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Proceedings of the 41st International Conference on Machine Learning

作者： Nianzu Yang Kaipeng Zeng Haotian Lu Yexin Wu Zexin Yuan Danni Chen Shengdian Jiang Jiaxiang Wu Yimin Wang Junchi Yan School of Artificial Intelligence & Department of Computer Science and Engineering & MoE Lab of AI Shanghai Jiao Tong University Guangdong Institute of Intelligence Science and Technology SEU-ALLEN Joint Center Institute for Brain and Intelligence Southeast University XVERSE Technology

Neuronal morphology is essential for studying brain functioning and understanding neurodegenerative disorders. As acquiring real-world morphology data is expensive, computational approaches for morphology generation have been studied. Traditional methods heavily rely on expert-set rules and parameter tuning, making it difficult to generalize across different types of morphologies. Recently, MorphVAE was introduced as the sole learning-based method, but its generated morphologies lack plausibility, i.e., they do not appear realistic enough and most of the generated samples are topologically invalid. To fill this gap, this paper proposes MorphGrower, which mimicks the neuron natural growth mechanism for generation. Specifically, MorphGrower generates morphologies layer by layer, with each subsequent layer conditioned on the previously generated structure. During each layer generation, MorphGrower utilizes a pair of sibling branches as the basic generation block and generates branch pairs synchronously. This approach ensures topological validity and allows for fine-grained generation, thereby enhancing the realism of the final generated morphologies. Results on four real-world datasets demonstrate that MorphGrower outperforms MorphVAE by a notable margin. Importantly, the electrophysiological response simulation demonstrates the plausibility of our generated samples from a neuroscience perspective. Our code is available at https://***/Thinklab-SJTU/MorphGrower.

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Mixed Attention Densely Residual Network for Single Image Super-Resolution

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computer Systems science & engineering 2021年第10期39卷 133-146页

作者： Jingjun Zhou Jing Liu Jingbing Li Mengxing Huang Jieren Cheng Yen-Wei Chen Yingying Xu Saqib Ali Nawaz School of Information and Communication Engineering Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570228China Research Center for Healthcare Data Science Zhejiang LabHangzhou311121China School of Computer Science and Cyberspace Security Hainan UniversityHaikou570228China Graduate School of Information Science and Engineering Ritsumeikan University5258577Japan College of Computer Science and Technology Zhejiang UniversityHangzhou311100China

Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented ***,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance *** present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial ***,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep *** structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature *** qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.

关键词： Channel attention Laplacian spatial attention residual in dense mixed attention

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A Scalable, Efficient, and Robust Dynamic Memory Management Library for HLS-based FPGAs

A Scalable, Efficient, and Robust Dynamic Memory Management ...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Qinggang Wang Long Zheng Zhaozeng An Shuyi Xiong Runze Wang Yu Huang Pengcheng Yao Xiaofei Liao Hai Jin Jingling Xue 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 Zhejiang Lab Hangzhou China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Nowadays, high-level synthesis (HLS) has gained prominence for FPGA-based architecture prototyping, enhancing productivity significantly. Despite this advancement, HLS tools are impeded by a critical drawback: they lack support for dynamic memory management (DMM), leading to static mem-ory allocation and suboptimal use of memory resources. In response, numerous efforts have been made to develop DMM solutions compatible with HLS. However, our analysis indicates that existing solutions fail to concurrently meet the desired trifecta of scalability (efficient management of memory of any size), efficiency (minimal latency in memory (de-)allocation), and robustness (low allocation failure rates). This limitation hampers their applicability in real-world scenarios. In this paper, we introduce GraDMM, a “three-birds-one- stone” solution that comprehensively enhances the scalability, efficiency, and robustness of DMM. The key insight is to formulate memory (de-)allocation as graph analytics and lever-age sophisticated FPGA-based graph processing techniques. To achieve scalability, GraDMM specializes a simplified pipeline that significantly suppresses resource utilization expansion caused by managed memory scaling. This is crucial for managing arbitrarily sized memory on resource-limited FPGA platforms. For efficiency, GraDMM implements a data-centric concurrent traversal scheme and a shortcut-assisted fast traversal policy to accelerate (de-)allocation-guided graph traversal, reducing mem-ory (de-)allocation latency. To enhance robustness, GraDMM incorporates an adaptive memory defragmenter that defragments managed memory to minimize fragmentation-induced allocation failures. GraDMM is encapsulated as a library, providing high- level interfaces for users and ensuring synthesizability with Vi- vado HLS. Experimental results demonstrate that GraDMM out-performs three state-of-the-art HLS-compatible DMM solutions by significant margins: 56.71 %-85.59% in resource consumption sa

