Generic Fitting Research Articles

Analysis of Primate Retinal System for Better Computational Modeling

Photo transduction in human eye can be segmented into different electrical components by proper analysis of their work on the action potential with electrophysiological studies. J. H. Van Hateren, designed a computational model of primate retinal system with the aim of mimicking the human visual system as an electrical device for retinal implants in the future, but the model suffered from chromaticity adaptation and the existence of after images. This article concentrates in studying the model by simulating with different values and stating why generic value fit is taken into consideration, while others are not. The choice of generic value for the inactivation reaction of photopigment and phosphodiesterase is reasoned in greater detail. This paper states the flaws present in the previous mentioned computational model and provided a suggestion that instead of assuming the photoreceptor as low pass filters it works as a band pass filter which might overcome chromaticity adaptation.

A weighted total least-squares algorithm for any fitting model with correlated variables

An algorithm able to deal with any desired fitting model was developed for regression problems with uncertain and correlated variables.A typical application concerns the determination of calibration curves, especially (i) in those cases in which the uncertainties on the independent variables xi cannot be considered negligible with respect to those associated with the dependent variables yi, and (ii) when correlations exist among xi and yi. In the metrological field, several types of software have already been dedicated to the determination of calibration curves, some being focused just on problem (i) and a few others considering also problem (ii) but only for a straight-line fitting model. The proposed algorithm is able to deal with problems (i) and (ii) at the same time, for a generic fitting model. The tool was developed in the MATLAB® environment and validated on several benchmark data sets, fitted with linear and non-linear regression models.A review of the most commonly applied approximations to the parameter uncertainty is also presented, together with a Monte Carlo method proposed for comparison purposes with the results provided by the formula for the uncertainty evaluation which is implemented in the software.

AGE AND MASS FOR 920 LARGE MAGELLANIC CLOUD CLUSTERS DERIVED FROM 100 MILLION MONTE CARLO SIMULATIONS

We present new age and mass estimates for 920 stellar clusters in the Large Magellanic Cloud (LMC) based on previously published broad-band photometry and the stellar cluster analysis package, MASSCLEANage. Expressed in the generic fitting formula, d^{2}N/dM dt ~ M^{\alpha} t^{\beta}, the distribution of observed clusters is described by \alpha = -1.5 to -1.6 and \beta = -2.1 to -2.2. For 288 of these clusters, ages have recently been determined based on stellar photometric color-magnitude diagrams, allowing us to gauge the confidence of our ages. The results look very promising, opening up the possibility that this sample of 920 clusters, with reliable and consistent age, mass and photometric measures, might be used to constrain important characteristics about the stellar cluster population in the LMC. We also investigate a traditional age determination method that uses a \chi^2 minimization routine to fit observed cluster colors to standard infinite mass limit simple stellar population models. This reveals serious defects in the derived cluster age distribution using this method. The traditional \chi^2 minimization method, due to the variation of U,B,V,R colors, will always produce an overdensity of younger and older clusters, with an underdensity of clusters in the log(age/yr)=[7.0,7.5] range. Finally, we present a unique simulation aimed at illustrating and constraining the fading limit in observed cluster distributions that includes the complex effects of stochastic variations in the observed properties of stellar clusters.

CIRCUMFERENTIAL STRESS CONCENTRATION FACTORS AT THE ASYMMETRIC SHALLOW NOTCHES OF THE LIFTING HOOKS OF TRAPEZOIDAL CROSS-SECTION

The paper presents the equations for the calculation of the circumferential stress conentration factors. The equations were obtained by fitting to the finite element analysis results of the lifting hooks with the shallow notches. The Neubers expression of the stress concentration factors at the shallow notches for the cylindrical beams and plates have been used as a generic fitting equation. The constructed simplified version of this equation, having just one fitting coefficient, is also presented. These equations allow to perform the fast evaluation of the circumferential stress concentration without the usage of the finite element models. The article also presents the preliminar investigation of the stress state triaxiality and the consequent reduction of the materials ductility at the shallow notches of the lifting hooks.DOI: http://dx.doi.org/10.5755/j01.mech.18.2.1574

Integrating mechanistic organism–environment interactions into the basic theory of community and evolutionary ecology

