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.
SDSR reconstructs species trees from genetic markers efficiently.
We consider the problem of estimating the evolutionary history of a set of species (phylogeny or species tree) from several genes. It is known that the evolutionary history of individual genes (gene trees) might be topologically distinct from each other and from the underlying species tree, possibly confounding phyloge…
Forest tree species mapped with high accuracy using satellite data.
Deep semi-supervised learning identifies tree species from natural images.
Deep learning improves tree species classification accuracy in imbalanced contexts.
SLIC-UAV monitors forest recovery using UAVs and machine learning.
The paper uses VGG-19 for plant species classification from leaf images.
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…
Bayesian supertrees method uses variational Bayes for overlapping taxon subsets.
Paper proposes an algorithm to reconstruct optimal model structure from graph adjacency matrix.
Uniform proof reconstructs spaces using cross ratio on boundary.
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…
SNJ recovers latent tree models from similarity matrices.
Framework uncovers symmetric and asymmetric species associations from data.
We discuss an autoencoder model in which the encoding and decoding functions are implemented by decision trees. We use the soft decision tree where internal nodes realize soft multivariate splits given by a gating function and the overall output is the average of all leaves weighted by the gating values on their path. …
GeoPhy uses geometric gradients to efficiently infer phylogenetic trees from molecular data.
Heterogeneous GNN improves species distribution modeling.
GeoLifeCLEF 2020 dataset pairs species observations with environmental data.
The tree reconstruction problem is to collect and analyze massive data at the th level of the tree, to identify whether there is non-vanishing information of the root, as goes to infinity. Its connection to the clustering problem in the setting of the stochastic block model, which has wide applications in machin…
The paper introduces a method to control false splits in tree-based data aggregation.
Microbial identification is a central issue in microbiology, in particular in the fields of infectious diseases diagnosis and industrial quality control. The concept of species is tightly linked to the concept of biological and clinical classification where the proximity between species is generally measured in terms o…
Image classification system identifies bumble bee species from images.
We propose a Bayesian expectation-maximization (EM) algorithm for reconstructing Markov-tree sparse signals via belief propagation. The measurements follow an underdetermined linear model where the regression-coefficient vector is the sum of an unknown approximately sparse signal and a zero-mean white Gaussian noise wi…
Mathematical foundation for phylogenetic tree uncertainty quantification.
We propose and study a multi-scale approach to vector quantization. We develop an algorithm, dubbed reconstruction trees, inspired by decision trees. Here the objective is parsimonious reconstruction of unsupervised data, rather than classification. Contrasted to more standard vector quantization methods, such as K-mea…
StatEcoNet models species distribution using neural networks to correct observation errors.
Phytoplankton plays an important role in marine ecosystem. It is defined as a biological factor to assess marine quality. The identification of phytoplankton species has a high potential for monitoring environmental, climate changes and for evaluating water quality. However, phytoplankton species identification is not …
Gaussian process framework learns interaction kernels in multi-species particle systems.
Until recently, transcriptomics was limited to bulk RNA sequencing, obscuring the underlying expression patterns of individual cells in favor of a global average. Thanks to technological advances, we can now profile gene expression across thousands or millions of individual cells in parallel. This new type of data has …
Deep learning identifies frog species and detects new ones.
We adopt data structure in the form of cover trees and iteratively apply approximate nearest neighbour (ANN) searches for fast compressed sensing reconstruction of signals living on discrete smooth manifolds. Levering on the recent stability results for the inexact Iterative Projected Gradient (IPG) algorithm and by us…
PHIBP models complex microbiome data with shared parameters.
Auto-encoding is an important task which is typically realized by deep neural networks (DNNs) such as convolutional neural networks (CNN). In this paper, we propose EncoderForest (abbrv. eForest), the first tree ensemble based auto-encoder. We present a procedure for enabling forests to do backward reconstruction by ut…
Interpretable neural network for plant traits and species identification.
Deep model tackles zero-inflated multi-species abundance estimation.
The evolution of drug-resistant microbial species is one of the major challenges to global health. The development of new antimicrobial treatments such as antimicrobial peptides needs to be accelerated to combat this threat. However, the discovery of novel antimicrobial peptides is hampered by low-throughput biochemica…
DeepMaxent uses neural networks to improve species distribution models.
In this article we extend the computational geometric curve reconstruction approach to curves in Riemannian manifolds. We prove that the minimal spanning tree, given a sufficiently dense sample, correctly reconstructs the smooth arcs and further closed and simple curves in Riemannian manifolds. The proof is based on th…
Method infers parameters in complex diffusion processes.
Bayesian method improves estimation of unseen species.
Develops probabilistic models for gene regulatory network inference.
The paper proposes a method to infer differentiation trees from RNA velocity data.
As the technology is advancing, audio recognition in machine learning is improved as well. Research in audio recognition has traditionally focused on speech. Living creatures (especially the small ones) are part of the whole ecosystem, monitoring as well as maintaining them are important tasks. Species such as animals …
A new method avoids overfitting in network reconstruction by using the minimum description length principle.
We consider the classical sparse regression problem of recovering a sparse signal given a measurement vector . We propose a tree search algorithm driven by the deep neural network for sparse regression (TSN). TSN improves the signal reconstruction performance of the deep neural network designed for sp…
The answers to many unsolved problems lie in the intractable chemical space of molecules and materials. Machine learning techniques are rapidly growing in popularity as a way to compress and explore chemical space efficiently. One of the most important aspects of machine learning techniques is representation through th…
Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic weed con…
Populations of species in ecosystems are often constrained by availability of resources within their environment. In effect this means that a growth of one population, needs to be balanced by comparable reduction in populations of others. In neutral models of biodiversity all populations are assumed to change increment…