This study aims to disentangle the often-confounded effects of input frequency and morphophonological complexity in the acquisition of inflection, by focusing on simple and complex verb forms in Japanese. Study 1 tested 28 children aged 3;3–4;3 on stative and simple past forms, and Study 2 tested 30 children aged 3;5–5;3 on completive and simple past forms, with both studies using a production priming paradigm. Mixed effects models for children's responses were built to test the prediction that children's verb (...) use is explained by the relative bias in input frequency between the two inflectional forms. Although Study 1 did not show a significant effect of input bias, Study 2, which corrected for this problem, yielded the predicted relationship. These findings suggest that input frequency effects, at the level of different inflectional forms of the same verb stem, hold even after controlling for morphophonological complexity. (shrink)

All other things being equal the parser favors attaching an ambiguous modifier to the most recent possible site. A plausible explanation is that locality preferences such as this arise in the service of minimizing memory costs—more distant sentential material is more difficult to reactivate than more recent material. Note that processing any sentence requires linking each new lexical item with material in the current parse. This often involves the construction of long‐distance dependencies. Under a resource‐limited view of language processing, lengthy (...) integrations should induce difficulty even in unambiguous sentences. To date there has been little direct quantitative evidence in support of this perspective. This article presents 2 self‐paced reading studies, which explore the hypothesis that dependency distance is a fundamental determinant of reading complexity in unambiguous constructions in English. The evidence suggests that the difficulty associated with integrating a new input item is heavily determined by the amount of lexical material intervening between the input item and the site of its target dependents. The patterns observed here are not straightforwardly accounted for within purely experience‐based models of complexity. Instead, this work supports the role of a memory bottleneck in language comprehension. This constraint arises because hierarchical linguistic relations must be recovered from a linear input stream. (shrink)

This collection of writings provides the only comprehensive introduction to the input-output model for which Leontief was awarded the Nobel Prize in 1973. The structural approach to economics developed by Leontief, and known as input-output analysis, paved the way for the transformation of economics into a truly empirical discipline that could utilize modern data processing technology. This thoroughly revised second edition includes twenty essays--twelve of which are new to this edition--that reflect the past developments and the present state (...) of the field. Beginning with an introductory chapter, the book leads the reader into an understanding of the input-output approach--not only as formal theory but also as a research strategy and powerful tool for dealing with a complex modern economy. (shrink)

This collection of writings provides the only comprehensive introduction to the input-output model for which Leontief was awarded the Nobel Prize in 1973. The structural approach to economics developed by Leontief, and known as input-output analysis, paved the way for the transformation of economics into a truly empirical discipline that could utilize modern data processing technology. This thoroughly revised second edition includes twenty essays--twelve of which are new to this edition--that reflect the past developments and the present state (...) of the field. Beginning with an introductory chapter, the book leads the reader into an understanding of the input-output approach--not only as formal theory but also as a research strategy and powerful tool for dealing with a complex modern economy. (shrink)

There are clear associations between the overall quantity of input children are exposed to and their vocabulary acquisition. However, by uncovering specific features of the input that matter, we can better understand the mechanisms involved in vocabulary learning. We examine whether exposure to wh-questions, a challenging quality of the communicative input, is associated with toddlers' vocabulary and later verbal reasoning skills in a sample of low-income, African-American fathers and their 24-month-old children. Dyads were videotaped in free play (...) sessions at home. Videotapes were transcribed and reliably coded for sheer quantity of fathers' input as well as the number of wh-questions fathers produce. Children's productive vocabulary was measured at 24 months using the McArthur Bates Communicative Development Inventory MCDI, and children's verbal reasoning skills were measured 1 year later using the Bayley Scales of Infant Development. Results indicate that the overall quantity of father talk did not relate to children's vocabulary or reasoning skills. However, fathers' use of wh-questions related to both vocabulary and reasoning outcomes. Children's responses to wh-questions were more frequent and more syntactically complex, measured using the mean length of utterance, than their responses to other questions. Thus, posing wh-questions to 2-year-olds is a challenging type of input, which elicits a verbal response from the child that likely helps build vocabulary and foster verbal reasoning abilities. (shrink)

