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SB-AGT: A stochastic beam search-enhanced attention-based Gumbel tree framework for drug-drug interaction extraction from biomedical literature

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computers in Biology and Medicine 2025年 189卷 110011-110011页

作者： Saad, Eman Kishk, Sherif Ali-Eldin, Amr Saleh, Ahmed I. Communication and Information Technology Center Mansoura University Egypt Electronics and Communication Engineering Dept. Mansoura University Egypt Computer Engineering & Control Systems Dept. Mansoura University Egypt

Drug-Drug Interactions (DDI) and Chemical-Protein Interactions (CPI) detection are crucial for patient safety, as unidentified interactions may lead to severe Adverse Drug Reactions (ADRs). While extensive DDI and CPI information exists within biomedical literature, manual extraction by experts proves time-intensive and resource-demanding. This study presents SB-AGT: Stochastic Beam Search-enhanced Attention-based Gumbel Tree an innovative approach to automated DDI and CPI extraction through an attention-based architecture incorporating a modified Gumbel-Tree method with stochastic beam search optimization. SB-AGT leverages multi-head attention mechanisms and enhanced latent tree structures to effectively capture complex syntactic features and drug relationship patterns. Also, the methodology includes comprehensive preprocessing protocols and addresses dataset imbalance through hybrid sampling techniques. Experimental validation on the DDIExtraction 2013 and CHEMPROT datasets demonstrates the model's effectiveness, achieving precision, surpassing traditional approaches and showing competitive performance compared to contemporary pre-trained models. Through parametric analysis, we establish optimal beam search configurations that maximize the model's extraction accuracy. © 2025 Elsevier Ltd

关键词： Stochastic models

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A latent representation of brain networks based on EEG ⁎

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IFAC-PapersOnLine 2024年第15期58卷 414-419页

作者： Lucia Falconi Giulia Cisotto Mattia Zorzi Automatic Control Laboratory ETH Zurich Physikstrasse 3 8092 Zurich Switzerland Dept. of Informatics Systems and Communication University of Milano-Bicocca Milan Italy Dept. of Information Engineering University of Padova Italy Dept. of Information Engineering University of Padova Italy

Electroencephalography (EEG) is one of the most popular techniques to investigate normal as well as pathological cerebral mechanisms, as it allows to measure, non-invasively and in real-time, the brain activity. However, modeling EEG is still extremely challenging, because of its high-dimensionality, low signal-to-noise ratio, and high individual variability. This paper proposes a novel latent representation to study brain networks using EEG by means of a robust dynamic factor analysis (RDFA) approach. We investigate the ability of this latent representation to discriminate between two groups of subjects, i.e. alcoholic and healthy. By RDFA, we can extract a limited number of highly explanatory factors, as low as 8, significantly discriminating between the two groups. Also, we show that different brain patterns can be identified across different stimulation scenarios and EEG locations. Although preliminary, this work could give support to domain experts while providing some clinically-meaningful insights to identify common patterns as well as individual characteristics in different groups of healthy and pathological subjects.

关键词： EEG system identification dynamic factor analysis

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A COMPARATIVE STUDY OF SUPPORT VECTOR MACHINE AND RISK DETERMINISTIC APPROACH ON A PHOTOVOLTAIC SYSTEM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2024年第3期86卷 67-80页

作者： Colbu, Ștefania-Cristiana Băncilă, Daniel-Marian Petrescuniță, Alina Popescu, Dumitru Olteanu, Severus-Constantin Dept. of Automatic Control and Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Mathematical Methods and Models National University of Science and Technology POLITEHNICA Bucharest Romania

The present paper introduces a comparative analysis of the management of a stochastic optimization problem using both a risk deterministic approach and a support vector machine strategy. This optimization problem is formulated in consideration of the influence produced by environmental factors, such as irradiance and temperature, on a photovoltaic panel. The problem will be solved by applying both methods to determine the Maximum Power Point (MPP), thereby to enhance the power generation efficiency. The primary objective is to conduct an analysis comparing the two approaches, with the aim of recommending the most suitable approach for Maximum Power Point Tracking (MPPT). © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： machine learning photovoltaics risk optimization support vector machine for regression

