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Record W2609364824 · doi:10.19086/da.4438

On the number of points in general position in the plane

2018· article· en· W2609364824 on OpenAlexfundno aff

Bibliographic record

VenueDiscrete Analysis · 2018
Typearticle
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Illinois at Urbana-ChampaignNational Science Foundation
KeywordsGeneral positionCliqueCollinearityType (biology)Plane (geometry)Upper and lower boundsPosition (finance)Discrete geometry

Abstract

fetched live from OpenAlex

On the number of points in general position in the plane, Discrete Analysis 2018:16, 20 pp. A recurring theme in combinatorics is questions of the following kind. Suppose that we have a combinatorial structure $S$ of size $n$ that contains no object of type $A$. Then how large a subset of $S$ can we find that contains no object of type $B$? For example, a graph with $n$ vertices that contains no clique of size 4 can be shown quite easily to have a triangle-free subgraph with $n^{1/2}$ vertices, and Wolfovitz has shown that there are graphs with no clique of size 4 and no triangle-free subgraph with more than $n^{1/2}(\log n)^{120}$ vertices. One of the main questions discussed in this paper is perhaps the first question of this type one would think of in discrete geometry: if $S$ is a set of $n$ points in the plane and if no four of these points are collinear, then how large a subset of $S$ can one find with no three collinear? A bound of $o(n)$ follows from the density Hales-Jewett theorem, which implies that a subset of $\{1,2,3\}^k$ of positive density contains three points in a line. It is not hard to project the set $\{1,2,3\}$ into the plane in such a way that collinearity is preserved, but no four points of the image lie in a line. However, the bound obtained this way is very weak -- roughly $n/\log_*(n)$. This paper obtains the first reasonable bound for the problem, namely $n^{5/6+o(1)}$. It is not clear whether 5/6 is the right exponent, but the authors suggest that their construction may be close to optimal and that the difficulty is to calculate the correct exponent for that example. Perhaps the most interesting aspect of the paper is that it uses the so-called method of containers. This method, developed by Saxton and Thomason, and independently by Balogh, Morris and Samotij, has already been used to solve a large number of important problems, but this appears to be the first time it has been used to solve a problem in discrete geometry, and it is used in a novel way. They also use containers to prove a second discrete geometry result, this time about epsilon-nets. Given a family $\mathcal F$ of subsets of a finite set $X$, an $\epsilon$-net $E$ of $\mathcal F$ is a subset $E$ of $X$ such that every $F\in\mathcal F$ of size at least $\epsilon|X|$ contains an element of $E$. There are many interesting questions about the sizes of $\epsilon$-nets when $X$ is a geometrical set such as a finite set of points in the plane, and $\mathcal F$ is some natural class of subsets such as the set of all intersections of $X$ with convex bodies. With this example, one can also define a _weak_ $\epsilon$-net as follows: it is a set of points $E$ in the plane, not necessarily a subset of $X$, such that every convex hull of at least $\epsilon|X|$ points of $X$ contains a point of $E$. Natural notions of weak $\epsilon$-nets can be defined in many other contexts too. An interesting open question, asked by Noga Alon, is whether there is some natural geometrically defined family $\mathbb F$ of bounded VC-dimension such that the smallest $\epsilon$-net has size at least $(c/\epsilon)\log(1/\epsilon)$. Also using the density Hales-Jewett theorem, Alon obtained a bound that was very slightly superlinear in the case where $X$ was a certain point set and $\mathcal F$ was the set of all intersections of lines with $X$. In this paper, Alon's bound is improved to $(1/\epsilon)\log(1/\epsilon)^{1/3-o(1)}$, which is much closer to the bound he suggests might be obtainable. They also obtain an improved bound for weak $\epsilon$-nets, but with a power of $\log\log(1/\epsilon)$ replacing the power of $\log(1/\epsilon)$. This construction has the additional feature that it works just as well in the projective plane.

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)0.004
metaresearch head score (Gemma)0.040
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: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.040
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0050.004
Science and technology studies0.0050.009
Scholarly communication0.0090.022
Open science0.0070.007
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0180.002

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 head0.011
GPT teacher head0.278
Teacher spread0.267 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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