552 research outputs found

### Finite-State Dimension and Lossy Decompressors

This paper examines information-theoretic questions regarding the difficulty
of compressing data versus the difficulty of decompressing data and the role
that information loss plays in this interaction. Finite-state compression and
decompression are shown to be of equivalent difficulty, even when the
decompressors are allowed to be lossy.
Inspired by Kolmogorov complexity, this paper defines the optimal
*decompression *ratio achievable on an infinite sequence by finite-state
decompressors (that is, finite-state transducers outputting the sequence in
question). It is shown that the optimal compression ratio achievable on a
sequence S by any *information lossless* finite state compressor, known as the
finite-state dimension of S, is equal to the optimal decompression ratio
achievable on S by any finite-state decompressor. This result implies a new
decompression characterization of finite-state dimension in terms of lossy
finite-state transducers.Comment: We found that Theorem 3.11, which was basically the motive for this
paper, was already proven by Sheinwald, Ziv, and Lempel in 1991 and 1995
paper

### Depth, Highness and DNR degrees

We study Bennett deep sequences in the context of recursion theory; in
particular we investigate the notions of O(1)-deepK, O(1)-deepC , order-deep K
and order-deep C sequences. Our main results are that Martin-Loef random sets
are not order-deepC , that every many-one degree contains a set which is not
O(1)-deepC , that O(1)-deepC sets and order-deepK sets have high or DNR Turing
degree and that no K-trival set is O(1)-deepK.Comment: journal version, dmtc

### Dimensions of Copeland-Erdos Sequences

The base-$k$ {\em Copeland-Erd\"os sequence} given by an infinite set $A$ of
positive integers is the infinite sequence \CE_k(A) formed by concatenating
the base-$k$ representations of the elements of $A$ in numerical order. This
paper concerns the following four quantities.
The {\em finite-state dimension} \dimfs (\CE_k(A)), a finite-state version
of classical Hausdorff dimension introduced in 2001.
The {\em finite-state strong dimension} \Dimfs(\CE_k(A)), a finite-state
version of classical packing dimension introduced in 2004. This is a dual of
\dimfs(\CE_k(A)) satisfying \Dimfs(\CE_k(A)) \geq \dimfs(\CE_k(A)).
The {\em zeta-dimension} \Dimzeta(A), a kind of discrete fractal dimension
discovered many times over the past few decades.
The {\em lower zeta-dimension} \dimzeta(A), a dual of \Dimzeta(A)
satisfying \dimzeta(A)\leq \Dimzeta(A).
We prove the following.
\dimfs(\CE_k(A))\geq \dimzeta(A). This extends the 1946 proof by Copeland
and Erd\"os that the sequence \CE_k(\mathrm{PRIMES}) is Borel normal.
\Dimfs(\CE_k(A))\geq \Dimzeta(A).
These bounds are tight in the strong sense that these four quantities can
have (simultaneously) any four values in $[0,1]$ satisfying the four
above-mentioned inequalities.Comment: 19 page

### Martingale families and dimension in P

AbstractWe introduce a new measure notion on small complexity classes (called F-measure), based on martingale families, that gets rid of some drawbacks of previous measure notions: it can be used to define dimension because martingale families can make money on all strings, and it yields random sequences with an equal frequency of 0â€™s and 1â€™s. On larger complexity classes (E and above), F-measure is equivalent to Lutz resource-bounded measure. As applications to F-measure, we answer a question raised in [E. Allender, M. Strauss, Measure on small complexity classes, with application for BPP, in: Proc. of the 35th Ann. IEEE Symp. on Found. of Comp. Sci., 1994, pp. 807â€“818] by improving their result to: for almost every language A decidable in subexponential time, PA=BPPA. We show that almost all languages in PSPACEÂ do not have small non-uniform complexity. We compare F-measure to previous notions and prove that martingale families are strictly stronger than Î“-measure [E. Allender, M. Strauss, Measure on small complexity classes, with application for BPP, in: Proc. of the 35th Ann. IEEE Symp. on Found. of Comp. Sci., 1994, pp. 807â€“818], we also discuss the limitations of martingale families concerning finite unions. We observe that all classes closed under polynomial many-one reductions have measure zero in EXPÂ iff they have measure zero in SUBEXP. We use martingale families to introduce a natural generalization of Lutz resource-bounded dimension [J.H. Lutz, Dimension in complexity classes, in: Proceedings of the 15th Annual IEEE Conference on Computational Complexity, 2000, pp. 158â€“169] on P, which meets the intuition behind Lutzâ€™s notion. We show that P-dimension lies between finite-state dimension and dimension on E. We prove an analogue of a Theorem of Eggleston in P, i.e. the class of languages whose characteristic sequence contains 1â€™s with frequency Î±, has dimension the Shannon entropy of Î± in P

### Resource-bounded Measure on Probabilistic Classes

We extend Lutzâ€™s resource-bounded measure to probabilistic classes, and obtain
notions of resource-bounded measure on probabilistic complexity classes such as
BPE and BPEXP. Unlike former attempts, our resource bounded measure notions
satisfy all three basic measure properties, that is every singleton {L} has measure
zero, the whole space has measure one, and "enumerable infinite unions" of measure
zero sets have measure zero

### Baire categories on small complexity classes and meagerâ€“comeager laws

We introduce two resource-bounded Baire category notions on small complexity classes such as P, QUASIPOLY, SUBEXP and PSPACE and on probabilistic classes such as BPP, which differ on how the corresponding finite extension strategies are computed. We give an alternative characterization of small sets via resource-bounded Banach-Mazur games. As an application of the first notion, we show that for almost every language A (i.e. all except a meager class) computable in subexponential time, PA = BPPA. We also show that almost all languages in PSPACE do not have small nonuniform complexity. We then switch to the second Baire category notion (called locally-computable), and show that the class SPARSE is meager in P. We show that in contrast to the resource-bounded measure case, meagerâ€“comeager laws can be obtained for many standard complexity classes, relative to locally-computable Baire category on BPP and PSPACE. Another topic where locally-computable Baire categories differ from resource-bounded measure is regarding weak-completeness: we show that there is no weak-completeness notion in P based on locally-computable Baire categories, i.e. every P-weakly-complete set is complete for P. We also prove that the class of complete sets for P under Turing-logspace reductions is meager in P, if P is not equal to DSPACE (log n), and that the same holds unconditionally for QUASIPOLY. Finally we observe that locally-computable Baire categories are incomparable with all existing resource-bounded measure notions on small complexity classes, which might explain why those two settings seem to differ so fundamentally

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