arXiv research

Super-resolution improves MRI resolution and accuracy for biomarker assessment.

We consider spacetimes solving the Einstein non-linear scalar field equations with T2-symmetry and show that they admit an areal time foliation in the expanding direction. In particular, we prove global existence and uniqueness of solutions to the corresponding system of evolution equations for all future times. The on…

A Minkowski class is a closed subset of the space of convex bodies in Euclidean space Rn which is closed under Minkowski addition and non-negative dilatations. A convex body in Rn is universal if the expansion of its support function in spherical harmonics contains non-zero harmonics of all orders. If K is universal, t…

New technique halves scan time for multi-echo MR images.

Bayesian model enhances phenotype discovery in asthma EHRs.

Study generates synthetic MR images to improve glioma segmentation.

New methods explain NE embeddings by identifying key variables.

Study predicts 2015 Chinese stock market bubble using LPPLS model.

In this paper are given examples of tori T2 embedded in R3 with all their principal lines dense. These examples are obtained by stereographic projection of deformations of the Clifford torus in S3.

Deep CNNs segment prostate from MRI images.

Stability of a special spacetime solution is proven under certain symmetries.

We consider the class of short rate interest rate models for which the short rate is proportional to the exponential of a Gaussian Markov process x(t) in the terminal measure r(t) = a(t) exp(x(t)). These models include the Black, Derman, Toy and Black, Karasinski models in the terminal measure. We show that such intere…

CollaGAN learns MR contrast imputation, revealing essential contrasts.

The study examines how modernizing settlement infrastructure affects inside money elasticity and network efficiency.

Estimate relaxation times in nonextensive systems using gradient flow for Tsallis entropy maximization.

Synthetic learning improves neonatal brain MRI segmentation robustness.

We prove that the classical set of moves for standard spines of 3-manifolds (i.e. the MP-move and the V-move) does not suffice to relate to each other any two standard skeleta of a 3-manifold with marked boundary. We also describe a condition on the 3-manifold with marked boundary that tells whether the generalised set…

In this work we study convex relaxations of quadratic optimisation problems over permutation matrices. While existing semidefinite programming approaches can achieve remarkably tight relaxations, they have the strong disadvantage that they lift the original $n {\times} n$-dimensional variable to an $n^2 {\times} n^2$-d…

New method improves neural network verification by considering multivariate input space of ReLU neurons.

SegAN-CAT improves brain tumor segmentation in MRI using adversarial networks.

We obtain new examples of non-symmetric Einstein solvmanifolds by combining two techniques. In \cite{T2}, H. Tamaru constructs new {\em attached} solvmanifolds, which are submanifolds of the solvmanifolds corresponding to noncompact symmetric spaces, endowed with a natural metric. Extending this construction, we apply …

Under weak regularity assumptions, only, we develop a fully geometric theory of vacuum Einstein spacetimes with T2 symmetry, establish the global well-posedness of the initial value problem for Einstein's field equations, and investigate the global causal structure of the constructed spacetimes. Our weak regularity ass…

New method predicts aphasia severity with narrower uncertainty intervals.

Many high dimensional sparse learning problems are formulated as nonconvex optimization. A popular approach to solve these nonconvex optimization problems is through convex relaxations such as linear and semidefinite programming. In this paper, we study the statistical limits of convex relaxations. Particularly, we con…

The Gromov-Hausdorff distance provides a metric on the set of isometry classes of compact metric spaces. Unfortunately, computing this metric directly is believed to be computationally intractable. Motivated by applications in shape matching and point-cloud comparison, we study a semidefinite programming relaxation of …

For a given complex n-fold M we present an explicit construction of all complex (n+1)-folds which are principal holomorphic T2-fibrations over M. For physical applications we consider the case of M being a Calabi-Yau 2-fold. We show that for such M, there is a subclass of the 3-folds that we construct, which has natura…

We show that the spectral norm of a random $n_1\times n_2\times \cdots \times n_K$ tensor (or higher-order array) scales as $O\left(\sqrt{(\sum_{k=1}^{K}n_k)\log(K)}\right)$ under some sub-Gaussian assumption on the entries. The proof is based on a covering number argument. Since the spectral norm is dual to the tensor…

Improved clustering speed for 20 clusters on CIFAR-100 dataset.

Statistical image reconstruction (SIR) methods are studied extensively for X-ray computed tomography (CT) due to the potential of acquiring CT scans with reduced X-ray dose while maintaining image quality. However, the longer reconstruction time of SIR methods hinders their use in X-ray CT in practice. To accelerate st…

Intensity augmentation improves breast MRI segmentation accuracy.

A new method relaxes molecules without needing non-equilibrium data.

New bounds for MCMC on discrete spaces without dimension dependence.

Study uses DRL with Lagrangian relaxation to solve temporal control tasks with STL constraints.

Unified convex relaxation framework for neural network robustness verification.

Combined ML and JVC-SENSE enable high-resolution imaging with fewer shots and less distortion.

We propose an SDP relaxation for the Gromov-Wasserstein distance, providing globally optimal solutions.

Proposes new convex relaxations for certifying spatial robustness of neural networks.

Improved patient risk stratification with relaxed parameter sharing in clinical time-series data.

A novel method relaxes binary constraints to non-negative spheres for multi-matching and clustering.

We study the problem of controlling linear time-invariant systems with known noisy dynamics and adversarially chosen quadratic losses. We present the first efficient online learning algorithms in this setting that guarantee $O(\sqrt{T})$ regret under mild assumptions, where $T$ is the time horizon. Our algorithms rely …

We study singular stochastic control of a two dimensional stochastic differential equation, where the first component is linear with random and unbounded coefficients. We derive existence of an optimal relaxed control and necessary conditions for optimality in the form of a mixed relaxed-singular maximum principle in a…

This article provides the first procedure for computing a fully data-dependent interval that traps the mixing time $t_{\text{mix}}$ of a finite reversible ergodic Markov chain at a prescribed confidence level. The interval is computed from a single finite-length sample path from the Markov chain, and does not require t…

This work optimizes RL algorithms using entropy regularisation for continuous-time LQ problems.

New algorithm solves large cardinality-constrained clustering problems.

The paper develops sum-of-squares relaxations for computing $f$-divergences.

Bounds on chemical reaction network relaxation rates using convex analysis.

GDM models time series with smoother transitions and interpretable states.

We study the relaxation dynamics of a financial market just after the occurrence of a crash by investigating the number of times the absolute value of an index return is exceeding a given threshold value. We show that the empirical observation of a power law evolution of the number of events exceeding the selected thre…