This paper presents an overview of how mechanistic knowledge of organism-environment interactions, including biomechanical interactions of heat, mass and momentum transfer, can be integrated into basic theoretical population biology through mechanistic functional responses that quantitatively describe how organisms respond to their physical environment. Integrating such functional responses into simple community and microevolutionary models allows scaling up of the organism-level understanding from biomechanics both ecologically and temporally. For community models, Holling-type functional responses for predator-prey interactions provide a classic example of the functional response affecting qualitative model dynamics, and recent efforts are expanding analogous models to incorporate environmental influences such as temperature. For evolutionary models, mechanistic functional responses dependent on the environment can serve as fitness functions in both quantitative genetic and game theoretic frameworks, especially those concerning function-valued traits. I present a novel comparison of a mechanistic fitness function based on thermal performance curves to a commonly used generic fitness function, which quantitatively differ in their predictions for response to environmental change. A variety of examples illustrate how mechanistic functional responses enhance model connections to biologically relevant traits and processes as well as environmental conditions and therefore have the potential to link theoretical and empirical studies. Sensitivity analysis of such models can provide biologically relevant insight into which parameters and processes are important to community and evolutionary responses to environmental change such as climate change, which can inform conservation management aimed at protecting response capacity. Overall, the distillation of detailed knowledge or organism-environment interactions into mechanistic functional responses in simple population biology models provides a framework for integrating biomechanics and ecology that allows both tractability and generality.

Multiple Adaptive Substitutions During Evolution in Novel Environments

We consider an asexual population under strong selection-weak mutation conditions evolving on rugged fitness landscapes with many local fitness peaks. Unlike the previous studies in which the initial fitness of the population is assumed to be high, here we start the adaptation process with a low fitness corresponding to a population in a stressful novel environment. For generic fitness distributions, using an analytic argument we find that the average number of steps to a local optimum varies logarithmically with the genotype sequence length and increases as the correlations among genotypic fitnesses increase. When the fitnesses are exponentially or uniformly distributed, using an evolution equation for the distribution of population fitness, we analytically calculate the fitness distribution of fixed beneficial mutations and the walk length distribution.

WMAP7 and future CMB constraints on annihilating dark matter: implications for GeV-scale WIMPs

(Context) We calculate constraints from current and future cosmic microwave background (CMB) measurements on annihilating dark matter (DM) with masses below the electroweak scale: m_{DM}=5-100 GeV. In particular, we focus our attention on the lower end of this mass range, as DM particles with masses m_{DM} ~ 10 GeV have been recently claimed to be consistent with the CoGeNT and DAMA/LIBRA results, while also providing viable DM candidates to explain the measurements of Fermi and WMAP haze. (Aims) We study the model (in)dependence of CMB spectrum on particle physics DM models, large scale structure formation and cosmological uncertainties. We attempt to find a simple and practical recipe for estimating current and future CMB bounds on a broad class of DM annihilation models. (Results) We show that in the studied DM mass range the CMB signal of DM annihilations is independent of the details of large scale structure formation, distribution and profile of DM halos and other cosmological uncertainties. All particle physics models of DM annihilation can be described with only one parameter, the fraction of energy carried away by neutrinos in DM annihilation. As the main result we provide a simple and rather generic fitting formula for calculating CMB constraints on the annihilation cross section of light WIMPs. We show that thermal relic DM in the CoGeNT, DAMA favoured mass range is in a serious conflict with present CMB data for the annihilation channels with few neutrinos, and will definitely be tested by the Planck mission for all possible DM annihilation channels. Also, our findings strongly disfavor the claim that thermal relic DM annihilations with m_{DM} ~ 10 GeV and $<sigma_av> ~ 9x10^{-25} cm^{3}s^{-1} could be a cause for Fermi and WMAP haze.

Erratum to: Revisiting the global electroweak fit of the Standard Model and beyond with Gfitter

