Aspects of the chemistry of some phosphorus halides and pseudohalides

The preparation of pseudohalogeno derivatives of the simple phosphorus(V) species PC1(_4)(^+), PC1(_5) and PC1(_6)(^-) has been attempted. In the case of the tetrachlorophosphonium ion only azido-derivatives are observable in normal organic solvents, cyano and thiocyanato derivatives being more stable in liquid halogen media. Isolation of these compounds was not possible. Molecular derivatives based on PC1(_5) seem to be particularly unstable and are only readily observable under forcing conditions for cyanide. The derivatives of the hexachlorophosphate ion are all observable, PX(_6)(^-) being readily formed for X = N(_3), NCS, NCO and OCN although these and the intermediate species are all unstable. The series of cyanides PC1(_6-n)(CN)(_n)(^-) (0 < n < 3) have been isolated as solids and fully characterised, and the presence of isomers for n = 2 and 3 has been clearly established. The six-coordinate fluorochlorophosphates PF(_3)Cl(_3)(^-), PF(_2)C1(_4)(^-) and PFC1(_5)(^-) have been isolated as pure tetraalkylammonium salts and the reactions of these anions studied with respect to substitution by pseudohalides. The observation of PF(_6-n)X(_n)(^-) (X = pseudohalogen) has been carried out by ligand exchange between PF(_6)(^-) and PX(_6)(^-) (where known) or PX(_3) and attempts have been made to isolate compounds, where feasible, by other reactions such as the addition of pseudohalide ions to PF(_5).The use of pairwise interactions has proved invaluable in assigning formulae in the tetrahedral systems, and in both assigning formulae and identifying specific isomers in many of the six-coordinate systems. The substitution patterns in the six-coordinate systems can be rationalised in terms of a simple steric model, or on the basis of ligand field theory for the cyanides. Other six-coordinate systems have been studied with respect to substitution by azide and several new species have been identified

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

First-year sea-ice contact predicts bromine monoxide (BrO) levels better than potential frost flower contact

International audienceReactive halogens are responsible for boundary-layer ozone depletion and mercury deposition in Polar Regions during springtime. To investigate the source of reactive halogens in the air arriving at Barrow, Alaska, we measured BrO, a marker of reactive halogen chemistry, and correlated its abundance with airmass histories derived from meteorological back trajectories and remotely sensed sea ice properties. The BrO is found to be positively correlated to first-year sea-ice contact (R2=0.55), and weakly negatively correlated to potential frost flower (PFF) contact (R2=0.04). These data indicate that snow contaminated with sea salts on first-year sea ice is a more probable bromine source than are frost flowers. Recent climate-driven changes in Arctic sea ice are likely to alter frost flower and first year sea ice prevalence, suggesting a significant change in reactive halogen abundance, which will alter the chemistry of the overlying Arctic atmosphere

The Stern-Gerlach Experiment Revisited

The Stern-Gerlach-Experiment (SGE) of 1922 is a seminal benchmark experiment of quantum physics providing evidence for several fundamental properties of quantum systems. Based on today's knowledge we illustrate the different benchmark results of the SGE for the development of modern quantum physics and chemistry. The SGE provided the first direct experimental evidence for angular momentum quantization in the quantum world and thus also for the existence of directional quantization of all angular momenta in the process of measurement. It measured for the first time a ground state property of an atom, it produced for the first time a `spin-polarized' atomic beam, it almost revealed the electron spin. The SGE was the first fully successful molecular beam experiment with high momentum-resolution by beam measurements in vacuum. This technique provided a new kinematic microscope with which inner atomic or nuclear properties could be investigated. The original SGE is described together with early attempts by Einstein, Ehrenfest, Heisenberg, and others to understand directional quantization in the SGE. Heisenberg's and Einstein's proposals of an improved multi-stage SGE are presented. The first realization of these proposals by Stern, Phipps, Frisch and Segr\`e is described. The set-up suggested by Einstein can be considered an anticipation of a Rabi-apparatus. Recent theoretical work is mentioned in which the directional quantization process and possible interference effects of the two different spin states are investigated. In full agreement with the results of the new quantum theory directional quantization appears as a general and universal feature of quantum measurements. One experimental example for such directional quantization in scattering processes is shown. Last not least, the early history of the `almost' discovery of the electron spin in the SGE is revisited.Comment: 50pp, 17 fig

