Ubenwa diagnoses birth asphyxia from infant cries.
problem Difficulty in early detection of asphyxia in resource-poor settings.
method Machine learning system for automated infant cry analysis.
result Reduction in time, cost, and skill required for accurate diagnoses.
Study improves infant cry-based asphyxia diagnosis using transfer learning.
problem Improving accuracy of diagnosing perinatal asphyxia in newborns.
method Neural transfer learning from adult speech to infant cries.
result Transfer learning models are resilient to noise and signal loss.
Method approximates first passage times for birth-death processes.
problem Approximating first passage times for birth-death processes.
method General method using birth-death process properties, Keilson's theorem, and Riemann sums.
result Closed-form expressions for first passage times.
Study uses machine learning to predict preterm birth.
problem Predicting different classes of preterm birth.
method Support vector machine (SVM) with linear and non-linear kernels, logistic regression, and decision rules.
result Significant improvement in predicting preterm birth.
Proposes a new birth-death process for better modeling of population dynamics.
problem Models of population or opinion dynamics with spurious long-range memory.
method Introduces Bessel-like birth-death process to address the spurious long-range memory.
result Derives equations for the burst and inter-burst duration of the new process.
Gradient flow connects two critical points near birth-death.
problem Connecting critical points near birth-death in gradient flows.
method Whitney normal form, Conley index construction, adiabatic limit analysis.
result Gradient trajectory connects two critical points up to time-shift.
New prediction rule for preterm births with high sensitivity and specificity.
problem Difficulty in predicting preterm births accurately.
method Automatically generated and selected interpretable prediction rule from high-dimensional data.
result Simplified prediction rule with 10 items has 62.3% sensitivity and 81.5% specificity.
A birth-death process improves BN structure learning.
problem Learning BN structure from data is NP-hard.
method Modeling BN structure changes as a birth-death process.
result The birth-death process mixes faster than Metropolis-Hastings.
Study birth-death dynamics for sampling Gibbs measures with nonconvex potentials.
problem Sampling Gibbs measures with nonconvex potentials.
method Birth-death dynamics, Kullback-Leibler divergence, χ 2 χ^2 χ 2 divergence, kernel-based approximations, Γ Γ Γ -convergence of gradient flows. result Probability density converges exponentially fast to Gibbs equilibrium measure with a universal rate.
Study estimates personalized effects of maternal PM2.5 exposure on birth weight.
problem Identify critical windows and heterogeneity in maternal PM2.5 exposure effects on birth weight.
method Heterogeneous Distributed Lag Models and Bayesian Additive Regression Trees.
result Evidence of heterogeneity in PM2.5-birth weight relationship, with some dyads showing 3x larger decrease.
A new sampling algorithm speeds up Langevin sampling for multimodal distributions.
problem Efficient sampling from multimodal distributions in Bayesian inference.
method Birth-death mechanism applied to Langevin diffusion.
result The algorithm accelerates mixing of Langevin diffusion, independent of potential barriers.
New method uses birth-death process and exploration component to accelerate sampling from multimodal distributions.
problem Sampling from multimodal probability distributions efficiently.
method Combines birth-death process and exploration component to accelerate sampling.
result Proves exponential asymptotic convergence under mild assumptions.
Study predicts infant mortality using birth certificate data.
problem High infant mortality rate in the U.S. and racial/ethnic disparities.
method Classification models trained on birth certificate features.
result Methodology outperforms standard classification methods.
Deep learning predicts preterm birth risk with improved accuracy.
problem Improving accuracy in predicting spontaneous preterm deliveries.
method U-Net segmentation network for automatic extraction of cervical length and anterior cervical angle.
result Combined markers reduce false-negative ratio from 30% to 18%
Fair quantile regression adjusts estimators to balance subpopulation quantiles.
problem Unfair quantile estimators for subpopulations defined by a protected attribute.
method Proposes a procedure to adjust quantile estimators on heldout samples with protected attribute information.
result Demonstrates n \sqrt{n} n -fairness, balancing target quantiles across subpopulations. New birth-death dynamics accelerates convergence in neural networks.
problem Accelerating convergence in neural networks with large parameters.
method Proposed a non-local mass transport dynamics as a stochastic neuronal birth-death process.
result Proved that the birth-death dynamics accelerates the rate of convergence in the mean-field limit.