The global fit of the Standard Model to electroweak precision data, routinely performed by the LEP electroweak working group and others, demonstrated impressively the predictive power of electroweak unification and quantum loop corrections. We have revisited this fit in view of (i) the development of the new generic fitting package Gfitter, (ii) the insertion of constraints from direct Higgs searches at LEP and the Tevatron, and (iii) a more thorough statistical interpretation of the results. Gfitter is a modular fitting toolkit, which features predictive theoretical models as independent plugins, and a statistical analysis of the fit results using toy Monte Carlo techniques. The state-of-the-art electroweak Standard Model is fully implemented, as well as generic extensions to it. This paper introduces the Gfitter project, and presents state-of-the-art results for the global electroweak fit in the Standard Model, and for a model with an extended Higgs sector (2HDM). Numerical and graphical results for fits with and without including the constraints from the direct Higgs searches at LEP and Tevatron are given. Perspectives for future colliders are analysed and discussed. Including the direct Higgs searches, we find M_H=(116.4 +18.3 -1.3) GeV, and the 2sigma and 3sigma allowed regions [114,145] GeV and [[113,168] and [180,225]] GeV, respectively. For the strong coupling strength at fourth perturbative order we obtain alpha_S(M_Z)=0.1193 +0.0028 -0.0027(exp) +- 0.0001(theo). Finally, for the mass of the top quark, excluding the direct measurements, we find m_t=(178.2 +9.8 -4.2) GeV. In the 2HDM we exclude a charged-Higgs mass below 240 GeV at 95% confidence level. This limit increases towards larger tan(beta), where e.g., M_H+-<780 GeV is excluded for tan(beta)=70.

Vibrational mode assignments for bundled single-wall carbon nanotubes using Raman spectroscopy at different excitation energies

This study establishes a generic fitting approach to assignment of nanotube chiralities based on radial breathing mode frequencies (ωRBM) of SWCNTs in as-produced bundles. Four laser lines with energies of 2.62 eV, 2.33 eV, 1.88 eV and 1.58 eV were employed. The observed RBM frequencies, ωRBM, were plotted as a function of the possible diameters, d, as identified from the so-called Kataura plot and reported values of the parameters A and B, where ωRBM=A/d+B, assuming that SWCNTs resonant at the respective laser frequencies dominate the spectrum. The refined values of A and B, obtained by the best fit of a linear regression between ωRBM and 1/d, were found to vary significantly for different laser frequencies. This variation is interpreted in terms of the differences in electronic properties of SWCNTs resonant at different frequencies. The assigned nanotubes match well with those identified in the Kataura plot, falling within a resonant line width of ±0.2 eV of the respective laser lines.

Designing a Best-in-Class Innovation Scorecard

During the first half of 2009, the Research & Technology Executive Council of the Corporate Executive Board interviewed and surveyed R&D executives (1) from over 100 global companies (2) about the biggest challenges facing them today, particularly related to managing R&D portfolios during a period of heightened uncertainty. One thing became clear from these conversations: R&D project valuation continues to vex even the most progressive organizations. To address this ongoing challenge, leading organizations are revisiting the way in which they measure and select R&D projects-namely, they are tweaking, if not redesigning, their innovation scorecards. The Top Ten List below summarizes what we learned from these conversations and provides some quick tips for R&D and strategy executives who wish to improve the quality of R&D project selection and portfolio balancing in their organizations. 1. Get agreement across your organization on what constitutes and incremental projects. When balancing your R&D portfolio, you probably look at the percentage of incremental versus projects, among several other parameters. But if everyone in your organization is defining breakthrough differently, how do you know how much you are truly innovating? Before you begin selecting criteria for your scorecard, clearly define your project classifications by a few critical business outcomes. We spoke with one progressive chemicals company that defines its classifications according to three critical metrics: IP protection, Peak Year Sales, and Marginal Income. Every company needs to decide which metrics are most important for its organization, and once you come to a consensus on what constitutes a breakthrough project, it is much easier to design an innovation scorecard that will help spot these projects early and facilitate more accurate portfolio balancing. 2. Ground your scorecard in the business perspective. Central R&D groups often become so focused on creating standards that they end up with scorecards or processes that ultimately have a generic fit across multiple diverse businesses. But the goal of creating a standard innovation scorecard isn't to find the lowest common denominator but to help companies make better R&D prioritization and funding decisions. This always needs to be fundamental to scorecard design. Companies should work closely with their business partners to ensure that they can meaningfully assess project value and help drive better decisions. 3. Don't ignore project interdependencies. Project scorecards usually assess the attractiveness of individual projects as independent workstreams. When designing scorecards and decision-making processes using scorecards, companies must account for project interrelationships and interdependencies. For example, while certain projects may not appear profitable at the surface level, they may be crucial in forming the foundational technology for other large-scale projects. We spoke with one company whose innovation questionnaire explicitly asks each project manager to rate the interdependency risk and opportunity associated with each project. 4. Keep the list of criteria short. There's only so much information that people can process to help inform meaningful decisions. We've seen some scorecards that examine over 20 different criteria and some with as few as three. Typically, companies include 5-7 criteria, and the most progressive companies try to limit the number to no more than 12. Companies should keep it short and look for opportunities to continually refine and cut down on the amount of information required to make decisions. 5. Secure early support from Finance, Marketing and Operations. It is absolutely critical that R&D involve other functions at the earliest stages of technology and product development. When developing standard project evaluation criteria, R&D should solicit input from other stakeholder functions to ensure that everyone can agree on how to prioritize innovation projects. …

