160,554 research outputs found
Error-Correcting Data Structures
We study data structures in the presence of adversarial noise. We want to
encode a given object in a succinct data structure that enables us to
efficiently answer specific queries about the object, even if the data
structure has been corrupted by a constant fraction of errors. This new model
is the common generalization of (static) data structures and locally decodable
error-correcting codes. The main issue is the tradeoff between the space used
by the data structure and the time (number of probes) needed to answer a query
about the encoded object. We prove a number of upper and lower bounds on
various natural error-correcting data structure problems. In particular, we
show that the optimal length of error-correcting data structures for the
Membership problem (where we want to store subsets of size s from a universe of
size n) is closely related to the optimal length of locally decodable codes for
s-bit strings.Comment: 15 pages LaTeX; an abridged version will appear in the Proceedings of
the STACS 2009 conferenc
Efficient and Error-Correcting Data Structures for Membership and Polynomial Evaluation
We construct efficient data structures that are resilient against a constant
fraction of adversarial noise. Our model requires that the decoder answers most
queries correctly with high probability and for the remaining queries, the
decoder with high probability either answers correctly or declares "don't
know." Furthermore, if there is no noise on the data structure, it answers all
queries correctly with high probability. Our model is the common generalization
of a model proposed recently by de Wolf and the notion of "relaxed locally
decodable codes" developed in the PCP literature.
We measure the efficiency of a data structure in terms of its length,
measured by the number of bits in its representation, and query-answering time,
measured by the number of bit-probes to the (possibly corrupted)
representation. In this work, we study two data structure problems: membership
and polynomial evaluation. We show that these two problems have constructions
that are simultaneously efficient and error-correcting.Comment: An abridged version of this paper appears in STACS 201
A Coding Theoretic Study on MLL proof nets
Coding theory is very useful for real world applications. A notable example
is digital television. Basically, coding theory is to study a way of detecting
and/or correcting data that may be true or false. Moreover coding theory is an
area of mathematics, in which there is an interplay between many branches of
mathematics, e.g., abstract algebra, combinatorics, discrete geometry,
information theory, etc. In this paper we propose a novel approach for
analyzing proof nets of Multiplicative Linear Logic (MLL) by coding theory. We
define families of proof structures and introduce a metric space for each
family. In each family, 1. an MLL proof net is a true code element; 2. a proof
structure that is not an MLL proof net is a false (or corrupted) code element.
The definition of our metrics reflects the duality of the multiplicative
connectives elegantly. In this paper we show that in the framework one
error-detecting is possible but one error-correcting not. Our proof of the
impossibility of one error-correcting is interesting in the sense that a proof
theoretical property is proved using a graph theoretical argument. In addition,
we show that affine logic and MLL + MIX are not appropriate for this framework.
That explains why MLL is better than such similar logics.Comment: minor modification
Improved Explicit Data Structures in the Bit-Probe Model Using Error-Correcting Codes
We consider the bit-probe complexity of the set membership problem: represent an n-element subset S of an m-element universe as a succinct bit vector so that membership queries of the form "Is x ? S" can be answered using at most t probes into the bit vector. Let s(m,n,t) (resp. s_N(m,n,t)) denote the minimum number of bits of storage needed when the probes are adaptive (resp. non-adaptive). Lewenstein, Munro, Nicholson, and Raman (ESA 2014) obtain fully-explicit schemes that show that
s(m,n,t) = ?((2^t-1)m^{1/(t - min{2?log n?, n-3/2})}) for n ? 2,t ? ?log n?+1 .
In this work, we improve this bound when the probes are allowed to be superlinear in n, i.e., when t ? ?(nlog n), n ? 2, we design fully-explicit schemes that show that
s(m,n,t) = ?((2^t-1)m^{1/(t-{n-1}/{2^{t/(2(n-1))}})}),
asymptotically (in the exponent of m) close to the non-explicit upper bound on s(m,n,t) derived by Radhakrishan, Shah, and Shannigrahi (ESA 2010), for constant n.
In the non-adaptive setting, it was shown by Garg and Radhakrishnan (STACS 2017) that for a large constant n?, for n ? n?, s_N(m,n,3) ? ?{mn}. We improve this result by showing that the same lower bound holds even for storing sets of size 2, i.e., s_N(m,2,3) ? ?(?m)
Syntactic Structures and Code Parameters
We assign binary and ternary error-correcting codes to the data of syntactic
structures of world languages and we study the distribution of code points in
the space of code parameters. We show that, while most codes populate the lower
region approximating a superposition of Thomae functions, there is a
substantial presence of codes above the Gilbert-Varshamov bound and even above
the asymptotic bound and the Plotkin bound. We investigate the dynamics induced
on the space of code parameters by spin glass models of language change, and
show that, in the presence of entailment relations between syntactic parameters
the dynamics can sometimes improve the code. For large sets of languages and
syntactic data, one can gain information on the spin glass dynamics from the
induced dynamics in the space of code parameters.Comment: 14 pages, LaTeX, 12 png figure
A correction for regression discontinuity designs with group-specific mismeasurement of the running variable
When the running variable in a regression discontinuity (RD) design is measured with error, identification of the local average treatment effect of interest will typically fail. While the form of this measurement error varies across applications, in many cases the measurement error structure is heterogeneous across different groups of observations. We develop a novel measurement error correction procedure capable of addressing heterogeneous mismeasurement structures by leveraging auxiliary information. We also provide adjusted asymptotic variance and standard errors that take into consideration the variability introduced by the estimation of nuisance parameters, and honest confidence intervals that account for potential misspecification. Simulations provide evidence that the proposed procedure corrects the bias introduced by heterogeneous measurement error and achieves empirical coverage closer to nominal test size than “naive” alternatives. Two empirical illustrations demonstrate that correcting for measurement error can either reinforce the results of a study or provide a new empirical perspective on the data
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