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 uses machine learning to predict predator-prey dynamics without prior knowledge.
Sustainability became the most important component of world development, as countries worldwide fight the battle against the climate change. To understand the effects of climate change, the ecological footprint, along with the biocapacity should be observed. The big part of the ecological footprint, the carbon footprin…
Improved forecasting of suicide attempts using LSGPs for patients with little data.
Hybrid model improves forest growth predictions.
SGNNs use simulations to train neural networks, improving scientific forecasting and interpretability.
Delay embedding---a method for reconstructing dynamical systems by delay coordinates---is widely used to forecast nonlinear time series as a model-free approach. When multivariate time series are observed, several existing frameworks can be applied to yield a single forecast combining multiple forecasts derived from va…
The study examines how language models learn to represent the world, identifying conditions for ecological veridicality.
CAST predicts distribution-valued time series by stabilizing and transporting simplex-supported successors.
EcoCast predicts biodiversity risks using satellite data and citizen science records.
Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms - including Support Vector Machines (SVMs) -- have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecologic…
Study creates open-access wildfire dataset for Russia.
What do binary (or probabilistic) forecasting abilities have to do with overall performance? We map the difference between (univariate) binary predictions, bets and "beliefs" (expressed as a specific "event" will happen/will not happen) and real-world continuous payoffs (numerical benefits or harm from an event) and sh…
Empirical mode modeling improves state-space analysis of noisy data.
Classification is an important supervised machine learning method, which is necessary and challenging issue for ecological research. It offers a way to classify a dataset into subsets that share common patterns. Notably, there are many classification algorithms to choose from, each making certain assumptions about the …
Ecological systems have a high level of complexity combined with stability and rich biodiversity. Recently, the analysis of their properties and evolution has been pushed forward on a basis of concept of mutualistic networks that provides a detailed understanding of their features being linked to a high nestedness of t…
StatEcoNet models species distribution using neural networks to correct observation errors.
We provide a machine learning solution that replaces the traditional methods for deciding the pesticide application time of Sunn Pest. We correlate climate data with phases of Sunn Pest in its life-cycle and decide whether the fields should be sprayed. Our solution includes two groups of prediction models. The first gr…
Evology models US equity mutual funds interactions for investment strategies.
Decodes neural activity to assess latent states in real-world driving tasks.
Study finds cryptocurrency market diversity patterns inconsistent with neutral models.
We combine fine-grained spatially referenced census data with the vote outcomes from the 2016 US presidential election. Using this dataset, we perform ecological inference using distribution regression (Flaxman et al, KDD 2015) with a multinomial-logit regression so as to model the vote outcome Trump, Clinton, Other / …
New insights into optimal portfolios and ecological equilibria reveal surprising complexity.
LLMs in financial markets show diverse behaviors, from stable to speculative, challenging rational expectations.
Market inefficiencies arise from density-dependent returns in a noisy environment.
Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait-matching for determining species in…
Framework for inferring latent structure from sparse, imperfectly detected bipartite networks.
The rise in online social networking has brought about a revolution in social relations. However, its effects on offline interactions and its implications for collective well-being are still not clear and are under-investigated. We study the ecology of online and offline interaction in an evolutionary game framework wh…
Earth observation embeddings can convert discrete biome maps into continuous representations that better capture ecological variation.
The paper uses a graph autoencoder to learn unbiased plant-pollinator interaction embeddings.
New hybrid model predicts carbon prices using blockchain data.
A review of ML and DL for ecological data analysis.
Reflective of income and wealth distributions, philanthropic gifting appears to follow an approximate power-law size distribution as measured by the size of gifts received by individual institutions. We explore the ecology of gifting by analysing data sets of individual gifts for a diverse group of institutions dedicat…
A policy compass indicates the direction in which an institution is going in terms of three general qualities. The three qualities are: suppression, harmony and passion. Any formal institution can develop a policy compass to examine the discrepancy between what the institution would like to do (suggested in its mandate…
High-dimensional random geometry shows phase transitions in various problems.
Bipartite networks provide an insightful representation of many systems, ranging from mutualistic networks of species interactions to investment networks in finance. The analysis of their topological structures has revealed the ubiquitous presence of properties which seem to characterize many - apparently different - s…
We consider a problem of ecological inference, in which individual-level covariates are known, but labeled data is available only at the aggregate level. The intended application is modeling voter preferences in elections. In Rosenman and Viswanathan (2018), we proposed modeling individual voter probabilities via a log…
Modeling vessel speed to balance efficiency and environmental risks in Arctic shipping.
The study analyzes river water quality using statistical and machine learning methods.
Exact results on power-law distributions in systems with resets.
The paper explores how innovative financing solutions boost Moroccan businesses' performance.
In this paper, we show how simple logistic growth that was studied intensively during the last 200 years in many domains of science could be extended in a rather simple way and with these extensions is capable to produce a collection of behaviors widely observed in an enormous number of real-life systems in Economics, …
Critiques causal reductionism in financial studies, suggesting alternative approaches.
Neural likelihood approximates integer time series data efficiently.
How are economic activities linked to geographic locations? To answer this question, we use a data-driven approach that builds on the information about location, ownership and economic activities of the world's 3,000 largest firms and their almost one million subsidiaries. From this information we generate a bipartite …
TMLE improves IPM estimation for ecological population dynamics.
The cryptocurrency market surpassed the barrier of \$100 billion market capitalization in June 2017, after months of steady growth. Despite its increasing relevance in the financial world, however, a comprehensive analysis of the whole system is still lacking, as most studies have focused exclusively on the behaviour o…
We present a model of an economy inspired by individual based model approaches in evolutionary ecology. We demonstrate that evolutionary dynamics in a space of companies interconnected through a correlated interaction matrix produces time dependencies of the total size of the economy total number of companies, companie…
Graph neural networks help assess how global changes affect plant-pollinator networks.