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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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Shared Incentive System for Clinical Pathway Experience

Shared Incentive System for Clinical Pathway Experience

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weiqi Dai Wenhao Zhao Xia Xie Song Wu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China 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 School of Computer Science and Technology Hainan University Haikou China

The phenomenon of unbalanced regional medical resources has led to large differences in the implementation experience of clinical pathways in hospitals with different medical levels. However, due to the fear of privacy leakage and the lack of sharing motivation, there is a lack of effective collaborative communication channels for clinical pathways among medical institutions. In response to these problems, we propose a blockchain-based sharing incentive scheme for clinical pathway experience, which uses the clinical pathway implementation effect evaluation system to evaluate the quality of clinical pathway experience data. Specifically, we designe a two-phase cross-domain shared transaction model for the transaction of clinical pathway experience data. Moreover, we introduce the shared transaction alliance committee to verify and review the transaction, and solve the problems in the transaction in the arbitration phase. Finally, the functional test results show that the system meets the experience sharing incentive requirements, and the performance test results show that the TPS of sharing clinical pathway experience read and writed can reach around 600 and 1000, and the transaction latency of each phase is within 3 s.

关键词： Privacy Hospitals Collaboration Communication channels data models Security Incentive schemes

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LLGS: Illuminating Gaussian Splatting via absorptance Modulation

LLGS: Illuminating Gaussian Splatting via absorptance Modula...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jianwen Gan Wenxin Li Bo Zheng Chengliang Wang Yingbo Wu National Elite Institute of Engineering Chongqing University China AI Lab Chongqing Changan Automobile Co. Ltd. China College of Computer Science Chongqing University China School of Big Data & Software Engineering Chongqing University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light images are typically characterized by low pixel intensity and color distortion, presenting a significant challenge for accurate 3D reconstruction with 3D Gaussian Splatting (3DGS). Traditional 2D enhancement methods fail to maintain consistent illumination, affecting reconstruction quality. We propose Low-Light Gaussian (LLGS), which can directly leverage low-light images for 3D reconstruction and synthesizing normal-light novel views. LLGS incorporates absorptance to simulate light behavior in low-light conditions, assuming objects maintain normal illumination while reflected light intensity attenuates due to absorptance during rendering. This approach enables the capture of authentic color information in dimly lit scenes. LLGS outperforms current enhancement algorithms and Neural Radiance Fields (NeRF) in image quality and processing efficiency, making it highly effective for low-light 3D scene reconstruction and novel view synthesis.

关键词： Image quality Three-dimensional displays Image color analysis Lighting Signal processing algorithms Modulation Speech enhancement Neural radiance field Rendering (computer graphics) Image reconstruction

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MHCN: A Hyperbolic Neural Network Model for Multi-view Hierarchical Clustering

MHCN: A Hyperbolic Neural Network Model for Multi-view Hiera...

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International Conference on Computer Vision (ICCV)

作者： Fangfei Lin Bing Bai Yiwen Guo Hao Chen Yazhou Ren Zenglin Xu University of Electronic Science and Technology of China China Tencent Security Big Data Lab China Independent Researcher University of California Davis USA Peng Cheng Lab China Harbin Institute of Technology Shenzhen China

Multi-view hierarchical clustering (MCHC) plays a pivotal role in comprehending the structures within multi-view data, which hinges on the skillful interaction between hierarchical feature learning and comprehensive representation learning across multiple views. However, existing methods often overlook this interplay due to the simple heuristic agglomerative strategies or the decoupling of multi-view representation learning and hierarchical modeling, thus leading to insufficient representation learning. To address these issues, this paper proposes a novel Multi-view Hierarchical Clustering Network (MHCN) model by performing simultaneous multi-view learning and hierarchy modeling. Specifically, to uncover efficient tree-like structures among all views, we derive multiple hyperbolic autoencoders with latent space mapped onto the Poincaré ball. Then, the corresponding hyperbolic embeddings are further regularized to achieve the multi-view representation learning principles for both view-common and view-private information, and to ensure hyperbolic uniformity with a well-balanced hierarchy for better interpretability. Extensive experiments on real-world and synthetic multi-view datasets have demonstrated that our method can achieve state-of-the-art hierarchical clustering performance, and empower the clustering results with good interpretability.

