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Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (' in silico') experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology. Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the 'Physics core' of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from its Theoretical Physics foundation. [ABSTRACT FROM AUTHOR]

A major component of flow cytometry (FCM) data analysis involves gating, which is the process of identifying homogeneous groups of cells. With the rapid development of the portable flow cytometer, manual gating techniques have been unable to meet the demand for accurate and rapid analysis of samples. To provide a practical application for portable devices, we propose a flexible, statistical model-based clustering approach for identifying cell populations in FCM data. This approach, which mimics the manual gating process, employs a finite mixture model with a density function of skew t distribution and estimates parameters via an expectation maximization algorithm. Data analysis from an experiment on a patient's peripheral blood samples have proven that the proposed methodology yields better results in terms of robustness against outliers than current state-of-the-art automated gating methods, has more flexibility in clustering symmetric data and leads to lower misclassification rates (misclassification rates of skew t method is 0.06442) when handling highly asymmetric data. The method we proposed will improve data analysis of portable flow cytometers, especially when the users have no professional training. [ABSTRACT FROM AUTHOR]

A global smoothing procedure is developed using B-spline function approximation for estimating the unknown functions of a functional coefficient regression model with spatial data. A general formulation is used to treat mean regression, median regression, quantile regression and robust mean regression in one setting. The global convergence rates of the estimators of unknown coefficient functions are established. Various applications of the main results, including estimating conditional quantile coefficient functions and robustifying the mean regression coefficient functions are given. Finite sample properties of our procedures are studied through Monte Carlo simulations. A housing data example is used to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Abstract: The trust region method which originated from the Levenberg–Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare the proposed methods with the conventional expectation-maximization (EM) algorithm based on a series of datasets (synthetic and real human fMRI datasets). From simulation studies, we found a higher damping factor for the LM algorithm is better than lower damping factor for the fMRI data analysis. More importantly, in most cases, the expectation trust region algorithm is superior to the EM algorithm in terms of accuracy if the random effect variance is large. We also compare these algorithms on real human datasets which comprise repeated measures of fMRI in phased-encoded and random block experiment designs. We observed that the proposed method is faster in computation and robust to Gaussian noise for the fMRI analysis. The advantages and limitations of the suggested methods are discussed. [Copyright &y& Elsevier]

For solving conflicting information between data and prior distributions, Bayesian modelling with heavy-tailed distributions is applied. Exploiting properties of regularly varying functions and distribution functions as well as their relationship with the finiteness of the moments, we establish results for both location and shape parameter structures. And, as a side result, rates of convergence are derived. [ABSTRACT FROM AUTHOR]

Abstract: Offshore jacket platforms are one of the most motivating structures for damage detection due to their importance and productivity. In this study, the application of finite element model updating in damage detection of an offshore jacket platform is investigated. The objective function of this method is based on the measured and analytical modal data, including natural frequencies and mode shapes. However, the measured data is expected to be noisy. Also, to avoid obtaining false damage results, a penalty term is added to the objective function. To update the model, genetic algorithm is utilized as a robust global searching tool. Afterward, the efficiency of this method is evaluated on several damage cases in presence of 0, 1, 2 and 3 percent noise with measured modal data. The results show that this method can detect the damage of this kind of structure satisfactorily even if modal data is not precisely obtained. [Copyright &y& Elsevier]

Abstract: This paper presents two watermarking methods for NURBS curves and surfaces. Both methods are blind, shape-preserving and data amount-preserving. These features are often required in watermarking of CAD models. The first method is based on the replacement of exterior knots of NURBS. Watermarks are embedded into the cross-ratio of four knots in the knot vector(s) and the method is robust to affine transformation, Möbius reparameterization, and interior knot insertion and removal operations. The second method is based on reparameterization using Möbius transformation. Watermarks are embedded into the ratio of two knot intervals selected from the knot vector(s) and the method is robust to affine transformation and linear reparameterization. The capacity of the first method is for a degree NURBS curve and for a degree NURBS surface, and the capacity of the second method is 1 for a curve and 2 for a surface. Experiments have been conducted to demonstrate the shape-preserving, data amount-preserving properties and robustness. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new road lane model based on linear fuzzy space mathematics, coupled with a robust road lane detection method using fuzzy c-means clustering. The fuzzy line based road lane model presented here describes a lane as a set of fuzzy collinear fuzzy points. The proposed algorithm for road line detection is able to deal with imprecise data and enables reduced computational complexity (proportional to the number of fuzzy points multiplied by the number of fuzzy lines) versus a standard Hough transformation. Experimental results show that the proposed method is fast, and robust enough for use in real-time applications. The proposed method has been implemented as an Android-based mobile phone application. [Copyright &y& Elsevier]

