Data-driven Parameter Research Articles

Erratum: Improved Monte Carlo Glauber predictions at present and future nuclear colliders [Phys. Rev. C 97 , 054910 (2018)

We present the results of an improved Monte Carlo Glauber (MCG) model of relevance for collisions involving nuclei at center-of-mass energies of BNL RHIC ($\sqrt{s_{\rm NN}}=0.2$ TeV), CERN LHC ($\sqrt{s_{\rm NN}}=2.76$-$8.8$ TeV), and proposed future hadron colliders ($\sqrt{s_{\rm NN}}\approx 10$-$63$ TeV). The inelastic pp cross sections as a function of $\sqrt{s_{\rm NN}}$ are obtained from a precise data-driven parametrization that exploits the many available measurements at LHC collision energies. We describe the nuclear transverse profile with two separated 2-parameter Fermi distributions for protons and neutrons to account for their different densities close to the nuclear periphery. Furthermore, we model the nucleon degrees of freedom inside the nucleus through a lattice with a minimum nodal separation, combined with a "recentering and reweighting" procedure, that overcomes some limitations of previous MCG approaches. The nuclear overlap function, number of participant nucleons and binary nucleon-nucleon collisions, participant eccentricity and triangularity, overlap area and average path length are presented in intervals of percentile centrality for lead-lead (PbPb) and proton-lead (pPb) collisions at all collision energies. We demonstrate for collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV that the central values of the Glauber quantities change by up to 7%, in a few bins of reaction centrality, due to the improvements implemented, though typically remain within the previously assigned systematic uncertainties, while their associated uncertainties are generally smaller (mostly below 5%) at all centralities than for earlier calculations. Tables for all quantities versus centrality at present and foreseen collision energies involving Pb nuclei, as well as for collisions of XeXe at $\sqrt{s_{\rm NN}}=5.44$, and AuAu and CuCu at $\sqrt{s_{\rm NN}}=0.2$ TeV, are provided.

Transdimensional ambient noise tomography of Bass Strait, southeast Australia, reveals the sedimentary basin and deep crustal structure beneath a failed continental rift

Debate is ongoing as to which tectonic model is most consistent with the known geology of southeast Australia, formerly part of the eastern margin of Gondwana. In particular, numerous tectonic models have been proposed to explain the enigmatic geological relationship between Tasmania and the mainland, which is separated by Bass Strait. One of the primary reasons for the lack of certainty is the limited exposure of basement rocks, which are masked by the sea and thick Mesozoic–Cenozoic sedimentary and volcanic cover sequences. We use ambient noise tomography recorded across Bass Strait to generate a new shear wave velocity model in order to investigate crustal structure. Fundamental mode Rayleigh wave phase velocity dispersion data extracted from long-term cross-correlation of ambient noise data are inverted using a transdimensional, hierarchical, Bayesian inversion scheme to produce phase velocity maps in the period range 2–30 s. Subsequent inversion for depth-dependent shear wave velocity structure across a dense grid of points allows a composite 3-D shear wave velocity model to be produced. Benefits of the transdimensional scheme include a data-driven parametrization that allows the number and distribution of velocity unknowns to vary, and the data noise to also be treated as an unknown in the inversion. The new shear wave velocity model clearly reveals the primary sedimentary basins in Bass Strait as slow shear velocity zones which extend down to 14 km in depth. These failed rift basins, which formed during the early stages of Australia–Antarctica break-up, appear to be overlying thinned crust, where high velocities of 3.8–4.0 km s−1 occur at depths greater than 20 km. Along the northern margin of Bass Strait, our new model is consistent with major tectonic boundaries mapped at the surface. In particular, we identify an east dipping velocity transition zone in the vicinity of the Moyston Fault, a major tectonic boundary between the Lachlan and Delamerian orogens, which are part of the Phanerozoic accretionary terrane that makes up eastern Australia. A pronounced lineament of high shear wave velocities (∼3.7–3.8 km s−1) in the lower crust of our new model may represent the signature of relict intrusive magmatism from failed rifting in the early stages of Australia–Antarctica break-up along a crustal scale discontinuity in the Selwyn Block microcontinent which joins Tasmania and Victoria.

