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…
On-device research index
arXiv research
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.
169,051 papers · 148 categories
Trend · papers per month
48 results for “ecological features”
StatEcoNet models species distribution using neural networks to correct observation errors.
problem Correcting observation errors in wildlife surveys for accurate species distribution modeling.
method StatEcoNet integrates a graphical generative model with neural networks to address SDM challenges.
result StatEcoNet outperforms traditional methods on simulated and real datasets.
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 / …
Paper improves geographic location embeddings using Flickr tags and structured data.
problem Lack of integration between Flickr metadata and structured scientific data.
method Learning vector space embeddings of geographic locations.
result Improved predictions of ecological features using the new method.
New machine learning algorithms inspired by ecological principles.
problem Improving machine learning performance.
method Inspired by ecological dynamics, developed new online SVM algorithms.
result New algorithms outperform traditional methods on the MNIST dataset.
The study examines how language models learn to represent the world, identifying conditions for ecological veridicality.
problem Understanding when language models learn to represent the world accurately and how this learning process can fail.
method Analyzes the Bayes-optimal next-token cross-entropy decomposition and the role of training ecology in shaping model representations.
result The minimum-complexity zero-excess solution is the quotient partition by training equivalence, and this solution is not preserved in in-context learning or per-task adaptation.
Time Series Vector Autoregression Prediction of the Ecological Footprint based on Energy Parametersphysics.soc-ph
Paper predicts ecological footprint using energy parameters.
problem Forecasting the ecological footprint using energy parameters.
method Time series vector autoregression model.
result Predictions indicate increasing consumption and declining coal energy.
This paper compares machine learning algorithms for ecological data.
problem Classifying ecological datasets into subsets with common patterns.
method Applied eight machine learning classification algorithms to ecological data.
result Linear Discriminant Analysis and k-nearest neighbors are the best methods.
A new testing method for random forests scales well for big data.
problem Efficiently assessing feature significance in random forests.
method Permutation-style testing approach, leveraging exchangeability arguments.
result The test maintains high power with significantly fewer computations.
From Ecology to Finance (and Back?): Recent Advancements in the Analysis of Bipartite Networksphysics.soc-ph
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…
Machine learning improves predicting species interactions based on traits.
problem Variability in empirical trait-matching studies for ecological networks.
method Compared conventional GLMs with ML models (Random Forest, Boosted Regression Trees, etc.) on simulated and real data.
result ML models outperform GLMs in predicting species interactions and identifying trait-matching combinations.
Evology models US equity mutual funds interactions for investment strategies.
problem Understanding complex interactions in financial markets.
method Agent-based model (ABM) of US stock market participants and their strategies.
result Trading strategies interact with other market participants and conditions.
Decodes neural activity to assess latent states in real-world driving tasks.
problem Understanding latent states during complex tasks in natural settings.
method Domain-generalized models trained on controlled lab paradigms applied to ecologically valid driving tasks.
result Changes in neural activity correlate with changes in behavior and task performance.
Confounding biases recommender systems, even when data seems fully observed.
problem Unmeasured features influencing both treatment and outcome in recommender systems.
method Illustrations and simulation studies showing how common practices introduce confounding.
result Standard recommender system practices can introduce confounding, reducing performance.
A compass guides institutions towards ecological economics goals.
problem Aligning institutional goals with ecological economics principles.
method Develops a policy compass for institutions, incorporating ecological economics principles.
result Adapted policy compass aligns institutional actions with ecological economics goals.
Study finds cryptocurrency market diversity patterns inconsistent with neutral models.
problem Cryptocurrency market diversity patterns not consistent with neutral models.
method Analysis borrowing methods from ecology, focusing on diversity patterns and community structure.
result Cryptocurrency market diversity patterns not consistent with neutral models, suggesting strong interactions between species.