Abstract: In this paper, we construct a nonparametric M-estimator of a regression function for a left truncated model when the data exhibit some kind of dependence. It is assumed that the observations form a stationary -mixing sequence. Under appropriate assumptions, we establish weak and strong consistency of the estimator as well as its asymptotic normality. Finite sample behavior of the estimators shows that the M-estimator is more robust than the Nadaraya–Watson type estimator. [Copyright &y& Elsevier]

Abstract: In this paper, we address a method to reduce the number of species equations that must be solved via application of Principal Component Analysis (PCA). This technique provides a robust methodology to reduce the number of species equations by identifying correlations in state-space and defining new variables that are linear combinations of the original variables. We show that applying this technique in the context of Large Eddy Simulation allows for a mapping between the reduced variables and the full set of variables that is insensitive to the size of filter used. This is notable since it provides a model to map state variables to progress variables that is a closed model. As a linear transformation, PCA allows us to derive transport equations for the principal components, which have source terms. These source terms must be parameterized by the reduced set of principal components themselves. We present results from a priori studies to show the strengths and weaknesses of such a modeling approach. Results suggest that the PCA-based model can identify manifolds that exist in state space which are insensitive to filtering, suggesting that the model is directly applicable for use in Large Eddy Simulation. However, the resulting source terms are not parameterized with an accuracy as high as the state variables. [Copyright &y& Elsevier]

Abstract: The advent of high throughput technologies, in particular microarrays, for biological research has revived interest in clustering, resulting in a plethora of new clustering algorithms. However, model selection, i.e., the identification of the correct number of clusters in a dataset, has received relatively little attention. Indeed, although central for statistics, its difficulty is also well known. Fortunately, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained prominence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of prediction, but the slowest in terms of time. It is very unfortunate that as fascinating and classic an area of statistics as model selection, with important practical applications, has received very little attention in terms of algorithmic design and engineering. In this paper, in order to partially fill this gap, we make the following contributions: (A) the first general algorithmic paradigm for stability-based methods for model selection; (B) reductions showing that all of the known methods in this class are an instance of the proposed paradigm; (C) a novel algorithmic paradigm for the class of stability-based methods for cluster validity, i.e., methods assessing how statistically significant is a given clustering solution; (D) a general algorithmic paradigm that describes heuristic and very effective speed-ups known in the literature for stability-based model selection methods. Since the performance evaluation of model selection algorithms is mainly experimental, we offer, for completeness and without even attempting to be exhaustive, a representative synopsis of known experimental benchmarking results that highlight the ability of stability-based methods for model selection and the computational resources that they require for the task. As a whole, the contributions of this paper generalize in several respects reference methodologies in statistics and show that algorithmic approaches can yield deep methodological insights into this area, in addition to practical computational procedures. [Copyright &y& Elsevier]

Abstract: This work presents the development of a rainfall–runoff model for the Iguaçu River basin in the south of Brazil. The model was developed to support the operational planning of hydroelectric power plants and is intended to compute natural flow predictions based on meteorological rain forecasts. A recurrent fuzzy system model was employed, with parameters estimated by a genetic algorithm using observed rainfall as input. This work presents the recurrent fuzzy model within a multi-model approach, where the input data are furnished as an envelope, resulting in a prediction envelope that has demonstrated the ability to produce robust results. [Copyright &y& Elsevier]

