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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Siyang Wu, Lianlian Xu, Yong Liu, Kunhong He, Song Bo, Xiaochen Xiamen University School of Informatics Xiamen China Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin China Fujian University of Technology School of Computer Science and Mathematics Fuzhou China Xiamen University School of Film Department of Digital Media Technology Xiamen China Institute of Health Service and Transfusion Medicine Department of Bioinformatics Beijing China

ISBN: (纸本)9798350337488

The interpretable machine learning method is important in drug discovery. Unlike traditional ensemble learning methods, this paper proposes an interpretable algorithm based on Bayesian rule extraction to obtain reliable and explainable results for hepatotoxicity prediction. To extract information from different types of omics data, our algorithm employs a multi-view learning strategy to enhance performance. Specifically, a random forest is trained in each view, and then the Bayesian rule extraction algorithm is designed to select an optimal rule subset, controlling the size and accuracy of the ruleset through probabilities. These rule sets are integrated through multi-view voting to get the final decisions. The performance of our algorithm is tested on the hepatotoxicity dataset, demonstrating that compared to traditional machine learning algorithms and rule-based algorithms, our approach maintains excellent performance while achieving high interpretability in most cases. Our python source code and the related Supplementary Materials are available at: ***/MLDMXM2017/MV-BRS. © 2023 IEEE.

关键词： Bayesian Ruleset Hepatotoxicity Prediction Interpretable Machine Learning Multi-view Learning

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What improves the generalization of graph transformers? a theoretical dive into the self-attention and positional encoding 24

What improves the generalization of graph transformers? a th...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongkang Li Meng Wang Tengfei Ma Sijia Liu Zaixi Zhang Pin-Yu Chen Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy NY Department of Biomedical Informatics Stony Brook University Stony Brook NY Department of Computer Science and Engineering Michigan State University East Lansing MI and MIT-IBM Watson AI Lab IBM Research MA Department of Computer Science and Engineering University of Science and Technology of China Hefei Anhui China IBM Thomas J. Watson Research Center Yorktown Heights NY

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks.

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Depth and Pitch Control of Autonomous Underwater Glider Using Model Predictive Control with ARX Model

Depth and Pitch Control of Autonomous Underwater Glider Usin...

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International Conference on Electrical engineering and informatics, ICEEI

作者： Jason Jeremy Egi Muhammad Idris Hidayat Simon Siregar Bambang Riyanto Trilaksono Natsir Habibullah School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Department Computer Engineering Faculty of Applied Science Telkom University Bandung Indonesia

Autonomous Underwater Gliders (AUGs) are extensively developed vehicles capable of prolonged exploration and observation in complex marine environments. Control of the AUG is challenging due to its slow response system and its constraints. In this research, a linear model representation of AutoRegressive eXogenous input (ARX) will be constructed using input and output data from the AUG system, built Model Predictive Control (MPC), analyzed the performance, comparing with traditional Proportional-Integral-Derivative (PID). The objective is to enhance setpoint tracking accuracy and minimize energy to extend exploration time while faced with constraints. Simulation results reveal MPC exhibits potential setpoint tracking, low overshoot as low as 0.6% up to 0.3m/s maximum depth rate, has relatively low input changes indicating good efficiency compared to PID. MPC approach effectively addresses slow response systems, managing momentum, and handling actuator constraints commonly encountered in AUG.

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Drug Repurposing Targeting COVID-19 3CL Protease using Molecular Docking and Machine Learning Regression Approach

arXiv

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arXiv 2023年

作者： Aqeel, Imra Majid, Abdul Biomedical Informatics Research Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan

The COVID-19 pandemic has initiated a global health emergency, with an exigent need for effective cure. Progressively, drug repurposing is emerging a promise solution as it saves the time, cost and labor. However, the number of drug candidates that have been identified as being repurposed for the treatment of COVID-19 are still insufficient, so more effective and thorough drug exploring strategies are required. In this study, we joint the molecular docking with machine learning regression approaches to find some prospective therapeutic candidates for COVID-19 treatment. We screened the 5903 approved drugs for their inhibition by targeting the main protease 3CL of SARS-CoV-2, which is responsible to replicate the virus. Molecular docking is used to calculate the binding affinities of these drugs to the main protease 3CL. We employed several machine learning regression approaches for QSAR modeling to find out some potential drugs with high binding affinities. Our outcomes demonstrated that the Decision Tree Regression (DTR) model with best scores of R2 and RMSE, is the most suitable model to explore the potential drugs. We shortlisted six favorable drugs with their respective Zinc IDs ZINC (3873365, 85432544, 203757351, 85536956, 8214470, and 261494640) within the range of -15 kcal/mol to -13 kcal/mol. These drugs have novel repurposing potential, except for one antiviral ZINC203757351 compound that has already been identified in other studies. We further examined the physiochemical and pharmacokinetic properties of these most potent drugs and their best binding interaction to specific target protease 3CLpro. Our study provides an efficient framework to explore the potential drugs against COVID-19, and establishes the impending combination of molecular docking with machine learning regression approaches to accelerate the identification of potential therapeutic candidates. Our verdicts contribute to the larger goal of finding effective cures for COVID-19, which is an a

