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.
Study creates open-access wildfire dataset for Russia.
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…
Model predicts short-term Amazon rainforest fires with high accuracy.
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…
The paper uses transformed ANOVA to identify important fire detection variables.
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.
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…
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…
Paper proposes decision-theoretic approach to combat wildfires.
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 …
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…
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…
New method predicts positive samples with missing labels.
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.
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…
Develops a new neural spike train decoding framework using topological data.
The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro. Based on a propagation of the membrane autocorrelation across spike bursts, we …
This study develops an unsupervised learning algorithm for products of expert capsules with dynamic routing. Analogous to binary-valued neurons in Restricted Boltzmann Machines, the magnitude of a squashed capsule firing takes values between zero and one, representing the probability of the capsule being on. This analo…
A new model predicts wildfire spread with wind and slope effects.
New method identifies algo trading strategies as liquidity consumers or providers.
Study identifies key drivers and spatio-temporal trends of extreme Mediterranean wildfires.
Pyrocast predicts pyrocumulonimbus clouds six hours before they form.
We present two Bayesian procedures to infer the interactions and external currents in an assembly of stochastic integrate-and-fire neurons from the recording of their spiking activity. The first procedure is based on the exact calculation of the most likely time courses of the neuron membrane potentials conditioned by …
The scope of financial systemic risk research encompasses a wide range of interbank channels and effects, including asset correlation shocks, default contagion, illiquidity contagion, and asset fire sales. This paper introduces a financial network model that combines the default and liquidity stress mechanisms into a "…
The creation of the Renewable Energy Law (Law 1715 of 2014) promotes the introduction of large-scale renewable energy generation in the Colombian electricity market. The new legislation aims to diversify the country's generation matrix, mainly composed of hydro and fuel-based generation, with a share of 66% and 34% res…