Equilibrium and eigenfunctions estimates in the semi-classical regime

We establish eigenfunctions estimates, in the semi-classical regime, for critical energy levels associated to an isolated singularity. For Schr\"odinger operators, the asymptotic repartition of eigenvectors is the same as in the regular case, excepted in dimension 1 where a concentration at the critical point occurs. This principle extends to pseudo-differential operators and the limit measure is the Liouville measure as long as the singularity remains integrable.Comment: 13 pages, 1 figure, perhaps to be revise

Trees of Unusual Size: Biased Inference of Early Bursts from Large Molecular Phylogenies

An early burst of speciation followed by a subsequent slowdown in the rate of diversification is commonly inferred from molecular phylogenies. This pattern is consistent with some verbal theory of ecological opportunity and adaptive radiations. One often-overlooked source of bias in these studies is that of sampling at the level of whole clades, as researchers tend to choose large, speciose clades to study. In this paper, we investigate the performance of common methods across the distribution of clade sizes that can be generated by a constant-rate birth-death process. Clades which are larger than expected for a given constant-rate branching process tend to show a pattern of an early burst even when both speciation and extinction rates are constant through time. All methods evaluated were susceptible to detecting this false signature when extinction was low. Under moderate extinction, both the gamma-statistic and diversity-dependent models did not detect such a slowdown but only because the signature of a slowdown was masked by subsequent extinction. Some models which estimate time-varying speciation rates are able to detect early bursts under higher extinction rates, but are extremely prone to sampling bias. We suggest that examining clades in isolation may result in spurious inferences that rates of diversification have changed through time.Comment: 17 pages, 5 figure

Constraining the photometric properties of MgII absorbing galaxies with the SDSS

Using a sample of nearly 700 quasars with strong (W_0(2796)>0.8 Angstrom) MgII absorption lines detected in the Early Data Release of the SDSS, we demonstrate the feasibility of measuring the photometric properties of the absorber systems by stacking SDSS imaging data. As MgII lines can be observed in the range 0.37<z_abs<2.2, the absorbing galaxies are in general not identified in SDSS images, but they produce systematic light excesses around QSOs which can be detected with a statistical analysis. In this Letter we present a 6-sigma detection of this effect over the whole sample in i-band, rising to 9.4-sigma for a low-redshift subsample with 0.37<z_abs<=0.82. We use a control sample of QSOs without strong MgII absorption lines to quantify and remove systematics with typical 10-20% accuracy. The signal varies as expected as a function of absorber redshift. For the low z_abs subsample we can reliably estimate the average luminosities per MgII absorber system in the g, r, and i bands and find them to be compatible with a few-hundred-Myr old stellar population of M_r ~ -21 in the rest frame. Colors are also consistent with typical absorbing galaxies resembling local Sb-c spirals. Our technique does not require any spectroscopic follow-up and does not suffer from confusion with other galaxies arising along the line-of-sight. It will be applied to larger samples and other line species in upcoming studies.Comment: Accepted on ApJ Letters, 5 pages, 2 figure

Classification of Stars from Redshifted Stellar Spectra utilizing Machine Learning

The classification of stellar spectra is a fundamental task in stellar astrophysics. There have been many explorations into the automated classification of stellar spectra but few that involve the Sloan Digital Sky Survey (SDSS). Stellar spectra from the SDSS are applied to standard classification methods such as K-Nearest Neighbors, Random Forest, and Support Vector Machine to automatically classify the spectra. Stellar spectra are high dimensional data and the dimensionality is reduced using standard Feature Selection methods such as Chi-Squared and Fisher score and with domain-specific astronomical knowledge because classifiers work in low dimensional space. These methods are utilized to classify the stellar spectra into the two standard star classification schemes, the Harvard Spectral Classification and the Morgan Keenan Luminosity Classes. If a star is classified into both of these schemes, many stellar properties can be approximated with ease, whereas the direct approaches can take up to months of observations. A physical phenomenon known as redshift causes machine learning complications through the feature matrix when classifying stellar spectra. However, classifiers utilizing redshifted stellar spectra performed with high accuracy. An approach for extracting redshift using predictions from the classification models is explored

Linguistic Self-Awareness and Poetry Preference

This paper examines the relationship between linguistic self-awareness and poetry preference in college students who don’t regularly read poetry. It addresses whether or not there are consistent phonological and semantic features that influence preference, and it observes whether or not students recognize linguistic factors as part of their preference. It also touches on syntactic play and the degree to which amateur readers understand that professional poets deliberately subvert linguistic tendencies

TEACHERS’ LIVED EXPERIENCES AND PERSPECTIVES OF SCHOOL-WIDE POSITIVE BEHAVIOR SUPPORT PLANS

The purpose of this case study was to examine how teachers from Title 1 elementary schools implemented school-wide positive behavior plans in Northeast Florida. This qualitative study was founded on the theory of planned behavior (TPB). According to TPB, an individual’s behavior can be predicted by attitude and purposes for the behavior. The research participants were six Title 1 elementary school teachers with whom semi-structured interviews were conducted to gather the data. Themes that emerged were professional development, consistency, teacher buy-in, administrative support, and the effectiveness of the plan. Participants indicated the importance of continued support through check-ins, collaborations, professional development, and explained expectations, especially when new administration implemented new expectations. Teachers also recognized that, when implementation of a SWPBS (school-wide positive behavior support plan) plan is done successfully, relationships are built, students can process their emotions with coping tools, and a classroom becomes a positive environment where students are learning and feel safe. Through examining the lived experiences of Title 1 elementary school teachers implementing a SWPBS plan, greater understanding may be gained through increased teacher buy-in to effectively implement a SWPBS plan

Asset Pricing with Incomplete Information under Stable Shocks

We study a consumption based asset pricing model with incomplete information and alpha-stable shocks. Incomplete information leads to a non-Gaussian filtering problem. Bayesian updating generates fluctuating confidence in the agents' estimate of the persistent component of the dividends’ growth rate. Similar results are obtained with alternate distributions exhibiting fat tails (Extreme Value distribution, Pearson Type IV distribution) while they are not with a thin-tail distribution (Binomial distribution). This has the potential to generate time variation in the volatility of model-implied returns, without relying on discrete shifts in the drift rate of dividend growth rates. A test of the model using US consumption data indicates strong support in the sense that the implied returns display significant volatility persistence of a magnitude comparable to that in the data.asset pricing, incomplete information, time-varying volatility, fat tails, stable distributions

Clustering-based Redshift Estimation: Comparison to Spectroscopic Redshifts

We investigate the potential and accuracy of clustering-based redshift estimation using the method proposed by M\'enard et al. (2013). This technique enables the inference of redshift distributions from measurements of the spatial clustering of arbitrary sources, using a set of reference objects for which redshifts are known. We apply it to a sample of spectroscopic galaxies from the Sloan Digital Sky Survey and show that, after carefully controlling the sampling efficiency over the sky, we can estimate redshift distributions with high accuracy. Probing the full colour space of the SDSS galaxies, we show that we can recover the corresponding mean redshifts with an accuracy ranging from $\delta$z=0.001 to 0.01. We indicate that this mapping can be used to infer the redshift probability distribution of a single galaxy. We show how the lack of information on the galaxy bias limits the accuracy of the inference and show comparisons between clustering redshifts and photometric redshifts for this dataset. This analysis demonstrates, using real data, that clustering-based redshift inference provides a powerful data-driven technique to explore the redshift distribution of arbitrary datasets, without any prior knowledge on the spectral energy distribution of the sources.Comment: 13 pages. Submitted to MNRAS. Comments welcom