A new spin on optimal portfolios and ecological equilibriacond-mat.stat-mech
New insights into optimal portfolios and ecological equilibria reveal surprising complexity.
problem Optimal portfolio construction with ecological constraints.
method Computational analysis of multispecies Lotka-Volterra equations with unit rank interaction matrices.
result Logarithm of the average number of solutions grows as \(N^{2/3}\), with most likely solutions being much smaller.
We introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of userdefined, spatial features. We describe an efficient stochastic gradient algorithm for fitting model parameters to da…
Market inefficiencies arise from density-dependent returns in a noisy environment.
problem Market inefficiencies and excess volatility.
method Developed a market model using ecological concepts.
result Market dynamics are density-dependent, leading to inefficiencies.
Sparse Network Inference under Imperfect Detection and its Application to Ecological Networksstat.ML
Framework for inferring latent structure from sparse, imperfectly detected bipartite networks.
problem Recovering latent structure from sparse, imperfectly detected bipartite networks in ecology.
method Structured sparse nonnegative low-rank factorization with detection probability estimation and ADMM-based algorithm.
result Improved recovery of latent factors and structure compared to existing methods.
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…
A new probabilistic model detects communities in networks using both structure and node features.
problem Detecting communities in networks with node features for more accurate results.
method Generative probabilistic model considering network structure and node features.
result The model accurately detects communities and determines feature strength.
Earth observation embeddings can convert discrete biome maps into continuous representations that better capture ecological variation.
problem Biome maps impose categorical boundaries that compress continuous variation in biotic communities.
method Fit a linear classifier on Earth observation embeddings to predict biome labels.
result Continuous biome representation outperforms discrete biome labels for predicting species occurrence.
The paper uses a graph autoencoder to learn unbiased plant-pollinator interaction embeddings.
problem Sampling bias in citizen science data affects ecological network analysis.
method Bipartite graph variational autoencoder with HSIC for fairness.
result The method mitigates sampling bias and provides unbiased embeddings.
New results for modeling voter probabilities in elections.
problem Modeling voter preferences with aggregate data and individual covariates.
method Maximum likelihood estimation for Poisson binomial distribution, approximated with heteroscedastic Gaussian.
result Existence and curvature results for the MLE of the Poisson binomial likelihood.
Machine learning detects frog calls in audio recordings with high accuracy.
problem Estimating frog presence in audio recordings from tropical environments.
method Gaussian mixture model classification of frog calls with 20 cepstral features.
result The algorithm achieved an average weighted error rate of 0.9% for nine species classification.
Spatially constrained clustering divides landscapes into homogeneous regions with spatial contiguity.
problem Dividing landscapes into homogeneous patches with spatial contiguity and hierarchy.
method Developed a spatially constrained spectral clustering framework using a flexible kernel and recursive bisection.
result The proposed framework outperforms baseline methods in balancing region contiguity and homogeneity.
Study uses machine learning to predict predator-prey dynamics without prior knowledge.
problem Predicting predator-prey interactions without prior knowledge of the system.
method Applied Neural Ordinary Differential Equations (Neural ODEs) and Universal Differential Equations (UDEs) to the Lotka-Volterra model.
result UDEs outperform Neural ODEs in predicting predator-prey dynamics, especially in noisy data.
Market Ecology, Pareto Wealth Distribution and Leptokurtic Returns in Microscopic Simulation of the LLS Stock Market Modelcond-mat.stat-mech
The LLS stock market model is a model of heterogeneous quasi-rational investors operating in a complex environment about which they have incomplete information. We review the main features of this model and several of its extensions. We study the effects of investor heterogeneity and show that predation, competition, o…
PolyILR: A Tree-Structured Orthonormal Decomposition of Compositional Data
problem Representing compositional data with hierarchical structure
method PolyILR: A canonical orthonormal decomposition of the Aitchison tangent space aligned with any tree topology
result PolyILR yields stable, interpretable features and enables inference at multiscale tree resolution
Hybrid model improves forest growth predictions.
problem Misspecified assumptions in mechanistic models.
method Forest Informed Neural Networks (FINN) combining DVM and DNN.
result DNN learned improved growth process functional form.
