Recoil excitation of vibrational structure in the carbon 1s photoelectron spectrum of CF4

The carbon 1s photoelectron spectrum of CF4 measured at photon energies from 330 to 1500 eV shows significant contributions from nonsymmetric vibrational modes. These increase linearly as the photon energy increases. The excitation of these modes, which is not predicted in the usual Franck-Condon point of view, arises from the recoil momentum imparted to the carbon atom in the ionization process. A theory is presented for quantitative prediction of the recoil effect; the predictions of this theory are in agreement to the measurements. The experiments also yield the vibrational frequencies of the symmetric and asymmetric stretching modes in core-ionized CF4, the change in CF bond length upon ionization, -0.61 pm, and the Lorentzian linewidth of the carbon 1s hole, 67 meV.     

A Type Calculus for Executable Modelling Languages

There is considerable current interest in the design and constructionof directly executable modelling languages for mathematicalprogramming. The present research extends contemporary modellinglanguages by specifying a type calculus for an extended dimensionalsystem that determines if the model is well formed in the sensethat the objective function and constraints consist of homogeneouscomponents. Each variable, coefficient, constant, function,constraint, input, and output of the model is assigned a typethat consists of its concepts, quantities, and units of measurementwith optional scale factors. In checking the composition offunctions and constraints, the system can do automatic unitconversions and apply user-supplied conversions. The systemallows a hierarchy of concepts that provides inheritance ofquantities and automatic concept conversion. In addition, eachset in a model is typed so the system can check the validityof operations on indices.     

The flat-maximum effect and generic linear scoring models: a test

Received on 1 July 1991. Predicting human behaviour patterns with linear correlationmodels has absorbed researchers for the past five decades. Althoughmost observers generally concede that humans are inferior tosuch models in combining information, linear scoring modelsare unfortunately, plagued by the flat-maximum effect or the‘curse of insensitivity’. As Lovie & Lovie(1986)observe: ‘The predictive ability of linear models is insensitiveto large variations in the size of regression weights and tothe number of predictors.’ In essence, seemingly differentscoringmodels tend to produce indistinguishable predictive outcomes. Since its demonstration by Dawes & Corrigan (1974), observershave cast the flat maximum in a decidedly negative light. Incontrast, Lovie & Lovie (1986) present a provocatively contrarianview of the flat maximum‘s positive potential. In thissame vein, we examine the predictive power of a generic credit-scoringmodel versus individual empirically derived systems. If, asWainer (1976) noted in regard to the flat maximum, ’itdon‘t make no nevermind’, generic credit-scoringmodels could provide cheaper alternatives to individual empiricallyderived models. During the period 1984–8, a series of linear credit-scoringmodels were developed for ten Southeastern U.S. credit unions.For each credit union, stepwise multiple regression was employedto select a subset of explanatory variables to be used in adiscriminant analysis. A generic credit-scoring equation wasdeveloped from the resulting discriminant analyses using weightedaverage coefficients from five systems. The predictive powerof the generic model was compared to the predictive power ofholdout sample of the five remaining credit-scoring models. In all cases, the generic model's performance was very closeto that of the empirically derived models. Thus, our findingssupport Lovie & Lovie's (1986) challenge to the conventionalwisdom that the flat maximum casts a pall on the successfulmodelling of judgement processes. Indeed, the flat maximum impliesa positive role for simpler, and hence cheaper, generic models.Although further research is needed, it should be possible todevelop hybrid models with generic cores that perform as wellas empirically derived linear models.