European Centre for Living Technology

We introduce the cooperative network architecture (CNA), a model that represents sensory signals using structured, recurrently connected networks of neurons, termed "nets." Nets are dynamically assembled from overlapping net fragments, which are learned based on statistical regularities in sensory input. This architecture offers robustness to noise, deformation, and generalization to out-of-distribution data, addressing challenges in current vision systems from a novel perspective. We demonstrate that net fragments can be learned without supervision and flexibly recombined to encode novel patterns, enabling figurecompletion and resilience to noise. Our findings establish CNA as a promising paradigm for developing neural representations that integrate local feature processing with global structure formation, providing a foundation for future research on invariant object recognition.

Wetware Network-Based Artificial Intelligence (WNAI) introduces a new approach to robotic cognition and artificial intelligence: autonomous cognitive agents built from synthetic chemical networks. Rooted in Wetware Neuromorphic Engineering, WNAI shifts the focus of this emerging field from disembodied computation and biological mimicry to reticular chemical self-organization as a substrate for cognition. At the theoretical level, WNAI integrates insights from network cybernetics, autopoietic theory and enaction to frame cognition as a materially grounded, emergent phenomenon. At the heuristic level, WNAI defines its role as complementary to existing leading approaches. On the one hand, it complements embodied AI and xenobotics by expanding the design space of artificial embodied cognition into fully synthetic domains. On the other hand, it engages in mutual exchange with neural network architectures, advancing cross-substrate principles of network-based cognition. At the technological level, WNAI offers a roadmap for implementing chemical neural networks and protocellular agents, with potential applications in robotic systems requiring minimal, adaptive, and substrate-sensitive intelligence. By situating wetware neuromorphic engineering within the broader landscape of robotics and AI, this article outlines a programmatic framework that highlights its potential to expand artificial cognition beyond silicon and biohybrid systems.

The development of efficient circularly polarized electrochemiluminescence (CP-ECL) probes is still at its infancy and examples are still very limited. Yet, their achievement would enable gathering a readout that carries privileged information on the probe's chiral environment by monitoring luminescence polarization bias with high signal-to-noise ratio. Notwithstanding, this is a highly challenging task and requires judicious chemical engineering of chiral ECL-active emitters. Herein, we aim at expanding the palette of CP-ECL luminophores by presenting a novel class of enantiopure heterobinuclear Ir(III)-Au(I) complexes, which are investigated thoroughly by means of chemical, structural, and (chiro-)optical techniques. The ground and excited state properties are also elucidated by using density functional theory (DFT) approaches including spin-orbital coupling (SOC) perturbation. The chiral-at-metal complexes display luminescence with a polarization bias of the emitted light that is function of the helical arrangement of the coordination sphere around the Ir(III) center. Overall, the photo- and electro-active complexes unraveled in this work combine unparallelly high photoluminescence quantum yield in the orange region, excellent circularly polarized luminescence (CPL) brightness up to 4.5M-1 cm-1 with a notable ECL activity. Finally, these features provide emitters with CP-ECL efficiency that encompass remarkably by a factor 3.5 that of the well-known benchmark tris-(2,2'-bipyridyl)ruthenium(II).

Modular microrobotics can potentially address many information-intensive microtasks in medicine, manufacturing, and the environment. However, surface area has limited the natural powering, communication, functional integration, and self-assembly of smart mass-fabricated modular robotic devices at small scales. We demonstrate the integrated self-folding and self-rolling of functionalized patterned interior and exterior membrane surfaces resulting in programmable, self-assembling, intercommunicating, and self-locomoting micromodules (smartlets ≤ 1 cubic millimeter) with interior chambers for onboard buoyancy control. The microrobotic divers, with 360° solar harvesting rolls, functioned with sufficient ambient power for communication and programmed locomotion in water via electrolysis. The interior folding faces carried rigid microcomponents, including silicon chiplets (Si chiplets) as microprocessors and micro-light-emitting diodes (LEDs) for communication. The exterior faces were able to engage in specific patterned docking interactions between smartlets. The heterogeneous integration is mass producible and affordable through two-dimensional (2D)-automated lithography and microchiplet bump-bonding processes, here shown to be compatible with subsequent autonomous 3D folding and rolling. The robotic modules functioned in natural aqueous environments, and the technology was analyzed as scalable down to microscopic dimensions. Selectively addressed communication with individual smartlets was enhanced via frequency-specific optical signals and enabled precise control, allowing each smartlet to be activated independently within a collective system. The work remodels modular microrobotics closer to the surface-rich modular autonomy of biological cells and provides an economical platform for microscopic applications.

