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Explore maximally recoverable codes for distributed storage systems, focusing on efficient repair, reliability, and storage optimization. Learn about different classes, bounds, and constructions.
Introducing fair-density parity-check codes for high-rate applications, featuring a novel base matrix selection method, weight distribution analysis, and an efficient MP-PL decoding algorithm for improved performance.
Explore algebraic codes for private information retrieval, focusing on capacity-approaching techniques and their applications in secure data access.
Explore GNN-based architecture for channel decoding, offering scalability and improved performance. Learn about its application to quantum LDPC codes and integration with belief propagation.
Explore advanced techniques for distributed matrix multiplication, focusing on sparsity and privacy considerations in large-scale computations.
Explore distributed system performance metrics through service rate regions of MDS codes, connecting them to fractional matching polytopes in quasi-uniform hypergraphs for improved data storage and retrieval.
Explores minimizing DNA sequencing coverage in storage systems, focusing on error-correcting codes and retrieval algorithms to reduce costs and latency while optimizing information retrieval efficiency.
Explore advanced techniques in 1-bit compressed sensing, focusing on combinatorial approaches to support recovery and their applications in coding theory.
Explores a framework for lossy distributed source coding using conditional VQ-VAE, achieving state-of-the-art performance on complex correlations and high-dimensional data without relying on hand-crafted source modeling.
Explore advanced techniques for high-dimensional matrix computations in distributed systems, addressing numerical stability, partial stragglers, and sparse input matrices.
Explore polar codes for IDS channels, addressing numerical instability, partial stragglers, and worker computation time in sparse matrix scenarios. Learn recent contributions to overcome these challenges.
Explore innovative approaches to coded matrix multiplication, focusing on approximate results for faster computation in optimization and learning applications.
Explores novel error-correction framework for constrained systems, introducing Quantized-Constraint Concatenation and analyzing covering radius to improve DNA storage coding techniques.
Explores advanced error correction coding for high-performance data transmission, focusing on generalized staircase codes with flexible bit protection and reduced decoding complexity.
Explore theoretical foundations for synthetic DNA data storage using nanopore sequencing, including channel models, noise characteristics, and coding schemes for practical archival applications.
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