Incorporation of parameter prediction models of different fidelity into job shop scheduling

Scheduling of industrial job shop processes is normally conducted using estimates of parameters (e.g. processing times) defining the optimization problem. Inaccuracy in these estimated parameters can significantly affect the optimality, or even feasibility, of the scheduling solution. In this work, we incorporate data-driven parameter prediction models of different fidelity into a unit-specific continuous time scheduling model, and investigate the dependency of the solution quality on the prediction model fidelity. Our high-fidelity prediction model is based on Gaussian processes (GP); more specifically we use the maximum a posteriori probability (MAP) estimate. The low and medium-fidelity prediction models rely on determining the average processing time or average processing rate, respectively, from the dataset. In our test case, involving prediction of taxi durations in New York City, the use of GP prediction model yielded, on average, 5.8% and 1.8% shorter realized make spans in comparison to using the low and medium-fidelity prediction models, respectively.

CO2-based grey-box model to estimate airflow rate and room occupancy

In the existing building stock, heating, cooling and ventilation often run on fixed schedules assuming maximal occupancy. However, fitting the control of the HVAC system to the building’s real demand offers large potential for energy savings over the status quo. Building occupants’ presence as well as mechanically supplied and infiltrated airflow rates provide information that enables to define tailored strategies for demand-controlled ventilation. Hence, real-time estimations of these quantities are a valuable input to demand-controlled built environments. In this work, the use of stochastic differential equations (SDE) to estimate the room occupancy, infiltration air-rate and ventilation air-rate is investigated. In particular, a grey-box model based on a carbon dioxide (CO2) mass balance equation is presented. The model combines knowledge about the physical system with statistical, data-driven parameter estimation. Furthermore, the proposed model contains uncertainty parameters. This is in contrast to purely deterministic models based on ordinary differential equations, where uncertainty is usually disregarded. The suggested model has been tested in a naturally ventilated and in a mechanically ventilated environment; the performance in these two cases has been compared. We show that the ability to address measurement errors and non-homogeneous conditions in the room air implies that the suggested SDE-based grey-box approach is suitable in the context of demand-controlled ventilation.

The Conundrum of Application of Intellectual Property (Ip) Strategy in Light of Business Strategy, and its Significance to the Corporate World in Contemporary Settings

Every leading firm of the current time cements their foundation with their IP portfolio. By integrating IP into every vertical — like R&D, strategic planning, product development cycles — a firm can seamlessly leverage multiple opportunities and enhance its value in the competitive market as well as protect itself from the same. In fact, reading one’s own IP portfolio and of the other entities working in the domain can be goaded in such a manner to derive practical strategic insights that can help firms positively. Thus, the issue being addressed in this paper is related to the parameters necessary for such strategizing. Specifically, it relates to utilizing more empirical and data-driven parameters, rather than plain theoretical hypotheses. Further, these parameters can be utilized to construct a representative matrix (a 3-dimensional graph) with respect to a subject company; this matrix would help in identifying the company’s position in a particular domain — against the other entities that also work in the domain. In effect, this matrix can be utilized to obtain insights and design strategies that the company can use with respect to these other entities in its vision.

