Computational Principles Research Articles

Self-Potential Inversion for Regularly Polarized Ore Deposits Anomalies in Different Models Based on Quantum Genetic Algorithm

The self-potential (SP) method is widely used in mineral exploration for its simplicity and cost-effectiveness. Traditional inversion approaches often face challenges with local optima and computational inefficiency in complex models. A quantum genetic algorithm (QGA) for SP data inversion is introduced, combining quantum computation principles with genetic algorithms. Modal analysis and parameters analysis of inversion in different physical models are conducted to determine parameter sensitivity and the optimal parameter range. The proposed optimizer is applied on synthetic data generated from diverse geological models. Gaussian noise is added to test robustness. Indoor experiments, using a controlled environment with a 2D electrode grid, validate QGA's effectiveness in layered models. The impact of data density on the algorithm's performance is evaluated by reducing the number of measurement points. Different field cases from Turkey, Germany and USA further confirmed QGA's practical applicability, with inversion results closely matching drilling data or research published before. Uncertainty appraisal analyses show the validity of the model parameter estimations. In summary, QGA significantly improves SP data inversion, offering better accuracy, efficiency, and robustness, making it a promising tool for complex geological conditions.

All Your Base Are Belong to Us: Sort Polymorphism for Proof Assistants

Proof assistants based on dependent type theory, such as Coq, Lean and Agda, use different universes to classify types, typically combining a predicative hierarchy of universes for computationally-relevant types, and an impredicative universe of proof-irrelevant propositions. In general, a universe is characterized by its sort, such as Type or Prop, and its level, in the case of a predicative sort. Recent research has also highlighted the potential of introducing more sorts in the type theory of the proof assistant as a structuring means to address the coexistence of different logical or computational principles, such as univalence, exceptions, or definitional proof irrelevance. This diversity raises concrete and subtle issues from both theoretical and practical perspectives. In particular, in order to avoid duplicating definitions to inhabit all (combinations of) universes, some sort of polymorphism is needed. Universe level polymorphism is well-known and effective to deal with hierarchies, but the handling of polymorphism between sorts is currently ad hoc and limited in all major proof assistants, hampering reuse and extensibility. This work develops sort polymorphism and its metatheory, studying in particular monomorphization, large elimination, and parametricity. We implement sort polymorphism in Coq and present examples from a new sort-polymorphic prelude of basic definitions and automation. Sort polymorphism is a natural solution that effectively addresses the limitations of current approaches and prepares the ground for future multi-sorted type theories.

Muhammad ibn Musa al-Khwarizmi: The Pioneer of Algorithms and His Enduring Legacy in Artificial Intelligence

Muhammad ibn Musa al-Khwarizmi, a pivotal figure of the Islamic Golden Age, established foundational principles for modern computation and artificial intelligence (AI). This article examines his transformative contributions to mathematics, particularly his seminal work, The Compendious Book on Calculation by Completion and Balancing, which introduced algebra as a systematic discipline. Al-Khwarizmi’s development of algorithms—structured problem-solving methods—shaped medieval and contemporary computational sciences. His introduction of the Hindu-Arabic numeral system, including the concept of zero, revolutionized arithmetic, enabling advancements in science, engineering, and technology. The article highlights the profound connection between Al-Khwarizmi’s methodologies and modern AI applications, such as machine learning and neural networks, which rely on systematic algorithms. It also explores the intellectual ecosystem of the Abbasid Caliphate’s House of Wisdom in Baghdad, emphasizing its role in fostering innovation and interdisciplinary collaboration. This study challenges Eurocentric narratives, shedding light on the contributions of non-Western civilizations to modern technological paradigms. Keywords: Al-Khwarizmi, Algorithms, Algebra, Artificial Intelligence, Islamic Golden Age.

Free-space optical spiking neural network.