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Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution

arXiv

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arXiv 2024年

作者： Lin, Junxiong Wang, Yan Tao, Zeng Wang, Boyang Zhao, Qing Wang, Haorang Tong, Xuan Mai, Xinji Lin, Yuxuan Song, Wei Yu, Jiawen Yan, Shaoqi Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China East China University of Science and Technology Shanghai China College of Information Technology Shanghai Ocean University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Pre-trained diffusion models utilized for image generation encapsulate a substantial reservoir of a priori knowledge pertaining to intricate textures. Harnessing the potential of leveraging this a priori knowledge in the context of image super-resolution presents a compelling avenue. Nonetheless, prevailing diffusion-based methodologies presently overlook the constraints imposed by degradation information on the diffusion process. Furthermore, these methods fail to consider the spatial variability inherent in the estimated blur kernel, stemming from factors such as motion jitter and out-of-focus elements in open-environment scenarios. This oversight results in a notable deviation of the image super-resolution effect from fundamental realities. To address these concerns, we introduce a framework known as Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution (SSR). Within the SSR framework, we propose a Spatially Variant Kernel Refinement (SVKR) module. SVKR estimates a Depth-Informed Kernel, which takes the depth information into account and is spatially variant. Additionally, SVKR enhance the accuracy of depth information acquired from LR images, allowing for mutual enhancement between the depth map and blur kernel estimates. Finally, we introduce the Adaptive Multi-Modal Fusion (AMF) module to align the information from three modalities: low-resolution images, depth maps, and blur kernels. This alignment can constrain the diffusion model to generate more authentic SR results. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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From "ai" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?

arXiv

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arXiv 2024年

作者： Inie, Nanna Druga, Stefania Zukerman, Peter Bender, Emily M. Denmark and University of Washington Paul G. Allen School of Computer Science & Engineering United States University of Chicago Center for Applied AI Research United States University of Washington Department of Linguistics United States

This paper investigates the influence of anthropomorphized descriptions of so-called "ai" (artificial intelligence) systems on people’s self-assessment of trust in the system. Building on prior work, we define four categories of anthropomorphization (1. Properties of a cognizer, 2. Agency, 3. Biological metaphors, and 4. Properties of a communicator). We use a survey-based approach (n=954) to investigate whether participants are likely to trust one of two (fictitious) "ai" systems by randomly assigning people to see either an anthropomorphized or a de-anthropomorphized description of the systems. We find that participants are no more likely to trust anthropomorphized over de-anthropmorphized product descriptions overall. The type of product or system in combination with different anthropomorphic categories appears to exert greater influence on trust than anthropomorphizing language alone, and age is the only demographic factor that significantly correlates with people’s preference for anthropomorphized or de-anthropomorphized descriptions. When elaborating on their choices, participants highlight factors such as lesser of two evils, lower or higher stakes contexts, and human favoritism as driving motivations when choosing between product A and B, irrespective of whether they saw an anthropomorphized or a de-anthropomorphized description of the product. Our results suggest that "anthropomorphism" in "ai" descriptions is an aggregate concept that may influence different groups differently, and provide nuance to the discussion of whether anthropomorphization leads to higher trust and over-reliance by the general public in systems sold as "ai". © 2024, CC BY.

