Sparse kernel modelling: a unified approach

A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic sparse kernel data modelling approach.

Orthogonal-least-squares regression: A unified approach for data modelling

A unified approach is proposed for data modelling that includes supervised regression and classification applications as well as unsupervised probability density function estimation. The orthogonal-least-squares regression based on the leave-one-out test criteria is formulated within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic data-modelling approach for constructing parsimonious kernel models with excellent generalisation capability. (C) 2008 Elsevier B.V. All rights reserved.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Asymmetric synthesis of cyclic β-amino acids and cyclic amines via sequential diastereoselective conjugate addition and ring closing metathesis

Diastereoselective conjugate addition of lithium (S)-N-allyl-N-alpha-methylbenzylamide to a range of alpha,beta-unsaturated esters followed by ring closing metathesis is used to afford efficiently a range of substituted cyclic beta-amino esters in high d.e. Alternatively, conjugate addition to alpha,beta-unsaturated Weinreb amides, functional group conversion and ring closing metathesis affords cyclic amines in high d.e. The further application of this methodology to the synthesis of a range of carbocyclic beta-amino esters via conjugate addition, enolate alkylation and ring closing metathesis is also described. Application of this methodology affords, after deprotection, (S)-homoproline, (S)-homopipecolic acid, (S)-coniine and (1S,2S)-trans-pentacin.

Time-resolved gas-phase kinetic study of the germylene addition reaction, GeH2 + C2D2

Time-resolved studies of germylene, GeH2, generated by laser. ash photolysis of 3,4-dimethyl-1-germacyclopent-3-ene, have been carried out to obtain rate coefficients for its bimolecular reaction with C2D2. The reaction was studied in the gas phase, mainly at a total pressure of 1.3 kPa (in SF6 bath gas) at five temperatures in the range 298-558 K. Pressure variation measurements over the range 0.13-13 kPa ( SF6) at 298, 397 and 558 K revealed a small pressure dependence but only at 558 K. After correction for this, the second-order rate coefficients gave the Arrhenius equation: log(k(infinity)/cm(3) molecule(-1) s(-1)) = (-10.96 +/- 0.05) + ( 6.16 +/- 0.37 kJ mol(-1))/RT ln 10 Comparison with the reaction of GeH2 + C2H2 (studied earlier) showed a similar behaviour with almost identical rate coefficients. The lack of a significant isotope effect is consistent with a rate-determining addition process and is explained by irreversible decomposition of the reaction intermediate to give Ge(P-3) + C2H4. This result contrasts with that for GeH2 + C2H4/C2D4 and those for the analogous silylene reactions. The underlying reasons for this are discussed.

Gas-phase kinetics of chlorosilylene reactions II. ClSiH + C2H4: absolute rate measurements and quantum chemical and RRKM calculations for the prototype pi addition reaction

Time-resolved studies of chlorosilylene, ClSiH, generated by the 193 nm laser flash photolysis of 1-chloro-1-silacyclopent-3-ene, are carried out to obtain rate constants for its bimolecular reaction with ethene, C2H4, in the gas-phase. The reaction is studied over the pressure range 0.13-13.3 kPa (with added SF6) at five temperatures in the range 296-562 K. The second order rate constants, obtained by extrapolation to the high pressure limits at each temperature, fitted the Arrhenius equation: log(k(infinity)/cm(3) molecule(-1) s(-1))=(-10.55 +/- 0.10) + (3.86 +/- 0.70) kJ mol(-1)/RT ln10. The Arrhenius parameters correspond to a loose transition state and the rate constant at room temperature is 43% of that for SiH2 + C2H4, showing that the deactivating effect of Cl-for-H substitution in the silylene is not large. Quantum chemical calculations of the potential energy surface for this reaction at the G3MP2//B3LYP level show that, as well as 1-chlorosilirane, ethylchlorosilylene is a viable product. The calculations reveal how the added effect of the Cl atom on the divalent state stabilisation of ClSiH influences the course of this reaction. RRKM calculations of the reaction pressure dependence suggest that ethylchlorosilylene should be the main product. The results are compared and contrasted with those of SiH2 and SiCl2 with C2H4.

Time-resolved gas-phase kinetic study of the germylene addition reaction, CH3C CCH3

Time-resolved kinetic studies of the reaction of germylene, GeH2, generated by laser. ash photolysis of 3,4-dimethyl-1-germacyclopent-3-ene, have been carried out to obtain rate constants for its bimolecular reaction with 2-butyne, CH3C CCH3. The reaction was studied in the gas phase over the pressure range 1-100 Torr in SF6 bath gas, at five temperatures in the range 300-556 K. The second order rate constants obtained by extrapolation to the high pressure limits at each temperature, fitted the Arrhenius equation: log(k(infinity)/cm(3) molecule(-1) s(-1)) = (-10.46 +/- 10.06) + (5.16 +/- 10.47) kJ mol(-1)/ RT ln 10 Calculations of the energy surface of the GeC4H8 reaction system were carried out employing the additivity principle, by combining previous quantum chemical calculations of related reaction systems. These support formation of 1,2-dimethylvinylgermylene (rather than 2,3-dimethylgermirene) as the end product. RRKM calculations of the pressure dependence of the reaction are in reasonable agreement with this finding. The reactions of GeH2 with C2H2 and with CH3CRCCH3 are compared and contrasted.

