Severe Departures Research Articles

To be, or not to be: Balmer breaks in high-z galaxies with JWST

ABSTRACT Standard models of structure formation allow us to predict the cosmic timescales relevant for the onset of star formation and the assembly history of galaxies at high redshifts (z > 10). The strength of the Balmer break represents a well-known diagnostic of the age and star formation history of galaxies, which enables us to compare observations with contemporary simulations – thus shedding light on the predictive power of our current models of star formation in the early Universe. Here, we measure the Balmer break strength for 23 spectroscopically confirmed galaxies at redshifts 6 ≲ z ≲ 12 using public JWST NIRSpec data from the cycle 1 GO 1433 and GO 2282 programmes (PI Coe), as well as public spectroscopic data from the JWST Deep Extragalactic Survey (JADES). We find that the range of observed Balmer break strengths agree well with that of current simulations given our measurement uncertainties. No cases of anomalously strong Balmer breaks are detected, and therefore no severe departures from the predictions of contemporary models of star formation. However, there are indications of a number of outliers in the observed distribution which have weaker Balmer breaks than predicted by simulations.

Competing Forecast Verification: Using the Power-Divergence Statistic for Testing the Frequency of “Better”

Abstract When testing hypotheses about which of two competing models is better, say A and B, the difference is often not significant. An alternative, complementary approach, is to measure how often model A is better than model B regardless of how slight or large the difference. The hypothesis concerns whether or not the percentage of time that model A is better than model B is larger than 50%. One generalized test statistic that can be used is the power-divergence test, which encompasses many familiar goodness-of-fit test statistics, such as the loglikelihood-ratio and Pearson X2 tests. Theoretical results justify using the distribution for the entire family of test statistics, where k is the number of categories. However, these results assume that the underlying data are independent and identically distributed, which is often violated. Empirical results demonstrate that the reduction to two categories (i.e., model A is better than model B versus model B is better than A) results in a test that is reasonably robust to even severe departures from temporal independence, as well as contemporaneous correlation. The test is demonstrated on two different example verification sets: 6-h forecasts of eddy dissipation rate (m2/3 s−1) from two versions of the Graphical Turbulence Guidance model and for 12-h forecasts of 2-m temperature (°C) and 10-m wind speed (m s−1) from two versions of the High-Resolution Rapid Refresh model. The novelty of this paper is in demonstrating the utility of the power-divergence statistic in the face of temporally dependent data, as well as the emphasis on testing for the “frequency-of-better” alongside more traditional measures.

Statistical power, accuracy, reproducibility and robustness of a graph clusterability test

Not all graphs are clusterable. Not all graphs have a clustered structure and can be meaningfully summarized through vertex clustering. Clusterable graphs are characterized by pockets of densely connected vertices that are only sparsely connected to the remaining graph. In this article, we re-introduce a very simple and intuitive, yet highly informative, statistical hypothesis test for graph clusterability that is based on vertex and neighborhood samples. The goal of this test is to determine if a graph meets the necessary structural conditions to be summarized meaningfully through vertex clusters. Our test is based on the hypothesis that a clusterable graph will display, on average, a local neighborhood induced subgraph density that is greater than the graph’s overall density. The test is also applied to graph comparisons, to test whether one graph has a stronger clustered structure than another. Significance is assessed using the t-statistic. Since it is based on sampling, we provide a focused examination of our test’s sensitivity to sample size. The main contribution of this article is a detailed examination of our test’s accuracy, sensitivity to sample size, conclusion reproducibility and robustness. Our empirical results remain consistent with our earlier conclusions and demonstrate the almost perfect accuracy of our test, even with very small samples of the graph. They also reveal that our test remains robust even under severe departures from the null hypothesis.