关键词： Semantics

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Tensor Dynamic Fusion Based Modality-Imbalanced Multimodal Federated Learning in Mobile Edge Computing for Consumer Applications

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IEEE Transactions on Consumer Electronics 2024年

作者： Nie, Xin Yi, Lingzhi Yang, Laurence T. Deng, Xianjun Fan, Fulan Zhang, Hongtao Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei EngineeringResearch Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Zhongnan University of Economics and Law School of Information and Safety Engineering Wuhan430074 China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China St. Francis Xavier University Department of Computer Science Antigonish Canada South-Central Minzu University School of Education Wuhan430074 China

Mobile edge computing extends the capabilities of computation and storage from the cloud to the edge, enabling local data storage and processing. This computing paradigm complements federated learning (FL), as it satisfies the need for privacy protection while providing efficiency and quick response. In consumer applications, multimodal data is experiencing explosive growth. Therefore, multimodal FL has emerged as a crucial research area. However, applying traditional FL methods directly to multimodal data poses certain challenges. Additionally, issues like sensor failures result in modality-imbalanced data among clients, i.e., the number and type of data modalities between client devices vary. Hence, it is imperative to design specialized FL frameworks and models specifically tailored for analyzing multimodal data. To address these challenges, we propose a tensor fusion-based multimodal federated learning (TenMFL). First, a tensor dynamic fusion network is designed for addressing the challenge of multimodal data fusion. This approach separates the parameter forward computation and the modal fusion computation, effectively resolving issues related to client-side modality imbalance. Second, we introduce a hybrid fusion method that combines feature fusion and decision fusion to further exploit the complementarity of multimodal data. We evaluate TenMFL on three multimodal datasets, experimental results demonstrate our superior performances. © 1975-2011 IEEE.

关键词： Federated learning

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An Overview of Calibration Technology of Industrial Robots

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 23-36页

作者： Zhibin Li Shuai Li Xin Luo the School of Computer Science and Technology Dongguan University of TechnologyDongguanGuangdong 523808 the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China the Department of Electronics and Electrical Engineering Swansea UniversitySwansea SA18EN the Hengrui(Chongqing)Artificial Intelligence Research Center Department of Big Data Analyses TechniquesCloudwalkChongqing 401331China the Department of Electronics and Electrical Engineering Swansea UniversitySwansea SA18ENUK

With the continuous improvement of automation,industrial robots have become an indispensable part of automated production *** widely used in a number of industrial production activities,such as spraying,welding,handling,etc.,and have a great role in these ***,the robotic technology is developing towards high precision,high *** calibration technology has a great significance to improve the accuracy of ***,it has much work to be done in the identification of robot *** parameter identification work of existing serial and parallel robots is *** the one hand,it summarizes the methods for parameter calibration and discusses their advantages and *** the other hand,the application of parameter identification is *** overview has a great reference value for robot manufacturers to choose proper identification method,points further research areas for ***,this paper analyzes the existing problems in robot calibration,which may be worth researching in the future.

关键词： Absolute pose accuracy identification method industrial robots parameter identification robot calibration technology robot parameters

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DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration

DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral M...

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International Conference on computer Vision (ICCV)

作者： Yuchun Miao Lefei Zhang Liangpei Zhang Dacheng Tao National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Luojia Laboratory State Key Lab. of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Sydney AI Centre School of Computer Science The University of Sydney

Diffusion models have recently received a surge of interest due to their impressive performance for image restoration, especially in terms of noise robustness. However, existing diffusion-based methods are trained on a large amount of training data and perform very well in-distribution, but can be quite susceptible to distribution shift. This is especially inappropriate for data-starved hyperspectral image (HSI) restoration. To tackle this problem, this work puts forth a self-supervised diffusion model for HSI restoration, namely Denoising Diffusion Spatio-Spectral Model (DDS2M), which works by inferring the parameters of the proposed Variational Spatio-Spectral Module (VS2M) during the reverse diffusion process, solely using the degraded HSI without any extra training data. In VS2M, a variational inference-based loss function is customized to enable the untrained spatial and spectral networks to learn the posterior distribution, which serves as the transitions of the sampling chain to help reverse the diffusion process. Benefiting from its self-supervised nature and the diffusion process, DDS2M enjoys stronger generalization ability to various HSIs compared to existing diffusion-based methods and superior robustness to noise compared to existing HSI restoration methods. Extensive experiments on HSI denoising, noisy HSI completion and super-resolution on a variety of HSIs demonstrate DDS2M’s superiority over the existing task-specific state-of-the-arts. Code is available at: https://***/miaoyuchun/DDS2M.

