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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Eduard Antonov Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Russia

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Personalized learning plays a critical role in enhancing educational outcomes by customizing experiences to meet individual learners' unique needs, preferences, and abilities. Although Large Language Models (LLMs) offer promising opportunities for personalization through natural language interactions, they face challenges related to maintaining long-term context and ensuring traceability of a learner's evolving knowledge. This paper introduces a composite AI framework that merges an LLM-powered conversational agent with Knowledge Graphs (KGs) structured according to Knowledge Space Theory (KST), enhanced by advanced retrieval tools and a robust long-term memory mechanism. This system autonomously orchestrates meaningful, context-aware dialogues while utilizing function calling to access various tools, effectively bridging the gap between dynamic interactions and structured knowledge representation. Our empirical evaluations indicate that the proposed system excels in maintaining coherent dialogues, accurately tracking learner progress, and providing reliable, personalized educational support. By addressing the limitations of standalone LLMs and traditional adaptive learning systems, this integrated approach signifies a notable advancement in intelligent educational technology.

关键词： Large language models Retrieval augmented generation Natural languages Knowledge graphs Educational technology Memory modules Learning (artificial intelligence) Reliability theory Robots Intelligent control

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Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem 20

Standardized feature extraction from pairwise conflicts appl...

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20th IEEE Jubilee World Symposium on applied Machine Intelligence and Informatics, SAMI 2022

作者： Kopacz, Aniko Mester, Agnes Kolumban, Sandor Csato, Lehel Babeş-Bolyai University Faculty of Mathematics and Computer Science Cluj-Napoca Romania Institute of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary

ISBN: (纸本)9781665497046

We propose a train rescheduling algorithm which applies a standardized feature selection based on pairwise conflicts in order to serve as input for the reinforcement learning framework. We implement an analytical method which identifies and optimally solves every conflict arising between two trains, then we design a corresponding observation space which features the most relevant information considering these conflicts. The data obtained this way then translates to actions in the context of the reinforcement learning framework. We test our preliminary model using the evaluation metrics of the Flatland Challenge. The empirical results indicate that the suggested feature space provides meaningful observations, from which a sensible scheduling policy can be learned. © 2022 IEEE.

关键词： Measurement Reinforcement learning Feature extraction Informatics Machine intelligence

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Improving Group Lasso for High-Dimensional Categorical Data 23rd

Improving Group Lasso for High-Dimensional Categorical Data

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23rd International Conference on Computational science, ICCS 2023

作者： Nowakowski, Szymon Pokarowski, Piotr Rejchel, Wojciech Soltys, Agnieszka Institute of Applied Mathematics and Mechanics University of Warsaw Warsaw Poland Faculty of Physics University of Warsaw Warsaw Poland Faculty of Mathematics and Computer Science Nicolaus Copernicus University Toruń Poland

ISBN: (纸本)9783031360206

Sparse modeling or model selection with categorical data is challenging even for a moderate number of variables, because roughly one parameter is needed to encode one category or level. The Group Lasso is a well known efficient algorithm for selection of continuous or categorical variables, but all estimates related to a selected factor usually differ. Therefore, a fitted model may not be sparse, which makes the model interpretation difficult. To obtain a sparse solution of the Group Lasso, we propose the following two-step procedure: first, we reduce data dimensionality using the Group Lasso;then, to choose the final model, we use an information criterion on a small family of models prepared by clustering levels of individual factors. In the consequence, our procedure reduces dimensionality of the Group Lasso and strongly improves interpretability of the final model. What is important, this reduction results only in the small increase of the prediction error. In the paper we investigate selection correctness of the algorithm in a sparse high-dimensional scenario. We also test our method on synthetic as well as the real data sets and show that it outperforms the state of the art algorithms with respect to the prediction accuracy, model dimension and execution time. Our procedure is contained in the R package DMRnet and available in the CRAN repository. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Physics-informed neural networks for modeling water flows in a river channel

