4,278 research outputs found
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 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
- …