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PubMed2005

Robustness mechanisms in primate societies: a perturbation study.

Conflict management mechanisms have a direct, critical effect on system robustness because they mitigate conflict intensity and help repair damaged relationships. However, robustness mechanisms can also have indirect effects on system integrity by facilitating interactions among components. We explore the indirect role that conflict management mechanisms play in the maintenance of social system robustness, using a perturbation technique to 'knockout' components responsible for effective conflict management. We explore the effects of knockout on pigtailed macaque (Macaca nemestrina) social organization, using a captive group of 84 individuals. This system is ideal in addressing this question because there is heterogeneity in performance of conflict management. Consequently, conflict managers can be easily removed without disrupting other control structures. We find that powerful conflict managers are essential in maintaining social order for the benefit of all members of society. We show that knockout of components responsible for conflict management results in system destabilization by significantly increasing mean levels of conflict and aggression, decreasing socio-positive interaction and decreasing the operation of repair mechanisms.

Aggression↗
PubMed2025

Protein overabundance is driven by growth robustness.

Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naïvely predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacterium Acinetobacter baylyi by characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale. In these experiments, cells proliferate for multiple generations as target protein levels are diluted from their endogenous levels. This approach facilitates a proteome-scale analysis of the fitness landscape with respect to protein abundance. We find that most essential proteins are subject to a threshold-like fitness landscape: growth is independent of protein abundance above a critical threshold and arrests below that threshold. We have recently analyzed the implications of this landscape for growth robustness. Confirming signature predictions of this model, we find that (i) roughly 70% of essential proteins are overabundant, (ii) overabundance increases as the expression level decreases and (iii) the lowest abundance proteins are in vast excess (>10×) of what is required for growth in the typical cell. These results reveal that robustness plays a fundamental role in determining the expression levels of essential genes and that overabundance is a key mechanism for ensuring robust growth.

Journal Article↗
PubMed2001

Robust chaos in smooth unimodal maps.

Robust chaos is defined by the absence of periodic windows and coexisting attractors in some neighborhood of the parameter space. It has been conjectured that robust chaos cannot occur in smooth systems [E. Barreto, B. Hunt, and C. Grebogi, Phys. Rev. Lett. 78, 4561 (1997); 80, 3049 (1998)]. Contrary to this conjecture, we describe a general procedure for generating robust chaos in smooth unimodal maps.

Journal Article↗
PubMed2005

Scale-free fuse network and its robustness.

The robustness and reliability of scale-free networks are tested as a fuse network. The idea is to examine the robustness of a scale-free network when links are irreversibly removed after failing. Due to inherent characteristics of the fuse network model, the sequence of links removal is deterministic and conditioned to fuse tolerance and connectivity of its ends. It is a different situation from classical robustness analysis of complex networks, when they are usually tested under random fails and deliberate attacks of nodes. The use of this system to study the fracture of elastic material brought some interesting results.

Journal Article↗
PubMed2000

Robustness of wave functions of interacting many bosons in a leaky Box

We study the robustness, against the leakage of bosons, of wave functions of interacting many bosons confined in a finite box by deriving and analyzing a general equation of motion for the reduced density operator. We identify a robust wave function that remains a pure state, whereas other wave functions, such as the Bogoliubov's ground state and the ground state with a fixed number of bosons, evolve into mixed states. Although these states all have the off-diagonal long-range order, and the same energy, we argue that only the robust state is realized as a macroscopic quantum state.

Journal Article↗
PubMed2005

A robust meta-classification strategy for cancer diagnosis from gene expression data.

One of the major challenges in cancer diagnosis from microarray data is to develop robust classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose a meta-classification scheme which uses a robust multivariate gene selection procedure and integrates the results of several machine learning tools trained on raw and pattern data. We validate our method by applying it to distinguish diffuse large B-cell lymphoma (DLBCL) from follicular lymphoma (FL) on two independent datasets: the HuGeneFL Affmetrixy dataset of Shipp et al. (www. genome.wi.mit.du/MPR /lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our meta-classification technique achieves higher predictive accuracies than each of the individual classifiers trained on the same dataset and is robust against various data perturbations. We also find that combinations of p53 responsive genes (e.g., p53, PLK1 and CDK2) are highly predictive of the phenotype.

