Výsledky vyhledávání - Data Compression. Algorithms. Data structures

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  1. 1

    Data structures and compression algorithms for genomic sequence data Autor Brandon, Marty C., Wallace, Douglas C., Baldi, Pierre

    ISSN: 1367-4803, 1367-4811, 1460-2059, 1367-4811
    Vydáno: Oxford Oxford University Press 15.07.2009
    Vydáno v Bioinformatics (15.07.2009)
    “…, but also for the storage, navigation and privacy of genomic data. Here, we develop data structures and algorithms for the efficient storage of genomic and other sequence data that may also facilitate querying and protecting the data. Results…”
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  2. 2

    Data structures and compression algorithms for high-throughput sequencing technologies Autor Daily, Kenny, Rigor, Paul, Christley, Scott, Xie, Xiaohui, Baldi, Pierre

    ISSN: 1471-2105, 1471-2105
    Vydáno: London BioMed Central 14.10.2010
    Vydáno v BMC bioinformatics (14.10.2010)
    “… Results We develop data structures and compression algorithms for HTS data. A processing stage maps short sequences to a reference genome or a large table of sequences…”
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  3. 3

    SpZip: Architectural Support for Effective Data Compression In Irregular Applications Autor Yang, Yifan, Emer, Joel S., Sanchez, Daniel

    ISSN: 2575-713X
    Vydáno: IEEE 01.06.2021
    “… applications.We present SpZip, an architectural approach that makes data compression practical for irregular algorithms…”
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  4. 4

    Faster and Stronger Lossless Compression with Optimized Autoregressive Framework Autor Mao, Yu, Li, Jingzong, Cui, Yufei, Xue, Jason Chun

    Vydáno: IEEE 09.07.2023
    “…Neural AutoRegressive (AR) framework has been applied in general-purpose lossless compression recently to improve compression performance…”
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  5. 5

    NRGC: a novel referential genome compression algorithm Autor Saha, Subrata, Rajasekaran, Sanguthevar

    ISSN: 1367-4803, 1367-4811, 1367-4811
    Vydáno: England Oxford University Press 15.11.2016
    Vydáno v Bioinformatics (Oxford, England) (15.11.2016)
    “… Data compression techniques are one of the most…”
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  6. 6

    SNAPPIX: Efficient-Coding-Inspired In-Sensor Compression for Edge Vision Autor Lin, Weikai, Ma, Tianrui, Boloor, Adith, Feng, Yu, Xing, Ruofan, Zhang, Xuan, Zhu, Yuhao

    Vydáno: IEEE 22.06.2025
    “…Energy-efficient image acquisition on the edge is crucial for enabling remote sensing applications where the sensor node has weak compute capabilities and must transmit data to a remote server/cloud for processing…”
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  7. 7

    ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression Autor Liu, Guangda, Li, Chengwei, Zhao, Jieru, Zhang, Chenqi, Guo, Minyi

    Vydáno: IEEE 22.06.2025
    “…) cache and increased latency due to extensive memory accesses. Recent works have proposed compressing KV cache to approximate computation, but these methods either…”
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  8. 8

    Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs Autor Mao, Yu, Li, Jingzong, Wang, Jun, Xu, Hong, Kuo, Tei-Wei, Guan, Nan, Xue, Chun Jason

    Vydáno: IEEE 22.06.2025
    “…Neural image compression, necessary in various machine-to-machine communication scenarios, suffers from its heavy encode-decode structures and inflexibility in switching between different compression levels…”
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  9. 9

    Efficient and robust search of microbial genomes via phylogenetic compression Autor Břinda, Karel, Lima, Leandro, Pignotti, Simone, Quinones-Olvera, Natalia, Salikhov, Kamil, Chikhi, Rayan, Kucherov, Gregory, Iqbal, Zamin, Baym, Michael

    ISSN: 1548-7105, 1548-7105
    Vydáno: United States 01.04.2025
    Vydáno v Nature methods (01.04.2025)
    “… algorithms and data structures. We show that, when applied to modern diverse collections approaching millions of genomes, lossless phylogenetic compression improves the compression ratios of assemblies, de Bruijn graphs and k-mer…”
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  10. 10

    MILLION: MasterIng Long-Context LLM Inference Via Outlier-Immunized KV Product QuaNtization Autor Wang, Zongwu, Xu, Peng, Liu, Fangxin, Hu, Yiwei, Sun, Qingxiao, Li, Gezi, Li, Cheng, Wang, Xuan, Jiang, Li, Guan, Haibing

    Vydáno: IEEE 22.06.2025
    “… KV cache mechanism alleviates this issue by storing pre-computed data, but introduces memory requirements that scale linearly with context length, hindering efficient LLM deployment…”
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  11. 11

