Search Results (432)

This essay explores the metaphysical and philosophical implications of Artificial Intelligence (AI) and Machine Learning (ML) through the intersecting insights of René Guénon (ʿAbd al-Wāḥid Yaḥiā), Martin Heidegger, and Ibn al-ʿArabī. It argues that modern AI systems, particularly in their statistical and data-centric forms, are not merely instrumental tools but expressions of a deeper metaphysical worldview-one rooted in quantification, abstraction, and utility. Guénon’s critique of the “reign of quantity” and Heidegger’s notion of Enframing (Gestell) converge in diagnosing the loss of qualitative and sacred dimensions in modern life. While Heidegger’s phenomenology provides a powerful immanent critique of technological reductionism from within the Western philosophical tradition, Guénon’s metaphysical traditionalism articulates a diagnosis of modernity that resonates with Islamic metaphysics, especially as articulated by Ibn al-ʿArabī. The essay includes Heidegger in the argument as a representative of a critique of modern technology issuing from the Western tradition itself, and by emphasizing his shared concerns with Guénon, whose metaphysics resonates with Ibn al-ʿArabī’s metaphysics. Through a comparative metaphysical framework, this paper proposes an Islamic response to AI that avoids both technophilia and technophobia, insisting instead on a spiritually grounded ethic of technology that preserves human’s dignity and mission. Methodologically, the essay restores a prior order often inverted in contemporary AI ethics: ontology (what AI is) grounds epistemology (what it can know), and only then can ethical evaluation be coherent. Full article

Regions of partial melt of Fe-S-Si alloys at high pressures may arise during planetary formation or at the boundary of the inner and outer cores of small terrestrial planetary bodies. Melting experiments were performed on Fe-16wt%S-2wt%Si samples in a multi-anvil apparatus across the range 2–13 GPa with quench temperatures in partial and complete melt regions. A phase diagram is constructed from electron microprobe analyses, back-scattered electron imaging, and electrical resistivity measurements. Microstructures arising in post-quench samples include Turing patterns of Fe and FeS in the partial melt, dendritic Fe structures with tertiary arms in the partial and complete melt, and Fe-S-Si miscible regions in the complete melt. These melt structures may arise broadly or locally in small Fe-S planetary cores with consequences for the energetics of the core. Full article

According to the Church–Turing thesis, the limit of what is computable is bounded by Turing machines. Following from this, given that general computable functions formally describe the notion of recursive mechanisms, it is sometimes argued that every organismic process that specifies consistent cognitive responses should be both limited to Turing machine capabilities and amenable to formalization. There is, however, a deep intuitive conviction permeating contemporary cognitive science, according to which mental phenomena, such as consciousness and agency, cannot be explained by resorting to this kind of framework. In spite of some exceptions, the overall tacit assumption is that whatever the mind is, it exceeds the reach of what is described by notions of computability. This issue, namely the nature of the relation between cognition and computation, becomes particularly pertinent and increasingly more relevant as a possible source of better understanding the inner workings of the mind, as well as the limits of artificial implementations thereof. Moreover, although it is often overlooked or omitted so as to simplify our models, it will probably define, or so we argue, the direction of future research on artificial life, cognitive science, artificial intelligence, and related fields. Full article

In this paper, we investigate the asymptotic behavior of solutions to a diffusive forest kinematic model, which describes the interactions among young trees, old trees, and airborne seeds. Our study focuses on the stability of the positive equilibrium, the occurrence of Hopf bifurcation yielding spatially homogeneous periodic solutions, and the subsequent Turing instability induced by diffusion in these periodic states. The analysis highlights that the juvenile tree mortality rate, represented by a quadratic function of mature tree density, plays a central dynamical role. Specifically, the parameter corresponding to the mature tree density at which juvenile mortality is minimized serves as a key Hopf bifurcation parameter. This indicates that the system’s transition to periodic solutions and later to diffusion-driven pattern formation can be effectively regulated through this parameter. From an ecological perspective, these results suggest that forest management strategies capable of indirectly influencing factors related to this critical parameter could help control the emergence of spatial patterns, such as forest patches. Furthermore, the functional form of the mortality rate offers a useful foundation for future studies examining how different assumptions regarding tree interaction morphology may influence ecosystem patterning. Full article

