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Fundamental Dynamics of Popularity-Similarity Trajectories in Real Networks

Physical Review Letters 2024年第25期132卷 257401-257401页

作者： Evangelos S. Papaefthymiou Costas Iordanou Fragkiskos Papadopoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 3036 Limassol Cyprus.

Real networks are complex dynamical systems, evolving over time with the addition and deletion of nodes and links. Currently, there exists no principled mathematical theory for their dynamics—a grand-challenge open problem. Here, we show that the popularity and similarity trajectories of nodes in hyperbolic embeddings of different real networks manifest universal self-similar properties with typical Hurst exponents H≪0.5. This means that the trajectories are predictable, displaying antipersistent or “mean-reverting” behavior, and they can be adequately captured by a fractional Brownian motion process. The observed behavior can be qualitatively reproduced in synthetic networks that possess a latent geometric space, but not in networks that lack such space, suggesting that the observed subdiffusive dynamics are inherently linked to the hidden geometry of real networks. These results set the foundations for rigorous mathematical machinery for describing and predicting real network dynamics.

关键词： Evolving networks Real world networks Evolving network models Self-similarity

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SGCL-LncLoc:An Interpretable Deep Learning Model for Improving IncRNA Subcellular Localization Prediction with Supervised Graph Contrastive Learning

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Big Data Mining and Analytics 2024年第3期7卷 765-780页

作者： Min Li Baoying Zhao Yiming Li Pingjian Ding Rui Yin Shichao Kan Min Zeng School of Computer Science and Engineering Central South UniversityChangsha 410083China Center for Artificial Intelligence in Drug Discovery Case Western Reserve UniversityClevelandOH 44106USA Department of Health Outcomes and Biomedical Informatics University of FloridaGainesvilleFL 32603USA

Understanding the subcellular localization of long non-coding RNAs(IncRNAs)is crucial for unraveling their functional *** previous computational methods have made progress in predicting IncRNA subcellular localization,most of them ignore the sequence order information by relying on k-mer frequency features to encode IncRNA *** the study,we develope SGCL-LncLoc,a novel interpretable deep learning model based on supervised graph contrastive ***-LncLoc transforms IncRNA sequences into de Bruijn graphs and uses the Word2Vec technique to learn the node representation of the ***,SGCL-LncLoc applies graph convolutional networks to learn the comprehensive graph ***,we propose a computational method to map the attention weights of the graph nodes to the weights of nucleotides in the IncRNA sequence,allowing SGCL-LncLoc to serve as an interpretable deep learning ***,SGCL-LncLoc employs a supervised contrastive learning strategy,which leverages the relationships between different samples and label information,guiding the model to enhance representation learning for *** experimental results demonstrate that SGCL-LncLoc outperforms both deep learning baseline models and existing predictors,showing its capability for accurate IncRNA subcellular localization ***,we conduct a motif analysis,revealing that SGCL-LncLoc successfully captures known motifs associated with IncRNA subcellular *** SGCL-LncLoc web server is available at http://***:8000/*** source code can be obtained from https://***/CSUBioGroup/SGCL-LncLoc.

关键词： supervised contrastive learning long non-coding RNA(IncRNA) subcellular localization prediction deep learning Graph Convolutional Network(GCN)

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Feature Matching via Topology-Aware Graph Interaction Model

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 113-130页

作者： Yifan Lu Jiayi Ma Xiaoguang Mei Jun Huang Xiao-Ping Zhang the Electronic Information School Wuhan UniversityWuhan 430072China IEEE Tsinghua Berkeley Shenzhen Institute Shenzhen 518071China the Department of Electrical Computer and Biomedical EngineeringToronto Metropolitan UniversityToronto ON M5B 2K3Canada

Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive *** paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://***/YifanLu2000/TIM.

