Unified Mathematical Framework Research Articles

Discrete-time differential systems

In this paper we formulate a coherent discrete-time signals and systems theory taking derivative concepts as basis. Two derivatives – nabla (forward) and delta (backward) – are defined and generalized to fractional orders, obtaining two formulations that are discrete versions of the well-known Grünwald–Letnikov derivatives. The eigenfunctions of such derivatives are the so-called nabla and delta exponentials. With these exponentials two generalized discrete-time Laplace transforms are deduced and their properties studied. These transforms are back compatible with the current Laplace and Z transforms. They are used to study the discrete-time linear systems defined by differential equations. These equations although discrete mimic the usual continuous-time equations that are uniformly approximated when the sampling interval becomes small. Impulse response and transfer function notions are introduced and obtained. The Fourier transform and the frequency response are also considered. This implies a unified mathematical framework that allows us to approximate the classic continuous-time case when the sampling rate is high or obtain the current discrete-time case based on difference equation.

Unified Mathematical Framework for Slicing and Symmetry Reduction over Event Structures

Nonclassical slicing and symmetry reduction can act as efficient structural abstract methods for pruning state space when dealing with verification problems. In this paper, we mainly address theoretical and algorithmic aspects for nonclassical slicing and symmetry reduction over prime event structures. We propose sliced and symmetric quotient reduction models of event structures and present their corresponding algorithms. To construct the underlying foundation of the proposed methodologies, we introduce strong and weak conflict concepts and a pair of mutually inverse operators and extend permutation group based symmetry notion of event structures. We have established a unified mathematical framework for slicing and symmetry reduction, and further investigated the translation, isomorphism, and equivalence relationship and other related basic facts from a theoretical point of view. The framework may provide useful guidance and theoretical exploration for overcoming verification challenges. This paper also demonstrates their practical applications by two cases.

Scalable Realistic Rendering with Many‐Light Methods

AbstractRecent years have seen increasing attention and significant progress in many‐light rendering, a class of methods for efficient computation of global illumination. The many‐light formulation offers a unified mathematical framework for the problem reducing the full lighting transport simulation to the calculation of the direct illumination from many virtual light sources. These methods are unrivaled in their scalability: they are able to produce plausible images in a fraction of a second but also converge to the full solution over time. In this state‐of‐the‐art report, we give an easy‐to‐follow, introductory tutorial of the many‐light theory; provide a comprehensive, unified survey of the topic with a comparison of the main algorithms; discuss limitations regarding materials and light transport phenomena and present a vision to motivate and guide future research. We will cover both the fundamental concepts as well as improvements, extensions and applications of many‐light rendering.

BEHAVIOR PREDICTION FOR DECISION AND CONTROL IN COGNITIVE AUTONOMOUS SYSTEMS

This paper presents an innovative concept of behavior prediction for decision and control in cognitive autonomous systems. The objective is to coordinate human–machine collaboration such that human operators can assess and enable autonomous systems to utilize their experiential and unmodeled domain knowledge and perception for mission execution. The concept of quantum probability is proposed to construct a unified mathematical framework for interfacing between models of human cognition and machine intelligence.

Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models

This paper presents a review of all existing discrete methods for computing the derivatives of computational models within a unified mathematical framework. This framework hinges on a new equation, the unifying chain rule, from which all the methods can be derived. The computation of derivatives is described as a two-step process: the evaluation of the partial derivatives and the computation of the total derivatives, which are dependent on the partial derivatives. Finite differences, the complex-step method, and symbolic differentiation are discussed as options for computing the partial derivatives. It is shown that these are building blocks with which the total derivatives can be assembled using algorithmic differentiation, the direct and adjoint methods, and coupled analytic methods for multidisciplinary systems. Each of these methods is presented and applied to a common numerical example to demonstrate and compare the various approaches. The paper ends with a discussion of current challenges and possible future research directions in this field.

