34,682 research outputs found
Semi-Transitive Orientations and Word-Representable Graphs
A graph is a \emph{word-representable graph} if there exists a word
over the alphabet such that letters and alternate in if and
only if for each .
In this paper we give an effective characterization of word-representable
graphs in terms of orientations. Namely, we show that a graph is
word-representable if and only if it admits a \emph{semi-transitive
orientation} defined in the paper. This allows us to prove a number of results
about word-representable graphs, in particular showing that the recognition
problem is in NP, and that word-representable graphs include all 3-colorable
graphs.
We also explore bounds on the size of the word representing the graph. The
representation number of is the minimum such that is a
representable by a word, where each letter occurs times; such a exists
for any word-representable graph. We show that the representation number of a
word-representable graph on vertices is at most , while there exist
graphs for which it is .Comment: arXiv admin note: text overlap with arXiv:0810.031
The Pagoda Sequence: a Ramble through Linear Complexity, Number Walls, D0L Sequences, Finite State Automata, and Aperiodic Tilings
We review the concept of the number wall as an alternative to the traditional
linear complexity profile (LCP), and sketch the relationship to other topics
such as linear feedback shift-register (LFSR) and context-free Lindenmayer
(D0L) sequences. A remarkable ternary analogue of the Thue-Morse sequence is
introduced having deficiency 2 modulo 3, and this property verified via the
re-interpretation of the number wall as an aperiodic plane tiling
A study and experiment plan for digital mobile communication via satellite
The viability of mobile communications is examined within the context of a frequency division multiple access, single channel per carrier satellite system emphasizing digital techniques to serve a large population of users. The intent is to provide the mobile users with a grade of service consistant with the requirements for remote, rural (perhaps emergency) voice communications, but which approaches toll quality speech. A traffic model is derived on which to base the determination of the required maximum number of satellite channels to provide the anticipated level of service. Various voice digitalization and digital modulation schemes are reviewed along with a general link analysis of the mobile system. Demand assignment multiple access considerations and analysis tradeoffs are presented. Finally, a completed configuration is described
Sparse approaches for the exact distribution of patterns in long state sequences generated by a Markov source
We present two novel approaches for the computation of the exact distribution
of a pattern in a long sequence. Both approaches take into account the sparse
structure of the problem and are two-part algorithms. The first approach relies
on a partial recursion after a fast computation of the second largest
eigenvalue of the transition matrix of a Markov chain embedding. The second
approach uses fast Taylor expansions of an exact bivariate rational
reconstruction of the distribution. We illustrate the interest of both
approaches on a simple toy-example and two biological applications: the
transcription factors of the Human Chromosome 5 and the PROSITE signatures of
functional motifs in proteins. On these example our methods demonstrate their
complementarity and their hability to extend the domain of feasibility for
exact computations in pattern problems to a new level
Revisiting LFSMs
Linear Finite State Machines (LFSMs) are particular primitives widely used in
information theory, coding theory and cryptography. Among those linear
automata, a particular case of study is Linear Feedback Shift Registers (LFSRs)
used in many cryptographic applications such as design of stream ciphers or
pseudo-random generation. LFSRs could be seen as particular LFSMs without
inputs.
In this paper, we first recall the description of LFSMs using traditional
matrices representation. Then, we introduce a new matrices representation with
polynomial fractional coefficients. This new representation leads to sparse
representations and implementations. As direct applications, we focus our work
on the Windmill LFSRs case, used for example in the E0 stream cipher and on
other general applications that use this new representation.
In a second part, a new design criterion called diffusion delay for LFSRs is
introduced and well compared with existing related notions. This criterion
represents the diffusion capacity of an LFSR. Thus, using the matrices
representation, we present a new algorithm to randomly pick LFSRs with good
properties (including the new one) and sparse descriptions dedicated to
hardware and software designs. We present some examples of LFSRs generated
using our algorithm to show the relevance of our approach.Comment: Submitted to IEEE-I
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching
Personalization in marketing aims at improving the shopping experience of
customers by tailoring services to individuals. In order to achieve this,
businesses must be able to make personalized predictions regarding the next
purchase. That is, one must forecast the exact list of items that will comprise
the next purchase, i.e., the so-called market basket. Despite its relevance to
firm operations, this problem has received surprisingly little attention in
prior research, largely due to its inherent complexity. In fact,
state-of-the-art approaches are limited to intuitive decision rules for pattern
extraction. However, the simplicity of the pre-coded rules impedes performance,
since decision rules operate in an autoregressive fashion: the rules can only
make inferences from past purchases of a single customer without taking into
account the knowledge transfer that takes place between customers. In contrast,
our research overcomes the limitations of pre-set rules by contributing a novel
predictor of market baskets from sequential purchase histories: our predictions
are based on similarity matching in order to identify similar purchase habits
among the complete shopping histories of all customers. Our contributions are
as follows: (1) We propose similarity matching based on subsequential dynamic
time warping (SDTW) as a novel predictor of market baskets. Thereby, we can
effectively identify cross-customer patterns. (2) We leverage the Wasserstein
distance for measuring the similarity among embedded purchase histories. (3) We
develop a fast approximation algorithm for computing a lower bound of the
Wasserstein distance in our setting. An extensive series of computational
experiments demonstrates the effectiveness of our approach. The accuracy of
identifying the exact market baskets based on state-of-the-art decision rules
from the literature is outperformed by a factor of 4.0.Comment: Accepted for oral presentation at 25th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD 2019
Fast algorithm for border bases of Artinian Gorenstein algebras
Given a multi-index sequence , we present a new efficient algorithm
to compute generators of the linear recurrence relations between the terms of
. We transform this problem into an algebraic one, by identifying
multi-index sequences, multivariate formal power series and linear functionals
on the ring of multivariate polynomials. In this setting, the recurrence
relations are the elements of the kerne l\sigma of the Hankel operator
$H$\sigma associated to . We describe the correspondence between
multi-index sequences with a Hankel operator of finite rank and Artinian
Gorenstein Algebras. We show how the algebraic structure of the Artinian
Gorenstein algebra \sigma\sigma yields the
structure of the terms $\sigma\alpha N nAK[x 1 ,. .. , xnIHIA$ and the tables of multiplication by the variables in these
bases. It is an extension of Berlekamp-Massey-Sakata (BMS) algorithm, with
improved complexity bounds. We present applications of the method to different
problems such as the decomposition of functions into weighted sums of
exponential functions, sparse interpolation, fast decoding of algebraic codes,
computing the vanishing ideal of points, and tensor decomposition. Some
benchmarks illustrate the practical behavior of the algorithm
Expressive Completeness of Existential Rule Languages for Ontology-based Query Answering
Existential rules, also known as data dependencies in Databases, have been
recently rediscovered as a promising family of languages for Ontology-based
Query Answering. In this paper, we prove that disjunctive embedded dependencies
exactly capture the class of recursively enumerable ontologies in
Ontology-based Conjunctive Query Answering (OCQA). Our expressive completeness
result does not rely on any built-in linear order on the database. To establish
the expressive completeness, we introduce a novel semantic definition for OCQA
ontologies. We also show that neither the class of disjunctive tuple-generating
dependencies nor the class of embedded dependencies is expressively complete
for recursively enumerable OCQA ontologies.Comment: 10 pages; the full version of a paper to appear in IJCAI 2016.
Changes (regarding to v1): a new reference has been added, and some typos
have been correcte
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