A unified titration formula

In a number of standard titrations, a volume of a monoprotic base MOH at a specific concentration is added to a volume of a monoprotic acid HA at a specific concentrations. Four different types of titration are possible, depending on whether the acid and base are strong or weak. A single unifying formula covering all four cases has been determined.

Metal cluster expansion by addition of low valent group 14 reagents: III. Reaction of bis[bis(trimethylsilyl)methyl]tin with M3(CO)12 or its derivatives (M = Fe, Ru, or Os): the crystal and molecular structures of M3(μ-SnR2)2(CO)10(R = CH(SiMe3)2; M = Ru or Os)

The stannylene [SnR2] (R = CH(SiMe3)2) reacts in different ways with the three dodecacarbonyls of the iron triad: [Fe3(CO)12] gives [Fe2(CO)8(μ-SnR2)], [Ru3(CO)12] gives the planar pentametallic cluster [Ru3(CO)10(μ-SnR2)2], for which a full structural analysis is reported, while [Os3(CO)12] fails to react. Different products are also obtained from three nitrile derivatives: [Fe3-(CO)11(MeCN)] gives [Fe2(CO)6(μ-SnR2)2], which has a structure significantly different from that of known Fe2Sn2 clusters, [Ru3(CO)10(MeCN)2] gives the pentametallic cluster described above, while [Os3(CO)10(MeCN)2] gives the isostructural osmium analogue, which shows the unusual feature of a CO group bridging two osmium atoms.

Metal cluster expansion by addition of low-valent group 4B reagents: crystal and molecular structure of [Os3SnH2(CO)10{CH(SiMe3)2}2]; a compound with hydrogen bridging osmium and tin

Cluster expansion of [Os3H2(CO)10] with [SnR2][R = CH(SiMe3)2] take place in high yield to give [Os3SnH2(CO)10R2], the first closed triosmium–main-group metal cluster to be structurally characterized; a novel feature is the presence of a hydrogen atom bridging the tin atom and one of the osmium atoms.

Alkenyl derivatives of the metals. Oxidative addition to intermediate tin(II) alkenyls; isolation, and crystal and molecular structure of n-butyltris(triphenylethenyl)tin(IV)

Reaction of tin(II) chloride with Li(CPhCPh2) at –78 °C in diethyl ether–hexane–tetrahydrofuran affords a deep red solution whose colour fades on warming, and which we believe contains the (unstable) first dialkenyltin(II) species. The latter survives long enough at low temperatures to undergo intermolecular oxidative addition, and one such adduct leads ultimately to the formation of Sn(CPhCPh2)3Bun, which has been fully characterised including a crystal and molecular structure study. The mechanism of formation of the final product has been examined and results are reported.

The HadGEM2 family of Met Office Unified Model climate configurations

We describe the HadGEM2 family of climate configurations of the Met Office Unified Model, MetUM. The concept of a model "family" comprises a range of specific model configurations incorporating different levels of complexity but with a common physical framework. The HadGEM2 family of configurations includes atmosphere and ocean components, with and without a vertical extension to include a well-resolved stratosphere, and an Earth-System (ES) component which includes dynamic vegetation, ocean biology and atmospheric chemistry. The HadGEM2 physical model includes improvements designed to address specific systematic errors encountered in the previous climate configuration, HadGEM1, namely Northern Hemisphere continental temperature biases and tropical sea surface temperature biases and poor variability. Targeting these biases was crucial in order that the ES configuration could represent important biogeochemical climate feedbacks. Detailed descriptions and evaluations of particular HadGEM2 family members are included in a number of other publications, and the discussion here is limited to a summary of the overall performance using a set of model metrics which compare the way in which the various configurations simulate present-day climate and its variability.

Generic unified modelling process for developing semantically rich, dynamic and temporal models

Models play a vital role in supporting a range of activities in numerous domains. We rely on models to support the design, visualisation, analysis and representation of parts of the world around us, and as such significant research effort has been invested into numerous areas of modelling; including support for model semantics, dynamic states and behaviour, temporal data storage and visualisation. Whilst these efforts have increased our capabilities and allowed us to create increasingly powerful software-based models, the process of developing models, supporting tools and /or data structures remains difficult, expensive and error-prone. In this paper we define from literature the key factors in assessing a model’s quality and usefulness: semantic richness, support for dynamic states and object behaviour, temporal data storage and visualisation. We also identify a number of shortcomings in both existing modelling standards and model development processes and propose a unified generic process to guide users through the development of semantically rich, dynamic and temporal models.