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We extend Ziv and Lempel's model of finite-state encoders to the realm of lossy compression of individual sequences. In particular, the model of the encoder includes a finite-state reconstruction codebook followed by an information lossless…

Information Theory · Computer Science 2024-01-04 Neri Merhav

We consider the problem of lossless compression of individual sequences using finite-state (FS) machines, from the perspective of the best achievable empirical cumulant generating function (CGF) of the code length, i.e., the normalized…

Information Theory · Computer Science 2016-05-05 Neri Merhav

We propose and study a family of universal sequential probability assignments on individual sequences, based on the incremental parsing procedure of the Lempel-Ziv (LZ78) compression algorithm. We show that the normalized log loss under any…

Information Theory · Computer Science 2025-12-15 Naomi Sagan , Tsachy Weissman

We obtain an index of the complexity of a random sequence by allowing the role of the measure in classical probability theory to be played by a function we call the generating mechanism. Typically, this generating mechanism will be a finite…

Machine Learning · Statistics 2008-12-11 Finn Macleod , James Gleeson

In analogy to the well-known notion of finite--state compressibility of individual sequences, due to Lempel and Ziv, we define a similar notion of "finite-state encryptability" of an individual plaintext sequence, as the minimum asymptotic…

Information Theory · Computer Science 2012-01-10 Neri Merhav

The problem of guessing a random string is revisited. A close relation between guessing and compression is first established. Then it is shown that if the sequence of distributions of the information spectrum satisfies the large deviation…

Information Theory · Computer Science 2010-08-12 Manjesh Kumar Hanawal , Rajesh Sundaresan

A guessing wiretapper's performance on a Shannon cipher system is analyzed for a source with memory. Close relationships between guessing functions and length functions are first established. Subsequently, asymptotically optimal encryption…

Information Theory · Computer Science 2016-11-17 Rajesh Sundaresan

We study universal decoding over unknown discrete additive channels determined by a finite-state (unifilar) random process. Aiming at low-complexity decoders, we study variants of noise-guessing decoders that use estimators for the…

Information Theory · Computer Science 2025-07-24 Henrique K. Miyamoto , Sheng Yang

In this paper, we consider the problem of guessing a sequence subject to a distortion constraint. Specifically, we assume the following game between Alice and Bob: Alice has a sequence $\bx$ of length $n$. Bob wishes to guess $\bx$, yet he…

Information Theory · Computer Science 2021-12-28 Asaf Cohen , Neri Merhav

The unrestricted LZ78 universal data-compression algorithm (as well as the LZ77 and LZW versions) achieves asymptotically, as the block-length tends to infinity, the FS compressibility, namely the best compression-ratio that may be achieved…

Information Theory · Computer Science 2015-03-16 Jacob Ziv

The problem of guessing subject to distortion is considered, and the performance of randomized guessing strategies is investigated. A one-shot achievability bound on the guessing moment (i.e., moment of the number of required queries) is…

Information Theory · Computer Science 2021-05-27 Shigeaki Kuzuoka

We present an efficient exact algorithm for estimating state sequences from outputs (or observations) in imprecise hidden Markov models (iHMM), where both the uncertainty linking one state to the next, and that linking a state to its…

Artificial Intelligence · Computer Science 2012-10-08 Jasper De Bock , Gert de Cooman

We study sequential state discrimination measurements performed on the same qubit by subsequent observers. Specifically, we focus on the case when the observers perform a kind of a minimum-error type state discriminating measurement where…

Quantum Physics · Physics 2020-05-26 Dov Fields , Árpad Várga , János A. Bergou

We propose a new method for the calculation of the statistical properties, as e.g. the entropy, of unknown generators of symbolic sequences. The probability distribution $p(k)$ of the elements $k$ of a population can be approximated by the…

chao-dyn · Physics 2009-10-28 Thorsten Pöschel , Werner Ebeling , Helge Rosé

We consider the problem of successive-refinement coding for lossy compression of individual sequences, namely, compression in two stages, where in the first stage, a coarse description at a relatively low rate is sent from the encoder to…

Information Theory · Computer Science 2025-02-25 Neri Merhav

We investigate the problem of guessing a discrete random variable $Y$ under a privacy constraint dictated by another correlated discrete random variable $X$, where both guessing efficiency and privacy are assessed in terms of the…

Information Theory · Computer Science 2017-04-13 Shahab Asoodeh , Mario Diaz , Fady Alajaji , Tamás Linder

We introduce a random coding technique for transmission over discrete memoryless channels, reminiscent of the basic construction attaining the Gilbert-Varshamov bound for codes in Hamming spaces. The code construction is based on drawing…

Information Theory · Computer Science 2019-03-15 Anelia Somekh-Baruch , Jonathan Scarlett , Albert Guillén i Fàbregas

We consider the Shannon cipher system in the framework of individual sequences and finite-state encrypters under the metric of maximal leakage of information. A lower bound and an asymptotically matching upper bound on the leakage are…

Information Theory · Computer Science 2025-05-01 Neri Merhav

State resetting is a fundamental but often overlooked capability of simulators. It supports sample-based planning by allowing resets to previously encountered simulation states, and enables calibration of simulators using real data by…

Machine Learning · Computer Science 2025-11-27 Nan Jiang

This article considers application of genetic algorithms for finite machine synthesis. The resulting genetic finite state machines synthesis algorithm allows for creation of machines with less number of states and within shorter time. This…

Neural and Evolutionary Computing · Computer Science 2013-08-06 Andrey Bereza , Maksim Lyashov , Luis Blanco
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