In this paper, we propose robust estimation of the generalised partial linear model with covariates missing at random. The developed approach integrated the robust method and the method for dealing with missing data. Under some regularity conditions, we establish the asymptotic normality of the proposed estimator of the regression coefficients and show that the proposed estimator of the nonparametric function can achieve the optimal rate of convergence. It can be observed that the regression spline approach avoids some of the intricacies associated with the kernel method, and the robust estimation and inference can be carried out operationally as if a generalised linear model were used. Simulation studies are conducted to investigate the robustness of the proposed method. At the end, the proposed method is applied to a real data analysis for illustration. [ABSTRACT FROM PUBLISHER]

The theory of fuzzy rough sets is claimed to be a powerful mathematical tool for dealing with uncertainty in data analysis. Unluckily, the classical model of fuzzy rough sets is sensitive to noisy information. This disadvantage limits the applicability of the model in practice. In this work, we present a robust fuzzy rough set model based on soft minimum enclosing ball, and introduce a new fuzzy dependency function with this model. Some properties of the new model are discussed. Finally, we conduct some experiments to test the effectiveness of the proposed model, and experimental results show that the soft minimum enclosing ball-based fuzzy rough set model is robust to noise. [ABSTRACT FROM AUTHOR]

Critical transitions are sudden, often irreversible, changes that can occur in a large variety of complex systems; signals that warn of critical transitions are therefore highly desirable. We propose a new method for early warning signals that integrates multiple sources of information and data about the system through the framework of a generalized model. We demonstrate our proposed approach through several examples, including a previously published fisheries model. We regard our method as complementary to existing early warning signals, taking an approach of intermediate complexity between model-free approaches and fully parameterized simulations. One potential advantage of our approach is that, under appropriate conditions, it may reduce the amount of time series data required for a robust early warning signal. INSET: Author Summary. [ABSTRACT FROM AUTHOR]

We propose a probabilistic model for clustering Boolean data where an object can be simultaneously assigned to multiple clusters. By explicitly modeling the underlying generative process that combines the individual source emissions, highly structured data are expressed with substantially fewer clusters compared to single-assignment clustering. As a consequence, such a model provides robust parameter estimators even when the number of samples is low. We extend the model with different noise processes and demonstrate that maximum-likelihood estimation with multiple assignments consistently infers source parameters more accurately than single-assignment clustering. Our model is primarily motivated by the task of role mining for role-based access control, where users of a system are assigned one or more roles. In experiments with real-world access-control data, our model exhibits better generalization performance than state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Abstract: Condition-based maintenance is concerned with the collection and interpretation of data to support maintenance decisions. The non-intrusive nature of vibration data enables the monitoring of enclosed systems such as gearboxes. It remains a significant challenge to analyze vibration data that are generated under fluctuating operating conditions. This is especially true for situations where relatively little prior knowledge regarding the specific gearbox is available. It is therefore investigated how an adaptive time series model, which is based on Bayesian model selection, may be used to remove the non-fault related components in the structural response of a gear assembly to obtain a residual signal which is robust to fluctuating operating conditions. A statistical framework is subsequently proposed which may be used to interpret the structure of the residual signal in order to facilitate an intuitive understanding of the condition of the gear system. The proposed methodology is investigated on both simulated and experimental data from a single stage gearbox. [Copyright &y& Elsevier]

Abstract: This paper addresses the problem of network reconstruction from data. Previous work identified necessary and sufficient conditions for network reconstruction of LTI systems, assuming perfect measurements (no noise) and perfect system identification. This paper assumes that the conditions for network reconstruction have been met but here we additionally take into account noise and unmodelled dynamics (including nonlinearities). In order to identify the network structure that generated the data, we compute the smallest distances between the measured data and the data that would have been generated by particular network structures. We conclude with biologically inspired network reconstruction examples which include noise and nonlinearities. [Copyright &y& Elsevier]

This paper introduces two real-time elastography techniques based on analytic minimization (AM) of regularized cost functions. The first method (1D AM) produces axial strain and integer lateral displacement, while the second method (2D AM) produces both axial and lateral strains. The cost functions incorporate similarity of radio–frequency (RF) data intensity and displacement continuity, making both AM methods robust to small decorrelations present throughout the image. We also exploit techniques from robust statistics to make the methods resistant to large local decorrelations. We further introduce Kalman filtering for calculating the strain field from the displacement field given by the AM methods. Simulation and phantom experiments show that both methods generate strain images with high SNR, CNR and resolution. Both methods work for strains as high as 10% and run in real-time. We also present in vivo patient trials of ablation monitoring. An implementation of the 2D AM method as well as phantom and clinical RF-data can be downloaded. [ABSTRACT FROM AUTHOR]

