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10th Symposium on Novel Optoelectronic Detection technology and Applications

作者： Zhang, Qian Zhu, Yihua Zhang, Ruiheng Chen, Ruidong Lan, Tian Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China Nanjing Institute of Information Technology Nanjing210000 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610000 China

ISBN: (数字)9781510688155

ISBN: (纸本)9781510688148

Modern anti-money laundering (AML) deep learning models often overlook temporal factors, focusing mainly on structural and transactional features, which limits their ability to detect evolving patterns in money laundering activities. This study emphasizes incorporating temporal elements and proposes an enhanced model: the Temporal Aware Graph Attention (TAGA) model, building on TGAT. TAGA uses graph structures and variational autoencoders (VAE) for temporal encoding, capturing time-sensitive dependencies to classify suspicious activities. Unlike traditional static approaches, TAGA improves the model's ability to recognize subtle temporal correlations, providing a more robust tool for real-time AML detection in dynamic cryptocurrency *** study demonstrates that TAGA's temporal encoding and graph-based feature extraction enhance transaction monitoring, offering promising directions for future AML systems in blockchain transaction analysis. © 2025 SPIE.

关键词： Cryptocurrency

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Unsupervised Adaptive Lesion Segmentation with Similarity Based Cross Pseudo Supervision 7

Unsupervised Adaptive Lesion Segmentation with Similarity Ba...

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Zhu, Yuxin Li, Shuhao Yang, Aodi Feng, Mei Pu, Xiaorong Ren, Yazhou University of Electronic Science And Technology of China School of Computer Science And Engineering China The Third People's Hospital of Sichuan Department of Medical Oncology China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China

ISBN: (纸本)9798350359145

Notable advancement has been made in lesion segmentation based on deep learning. Nonetheless, the effectiveness of deep learning heavily relies on the availability of a substantial amount of high-quality labeled data, which is labor-intensive and expensive to require. Moreover, when applied to a different domain, deep learning-based lesion segmentation methods often experience a decline in performance due to variations in device vendors and patient populations. The unsupervised domain adaptation method is proposed to tackle the domain discrepancy problem. There are certain similarities between semi-supervised learning and unsupervised domain adaptation. From an application perspective, semi-supervised learning models can be used to solve unsupervised domain adaptation *** this end, we introduce the cross-pseudo-supervision mechanism in the semi-supervised field for unsupervised adaptive lesion segmentation. However, the method ignores the similar characteristics in medical images. To fully leverage the semantic similarity between domains, a novel similarity-based cross-pseudo-supervised lesion segmentation model is proposed. The proposed model employs a U-Net encoder, trained on source domain, to extract features from both the source and target domains. Subsequently, cosine similarity is computed based on these extracted features. The obtained similarity scores are then employed to weigh the source samples during the training process. The effectiveness of the proposed method is demonstrated through cross-domain experiments conducted on two publicly available COVID-19 lesion segmentation datasets. © 2023 IEEE.

关键词： Population statistics

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Knowledge Graph Error Detection With Unsupervised Triplet Network 7

Knowledge Graph Error Detection With Unsupervised Triplet Ne...

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7th Global Intelligent Industry Conference, GIIC 2024

作者： Wan, Tianqi Song, Faren Zheng, Xu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Aerospace Measurement and Testing Technology Beijing China Shenzhen Institute for Advanced Study UESTC Shenzhen China

ISBN: (纸本)9781510682986

Knowledge Graphs (KGs) are essential in various application areas. However, their quality is often compromised due to a large number of errors. Existing KG error detection methods have limitations in versatility and practicality. They mainly rely on supervision information such as entity types or error labels, which is not always easy to obtain in real scenarios. To address this issue, the paper proposes SymNet, an innovative framework for efficiently detecting errors in KG. SymNet utilises a triple embedding strategy, treating each triple as a node, and constructs a dynamic triple network through relational symmetric triples. To overcome the challenges posed by the special data characteristics and label scarcity of KG, a multi-layer information integration design is introduced. The BiLSTM module captures local-level intra-triplet translation information, while the graph attention network collects global-level inter-triplet contextual information. This multi-layer information integration strategy enables SymNet to more comprehensively understand the associated information in the knowledge graph, resulting in a significant improvement in error detection accuracy. The experimental results demonstrate that SymNet outperforms current state-of-the-art error detection algorithms in terms of both accuracy and efficiency when tested on two real-world knowledge graphs. Our research offers novel concepts and efficient solutions for addressing error detection issues in knowledge graphs, providing robust backing for enhancing the quality of knowledge graphs in practical scenarios. © 2024 SPIE.