Algorithms↗
PubMed2006

Robust photometric invariant features from the color tensor.

Luminance-based features are widely used as low-level input for computer vision applications, even when color data is available. The extension of feature detection to the color domain prevents information loss due to isoluminance and allows us to exploit the photometric information. To fully exploit the extra information in the color data, the vector nature of color data has to be taken into account and a sound framework is needed to combine feature and photometric invariance theory. In this paper, we focus on the structure tensor, or color tensor, which adequately handles the vector nature of color images. Further, we combine the features based on the color tensor with photometric invariant derivatives to arrive at photometric invariant features. We circumvent the drawback of unstable photometric invariants by deriving an uncertainty measure to accompany the photometric invariant derivatives. The uncertainty is incorporated in the color tensor, hereby allowing the computation of robust photometric invariant features. The combination of the photometric invariance theory and tensor-based features allows for detection of a variety of features such as photometric invariant edges, corners, optical flow, and curvature. The proposed features are tested for noise characteristics and robustness to photometric changes. Experiments show that the proposed features are robust to scene incidental events and that the proposed uncertainty measure improves the applicability of full invariants.

Algorithms↗
PubMed2006

Robust and efficient image alignment based on relative gradient matching.

In this paper, we present a robust image alignment algorithm based on matching of relative gradient maps. This algorithm consists of two stages; namely, a learning-based approximate pattern search and an iterative energy-minimization procedure for matching relative image gradient. The first stage finds some candidate poses of the pattern from the image through a fast nearest-neighbor search of the best match of the relative gradient features computed from training database of feature vectors, which are obtained from the synthesis of the geometrically transformed template image with the transformation parameters uniformly sampled from a given transformation parameter space. Subsequently, the candidate poses are further verified and refined by matching the relative gradient images through an iterative energy- minimization procedure. This approach based on the matching of relative gradients is robust against nonuniform illumination variations. Experimental results on both simulated and real images are shown to demonstrate superior efficiency and robustness of the proposed algorithm over the conventional normalized correlation method.

Algorithms↗
PubMed2004

Robust model-based vasculature detection in noisy biomedical images.

This paper presents a set of algorithms for robust detection of vasculature in noisy retinal video images. Three methods are studied for effective handling of outliers. The first method is based on Huber's censored likelihood ratio test. The second is based on the use of a alpha-trimmed test statistic. The third is based on robust model selection algorithms. All of these algorithms rely on a mathematical model for the vasculature that accounts for the expected variations in intensity/texture profile, width, orientation, scale, and imaging noise. These unknown parameters are estimated implicitly within a robust detection and estimation framework. The proposed algorithms are also useful as nonlinear vessel enhancement filters. The proposed algorithms were evaluated over carefully constructed phantom images, where the ground truth is known a priori, as well as clinically recorded images for which the ground truth was manually compiled. A comparative evaluation of the proposed approaches is presented. Collectively, these methods outperformed prior approaches based on Chaudhuri et al. (1989) matched filtering, as well as the verification methods used by prior exploratory tracing algorithms, such as the work of Can et aL (1999). The Huber censored likelihood test yielded the best overall improvement, with a 145.7% improvement over the exploratory tracing algorithm, and a 43.7% improvement in detection rates over the matched filter.

Algorithms↗
PubMed2005

On FastMap and the convex hull of multivariate data: toward fast and robust dimension reduction.

FastMap is a dimension reduction technique that operates on distances between objects. Although only distances are used, implicitly the technique assumes that the objects are points in a p-dimensional Euclidean space. It selects a sequence of k < or = p orthogonal axes defined by distant pairs of points (called pivots) and computes the projection of the points onto the orthogonal axes. We show that FastMap uses only the outer envelope of a data set. Pivots are taken from the faces, usually vertices, of the convex hull of the data points in the original implicit Euclidean space. This provides a bridge to results in robust statistics, where the convex hull is used as a tool in multivariate outlier detection and in robust estimation methods. The connection sheds new light on the properties of FastMap, particularly its sensitivity to outliers, and provides an opportunity for a new class of dimension reduction algorithms, RobustMaps, that retain the speed of FastMap and exploit ideas in robust statistics.

