Excited light and strange hadrons from the lattice with two Chirally Improved quarks

Results for excited light and strange hadrons from the lattice with two flavors of Chirally Improved sea quarks are presented. We perform simulations at several values of the pion mass ranging from 250 to 600 MeV and extrapolate to the physical pion mass. The variational method is applied to extract excited energy levels but also to discuss the content of the states. Among others, we explore the flavor singlet/octet content of Lambda states. In general, our results agree well with experiment, in particular we confirm the Lambda(1405) and its dominant flavor singlet structure.Comment: Contribution to the XV International Conference on Hadron Spectroscopy "Hadron 2013", 4-8 November 2013, Nara, Japa

An FTIR spectrometer for remote measurements of atmospheric composition

The JPL IV interferometer, and infrared Michelson interferometer, was built specifically for recording high resolution solar absorption spectra from remote ground-based sites, aircraft and from stratospheric balloons. The instrument is double-passed, with one fixed and one moving corner reflector, allowing up to 200-cm of optical path difference (corresponding to an unapodised spectral resolution of 0.003/cm). The carriage which holds the moving reflector is driven by a flexible nut riding on a lead screw. This arrangement, together with the double-passed optical scheme, makes the instrument resistant to the effects of mechanical distortion and shock. The spectral range of the instrument is covered by two liquid nitrogen-cooled detectors: an InSb photodiode is used for the shorter wavelengths (1.85 to 5.5 microns, 1,800 to 5,500/cm) and a HgCdTe photoconductor for the range (5.5 to 15 microns, 650 to 1,800/cm). For a single spectrum of 0.01/cm resolution, which requires a scan time of 105 seconds, the signal/noise ratio is typically 800:1 over the entire wavelength range

Nonparametric Markovian Learning of Triggering Kernels for Mutually Exciting and Mutually Inhibiting Multivariate Hawkes Processes

In this paper, we address the problem of fitting multivariate Hawkes processes to potentially large-scale data in a setting where series of events are not only mutually-exciting but can also exhibit inhibitive patterns. We focus on nonparametric learning and propose a novel algorithm called MEMIP (Markovian Estimation of Mutually Interacting Processes) that makes use of polynomial approximation theory and self-concordant analysis in order to learn both triggering kernels and base intensities of events. Moreover, considering that N historical observations are available, the algorithm performs log-likelihood maximization in $O(N)$ operations, while the complexity of non-Markovian methods is in $O(N^{2})$. Numerical experiments on simulated data, as well as real-world data, show that our method enjoys improved prediction performance when compared to state-of-the art methods like MMEL and exponential kernels

Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives

A combination of federal and state-level decision making has shaped the response to COVID-19 in the United States. In this paper, we analyze the Twitter narratives around this decision making by applying a dynamic topic model to COVID-19 related tweets by U.S. Governors and Presidential cabinet members. We use a network Hawkes binomial topic model to track evolving sub-topics around risk, testing, and treatment. We also construct influence networks amongst government officials using Granger causality inferred from the network Hawkes process

Reducing Bias in Estimates for the Law of Crime Concentration

Objectives The law of crime concentration states that half of the cumulative crime in a city will occur within approximately 4% of the city’s geography. The law is demonstrated by counting the number of incidents in each of N spatial areas (street segments or grid cells) and then computing a parameter based on the counts, such as a point estimate on the Lorenz curve or the Gini index. Here we show that estimators commonly used in the literature for these statistics are biased when the number of incidents is low (several thousand or less). Our objective is to significantly reduce bias in estimators for the law of crime concentration. Methods By modeling crime counts as a negative binomial, we show how to compute an improved estimate of the law of crime concentration at low event counts that significantly reduces bias. In particular, we use the Poisson–Gamma representation of the negative binomial and compute the concentration statistic via integrals for the Lorenz curve and Gini index of the inferred continuous Gamma distribution. Results We illustrate the Poisson–Gamma method with synthetic data along with homicide data from Chicago. We show that our estimator significantly reduces bias and is able to recover the true law of crime concentration with only several hundred events. Conclusions The Poisson–Gamma method has applications to measuring the concentration of rare events, comparisons of concentration across cities of different sizes, and improving time series estimates of crime concentration

Beverage preferences and associated drinking patterns, consequences and other substance use behaviours.

BACKGROUND: Studies about beverage preferences in a country in which wine drinking is relatively widespread (like Switzerland) are scarce. Therefore, the main aims of the present study were to examine the associations between beverage preferences and drinking patterns, alcohol-related consequences and the use of other substances among Swiss young men. METHODS: The analytical sample consisted of 5399 Swiss men who participated in the Cohort Study on Substance Use Risk Factors (C-SURF) and had been drinking alcohol over the preceding 12 months. Logistic regression analyses were conducted to study the associations between preference for a particular beverage and (i) drinking patterns, (ii) negative alcohol-related consequences and (iii) the (at-risk) use of cigarettes, cannabis and other illicit drugs. RESULTS: Preference for beer was associated with risky drinking patterns and, comparable with a preference for strong alcohol, with the use of illicit substances (cannabis and other illicit drugs). In contrast, a preference for wine was associated with low-risk alcohol consumption and a reduced likelihood of experiencing at least four negative alcohol-related consequences or of daily cigarette smoking. Furthermore, the likelihood of negative outcomes (alcohol-related consequences; use of other substances) increased among people with risky drinking behaviours, independent of beverage preference. CONCLUSIONS: In our survey, beer preference was associated with risky drinking patterns and illicit drug use. Alcohol polices to prevent large quantities of alcohol consumption, especially of cheaper spirits like beer, should be considered to reduce total alcohol consumption and the negative consequences associated with these beverage types

A Penalized Likelihood Method for Balancing Accuracy and Fairness in Predictive Policing

Racial bias of predictive policing algorithms has been the focus of recent research and, in the case of Hawkes processes, feedback loops are possible where biased arrests are amplified through self-excitation, leading to hotspot formation and further arrests of minority populations. In this article we develop a penalized likelihood approach for introducing fairness into point process models of crime. In particular, we add a penalty term to the likelihood function that encourages the amount of police patrol received by each of several demographic groups to be proportional to the representation of that group in the total population. We apply our model to historical crime incident data in Indianapolis and measure the fairness and accuracy of the two approaches across several crime categories. We show that fairness can be introduced into point process models of crime so that patrol levels proportionally match demographics, though at a cost of reduced accuracy of the algorithms