propto V^2$ in $k_BT \ll eV$ limit, if the SC and IQH bulks are fully gapped.Comment: 6 pages, 4 figures. The T=0 calculation is updated for a more general situation (k_F=/=0). The results are not affecte

Mining Frequent Neighborhood Patterns in Large Labeled Graphs

Over the years, frequent subgraphs have been an important sort of targeted patterns in the pattern mining literatures, where most works deal with databases holding a number of graph transactions, e.g., chemical structures of compounds. These methods rely heavily on the downward-closure property (DCP) of the support measure to ensure an efficient pruning of the candidate patterns. When switching to the emerging scenario of single-graph databases such as Google Knowledge Graph and Facebook social graph, the traditional support measure turns out to be trivial (either 0 or 1). However, to the best of our knowledge, all attempts to redefine a single-graph support resulted in measures that either lose DCP, or are no longer semantically intuitive. This paper targets mining patterns in the single-graph setting. We resolve the "DCP-intuitiveness" dilemma by shifting the mining target from frequent subgraphs to frequent neighborhoods. A neighborhood is a specific topological pattern where a vertex is embedded, and the pattern is frequent if it is shared by a large portion (above a given threshold) of vertices. We show that the new patterns not only maintain DCP, but also have equally significant semantics as subgraph patterns. Experiments on real-life datasets display the feasibility of our algorithms on relatively large graphs, as well as the capability of mining interesting knowledge that is not discovered in prior works.Comment: 9 page

PRICE UNCERTAINTY AND AGRICULTURAL PRODUCTIVITY

This paper examines the effects of price uncertainty on agricultural productivity. Appelbaum(1991) provided an empirical framework to analyze the effects of uncertainty on firm behavior. We apply the model to the U.S. agricultural sector, using a parametric rather than a nonparametric approach to obtain the measurement of price uncertainty and risk. Keywords: risk, uncertainty, productivityrisk, uncertainty, productivity, Productivity Analysis,

Image classification by visual bag-of-words refinement and reduction

This paper presents a new framework for visual bag-of-words (BOW) refinement and reduction to overcome the drawbacks associated with the visual BOW model which has been widely used for image classification. Although very influential in the literature, the traditional visual BOW model has two distinct drawbacks. Firstly, for efficiency purposes, the visual vocabulary is commonly constructed by directly clustering the low-level visual feature vectors extracted from local keypoints, without considering the high-level semantics of images. That is, the visual BOW model still suffers from the semantic gap, and thus may lead to significant performance degradation in more challenging tasks (e.g. social image classification). Secondly, typically thousands of visual words are generated to obtain better performance on a relatively large image dataset. Due to such large vocabulary size, the subsequent image classification may take sheer amount of time. To overcome the first drawback, we develop a graph-based method for visual BOW refinement by exploiting the tags (easy to access although noisy) of social images. More notably, for efficient image classification, we further reduce the refined visual BOW model to a much smaller size through semantic spectral clustering. Extensive experimental results show the promising performance of the proposed framework for visual BOW refinement and reduction