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.
Spatial refinement of Bar-Natan homology constructed.
Convolutional GANs favor low spatial frequencies, affecting fine detail generation.
A neural network improves DOA estimation from a single snapshot.
Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. In the classification context, the extracted features are commonly concatenated into a lon…
To improve the classification performance in the context of hyperspectral image processing, many works have been developed based on two common strategies, namely the spatial-spectral information integration and the utilization of neural networks. However, both strategies typically require more training data than the cl…
We present an approach to model time series data from resting state fMRI for autism spectrum disorder (ASD) severity classification. We propose to adopt kernel machines and employ graph kernels that define a kernel dot product between two graphs. This enables us to take advantage of spatio-temporal information to captu…
In this work we study the non-parametric reconstruction of spatio-temporal dynamical Gaussian processes (GPs) via GP regression from sparse and noisy data. GPs have been mainly applied to spatial regression where they represent one of the most powerful estimation approaches also thanks to their universal representing p…
Study on how soliton equations form singularities using L,A,B-triples.
Sparse-mode DMD disambiguates local and global modes in spatiotemporal data.
Paper presents a method for identifying isotope envelopes in MALDI-ToF data.
The complex wave representation (CWR) converts unsigned 2D distance transforms into their corresponding wave functions. Here, the distance transform S(X) appears as the phase of the wave function φ(X)---specifically, φ(X)=exp(iS(X)/τwhere τis a free parameter. In this work, we prove a novel result using the higher-orde…
Researchers develop tests to assess quality of GAN-generated images.
Study on infinitely-wide CNNs and their adaptability to function spatial scales.
This paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on mon…
Diagnosing Autism Spectrum Disorder (ASD) is a challenging problem, and is based purely on behavioral descriptions of symptomology (DSM-5/ICD-10), and requires informants to observe children with disorder across different settings (e.g. home, school). Numerous limitations (e.g., informant discrepancies, lack of adheren…
We define a new spectrum for compact length spaces and Riemannian manifolds called the "covering spectrum" which roughly measures the size of the one dimensional holes in the space. More specifically, the covering spectrum is a set of real numbers which identify the distinct covers of the space. We investigat…
Case vs control comparisons have been the classical approach to the study of neurological diseases. However, most patients will not fall cleanly into either group. Instead, clinicians will typically find patients that cannot be classified as having clearly progressed into the disease state. For those subjects, very lit…
The subject of this paper is the relationship among the marked length spectrum, the length spectrum, the Laplace spectrum on functions, and the Laplace spectrum on forms on Riemannian nilmanifolds. In particular, we show that for a large class of three-step nilmanifolds, if a pair of nilmanifolds in this class has the …
The subject of this paper is the relationship among the marked length spectrum, the length spectrum, the Laplace spectrum on functions, and the Laplace spectrum on forms on Riemannian nilmanifolds. In particular, we show that for a large class of three-step nilmanifolds, if a pair of nilmanifolds in this class has the …
Study the energy spectrum of metrics on surfaces and its relation to simple length spectrum.
Study shows spectrum properties for specific Hadamard manifolds.
Proofs high-dimensional spectrum convergence of weighted sample covariance.
Iterative method 'Concent' corrects spectrum bias in covariance matrices.
Develops a new spectrum for annular links, recovering a transverse invariant at extreme gradings.
Deep learning methods are increasingly being used with neuroimaging data like structural and function magnetic resonance imaging (MRI) to predict the diagnosis of neuropsychiatric and neurological disorders. For psychiatric disorders in particular, it is believed that one of the most promising modality is the resting-s…
The spectrum of certain manifolds matches that of hyperbolic space if the bottom spectrum is maximal.
Lower bounds for Hodge-Laplacian spectrum on orbifolds.
How to generalize the concept of eigenvalues of quadratic forms to eigenvalues of arbitrary, even, homogeneous continuous functionals, if stability of the set of eigenvalues under small perturbations is required? We compare two possible generalizations, Gromov's homotopy significant spectrum and the Krasnoselskii spect…
Spatial blind source separation simplifies multivariate spatial prediction.
We construct Riemannian manifolds with singular continuous spectrum embedded in the absolutely continuous spectrum of the Laplacian. Our manifolds are asymptotically hyperbolic with sharp curvature bounds.
The paper introduces groupoid racks for spatial surfaces.
Trapezoids uniquely identified by their Dirichlet Laplace spectrum.
Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is problematic and results in models that can reproduce training data …
In 2004, Sormani and Wei introduced the covering spectrum: a geometric invariant that isolates part of the length spectrum of a Riemannian manifold. In their paper they observed that certain Sunada isospectral manifolds share the same covering spectrum, thus raising the question of whether the covering spectrum is a sp…
STICC clusters geographic objects considering both spatial contiguity and attributes.
Survey on bottom of spectrum of Hodge Laplacian on complete noncompact Kähler manifolds
The paper extends decay estimates to graphs with positive spectrum.
Upper bounds for volume spectrum depend on volume, dimension, and a conformal invariant.
Upper bounds for essential spectrum of minimal submanifolds linked to volume growth.
Study on magnetic Dirac operators and their spectrum.
Spatial understanding is a fundamental problem with wide-reaching real-world applications. The representation of spatial knowledge is often modeled with spatial templates, i.e., regions of acceptability of two objects under an explicit spatial relationship (e.g., "on", "below", etc.). In contrast with prior work that r…
Notes on continuity of discrete-spectrum Fredholm operators.
Defines non-parabolic curves in spatial hybrid space with applications.
The rigidity of marked length spectrum for closed hyperbolic surfaces due to Fricke-Klein [7] has been the motivation of many different rigidity results, specially for manifolds of negative curvature. From the works of Vigneras [18], Sunada [17] and many other authors this result is far from being true for the unmarked…
Study essential spectrum of differential operators on geometrically finite orbifolds.
ManifoldFlow relaxes fixed-spectrum Stiefel layers to learn a positive spectrum.
Paper proves flat metrics from holomorphic quadratic differentials can be identified by length spectrum.
Study on length spectrum of random hyperbolic 3-manifolds.