关键词： Knowledge graph

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LIRA: A Learning-based Query-aware Partition Framework for Large-scale ANN Search 25

LIRA: A Learning-based Query-aware Partition Framework for L...

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34th ACM Web Conference, WWW 2025

作者： Zeng, Ximu Deng, Liwei Chen, Penghao Chen, Xu Su, Han Zheng, Kai University of Electronic Science and Technology of China Chengdu China University of Electronic Science and Technology of China Quzhou China School of Computer Science and Engineering UESTC China Shenzhen Institute for Advanced Study UESTC China

ISBN: (纸本)9798400712746

Approximate nearest neighbor search is fundamental in information retrieval. Previous partition-based methods enhance search efficiency by probing partial partitions, yet they face two common issues. In the query phase, a common strategy is to probe partitions based on the distance ranks of a query to partition centroids, which inevitably probes irrelevant partitions as it ignores data distribution. In the partition construction phase, all partition-based methods face the boundary problem that separates a query’s nearest neighbors to multiple partitions, resulting in a long-tailed kNN distribution and degrading the optimal nprobe (i.e., the number of probing partitions). To address this gap, we propose LIRA, a LearnIng-based queRy-aware pArtition framework. Specifically, we propose a probing model to directly probe the partitions containing the kNN of a query, which can reduce probing waste and allow for query-aware probing with nprobe individually. Moreover, we incorporate the probing model into a learning-based redundancy strategy to mitigate the adverse impact of the long-tailed kNN distribution on search efficiency. Extensive experiments on real-world vector datasets demonstrate the superiority of LIRA in the trade-off among accuracy, latency, and query fan-out. © 2025 Copyright held by the owner/author(s).

关键词： Online searching

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Segmenting Anything in the Dark via Depth Perception

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IEEE Transactions on Multimedia 2025年 27卷 2975-2986页

作者： Liu, Peng Deng, Jinhong Duan, Lixin Li, Wen Lv, Fengmao University of Electronic Science and Technology of China Chengdu611731 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China UESTC Shenzhen Institute for Advanced Study Shenzhen518110 China University of Electronic Science and Technology of China Sichuan Provincial Key Laboratory for Human Disease Gene Study Center for Medical Genetics Department of Laboratory Medicine Sichuan Academy of Medical Sciences Sichuan Provincial People's Hospital Chengdu610032 China Southwest Jiaotong University Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education School of Computing and Artificial Intelligence Chengdu611756 China

Image segmentation under low-light conditions is essential in real-world applications, such as autonomous driving and video surveillance systems. The recent Segment Anything Model (SAM) exhibits strong segmentation capability in various vision applications. However, its performance could be severely degraded under low-light conditions. On the other hand, multimodal information has been exploited to help models construct more comprehensive understanding of scenes under low-light conditions by providing complementary information (e.g., depth). Therefore, in this work, we present a pioneer attempt that elevates a unimodal vision foundation model (e.g., SAM) to a multimodal one, by efficiently integrating additional depth information under low-light conditions. To achieve that, we propose a novel method called Depth Perception SAM (DPSAM) based on the SAM framework. Specifically, we design a modality encoder to extract the depth information and the Depth Perception Layers (DPLs) for mutual feature refinement between RGB and depth features. The DPLs employ the cross-modal attention mechanism to mutually query effective information from both RGB and depth for the subsequent feature refinement. Thus, DPLs can effectively leverage the complementary information from depth to enrich the RGB representations and obtain comprehensive multimodal visual representations for segmenting anything in the dark. To this end, our DPSAM maximally maintains the instinct expertise of SAM for RGB image segmentation and further leverages on the strength of depth for enhanced segmenting anything capability, especially for cases that are likely to fail with RGB only (e.g., low-light or complex textures). As demonstrated by extensive experiments on four RGBD benchmark datasets, DPSAM clearly improves the performance for the segmenting anything performance in the dark, e.g., +12.90% mIoU and +16.23% mIoU on LLRGBD and DeLiVER, respectively. © 1999-2012 IEEE.

