RAFAEL PRIETO-CURIEL , GIAN MARIA CAMPEDELLI, AND ALEJANDRO HOPE
SCIENCE 21 Sep 2023 Vol 381, Issue 6664 pp. 1312-1316 DOI: 10.1126/science.adh2888

Mexican cartels lose many members as a result of conflict with other cartels and incarcerations. Yet, despite their losses, cartels manage to increase violence for years. We address this puzzle by leveraging data on homicides, missing persons, and incarcerations in Mexico for the past decade along with information on cartel interactions. We model recruitment, state incapacitation, conflict, and saturation as sources of cartel size variation. Results show that by 2022, cartels counted 160,000 to 185,000 units, becoming one of the country’s top employers. Recruiting between 350 and 370 people per week is essential to avoid their collapse because of aggregate losses. Furthermore, we show that increasing incapacitation would increase both homicides and cartel members. Conversely, reducing recruitment could substantially curtail violence and lower cartel size.

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Measurement and entanglement both have a “spooky” nonlocal flavor to them. Now physicists are harnessing that nonlocality to probe the spread of quantum information and control it.

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ARYO JAMSHIDPEY, MARCO DORIGO, AND MARY KATHERINE HEINRICH

INTELLIGENT COMPUTING 13 Sep 2023 Vol 2 Article ID: 0044 DOI: 10.34133/icomputing.0044

In collective perception, agents sample spatial data and use the samples to agree on some estimate. In this paper, we identify the sources of statistical uncertainty that occur in collective perception and note that improving the accuracy of fully decentralized approaches, beyond a certain threshold, might be intractable. We propose self-organizing hierarchy as an approach to improve accuracy in collective perception by reducing or eliminating some of the sources of uncertainty. Using self-organizing hierarchy, aspects of centralization and decentralization can be combined: robots can understand their relative positions system-wide and fuse their information at one point, without requiring, e.g., a fully connected or static communication network. In this way, multi-sensor fusion techniques that were designed for fully centralized systems can be applied to a self-organized system for the first time, without losing the key practical benefits of decentralization. We implement simple proof-of-concept fusion in a self-organizing hierarchy approach and test it against three fully decentralized benchmark approaches. We test the perceptual accuracy of the approaches for absolute conditions that are uniform time-invariant, time-varying, and spatially nonuniform with high heterogeneity, as well as the scalability and fault tolerance of their accuracy. We show that, under our tested conditions, the self-organizing hierarchy approach is generally more accurate, more consistent, and faster than the other approaches and also that its accuracy is more scalable and comparably fault-tolerant. Under spatially nonuniform conditions, our results indicate that the four approaches are comparable in terms of similarity to the reference samples. In future work, extending these results to additional methods, such as collective probability distribution fitting, is likely to be much more straightforward in the self-organizing hierarchy approach than in the decentralized approaches.

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Bálint Hartmann, István Papp, Kristóf Benedek, Shengfeng Deng, Géza Ódor, Jeffrey Kelling

While weak, tuned asymmetry can improve, strong heterogeneity destroys synchronization in the electric power system. We study the level of heterogeneity, by comparing large high voltage (HV) power-grids of Europe and North America. We provide an analysis of power capacities and loads of various energy sources from the databases and found heavy tailed distributions with similar characteristics. Graph topological measures, community structures also exhibit strong similarities, while the cable admittance distributions can be well fitted with the same power-laws (PL), related to the length distributions. The community detection analysis shows the level of synchronization in different domains of the European HV power grids, by solving a set of swing equations. We provide numerical evidence for frustrated synchronization and Chimera states and point out the relation of topology and level of synchronization in the subsystems. We also provide empirical data analysis of the frequency heterogeneities within the Hungarian HV network and find q-Gaussian distributions related to super-statistics of time-lagged fluctuations, which agree well with former results on the Nordic Grid.

