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1. The fractal nature as new frontier in microstructural characterization and relativization of scale sizes within space

   https://doi.org/10.1142/S0217984920504217  

   Mitic, Vojislav V.; Lazovic, Goran; Mirjanic, Dragoljub; Fecht, Hans; Vlahovic, Branislav; Arnold, Walter (July 2020, Modern Physics Letters B)

   Today in the age of advanced ceramic civilization, there are a variety of applications for modern ceramics materials with specific properties. Our up-to date research recognizes that ceramics have a fractal configuration nature on the basis of different phenomena. The key property of fractals is their scale-independence. The practical value is that the fractal objects’ interaction and energy is possible at any reasonable scale of magnitude, including the nanoscale and may be even below. This is a consequence of fractal scale independence. This brings us to the conclusion that properties of fractals are valid on any scale (macro, micro, or nano). We also analyzed these questions with experimental results obtained from a comet, here 67P, and also from ceramic grain and pore morphologies on the microstructure level. Fractality, as a scale-independent morphology, provides significant variety of opportunities, for example for energy storage. From the viewpoint of scaling, the relation between large and small in fractal analysis is very important. An ideal fractal can be magnified endlessly but natural morphologies cannot, what is the new light in materials sciences and space. 

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2. Revisiting scale invariance and scaling in ecology: River fractals as an example

   https://doi.org/10.1002/1438-390X.12163  

   Terui, Akira (August 2023, Population Ecology)

   Abstract Scale invariance, which refers to the preservation of geometric properties regardless of observation scale, is a prevalent phenomenon in ecological systems. This concept is closely associated with fractals, and river networks serve as prime examples of fractal systems. Quantifying river network complexity is crucial for unveiling the role of river fractals in riverine ecological dynamics, and researchers have used a metric of “branching probability” to do so. Previous studies showed that this metric reflects the fractal nature of river networks. However, a recent article by Carraro and Altermatt (2022) contradicted this classical observation and concluded that branching probability is “scale dependent.” I dispute this claim and argue that their major conclusion is derived merely from their misconception of scale invariance. Their analysis in the original article (fig. 3a) provided evidence that branching probability is scale‐invariant (i.e., branching probability exhibits a power‐law scaling), although the authors erroneously interpreted this result as a sign of scale dependence. In this article, I re‐introduce the definition of scale invariance and show that branching probability meets this definition. This provided an opportunity to address the divergent use of “scale invariance” and “scaling” between fractal theory and ecology. 

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3. Bridging the Gap Between Neurons and Cognition Through Assemblies of Neurons

   https://doi.org/10.1162/neco_a_01463  

   Papadimitriou, Christos H.; Friederici, Angela D. (January 2022, Neural Computation)

   Abstract During recent decades, our understanding of the brain has advanced dramatically at both the cellular and molecular levels and at the cognitive neurofunctional level; however, a huge gap remains between the microlevel of physiology and the macrolevel of cognition. We propose that computational models based on assemblies of neurons can serve as a blueprint for bridging these two scales. We discuss recently developed computational models of assemblies that have been demonstrated to mediate higher cognitive functions such as the processing of simple sentences, to be realistically realizable by neural activity, and to possess general computational power. 

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4. Female‐directed aggression by adolescent male chimpanzees primarily constitutes dominance striving, not sexual coercion

   https://doi.org/10.1002/ajpa.24296  

   Enigk, Drew K.; Emery Thompson, Melissa; Machanda, Zarin P.; Wrangham, Richard W.; Muller, Martin N. (May 2021, American Journal of Physical Anthropology)

   Abstract ObjectivesChimpanzees (Pan troglodytes) are notable for exhibiting high levels of male‐to‐female aggression. Much of this aggression from adult males serves sexually coercive functions. Despite being smaller and lower‐ranking than adult males, adolescent males also engage in regular aggression against adult females. Here, we test whether the primary function of this aggression is sexual coercion, as in adult males, or, alternatively, whether adolescent males use aggression to establish social dominance over females. Materials and MethodsWe analyzed 1771 copulations and 1812 instances of male‐initiated aggression between adolescent males (aged nine through 14 years) and adult females across 21 years of observation of the Kanyawara chimpanzee community in Kibale National Park, Uganda. ResultsOur test of the sexual coercion hypothesis revealed that adolescent males did not selectively target cycling females for aggression, nor did aggression against cycling females predict rates of copulation with those females. Our test of the social dominance hypothesis showed that males succeeded in dominating all adult females before, or soon after, dominating their first adult male. Additionally, we found that adolescent males dominated females approximately in the order of the females' own ranks, from the bottom to the top of the female hierarchy. DiscussionOur data illustrate that the establishment of social dominance was more important than sexual coercion in explaining patterns of adolescent male aggression toward females. In comparison, evidence for sexual coercion was clear and compelling in adult males. These findings highlight that the primary function of male‐to‐female aggression differs between adolescent and adult males. 

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5. Learning Fractals by Gradient Descent

   https://doi.org/10.1609/aaai.v37i2.25342  

   Tu, Cheng-Hao; Chen, Hong-You; Carlyn, David; Chao, Wei-Lun (June 2023, Proceedings of the AAAI Conference on Artificial Intelligence)

   Fractals are geometric shapes that can display complex and self-similar patterns found in nature (e.g., clouds and plants). Recent works in visual recognition have leveraged this property to create random fractal images for model pre-training. In this paper, we study the inverse problem --- given a target image (not necessarily a fractal), we aim to generate a fractal image that looks like it. We propose a novel approach that learns the parameters underlying a fractal image via gradient descent. We show that our approach can find fractal parameters of high visual quality and be compatible with different loss functions, opening up several potentials, e.g., learning fractals for downstream tasks, scientific understanding, etc. 

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