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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Cheng, Xi Zhao, Yunjie Zhang, Kun Wang, Xiaohua Shao, Wei Peng, Lin Wiart, Joe Xinjiang Agricultural University School of Computer and Information Engineering Urumqi830052 China University of Electronic Science and Technology of China School of Physics Chengdu611731 China Guilin University of Electronic Technology Key Laboratory of Cognitive Radio and Information Processing Guilin541004 China Ltci Télécom Paris C2M 19 Place Marguerite Perey Palaiseau91120 France

ISBN: (纸本)9798350326277

The finite-difference time-domain (FDTD) method is the most commonly used method in electromagnetic simulations due to its advantages of easy implementation and stabilization. However, in the ground penetrating radar (GPR) simulation, it requires performing multiple FDTD calculations on the same physical model according to the number of GPR emitters, which brings heavy calculation burden, making it difficult to implement in practical application. This paper proposes a GPR simulation method based on the recurrent neural network (RNN) architecture. The new method reconstructs the FDTD algorithm through deep learning theory and operators without training. The proposed method can maintain the same accuracy as FDTD while speeding up the simulation. © 2023 IEEE.

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A robust incomplete large-scale group decision-making model for metaverse metro operations and maintenance

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Applied Soft Computing 2024年 156卷

作者： Bai, Wenhui Zhang, Chao Zhai, Yanhui Sangaiah, Arun Kumar School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

The metaverse, constructed through digital technology, serves as a virtual realm intertwining with reality. Within this context, the challenge of evaluating data from diverse sources arises, and the application of large-scale group decision-making (LSGDM) methods emerges as a viable solution. Handling incomplete information and reducing dimensionality for large-scale decision-makers (DMs) is crucial in addressing complex decision-making problems. Moreover, addressing missing data is a fundamental and pivotal concern in tackling real-world decision challenges, given the ubiquitous presence of information gaps that cannot be straightforwardly integrated into decision models. Besides, the intricacies of LSGDM amplify this challenge by introducing a wealth of DMs, thereby augmenting the complexity and diversity of decision-related information. This paper proposes an approach to supplement missing data by double-dimensions. This paper explores various facets of similarity relationships within the data to enhance data completeness. Additionally, this paper categorizes DMs into clusters based on their relevance and establishes a two-stage consensus-reaching process (CRP) that takes into account both group sizes and individual consensus contributions. These CRPs play a crucial role in enhancing the overall consistency and consensus within the decision group. Subsequently, this paper applies a robust decision-making method rooted in MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the complete MULTIplicative form) to rank decision objects. Finally, this paper employs this proposed methodology in a practical case study that involves evaluating the operational status of a metaverse's urban construction metro system. Following these considerations, a comprehensive stability analysis of relevant parameters is conducted to guarantee the robustness and reliability of the decision-making process. © 2024 Elsevier B.V.

关键词： Multiobjective optimization

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Soft-BAC: Soft Bidirectional Alignment Cost for End-to-End Automatic Speech Recognition 18th

Soft-BAC: Soft Bidirectional Alignment Cost for End-to-End A...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Wang, Yonghe Zhang, Hui Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Huhhot010021 China

ISBN: (纸本)9783030893620

Connectionist temporal classification (CTC) has gained success in both end-to-end ASR model and as an auxiliary task for attention-based sequence-to-sequence (S2S) system. However, the special topological structure of CTC and the modeling form that a redundant blank symbol to be optionally inserted between each modeling units makes the CTC inclined to model blank symbols, resulting in a worse than expected model alignment effect, and frames are usually aligned with redundant symbols. In this paper, we design a new simple topology and propose a novel smooth alignment optimization method named soft bidirectional alignment cost (soft-BAC), which is an alternative to the CTC. We propose a scheme that only inserts identifiers between consecutive repetitive labels and solve the alignment problem between two time series of speech-transcription pair by minimizing all costs of the left-to-right and right-to-left alignment process. Experiments on the LibriSpeech corpus show that the proposed soft-BAC method achieves significant improvement in word error rate and alignment effect over the CTC-based baseline model. © 2021, Springer Nature Switzerland AG.