    KVO-LLM: Boosting Long-Context Generation Throughput for Batched LLM Inference Autor Li, Zhenyu, Lyu, Dongxu, Wang, Gang, Chen, Yuzhou, Chen, Liyan, Li, Wenjie, Jiang, Jianfei, Sun, Yanan, He, Guanghui

    Vydáno: IEEE 22.06.2025
    “… cache compression algorithms struggle with hardware inefficiency or significant accuracy degradation…”
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  12. 12

    DuQTTA: Dual Quantized Tensor-Train Adaptation with Decoupling Magnitude-Direction for Efficient Fine-Tuning of LLMs Autor Dong, Haoyan, Chen, Hai-Bao, Chang, Jingjing, Yang, Yixin, Gao, Ziyang, Ji, Zhigang, Wang, Runsheng, Huang, Ru

    Vydáno: IEEE 22.06.2025
    “…Recent parameter-efficient fine-tuning (PEFT) techniques have enabled large language models (LLMs) to be efficiently fine-tuned for specific tasks, while…”
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  13. 13

    NDSEARCH: Accelerating Graph-Traversal-Based Approximate Nearest Neighbor Search through Near Data Processing Autor Wang, Yitu, Li, Shiyu, Zheng, Qilin, Song, Linghao, Li, Zongwang, Chang, Andrew, Li, Hai lHelenr, Chen, Yiran

    Vydáno: IEEE 29.06.2024
    “…Approximate nearest neighbor search (ANNS) is a key retrieval technique for vector database and many data center applications, such as person re-identification and recommendation systems…”
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  14. 14

    PISA: Efficient Precision-Slice Framework for LLMs with Adaptive Numerical Type Autor Yang, Ning, Wang, Zongwu, Sun, Qingxiao, Lu, Liqiang, Liu, Fangxin

    Vydáno: IEEE 22.06.2025
    “… Previous efforts addressed this issue by employing compression-based encoding for mixed-precision quantization…”
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  15. 15

    SHM data compression and reconstruction based on IGWO-OMP algorithm Autor Zhang, Longguan, Jia, Junfeng, Bai, Yulei, Du, Xiuli, Lin, Ping, Guo, He

    ISSN: 0141-0296
    Vydáno: Elsevier Ltd 01.09.2024
    Vydáno v Engineering structures (01.09.2024)
    “…) algorithm for SHM data compression and reconstruction. The effectiveness of the proposed algorithm was validated using four SHM datasets including ultrasonic guided wave (UGW…”
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  16. 16

    Seprox: Sequence-based Approximations for Compressing Ultra-Low Precision Deep Neural Networks Autor Parvathy, Aradhana Mohan, Krithivasan, Sarada, Sen, Sanchari, Raghunathan, Anand

    ISSN: 1558-2434
    Vydáno: ACM 29.10.2022
    “…Compression techniques such as quantization and pruning are indispensable for deploying state-of-the-art Deep Neural Networks (DNNs…”
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  17. 17

    Processing-In-Hierarchical-Memory Architecture for Billion-Scale Approximate Nearest Neighbor Search Autor Zhu, Zhenhua, Liu, Jun, Dai, Guohao, Zeng, Shulin, Li, Bing, Yang, Huazhong, Wang, Yu

    Vydáno: IEEE 09.07.2023
    “… Because of the irregular and large-volume data access, existing CPU-based systems suffer from heavy data movements when dealing with graph-based ANNS algorithms…”
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  18. 18

    On The Efficiency of Sparse-Tiled Tensor Graph Processing For Low Memory Usage Autor Cipolletta, Antonio, Calimera, Andrea

    Vydáno: IEEE 05.12.2021
    “… Even though many data-driven compression pipelines have proven their efficacy, this work shows there is still room for optimization at the intersection with compute-oriented optimizations…”
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  19. 19

    Behavioral Study of Data Structures on Lempel Ziv Welch (LZW) Data Compression Algorithm and ITS Computational Complexity Autor Nishad, P. M., Chezian, R. Manika

    Vydáno: IEEE 01.03.2014
    “… The LZW algorithm uses dictionary while decoding and encoding. The computational and space complexity of LZW data compression algorithm is purely depends on the effective implementation of data structure…”
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  20. 20

    On system behaviour using complex networks of a compression algorithm Autor Walker, David M, Correa, Debora C, Small, Michael

    ISSN: 1089-7682, 1089-7682
    Vydáno: United States 01.01.2018
    Vydáno v Chaos (Woodbury, N.Y.) (01.01.2018)
    “…We construct complex networks of scalar time series using a data compression algorithm…”
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