Improved Monte Carlo Glauber predictions at present and future nuclear colliders

We present the results of an improved Monte Carlo Glauber (MCG) model of relevance for collisions involving nuclei at center-of-mass energies of the BNL Relativistic Heavy Ion Collider ($\sqrt{{s}_{\mathit{NN}}}=0.2$ TeV), CERN Large Hadron Collider (LHC) ($\sqrt{{s}_{\mathit{NN}}}=2.76--8.8\phantom{\rule{0.16em}{0ex}}\mathrm{TeV}$), and proposed future hadron colliders ($\sqrt{{s}_{\mathit{NN}}}\ensuremath{\approx}10--63$ TeV). The inelastic $pp$ cross sections as a function of $\sqrt{{s}_{\mathit{NN}}}$ are obtained from a precise data-driven parametrization that exploits the many available measurements at LHC collision energies. We describe the nuclear density of a lead nucleus with two separated two-parameter Fermi distributions for protons and neutrons to account for their different densities close to the nuclear periphery. Furthermore, we model the nucleon degrees of freedom inside the nucleus through a lattice with a minimum nodal separation, combined with a ``recentering and reweighting'' procedure, that overcomes some limitations of previous MCG approaches. The nuclear overlap function, number of participant nucleons and binary nucleon-nucleon collisions, participant eccentricity and triangularity, overlap area, and average path length are presented in intervals of percentile centrality for lead-lead (PbPb) and proton-lead ($p\mathrm{Pb}$) collisions at all collision energies. We demonstrate for collisions at $\sqrt{{s}_{\mathit{NN}}}=5.02\phantom{\rule{0.16em}{0ex}}\mathrm{TeV}$ that the central values of the Glauber quantities change by up to 7% in a few bins of reaction centrality, due to the improvements implemented, though typically they remain within the previously assigned systematic uncertainties, while their new associated uncertainties are generally smaller (mostly below 5%) at all centralities than for earlier calculations. Tables for all quantities versus centrality at present and foreseen collision energies involving Pb nuclei, as well as for collisions of XeXe at $\sqrt{{s}_{\mathit{NN}}}=5.44\phantom{\rule{0.16em}{0ex}}\mathrm{TeV}$, and AuAu and CuCu at $\sqrt{{s}_{\mathit{NN}}}=0.2\phantom{\rule{0.16em}{0ex}}\mathrm{TeV}$, are provided. The source code for the improved Monte Carlo Glauber model is made publicly available.

Maximum likelihood estimation of skew-t copulas with its applications to stock returns

ABSTRACTThe multivariate Student-t copula family is used in statistical finance and other areas when there is tail dependence in the data. It often is a good-fitting copula but can be improved on when there is tail asymmetry. Multivariate skew-t copula families can be considered when there is tail dependence and tail asymmetry, and we show how a fast numerical implementation for maximum likelihood estimation is possible. For the copula implicit in a multivariate skew-t distribution, the fast implementation makes use of (i) monotone interpolation of the univariate marginal quantile function and (ii) a re-parametrization of the correlation matrix. Our numerical approach is tested with simulated data with data-driven parameters. A real data example involves the daily returns of three stock indices: the Nikkei225, S&P500 and DAX. With both unfiltered returns and GARCH/EGARCH filtered returns, we compare the fits of the Azzalini–Capitanio skew-t, generalized hyperbolic skew-t, Student-t, skew-Normal and Normal copulas.

Experimental Data-Driven Parameter Identification and State of Charge Estimation for a Li-Ion Battery Equivalent Circuit Model

It is well known that accurate identification of the key state parameters and State of Charge (SOC) estimation method for a Li-ion battery cell is of great significance for advanced battery management system (BMS) of electric vehicles (EVs), which further facilitates the commercialization of EVs. This study proposed a systematic experimental data-driven parameter identification scheme and an adaptive extended Kalman Filter (AEKF)-based SOC estimation algorithm for a Li-Ion battery equivalent circuit model in EV applications. The key state parameters of Li-ion battery cell were identified based on the second-order resistor capacitor (RC) equivalent circuit model and the experimental battery test data using genetic algorithm (GA). Meanwhile, the proposed parameter identification procedure was validated by carrying out a comparative study of the simulated and experimental output voltage under the same input current profile. Then, SOC estimation was performed based on the AEKF algorithm. Finally, the effectiveness and feasibility of the proposed SOC estimator was verified by loading different operating profiles.

Hydrocyclone Separation Efficiency Modeled by Flow Resistances and Droplet Trajectories

The growing demand to optimize the deoiling performance for offshore oil & gas gives the incentive to improve existing control solutions by means of model-based control solutions. This paper proposes a separation efficiency grey-box model of a deoiling hydrocyclone. Grey-box modeling of deoiling hydrocyclones aims to combine knowledge from fluid dynamics with the data-driven parameter estimation to yield better accuracy than black-box derived models while keeping the computational load much lower than CFD-simulations. The model has to be reasonably accurate in all likely operating conditions and be computed in real-time, in order to be beneficial for advanced model-based control. The developed grey-box model is based on flow resistance and oil droplet trajectory analysis. The model functionally describes how the valve openings, inflow rates, and PDR set-points affect separation efficiency of the considered generic deoiling hydrocyclone. The results are reasonable and provide a fundamental overview of how the operational conditions affect separation efficiency. The model can be extended to account for changes in the axial velocity distribution, coalescence and breakup of droplets.

