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In the process of evolution, the brain has achieved such perfection that artificial intelligence systems do not have and which needs its own mathematics. The concept of cognitome, introduced by the...
We study operator learning for a nonlinear dynamical system describing symplastic plant leaf growth with multiple interacting cell files and stochastic cell division. The biomechanical model consists...
Hyperspectral images contain a large volume of source data that exhibits high correlations along neighboring spectral bands. This makes it necessary to select the most informative features among corre...
Large language models (LLMs) have proven themselves to be powerful tools for many natural language tasks — from being a high-quality text classifiers to acting as agents in complex retrieval-augmented...
Modern metaverse platforms, populated by heterogeneous multi-agent systems (MAS), generate vast streams of experiential data whose epistemic value remains largely untapped. This paper introduces the E...
Public spaces and commercial environments face persistent challenges regarding human misconduct. Traditional surveillance remains passive, while manual monitoring is labor-intensive and inefficient. C...
We investigate whether structured knowledge retrieval from a mathematical library's dependency graph can improve neural theorem proving at inference time while maintaining explainability of the retrie...
The integration of neural network methods in computer vision with logical infer- ence based on a Mivar expert system allows leveraging the advantages of both paradigms: high efficiency in processing...
The article proposes a method for assessing the neutron energy spectrum and effective dose rate of personnel based on the readings of a Bonner spectrometer (BSS) for high-energy neutron fields. Neutro...
Semantic modelling plays an important role in data processing, enabling a deep understanding of information and the development of intelligent systems. One of the methods is a four-level model of know...
We present a two-layer construction for image hashing. First, a \emph{perceptual} binary code $c(x)$ is derived from a ResNet-18 embedding (after global average pooling, $d=512$) via a linear projecti...
We study semi-supervised classification in a dynamic data-stream setting, where objects and their relations evolve over time while only a small fraction of observations is labeled. Classical graph-bas...
Group Relative Policy Optimization (GRPO) has significantly advanced the training of large language models and enhanced their reasoning capabilities, while it remains susceptible to instability due to...
Emotion attribution in social graphs requires inferring directed emotional attitudes between entities in complex, multi-turn dialogues. While transformer models dominate the field, they often lack the...
Modern automated resume screening systems are typically based on neural text classification models that encode a resume as a feature representation and predict a discrete label corresponding to candid...
Artificial intelligence systems are now integral to virtually every facet of our lives, exhibiting an ability to reason and solve problems within defined formal frameworks. However, challenges remai...
Temporal graphs provide a natural model for dynamic relational data arising in modern AI systems, including event streams, temporal knowledge graphs, interaction networks, and transaction systems. Eff...
The subject of the study is the problem of adapting language models to scientific subject areas. The issues of expanding language models to mathematical subject areas are considered. It is proposed to...
It is well known that the theory of monotone systems transforms clustering from a global optimization problem (which is often NP-hard) into a successive elimination problem solvable in polynomial time...
The paper addresses the "black box" problem of neural networks by analyzing the approximation properties of latent layers. It proposes that a key limitation preventing the practical achievement of uni...