Gradient-based iterative parameter estimation for bilinear-in-parameter systems using the model decomposition technique

The parameter estimation issues of a block-oriented non-linear system that is bilinear in the parameters are studied, i.e. the bilinear-in-parameter system. Using the model decomposition technique, the bilinear-in-parameter model is decomposed into two fictitious submodels: one containing the unknown parameters in the non-linear block and the other containing the unknown parameters in the linear dynamic one and the noise model. Then a gradient-based iterative algorithm is proposed to estimate all the unknown parameters by formulating and minimising two criterion functions. The stochastic gradient algorithms are provided for comparison. The simulation results indicate that the proposed iterative algorithm can give higher parameter estimation accuracy than the stochastic gradient algorithms

Improved gradient descent algorithms for time-delay rational state-space systems: Intelligent search method and momentum method

This study proposes two improved gradient descent parameter estimation algorithms for rational state-space models with time-delay. These two algorithms, based on intelligent search method and momentum method, can simultaneously estimate the time-delay and parameters without the matrix eigenvalue calculation in each iteration. Compared with the traditional gradient descent algorithm, the improved algorithms come with two advantages: having quicker convergence rates and less computational efforts, particularly meaningful for those large scale systems. A simulated example is selected to illustrate the efficiency of the proposed algorithms

State filtering and parameter estimation for two input two output systems with time delay

This paper focuses on presenting a new identification algorithm to estimate the parameters and state variables for two-input two-output dynamic systems with time delay based on canonical state space models. First, the related input-output equation is determined and transformed into an identification oriented model, which does not involve in the unmeasurable states, and then a residual based least squares identification algorithm is presented for the estimations. After the parameters being estimated, the system states are subsequently estimated by using the estimated parameters. Through theoretical analysis, the convergence of the algorithm is derived to provide assurance for applicability. Finally, a selected simulation example is given for a meaningful case study to show the effectiveness of the proposed algorithm

Learning Multimodal Structures in Computer Vision

A phenomenon or event can be received from various kinds of detectors or under different conditions. Each such acquisition framework is a modality of the phenomenon. Due to the relation between the modalities of multimodal phenomena, a single modality cannot fully describe the event of interest. Since several modalities report on the same event introduces new challenges comparing to the case of exploiting each modality separately. We are interested in designing new algorithmic tools to apply sensor fusion techniques in the particular signal representation of sparse coding which is a favorite methodology in signal processing, machine learning and statistics to represent data. This coding scheme is based on a machine learning technique and has been demonstrated to be capable of representing many modalities like natural images. We will consider situations where we are not only interested in support of the model to be sparse, but also to reflect a-priorily known knowledge about the application in hand. Our goal is to extract a discriminative representation of the multimodal data that leads to easily finding its essential characteristics in the subsequent analysis step, e.g., regression and classification. To be more precise, sparse coding is about representing signals as linear combinations of a small number of bases from a dictionary. The idea is to learn a dictionary that encodes intrinsic properties of the multimodal data in a decomposition coefficient vector that is favorable towards the maximal discriminatory power. We carefully design a multimodal representation framework to learn discriminative feature representations by fully exploiting, the modality-shared which is the information shared by various modalities, and modality-specific which is the information content of each modality individually. Plus, it automatically learns the weights for various feature components in a data-driven scheme. In other words, the physical interpretation of our learning framework is to fully exploit the correlated characteristics of the available modalities, while at the same time leverage the modality-specific character of each modality and change their corresponding weights for different parts of the feature in recognition

