Hub-and-spoke systems from symbolic dynamics can have completely positive mean dimension without uniformly positive mean dimension or entropy, with proofs linking entropy and mean dimension properties at the level of fixed covers.
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Compressed sensing
17 Pith papers cite this work. Polarity classification is still indexing.
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Introduces zero-inflated Gaussian distributions for EDAs to jointly optimize sparsity patterns and active parameter values without bi-level schemes or custom operators.
Polynomial-time SDP and ellipsoid-based approximation of Kolmogorov widths yields efficient robust detection boundaries matching upper bounds up to polylog factors for structured constrained signals.
CSGuard binds diffusion-model watermarks to a secret matrix via compressed sensing, cutting forgery attack success from 100% to 28.12% while preserving 100% detection on legitimate images.
DUSG-Tomo-Net performs super-resolved gridless TomoSAR inversion by learning a Toeplitz-structured covariance representation from single-look nonuniform-baseline data via deep unfolding and projection enforcement.
Sparse autoencoders resolve superposition in image-based neuron representations, recovering geometric fidelity and enabling scRNA-seq adaptation plus GW-map alignment to reconstruct pathology pathways without spatial transcriptomics.
Differential Unfolding replaces uniform stacking in deep unfolding networks with a heterogeneous structure of anchoring and differential evolution stages to achieve better accuracy-efficiency trade-offs in video SCI reconstruction.
Extends variable projection to constrained separable nonlinear least-squares via bilevel collapse, yielding exact reduced gradients and a convergent conditional-gradient algorithm.
Introduces a local measurement-manifold compatibility measure that bounds stable reconstruction error under generative priors and motivates fixed and adaptive acquisition rules.
The paper formalizes residual margin stability via span-level idealizations for SRC, derives a lower bound under coverage and separation assumptions, and proposes geometry-shaping training objectives evaluated under fixed SRC/OMP inference.
Random undersampling reduces SPR measurements by a factor of 6 on a carbon fibre-aluminium composite using weighted random sampling on sparse signals.
Introduces group sparsity constraint and soft energy lower bound in compressed sensing to reconstruct directional wave spectra from sparse multi-channel buoy data.
Mathematical analysis shows sparse linear regression mitigates output dimension collapse in brain-to-image reconstruction at small data scales by exploiting sparsity in the brain-to-feature mapping.
New inconsistent alternating projection scheme for basis pursuit with linear convergence proofs and competitive benchmarks.
Extends Fano bounds to sufficiency of low conditional entropy and defines a quantum entanglement task for infinite-dimensional systems with bounds via maximal singlet fraction of finite-dimensional approximations.
A review of Faraday Rotation Measure Synthesis techniques and SKA Array Assembly stages for high-resolution Faraday tomography of cosmic magnetic structures.
Review of cosmic ray production and radio emission in galaxy clusters with recommendations for SKA observations of magnetic fields and low-energy particles.
citing papers explorer
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Uniformly Positive Mean Dimension
Hub-and-spoke systems from symbolic dynamics can have completely positive mean dimension without uniformly positive mean dimension or entropy, with proofs linking entropy and mean dimension properties at the level of fixed covers.
-
Zero-Inflated Gaussian Distributions Enable Parameter-Space Sparsity in Estimation-of-Distribution Algorithms
Introduces zero-inflated Gaussian distributions for EDAs to jointly optimize sparsity patterns and active parameter values without bi-level schemes or custom operators.
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Efficient Robust Constrained Signal Detection via Kolmogorov Width Approximations
Polynomial-time SDP and ellipsoid-based approximation of Kolmogorov widths yields efficient robust detection boundaries matching upper bounds up to polylog factors for structured constrained signals.
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CSGuard: Toward Forgery-Resistant Watermarking in Diffusion Models via Compressed Sensing Constraint
CSGuard binds diffusion-model watermarks to a secret matrix via compressed sensing, cutting forgery attack success from 100% to 28.12% while preserving 100% detection on legitimate images.
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DUSG-Tomo-Net: A Deep Unfolded Neural Network for Super-Resolving Gridless Spaceborne SAR Tomography via Learned Toeplitz-Structured Covariance Representation
DUSG-Tomo-Net performs super-resolved gridless TomoSAR inversion by learning a Toeplitz-structured covariance representation from single-look nonuniform-baseline data via deep unfolding and projection enforcement.
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Resolving superposition in AI for interpretability and cross-modal alignment in patient-neuronal images
Sparse autoencoders resolve superposition in image-based neuron representations, recovering geometric fidelity and enabling scRNA-seq adaptation plus GW-map alignment to reconstruct pathology pathways without spatial transcriptomics.
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Differential Unfolding: Efficient Unfolding Reconstruction for Video Snapshot Compressive Imaging
Differential Unfolding replaces uniform stacking in deep unfolding networks with a heterogeneous structure of anchoring and differential evolution stages to achieve better accuracy-efficiency trade-offs in video SCI reconstruction.
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Constrained Variable Projection for Structured Problems
Extends variable projection to constrained separable nonlinear least-squares via bilevel collapse, yielding exact reduced gradients and a convergent conditional-gradient algorithm.
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Measurement Geometry and Design for Trustworthy Generative Inverse Problems
Introduces a local measurement-manifold compatibility measure that bounds stable reconstruction error under generative priors and motivates fixed and adaptive acquisition rules.
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A Geometric View of SRC: Learning Representations for Stable Residual Inference
The paper formalizes residual margin stability via span-level idealizations for SRC, derives a lower bound under coverage and separation assumptions, and proposes geometry-shaping training objectives evaluated under fixed SRC/OMP inference.
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Thermal characterisation by Scanning Photothermal Radiometry using a random undersampled measurement scheme
Random undersampling reduces SPR measurements by a factor of 6 on a carbon fibre-aluminium composite using weighted random sampling on sparse signals.
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Recovery of directional wave spectrum from sparse data with compressed sensing
Introduces group sparsity constraint and soft energy lower bound in compressed sensing to reconstruct directional wave spectra from sparse multi-channel buoy data.
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Faraday Tomography with the SKA: A New Era of Cosmic Magnetism Studies
A review of Faraday Rotation Measure Synthesis techniques and SKA Array Assembly stages for high-resolution Faraday tomography of cosmic magnetic structures.
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The Cosmic Ray Life Cycle in Galaxy Clusters
Review of cosmic ray production and radio emission in galaxy clusters with recommendations for SKA observations of magnetic fields and low-energy particles.