Modeling Yield-Factor Volatility

The term structure of interest rates is often summarized using a handful of yield factors that capture shifts in the yield curve. Despite their wide application in financial economics, very little is known on the time-series properties of the yield-factor volatilities. We examine three common yield-factors: the level of short-term interest rates, the slope and curvature in the yield curve. We model the volatility dynamics in these yield factors using both GARCH and level effects and find that both are needed to adequately model yield-factor volatility. The level effect is routinely used when modeling volatility in short-term interest rates and we find that the level of the short-rate is useful in modeling the volatility of the slope and curvature too. We also examine the effect of volatility on the dynamics of the yield-factors and find that the GARCH-based volatility of the short-rate is negatively related to future interest rates and positively related to the slope of the yield curve. This volatility-in-mean effect is much weaker when a level effect is introduced. We also examine regime switching models that recognize different economic regimes and find that this dramatically improves the model's fit. Interestingly, the level effect is strengthened and the GARCH effects is weakened somewhat. The Bayesian information criteria suggests that the correct model is a regime-switching model with level effecC32, C51, G12

Distortion maps for genus two curves

Distortion maps are a useful tool for pairing based cryptography. Compared with elliptic curves, the case of hyperelliptic curves of genus g > 1 is more complicated since the full torsion subgroup has rank 2g. In this paper we prove that distortion maps always exist for supersingular curves of genus g>1 and we construct distortion maps in genus 2 (for embedding degrees 4,5,6 and 12).Comment: 16 page

Recommending Learning Algorithms and Their Associated Hyperparameters

The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters. Selecting an appropriate learning algorithm and setting its hyperparameters for a given data set can be a challenging task, especially for users who are not experts in machine learning. Previous work has examined using meta-features to predict which learning algorithm and hyperparameters should be used. However, choosing a set of meta-features that are predictive of algorithm performance is difficult. Here, we propose to apply collaborative filtering techniques to learning algorithm and hyperparameter selection, and find that doing so avoids determining which meta-features to use and outperforms traditional meta-learning approaches in many cases.Comment: Short paper--2 pages, 2 table

An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage

The results from most machine learning experiments are used for a specific purpose and then discarded. This results in a significant loss of information and requires rerunning experiments to compare learning algorithms. This also requires implementation of another algorithm for comparison, that may not always be correctly implemented. By storing the results from previous experiments, machine learning algorithms can be compared easily and the knowledge gained from them can be used to improve their performance. The purpose of this work is to provide easy access to previous experimental results for learning and comparison. These stored results are comprehensive -- storing the prediction for each test instance as well as the learning algorithm, hyperparameters, and training set that were used. Previous results are particularly important for meta-learning, which, in a broad sense, is the process of learning from previous machine learning results such that the learning process is improved. While other experiment databases do exist, one of our focuses is on easy access to the data. We provide meta-learning data sets that are ready to be downloaded for meta-learning experiments. In addition, queries to the underlying database can be made if specific information is desired. We also differ from previous experiment databases in that our databases is designed at the instance level, where an instance is an example in a data set. We store the predictions of a learning algorithm trained on a specific training set for each instance in the test set. Data set level information can then be obtained by aggregating the results from the instances. The instance level information can be used for many tasks such as determining the diversity of a classifier or algorithmically determining the optimal subset of training instances for a learning algorithm.Comment: 7 pages, 1 figure, 6 table

Kekule versus hidden superconducting order in graphene-like systems: Competition and coexistence

We theoretically study the competition between two possible exotic superconducting orders that may occur in graphene-like systems, assuming dominant nearest-neighbor attraction: the gapless hidden superconducting order, which renormalizes the Fermi velocity, and the Kekule order, which opens a superconducting gap. We perform an analysis within the mean-field theory for Dirac electrons, at finite-temperature and finite chemical potential, as well as at half filling and zero-temperature, first excluding the possibility of the coexistence of the two orders. In that case, we find the dependence of the critical (more precisely, crossover) temperature and the critical interaction on the chemical potential. As a result of this analysis, we find that the Kekule order is preferred over the hidden order at both finite temperature and finite chemical potential. However, when the coexistence of the two superconducting orders is allowed, using the coupled mean-field gap equations, we find that above a critical value of the attractive interaction a mixed phase sets in, in which these orders coexist. We show that the critical value of the interaction for this transition is greater than the critical coupling for the hidden superconducting state in the absence of the Kekule order, implying that there is a region in the phase diagram where the Kekule order is favored as a result of the competition with the hidden superconducting order. The latter, however, eventually sets in and coexists with the Kekule state. According to our mean-field calculations, the transition from the Kekule to the mixed phase is of the second order, but it may become first order when fluctuations are considered. Finally, we investigate whether these phases could be possible in honeycomb superlattices of self-assembled semiconducting nanocrystals, which have been recently experimentally realized with CdSe and PbSe.Comment: 15 pages, 9 figures, published version. Minor changes, new references adde

Localization of aeroelastic modes in mistuned high-energy turbines

The effects of blade mistuning on the aerodynamic characteristics of a class of bladed-disk assemblies, namely high energy turbines, are discussed. The specific rotor analyzed is the first stage of turbine blades of the oxidizer turbopump in the Space Shuttle Main Engine. The common occurrence of fatigue cracks for these turbine blades indicates the possibility of high dynamic loading. Since mistuning under conditions of weak interblade coupling has been shown to increase blade response amplitudes drastically for simple structural models of blade assemblies, it provides a plausible explanation for the occurrence of cracks. The focus here is on the effects of frequency mistuning on the aeroelastic stability of the assembly and on the aeroelastic mode shapes

Mortality and magnitude of the "wild effect" in chimpanzee tooth emergence

Age of tooth emergence is a useful measure of the pace of life for primate species, both living and extinct. A recent study combining wild chimpanzees of the Taï Forest, Gombe, and Bossou by Zihlman et al. (2004) suggested that wild chimpanzees erupt teeth much later than captives, bringing into question both comparisons within the hominin fossil record and assessment of chimpanzees. Here, we assess the magnitude of the “wild effect” (the mean difference between captive and wild samples expressed in standard deviation units) in these chimpanzees. Tooth emergence in these wild individuals is late,although at a more moderate level than previously recorded, with a mean delay conservatively estimated at about 1 SD compared to the captive distributions. The effect rises to 1.3 SD if we relax criteria for age estimates. We estimate that the mandibular M1 of these wild chimpanzees emerges at about 3 2/3-3 3/4 years of age. An important point, often ignored, is that these chimpanzees are largely dead of natural causes, merging the effect of living wild with the effect of early death. Evidence of mortality selection includes, specifically: younger deaths appear to have been more delayed than the older in tooth emergence, more often showed evidence of disease or debilitation, and revealed a higher occurrence of dental anomalies. Notably, delay in tooth emergence for live-captured wild baboons appears lower in magnitude (ca. 0.5 SD) and differs in pattern. Definitive ages of tooth emergence times in living wild chimpanzees must be established from the study of living animals. The fossil record, of course, consists of many dead juveniles; the present study has implications for how we evaluate them. 2010 Elsevier Ltd. All rights reserved.Max Planck Institute of Evolutionary Anthropology, GermanyPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87989/1/Smith Boesch 2010 final.pd

A stereodivergent asymmetric approach to difluorinated aldonic acids

A (bromodifluoromethyl)alkyne has been deployed in a stereoselective route to difluorinated aldonic acid analogues, in which a Sharpless asymmetric dihydroxylation reaction and diastereoisomer separation set the stage for phenyl group oxidation