Nonlinear Systems

Open Mathematics is a challenging notion for theoretical modeling, technical analysis, and numerical simulation in physics and mathematics, as well as in many other fields, as highly correlated nonlinear phenomena, evolving over a large range of time scales and length scales, control the underlying systems and processes in their spatiotemporal evolution. Indeed, available data, be they physical, biological, or financial, and technologically complex systems and stochastic systems, such as mechanical or electronic devices, can be managed from the same conceptual approach, both analytically and through computer simulation, using effective nonlinear dynamics methods. The aim of this Special Issue is to highlight papers that show the dynamics, control, optimization and applications of nonlinear systems. This has recently become an increasingly popular subject, with impressive growth concerning applications in engineering, economics, biology, and medicine, and can be considered a veritable contribution to the literature. Original papers relating to the objective presented above are especially welcome subjects. Potential topics include, but are not limited to: Stability analysis of discrete and continuous dynamical systems; Nonlinear dynamics in biological complex systems; Stability and stabilization of stochastic systems; Mathematical models in statistics and probability; Synchronization of oscillators and chaotic systems; Optimization methods of complex systems; Reliability modeling and system optimization; Computation and control over networked systems

Gene Regulatory Compatibility in Bacteria: Consequences for Synthetic Biology and Evolution

Mechanistic understanding of gene regulation is crucial for rational engineering of new genetic systems through synthetic biology. Genetic engineering efforts in new organisms are often hampered by a lack of knowledge about how regulatory components function in new host contexts. This dissertation focuses on efforts to overcome these challenges through the development of generalizable experimental methods for studying the behavior of DNA regulatory sequences in diverse species at large-scale. Chapter 2 describes experimental approaches for quantitatively assessing the functions of thousands of diverse natural regulatory sequences through a combination of metagenomic mining, high-throughput DNA synthesis and deep sequencing. By employing these methods in three distinct bacterial species, we revealed striking functional differences in gene regulatory capacity. We identified regulatory sequences with activity levels with activity levels spanning several orders of magnitude, which will aid in efforts to engineer diverse bacterial species. We also demonstrate functional species-selective gene circuits with programmable host behaviors that may be useful for microbial community engineering. In Chapter 3 we provide evidence for the evolution of altered stringency in σ70-mediated transcriptional activation based on patterns of initiation and activity from promoters of diverse compositions. We show that the contrast in GC content between a regulatory element and the host genome dictates both the likelihood and the magnitude of expression. We also discuss the potential implications of this proposed mechanism on horizontal gene transfer. The next two chapters focus on efforts aimed at extending the high-throughput methods described in earlier chapters to new organisms. Chapter 4 presents an in vitro approach for multiplexed gene expression profiling. Through the development and use of cell-free expression systems made from diverse bacteria, it was possible to rapidly acquire thousands of transcriptional measurements in small volume reactions, enabling functional comparisons of regulatory sequence function across multiple species. In Chapter 5 we characterize the restriction-modification system repertoires of several commensal bacterial species. We also describe ongoing efforts to develop methods for bypassing these systems in order to increase transformation efficiencies in species that are difficult or impossible to transform using current approaches

A Mutualistic Approach to Morality: The Evolution of Fairness by Partner Choice

What makes humans moral beings? This question can be understood either as a proximate “how” question or as an ultimate “why” question. The “how” question is about the mental and social mechanisms that produce moral judgments and interactions, and has been investigated by psychologists and social scientists. The “why” question is about the fitness consequences that explain why humans have morality, and has been discussed by evolutionary biologists in the context of the evolution of cooperation. Our goal here is to contribute to a fruitful articulation of such proximate and ultimate explanations of human morality. We develop an approach to morality as an adaptation to an environment in which individuals were in competition to be chosen and recruited in mutually advantageous cooperative interactions. In this environment, the best strategy is to treat others with impartiality and to share the costs and benefits of cooperation equally. Those who offer less than others will be left out of cooperation; conversely, those who offer more will be exploited by their partners. In line with this mutualistic approach, the study of a range of economic games involving property rights, collective actions, mutual help and punishment shows that participants\u27 distributions aim at sharing the costs and benefits of interactions in an impartial way. In particular, the distribution of resources is influenced by effort and talent, and the perception of each participant\u27s rights on the resources to be distributed

The emotional shape of our moral life: Anger-related emotions and mutualistic anthropology

The evolutionary hypothesis advanced by Baumard et al. makes precise predictions on which emotions should play the main role in our moral lives: morality should be more closely linked to "avoidance” emotions (like contempt and disgust) than to "punitive” emotions (like anger). Here, we argue that these predictions run contrary to most psychological evidenc