Reconstruction of a Single String from a Part of its Composition Multiset
Zuo Ye, Ohad Elishco

TL;DR
This paper investigates the problem of reconstructing strings from their composition multisets, characterizes non-uniquely reconstructable strings, and develops codes for error correction and partial information scenarios, with applications in polymer data storage.
Contribution
It provides a complete structural description of strings not uniquely reconstructable from prefix-suffix compositions and constructs optimal and error-correcting composition codes.
Findings
Existence of non-uniquely reconstructable strings for all lengths n ≥ 6.
Explicit construction of all uniquely reconstructable strings for each length n ≥ 6.
Development of composition codes capable of correcting errors and handling partial composition information.
Abstract
Motivated by applications in polymer-based data storage, we study the problem of reconstructing a string from part of its composition multiset. We give a full description of the structure of the strings that cannot be uniquely reconstructed (up to reversal) from their multiset of all of their prefix-suffix compositions. Leveraging this description, we prove that for all , there exists a string of length that cannot be uniquely reconstructed up to reversal. Moreover, for all , we explicitly construct the set consisting of all length strings that can be uniquely reconstructed up to reversal. As a by product, we obtain that any binary string can be constructed using Dyck strings and Catalan-Bertrand strings. For any given string , we provide a method to explicitly construct the set of all strings with the same prefix-suffix composition multiset as…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDNA and Biological Computing · Algorithms and Data Compression · Network Packet Processing and Optimization
