Volumes of highly twisted knots and links

We show that for a large class of hyperbolic knots and links, we can determine bounds on the volume of the link complement from combinatorial information given by a link diagram. Specifically, there is a universal constant C such that if a knot or link admits a prime, twist reduced diagram with at least 2 twist regions and at least C crossings per twist region, then the link complement is hyperbolic with volume bounded below by 3.3515 times the number of twist regions in the diagram. C is at most 113.Comment: 14 pages, 4 figures. Minor changes to clarify exposition, fix typos, and correct a historical inaccuracy in the introduction. Paper has now appeared in AG

The length of unknotting tunnels

We show there exist tunnel number one hyperbolic 3-manifolds with arbitrarily long unknotting tunnel. This provides a negative answer to an old question of Colin Adams.Comment: 20 pages, 6 figures. 20 pages, 6 figures. Daryl Cooper added as author. V2 contains two new sections, including a second proof of the main result, and a proof that the result holds for knots in homology sphere

The Roles of Women, Animals, and Nature in Traditional Japanese and Western Folk Tales Carry Over into Modern Japanese and Western Culture.

The roles of women, animals, and nature in traditional Japanese and Western folk tales continue to be parallel to the roles of women, animals, and nature in modern Japanese and Western Culture. This is a result of the values and morals that are encapsulated within these folk tales

Improving end-use quality in hard winter wheat through glutenin allele combinations and genomic selection

2014 Fall.Wheat (Triticum aestivum L.) has unique properties that allow for a variety of end products, such as pan bread, steamed bread, cookies, cakes, and tortillas. Most wheat-breeding programs focus on increasing yield and yield-related traits as primary objectives. However, end-use quality is also crucial as quality characteristics influence grain sale price and market success of a variety. Large-effect quantitative trait loci (QTL) have been identified for quality related traits. The Glu-1 loci encoding high molecular weight glutenin subunits (HMWGS) have a major effect on dough mixing properties. However, many quality traits are too complex to be controlled by only a small number of loci. These traits may benefit from genomic selection (GS), which utilizes all effective loci regardless of effect size. Genomic selection can accelerate genetic progress especially for traits that are costly or time consuming to phenotype, like quality-related traits. This research focused on the genetic improvement of end-use quality in hard winter wheat by targeting specific loci with known effects or by using all loci in a GS approach. The objectives of this study were to: i) evaluate agronomic and quality effects associated with different combinations of HMW-GS at the Glu-B1 and Glu-D1 loci among a set of near isogenic lines (NILs); ii) use a genome-wide association approach to identify QTL and develop predictive models for pre-harvest sprouting tolerance (PHST) and iii) assess GS models for milling and baking traits in hard winter wheat lines representative of west-central U.S. Great Plains germplasm. A set of NILs that varied for alleles at the Glu-B1 and Glu-D1 loci were evaluated for dough mixing properties, kernel characteristics, and agronomic effects. Results confirmed the Bx7OE + By8 HMW-GS (Glu-B1a1 allele) at Glu-B1 contributed to greater dough strength compared to the common Bx7 + By8 HMW-GS (Glu-B1b allele); however, the effect was not as significant as that conferred by Dx5 + Dy10 subunits (Glu-D1d allele). Near isogenic lines with the combination of both favorable alleles at Glu-B1 and Glu-D1 had the largest mixograph mixing time. However, a decrease in yield was observed for groups containing the Bx7OE + By8 subunits. These results suggest glutenin allele combinations are useful for improving bread-making characteristics in winter wheat but some combinations may be associated with negative effects on yield. Pre-harvest sprouting (PHS) is a major problem in wheat that results in decreased yield and quality. Genomic selection was evaluated as a potential breeding method for PHST given the complex inheritance and phenotyping difficulty of this trait. In this study, genotyping-by-sequencing (GBS) markers were used to identify QTL associated with PHST among a panel of hard red and white winter wheat lines. Genomic selection models were developed with the GBS data and phenotype data collected across seven growing seasons. The effect of including identified QTL and kernel color as fixed effects in the model was assessed, as kernel color has been generally associated with sprouting tolerance. Optimum marker number was also determined as accuracy can vary with different numbers of markers. Results showed model accuracy did not improve with kernel color information but weighting major QTL increased predictive performance. Optimum marker number was 4,000 with no improvement in accuracy above this threshold. Overall, model accuracies were promising and confirmed wheat breeding programs would benefit from incorporating GS models for PHST. Lastly, the accuracy of GS models for 11 end-use quality traits in a panel of hard red and white winter wheat breeding lines phenotyped across multiple years and locations was assessed. Trait heritability, marker number, and marker imputation method were evaluated for their effect on model accuracy. Traits measured included flour yield, single kernel characteristics, protein concentration, mixograph mixing time and tolerance, bake absorption, bake mixing time, crumb grain score, and loaf volume. Genotyping-by-sequencing marker data varied for marker density and imputation method used for missing data. Across traits, model accuracies ranged from 0.30 to 0.63 and trait heritability ranged from 0.03 to 0.61. Imputation method and marker density had little to no effect on model accuracy. Heritability appeared to have the greatest effect on accuracy as GS models for traits with higher heritability had higher accuracies. Additionally, GS models for moderate to high heritability traits performed better than expected when predicting a set of genotypes separate from the training panel. Results showed model accuracies for end-use quality traits were sufficient for increasing genetic gain in a wheat breeding program. In summary, genetic improvement in end-use quality can be made by utilizing both large effect and small effect loci in the wheat genome for such traits and will reduce phenotyping costs while increasing efficiency in a breeding program. In many winter wheat breeding programs, particularly those at higher latitudes, phenotypic quality evaluations from one season cannot be used for planting decisions of the next season due to the short turn-around time from harvest to planting. Genomic selection potentially solves this problem as selection decisions based on genotypic data can be implemented before the next season of planting. Thus, results from this study support the implementation of GS to reduce phenotyping costs and increase the rate of genetic gain for end-use quality in wheat

