Search Results (12)

The analysis of brain data through electroencephalography (EEG) has become essential in neuroscience, affective computing, and brain–computer interfaces. Recent work associates EEG features with artificial neurotransmitter models, simulating emotions and rational–emotional decision-making using complexity theory. However, current methods face limitations: (1) linear temporal representations lacking memory and anticipation, (2) limited contextual adaptation, (3) difficulty with paradoxical affective states, and (4) absence of ethical reasoning in decision-making. We present a framework based on Sophimatics, using complex time ($t=t_{real}+i\cdot t_{imag}\in \mathbb{C}$) where $t_{real}$ represents chronology and $t_{imag}$ encodes experiential dimensions including memory depth and anticipatory imagination. The Super Time Cognitive Neural Network (STCNN) architecture enables the parallel processing of objective time sequences and subjective cognitive experiences. Our Sophimatics-assisted EEG analysis achieves: (1) two-dimensional temporal coherence integrating past experiences and future projections, (2) context-sensitive adaptation via ontological knowledge graphs, (3) interpretable symbolic reasoning compatible with clinical psychology, (4) mechanisms for resolving affective paradoxes, and (5) ethical constraints ensuring value-based decision-making. Across three case studies (emotion recognition, meditation-induced transitions, and brain–computer interface decision support), integrated Sophimatics models outperform traditional machine learning (15–22% accuracy improvement) and complexity theory models (8–14% improvement), while offering greater cognitive richness and immunity to incomplete data. Results establish a post-generative AI framework with computational wisdom: relationally interactive, ethically informed, and temporally consistent with human cognitive and affective life. The framework outlines paths toward next-generation neuromorphic systems achieving genuine understanding beyond pattern recognition. Full article

This work represents the natural continuation of the development of the cognitive architecture developed and named Sophimatics, organically integrating the spatio-temporal processing mechanisms of the Super Time Cognitive Neural Network (STCNN) with the advanced principles of Sophimatics. Sophimatics’ goal is as challenging as it is fraught with obstacles, but its ultimate aim is to achieve a more humanized post-generative artificial intelligence, capable of understanding and analyzing context and evaluating the user’s purpose and intent, viewing time not only as a chronological sequence but also as an experiential continuum. The path to achieving this extremely ambitious goal has been made possible thanks to some previous work in which the philosophical thinking of interest in AI was first inherited as the inspiration for the aforementioned capabilities of the Sophimatic framework, then the issue of mapping concepts and philosophical thinking in Sophimatics’ AI infrastructure was addressed, and finally a cognitive-inspired network such as STCNN was created. This work, on the other hand, addresses the challenge of how to endow the infrastructure with both chronological and experiential time and its powerful implications, such as the innate ability to resolve paradoxes, which generative AI does not have among its prerogatives precisely because of structural limitations. To reach these results, the model operates in the two-dimensional complex time domain ℂ², extending cognitive processing capabilities through the implementation of dual temporal operators that simultaneously manage the real temporal dimension, where past, present, and future are managed and the imaginary one, that considers memory, creativity, and imagination. The resulting architecture demonstrates superior capabilities in resolving informational paradoxes and integrating apparently contradictory cognitive states, maintaining computational coherence through adaptive Sophimatic mechanisms. In conclusion, this work introduces Phase 4 of the Sophimatic framework, enabling management of two-dimensional time within a novel cognitively inspired neural architecture grounded in philosophical concepts. It connects with existing research on temporal cognition, hybrid symbolic–connectionist models, and ethical AI. The methodology translates philosophical insights into formal computational systems, culminating in a mathematical formalization that supports two-dimensional temporal reasoning and paradox resolution. Experimental results demonstrate efficiency, predictive accuracy, and computational feasibility, highlighting potential real-world applications, future research directions, and present limitations. Full article

Current generative AI systems, despite extraordinary progress, face fundamental limitations in temporal reasoning, contextual understanding, and ethical decision-making. These systems process information statistically without authentic comprehension of experiential time or intentional context, limiting their applicability in security-critical domains where reasoning about past experiences, present situations, and future implications is essential. We present Phase 3 of the Sophimatics framework: Super Time-Cognitive Neural Networks (STCNNs), which address these limitations through complex-time representation T ∈ ℂ where chronological time (Re(T)) integrates with experiential dimensions of memory (Im(T) < 0), present awareness (Im(T) ≈ 0), and imagination (Im(T) > 0). The STCNN architecture implements philosophical constraints through geometric parameters α and β that bound memory accessibility and creative projection, enabling neural systems to perform temporal-philosophical reasoning while maintaining computational tractability. We demonstrate STCNN’s effectiveness across five security-critical applications: threat intelligence (AUC 0.94, 1.8 s anticipation), privacy-preserving AI (84% utility at ε = 1.0), intrusion detection (96.3% detection, 2.1% false positives), secure multi-party computation (ethical compliance 0.93), and blockchain anomaly detection (94% detection, 3.2% false positives). Empirical evaluation shows 23–45% improvement over baseline systems while maintaining temporal coherence > 0.9, demonstrating that integration of temporal-philosophical reasoning with neural architectures enables AI systems to reason about security threats through simultaneous processing of historical patterns, current contexts, and projected risks. Full article

