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arXiv 2025年

作者： Chen, Fu Zhao, Qinglin Feng, Li Tang, Longfei Lin, Yangbin Huang, Haitao Faculty of Innovation Engineering Macau University of Science and Technology 999078 China College of Electrical Engineering and Automation Fuzhou University Fuzhou350108 China College of Computer Engineering College Jimei Univeristy Xiamen361021 China

The self-attention mechanism has revolutionized classical machine learning, yet its quantum counterpart remains underexplored in fully harnessing the representational power of quantum states. Current quantum self-attention models exhibit a critical limitation by neglecting the indispensable phase information inherent in quantum systems when compressing attention weights into real-valued overlaps. To address this fundamental gap, we propose the Quantum Complex-Valued Self-Attention Model (QCSAM), the first framework that explicitly leverages complex-valued similarities between quantum states to capture both amplitude and phase relationships. Simultaneously, we enhance the standard Linear Combination of Unitaries (LCUs) method by introducing a Complex LCUs (CLCUs) framework that natively supports complex-valued coefficients. This framework enables the weighting of corresponding quantum values using fixed quantum complex self-attention weights, while also supporting trainable complex-valued parameters for value aggregation and quantum multi-head attention. Experimental evaluations on MNIST and Fashion-MNIST demonstrate our model’s superiority over recent quantum self-attention architectures including QKSAN, QSAN, and GQHAN, with multi-head configurations showing consistent advantages over single-head variants. We systematically evaluate model scalability through qubit configurations ranging from 3 to 8 qubits and multi-class classification tasks spanning 2 to 4 categories. Through comprehensive ablation studies, we establish the critical advantage of complex-valued quantum attention weights over real-valued alternatives. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Sparse Attention-based Hierarchical Node Representation for Spatial Domain Identification

Sparse Attention-based Hierarchical Node Representation for ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Peng Zhihao Ping Wei Dai Xiaodong Fu Li Liu Lijun Liu Ning Yu Faculty of Information Engineering and Automation Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Department of Computing Sciences The College at Brockport State University of New York Brockport NY

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://***/weiba/SpaNFM

关键词： Biological system modeling Biological tissues Transcriptomics Contrastive learning Feature extraction Brain modeling Spatial databases Gene expression Resource management Image reconstruction

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Efficient Visual Recognition:A Survey on Recent Advances and Brain-inspired Methodologies

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Machine Intelligence Research 2022年第5期19卷 366-411页

作者： Yang Wu Ding-Heng Wang Xiao-Tong Lu Fan Yang Man Yao Wei-Sheng Dong Jian-Bo Shi Guo-Qi Li Applied Research Center Laboratory Tencent Platform and Content Group Shenzhen 518057China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi′an Jiaotong UniversityXian710049China School of Artificial Intelligence Xidian UniversityXi'an 710071China Division of Information Science Nara Institute of Science and TechnologyNara 6300192Japan Peng Cheng Laboratory Shenzhen 518000China Department of Computer and Information Science University of PennsylvaniaPhiladelphia PA19104-6389USA Institute of Automation Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

Visual recognition is currently one of the most important and active research areas in computer vision,pattern recognition,and even the general field of artificial *** has great fundamental importance and strong industrial needs,particularly the modern deep neural networks(DNNs)and some brain-inspired methodologies,have largely boosted the recognition performance on many concrete tasks,with the help of large amounts of training data and new powerful computation *** recognition accuracy is usually the first concern for new progresses,efficiency is actually rather important and sometimes critical for both academic research and industrial ***,insightful views on the opportunities and challenges of efficiency are also highly required for the entire *** general surveys on the efficiency issue have been done from various perspectives,as far as we are aware,scarcely any of them focused on visual recognition systematically,and thus it is unclear which progresses are applicable to it and what else should be *** this survey,we present the review of recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related and brain-inspired visual recognition approaches,including efficient network compression and dynamic brain-inspired *** investigate not only from the model but also from the data point of view(which is not the case in existing surveys)and focus on four typical data types(images,video,points,and events).This survey attempts to provide a systematic summary via a comprehensive survey that can serve as a valuable reference and inspire both researchers and practitioners working on visual recognition problems.

关键词： Visual recognition deep neural networks(DNNS) brain-inspired methodologies network compression dynamic inference survey

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Method for Determination Rotor Eccentrity of an Electric Machine

Method for Determination Rotor Eccentrity of an Electric Mac...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Orazovna, Yussupova Assel Neftissov, Alexandr Olegovna, Potapenko Alexandra Sansyzbayevna, Urazalimova Dariya Toraighyrov University Department Of Electrical Engineering And Automation Pavlodar Kazakhstan Research And Innovation Center 'Industry 4.0' Astana It University Nur-Sultan Kazakhstan Toraighyrov University Faculty Of Computer Science Pavlodar Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

Modern diagnostic systems for asynchronous motors are widely used in the power industry. The most promising diagnostic systems for rotor eccentricity of induction motors are systems with capacitive measuring transducers in view of their simplicity, reliability and sufficient sensitivity. In the proposed work, the selection of the information sign of damage for the diagnosis system of rotor eccentricity with open and closed slots of an induction motor with a capacitive measuring transducer. © 2022 IEEE.

关键词： Induction motors

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Coreference disambiguation based on two-layer label dependence analysis 7

Coreference disambiguation based on two-layer label dependen...