I have two aims for the present paper, one narrow and one broad. The narrow aim is to show that a class of data originally described by Lynsey Wolter empirically undermine the leading treatments of complex demonstratives that have been described in the literature. The broader aim of the paper is to show that Wolter demonstratives, as I will call the constructions I focus on, are a threat not just to existing treatments, but to any possible theory that retains the (...) uncontroversial assumptions that relative clauses always form a constituent with the nouns they modify, and that semantic composition proceeds sequentially and locally, with the inputs to interpretation having the structure syntax tells us they do. (shrink)

In this paper we study generic complexity of undecidable problems. It turns out that some classical undecidable problems are, in fact, strongly undecidable, i.e., they are undecidable on every strongly generic subset of inputs. For instance, the classical Halting Problem is strongly undecidable. Moreover, we prove and analog of the Rice theorem for strongly undecidable problems, which provides plenty of examples of strongly undecidable problems. Then we show that there are natural super-undecidable problems. i.e., problem which are undecidable on (...) every generic (not only strongly generic) subset of inputs. In particular, there are finitely presented semigroups with super-undecidable word problem. To construct strongly- and super-undecidable problems we introducea method of generic amplification (an analog of the amplification in complexity theory). Finally, we construct absolutely undecidable problems, which stay undecidable on every non-negligible set of inputs. Their construction rests on generic immune sets. (shrink)

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable. In cognitive science and philosophy, this tractability result has been used to argue that only functions in P can feasibly (...) work as computational models of human cognitive capacities. One interesting area of computational complexity theory is descriptive complexity, which connects the expressive strength of systems of logic with the computational complexity classes. In descriptive complexity theory, it is established that only first-order systems are connected to P, or one of its subclasses. Consequently, second-order systems of logic are considered to be computationally intractable, and may therefore seem to be unfit to model human cognitive capacities. This would be problematic when we think of the role of logic as the foundations of mathematics. In order to express many important mathematical concepts and systematically prove theorems involving them, we need to have a system of logic stronger than classical first-order logic. But if such a system is considered to be intractable, it means that the logical foundation of mathematics can be prohibitively complex for human cognition. In this paper I will argue, however, that this problem is the result of an unjustified direct use of computational complexity classes in cognitive modelling. Placing my account in the recent literature on the topic, I argue that the problem can be solved by considering computational complexity for humanly relevant problem solving algorithms and input sizes. (shrink)

In this paper we study the Kolmogorov complexity for non-effective computations, that is, either halting or non-halting computations on Turing machines. This complexity function is defined as the length of the shortest input that produce a desired output via a possibly non-halting computation. Clearly this function gives a lower bound of the classical Kolmogorov complexity. In particular, if the machine is allowed to overwrite its output, this complexity coincides with the classical Kolmogorov complexity for (...) halting computations relative to the first jump of the halting problem. However, on machines that cannot erase their output –called monotone machines–, we prove that our complexity for non effective computations and the classical Kolmogorov complexity separate as much as we want. We also consider the prefix-free complexity for possibly infinite computations. We study several properties of the graph of these complexity functions and specially their oscillations with respect to the complexities for effective computations. (shrink)

We present results from numerical studies of supervised learning operations in recurrent networks considered as graphs, leading from a given set of input conditions to predetermined outputs. Graphs that have optimized their output for particular inputs with respect to predetermined outputs are asymptotically stable and can be characterized by attractors which form a representation space for an associative multiplicative structure of input operations. As the mapping from a series of inputs onto a series of such attractors generally depends (...) on the sequence of inputs, this structure is generally noncommutative. Moreover, the size of the set of attractors, indicating the complexity of learning, is found to behave non-monotonically as learning proceeds. A tentative relation between this complexity and the notion of pragmatic information is indicated. (shrink)

This study focused on the identification problems of two-input single-output system with moving average noises based on unsupervised learning methods applied to the input signals. The input signal to the autoregressive moving average model is proposed to be arriving from a source with continuous technical and environmental changes as two separate featured input signals. These two input signals were grouped in a number of clusters using the K-means clustering algorithm. The clustered input signals were (...) supplied to the model in an orderly fashion from cluster-1 up to cluster-K. To ensure that the output signal can be best predicted from the input signal which in turn leads to selecting good enough model for its intended use, the magnitude-squared coherence measure is applied to the input/output signals in the cases of clustered and nonclustered inputs, which indicates best correlation coefficient when measured with clustered inputs. From collected input-output signals, we deduce a K-means clustering based recursive least squares method for estimating the parameter of autoregressive moving average system. The simulation results indicate that the suggested method is effective. (shrink)

