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2022 Scholar's Yearly Symposium of Technology, Engineering and mathematics, SYSTEM 2022

作者： Ponzi, Valerio Russo, Samuele Wajda, Agata Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Psychology Sapienza University of Rome Via dei Marsi 78 Roma00185 Italy Institute of Energy and Fuel Processing Technology Zabrze41-803 Poland Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Principal component analysis

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Scenario-Assisted Deep Reinforcement Learning

arXiv

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arXiv 2022年

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 Rehovot76100 Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Givat Ram Jerusalem91904 Israel Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (specifically, its reward calculation), in a way that allows human engineers to directly contribute their expert knowledge, making the agent under training more likely to comply with various relevant constraints. Moreover, our proposed approach allows formulating these constraints using advanced model engineering techniques, such as scenario-based modeling. This mix of black-box learning-based tools with classical modeling approaches could produce systems that are effective and efficient, but are also more transparent and maintainable. We evaluated our technique using a case-study from the domain of internet congestion control, obtaining promising results. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis

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Procedia computer science 2023年 225卷 4930-4942页

作者： Joanna Kołodziejczyk Yuliia Tarasenko Piotr Sulikowski Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. żołnierska 49 71-210 Szczecin Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw 04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. żołnierska 49 71-210 Szczecin Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment.

关键词： Particle Swarm Optimization random number distribution stochastic optimization

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Structure-aware E(3)-invariant molecular conformer aggregation networks 24

Structure-aware E(3)-invariant molecular conformer aggregati...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Duy M. H. Nguyen Nina Lukashina Tai Nguyen An T. Le TrungTin Nguyen Nhat Ho Jan Peters Daniel Sonntag Viktor Zaverkin Mathias Niepert Department of Computer Science University of Stuttgart Germany and Max Planck Research School for Intelligent Systems (IMPRS-IS) and German Research Center for Artificial Intelligence (DFKI) Department of Computer Science University of Stuttgart Germany and Max Planck Research School for Intelligent Systems (IMPRS-IS) German Research Center for Artificial Intelligence (DFKI) Department of Computer Science Technische Universitat Darmstadt Germany School of Mathematics and Physics University of Queensland Australia Department of Statistics and Data Sciences University of Texas at Austin German Research Center for Artificial Intelligence (DFKI) and Department of Computer Science Technische Universitat Darmstadt Germany and Hessian.AI German Research Center for Artificial Intelligence (DFKI) and Department of Applied Artificial Intelligence Oldenburg University Germany NEC Laboratories Europe Department of Computer Science University of Stuttgart Germany and Max Planck Research School for Intelligent Systems (IMPRS-IS) and NEC Laboratories Europe

A molecule's 2D representation consists of its atoms, their attributes, and the molecule's covalent bonds. A 3D (geometric) representation of a molecule is called a conformer and consists of its atom types and Cartesian coordinates. Every conformer has a potential energy, and the lower this energy, the more likely it occurs in nature. Most existing machine learning methods for molecular property prediction consider either 2D molecular graphs or 3D conformer structure representations in isolation. Inspired by recent work on using ensembles of conformers in conjunction with 2D graph representations, we propose E(3)-invariant molecular conformer aggregation networks. The method integrates a molecule's 2D representation with that of multiple of its conformers. Contrary to prior work, we propose a novel 2D-3D aggregation mechanism based on a differentiable solver for the Fused Gromov-Wasserstein Barycenter problem and the use of an efficient conformer generation method based on distance geometry. We show that the proposed aggregation mechanism is E(3) invariant and propose an efficient GPU implementation. Moreover, we demonstrate that the aggregation mechanism helps to significantly outperform state-of-the-art molecule property prediction methods on established datasets. Our implementation is available at this https://***/duyhominhnguyen/conan-fgw/tree/main.

