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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Amirian, Mohammad Mahdi Ghidary, Saeed Shiry Department of Computer Engineering Amirkabir University of Technology Tehran Iran Department of Math and Computer Science Amirkabir University of Technology Tehran Iran

ISBN: (纸本)9780999241165

We present improvements in maximum a-posteriori inference for Markov Logic, a widely used SRL formalism. Several approaches, including Cutting Plane Aggregation (CPA), perform inference through translation to Integer Linear Programs. Aggregation exploits context-specific symmetries independently of evidence and reduces the size of the program. We illustrate much more symmetries occurring in long ground clauses that are ignored by CPA and can be exploited by higher-order aggregations. We propose Full-Constraint-Aggregation, a superior algorithm to CPA which exploits the ignored symmetries via a lifted translation method and some constraint relaxations. RDBMS and heuristic techniques are involved to improve the overall performance. We introduce Xeggora as an evolutionary extension of RockIt, the query engine that uses CPA. Xeggora evaluation on real-world benchmarks shows progress in efficiency compared to RockIt especially for models with long formulas. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Integer programming

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A structure-activity relationship modelling of opioid compounds by using machine learning 15

A structure-activity relationship modelling of opioid compou...

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15th Conference of the Euro-American Consortium for Promoting the Application of mathematics in Technical and Natural sciences, AMiTaNS 2023

作者： Sapundzhi, Fatima Lazarova, Meglena Dzimbova, Tatyana Georgiev, Slavi Ivanova, Antonina Department of Communication and Computer Engineering Faculty of Engineering South-West University Neofit Rilski 66 Ivan Myhailov Str. Blagoevgrad2700 Bulgaria Faculty of Applied Mathematics and Informatics Technical University of Sofia 8 St. Kliment Ohridski Blvd. Sofia1000 Bulgaria Institute of Molecular Biology Bulgarian Academy of Sciences Acad. G.Bonchev Str bl. 21 Sofia1113 Bulgaria Department of Information Modeling Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev Str. bl. 8 Sofia1113 Bulgaria Department of Applied Mathematics and Statistics Faculty of Natural Sciences and Education University of Ruse 8 Studentska Str. Ruse7004 Bulgaria Department of Computer Science Faculty of International Economics and Administration Varna Free University Chernorizets Hrabar 84 Yanko Slavchev Str. Chaika Resort Varna9007 Bulgaria

Opiates are among the oldest drugs that are used to treat many medical problems. They are analgesic and sedative drugs that contain opium. The morphine is its most active ingredient and it is a widely used pain reliever despite its side effects. The main objective of this study is to construct a model which gives the structure-activity relationship among a series of mu-opioid ligands and molecular docking results. For this purpose, a model of mu-opioid receptors using machine learning is introduced. By obtaining a relationship between the docking results and the in vivo test, we could predict the biological effect of the newly synthesized ligands. © Published under licence by IOP Publishing Ltd.

关键词： Medical problems

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Analyzing Generalized Pólya Urn Models Using Martingales, with an Application to Viral Evolution

SSRN

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SSRN 2023年

作者： Specht, Ivan Mitzenmacher, Michael Harvard College Faculty of Arts and Sciences Harvard University CambridgeMA02138 United States Department of Computer Science School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States

The randomized play-the-winner (RPW) model is a generalized Pólya Urn process with broad applications ranging from viral genomics to clinical trials. We derive an exact expression for the variance of the RPW model by transforming the Pólya Urn process into a martingale, correcting an earlier result of Matthews and Rosenberger (1997). We then use this result to approximate the full probability mass function of the RPW model for certain parameter values relevant to genetic applications. Finally, we fit our model to genomic sequencing data of SARS-CoV-2, demonstrating a novel method of estimating the viral mutation rate that delivers comparable results to existing scientific literature. © 2023, The Authors. All rights reserved.

关键词： Genome

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Achievable Rate of Multi-Cell Downlink Massive MIMO Systems with D2D Underly 91

Achievable Rate of Multi-Cell Downlink Massive MIMO Systems ...

