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.
Models predict fire and other emergencies in Edmonton.
SAE-FiRE extracts key financial info from long documents, improving earnings surprise predictions.
Study creates open-access wildfire dataset for Russia.
Model predicts short-term Amazon rainforest fires with high accuracy.
Paper compares GRU and LSTM for predicting wildfire spread direction.
Machine learning improves wildfire science and management, but requires expert knowledge.
FIRES framework selects stable features from online data.
Paper proposes decision-theoretic approach to combat wildfires.
SVMs improve forest fire detection accuracy on challenging datasets.
FIRE extracts interpretable rules from tree ensembles.
Dynamic risk assessment method for WUI fires improves upon static frameworks.
Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competence as being from the same class. The Frienemy Indecision REgion DES (FIRE-DES) tackles this problem by pre-selecting classifiers that correc…
New method predicts positive samples with missing labels.
Forest Fire Clustering discovers cell types from single-cell data.
The firefighter game problem on locally finite connected graphs was introduced by Bert Hartnell. The game on a graph can be described as follows: let be a sequence of positive integers; an initial fire starts at a finite set of vertices; at each (integer) time , vertices which are not on fire b…
Pyrocast predicts pyrocumulonimbus clouds six hours before they form.
The paper uses transformed ANOVA to identify important fire detection variables.
Paper analyzes fire sales in a network of banks using VWAP and LOB pricing.
Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood …
Complex contagion model explains financial fire sales through continuous asset prices.
Detects project management anti-patterns using code and issue data.
This paper proposes a vision-based fire and smoke segmentation system which use spatial, temporal and motion information to extract the desired regions from the video frames. The fusion of information is done using multiple features such as optical flow, divergence and intensity values. These features extracted from th…
Mathematical framework investigates fire sales amplification and stability.
Neural population activity often exhibits rich variability and temporal structure. This variability is thought to arise from single-neuron stochasticity, neural dynamics on short time-scales, as well as from modulations of neural firing properties on long time-scales, often referred to as "non-stationarity". To better …
Team aims to predict particulate matter levels on ISS using Bi-GRU.
A new model predicts wildfire spread with wind and slope effects.
Develops a new neural spike train decoding framework using topological data.
Chronnet models spatiotemporal data using chronological networks.
This paper provides a framework for modeling financial contagion in a network subject to fire sales and price impacts, but allowing for firms to borrow to cover their shortfall as well. We consider both uncollateralized and collateralized loans. The main results of this work are providing sufficient conditions for exis…
FIRE PBT improves neural network training by focusing on long-term performance.
This paper provides a framework for modeling the financial system with multiple illiquid assets when liquidation of illiquid assets is caused by failure to meet a leverage requirement. This extends the network model of Cifuentes, Shin & Ferrucci (2005) which incorporates a single asset with fire sales and capital adequ…
Study shows how capital constraints can lead to systemic crises in financial systems.
Recently, it has been shown that spiking neural networks (SNNs) can be trained efficiently, in a supervised manner, using backpropagation through time. Indeed, the most commonly used spiking neuron model, the leaky integrate-and-fire neuron, obeys a differential equation which can be approximated using discrete time st…
FIRE method improves model performance in federated learning by penalizing fragmentation-induced covariate shifts.
We introduce tools to capture the dynamics of three different pathways, in which the synchronization of human decision-making could lead to turbulent periods and contagion phenomena in financial markets. The first pathway is caused when stock market indices, seen as a set of coupled integrate-and-fire oscillators, sync…
Storage has become a constrained resource on smartphones. Gaming is a popular activity on mobile devices and the explosive growth in the number of games coupled with their growing size contributes to the storage crunch. Even where storage is plentiful, it takes a long time to download and install a heavy app before it …
Neuroscientific theory suggests that dopaminergic neurons broadcast global reward prediction errors to large areas of the brain influencing the synaptic plasticity of the neurons in those regions. We build on this theory to propose a multi-agent learning framework with spiking neurons in the generalized linear model (G…
Machine learning helps predict smoke types for safer forest burns.
Maximal Rate of Stepwise Uncertainty Reduction selects simulations to reduce uncertainty efficiently.
DIET-SNN optimizes SNNs for faster, lower-energy image classification.
The seemingly stochastic transient dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference. In vitro neurons, on the other hand, exhibit a highly deterministic response to various types of stimulation. We show that an ensemble of deterministic le…
The mushroom body is the key network for the representation of learned olfactory stimuli in Drosophila and insects. The sparse activity of Kenyon cells, the principal neurons in the mushroom body, plays a key role in the learned classification of different odours. In the specific case of the fruit fly, the sparseness o…
Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian …
One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hi…
This work bridges theory and practice in spiking reservoirs, identifying robust parameter ranges.
Assessing systemic risk in financial markets is of great importance but it often requires data that are unavailable or available at a very low frequency. For this reason, systemic risk assessment with partial information is potentially very useful for regulators and other stakeholders. In this paper we consider systemi…
Recently, large-scale cascading failures in complex systems have garnered substantial attention. Such extreme events have been treated as an integral part of the self-organized criticality (SOC). Recent empirical work has suggested that some extreme events systematically deviate from the SOC paradigm, requiring a diffe…
Improved model predicts wildfire spread on slopes.