arXiv research

New photometric stereo method using dictionary learning for better normal vector reconstruction.

Paper proposes RAN for better anomaly detection in time series data.

A new method for reconstructing flows from perturbed distributions.

Paper presents a method for robust surface reconstruction from noisy gradients using adaptive dictionary learning.

A singular foliation on a complete riemannian manifold M is said to be riemannian if each geodesic that is perpendicular at one point to a leaf remains perpendicular to every leaf it meets. We prove that the regular leaves are equifocal, i.e., the end point map of a normal foliated vector field has constant rank. This …

We give reconstruction formulas inverting the geodesic X-ray transform over functions (call it $I_0$) and solenoidal vector fields on surfaces with negative curvature and strictly convex boundary. These formulas generalize the Pestov-Uhlmann formulas in [Pestov-Uhlmann, IMRN '04] (established for simple surfaces) to ca…

This article concerns cotangent-lifted Lie group actions; our goal is to find local and ``semi-global'' normal forms for these and associated structures. Our main result is a constructive cotangent bundle slice theorem that extends the Hamiltonian slice theorem of Marle, Guillemin and Sternberg. The result applies to a…

The paper reconstructs Lorentzian spacetimes from causal sets.

The paper proposes methods to find a shared active subspace for multivariate vector-valued functions.

Fine-tuning normalization layers can reconstruct smaller networks.

Improved physics-integrated generative models with noise robustness and fidelity.

This work compares meta-embeddings for natural language processing tasks.

Proves formula for reconstruction performance in generalized linear models.

This paper is concerned with the question of reconstructing a vector in a finite-dimensional real Hilbert space when only the magnitudes of the coefficients of the vector under a redundant linear map are known. We analyze various Lipschitz bounds of the nonlinear analysis map and we establish theoretical performance bo…

ABC uses autoencoders for better anomaly detection.

Reconstructing signature features from randomized vector fields in differential equations.

Normalizing flows improve ptychography reconstruction quality and uncertainty quantification.

In this note we answer a question of G. Lecué, by showing that column normalization of a random matrix with iid entries need not lead to good sparse recovery properties, even if the generating random variable has a reasonable moment growth. Specifically, for every $2 \leq p \leq c_1\log d$ we construct a random vector …

A new multi-scale vector quantization method for unsupervised data.

RADAR uses diffusion models to detect anomalies without reconstruction, improving accuracy and efficiency.

Proposes a novel method for generating hard negatives near time series data boundaries.

SaR-SVM-STV improves hyperspectral image classification with shape-adaptive reconstruction and denoising.

Reconstructing polytopes with fixed facet directions from support function evaluations.

We introduce the problem of reconstructing a sequence of multidimensional real vectors where some of the data are missing. This problem contains regression and mapping inversion as particular cases where the pattern of missing data is independent of the sequence index. The problem is hard because it involves possibly m…

Probabilistic Autoencoder learns latent space weights' distribution.

The paper explores Parseval frames on vector bundles, proving their existence for certain cases.

Proposes a novel method for detecting novelty in multi-modal data.

Boosts neural network performance by improving weight separability.

Auto-encoder model simplifies industrial sensor data into fixed-length vectors.

3D dust map of the Milky Way improves resolution and accuracy.

Paper explores CapsNet for anomaly detection in images.

The Funk-Minkowski transform and spherical convolution reconstruct functions and vector fields on the sphere.

We present a generalization of Minkowski's classic theorem on the reconstruction of tetrahedra from algebraic data to homogeneously curved spaces. Euclidean notions such as the normal vector to a face are replaced by Levi-Civita holonomies around each of the tetrahedron's faces. This allows the reconstruction of both s…

Reconstructs fundamental groups from liquid local systems.

The paper studies polar normalizations of skew ruled surfaces in 3D space.

The paper proves a Minkowski-like theorem for tetrahedra in dS3 and AdS3.

We develop a non-relativistic twistor theory, in which Newton--Cartan structures of Newtonian gravity correspond to complex three-manifolds with a four-parameter family of rational curves with normal bundle ${\mathcal O}\oplus{\mathcal O}(2)$. We show that the Newton--Cartan space-times are unstable under the general K…

The paper recovers contact forms from boundary data using vector fields and Lyapunov functions.

Belief Propagation outperforms other algorithms in reconstructing binary symmetric channel trees.

Two strategies for embedding new data points from proximity data are explored.

Paper proves extension of unit normal vector field from a hypersurface.

Improved image reconstruction and anomaly detection using hierarchical VAEs.

The normal map of curves is analyzed as a vector field on a cylinder.

Study improves self-normalized bounds for vector-valued processes beyond sub-Gaussianity.

The paper explores holographic structures on exotic spheres and their implications.

Article studies symmetry in smooth vector bundles using advanced operations.

New method for mesh denoising using TGV of normal vector field.

Improved bipartite link prediction using 2-hop paths.