The paper studies curvature conditions on birth-death processes and graphs.
problem Curvature dimension conditions on birth-death processes and linear graphs.
method Combinatorial characterization and proof of conditions for linear graphs.
result Volume doubling property and Poincaré inequality for graphs with non-negative curvature.
New method improves phylogenetic model inference by 30x.
problem Improving phylogenetic model inference for birth-death processes.
method Combines alive particle filter with delayed sampling.
result Significant improvement in effective sample size and acceptance rate.
FS&P uses birth-death process to ensure global convergence of stochastic conic particle gradient descent.
problem Global optimization of non-convex objective functions over measure space.
method Introduces Fast Spawn\&Prune (FS\&P) combining CPGD with birth-death process.
result First theoretical guarantee of global convergence for discrete-time stochastic algorithms.
Paper improves preterm birth prediction using neural networks with noisy labels.
problem Predicting preterm birth from noisy EHR diagnosis codes.
method Developed ALC method to correct label noise in deep learning models.
result Improved prediction performance compared to baseline methods.
New RL algorithm reduces regret in birth-death queueing problems.
problem Efficiency of reinforcement learning in MDPs with large state spaces.
method Modified Ucrl2 algorithm exploiting birth-death structure.
result Regret bound of i l d e O ( E 2 A T ) ilde{\mathcal{O}}(\sqrt{E_2AT}) i l d e O ( E 2 A T ) independent of state space size. Classifies and analyzes the stability of black hole event horizon birth points using contact geometry.
problem Classifying and understanding the structural possibilities of black hole crease sets.
method Contact geometry approach, focusing on BigFronts and their Legendrian projections.
result Refined stability discussion of the event horizon birth component and identification of additional components.
Paper disproves conjecture about log-Sobolev constants.
problem Log-Sobolev constants and curvature bounds.
method Counterexample on birth-death chains.
result Conjecture about Ollivier curvature is incorrect.
New MCMC algorithm improves convergence of Bayesian regression trees.
problem Local mode stickiness and poor mixing in MCMC algorithms for Bayesian regression trees.
method Continuous-time birth-death MCMC algorithm for Bayesian regression tree models.
result The new algorithm dramatically improves convergence and mixing properties of MCMC.
Paper uses AI to improve medical diagnosis accuracy.
problem Improving accuracy of medical diagnoses.
method Heuristic frequentist and Bayesian approaches applied to a nationwide dataset.
result Algorithm outperforms human doctors in detecting abnormal births.
New metrics and coordinates for barcode space using group theory.
problem Describing and measuring the space of barcodes.
method Geometric group theory applied to barcodes.
result Stratification of barcode space into regions with similar statistical properties.
Paper proposes scalable method for analyzing multi-omic data.
problem Integrating high-dimensional multi-omic data for cancer subtyping.
method Mixed graphical model approach using Birth-Death MCMC algorithm.
result Our method outperforms LASSO and standard BDMCMC in computational efficiency and model selection accuracy.
Paper introduces a Bayesian nonparametric approach for tracking multiple objects with spawning events.
problem Tracking multiple objects with birth and death events (spawning).
method Bayesian nonparametric approach with MCMC sampling for unknown number of objects.
result Advantages of nonparametric modeling for scenarios with spawning events.
Topic models have proven to be a useful tool for discovering latent structures in document collections. However, most document collections often come as temporal streams and thus several aspects of the latent structure such as the number of topics, the topics' distribution and popularity are time-evolving. Several mode…
Thirty years after the birth of foliations in the 1950's, André Haefliger has introduced a special property satisfied by holonomy pseudogroups of foliations on compact manifolds, called compact generation. Up to now, this is the only general property known about holonomy on compact manifolds. In this article, we give a…
Enhances early risk assessments for pediatric outcomes using contrastive learning.
problem Improving risk assessments in early stages of pediatric development.
method Contrastive multi-modal framework that treats each time window as a distinct modality, training on all available data.
result Consistent improvements in early-stage risk assessments validated on real-world tasks.
Explains the history and challenges of minimal surfaces.
problem Understanding the regularity of minimal surfaces.
method Historical overview and technical analysis.
result Outlines the evolution and current state of minimal surfaces.
