Statistical learning (SL) involving sensitivity to distributional regularities in the environment has been suggested to be an important factor in many aspects of cognition, including language. However, the degree to which statistically-learned information is retained over time is not well understood. To establish whether or not learners are able to preserve such regularities over time, we examined performance on an artificial second language learning task both immediately after training and also at a follow-up session 2 weeks later. Participants were exposed (...) to an artificial language (Brocanto2), half of them receiving simplified training items in which only 20% of sequences contained complex structures, whereas the other half were exposed to a training set in which 80% of the items were composed of complex sequences. Overall, participants showed signs of learning at the first session and retention at the second, but the degree of learning was affected by the nature of the training they received. Participants exposed to the simplified input outperformed those in the more complex training condition. A GLMM was used to model the relationship between stimulus properties and participants’ endorsement strategies across both sessions. The results indicate that participants in the complex training condition relied more on an item’s chunk strength than those in the simple training condition. Taken together, this set of findings shows that statistically learned regularities are retained over the course of 2 weeks. The results also demonstrate that training on input featuring simple items leads to improved learning and retention of grammatical regularities. (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 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)

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 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)

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)

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)

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)

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)

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)

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 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)

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)

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)

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 (classical) 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)

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)

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)

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)

The Hoffmann reflex (H-wave) is produced by alpha-motoneuron activation in the spinal cord. A feature of this electromyography response is that it exhibits fluctuations in amplitude even during repetitive stimulation with the same intensity of current. We herein explore the hypothesis that physical training induces plastic changes in the motor system. Such changes are evaluated with the fractal dimension (FD) analysis of the H-wave amplitude-fluctuations (H-wave FD) and the cross-covariance (CCV) between the bilateral H-wave amplitudes. The aim of this study (...) was to compare the H-wave FD as well as the CCV before and after track training in sedentary individuals and athletes. The training modality in all subjects consisted of running three times per week (for 13 weeks) in a concrete road of 5 km. Given the different physical condition of sedentary vs. athletes, the running time between sedentary and athletes was different. After training, the FD was significantly increased in sedentary individuals but significantly reduced in athletes, although there were no changes in spinal excitability in either group of subjects. Moreover, the CCV between bilateral H-waves exhibited a significant increase in athletes but not in sedentary individuals. These differential changes in the FD and CCV indicate that the plastic changes in the complexity of the H-wave amplitude fluctuations as well as the synaptic inputs to the Ia-motoneuron systems of both legs were correlated to the previous fitness history of the subjects. Furthermore, these findings demonstrate that the FD and CCV can be employed as indexes to study plastic changes in the human motor system. (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.

some years, the German public has been debating the case of migrant workers receiving German benefits for children living abroad, which has been scandalised as a case of “benefit tourism.” This points to a failure to recognise a striking imbalance between the output of the German welfare state to migrants and the input it receives from migrant domestic workers. In this article I discuss how this input is being rendered invisible or at least underappreciated by sexist, racist, and (...) classist practices of othering. To illustrate the point, I will use examples from two empirical research projects that looked into how families in Germany outsource various forms of reproductive work to both female and male migrants from Eastern Europe. Drawing on the concept of othering developed in feminist and postcolonial literature and their ideas of how priv‐ ileges and disadvantages are interconnected, I will put this example into the context of literature on racism, gender, and care work migration. I show how migrant workers fail to live up to the normative standards of work, family life, and gender relations and norms set by a sedentary society. A complex interaction of supposedly “natural” and “objective” differences between “us” and “them” are at work to justify everyday discrimination against migrants and their institutional exclusion. These processes are also reflected in current political and public debates on the commodification and transnationalisation of care. (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)

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)

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)

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)

With the innovation of computer, virtual clothing has also emerged. This research mainly discusses the real-time animation complex of interactive clothing design based on computer simulation. In the process of realizing virtual clothing, the sample interpolation synthesis method is used, and the human body sample library is constructed using the above two methods first, and then, the human body model is obtained by interpolation calculation according to the personalized parameters. Building a clothing model is particularly important for the effect of (...) trying on. The clothing that needs to be displayed can be scanned and then input into the computer to build the model. The model can be directly built in 3DMAX and other software and then its surface texture can be mapped, or the clothing model can be directly built. The 3D model in the 3ds file is loaded by the loop body nested switch branch selection structure. Correspondingly, the write-back operation of 3ds files is similar. Just follow the general structure of the 3ds file and write the root block, version information block, edit information block, key frame information block, etc. to a brand new file in sequence. The main reason for this article to perform the 3ds file write-back operation is that, after the clothing model is dynamically simulated through the dynamic principle, the deformed key animation frame needs to be saved as a 3ds file so that it can be further imported into the 3DSMAX software and generated by the renderer, form high-quality picture information, and finally get high-definition animation video. In the CPU-GPU hybrid method, modules such as force calculation, collision processing, and position update use the GPU method, while overstretching is processed by the CPU method, making the overall performance 10 times higher than the pure CPU method. This research helps to promote the development of 3D virtual clothing design. (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 use of artificial intelligence as an instrument to assist judges in determining sentences in criminal cases is an issue that gives rise to many theoretical challenges. The purpose of this article is to examine one of these challenges known as the “input problem.” This problem arises supposedly due to two reasons: that in order for an algorithm to be able to provide a sentence recommendation, it needs to be inputted with case specific information; and that the task of (...) presenting an adequate picture of a crime often turns out to be highly complex. Even though this problem has been noted since the earliest attempts at developing sentencing support systems, almost no one has considered the ethical nature of this challenge. The aim of this article is to fill that void. First, it is shown that the input problem has been subject to somewhat different interpretations. Second, several possible answers as to when and why the problem constitutes an ethical challenge are considered. Third, a few suggestions are presented as to how undesirable implications of complexity at the input stage might be ameliorated by tailoring the way sentencing algorithms are developed and used in the work of criminal courts. (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.