Neuromorphic engineering has emerged as a promising avenue for developing brain-inspired computational systems. However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal dissipation. As an alternative, optical implementations of such processors have been proposed, capitalizing on the intrinsic information-processing capabilities of light. Among the various Optical Neural Networks (ONNs) explored within the realm of optical neuromorphic engineering, Spiking Neural Networks (SNNs) have exhibited notable success in emulating the computational principles of the human brain. The event-based spiking nature of optical SNNs offers capabilities in low-power operation, speed, temporal processing, analog computing, and hardware efficiency that are difficult or impossible to match with other ONN types. In this work, we introduce the pioneering Free-space Optical Deep Spiking Convolutional Neural Network (OSCNN), a novel approach inspired by the computational model of the human eye. Our OSCNN leverages free-space optics to enhance power efficiency and processing speed while maintaining high accuracy in pattern detection. Specifically, our model employs Gabor filters in the initial layer for effective feature extraction, and utilizes optical components such as Intensity-to-Delay conversion and a synchronizer, designed using readily available optical components. The OSCNN was rigorously tested on benchmark datasets, including MNIST, ETH80, and Caltech, demonstrating competitive classification accuracy. Our comparative analysis reveals that the OSCNN consumes only 1.6 W of power with a processing speed of 2.44 ms, significantly outperforming conventional electronic CNNs on GPUs, which typically consume 150-300 W with processing speeds of 1-5 ms, and competing favorably with other free-space ONNs. Our contributions include addressing several key challenges in optical neural network implementation. To ensure nanometer-scale precision in component alignment, we propose advanced micro-positioning systems and active feedback control mechanisms. To enhance signal integrity, we employ high-quality optical components, error correction algorithms, adaptive optics, and noise-resistant coding schemes. The integration of optical and electronic components is optimized through the design of high-speed opto-electronic converters, custom integrated circuits, and advanced packaging techniques. Moreover, we utilize highly efficient, compact semiconductor laser diodes and develop novel cooling strategies to minimize power consumption and footprint.

Empowering DNA-Based Information Processing: Computation and Data Storage.

Information processing is a critical topic in the digital age, as silicon-based circuits face unprecedented challenges such as data explosion, immense energy consumption, and approaching physical limits. Deoxyribonucleic acid (DNA), naturally selected as a carrier for storing and using genetic information, possesses unique advantages for information processing, which has given rise to the emerging fields of DNA computing and DNA data storage. To meet the growing practical demands, a wide variety of materials and interfaces have been introduced into DNA information processing technologies, leading to significant advancements. This review summarizes the advances in materials and interfaces that facilitate DNA computation and DNA data storage. We begin with a brief overview of the fundamental functions and principles of DNA computation and DNA data storage. Subsequently, we delve into DNA computing systems based on various materials and interfaces, including microbeads, nanomaterials, DNA nanostructures, hydrophilic-hydrophobic compartmentalization, hydrogels, metal-organic frameworks, and microfluidics. We also explore DNA data storage systems, encompassing encapsulation materials, microfluidics techniques, DNA nanostructures, and living cells. Finally, we discuss the current bottlenecks and obstacles in the fields and provide insights into potential future developments.

Orthogonalization of spontaneous and stimulus-driven activity by hierarchical neocortical areal network in primates

How biological neural networks reliably process information in the presence of spontaneous activity remains controversial. In mouse primary visual cortex (V1), stimulus-evoked and spontaneous activity show orthogonal (dissimilar) patterns, which is advantageous for separating sensory signals from internal noise. However, studies in carnivore and primate V1, which have functional columns, have reported high similarity between stimulus-evoked and spontaneous activity. Thus, the mechanism of signal-noise separation in the columnar visual cortex may be different from that in rodents. To address this issue, we compared spontaneous and stimulus-evoked activity in marmoset V1 and higher visual areas. In marmoset V1, spontaneous and stimulus-evoked activity showed similar patterns as expected. However, in marmoset higher visual areas, spontaneous and stimulus-evoked activity were progressively orthogonalized along the cortical hierarchy, eventually reaching levels comparable to those in mouse V1. These results suggest that orthogonalization of spontaneous and stimulus-evoked activity is a general principle of cortical computation.

Computational Models Based on Working Memory

Understanding the intricacies of working memory (WM) is crucial for unraveling cognitive processes. This paper explores the relationship between working memory and computational neuroscience, aiming to shed light on how computational models contribute to our comprehension of WM. The discussion delves into the processes involved in working memory, the computational principles that underlie it, and the potential downsides of relying on computational models.