In the context of the debate on the labor-market consequences of globalization, we adopt an original approach toward the identification of the wage differences between foreign and domestic firms: worker mobility. Using matched employer-employee panel data for Portugal, we consider virtually all spells of interfirm mobility over a period of 10 yr. We find that foreign firms offer significantly more generous wage policies, although there is also a (smaller) selection effect. The results are robust to the consideration of displaced workers, wage growth differences in the new firms, and different subsets of workers. ( JEL J31, J63, F23) [ABSTRACT FROM AUTHOR]

Abstract: In this study, primary channel mismatch scenario between enrollment and test conditions in a speaker verification task are analyzed and modeled. A novel Gaussian mixture modeling with a universal background model (GMM–UBM) frame based compensation model related to the mismatch is formulated and evaluated using National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) 2008 data, along with a comparison to the well-known eigenchannel model. Proposed compensation method show significant improvement versus an eigenchannel model when only the supervector of the UBM is employed. Here, the supervector of the enrollment speaker model is not included for estimation of the mismatch since it is difficult to obtain the real supervector of the speaker based on the limited 5min, channel dependent speech data only. The proposed mismatch compensation model, therefore show that construction of the supervector obtained from a UBM model can more accurately describe the mismatch between enrollment and test data, resulting in effective classification performance improvement for speaker/speech applications. [ABSTRACT FROM AUTHOR]

We use two large samples of firms to assess the effects of business environment constraints, competition, export orientation, and ownership on firm performance. We deal with omitted variables, errors in variables, and endogeneity, and find that few business constraints affect performance. Replicating the analysis with Doing Business and Heritage Foundation indicators of the business environment yields similar results. In fact, country fixed effects, reflecting time-invariant differences in the business environment as well as other factors such as health care and education, matter more for firm performance than differences in the business environment across firms within countries. [ABSTRACT FROM AUTHOR]

Does hiring workers with experience from multinationals (MNEs) increase productivity in non-MNEs? Tracing worker flows between plants in Norwegian manufacturing during the 1990s, I find a positive correlation between the share of workers with MNE experience in non-MNEs and the productivity of these plants. Workers with MNE experience contribute 20% more to the productivity of their plant than workers without such experience, even after controlling for differences in unobservable worker characteristics. The private return to mobility is smaller than the productivity effect at the plant level, which suggests that labor mobility from MNEs to non-MNEs represents a true knowledge externality. [ABSTRACT FROM AUTHOR]

Abstract: This article introduces a parametric robust way of determining the mean–variance relationship in the setting of generalized linear models. More specifically, the normal likelihood is properly amended to become asymptotically valid even if normality fails. Consequently, legitimate inference for the parametric relationship between mean and variance could be derived under model misspecification. More details are given to the scenario when the variance is proportional to an unknown power of the mean function. The efficacy of the novel technique is demonstrated via simulations and the analysis of two real data sets. [Copyright &y& Elsevier]

Free media may reduce incidents of socio-political instability. Different types of sociopolitical instability have been shown to have a negative effect on investment and economic growth. This study examines the effect of free media on various indicators of socio-political instability. Using a panel of 98 countries over 1994-2005, this study shows that media free from government control and interference may decrease different forms of socio-political instability because it puts internal and external pressure on self-interested governments to act in the best interests of citizens--rather than their own. The empirical results suggest that a freer media is associated with lower levels of socio-political instability as measured by ethnic tensions, external and internal conflicts, crime and disorder, military participation in government and religious tensions. The estimates are robust to several sensitivity tests. [ABSTRACT FROM AUTHOR]