Interaction between financial risk measures and machine learning methods

The purpose of this article is to review the similarity and difference between financial risk minimization and a class of machine learning methods known as support vector machines, which were independently developed. By recognizing their common features, we can understand them in a unified mathematical framework. On the other hand, by recognizing their difference, we can develop new methods. In particular, employing the coherent measures of risk, we develop a generalized criterion for two-class classification. It includes existing criteria, such as the margin maximization and \(\nu \)-SVM, as special cases. This extension can also be applied to the other type of machine learning methods such as multi-class classification, regression and outlier detection. Although the new criterion is first formulated as a nonconvex optimization, it results in a convex optimization by employing the nonnegative \(\ell _1\)-regularization. Numerical examples demonstrate how the developed methods work for bond rating.

Unraveling dynamics of human physical activity patterns in chronic pain conditions

Chronic pain is a complex disabling experience that negatively affects the cognitive, affective and physical functions as well as behavior. Although the interaction between chronic pain and physical functioning is a well-accepted paradigm in clinical research, the understanding of how pain affects individuals' daily life behavior remains a challenging task. Here we develop a methodological framework allowing to objectively document disruptive pain related interferences on real-life physical activity. The results reveal that meaningful information is contained in the temporal dynamics of activity patterns and an analytical model based on the theory of bivariate point processes can be used to describe physical activity behavior. The model parameters capture the dynamic interdependence between periods and events and determine a ‘signature’ of activity pattern. The study is likely to contribute to the clinical understanding of complex pain/disease-related behaviors and establish a unified mathematical framework to quantify the complex dynamics of various human activities.

A unified mathematical framework and an adaptive numerical method for fluid–structure interaction with rigid, deforming, and elastic bodies

Many problems of interest in biological fluid mechanics involve interactions between fluids and solids that require the coupled solution of momentum equations for both the fluid and the solid. In this work, we develop a mathematical framework and an adaptive numerical method for such fluid–structure interaction (FSI) problems in which the structure may be rigid, deforming, or elastic. We employ an immersed boundary (IB) formulation of the problem that permits us to avoid body conforming discretizations and to use fast Cartesian grid solvers. Rigidity and deformational kinematic constraints are imposed using a formulation based on distributed Lagrange multipliers, and a conventional IB method is used to describe the elasticity of the immersed body. We use Cartesian grid adaptive mesh refinement (AMR) to discretize the equations of motion and thereby obtain a solution methodology that efficiently captures thin boundary layers at fluid–solid interfaces as well as flow structures shed from such interfaces. This adaptive methodology is validated for several benchmark problems in two and three spatial dimensions. In addition, we use this scheme to simulate free swimming, including the maneuvering of a two-dimensional model eel and a three-dimensional model of the weakly electric black ghost knifefish.

Predictive Modeling in a Polyhedral Optimization Space

High-level program optimizations, such as loop transformations, are critical for high performance on multi-core targets. However, complex sequences of loop transformations are often required to expose parallelism (both coarse-grain and fine-grain) and improve data locality. The polyhedral compilation framework has proved to be very effective at representing these complex sequences and restructuring compute-intensive applications, seamlessly handling perfectly and imperfectly nested loops. It models arbitrarily complex sequences of loop transformations in a unified mathematical framework, dramatically increasing the expressiveness (and expected effectiveness) of the loop optimization stage. Nevertheless identifying the most effective loop transformations remains a major challenge: current state-of-the-art heuristics in polyhedral frameworks simply fail to expose good performance over a wide range of numerical applications. Their lack of effectiveness is mainly due to simplistic performance models that do not reflect the complexity today’s processors (CPU, cache behavior, etc.). We address the problem of selecting the best polyhedral optimizations with dedicated machine learning models, trained specifically on the target machine. We show that these models can quickly select high-performance optimizations with very limited iterative search. We decouple the problem of selecting good complex sequences of optimizations in two stages: (1) we narrow the set of candidate optimizations using static cost models to select the loop transformations that implement specific high-level optimizations (e.g., tiling, parallelism, etc.); (2) we predict the performance of each high-level complex optimization sequence with trained models that take as input a performance-counter characterization of the original program. Our end-to-end framework is validated using numerous benchmarks on two modern multi-core platforms. We investigate a variety of different machine learning algorithms and hardware counters, and we obtain performance improvements over productions compilers ranging on average from $$3.2\times $$ to $$8.7\times $$ , by running not more than $$6$$ program variants from a polyhedral optimization space.