Quantum Ant Colony Algorithm for Solving the Traveling Salesman Problem: A Theoretical and Practical Analysis

The quantum ant colony algorithm (QACO) is explored as a solution to the traveling salesman problem (TSP), targeting inefficiencies such as slow convergence and local optima entrapment found in traditional ant colony optimization (ACO) methods. By integrating quantum computing elements, specifically quantum rotation gates and qubits, into the ACO framework, QACO demonstrates enhanced efficiency and faster convergence. This paper delves into the foundational principles of quantum computing and their application to refine ACO, effectively solving the TSP. An in-depth analysis is provided, showcasing QACO's advantages over traditional ACO through empirical examples. These examples illustrate QACO's potential to improve path planning for applications within the rapidly growing e-commerce and logistics sectors. The integration of quantum mechanisms facilitates a more dynamic pheromone update process, which significantly reduces the likelihood of premature convergence and enhances solution quality in complex optimization scenarios. This advancement indicates a promising direction for future research in algorithmic optimization in both theoretical and practical applications.

Autism spectrum disorder variation as a computational trade-off via dynamic range of neuronal population responses

Individuals diagnosed with autism spectrum disorder (ASD) show neural and behavioral characteristics differing from the neurotypical population. This may stem from a computational principle that relates inference and computational dynamics to the dynamic range of neuronal population responses, reflecting the signal levels for which the system is responsive. In the present study, we showed that an increased dynamic range (IDR), indicating a gradual response of a neuronal population to changes in input, accounts for neural and behavioral variations in individuals diagnosed with ASD across diverse tasks. We validated the model with data from finger-tapping synchronization, orientation reproduction and global motion coherence tasks. We suggested that increased heterogeneity in the half-activation point of individual neurons may be the biological mechanism underlying the IDR in ASD. Taken together, this model provides a proof of concept for a new computational principle that may account for ASD and generates new testable and distinct predictions regarding its behavioral, neural and biological foundations.

Prospects and Challenges of Affective AI Technology in Supporting Remote Learning

This study analyzes the potential and challenges of introducing affective AI technologies to promote remote education. The growing popularity of remote learning highlights the significant upside of affective AI in real-time emotional monitoring and feedback, especially for improving learning motivation, optimizing personalized educational paths, and supporting students' mental health. This study explores the particular applications of affective AI technologies and the ethical and privacy concerns they present. The research indicates that affective AI technology, based on the principles of affective computing, may strengthen students' emotional management abilities, improve the overall educational experience by identifying their emotional states in real time, and create more possibilities for teacher-student interactions. Affective AI enhances student motivation and optimizes personalized learning paths in remote education, especially for fostering positive emotional regulation and mental health support outcomes. However, affective AI encounters challenge related to technical prejudice, privacy protection, ethical fairness, and user acceptance in extensive applications, particularly notable disparities in multicultural contexts, which require solutions through privacy safeguards, algorithm refinement, and culturally adaptive design. This study is significant as it systematically reveals the potential of affective AI technologies in remote learning, identifies their critical challenges, and offers valuable recommendations and references for future advancement in global education.

Modular Stimuli‐Responsive Valves for Pneumatic Soft Robots

Pneumatic soft robots have several advantages, including facile fabrication, versatile deformation modes, and safe human–machine interaction. However, pneumatic soft robots typically rely on mechatronics to interact with their environment, which can limit their form factors and reliability. Researchers have considered how to achieve autonomous behaviors using the principles of mechanical computing and physical intelligence. Herein, modular responsive valves that can autonomously regulate airflow within pneumatic soft robots in response to various environmental stimuli, including light, water, and mechanical forces, are described. By combining multiple types of valves, autonomous logic gates and more advanced logical operations can be realized. Finally, it is demonstrated that responsive valves can be integrated with pneumatic soft robots, allowing autonomous morphing and navigation. This framework provides a strategy for creating autonomous pneumatic robots that can respond to multiple stimuli in their environment.

