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Record W2974476133 · doi:10.48550/arxiv.1909.07580

Design Theory and Some Non-simple Forbidden Configurations

2019· preprint· en· W2974476133 on OpenAlexaff
R.P. Anstee, Farzin Barekat

Bibliographic record

VenuearXiv (Cornell University) · 2019
Typepreprint
Languageen
FieldEngineering
Topicgraph theory and CDMA systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsColumn (typography)CombinatoricsPermutation (music)Matrix (chemical analysis)Simple (philosophy)MathematicsPermutation matrixPhysicsGeometryChromatographyChemistry

Abstract

fetched live from OpenAlex

Let 1_k 0_l denote the (k+l)\times 1 column of k 1's above l 0's. Let q. (1_k 0_l) $ denote the (k+l)xq matrix with q copies of the column 1_k0_l. A 2-design S_λ(2,3,v) can be defined as a vx(λ/3)\binom{v}{2} (0,1)-matrix with all column sums equal 3 and with no submatrix (λ+1).(1_20_0). Consider an mxn matrix A with all column sums in {3,4,... ,m-1}. Assume m is sufficiently large (with respect to λ) and assume that A has no submatrix which is a row permutation of (λ+1). (1_2 0_1). Then we show the number of columns in A is at most (λ)/3)\binom{m}{3} with equality for A being the columns of column sum 3 corresponding to the triples of a 2-design S_λ(2,3,m). A similar results holds for(λ+1). (1_2 0_2). Define a matrix to be simple if it is a (0,1)-matrix with no repeated columns. Given two matrices A, F, we define A to have F as a configuration if and only if some submatrix of A is a row and column permutation of F. Given m, let forb(m,q.(1_k 0_l)) denote the maximum number of possible columns in a simple m-rowed matrix which has no configuration q.(1_k 0_l). For m sufficiently large with respect to q, we compute exact values for forb(m,q.(1_1 0_1)), forb(m,q.(1_2 0_1)), forb(m,q.(1_2 0_2)). In the latter two cases, we use a construction of Dehon (1983) of simple triple systems S_λ(2,3,v) for λ>1. Moreover for l=1,2, simple mxforb(m,q.(1_2 0_l)) matrices with no configuration q.(1_2 0_l) must arise from simple 2-designs S_λ(2,3,m) of appropriate λ. The proofs derive a basic upper bound by a pigeonhole argument and then use careful counting and Turan's bound, for large m, to reduce the bound. For small m, the larger pigeonhole bounds are sometimes the exact bound. There are intermediate values of m for which we do not know the exact bound.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.165
Teacher spread0.122 · 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 teacher head, not a consensus.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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