arXiv research

This is the third companion paper of arXiv:1601.03586. When a gauge theory has a flavor symmetry group, we construct a partial resolution of the Coulomb branch as a variant of the definition. We identify the partial resolution with a partial resolution of a generalized slice in the affine Grassmannian, Hilbert scheme o…

Neural DEs improve single image super-resolution.

MeshfreeFlowNet generates high-resolution spatio-temporal solutions from low-resolution inputs.

A Monte Carlo k-nearest neighbours (KNN) and a multi-resolution convolutional neural network (CNN) were developed to detect the presences of multiple gasses in near infrared (IR) spectrums. High Resolution Transmission database was used to synthesize the near IR spectrums. Monte Carlo KNN determined the optimal kernel …

Study uses multi-agent reinforcement learning to control self-assembly with high-resolution external control.

Physics-informed neural operator learns from coarse to fine discretized data.

Study on cohomology of SL_n(Z) for n>=3, proving vanishing of certain cohomology groups.

We study the partial resolutions of singularities related to Hilbert schemes of points on an affine space. Consider a quotient of a vector space $V$ by an action of a finite group $G$ of linear transforms. Under some additional assumptions, we prove that the partial desingularization of Hilbert type is smooth only if t…

We describe partial semi-simplicial resolutions of moduli spaces of surfaces with tangential structure. This allows us to prove a homological stability theorem for these moduli spaces, which often improves the known stability ranges and give explicit stability ranges in many new cases. In each of these cases the stable…

Partial proof of a conjecture about knot concordance maps.

The metrics of S. Y. Cheng and S.-T. Yau are considered on a strictly pseudoconvex domains in a complex manifold. Such a manifold carries a complete Kähler-Einstein metric if and only if its canonical bundle is positive. We consider the restricted case in which the CR structure on $\partial M$ is normal. In this case M…

We develop a framework for understanding the existence of asymptotically flat solutions to the static vacuum Einstein equations with prescribed boundary data consisting of the induced metric and mean curvature on a 2-sphere. A partial existence result is obtained, giving a partial resolution of a conjecture of Bartnik …

Let $V$ be a compact and irreducible complex space of complex dimension $v$ whose regular part is endowed with a complete Hermitian metric $h$. Let $π:M\rightarrow V$ be a resolution of $V$. Under suitable assumptions on $h$ we prove that $$H^{v,q}_{2,\overline{\partial}}(\operatorname{reg}(V),g)\cong H^{v,q}_{\overlin…

We prove that given any smooth metric $γ$ and smooth positive function $H$ on $S^{2}$, there is a constant $λ> 0$, depending on $(γ, H)$, and an asymptotically flat solution $(M, g, u)$ of the static vacuum Einstein equations on $M = {\mathbb R}^{3} \setminus B^{3}$, such that the induced metric and mean curvature of $…

In this thesis, we show the existence of a sequence of differential operators starting with with the Dirac operator in k Clifford variables, $D=(D_1,..., D_k)$, where $D_i=\sum_j e_j\cdot \partial_{ij}: C^\infty((\R^n)^k,§)\to C^\infty((\R^n)^k,§)$ ($§$ is the spinor module). This operator is the Cauchy-Riemann operato…

New non-Kähler manifolds constructed with specific properties.

Let $Γ$ be a finite group acting linearly on $\C^n$, freely outside the origin, and let $N$ be the number of conjugacy classes of $Γ$ minus one. A construction of Kronheimer of moduli spaces $X_ζ$ of translation-invariant $Γ$-equivariant instantons on $\C^2$ is generalised to $\C^n$. The moduli spaces $X_ζ$ depend on a…

We introduce a Bayesian Gaussian process latent variable model that explicitly captures spatial correlations in data using a parameterized spatial kernel and leveraging structure-exploiting algebra on the model covariance matrices for computational tractability. Inference is made tractable through a collapsed variation…

The resolution and calibration of pure spectra of minority components in measurements of chemical mixtures without prior knowledge of the mixture is a challenging problem. In this work, a combination of band target entropy minimization (BTEM) and target partial least squares (T-PLS) was used to obtain estimates for sin…

The study optimizes Gaussian process approximations for finite-rank models.

Framework learns surrogates for molecular dynamics across multiple time-scales.

NKN deep neural network learns governing equations and classifies images.

Novel neural operator predicts complex spatiotemporal dynamics from partial observations.

Within a financial model with linear price impact, we study the problem of hedging a covered European option under gamma constraint. Using stochastic target and partial differential equation smoothing techniques, we prove that the super-replication price is the viscosity solution of a fully non-linear parabolic equatio…

Sig-DEG speeds up diffusion models by distilling them into faster approximations.

Torus covers have controlled volume and diameter under curvature and diameter bounds.

The paper constructs Ricci-flat Kähler manifolds with specific decay properties.

LUNO linearizes neural operators to quantify their predictive uncertainty.

New method learns PDE solutions from low-fidelity data.

Study portfolio optimization with partial info and drawdown constraints using deep learning.

Cut-DeepONet handles discontinuities and sharp transitions in neural operators.

Partial AHS-structures extend G-structures and Cartan geometries to manifolds with involutive distributions.

Let S be a K3 surface that admits a non-symplectic automorphism $ρ$ of order 3. We divide $S\times \mathbb{P}^1$ by $ρ\timesψ$ where $ψ$ is an automorphism of order 3 of $\mathbb{P}^1$. There exists a threefold ramified cover of a partial crepant resolution of the quotient that is a Calabi-Yau orbifold. We compute the …

A new method uses PDEs to predict spatiotemporal phenomena.

SR-NAM maps low-res images to multiple high-res images realistically.

Deep learning speeds up whole heart MRI to 30 seconds.

New model tackles complex spatio-temporal causal inference with dynamic confounders and functional data.

This paper solves PDEs for embedding discrete lattices into smooth manifolds.

The abstract discusses p-harmonic forms and their geometric properties, proving new theorems about Lp-cohomology.

Paper uses neural nets for financial optimization problems.

Paper introduces CRP-O framework for uncertainty quantification in deep operators.

We present explicit constructions of complete Ricci-flat Kahler metrics that are asymptotic to cones over non-regular Sasaki-Einstein manifolds. The metrics are constructed from a complete Kahler-Einstein manifold (V,g_V) of positive Ricci curvature and admit a Hamiltonian two-form of order two. We obtain Ricci-flat Ka…

State-free reversible VAMPnets (SRVs) are a neural network-based framework capable of learning the leading eigenfunctions of the transfer operator of a dynamical system from trajectory data. In molecular dynamics simulations, these data-driven collective variables (CVs) capture the slowest modes of the dynamics and are…

In this article we extend earlier work on the jump-diffusion risk-sensitive asset management problem [SIAM J. Fin. Math. (2011) 22-54] by allowing jumps in both the factor process and the asset prices, as well as stochastic volatility and investment constraints. In this case, the HJB equation is a partial integro-diffe…

A new deep metric learning method for defect classification in threaded pipe connections.

Paper introduces a Gaussian Process for operator learning in computational mechanics.

The paper proposes a method to improve fine-resolution predictions using coarse-resolution data.

Proposes ENOs for learning PDE solutions that conserve energy.