arXiv research

A new model improves homogeneity in burn patient reimbursement.

Bitcoin reacts positively to USDT minting but not burning, showing state-dependence.

In this paper we develop a method to compute the Burns-Epstein invariant of a spherical CR homology sphere, up to an integer, from its holonomy representation. As application, we give a formula for the Burns-Epstein invariant, modulo an integer, of a spherical CR structure on a Seifert fibered homology sphere in terms …

Optimal algorithms for non-linear ridge bandits reduce burn-in cost.

Interpool solves interoperability issues by minting, exchanging, and burning tokens within a single liquidity pool.

The profitability of CPMMs is significantly impacted by mint and burn fees.

Machine learning helps predict smoke types for safer forest burns.

We define a renormalized characteristic class for Einstein asymptotically complex hyperbolic (ACHE) manifolds of dimension 4: for any such manifold, the polynomial in the curvature associated to the characteristic class euler-3signature is shown to converge. This extends a work of Burns and Epstein in the Kahler-Einste…

The paper provides formulae for CR invariants in Sasakian η-Einstein manifolds.

Study fast learning rates for square loss in dependent data with hypercontractivity condition.

New RL algorithms reduce costs for single-agent and federated learning.

New algorithm learns optimal policies with minimal memory and time.

Study Kähler-Einstein manifolds with holomorphic isometries into blow-ups of complex spaces.

New algorithm reduces cold-start costs in multi-armed bandits for many products.

Recent work on imitation learning has generated policies that reproduce expert behavior from multi-modal data. However, past approaches have focused only on recreating a small number of distinct, expert maneuvers, or have relied on supervised learning techniques that produce unstable policies. This work extends InfoGAI…

ULA estimates covariance of log-concave distributions efficiently.

Proves a higher rank rigidity theorem for convex real projective manifolds.

There is a well known link between (maximal) polar representations and isotropy representations of symmetric spaces provided by Dadok. Moreover, the theory by Tits and Burns-Spatzier provides a link between irreducible symmetric spaces of non-compact type of rank at least three and irreducible topological spherical bui…

Parallelized bandit algorithms speed up decision-making.

The paper studies scalar flat Kähler metrics on line bundles and proves their properties.

Paper compares GRU and LSTM for predicting wildfire spread direction.

A new algorithm reduces memory and computational needs for reinforcement learning.

SGD shows distinct phases in learning single-index models, achieving optimal sample complexity and regret.

New bounds on trajectory safety in training models with Langevin Dynamics.

GD with large, adaptive stepsizes achieves optimal risk in logistic regression.

In this paper we extend our previous work on singularities of Monge-Ampère foliations to the case of pseudoconvex finite type domains. We are able to answer the questin of Burns on homogeneous polynomials whose logarithm satisfies the complex Monge-Ampère equation completely in dimension 2 . We are also able to general…

Improved forecasting of suicide attempts using LSGPs for patients with little data.

Efficiently fine-tunes patient-independent seizure detection models with tensor kernel machine.

Observer learns optimal policy from learner's actions without rewards.

Partial answer to affineness of entire Grauert tubes, with Stein manifold criterion.

We extend a recent result of Burns, Guillemin and Uribe on the asymptotics of the spectral measure for the reduction metric on a toric variety to any toric metric on a toric variety. We show how this extended result together with the Tian-Yau-Zelditch asymptotic expansion can be used to deduce Abreu's formula for the s…

Personalized models explain TB treatment outcomes considering patient context.

Two algorithms learn Gaussian graphical models from Glauber dynamics trajectories.

Simulates patient pathways to detect delayed rare disease diagnoses.

Researchers embed gravitational instantons in higher-dimensional spaces.

Model predicts wound and episode-level readmission risk and time to re-admit.

Currently, approximately 30% of epileptic patients treated with antiepileptic drugs (AEDs) remain resistant to treatment (known as refractory patients). This project seeks to understand the underlying similarities in refractory patients vs. other epileptic patients, identify features contributing to drug resistance acr…

Cluster analysis aims at separating patients into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. It is an important approach in data-driven disease classification and subtyping. Acute coronary syndrome (ACS) is a syndrome due to sudden decrease of coronary artery blood f…

The health outcomes of high-need patients can be substantially influenced by the degree of patient engagement in their own care. The role of care managers includes that of enrolling patients into care programs and keeping them sufficiently engaged in the program, so that patients can attain various goals. The attainmen…

The paper proves properties of Kähler surfaces with zero scalar curvature.

Deep learning model creates patient representations for scalable EHR-based stratification.

We study the problem of the existence and the holomorphicity of the Monge-Ampère foliation associated to a plurisubharmonic solutions of the complex homogeneous Monge-Ampère equation even at points of arbitrary degeneracy. We obtain good results for real analytic unbounded solutions. As a consequence we also provide a …

Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment. However, commonly used sepsis diagnostic criteria fail to account for significant underlying heterogeneity, both between patients as well as over time in a single patient. W…

We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims t…

Longitudinal patient data has the potential to improve clinical risk stratification models for disease. However, chronic diseases that progress slowly over time are often heterogeneous in their clinical presentation. Patients may progress through disease stages at varying rates. This leads to pathophysiological misalig…

Study automates detection of visitation disruptions in ICU patients.

Bayesian methods improve group testing for identifying infected patients.

To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients' behaviors in real-time, is proposed. In this study, we use an mmWave radar to t…