Imprints of gravitational lensing in the Planck CMB data at the location of WISExSCOS galaxies

We detect weak gravitational lensing of the cosmic microwave background (CMB) at the location of the WISExSCOS (WxS) galaxies using the publicly available Planck lensing convergence map. By stacking the lensing convergence map at the position of 12.4 million galaxies in the redshift range $0.1\le z \le 0.345$, we find the average mass of the galaxies to be M$_{200_{\rm crit}}$ = 6.25 $\pm$ 0.6 $\times 10^{12}\ M_{\odot}$. The null hypothesis of no-lensing is rejected at a significance of $17\sigma$. We split the galaxy sample into three redshift slices each containing $\sim$4.1 million objects and obtain lensing masses in each slice of 4.18 $\pm$ 0.8, 6.93 $\pm$ 0.9, and 18.84 $\pm$ 1.2 \times\ 10^{12}\ \mbox{M}_{\odot}. Our results suggest a redshift evolution of the galaxy sample masses but this apparent increase might be due to the preferential selection of intrinsically luminous sources at high redshifts. The recovered mass of the stacked sample is reduced by 28% when we remove the galaxies in the vicinity of galaxy clusters with mass M$_{200_{\rm crit}}$ = 2 \times 10^{14}\ \mbox{M}_{\odot}. We forecast that upcoming CMB surveys can achieve 5% galaxy mass constraints over sets of 12.4 million galaxies with M$_{200_{\rm crit}}$ = $1 \times 10^{12}\ M_{\odot}$ at $z=1$.Comment: 7 pages, 2 figures, 2 tables: updates: correlations between z-bins included: accepted for publication in PR

Weather Cycles, Production Yields and Georgia's Muscadine

This paper looks at the relationship between weather, crop yield, and market price of muscadines using a dynamic panel data that spans from the 2000 to 2005 and across the state of Georgia. We use a Generalized Methods of Moments technique to estimate the impact of weather on the price of muscadines with the yield per acre as the instrumented variable. The results suggest that there is a relationship between the price and weather for muscadines, which provide important implications for the potential relevance of a weather derivative for muscadine production.muscadines, weather cycles, price, production yields, Georgia, Generalized Method of Moments, Farm Management, Risk and Uncertainty,

Gender Bias Claims in Farm Service Agency’s Lending Decisions

This study analyzes the courts’ denial of women farmers’ motion for class-action certification of their lawsuits alleging gender discrimination in Farm Service Agency (FSA) lending decisions. The plaintiffs’ claim of “commonality†of circumstances in women farmers’ dealings with FSA is tested using a four-year sampling of Georgia FSA loan applications. The econometric framework has been developed after accounting for the separability of loan approval and amount decisions, as well as endogeneity issues through instrumental variable estimation. This study’s results do not produce overwhelming evidence of gender bias in FSA loan approval decisions and in favor of the “commonality†argument among Georgia FSA farm loan applicants.class-action suit, credit risk, creditworthiness, gender discrimination, Heckman selection, instrumental variable probit, Labor and Human Capital,

Grass-Fed versus Organic Dairy Production: Southeastern US Willingness to Pay

This paper examines determinants of consumers’ willingness to pay a premium for grass-fed and organic dairy by using a survey data from the southeastern United States. We use ordered and Heckman probit regression techniques to estimate the impact of consumer characteristics on their willingness to pay premiums. The results suggest that some of relevant determinants are: age, income, gender, and geographical variables. This research has important implications for the large dairy industries in Florida and also as provides important information for the growing dairy industries in the rest of the southeastern United States.Grass-fed Dairy, Heckman Probit Regression, Organic Dairy, WTP, Livestock Production/Industries,

A structure in the early Universe at z 1.3 that exceeds the homogeneity scale of the R-W concordance cosmology

A Large Quasar Group (LQG) of particularly large size and high membership has been identified in the DR7QSO catalogue of the Sloan Digital Sky Survey. It has characteristic size (volume^1/3) ~ 500 Mpc (proper size, present epoch), longest dimension ~ 1240 Mpc, membership of 73 quasars, and mean redshift = 1.27. In terms of both size and membership it is the most extreme LQG found in the DR7QSO catalogue for the redshift range 1.0 = 1.28, which is itself one of the more extreme examples. Their boundaries approach to within ~ 2 deg (~ 140 Mpc projected). This new, huge LQG appears to be the largest structure currently known in the early universe. Its size suggests incompatibility with the Yadav et al. scale of homogeneity for the concordance cosmology, and thus challenges the assumption of the cosmological principle

