eess.SP · 2026-07-09 · No. 48

Signal Processing, 2026-07-09.

2 new papers in eess.SP. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

2 entries
  1. 01

    Stability of Flow Models for Graph Signals

    Martin Schmidt, Gonzalo Mateos

    eess.SP · cs.AI · cs.LG

    Generating signals on graphs requires permutation-equivariant models that exhibit stability with respect to relative structural perturbations. While favorable stability properties of Graph Neural Networks (GNNs) have been well documented, it is unclear how structural errors propagate through the dynamics of continuous generative flow models that are gaining traction for graph signal generation. In this paper, we analyze continuous normalized...

    arxiv.org/abs/2607.07510 · PDF

  2. 02

    A Multi-Analyst LLM Pipeline for Auditable Rule Discovery Across 68 Public Physiological Corpora

    Dovy Paukstys

    eess.SP · cs.AI

    Open physiological corpora are heterogeneous: they use different sensors, labels, sampling rates, recording settings, and clinical endpoints. They can support detector design, but they do not directly specify which detector rules should be built for a new contactless monitoring platform. We report a controlled four-analyst large-language-model (LLM) workflow for converting 68 public physiological corpora, screened for commercial-use...

    arxiv.org/abs/2607.06802 · PDF

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