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Boolean modeling has been successfully applied to the budding yeast cell cycle to demonstrate that both its structure and its timing are robustly designed. However, from these studies few conclusions can be drawn how robust the cell cycle arrest upon osmotic stress and pheromone exposure might be. We therefore implement a compact Boolean model of the S. cerevisiae cell cycle including its interfaces with the High Osmolarity Glycerol (HOG) and the pheromone pathways. We show that all initial states of our model robustly converge to a cyclic attractor in the absence of stress inputs whereas pheromone exposure and osmotic stress lead to convergence to singleton states which correspond to G1 and G2 arrest in silico. A comparison with random Boolean networks reveals, that cell cycle arrest under osmotic stress is a highly robust property of the yeast cell cycle. We implemented our model using the novel frontend booleannetGUI to the python software booleannet.

Estimation, testing and robustness are three common problems in two-level structural equation models. Liang & Bentler (2004a) developed the EM approach to estimation of model parameters and studied the asymptotic properties of the estimators. In a series of articles, Yuan and Bentler studied the problems of testing model fit and the robustness of tests and standard errors. This paper reviews some recent advances in research on the three problems, and provides some numerical comparisons between our methodology and selected existing ones.

After light absorption the primary process in light harvesting is the transfer of excitation to a reaction center which facilitates a separation of charge across a cell membrane. The physical principles underlying excitation transfer are explained. Theoretical methods for the description of the excitation migration process, including an expansion for excitation lifetime in terms of repeated trapping and subsequent detrapping events, and the construction of representative pathways for excitation transfer based on mean first passage times, are presented. Measures for robustness and optimality of excitation transfer in terms of quantum yield are introduced. Photosystem I (PSI) is used as an example to illustrate the methods discussed. Some conclusions for the design of artificial light harvesting systems are also discussed.

The explosive growth in medical imaging technologies has been matched by a tremendous increase in the number of investigations centered on the structural and functional organization of the human body. A pivotal first step towards elucidating the correlation between structure and function, accurate and robust segmentation is a major objective of computerized medicine. It is also a substantial challenge in view of the wide variety of shapes and appearances that organs, anatomical structures and tissues can exhibit in medical images.

This chapter surveys the actively expanding field of medical image segmentation. We discuss the main issues that pertain to the remarkably diverse range of proposed techniques. Among others, the characteristics of a suitable segmentation paradigm, the introduction of a priori knowledge, robustness and validation are detailed and illustrated with relevant techniques and applications.

Based on completely different properties of the signal and noise in wavelet transform, the noise confidence factor is introduced to estimate the clutter power level by means of CM (Censored Method). A radar signal CFAR (Constant False Alarm Rate) detection method with soft threshold is proposed. The CFAR characteristic of the method under background with Gaussian distribution clutter is studied theoretically. And the experiment results of radar signal processing demonstrate that this method can detect the targets in other different clutters effectively, which shows the method's robustness and effectiveness.

A new approach to blind digital watermarking based on the DMWT is presented in this paper. OBSA multi-wavelets have randomicity as to selection of the prefilter. A prefilter Q can be constructed through genetic algorithm according to fitness function for different images. A grey watermark image is decomposed to bit-planes. One level DMWT is realized by using the prefilter Q and OBSA multiwavelet. The watermark bit-planes are embedded in low frequency parts. "0" or "1" of the watermark bit-planes is represented by even or odd number respectively, and vice versa. The experiment results have shown that the proposed approach has the desired properties such as invisibility, reliable detection, and robustness against cropping operation.

This paper establishes an asymptotic representation for regression and autoregression rank score statistics of the serial type. Applications include rank-based versions of the Durbin-Watson test, tests of AR(p) against AR(p + 1) dependence, or detection of the presence of random components in AR processes.

Our understanding of the input-output function of single neurons has been advanced by biophysically accurate multi-compartmental models. The large number of free parameters in these models requires the use of automated approaches for finding their optimal values in a very multi dimensional parameter-plane. Due to inherent noise in measuring equipment and surroundings determination of the accuracy of the obtained parameters is near impossible. Here we show that finding the parameters distribution via Monte Carlo approach simulations reveals the sensitivity of each parameter to noise. This method allows for the reduction of the complexity of the parameter-plane which in turn enables finding the sensitivity of the model to each parameter and the sensitivity of each parameter to noise.

This paper provides a survey of the theory of patchy feedbacks, and its applications to asymptotic stabilization and optimal control. It also contains two new results, showing the robustness of sub-optimal patchy feedbacks both in the case of (internal and external) deterministic disturbances, and of random perturbations modelled by stochastic Brownian motion.

Human solid tumors are believed to be caused by a sequence of genetic abnormalities arising in normal and premalignant cells. The understanding of these sequences is important for improving cancer treatment. Models for the occurrence of the abnormalities include linear structure and a recently proposed tree-based structure. We will describe the oncogenetic tree model and an efficient algorithm for its estimation. We also discuss methods for estimating the reliability and goodness-of-fit of this reconstruction. An R package “Oncotree” implementing the described methodology is available from the authors.