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7th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Xu, Hongfei Li, Yingna Faculty of Information Engineering and Automation Kunming University of Science and Technology 727 Jingming South Rd.Chenggong District Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications 727 Jingming South Rd.Chenggong District Kunming650500 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

A two-layer labeling-based dependency analysis model is proposed to introduce the location features of words for the coreference disambiguation of entities. Firstly, two layers of labels are used for labeling, the second layer labels the location information of words, the features of sentences are learned using a bidirectional long short-Term memory network, the dependency syntax analysis based on deep graph decoding obtains the dependency tree of sentences, and the two layers of labels are fused to improve the performance and accuracy of coreference disambiguation. Experiments are conducted on the text dataset of power security entity relationship extraction, and the results show that the two-layer labeled dependency analysis has an improved effect on co-finger disambiguation, which verifies the effectiveness of the model for cofinger disambiguation on the experimental dataset. © 2023 SPIE. All rights reserved.

关键词： Trees (mathematics)

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Overview of Load Disaggregation Models, Methods and Hardware

Overview of Load Disaggregation Models, Methods and Hardware

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International Universities Power engineering Conference (UPEC)

作者： Mircea N. Lancranjan Alexandru Muresan Dacian I. Jurj Alexandru Berciu Alexis Polycarpou Levente Czumbil Dan D. Micu Department Electrical Engineering Technical University of Cluj-Napoca Cluj-Napoca Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Department of Electrical and Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (数字)9798350379730

ISBN: (纸本)9798350379747

In the dynamic landscape of energy efficiency, a domain garnering ever increasing attention and urgency, the cornerstone of informed decision-making resides in the acquisition of reliable data. This paper summarizes the main approaches, methods and hardware used to gather, process and disaggregate such data on an appliance-by-appliance basis. The paper explores a critical analysis of the various models and techniques that support the gathering of energy data at the appliance level. A range of approaches, including hardware-based solutions such as smart meters, submeters, and sensor arrays, placing a special focus on intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM) algorithms that employ advanced signal processing and machine learning techniques, are examined for their effectiveness, scalability, and suitability for both industrial and commercial contexts. Furthermore, the challenges of data acquisition capabilities, reconciling scalability, cost-effectiveness, and user-friendliness - prerequisites for widespread acceptance and uptake are also investigated.

关键词： Load monitoring Costs Scalability Signal processing algorithms Hardware Smart meters Energy efficiency Transient analysis Time complexity Load modeling

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A Deep Learning Approach For Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants

A Deep Learning Approach For Faults Recognition of Dissolved...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Liliana Maria Ghinea Mihaela Miron Harsha Ratnaweera Department of Automatic Control and Electrical Engineering Department Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Faculty of Science and Technology Norwegian University of Life Sciences Oslo Norway

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing.

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Classification of skin lesions using morpho-granulometry features derived from color clusters

Classification of skin lesions using morpho-granulometry fea...

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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics "Dunarea de Jos" University of Galati Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics The Modelling & Simulation Laboratory "Dunarea de Jos" University of Galati Galati Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesion, this novelty is highlighted in this study. Important features of skin lesions can be modulated by fusing neural networks (NN) and machine learning (ML). By choosing the nevus and melanoma classes, the primary goal was accomplished, and three databases were used to test the methodology. The characteristics based on morpho-granulometry allowed for the identification of microstructure within the images, which can be very helpful in characterizing the biological system. Based on random forest (RF) and extreme gradient boosting (XGboost) classifiers, this work aimed to improve the classification performance of important feature selection. The selected features from three free image databases with three NNs were classified. In a binary classification of nevus vs. melanoma, the results showed that the pattern recognition neural network (PRNN), according to the PH2 database, provided an accuracy of 0.923 and an F1-score of 0.876. The classification is interpretable if it is not validated. In our study, the best results were verified with a logistic regression (LR) classifier.

关键词： Accuracy Recurrent neural networks Image color analysis Melanoma Biological systems Artificial neural networks Feature extraction Skin Lesions Classification tree analysis

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Distance-based Formation-Motion Control for Unicycle Agents

Distance-based Formation-Motion Control for Unicycle Agents

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American Control Conference (ACC)

作者： Jin Chen Bayu Jayawardhana Hector Garcia de Marina Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen The Netherlands Department of Computer Engineer Automation and Robotics and With CITIC The University of Granada Spain

We present a distance-based distributed formation-motion control law for unicycle agents that are required to move together at a constant reference speed. The distributed control law consists of the standard distance-based formation gradient control law, which is projected to the longitudinal and angular velocity inputs of the unicycle, and of a linear term that depends on the reference speed. The main contribution of this paper is to realize the consistency of unicycle agents’ orientations without orientation measurement, thereby reducing the possibility of significant orientation measurement errors in real-world applications. We prove and show numerically the local exponential convergence of the unicycle agents to the desired formation, where they eventually move in unison at a constant reference speed.

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Scaling up the formation of agents with heterogeneous sensing: mixed distance and bearing-only

arXiv

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arXiv 2023年

作者： Chen, Jin Jayawardhana, Bayu de Marina, Hector Garcia The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Netherlands Department of Computer Engineer Automation and Robotics CITIC The University of Granada Spain

Unlike the case with identical neighboring agents whose actions are mirrored, the problem of distributed formation control design with heterogeneous sensing is not straightforward. In this paper, we consider the problem of distributed formation control where each agent can only control distances or bearings with its neighbors. We firstly develop a rigidity theory with heterogeneous sensing to ensure that the desired shape is well-posed. Secondly, we propose an iterative method that allows us to scale up the number of agents with heterogeneous sensing. Finally, numerical simulations show the effectiveness of our method. © 2023, CC BY.

关键词： Iterative methods

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