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Operation of an ambulance fleet under uncertainty

arXiv

引用

arXiv 2022年

作者： Guigues, Vincent Kleywegt, Anton J. Nascimento, Victor Hugo School of Applied Mathematics FGV Praia de Botafogo Rio de Janeiro Brazil Industrial and Systems Engineering Georgia Institute of Technology Atlanta United States Systems Engineering and Computer Science UFRJ Rio de Janeiro Brazil

We introduce new optimization models for the dispatch of ambulances in two types of situations. In the first type of situation, an emergency call arrives, and a decision has to be made whether an ambulance should be dispatched for that call, and if so, which ambulance should be dispatched, or whether the request should be put in a queue of waiting requests. In the second type of situation, an ambulance finishes its current task, and a decision has to be made whether the ambulance should be dispatched to a request waiting in queue, and if so, which request, or whether the ambulance should be dispatched to an ambulance staging location, and if so, which ambulance staging location. These ambulance dispatch decisions affect not only the emergency call and ambulance under consideration, but also the ability of the ambulance fleet to respond to future emergencies. There is uncertainty regarding the locations, arrival times, types, service needs, and service times of future emergencies, as well as uncertainty regarding travel times. The types and service needs of emergencies are important, because most emergency medical services have crew members with different qualifications and skills and ambulances with different capabilities, and emergencies should be served by crews and ambulances appropriate for the type of emergency. A number of methods have been proposed in the literature for making ambulance dispatch decisions, none of which takes into account the importance of dispatching a crew and ambulance with the right capabilities for the emergency at hand. Also, it is well known that response time is important. However, response time is not of equal importance for all emergencies, and sometimes there is a trade-off between dispatching the ambulance/crew with the shortest response time and dispatching the ambulance/crew with the best capabilities for the emergency. The methods proposed in this paper are the first that take into account how well the capabilities of each ambu

关键词： Travel time

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Towards the Identification of Continuous Decisional Model: the Accuracy Testing in the SITCOM Approach

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Procedia computer science 2022年 207卷 4390-4400页

作者： Bartłomiej Kizielewicz Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Z˙ołnierska 49 71-210 Szczecin Poland

Most Multi-Criteria Decision Analysis (MCDA) methods identify only discrete, defined sets of alternatives. Such identification does not provide an opportunity to consider a more significant number of samples acquired over long periods, which is not a desirable effect. An additional aspect of identification is the frequent application of expert knowledge, whereby a continuous model would have to be defined to consider further alternatives that would be suitable for further use. This paper considers a new approach for identifying continuous decision models named SITCOM. The purpose of this paper was to conduct a study on the accuracy of the proposed approach. The accuracy of Stochastic IdenTifiCation Of Models (SITCOM) was tested using sample evaluations obtained from fuzzy reference models (FRM). Spearman's weighted correlation coefficient was used to measure the accuracy of the created models. The experiments showed the dependence of accuracy on the number of characteristic objects. Furthermore, the obtained results prove the reliability of the obtained results, where the trends of accuracy decrease based on the number of characteristic values.

关键词： Optimization Genetic algorithm, Multi-Criteria Decision Analysis, SITCOM, Decision Support System, Fuzzy Logic

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An exploration of the balance game

arXiv

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arXiv 2024年

作者： Dorbec, Paul Henning, Michael A. Tuza, Zsolt Versteegen, Leo Normandie Univ UNICAEN ENSICAEN CNRS GREYC Caen14000 France Department of Mathematics and Applied Mathematics University of Johannesburg Auckland Park2006 South Africa HUN-REN Alfréd Rényi Institute of Mathematics Reáltanoda u. 13-15 BudapestH-1053 Hungary Department of Computer Science and Systems Technology University of Pannonia Veszprém Hungary Department of Mathematics University College London LondonWC1E 6BT United Kingdom

The balance game is played on a graph G by two players, Admirable (A) and Impish (I), who take turns selecting unlabeled vertices of G. Admirable labels the selected vertices by 0 and Impish by 1, and the resulting label on any edge is the sum modulo 2 of the labels of the vertices incident to that edge. Let e0 and e1 denote the number of edges labeled by 0 and 1 after all the vertices are labeled. The discrepancy in the balance game is defined as d = e1 − e0. The two players have opposite goals: Admirable attempts to minimize the discrepancy d while Impish attempts to maximize d. When (A) makes the first move in the game, the (A)-start game balance number, bAg (G), is the value of d when both players play optimally, and when (I) makes the first move in the game, the (I)-start game balance number, bIg(G), is the value of d when both players play optimally. Among other results, we show that if G has order n, then (formula presented) if n is even and (formula presented) if n is odd. Moreover we show that bAg (G) + bIg(G) = ⌊n/2⌋. © 2024, CC BY-NC-ND.

