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This submission is a duplicate of arXiv:q-bio/0602024 and has been removed.

This submission has been withdrawn by arXiv administrators because it is a duplicate of 0704.2182.

This paper has been withdrawn because it is a duplicate of [hep-ph/0609266].

There is a technical issue in the analysis that is not easily fixable. We, therefore, withdraw the submission. Sorry for the inconvenience.

This paper has been withdrawn because it is a duplicate of [math/0609208].

This paper has been retracted, for obvious reasons.

This paper has been withdrawn by the author.

This paper has been withdrawn by the author. This draft is withdrawn for its poor quality in english, unfortunately produced by the author when he was just starting his science route. Look at the ICML version instead:…

This manuscript has been withdrawn by the author. Some of the material has been included in the manuscript 'Dualizing the Dual Standard Model' hep-ph/0102084.

This article was withdrawn because (1) it was uploaded without the co-authors' knowledge or consent, and (2) there are allegations of plagiarism.

The author decided to withdraw this paper by 1) an error in Lemma 5.11 (and 5.12) which requires some justification; 2) the main result of this paper suffers overlap with arXiv:1203.5254; 3) the author decided to split arXiv:1203.5254 into…

Background: Many published machine learning studies are irreproducible. Issues with methodology and not properly accounting for variation introduced by the algorithm themselves or their implementations are attributed as the main…

This paper was withdrawn by arXiv administrators. It is an erroneous duplicate submission of math.NA/0405095.

Reproducibility of modeling is a problem that exists for any machine learning practitioner, whether in industry or academia. The consequences of an irreproducible model can include significant financial costs, lost time, and even loss of…

Paper erroneously re-submitted as duplicte. Readers should look at math-ph/9909004.

This paper has been withdrawn, because it is subsumed by the new preprint arXiv:0806.4540 .

This paper has been withdrawn by the author, since one of the key results duplicates existing work, as pointed out by a reader. I am currently revising the manuscript.

Reproducibility is one of the core dimensions that concur to deliver Trustworthy Artificial Intelligence. Broadly speaking, reproducibility can be defined as the possibility to reproduce the same or a similar experiment or method, thereby…

In this paper, we reproduce the experimental results presented in our previous work titled "Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems," which was published in the proceedings of the 31st ACM…

This paper has been withdrawn by the authors due to a major rewriting.