Algorithms↗
PubMed2004

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality.

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Journal Article↗
PubMed2005

Relay feedback tuning of robust PID controllers with iso-damping property.

A new tuning method for proportional-integral-derivative (PID) controller design is proposed for a class of unknown, stable, and minimum phase plants. We are able to design a PID controller to ensure that the phase Bode plot is flat, i.e., the phase derivative w.r.t. the frequency is zero, at a given frequency called the "tangent frequency" so that the closed-loop system is robust to gain variations and the step responses exhibit an iso-damping property. At the "tangent frequency," the Nyquist curve tangentially touches the sensitivity circle. Several relay feedback tests are used to identify the plant gain and phase at the tangent frequency in an iterative way. The identified plant gain and phase at the desired tangent frequency are used to estimate the derivatives of amplitude and phase of the plant with respect to frequency at the same frequency point by Bode's integral relationship. Then, these derivatives are used to design a PID controller for slope adjustment of the Nyquist plot to achieve the robustness of the system to gain variations. No plant model is assumed during the PID controller design. Only several relay tests are needed. Simulation examples illustrate the effectiveness and the simplicity of the proposed method for robust PID controller design with an iso-damping property.

Algorithms↗
PubMed2005

An improved robust fuzzy-PID controller with optimal fuzzy reasoning.

Many fuzzy control schemes used in industrial practice today are based on some simplified fuzzy reasoning methods, which are simple but at the expense of losing robustness, missing fuzzy characteristics, and having inconsistent inference. The concept of optimal fuzzy reasoning is introduced in this paper to overcome these shortcomings. The main advantage is that an integration of the optimal fuzzy reasoning with a PID control structure will generate a new type of fuzzy-PID control schemes with inherent optimal-tuning features for both local optimal performance and global tracking robustness. This new fuzzy-PID controller is then analyzed quantitatively and compared with other existing fuzzy-PID control methods. Both analytical and numerical studies clearly show the improved robustness of the new fuzzy-PID controller.

Algorithms↗
PubMed2005

Improved global robust asymptotic stability criteria for delayed cellular neural networks.

This paper considers the problem of global robust stability analysis of delayed cellular neural networks (DCNNs) with norm-bounded parameter uncertainties. In terms of a linear matrix inequality, a new sufficient condition ensuring a nominal DCNN to have a unique equilibrium point which is globally asymptotically stable is proposed. This condition is shown to be a generalization and improvement over some previous criteria. Based on the stability result, a robust stability condition is developed, which contains an existing robust stability result as a special case. An example is provided to demonstrate the reduced conservativeness of the proposed results.

Algorithms↗
PubMed2006

Mode-independent robust stabilization for uncertain Markovian jump nonlinear systems via fuzzy control.

This paper is concerned with the robust-stabilization problem of uncertain Markovian jump nonlinear systems (MJNSs) without mode observations via a fuzzy-control approach. The Takagi and Sugeno (T-S) fuzzy model is employed to represent a nonlinear system with norm-bounded parameter uncertainties and Markovian jump parameters. The aim is to design a mode-independent fuzzy controller such that the closed-loop Markovian jump fuzzy system (MJFS) is robustly stochastically stable. Based on a stochastic Lyapunov function, a robust-stabilization condition using a mode-independent fuzzy controller is derived for the uncertain MJFS in terms of linear matrix inequalities (LMIs). A new improved LMI formulation is used to alleviate the interrelation between the stochastic Lyapunov matrix and the system matrices containing controller variables in the derivation process. Finally, a simulation example is presented to illustrate the effectiveness of the proposed design method.

Algorithms↗
PubMed2001

Options in radioactive waste management revisited: a proposed framework for robust decision making.

