Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

Management Aspects of Software Clone Detection and Analysis

Copying a code fragment and reusing it by pasting with or without minor modifications is a common practice in software development for improved productivity. As a result, software systems often have similar segments of code, called software clones or code clones. Due to many reasons, unintentional clones may also appear in the source code without awareness of the developer. Studies report that significant fractions (5% to 50%) of the code in typical software systems are cloned. Although code cloning may increase initial productivity, it may cause fault propagation, inflate the code base and increase maintenance overhead. Thus, it is believed that code clones should be identified and carefully managed. This Ph.D. thesis contributes in clone management with techniques realized into tools and large-scale in-depth analyses of clones to inform clone management in devising effective techniques and strategies. To support proactive clone management, we have developed a clone detector as a plug-in to the Eclipse IDE. For clone detection, we used a hybrid approach that combines the strength of both parser-based and text-based techniques. To capture clones that are similar but not exact duplicates, we adopted a novel approach that applies a suffix-tree-based k-difference hybrid algorithm, borrowed from the area of computational biology. Instead of targeting all clones from the entire code base, our tool aids clone-aware development by allowing focused search for clones of any code fragment of the developer's interest. A good understanding on the code cloning phenomenon is a prerequisite to devise efficient clone management strategies. The second phase of the thesis includes large-scale empirical studies on the characteristics (e.g., proportion, types of similarity, change patterns) of code clones in evolving software systems. Applying statistical techniques, we also made fairly accurate forecast on the proportion of code clones in the future versions of software projects. The outcome of these studies expose useful insights into the characteristics of evolving clones and their management implications. Upon identification of the code clones, their management often necessitates careful refactoring, which is dealt with at the third phase of the thesis. Given a large number of clones, it is difficult to optimally decide what to refactor and what not, especially when there are dependencies among clones and the objective remains the minimization of refactoring efforts and risks while maximizing benefits. In this regard, we developed a novel clone refactoring scheduler that applies a constraint programming approach. We also introduced a novel effort model for the estimation of efforts needed to refactor clones in source code. We evaluated our clone detector, scheduler and effort model through comparative empirical studies and user studies. Finally, based on our experience and in-depth analysis of the present state of the art, we expose avenues for further research and development towards a versatile clone management system that we envision

Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion

Detecting change and dealing with uncertainty in imperfect evolutionary environments

Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high

Toward an Ising Model of Cancer and Beyond

Theoretical and computational tools that can be used in the clinic to predict neoplastic progression and propose individualized optimal treatment strategies to control cancer growth is desired. To develop such a predictive model, one must account for the complex mechanisms involved in tumor growth. Here we review resarch work that we have done toward the development of an "Ising model" of cancer. The review begins with a description of a minimalist four-dimensional (three in space and one in time) cellular automaton (CA) model of cancer in which healthy cells transition between states (proliferative, hypoxic, and necrotic) according to simple local rules and their present states, which can viewed as a stripped-down Ising model of cancer. This model is applied to model the growth of glioblastoma multiforme, the most malignant of brain cancers. This is followed by a discussion of the extension of the model to study the effect on the tumor dynamics and geometry of a mutated subpopulation. A discussion of how tumor growth is affected by chemotherapeutic treatment is then described. How angiogenesis as well as the heterogeneous and confined environment in which a tumor grows is incorporated in the CA model is discussed. The characterization of the level of organization of the invasive network around a solid tumor using spanning trees is subsequently described. Then, we describe open problems and future promising avenues for future research, including the need to develop better molecular-based models that incorporate the true heterogeneous environment over wide range of length and time scales (via imaging data), cell motility, oncogenes, tumor suppressor genes and cell-cell communication. The need to bring to bear the powerful machinery of the theory of heterogeneous media to better understand the behavior of cancer in its microenvironment is presented.Comment: 55 pages, 21 figures and 3 tables. To appear in Physical Biology. Added reference

Mathematical Models of Immune Regulation and Cancer Vaccines

An array of powerful mathematical tools can be used to identify the key underlying components and interactions that determine the mechanics of biological systems such as the immune system and its interaction with cancer. In this dissertation, we develop mathematical models to study the dynamics of immune regulation in the context of the primary immune response and tumor growth. Regulatory T cells play a key role in the contraction of the immune response, a phase that follows the peak response to bring cell levels back to normal. To understand how the immune response is regulated, it is imperative to study the dynamics of regulatory cells, and in particular, the conditions under which they are functionally stable. There is conflicting biological evidence regarding the ability of regulatory cells to lose their regulatory capabilities and possibly turn into immune promoting cells. We develop dynamical models to investigate the effects of an unstable regulatory T cell population on the immune response. These models display the usual characteristics of an immune response with the added capabilities of being able to correct for initial imbalances in T cell populations. We also observe an increased robustness of the immune response with respect to key parameters. Similar conclusions are demonstrated with regards to the effects of regulatory T cell switching on immunodominance. TGF-beta is an immunoregulatory protein that contributes to inadequate anti-tumor immune responses in cancer patients. Recent experimental data suggests that TGF-beta inhibition alone, provides few clinical benefits, yet it can significantly amplify the anti-tumor immune response when combined with a tumor vaccine. We develop a mathematical model to gain insight into the cooperative interaction between anti-TGF-beta and vaccine treatments. Using numerical simulations and stability analysis we study the following scenarios: a control case of no treatment, anti-TGF-beta treatment, vaccine treatment, and combined anti-TGF-beta vaccine treatments. Consistent with experimental data, we show that monotherapy alone cannot successfully eradicate a tumor. Tumor eradication requires the combination of these therapeutic approaches. We also demonstrate that our model captures the observed experimental results, and hence can be potentially used in designing future experiments involving this approach to immunotherapy

NASA/ASEE Summer Faculty Fellowship Program, 1990, Volume 1

The 1990 Johnson Space Center (JSC) NASA/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by the University of Houston-University Park and JSC. A compilation of the final reports on the research projects are presented. The topics covered include: the Space Station; the Space Shuttle; exobiology; cell biology; culture techniques; control systems design; laser induced fluorescence; spacecraft reliability analysis; reduced gravity; biotechnology; microgravity applications; regenerative life support systems; imaging techniques; cardiovascular system; physiological effects; extravehicular mobility units; mathematical models; bioreactors; computerized simulation; microgravity simulation; and dynamic structural analysis

Applicability of the Future State Maximization Paradigm to Agent-Based Modeling: A Case Study on the Emergence of Socially Sub-Optimal Mobility Behavior

Novel developments in artificial intelligence excel in regard to the abilities of rule-based agent-based models (ABMs), but are still limited in their representation of bounded rationality. The future state maximization (FSX) paradigm presents a promising methodology for describing the intelligent behavior of agents. FSX agents explore their future state space using “walkers” as virtual entities probing for a maximization of possible states. Recent studies have demonstrated the applicability of FSX to modeling the cooperative behavior of individuals. Applied to ABMs, the FSX principle should also represent non-cooperative behavior: for example, in microscopic traffic modeling, there is a need to model agents that do not fully adhere to the traffic rules. To examine non-cooperative behavior arising from FSX, we developed a road section model populated by agent-cars endowed with an augmented FSX decision making algorithm. Simulation experiments were conducted in four scenarios modeling various traffic settings. A sensitivity analysis showed that cooperation among the agents was the result of a balance between exploration and exploitation. We showed that our model reproduced several patterns observed in rule-based traffic models. We also demonstrated that agents acting according to FSX can stop cooperating. We concluded that FSX can be useful for studying irrational behavior in certain traffic settings, and that it is suitable for ABMs in general