Molecular dynamics simulations of lanthanum chloride by deep learning potential

It has been known for many years that mixtures of lanthanum chloride (LaCl3) with alkali chlorides in spent nuclear fuel show severe departures from ideality. To go deep into this non-ideality, the local structure of molten LaCl3 was investigated by experiments and classic molecular dynamics simulations. However, there appears to be some controversy concerning conclusions of these research methodology such as coordination numbers of the first coordination shell. In this work, interatomic potential driven by machine learning is developed based on data sets generated by ab initio calculations in order to research the local structure and property of molten LaCl3 at 1200 K, 1300 K, 1400 K and 1500 K. The machine learning potential enables higher efficiency and similar accuracy relative to DFT and yields precise descriptions of microstructures and properties. Microstructural evolution with target temperatures is analyzed through partial radial distribution functions, coordination numbers, angular distribution functions and total neutron structural factors. We observe short- and intermediate-range order and the latter disappears at high temperature. The sevenfold and eightfold coordinated structures are dominant in LaCl3 melt and the network structure is composed of corner-sharing, edge-sharing and face-sharing configurations. Evolution of properties including density and self-diffusion coefficient over the entire operating temperature range are documented. This work exhibits a thorough understanding of the local structure and property of LaCl3 melt and reveals the accuracy of machine learning potential on molten trivalent metal chlorides for first time.

How Does Lane Departure Warning Effectiveness Vary by Severity of Departure

Lane departures are a significant traffic safety problem. They can be attributed to a variety of types of impairment including the increasing prevalence of distracted driving. Research to date has focused on how drivers respond to imminent lane departure warnings, often with short time to line crossing at the onset of the alert. This paper examines how this effectiveness changes over a range of severities associated with various times to line crossing. Although warning systems are generally beneficial in reducing the severity of lane departures, time to line crossing had mixed effects on lane departures. Longer time to line crossing was associated with a greater likelihood of departing the lane but with less severe lane departures. Additionally, an active warning that provides steering torque to help correct lane departures showed decreased effectiveness with increasing time to line crossing in terms of likelihood of lane departure and duration and area of lane departure. These results point to the importance of considering the range of situations in which a warning may be issued, not just the most severe cases.

Locations Where Space Weather Energy Impacts the Atmosphere

In this review we consider aspects of space weather that can have a severe impact on the terrestrial atmosphere. We begin by identifying the pre-conditioning role of the Sun on the temperature and density of the upper atmosphere. This effect we define as “space climatology”. Space weather effects are then defined as severe departures from this state of the atmospheric energy and density. Three specific forms of space weather are reviewed and we show that each generates severe space weather impacts. The three forms of space weather being considered are the solar photon flux (flares), particle precipitation (aurora), and electromagnetic Joule heating (magnetosphere–ionospheric (M-I) coupling). We provide an overview of the physical processes associated with each of these space weather forms. In each case a very specific altitude range exists over which the processes can most effectively impact the atmosphere. Our argument is that a severe change in the local atmosphere’s state leads to atmospheric heating and other dynamic changes at locations beyond the input heat source region. All three space weather forms have their greatest atmospheric impact between 100 and 130 km. This altitude region comprises the transition between the atmosphere’s mesosphere and thermosphere and is the ionosphere’s E-region. This region is commonly referred to as the Space Atmosphere Interaction Region (SAIR). The SAIR also acts to insulate the lower atmosphere from the space weather impact of energy deposition. A similar space weather zone would be present in atmospheres of other planets and exoplanets.

Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence

Most existing reduced-form macroeconomic multivariate time series models employ elliptical disturbances, so that the forecast densities produced are symmetric. In this article, we use a copula model with asymmetric margins to produce forecast densities with the scope for severe departures from symmetry. Empirical and skew t distributions are employed for the margins, and a high-dimensional Gaussian copula is used to jointly capture cross-sectional and (multivariate) serial dependence. The copula parameter matrix is given by the correlation matrix of a latent stationary and Markov vector autoregression (VAR). We show that the likelihood can be evaluated efficiently using the unique partial correlations, and estimate the copula using Bayesian methods. We examine the forecasting performance of the model for four U.S. macroeconomic variables between 1975:Q1 and 2011:Q2 using quarterly real-time data. We find that the point and density forecasts from the copula model are competitive with those from a Bayesian VAR. During the recent recession the forecast densities exhibit substantial asymmetry, avoiding some of the pitfalls of the symmetric forecast densities from the Bayesian VAR. We show that the asymmetries in the predictive distributions of GDP growth and inflation are similar to those found in the probabilistic forecasts from the Survey of Professional Forecasters. Last, we find that unlike the linear VAR model, our fitted Gaussian copula models exhibit nonlinear dependencies between some macroeconomic variables. This article has online supplementary material.