This paper examines the role of migration networks in determining self-selection patterns of Mexico-U.S. migration. A simple theoretical framework shows the impact of networks on migration incentives at different education levels and how this affects the composition of migrant skills. Empirically, we find positive or education-neutral selection in communities with weak migrant networks but negative self-selection in communities with stronger networks. This is consistent with high migration costs driving positive or intermediate self-selection, as advocated by Chiquiar and Hanson (2005), and with negative self-selection being driven by lower returns to education in the United States than in Mexico, as advocated by Borjas (1987). [ABSTRACT FROM AUTHOR]

This paper compares economic outcomes across areas in India that were under direct British colonial rule with areas that were under indirect colonial rule. Controlling for selective annexation using a specific policy rule, I find that areas that experienced direct rule have significantly lower levels of access to schools, health centers, and roads in the postcolonial period. I find evidence that the quality of governance in the colonial period has a significant and persistent effect on postcolonial outcomes. [ABSTRACT FROM AUTHOR]

If pricing is state dependent, firms are more likely to adjust whenever aggregate and idiosyncratic shocks reinforce each other and trigger desired price changes in the same direction. Using measures of technology shocks derived from production function estimates for four-digit U.S. manufacturing industries, I find that sectoral inflation rates are more sensitive to negative, as opposed to positive, technology disturbances in periods of higher economy-wide inflation, commodity price increases, and expansionary monetary policy shocks. I argue, using a state-dependent sticky price model that matches salient features of the U.S. microprice data, that these results suggest that pricing is state-dependent in U.S. manufacturing. [ABSTRACT FROM AUTHOR]

Why does schooling attainment vary widely across countries? Why are differences in schooling attainment highly persistent? I show that cross-country differences in schooling are related to political institutions, such as democracy and local democracy (political decentralization), which are affected by colonial factors. By using the number of native cultures before colonization as an instrument for political decentralization, I show that after controlling for the causal effect of income on schooling, the degree of democratization positively affects the development of primary education, whereas political decentralization has a positive and significant impact on more advanced levels of schooling. [ABSTRACT FROM AUTHOR]

Abstract: Robustness is one of the most important topics for automatic speech recognition (ASR) in practical applications. Monaural speech separation based on computational auditory scene analysis (CASA) offers a solution to this problem. In this paper, a novel system is presented to separate the monaural speech of two talkers. Gaussian mixture models (GMMs) and vector quantizers (VQs) are used to learn the grouping cues on isolated clean data for each speaker. Given an utterance, speaker identification is firstly performed to identify the two speakers presented in the utterance, then the factorial-max vector quantization model (MAXVQ) is used to infer the mask signals and finally the utterance of the target speaker is resynthesized in the CASA framework. Recognition results on the 2006 speech separation challenge corpus prove that this proposed system can improve the robustness of ASR significantly. [Copyright &y& Elsevier]

Summary: Graphical Gaussian models (GGMs) are a promising approach to identify gene regulatory networks. Such models can be robustly inferred by solving the sparse inverse covariance selection (SICS) problem. With the high dimensionality of genomics data, fast methods capable of solving large instances of SICS are needed.We developed a novel network modeling tool, Ultranet, that solves the SICS problem with significantly improved efficiency. Ultranet combines a range of mathematical and programmatical techniques, exploits the structure of the SICS problem and enables computation of genome-scale GGMs without compromising analytic accuracy.Availability and implementation: Ultranet is implemented in C++ and available at www.broadinstitute.org/ultranet.Contact: bnilsson@broadinstitute.org or bjorn.nilsson@med.lu.seSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

In this paper, we propose an effective solution to reconstruct solid models of existing objects. Specifically, we convert the model reconstruction problem into the issue of feature parameter extraction, and thereby design diverse methods to extract the parameters of basic design features from input surface meshes. After extracting the feature parameters, the corresponding features are constructed. By performing modeling operations on those features, the final solid model is constructed, and meanwhile the complete history of the model building operations is recorded. By introducing the concepts of “feature,” “constraint,” and “modeling history” into the reconstruction process, the design intent is captured and hence represented in the reconstructed model. As a result, the model is geometrically accurate and topologically consistent, and moreover it is flexibly editable, which makes it convenient to carry out model redesign and modification for the innovation applications. A variety of experimental results demonstrate the effectiveness and robustness of this solution. [ABSTRACT FROM AUTHOR]