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Two-dimensional Parameter Relationships for W UMa-type Systems Revisited

Research in Astronomy and Astrophysics 2024年第1期24卷 51-58页

作者： Atila Poro Ehsan Paki Ailar Alizadehsabegh Mehdi Khodadadilori Selda Ranjbar Salehian Mahya Hedayatjoo Fatemeh Hashemi Yasaman Dashti Fatemeh Mohammadizadeh Astronomy Department of the Raderon AI Lab. BurnabyBCCanada Faculty of Electrical and Computer Engineering Department of PhotonicsUniversity of TabrizTabriz 51664Iran Department of Electrical Engineering Amirkabir University of TechnologyTehranIran Astronomy Students'Scientific Association University of TabrizTabriz 51664Iran Department of Physics Iran University of Science and TechnologyTehranIran Institute for the Intellectual Development(Kanoon) Astronomy GroupBojnordIran Department of Mechanical Engineering Amirkabir University of TechnologyTehranIran Aerospace Engineering Department Science and Research Branch of Islamic Azad UniversityTehranIran

Reviewing the empirical and theoretical parameter relationships between various parameters is a good way to understand more about contact binary *** this investigation,two-dimensional(2D)relationships for P–MV(system),P–L1,2,M1,2–L1,2,and q–Lratiowere *** sample used is related to 118 contact binary systems with an orbital period shorter than 0.6 days whose absolute parameters were estimated based on the Gaia Data Release 3 *** reviewed previous studies on 2D relationships and updated six parameter ***,Markov chain Monte Carlo and Machine Learning methods were used,and the outcomes were *** selected 22 contact binary systems from eight previous studies for comparison,which had light curve solutions using spectroscopic *** results show that the systems are in good agreement with the results of this study.

关键词： (stars:)binaries(including multiple):close stars:fundamental parameters methods:data analysis

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Heat transfer analysis of TiO2-H2O based nanofluid flow across a porous stretching sheet with heat source/sink using Response Surface Methodology

Heat transfer analysis of TiO2-H2O based nanofluid flow acro...

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2024 International Conference on Thermofluids and Manufacturing science, ICTMS 2024

作者： Reshmi, V.P. Nagaraja, K.V. Shankara Department of Computer Science &engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Computational Science Lab Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Civil Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

Efforts are focused on optimizing thermal energy control and utilization within the system. The analysis is done on heat transmission characteristics of a TiO2.H2O nanofluid flowing over a stretching sheet with an integrated heat source/sink. Sensitivity analysis is performed using Response Surface Methodology (RSM) to understand heat transfer behaviour effectively. Governing equations are transformed into ordinary differential equations using suitable similarity variables. Mathematical formulation for steady two-dimensional flow of nanofluid in a porous medium along with a stretching sheet is established, and numerical outcomes are optimized using RSM. Influence of porous medium (A), volume fraction (φ), and heat source/sink (Hs) on Nusselt number (Nu) and skin friction factor (Cf) is analyzed through normal, response surface, and contour plots and it is found that for increasing Hs decreases Nu for higher φ values. © Published under licence by IOP Publishing Ltd.

关键词： Porous materials

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iNet:visual analysis of irregular transition in multivariate dynamic networks

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Frontiers of computer science 2022年第2期16卷 124-139页

作者： Dongming HAN Jiacheng PAN Rusheng PAN Dawei ZHOU Nan CAO Jingrui HE Mingliang XU Wei CHEN State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Department of Computer Science and Engineering Arizona State UniversityArizona 85287America Tong Ji Intelligent Big Data Visualisation Lab(iDVx Lab) TongJi UniversityShanghai 200082China Department of Computer Science and Technology Zhengzhou UniversityZhengzhou 450001China

Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along *** are common in multimedia *** detection is one of the essential tasks in analyzing these networks though it is not well *** this paper,we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic *** conclude features of rare categories and two types of anomalies of rare *** we present a novel rare category detection method,called DIRAD,to detect rare category candidates with *** develop a prototype system called iNet,which integrates two major visualization components,including a glyph-based rare category identifier,which helps users to identify rare categories among detected substructures,a major view,which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex ***,including an algorithm performance evaluation,a case study,and a user study,are conducted to test the effectiveness of proposed methods.