关键词： Feature matching graph cut outlier filtering topology preserving

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Using Goal-Conditioned Reinforcement Learning with Deep Imitation to Control Robot Arm in Flexible Flat Cable Assembly Task

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IEEE Transactions on Automation Science and engineering 2024年第4期21卷 6217-6228页

作者： Li, Jingchen Shi, Haobin Hwang, Kao-Shing Northwestern Polytechnical University School of Computer Science Xi'an710072 China National Sun Yat-sen University Department of Electrical Engineering Kaohsiung81164 Taiwan Kaohsiung Medical University Department of Healthcare Administration and Medical Informatics Kaohsiung80708 Taiwan

Leveraging reinforcement learning on high-precision decision-making in Robot Arm assembly scenes is a desired goal in the industrial community. However, tasks like Flexible Flat Cable (FFC) assembly, which require highly trained workers, pose significant challenges due to sparse rewards and limited learning conditions. In this work, we propose a goal-conditioned self-imitation reinforcement learning method for FFC assembly without relying on a specific end-effector, where both perception and behavior plannings are learned through reinforcement learning. We analyze the challenges faced by Robot Arm in high-precision assembly scenarios and balance the breadth and depth of exploration during training. Our end-to-end model consists of hindsight and self-imitation modules, allowing the Robot Arm to leverage futile exploration and optimize successful trajectories. Our method does not require rule-based or manual rewards, and it enables the Robot Arm to quickly find feasible solutions through experience relabeling, while unnecessary explorations are avoided. We train the FFC assembly policy in a simulation environment and transfer it to the real scenario by using domain adaptation. We explore various combinations of hindsight and self-imitation learning, and discuss the results comprehensively. Experimental findings demonstrate that our model achieves fast and advanced flexible flat cable assembly, surpassing other reinforcement learning-based methods. Note to Practitioners - The motivation of this article stems from the need to develop an efficient and accurate FFC assembly policy for 3C (computer, Communication, and Consumer Electronic) industry, promoting the development of intelligent manufacturing. Traditional control methods are incompetent to complete such a high-precision task with Robot Arm due to the difficult-to-model connectors, and existing reinforcement learning methods cannot converge with restricted epochs because of the difficult goals or trajectories. To

关键词： Manipulators

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Exploring Ensemble Machine Learning Models for Attention and Memory Assesment 46

Exploring Ensemble Machine Learning Models for Attention and...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Gramouseni, Foteini Prapas, Georgios Bellos, Christos Angelidis, Pantelis Giannakeas, Nikolaos Tsipouras, Markos G. University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece University of Ioannina Department of Informatics and Telecommunications Ioannina Greece

ISBN: (纸本)9798350303964

EEG analysis for attention and memory assessment is an important research topic that has gained a lot of attention in recent years. Virtual Reality (VR) is an innovative technology that provides the ability to design experiments suitable for research in this field. This study explores the feasibility of utilizing ensemble classification algorithms to analyze EEG signals obtained during VR stimulation for the purpose of evaluating memory encoding and attention orienting. The obtained results indicate that EEG analysis can accurately assess memory and attention levels in VR environment setting with a high degree of certainty (84.90% accuracy, and an average of 0.86 true positive rate, 0.88 positive predicted value, 0.89 area under the ROC curve, and 0.89 true negative rate). © 2023 IEEE.

关键词： Virtual reality

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Accuracy Testing of PQ Analyzers Using the OMICRON CMC 356 23

Accuracy Testing of PQ Analyzers Using the OMICRON CMC 356

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23rd International Scientific Conference on Electric Power engineering, EPE 2023

作者： Hlavaty, Lukas Bilik, Petr VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical Engineering Ostrava Czech Republic

ISBN: (纸本)9798350335934

This article deals with the accuracy check of the PQ analyzers using the OMICRON CMC 356 calibrator. This article describes the principle and concept of three applications used to test the accuracy of various quantities important in PQ measurements. Furthermore, the GUI of each application, its principle, and description of functionality, software architecture, and implementation in the LabVIEW graphical programming environment are described. The purpose of the applications is the automated generation of a three-phase voltage at the output of the OMICRON CMC 356 calibrator with parameters defined by the operator to test the accuracy of the PQ analyzers. The first application is used to generate the harmonic components according to EN 50160 with definable deviations from the standard and duration intervals. The second application is to generate defined sequence of events (overvoltages, undervoltages, faults) with a definable duration. The third application is used to simulate different types of voltage fluctuations called flicker at the output of the calibrator. © 2023 IEEE.

