Related papers: Upper limits based on "loudest" events

this paper has been withdrawn. A crucial estimate on a Lipschitz variant of the Kakeya maximal function has an incomplete proof.

This paper has been withdraw by the auhor due to a mistake in classification of the algebra

This paper has been withdrawn by the author due to an error.

This paper has been withdrawn by the author, due to a significant error in section 4.3.1.

This paper enhances the result of the work [G. Kozma, B. T\'oth, Ann. Probab. vol. 45 (2017) 4307-4347] . We prove the central limit theorem (in probability w.r.t. the environment) for the displacement of a random walker in divergence-free…

This paper has been withdrawn by the authors due to missing references to earlier work and an error in the interpretation of Figures 2 and 3. A corrected version is in preparation.

Parameter estimation via unbinned maximum likelihood fits is central for many analyses performed in high energy physics. Unbinned maximum likelihood fits using event weights, for example to statistically subtract background contributions…

Let $X$ be the number of $k$-term arithmetic progressions contained in the $p$-biased random subset of the first $N$ positive integers. We give asymptotically sharp estimates on the logarithmic upper-tail probability $\log \Pr(X \ge E[X] +…

This paper has been withdrawn by the author due to a gap in the proof of the main result.

This paper has been withdrawn due to crucial errors.

In this paper has been withrawn by the author due the error in the proof of theoem 1.

The paper has been withdrawn due to an error in Lemma 1.

This paper has been withdrawn by the authors because of an error in the proof. We can, however, prove a weaker spatial fall-off that is still superlinear, namely exp[-x log x].

For scattering amplitudes in strong background fields, it is -- at least in principle -- possible to perturbatively expand the background to obtain higher-point vacuum amplitudes. In the case of self-dual plane wave backgrounds we consider…

We discuss how to determine and combine upper limits based on observed events and estimated backgrounds with a Bayesian method, when insignificant signals are observed in independent measurements. In addition to some general features…

This paper has been withdrawn by the author(s), due an error in the proof.

Large Language Models (LLMs) have benefited enormously from scaling, yet these gains are bounded by five fundamental limitations: (1) hallucination, (2) context compression, (3) reasoning degradation, (4) retrieval fragility, and (5)…

This paper has been withdrawn by the author due to an erro thereon line -2 of page 4.

This paper has been withdrawn by the author because the conclusions reached in it are incorrect.

This paper has been withdrawn.