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ParaSAT: A scalable parallel framework for sequence alignment

ParaSAT: A scalable parallel framework for sequence alignmen...

引用

2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Wenjuan Xu, Jianbang Pan, Liangrui Cai, Tie Peng, Shaoliang Hunan University College of Computer Science and Electronic Engineering Changsha China Shenzhen China

ISBN: (纸本)9798350386226

Sequence alignment is a fundamental step in genomic data analysis. Third-generation sequencing technology facilitates the acquisition of high-quality genomic data but the explosive growth of sequencing data poses huge challenges to current sequence alignment. To reduce sequence alignment time and enhance alignment performance, a parallelization framework based on Minimap2 is proposed, called ParaSAT, aiming to expedite sequence alignment and offer insights to researchers in the field. In order to achieve load balancing among multi-nodes, We design a task pool scheduling strategy which can dynamically distribute tasks according to the status of compute nodes. To evaluate the performance of the framework, We choose 6 dataset to conduct experiments on TH-1 supercomputer. The outcomes confirm that the parallel framework ensures sequence alignment accuracy, and demonstrates a notable speedup on large datasets, approaching linearity, with parallel efficiency consistently above 80%. The framework also exhibits strong and weak scalability, effectively enhancing the efficiency of sequence alignment and offering guidance for high-performance genomic data processing. © 2024 IEEE.

关键词： Supercomputers

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SSTHyper: Sparse Spectral Transformer for Hyperspectral Image Reconstruction 17th

SSTHyper: Sparse Spectral Transformer for Hyperspectral Ima...

引用

17th Asian conference on computer Vision, ACCV 2024

作者： Xu, Meng Lin, Mingying Ren, Qi Jia, Sen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9789819609109

Transformer-based methods have improved the quality of hyperspectral images (HSIs) reconstructed from RGB by effectively capturing their remote relationships. The self-attention mechanisms in existing Transformer models have not fully considered the spatial sparsity and spectral continuity characteristics of HSIs and fail to effectively filter out significant features, resulting in lower-quality reconstruction results. This paper proposes a sparse spectral transformer model for HSI reconstruction (SSTHyper) to address this limitation, adaptively preserving crucial features. The network consists of an encoder-decoder structure capable of learning shallow and deep spatial-spectral priors, primarily composed of sparse spectral self-attention groups. Introducing a sparse spectral self-attention mechanism allows adaptive masking of non-significant details, enhancing reconstruction accuracy. Meanwhile, a lightweight cross-level fusion network is proposed to reduce model parameters and computational costs to enhance spatial-spectral feature extraction. Experimental results on two benchmark datasets demonstrate the outstanding performance of the proposed method. The code will be released at SSTHyper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image reconstruction

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

引用

2024 International Telecommunications conference, ITC-Egypt 2024

作者： Noureldeen, Rana M. Elshshtawy, Rahma A. Elgohary, Omnia A. Wahba, Fatma Alzahraa A. Attia, Marco K. Abo Elazm, Zeinab B. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %. © 2024 IEEE.

关键词： Convolutional neural networks

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HL-LSM: A LSM-Tree Combined with Read Hotness and Learned Index

HL-LSM: A LSM-Tree Combined with Read Hotness and Learned In...

引用

2024 IEEE International conference on Big Data, BigData 2024

作者： Tan, Hong Li, Hui Wang, Xiaopeng Huang, Weixiang Huang, Runhuai Deng, Shaowen Wu, Jianping Peking University Shenzhen Graduate School Shenzhen China Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology China FuYao Univ. of Sci. &tech School of Computer & Ai Fuzhou China China Mobile Internet Co. Ltd Capability & Platform Business Dept Guangzhou China China Telecom Co. Ltd China Telecom Cloud Computing Beijing China China Unicom Global Limited Hong Kong China Communications Services Construction Co. Ltd GuangZhou China

ISBN: (纸本)9798350362480

Log-Structured Merge Tree (LSM-Tree) is widely employed in key-value (kv) store owing to its exceptional performance in write-intensive workloads. LSMTree appends kv pairs to memory initially, and only when the memory capacity reaches a threshold does it flush the data to disk. However, in comparison to write, read operations often entail more disk access, resulting in relatively a slower response speed. Although LSM-Tree utilizes an index structure to expedite read operations, its performance enhancement remains limited. In addition, LSM-Tree fails to consider the read hotness of each sorted string table (SSTable) during compaction, leading to significant fluctuations in performance. This paper introduces HL-LSM, a novel LSM-Tree. HL-LSM utilizes learned index to replace original index to save system overhead and improve read performance. Moreover, a lightweight read hotness statistics is devised to optimize the compaction and assess the feasibility of each SSTable in constructing a learned index. In order to evaluate the performance of HL-LSM, we constructed a system prototype, HLDB, based on WiscKey. Experiment demonstrates that in read-intensive scenarios, HLDB is able to reduce the overall system latency by nearly 10% compared to WiscKey. Furthermore, it was observed that the optimization effect of HL-LSM improves as the variance of hotness value distribution increases. © 2024 IEEE.

