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.
A scheme for UAVs to borrow spectrum from terrestrial networks for disaster relief.
Bayesian model uses mobile data to assess business resilience after hurricanes.
Model analyzes mortgage relief during financial hardship.
ReliefE ranks features faster and better in high-dimensional data.
Paper presents a model for identifying informative COVID-19 tweets.
Basel III introduces new capital charges for CVA. These charges, and the Basel 2.5 default capital charge can be mitigated by CDS. Therefore, to price in the capital relief that CDS contracts provide, we introduce a CDS pricing model with three legs: premium; default protection; and capital relief. If markets are compl…
In this paper, the authors aim to combine the latest state of the art models in image recognition with the best publicly available satellite images to create a system for landslide risk mitigation. We focus first on landslide detection and further propose a similar system to be used for prediction. Such models are valu…
Estimates funding impact from an algorithmic relief rule, finding little effect on hospital activities.
Paper examines how income support affects retirement decisions for low-income individuals.
Monitoring of disasters is crucial for mitigating their effects on the environment and human population, and can be facilitated by the use of unmanned aerial vehicles (UAV), equipped with camera sensors that produce aerial photos of the areas of interest. A modern technique for recognition of events based on aerial pho…
In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental, and human losses. The unpredictable nature of natural disasters' behavior makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys…
NDI aims to forecast future natural disasters risk for insurers.
Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in advanced but computationally expensive methodologies able to model complex associations. Specifically, there is a need for feature selection methods that are computat…
Study proposes a tax-based system to share disaster risk among regions.
This paper improves typhoon intensity prediction using social media data and semantic word embeddings.
Automatic post-disaster damage detection using aerial imagery is crucial for quick assessment of damage caused by disaster and development of a recovery plan. The main problem preventing us from creating an applicable model in practice is that damaged (positive) examples we are trying to detect are much harder to obtai…
Proposes a new Hawkes process bandit model for disaster search and rescue.
This paper classifies typhoon damage features using aerial photography.
Unified framework for growth models with environmental risk and pollution-dependent disasters.
In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite image…
Stocks of more resilient firms outperformed during the pandemic, reflecting disaster risk.
DASC combines social media and car sensors to improve disaster response.
Graph Attention Networks predict power outage durations from natural disasters.
I-AID categorizes disaster tweets into useful information types.
Nostradamus links climate and stock market performance.
Paper detects important sub-events in disaster tweets.
A crucial and time-sensitive task when any disaster occurs is to rescue victims and distribute resources to the right groups and locations. This task is challenging in populated urban areas, due to the huge burst of help requests generated in a very short period. To improve the efficiency of the emergency response in t…
Archetypal analysis represents a set of observations as convex combinations of pure patterns, or archetypes. The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real valued. This, unfortunately, is not compatible with many practical situations…
RGRR allocates between QQQ and DIA based on relative states, improving Sharpe and CAGR.
Machine learning improves ASD diagnosis accuracy.
Study uses SVM to predict weather-induced home insurance claims and losses.
Modeling complex systems with multi-resolution data and causal dependencies.
The study examines how formal index insurance compares to informal risk sharing in managing natural disasters.
In this paper we introduce a new feature selection algorithm to remove the irrelevant or redundant features in the data sets. In this algorithm the importance of a feature is based on its fitting to the Catastrophe model. Akaike information crite- rion value is used for ranking the features in the data set. The propose…
It is a challenging and complex task to acquire information from different regions of a disaster-affected area in a timely fashion. The extensive spread and reach of social media and networks allow people to share information in real-time. However, the processing of social media data and gathering of valuable informati…
Relief based algorithms have often been claimed to uncover feature interactions. However, it is still unclear whether and how interaction terms will be differentiated from marginal effects. In this paper, we propose IMMIGRATE algorithm by including and training weights for interaction terms. Besides applying the large …
This paper analyzes extreme flooding risks and proposes insurance and bond solutions.
Many post-disaster and -conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of aging and deteriorating bridges increase, it is necessary to quantify their load characteristics in order to inform maintenance …
We develop a simple test for deviations from power law tails, which is based on the asymptotic properties of the empirical distribution function. We use this test to answer the question whether great natural disasters, financial crashes or electricity price spikes should be classified as dragon kings or 'only' as black…
This study tackles basis risk in weather parametric insurance using Monte Carlo simulations.
Proposes semi-supervised feature ranking for handling high-dimensional, unlabeled data.
Investigates how extreme temperature events affect global equity portfolios.
Spatially-aware model improves earthquake hazard assessment accuracy.
A framework for cost of belief revision in uncertain agents.
Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements. We co-create products with UK and in-country commercial, GO and NGO pa…
Natural disasters can have catastrophic impacts on the functionality of infrastructure systems and cause severe physical and socio-economic losses. Given budget constraints, it is crucial to optimize decisions regarding mitigation, preparedness, response, and recovery practices for these systems. This requires accurate…
Between 2003 and 2015 the prices of apartments in Hong Kong (adjusted for inflation) increased by a factor of 3.8. This is much higher than in the United States prior to the so-called subprime crisis of 2007. The analysis of this speculative episode confirms the mechanism and regularities already highlighted by the pre…
Study explores rumor spread on Twitter using supervised learning.