Advancing civil engineering: The transformative impact of neuromorphic computing on infrastructure resilience and sustainability

The resilience and adaptability of civil infrastructure are paramount for societal well-being and economic stability. This paper examines the transformative impact of neuromorphic computing on enhancing the resilience of infrastructure systems and advancing sustainable engineering practices. Drawing inspiration from the human brain's processing capabilities, neuromorphic computing has been demonstrated to significantly enhance the efficiency of real-time data analysis, learning, and prediction of structural integrity. We detail the fundamental principles of neuromorphic computing, its practical applications within civil engineering, and notable implementations, underscoring its substantial contributions to predictive maintenance, disaster preparedness, and environmental conservation. Our findings reveal that neuromorphic systems, through their capability to continuously learn and adapt, have effectively improved the accuracy of predictive models for infrastructure behavior under various environmental stressors, thereby facilitating more robust disaster response strategies and reducing maintenance costs by up to 30 %. Despite facing technological and integration challenges, neuromorphic computing emerges as a pioneering force in the field of civil engineering, offering a future where infrastructure is not only more durable but also inherently intelligent and responsive to changing conditions. The integration of neuromorphic computing holds the promise of revolutionizing civil engineering practices by fostering greater interdisciplinary collaboration and driving a commitment to environmental sustainability. This study advocates for broader adoption of neuromorphic technologies to cultivate a resilient, adaptive, and sustainable built environment.

Оптимізація обробки великих даних за допомогою «лінивих обчислень»: систематичний огляд технологій та застосування

The article examines the concept of lazy operations and its application for efficient processing of large volumes of data. The main principles of lazy computations, their implementation in various programming languages, and strategies for effective use in Big Data processing are analyzed. The advantages and limitations of the lazy approach are investigated, particularly regarding memory savings, performance improvement, and the ability to work with infinite data streams. A concept for selecting computation strategies based on data size and computational complexity is proposed.

High-throughput detection of RNA modifications at single base resolution.

RNA is modified by > 170 chemical modifications that affect its structure and function. Accordingly, RNA modifications have been implicated in regulation of gene expression and cellular outcomes in a variety of species spanning the phylogenetic tree. The study of RNA modifications is accelerated by generation of high-throughput methods for detecting RNA modifications at single base resolution. Here, we review recent advancement in next generation sequencing based approaches for detection of 14 distinct RNA modifications present in rRNA, tRNA and mRNA. We further outline the molecular and computational principles underlying currently available methods.

Enhancing Data Processing Efficiency : The Synergy of Edge Computing and Hybrid Cloud Storage

This comprehensive article explores the transformative integration of edge computing and hybrid cloud storage, a technological convergence that is reshaping data processing architectures in the era of exponential data growth. The research delves into the fundamental principles of edge computing and hybrid cloud storage, examining their synergistic relationship in addressing the limitations of traditional centralized cloud computing. By bringing computational resources closer to data sources, this integrated approach significantly reduces latency, enhances processing efficiency by up to 50%, and improves overall system reliability. The article presents detailed case studies in autonomous driving and smart city infrastructure, showcasing real-world applications and benefits. It critically analyzes the challenges inherent in this integration, including security concerns in decentralized architectures, data consistency issues, and cost implications. Furthermore, the article explores future directions, discussing emerging technologies such as AI-powered edge devices, evolving hybrid cloud solutions, and the potential for further optimization. This research provides valuable insights for organizations and researchers navigating the complex landscape of distributed computing, offering a roadmap for leveraging edge computing and hybrid cloud storage to achieve unprecedented levels of performance, scalability, and flexibility in data management and processing.

Quantum support vector machines: theory and applications

Abstract. Quantum Support Vector Machines (QSVMs) combine the fundamental principles of quantum computing and classical Support Vector Machines (SVMs) to improve machine learning performance. In this paper, the author will further explore QSVM. Firstly, introduce the basics of classical SVM, including hyperplane, margin, support vector, and kernel methods. Then, introduce the basic theories of quantum computing, including quantum bits, entanglement, quantum states, superposition, and some related quantum algorithms. Focuses on the concept of QSVM, quantum kernel methods, and how SVM runs on a quantum computer. Key topics include quantum state preparation, measurement, and output interpretation. Theoretical advantages of QSVMs, such as faster computation speed, stronger performance to process high-dimensional data, and kernal computation. In addition, the author discussed the implementation of QSVM, quantum algorithms, quantum gradient descent, and optimization techniques for SVM training. The article also discusses practical issues such as error mitigation and quantum hardware requirements. The purpose of this paper is to show the advantages of QSVM by comparing SVM and QSVM.