Rome, historically regarded as a monumental center of Catholic Christendom, now stands as a multi-layered environment shaped by diverse religious communities whose overlapping architectures, rites, and narratives expand the city’s cultural memory. This article employs Warburg’s Mnemosyne methodology to investigate how symbolic motifs, architectural forms, and intangible practices—from Eastern Orthodox iconography to the Great Mosque of Rome’s transnational design—migrate, adapt, and reconfigure within Rome’s urban fabric. Drawing on interdisciplinary approaches from cultural memory studies, religious studies, and urban geography, it reveals how minority communities—Jewish, Muslim, Orthodox Christian, Protestant, Methodist, and Scientology—act as “memory agents”, negotiating visibility and introducing new heritage layers that challenge monolithic perceptions of Rome’s identity. The analysis underscores that intangible heritage, such as chanting, prayer, and interfaith festivals, is equally central to understanding how collective memory is produced and transmitted. Tensions arise when key stakeholders do not validate these emerging cultural forms or question their “authenticity”, reflecting the contested nature of heritage-making. Ultimately, Rome’s religious plurality, shaped by migration and historical transformations, emerges as a dynamic memoryscape. By recognizing the vital role of minority faiths in heritage-making, this study contributes to broader debates on cultural pluralism, super-diversity, and the evolving definitions of religious and cultural heritage in contemporary global cities. Full article

The traditional probe–pass integration system embeds communication information into a radar waveform, which leads to a high level of waveform autocorrelation sidelobes and a poor false symbol rate at low signal-to-noise ratios. This article proposes a three-dimensional indexed modulation-based design method for probe–pass integration waveforms. This method realises communication information modulation and demodulation by simultaneously indexing orthogonal waveform selection, transmitting pulse PRI changes and carrier frequency changes in three dimensions, and applying compressed perception technology to solve the problems of PRI shortcuts and carrier frequency, resulting in a velocity term in the received waveform that cannot be accumulated by phase reference to realise velocity super-resolution. Finally, the radar detection performance and communication performance are simulated and analysed, and the simulation results reveal that the method proposed in this paper can not only satisfy the radar detection performance requirements but also achieve a lower unsigned rate on the basis of an improved communication rate. Full article

Wake-up radio is a promising solution to reduce the energy wasted by mobile devices during an idle state. In this paper, we propose a new wake-up radio solution for 5G mobile devices based on a super-regenerative receiver characterized by its low cost and low power consumption and investigate how to build on the orthogonal frequency-division multiplexing (OFDM) modulation capability at the base station to generate optimal wake-up signals. After presenting the relevant features and limitations of super-regenerative receivers operating in different 5G New Radio (NR) frequency bands, we evaluate how the numerology, the number of resource blocks, and the quadrature amplitude modulation (QAM) scheme used affect the sensitivity of the super-regenerative wake-up receiver. The results show that a 256-QAM modulation scheme, together with the highest numerology values, achieves optimal receiver sensitivity with a minimal number of resource blocks, yielding higher duty cycle pulses that also facilitate symbol synchronization tasks. Full article

With 500+ papers and 20+ books spanning many scientific disciplines, Mark Burgin has left an indelible mark and legacy for future explorers of human thought and information technology professionals. In this paper, I discuss his contribution to the evolution of machine intelligence using his general theory of information (GTI) based on my discussions with him and various papers I co-authored during the past eight years. His construction of a new class of digital automata to overcome the barrier posed by the Church–Turing Thesis, and his contribution to super-symbolic computing with knowledge structures, cognizing oracles, and structural machines are leading to practical applications changing the future landscape of information systems. GTI provides a model for the operational knowledge of biological systems to build, operate, and manage life processes using 30+ trillion cells capable of replication and metabolism. The schema and associated operations derived from GTI are also used to model a digital genome specifying the operational knowledge of algorithms executing the software life processes with specific purposes using replication and metabolism. The result is a digital software system with a super-symbolic computing structure exhibiting autopoietic and cognitive behaviors that biological systems also exhibit. We discuss here one of these applications. Full article