关键词： Compaction

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RTGA: A Redundancy-free Accelerator for high-performance Temporal Graph Neural Network Inference 24

RTGA: A Redundancy-free Accelerator for High-Performance Tem...

引用

61st ACM/IEEE Design Automation conference, DAC 2024

作者： Yu, Hui Zhang, Yu Tan, Andong Lu, Chenze Zhao, Jin Liao, Xiaofei Jin, Hai Liu, Haikun National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9798400706011

Temporal Graph Neural Network (TGNN) has attracted much research attention because it can capture the dynamic nature of complex networks. However, existing solutions suffer from redundant computation overhead and excessive off-chip communications for TGNN inference because they often rely on redundant graph sampling and repeatedly fetching the features and vertex memory. This paper proposes a redundancy-free accelerator, RTGA, for high-performance TGNN inference. Specifically, RTGA proposes a redundancy-aware execution approach with temporal tree into a novel accelerator design to effectively eliminate unnecessary data processing for fewer redundant computations and off-chip communications and also designs a temporal-aware data caching method to improve data locality for TGNN. We have implemented and evaluated RTGA on a Xilinx Alveo U280 FPGA card. Compared with cutting-edge software solutions (i.e., TGN and TGL) and hardware solutions (i.e., BlockGNN and FlowGNN), RTGA improves the performance of TGNN inference by an average of 473.2x, 87.4x, 8.2x, and 6.9x and saves energy by 542.8x, 102.2x, 9.4x, and 8.3x, respectively. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Spatio-temporal data

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Neural encoding of multidimensional handwriting movements for brain-computer interfaces 24

Neural encoding of multidimensional handwriting movements fo...

引用

11th International conference on Biomedical and Bioinformatics Engineering, ICBBE 2024

作者： Wang, Zebin Xu, Guangxiang Wang, Yueming Hao, Yaoyao The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Nanhu Brain-computer Interface Institute Hangzhou China

ISBN: (纸本)9798400718274

Neural representations of handwriting persist even years after paralysis, which was previously employed to build high performance brain-computer interfaces (BCI) for brain-to-text communication. However, handwriting was traditionally viewed as two-dimensional (2D) movements, ignoring other key kinematic and kinetic dimensions. How brain encodes these additional dimensions during handwriting and the potential of this information to enhance BCI performance were largely unexplored. Here, we recorded intracortical neural signals from a paralyzed individual engaged in handwriting imaginary and tried to align these signals with the multidimensional handwriting movements from healthy subjects. We achieved successful decoding of neural signals from the paralyzed individual, enabling reconstruction of 2D handwriting trajectories with high accuracy. We then characterized handwriting from heathy subjects as multidimensional movements which include 3D velocity of pen tip, pen grip strength, pen tip pressure on the paper, and 8-channel electromyography (EMG) on forearm. Employing a neural encoding model, we found that more variance of the neural signals could be accounted by these additional variables, suggesting brain encodes handwriting in multiple dimensions rather than solely in 2D. Together, these results shed light on the complex neural mechanisms underlying handwriting, offering new insights into the potential for improving related BCI applications. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Electromyography

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Research on high-Speed Gait Planning and Control of Quadruped Robot

Research on High-Speed Gait Planning and Control of Quadrupe...

引用

2023 IEEE International conference on Image Processing and computer Applications, ICIPCA 2023

作者： Li, Yufeng Jiang, Lei Xu, Peng Xu, Wei Liu, Yufei Xing, Boyang Yan, Tong China North Vehicle Research Institute Beijing100072 China Collective Intelligence &collaboration Laboratory Beijing100072 China

ISBN: (纸本)9798350314670

In order to improve the high-speed adaptive performance of quadruped robot in complex terrain, a high-speed motion planning and control method for quadruped robot is studied. The model predictive control method is adopted and the whole-body dynamics control strategy is integrated, two kinds of swing trajectories are planned and designed, and high-speed stable control of the robot under two kinds of gaits are achieved. Finally, the feasibility of the method is verified by experiments and the power consumption is compared for different high-speed gaits. © 2023 IEEE.