Generic Versus Small-sided Game Training in Soccer

The aim of this study was to compare 7 weeks of soccer-specific small-sided game (SSG) and mixed generic fitness training, on selected physiological, perceptual and performance variables. Twenty-five elite youth players were randomly allocated to either a SSG (coach selected) or generic training group (GTG), in a randomised, parallel matched-group design. In addition to normal training, each group completed two fitness training sessions per week of equal duration. Players completed a V O (2 max) treadmill test, Multistage Fitness Test (MSFT), Yo-Yo Intermittent Recovery Test Level 1 (YYIRTL1), 12x20 m test of repeated-sprint ability (RSA) and 20-m sprint test pre and post training. Training heart rate, perceived training intensity and perceptual fatigue measures were recorded throughout the training period. There were no differences in training heart rate or perceptual well-being measures. However, the GTG did perceive their training to be more intense than SSG. There were no changes in either group for V O (2 max), MSFT, RSA or sprint performance. However, there were improvements in YYIRTL1 performance for both groups over time, but not between groups. The results show that both types of training are equally effective at improving pre-season YYIRTL1 performance, despite GTG being perceived to be more intense.

Revisiting the global electroweak fit of the Standard Model and beyond with Gfitter

The global fit of the Standard Model to electroweak precision data, routinely performed by the LEP electroweak working group and others, demonstrated impressively the predictive power of electroweak unification and quantum loop corrections. We have revisited this fit in view of (i) the development of the new generic fitting package, Gfitter, allowing for flexible and efficient model testing in high-energy physics, (ii) the insertion of constraints from direct Higgs searches at LEP and the Tevatron, and (iii) a more thorough statistical interpretation of the results. Gfitter is a modular fitting toolkit, which features predictive theoretical models as independent plug-ins, and a statistical analysis of the fit results using toy Monte Carlo techniques. The state-of-the-art electroweak Standard Model is fully implemented, as well as generic extensions to it. Theoretical uncertainties are explicitly included in the fit through scale parameters varying within given error ranges. This paper introduces the Gfitter project, and presents state-of-the-art results for the global electroweak fit in the Standard Model (SM), and for a model with an extended Higgs sector (2HDM). Numerical and graphical results for fits with and without including the constraints from the direct Higgs searches at LEP and Tevatron are given. Perspectives for future colliders are analysed and discussed. In the SM fit including the direct Higgs searches, we find M H =116.4 −1.3 +18.3 GeV, and the 2σ and 3σ allowed regions [114,145] GeV and [[113,168] and [180,225]] GeV, respectively. For the strong coupling strength at fourth perturbative order we obtain α S (M 2 )=0.1193 −0.0027 +0.0028 (exp )±0.0001 (theo). Finally, for the mass of the top quark, excluding the direct measurements, we find m t =178.2 −4.2 +9.8 GeV. In the 2HDM we exclude a charged-Higgs mass below 240 GeV at 95% confidence level. This limit increases towards larger tan β, e.g., $M_{H^{\pm}}<780 \mbox{GeV}$ is excluded for tan β=70.

Pinning down the new minimal supersymmetric GUT

We show that generic 10⊕120⊕126¯ fits of fermion masses and mixings, using real superpotential couplings but with complex ‘Higgs fractions’ leading to complex Yukawa couplings in the effective MSSM, overdetermine (by one extra constraint) the superpotential parameters of the new minimal supersymmetric SO(10) GUT(NMSGUT) [C.S. Aulakh, S.K. Garg, hep-ph/0612021]. Therefore fits should properly be done by generating the 24 generic fit parameters from the 23 parameters of the NMSGUT superpotential, given tanβ as input. Each numerical fit then fully specifies the parameters of the NMSGUT. Thus the NMSGUT offers the possibility of a mutual confrontation between gauge unification, the fit to fermion masses and proton decay calculations due to their extractable common dependence on the NMSGUT parameters. If and when ‘smoking gun’ discoveries of supersymmetry and proton decay occur they will find the NMSGUT vulnerable to falsification.