The Super Cryogenic Dark Matter Search (SuperCDMS) experiment is used to search for Weakly Interacting Massive Particles (WIMPs)—candidates for dark matter particles. In this experiment, the WIMPs interact with nuclei in the detector; however, there are many other interactions (background interactions). To separate background interactions from the signal, it is necessary to measure the interaction energy and to reconstruct the location of the interaction between WIMPs and the nuclei. In recent years, some research papers have been investigating the reconstruction of interaction locations using artificial intelligence (AI) methods. In this paper, a genetic programming-symbolic regression (GPSR), with randomly tuned hyperparameters cross-validated via a five-fold procedure, was applied to the SuperCDMS experiment to estimate the interaction locations with high accuracy. To measure the estimation accuracy of obtaining the SEs, the mean and standard deviation ($\sigma$) values of $R^2$, the root-mean-squared error ($RMSE$), and finally, the mean absolute error ($MAE$) were used. The investigation showed that using GPSR, SEs can be obtained that estimatethe interaction locations with high accuracy. To improve the solution, the five best SEs were combined from the three best cases. The results demonstrated that a very high estimation accuracy can be achieved with the proposed methodology. Full article

The goal of the paper is the introduction and exploration of new types of information processing. Starting with the typology developed in such an important class of information processing as computation, we extend this typology by analyzing information representations used in computational processes and delineating novel forms of information representations. While the traditional approach deals only with two dimensions of information processing—symbolic and sub-symbolic, our analysis explicated one more dimension—super-symbolic information processing. Information processing in biological systems is both symbolic and sub-symbolic, having the form of genes and neural networks. Nevertheless, in their evolution, biological systems have advanced their abilities one step further by developing super-symbolic information processing and evolving symbiotic information processing that performs information processing on the combined knowledge in the brain from both symbolic, subsymbolic, and super-symbolic information processing to derive higher order autopoietic and cognitive behaviors. Performing all forms of information processing, biological systems achieve much higher cognitive and intelligence level. That is why here we also consider a new type of computing automata called structural machines with the goal of transferring these advantageous features of biological systems to the existing information processing technology. Full article

All living beings use autopoiesis and cognition to manage their “life” processes from birth through death. Autopoiesis enables them to use the specification in their genomes to instantiate themselves using matter and energy transformations. They reproduce, replicate, and manage their stability. Cognition allows them to process information into knowledge and use it to manage its interactions between various constituent parts within the system and its interaction with the environment. Currently, various attempts are underway to make modern computers mimic the resilience and intelligence of living beings using symbolic and sub-symbolic computing. We discuss here the limitations of classical computer science for implementing autopoietic and cognitive behaviors in digital machines. We propose a new architecture applying the general theory of information (GTI) and pave the path to make digital automata mimic living organisms by exhibiting autopoiesis and cognitive behaviors. The new science, based on GTI, asserts that information is a fundamental constituent of the physical world and that living beings convert information into knowledge using physical structures that use matter and energy. Our proposal uses the tools derived from GTI to provide a common knowledge representation from existing symbolic and sub-symbolic computing structures to implement autopoiesis and cognitive behaviors. Full article

In this paper, we consider the validity of the strong maximum principle for weakly coupled, degenerate and cooperative elliptic systems in a bounded domain. In particular, we are interested in the viscosity solutions of elliptic systems with fully nonlinear degenerated principal symbol. Applying the method of viscosity solutions, introduced by Crandall, Ishii and Lions in 1992, we prove the validity of strong interior and boundary maximum principle for semi-continuous viscosity sub- and super-solutions of such nonlinear systems. For the first time in the literature, the strong maximum principle is considered for viscosity solutions to nonlinear elliptic systems. As a consequence of the strong interior maximum principle, we derive comparison principle for viscosity sub- and super-solutions in case when on of them is a classical one. The main novelty of this work is the reduction of the smoothness of the solution. In the literature the strong maximum principle is proved for classical $C^2$ or generalized $C^1$ solutions, while we prove it for semi-continuous ones. Full article

This paper introduces the principle and key technology of single radio frequency (RF) link Multiple-Input Multiple-Output (MIMO) system based on a switched parasitic antenna (SPA). The software SystemVue is adopted for signal processing and system-level simulation with merit of strong operability and high efficiency, which provides tools for the single RF link MIMO system research. A single RF link of a 2 × 2 MIMO system based on the switch parasitic antenna is proposed in this paper. The binary codes are modulated to the baseband Binary Phase Shift Keying (BPSK) signals and transmitted with a 2.4 GHz carrier frequency. The receiver based on the super-heterodyne prototype adopts the channel equalization algorithm for restoring symbols, and it can effectively reduce the system error rate. The simulation results show that the MIMO system built on the platform can achieve equivalent performance with traditional MIMO system, which validates the effectiveness of the proposed scheme. The switched parasitic antenna and equalization algorithm provide new research ideas for single RF link MIMO system and have theoretical significance for further research. Full article