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AN INTEGRATED BREAST CANCER MICROARRAY ANALYSIS APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2022年第2期84卷 79-90页

作者： Lixandru-Petre, Irina-Oana Buiu, Catalin Dept. of Automatic Control and Systems Engineering University POLITEHNICA of Bucharest Romania

As knowledge of the biological and biophysical basis of cellular function has increased, opportunities to understand the cellular and molecular functioning of organic matter have expanded, and gene expression microarrays have become a widely used technique to study the dynamics of biological processes. In this paper, we present a gene expression analysis approach using R programming, starting from mRNA oligonucleotide matrices ending with interrelated genes, analyzed within a chosen biological process. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Microarrays

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Adaptive control Using a Quaternion Wavelet Neural Network

Adaptive Control Using a Quaternion Wavelet Neural Network

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International Workshop on Robot Motion and control (RoMoCo)

作者： Gerardo Martínez-Terán Eduardo Bayro-Corrochano Dept. Electrical Engineering and Computer Science CINVESTAV Guadalajara Mexico Institute of Automatic Control and Robotics Poznan University of Technology

ISBN: (数字)9798350393965

ISBN: (纸本)9798350393972

Robotics and haptic systems have allowed new and diverse applications in the field of medicine, such as assisted surgery and teleoperation which have increasingly stringent requirements for accuracy, convergence, and low computational consumption. In this paper an adaptive PID control law (Proportional Integral Derivative controller, PID), of indirect architecture is presented for movement paths in a haptic system of open chain, where the identification of the plant is through a quaternionic wavelet neural network (Quaternion Wavelet Neural Network, QWNN) for tune the PID values, this allows the optimal movement into the regions of the workspace.

关键词： Uncertainty Quaternions Neural networks Surgery Robustness Haptic interfaces Topology

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Newton-Euler Modeling of Robot Dynamics Using Geometric Algebra

Newton-Euler Modeling of Robot Dynamics Using Geometric Alge...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Eduardo Bayro-Corrochano Daniel Alvarado-Mejia Institute of Automatic Control and Robotics Poznan University of Technology Dept. Electrical Engineering and Computer Science CINVESTAV Guadalajara Mexico

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

In this work, we present the modeling of the dynamics of a robot manipulator using the Newton-Euler algorithm in the conformal algebra framework. The modeling of the dynamics of robot manipulators is currently done using the Euler-Lagrange formulation which is a batch type of computation. In contrast, in this paper, we propose a recursive algorithm for the modeling of the dynamics of robot manipulators using the Newton-Euler algorithm in the conformal geometric algebra framework.

关键词： Algebra Heuristic algorithms Computational modeling Optimal control Motors Real-time systems Mathematical models

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Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods 4

Forecasting Breakdowns of Electric Motors of a Sugar Factory...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Zaiets, Nataliia Vlasenko, Lidiia Lutska, Nataliia Zhyltsov, Andrii Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350395532

The paper demonstrates the process of developing mathematical models for identifying breakdowns of electric motors using machine learning methods. The authors have developed three mathematical models for identifying breakdowns of electric drives with a power of 55 kW, 1500 rpm on the example of sugar production: model for quadratic discriminant analysis;binary classification decision tree;feedforward neural network model. The best structures and parameters of the models were determined by machine learning methods. The performance of the models on the training and test sets was more than 96% by F-score. It was proposed to use all three models independently for diagnosing breakdowns of electric drives, while the decision maker makes the final decision on the replacement or repair of electrical equipment. This approach is justified and effective because of the diversity of models. The developed models, except for assessing the state of the engine, can also be used for simulation modelling, forecasting, and serve as information support for process operators in organizing equipment maintenance. The availability of real-time information about the state of equipment in production will allow timely repair or replacement of equipment, thereby reducing the risks of stopping production and increasing the resource efficiency of technological processes. © 2023 IEEE.