In complexity theory, both the brain and consciousness are understood as trophic systems—they consume metabolic energy when they function. Complex systems are dynamic and nonlinear and comprise diverse entities that are interdependent and interconnected in such a way that information is shared and that entities adapt to one another. Some natural complex systems are complex adaptive systems (CAS), which are sensitive to change in relation to their environments and are often chaotic. Consciousness and the neural systems mediating consciousness may (...) be modeled as CAS and, more specifically, as intelligent complex adaptive systems (ICAS), where intelligence means that a nervous system can solve problems successfully by intervening between sensory input and behavioral output. Evolution of any ICAS will result in emergent properties, particularly advanced brains. Two processes are involved in integrating experience and knowledge: the effort after meaning and the effort after truth. These efforts are mediated by the predominance given to direct experience presented to the brain's sensorium and modeling processes mediated by higher cognitive functions. Understanding consciousness as an ICAS has profound repercussions in how anthropology conceives of culture. (shrink)

Learning theory has frequently been applied to language acquisition, but discussion has largely focused on information theoretic problems—in particular on the absence of direct negative evidence. Such arguments typically neglect the probabilistic nature of cognition and learning in general. We argue first that these arguments, and analyses based on them, suffer from a major flaw: they systematically conflate the hypothesis class and the learnable concept class. As a result, they do not allow one to draw significant conclusions about the learner. (...) Second, we claim that the real problem for language learning is the computational complexity of constructing a hypothesis from input data. Studying this problem allows for a more direct approach to the object of study—the language acquisition device—rather than the learnable class of languages, which is epiphenomenal and possibly hard to characterize. The learnability results informed by complexity studies are much more insightful. They strongly suggest that target grammars need to be objective, in the sense that the primitive elements of these grammars are based on objectively definable properties of the language itself. These considerations support the view that language acquisition proceeds primarily through data-driven learning of some form. (shrink)

Leech et al. present a connectionist algorithm as a model of (the development) of analogizing, but they do not specify the algorithm's associated computational-level theory, nor its computational complexity. We argue that doing so may be essential for connectionist cognitive models to have full explanatory power and transparency, as well as for assessing their scalability to real-world input domains.

This paper presents a computational account of nonderived environment blocking (NDEB) that indicates the challenges it has posed for phonological theory do not stem from any inherent complexity of the patterns themselves. Specifically, it makes use of input strictly local (ISL) functions, which are among the most restrictive (i.e., lowest computational complexity) classes of functions in the subregular hierarchy (Heinz 2018) and shows that NDEB is ISL provided the derived and nonderived environments correspond to unique substrings in (...) the input structure. Using three classic examples of NDEB from Finnish, Polish, and Turkish, it is shown that the distinction between derived and nonderived sequences is fully determined by the input structure and can be achieved without serial derivation or intermediate representations. This result reveals that such cases of NDEB are computationally unexceptional and lends support to proposals in rule- and constraint-based theories that make use of its input-oriented nature. (shrink)

The established definition of replication in terms of the conditions of causality, similarity and information transfer is very broad. We draw inspiration from the literature on self-reproducing automata to strengthen the notion of information transfer in replication processes. To the triple conditions of causality, similarity and information transfer, we add a fourth condition that defines a “generative replicator” as a conditional generative mechanism, which can turn input signals from an environment into developmental instructions. Generative replication must have the potential (...) to enhance complexity, which in turn requires that developmental instructions are part of the information that is transmitted in replication. Demonstrating the usefulness of the generative replicator concept in the social domain, we identify social generative replicators that satisfy all of the four proposed conditions. (shrink)

How to identify the key nodes effectively in urban traffic networks to achieve the equitable resource allocation face to the complex traffic network? This issue needs to be solved in current traffic management. This study considered the urban traffic network topology and network traffic status, put forward an improved model based on the economics of the input-output method by introducing a virtual node to the selected network set up with the flow of urban traffic network, sensor nodes by Leontief (...) inverse matrix calculation coefficient to determine node importance, according to the node importance to deliberate attack traffic network to analyze its robustness, to test the accuracy and practicability of the method. The results show that this improved method adopted to measure the importance of traffic nodes from the global scope has the advantages of fast calculation and simple process and provides a more reliable basis for rational allocation of transport resources. (shrink)

