“Chaos” in superregenerative receivers

The superregenerative principle has been known since the early 1920s. The circuit is extremely simple and extremely sensitive. Today, superheterodyne receivers generally supplant superregenerative receivers in most applications because there are several undesirable characteristics: poor selectivity, reradiation, etc. Superregenerative receivers undergo a revival in recent papers for wireless systems, where low cost and very low power consumption are relevant: house/building meters (such as water, energy, gas counter), personal computer environment (keyboard, mouse), etc. Another drawback is the noise level which is higher than that of a well-designed superheterodyne receiver; without an antenna input signal, the output of the receiver hears in an earphone as a waterfall noise; this sound principally is the inherent input noise amplified and detected by the circuit; however, when the input noise is negligible with respect of an antenna input signal, we are faced to an other source of “noise” self-generated by the superregenerative working. The main objective of this paper concerns this self-generated noise coming from an exponential growing followed by a re-injection process for which the final state is a function of the phase of the input signal.     

Serendipity

The aim of this short paper, is to show how serendipity was a key input in the research that led Ivar’s to two of his main contributions: proof of an old Hamiltonian systems conjecture using devices from the math eco tool box, then smart investigation of family’s budget decision process using symplectic geometry. Of course anyone is expected to say: “you mean: symplectic tools for Hamiltonians, and math eco toolkit for family budget.” Here is the point: these Ivar’s works illuminate how much serendipity creates unexpected brilliant results.     

Optimal Feedback Control for Linear Systems with Input Delays Revisited

The design problem of optimal feedback control for linear systems with input delays is very important in many engineering applications. Usually, the linear systems with input delays are firstly converted into linear systems without delays, and then all the design procedures are based on the delay-free linear systems. In this way, the feedback controllers are not designed in terms of the original states. This paper presents some new closed-form formula in terms of the original states for the delayed optimal feedback control of linear systems with input delays. We firstly reveal the essential role of the input delay in the optimal control design of the linear system with a single input delay: the input delay postpones the action of the optimal control only. Based on this fact, we calculate the delayed optimal control and find that the optimal state can be represented by a simple closed-form formula, so that the delayed optimal feedback control can be obtained in a simple way. We show that the delayed feedback gain matrix can be “smaller” than that for the controlled system with zero input delay, which implies that the input delay can be considered as a positive factor. In addition, we give a general formula for the delayed optimal feedback control of time-variant linear systems with multiple input delays. To show the effectiveness and advantages of the main results, we present five illustrative examples with detailed numerical simulation and comparison.     

Décomposition de domaine pour le contrôle de systèmes gouvernés par des équations d'évolution

We present a non overlapping iterative domain decomposition method with “coupled” Robin transmission conditions. We prove its convergence on an optimal control problem for the wave equation. The linear part of the “feed-back” law associated to the local optimal control problems set on subdomains is independent of the iterative process. The method can be applied, at least formally, to the optimal control of systems governed by evolution equations.     

省域审计供求关系与审计质量——基于审计投入的中介效应视角

文章基于我国上市公司年度样本,利用审计工时数据研究探索审计市场供需不平衡对审计质量的影响作用机制。结果表明,审计投入在事务所“异常”供给与审计质量间起中介作用,具体而言,审计投入在Big4事务所“异常”供给对审计质量的影响中发挥了完全中介效应,在Next6和Small事务所“异常”供给对审计质量的影响中发挥了部分中介效应。文章丰富了审计质量相关研究,并为深入分析审计质量影响因素的路径机制提供了一些经验证据。     

From one-dimensional to infinite-dimensional dynamical systems: Ideal turbulence

There is a very short chain that joins dynamical systems with the simplest phase space (real line) and dynamical systems with the “most complicated” phase space containing random functions, as well. This statement is justified in this paper. By using “simple” examples of dynamical systems (one-dimensional and two-dimensional boundary-value problems), we consider notions that generally characterize the phenomenon of turbulence—first of all, the emergence of structures (including the cascade process of emergence of coherent structures of decreasing scales) and self-stochasticity.     

The converse of the Dominated Ergodic Theorem

The aim of the present paper is to derive a recursive formula of state estimation for a parabolic differential system with a “partially unknown input.” Here the word “partially” unknown input means that the input term of the system is a product of two functions: one is a known function of time variable and of spatial variable; the other is an unknown function of spatial variable. It is desired to perform the state estimation by identifying the latter function. The formula is derived applying the results of J. L. Lions for the decoupling of Riccati type.     