This chapter aims at presenting our data mining vision on Statistical Process Control (SPC) analysis, specifically on the design of multivariate control charts for individual observations in the case of independent data and continuous variables. We first argue why the classic multivariate SPC tool, namely the Hotelling T² chart, might not be appropriate for large data sets, and then we provide an up-to-date critical review of the methods suitable for dealing with data mining issues in control chart design. In order to address new SPC issues such as the presence of multiple outliers and incorrect model assumptions in the context of large data sets, we suggest exploitation of some multivariate nonparametric statistical methods. In a model-free environment, we present the way we handle large data sets: a multivariate control scheme based on the data depth approach. We first present the general framework, and then our specific idea on how to design a proper control chart. There follows an example, a simulation study, and some remarks on the choice of the depth function from a data mining perspective. A brief discussion of some open issues in data mining SPC closes the chapter.

We derive the asymptotic distribution of the maximal depth regression estimator recently proposed in Rousseeuw and Hubert. The estimator is obtained by maximizing a projection-based depth and the limiting distribution is characterized through a max–min operation of a continuous process. The same techniques can be used to obtain the limiting distribution of some other depth estimators including Tukey's deepest point based on half-space depth. Results for the special case of two-dimensional problems have been available, but the earlier arguments have relied on some special geometric properties in the low-dimensional space. This paper completes the extension to higher dimensions for both regression and multivariate location models.

A large number of image and spatial information processing problems involves the estimation of the intrinsic image information from observed images, for instance, image restoration, image registration, image partition, depth estimation, shape reconstruction and motion estimation. These are inverse problems and generally ill-posed. Such estimation problems can be readily formulated by Bayesian models which infer the desired image information from the measured data. Bayesian paradigms have played a very important role in spatial data analysis for over three decades and have found many successful applications. In this paper, we discuss several aspects of Bayesian paradigms: uncertainty present in the observed image, prior distribution modeling, Bayesian-based estimation techniques in image processing, particularly, the maximum a posteriori estimator and the Kalman filtering theory, robustness, and Markov random fields and applications.

Our objective is to provide an overview of the minimum sum of absolute errors (MSAE) regression. Although proposed fifty years before the concept of least squares regression, the MSAE regression did not receive much attention until the second half of the last century. During this period, several very effective and efficient algorithms to compute the MSAE estimates of the unknown parameters of the multiple linear regression model were proposed and studied. Today a number of very good computer programs are available in the open literature and in publicly available computer packages. Efficient algorithms and computer programs for the selection of models with fewer variables are also available.

The asymptotic and small sample properties of the MSAE estimators have been studied. Based on these results, formulae for confidence intervals and procedures for testing hypotheses have been developed.

Some of these results have appeared in survey articles in the literature. However, since their publication, a number of new results have appeared in he literature that makes the MSAE regression procedure more attractive. For example, an R² like measure for the MSAE regression is now available. We now understand how special characteristics unique to the MSAE regression explain its robustness to certain type of outliers. And unlike least squares regression, variations in the values of the response and predictor variables within definable limits do not change the fitted MSAE regression. We highlighted those features of MSAE regression in this paper.

To solve the problem of efficiently and securely transmitting perceptual hash values in speech perceptual hashing authentication systems, a transparent and robust audio reversible watermarking algorithm is proposed in this paper. This algorithm, which is based on phase coding improved by bipolar quantization, guarantees its security by Logistic mapping. Experimental results show that the proposed algorithm has good reversibility, transparency, robustness and security, and has better comprehensive properties.

The robustness analysis of intuitionistic fuzzy difference (IFD) is based on an evaluation of the δ sensitivity in representable fuzzy negations, triangular norms and conorms. The results in the class of IFD operators preserve projections and dual constructions related to their intuitionistic approach.

By analyzing the mathematical model of a doubly-fed induction generator (DFIG) and vector control scheme, an adaptive rotor flux estimator and resistance and speed estimators for DFIG which can realizes the independent regulation of the active power and reactive power of DFIG are put forward. The method is based on model reference adaptive principle. The adaptive law has been obtained from the Lyapunov stability conduction. It has been proved that the estimating error caused by parameters mismatch is convergent. Results of simulation show that the estimator presented above is effective and the accuracy and robustness of control system are improved at the same time.

Increasing integrated avionics electronic system makes integrated avionics system structure increasingly complex, makes the demand for network topology and dynamics performance increasingly urgent, and the robustness of the dynamics of the network is very important performance. In this paper, using complex networks and network dynamics as a theoretical basis, based on TTE and AFDX network topology, we establish an integrated avionics system network in dual bus architecture model, and use the maximum communication subgraph and percolation theory to analyze the robustness of non-weighted and weighted network model.

In this paper, a new distance metric between interval-valued fuzzy sets is pro-posed. The four special interval-valued fuzzy metric spaces are studied, which are induced by four well-known interval-valued residual implication operations. It proved that the interval-valued fuzzy metric spaces induced by Łukasiewicz implication and Goguen implication are more suitable for interval-valued fuzzy reasoning. Moreover, based on these two interval-valued fuzzy metric spaces, we discuss the robustness of interval-valued fuzzy reasoning triple I methods.

A new distance metric between interval-valued fuzzy sets is proposed. And based on this metric, we analyze robustness of the interval-valued fuzzy reasoning algorithms based on two well-known interval-valued residual implication operations. It was proved that the interval-valued fuzzy reasoning algorithm based the Łukasiewicz interval-valued implication and Goguen interval-valued implication are robust.