A search for Mg-poor stars using LAMOST survey data

Most Galactic metal-poor stars exhibit enhanced α-abundances (e.g. [Mg/Fe] ∼ +0.4) according to previous studies of stellar chemical compositions. However, a handful of metal-poor stars with large deficiencies in Mg (e.g. [Mg/Fe] ∼ −0.2) show severe departures from this α-enhancement trend. The sub-solar [Mg/Fe] ratios of these anomalous stars indicate that they possess different chemical enrichment histories than the majority of Galactic metal-poor stars. In previous work, we presented a method to select Mg-poor metal-poor stars from low-resolution SDSS spectra based on a spectral matching technique. In this paper, a similar method is applied to low-resolution (R ∼ 1800) LAMOST spectra. Stellar [Mg/Fe] abundances are determined by using stellar parameters delivered by the LAMOST Data Release 2 catalog. From a sample of ∼ 60 000 stars with atmospheric parameters in the range Teff = [5500, 6500] K and [Fe/H] = [−2.4, +0.5], we select 15 candidate Mg-poor metal-poor stars.

Robust Alcohol Policy in Russia: Some Aspirational Measures

Alcohol consumption is associated with severe externalities as well as negative effects suffered by drinkers themselves. The usual economics prescription for promoting social well-being in the face of externalities is to apply regulations, including taxes, to force decision makers to confront the full social costs of their choices. Self-harms suffered by rational and informed adult alcohol drinkers, alternatively, offer little rationale (by the lights of traditional economics) for coercive policies aimed at stemming alcohol consumption. The inherent unknowability of the extent of rationality in drinking decisions suggests that the “first best” policy of leaving rational drinking unfettered, while attempting to adjust potentially irrational choices, is not available in practice. The contention of this paper is that a second-best approach should be robust, in the sense that the policy regime will work fairly well when drinkers are fully rational, and also will do a creditable job of serving social well-being when alcohol-related choices are marked by substantial rationality shortfalls. In the case of the Russian Federation, some policies that mark rather severe departures from the current alcohol system, but are consistent with robustness, include: voluntary self-exclusion from either alcohol purchases or consumption; mandatory exclusion for those who produce external alcohol-related harms; and, a buyer licensing scheme that relies on a “double default” system to reduce both alcohol prevalence and the harms that stem from the drinking that does take place. This paper explicates these “aspirational” alcohol control policies.

MINING THE SLOAN DIGITAL SKY SURVEY IN SEARCH OF EXTREMELY α-POOR STARS IN THE GALAXY

As we know, the majority of metal-poor Galactic halo stars appear to have chemical abundances that were enhanced by α-elements (e.g., O, Mg, Si, Ca, and Ti) during the early stage of the Galaxy. Observed metal-poor halo stars preserved this pattern by exhibiting abundance ratios [α/Fe] ~+0.4. A few striking exceptions that show severe departures from the general enhanced α-element chemical abundance trends of the halo have been discovered in recent years. They possess relatively low [α/Fe] compared to other comparable-metallicity stars, with abundance ratios over 0.5 dex lower. These stars may have a different chemical enrichment history from the majority of the halo. Similarly, low-α abundances are also displayed by satellite dwarf spheroidal (dSph) galaxies. We present a method to select extremely α-poor (EAP) stars from the SDSS/SEGUE survey. The method consists of a two-step approach. In the first step, we select suspected metal-poor ([Fe/H] <–0.5) and α-poor ([Mg/Fe] <0) stars as our targets. In the second step, we determine [Mg/Fe] from low-resolution (R = 2000) stellar spectra for our targets and select stars with [Mg/Fe] <–0.1 as candidate EAP stars. In a sample of 40,000 stars with atmospheric parameters in the range of T eff = [4500, 7000] K, log g = [1.0, 5.0], and [Fe/H] = [–4.0, +0.5], 14 candidate stars were identified. Three of these stars are found to have already been confirmed by other research.

UsingExcelfor White's Test-An Important Technique for Evaluating the Equality of Variance Assumption and Model Specification in a Regression Analysis

There is consensus in the statistical literature that severe departures from its assumptions invalidate the use of regression modeling for purposes of inference. The assumptions of regression modeling are usually evaluated subjectively through visual, graphic displays in a residual analysis but such an approach, taken alone, may be insufficient for assessing the appropriateness of the fitted model. Here, an easy-to-use test of the assumption of equal variance (i.e., homoscedasticity) as well as model specification is provided. Given the importance of the equal-variance assumption (i.e., if uncorrected, severe violations preclude the use of statistical inference and moderate violations result in a loss of statistical power) and given the fact that, if uncorrected, a misspecified or underspecified model could invalidate an entire study, the test developed by Halbert White in 1980 is recommended for supplementing a graphic residual analysis when teaching regression modeling to business students at both the undergraduate and graduate levels. Using this confirmatory approach to supplement a traditional residual analysis has value because students often find that graphic displays are too subjective for determining what constitutes severe from moderate departures from the equal variance assumption or for assessing patterns in plots that might indicate model misspecification or underspecification.

Searching for extremely alpha-poor stars in the Galactic Halo

AbstractA few alpha-poor stars that show severe departures (over 0.4 dex deficiency in alpha-element abundance) from the general enhanced alpha-element chemical abundance trends of the halo have been discovered in recent years, such as BD +80°245, G4-36 and CS 22966-043. These ratios suggest a different chemical enrichment history for these stars than for the majority of the halo. Similarly low-alpha abundance patterns are also seen in the Sagittarius dSph galaxy. We present a method for searching of extremely alpha-poor stars from low-resolution stellar spectra of LAMOST pilot survey and attempt to create a large sample of these particular Galactic halo stars.

The trend odds model for ordinal data

Ordinal data appear in a wide variety of scientific fields. These data are often analyzed using ordinal logistic regression models that assume proportional odds. When this assumption is not met, it may be possible to capture the lack of proportionality using a constrained structural relationship between the odds and the cut-points of the ordinal values. We consider a trend odds version of this constrained model, wherein the odds parameter increases or decreases in a monotonic manner across the cut-points. We demonstrate algebraically and graphically how this model is related to latent logistic, normal, and exponential distributions. In particular, we find that scale changes in these potential latent distributions are consistent with the trend odds assumption, with the logistic and exponential distributions having odds that increase in a linear or nearly linear fashion. We show how to fit this model using SAS Proc NLMIXED and perform simulations under proportional odds and trend odds processes. We find that the added complexity of the trend odds model gives improved power over the proportional odds model when there are moderate to severe departures from proportionality. A hypothetical data set is used to illustrate the interpretation of the trend odds model, and we apply this model to a swine influenza example wherein the proportional odds assumption appears to be violated.

Monocrystalline Al(As1–xNx)Si3 and Al(P1–xNx)ySi5–2y Alloys with Diamond-like Structures: New Chemical Approaches to Semiconductors Lattice Matched to Si

The reaction of M(SiH3)3 (M = P, As) with Al atoms from a Knudsen cell has recently led to an entirely new approach to the synthesis of (III–V)–(IV) alloys on Si substrates using gas-source molecular beam epitaxy. The main feature of these new materials is the presence of isolated “donor–acceptor” pairs within fully interlinked III–V–(IV)3 “building blocks”, leading to highly stable and crystalline structures with average diamond-like symmetry. Examples include stoichiometric compounds Al(P–As)Si3 as well as silicon-rich alloys (Si)5–2y(AlP)y. While these materials can be grown on Si with small mismatch strains, the complete elimination of the lattice mismatch may be important for applications such as photovoltaics which require thick films. Accordingly, in this paper we expand the above compositional space and explore the optical tuning potential in this new class of materials by introducing nitrogen onto the group V sublattice. We demonstrate that the addition of N(SiH3)3 into the reaction mixture under appropriate conditions readily yields novel Al(As1–xNx)Si3 and Al(P1–xNx)ySi5–2y alloys which exhibit enhanced optical absorption with respect to silicon as evidenced by spectroscopic measurements indicating potential applications in Si-based photovoltaics. These materials can be grown as monocrystalline layers completely lattice matched on Si via substitution of As and P by the markedly smaller N atoms. First-principles studies of the reactivity trends are consistent with the finding that a large excess of the less reactive N(SiH3)3 is needed to incorporate even minor amounts of the N atoms in the alloy products, up to ∼11% in the case of AlAs1–xNxSi3 but only ∼3% in the case of AlP1–xNxSi3. Molecular calculations are then combined with solid-state simulations to elucidate the path from “molecule to solid” by quantifying the structural changes associated with the incorporation of the Al–P–Si3, Al–As–Si3, and Al–N–Si3 tetrahedral cores into the solid. The main conclusions of this study are that severe departures from tetrahedral symmetry in the Al–N–Si3 building block thwart the assembly of a regular diamond lattice. This is in contrast to the AlAsSi3 counterpart, which is found to exhibit near perfect tetrahedral symmetry in the crystalline state. All compounds are predicted to be thermodynamically stable relative to their elemental reference states.

Simulating non‐normal distributions with specifiedL‐moments andL‐correlations

This paper derives a procedure for simulating continuous non‐normal distributions with specifiedL‐moments andL‐correlations in the context of power method polynomials of order three. It is demonstrated that the proposed procedure has computational advantages over the traditional product‐moment procedure in terms of solving for intermediate correlations. Simulation results also demonstrate that the proposedL‐moment‐based procedure is an attractive alternative to the traditional procedure when distributions with more severe departures from normality are considered. Specifically, estimates ofL‐skew andL‐kurtosis are superior to the conventional estimates of skew and kurtosis in terms of both relative bias and relative standard error. Further, theL‐correlation also demonstrated to be less biased and more stable than the Pearson correlation. It is also shown how the proposedL‐moment‐based procedure can be extended to the larger class of power method distributions associated with polynomials of order five.

An extension of parametric ROC analysis for calculating diagnostic accuracy when underlying distributions are mixture of Gaussian

The semiparametric LABROC approach of fitting binormal model for estimating AUC as a global index of accuracy has been justified (except for bimodal forms), while for estimating a local index of accuracy such as TPF, it may lead to a bias in severe departure of data from binormality. We extended parametric ROC analysis for quantitative data when one or both pair members are mixture of Gaussian (MG) in particular for bimodal forms. We analytically showed that AUC and TPF are a mixture of weighting parameters of different components of AUCs and TPFs of a mixture of underlying distributions. In a simulation study of six configurations of MG distributions:{bimodal, normal} and {bimodal, bimodal} pairs, the parameters of MG distributions were estimated using the EM algorithm. The results showed that the estimated AUC from our proposed model was essentially unbiased, and that the bias in the estimated TPF at a clinically relevant range of FPF was roughly 0.01 for a sample size of n=100/100. In practice, with severe departures from binormality, we recommend an extension of the LABROC and software development for future research to allow for each member of the pair of distributions to be a mixture of Gaussian that is a more flexible parametric form.

Tetrapalladium(II) Bisazobenzene and Azoazoxybenzene Complexes: Syntheses, Electronic Structures, and Optical Properties

Three new tetranuclear, chloride-bridged PdII complexes have been prepared by using 1,4-bis[(E)-phenylazo]benzenes (BPABs) and an azoazoxybenzene derivative. The latter was obtained unexpectedly by reacting N,N-dimethyl-4-nitrosoaniline with 2,3,5,6-tetramethylphenylenediamine in glacial acetic acid. Characterization includes 1H NMR and UV–vis spectroscopies, and single-crystal X-ray structures have been obtained for two of the proligands and one complex. As expected, the methyl groups on the central aryl rings cause severe departures from planarity. The proligands show an intense UV absorption (λmax ≈ 350 nm) due to π → π* intramolecular charge transfer, together with weaker n → π* bands in the visible region. The complexes display an intense visible band (λmax = 449–475 nm) and multiple near-UV absorptions. While the electronic absorption spectra of the BPAB complexes are quite similar, the azoazoxybenzene complex shows a doubly intense visible band. Dichroic ratios (DRs) measured in two different liqui...

Inverse probability weighted estimation of social tariffs: An illustration using the SF-6D value sets

This paper presents a novel approach to model health state valuations using inverse probability weighting techniques. Our approach makes no assumption on the distribution of health state values, accommodates covariates in a flexible way, eschews parametric assumptions on the relationship between the outcome and the covariates, allows for an undetermined amount of heterogeneity in the estimates and it formally tests and corrects for sample selection biases. The proposed model is semi-parametrically estimated and it is illustrated with health state valuation data collected for Spain using the SF-6D descriptive system. Estimation results indicate that the standard regression model underestimates the utility loss that the Spanish general population assigns to departures from full health, particularly so for severe departures.

Reconsidering the asymptotic null distribution of likelihood ratio tests for genetic linkage in multivariate variance components models under complete pleiotropy

Accurate knowledge of the null distribution of hypothesis tests is important for valid application of the tests. In previous papers and software, the asymptotic null distribution of likelihood ratio tests for detecting genetic linkage in multivariate variance components models has been stated to be a mixture of chi-square distributions with binomial mixing probabilities. For variance components models under the complete pleiotropy assumption, we show by simulation and by theoretical arguments based on the geometry of the parameter space that all aspects of the previously stated asymptotic null distribution are incorrect-both the binomial mixing probabilities and the chi-square components. Correcting the null distribution gives more conservative critical values than previously stated, yielding P values that can easily be 10 times larger. The true mixing probabilities give the highest probability to the case where all variance parameters are estimated positive, and the mixing components show severe departures from chi-square distributions. Thus, the asymptotic null distribution has complex features that raise challenges for the assessment of significance of multivariate linkage findings. We propose a method to generate an asymptotic null distribution that is much faster than other empirical methods such as permutation, enabling us to obtain P values with higher precision more efficiently.

Testing Lagrangian Theories of Internal Wave Spectra. Part I: Varying the Amplitude and Wavenumbers

Abstract A growing body of literature has been built on the premise that kinematic advection produced by linear superpositions of sinusoidal Lagrangian gravity waves confined to lower vertical wavenumbers can provide an explanation for quasi-universal Eulerian spectral tails commonly found in the oceans and the atmosphere. Recently, Hines has established criteria delineating the circumstances in which Eulerian and Lagrangian spectra differ. For conditions in which Hines claims Lagrangian linearity and the production of quasi-universal Eulerian m−3 spectra, a kinematic advection model based on ensembles of seven nonstanding Lagrangian waves reveals the presence of gross violations of continuity and adiabaticity as well as severe departures from hydrostatic balance. Similar infractions are found for other seven-wave ensembles having a broad range of amplitudes and wavenumbers typical of saturated wave fields in the middle atmosphere. Furthermore, m−3 spectra are found only as the Lagrangian wave field approaches a singular state. The singularities in the Lagrangian to Eulerian transformation are induced by stretching deformation fields that form during the superposition of sinusoidal waves with nonparallel wave vectors. Such deformation fields are known to be unstable with respect to three-dimensional vortices. The results strongly suggest that saturated middle atmosphere wave fields are frequently accompanied by small-scale turbulent eddies.