Results 321 to 330 of about 22,970,744 (375)

Data Compression:

Nine Algorithms That Changed the Future, 2020
2. Saywood’s book, chapter 2, question #4. Suppose we have a source with a probability model P={p0, p1, ..., pm} and entropy HP. Suppose we have another source with probability model Q={q0, q1, ..., qm} and entropy HQ, where qi = pi, i = 0, 1, ..., j-2 ...

semanticscholar   +1 more source

LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression

Annual Meeting of the Association for Computational Linguistics
This paper focuses on task-agnostic prompt compression for better generalizability and efficiency. Considering the redundancy in natural language, existing approaches compress prompts by removing tokens or lexical units according to their information ...
Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, Qingwei Lin, Victor Rühle, Yuqing Yang, Chin-Yew Lin, H. V. Zhao, Lili Qiu, Dongmei Zhang, K. Cobbe, Vineet Kosaraju, Mo Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano   +22 more
semanticscholar   +1 more source

Quantum data compression by principal component analysis

Quantum Information Processing, 2018
Data compression can be achieved by reducing the dimensionality of high-dimensional but approximately low-rank datasets, which may in fact be described by the variation of a much smaller number of parameters.
Chao-Hua Yu, F. Gao, Song Lin, Jingbo Wang   +3 more
semanticscholar   +1 more source

Compression and data mining

2015 International Conference on Computing, Networking and Communications (ICNC), 2015
Data compression plays an important role in data mining in assessing the minability of data and a modality of evaluating similarities between complex objects. We focus on compressibility of strings of symbols and on using compression in computing similarity in text corpora; also we propose a novel approach for assessing the quality of text ...
Ping Chen, Dan A. Simovici, Tong Wang, Dan Pletea   +3 more
openaire   +3 more sources

Identification via compressed data

Proceedings of 1994 Workshop on Information Theory and Statistics, 1997
Summary: A new coding problem is introduced for a correlated source \((X^n,Y^n)_{n=1}^\infty\). The observer of \(X^n\) can transmit data depending on \(X^n\) at a prescribed rate \(R\). Based on these data the observer of \(Y^n\) tries to identify whether for some distortion measure \(\rho\) (like the Hamming distance) \(n^{-1}\rho (X^n,Y^n)\leq d ...
Ahlswede, Rudolf, Yang, En-Hui, Zhang, Zhen   +2 more
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Data compression of stereopairs

IEEE Transactions on Communications, 1992
Two fundamentally different techniques for compressing stereopairs are discussed. The first technique, called disparity-compensated transform-domain predictive coding, attempts to minimize the mean-square error between the original stereopair and the compressed stereopair.
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Data Compression

Compare Huffman coding and DEFLATE to understand their efficiency in compressing different types of text data.
Tyler Estro, Erez Zadok
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Lossless Compression of Cytometric Data

Cytometry Part A, 2019
AbstractNowadays, most cytometrists apply lossless compression by storing their FCS files in ZIP archives. Unfortunately, ZIP only achieves modest space savings in cytometric data, due to DEFLATE being used as the underlying lossless compression algorithm (LCA).
Anne E. Bras, Vincent H. J. van der Velden   +1 more
openaire   +4 more sources

EEG data compression techniques

IEEE Transactions on Biomedical Engineering, 1997
In this paper, electroencephalograph (EEG) and Holter EEG data compression techniques which allow perfect reconstruction of the recorded waveform from the compressed one are presented and discussed. Data compression permits one to achieve significant reduction in the space required to store signals and in transmission time. The Huffman coding technique
G. Antoniol, Tonella, Paolo
openaire   +3 more sources

ECG Data Compression by Modeling

Computers and Biomedical Research, 1993
This paper presents a novel algorithm for data compression of single lead Electrocardiogram (ECG) data. The method is based on Parametric modeling of the Discrete Cosine Transformed ECG signal. Improved high frequency reconstruction is achieved by separately modeling the low and the high frequency regions of the transformed signal.
I. S. N. Murthy, Budagavi Madhukar
openaire   +3 more sources