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.
DDPMs can reproduce medical image context, showing interpolation between samples.
Paper proposes dp-VAE for preserving spatial context in gene expression data.
Spatially-aware metrics improve uncertainty evaluation in segmentation.
Tiled Squeeze-and-Excite improves channel attention with local spatial context.
GSNE improves house price predictions by embedding geo-spatial context.
Spatial graphs study tangle replacement with equivalence classes.
Cellina uses supervised disentanglement to predict cell behavior in tissues.
We review the construction and context of a stable homotopy refinement of Khovanov homology.
MSFA clusters high-dimensional spatial data using spline-based covariance structures.
The consideration of spatial effects at a regional level is becoming increasingly frequent and the work of Anselin (1988), among others, has contributed to this. This study analyses, through cross-section estimation methods, the influence of spatial effects in productivity (product per worker) in the NUTs III economic …
Researchers develop tests to assess quality of GAN-generated images.
This study analyses, through cross-section estimation methods, the influence of spatial effects in productivity (product per worker), at economic sectors level of the NUTs III of mainland Portugal, from 1995 to 1999 and from 2000 to 2005 (taking in count the data availability and the Portuguese and European context), c…
Data driven segmentation is an important initial step of shape prior-based segmentation methods since it is assumed that the data term brings a curve to a plausible level so that shape and data terms can then work together to produce better segmentations. When purely data driven segmentation produces poor results, the …
Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of …
New method reduces spatial graphs while preserving their topological features.
Spatially-aware model improves earthquake hazard assessment accuracy.
This work creates a system for understanding human movement in spaces.
Spatial machine learning improves poverty targeting in Indonesia.
S-SIRUS explains RF for spatial data, improving accuracy and interpretability.
TransST improves spatial transcriptomics data analysis by identifying cell clusters and biomarkers.
PriorVAE uses VAEs to efficiently encode spatial priors for small-area estimation.
Unsupervised text encoding models have recently fueled substantial progress in NLP. The key idea is to use neural networks to convert words in texts to vector space representations based on word positions in a sentence and their contexts, which are suitable for end-to-end training of downstream tasks. We see a striking…
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
STOIC improves energy demand forecasting with reliable uncertainty estimates.
Decentralized framework for spatial data inference over vulnerabilities.
Paper develops Dense NN models for temporal-spatial data with improved performance.
Geographic object-based image analysis (GEOBIA) framework has gained increasing interest recently. Following this popular paradigm, we propose a novel multiscale classification approach operating on a hierarchical image representation built from two images at different resolutions. They capture the same scene with diff…
Spatially relaxed inference tackles high-dimensional linear models with correlated covariates.
Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becomes crucial for building modern smart cities. In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factoriz…
Efficiently infers gene regulatory networks from spatial data.
In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance. However, conventional spatial context-based methods simply assume that spatially neighboring pixels should correspond to the same land-cover class, so they often fail to correctly discover …
Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distribution…
EFA extends self-attention to handle mixed data types and dynamic relevance.
Model-based approaches bear great promise for decision making of agents interacting with the physical world. In the context of spatial environments, different types of problems such as localisation, mapping, navigation or autonomous exploration are typically adressed with specialised methods, often relying on detailed …
Understanding and reasoning about places and their relationships are critical for many applications. Places are traditionally curated by a small group of people as place gazetteers and are represented by an ID with spatial extent, category, and other descriptions. However, a place context is described to a large extent…
GWRBoost improves GWR for better spatial relationship quantification.
Graph neural networks improve residential location choice predictions.
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…
New method learns time-invariant rewards from demonstrations.
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…
The paper examines uncertainty calibration for object detection models in autonomous driving.
Model traffic congestion events using multi-modal data and attention-based neural networks.
This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation. Our neural networks for separation use an advanced convolutional architecture trained with a novel stabilized signal-to-noise ratio loss function. For beamformi…
In this expository article, we introduce the topological ideas and context central to the Poincare Conjecture. Our account is intended for a general audience, providing intuitive definitions and spatial intuition whenever possible. We define surfaces and their natural generalizations, manifolds. We then discuss the cla…
Accurate taxi demand-supply forecasting is a challenging application of ITS (Intelligent Transportation Systems), due to the complex spatial and temporal patterns. We investigate the impact of different spatial partitioning techniques on the prediction performance of an LSTM (Long Short-Term Memory) network, in the con…
Study uses trajectory embedding to measure place function similarity at fine spatial granularity.
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…
This article addresses the modeling of reverberant recording environments in the context of under-determined convolutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random variable whose covariance encodes the spatial cha…