The model-checking problem for a logic L on a class C of structures asks whether a given L-sentence holds in a given structure in C. In this paper, we give super-exponential lower bounds for fixed-parameter tractable model-checking problems for first-order and monadic second-order logic. We show that unless PTIME=NP, the model-checking problem for monadic second-order logic on finite words is not solvable in time f·p, for any elementary function f and any polynomial p. Here k denotes the size of the (...) input sentence and n the size of the input word. We establish a number of similar lower bounds for the model-checking problem for first-order logic, for example, on the class of all trees. (shrink)

We propose that a top priority of the cerebral cortex must be the discovery and explicit representation of the environmental variables that contribute as major factors to environmental regularities. Any neural representation in which such variables are represented only implicitly (thus requiring extra computing to use them) will make the regularities more complex and therefore more difficult, if not impossible, to learn. The task of discovering such important environmental variables is not an easy one, since their existence is only indirectly (...) suggested by the sensory input patterns the cortex receives – these variables are “hidden.” We present a candidate computational strategy for (1) discovering regularity-simplifying environmental variables, (2) learning the regularities, and (3) using regularities in perceptual and decision-making tasks. The SINBAD computational model discovers useful environmental variables through a search for different, but nevertheless highly correlated functions of any kind over non-overlapping subsets of the known variables, this being indicative of some important environmental variable that is responsible for the correlation. We suggest that such a search is performed in the neocortex by the dendritic trees of individual pyramidal cells. According to the SINBAD model, the basic function of each pyramidal cell is (1) to discover and represent one of the regularity-simplifying environmental variables, and (2) to learn to infer the state of its variable from the states of other variables, represented by other pyramidal cells. A network of such cells – each cell just attending to representation of its variable – can function as a sophisticated and useful inferential model of the outside world. (shrink)

In this article, we formally define and investigate the computational complexity of the definability problem for open first-order formulas with equality. Given a logic $\boldsymbol{\mathcal{L}}$, the $\boldsymbol{\mathcal{L}}$-definability problem for finite structures takes as an input a finite structure $\boldsymbol{A}$ and a target relation $T$ over the domain of $\boldsymbol{A}$ and determines whether there is a formula of $\boldsymbol{\mathcal{L}}$ whose interpretation in $\boldsymbol{A}$ coincides with $T$. We show that the complexity of this problem for open first-order formulas is (...) coNP-complete. We also investigate the parametric complexity of the problem and prove that if the size and the arity of the target relation $T$ are taken as parameters, then open definability is $\textrm{coW}[1]$-complete for every vocabulary $\tau $ with at least one, at least binary, relation. (shrink)

We explore the relationship between analytic behavior of a computable real valued function and the computability-theoretic complexity of the individual values of its derivative almost-everywhere. Given a computable function f, the values of its derivative \\), where they are defined, are uniformly computable from \, the Turing jump of the input. It is known that when f is \, the values of \\) are actually computable from x. We construct a \ function f so that, almost everywhere, \\ge (...) _T x'\). Although the values \\) at each point x cannot uniformly compute the corresponding jumps \ of the inputs x almost everywhere for any \ function f, we produce an example of a \ function f such that \\ge _T \emptyset '\) uniformly on subsets of arbitrarily large measure, effectively. We also explore analogous questions for weaker smoothness conditions, such as for f differentiable everywhere, and f differentiable almost everywhere. (shrink)

The present event‐related potential (ERP) study used picture–sentence verification to investigate the neurolinguistic correlates of the online processing of compositional‐semantic information. To this end, we examined context effects on sentences involving temporal adverbial quantification likeJana war jeden Morgen schwimmen an den Arbeitstagen (“Jana went for a swim every morning during the working week”). We tested whether the conceptual complexity associated with quantifying over time intervals leads to delayed predictions regarding the upcoming words in a sentence. The present study replicated (...) previous results relating to quantification over individuals, which are conceptually less complex than time intervals. Analogous to previous studies, false vs. true sentences elicited an N400 whenever contextual cues did not permit a potential revision of a locally assigned truth value. The present results are compatible with an approach under which contextual cues are immediately considered for predicting how a sentence continues. The fact that the contextual complexity did not lead to processing delays indicates that the processing system quickly abstracts away from the conceptual complexity associated with the linguistic input if such an abstraction is possible. (shrink)