Analysis of the Principles of Quantum Computing and State-of-the-Art Applications

Abstract. Contemporarily, quantum computing has emerged as a promising field, offering potential breakthroughs in various computational tasks that are currently limited by classical computing. With this in mind, this study delves into the principles of quantum computing, exploring the fundamental concept of quantum entanglement and its implications for computation. After outlining the historical development and research significance of quantum computing, this research presents an overview of the latest advancements in the field. The paper then focuses on the principles of quantum computation, including the use of qubits and quantum gates, illustrated with relevant mathematical formulations and diagrams. Furthermore, this study discusses the state-of-the-art applications of quantum computing, showcasing recent achievements and results obtained from these cutting-edge technologies. A comparative analysis with traditional algorithms highlights the advantages and potential gains offered by quantum computing. Finally, the current limitations of quantum computing are discussed and the insights into future research directions and prospects are proposed for this exciting field.

A large-scale examination of inductive biases shaping high-level visual representation in brains and machines

The rapid release of high-performing computer vision models offers new potential to study the impact of different inductive biases on the emergent brain alignment of learned representations. Here, we perform controlled comparisons among a curated set of 224 diverse models to test the impact of specific model properties on visual brain predictivity – a process requiring over 1.8 billion regressions and 50.3 thousand representational similarity analyses. We find that models with qualitatively different architectures (e.g. CNNs versus Transformers) and task objectives (e.g. purely visual contrastive learning versus vision- language alignment) achieve near equivalent brain predictivity, when other factors are held constant. Instead, variation across visual training diets yields the largest, most consistent effect on brain predictivity. Many models achieve similarly high brain predictivity, despite clear variation in their underlying representations – suggesting that standard methods used to link models to brains may be too flexible. Broadly, these findings challenge common assumptions about the factors underlying emergent brain alignment, and outline how we can leverage controlled model comparison to probe the common computational principles underlying biological and artificial visual systems.

Goal-directed action preparation in humans entails a mixture of corticospinal neural computations.

The seemingly effortless ability of humans to transition from thinking about actions to initiating them relies on sculpting corticospinal output from primary motor cortex. This study tested whether canonical additive and multiplicative neural computations, well-described in sensory systems, generalize to the corticospinal pathway during human action preparation. We used non-invasive brain stimulation to measure corticospinal input-output across varying action preparation contexts during instructed-delay finger response tasks. Goal-directed action preparation was marked by increased multiplicative gain of corticospinal projections to task-relevant muscles and additive suppression of corticospinal projections to non-selected and task-irrelevant muscles. Individuals who modulated corticospinal gain to a greater extent were faster to initiate prepared responses. Our findings provide physiological evidence of combined additive suppression and gain modulation in the human motor system. We propose these computations support action preparation by enhancing the contrast between selected motor representations and surrounding background activity to facilitate response selection and execution.

Spike-based computational primitives to reproduce neural dynamics in the parietal cortex during motor preparation

Abstract Biologically plausible spiking neural network models of sensory cortices can be instrumental in understanding and validating their principles of computation. Models based on Cortical Computational Primitives (CCPs), such as Hebbian plasticity and Winner-Take-All (WTA) networks, have already been successful in this approach. However, the specific nature and roles of CCPs in sensorimotor cortices during cognitive tasks are yet to be fully deciphered. The evolution of motor intention in the Posterior Parietal Cortex (PPC) before arm-reaching movements is a well-suited cognitive process to assess the effectiveness of different CCPs. To this end, we propose a biologically plausible model composed of heterogeneous spiking neurons which implements and combines multiple CCPs, such as multi-timescale learning and soft WTA modules. By training the model to replicate the dynamics of in-vivo recordings from non-human primates, we show how it is effective in generating meaningful representations from unbalanced input data, and in faithfully reproducing the transition from motor planning to action selection. Our findings elucidate the importance of distributing spike-based plasticity across multi-timescales, and provide an explanation for the role of different CCPs in models of frontoparietal cortical networks for performing multisensory integration to efficiently inform action execution.