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.
The paper develops a new model for high-dimensional spatial arbitrage pricing.
MSFA clusters high-dimensional spatial data using spline-based covariance structures.
MRTL learns interpretable spatial patterns efficiently.
DMSTF models spatio-temporal data with deep Markov priors.
We develop a machine learning approach to represent and analyze the underlying spatial structure that governs shot selection among professional basketball players in the NBA. Typically, NBA players are discussed and compared in an heuristic, imprecise manner that relies on unmeasured intuitions about player behavior. T…
SPACY discovers causal graphs from spatiotemporal data using variational inference.
Connected sum and trivalent vertex sum are natural operations on genus 2 spatial graphs and, as with knots, tunnel number behaves in interesting ways under these operations. We prove sharp Scharlemann-Schultens type bounds for the tunnel number of a composite genus 2 spatial graph. For the tunnel number of a composite …
This paper improves MARL for networked systems through new protocols and discount factors.
Dynamic model captures spatial, temporal, and spatiotemporal volatility effects.
We consider the vacuum Einstein flow with a positive cosmological constant on spatial manifolds of product form. In spatial dimension at least four we show the existence of continuous families of recollapsing models whenever at least one of the factors or admits a Riemannian Einstein metric with positive Einstein const…
Spatial ABM predicts housing market trends in Sydney.
Proposes TS-NMF for 2D clustering, preserving spatial info.
Spatially-aware metrics improve uncertainty evaluation in segmentation.
A framework converts spatial data into embeddings for insurance risk modelling.
Finding the most effective way to aggregate multi-subject fMRI data is a long-standing and challenging problem. It is of increasing interest in contemporary fMRI studies of human cognition due to the scarcity of data per subject and the variability of brain anatomy and functional response across subjects. Recent work o…
Spatial Deconfounder tackles interference and confounding in spatial data.
A3T-GCN improves traffic forecasting by capturing spatial and temporal dependencies.
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different dom…
Bayesian model tackles spatial count data issues with flexible non-parametric techniques.
It is widely acknowledged that addiction relapse is highly associated with spatial-temporal factors such as some specific places or time periods. Current studies suggest that those factors can be utilized for better relapse interventions, however, there is no relapse prevention application that makes use of those facto…
Paper analyzes tech adoption in financial networks, finding key leadership and diffusion dynamics.
The paper studies the past inextendibility of FLRW spacetimes using the VDR asymptote.
Spatial Adapter adds structured spatial representation to frozen predictors.
Accurately forecasting urban development and its environmental and climate impacts critically depends on realistic models of the spatial structure of the built environment, and of its dependence on key factors such as population and economic development. Scenario simulation and sensitivity analysis, i.e., predicting ho…
Bayesian spatial predictive synthesis improves spatial data predictions.
The paper proves that certain FLRW spacetimes cannot be extended past the big bang.
Deep learning method for semiparametric regression of spatial data.
TransST improves spatial transcriptomics data analysis by identifying cell clusters and biomarkers.
A scalable method for estimating spatial data using VREML.
Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production. However, such a task is challenged by the complex interaction between crop growth and environmental and…
Product Kanerva Machines dynamically combine smaller models for better memory organization.
Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a group level scalable probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component pruning using automa…
In this paper we utilize symmetries in order to exhibit exact solutions to Einstein's equation of a perfect fluid on a static manifold all of whose spatial factor belongs to the conformal class of a Riemannian space of constant curvature.
Spatio-temporal (ST) data, which represent multiple time series data corresponding to different spatial locations, are ubiquitous in real-world dynamic systems, such as air quality readings. Forecasting over ST data is of great importance but challenging as it is affected by many complex factors, including spatial char…
NoTMF forecasts sparse urban road movement speeds with nonstationary temporal matrix factorization.
Efficiently infers gene regulatory networks from spatial data.
Neural networks speed up covariance estimation in spatial Gaussian processes.
We introduce a novel approach for parallelizing MCMC inference in models with spatially determined conditional independence relationships, for which existing techniques exploiting graphical model structure are not applicable. Our approach is motivated by a model of seismic events and signals, where events detected in d…
We consider calculation of capital requirements when the underlying economic scenarios are determined by simulatable risk factors. In the respective nested simulation framework, the goal is to estimate portfolio tail risk, quantified via VaR or TVaR of a given collection of future economic scenarios representing factor…
Three methods solve spatial rational curves with rational arc length.
We study large-scale spatial systems that contain exogenous variables, e.g. environmental factors that are significant predictors in spatial processes. Building predictive models for such processes is challenging because the large numbers of observations present makes it inefficient to apply full Kriging. In order to r…
This paper describes a versatile method that accelerates multichannel source separation methods based on full-rank spatial modeling. A popular approach to multichannel source separation is to integrate a spatial model with a source model for estimating the spatial covariance matrices (SCMs) and power spectral densities…
We describe a model for capturing the statistical structure of local amplitude and local spatial phase in natural images. The model is based on a recently developed, factorized third-order Boltzmann machine that was shown to be effective at capturing higher-order structure in images by modeling dependencies among squar…
Study on future stability of FLRW spacetime solutions with decelerated expansion.
FPGs use structure to improve policy learning in complex tasks.
We develop a cross-sectional research design to identify causal effects in the presence of unobservable heterogeneity without instruments. When units are dense in physical space, it may be sufficient to regress the "spatial first differences" (SFD) of the outcome on the treatment and omit all covariates. The identifyin…
Proposes a new tensor decomposition method for functional temporal data with adaptive complexity.
The ability to decompose complex multi-object scenes into meaningful abstractions like objects is fundamental to achieve higher-level cognition. Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability…