Impact of large-scale dynamic versus thermodynamic climate conditions on contrasting tropical cyclone genesis frequency

Significant advances have been made in understanding the key climate factors responsible for tropical cyclone (TC) activity, yet any theory that estimates likelihood of observed TC formation rates from mean climate states remains elusive. The present study investigates how the extremes of observed TC genesis (TCG) frequency during peak TC seasons are interrelated with distinct changes in the large-scale climate conditions over different ocean basins using the global International Best Track Archive for Climate Stewardship (IBTrACS) dataset and ERA-Interim for the period 1979–2014. Peak TC seasons with significantly high and low TCG frequency are identified for five major ocean basins, and their substantial spatial changes in TCG are noted with regionally distinct differences. To explore the possible climate link behind such changes, a suite of potentially relevant dynamic and thermodynamic climate conditions is analyzed. Results indicate that the observed changes in extreme TCG frequency are closely linked with distinct dominance of specific dynamic and thermodynamic climate conditions over different regions. While the combined influences of dynamic and thermodynamic climate conditions are found to be necessary for modulating TC formation rate over the North Atlantic, eastern Pacific, and southern Indian Oceans, significant changes in large-scale dynamic conditions appear to solely control the TCG frequency over the western Pacific and South Pacific basins. Estimation of the fractional changes in genesis-weighted climate conditions also indicates the coherent but distinct competing effects of different climate conditions on TCG frequency. The present study further points out the need for revising the existing genesis indices for estimating TCG frequency over individual basins

Author Correction: Task-dependent representations of stimulus and choice in mouse parietal cortex.

In the original version of this Article, the Acknowledgements section was inadvertently omitted. This has now been corrected in both the PDF and HTML versions of the Article

An EMTP system level model of the PMAD DC test bed

A power management and distribution direct current (PMAD DC) test bed was set up at the NASA Lewis Research Center to investigate Space Station Freedom Electric Power Systems issues. Efficiency of test bed operation significantly improves with a computer simulation model of the test bed as an adjunct tool of investigation. Such a model is developed using the Electromagnetic Transients Program (EMTP) and is available to the test bed developers and experimenters. The computer model is assembled on a modular basis. Device models of different types can be incorporated into the system model with only a few lines of code. A library of the various model types is created for this purpose. Simulation results and corresponding test bed results are presented to demonstrate model validity

Spacetime torsion and parity violation: a gauge invariant formulation

The possibility of parity violation through spacetime torsion has been explored in a scenario containing fields with different spins. Taking the Kalb-Ramond field as the source of torsion, an explicitly parity violating $U(1)_{EM}$ gauge invariant theory has been constructed by extending the KR field with a Chern-Simons term.Comment: 4 pages, RevTe

Three-coordinate iron(II) expanded ring N-heterocyclic carbene complexes

A sterically demanding seven-membered expanded ring N-heterocyclic carbene (NHC) ligand allows access to rare examples of three-coordinate iron(II)-NHC complexes incorporating only halide coligands of the general formula [Fe(NHC)X 2 ] (NHC = 7-DiPP; X = Br (1) Cl (2)). Reducing the steric influence of the ancillary NHC ligand through modulation of the N-aryl substituents leads to either four- or three-coordinate complexes of the general formula [Fe(NHC)Br 2 (THF)] (3) or [Fe(NHC)Br 2 ] (4) (NHC = 7-Mes), dependent upon the solvent of recrystallization. The further reduction of NHC steric influence results in four-coordinate geometries at iron in the form of the dimeric species [Fe(NHC)Br(μ-Br)] 2 (5) or [Fe(NHC)Br 2 (THF)] (6) (NHC = SDiPP), again dependent upon the solvent of recrystallization. Compounds 1-6 have been analyzed by 1 H NMR spectroscopy, X-ray crystallography, elemental microanalysis, Mössbauer spectroscopy (for 1 and 3-5), and Evans method magnetic susceptibility. In addition to these measurements the three-coordinate species 1 and 4 have been further analyzed by SQUID magnetometry and CASSCF calculations, which show significant magnetic anisotropy that is extremely sensitive to the coordination geometry

Gravitational Redshift in Einstein-Kalb-Ramond Spacetime and Randall-Sundrum Scenario

It is shown that the gravitational redshift as predicted by Einstein's theory, is modified in presence of second rank antisymmetric tensor (Kalb-Ramond) field in a string inspired background spacetime.In presence of extra dimensions, the Randall-Sundrum brane world scenario is found to play a crucial role in suppressing this additional shift. The bound on the value of the warp factor is determined from the redshift data and is found to be in good agreement with that determined from the requirements of Standard model.Comment: 4 Pages, Revtex, No figures, version thoroughly revise

A modern maximum-likelihood theory for high-dimensional logistic regression

Every student in statistics or data science learns early on that when the sample size largely exceeds the number of variables, fitting a logistic model produces estimates that are approximately unbiased. Every student also learns that there are formulas to predict the variability of these estimates which are used for the purpose of statistical inference; for instance, to produce p-values for testing the significance of regression coefficients. Although these formulas come from large sample asymptotics, we are often told that we are on reasonably safe grounds when $n$ is large in such a way that $n \ge 5p$ or $n \ge 10p$. This paper shows that this is far from the case, and consequently, inferences routinely produced by common software packages are often unreliable. Consider a logistic model with independent features in which $n$ and $p$ become increasingly large in a fixed ratio. Then we show that (1) the MLE is biased, (2) the variability of the MLE is far greater than classically predicted, and (3) the commonly used likelihood-ratio test (LRT) is not distributed as a chi-square. The bias of the MLE is extremely problematic as it yields completely wrong predictions for the probability of a case based on observed values of the covariates. We develop a new theory, which asymptotically predicts (1) the bias of the MLE, (2) the variability of the MLE, and (3) the distribution of the LRT. We empirically also demonstrate that these predictions are extremely accurate in finite samples. Further, an appealing feature is that these novel predictions depend on the unknown sequence of regression coefficients only through a single scalar, the overall strength of the signal. This suggests very concrete procedures to adjust inference; we describe one such procedure learning a single parameter from data and producing accurate inferenceComment: 29 pages, 14 figures, 4 table