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.
New algorithm predicts geolocation of fungi samples with high accuracy.
Pattern ensembling fills in missing or inaccurate trajectory data.
The problem of predicting the location of users on large social networks like Twitter has emerged from real-life applications such as social unrest detection and online marketing. Twitter user geolocation is a difficult and active research topic with a vast literature. Most of the proposed methods follow either a conte…
Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class membership labels are unavailable. Probabilistic models for predicting the protected class based on observable proxies, such as surname and geolocation for race, are sometimes used to …
Post-pandemic, work patterns shifted with fewer days in offices and a new midweek mountain.
New method generates geolocated synthetic populations from real data.
Data on human spatial distribution and movement is essential for understanding and analyzing social systems. However existing sources for this data are lacking in various ways; difficult to access, biased, have poor geographical or temporal resolution, or are significantly delayed. In this paper, we describe how geoloc…
In this paper we tackle the issue of clustering trajectories of geolocalized observations. Using clustering technics based on the choice of a distance between the observations, we first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then based on the limitation…
Users form information trails as they browse the web, checkin with a geolocation, rate items, or consume media. A common problem is to predict what a user might do next for the purposes of guidance, recommendation, or prefetching. First-order and higher-order Markov chains have been widely used methods to study such se…
Spatial variable selection is crucial for reliable spatial predictions in machine learning.
Chagas disease is a neglected disease, and information about its geographical spread is very scarse. We analyze here mobility and calling patterns in order to identify potential risk zones for the disease, by using public health information and mobile phone records. Geolocalized call records are rich in social and mobi…
Traditional semantic similarity models often fail to encapsulate the external context in which texts are situated. However, textual datasets generated on mobile platforms can help us build a truer representation of semantic similarity by introducing multimodal data. This is especially important in sparse datasets, maki…
A new platform predicts object movements without needing specific context.
The paper classifies U.S. crop types using hyperspectral satellite imagery.
This work improves local differential privacy by considering context to make it more effective.
This study maps cycling risks and discomfort in Zurich, offering personalized route recommendations.
Paper tackles fairness in algorithms by predicting protected class from auxiliary data.
Statistical learning approach for spatial data prediction.
Study uses satellite and lidar data to map forest height and biomass in France.