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Søren Nors Nielsen, Felix Müller

Entropy 2023, 25(9), 1288

In the last few decades, the number of published papers that include search terms such as thermodynamics, entropy, ecology, and ecosystems has grown rapidly. Recently, background research carried out during the development of a paper on “thermodynamics in ecology” revealed huge variation in the understanding of the meaning and the use of some of the central terms in this field—in particular, entropy. This variation seems to be based primarily on the differing educational and scientific backgrounds of the researchers responsible for contributions to this field. Secondly, some ecological subdisciplines also seem to be better suited and applicable to certain interpretations of the concept than others. The most well-known seems to be the use of the Boltzmann–Gibbs equation in the guise of the Shannon–Weaver/Wiener index when applied to the estimation of biodiversity in ecology. Thirdly, this tendency also revealed that the use of entropy-like functions could be diverted into an area of statistical and distributional analyses as opposed to real thermodynamic approaches, which explicitly aim to describe and account for the energy fluxes and dissipations in the systems. Fourthly, these different ways of usage contribute to an increased confusion in discussions about efficiency and possible telos in nature, whether at the developmental level of the organism, a population, or an entire ecosystem. All the papers, in general, suffer from a lack of clear definitions of the thermodynamic functions used, and we, therefore, recommend that future publications in this area endeavor to achieve a more precise use of language. Only by increasing such efforts it is possible to understand and resolve some of the significant and possibly misleading discussions in this area.

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J. G. Oliveira, S. N. Dorogovtsev, J. F. F. Mendes

We delve into the statistical properties of regions within complex networks that are distant from vertices with high centralities, such as hubs or highly connected clusters. These remote regions play a pivotal role in shaping the asymptotic behaviours of various spreading processes and the features of associated spectra. We investigate the probability distribution P≥m(s) of the number s of vertices located at distance m or beyond from a randomly chosen vertex in an undirected network. Earlier, this distribution and its large m asymptotics 1/s2 were obtained theoretically for undirected uncorrelated networks [S. N. Dorogovtsev, J. F. F. Mendes, A. N. Samukhin, Nucl. Phys. B 653 (2003) 307]. Employing numerical simulations and analysing empirical data, we explore a wide range of real undirected networks and their models, including trees and loopy networks, and reveal that the inverse square law is valid even for networks with strong correlations. We observe this law in the networks demonstrating the small-world effect and containing vertices with degree 1 (so-called leaves or dead ends). We find the specific classes of networks for which this law is not valid. Such networks include the finite-dimensional networks and the networks embedded in finite-dimensional spaces. We notice that long chains of nodes in networks reduce the range of m for which the inverse square law can be spotted. Interestingly, we detect such long chains in the remote regions of the undirected projection of a large Web domain.

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Felix Wagner, Florian Nachtigall, Lukas Franken, Nikola Milojevic-Dupont, Rafael H.M. Pereira, Nicolas Koch, Jakob Runge, Marta Gonzalez, Felix Creutzig

Global sustainability requires low-carbon urban transport systems, shaped by adequate infrastructure, deployment of low-carbon transport modes and shifts in travel behavior. To adequately implement alterations in infrastructure, it's essential to grasp the location-specific cause-and-effect mechanisms that the constructed environment has on travel. Yet, current research falls short in representing causal relationships between the 6D urban form variables and travel, generalizing across different regions, and modeling urban form effects at high spatial resolution. Here, we address all three gaps by utilizing a causal discovery and an explainable machine learning framework to detect urban form effects on intra-city travel based on high-resolution mobility data of six cities across three continents. We show that both distance to city center, demographics and density indirectly affect other urban form features. By considering the causal relationships, we find that location-specific influences align across cities, yet vary in magnitude. In addition, the spread of the city and the coverage of jobs across the city are the strongest determinants of travel-related emissions, highlighting the benefits of compact development and associated benefits. Differences in urban form effects across the cities call for a more holistic definition of 6D measures. Our work is a starting point for location-specific analysis of urban form effects on mobility behavior using causal discovery approaches, which is highly relevant for city planners and municipalities across continents.

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David Fajardo-Ortiz, Bart Thijs, Wolfgang Glanzel, Karin R. Sipido

The historical research-funding model, based on the curiosity and academic interests of researchers, is giving way to new strategic funding models that seek to meet societal needs. We investigated the impact of this trend on health research funded by the two leading funding bodies worldwide, i.e. the National Institutes of Health (NIH) in the United States, and the framework programs of the European Union (EU). To this end, we performed a quantitative analysis of the content of projects supported through programmatic funding by the EU and NIH, in the period 2008-2014 and 2015-2020. We used machine learning for classification of projects as basic biomedical research, or as more implementation directed clinical therapeutic research, diagnostics research, population research, or policy and management research. In addition, we analyzed funding for major disease areas (cancer, cardio-metabolic and infectious disease). We found that EU collaborative health research projects clearly shifted towards more implementation research. In the US, the recently implemented UM1 program has a similar profile with strong clinical therapeutic research, while other NIH programs remain heavily oriented to basic biomedical research. Funding for cancer research is present across all NIH and EU programs, and in biomedical as well as more implementation directed projects, while infectious diseases is an emerging theme. We conclude that demand for solutions for medical needs leads to expanded funding for implementation- and impact-oriented research. Basic biomedical research remains present in programs driven by scientific initiative and strategies based on excellence, but may be at risk of declining funding opportunities.