关键词： COVID-19

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Implementation of PSO-Optimized PI Control Algorithm for SMES Based DVR in Power Quality Mitigation

Implementation of PSO-Optimized PI Control Algorithm for SME...

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Delhi Section Conference (DELCON), IEEE

作者： Jeba Singh O D. Godwin Immanuel Sheila Mahapatra Rajesh Sharma R Akey Sungheetha G. S. Pradeep Ghantasala Centre for Academic Research Alliance University Bengaluru India School of Electrical and Electronics Sathyabama Institute of Science of Technology Chennai India Department of Electrical and Electronics Engineering Alliance University Bengaluru India Department of Computer Sciences and Engineering Alliance University Bengaluru India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Nowadays, Power Quality (PQ) problems should be more concentrated in distribution systems of power sector. To maintain the power quality in the distribution system the occurrence of PQ disturbances must be identified and compensated. Disturbances like voltage sag and swell are the major issues that cause failure in electrical devices. In this paper, a Superconducting Magnetic Energy Storage (SMES) integrated Dynamic Voltage Restorer (DVR)is implemented to protect the grid voltage from sag, swell and transients. A better solution to PQ disturbances and mitigation related issues are provided through DVR topology. Voltage sag is one of the dominant problems which cause the PQ issues. In this proposed work, sag, swell and outages are mitigated thus the real power is recovered so that the power wastage is prevented. Moreover, this SMES technology-based DVR method compensates the grid from balanced and unbalanced sag, mitigation of outages, interruption, single phase and two-phase swell conditions. The principle, design and working of the SMES based DVR is implemented using MATLAB / SIMULINK. The testing of the established model and performance results are presented.

关键词： Voltage fluctuations Software packages Prevention and mitigation Power quality Reliability engineering Topology Power system reliability Superconducting magnetic energy storage Transient analysis Testing

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MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal

arXiv

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arXiv 2024年

作者： Hung, Kuo-Hsuan Wang, Kuan-Chen Liu, Kai-Chun Chen, Wei-Lun Lu, Xugang Tsao, Yu Lin, Chii-Wann Department of Biomedical Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academic Sinica Taiwan College of Information and Computer Sciences University of Massachusetts Amherst United States National Institute of Information and Communications Technology Japan

Electrocardiogram (ECG) is an important noninvasive method for diagnosing cardiovascular disease. However, ECG signals are susceptible to noise contamination, such as electrical interference or signal wandering, which reduces diagnostic accuracy. Various ECG denoising methods have been proposed, but most existing methods yield suboptimal performance under very noisy conditions or require several steps during inference, leading to latency during online processing. In this paper, we propose a novel ECG denoising model, namely Mamba-based ECG Enhancer (MECG-E), which leverages the Mamba architecture known for its fast inference and outstanding nonlinear mapping capabilities. Experimental results indicate that MECG-E surpasses several well-known existing models across multiple metrics under different noise conditions. Additionally, MECG-E requires less inference time than state-of-the-art diffusion-based ECG denoisers, demonstrating the model’s functionality and efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Electrocardiograms

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Instrument-tissue Interaction Detection Framework for Surgical Video Understanding

arXiv

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arXiv 2024年

作者： Lin, Wenjun Hu, Yan Fu, Huazhu Yang, Mingming Chng, Chin-Boon Kawasaki, Ryo Chui, Cheekong Liu, Jiang The Department of Mechanical Engineering National University of Singapore Singapore The Research Institute of Trustworthy Autonomous Systems The Department of Computer Science and Engineering Southern University of Science and Technology China The Agency for Science Technology and Research Singapore The Department of Informatics Osaka University Graduate School of Medicine Japan The Department of Ophthalmology Shenzhen Peoples Hospital China