关键词： multivariate dynamic networks rare categories anomaly detection visual analysis

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Exploring the Potential of Large Language Models (LLMs) for Low-resource Languages: A Study on Named-Entity Recognition (NER) and Part-Of-Speech (POS) Tagging for Nepali Language 30

Exploring the Potential of Large Language Models (LLMs) for ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Subedi, Bipesh Regmi, Sunil Bal, Bal Krishna Acharya, Praveen Information and Language Processing Research Lab Department of Computer Science & Engineering Kathmandu University Nepal School of Computing Dublin City University Ireland

ISBN: (纸本)9782493814104

Large Language Models (LLMs) have made significant advancements in Natural Language Processing (NLP) by excelling in various NLP tasks. This study specifically focuses on evaluating the performance of LLMs for Named Entity Recognition (NER) and Part-of-Speech (POS) tagging for a low-resource language, Nepali. The aim is to study the effectiveness of these models for languages with limited resources by conducting experiments involving various parameters and fine-tuning and evaluating two datasets namely, ILPRL and EBIQUITY. In this work, we have experimented with eight LLMs for Nepali NER and POS tagging. While some prior works utilized larger datasets than ours, our contribution lies in presenting a comprehensive analysis of multiple LLMs in a unified setting. The findings indicate that NepBERTa, trained solely in the Nepali language, demonstrated the highest performance with F1-scores of 0.76 and 0.90 in ILPRL dataset. Similarly, it achieved 0.79 and 0.97 in EBIQUITY dataset for NER and POS respectively. This study not only highlights the potential of LLMs in performing classification tasks for low-resource languages but also compares their performance with that of alternative approaches deployed for the tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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MASSFormer: Mobility-Aware Spectrum Sensing using Transformer-Driven Tiered Structure

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IEEE Communications Letters 2025年第6期29卷 1220-1224页

作者： Janu, Dimpal Mushtaq, Faisel Mandia, Sandeep Singh, Kuldeep Kumar, Sandeep Malaviya National Institute of Technology Electronics and Communication Engineering department Jaipur302017 India SRM University AP Electronics and Communication Engineering department Amaravati522502 India Malaviya National Institute of Technology Department of Computer Science and Engineering Jaipur302017 India Thapar Institute of Engineering and Technology Patiala147004 India Bharat Electronics Ltd Central Research Lab Ghaziabad201010 India

In this paper, we develop a novel mobility-aware transformer-driven tiered structure (MASSFormer) based co-operative spectrum sensing method that effectively models the spatio-temporal dynamics of user movements. Unlike existing methods, our method considers a dynamic scenario involving mobile primary users (PUs) and secondary users (SUs) and addresses the complexities introduced by user mobility. The transformer architecture utilizes an attention mechanism, allowing the proposed method to model the temporal dynamics of user mobility by effectively capturing long-range dependencies. The proposed method first computes tokens from the sequence of covariance matrices (CMs) for each SU. It processes them in parallel using the SU-transformer to learn the spatio-temporal features at SU-level. Subsequently, the collaborative transformer learns the group-level PU state from all SU-level feature representations. The main goal of predicting the PU states at each SU-level and group-level is to improve detection performance even more. The proposed method is tested under imperfect reporting channel scenarios to show robustness. The efficacy of our method is validated with simulation results that demonstrate its higher performance compared to existing methods in terms of detection probability Pd, sensing error, and classification accuracy (CA). © 1997-2012 IEEE.

关键词： Fading channels

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SpatialHD: Spatial Transformer Fused with Hyperdimensional Computing for AI Applications 5

SpatialHD: Spatial Transformer Fused with Hyperdimensional C...

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5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Bettayeb, Meriem Hassan, Eman Mohammad, Baker Saleh, Hani Khalifa University of Science and Technology System on Chip Lab Electrical Engineering and Computer Science Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350332674

Brain-inspired computing methods have shown remarkable efficiency and robustness compared to deep neural networks (DNN). In particular, HyperDimensional Computing (HDC) and Vision Transformer (ViT) have demonstrated promising achievements in facilitating effective and reliable cognitive learning. This paper proposes SpatialHD, the first framework that combines spatial transformer networks (STN) and HDC. First, SpatialHD exploits the STN, which explicitly allows the spatial manipulation of data within the network. Then, it employs HDC to operate over STN output by mapping feature maps into high-dimensional space, learning abstracted information, and classifying data. In addition, the STN output is resized to generate a smaller input feature map. This further reduces computing complexity and memory storage compared to HDC alone. Finally, to test the model's functionality, we applied spatial HD for image classification, utilizing the MNIST and Fashion-MNIST datasets, using only 25% of the dataset for training. Our results show that SpatialHD improves accuracy by ≈ 8% and enhances efficiency by approximately 2.5x compared to base-HDC. © 2023 IEEE.

关键词： Image classification

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Study on the Perception of Mouse Weight Based on Moment of Inertia and Center of Mass

Study on the Perception of Mouse Weight Based on Moment of I...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Baek, Minhyeok Ryu, Yeonsoo Heo, Daehyun Kim, Sunjun Department of Electrical Engineering and Computer Science Smart Input Device Lab. Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu South Korea Department of Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science & Technology Daegu South Korea

ISBN: (纸本)9798400713958

Video games have become a cultural phenomenon that captivate millions of people worldwide. Alongside the growth of gaming culture, the demand for better gaming equipment has led to diverse research on mice. However, there is a lack of research on how mass distribution affects user perception and performance. In this study, we explored the effects of varying mass distributions on user perception of mouse weight. We fabricated mice with variable centers of mass (CoM) and moments of inertia while keeping the actual weight constant. The results showed that changes in CoM can alter perceived weight, but changes in moments of inertia have only a limited effect. The results of this study could provide a preliminary understanding of how weight perception varies with different mass distributions of a mouse. © 2025 Copyright held by the owner/author(s).