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Affine Equivariant Networks Based on Differential Invariants

Affine Equivariant Networks Based on Differential Invariants

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yikang Li Yeqing Qiu Yuxuan Chen Lingshen He Zhouchen Lin National Key Lab of General AI School of Intelligence Science and Technology Peking University The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Institute for Artificial Intelligence Peking University Peng Cheng Laboratory

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Convolutional neural networks benefit from translation equivariance, achieving tremendous success. Equivariant networks further extend this property to other transformation groups. However, most existing methods require dis-cretization or sampling of groups, leading to increased model sizes for larger groups, such as the affine group. In this paper, we build affine equivariant networks based on differential invariants from the viewpoint of symmetric PDEs, without discretizing or sampling the group. To ad-dress the division-by-zero issue arising from fractional dif-ferential invariants of the affine group, we construct a new kind of affine invariants by normalizing polynomial relative differential invariants to replace classical differential invariants. For further flexibility, we design an equivariant layer, which can be directly integrated into convolutional networks of various architectures. Moreover, our frame-work for the affine group is also applicable to its continu-ous subgroups. We implement equivariant networks for the scale group, the rotation-scale group, and the affine group. Numerical experiments demonstrate the outstanding performance of our framework across classification tasks involving transformations of these groups. Remarkably, under the out-of-distribution setting, our model achieves a 3.37% im-provement in accuracy over the main counterpart affConv on the affNIST dataset.

关键词： Manifolds Computer vision Three-dimensional displays Accuracy Lie groups Polynomials Pattern recognition

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CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation

arXiv

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arXiv 2023年

作者： Liao, Weibin Li, Xuhong Wang, Qingzhong Xu, Yanwu Yin, Zhaozheng Xiong, Haoyi Big Data Lab Baidu Inc. Beijing China HDMI Lab South China University of Technology Guangdong China Department of Computer Science Stony Brook University Stony BrookNY United States

—While pre-training on object detection tasks, such as Common Objects in Contexts (COCO) [1], could significantly boost the performance of cell segmentation, it still consumes on massive fine-annotated cell images [2] with bounding boxes, masks, and cell types for every cell in every image, to fine-tune the pre-trained model. To lower the cost of annotation, this work considers the problem of pre-training DNN models for few-shot cell segmentation, where massive unlabeled cell images are available but only a small proportion is annotated. Hereby, we propose Cross-domain Unsupervised Pre-training, namely CUPre, transferring the capability of object detection and instance segmentation for common visual objects (learned from COCO) to the visual domain of cells using unlabeled images. Given a standard COCO pre-trained network with backbone, neck, and head modules, CUPre adopts an alternate multi-task pre-training (AMT2) procedure with two sub-tasks — in every iteration of pre-training, AMT2 first trains the backbone with cell images from multiple cell datasets via unsupervised momentum contrastive learning (MoCo) [3], and then trains the whole model with vanilla COCO datasets via instance segmentation. After pre-training, CUPre fine-tunes the whole model on the cell segmentation task using a few annotated images. We carry out extensive experiments to evaluate CUPre using LIVECell [2] and BBBC038 [4] datasets in few-shot instance segmentation settings. The experiment shows that CUPre can outperform existing pre-training methods, achieving the highest average precision (AP) for few-shot cell segmentation and detection. Specifically, when using only 5% annotated images from LIVECell for training, CUPre achieves 41.5% APbbox versus 40.0% and 8.3% obtained by the COCO pre-trained and MoCo-based LIVECell pre-trained models respectively. External validation using out-of-distribution datasets further confirms the generalization superiority of CUPre, which outperforms baselines w

关键词： Object detection

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MeG2: In-Memory Acceleration for Genome Graphs Analysis

MeG2: In-Memory Acceleration for Genome Graphs Analysis

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Design Automation Conference

作者： Yu Huang Long Zheng Haifeng Liu Zhuoran Zhou Dan Chen Pengcheng Yao Qinggang Wang Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China

Genome graphs analysis has emerged as an effective means to enable mapping DNA fragments (known as reads) to the reference genome. It replaces the traditional linear reference with a graph-based representation to augment the genetic variations and diversity information, significantly improving the quality of genotyping. The in-depth characterization of genome graphs analysis uncovers that it is bottlenecked by the irregular seed index access and the intensive alignment operation, stressing both the memory system and computing *** on these observations, we propose MeG 2 , a lightweight, commodity DRAM-compliant, processing-in-memory architecture to accelerate genome graphs analysis. MeG 2 is specifically integrated with the capabilities of both near-memory processing and bitwise in-situ computation. Specifically, MeG 2 leverages the low access latency of near-memory processing with the index-centric offload mechanism to alleviate the irregular memory access in the seeding procedure, and harnesses the row-parallel capacity of in-situ computation with the distance-aware technique to exploit the intensive computational parallelism in the alignment process. Results show that MeG 2 outperforms the CPU-, GPU-, and ASIC-based genome graphs analysis solutions by 502× (30.2×), 272× (15.1× ), and 5.5× (8.3×) for short (long) reads, while reducing energy consumption by 1628× (85.6×), 1443× (77.1×), and 7.8× (11.7×), respectively. We also demonstrate that MeG 2 offers significant improvements over existing PIM-based genome sequence analysis accelerators.