In the last 20 years, a stream of research emerged under the label of „complex problem solving“ (CPS). This research was intended to describe the way people deal with complex, dynamic, and intransparent situations. Complex computer-simulated scenarios were as stimulus material in psychological experiments. This line of research lead to subtle insights into the way how people deal with complexity and uncertainty. Besides these knowledge-rich, realistic, intransparent, complex, dynamic scenarios with many variables, a second line of research used more (...) simple, knowledge-lean scenarios with a low number of variables („minimal complex systems“, MCS) that have been proposed recently in problem-solving research for the purpose of educational assessment. In both cases, the idea behind the use of microworlds is to increase validity of problem solving tasks by presenting interactive environments that can be explored and controlled by participants while pursuing certain action goals. The main argument presented here is: both types of systems - CPS and MCS – can only be dealt with successfully if causal dependencies between input and output variables are identified and used for system control. System knowledge is necessary for control and intervention. But CPS and MCS differ in their way of how causal dependencies are identified and how the mental model is constructed; therefore, they cannot be compared directly to each other with respect to the cognitive processes that are necessary for solving the tasks. Knowledge-poor MCS tasks address only a small fraction of the cognitive processes and structures needed for knowledge-rich CPS situations. (shrink)

In this article, we consider infants’ acquisition of foundational aspects of language and emotion through the lens of statistical learning. By taking a comparative developmental approach, we highlight ways in which the learning problems presented by input from these two rich communicative domains are both similar and different. Our goal is to encourage other scholars to consider multiple domains of human experience when developing theories in developmental cognitive science.

This paper investigates the vehicle platoon control problems with both velocity constraints and input saturation. Firstly, radial basis function neural networks are employed to approximate the unknown driving resistance of a vehicle’s dynamic model. Then, a bidirectional topology, where vehicles can only communicate with their direct preceding and following neighbors, is used to depict the relationship among the vehicles in the platoon. On this basis, a neural adaptive sliding-mode control algorithm with an anti-windup compensation technique is proposed to maintain (...) the vehicle platoon with desired distance. Moreover, the string stability and the strong string stability of the whole vehicle platoon are proven through the stability theorem. Finally, numerical simulations verify the feasibility and effectiveness of the proposed control method. (shrink)

The meaning and function of the image has been evolving over all time. This has been separated from mimesis and given greater openness and an emergent meaning to be adapted to the fluxes that characterize our contemporary society. Throughout this process Art has lost the primacy and exclusivity of the image to share it with science and its visualization procedures and with a social function, now disseminated trough social networks. This article points out the decisive moments – and illustrates them (...) with examples – through the evolution from the image in Postmodernity to the open and co-participated meaning of the Postmedia age image, and the new relation with the real world that characterizes the image of the Postdigital situation. Finally, we propose that the Postdigital Condition, related to new advances such as augmented reality (AR), geopositioning devices and ubiquitous informatics, is making the appearance of a new image type possible, here called Tracking-Image. This Tracking-Image is self-generated with data, from the flux of inputs generated in physical space and registered by networked users and sensors, becoming the protagonist of a new encounter with the physical fluid reality that characterizes the Postdigital situation. (shrink)

This study examines the distribution of complex nominal constructions in Hebrew texts produced by non-expert schoolage and adult writers, compared with their distribution in expert-written encyclopedic texts. One aim of the paper was to determine young writers’ ability to distinguish text types through their usage of genre-appropriate morpho-syntactic forms. Another aim was to investigate the distribution of these constructions in expert school-related texts so as to confirm or refute the hypothesis of “resonance” between input and output texts. The study (...) population consisted of native Hebrew-speaking gradeschoolers, adolescents and adults who each produced 2 written texts, one biographical, one expository. The same text types were analyzed in three encyclopedias, targeted for use in each of the age groups under discussion. Our analysis indicates that non-expert writers’ textual output undergoes marked developmental changes during later childhood and adolescence, but no developmental resonance was found in expert texts, which constitute part of the input that novice and non-expert writers are exposed to. Implications for the nature of the mental lexicon and its relationship with grammar are discussed. (shrink)