关键词： Speech recognition

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Spatiality-Aligned Graph Convolution Hashing for Unsupervised Cross-Modal Retrieval

Spatiality-Aligned Graph Convolution Hashing for Unsupervise...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Fang Li Liulian Dai Rushi Lan Rui Yang Xiaonan Luo Center for Applied Mathematics of Guangxi(GUET) Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Unsupervised cross-modal retrieval leveraging hash learning has attracted a lot of interest from academics, primarily due to its minimal storage requirements, swift retrieval speeds, and label-free nature, making it a prime candidate for real-world applications. Nevertheless, the inherent "heterogeneous differences" and "semantic gap" within cross-modal data features pose significant challenges. Despite various attempts by scholars to address these issues, the existing solutions often fall short in terms of effectiveness. This can be attributed to several reasons: Firstly, image features, in contrast to text features, possess a plethora of attributes, making it challenging to extract features that possess strong expressive capabilities. Secondly, each modality of data exhibits not only inter-node correlations but also intricate relationships between individual nodes and their neighboring nodes. Consequently, a straightforward semantic correlation matrix is inadequate in capturing the intricate structural relationships within the data. Thirdly, the neglect of internal structural associations results in insufficient representation capabilities of binary hash codes in the Hamming space, hampering the learning of effective hash functions. To address these limitations, we introduce a Spatiality-Aligned Graph Convolution Hashing (SAGCH) algorithm. Extensive experiments demonstrate that our SAGCH method excels in both "image-to-text retrieval" and "text-to-image retrieval" tasks.

关键词： Hash functions Correlation Codes Convolution Graph convolutional networks Semantics Text to image Feature extraction Data processing Cross modal retrieval

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Application of functional near-infrared spectroscopy(fNIRS) for studying the influence of capsaicin stimulation on brain perception

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Food Science and Human Wellness 2025年

作者： Ran Tao Hang Liang Huiting Qiao Xin Liu Na Xu Ling Wang Xinglian Xu Lingqi Li Yujia Yang Peng Wang State Key Laboratory of Meat Quality Control and Cultured Meat Development， Ministry of Education China， Jiangsu Collaborative Innovation Center of Meat Production and Processing， Quality and Safety Control， College of Food Science and Technology， Nanjing Agricultural University Danyang SuHuichuang Brain Scientific Research Center School of Biological Science and Medical Engineering， Beihang University College of Computer Science， Sichuan Normal University

Spicy taste have been utilized in the food industry as appetizers， stomachics， and beverages and in the health sector to alleviate high cholesterol and hypertension. However， an understanding of neural mechanisms and cognitive processes associated with spicy diets remains limited due to the lack of instruments that directly monitoring the brain’s reaction to spicy sensations. This study aimed to examine the neural responses of participants to oral capsaicin solutions through functional near-infrared spectroscopy and reveal distinct neural activation patterns associated with spiciness induction and relief phases. Results showed that the perception of spiciness is a dynamic process， and significant differences in sensory intensity and emotion were observed between the phases. The prefrontal regions of the brain were activated during spicy flavor perception and presented clear lateralization effects. Further spatial localization results indicated that the orbitofrontal， dorsolateral prefrontal， and ventral lateral prefrontal cortices were the main causes of difference in activation among the phases. In addition， correlational analyses indicated that spicy flavor perception activated cortical areas related to emotion and reward processing. Overall， this research enhances our understanding of the neural mechanisms underlying addiction to spicy food and may be useful in the development of food on the basis of consumers’ preferences and behavioral responses.

关键词： Taste Capsaicin fNIRS Prefrontal cortex Oral irritation

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Classification-Detection of Medical Images by Visualizing Hidden-Layer Features of a Deep Learning Approach 12th

Classification-Detection of Medical Images by Visualizing Hi...

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Tong, Ziyu Deng, Xing Shao, Haijian Wang, Xia School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China School of Automation Key Laboratory of Measurement and Control for CSE Ministry of Education Southeast University Nanjing Jiangsu210096 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Deep learning algorithms, such as CNN, have been widely used on a variety of computer vision problems due to their powerful capacity to extract nonlinear features for medical picture analysis. However, due to the decreased identification rate for medical images, CNNs continue to fail to offer an interpretable depiction of the hidden-layers’ features. Furthermore, the enormous amount of redundant parameters from the convolutional to fully connected layers will surely add to the computational complexity. This work provides a set of fast deep learning architectures for visualizing and analyzing hidden-layer properties in medical images. The clustered feature map is used to simplify the model parameters and minimize redundancy parameters in order to meet the goal of a streamlined trade-off between model performance and architecture. The testing results on the medial images reveal that 41% of redundant channels may be removed while 100% of performance can be preserved. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Multimodal Brain-computer Interfaces: AI-powered Decoding Methodologies

arXiv

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arXiv 2025年

作者： Li, Siyang Wang, Hongbin Chen, Xiaoqing Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the field. We emphasize algorithmic advancements in cross-modality mapping, sequential modeling, besides classic multi-modality fusion, illustrating how these novel AI approaches enhance decoding of brain data. The current literature of BCI applications on visual, speech, and affective decoding are comprehensively explored. Looking forward, we draw attention on the impact of emerging architectures like multimodal Transformers, and discuss challenges such as brain data heterogeneity and common errors. This review also serves as a bridge in this interdisciplinary field for experts with neuroscience background and experts that study AI, aiming to provide a comprehensive understanding for AI-powered multimodal BCIs. © 2025, CC BY-NC-ND.