Mobility and agility analysis of a multi-legged subsea robot system

This paper presents a unified mathematical framework for the analysis of mobility and agility of a multi-legged subsea robot. The aim of this research is to analyze a dynamic performance on the multi-legged robot in an underwater environment conditions with hydrodynamic forces, seabed friction, gravity, buoyancy, and so on. The dynamic performance is represented by both translational acceleration (mobility) and rotational acceleration (agility) that ensures no slip at each foot of robot under given a robot's configuration, joint torque boundaries, and environment conditions. To analyze the performance, this work includes the derivation of a dynamic equation and a joint torque constraint equation and the establishment of mathematical framework by using its two equations. The dynamic equation is the relationship equation between joint actuator torques and body acceleration, which is derived from subsea robot dynamics and force and acceleration relationship of body and leg. The joint torque constraint equation for ensuring no slip at the contact point is derived from frictional force constraint conditions. In this paper, the resultant accelerations are represented as polytopes through the mathematical framework and given joint torque bounds. To verify the proposed method, the simulations of 4 and 6-legged robots were performed with the consideration of differential pose, environment, and tidal current.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology.

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental disturbances, is also proposed, together with a simulation example.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Tradeoff on Phenotype Robustness in Biological Networks Part II: Ecological Networks.

In ecological networks, network robustness should be large enough to confer intrinsic robustness for tolerating intrinsic parameter fluctuations, as well as environmental robustness for resisting environmental disturbances, so that the phenotype stability of ecological networks can be maintained, thus guaranteeing phenotype robustness. However, it is difficult to analyze the network robustness of ecological systems because they are complex nonlinear partial differential stochastic systems. This paper develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance sensitivity in ecological networks. We found that the phenotype robustness criterion for ecological networks is that if intrinsic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations and environmental disturbances. These results in robust ecological networks are similar to that in robust gene regulatory networks and evolutionary networks even they have different spatial-time scales.

Dependence properties of dynamic credit risk models

Abstract We give a unified mathematical framework for reduced-form models for portfolio credit risk and identify properties which lead to positive dependence of default times. Dependence in the default hazard rates is modeled by common macroeconomic factors as well as by inter-obligor links. It is shown that popular models produce positive dependence between defaults in terms of association. Implications of these results are discussed, in particular when we turn to pricing of credit derivatives. In mathematical terms our paper contains results about association of a class of non-Markovian processes.

Pattern matching through Chaos Game Representation: bridging numerical and discrete data structures for biological sequence analysis

BackgroundChaos Game Representation (CGR) is an iterated function that bijectively maps discrete sequences into a continuous domain. As a result, discrete sequences can be object of statistical and topological analyses otherwise reserved to numerical systems. Characteristically, CGR coordinates of substrings sharing an L-long suffix will be located within 2-L distance of each other. In the two decades since its original proposal, CGR has been generalized beyond its original focus on genomic sequences and has been successfully applied to a wide range of problems in bioinformatics. This report explores the possibility that it can be further extended to approach algorithms that rely on discrete, graph-based representations.ResultsThe exploratory analysis described here consisted of selecting foundational string problems and refactoring them using CGR-based algorithms. We found that CGR can take the role of suffix trees and emulate sophisticated string algorithms, efficiently solving exact and approximate string matching problems such as finding all palindromes and tandem repeats, and matching with mismatches. The common feature of these problems is that they use longest common extension (LCE) queries as subtasks of their procedures, which we show to have a constant time solution with CGR. Additionally, we show that CGR can be used as a rolling hash function within the Rabin-Karp algorithm.ConclusionsThe analysis of biological sequences relies on algorithmic foundations facing mounting challenges, both logistic (performance) and analytical (lack of unifying mathematical framework). CGR is found to provide the latter and to promise the former: graph-based data structures for sequence analysis operations are entailed by numerical-based data structures produced by CGR maps, providing a unifying analytical framework for a diversity of pattern matching problems.