Search on a Hypercubic Lattice through a Quantum Random Walk: II. d=2

We investigate the spatial search problem on the two-dimensional square lattice, using the Dirac evolution operator discretised according to the staggered lattice fermion formalism. $d=2$ is the critical dimension for the spatial search problem, where infrared divergence of the evolution operator leads to logarithmic factors in the scaling behaviour. As a result, the construction used in our accompanying article \cite{dgt2search} provides an $O(\sqrt{N}\log N)$ algorithm, which is not optimal. The scaling behaviour can be improved to $O(\sqrt{N\log N})$ by cleverly controlling the massless Dirac evolution operator by an ancilla qubit, as proposed by Tulsi \cite{tulsi}. We reinterpret the ancilla control as introduction of an effective mass at the marked vertex, and optimise the proportionality constants of the scaling behaviour of the algorithm by numerically tuning the parameters.Comment: Revtex4, 5 pages (v2) Introduction and references expanded. Published versio

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instant out-of-sample predictions for proton and carbon nuclear chemical shifts, atomic core level excitations, and forces on atoms reach accuracies on par with density functional theory reference. Locality is exploited within non-linear regression via local atom-centered coordinate systems. The approach is validated on a diverse set of 9k small organic molecules. Linear scaling of computational cost in system size is demonstrated for saturated polymers with up to sub-mesoscale lengths

Measuring galaxy cluster masses with CMB lensing using a Maximum Likelihood estimator: Statistical and systematic error budgets for future experiments

We develop a Maximum Likelihood estimator (MLE) to measure the masses of galaxy clusters through the impact of gravitational lensing on the temperature and polarization anisotropies of the cosmic microwave background (CMB). We show that, at low noise levels in temperature, this optimal estimator outperforms the standard quadratic estimator by a factor of two. For polarization, we show that the Stokes Q/U maps can be used instead of the traditional E- and B-mode maps without losing information. We test and quantify the bias in the recovered lensing mass for a comprehensive list of potential systematic errors. Using realistic simulations, we examine the cluster mass uncertainties from CMB-cluster lensing as a function of an experiment's beam size and noise level. We predict the cluster mass uncertainties will be 3 - 6% for SPT-3G, AdvACT, and Simons Array experiments with 10,000 clusters and less than 1% for the CMB-S4 experiment with a sample containing 100,000 clusters. The mass constraints from CMB polarization are very sensitive to the experimental beam size and map noise level: for a factor of three reduction in either the beam size or noise level, the lensing signal-to-noise improves by roughly a factor of two.Comment: 28 pages, 5 figures: figs 2, 3 updated, references added: accepted for publication in JCA

Phenomenological modelling of first order phase transitions in magnetic systems

First order phase transitions may occur in several magnetic systems, with two structural phases having different magnetic properties each and a structural transition between them. Here, a novel physics based phenomenological model of such systems is proposed, in which magnetization is represented by the volumetric amounts of ferromagnetism (described by extended Jiles-Atherton theory) and paramagnetism (described by the Curie-Weiss law) in respective phases. An identification procedure to extract material parameters from experimental data is proposed. The proposed phenomenological approach was successfully applied to magnetocaloric Gd5(Six Ge 1−x)4 system and also has the potential to describe the behavior of Griffiths phase magnetic systems

Genome-wide Profiling of RNA splicing in prostate tumor from RNA-seq data using virtual microarrays

BACKGROUND: Second generation RNA sequencing technology (RNA-seq) offers the potential to interrogate genome-wide differential RNA splicing in cancer. However, since short RNA reads spanning spliced junctions cannot be mapped contiguously onto to the chromosomes, there is a need for methods to profile splicing from RNA-seq data. Before the invent of RNA-seq technologies, microarrays containing probe sequences representing exon-exon junctions of known genes have been used to hybridize cellular RNAs for measuring context-specific differential splicing. Here, we extend this approach to detect tumor-specific splicing in prostate cancer from a RNA-seq dataset. METHOD: A database, SPEventH, representing probe sequences of under a million non-redundant splice events in human is created with exon-exon junctions of optimized length for use as virtual microarray. SPEventH is used to map tens of millions of reads from matched tumor-normal samples from ten individuals with prostate cancer. Differential counts of reads mapped to each event from tumor and matched normal is used to identify statistically significant tumor-specific splice events in prostate. RESULTS: We find sixty-one (61) splice events that are differentially expressed with a p-value of less than 0.0001 and a fold change of greater than 1.5 in prostate tumor compared to the respective matched normal samples. Interestingly, the only evidence, EST (BF372485), in the public database for one of the tumor-specific splice event joining one of the intron in KLK3 gene to an intron in KLK2, is also derived from prostate tumor-tissue. Also, the 765 events with a p-value of less than 0.001 is shown to cluster all twenty samples in a context-specific fashion with few exceptions stemming from low coverage of samples. CONCLUSIONS: We demonstrate that virtual microarray experiments using a non-redundant database of splice events in human is both efficient and sensitive way to profile genome-wide splicing in biological samples and to detect tumor-specific splicing signatures in datasets from RNA-seq technologies. The signature from the large number of splice events that could cluster tumor and matched-normal samples into two tight separate clusters, suggests that differential splicing is yet another RNA phenotype, alongside gene expression and SNPs, that can be exploited for tumor stratification