A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This paper tabulates prime knot projections up to eight double points.
Study of curves and surfaces from single-direction projections.
MimicGAN improves robustness of image projections under corruption.
New algorithm improves sparse-view tomography without needing ground-truth data.
MarmoNet automates analysis of marmoset brain axonal projections.
We examine the relationships between the differential invariants of objects and of their images under a surjective map. We analyze both the case when the underlying transformation group is projectable and hence induces an action on the image, and the case when only a proper subgroup of the entire group acts projectably…
The paper extends a formula linking surface curvature to projection invariants.
A new snake model improves segmentation of SEM images.
Study slopes of direct images in complex manifolds, proving a Mehta-Ramanathan type theorem.
Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism implemented by an appropriate projection matrix. The CS theory is based on random Gaussian projection matrices, which satisfy recovery guara…
The paper studies volumes of direct images for high tensor powers of ample bundles.
We present a novel algorithm for deciding whether a given planar curve is an image of a given spatial curve, obtained by a central or a parallel projection with unknown parameters. The motivation comes from the problem of establishing a correspondence between an object and an image, taken by a camera with unknown posit…
A new method for comparing image probability measures using convolution operators.
The paper addresses speckle noise in coherent imaging systems.
New real algebraic maps with prescribed images and compositions are constructed locally like moment maps.
New model uses PEPS for image classification, outperforming tree-like networks.
In this paper, we pose several conjectures on structures and images of maximal rationally connected fibrations of smooth projective varieties admitting semi-positive holomorphic sectional curvature. Toward these conjectures, we prove that the canonical bundle of images of such fibrations is not big. Our proof gives a g…
Max-sliced Wasserstein distance improves GAN training on high-dimensional images.
Information theoretical measures, such as entropy, mutual information, and various divergences, exhibit robust characteristics in image registration applications. However, the estimation of these quantities is computationally intensive in high dimensions. On the other hand, consistent estimation from pairwise distances…
3D ConvNets improved with Project & Excite for medical imaging segmentation.
We discuss holomorphic isometric embeddings of the projective line into quadrics using a generalisation of the theorem of do Carmo--Wallach to provide a description of their moduli spaces up to image and gauge--equivalence. Moreover, we show rigidity of the real standard map from the projective line into quadrics.
In this project we analysed how much semantic information images carry, and how much value image data can add to sentiment analysis of the text associated with the images. To better understand the contribution from images, we compared models which only made use of image data, models which only made use of text data, an…
We determine all complete projective special real surfaces. By the supergravity r-map, they give rise to complete projective special Kähler manifolds of dimension 6, which are distinguished by the image of their scalar curvature function. By the supergravity c-map, the latter manifolds define in turn complete quaternio…
Ng and Schauenburg proved that the kernel of a -dimensional topological quantum field theory representation of is a congruence subgroup. Motivated by their result, we explore when the kernel of an irreducible representation of the braid group with finite image enjoys a congruen…
Study assesses CNN model robustness to noise in low-cost CT scans.
Deep learning system speeds up wildlife species identification from camera trap images.
New method uses contours of segmented images for X-ray classification.
Formula for analytic torsion forms in fibrations by projective curves.
Develops PRPCA for smooth image recovery combining low-rank and smoothness.
Paper proves structure of compact Kähler 3-folds with specific bundles.
Proves theorem about meromorphic projective structures with complex poles.
We extend some results of [BF12] on subfactor projections to show that the projection of a free factor B to the free factor complex of the free factor A is well-defined with uniformly bound diameter, unless either A is contained in B or A and B are vertex stabilizers of a single splitting of F_n, i.e. they are disjoint…
We consider projective varieties with degenerate Gauss image whose focal hypersurfaces are non-reduced schemes. Examples of this situation are provided by the secant varieties of Severi and Scorza varieties. The Severi varieties are moreover characterized by a uniqueness property.
New algorithm uses untrained neural networks for image recovery, offering better compression.
We study surface knots in 4-space by using generic planar projections. These projections have fold points and cusps as their singularities and the image of the singular point set divides the plane into several regions. The width (or the total width) of a surface knot is a numerical invariant related to the number of po…
32 knot projections classified based on forbidden Reidemeister moves.
A dataset of 10 molecule types for machine learning studies.
Link projections with the same circle arrangement can be transformed by specific moves.
We give a combinatorial proof, using the hyperbolicity of the curve graphs, of the bounded geodesic image theorem of Masur and Minsky. Recently it has been shown that curve graphs are uniformly hyperbolic, thus a universal bound can be given for the diameter of the geodesic image. We also generalize the theorem for pro…
We formulate a correspondence between affine and projective special Kähler manifolds of the same dimension. As an application, we show that, under this correspondence, the affine special Kähler manifolds in the image of the rigid r-map are mapped to one-parameter deformations of projective special Kähler manifolds in t…
A variety of real-world tasks involve the classification of images into pre-determined categories. Designing image classification algorithms that exhibit robustness to acquisition noise and image distortions, particularly when the available training data are insufficient to learn accurate models, is a significant chall…
We consider an important class of signal processing problems where the signal of interest is known to be sparse, and can be recovered from data given auxiliary information about how the data was generated. For example, a sparse Green's function may be recovered from seismic experimental data using sparsity optimization…
The study shows how stabilizing manifolds with projective spaces affects their homotopy structure.
New method sets explicit sparsity for groups of vectors in deep learning and NMF.
Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance. Taking into account manifold geometry is typically done via (1) embedding the manifolds in tangent space…
Generative adversarial networks (GANs) transform latent vectors into visually plausible images. It is generally thought that the original GAN formulation gives no out-of-the-box method to reverse the mapping, projecting images back into latent space. We introduce a simple, gradient-based technique called stochastic cli…
A rapidly growing area of work has studied the existence of adversarial examples, datapoints which have been perturbed to fool a classifier, but the vast majority of these works have focused primarily on threat models defined by norm-bounded perturbations. In this paper, we propose a new threat model for adver…
Optimal square matrices for image approximation under translation and rotation.