According to, when forming complex yes/no questions, children do not make errors such as Is the boy who smoking is crazy? because they have innate knowledge of structure dependence and so will not move the auxiliary from the relative clause. However, simple recurrent networks are also able to avoid such errors, on the basis of surface distributional properties of the input (; ). Two new elicited production studies revealed that (a) children occasionally produce structure‐dependence errors and (b) the pattern (...) of children's auxiliary‐doubling errors (Is the boy who is smoking is crazy?) suggests a sensitivity to surface co‐occurrence patterns in the input. This article concludes that current data do not provide any support for the claim that structure dependence is an innate constraint, and that it is possible that children form a structure‐dependent grammar on the basis of exposure to input that exhibits this property. (shrink)

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 paper addresses the robust output feedback model predictive control schemes for networked control systems with input quantization. The logarithmic quantizer is considered in this paper, and the sector bound approach is applied, which appropriately treats the quantization error as a sector-bounded uncertainty. The presented method involves an offline designed state observer using linear matrix inequality and online robust output feedback MPC algorithms which optimize one free control move followed by the output feedback using the estimated state. Moreover, due (...) to the uncertainty of estimation error, a technique of refreshing the bound of estimation error which involves the quantization error is provided so as to guarantee the recursive feasibility of the optimization problem. The proposed MPC schemes inherit the characteristics of the synthesis approach of MPC, guaranteeing the recursive feasibility of the optimization problem and the stability of a closed-loop system, and explicitly account for quantization error. Two simulation examples are given to illustrate the effectiveness of the proposed methods. (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.

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)

Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex (...) ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished. Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer, NBEATS and Echo State Network. (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)

A sector is a basic unit of airspace whose operation is managed by air traffic controllers. The operation complexity of a sector plays an important role in air traffic management system, such as airspace reconfiguration, air traffic flow management, and allocation of air traffic controller resources. Therefore, accurate evaluation of the sector operation complexity is crucial. Considering there are numerous factors that can influence SOC, researchers have proposed several machine learning methods recently to evaluate SOC by mining the (...) relationship between factors and complexity. However, existing studies rely on hand-crafted factors, which are computationally difficult, specialized background required, and may limit the evaluation performance of the model. To overcome these problems, this paper for the first time proposes an end-to-end SOC learning framework based on deep convolutional neural network specifically for free of hand-crafted factors environment. A new data representation, i.e., multichannel traffic scenario image, is proposed to represent the overall air traffic scenario. A MTSI is generated by splitting the airspace into a two-dimension grid map and filled with navigation information. Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high-level traffic features from MTSIs and learn the SOC pattern. Thus, the model input is determined by combining multiple image channels composed of air traffic information, which are used to describe the traffic scenario. The model output is SOC levels for the target sector. The experimental results using a real dataset from the Guangzhou airspace sector in China show that our model can effectively extract traffic complexity information from MTSIs and achieve promising performance than traditional machine learning methods. In practice, our work can be flexibly and conveniently applied to SOC evaluation without the additional calculation of hand-crafted factors. (shrink)

This study investigates the sampled-data admissibility problem for a singular system. The objective of this paper is to design a sampled-data controller to ensure the admissibility of a singular system and to construct an appropriate Lyapunov–Krasovskii functional to get less conservative results for the sampled-data singular system. To accomplish these objectives, the system is converted into a time-delay system by the input delay approach firstly, and both lower and upper bounds of the delay are considered. Secondly, by introducing a (...) suitable LKF, the admissibility criteria are obtained. Then, when estimating the derivative of the LKF, a relaxation variable is introduced by the method of reciprocally convex inequality, and it is proved that the conservatism is reduced. Finally, two numerical examples are given to prove that the designed sampled-data controller can ensure the states of the systems under the influence of external interference. (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)

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)

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)

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.