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91st IEEE Vehicular Technology Conference, VTC Spring 2020

作者： Al-Rimawi, Ashraf Ibrahim, Laith Ajib, Wessam Birzeit University Department of Electrical and Computer Engineering Palestine Al-Quds Open University Department of Technology and Applied Science Palestine University of Quebec in Montreal Department of Computer Science Canada

ISBN: (纸本)9781728152073

In this paper, a new analytical framework model based on stochastic geometry for Device-To-Device (D2D) communication underlaying multi-cell massive Multi-Input-Multi-Output (MIMO) system is proposed. Assuming Maximum Ratio Transmission or Zero Forcing precoding scheme for cellular downlink transmission, the impact of RF mismatches and achievable rate of cellular user are analytically derived. The studied model assumes truncated Gaussian distribution to model RF mismatches, D2D interference, inter cell interference, and intra-cell interference. Accordingly, closed form expressions of lower-bound achievable data rate for cellular users is derived. Moreover, asymptotic performance analysis under the assumption of large number of antennas has been performed. Simulation results are found to coinside with the theoritical results and validated our model. © 2020 IEEE.

关键词： Stochastic systems

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Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine

arXiv

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arXiv 2023年

作者： Chen, Emma Kansal, Aman Chen, Julie Jin, Boyang Tom Reisler, Julia Rachel Kim, David A. Rajpurkar, Pranav Department of Biomedical Informatics Harvard Medical School United States Department of Computer Science Stanford University United States Department of Emergency Medicine Stanford University School of Medicine United States Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University United States

We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+ continuously monitored Emergency department visits from 2020-2022. MC-BEC focuses on clinically relevant prediction tasks at timescales from minutes to days, including predicting patient decompensation, disposition, and emergency department (ED) revisit, and includes a standardized evaluation framework with train-test splits and evaluation metrics. The multimodal dataset includes a wide range of detailed clinical data, including triage information, prior diagnoses and medications, continuously measured vital signs, electrocardiogram and photoplethysmograph waveforms, orders placed and medications administered throughout the visit, free-text reports of imaging studies, and information on ED diagnosis, disposition, and subsequent revisits. We provide performance baselines for each prediction task to enable the evaluation of multimodal, multitask models. We believe that MC-BEC will encourage researchers to develop more effective, generalizable, and accessible foundation models for multimodal clinical data. © 2023, CC BY.

关键词： Emergency rooms

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An Overall Disease Analysis of Mango Using Neural Network with Hybrid Feature model

An Overall Disease Analysis of Mango Using Neural Network wi...

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applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Jatinder Kaur Ashwani K Sharma Manudeep Kaushal Arti Badhoutiya Nirmala Reddy Ahmed Alkhayyat Department of ECE Chandigarh Engineering College Jhanjeri Mohali India Department of Applied Sciences Chandigarh Engineering College Jhanjeri Mohali India Dean Professional Studies and Dean Corporate Affairs Gulzar Group of Institutes Khanna Punjab India Department of Electrical Engineering GLA University Mathura India Department of Computer Science & Engineering IES College of Technology Bhopal IES University Bhopal India College of Technical Engineering The Islamic University Najaf Iraq

Mango is a highly valued fruit crop, with arable land covering about 36% of the total fruit area. With roughly 23% of the total area under mango, Uttar Pradesh and Andhra Pradesh lead the way, followed by Tamil Nadu, Karnataka, Gujarat, and Bihar. Global food security is affected a lot due to crop plant diseases. These diseases directly affect the quality of food, fruits, etc., resulting in a fall in agricultural productivity. Farmers must assess the leaf, fruit, or tree through visual interpretation to determine whether any fruit crop part is contaminated or blooming properly. But this traditional technique has its limitations; it is inconsistent, unreliable, and prone to mistakes. Researchers have proposed a variety of techniques for resolving the above-stated issues. Most of them preferred the modelling of convolutional neural network models according to problem areas by giving low-resolution images as input, resulting in low disease detection accuracy. In this work, we use an artificial neural network (ANN) technique with the objective of early detection of disease in any part of the fruit crop with tiny disease blobs that can only be seen with better resolution images. All the contaminated blobs are segmented for the entire dataset after a pre-processing phase using a contrast enhancement approach.

