Persistent homology on spatio-temporal cubical complexes from address records quantifies urban population displacement and identifies affected neighborhoods and years in a Madrid case study.
Barcodes: the persistent topology of data
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RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.
Hybrid models that add persistent-homology features from fixation time series to traditional statistical features outperform purely statistical baselines for dyslexia detection on the Copenhagen Corpus.
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Quantifying displacement: an urban expansion consequence via persistent homology
Persistent homology on spatio-temporal cubical complexes from address records quantifies urban population displacement and identifies affected neighborhoods and years in a Madrid case study.
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A renormalization-group inspired lattice-based framework for piecewise generalized linear models
RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.
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Fixation Sequences as Time Series: A Topological Approach to Dyslexia Detection
Hybrid models that add persistent-homology features from fixation time series to traditional statistical features outperform purely statistical baselines for dyslexia detection on the Copenhagen Corpus.