关键词： center of mass computer mouse e-sports gaming mass distribution moment of inertia weight perception

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Maize Phenotype Classification Using GNN: Research Perspective 14

Maize Phenotype Classification Using GNN: Research Perspecti...

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14th IEEE Integrated STEM Education Conference, ISEC 2024

作者： Vatsa, Avimanyou Zhang, Kevin Quinche-Dutan, Kris G. Lab Department of Computer Science Gildart Haase School of Computer Sciences & Engineering Fairleigh Dickinson University TeaneckNJ07666 United States Deep Chain Lab Fairleigh Dickinson University TeterboroNJ07608 United States The Department of Computer Science Fairleigh Dickinson University TeaneckNJ07666 United States

ISBN: (纸本)9798350352801

To address the scarcity of food by 2050 for the world population and increase food productivity, this study considered the maize plant as an organism [24]. This study used its phenotypes (lesions), observable and measurable traits of an organism of six different inbred lines (genetic backgrounds). The phenotypes are complex due to interaction among biochemical reactions, environmental conditions (temperature, humidity, soil quality, rainfall, etc.), and genomic expressions. The maize phenotype images are collected from field experiments over fifteen pedigrees. These image datasets are input datasets [7], [8], [15] to find a network among phenotypes and classify them. Therefore, this research uses a Deep Learning method called Graph Neural Network (GNN)), and other variants of GNN (Convolutional GNN, Graph Autoencoder (GAE), Recurrent GNN, Gated GNN, Spatial-Temporal GNN (STGNNs), etc.), and Python packages, including OpenCV Python, on the Keras framework. These identify nodes and edges to find the network among phenotypes. Also, this research provides a framework for predicting nodes, edges, network mining among maize phenotypes, and graph-related problems. Later, this outcome will help identify a group of genes responsible for this phenotype, including the yield of maize kernels. © 2024 IEEE.

关键词： Plant diseases

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A Traffic Sign Recognition qnd Speech-BasedFeedback Generation System to Assist Drivers 5

A Traffic Sign Recognition qnd Speech-BasedFeedback Generati...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Sarma, Himangshu Saharia, Navanath IIIT Department of Computer Science & Engineering Andhra PradeshSri City India IIIT Data Engineering Lab CSE Senapati Manipur India

ISBN: (纸本)9798350338287

According to the World Health Organization (WHO), 1.3 million people loss their life annually as a result of road accidents. By alerting or assisting the driver while they are driving, information technology such as computer vision can play a significant part in reducing the accident rate. Keeping that in mind, in this article we presents one traffic sign recognition tool using deep neural network (DNN) models with speech-based feedback for vehicle drivers. The proposed system can give voice-based feedback to driver to direct or inform them to take the appropriate action while driving, such as 'Drive slow, speed breaker ahead', 'Maximum Speed Limit is 80km/hr', 'School Ahead, drive slow', 'Don't take overtake' etc. © 2023 IEEE.

关键词： Deep neural networks

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Unguided Self-exploration in Narrow Spaces with Safety Region Enhanced Reinforcement Learning for Ackermann-steering Robots 2

Unguided Self-exploration in Narrow Spaces with Safety Regio...

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2nd IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Tian, Zhaofeng Liu, Zichuan Zhou, Xingyu Shi, Weisong University of Delaware The CAR Lab Newark United States Wayne State University Department of Computer Science Detroit United States Wayne State University Department of Electrical and Computer Engineering Detroit United States

ISBN: (纸本)9798350307733

In narrow spaces, motion planning based on the traditional hierarchical autonomous system could cause collisions due to mapping, localization, and control noises, especially for car-like Ackermann-steering robots which suffer from non-convex and non-holonomic kinematics. To tackle these problems, we leverage deep reinforcement learning which is verified to be effective in self-decision-making, to self-explore in narrow spaces without a given map and destination while avoiding collisions. Specifically, based on our Ackermann-steering rectangular-shaped ZebraT robot and its Gazebo simulator, we propose the rectangular safety region to represent states and detect collisions for rectangular-shaped robots, and a carefully crafted reward function for reinforcement learning that does not require the waypoint guidance. For validation, the robot was first trained in a simulated narrow track. Then, the well-trained model was transferred to other simulation tracks and could outperform other traditional methods including classical and learning methods. Finally, the trained model is demonstrated in the real world with our ZebraT robot. https://***/view/rl4exploration © 2024 IEEE.

关键词： Reinforcement learning

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