Instrument-tissue interaction detection task, which helps understand surgical activities, is vital for constructing computer-assisted surgery systems but with many challenges. Firstly, most models represent instrument-tissue interaction in a coarse-grained way which only focuses on classification and lacks the ability to automatically detect instruments and tissues. Secondly, existing works do not fully consider relations between intra- and inter-frame of instruments and tissues. In the paper, we propose to represent instrument-tissue interaction as 〈instrument class, instrument bounding box, tissue class, tissue bounding box, action class〉 quintuple and present an Instrument-Tissue Interaction Detection Network (ITIDNet) to detect the quintuple for surgery videos understanding. Specifically, we propose a Snippet Consecutive Feature (SCF) Layer to enhance features by modeling relationships of proposals in the current frame using global context information in the video snippet. We also propose a Spatial Corresponding Attention (SCA) Layer to incorporate features of proposals between adjacent frames through spatial encoding. To reason relationships between instruments and tissues, a Temporal Graph (TG) Layer is proposed with intra-frame connections to exploit relationships between instruments and tissues in the same frame and inter-frame connections to model the temporal information for the same instance. For evaluation, we build a cataract surgery video (PhacoQ) dataset and a cholecystectomy surgery video (CholecQ) dataset. Experimental results demonstrate the promising performance of our model, which outperforms other state-of-the-art models on both datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Tissue

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Mining Intelligence Hierarchical Feature for Malware Detection 15th

Mining Intelligence Hierarchical Feature for Malware Detecti...

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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Davuluri, Sandeep Kumar Soni, Mukesh ALMahadin, Ghayth Rivera, Richard Upadhyay, Jinal Patel, Pavan Department of Information Technology University of the Cumberlands Williamsburg United States Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Faculty of Information Technology Al Ahliyya Amman University Amman Jordan Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador HasmukhGoswami College of Engineering Gujarat Vahelal India Ahmedabad Institute of Technology Gujarat Ahmedabad India

ISBN: (纸本)9789819786305

In recent years, a new strain of malware, known as mining malware, has emerged, resulting in significant losses for its victims due to its encryption techniques. This research proposes an innovative approach to detect mining malware by integrating hierarchical threat intelligence features, focusing on the static attributes of mining malware. From the perspective of mining malware threat intelligence, we train classifiers using distinct layers of features, including byte-level features, Portable Executable (PE) structure features, and mining operation execution features. By leveraging diverse malicious software attributes for detection preferences, we develop a comprehensive mining malware detector based on a hierarchical feature integration methodology. To evaluate our approach, we conducted experiments in both controlled lab settings and real-world datasets. The results indicate that our hierarchical feature-integrated mining malware detection method achieves an impressive accuracy rate of 97.01% on the simulated real-world dataset, demonstrating a substantial 6.13% accuracy improvement compared to the baseline mining malware detection method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Correlation Analysis and Predictive Factors for Building a Mathematical Model 1

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7th Computational Methods in Systems and Software, CoMeSySo 2023

作者： Nelyub, V.A. Tynchenko, V.S. Gantimurov, A.P. Degtyareva, K.V. Kukartseva, O.I. Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow105005 Russia Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk660041 Russia Department of Information Economic System Institute of Engineering and Economics Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Systems Analysis and Operations Research Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia

ISBN: (数字)9783031535499

ISBN: (纸本)9783031535482

The study, published in the journal Nature Medicine, looked at data on 1,000 people from China who were tracked over an average period of six years. The participants were divided into two groups: those who lived in areas with high levels of air pollution and those who lived in areas with low levels of air pollution. The study analyzed data on patients with lung cancer, including their age, gender, exposure to air pollution, alcohol consumption, dust allergy, occupational hazards, genetic risk, chronic lung disease, balanced diet, obesity, smoking, passive smoking, chest pain, cough, hemoptysis, fatigue, weight loss, shortness of breath, wheezing, difficulty swallowing, nail thickening and snoring. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Air pollution

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AlGaAs soliton microcombs at room temperature

AlGaAs soliton microcombs at room temperature

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Wu, Lue Xie, Weiqiang Xiang, Chao Chang, Lin Yu, Yan Chen, Hao-Jing Yamamoto, Yoshihisa Bowers, John E. Vahala, Kerry J. Suh, Myoung-Gyun T. J. Watson Laboratory of Applied Physics California Institute of Technology PasadenaCA91125 United States Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA93106 United States Physics & Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States

Soliton generation in AlGaAs microresonators at room temperation is reported for the first time. The destabilizing thermo-optic effect is shown to instead provide stability for high soliton repetition rates. The optic... 详细信息

ISBN: (纸本)9781957171258

关键词： Aluminum gallium arsenide

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