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.
ART automates synthetic biology design with machine learning.
DECAT framework evaluates multimodal models for shared biology, detecting confounders and false positives.
Optimal algorithm selects biological models without prior info.
Two simulation-based methods improve optimal sampling design in systems biology.
Quantum computing promises faster bioinformatics, but challenges remain.
A scalable Bayesian inference method for mixed-effects models in systems biology.
ProGen models protein sequences for synthetic biology.
Method uses network biology to construct gene expression models for cancer.
In this paper, we provide conditions which ensure that stochastic Lipschitz BSDEs admit Malliavin differentiable solutions. We investigate the problem of existence of densities for the first components of solutions to general path-dependent stochastic Lipschitz BSDEs and obtain results for the second components in part…
nUDEs use neural networks to model biology without negative values.
New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of…
hyperSBINN improves drug cardiosafety assessment by efficiently modeling cardiac action potentials.
We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden Markov models and propose a structured variational inference approach to infer the structure and states of the model. We start from the class…
X-SHAP assesses multiplicative variable contributions in machine learning models.
BioBO optimizes gene perturbation design using Bayesian optimization with biological priors.
Geometric modeling for human food and chemical sensitivities.
New algorithm optimizes matrix reordering for noisy disordered matrices.
DAG-FOCI learns causal relationships without parametric assumptions.
COMRECGC finds common recourse for global counterfactual explanations in GNNs.
Physics analogies explain machine learning overfitting control.
We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as these are continuous tim…
We address the problem of parameter estimation in models of systems biology from noisy observations. The models we consider are characterized by simultaneous deterministic nonlinear differential equations whose parameters are either taken from in vitro experiments, or are hand-tuned during the model development process…
Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate…
Graphical modelling has a long history in statistics as a tool for the analysis of multivariate data, starting from Wright's path analysis and Gibbs' applications to statistical physics at the beginning of the last century. In its modern form, it was pioneered by Lauritzen and Wermuth and Pearl in the 1980s, and has si…
Quantitative modeling of post-transcriptional regulation process is a challenging problem in systems biology. A mechanical model of the regulatory process needs to be able to describe the available spatio-temporal protein concentration and mRNA expression data and recover the continuous spatio-temporal fields. Rigorous…
We compute Khovanov homology for tangles using TQFT.
New method learns cell trajectories and network interactions from single-cell data.
This thesis tackles data fusion issues across different biological scales and types.
DHRL learns interpretable features from visual data.
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, …
MSBM extends SB for multi-marginal trajectory inference.
A new method for inferring latent states in Markov jump processes.
Deep learning quantifies butterfly phenotypes, validating evolutionary theory.
Applications of Quantum Tunneling effect have long gone beyond the traditional physical meaning. Initially created by Gamow to explain α-decay of nuclear particles, along the time, quantum tunneling found fertile domain of research in chemistry and recently in biology, where the new discipline of Quantum Biology emerge…
CHANI learns classification tasks with local transformations inspired by biology.
Active learning selects optimal measurement times for inferring continuous paths from sparse data.
New method learns SDEs with structured noise from data.
These lecture notes in the De Rham-Hodge theory are designed for a 1-semester undergraduate course (in mathematics, physics, engineering, chemistry or biology). This landmark theory of the 20th Century mathematics gives a rigorous foundation to modern field and gauge theories in physics, engineering and physiology. The…
New research connects evolutionary dynamics to Bayesian learning.
Polynomial-time algorithm finds short non-orientable loops intersecting graph edges up to 30 times.
ABI adapts to graph data for fast, scalable inference.
Despite an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited. Whether performance in such tasks can be improved by using large repositories of tangentially related structural data remains an open question. …
The paper studies conformally flat cubic metrics with isotropic curvature, finding they must be Minkowski.
The aim of this paper is to construct a natural Riemann-Lagrange differential geometry on 1-jet spaces, in the sense of nonlinear connections, generalized Cartan connections, d-torsions, d-curvatures, jet electromagnetic fields and jet electromagnetic Yang-Mills energies, starting from some given nonlinear evolution OD…
New bound for neural nets on non-iid data.
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathematical and statistical foundations of graphical models, along with their fundamental properties, estimation and basic inference procedures. In particular we will develop Markov networks (also known as Markov random field…
Several real problems ranging from text classification to computational biology are characterized by hierarchical multi-label classification tasks. Most of the methods presented in literature focused on tree-structured taxonomies, but only few on taxonomies structured according to a Directed Acyclic Graph (DAG). In thi…
These lecture notes in Lie Groups are designed for a 1--semester third year or graduate course in mathematics, physics, engineering, chemistry or biology. This landmark theory of the 20th Century mathematics and physics gives a rigorous foundation to modern dynamics, as well as field and gauge theories in physics, engi…