Sequence Reconstruction: Error Models and Frameworks
Beschreibung
When a sequence is transmitted over several noisy channels, or stored and read many times as in DNA data storage, the receiver sees multiple corrupted copies and must recover the original. A central question is how many copies are needed to do this. The answer depends on the type of errors (substitutions, insertions or deletions) and on how the problem is modeled.
Combinatorial approaches, known as sequence reconstruction, assume a bounded number of adversarial errors. Probabilistic approaches, known as trace reconstruction, assume random errors. Coding the transmitted sequence can further reduce the number of copies required.
This seminar gives an overview of the area, its main problem formulations, and its key results.
Suggested Literature:
[1] V. Levenshtein, "Efficient Reconstruction of Sequences", IEEE Trans. on Information Theory, vol. 47, no.1, pp. 2-22, Jan. 2001, doi: 10.1109/18.904499.
[2] O. Sabary, H. M. Kiah, P. H. Siegel and E. Yaakobi, "Survey for a Decade of Coding for DNA Storage", IEEE Trans. on Molecular, Biological, and Multi-Scale Communications, vol. 10, no. 2, pp. 253-271, June 2024. doi: 10.1109/TMBMC.2024.3403488.
[3] V. Bhardwaj, P.A. Pevzner, C. Rashtchian and Y. Safanova, "Trace Reconstruction Problems in COmputational Biology", IEEE Trans. on Information Theory, vol. 67, no. 6, pp. 3295-3314, June 2021. doi: 10.1109/TIT.2020.3030569.
[4] M. Cheraghchi, J. Ribeiro, R. Gabrys, O. Milenkovic, "Coded Trace Reconstruction", 2019 IEEE Information Theory Workshop (ITW), pp. 1-5. doi: 10.1109/ITW44776.2019.8989261.
Voraussetzungen
It is nice for the student to have some background knowledge on linear algebra, combinatorics and channel coding. However, questions on topics that are unclear to the student are always welcome.
In general, this is a student-driven task, therefore it is the student's job to plan and execute the review of the given papers. Support and guidance will be provided if requested. There will also be a clear discussion of what is required in the final presentation as well as evaluation points, directly after the topic assignment.
Betreuer:
External Master Thesis at Huawei: AI-Driven Optimization for Reconfigurable Quantum Communication Networks
Develop AI-based methods for optimizing switching decisions in reconfigurable quantum communication networks. Design and implement machine learning, reinforcement learning, or graph?based models that exploit the network’s inherent structure. Integrate your approach into our existing simulation and optimization framework, and benchmark it against established baselines. Work in close collaboration with a multidisciplinary research team spanning quantum communication, optimization, and machine learning Publish and present research findings at academic conferences. Contribute to intellectual property generation.
Beschreibung
See more information and apply directly here: Intern/Master Thesis Student - Topic/Area - Huawei Research Center Germany