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arXiv 2024年

作者： Lou, Siyu Liu, Chengchun Chen, Yuntian Mo, Fanyang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China School of Materials Science and Engineering Peking University Beijing100871 China -Preferred Program Peking University Shenzhen Graduate School Shenzhen518500 China

Thin-layer chromatography (TLC) is a crucial technique in molecular polarity analysis. Despite its importance, the interpretability of predictive models for TLC, especially those driven by artificial intelligence, remains a challenge. Current approaches, utilizing either high-dimensional molecular fingerprints or domain-knowledge-driven feature engineering, often face a dilemma between expressiveness and interpretability. To bridge this gap, we introduce Unsupervised Hierarchical Symbolic Regression (UHiSR), combining hierarchical neural networks and symbolic regression. UHiSR automatically distills chemical-intuitive polarity indices, and discovers interpretable equations that link molecular structure to chromatographic behavior. Copyright © 2024, The Authors. All rights reserved.

关键词： Thin layer chromatography

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Spectral-Spatial Fusion Hypergraph Convolutional Network for Multispectral Point Cloud Classification

Spectral-Spatial Fusion Hypergraph Convolutional Network for...

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Information Systems and Computing technology (ISCTech), International Conference on

作者： Jiangbo Huang Qingwang Wang Zifeng Zhang Sivang Liu Zhifeng Wang Tao Shen Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China School of Information Engineering and Automation Kunming University of Science and Technology Kunming China General Logistics Department of PLA Real Estate Resource Management Center Beijing China Beijing Anlu International Technology Co Beijing China

Graph Neural Networks (GNNs) based on simple graph can directly model 3D multispectral point clouds, thus avoiding the problem of 3D spatial information loss when converting 3D point clouds to 2D feature images. However, considering the dense distribution of objects in remote sensing scenes, GNNs only consider the association between pairs of vertices, which is insufficient for exploring high-order correlations and long-term dependence correlations. Hypergraph neural network (HGNN) is a more generalized framework that overcomes the limitations of traditional simple graph. In this paper, we propose a spectral-spatial fusion hypergraph convolutional network (S2FHGCN) for 3D multispectral point cloud classification. S2FHGCN first generates two hypergraphs from multidimensional features including spectral feature and spatial feature, respectively. The hypergraph structure can better handle complex high-order correlations for representation learning. Then the two hypergraphs are fused to obtain global representation information. Finally, the multidimensional features and the fused hypergraph are input to the hypergraph convolution network. The experimental results on real multispectral point clouds demonstrate the effectiveness of our proposed S2FHGCN for multispectral point cloud classification.

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A Hierarchical DRL Approach for Resource Optimization in Multi-RIS Multi-Operator Networks

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4981-4995页

作者： Zhang, Haocheng Wang, Wei Zhou, Hao Lu, Zhiping Li, Ming Dalian University of Technology School of Information and Communication Engineering Dalian116024 China McGill University Department of Computer Science MontréalQCH3A 0G4 Canada China Information Communication Technologies Group Corporation State Key Laboratory of Wireless Mobile Communications Beijing100191 China

As reconfigurable intelligent surfaces (RIS) emerge as a pivotal technology in the upcoming sixth-generation (6G) networks, its deployment within practical multiple operator (OP) networks presents significant challenges, including the coordination of RIS configurations among OPs, interference management, and privacy maintenance. A promising strategy is to treat RIS as a public resource managed by an RIS provider (RP), which can enhance resource allocation efficiency by allowing dynamic access for multiple OPs. However, the intricate nature of coordinating management and optimizing RIS configurations significantly complicates the implementation process. In this paper, we propose a hierarchical deep reinforcement learning (HDRL) approach that decomposes the complicated RIS resource optimization problem into several subtasks. Specifically, a top-level RP-agent is responsible for RIS allocation, while low-level OP-agents control their assigned RISs and handle beamforming, RIS phase-shifts, and user association. By utilizing the semi-Markov decision process (SMDP) theory, we establish a sophisticated interaction mechanism between the RP and OPs, and introduce an advanced hierarchical proximal policy optimization (HPPO) algorithm. Furthermore, we propose an improved sequential-HPPO (S-HPPO) algorithm to address the curse of dimensionality encountered with a single RP-agent. Experimental results validate the stability of the HPPO algorithm across various environmental parameters, demonstrating its superiority over other benchmarks for joint resource optimization. Finally, we conduct a detailed comparative analysis between the proposed S-HPPO and HPPO algorithms, showcasing that the S-HPPO algorithm achieves faster convergence and improved performance in large-scale RIS allocation scenarios. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Vehicular Communication Network Enabled CAV Data Offloading: A Review