One of Wittgenstein’s most quoted passages from his Remarks on the Philosophy of Psychology concerns Turing’s “machines” and says verbatim: “These machines are humans who calculate. And one might express what he [Turing] says also in the form of games.” This passage not only captures the kernel of Turing’s conceptual argument for the adequacy of his definition of “computability”, as presented in his article On Computable Numbers (1936), but also helps clarify Turing’s idea of “mechanical intelligence.” Indeed, the notion of game provides an ideal means to focus on similarities and differences between Turing and Wittgenstein’s views of mechanical procedures, mathematical understanding, and thinking activity. The live encounter between Ludwig Wittgenstein and Alan Turing took place in Cambridge in 1939, when Wittgenstein’s Lectures on the Foundations of Mathematics were regularly attended by Turing. Interestingly, during the conversations between the two, Turing seems to play the role of the Wittgenstein of the Tractatus, to allow the present Wittgenstein to reassess what he deplores as mistaken or misleading in his early work. As for Turing himself, his reflection on thinking machines from the late 1940s demonstrates the significance of his dialogue with Wittgenstein. Full article

The design of low-complexity and efficient constrained codes has been a major research item for many years. This paper reports on a versatile method named concatenated constrained codes for designing efficient fixed-length constrained codes with small complexity. A concatenated constrained code comprises two (or more) cooperating constrained codes of low complexity enabling long constrained codes that are not practically feasible with prior art methods. We apply the concatenated coding approach to two case studies, namely the design of constant-weight and low-weight codes. In a binary constant-weight code, each codeword has the same number, w, of 1’s, where w is called the weight of a codeword. We specifically focus on the trading between coder complexity and redundancy. Full article

Kolmogorov complexity asks whether a string can be outputted by a Turing Machine (TM) whose description is shorter. Analogously, a real number is considered computable if a Turing machine can generate its decimal expansion. The modern $ϵ$-approximation definition of computability, widely used in practical computation, ensures that computable reals are constructively closed under addition. However, Turing’s original 1936 digit-by-digit notion, which demands the direct output of the $n\text{-th}$ digit, presents a stark divergence. Though the set of Turing-computable reals is not constructively closed under addition, we prove that a Turing machine capable of computing $x+y$ non-constructively exists. The core constructive computational barrier arises from determining the ones digit of a sum like $0.33\overline{3}+0.66\overline{6}=0.99\overline{9}=1.00\overline{0}$. This particular example is ambiguous because both $0.99\overline{9}$ and $1.00\overline{0}$ are legitimate decimal representations of the same number. However, if any of the infinite number of 3s in the first term is changed to a 2 (e.g., $0.33\dots 32\dots +0.66\overline{6}$), the sum’s leading digit is definitely zero. Conversely, if it is changed to a 4 (e.g., $0.33\dots 34\dots +0.66\overline{6}$), the leading digit is definitely one. This implies an inherent undecidability in determining these digits. Recent papers and our work address this issue. Hamkins provides an informal argument, while Berthelette et al. present more complicated formal proof, and our contribution offers a simple reduction to the Halting Problem. We demonstrate that determining when carry propagation stops can be resolved with a single query to an oracle that tells if and when a given TM halts. Because a concrete answer to this query exists, so does a TM computing the digits of $x+y$, though the proof is non-constructive. As far as we know, the analogous question for multiplication remains open. This, we feel, is an interesting addition to the story. This reveals a subtle but significant difference between the modern $ϵ$-approximation definition and Turing’s original 1936 digit-by-digit notion of a computable number, as well as between constructive and non-constructive proof. This issue of computability and numerical precision ties into algorithmic information and Kolmogorov complexity. Full article

