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.
OpFlow predicts robust OD flows by learning choice potentials conditioned on spatial exposures.
Unified framework detects shifts in climate boundaries using GP regression and MAD test.
New method improves traffic forecasting models by adapting to spatial shifts.
MIP framework improves urban flow prediction by adapting to distribution shifts.
New method improves spatial prediction validation accuracy.
Geostatistical learning faces unique challenges due to spatial correlation and covariate shifts.
WBCP improves conformal prediction for distribution shifts using weighted Dirichlet posteriors.
We give a topological condition for a generic sliced space to be globally hyperbolic, without any hypothesis on the lapse function, shift function and spatial metric.
Comparing data defined over space and time is notoriously hard, because it involves quantifying both spatial and temporal variability, while at the same time taking into account the chronological structure of data. Dynamic Time Warping (DTW) computes an optimal alignment between time series in agreement with the chrono…
Using open source data, we observe the fascinating dynamics of nighttime light. Following a global economic regime shift, the planetary center of light can be seen moving eastwards at a pace of about 60 km per year. Introducing spatial light Gini coefficients, we find a universal pattern of human settlements across dif…
Paper introduces a new metric to select optimal Graph Shift Operator for GNNs.
Model predicts unseen climate extremes to inform risk planning.
New equivariant filters improve graph classification.
This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate s…
FLUXtrapolation benchmarks machine learning for extrapolating ecosystem fluxes under distribution shifts.
Proposes FSM-IRL to learn invariant network representations considering feature and structural shifts.
Paper tackles estimating initial conditions of spatio-temporal processes from sparse data.
Scattering representations simplify SBI for images without extra compression.
Bayesian Empirical Bayes extends EB to complex structures using probabilistic symmetry.
Current studies about motor imagery based rehabilitation training systems for stroke subjects lack an appropriate analytic method, which can achieve a considerable classification accuracy, at the same time detects gradual changes of imagery patterns during rehabilitation process and disinters potential mechanisms about…
As one of the important functions of the intelligent transportation system (ITS), supply-demand prediction for autonomous vehicles provides a decision basis for its control. In this paper, we present two prediction models (i.e. ARLP model and Advanced ARLP model) based on two system environments that only the current d…
The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI sys…
Unsupervised image segmentation aims at clustering the set of pixels of an image into spatially homogeneous regions. We introduce here a class of Bayesian nonparametric models to address this problem. These models are based on a combination of a Potts-like spatial smoothness component and a prior on partitions which is…
GOPSA optimizes EEG data for cross-site age prediction, improving performance on multiple metrics.
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 …
ProGen improves spatiotemporal forecasting with SDEs and diffusion models.
Deep convolutional neural networks are known to be unstable during training at high learning rate unless normalization techniques are employed. Normalizing weights or activations allows the use of higher learning rates, resulting in faster convergence and higher test accuracy. Batch normalization requires minibatch sta…
Efficient neural Bayes estimators for censored peaks-over-threshold models improve inference speed and accuracy.
In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interact…
Improves AI agents' 3D navigation by learning from failures and 3D spatial relationships.
Nuclear magnetic resonance (NMR) spectroscopy is one of the leading techniques for protein studies. The method features a number of properties, allowing to explain macromolecular interactions mechanistically and resolve structures with atomic resolution. However, due to laborious data analysis, a full potential of NMR …
SA-BCP combines long-term and local evidence for efficient, adaptive online prediction.
In this paper, we consider domain-invariant deep learning by explicitly modeling domain shifts with only a small amount of domain-specific parameters in a Convolutional Neural Network (CNN). By exploiting the observation that a convolutional filter can be well approximated as a linear combination of a small set of dict…
New model maps malaria prevalence across Kenya's changing administrative boundaries.
Paper models spatio-temporal extremes using conditional variational autoencoders.
Modeling wind dynamics in Saudi Arabia using deep learning and stochastic PDEs.
We investigate the set of spacetime general coordinate transformations (G.C.T.) which leave the line element of a generic Bianchi Type Geometry, quasi-form invariant; i.e. preserve manifest spatial Homogeneity. We find that these G.C.T.'s, induce special time-dependent automorphic changes, on the spatial scale factor m…
Spatial blind source separation simplifies multivariate spatial prediction.
Recurrent and convolutional neural networks are the most common architectures used for time series forecasting in deep learning literature. These networks use parameter sharing by repeating a set of fixed architectures with fixed parameters over time or space. The result is that the overall architecture is time-invaria…
The paper introduces groupoid racks for spatial surfaces.
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 …
STICC clusters geographic objects considering both spatial contiguity and attributes.
Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step in isolation. With the advent…
A new framework for averaging spatio-temporal signals using optimal transport and soft alignments.
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…
Defines non-parabolic curves in spatial hybrid space with applications.
Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused b…
A framework converts spatial data into embeddings for insurance risk modelling.