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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Luo, Xiaoling Xu, Qihao Wu, Huisi Liu, Chengliang Lai, Zhihui Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Diabetic retinopathy (DR), with its large patient population, has become a formidable threat to human visual health. In the clinical diagnosis of DR, multi-view fundus images are considered to be more suitable for DR diagnosis because of the wide coverage of the field of view. Therefore, different from the previous single-view DR grading methods, we design a dynamic selection-driven multi-view DR grading method to fit clinical scenarios better. Since lesion information plays a key role in DR diagnosis, previous methods usually boost the model performance by enhancing the lesion feature. However, during the actual diagnosis, ophthalmologists not only focus on the crucial parts, but also exclude irrelevant features to ensure the accuracy of judgment. To this end, we introduce the idea of dynamic selection and design a series of selection mechanisms from fine granularity to coarse granularity. In this work, we first introduce an Ophthalmic Image Reader (OIR) agent to provide the model with pixel-level prompts of suspected lesion areas. Moreover, we design a Multi-View Token Selection Module (MVTSM) that prunes redundant feature tokens and dynamically selects key information. In the final decision stage, we dynamically fuse multi-view features through the novel Multi-View Mixture of Experts Module (MVMoEM), to enhance key views and reduce the impact of conflicting views. Extensive experiments on a large multi-view fundus image dataset with 34,452 images prove that our method performs favorably against state-of-the-art models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Ophthalmology

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WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration

arXiv

引用

arXiv 2024年

作者： Cheng, Xinxing Jia, Xi Lu, Wenqi Li, Qiufu Shen, Linlin Krull, Alexander Duan, Jinming School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom Department of Computing and Mathematics Manchester Metropolitan University ManchesterM15 6BH United Kingdom National Engineering Laboratory for Big Data System Computing Technology Shenzhen University 518060 China

Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense deformation fields in a coarse-to-fine manner. However, due to the cascaded nature and repeated composition/warping operations on feature maps, these methods negatively increase memory usage during training and testing. Moreover, such approaches lack explicit constraints on the learning process of small deformations at different scales, thus lacking explainability. In this study, we introduce a model-driven WiNet that incrementally estimates scale-wise wavelet coefficients for the displacement/velocity field across various scales, utilizing the wavelet coefficients derived from the original input image pair. By exploiting the properties of the wavelet transform, these estimated coefficients facilitate the seamless reconstruction of a full-resolution displacement/velocity field via our devised inverse discrete wavelet transform (IDWT) layer. This approach avoids the complexities of cascading networks or composition operations, making our WiNet an explainable and efficient competitor with other coarse-to-fine methods. Extensive experimental results from two 3D datasets show that our WiNet is accurate and GPU efficient. Code is available at https://***/x-xc/WiNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Image registration

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CPSAA: Accelerating Sparse Attention using Crossbar-based Processing-In-Memory Architecture

arXiv

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arXiv 2022年

作者： Li, Huize Jin, Hai Zheng, Long Liao, Xiaofei Huang, Yu Liu, Cong Xu, Jiahong Duan, Zhuohui Chen, Dan Gui, Chuangyi National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

The attention-based neural network attracts great interest due to its excellent accuracy enhancement. However, the attention mechanism requires huge computational efforts to process unnecessary calculations, significantly limiting the system’s performance. To reduce the unnecessary calculations, researchers propose sparse attention to convert some dense-dense matrices multiplication (DDMM) operations to sampled dense-dense matrix multiplication (SDDMM) and sparse matrix multiplication (SpMM) operations. However, current sparse attention solutions introduce massive off-chip random memory access since the sparse attention matrix is generally unstructured. We propose CPSAA, a novel crossbar-based processing-in-memory (PIM)-featured sparse attention accelerator to eliminate off-chip data transmissions. First, we present a novel attention calculation mode to balance the crossbar writing and crossbar processing latency. Second, we design a novel PIM-based sparsity pruning architecture to eliminate the pruning phase’s off-chip data transfers. Finally, we present novel crossbar-based SDDMM and SpMM methods to process unstructured sparse attention matrices by coupling two types of crossbar arrays. Experimental results show that CPSAA has an average of 89.6×, 32.2×, 17.8×, 3.39×, and 3.84× performance improvement and 755.6×, 55.3×, 21.3×, 5.7×, and 4.9× energy-saving when compare with GPU, FPGA, SANGER, ReBERT, and ReTransformer. Copyright © 2022, The Authors. All rights reserved.

关键词： Memory architecture

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Design and Simulation of Multi-tiered Heterogeneous Memory Architecture

Design and Simulation of Multi-tiered Heterogeneous Memory A...

引用

International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication systems (MASCOTS)

作者： Jinyuan Hu Haikun Liu Hai Jin Xiaofei Liao National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Heterogeneous memory systems have become increasingly popular in recent years. Because heterogeneous storage media often show significantly different characteristics in terms of bandwidth, latency, capacity, and energy consumption, it is still challenging to best utilize them for cost-efficient and energy-efficient heterogeneous memory systems. In this paper, we propose a simulation framework for multi-tiered heterogeneous memory architectures based on GEM5 and DRAMsim3 simulators. We design a heterogeneous memory controller to architect Non-Volatile Memory (NVM) as main memory, and architect both Dynamic Random Access Memory (DRAM) and High-Bandwidth Memory (HBM) as a hybrid cache of NVM. Specifically, HBM, DRAM, and NVM are managed in a single (flat) address space. However, we use an address remapping table to maintain the mappings between NVM pages and HBM/DRAM pages, and logically manage HBM/DRAM/NVM as a three-tiered hybrid memory system. We also design a hardware-supported hot page monitor based on Majority Element Algorithm (MEA) to identify the hottest pages in the DRAM, and a dynamic threshold adjustment scheme for hot page migration to balance the memory bandwidth between DRAM and HBM. Our multi-tiered heterogeneous memory architecture can take advantage of the large capacity of NVM, the low latency of DRAM, and the high bandwidth of HBM concurrently. Experimental results show that our tiered memory architecture can improve application performance by an average of $2.5\times$ compared with an NVM-only architecture, and up to 57.4% compared with a DRAM-only architecture. Moreover, the performance gap between our HBM/DRAM/NVM architecture and a HBM-only architecture is less than 10%.

关键词： Analytical models Nonvolatile memory Heuristic algorithms Computational modeling Memory architecture Random access memory Computer architecture

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Downstream-agnostic Adversarial Examples

arXiv

引用

arXiv 2023年

作者： Zhou, Ziqi Hu, Shengshan Zhao, Ruizhi Wang, Qian Zhang, Leo Yu Hou, Junhui Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China School of Information and Communication Technology Griffith University Australia Department of Computer Science City University of Hong Kong Hong Kong National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial use. In this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://github. com/CGCL-codes/AdvEncoder. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Achieving High Open-Circuit Voltage in Efficient Kesterite Solar Cells Via Lanthanide Europium Ion Induced Carrier Lifetime Enhancement

SSRN

引用

SSRN 2024年

作者： Chen, Xingye Zhao, Yunhai Ahmad, Nafees Zhao, Jun Zheng, Zhuanghao Su, Zhenghua Peng, Xiaogang Li, Xuejin Zhang, Xianghua Fan, Ping Liang, Guangxing Chen, Shuo Shenzhen Key Laboratory of Advanced Thin Films and Applications Key Laboratory of Optoelectronic Devices and Systems Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China UMR 6226 Université de Rennes Rennes F-35000 France Shenzhen518060 China

Short carrier lifetimes is a key challenge limiting the open-circuit voltage (VOC) and power conversion efficiency (PCE) of kesterite Cu2ZnSn(S,Se)4 (CZTSSe) solar cells. In this work, for the first time, lanthanide europium (Eu) ions have been introduced into Ag-incorporated CZTSSe absorber layer, which can modify the selenization reaction pathway during the initial selenization process, leading to high-quality (Ag, Eu)-CZTSSe absorber with large compact crystal grains and benign surface potential fluctuations. This innovative absorber engineering can also optimize defects dynamics with less detrimental defects and defects-assisted non-radiative recombination. Thus, significantly enhanced minority carrier lifetimes from 0.97 to 3.15 ns can be achieved, representing one of the top values among various bulk and/or interface engineering treated CZTSSe. The champion CZTSSe thin-film solar cell exhibits an impressive VOC of 545.6 mV, accompanied with a stimulating increase in PCE from 10.61% to 13.30%. These findings underscore the potential of lanthanide cation doping to drive significant advancements in CZTSSe photovoltaic technology, and therefore promoting its further development and future applications. © 2024, The Authors. All rights reserved.

关键词： Open circuit voltage

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Dual Graph Inference Network for Weakly Supervised Semantic Segmentation

引用

IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Zhang, Jia Peng, Bo Wu, Xi Southwest Jiaotong University School of Computing and Artificial Intelligence Sichuan Chengdu611756 China Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Chengdu University of Information Technology School of Computer Science Sichuan Chengdu610225 China

Establishing global contextual relationships between objects is crucial for weakly supervised semantic segmentation (WSSS) tasks that lack pixel-level labels. Limited by the efficiency of convolutional operations in capturing long-range dependencies with a limited receptive field and to bridge the gap between image-level annotations and pixel-level labels, we propose a Dual Graph Reasoning Mapping (DGRM) module. When integrated into a convolutional network, it conducts contextual graph reasoning on both spatial and interaction spaces of visual features. The first component of this graph reasoning module involves incorporating commonsense knowledge extracted from an external knowledge base into visual features to promote global contextual reasoning for visual graphs. The second component focuses on reasoning in the projected interaction space, utilizing abstracted object class attributes from high-level visual features to establish dependencies among channels in a potential low-dimensional space. Moreover, to capture correspondences at different semantic levels, we model the feature maps in a pyramid-like structure for graph reasoning at various levels. Extensive experiments on popular datasets, such as PASCAL VOC 2012 and MS COCO 2014, demonstrate the superiority of our approach. Our code is provided at https://***/JIAZHANG666/DGRM. © 2025 IEEE.

关键词： Semantic Segmentation

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Energy-efficient and Privacy-preserving Edge Intelligence for 6G-Empowered Intelligent Transportation systems

引用

IEEE Network 2025年

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Xiao, Ziheng Guo, Tingting Liu, Shenghao Sun, Jiaqi Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wuhan Kedao Geographic Information Engineering Co. Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of

6G-empowered Intelligent Transportation systems (ITS) generate large amounts of data through millions of devices and sensors at the terminal and network edge. Edge intelligence advances the frontier of data-driven artificial intelligence (AI) to the network edge, which enables AI applications to better utilize edge data. Broader connectivity, higher device density, and stronger data exchange present challenges for reliable device communication and privacy data protection in 6G-empowered ITS. To address these challenges, this paper proposes an energy-efficient and privacy-preserving edge intelligence framework named EP-EI. EP-EI deploys a deep reinforcement learning-based cluster head selection model at the edge to ensure energy-efficient communication for terminal devices. Meanwhile, EP-EI designs a privacy-aware hierarchical computing framework for terminal-edge-cloud to prevent privacy leakage. Furthermore, the application scenarios, challenges, and future directions of EP-EI are discussed. Finally, simulation experiments in a specific intelligent transportation case study demonstrate that EP-EI can provide energy-efficient and privacy-protecting services. © 2025 IEEE.

关键词： Differential privacy

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NaFV-Net: An Adversarial Four-view Network for Mammogram Classification 39

NaFV-Net: An Adversarial Four-view Network for Mammogram Cla...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Feng Hou, Yuxiang Li, Wei Yang, Xiangying Zheng, Haibo Luo, Wenxi Chen, Leqing Cao, Yuyang Liao, Xiaofei Zhang, Yu Yang, Fan Zomaya, Albert Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies Centre for Distributed and High Performance Computing School of Computer Science University of Sydney Australia Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)157735897X

Breast cancer remains a leading cause of mortality among women, with millions of new cases diagnosed annually. Early detection through screening is crucial. Using neural networks to improve the accuracy of breast cancer screening has become increasingly important. In accordance with radiologists’ practices, we propose using images from the unaffected side to create adversarial samples with critical medical implications in our adversarial learning process. By introducing beneficial perturbations, this method aims to reduce overconfidence and improve the precision and robustness of breast cancer classification. Our proposed framework is an adversarial four-view classification network (NaFV-Net) incorporating images from both affected and unaffected perspectives. By comprehensively capturing local and global information and implementing adversarial learning from four mammography views, this framework allows for the fusion of features and the integration of medical principles and radiologist evaluation techniques, thus facilitating the accurate identification and characterization of breast tissues. Extensive experiments have shown the high effectiveness of our model in accurately distinguishing between benign and malignant findings, demonstrating state-of-the-art classification performance on both internal and public datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mammography

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Urban Water Sprinkler Routing: A Multi-Depot Mixed Capacitated Arc Routing Problem Incorporating Real-Time Demands

arXiv

引用

arXiv 2024年

作者： Yang, Hongtai Liu, Luna Han, Ke Lei, Boyi School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Institute of System Science and Engineering Southwest Jiaotong University Chengdu611756 China School of Economics and Management Southwest Jiaotong University Chengdu611756 China

Fugitive road dust (FRD), as one of the major pollutants in the city, poses great harm to the environment and the physical health of citizens. A common countermeasure adopted by government agencies is employing on-road water trucks (sprinklers) to spray water (sprinkle) on urban streets to reduce the FRD. Currently, the traveling routes of sprinklers are usually planned based on drivers’ experience, which may lead low operation efficiency and could not respond to the real-time sprinkling demands. To address these issues, this study formulates the routes planning of sprinklers as a multi-depot mixed capacitated arc routing problem with real-time demands with the aim of minimizing the sprinklers’ travel distance. We develop an improved adaptive large neighborhood search (ALNS) algorithm that incorporates a tabu-list and a perturbation mechanism to solve this problem. Furthermore, a problem-specific acceleration mechanism is designed to reduce unnecessary search domains to improve the efficiency of the algorithm. Empirical experiments are conducted based on various scenarios and the results demonstrate that the proposed algorithm generates solutions that are superior or at least comparable to the solutions generated by the traditional ALNS algorithm but with significantly lower computation time. Sensitivity analysis is conducted to explore the effects of relevant parameters on the results. This study is the first to incorporate real-time FRD pollution information, gathered through multiple data sources via IoT technology, into urban sprinkling operations, extending the traditional CARP from a tactical planning to a real-time operational environment. A real-world implementation case is also presented. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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