关键词： Power quality

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Stochastic dynamics of the logistic growth model subjected to environmental perturbations

Stochastic dynamics of the logistic growth model subjected t...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Rezaee, Sayeh Nieto, Cesar Vahdat, Zahra Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE United States University of Delaware Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology Department of Electrical and Computer Engineering NewarkDE United States

ISBN: (纸本)9798350335446

The stochastic logistic model is widely used to capture random fluctuations arising from birth-death processes in ecological populations. We use this model to study the impact of environmental perturbations that may occur naturally or as a consequence of population harvesting. In our model formulation, environmental perturbations occur randomly as per a Poisson process, and the perturbations result in each individual dying with a certain probability of death. Moment closure schemes are employed to derive expressions for the mean and variability in population numbers. Moreover, to quantify the impact of population extinction in our model we compute the probability of extinction using the Finite State Projection (FSP) numerical method. Our analysis shows that rare environmental perturbations with a high probability of death lead to overall larger random fluctuations and extinction risk as compared to frequent perturbations with a low probability of death. Finally, we formulate the problem in the context of population harvesting to find the optimal harvesting rate that maximizes the cumulative yield. © 2023 IEEE.

关键词： Harvesting

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Face image authentication scheme based on MTCNN and SLT

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Multimedia Tools and Applications 2025年 1-43页

作者： Thabit, Rasha Al-Askari, Mohanad A. Mohammed, Dunya Zeki Anaam, Elham Abdulwahab Mahmood, Zainab H. Jabbar, Dina Jamal Salih, Zahraa Aqeel Information System Department College of Computer Sciences and Information Technology University of Al-Anbar Al-Anbar Iraq Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Gilgamesh University Baghdad Iraq Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Department of Cybersecurity Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Electrical Engineering Technical Collage Middle Technical University Baghdad Iraq Department of Business Administration Alamaal University College Baghdad Iraq

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specific conditions, however, many limitations and challenges can be encountered in this field. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Different block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for different face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efficiency of the proposed scheme in detecting and localizing different face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications. © The Author(s), un

关键词： Authentication

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Optimizing Precision in Cellular Clocks Through Self-Regulated Accumulation Of Molecules 27

Optimizing Precision in Cellular Clocks Through Self-Regulat...

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27th International Conference on System Theory, Control and Computing, ICSTCC 2023

作者： Rezaee, Sayeh Nieto, César Singh, Abhyudai Department of Electrical and Computer Engineering University of Delaware NewarkDE United States Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology University of Delaware NewarkDE United States

ISBN: (纸本)9798350337983

The timing of cellular events is often determined by the instant when the level of a chemical species crosses a threshold from an initial value of zero. To attenuate the inherent random fluctuations in this first-passage time, the production rate can be a function of the current species level by implementing a feedback or self-regulation. In this study, we aim to identify the self-regulation strategy that minimizes timing noise around a specified mean first-passage time. Previous results showed that no-feedback is the optimal strategy in the absence of molecule degradation. Based on this result, we study the case where the species may undergo degradation. First, we set the feedback to be a linear function of the number of molecules. We derive analytical formulas concluding that the optimal strategy involves positive feedback, whereby the production rate increases with the number of molecules. When feedback constraints are removed, our numerical methods suggest that a mixed self-regulation scheme is optimal. This strategy involves an initial positive feedback phase, followed by a sharp transition to a negative feedback scheme in proximity to the threshold. In summary, this research quantifies the fundamental limits of timing noise reduction, considering different strategies of self-regulation in situations where time-keeping species may be subjected to degradation. © 2023 IEEE.

关键词： Molecules

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Image Enhancement Algorithm for Liver CT Scan using Digital Image Processing 6

Image Enhancement Algorithm for Liver CT Scan using Digital ...

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6th International Conference on Digital Medicine and Image Processing, DMIP 2023

作者： Linsangan, Noel B. Martin, Melissa B. Cacnio, Jose Lorenzo I. Su, Jenn-Lung School of Electrical Electronics and Computer Engineering Mapua University Philippines Department of Biomedical Engineering Chung Yuan Christian University Taiwan

ISBN: (纸本)9798400709425

Image Enhancement is defined as the process of improving the quality of a digitally-stored image by manipulating it by means of image processing. Image enhancement is often applied in various images such as a Computed Tomography scan, a medical image employing tomography created by computer processing. The image produced in these scans are grayscale in nature and requires a much simpler image processing technique compared to colored images. The techniques that will be used for the enhancement of a Liver CT scan shall follow the standards of "Digital Image Processing"and shall be coded in computer programming software (Borland C++ Builder 6). Digital image processing is the use of computer algorithms to perform image processing on digital images, the word algorithm being a series of steps, reoccurring or not. This research will demonstrate and describe in detail the different algorithms that were applied (e.g. grayscale transformation, histograms, histogram equalization, etc.) and the resulting recommended algorithm that will be used to reduce the noise of a liver CT scan image. © 2023 ACM.

关键词： computerized tomography

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