Underlying System Behavior Research Articles

Improving the Process of Early-Warning Detection and Identifying the Most Affected Markets: Evidence from Subprime Mortgage Crisis and COVID-19 Outbreak-Application to American Stock Markets.

Stock-market-crash predictability is of particular interest in the field of financial time-series analysis. Famous examples of major stock-market crashes are the real-estate bubble in 2008 and COVID-19 in 2020. Several studies have studied the prediction process without taking into consideration which markets might be falling into a crisis. To this end, a combination analysis is utilized in this manuscript. Firstly, the auto-regressive estimation (ARE) algorithm is successfully applied to electroencephalography (EEG) brain data for detecting diseases. The ARE algorithm is employed based on state-space modelling, which applies the expectation-maximization algorithm and Kalman filter. This manuscript introduces its application, for the first time, to stock-market data. For this purpose, a time-evolving interaction surface is constructed to observe the change in the surface topology. This enables tracking of the stock market's behavior over time and differentiates between different states. This provides a deep understanding of the underlying system behavior before, during, and after a crisis. Different patterns of the stock-market movements are recognized, providing novel information regarding detecting an early-warning sign. Secondly, a Granger-causality time-domain technique, called directed partial correlation, is employed to infer the underlying interconnectivity structure among markets. This information is crucial for investors and market players, enabling them to differentiate between those markets which will fall in a catastrophic loss, and those which will not. Consequently, they can make successful decisions towards selecting less risky portfolios, which guarantees lower losses. The results showed the effectiveness of the use of this methodology in the framework of the process of early-warning detection.

Zeroth-order finite similitude and scaling of complex geometries in biomechanical experimentation.

Scaled experimentation provides an alternative approach to full-scale biomechanical (and biological) testing but is known to suffer from scale effects, where the underlying system behaviour changes with scale. This phenomenon is arguably the overriding principal obstacle to the many advantages that scaled experimentation provides. These include reduced costs, materials and time, along with the eschewal of ethical compliance concerns with the application of substitute artificial materials as opposed to the use of hazardous biological agents. This paper examines the role scale effects play in biomechanical experimentation involving strain measurement and introduces a formulation that overtly captures scale dependencies arising from geometrical change. The basic idea underpinning the new scaling approach is the concept of space scaling, where a biomechanical experiment is scaled by the metaphysical mechanism of space contraction. The scaling approach is verified and validated with finite-element (FE) models and actual physical-trial experimentation using digital image correlation software applied to synthetic composite bone. The experimental design aspect of the approach allows for the selection of three-dimensional printing materials for trial-space analysis in a complex pelvis geometry. This aspect takes advantage of recent advancements in additive manufacturing technologies with the objective of countering behavioural distorting scale effects. Analysis is carried out using a laser confocal microscope to compare the trial and physical space materials and subsequently measured using surface roughness parameters. FE models were constructed for the left hemipelvis and results show similar strain patterns (average percentage error less than 10%) for two of the three trial-space material combinations. A Bland-Altman statistical analysis shows a good agreement between the FE models and physical experimentation and a good agreement between the physical-trial experimentation, providing good supporting evidence of the applicability of the new scaling approach in a wider range of experiments.

Spotting Key Members in Networks: Clustering-Embedded Eigenvector Centrality

Identifying key members in a social network is critical to understand the underlying system behavior. Whereas there are several measures designed to discern the most central member, they fail to identify a central set of members and at the same time reveal the spheres of influence of the individuals in such central set. Here, we combine eigenvector centrality with clustering to design a mathematical programming formulation capable of detecting key members while preventing their spheres of influence from overlapping. Our computational experience reproduces these two features as different aspects of the same phenomenon. The optimal set of key members and their spheres of influence are identified in real-life networks and synthetic ones. For the former, community structures are consistent with existing knowledge about the instances. For the latter, network underlying organization is known a priori and it is perfectly uncovered. Experiments further reveal previously neglected nodes to be optimal key members. The size of the instances tested reach several hundreds of nodes and thousands of links.