Machine Learning Predicts Reach-Scale Channel Types From Coarse-Scale Geospatial Data in a Large River Basin

Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin-wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, this study leverages machine learning to predict reach-scale geomorphic channel types using publicly available geospatial data. A bottom-up machine learning approach selects the most accurate and stable model among∼20,000 combinations of 287 coarse geospatial predictors, preprocessing methods, and algorithms in a three-tiered framework to (i) define a tractable problem and reduce predictor noise, (ii) assess model performance in statistical learning, and (iii) assess model performance in prediction. This study also addresses key issues related to the design, interpretation, and diagnosis of machine learning models in hydrologic sciences. In an application to the Sacramento River basin (California, USA), the developed framework selects a Random Forest model to predict 10 channel types previously determined from 290 field surveys over 108,943 two hundred-meter reaches. Performance in statistical learning is reasonable with a 61% median cross-validation accuracy, a sixfold increase over the 10% accuracy of the baseline random model, and the predictions coherently capture the large-scale geomorphic organization of the landscape. Interestingly, in the study area, the persistent roughness of the topography partially controls channel types and the variation in the entropy-based predictive performance is explained by imperfect training information and scale mismatch between labels and predictors

Flexible parameter-sparse global temperature time-profiles that stabilise at 1.5C and 2.0C

The meeting of the United Nations Framework Convention on Climate Change (UNFCCC) in December 2015 committed parties to the Convention to hold the rise in global average temperature to well below 2.0 C above pre-industrial levels. It also committed the parties to pursue efforts to limit warming to 1.5 C. This leads to two key questions. First,what extent of emission reductions will achieve either target? Second, what is the benefit of the reduced climate impacts by keeping warming at or below 1.5 C? To provide answers, climate model simulations need to follow trajectories consistent with these global temperature limits. It is useful to operate models in an inverse mode to make model-specific estimates of greenhouse gas (GHG) concentration pathways consistent with the prescribed temperature profiles. Further inversion derives related emissions pathways for these concentrations. For this to happen, and to enable climate research centres to compare GHG concentrations and emissions estimates, common temperature trajectory scenarios are required. Here we define algebraic curves which asymptote to a stabilised limit, while also matching the magnitude and gradient of recent warming levels. The curves are deliberately parameter-sparse, needing prescription of just two parameters plus the final temperature. Yet despite this simplicity, they can allow for temperature overshoot and for generational changes where more effort to decelerate warming change is needed by future generations. The curves capture temperature profiles from the existing Representative Concentration Pathway (RCP2.6) scenario projections by a range of different earth system models (ESMs), which have warming amounts towards the lower levels of those that society is discussing

Rapidity distributions around mid-rapidity of strange particles in Pb-Pb collisions at 158 AA GeV/c

The production at central rapidity of K0s, Lambda, Xi and Omega particles in Pb-Pb collisions at 158 A GeV/c has been measured by the NA57 experiment over a centrality range corresponding to the most central 53% of the inelastic Pb-Pb cross section. In this paper we present the rapidity distribution of each particle in the central rapidity unit as a function of the event centrality. The distributions are analyzed based on hydrodynamical models of the collisions.Comment: 15 pages, 10 figure

Strangeness enhancements at central rapidity in 40 A GeV/c Pb-Pb collisions

Results are presented on neutral kaon, hyperon and antihyperon production in Pb-Pb and p-Be interactions at 40 GeV/c per nucleon. The enhancement pattern follows the same hierarchy as seen in the higher energy data - the enhancement increases with the strangeness content of the hyperons and with the centrality of collision. The centrality dependence of the Pb-Pb yields and enhancements is steeper at 40 than at 158 A GeV/c. The energy dependence of strangeness enhancements at mid-rapidity is discussed.Comment: 15 pages, 10 figures and 3 tables. Presented at International Conference on Strangeness in Quark Matter (SQM2009), Buzios, Rio de Janeiro, Brazil, 27 Sept - 2 Oct 2009. Submitted to J.Phys.G: Nucl.Part.Phys, one reference adde