关键词： Brain computer interface

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Subgraph Aggregation for Out-of-Distribution Generalization on Graphs

arXiv

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arXiv 2024年

作者： Liu, Bowen Li, Haoyang Wang, Shuning Nie, Shuo Zhang, Shanghang State Key Laboratory of Multimedia Information Processing School of Computer Science Peking University China Harbin Institute of Technology China Weill Cornell Medicine Cornell University United States

Out-of-distribution (OOD) generalization in Graph Neural Networks (GNNs) has gained significant attention due to its critical importance in graph-based predictions in real-world scenarios. Existing methods primarily focus on extracting a single causal subgraph from the input graph to achieve generalizable predictions. However, relying on a single subgraph can lead to susceptibility to spurious correlations and is insufficient for learning invariant patterns behind graph data. Moreover, in many real-world applications, such as molecular property prediction, multiple critical subgraphs may influence the target label property. To address these challenges, we propose a novel framework, SubGraph Aggregation (SuGAr), designed to learn a diverse set of subgraphs that are crucial for OOD generalization on graphs. Specifically, SuGAr employs a tailored subgraph sampler and diversity regularizer to extract a diverse set of invariant subgraphs. These invariant subgraphs are then aggregated by averaging their representations, which enriches the subgraph signals and enhances coverage of the underlying causal structures, thereby improving OOD generalization. Extensive experiments on both synthetic and real-world datasets demonstrate that SuGAr outperforms state-of-the-art methods, achieving up to a 24% improvement in OOD generalization on graphs. To the best of our knowledge, this is the first work to study graph OOD generalization by learning multiple invariant subgraphs. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Learning to Robustly Reconstruct Dynamic Scenes from Low-light Spike Streams

arXiv

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arXiv 2024年

作者： Hu, Liwen Ding, Ziluo Liu, Mianzhi Ma, Lei Huang, Tiejun State Key Laboratory of Multimedia Information Processing School of Computer Science Peking University China Beijing Academy of Artificial Intelligence China College of Future Technology Peking University China

Spike camera with high temporal resolution can fire continuous binary spike streams to record per-pixel light intensity. By using reconstruction methods, the scene details in high-speed scenes can be restored from spike streams. However, existing methods struggle to perform well in low-light environments due to insufficient information in spike streams. To this end, we propose a bidirectional recurrent-based reconstruction framework to better handle such extreme conditions. In more detail, a light-robust representation (LR-Rep) is designed to aggregate temporal information in spike streams. Moreover, a fusion module is used to extract temporal features. Besides, we synthesize a reconstruction dataset for high-speed low-light scenes where light sources are carefully designed to be consistent with reality. The experiment shows the superiority of our method. Importantly, our method also generalizes well to real spike streams. Our project is: https://***/Acnext/Learning-to-Robustly-Reconstruct-Dynamic-Scenes-from-Low-light-Spike-Streams/. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

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Neuroscience Bulletin 2023年第6期39卷 893-910页

作者： Feng Su Yangzhen Wang Mengping Wei Chong Wang Shaoli Wang Lei Yang Jianmin Li Peijiang Yuan Dong-Gen Luo Chen Zhang Department of Neurobiology School of Basic Medical SciencesBeijing Key Laboratory of Neural Regeneration and RepairCapital Medical UniversityBeijing100069China Chinese Institute for Brain Research Beijing102206China State Key Laboratory of Translational Medicine and Innovative Drug Development Nanjing210000China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing100871China School of Life Sciences Tsinghua UniversityBeijing100084China School of Biological Science and Medical Engineering Beihang UniversityBeijing100191China The Key Laboratory of Developmental Genes and Human Disease Institute of Life SciencesSoutheast UniversityNanjing210096JiangsuChina Institute for Artificial Intelligence the State Key Laboratory of Intelligence Technology and SystemsBeijing National Research Center for Information Science and TechnologyDepartment of Computer Science and TechnologyTsinghua UniversityBeijing100084China School of Mechanical Engineering and Automation Beihang UniversityBeijing100191China

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social *** tracking methods(e.g.,marking each animal with dye or surgically implanting microchips)can be invasive and may have an impact on the social behavior being *** overcome these shortcomings,video-based methods for tracking unmarked animals,such as fruit flies and zebrafish,have been ***,tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction *** this study,we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network(R-CNN)deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion *** system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing *** proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.

关键词： Noninvasive tracking Deep learning Multi-camera Mouse group Social interaction

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