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1001-1015页

作者： Nazari, Luis Fernando Camponogara, Eduardo Seman, Laio Oriel Department of Mathematics Federal Institute of Education Science and Technology Catarinense Rio do Sul89160-202 Brazil Department of Automation and Systems Engineering Federal University of Santa Catarina Florianopolis88040-900 Brazil Program in Applied Computer Science Universidade Do Vale Do Itajai Itajai88302-901 Brazil

The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained fromconceptual or data-drivenmethods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-Time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. Impact Statement-The research seeks to contribute to the hydrological modeling area with a surrogate model based on physicsinformed neural networks (PINNs) to water flow in a watershed. In practice, thesemodels use conceptual or *** models to reach the precision provided by themethodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system *** introduce PINNs as a viable solution for

关键词： Floods

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Deformation of the Heisenberg–Weyl algebra and the Lie superalgebra : exact solution for the quantum harmonic oscillator with a position-dependent mass

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The European Physical Journal Plus 2025年第4期140卷 1-13页

作者： Jafarov, E. I. Nagiyev, S. M. Van der Jeugt, J. Institute of Physics Ministry of Science and Education Baku Azerbaijan Department of Applied Mathematics Computer Science and Statistics Ghent University Gent Belgium

We propose a new deformation of the quantum harmonic oscillator Heisenberg–Weyl algebra with a parameter $$a>-1$$ . This parameter is introduced through the replacement of the homogeneous mass $$m_0$$ in the definition of the momentum operator $$\hat{p}_x$$ as well as in the creation–annihilation operators $${\hat{a}}^\pm$$ with a mass varying with position x. The realization of such a deformation is shown through the exact solution of the corresponding Schrödinger equation for the non-relativistic quantum harmonic oscillator within the canonical approach. The obtained analytical expression of the energy spectrum consists of an infinite number of equidistant levels, whereas the wavefunctions of the stationary states of the problem under construction are expressed through the Hermite polynomials. Then, the Heisenberg–Weyl algebra deformation is generalized to the case of the Lie superalgebra $$\mathfrak {osp}\left( {1|2} \right)$$ . It is shown that the realization of such a generalized superalgebra can be performed for the parabose quantum harmonic oscillator problem, the mass of which possesses a behavior completely overlapping with the position-dependent mass of the canonically deformed harmonic oscillator problem. This problem is solved exactly for both even and odd stationary states. It is shown that the energy spectrum of the deformed parabose oscillator is still equidistant; however, both even- and odd-state wavefunctions are now expressed through the Laguerre polynomials. Some basic limit relations recovering the canonical harmonic oscillator with constant mass are also discussed briefly.

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 356-368页

作者： Jiaxing Li Zidong Wang Jun Hu Hongli Dong Hongjian Liu the Department of Applied Mathematics Harbin University of Science and Technology IEEE the Department of Computer Science Brunel University London the School of Automation Harbin University of Science and Technology the Artificial Intelligence Energy Research Institute Northeast Petroleum University the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University the National Key Laboratory of Continental Shale Oil of China Northeast Petroleum University the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University the School of Mathematics and Physics Anhui Polytechnic University

In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

关键词： Amplify-and-forward (AaF) relays covariance intersection fusion cubature Kalman filtering multi-sensor systems uniform boundedness

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Enhancing Deep Reinforcement Learning with Scenario-Based Modeling

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SN computer science 2023年第2期4卷 1-13页

作者： Yerushalmi, Raz Amir, Guy Elyasaf, Achiya Harel, David Katz, Guy Marron, Assaf Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot 76100 Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem 91904 Israel Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva 8410501 Israel