关键词： Image segmentation

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Automatic Bone Metastasis Diagnosis Framework via Lightweight Transformer-Based Network 7

Automatic Bone Metastasis Diagnosis Framework via Lightweigh...

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Wei, Xiaoyu Jiang, Guo Huang, Zongmo Pu, Xiaorong Ren, Yazhou Zhu, Liqin He, Chuandong University of Electronic Science And Technology of China School of Computer Science And Engineering Chengdu China Shenzhen Institute For Advanced Study University of Electronic Science And Technology of China School of Computer Science And Engineering Chengdu China Mianyang Central Hospital Nhc Key Laboratory of Nuclear Technology Medical Transformation Mianyang China

ISBN: (纸本)9798350359145

Bone metastasis is one of the main cancer complications whose diagnosis is mainly based on bone scintigraphy. As an assistance for the diagnosis of bone metastasis, researches based on convolutional neural networks have achieved impressive performance. However, due to the absence of relevant dataset, most existing methods could only diagnose whether metastasis occurred without indicating the general location of its occurrence or make diagnoses of only few specific organs. Such models may lose the insights of regional information and deviate from clinical practice. To address this issue, we construct the first bone scintigraphy dataset where whole body images in the anterior view are segmented into 13 distinct body parts and all subsets of body parts are carefully annotated according to formal diagnoses. With comprehensively annotated dataset, we propose an automatic diagnosis framework via lightweight transformer-based network with early convolutions and spatial dimension reduction to make detailed diagnoses for all body parts. The results reveal that our overall diagnosis achieves higher recall, satisfying accuracy and with fewer parameters compared to popular models for medical image classification. Dataset and code would be made public upon acceptance. © 2023 IEEE.

关键词： Convolution

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New EVENODD+ Codes with More Flexible Parameters and Lower Complexity

New EVENODD+ Codes with More Flexible Parameters and Lower C...

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2024 IEEE International Symposium on Information Theory, ISIT 2024

作者： Zhu, Panyu Lv, Jingjie Han, Yunghsiang S. Song, Linqi Hou, Hanxu School of Electrical Engineering & Intelligentization Dongguan University of Technology China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China City University of Hong Kong Department of Computer Science Hong Kong

ISBN: (纸本)9798350382846

EVENODD+ codes are binary maximum distance separable (MDS) array codes for correcting double disk failures in RAID-6 with asymptotically optimal encoding/decoding/update complexities. However, the number of bits stored in each disk of EVENODD+ codes should be an odd number minus one. In this paper, we present a new construction of EVENODD+ codes that have more flexible parameters. The number of bits stored in each disk of our codes is an odd minus one times any positive integer. Moreover, our codes not only have asymptotically optimal encoding/decoding/update complexities but also have lower encoding/decoding/update complexities than the existing EVENODD+ codes. © 2024 IEEE.

关键词： Encoding (symbols)

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La-substituted W-type barium-nickel ferrites for tunable and high-performance electromagnetic wave absorption

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International Journal of Minerals,Metallurgy and Materials 2025年第3期32卷 645-656页

作者： Long Wang Jiurong Liu Shenghui Xie Yanli Deng Zhou Wang Shanyue Hou Shengying Yue Gang Wang Na Wu Zhihui Zeng Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials(Ministry of Education) School of Materials Science and EngineeringShandong UniversityJinan 250061China Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education and International Center for Dielectric ResearchSchool of Electronic Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China Army Engineering University of PLA College of Field Engineering Nanjing 210007China Laboratory for Multiscale Mechanics and Medical Science State Key Laboratory for Strength and Vibration of Mechanical StructuresSchool of AerospaceXi’an Jiaotong UniversityXi’an 710049China Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbana 61801USA School of Chemistry and Chemical Engineering Shandong UniversityJinan 250100China