The undecidability of first-order logic implies that there is no computable bound on the length of shortest proofs of valid sentences of first-order logic. Some valid sentences can only have quite long proofs. How hard is it to prove such "hard" valid sentences? The polynomial time tractability of this problem would imply the fixed-parameter tractability of the parameterized problem that, given a natural number n in unary as input and a first-order sentence φ as parameter, asks whether φ has (...) a proof of length ≤ n. As the underlying classical problem has been considered by Gödel we denote this problem by p-Gödel. We show that p-Gödel is not fixed-parameter tractable if DTIME(h O(1) ) ≠ NTIME(h O(1) ) for all time constructible and increasing functions h. Moreover we analyze the complexity of the construction problem associated with p-Gödel. (shrink)

Applying Behrendt & Young's (B&Y's) model of thalamocortical synchrony to complex visual hallucinations in neurodegenerative disorders, such as dementia with Lewy bodies and progressive supranuclear palsy, leads us to propose that the primary pathology may be cortical rather than thalamic. Additionally, the extinction of active hallucinations by eye closure challenges their conception of the role of reduced sensory input.

Cerebellar Purkinje cells generate two distinct types of spikes, complex and simple spikes, both of which have conventionally been considered to be highly irregular, suggestive of certain types of stochastic processes as underlying mechanisms. Interestingly, however, the interspike interval structures of complex spikes have not been carefully studied so far. We showed in a previous study that simple spike trains are actually composed of regular patterns and single interspike intervals, a mixture that could not be explained by a simple rate-modulated (...) Poisson process. In the present study, we systematically investigated the interspike interval structures of separated complex and simple spike trains recorded in anaesthetized rats, and derived an appropriate stochastic model. We found that: (i) complex spike trains do not exhibit any serial correlations, so they can effectively be generated by a renewal process, (ii) the distribution of intervals between complex spikes exhibits two narrow bands, possibly caused by two oscillatory bands (0.5–1 and 4–8 Hz) in the input to Purkinje cells and (iii) the regularity of regular patterns and single interspike intervals in simple spike trains can be represented by gamma processes of orders, which themselves are drawn from gamma distributions, suggesting that multiple sources modulate the regularity of simple spike trains. (shrink)

This study explores the emergence and productivity of word order usage in Mandarin-speaking typically-developing children and children with autism spectrum disorder, and examines how this emergence relates to frequency of use in caregiver input. Forty-two caregiver-child dyads participated in video-recorded 30-min semi-structured play sessions. Eleven children with ASD were matched with 10 20-month-old TD children and another 11 children with ASD were matched with 10 26-month-old TD children, on expressive language. We report four major findings: Preschool Mandarin-speaking children with (...) ASD produced word order structures with pervasive ellipsis at similar rates to language-matched TD children, but also displayed differences from TD children in their usage of SVt and VtO frames; Grammatical productivity was observed in both TD children and children with ASD; moreover, children with ASD with higher expressive language produced less stereotyped language; Both TD children and children with ASD heard a range of word orders in their caregivers’ input, with TD children’s input greater in amount and complexity; however, caregivers of both groups also showed no age/language-related changes in word order usage; Few word-order-specific correlations emerged between caregivers and their children; however, strong correlations were observed for mean length of utterances for both groups: Caregivers who produced longer/more complex utterances had children who did the same. Taken together, it seems that despite their pragmatic deficits, the early grammatical knowledge of word order in Mandarin-exposed children with ASD is well preserved and in general follows the typical developmental pattern. Moreover, caregiver input is broadly rather than finely tuned to the linguistic development of TD children and children with ASD, and plays a more important role in children’s general syntactic development than in specific word order acquisition. Thus, early word order usage in preschool Mandarin-speaking TD children and children with ASD may be influenced by both caregiver input and child abilities. (shrink)

The process of gene expression is affected by many extracellular stimulus signals, and the stochasticity of these signals reshapes gene expression. To adapt the fluctuation of the extracellular environment, genes have many strategies for augmenting their survival probability, frequency modulation, and amplitude modulation. However, it is unclear how genes utilize the stochasticity of signals to regulate gene expression and which strategy will be chosen to maximize cellular function. Here, we analyze a simple mechanistic model to clarify the effect of extracellular (...) random input on gene expression and burst kinetics at different timescales. We can see that in different contexts, extracellular noise has different effects on downstream gene expression, effects which include the following: extracellular noise will make the ON-OFF-state dwell time drift, which will influence the burst frequency and burst size of downstream gene expression under different modulation paradigms; comparing the burst parameter or gene expression products under different modulation paradigms, we can see that the amplitude signal is more sensitive in the case of extracellular noise input, whereas the signal in noiseless conditions is more sensitive when the random input is a fast process, which indicates that the amplitude signal is a superior and common signal in gene expression; and extracellular random input will change the bimodality for gene expression, but its influence is different for gene expression products under different modulation paradigms. These qualitative results reveal that extracellular random input can prompt the gene to achieve its function quickly under different modulation paradigms. (shrink)