Deregulation, with concurrent pressure on electricity utilities, has fundamentally changed the once-"closed" radioactive waste management system controlled by the so-called "nuclear establishment." Advocacy coalitions may change-who knows in which direction-but policy learning may also take place. This article presents a framework to evaluate the management options for a specified concept of "sustainability." When weighing the different objectives in view of the long-lasting potential danger of radiotoxic substances, the overall goal of a sound waste management system is to demonstrate safety. The first-priority objective of a disposal system, therefore, is its stability so that it can comply with the protection goal, that is, the long-term protection of humans and the environment from ionizing radiation. The complementary objective is flexibility, defined here as intervention potential. Because trade-offs within the "sustainability triangle" of ecology, economy, and society are unavoidable, the concept of "integral robustness"-both technical and societal-is introduced into radioactive waste management. A system is robust if it is not sensitive to significant parameter changes. In the present case, it has to have a conservative, passively stable design with built-in control and intervention mechanisms. With regard to technical implementation, a concept called "monitored long-term geological disposal" is presented. Such an "extended" final disposal concept emphasizes technical robustness, recognizes evaluation demands (for a potential break-off of a project), and enhances process-based transparency. This open approach admittedly sets high challenges with regard to technicalities as well as the institutional setting and the management process. It requires "mutual learning" by and from all stakeholders to achieve a truly sustainable radioactive waste management system.

Decision Making↗
PubMed2004

Accuracy, efficiency and robustness of four algorithms allowing full sibship reconstruction from DNA marker data.

In the problem of reconstructing full sib pedigrees from DNA marker data, three existing algorithms and one new algorithm are compared in terms of accuracy, efficiency and robustness using real and simulated data sets. An algorithm based on the exclusion principle and another based on a maximization of the Simpson index were very accurate at reconstructing data sets comprising a few large families but had problems with data sets with limited family structure, while a Markov Chain Monte Carlo (MCMC) algorithm based on the maximization of a partition score had the opposite behaviour. An MCMC algorithm based on maximizing the full joint likelihood performed best in small data sets comprising several medium-sized families but did not work well under most other conditions. It appears that the likelihood surface may be rough and presents challenges for the MCMC algorithm to find the global maximum. This likelihood algorithm also exhibited problems in reconstructing large family groups, due possibly to limits in computational precision. The accuracy of each algorithm improved with an increasing amount of information in the data set, and was very high with eight loci with eight alleles each. All four algorithms were quite robust to deviation from an idealized uniform allelic distribution, to departures from idealized Mendelian inheritance in simulated data sets and to the presence of null alleles. In contrast, none of the algorithms were very robust to the probable presence of error/mutation in the data. Depending upon the type of mutation or errors and the algorithm used, between 70 and 98% of the affected individuals were classified improperly on average.

Algorithms↗
PubMed2005

Rapid categorization of achromatic natural scenes: how robust at very low contrasts?

The human visual system is remarkably good at categorizing objects even in challenging visual conditions. Here we specifically assessed the robustness of the visual system in the face of large contrast variations in a high-level categorization task using natural images. Human subjects performed a go/no-go animal/nonanimal categorization task with briefly flashed grey level images. Performance was analysed for a large range of contrast conditions randomly presented to the subjects and varying from normal to 3% of initial contrast. Accuracy was very robust and subjects were performing well above chance level (approximately 70% correct) with only 10-12% of initial contrast. Accuracy decreased with contrast reduction but reached chance level only in the most extreme condition (3% of initial contrast). Conversely, the maximal increase in mean reaction time was approximately 60 ms (at 8% of initial contrast); it then remained stable with further contrast reductions. Associated ERPs recorded on correct target and distractor trials showed a clear differential effect whose amplitude and peak latency were correlated respectively with task accuracy and mean reaction times. These data show the strong robustness of the visual system in object categorization at very low contrast. They suggest that magnocellular information could play a role in ventral stream visual functions such as object recognition. Performance may rely on early object representations which lack the details provided subsequently by the parvocellular system but contain enough information to reach decision in the categorization task.

Adult↗