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The common use of several disciplines for the realization of practical purposes and their usual coworking favour such meetings involving different disciplines. Some old and recent successful examples of effective interferences between different disciplines are reported in order to evidence the potential fruitfulness of this process. The theme complexity and disorder are shown to be a central point of convergence between disciplines shown from the observation of past and future projects.

This article traces the life and work of architect Lina Bo Bardi to explore the more humane, free, unprejudiced and playful side of her architectural work. This will lead us to unveil a much more complex past than one might initially think, where the architect applies a particular human sensitivity that is perceptible in all of her work and where people stand at the centre of her priorities. Behind her wild fantasy and imagination lies the complexity and depth of focused, conscientious, coherent, self-critical and socially committed work. This has produced an open, porous architecture that ties in the joy and unpredictable essence of everyday life, nourishing collective spaces and embracing people's way of life.

The LMC statistical measure of complexity (and other related measures that, in this work, are collectively referred to as “LMC measures”) have been applied, by researchers in physics and other areas, to the study of diverse problems. In spite of the intriguing results reported in those studies, relatively little attention has been devoted to characterize what types of dynamics lead to the optimization of the LMC measures. As a first, exploratory step in that direction, we consider an example of a family of evolution equations admitting an H -like theorem related to the LMC measures.

This paper assesses the role of socio-demographic triggers on Kolmogorov-based complexity in spoken English varieties. It thus contributes to the ongoing debate on contact and complexity in the sociolinguistic typological research community. Currently, evidence on whether socio-demographic triggers influence the morphosyntactic complexity of languages is controversial and inconclusive. Particularly controversial is the influence of the proportion of non-native speakers and the number of native speakers, which are both common proxies for language contact. In order to illuminate the issue from an English-varieties perspective, I use regression analysis to test several socio-demographic triggers in a corpus database of spoken English varieties. Language complexity here is operationalised in terms of Kolmogorov-based morphological and syntactic complexity. The results only partially support the idea that socio-demographic triggers influence morphosyntactic complexity in English varieties, i.e., speaker-related triggers turn out to be negative but non-significant. Yet, net migration rate shows a positive significant effect on morphological complexity which needs to be seen in the global context of English as a commodity and unequal access to English. I thus argue that socioeconomic triggers are better predictors for complexity than demographic speaker numbers. In sum, the paper opens up new horizons for research on language complexity.

One of the most challenging tasks in studying streamflow is quantifying how the complexities of environmental and dynamic parameters contribute to the overall system complexity. To address this, we employed Kolmogorov complexity (KC) metrics, specifically the Kolmogorov complexity spectrum (KC spectrum) and the Kolmogorov complexity plane (KC plane). These measures were applied to monthly streamflow time series averaged across 1879 gauge stations on U.S. rivers over the period 1950–2015. The variables analyzed included streamflow as a complex physical system, along with its key components: temperature, precipitation, and the Lyapunov exponent (LEX), which represents river dynamics. Using these metrics, we calculated normalized KC spectra for each position within the KC plane, visualizing interactive master amplitudes alongside individual amplitudes on overlapping two-dimensional planes. We further computed the relative change in complexities (RCC) of the normalized master and individual components within the KC plane, ranging from 0 to 1 in defined intervals. Based on these results, we analyzed and discussed the complexity patterns of U.S. rivers corresponding to each interval of normalized amplitudes.

Complexity is much talked about but sub-optimally studied in health services research. Although the significance of the complex system as an analytic lens is increasingly recognised, many researchers are still using methods that assume a closed system in which predictive studies in general, and controlled experiments in particular, are possible and preferred. We argue that in open systems characterised by dynamically changing inter-relationships and tensions, conventional research designs predicated on linearity and predictability must be augmented by the study of how we can best deal with uncertainty, unpredictability and emergent causality. Accordingly, the study of complexity in health services and systems requires new standards of research quality, namely (for example) rich theorising, generative learning, and pragmatic adaptation to changing contexts. This framing of complexity-informed health services research provides a backdrop for a new collection of empirical studies. Each of the initial five papers in this collection illustrates, in different ways, the value of theoretically grounded, methodologically pluralistic, flexible and adaptive study designs. We propose an agenda for future research and invite researchers to contribute to this on-going series.

A bstract Quantum error correction has given us a natural language for the emergence of spacetime, but the black hole interior poses a challenge for this framework: at late times the apparent number of interior degrees of freedom in effective field theory can vastly exceed the true number of fundamental degrees of freedom, so there can be no isometric (i.e. inner-product preserving) encoding of the former into the latter. In this paper we explain how quantum error correction nonetheless can be used to explain the emergence of the black hole interior, via the idea of “non-isometric codes protected by computational complexity”. We show that many previous ideas, such as the existence of a large number of “null states”, a breakdown of effective field theory for operations of exponential complexity, the quantum extremal surface calculation of the Page curve, post-selection, “state-dependent/state-specific” operator reconstruction, and the “simple entropy” approach to complexity coarse-graining, all fit naturally into this framework, and we illustrate all of these phenomena simultaneously in a soluble model.

Variation in communicative complexity has been conceptually and empirically attributed to social complexity, with animals living in more complex social environments exhibiting more signals and/or more complex signals than animals living in simpler social environments. As compelling as studies highlighting a link between social and communicative variables are, this hypothesis remains challenged by operational problems, contrasting results, and several weaknesses of the associated tests. Specifically, how to best operationalize social and communicative complexity remains debated; alternative hypotheses, such as the role of a species’ ecology, morphology, or phylogenetic history, have been neglected; and the actual ways in which variation in signaling is directly affected by social factors remain largely unexplored. In this review, we address these three issues and propose an extension of the “social complexity hypothesis for communicative complexity” that resolves and acknowledges the above factors. We specifically argue for integrating the inherently multimodal nature of communication into a more comprehensive framework and for acknowledging the social context of derived signals and the potential of audience effects. By doing so, we believe it will be possible to generate more accurate predictions about which specific social parameters may be responsible for selection on new or more complex signals, as well as to uncover potential adaptive functions that are not necessarily apparent from studying communication in only one modality.

Since the introduction of the Kolmogorov complexity of binary sequences in the 1960s, there have been significant advancements on the topic of complexity measures for randomness assessment, which are of fundamental importance in theoretical computer science and of practical interest in cryptography. This survey reviews notable research from the past four decades on the linear, quadratic and maximum-order complexities of pseudo-random sequences, and their relations with Lempel–Ziv complexity, expansion complexity, 2-adic complexity and correlation measures.

On solving a convex-concave bilinear saddle-point problem (SPP), there have been many works studying the complexity results of first-order methods. These results are all about upper complexity bounds, which can determine at most how many iterations would guarantee a solution of desired accuracy. In this paper, we pursue the opposite direction by deriving lower complexity bounds of first-order methods on large-scale SPPs. Our results apply to the methods whose iterates are in the linear span of past first-order information, as well as more general methods that produce their iterates in an arbitrary manner based on first-order information. We first work on the affinely constrained smooth convex optimization that is a special case of SPP. Different from gradient method on unconstrained problems, we show that first-order methods on affinely constrained problems generally cannot be accelerated from the known convergence rate O(1 / t) to O(1/t2), and in addition, O(1 / t) is optimal for convex problems. Moreover, we prove that for strongly convex problems, O(1/t2) is the best possible convergence rate, while it is known that gradient methods can have linear convergence on unconstrained problems. Then we extend these results to general SPPs. It turns out that our lower complexity bounds match with several established upper complexity bounds in the literature, and thus they are tight and indicate the optimality of several existing first-order methods.