Deep reinforcement learning agents have achieved unprecedented results when learning to generalize from unstructured data. However, the “black-box” nature of the trained DRL agents makes it difficult to ensure that they adhere to various requirements posed by engineers. In this work, we put forth a novel technique for enhancing the reinforcement learning training loop, and specifically—its reward function, in a way that allows engineers to directly inject their expert knowledge into the training process. This allows us to make the trained agent adhere to multiple constraints of interest. Moreover, using scenario-based modeling techniques, our method allows users to formulate the defined constraints using advanced, well-established, behavioral modeling methods. This combination of such modeling methods together with ML learning tools produces agents that are both high performing and more likely to adhere to prescribed constraints. Furthermore, the resulting agents are more transparent and hence more maintainable. We demonstrate our technique by evaluating it on a case study from the domain of internet congestion control, and present promising results. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep reinforcement learning Domain expertise Machine learning Rule-based specifications Scenario-based modeling

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A Short Note on Solving Partial Differential Equations Using Convolutional Neural Networks 27th

A Short Note on Solving Partial Differential Equations Using...

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27th International Conference on Domain Decomposition Methods in science and Engineering, DD 2022

作者： Grimm, Viktor Heinlein, Alexander Klawonn, Axel Department of Mathematics and Computer Science University of Cologne Weyertal 86-90 Koln50931 Germany Delft University of Technology Delft Institute of Applied Mathematics Mekelweg 4 Delft2628 CD Netherlands Center for Data and Simulation Science University of Cologne Cologne Germany

ISBN: (纸本)9783031507687

Solving partial differential equations (PDEs) is a common task in numerical mathematics and scientific computing. Typical discretization schemes, for example, finite element (FE), finite volume (FV), or finite difference (FD) methods, have the disadvantage that the computations have to be repeated once the boundary conditions (BCs) or the geometry change slightly;typical examples requiring the solution of many similar problems are time-dependent and inverse problems or uncertainty quantification. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Boundary conditions

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Acoustic cavitation bubble under influence of frequency ultrasound: Numerical analysis 3

Acoustic cavitation bubble under influence of frequency ultr...

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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Abu-Nab, A.K. Abu-Bakr, A.F. Abushaeer, Zain Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Moscow Institute of Physics and Technology Phystech School of Applied Mathematics and Informatics Moscow141700 Russia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Higher Institute of Engineering and Technology Department of Basic Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350323511

This work presents a numerical investigation of acoustic cavitation in Newtonian fluid based on frequency ultrasound. The model of ultrasonic cavitation dynamics is formulated by a modified Rayleigh-Plesset equation and an acoustic pressure equation. The exponential B-Spline collection method was employed to obtain the numerical solutions of the given cavitation bubble model, considering the effect of acoustic pressure. The behaviour of the cavitation microbubbles' radius and the internal pressure of acoustic cavitation were examined at different values of frequencies. The obtained results reveal the importance of frequencies in ultrasound, which play a vital role in changing the behaviour of cavitation bubbles. © 2023 IEEE.

关键词： Numerical methods

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Preparing for Super-Reactivity: Early Fault-Detection in the Development of Exceedingly Complex Reactive Systems

arXiv

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arXiv 2024年

作者： Harel, David Marron, Assaf Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

We introduce the term Super-Reactive Systems to refer to reactive systems whose construction and behavior are complex, constantly changing and evolving, and heavily interwoven with other systems and the physical world. Finding hidden faults in such systems early in planning and development is critical for human safety, the environment, society and the economy. However, the complexity of the system and its interactions and the absence of adequate technical details pose a great obstacle. We propose an architecture for models and tools to overcome such barriers and enable simulation, systematic analysis, and fault detection and handling, early in the development of super-reactive systems. The approach is facilitated by the inference and abstraction capabilities and the power and knowledge afforded by large language models and associated AI tools. It is based on: (i) deferred, just-in-time interpretation of model elements that are stored in natural language form, and (ii) early capture of tacit interdependencies among seemingly orthogonal requirements. Copyright © 2024, The Authors. All rights reserved.

关键词： Large scale systems

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