On the origin of the mitochondrial genetic code: Towards a unified mathematical framework for the management of genetic information

The origin of the genetic code represents one of the most challenging problems in molecular evolution. The genetic code is an important universal feature of extant organisms and indicates a common ancestry of different forms of life on earth. Known variants of the genetic code can be mainly divided in mitochondrial and nuclear classes. Here we provide a new insight on the origin of the mitochondrial genetic code: we found that its degeneracy distribution can be explained by using a mathematical approach recently developed for the description of the Euplotes nuclear variant of the genetic code. The results point to a primeval mitochondrial genetic code composed of four base codons, which we call tesserae, that, among other features, exhibit outstanding error detection capabilities. The theoretical description suggests also a formulation of a plausible biological theory about the origin of protein coding. Such theory is based on the symmetry properties of hypothetical primeval chemical adaptors between nucleic acids and amino acids (ancient tRNA’s). Our paper provides a unified mathematical framework for different hypotheses on the origin of genetic coding. Also, it contributes to revisit our present view about the evolutionary steps that led to extant genetic codes by giving a new first-principles perspective on the difficult problem of the origin of the genetic code, and consequently, on the origin of life on earth.

On the origin of the mitochondrial genetic code: Towards a unified mathematical framework for the management of genetic information

AbstractThe origin of the genetic code represents one of the most challenging problems in molecular evolution. The genetic code is an important universal feature of extant organisms and indicates a common ancestry of different forms of life on earth. Known variants of the genetic code can be mainly divided in mitochondrial and nuclear classes. Here we provide a new insight on the origin of the mitochondrial genetic code: we found that its degeneracy distribution can be explained by using a mathematical approach recently developed for the description of the Euplotes nuclear variant of the genetic code. The results point to a primeval mitochondrial genetic code composed of four base codons, which we call tesserae, that, among other features, exhibit outstanding error detection capabilities. The theoretical description suggests also a formulation of a plausible biological theory about the origin of protein coding. Such theory is based on the symmetry properties of hypothetical primeval chemical adaptors between nucleic acids and amino acids (ancient tRNA’s). Our paper provides a unified mathematical framework for different hypotheses on the origin of genetic coding. Also, it contributes to revisit our present view about the evolutionary steps that led to extant genetic codes by giving a new first-principles perspective on the difficult problem of the origin of the genetic code, and consequently, on the origin of life on earth.

Using vorticity to quantify the relative importance of heterogeneity, viscosity ratio, gravity and diffusion on oil recovery

The vorticity of the displacement velocity is used to derive dimensionless numbers that can be used to quantify the relative importance of viscosity ratio, gravity, diffusion/dispersion and permeability heterogeneity on secondary hydrocarbon recovery. Using this approach, a new objective measure of the impact of permeability and porosity heterogeneity on reservoir performance is obtained. This is used, in conjunction with other dimensionless numbers, to analyse the relative impact of heterogeneity, buoyancy effects, mobility ratio and dispersion on breakthrough time and recovery at 1 pore volume injected during first contact miscible gas injection. This is achieved using results obtained from detailed simulation of miscible displacements through a range of geologically realistic reservoir models. This study goes some way towards developing a unified mathematical framework to determine under which flow conditions reservoir heterogeneity becomes more important than other physical processes. We propose that comparison of these dimensionless numbers can be used to identify the key factors controlling recovery and thus assist the engineer in determining appropriate enhanced oil recovery techniques to improve recovery.

Reducing sequencing complexity in dynamical quantum error suppression by Walsh modulation

We study dynamical error suppression from the perspective of reducing sequencing complexity, with an eye toward facilitating the development of efficient semiautonomous quantum-coherent systems. To this end, we focus on digital sequences where all interpulse time periods are integer multiples of a minimum clock period and compatibility with digital classical control circuitry is intrinsic. We use so-called Walsh functions as a unifying mathematical framework; the Walsh functions are an orthonormal set of basis functions which may be associated directly with the control propagator for a digital modulation scheme. Using this insight, we characterize the suite of resulting Walsh dynamical decoupling sequences---including both familiar and novel control sequences---and identify the number of periodic square-wave (Rademacher) functions required to generate the associated Walsh function as the key determinant of the error-suppressing features. We also show how Walsh modulation may be employed for the protection of certain nontrivial logic gates. Based on these insights, we identify Walsh modulation as a digital-efficient approach for physical-layer error suppression.

Contributors

Contributors