Heavy Tails in Program Structure

Designing and optimizing computer systems require deep understanding of the underlying system behavior. Historically many important observations that led to the development of essential hardware and software optimizations were driven by empirical observations about program behavior. In this paper, we report an interesting property of program structures by viewing dynamic program execution as a changing network. By analyzing the communication network created as a result of dynamic program execution, we find that communication patterns follow heavy-tailed distributions. In other words, a few instructions have consumers that are orders of magnitude larger than most instructions in a program. Surprisingly, these heavy-tailed distributions follow the iconic power law previously seen in man-made and natural networks. We provide empirical measurements based on the SPEC CPU2006 benchmarks to validate our findings as well as perform semantic analysis of the source code to reveal the causes of such behavior.

High dimensional model representation (HDMR) coupled intelligent sampling strategy for nonlinear problems

High-dimensional model representation (HDMR) is a general set of metamodel assessment and analysis tools to improve the efficiency of high dimensional underlying system behavior. Compared with the current popular modeling methods, such as Kriging (KG), radial basis function (RBF), and the moving least square approximation method (MLS), the distinctive characteristic of the HDMR is to decouple the input variables. Therefore, a high dimensional problem can be transformed as a low, middle or combination of middle dimensional function. Although the HDMR is a feasible method for high dimensional problems, the computational cost is still a bottleneck for complex engineering problems. To improve the efficiency of the HDMR method further, the purpose of this study is to use an intelligent sampling method for the HDMR. Because the HDMR cannot be integrated with the sampling method directly, a projection-based intelligent method is suggested. Compared with the popular HDMR methods, the construction procedure for the HDMR-based model is optimized. To validate the performance of the suggested method, multiple mathematical test functions are given to illustrate the modeling principles, procedures, and the efficiency and accuracy of HDMR models with problems of a wide scope of dimensionalities.

Phi-array: A novel method for fitness visualization and decision making in evolutionary design optimization

There is a growing interest in integrating model based evolutionary optimization in engineering design decision making for effective search of the solution space. Most applications of evolutionary optimization are concerned with the search for optimal solutions satisfying pre-defined constraints while minimizing or maximizing desired goals. A few have explored post-optimization decision making using concepts such as Pareto optimality, but mostly in multi-objective problems. Sub-optimal solutions are usually discarded and do not contribute to decision making after optimization runs. However, the discarded ‘inferior’ solutions and their fitness contain useful information about underlying sensitivities of the system and can play an important role in creative decision making. The need for understanding the underlying system behavior is more pronounced in cases where variations in the genotype space can cause non-deterministic changes in either the fitness or phenotype space and where fitness evaluations are computationally expensive. The optimized design of an artificial lighting environment of a senior living room is used as a test case to demonstrate the need for and application of fitness visualization in genotype and phenotype spaces for effective decision making. Sub-optimal solutions are retained during optimization and visualized along with the optimum solution in a fitness array visualization system called phi-array, developed as part of this research. The optimization environment is based on genetic algorithm (GA) in which a compute-intensive raytracing rendering engine, RADIANCE, is used to evaluate the fitness of prospective design solutions. Apart from describing the development of the optimization system and demonstrating the utility of phi-array in effective decision making, this article explores optimization parameters and their effectiveness for artificial lighting design problems and the nature of their rugged fitness and constraint landscapes.

Adaptive MLS-HDMR metamodeling techniques for high dimensional problems

Metamodeling technique is to represent the approximation of input variables and output variables. With the exponential increase of dimension of assigned problems, accurate and robust model is difficult to achieve by popular regression methodologies. High-dimensional model representation (HDMR) is a general set of metamodel assessment and analysis tools to improve the efficiency of deducing high dimensional underlying system behavior. In this paper, a new HDMR, based on moving least square (MLS), termed as MLS-HDMR, is introduced. The MLS-HDMR naturally explores and exploits the linearity/nonlinearity and correlation relationships among variables of the underlying function, which is unknown or computationally expensive. Furthermore, to improve the efficiency of the MLS-HDMR, an intelligent sampling strategy, DIviding RECTangles (DIRECT) method is used to sample points. Multiple mathematical test functions are given to illustrate the modeling principles, procedures, and the efficiency and accuracy of the MLS-HDMR models with problems of a wide scope of dimensionalities.