Monte Carlo Computation of the Fisher Information Matrix in Nonstandard Settings

The Fisher information matrix summarizes the amount of information in the data relative to the quantities of interest. There are many applications of the information matrix in modeling, systems analysis, and estimation, including confidence region calculation, input design, prediction bounds, and “noninformative” priors for Bayesian analysis. This article reviews some basic principles associated with the information matrix, presents a resampling-based method for computing the information matrix together with some new theory related to efficient implementation, and presents some numerical results. The resampling-based method relies on an efficient technique for estimating the Hessian matrix, introduced as part of the adaptive (“second-order”) form of the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm.     

Convergence of powers of a fuzzy matrix

A Boolean matrix is a matrix with elements having values of either 1 or 0; a fuzzy matrix is a matrix with elements having values in the closed interval [0, 1]. Fuzzy matrices occur in the modeling of various fuzzy systems, with products usually determined by the “max(min)” rule arising from fuzzy set theory. In this paper, some sufficient conditions for convergence under “max(min)” products of the powers of a square fuzzy matrix and of a fuzzy state process are established.     

Fuzzy part coding for group technology

Contemporary Group Technology (GT) methods apply coding schemes as a popular method for capturing the design and manufacturing information pertinent to the parts to be grouped. Coding schemes are very popular and many different coding systems are commercially available. The main disadvantage of current coding systems, however, is their generality and lack of informative representation of the parts.This paper presents a new methodology for coding parts using fuzzy codes. The methodology is general and applies to attributes that have a crisp value (e.g., “length”, “ratio of length to diameter”), an interval value (e.g., “tolerance”, “surface roughness”) or a fuzzy value (e.g., “primary shape”). The methodology considers the range of attributes' values relevant for the grouping, and therefore, is tuned and adjusted to the specific collection of parts of interest. This method creates a more informative coding scheme which leads to improved variant process planning methods, scheduling and inventory control as well as other manufacturing functions that utilize GT.     

ABC-Miner+: constructing Markov blanket classifiers with ant colony algorithms

ABC-Miner is a Bayesian classification algorithm based on the Ant colony optimization (ACO) meta-heuristic. The algorithm learns Bayesian network Augmented Naïve-Bayes (BAN) classifiers, where the class node is the parent of all the nodes representing the input variables. However, this assumes the existence of a dependency relationship between the class variable and all the input variables, and this relationship is always a type of “causal” (rather than “effect”) relationship, which restricts the flexibility of the algorithm to learn. In this paper, we extended the ABC-Miner algorithm to be able to learn the Markov blanket of the class variable. Such a produced model has a more flexible Bayesian network classifier structure, where it is not necessary to have a (direct) dependency relationship between the class variable and each of the input variables, and the dependency between the class and the input variables varies from “causal” to “effect” relationships. In this context, we propose two algorithms: \({\hbox {ABC-Miner}+_1}\) , in which the dependency relationships between the class and the input variables are defined in a separate phase before the dependency relationships among the input variables are defined, and \({\hbox {ABC-Miner}+_2}\) , in which the two types of dependency relationships in the Markov blanket classifier are discovered in a single integrated process. Empirical evaluations on 33 UCI benchmark datasets show that our extended algorithms outperform the original version in terms of predictive accuracy, model size and computational time. Moreover, they have shown a very competitive performance against other well-known classification algorithms in the literature.     

Stochastic processes with imbedded marked point processes (pmp) and thcir application in queneing

In generalization of special cases of the literature a class of stochastic processes (PMP) is defined with an imbedded stochastic marked point process of “basic points” which must not be renewal points. A theorem (“intensity conservation principle”) has been proved concerning a relation between stationary distribution of PMP at arbitrary points in time and distributions and intensities connected with the basic points. This relationship simultaneously yields a general method for determination of stationary quantities at arbitrary points in time by means of the corresponding “imbedded” quantities. Some applications to concrete queueing systems have been demonstrated, where arrival or departure epochs of customers are used as basic points. Under weaker independence assumptions as till now done in the literature, new relations are given.     

Learning gradients via an early stopping gradient descent method

We propose an early stopping algorithm for learning gradients. The motivation is to choose “useful” or “relevant” variables by a ranking method according to norms of partial derivatives in some function spaces. In the algorithm, we used an early stopping technique, instead of the classical Tikhonov regularization, to avoid over-fitting.After stating dimension-dependent learning rates valid for any dimension of the input space, we present a novel error bound when the dimension is large. Our novelty is the independence of power index of the learning rates on the dimension of the input space.     

