Alexander Sauter

Alexander Sauter, Riccardo Schiavone, Lucía Balsa Picado et al.

This paper proposes the design of polar and convolutional coset codes for the unequal message protection (UMP) in the short blocklength regime, to overcome the rate loss introduced by preamble-based solutions. After providing conditions to ensure message class disjointness, a two-step decoding architecture is proposed: it first identifies the message class via a likelihood ratio test--computable exactly for convolutional codes and approximated for polar codes--and subsequently performs maximum (or near) likelihood decoding among the codewords of the chosen message class. Numerical results show that our construction closely tracks finite-length benchmarks. Specifically, the analyzed CRC-aided polar codes perform comparable to existing polar code approaches, without requiring specific code design, while offering a robust and spectrally efficient solution for UMP scenarios.

Laura Manduchi, Ričards Marcinkevičs, Michela C. Massi et al.

In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference. In contrast to previous work, our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and censored survival times. We compare our model to the related work on clustering and mixture models for survival data in comprehensive experiments on a wide range of synthetic, semi-synthetic, and real-world datasets, including medical imaging data. Our method performs better at identifying clusters and is competitive at predicting survival times. Relying on novel generative assumptions, the proposed model offers a holistic perspective on clustering survival data and holds a promise of discovering subpopulations whose survival is regulated by different generative mechanisms.