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In Situ Formed Bimetal Metal-Organic Frameworks in Polyvinyl Alcohol/Sodium Alginate-Based Conductive Hydrogels Towards Soft Electronics

SSRN

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SSRN 2024年

作者： Li, Jiongru Wei, Huige Cui, Shuaichuan Hou, Hua Zhang, Yifan Zhang, Yingying Xu, Ben Bin El-Bahy, Zeinhom M. Melhi, Saad Sellami, Rahma Guo, Zhanhu Tianjin Key Laboratory of Brine Chemical Engineering and Resource Eco-utilization College of Chemical Engineering and Materials Science Tianjin University of Science and Technology Tianjin300457 China State Key Laboratory of Polymer Physics and Chemistry Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun130022 China College of Materials Science and Engineering Taiyuan University of Science and Technology Taiyuan030024 China Tianjin University of Science and Technology China Department of Mechanical and Construction Engineering Northumbria University Newcastle Upon TyneNE1 8ST United Kingdom Department of Chemistry Faculty of Science Al-Azhar University Nasr City Cairo11884 Egypt Department of Computer Science Applied College Northern Border University Rafha91911 Saudi Arabia University of Bisha Saudi Arabia

Zeolitic imidazolate frameworks (ZIFs) have received enormous attention due to unique physi-chemical properties, but are rarely reported for applications in electrically conductive hydrogels (ECHs) arising from low intrinsic conductivity and poor dispersion. Herein, we propose an innovative strategy to prepare highly conductive and mechanically robust ECHs by in situ growing Ni/Co bimetallic ZIFs within the polyvinyl alcohol/sodium alginate dual network. 2-methylimidazole (MeIM) ligands are creatively copolymerized with pyrrole monomers to form Py-co-MeIM conductive fillers and improve the conjugation degree of the polymer chains and thereby increase the conductivity of the hydrogel. Meanwhile, MeIM ligands act as anchor points for the in-situ formation of bimetallic ZIFs by trapping targeted metal ions, effectively avoiding phase-to-phase interfacial resistance and ensuring a uniform distribution in the hydrogel network. Due to the synergism of Ni/Co bimetallic ZIFs, the PZPS hydrogel exhibits a high areal capacitance, promising for flexible energy storage devices. In addition, the hydrogel shows excellent mechanical strength and toughness, a high gauge factor and a pressure sensitivity over an extremely wide stress range when used as flexible wearable strain/pressure sensors. This study provides new insights to incorporating highly conductive and uniformly dispersed ZIFs into hydrogels for flexible wearable electronics. © 2024, The Authors. All rights reserved.

关键词： Hydrogels

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A novel control scheme for automatic voltage regulator using novel modified artificial rabbits optimizer

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2023年 6卷

作者： Izci, Davut Rizk-Allah, Rizk M. Snášel, Václav Ekinci, Serdar Hashim, Fatma A. Abualigah, Laith Department of Computer Engineering Batman University Batman 72100 Turkey MEU Research Unit Middle East University Amman Jordan Basic Engineering Science Department Faculty of Engineering Menoufia University Shebin El-Kom 32511 Egypt Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Faculty of Engineering Helwan University Egypt Prince Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Applied Science Research Center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

Automatic voltage regulators (AVRs) are essential components in electrical systems to maintain stable voltage output, ensuring optimal performance and equipment protection. The effectiveness of AVRs rely on key parameters such as voltage regulation, response time, stability, and efficiency. Integrating controllers with AVRs offers centralized monitoring and regulation, enhancing voltage output efficiency. This study therefore first discusses various controller types which showcase distinctive capabilities based on their tuning strategies and then introduces an intelligent optimization approach using fractional-order proportional-integral-derivative with double derivative (FOPIDD²) controller tailored for AVRs. The study adopts the artificial rabbits optimization (ARO) algorithm, fortified with an adaptive local search (ALS) mechanism and experience-based perturbed learning (EPL) strategy. The modified version (m-ARO) enhances solution diversity and navigational efficacy, resulting in improved optimization quality. The study's core objective is to demonstrate the superior performance of the m-ARO-based FOPIDD² controller in addressing the multifaceted challenges of AVR control, outperforming conventional techniques in stability, speed of response, robustness, and efficiency. To validate the method's efficacy, a comparative analysis is conducted using existing controllers with different tuning algorithms. Results indicate that the proposed m-ARO-based FOPIDD² controller achieves superior performance metrics, demonstrating its capability. The study also includes a wider perspective by considering nineteen different controllers, reported in the literature, for comprehensive comparison. Notably, the proposed method exhibits the best stability, further affirming its effectiveness. © 2023 The Author(s)