关键词： Decision trees

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Contagion Propagation with Rule-Based Reasoning and Decentralized control in an Agent-Based Susceptible-Infected-Recovered-Susceptible Infodemic Model 8

Contagion Propagation with Rule-Based Reasoning and Decentra...

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8th International Conference on Big Data, Knowledge and control systems engineering, BdKCSE 2023

作者： Berceanu, Cristian Arshad, Nadia Patrascu, Monica University Politehnica of Bucharest Complex Systems Laboratory Dept. of Automatic Control and Systems Engineering Bucharest Romania University of Bergen Section for Epidemiology and Medical Statistics Dept. of Global Public Health and Primary Care Bergen Norway University Politehnica of Bucharest Complex Systems Laboratory Romania University of Bergen Centre for Elderly and Nursing Home Medicine Dept. of Global Public Health and Primary Care Bergen Norway

ISBN: (纸本)9798350313246

In recent years, and especially during the on-going pandemic, the interest in investigating the dynamics of infodemics has increased to unprecedented levels. In this study we propose an agent-based model for the spread of misinformation, disinformation, and other similar information. The model is based on the susceptible-infected-recovered-susceptible epidemic interpretation. We introduce a rule-based reasoning module to model individual bias in the perception of information based on the psychology concept of illusion of knowledge. We integrate this module into a deliberative-reactive agent controller structure and a decentralized control scheme for the multi-agent system. Results show significant differences between the outputs of the proposed model and the classical version, impressing on the effects of considering local agent individuality when modeling human psycho-social interactions. © 2023 IEEE.

关键词： Multi agent systems

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Circuit Design Method of Rate Increase in Operational Amplifiers Based on the MH2XA031 Array Chip and CBJT Technological Routes

Circuit Design Method of Rate Increase in Operational Amplif...

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Alexey E. Titov Dmitriy V. Kleimenkin Nikolay N. Prokopenko dept. of Automatic Control Systems Southern Federal University Rostov-on-Don Russia dept. of Scientific Research dept. of Information Systems and Radio Engineering Don State Technical University Rostov-on-Don Russia

It has been revealed that in the conditions of small-scale production of discrete-analog filters on switched capacitors and ADC-drivers, it is most advisable to design high-speed operational amplifiers (OAs) based on the CBJT technological route and the MH2XA031 array chip, which allows operation in conditions of low temperatures and exposure to radiation. It has been established that most commercially produced $O A s$ provide an average value of the slew rate (SR, up to $200 \div 300 \mathrm{~V} / \mu \mathrm{s}$). This is largely due to the limitations of the technologies used, and most importantly, to the irrational design of circuits. We study the maximum performance parameters of a CBJT OA with one integrating capacitor, which ensures the stability of the $O A$, and a differentiating transient correction circuit, which is implemented by connecting additional small capacitors to the original circuit. In this case, the $S R$ of the $O A$ increases by more than 500 times (up to $4000 \mathrm{~V} / \mu \mathrm{s}$).

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Multi-Point Positional Insertion Tuning for Small Object Detection

Multi-Point Positional Insertion Tuning for Small Object Det...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kanoko Goto Takumi Karasawa Takumi Hirose Rei Kawakami Nakamasa Inoue Dept. of Computer Science Institute of Science Tokyo Tokyo Japan Dept. of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object detection models has emerged as a promising approach. However, finetuning large models is computationally and memory expensive. To address this issue, this paper introduces multi-point positional insertion (MPI) tuning, a parameter-efficient finetuning (PEFT) method for small object detection. Specifically, MPI incorporates multiple positional embeddings into a frozen pretrained model, enabling the efficient detection of small objects by providing precise positional information to latent features. Through experiments, we demonstrated the effectiveness of the proposed method on the SODA-D dataset. MPI performed comparably to conventional PEFT methods, including CoOp and VPT, while significantly reducing the number of parameters that need to be tuned.

关键词： Computational modeling Object detection Signal processing Feature extraction Encoding Acoustics Speech processing Tuning

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