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.
Machine learning predicts homicide clearance rates with SHAP explaining key features.
Homicide mortality is a worldwide concern and has occupied the agenda of researchers and public managers. In Brazil, homicide is the third leading cause of death in the general population and the first in the 15-39 age group. In South America, Brazil has the third highest homicide mortality, behind Venezuela and Colomb…
RestoreAI predicts landmine risk from patterns, improving clearance efficiency.
stCEG models spatial events using Chain Event Graphs in R.
Causal discovery algorithms can help generate legal arguments.
Paper proposes a human-algorithm approach to reduce medical device recall risk and workload.
Understanding the causes of crime is a longstanding issue in researcher's agenda. While it is a hard task to extract causality from data, several linear models have been proposed to predict crime through the existing correlations between crime and urban metrics. However, because of non-Gaussian distributions and multic…
We present a probabilistic method for linking multiple datafiles. This task is not trivial in the absence of unique identifiers for the individuals recorded. This is a common scenario when linking census data to coverage measurement surveys for census coverage evaluation, and in general when multiple record-systems nee…
Robust feature-weighted jump models for time-dependent clustering
Adapts MBDOE for real-time parameter estimation in complex systems.
This paper develops an XVA (costs) analysis of centrally cleared trading, parallel to the one that has been developed in the last years for bilateral transactions. We introduce a dynamic framework that incorporates the sequence of cash-flows involved in the waterfall of resources of a clearing house. The total cost of …
Study reveals finite-size effects and sensitivity to random numbers in Levy-Levy-Solomon model.
Predicting traffic incident duration is a major challenge for many traffic centres around the world. Most research studies focus on predicting the incident duration on motorways rather than arterial roads, due to a high network complexity and lack of data. In this paper we propose a bi-level framework for predicting th…
LDF combines neural networks with probabilistic models for data fusion.
This paper addresses AMMs for expiring assets, ensuring liquidity and risk management.
Buried landmines and unexploded remnants of war are a constant threat for the population of many countries that have been hit by wars in the past years. The huge amount of human lives lost due to this phenomenon has been a strong motivation for the research community toward the development of safe and robust techniques…
Paper derives policy rules from observational data for hepatitis C treatment.