TechRxiv

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TechRxiv 2023年

作者： Khan, Wali Ullah Ahmed, Manzoor Mirza, Muhammad Ayzed Raza, Salman Ahmad, Haseeb Xu, Fang Lin, Qing Han, Zhu The School of Computer and Information Science United Kingdom Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China University of Luxembourg Luxembourg BUPT-QMUL EM Theory and Application International Research Lab Beijing University of Posts and Telecommunications Beijing100876 China The College of Computer Science and Technology Qingdao University Qingdao266071 China Department of Computer Science National Textile University Faisalabad Pakistan University of Luxembourg Luxembourg City1855 Luxembourg The University of Houston TX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks’ traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading’s category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends. © 2023, CC BY.

关键词： Vehicle to Everything

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Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with applications

arXiv

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arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Motivated by recent developments, we introduce a new MLMC estimator of expectations, which does not require simulation of intractable Lévy areas but has a weak error of order 2 and achieves the optimal computational complexity. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate with numerical simulations that our new approaches provide efficiency gains for several problems relative to some existing methods. © 2024, CC BY.

关键词： Diffusion

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Fetal Health Classification using AI from Cardiotocography Features

Fetal Health Classification using AI from Cardiotocography F...

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International Conference on Innovative Mechanisms for Industry applications (ICIMIA)

作者： G Jyostna Himanshu Rai Goyal Manzoore Elahi M. Soudagar Swapnil Parikh Harshal Patil Kamal Alaskar Department of CSE-DS Potti Sriramulu Chalavadi Mallikarjuna Rao College of Engineering & Technology Vijayawada Andhra Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Application BharatiVidyapeeth (Deemed To Be University) Institute of Management Kolhapur Maharashtra India

Maternal health issues during pregnancy are a major global concern because they result in fetal death, which is more frequent in rising and poor countries. Cardiotocography (CTG) is the major non-invasive and cost-effective method for monitoring fetal health. CTG delivers critical parameters such as Fetal Heart Rate (FHR) and Uterine Contraction (UC), with a total of 21 features for accurate monitoring. These characteristics assist obstetricians in categorizing fetal health as normal, suspect, or pathological. However, differences in CTG interpretation among obstetricians frequently result in incorrect procedures. This work presents a unique way to detect deficient fetal states using a hybrid Machine Learning (ML) model that combines Support Vector Machine (SVM) and Random Forest (RF). The UCI repository dataset is pre-processed, which includes balancing, outlier reduction, and feature selection. The hybrid model’s performance is then compared to that of classic ML models such as SVM, K-Nearest Neighbour (KNN), RF, and Decision Tree (DT). The hybrid model outperforms the other models in simulation, attaining the greatest accuracy rate of 98.83%. This novel ML-based technique has the potential to improve fetal health detection while reducing the influence of human CTG interpretation discrepancies across healthcare providers.

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High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor

High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Nu-Ri Park Min-Joong Kim Jongsu Lee Minah Seo Yong-Sang Ryu Myung-Ki Kim KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA School of Biomedical Engineering Korea University Seoul Republic of Korea Interdisciplinary Program in Precision Public Health Korea University Seoul Republic of Korea

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen (<3%... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Electric potential Accuracy Hydrogen Lasers and electrooptics Optical sensors Electro-optical waveguides

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Improved Piano Music Recognition Algorithm Based on Artificial Intelligence

Improved Piano Music Recognition Algorithm Based on Artifici...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： M. Sahaya Sheela S. Surendran P. Muthu Krishnammal Vinothkumar K Surendra Shetty B. Venkataramanaiah Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Tagore Engineering College Chennai Tamil Nadu India School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh Department Of ECE PSNA College Of Engineering And Technology Dindigul Tamilnadu India Department of Master of Computer Application NMAM Institute of Technology Nitte(Deemed University) Karnataka India

