arXiv research

SVMs improve forest fire detection accuracy on challenging datasets.

Forest Fire Clustering discovers cell types from single-cell data.

The paper uses transformed ANOVA to identify important fire detection variables.

Machine learning helps predict smoke types for safer forest burns.

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…

New method predicts positive samples with missing labels.

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a sco…

Paper proposes decision-theoretic approach to combat wildfires.

A new model predicts wildfire spread with wind and slope effects.

Pyrocast predicts pyrocumulonimbus clouds six hours before they form.

Study creates open-access wildfire dataset for Russia.

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…

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…

Model predicts short-term Amazon rainforest fires with high accuracy.

The firefighter game problem on locally finite connected graphs was introduced by Bert Hartnell. The game on a graph $G$ can be described as follows: let $f_n$ be a sequence of positive integers; an initial fire starts at a finite set of vertices; at each (integer) time $n\geq 1$, $f_n$ vertices which are not on fire b…

SAE-FiRE extracts key financial info from long documents, improving earnings surprise predictions.

Paper analyzes fire sales in a network of banks using VWAP and LOB pricing.

Models predict fire and other emergencies in Edmonton.

Complex contagion model explains financial fire sales through continuous asset prices.

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 …

This paper addresses the problem of learning the concept of "propagation" in the pretopology theoretical formalism. Our proposal is first to define the pseudo-closure operator (modeling the propagation concept) as a logical combination of neighborhoods. We show that learning such an operator lapses into the Multiple In…

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.

Paper compares GRU and LSTM for predicting wildfire spread direction.

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…

Managers of US National Forests must decide what policy to apply for dealing with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber companies, home owners, and wildlife biologists) have often led to spirited political debates and even violent eco-terrorism. One way to transform these conflicts into…

FIRES framework selects stable features from online data.

Detects project management anti-patterns using code and issue data.

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 …

This work bridges theory and practice in spiking reservoirs, identifying robust parameter ranges.

iMondrian forest combines isolation forest and Mondrian forest for better anomaly detection.

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…

Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random forests achieve competitive predictive performance and are computationally efficient to train and test, making them excellent candidates for r…

New random forest method provides optimal rates and confidence bands.

RFpredInterval package builds prediction intervals for random forests and boosted forests.