EDBT 2026 Demo / reviewers in the wild / expert
Niv Beeri
dblp:283/5278
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Coding theory · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › error-correcting codes › storage coding › DNA storage
DNA storage codes |
0.6 | 1 | 2022 | Improved Rank-Modulation Codes for DNA Storage With Shotgun Sequencing · IEEE Trans. Inf. Theory 2022 |
Coding theory › error-correcting codes › flash memory coding
rank modulation codes |
0.6 | 1 | 2022 | Improved Rank-Modulation Codes for DNA Storage With Shotgun Sequencing · IEEE Trans. Inf. Theory 2022 |
Methods — techniques the papers use, named apart from their topics
shotgun sequencing · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Improved Rank-Modulation Codes for DNA Storage With Shotgun SequencingabstractA common method for reading information stored in DNA molecules is shotgun sequencing. This method outputs a histogram of the frequencies of all the molecules’ substrings of a given length$\ell $. To protect against noisy readings, the rank-modulation scheme encodes the information in the relative ranking of the substring frequencies, instead of their absolute values. However, the best rank-modulation codes for shotgun sequencing have low rates which are asymptotically vanishing. In this paper we propose new constructions of rank-modulation codes for shotgun sequencing. The first code construction is systematic, allowing the user to arbitrarily set the frequencies of a large subset of the substrings, which the encoder then completes to a permutation that may be realized by a DNA molecule. The construction is then improved by allowing the user to set the frequencies of additional substrings, at the cost of imposing constraints on the frequencies. The resulting codes have higher, non-vanishing rates, compared with previously known codes. As an example, for histograms of substrings of length$\ell =2$, and an alphabet of size 4 (as in DNA molecules), we are able to construct a code with rate$\approx 0.909$, whereas previously, the best construction resulted in a code with rate$\approx 0.654$. Additionally, the encoded information in our construction may be written to shorter DNA molecules than possible before. We also prove that the systematic codes constructed in this paper are the largest possible among all systematic codes. Niv Beeri, Moshe Schwartz 0001 |
IEEE Trans. Inf. Theory | 1 |