Abstract Oxygen evolution reaction (OER) is a key step affecting the large‐scale hydrogen fuel production from water splitting due to its slow kinetics. Despite numerous OER electrocatalysts, there is still a need for cheap, robust, and efficient catalysts. Here, a new facile protocol aimed at synthesizing composite nanostructures is described in which the characteristics of Prussian Blue Analogues (PBAs), namely nickel hexacyanoferrate (Ni‐HCF), and trigonal selenium ( t‐ Se) are merged. A novel surface‐structure modulation strategy is proposed that allows the PBA Ni‐HCF to develop a new morphology while integrating t‐ Se. Increasing selenization rates influence the shape, size, composition, and electronic and functional properties of the materials with a cube‐to‐sphere transition at high t‐ Se content. The catalyst with the second highest t‐ Se content exhibits an overpotential of 180 mV, achieving a current density of 10 mA cm −2 in 1 m KOH with a Tafel slope of 59.9 mV dec −1 . This performance, attributable to the synergistic effect of PBA‐based nanomaterial and trigonal selenium, rivals the best results described in the literature. The study suggests a simplified route for developing effective OER electrodes using economically affordable and abundant materials like Ni, Fe, and Se in Ni‐HCF/ t‐ Se, potentially replacing expensive Ru‐ and Ir‐based commercial catalysts.

ABSTRACTThe Sustainable Finance Disclosure Regulation (SFDR) imposes on investment companies the publication of sustainability related profiles of the portfolio creation process. This obligation seeks to enhance the information provided to customers on financial products with regard to the Environmental, Social and Governance (ESG) criteria. In order to understand to which extent this information is relevant for investment decisions, the reliability of the classification among funds with a different ESG focus (articles 6, 8, and 9) is studied from a financial perspective and verified through a hierarchical cluster analysis on principal components of the performance measures of various funds distributed in Europe.

Abstract This study examines the key factors that affect Europeans’ reactions to artificial intelligence (AI) in the context of both full and flawed democracies in Europe. AI applications have increasingly been integrated into democratic practices, ranging from micro-targeting of voters to election information campaigns and protests, as well as various administrative functions and services provided by governments. However, the impact of AI on democracy and democratic institutions has yielded mixed outcomes. Drawing upon a dataset of 4004 respondents, categorised into full democracies and flawed democracies based on The Democracy Index developed by the Economist Intelligence Unit (EIU), this research identifies crucial factors that shape Europeans’ attitudes toward AI in these two types of democracies. The analysis reveals noteworthy findings. First, flawed democracies tend to exhibit higher levels of trust in government entities compared to their counterparts in full democracies. Furthermore, individuals residing in flawed democracies demonstrate a more positive attitude toward AI when compared to respondents from full democracies. However, the study does not find significant differences in AI awareness between the two types of democracies, indicating a similar level of general knowledge about AI technologies amongst European citizens. Moreover, the study reveals that trust in AI measures, specifically “Trust AI Solution,” does not vary significantly between full and flawed democracies. This suggests that despite differences in democratic quality, both types of democracy have similar levels of confidence in AI solutions. Furthermore, employing regression models, the study uncovers the relative impact of these key factors and their correlations can reflect on policy implications. These findings contribute to a better understanding of the factors that shape the reactions of Europeans to AI in the democratic context, providing valuable information to policymakers and stakeholders in designing effective AI governance frameworks and strategies.

One of the fundamental problems of information theory, since its foundation by C. Shannon, has been the computation of the capacity of a discrete memoryless channel, a quantity expressing the maximum rate at which information can travel through the channel. In this paper, we investigate the properties of a novel approach to computing the capacity, based on a continuous-time dynamical system. Interestingly, the proposed dynamical system can be regarded as a continuous-time version of the classical Blahut-Arimoto algorithm, and we can prove that the former shares with the latter an exponential rate of convergence if certain conditions are met. Moreover, a circuit design is presented to implement the dynamics, hence enabling analog computation to estimate the capacity.

Abstract American archaeology has long been focused on reconstructing past cultures through the description and chronological ordering of items found in the archaeological record. This goal was most evident starting in the early 20th century through what became known as culture history, which in retrospect produced results based on common sense and ethnographic analogues rather than on formal theory. By the mid-1930s, some culture historians realized the lack of testability in their conclusions and began exploring Darwinian evolutionary theory as an alternative. However, their efforts were often ignored or ridiculed, and it wasn’t until the early 1980s that evolutionary theory and associated methods began to play significant roles in archaeology. This acceptance grew from the development of a genetics-based theory of cultural transmission and the introduction of phylogenetic methods into anthropology and archaeology. These methods offered the necessary means for distinguishing between simple historical continuity—one thing following another chronologically—and heritable continuity—how one thing is related to another in terms of descent. Two concepts that play key roles in the reconstruction of cultural phylogenies are tradition and lineage, the former representing patterns of phylogenetic relationship and the latter patterns of genealogical descent.