Kernel methods identify treatment effects with unobserved confounding using negative controls.
problem Learning causal relationships with unmeasured confounding.
method Kernel ridge regression algorithms for nonparametric treatment effects.
result Uniform consistency and finite sample rates of convergence proved.
Study shows over-sampling biases prediction results on imbalanced datasets.
problem Over-optimistic prediction results on imbalanced data.
method Applying over-sampling before partitioning training and testing sets.
result Over-sampling causes biased results and reduces predictive performance.
Flow Matching for count data improves sample quality and efficiency.
problem Mapping between count distributions across batches or time points in high-dimensional count data.
method count-FM, a flow-matching framework based on a continuous-time birth-death process with local unit jumps.
result count-FM achieves better sample quality than representative baselines while using fewer parameters.
Review of latest DRL algorithms with theoretical and practical insights.
problem Challenges in reinforcement learning and deep learning.
method Theoretical justification and empirical analysis of DRL algorithms.
result Empirical properties and practical limitations of DRL algorithms are discussed.
Bayesian method estimates QTEs from observational data.
problem Estimating nuanced characteristics of counterfactual distributions.
method Bayesian semiparametric conditional distribution regression model with double balancing score.
result Proposed method provides more accurate QTE estimates than other methods.
The surgery technique of Gromov and Lawson may be used to construct families of positive scalar curvature metrics which are parameterised by Morse functions. This has played an important role in the study of the space of metrics of positive scalar curvature on a smooth manifold and its corresponding moduli spaces. In t…
We propose a new Bayesian tracking and parameter learning algorithm for non-linear non-Gaussian multiple target tracking (MTT) models. We design a Markov chain Monte Carlo (MCMC) algorithm to sample from the posterior distribution of the target states, birth and death times, and association of observations to targets, …
Computational topology has recently known an important development toward data analysis, giving birth to the field of topological data analysis. Topological persistence, or persistent homology, appears as a fundamental tool in this field. In this paper, we study topological persistence in general metric spaces, with a …
The paper develops a stationary-distribution theory for Random Forest ensemble size selection.
problem Determining the optimal number of trees in Random Forests.
method Modeling the ensemble size as a birth-death Markov chain and deriving its stationary distribution.
result The stationary ensemble size B ∗ B_* B ∗ scales as O ( ε − 2 ) O(\varepsilon^{-2}) O ( ε − 2 ) as ε ↓ 0 \varepsilon\downarrow 0 ε ↓ 0 . Survey on Finsler manifolds with weighted Ricci curvature, focusing on geometric analysis.
problem Analysis of Finsler manifolds with weighted Ricci curvature.
method Nonlinear geometric analysis based on the Bochner inequality.
result Gradient estimates, functional inequalities, and isoperimetric inequalities.
Two singular links are cobordant if one can be obtained from the other by singular link isotopy together with a combination of births or deaths of simple unknotted curves, and saddle point transformations. A movie description of a singular link cobordism in 4-space is a sequence of singular link diagrams obtained from …
Sketch-based approach detects community events in evolving networks.
problem Community detection in time-varying networks.
method Maintains a small sketch graph to capture essential community structure.
result Efficiently identifies six key community events during network evolution.
The Hawkes process is a simple point process, whose intensity function depends on the entire past history and is self-exciting and has the clustering property. The Hawkes process is in general non-Markovian. The linear Hawkes process has immigration-birth representation. Based on that, Fierro et al. recently introduced…
Deep CNNs identify age-related patterns in fetal brain activity.
problem Understanding age effects in fetal brain development.
method Supervised 3D Convolutional Neural Networks applied to fetal fMRI data.
result Deep CNNs can distinguish age groups in fetal brain activity.
New method speeds up uncertainty estimation for large datasets in causal inference.
problem Computational infeasibility of bootstrap-based uncertainty quantification for large datasets.
method Extends cBLB algorithm to kernel methods, combining subsampling and resampling.
result Achieves computational scalability with nominal coverage.
Neurogenesis-inspired online learning adapts model architecture in changing environments.
problem Continuous adaptation of model architecture in non-stationary environments.
method Online dictionary-learning framework with adaptive addition and deletion of units, inspired by neurogenesis.
result Significant improvement in performance on nonstationary data compared to fixed-size online sparse coding.