A review of ML and DL for ecological data analysis.
problem Understanding the strengths and limitations of ML and DL in ecological research.
method Historical overview, algorithm families, differences, universal principles, and emerging trends.
result ML and DL excel in prediction tasks but are still debated for causal inference.
Improved forecasting of suicide attempts using LSGPs for patients with little data.
problem Challenges in predicting suicide attempts due to their rarity and patient heterogeneity.
method Introduced Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity.
result LSGPs outperform baseline models, even without kernel-design, and offer new insights into patient similarity.
Magnitude-based features capture interactions between different entities in multispecies spatial data.
problem Capturing interactions between different entities in multispecies spatial data.
method Developing magnitude-based features for multispecies spatial data.
result Identifies distinct neighbourhood types and spatial heterogeneity.
Machine learning detects regime shifts in online game-experiments with high accuracy.
problem Detecting regime shifts in online social systems.
method Gradient-boosted decision trees with memory-retaining features.
result Significantly outperforms standard early warning indicators.
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…
Aspects of a phase transition in high-dimensional random geometrycond-mat.stat-mech
High-dimensional random geometry shows phase transitions in various problems.
problem Phase transitions in high-dimensional random geometry.
method Analysis of various financial, optimization, and ecological problems.
result Links between seemingly distant fields and further ramifications.
We develop a novel advanced Particle Markov chain Monte Carlo algorithm that is capable of sampling from the posterior distribution of non-linear state space models for both the unobserved latent states and the unknown model parameters. We apply this novel methodology to five population growth models, including models …
Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Speci…
Novel Bayesian method for high-dimensional count data prediction.
problem Count data in high-dimensional settings requires feature selection.
method Pseudo-Bayesian framework with scaled Student prior and exponential weights.
result Strong performance compared to Lasso in various settings.
Are large complex economic systems unstable ?physics.soc-ph
Although classical economic theory is based on the concept of stable equilibrium, real economic systems appear to be always out of equilibrium. Indeed, they share many of the dynamical features of other complex systems, e.g., ecological food-webs. We focus on the relation between increasing complexity of the economic n…
Modeling vessel speed to balance efficiency and environmental risks in Arctic shipping.
problem Balancing vessel speed with environmental and ecological risks in Arctic shipping.
method Inverse control constrained optimization framework with risk parameters estimated from AIS data.
result Distinct decision-making patterns across vessel types and navigational statuses, with varying sensitivity to ice and whale risks.
Exact results on power-law distributions in systems with resets.
problem Understanding power-law distributions in systems with resets.
method Exact mathematical analysis of multiplicative processes with resets.
result Power-law distributions are built up over time and their moments behave quantitatively determined by parameters.
The paper explores how innovative financing solutions boost Moroccan businesses' performance.
problem Market volatility, ecological transitions, and technological change pose challenges to business sustainability.
method Examines innovative financing solutions like venture capital, green finance, crowdfunding, and blockchain.
result Embracing innovative financial strategies can transform business challenges into opportunities.
Infinite hierarchical contrastive clustering identifies personal environments linked to health outcomes.
problem Identifying meaningful relationships between environmental features and health outcomes on an individual level.
method Contrastive clustering framework with stick-breaking prior and participant-specific prediction loss.
result Model effectively identifies distinct personal environments and groups them into meaningful types linked to health outcomes.
Intermittency and Localizationphysics.soc-ph
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.
problem Limitations of unidirectional causation in self-referencing systems like finance.
method Critical assessment of causal inference in empirical finance, using ecological models.
result Current financial tools may be limited to ex post inference, especially in reflexive contexts.
Neural likelihood approximates integer time series data efficiently.
problem Inference of parameters for integer-valued stochastic processes is challenging.
method Constructs a neural likelihood approximation for inference of parameters from time series data.
result Accurately approximates the true posterior with significant computational speed-ups.