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Record W3083911802 · doi:10.1214/20-ejp507

On the boundary local time measure of super-Brownian motion

2020· article· en· W3083911802 on OpenAlexafffund
Jieliang Hong

Bibliographic record

VenueElectronic Journal of Probability · 2020
Typearticle
Languageen
FieldMathematics
TopicStochastic processes and statistical mechanics
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsMathematicsMeasure (data warehouse)Local timeBrownian motionBoundary (topology)Mathematical analysisReflected Brownian motionMotion (physics)Geometric Brownian motionDiffusion processClassical mechanicsStatisticsData mining

Abstract

fetched live from OpenAlex

In [9] the Hausdorff dimension, $d_{f}$, of $\partial \mathcal {R}$, the topological boundary of the range of super-Brownian motion for dimensions $d=2,3$ was found; $d_{f}=4-2\sqrt {2}$ if $d=2$, and $d_{f}=(9-\sqrt {17})/2$ if $d=3$. We will refine these dimension estimates in a number of ways. If $L^{x}$ is the total occupation local time of $d$-dimensional super-Brownian motion, $X$, for $d=2$ and $d=3$, we construct a random measure $\mathcal {L}$, called the boundary local time measure, as a rescaling of $L^{x} e^{-\lambda L^{x}} dx$ as $\lambda \to \infty $, thus confirming a conjecture of [19] and further show that the support of $\mathcal {L}$ equals $\partial \mathcal {R}$. This latter result uses a second construction of a boundary local time $\widetilde {\mathcal {L}}$ given in terms of exit measures and we prove that $\widetilde {\mathcal {L}}=c\mathcal {L}$ a.s. for some constant $c>0$. We derive reasonably explicit first and second moment measures for $\mathcal {L}$ in terms of negative dimensional Bessel processes and use them with the energy method to give a more direct proof of the lower bound of the Hausdorff dimension of $\partial \mathcal {R}$ in [9]. The construction requires a refinement of the $L^{2}$ upper bounds in [19] and [9] to exact $L^{2}$ asymptotics. The methods also refine the left tail bounds for $L^{x}$ in [19] to exact asymptotics. We conjecture that the $d_{f}$-dimensional Minkowski content of $\partial \mathcal {R}$ is equal to the total mass of the boundary local time $\mathcal {L}$ up to some constant.

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)0.004
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.002
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 head0.036
GPT teacher head0.261
Teacher spread0.226 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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