For linear discrete-time systems with randomly variable input trail length, a proportional- type iterative learning control law is proposed. To tackle the randomly variable input trail length, a modified control input at the desirable trail length is introduced in the proposed ILC law. Under the assumption that the initial state fluctuates around the desired initial state with zero mean, the designed ILC scheme can drive the ILC tracking errors to zero at the desirable trail length in expectation (...) sense. The designed ILC algorithm allows the trail length of control input which is different from system state and output at a specific iteration. In addition, the identical initial condition widely used in conventional ILC design is also mitigated. An example manifests the validity of the proposed ILC algorithm. (shrink)

This article explores how both corporate governance and corporate social responsibility can be improved by using insights from complexity theory. Complexity theory reveals that decentralized governance architecture is required for firms to absorb competently the increased intricacies, variety of variables, and objectives introduced by CSR. The current predominant form of centralized governance based on command-and-control hierarchies copes with complexities by reducing data inputs. This approach results in firms reducing their objectives, concerns, and insights about CSR. Firms with a (...) decentralized “network” form of governance architecture are used to illustrate how the data inputs of each manager can be reduced through the decomposition of decision-making labor to improve the capability of the firm to intelligently absorb and manage complexity. Network governance also introduces a division of powers with stakeholders to facilitate information flow and strengthen incentives to manage the enterprise to enhance both shareholder value and CSR. (shrink)

For decades, the dominant research paradigm has included trials conducted in clinical settings with little involvement from communities. However, concerns about the relevance and applicability of the processes or outcomes of such research have led to calls for greater community engagement in the research process. As such, there has been a shift in emphasis from simply recruiting research participants from community settings to engaging community members more broadly in all aspects of the research process. The move toward community engaged research (...) is in part driven by the recognition that inclusion of diverse perspectives in multidisciplinary teams is essential to addressing complex problems. Investigators have come to recognize the inherent value of engaging community members as collaborators in multidisciplinary teams that are conducting research on issues of concern to communities. The insider perspective from community members is now recognized as essential in designing effective and well-received recruitment strategies, culturally appropriate measures, and identifying meaningful and broad-reaching venues for dissemination. (shrink)

In the current study, we aimed at understanding the effect of exposure to complex input on speech sound development, by conducting a systematic meta-analysis review of the existing treatment-based studies employing complex input in children with speech sound disorders. In the meta-analysis review, using a list of inclusion criteria, we narrowed 280 studies down to 12 studies. Data from these studies were extracted to calculate effect sizes that were plotted as forest plots to determine the efficacy of (...) class='Hi'>complexity-based treatment approaches. The outcome variables of interest were improvement on the treated and generalization to the untreated sounds. Meta-analysis revealed that the exposure to complex input not only promoted improvement in production of complex speech sounds but also facilitated the production of untreated simple speech sounds. Overall, the current findings revealed that the exposure to complex input promotes acquisition of both complex and simple speech sounds. The current findings are in line with the models of language learnability. The current findings have implications in the treatment of speech sound disorders. (shrink)