关键词： Motion planning

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Revisiting Network Coding for Warm Blob Storage 23

Revisiting Network Coding for Warm Blob Storage

引用

23rd USENIX conference on File and Storage Technologies, FAST 2025

作者： Gan, Chuang Hu, Yuchong Zhao, Leyan Zhao, Xin Gong, Pengyu Feng, Dan School of Computer Science and Technology Huazhong University of Science and Technology China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9781939133458

Minimum-storage regenerating (MSR) codes are repair-optimal erasure codes that minimize the bandwidth for repairing a failed node, while minimizing the storage redundancy necessary for fault tolerance. Recent studies in the literature, both from coding theory and systems communities, mainly examine MSR codes in systematic form, which keeps the original data blocks as part of the encoded blocks for direct access. However, systematic MSR codes manage encoded blocks at the sub-block granularity and access non-contiguous sub-blocks during repairs to achieve bandwidth optimality. Thus, their actual repair performance is impaired by non-contiguous I/Os, especially when the block size is small. In this paper, we explore how non-systematic MSR codes, which generate purely coded blocks based on random linear coding in the classical network coding theory, can improve I/O efficiency in repair for practical warm blob (binary large object) storage systems that are dominated by a large fraction of small blobs. To this end, we design NCBlob, a network-coding-based warm blob storage system that encodes small blobs non-systematic MSR codes to achieve high repair performance, while leveraging the access locality of small blobs to maintain high normal read performance. Experiments on Alibaba Cloud show that NCBlob reduces the single-block repair time by up to 45.0%, and the full-node repair time by up to 38.4%, with as low as 2.1% read throughput loss, compared with state-of-the-art systematic MSR codes. © 2025 FAST. All Rights Reserved.

关键词： Turbo codes

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Spiking high-Dimensional Information Coding Based on Wavelet Decomposition Technique 21

Spiking High-Dimensional Information Coding Based on Wavelet...

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21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： He, Liu Ao, Ren Jianping, Li College of Computer Science Chongqing University Chongqing401331 China Chongqing Academy of Educational Sciences Chongqing400015 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

This paper studies the Spiking high-dimensional information coding method based on wavelet decomposition technology. Firstly, the wavelet decomposition technique is used to extract the multi-scale features of the input signal to capture the time-frequency characteristics of the signal. Subsequently, the extracted features are mapped to the pulse sequence of the Spiking neural network to achieve efficient coding of highdimensional information. In this paper, the influence of wavelet decomposition parameters on coding performance is analyzed, and the advantages of the proposed method in processing highdimensional data are verified by experiments. The experimental results show that the proposed method can significantly reduce the data dimension and computational complexity while maintaining the accuracy of information, which provides a new effective way for high-dimensional information processing. © 2024 IEEE.

关键词： Network coding

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HAZE-Net: high-Frequency Attentive Super-Resolved Gaze Estimation in Low-Resolution Face Images 16th

HAZE-Net: High-Frequency Attentive Super-Resolved Gaze Estim...

引用

16th Asian conference on computer Vision (ACCV)

作者： Yun, Jun-Seok Na, Youngju Kim, Hee Hyeon Kim, Hyung-Il Yoo, Seok Bong Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju South Korea Elect & Telecommunicat Res Inst Daejeon South Korea

ISBN: (纸本)9783031263477;9783031263484

Although gaze estimation methods have been developed with deep learning techniques, there has been no such approach as aim to attain accurate performance in low-resolution face images with a pixel width of 50 pixels or less. To solve a limitation under the challenging low-resolution conditions, we propose a high-frequency attentive super-resolved gaze estimation network, i.e., HAZE-Net. Our network improves the resolution of the input image and enhances the eye features and those boundaries via a proposed super-resolution module based on a high-frequency attention block. In addition, our gaze estimation module utilizes high-frequency components of the eye as well as the global appearance map. We also utilize the structural location information of faces to approximate head pose. The experimental results indicate that the proposed method exhibits robust gaze estimation performance even in low-resolution face images with 28x28 pixels. The source code of this work is available at https://***/dbseorms16/HAZE Net/.

关键词： Pixels

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