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CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation

arXiv

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arXiv 2023年

作者： Jiang, Xiaoheng Guo, Kaiyi Lu, Yang Yan, Feng Liu, Hao Cao, Jiale Xu, Mingliang Tao, Dacheng School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou China National Supercomputing Center in Zhengzhou Zhengzhou China School of Electrical and Information Engineering Tianjin University Tianjin China Sydney AI Centre the School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress, there are still some challenges for these methods, such as indistinguishable weak defects and defect-like interference in the background. To address these issues, we propose a transformer network with multi-stage CNN (Convolutional Neural Network) feature injection for surface defect segmentation, which is a UNet-like structure named CINFormer. CINFormer presents a simple yet effective feature integration mechanism that injects the multilevel CNN features of the input image into different stages of the transformer network in the encoder. This can maintain the merit of CNN capturing detailed features and that of transformer depressing noises in the background, which facilitates accurate defect detection. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects, so as to further reduce the impact of the redundant background. Extensive experiments conducted on the surface defect datasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormer achieves state-of-the-art performance in defect detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Surface defects

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Towards next-generation diagnostic pathology:aiempowered label-free multiphoton microscopy

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Light(science & Applications) 2024年第12期13卷 2887-2911页

作者： Shu Wang Junlin Pan Xiao Zhang Yueying Li Wenxi Liu Ruolan Lin Xingfu Wang Deyong Kang Zhijun Li Feng Huang Liangyi Chen Jianxin Chen School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory of Photonics TechnologyFujian Normal UniversityFuzhou 350007China College of Computer and Data Science Fuzhou UniversityFuzhou 350108China Department of Radiology Fujian Medical University Union HospitalFuzhou 350001China Department of Pathology The First Affiliated Hospital of Fujian Medical UniversityFuzhou 350005China Department of Pathology Fujian Medical University Union HospitalFuzhou 350001China New Cornerstone Laboratory State Key Laboratory of Membrane BiologyBeijing Key Laboratory of Cardiometabolic Molecular MedicineInstitute of Molecular MedicineNational Biomedical Imaging CenterSchool of Future TechnologyPeking UniversityBeijing 100091China

Diagnostic pathology,historically dependent on visual scrutiny by experts,is essential for disease *** digital pathology and developments in computer vision technology have led to the application of artificialintelligence(ai)in this *** these advancements,the variability in pathologists’subjective interpretations ofdiagnostic criteria can lead to inconsistent *** meet the need for precision in cancer therapies,there is anincreasing demand for accurate pathological ***,traditional diagnostic pathology is evolvingtowards“next-generation diagnostic pathology”,prioritizing on the development of a multi-dimensional,intelligentdiagnostic *** nonlinear optical effects arising from the interaction of light with biological tissues,multiphoton microscopy(MPM)enables high-resolution label-free imaging of multiple intrinsic components acrossvarious human pathological ***-empowered MPM further improves the accuracy and efficiency of diagnosis,holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic *** review,we systematically outline the applications of MPM in pathological diagnosis across various human diseases,and summarize common multiphoton diagnostic ***,we examine the significant role of ai inenhancing multiphoton pathological diagnosis,including aspects such as image preprocessing,refined differentialdiagnosis,and the prognostication of *** also discuss the challenges and perspectives faced by theintegration of MPM and ai,encompassing equipment,datasets,analytical models,and integration into the existingclinical ***,the review explores the synergy between ai and label-free MPM to forge novel diagnosticframeworks,aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems inclinical settings.

关键词： multiphoton refined diagnosis

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