Not only do people enjoy listening to piano music, but it may also be differentiated from vocal music in the realm of digital music. With its ongoing development, the piano music recommendation algorithm has found solutions to a great number of practical issues, and its technical path has also progressed from its initial recommendation based on the behavioral preferences of users to the correlation recommendation among users to the mining of users' potential preferences in recent years. The birth of The ONE smart piano means that a new teaching mode will be produced, that is, piano learning and user experience will be integrated, and the latest achievements of modern science and technology will be applied and rooted in the field of music education and teaching, so that the piano, a large musical instrument, can be easily combined with a variety of intelligent devices. This is significant in terms of the combination of offline artificial intelligence and music education because it means that a new teaching mode will be produced. Additionally, this new teaching mode will The improved piano music recognition system is investigated in this study, which uses artificial intelligence as its foundation. In the beginning, both the overall architecture of the system and the functional module design plan are shown. It is well known that the feature matrix of a piano is distinct from that of other musical instruments. This distinction was discovered when a digital recognition approach was used to extract the music timbre feature matrix of a piano. The findings of the experiments indicate that the suggested system has a high recognition accuracy for the purpose of timbre recognition of piano arrangements, which demonstrates that it is deserving of further promotion and application.

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Breast Cancer Classification in Mammograms Using Support Vector Machines (SVMs) and Histogram of Oriented Gradients (HOG)

Breast Cancer Classification in Mammograms Using Support Vec...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Choudhry, Rashmi Sivadharshini, B. Dua, Madhav Nirmala, V. Husain, Saif O. Arunkumar, R.S. Ies University Department of Information Technology M.P. Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Karpagam Academy of Higher Education Coimbatore Faculty of Engineering Department of Science and Humanities Tamilnadu India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Saveetha School of Engineering Department of Cse Chennai India

ISBN: (纸本)9798350390759

This work focuses on breast cancer classification in mammograms using SVMs classifier and histogram of oriented gradients features. The envisioned system is designed in such a way to enhance the diagnostic precision through integration of the virtues of both SVMs and HOG in identifying discriminative characteristics of breast tissue. Special emphasis was given on comparing and testing the performance of the intended model with other models on a public mammogram dataset containing both malignant and benign cases;the optimized SVM model provided an accuracy of 92%. 5%, sensitivity of 91. 0%, specificity of 93. 74 (fig 5) and 8%, and an AUC-ROC of 0. 95. This study shows that the method is efficient in discriminating between the malignant and benign first axillary lymph node tissue. The developed CAD system was found to possess the prospect of becoming a valuable support system for radiologists in the early detection of breast cancer. © 2024 IEEE.

关键词： Lung cancer

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An Industrial Internet Cloud-Edge Collaborative Scheme and Evaluation System

An Industrial Internet Cloud-Edge Collaborative Scheme and E...

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Cloud Computing, Big Data applications and Software Engineering (CBASE), International Conference on

作者： Tianwen Sun Yushen Wang Guang Yang Runqun Xiong Kai Yang Wenhua Zhang System integration center China Mobile Group Jiangsu Co. Ltd Nanjing China Group Customer Department China Mobile Group Jiangsu Co. Ltd Nanjing China School of Computer Science Southeast University Nanjing China Cloud network technology Department Migu Interactive Entertainment Co. Ltd Nanjing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

The scale of the global cloud computing market is growing rapidly. In the context of the global digital economy, cloud computing has become an inevitable choice for digital transformation of enterprises. With the rapid development of 5G, Internet of things and other technologies, edge computing has attracted more and more attention, and cloud-edge collaboration has become an important trend. Only cloud computing and edge computing can meet the needs of various scenarios through close collaboration, so as to maximize the application value of cloud computing and edge computing. This paper systematically puts forward a cloud edge collaboration scheme in the field of industrial Internet, and provides a standard industrial Internet cloud edge collaboration depth integration rating standard for manufacturing enterprises, which is convenient for scientific evaluation of cloud edge synergy effect.

关键词： Cloud computing Job shop scheduling Processor scheduling Architecture Collaboration computer architecture Real-time systems Internet of Things Standards Edge computing

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