State/Model-Free Variable-Gain Discrete Sliding Mode Control for an Ultraprecision Wafer Stage

Wafer stage is an important mechatronic unit of industrial lithography tool for manufacturing integrated circuits. This paper presents a novel state/model-free variable-gain discrete sliding mode control (DSMC) to suppress the unmolded position-dependent dynamics and disturbances in the nanopositioning wafer stage. The proposed DSMC is essentially composed of feedforward control term, linear feedback control term, and nonlinear switching control term, which can be designed separately. The gain of the switching control term is meaningfully designed to be variable to balance the tradeoff between the robustness and the chattering. Data-driven parameter optimization approach is employed to achieve the optimal controller parameters of the typically nonlinear controller, where off-line parameter updating is iteratively carried out based on the input/output data to minimize a predefined objective function. This scheme facilitates a rapid implementation without either a parameter model or a state observer, and excellent tracking performance with the optimal controller parameters. Moreover, the closed-loop stability is analyzed, and the proposed DSMC is finally implemented on an ultraprecision wafer stage developed in our lab. Comparative experimental results demonstrate that it not only achieves nanoscale tracking accuracy but also possesses promising robustness to position-dependent dynamics and disturbances.

Tikhonov–Phillips regularizations in linear models with blurred design

The paper deals with recovering an unknown vector β ∈ ℝ p based on the observations Y = Xβ + є ξ and Z = X + σζ, where X is an unknown n × p matrix with n ≥ p, ξ ∈ ℝ p is a standard white Gaussian noise, ζ is an n × p matrix with i.i.d. standard Gaussian entries, and є, σ ∈ ℝ+ are known noise levels. It is assumed that X has a large condition number and p is large. Therefore, in order to estimate β, the simple Tikhonov–Phillips regularization (ridge regression) with a data-driven regularization parameter is used. For this estimation method, we study the effect of noise σζ on the quality of recovering Xβ using concentration inequalities for the prediction error.

Hodgkin-Huxley revisited: reparametrization and identifiability analysis of the classic action potential model with approximate Bayesian methods.

As cardiac cell models become increasingly complex, a correspondingly complex ‘genealogy’ of inherited parameter values has also emerged. The result has been the loss of a direct link between model parameters and experimental data, limiting both reproducibility and the ability to re-fit to new data. We examine the ability of approximate Bayesian computation (ABC) to infer parameter distributions in the seminal action potential model of Hodgkin and Huxley, for which an immediate and documented connection to experimental results exists. The ability of ABC to produce tight posteriors around the reported values for the gating rates of sodium and potassium ion channels validates the precision of this early work, while the highly variable posteriors around certain voltage dependency parameters suggests that voltage clamp experiments alone are insufficient to constrain the full model. Despite this, Hodgkin and Huxley's estimates are shown to be competitive with those produced by ABC, and the variable behaviour of posterior parametrized models under complex voltage protocols suggests that with additional data the model could be fully constrained. This work will provide the starting point for a full identifiability analysis of commonly used cardiac models, as well as a template for informative, data-driven parametrization of newly proposed models.

Prior-knowledge-independent equalization to improve battery uniformity with energy efficiency and time efficiency for lithium-ion battery

To improve battery uniformity as well as energy efficiency and time efficiency, a SOC (state of charge)-based equalization by AGA (adaptive genetic algorithm) is proposed on basis of two-stage DC/DC converters. The simulation results indicate that compared with FLC (fuzzy logic controller) equalization, the standard deviation of final SOC is improved by 78.7% while energy efficiency is improved by 6.01% and equalization time is decreased by 20% for AGA equalization of extreme dispersion. Additionally, AGA improves the battery uniformity by 30.77% with shortening equalization time by 16.29% and saving energy loss by 1.51% compared with FLC for equalization of regular dispersion. For further validation, the equalization optimization is verified by experiment based on the data-driven parameter identification method which is used to enhance the real-time capability of AGA. For AGA equalization of extreme dispersion, the standard deviation of final SOC is just 0.41% while equalization time prolongs only 14 min and energy efficiency is decreased by 0.81% compared with simulation results. Moreover, not only the standard deviation of final SOC is just 0.28% but also the energy efficiency is decreased by 0.69% and equalization time prolongs by 10.4 min compared with the simulation results for equalization of regular dispersion.