Novel scoring function for modeling structures of oligomers of transmembrane alpha-helices.

Specific non-covalent interactions between transmembrane (TM) alpha-helices are important in a variety of biological processes. Experimental and computational studies have shown that van der Waals interactions play an important role in the tight packing between TM alpha-helices, although polar interactions can also be important in some instances. Based on the assumption that van der Waals interaction alone is sufficient for a meso-scale (residue-scale) description of the interaction between TM alpha-helices, we have designed a novel residue-scale scoring function for modeling structures of oligomers of TM alpha-helices. We first calculated atomistic van der Waals interaction energies between two amino acids, X and Y, of a pair of parallel alpha-helices, glycine-X-glycine and glycine-Y-glycine and compiled them according to three variables, the distance between the two C(alpha) atoms and the rotational angles of X and Y about their helical axes. Upon averaging over the rotational angles, we obtained one-dimensional interaction energy profiles that are functions of the distance between C(alpha) atoms only. Each of the interaction energy profiles was fitted with a generic fitting function of the distance between C(alpha) atoms, yielding analytical scoring functions for all possible amino acid pairs. For glycophorin A, neu/erbB-2, and phospholamban, lowest-energy conformations obtained through exhaustive scanning of the entire conformational space using the scoring functions were compatible with available experimental data.

Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning

In this paper, we address the automated tuning of input specification for supervised inductive learning and develop combinatorial optimization solutions for two such tuning problems. First, we present a framework for selection and reordering of input variables to reduce generalization error in classification and probabilistic inference . One purpose of selection is to control overfitting using validation set accuracy as a criterion for relevance. Similarly, some inductive learning algorithms, such as greedy algorithms for learning probabilistic networks , are sensitive to the evaluation order of variables. We design a generic fitness function for validation of input specification, then use it to develop two genetic algorithm wrappers : one for the variable selection problem for decision tree inducers and one for the variable ordering problem for Bayesian network structure learning. We evaluate the wrappers, using real-world data for the selection wrapper and synthetic data for both, and discuss their limitations and generalizability to other inducers.

Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning

In this paper, we address the automated tuning of input specification for supervised inductive learning and develop combinatorial optimization solutions for two such tuning problems. First, we present a framework for selection and reordering of input variables to reduce generalization error in classification and probabilistic inference. One purpose of selection is to control overfitting using validation set accuracy as a criterion for relevance. Similarly, some inductive learning algorithms, such as greedy algorithms for learning probabilistic networks, are sensitive to the evaluation order of variables. We design a generic fitness function for validation of input specification, then use it to develop two genetic algorithm wrappers: one for the variable selection problem for decision tree inducers and one for the variable ordering problem for Bayesian network structure learning. We evaluate the wrappers, using real-world data for the selection wrapper and synthetic data for both, and discuss their limitations and generalizability to other inducers.

High speed laser shadowgraphy for electromagnetically driven cylindrical implosions

A laser shadowgraphy system for high-speed imaging of a convergent cylindrical shockwave generated by an electromagnetically driven solid density liner implosion in Lucite is described. The laser shadowgraphy system utilizes an advanced high-energy, long-pulse, frequency-doubled Nd:YAG laser for target illumination and a fast framing camera for multiple frame imaging of the shockwave as it radially converges and transits the Lucite. The time window resolution is 10 ns as determined by the fastest exposure time capable with the camera. Two on-axis symmetric implosions and two off-axis asymmetric implosion experiments were fielded at the Air Force Research Laboratory’s Shiva Star 4.2 MJ capacitor bank z-pinch facility. For each experimental shot, the shadowgraphy system captured several frames of shadowgraph images as the shockwave moved through the Lucite. Analysis of the shockwave shadowgraph image shapes is done by fitting each shadowgraph image to a generic elliptical fit function and plotting the resultant two-dimensional image fits for comparison. For the on-axis symmetric implosion shots, a radial trajectory plot is extracted and a radial shock velocity is calculated. The Lucite shock speed is seen to increase monotonically from an initial velocity of 7.9 mm/μs to a near final velocity of 13.4 mm/μs as convergence effects dominate the shock speed calculated at small radii.