关键词： Adaptive local search Artificial rabbits optimization algorithm Automatic voltage regulator Experience-based perturbed learning Optimization

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Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images

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Discover applied sciences 2024年第1期6卷 1-14页

作者： Byeon, Haewon Patel, Ruchi Kshatri Vidhate, Deepak A. Kiyosov, Sherzod Rahin, Saima Ahmed Keshta, Ismail Lakshmi, T. R. Vijaya Department of Digital Anti-Aging Healthcare Inje University Gimhae Republic of Korea Shri Ram Institute of Technology Jabalpur India Department of Information Technology Dr Vithalrao Vikhe Patil College of Engineering Ahmednagar India The Department of Tax and Taxation Tashkent Institute of Finance Tashkent Uzbekistan United International University Dhaka Bangladesh Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Mahatma Gandhi Institute of Technology Gandipet Hyderabad India

The use of a light-weight deep learning Convolutional Neural Network (CNN) augmented with the power of Fuzzy Non-Sample Shearlet Transformation (FNSST) has successfully solved the problem of reducing noise and artifacts in Low-Dose Computed Tomography (LDCT) pictures. Both the Normal-Dose Computed Tomography (NDCT) and the Low-Dose Computed Tomography (LDCT) images from the dataset are subjected to the FNSST decomposition procedure during the training phase, producing high-frequency sub-images that act as input for the CNN. The CNN creates a meaningful connection between the high-frequency sub-images from LDCT and their corresponding residual sub-images during the training operation. The CNN is given the capacity to distinguish between LDCT high-frequency sub-images and expected high-frequency sub-images, which frequently have varying levels of noise or artifacts, especially in a fuzzy setting. The FNSST-CNN then successfully distinguishes LDCT high-frequency sub-images from the expected high-frequency sub-images during the testing phase, thereby reducing noise and artifacts. When compared to other approaches like KSVD, BM3D, and conventional image domain CNNs, the performance of FNSST-CNN is impressive as shown by better peak signal-to-noise ratios, stronger structural similarity, and a closer likeness to NDCT pictures. 1. A CNN model has been proposed to reduce the noise and artifacts in Low-Dose Computed Tomography images. 2. During the testing phase, the proposed model successfully distinguishes between high frequency sub-images. 3. CNN performs better than KSVD, BM3D and conventional CNN models in terms of better Signal-to-noise ratio.

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High-order ADER Discontinuous Galerkin schemes for a symmetric hyperbolic model of compressible barotropic two-fluid flows

arXiv

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arXiv 2023年

作者： Río-Martín, Laura Dumbser, Michael Department of Information Engineering and Computer Science University of Trento via Sommarive 9 Povo Trento38123 Italy Laboratory of Applied Mathematics DICAM University of Trento Via Mesiano 77 Trento38123 Italy

This paper presents a high-order discontinuous Galerkin finite element method to solve the barotropic version of the conservative symmetric hyperbolic and thermodynamically compatible (SHTC) model of compressible two-phase flow, introduced by Romenski et al. in [28, 29], in multiple space dimensions. In the absence of algebraic source terms, the model is endowed with a curl constraint on the relative velocity field. In this paper, the hyperbolicity of the system is studied for the first time in the multidimensional case, showing that the original model is only weakly hyperbolic in multiple space dimensions. To restore strong hyperbolicity, two different methodologies are used: i) the explicit symmetrization of the system, which can be achieved by adding terms that contain linear combinations of the curl involution, similar to the Godunov-Powell terms in the MHD equations;ii) the use of the hyperbolic generalized Lagrangian multiplier (GLM) curl-cleaning approach forwarded. The PDE system is solved using a high-order ADER discontinuous Galerkin method with a posteriori sub-cell finite volume limiter to deal with shock waves and the steep gradients in the volume fraction commonly appearing in the solutions of this type of model. To illustrate the performance of the method, several different test cases and benchmark problems have been run, showing the high-order of the scheme and the good agreement when compared to reference solutions computed with other well-known methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Two phase flow

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