Formal models for user interface design artefacts

There are many different ways of building software applications and of tackling the problems of understanding the system to be built, designing that system and finally implementing the design. One approach is to use formal methods, which we can generalise as meaning we follow a process which uses some formal language to specify the behaviour of the intended system, techniques such as theorem proving or model-checking to ensure the specification is valid (i.e., meets the requirements and has been shown, perhaps by proof or other means of inspection, to have the properties the client requires of it) and a refinement process to transform the specification into an implementation. Conversely, the approach we take may be less structured and rely on informal techniques. The design stage may involve jotting down ideas on paper, brainstorming with users etc. We may use prototyping to transform these ideas into working software and get users to test the implementation to find problems. Formal methods have been shown to be beneficial in describing the functionality of systems, what we may call application logic, and underlying system behaviour. Informal techniques, however, have also been shown to be useful in the design of the user interface to systems. Given that both styles of development are beneficial to different parts of the system we would like to be able to use both approaches in one integrated software development process. Their differences, however, make this a challenging objective. In this paper we describe models and techniques which allow us to incorporate informal design artefacts into a formal software development process.

Formal Models for Informal GUI Designs

Many different methods exist for the design and implementation of software systems. These methods may be fully formal, such as the use of formal specification languages and refinement processes, or they may be totally informal, such as jotting design ideas down on paper prior to coding, or they may be somewhere in between these two extremes. Formal methods are naturally suited to underlying system behaviour while user-centred approaches to user interface design fit comfortably with more informal approaches. The challenge is to find ways of integrating user-centred design methods with formal methods so that the benefits of both are fully realised. This paper presents a way of capturing the intentions behind informal design artefacts within a formal environment and then shows several applications of this approach.

Visualization of randomly ordered numeric data sets using spherical self-organizing feature maps

Scientific data visualization requires a variety of mathematical techniques to transform multivariate data sets into simple graphical objects, or glyphs, that provide scientists and engineers with a clearer understanding of the underlying system behaviour. The spherical self-organizing feature map (SOFM) described in this paper exploits an unsupervised clustering algorithm to map randomly organized N-dimensional data into a lower three-dimensional (3D) space for visual pattern analysis. Each node on the spherical lattice corresponds to a cluster of input vectors that lie in close spatial proximity within the original feature space, and neighbouring nodes on the lattice represent cluster centres with a high degree of vector similarity. Simple metrics are used to extract associations between the cluster units and the input vectors assigned to them. These are then graphically displayed on the spherical SOFM as either surface elevations or colourized facets. The resulting colourized graphical objects are displayed and manipulated within 3D immersive virtual reality (IVR) environments for interactive data analysis. The ability of the proposed algorithm to transform arbitrarily arranged numeric strings into unique, reproducible shapes is illustrated using chaotic data generated by the Lozi, Hénon, Rössler, and Lorenz attractor functions under varying initial conditions. Implementation of the basic data visualization technique is further demonstrated using the more common Wisconsin breast cancer data and multi-spectral satellite data.

Feature specification and automated conflict detection

Large software systems, especially in the telecommunications field, are often specified as a collection of features. We present a formal specification language for describing features, and a method of automatically detecting conflicts ("undesirable interactions") amongst features at the specification stage. Conflict detection at this early stage can help prevent costly and time consuming problem fixes during implementation. Features are specified using temporal logic; two features conflict essentially if their specifications are mutually inconsistent under axioms about the underlying system behavior. We show how this inconsistency check may be performed automatically with existing model checking tools. In addition, the model checking tools can be used to provide witness scenarios, both when two features conflict as well as when the features are mutually consistent. Both types of witnesses are useful for refining the specifications. We have implemented a conflict detection tool, FIX (Feature Interaction eXtractor), which uses the model checker COSPAN for the inconsistency check. We describe our experience in applying this tool to a collection of telecommunications feature specifications obtained from the Telcordia (Bellcore) standards. Using FIX, we were able to detect most known interactions and some new ones, fully automatically, in a few hours processing time.

Visualization of dynamical systems

The visualization of analytically defined dynamical systems is important for a thorough understanding of the underlying system behavior. An introduction to analytically defined dynamical systems is given. Various visualization techniques for dynamical systems are discussed. Several current research directions concerning the visualization of dynamical systems are treated in more detail. These are: texture-based techniques, visualization of high-dimensional dynamical systems, advanced streamsurface representations, local analyses – Poincaré sections, visualizing econometric models.