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.
A framework converts spatial data into embeddings for insurance risk modelling.
New method generates geolocated synthetic populations from real data.
Extracts intrinsic spatial coordinates for complex agent systems to learn PDEs.
Machine learning models perform better with location coordinates alone, not Moran Eigenvectors.
DNNs improve SIMP method but not spatially invariant, study shows.
To date, pavement management software products and studies on optimizing the prioritization of pavement maintenance and rehabilitation (M&R) have been mainly focused on three parameters; the pre-treatment pavement condition, the rehabilitation cost, and the available budget. Yet, the role of the candidate projects' spa…
A scalable method for estimating spatial data using VREML.
We use planar coordinates as well as hyperbolic coordinates to separate the de Sitter spacetime into two parts. These two ways of cutting the de Sitter give rise to two different spatial infinities. For spacetimes which are asymptotic to either half of the de Sitter spacetime, we are able to provide definitions of the …
Spatially constrained Gaussian mixture models reduce covariance complexity.
MSFA clusters high-dimensional spatial data using spline-based covariance structures.
We study the limit of quasilocal mass defined in [4] and [5] for a family of spacelike 2-surfaces in spacetime. In particular, we show the limit coincides with the ADM mass at spatial infinity. The limit for coordinate spheres of a boosted slice of the Schwarzchild solution is computed explicitly and shown to give the …
A2-SBNN models spatial data with copulas for non-Gaussian dependencies.
Hybrid framework merges data and domain knowledge for better spatial interpolation.
SpaCE tackles spatial confounding in scientific studies.
Humans can only interact with part of the surrounding environment due to biological restrictions. Therefore, we learn to reason the spatial relationships across a series of observations to piece together the surrounding environment. Inspired by such behavior and the fact that machines also have computational constraint…
Paper uses deep learning to detect cyclone rapid intensification.
Future stability of FLRW solutions in expanding 3D space is shown for compact perturbations.
State-of-the-art deep learning methods have shown a remarkable capacity to model complex data domains, but struggle with geospatial data. In this paper, we introduce SpaceGAN, a novel generative model for geospatial domains that learns neighbourhood structures through spatial conditioning. We propose to enhance spatial…
Automated rock fragmentation assessment using deep learning and spatial statistics.
Pursuit-evasion is a multi-agent sequential decision problem wherein a group of agents known as pursuers coordinate their traversal of a spatial domain to locate an agent trying to evade them. Pursuit evasion problems arise in a number of import application domains including defense and route planning. Learning to opti…
Paper proposes dp-VAE for preserving spatial context in gene expression data.
Proposes a neural network method to correct residual distortions in coordinate transformations.
DeepKriging uses DNNs to predict spatial data with improved accuracy and scalability.
In a spatially embedded network, that is a network where nodes can be uniquely determined in a system of coordinates, links' weights might be affected by metric distances coupling every pair of nodes (dyads). In order to assess to what extent metric distances affect relationships (link's weights) in a spatially embedde…
Few ideas have enjoyed as large an impact on deep learning as convolution. For any problem involving pixels or spatial representations, common intuition holds that convolutional neural networks may be appropriate. In this paper we show a striking counterexample to this intuition via the seemingly trivial coordinate tra…
We introduce a multiscale supervised dimension reduction method for SPatial Interaction Network (SPIN) data, which consist of a collection of spatially coordinated interactions. This type of predictor arises when the sampling unit of data is composed of a collection of primitive variables, each of them being essentiall…
Stability proved for open Milne spacetime, showing gravity's long-term behavior.
SE-KGE embeds spatial data into KGs for better spatial reasoning.
Nonlocal Bayesian modeling for continuous spatio-temporal dynamics
Researchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of interest (ROIs). This paper presents two machine learning approaches, transfer learning and selection transfer, that are compared upon their…
We study the general structure of formal perturbative solutions to the Hamiltonian perturbations of spatially one-dimensional systems of hyperbolic PDEs. Under certain genericity assumptions it is proved that any bihamiltonian perturbation can be eliminated in all orders of the perturbative expansion by a change of coo…
SCNode improves node embeddings for GNNs in both homophilic and heterophilic graphs.
We present a simple neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations. Instead of the deconvolutional network typically used in the decoder of VAEs, we tile (broadcast) the latent vector across space, concatenate fixed X- and Y-"coordinate" channels, and apply a…
Predicts local AQI using mobile sensor data, improving accuracy by 71.654 MSE.
Smooth metrics satisfying Penrose inequality are necessarily smooth.
A-BLINK speeds up Gaussian process covariance estimation.
We review recent work on the local geometry and optimal regularity of Lorentzian manifolds with bounded curvature. Our main results provide an estimate of the injectivity radius of an observer, and a local canonical foliations by CMC (Constant Mean Curvature) hypersurfaces, together with spatially harmonic coordinates.…
Study shows inflation in 3+1D cosmologies with bounded scalar potential and specific symmetry.
RFN models urban mobility demand by separating temporal and spatial variability.
We investigate the local regularity of pointed spacetimes, that is, time-oriented Lorentzian manifolds in which a point and a future-oriented, unit timelike vector (an observer) are selected. Our main result covers the class of Einstein vacuum spacetimes. Under curvature and injectivity bounds only, we establish the ex…
This paper concerns the numerical solution of the finite-horizon Optimal Investment problem with transaction costs under Potential Utility. The problem is initially posed in terms of an evolutive HJB equation with gradient constraints. In Finite-Horizon Optimal Investment with Transaction Costs: A Parabolic Double Obst…
The geometric approach to diffeomorphic image registration known as "large deformation by diffeomorphic metric mapping" (LDDMM) is based on a left action of diffeomorphisms on images, and a right-invariant metric on a diffeomorphism group, usually defined using a reproducing kernel. We explore the use of left-invariant…
These lecture notes provide some introduction to the 3+1 formalism of general relativity, which is the foundation of most modern numerical relativity. The text is rather self-contained, with detailed calculations and numerous examples. Contents: 1. Introduction, 2. Geometry of hypersurfaces, 3. Geometry of foliations, …
A multi-neck spacetime wormhole is constructed with a simple metric tensor.
A spacetime is a connected 4-dimensional semi-Riemannian manifold endowed with a metric with signature . The geometry of a spacetime is described by the metric tensor and the Ricci tensor of type whereas the energy momentum tensor of type describes the physical contents of the sp…
The paper proves stability of certain graph types in Euclidean space with specific densities.
New framework for diffusion geometry simplifies complex calculations.
Graph convolution network based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing. Each relationship represents a kind of spatial dependency, like region-wise distance or functional similarity. To incorporate multiple relationships into spatial …