BRANE Cut: biologically-related a priori network enhancement with graph cuts for gene regulatory network inference.

BackgroundInferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large number of available Gene Regulatory Network inference methods, the problem remains challenging: the underdetermination in the space of possible solutions requires additional constraints that incorporate a priori information on gene interactions.MethodsWeighting all possible pairwise gene relationships by a probability of edge presence, we formulate the regulatory network inference as a discrete variational problem on graphs. We enforce biologically plausible coupling between groups and types of genes by minimizing an edge labeling functional coding for a priori structures. The optimization is carried out with Graph cuts, an approach popular in image processing and computer vision. We compare the inferred regulatory networks to results achieved by the mutual-information-based Context Likelihood of Relatedness (CLR) method and by the state-of-the-art GENIE3, winner of the DREAM4 multifactorial challenge.ResultsOur BRANE Cut approach infers more accurately the five DREAM4 in silico networks (with improvements from 6 % to 11 %). On a real Escherichia coli compendium, an improvement of 11.8 % compared to CLR and 3 % compared to GENIE3 is obtained in terms of Area Under Precision-Recall curve. Up to 48 additional verified interactions are obtained over GENIE3 for a given precision. On this dataset involving 4345 genes, our method achieves a performance similar to that of GENIE3, while being more than seven times faster. The BRANE Cut code is available at: http://www-syscom.univ-mlv.fr/~pirayre/Codes-GRN-BRANE-cut.html.ConclusionsBRANE Cut is a weighted graph thresholding method. Using biologically sound penalties and data-driven parameters, it improves three state-of-the art GRN inference methods. It is applicable as a generic network inference post-processing, due to its computational efficiency.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0754-2) contains supplementary material, which is available to authorized users.

Migraineurs were reliably identified using administrative data

BackgroundMigraine is a common and important source of pain and disability in society. Accurately identifying such people using routinely collected health data would be beneficial for health services research. ObjectiveExternally validate a previously published method to identify migraineurs using health administrative data; and determine if a better model can be derived using data-mining techniques. MethodsMigraine status was determined for Ontarians participating in a population-based, cross-sectional survey. Consenting participants were linked to population-based health administrative data to identify age, sex, and coded diagnoses. Discrimination and calibration measures were used to appraise the models. A de novo technique we term “double threshold analysis” was used to determine optimal lower and upper expected probabilities to identify migraine status in the newly derived model. ResultsA total of 1,01,114 people (mean age 46 years, 46% male) were included in the study, of which 11,314 (11.2%) had migraines. Using data-driven parameter estimates, the previous model to identify migraineurs had adequate discrimination (c-statistic 0.707 [95% CI 0.701–0.712]) and calibration (Hosmer–Lemeshow [H–L] statistic 20.8). A new model that included diagnostic code scores for physician visits, emergency visits, and hospitalizations with nonlinear terms for age and interactions significantly improved the model (c-statistic 0.724 [0.716–0.733], 16.4). Categorizing all people with a predicted migraine probability less than 10% or greater than 90% as without and having the disease, respectively, resulted in a sensitivity of 3.1%, a specificity of 99.96%, and a positive predictive value of 81.0% while capturing 57.0% of the cohort and 29.3% of migraineurs. ConclusionA previously derived model to identify migraineurs was improved using data-mining techniques permitting accurate cohort identification using routinely collected health administrative data.