Let us start by some general definitions of the concept of complexity. We take a complex system to be one composed by a large number of parts, and whose properties are not fully explained by an understanding of its components parts. Studies of complex systems recognized the importance of “wholeness”, defined as problems of organization (and of regulation), phenomena non resolvable into local events, dynamics interactions in the difference of behaviour of parts when isolated or in higher configuration, etc., (...) in short, systems of various orders (or levels) not understandable by investigation of their respective parts in isolation. In a complex system it is essential to distinguish between ‘global’ and ‘local’ properties. Theoretical physicists in the last two decades have discovered that the collective behaviour of a macro-system, i.e. a system composed of many objects, does not change qualitatively when the behaviour of single components are modified slightly. Conversely, it has been also found that the behaviour of single components does change when the overall behaviour of the system is modified. There are many universal classes which describe the collective behaviour of the system, and each class has its own characteristics; the universal classes do not change when we perturb the system. The most interesting and rewarding work consists in finding these universal classes and in spelling out their properties. This conception has been followed in studies done in the last twenty years on second order phase transitions. The objective, which has been mostly achieved, was to classify all possible types of phase transitions in different universality classes and to compute the parameters that control the behaviour of the system near the transition (or critical or bifurcation) point as a function of the universality class. This point of view is not very different from the one expressed by Thom in the introduction of Structural Stability and Morphogenesis (1975). It differs from Thom’s program because there is no a priori idea of the mathematical framework which should be used. Indeed Thom considers only a restricted class of models (ordinary differential equations in low dimensional spaces) while we do not have any prejudice regarding which models should be accepted. One of the most interesting and surprising results obtained by studying complex systems is the possibility of classifying the configurations of the system taxonomically. It is well-known that a well founded taxonomy is possible only if the objects we want to classify have some unique properties, i.e. species may be introduced in an objective way only if it is impossible to go continuously from one specie to another; in a more mathematical language, we say that objects must have the property of ultrametricity. More precisely, it was discovered that there are conditions under which a class of complex systems may only exist in configurations that have the ultrametricity property and consequently they can be classified in a hierarchical way. Indeed, it has been found that only this ultrametricity property is shared by the near-optimal solutions of many optimization problems of complex functions, i.e. corrugated landscapes in Kauffman’s language. These results are derived from the study of spin glass model, but they have wider implications. It is possible that the kind of structures that arise in these cases is present in many other apparently unrelated problems. Before to go on with our considerations, we have to pick in mind two main complementary ideas about complexity. (i) According to the prevalent and usual point of view, the essence of complex systems lies in the emergence of complex structures from the non-linear interaction of many simple elements that obey simple rules. Typically, these rules consist of 0–1 alternatives selected in response to the input received, as in many prototypes like cellular automata, Boolean networks, spin systems, etc. Quite intricate patterns and structures can occur in such systems. However, what can be also said is that these are toy systems, and the systems occurring in reality rather consist of elements that individually are quite complex themselves. (ii) So, this bring a new aspect that seems essential and indispensable to the emergence and functioning of complex systems, namely the coordination of individual agents or elements that themselves are complex at their own scale of operation. This coordination dramatically reduces the degree of freedom of those participating agents. Even the constituents of molecules, i.e. the atoms, are rather complicated conglomerations of subatomic particles, perhaps ultimately excitations of patterns of superstrings. Genes, the elementary biochemical coding units, are very complex macromolecular strings, as are the metabolic units, the proteins. Neurons, the basic elements of cognitive networks, themselves are cells. In those mentioned and in other complex systems, it is an important feature that the potential complexity of the behaviour of the individual agents gets dramatically simplified through the global interactions within the system. The individual degrees of freedom are drastically reduced, or, in a more formal terminology, the factual space of the system is much smaller than the product of the state space of the individual elements. That is one key aspect. The other one is that on this basis, that is utilizing the coordination between the activities of its members, the system then becomes able to develop and express a coherent structure at a higher level, that is, an emergent behaviour (and emergent properties) that transcends what each element is individually capable of. (shrink)

This paper investigates the exponential synchronization of complex dynamical networks based on the sampled-data control method. The sampled-data control means that the control input remains unchanged for a long time after each sampling, which can reduce the sampling number. By using the stability theory of the dynamical systems, this paper provides a novel sampling controller and estimates the bound of the sampling interval. Finally, a numerical example is given to demonstrate the effectiveness of the proposed design technique.

For decades, the dominant research paradigm has included trials conducted in clinical settings with little involvement from communities. The move toward community engaged research (CEnR) necessitates the inclusion of diverse perspectives to address complex problems. Using a relationship paradigm, CEnR reframes the context, considerations, practical steps, and outcomes of research.

A tracking control algorithm of nonlinear multiple agents with undirected communication is studied for each multiagent system affected by external interference and input saturation. A control design scheme combining iterative learning and adaptive control is proposed to perform parameter adaptive time-varying adjustment and prove the effectiveness of the control protocol by designing Lyapunov functions. Simulation results show that the high-precision tracking control problem of the nonlinear multiagent system based on adaptive iterative learning control can be well realized even when (...) the input is saturated. Finally, the validity of the proposed algorithm is verified by numerical analysis. (shrink)