The Selectivity Gap in United States Responses to Human Rights Violations: Personal Integrity Violations between 1991 and 2001

Recent quantitative scholarship on international human rights law has revealed a trend in which countries will ratify human rights law in order to improve their international standings, but won’t make the policy changes necessary to implement the law because the policy changes would be more expensive than the costs of noncompliance. This raises the question as to in what circumstances will international law enforcement be costly enough to force countries to comply with international human rights law. Previous quantitative research demonstrates the importance of security concerns in determining US decisions to intervene. Additionally, previous research indicates that economic concerns have long term impacts on the likelihood of intervention, though the exact impacts are unclear. This article will thus treat economic variables as indicators of humanitarian intervention that require further exploration. Further, none of the scholars cited in the literature review focus on political explanations of humanitarian intervention. This is surprising given that political interests have a large impact on state behavior. Therefore, this study will focus on political concerns as a second indicator of humanitarian intervention using UN voting data as an indicator of political alignment

A Functional Analysis of Yadkin Bifaces in the Middle Savannah River Valley

The Woodland period was a time of changing settlement patterns, social structure, and technology. Increasing sedentism and social complexity begin during this period in the Savannah River valley and triangular bifaces enter the technological repertoire for the first time in the form of Yadkin bifaces. Yadkins are found exclusively in Middle Woodland contexts suggesting they played an important role in the changes occurring during this time. This thesis establishes the presence of the bow and arrow during the Middle Woodland period through a functional analysis of Yadkin and Eared Yadkin bifaces from South Carolina. This analysis shows that the evolutionary approaches used to explain the relationship between social complexity and the bow and arrow are inadequate for the Savannah River valley and other perspectives must be employed

Understanding ancient coin images

In recent years, a range of problems within the broad umbrella of automatic, computer vision based analysis of ancient coins has been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in the published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. Firstly, we explain that the approach of visual matching of coins, universally adopted in all existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g. online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on the understanding of the semantic content of coins. Hence, we describe a novel method which uses real-world multimodal input to extract and associate semantic concepts with the correct coin images and then using a novel convolutional neural network learn the appearance of these concepts. Empirical evidence on a real-world and by far the largest data set of ancient coins, we demonstrate highly promising results.Postprin