Estimates of eigenvalues of the Laplacian by a reduced number of subsets

Chung–Grigor’yan–Yau’s inequality describes upper bounds of eigenvalues of the Laplacian in terms of subsets (“input”) and their volumes. In this paper we will show that we can reduce “input” in Chung–Grigor’yan–Yau’s inequality in the setting of Alexandrov spaces satisfying CD(0,∞). We will also discuss a related conjecture for some universal inequality among eigenvalues of the Laplacian.     

Assessing the of potential of e-business models: towards a framework for assisting decision-makers

Decision makers are faced with an enormous range of electronic business models from which to choose. The process of fully researching each of these models can prove daunting. Such research is a feature of what has been termed the “intelligence phase” of decision making. This phase is important as options excluded at this stage do not get considered at a later stage. This paper develops a prerequisites framework for use at the intelligence phase to exclude models that are incompatible with prevailing organisational and supply chain characteristics. The framework assesses the following characteristics: economic control, supply chain integration, functional integration, innovation and input sourcing. The paper utilises a series of five point Likert scales to operationalise these characteristics so that they can be used by decision makers to efficiently manage “intelligence phase” activities.     

Quantum Transport in Degenerate Systems

Transport in nonequilibrium degenerate quantum systems is investigated. The transfer rate depends on the parameters of the system. In this paper we investigate the dependence of the flow (transfer rate) on the angle between “bright” vectors (which define the interaction of the system with the environment). We show that in some approximation for the system under investigation the flow is proportional to the cosine squared of the angle between the “bright” vectors. Earlier the author has shown that in this degenerate quantum system excitation of nondecaying quantum “dark” states is possible; moreover, the effectiveness of this process is proportional to the sine squared of the angle between the “bright” vectors (this phenomenon was discussed as a possible model of excitation of quantum coherence in quantum photosynthesis). Thus quantum transport and excitation of dark states are competing processes; “dark” states can be considered as a result of leakage of quantum states in a quantum thermodynamic machine which performs the quantum transport.     

A Canonical Theory of Origins and Development of Social Complexity

The puzzle of origins and future of government and social complexity in human and social dynamics, arguably a characteristic feature of the emergence and long-term evolution of hierarchy and power in the history of civilizations, is an enduring topic that has challenged political scientists, anthropological archaeologists, and other social scientists and historians. This paper proposes a new computational theory for the emergence of social complexity that accounts for the earliest formation of systems of government (pristine polities) in prehistory and early antiquity, as well as present and future political development. This general social theory is based on a “fast process” of crisis and opportunistic decision-making through collective action, which feeds a “slow” process of political development or decay. The “fast” core iterative process is “canonical” in the sense that it undergoes variations on a recurring theme of signal detection, information-processing, problem-solving, successful adaptation and occasional failure. When a group is successful in managing or overcoming serious situational changes (stresses or opportunities, endogenous or exogenous, social or physical) a probabilistic phase transition may occur, under a specified set of conditions, yielding a long-term (slow) probabilistic accrual process of emergent sociopolitical complexity and development. A reverse process may account for decay. The canonical theory is being formally implemented through the “PoliGen” agent-based model (ABM), based on the new Multi-Agent Simulator of Networks and Neighborhoods (MASON). Empirically, the theory is testable with the datasets on polities developed by the Long-Range Analysis of War (LORANOW) Project. This paper focuses on the concepts, mechanisms, and basic formal structure that constitute the canonical theory and inform the subsequent simulation model.     

Challenges in data science: a complex systems perspective

The ability to process and manage large data volumes has been proven to be not enough to tackle the current challenges presented by “Big Data”. Deep insight is required for understanding interactions among connected systems, space- and time- dependent heterogeneous data structures. Emergence of global properties from locally interacting data entities and clustering phenomena demand suitable approaches and methodologies recently developed in the foundational area of Data Science by taking a Complex Systems standpoint. Here, we deal with challenges that can be summarized by the question: “What can Complex Systems Science contribute to Big Data? ”. Such question can be reversed and brought to a superior level of abstraction by asking “What Knowledge can be drawn from Big Data?” These aspects constitute the main motivation behind this article to introduce a volume containing a collection of papers presenting interdisciplinary advances in the Big Data area by methodologies and approaches typical of the Complex Systems Science, Nonlinear Systems Science and Statistical Physics.