arXiv research

Paper uses AI to improve medical diagnosis accuracy.

Novel CTG analysis splits signals into balanced windows and uses 1DCNN for automatic feature extraction.

Study estimates personalized effects of maternal PM2.5 exposure on birth weight.

Deep CNNs identify age-related patterns in fetal brain activity.

Embeddings of lab test codes improve mortality prediction and preserve ordinality.

Investment disputes increase stock volatility, especially for companies with negative outcomes.

Enhances early risk assessments for pediatric outcomes using contrastive learning.

Kernel methods identify treatment effects with unobserved confounding using negative controls.

The study uses transfer learning to compare surgical outcomes across racial/ethnic subgroups.

Proposes a new birth-death process for better modeling of population dynamics.

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…

We propose a general method to obtain approximation of the first passage time distribution for the birth-death processes. We rely on the general properties of birth-death processes, Keilson's theorem and the concept of Riemann sum to obtain closed-form expressions. We apply the method to the three selected birth-death …

Paper proposes scalable method for analyzing multi-omic data.

We describe an application of machine learning to the problem of predicting preterm birth. We conduct a secondary analysis on a clinical trial dataset collected by the National In- stitute of Child Health and Human Development (NICHD) while focusing our attention on predicting different classes of preterm birth. We com…

Study birth-death dynamics for sampling Gibbs measures with nonconvex potentials.

A new sampling algorithm speeds up Langevin sampling for multimodal distributions.

Near a birth-death critical point in a one-parameter family of gradient flows, there are precisely two Morse critical points of index difference one on the birth side. This paper gives a self-contained proof of the folklore theorem that these two critical points are joined by a unique gradient trajectory up to time-shi…

Study introduces a new method to estimate causal mediation with multiple mediators.

ODE trajectories become abnormal curves in Carnot groups.

Preterm births occur at an alarming rate of 10-15%. Preemies have a higher risk of infant mortality, developmental retardation and long-term disabilities. Predicting preterm birth is difficult, even for the most experienced clinicians. The most well-designed clinical study thus far reaches a modest sensitivity of 18.2-…

New method uses birth-death process and exploration component to accelerate sampling from multimodal distributions.

Study predicts infant mortality using birth certificate data.

Deep learning predicts preterm birth risk with improved accuracy.

Fair quantile regression adjusts estimators to balance subpopulation quantiles.

Bayesian networks (BNs) are graphical models that are useful for representing high-dimensional probability distributions. There has been a great deal of interest in recent years in the NP-hard problem of learning the structure of a BN from observed data. Typically, one assigns a score to various structures and the sear…

Study finds abnormal paths on specific Lie groups using algebraic structures.

After birth, extremely preterm infants often require specialized respiratory management in the form of invasive mechanical ventilation (IMV). Protracted IMV is associated with detrimental outcomes and morbidities. Premature extubation, on the other hand, would necessitate reintubation which is risky, technically challe…

Study on abnormal curves in sub-Riemannian manifolds, proving length-minimizing properties.

The paper bounds abnormal and Goh-abnormal sets for metabelian Lie groups with polarizations.

New method improves phylogenetic model inference by 30x.

FS&P uses birth-death process to ensure global convergence of stochastic conic particle gradient descent.

We prove the smoothness of abnormal minimizers of subriemannian manifolds of step 3 with a nilpotent basis. We prove that rank 2 Carnot groups of step 4 admit no strictly abnormal minimizers. For any subriemannian manifolds of step less than 7, we show all abnormal minimizers have no corner type singularities, which pa…

Researchers found abnormal extremals on specific Lie groups.

We address the problem of abnormal event detection from trajectory data. In this paper, a new adversarial approach is proposed for building a deep neural network binary classifier, trained in an unsupervised fashion, that can distinguish normal from abnormal trajectory-based events without the need for setting manual d…

New optimality conditions for sub-Riemannian geodesics derived.

New method speeds up uncertainty estimation for large datasets in causal inference.

New methods estimate causal effects through mediators, handling confounding without strict assumptions.

Study on sub-Riemannian geometry in 4D, focusing on abnormal geodesics.

What happens when the Supreme Court of the United States decides a case impacting one or more publicly-traded firms? While many have observed anecdotal evidence linking decisions or oral arguments to abnormal stock returns, few have rigorously or systematically investigated the behavior of equities around Supreme Court…

Media tone around earnings announcements predicts stock returns.

There is bountiful evidence that political uncertainty stemming from presidential elections or doubt about the direction of future policy make financial markets significantly volatile, especially in proximity to close elections or elections that may prompt radical policy changes. Although several studies have examined …

New RL algorithm reduces regret in birth-death queueing problems.

Enhanced deep CNNs improve cardiac abnormality diagnosis from ECGs.

This study examines abnormal geodesics in 2D-Zermelo navigation problems, revealing their role in separating time minimal and maximal curves.

In this paper, we focus on the development of a method that detects abnormal trajectories of road users at traffic intersections. The main difficulty with this is the fact that there are very few abnormal data and the normal ones are insufficient for the training of any kinds of machine learning model. To tackle these …

Given a five dimensional space endowed with a Cartan distribution, the abnormal geodesics form another five dimensional space with a cone structure. Then it is shown, if the cone structure is regarded as a control system, then, the space of abnormal geodesics of the cone structure is naturally identified with the origi…

We provide the first known family of examples of integrable homogeneous sub-Riemannian structures admitting strictly abnormal geodesics. These examples were obtained through the analysis of the equivalence problem for sub-Riemannian Engel structures. We formulate a criterion of strict abnormality in terms of structure …

Classifies and analyzes the stability of black hole event horizon birth points using contact geometry.