arXiv research

Study expands classical harmonic function results to Riemannian manifolds.

Logifold improves ensemble machine learning by identifying fuzzy domains.

New GL-GP models learn covariance respecting domain geometry.

Given a Laplace eigenfunction on a surface, we study the distribution of its extrema on the nodal domains. It is classically known that the absolute value of the eigenfunction is asymptotically bounded by the 4-th root of the eigenvalue. It turns out that the number of nodal domains where the eigenfunction has an extre…

Study on unique minimal hypersurfaces in rotational domains.

In \cite{kamz} the author proved that every quasiconformal harmonic mapping between two Jordan domains with $C^{1,α}$, $0<α\le 1$, boundary is bi-Lipschitz, providing that the domain is convex. In this paper we avoid the restriction of convexity. More precisely we prove: any quasiconformal harmonic mapping between two …

Upper bounds and lower bounds show ERM outperforms DG methods in various settings.

Develops a logifold structure for understanding datasets.

New method tackles MNAR missingness in domain adaptation.

ManifoldShap improves model explanations by restricting evaluations to the data manifold.

There is a growing body of literature showing that deep neural networks are vulnerable to adversarial input modification. Recently this work has been extended from image classification to malware classification over boolean features. In this paper we present several new methods for training restricted networks in this …

Surface Electromyography (sEMG/EMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approxima…

Many efforts have been made to use various forms of domain knowledge in malware detection. Currently there exist two common approaches to malware detection without domain knowledge, namely byte n-grams and strings. In this work we explore the feasibility of applying neural networks to malware detection and feature lear…

New method eliminates domain size restrictions for X-ray transform inversion.

We study how the existence of a negatively pinched Kähler metric on a domain in complex Euclidean space restricts the geometry of its boundary. In particular, we show that if a convex domain admits a complete Kähler metric, with pinched negative holomorphic bisectional curvature outside a compact set, then the boundary…

Our goal is to better understand the relationship between the polyhedron and the group associated with a fundamental domain in H^3. In this paper, we will study torsion-free groups and determine a formula for how many edge classes a given abstract polyhedron must have. We will use that result to classify all fundamenta…

Two Kähler structures are PCR equivalent in the Siegel domain.

DAFNO learns surrogates for complex systems on irregular geometries.

Research explores hyperbolic space groups and their fundamental domains.

NTL protects AI models by restricting their generalization ability to specific domains.

Characterizes symplectic rational homology ball fillings of Seifert fibered spaces.

In this paper we study domains in flag manifolds which are bounded in an affine chart and whose projective automorphism group acts co-compactly. In contrast to the many examples in real projective space, we will show that no examples exist in many flag manifolds. Moreover, in the cases where such domains can exist, we …

Generalized score matching for densities on general domains.

We consider a global, nonlinear version of the Whitney extension problem for manifold-valued smooth functions on closed domains $C$, with non-smooth boundary, in possibly non-compact manifolds. Assuming $C$ is a submanifold with corners, or is compact and locally convex with rough boundary, we prove that the restrictio…

Much work has been done refining and characterizing the receptive fields learned by deep learning algorithms. A lot of this work has focused on the development of Gabor-like filters learned when enforcing sparsity constraints on a natural image dataset. Little work however has investigated how these filters might expan…

In domain adaptation, classifiers with information from a source domain adapt to generalize to a target domain. However, an adaptive classifier can perform worse than a non-adaptive classifier due to invalid assumptions, increased sensitivity to estimation errors or model misspecification. Our goal is to develop a doma…

A cost-effective framework for gradual domain adaptation using multifidelity.

The Novikov complex of a circle-valued Morse function is constructed algebraically from the Morse-Smale complex of the restriction to a fundamental domain of the real-valued Morse function on the pullback infinite cyclic cover.

SIG model identifies invariant variables for MSDA with fewer domain constraints.

Current supervised learning models cannot generalize well across domain boundaries, which is a known problem in many applications, such as robotics or visual classification. Domain adaptation methods are used to improve these generalization properties. However, these techniques suffer either from being restricted to a …

New approach to solving minimal surface system Dirichlet problem on smooth domains.

Structural RBM reduces parameters for image denoising and classification.

In this paper we prove the infinitesimal uniqueness theorem for the Newton potential of non simply connected bodies using the singularity theory approach. We consider the Newtonian potentials of the domains in ${\bf R}^n$ boundaries of which are the vanishing cycles on the level hypersurface of a holomorphic function w…

We shall discuss the inhomogeneous Dirichlet problem for: $f(x,u, Du, D^2u) = ψ(x)$ where $f$ is a "natural" differential operator, with a restricted domain $F$, on a manifold $X$. By "natural" we mean operators that arise intrinsically from a given geometry on $X$. An important point is that the equation need not be c…

Negative curvature restricts the gap between the first and second eigenvalues of convex domains.

Domain adaptation framework identifies latent variables for target distribution identifiability.

Study proves spectral determination of triangles and quadrilaterals, with restrictions on higher-order polygons.

Paper proposes MDAT to stabilize domain alignment in label-scarce settings.

Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs). Although the use of deep-learning techniques has been proposed, actual applications have been restricted by the fact the training data are obtained using traditional PDE solvers. Thereby, the uses of deep-le…

Distributions over exchangeable matrices with infinitely many columns, such as the Indian buffet process, are useful in constructing nonparametric latent variable models. However, the distribution implied by such models over the number of features exhibited by each data point may be poorly- suited for many modeling tas…

The paper develops quantitative estimates for holomorphic sections over bounded domains.

Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and …

Guarantees for third-person imitation learning from offline data.

Framework improves target domain prediction using quantile matching.

For a real symmetric domain $G_{\mathbb R}/K_{\mathbb R}$, with complexification $G_{\mathbb C}/K_{\mathbb C}$, we introduce the concept of "star-restriction" (a real analogue of the "star-products" for quantization of Kähler manifolds) and give a geometric construction of the $G_{\mathbb R}$-invariant differential ope…

Research on refined algebraic domains respecting differential geometry.

The article explores constructing biharmonic and conformal biharmonic maps to spheres.

This work defines a categorical notion of principal bundles.