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MICHAŁ B. PARADOWSKI, AGNIESZKA CIERPICH–KOZIEŁ, CHIH–CHUN CHEN, JEREMI K. OCHAB

MLJ Volume106, Issue4 Winter 2022 Pages 694-725

This data-driven study framed in the interactionist approach investigates the influence of social graph topology and peer interaction dynamics among foreign exchange students enrolled in an intensive German language course on second language acquisition (SLA) outcomes. Applying the algorithms and metrics of computational social network analysis (SNA), we find that (a) the best predictor of target language (TL) performance is reciprocal interactions in the language being acquired, (b) the proportion of output in the TL is a stronger predictor than input (Principle of Proportional Output), (c) there is a negative relationship between performance and interactions with same-first-language speakers, (d) a significantly underperforming English native-speaker dominated cluster is present, and (e) there are more intense interactions taking place between students of different proficiency levels. Unlike previous study abroad social network research concentrating on the microlevel of individual learners’ egocentric networks and presenting an emic view only, this study constitutes the first application of computational SNA to a complete learner network (sociogram). It provides new insights into the link between social relations and SLA with an etic perspective, showing how social network configuration and peer learner interaction are stronger predictors of TL performance than individual factors such as attitude or motivation, and offering a rigorous methodology for investigating the phenomenon.

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Arash Sadeghi Amjadi, Cem Bilaloğlu, Ali Emre Turgut, Seongin Na, Erol Şahin, Tomáš Krajník, Farshad Arvin
Adaptive Behavior

Aggregation, the gathering of individuals into a single group as observed in animals such as birds, bees, and amoeba, is known to provide protection against predators or resistance to adverse environmental conditions for the whole. Cue-based aggregation, where environmental cues determine the location of aggregation, is known to be challenging when the swarm density is low. Here, we propose a novel aggregation method applicable to real robots in low-density swarms. Previously, Landmark-Based Aggregation (LBA) method had used odometric dead-reckoning coupled with visual landmarks and yielded better aggregation in low-density swarms. However, the method’s performance was affected adversely by odometry drift, jeopardizing its application in real-world scenarios. In this article, a novel Reinforcement Learning-based Aggregation method, RLA, is proposed to increase aggregation robustness, thus making aggregation possible for real robots in low-density swarm settings. Systematic experiments conducted in a kinematic-based simulator and on real robots have shown that the RLA method yielded larger aggregates, is more robust to odometry noise than the LBA method, and adapts better to environmental changes while not being sensitive to parameter tuning, making it better deployable under real-world conditions.

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Kirsty Y. Wan

Animal Cognition

Living organisms routinely navigate their surroundings in search of better conditions, more food, or to avoid predators. Typically, animals do so by integrating sensory cues from the environment with their locomotor apparatuses. For single cells or small organisms that possess motility, fundamental physical constraints imposed by their small size have led to alternative navigation strategies that are specific to the microscopic world. Intriguingly, underlying these myriad exploratory behaviours or sensory functions is the onset of periodic activity at multiple scales, such as the undulations of cilia and flagella, the vibrations of hair cells, or the oscillatory shape modes of migrating neutrophils. Here, I explore oscillatory dynamics in basal microeukaryotes and hypothesize that these active oscillations play a critical role in enhancing the fidelity of adaptive sensorimotor integration.