关键词： Game theory

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Multi-Angle QAOA Does Not Always Need All Its Angles

arXiv

引用

arXiv 2022年

作者： Shi, Kaiyan Herrman, Rebekah Shaydulin, Ruslan Chakrabarti, Shouvanik Pistoia, Marco Larson, Jeffrey Mathematics and Computer Science Division Argonne National Laboratory United States Dept. of Computer Science University of Maryland United States Industrial and Systems Engineering University of Tennessee Knoxville United States Global Technology Applied Research JPMorgan Chase United States

Introducing additional tunable parameters to quantum circuits is a powerful way of improving performance without increasing hardware requirements. A recently introduced multiangle extension of the quantum approximate optimization algorithm (ma-QAOA) significantly improves the solution quality compared with QAOA by allowing the parameters for each term in the Hamiltonian to vary independently. Prior results suggest, however, considerable redundancy in parameters, the removal of which would reduce the cost of parameter optimization. In this work we show numerically the connection between the problem symmetries and the parameter redundancy by demonstrating that symmetries can be used to reduce the number of parameters used by ma-QAOA without decreasing the solution quality. We study Max-Cut on all 7,565 connected, non-isomorphic 8-node graphs with a nontrivial symmetry group and show numerically that in 67.4% of these graphs, symmetry can be used to reduce the number of parameters with no decrease in the objective, with the average ratio of parameters reduced by 28.1%. Moreover, we show that in 35.9% of the graphs this reduction can be achieved by simply using the largest symmetry. For the graphs where reducing the number of parameters leads to a decrease in the objective, the largest symmetry can be used to reduce the parameter count by 37.1% at the cost of only a 6.1% decrease in the objective. We demonstrate the central role of symmetries by showing that a random parameter reduction strategy leads to much worse performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Redundancy

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The dynamics simulation of knowledge potentials of agents including the feedback 12

The dynamics simulation of knowledge potentials of agents in...

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12th International Conference on mathematics, science and Technology Education, ICon-MaSTEd 2020

作者： Pasichnyk, V.V. Bomba, A.Y. Nazaruk, M.V. Kunanets, N.E. Information Systems and Networks Department Lviv Polytechnic National University 12 Bandera Str. Lviv79000 Ukraine Computer Science and Applied Mathematics Department National University of Water and Environmental Engineering 11 Soborna Str. Rivne33028 Ukraine Informatics and Applied Mathematics Department Rivne State Humanitarian University 31 Plastova Str. Rivne33000 Ukraine

The conceptual and mathematical models of the agents' knowledge potential redistribution considering their constituent components are constructed in the paper. A nonlinear model describing the dynamics of agents' knowledge potentials is developed taking into account the feedback effects (agent-student) on the source of knowledge (teacher / lecturer). In particular, the procedure of constructing a multicomponent two-dimensional array of discrete values of the knowledge potential constituent components for generating procedures to enhance (improve) the professional competences of knowledge sources, is generalized and specified. © 2021 Published under licence by IOP Publishing Ltd.

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Improving Cause-of-Death Classification from Verbal Autopsy Reports

arXiv

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arXiv 2022年

作者： Manaka, Thokozile van Zyl, Terence Kar, Deepak School of Computer Science and Applied Mathematics University of The Witwatersrand Johannesburg South Africa Institute for Intelligent Systems University of Johannesburg Johannesburg South Africa School of Physics University of The Witwatersrand Johannesburg South Africa

In many lower-and-middle income countries including South Africa, data access in health facilities is restricted due to patient privacy and confidentiality policies. Further, since clinical data is unique to individual institutions and laboratories, there are insufficient data annotation standards and conventions. As a result of the scarcity of textual data, natural language processing (NLP) techniques have fared poorly in the health sector. A cause of death (COD) is often determined by a verbal autopsy (VA) report in places without reliable death registration systems. A non-clinician field worker does a VA report using a set of standardized questions as a guide to uncover symptoms of a COD. This analysis focuses on the textual part of the VA report as a case study to address the challenge of adapting NLP techniques in the health domain. We present a system that relies on two transfer learning paradigms of monolingual learning and multi-source domain adaptation to improve VA narratives for the target task of the COD classification. We use the Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMo) models pre-trained on the general English and health domains to extract features from the VA narratives. Our findings suggest that this transfer learning system improves the COD classification tasks and that the narrative text contains valuable information for figuring out a COD. Our results further show that combining binary VA features and narrative text features learned via this framework boosts the classification task of COD. © 2022, CC BY.

关键词： Health

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