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Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning

arXiv

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arXiv 2024年

作者： Wang, Shuai Chen, Zhengyang Lee, Kong Aik Qian, Yanmin Li, Haizhou Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Guangdong Shenzhen518172 China Auditory Cognition and Computational Acoustics Lab The Department of Computer Science and Engineering The MoE Key Laboratory of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong

Speaker individuality information is among the most critical elements within speech signals. By thoroughly and accurately modeling this information, it can be utilized in various intelligent speech applications, such as speaker recognition, speaker diarization, speech synthesis, and target speaker extraction. In this overview, we present a comprehensive review of neural approaches to speaker representation learning from both theoretical and practical perspectives. Theoretically, we discuss speaker encoders ranging from supervised to self-supervised learning algorithms, standalone models to large pretrained models, pure speaker embedding learning to joint optimization with downstream tasks, and efforts toward interpretability. Practically, we systematically examine approaches for robustness and effectiveness, introduce and compare various open-source toolkits in the field. Through the systematic and comprehensive review of the relevant literature, research activities, and resources, we provide a clear reference for researchers in the speaker characterization and modeling field, as well as for those who wish to apply speaker modeling techniques to specific downstream tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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LightDAG: A Low-latency DAG-based BFT Consensus through Lightweight Broadcast

LightDAG: A Low-latency DAG-based BFT Consensus through Ligh...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiaohai Dai Guanxiong Wang Jiang Xiao Zhengxuan Guo Rui Hao Xia Xie Hai Jin 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 School of Computer Science and Artificial Intelligence Wuhan University of Technology China School of Computer Science and Technology Hainan University China

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

To improve the throughput of Byzantine Fault Tolerance (BFT) consensus protocols, the Directed Acyclic Graph (DAG) topology has been introduced to parallel data processing, leading to the development of DAG-based BFT consensus. However, existing DAG-based works heavily rely on Reliable Broadcast (RBC) protocols for block broadcasting, which introduces significant latency due to the three communication steps involved in each RBC. For instance, DAGRider, a representative DAG-based protocol, exhibits the best latency of 12 steps, considerably higher than non-DAG protocols like PBFT, which only requires 3 steps. To tackle this issue, we propose LightDAG, which replaces RBC with lightweight broadcasting protocols such as Consistent Broadcast (CBC) and Plain Broadcast (PBC). Since CBC and PBC can be implemented in two and one communication steps, respectively, LightDAG achieves low *** our proposal, we present two variants of LightDAG, namely LightDAG1 and LightDAG2, each providing a trade-off between the best latency and the expected worst latency. In LightDAG1, every block is broadcast using CBC, which exhibits a best latency of 5 steps and an expected worst latency of 14 steps. Since CBC cannot guarantee the totality property, we design a block retrieval mechanism in LightDAG1 to assist replicas in retrieving missing blocks. LightDAG2 utilizes a combination of PBC and CBC for block broadcasting, resulting in the best latency of 4 steps and an expected worst latency of 12(t+1) steps, where t represents the number of actual Byzantine replicas. Since a Byzantine replica may equivocate through PBC, LightDAG2 prohibits blocks from directly referencing contradictory blocks. To ensure liveness, we propose a mechanism to identify and exclude Byzantine replicas if they engage in equivocation attacks. Extensive experiments have been conducted to evaluate LightDAG, and the results demonstrate its feasibility and efficiency.

关键词： Fault tolerance Distributed processing Directed acyclic graph Fault tolerant systems Broadcasting Throughput data processing

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Hyperspectral Classification of Coastal Wetland Based on Multi-objective CNN and Decision Fusion Model 11

Hyperspectral Classification of Coastal Wetland Based on Mul...

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11th International Conference on Information Systems and Computing Technology, ISCTech 2023

作者： Hu, Yabin Yang, Junfang Ren, Guangbo Ma, Yi Wang, Jianbu Zhu, Xiaoheng Ministry of Natural Resources Lab of Marine Physics and Remote Sensing First Institute of Oceanography Qingdao China Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xian China China University of Petroleum College of Oceanography and Space Informatics Qingdao China ShanDong University of Science and Technology College of Geodesy and Geomatics Qingdao China

ISBN: (纸本)9798350342406

Coastal wetland play an irreplaceable role in maintaining biodiversity and improving climate. In this paper, we proposed a hyperspectral classification algorithm of coastal wetland based on multi-objective Convolutional Neural Networks (CNN) and decision fusion model. The algorithm is named that the multi-objective spatial-spectral CNN classification with binary morphological strategy decision fusion model algorithm (MB2SCNN-BMSDF). The multi-objective hyperspectral feature band selection model of coastal wetland based on spectral deviation algorithm is developed and constructed to effectively obtain hyperspectral sensitive bands for single target surface object types. On this basis, a CNN hyperspectral classification model that integrates multi-objective spatial spectral information, and a binary morphological decision fusion algorithm that combines spatial position information and fuzzy membership degrees are proposed, which solves the problems of salt and pepper noise and sand eye effect in the classification process, and reduces noise information. The results showed that the CNN classification of multi-objective spatial spectral information and the decision fusion model of binary morphology strategy have better classification effect and performance. © 2023 IEEE.

关键词： Biodiversity

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