MétaCan
Menu
Back to cohort
Record W2752188301 · doi:10.22215/etd/2017-11936

Covering Arrays from Maximal Sequences over Finite Fields

2017· preprint· en· W2752188301 on OpenAlexaff
Georgios Tzanakis

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsCarleton University
Fundersnot available
KeywordsFinite fieldMathematicsAlphabetCombinatoricsFocus (optics)Orthogonal arrayCombinatorial designComplementary sequencesDiscrete mathematicsAlgorithmPhysics

Abstract

fetched live from OpenAlex

The focus of this thesis is the study and construction of covering arrays, relying on maximal period sequences and other tools from finite fields.A covering array of strength t, denoted CA(N; t, k, v), is an N × k array with entries from an alphabet A of size v, with the property that in the N × t subarray defined by any t columns, each of the v t vectors in A t appears at least once as a row.Covering arrays generalize orthogonal arrays, which are classic combinatorial objects that have been studied extensively.Constructing covering arrays with a small rowto-column ratio is important in the design of statistical experiments, however it is also a challenging mathematical problem.Linear feedback shift register (LFSR) sequences are sequences of elements from a finite field that satisfy a linear recurrence relation.It is well-known that these are periodic; LFSR sequences that attain the maximum possible period are maximal (period) sequences, often abbreviated to m-sequences in the literature.Arrays constructed from cyclic shifts of maximal sequences possess strong combinatorial properties and have been previously used to construct orthogonal and covering arrays [62], although only one of the known constructions is for covering arrays that are not orthogonal arrays [75].In this thesis we present several new such constructions.The cornerstone of our results is a study of the combinatorial properties of arrays constructed from maximal sequences, where we make fundamental connections with concepts from diverse areas of discrete mathematics, such as orthogonal arrays, error-correcting codes, divisibility of polynomials and structures of finite geometry.One aspect of our work involves concatenating arrays corresponding to different maximal sequences and finding subarrays that are covering arrays.We express this as an optimization problem, to which we give an algorithmic solution based on backtracking, an underlying finite field theory and connections to other combinatorial objects.The results of our experiments include 37 new covering arrays of strength 4 and one of strength 5.For integers v ≥ 2, we introduce cyclic trace arrays modulo v, a variation of arrays from maximal sequences that we study using finite field characters -homomorphisms from the finite field to the unit circle of complex numbers.In particular, we use well-known bounds on character sums to derive conditions subject to which cyclic trace arrays modulo v are covering arrays, and we present new infinite families of covering arrays of strengths 3 and 4, as well as one of arbitrary strength which appears to be the second such family in the known literature [25].We also express the number of times that different vectors appear in the rows of a cyclic trace array modulo v as the solution of a linear program.iv To my parents, Maro and Nikos vi First and foremost, I would like to thank Daniel Panario.Having him as my advisor was a privilege; the guidance, opportunities and friendship that he offered will always be deeply appreciated.I am also most grateful for having met and worked with Lucia Moura and Brett Stevens.I owe this piece of work and much more to the support, knowledge and enthusiasm of these three people.I would like to extend my thanks to the members of

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.274
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicVLSI and Analog Circuit TestingFrench-language works237,207