Modified balanced iterative reducing and clustering using hierarchies (m-BIRCH) for visual clustering

In modern visual clustering applications where datasets are large and updates with new data may be ongoing, methods of online clustering are extremely important. Online clustering algorithms incrementally cluster the data points, use a fraction of the dataset memory, and update the clustering decisions when new data comes in. In this paper we adapt a classic online clustering algorithm called balanced iterative reducing and clustering using hierarchies (BIRCH) to incrementally cluster large datasets of features commonly used in visual clustering, e.g., 840 K color SIFT descriptors, 1.09 million color patches, 60 K outlier corrupted grayscale patches, and 700 K grayscale SIFT descriptors. We use the algorithm to cluster datasets consisting of non-convex clusters, e.g., Hopkins 155 3D motion segmentation dataset. We call the adapted version modified-BIRCH (m-BIRCH). BIRCH was originally developed by the database management community, but has not been used in computer vision. Modifications made in m-BIRCH enable data-driven parameter selection and effectively handle varying density regions in the feature space. Data-driven parameter selection automatically controls the level of coarseness of the data summarization. Effective handling of varying density regions is necessary to well represent the different density regions in the data summarization. Our implementation of the algorithm provides a useful clustering tool and is made publicly available.

A New Adaptive Image Denoising Method

In this paper, a new adaptive image denoising method is proposed that follows the soft-thresholding technique. In our method, a new threshold function is also proposed, which is determined by taking the various combinations of noise level, noise-free signal variance, subband size, and decomposition level. It is simple and adaptive as it depends on the data-driven parameters estimation in each subband. The state-of-the-art denoising methods viz. VisuShrink, SureShrink, BayesShrink, WIDNTF and IDTVWT are not able to modify the coefficients in an efficient manner to provide the good quality of image. Our method removes the noise from the noisy image significantly and provides better visual quality of an image.

Efficiency classification by hybrid Bayesian networks—The dynamic multidimensional models

This study develops the hybrid models of dynamic multidimensional efficiency classification. By integrating data envelopment analysis (DEA), naïve Bayesian networks (NBN) and dynamic Bayesian networks (DBN), this work proposes a five-step design for efficiency classification: (1) performance evaluation with DEA model, (2) efficiency discretization, (3) intra-period classification by NBN, (4) inter-period classification by DBN, (5) testing and validation. Due to the Markovian property of the dynamic models, the inter-period dependency is assumed invariant over time. In data-driven parameter learning, the fuzzy parameters for incorporating the variation in dynamic dependencies are introduced. We conduct an empirical case study of higher education in Taiwan to demonstrate the usability of this design.

Automated and weighted self-organizing time maps

This paper proposes schemes for automated and weighted self-organizing time maps (SOTMs). The SOTM provides means for a visual approach to evolutionary clustering, which aims at producing a sequence of clustering solutions. This task we denote as visual dynamic clustering. The implication of an automated SOTM is not only a data-driven parametrization of the SOTM, but also the feature of adjusting the training to the characteristics of the data at each time step. The aim of the weighted SOTM is to improve learning from more trustworthy or important data with an instance-varying weight. The schemes for automated and weighted SOTMs are illustrated on two real-world datasets: (i) country-level risk indicators to measure the evolution of global imbalances and (ii) credit applicant data to measure the evolution of firm-level credit risks.

Bayesian Nonparametric Instrumental Variables Regression Based on Penalized Splines and Dirichlet Process Mixtures

We propose a Bayesian nonparametric instrumental variable approach under additive separability that allows us to correct for endogeneity bias in regression models where the covariate effects enter with unknown functional form. Bias correction relies on a simultaneous equations specification with flexible modeling of the joint error distribution implemented via a Dirichlet process mixture prior. Both the structural and instrumental variable equation are specified in terms of additive predictors comprising penalized splines for nonlinear effects of continuous covariates. Inference is fully Bayesian, employing efficient Markov chain Monte Carlo simulation techniques. The resulting posterior samples do not only provide us with point estimates, but allow us to construct simultaneous credible bands for the nonparametric effects, including data-driven smoothing parameter selection. In addition, improved robustness properties are achieved due to the flexible error distribution specification. Both these features are challenging in the classical framework, making the Bayesian one advantageous. In simulations, we investigate small sample properties and an investigation of the effect of class size on student performance in Israel provides an illustration of the proposed approach which is implemented in an R package bayesIV. Supplementary materials for this article are available online.