W-type barium-nickel ferrite(BaNi_(2)Fe_(16)O_(27))is a highly promising material for electromagnetic wave(EMW)absorption be-cause of its magnetic loss capability for EMW,low cost,large-scale production potential,high-temperature resistance,and excellent chemical ***,the poor dielectric loss of magnetic ferrites hampers their utilization,hindering enhancement in their EMW-absorption *** efficient strategies that improve the EMW-absorption performance of ferrite is highly desired but re-mains ***,an efficient strategy substituting Ba^(2+)with rare earth La^(3+)in W-type ferrite was proposed for the preparation of novel La-substituted ferrites(Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)).The influences of La^(3+)substitution on ferrites’EMW-absorption performance and the dissipative mechanism toward EMW were systematically explored and ***^(3+)efficiently induced lattice defects,enhanced defect-induced polarization,and slightly reduced the ferrites’bandgap,enhancing the dielectric properties of the ***^(3+)also enhanced the ferromagnetic resonance loss and strengthened magnetic *** effects considerably improved the EMW-absorption perform-ance of Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)compared with pure W-type *** x=0.2,the best EMW-absorption performance was achieved with a minimum reflection loss of-55.6 dB and effective absorption bandwidth(EAB)of 3.44 GHz.

关键词： electromagnetic wave absorption W-type barium ferrite rare earth dielectric

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Single-shot single-beam coherent Raman scattering thermometry based on optically induced air lasing

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Light(science & Applications) 2024年第12期13卷 3253-3260页

作者： Xu Lu Yewei Chen Francesco Mazza Siyi He Zihan Li Shunlin Huang Quanjun Wang Ning Zhang Bo Shen Yuzhu Wu Jinping Yao Ya Cheng State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Faculty of Aerospace Engineering Delft University of TechnologyKluyverweg 12629 HS DelftThe Netherlands Institute of Modern Optics Nankai UniversityTianjin 300350China School of Microelectronics Shanghai UniversityShanghai 200444China

Thermometric techniques with high accuracy,fast response and ease of implementation are desirable for the study of dynamic combustion environments,transient reacting flows,and non-equilibrium ***,single-shot singlebeam coherent Raman scattering(SS-CRS)thermometry is developed,for the first time to our knowledge,by using air lasing as a *** show that the air-lasing-assisted CRS signal has a high signal-to-noise ratio enabling single-shot measurements at a 1 kHz repetition *** SS-CRS thermometry consistently exhibits precision of<2.3%at different temperatures,but the inaccuracy grows with the increase in *** high measurement repeatability,1 kHz acquisition rate and easy-to-implement single-beam scheme are achieved thanks to the unique temporal,spectral and spatial characteristics of air *** work opens a novel avenue for high-speed CRS thermometry,holding tremendous potential for fast diagnostics of transient reacting flows and plasmas.

关键词： scattering lasing beam

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Reed-Solomon Codes over Ring with Lower Computational Complexity

Reed-Solomon Codes over Ring with Lower Computational Comple...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Tan, Shicheng Hou, Hanxu Yu, Leilei Dongguan University of Technology School of Computer Science & Technology Dongguan China Dongguan University of Technology School of Electrical Engineering & Intelligentization Dongguan China University of Electronic Science & Technology of China Shenzhen Institute for Advanced Study China

ISBN: (纸本)9798350324457

Reed-Solomon (RS) codes are widely used in storage systems to provide high data reliability. The existing RS codes are constructed over finite field with field size larger than the code length that have high encoding/decoding complexity. In this paper, we propose a new construction for RS codes over a cyclic ring instead of finite field. Our new RS codes only incurs XOR and cyclic-shift operation in the encoding/decoding processes, and therefore have lower encoding/decoding complexity than the existing RS codes. In addition, we show that we can employ the efficient Reed-Muller (RM) transform in our new RS codes that reduces the encoding/decoding complexity. Moreover, we implement our new RS codes by C++ and show that our new RS codes have better encoding/decoding performance than the existing RS codes for the evaluated parameters. © 2023 IEEE.

关键词： cyclic ring cyclic-shift encoding/decoding complexity Reed-Muller transform Reed-Solomon codes

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