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Guim Aguadé-Gorgorió, Jean-François Arnoldi, Matthieu Barbier, Sonia Kéfi

Many natural and man-made systems, from financial markets to ecosystems or the human brain, are built from multiple interconnected units. This complex high-dimensionality hinders our capacity to understand and predict the dynamics, functioning and fragility of these systems. One fragility scenario, particularly relevant to ecological communities of interacting species, concerns so-called regime shifts: abrupt and unexpected transitions from healthy, species-rich communities towards states of degraded ecosystem function and services. The accepted explanation for these shifts is that they arise as abrupt transitions between alternative stable states: multiple stable configurations of a system under the same internal and external conditions. These alternative states are well-understood in low-dimensional systems, but how they upscale with system complexity remains a debated question. In the present work we investigate the emergence of multiple stable states in a number of complex system models. We find that high-dimensional models with random interactions can unfold at least four different regimes of multistability, each emerging under a specific interaction scheme. More importantly, each multistability regime leaves a distinct and quantifiable fingerprint, providing a framework to analyze experimental evidence of abrupt shifts. By bridging previous results and studying multistability regimes, their fingerprints and their correlation with empirical evidence in ecology, our study helps define a common ground to understand and classify multiple stable states in complex systems.

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Artemy Kolchinsky

Phys. Rev. E 108, 034101

We consider the minimal thermodynamic cost of an individual computation, where a single input x is mapped to a single output y. In prior work, Zurek proposed that this cost was given by K(x|y), the conditional Kolmogorov complexity of x given y (up to an additive constant that does not depend on x or y). However, this result was derived from an informal argument, applied only to deterministic computations, and had an arbitrary dependence on the choice of protocol (via the additive constant). Here we use stochastic thermodynamics to derive a generalized version of Zurek's bound from a rigorous Hamiltonian formulation. Our bound applies to all quantum and classical processes, whether noisy or deterministic, and it explicitly captures the dependence on the protocol. We show that K(x|y) is a minimal cost of mapping x to y that must be paid using some combination of heat, noise, and protocol complexity, implying a trade-off between these three resources. Our result is a kind of “algorithmic fluctuation theorem” with implications for the relationship between the second law and the Physical Church-Turing thesis.

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Keith D. Farnsworth

Biosystems Volume 232, October 2023, 105013

Autonomy, meaning freedom from exogenous control, requires independence of both constitution and cybernetic regulation. Here, the necessity of biological codes to achieve both is explained, assuming that Aristotelian efficient cause is ‘formal cause empowered by physical force’. Constitutive independence requires closure to efficient causation (in the Rosen sense); cybernetic independence requires transformation of cause–effect into signal-response relations at the organism boundary; the combination of both kinds of independence enables adaptation and evolution. Codes and cyphers translate information from one form of physical embodiment (domain) to another. Because information can only contribute as formal cause to efficient cause within the domain of its embodiment, translation can extend or restrict the range over which information is effective. Closure to efficient causation requires internalised information to be isolated from the cycle of efficient causes that it informs: e.g. Von Neumann self-replicator requires a (template) source of information that is causally isolated from the physical replication system. Life operationalises this isolation with the genetic code translating from the (isolated) domain of codons to that of protein interactions. Separately, cybernetic freedom is achieved at the cell boundary because transducers, which embody molecular coding, translate exogenous information into a domain where it no longer has the power of efficient cause. Information, not efficient cause, passes through the boundary to serve as stimulus for an internally generated response. Coding further extends freedom by enabling historically accumulated information to be selectively transformed into efficient cause under internal control, leaving it otherwise stored inactive. Code-based translation thus enables selective causal isolation, controlling the flow from cause to effect. Genetic code, cell-signalling codes and, in eukaryotes, the histone code, signal sequence based protein sorting and other code-dependent processes all regulate and separate causal chains. The existence of life can be seen as an expression of the power of molecular codes to selectively isolate and thereby organise causal relations among molecular interactions to form an organism.

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Alessandro Scirè & Valerio Annovazzi-Lodi 

Theory in Biosciences volume 142, pages 291–299 (2023)

This work concerns a many-body deterministic model that displays life-like properties such as emergence, complexity, self-organization, self-regulation, excitability and spontaneous compartmentalization. The model portraits the dynamics of an ensemble of locally coupled polar phase oscillators, moving in a two-dimensional space, that under certain conditions exhibit emergent superstructures. Those superstructures are self-organized dynamic networks, resulting from a synchronization process of many units, over length scales much greater than the interaction range. Such networks compartmentalize the two-dimensional space with no a priori constraints, due to the formation of porous transport walls, and represent a highly complex and novel non-linear behavior. The analysis is numerically carried out as a function of a control parameter showing distinct regimes: static pattern formation, dynamic excitable networks formation, intermittency and chaos. A statistical analysis is drawn to determine the control parameter ranges for the various behaviors to appear. The model and the results shown in this work are expected to contribute to the field of artificial life.

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