The artificial generation of text by computers has been a field of study in computer science since the beginning of the twentieth century, from Markov chains to Turing tests. This has evolved into automatic summarization and marketing chatbots. The generation of literary texts by Large Language Models (LLMs) has also been an area of scholarly inquiry for over six decades. The literary quality of AI-generated text can be evaluated with GrAImes, an evaluation protocol grounded in literary theory and inspired by the editorial process of book publishers. This evaluation can also be framed as part of broader editorial practices within publishing, emphasizing both theoretical grounding and applied assessment. This protocol necessitates the involvement of human judges to validate the texts generated, a process that is often resource-intensive in terms of both time and financial investment, primarily due to the specialized credentials and expertise required of these evaluators. In this paper, we propose an alternative approach by employing LLMs themselves as evaluators within the GrAImes framework. We apply this methodology to assess human-written and AI-generated microfictions in Spanish, to five PhD professors in literature and sixteen literary enthusiasts, and to short stories in both Spanish and English. By comparing the evaluations performed by LLMs with those of human judges, we examine the degree of alignment and divergence between both perspectives, thereby assessing the feasibility of LLMs as auxiliary literary evaluators. Our analysis focuses on the alignment of responses from LLMs with those of human evaluators, providing insights into the potential of LLMs in literary assessment. The conducted experiments reveal that while LLMs cannot be regarded as substitutes for human judges in the evaluation of literary microfictions and short stories, with a Krippendorff’a alpha reliability coefficient less than 0.66, they can serve as a valuable tool that offers an initial perspective on the editorial quality of the texts in question. Overall, this study contributes to the ongoing discourse on the role of artificial intelligence in literature, underlining both its methodological constraints and its potential as a complementary resource for literary evaluation. Full article

Indirect fear effects profoundly influence predator–prey dynamics by reducing prey reproduction. Whereas previous studies have investigated fear effects or self-diffusion separately in Leslie–Gower models, the novelty of this work lies in their simultaneous incorporation into a modified Leslie–Gower predator–prey system with Allee effect, leading to previously unreported bifurcations and spatiotemporal pattern selection. The temporal system exhibits up to six equilibria and undergoes a codimension-2 Bogdanov–Takens bifurcation. In the spatial extension, Turing instability is triggered when the predator diffusion coefficient exceeds a critical threshold. Using weak nonlinear multiple-scale analysis, amplitude equations are derived, and their stability analysis classifies stationary patterns into spots, stripes, and spot–stripe mixtures depending on the distance from the Turing onset. Numerical simulations confirm that low, moderate, and high predator diffusivity, respectively, favour spotted, mixed, and striped prey distributions. These results emphasise the critical role of fear-mediated indirect interactions and diffusion in driving spatial heterogeneity and ecosystem stability. Full article

Road marking lines can be extracted from aerial images using semantic segmentation (SS) models; however, in this work, a conditional generative adversarial network, RoadMark-cGAN, is proposed for direct extraction of these representations with image-to-image translation techniques. The generator features residual and attention blocks added in a functional bottleneck, while the discriminator features a modified PatchGAN, with an optimized encoder and an attention block added. The proposed model is improved in three versions (v2 to v4), in which dynamic dropout techniques and a novel “Morphological Boundary-Sensitive Class-Balanced” (MBSCB) loss are progressively added to better handle the high class imbalance present in the data. All models were trained on a novel “RoadMarking-binary” dataset (29,405 RGB orthoimage tiles of 256 × 256 pixels and their corresponding ground truth masks) to learn the distribution of road marking lines found on pavement. The metrical evaluation on the test set containing 2045 unseen images showed that the best proposed model achieved average improvements of 45.2% and 1.7% in the Intersection-over-Union (IoU) score for the positive, underrepresented class when compared to the best Pix2Pix and SS models, respectively, trained for the same task. Finally, a qualitative, visual comparison was conducted to assess the quality of the road marking predictions of the best models and their mapping performance. Full article

Turing patterns, characterised by spatial self-organisation in reaction–diffusion systems, exhibit sensitivity to initial conditions. This sensitivity, known as the robustness problem, results in different final patterns emerging even from small initial perturbations. In this paper, we introduce a mechanism of pattern mode isolation, where we investigate parameter regimes that promote the isolation of bifurcation branches, thereby delineating the conditions under which distinct pattern modes emerge and evolve independently. Pattern mode isolation can provide a means of enhancing the predictability of Turing pattern mode transitions and enhance the robustness and reproducibility of the patterning outputs. Full article

