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.
FCNv2 robustness tested under noise and random initial conditions.
Hurricanes are cyclones circulating about a defined center whose closed wind speeds exceed 75 mph originating over tropical and subtropical waters. At landfall, hurricanes can result in severe disasters. The accuracy of predicting their trajectory paths is critical to reduce economic loss and save human lives. Given th…
Predict real-time crash risks during hurricane evacuations using connected vehicle data.
Improved neural network predicts tropical storm trajectories and Bayesian intervals.
Bayesian model uses mobile data to assess business resilience after hurricanes.
Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people ``on the ground'' post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emerg…
In the first of these two lectures, I describe a gauge theory approach to understanding quantum knot invariants as Laurent polynomials in a complex variable q. The two main steps are to reinterpret three-dimensional Chern-Simons gauge theory in four dimensional terms and then to apply electric-magnetic duality. The var…
Standard supervised learning procedures are validated against a test set that is assumed to have come from the same distribution as the training data. However, in many problems, the test data may have come from a different distribution. We consider the case of having many labeled observations from one distribution, $P_…
Graph Attention Networks predict power outage durations from natural disasters.
Global physical event detection has traditionally relied on dense coverage of physical sensors around the world; while this is an expensive undertaking, there have not been alternatives until recently. The ubiquity of social networks and human sensors in the field provides a tremendous amount of real-time, live data ab…
Transfer learning framework for fragility modeling under domain shift and class imbalance
Transforms curves and surfaces for efficient geometric analysis.
Proposes ridge regression on Riemannian manifolds for time-series prediction.
In this paper we study the shape space of curves with values in a homogeneous space , where is a Lie group and is a compact Lie subgroup. We generalize the square root velocity framework to obtain a reparametrization invariant metric on the space of curves in . By identifying curves in with thei…
We use splines and the Sasaki metric to analyze and compare manifold-valued trajectories.
Proposes a new Hawkes process bandit model for disaster search and rescue.
New method explains anomalies in multivariate time series data.
Anomaly-aware forecast improves accuracy for extreme events.
Efficiently samples conformal boundaries in high dimensions using flows.
Study optimizes climate adaptation strategies for NYC.
The forecast of tropical cyclone trajectories is crucial for the protection of people and property. Although forecast dynamical models can provide high-precision short-term forecasts, they are computationally demanding, and current statistical forecasting models have much room for improvement given that the database of…
New method predicts spatial events like hurricanes and earthquakes with uncertainty.
Event detection has long been the domain of physical sensors operating in a static dataset assumption. The prevalence of social media and web access has led to the emergence of social, or human sensors who report on events globally. This warrants development of event detectors that can take advantage of the truly dense…
Paper improves predictive distributions for rare events using a simple framework.
Learning with rejection (LWR) allows development of machine learning systems with the ability to discard low confidence decisions generated by a prediction model. That is, just like human experts, LWR allows machine models to abstain from generating a prediction when reliability of the prediction is expected to be low.…
Then detection and identification of extreme weather events in large-scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs…
Modeling complex systems with multi-resolution data and causal dependencies.
We propose a method for causal inference using satellite image time series, in order to determine the treatment effects of interventions which impact climate change, such as deforestation. Simply put, the aim is to quantify the 'before versus after' effect of climate related human driven interventions, such as urbaniza…
The paper introduces diagnostic transport maps to improve the reliability of rare event predictions.
Hybrid deep learning model predicts urban floods with high accuracy.
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…