Water resources are fundamental for human development in every possible sense; from natural development, since they are the main biological factor necessary for the development of life, to economic development, since they are essential for a large number of productive systems, especially in the primary and secondary sectors. This makes them a resource which, although at first glance may seem unlimited, is critical since their scarcity and unavailability compromise the whole of human development, greatly limiting productive and economic activity and, ultimately, social welfare. The current development of IoT technology, on the other hand, provides tools to face this problem in a technical way, allowing the adoption of distributed and automated solutions that, together with the knowledge provided by disciplines such as agricultural and alimentary engineering, make viable the development of a system that allows us to monitor and control water distribution networks (WDNs). Next, the situations that involve the mentioned problem will be detailed and different aspects will be proposed in which the implementation of the presented system is intended to have a direct impact. Full article

Based on the two-dimensional reaction–diffusion model, the spatiotemporal dynamical characteristics of the semi-discrete pine–Monochamus alternatus system with cross-diffusion and convection effect were studied in this work. Firstly, the stability conditions of the equilibrium point were obtained through linear stability analysis and Lyapunov coefficients, as well as the Andronov–Hopf bifurcation, which explained the reason for the periodic outbursts of the Lyapunov population from a dynamic perspective. Subsequently, through the characteristic equations of the Laplace operator ${\mathrm{\nabla }}^2$ and the gradient operator ∇, the critical discrimination conditions for the occurrence of Turing instability in the system were obtained and revealed that the phenomenon of frequent damage to pine caused by the pink Monochamus is in the form of patches. Finally, the reliability of the theoretical analysis was verified through numerical simulation, and the dual effect of convection was clearly found in the system. Moderate convection can change the pattern shape, while strong convection produces a “washout effect”, completely inhibiting the formation of the pattern. This indicates that factors such as wind or the directional migration of Monochamus alternatus significantly affect the spatial distribution pattern of pests. Therefore, the theoretical research on Turing instability of models with convection terms may provide inspiration for subsequent studies. Full article

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore less about raw performance or scaling, and more about an architectural limitation: knowledge is treated as an after-the-fact annotation on computation, rather than as an organizing substrate that shapes computation. This paper introduces Mindful Machines, a computational paradigm that operationalizes coherence as an architectural property rather than an emergent afterthought. A Mindful Machine is specified by a Digital Genome (encoding purposes, constraints, and knowledge structures) and orchestrated by an Autopoietic and Meta-Cognitive Operating System (AMOS) that runs a continuous Discover–Reflect–Apply–Share (D-R-A-S) loop. Instead of a static model embedded in a one-shot ML pipeline or deep learning neural network, the architecture separates (1) a structural knowledge layer (Digital Genome and knowledge graphs), (2) an autopoietic control plane (health checks, rollback, and self-repair), and (3) meta-cognitive governance (critique-then-commit gates, audit trails, and policy enforcement). We validate this approach on the classic Credit Default Prediction problem by comparing a traditional, static Logistic Regression pipeline (monolithic training, fixed features, external scripting for deployment) with a distributed Mindful Machine implementation whose components can reconfigure logic, update rules, and migrate workloads at runtime. The Mindful Machine not only matches the predictive task, but also achieves autopoiesis (self-healing services and live schema evolution), explainability (causal, event-driven audit trails), and dynamic adaptation (real-time logic and threshold switching driven by knowledge constraints), thereby reducing the coherence debt that characterizes contemporary ML- and LLM-centric AI architectures. The case study demonstrates “a hybrid, runtime-switchable combination of machine learning and rule-based simulation, orchestrated by AMOS under knowledge and policy constraints”. Full article

Hypergraphs, a generalisation of traditional graphs in which hyperedges may connect more than two vertices, provide a natural framework for modeling higher-order interactions in complex biological systems. In the context of protein complexes, hypergraphs capture relationships in which a single protein may participate in multiple complexes simultaneously. A fundamental question is how such protein complex hypergraphs evolve over time. Motivated by duplication–divergence–deletion models often used for protein–protein interaction networks, we propose a novel Duplication–Divergence Hypergraph (DDH) model for the evolutionary dynamics of protein complex hypergraphs. To evaluate network resilience, we simulate targeted attack strategies analogous to drug treatments or genetic knockouts that remove selected proteins and their associated hyperedges. We measure the resulting structural changes using hypergraph-based efficiency metrics, comparing synthetic networks generated by the DDH model with empirical E. coli protein complex data. This framework demonstrates closer alignment with empirical observations than standard pairwise duplication–divergence models, suggesting that hypergraphs provide a more realistic representation of protein interactions. Full article