arXiv research

Study curves evolving by gradient flow of elastic energy, proving existence, smoothing, and convergence.

Proves Payne conjecture for buckling and membrane eigenvalues.

Euler's elastica with monotone curvature is uniquely minimal.

Extends plate problems to differential forms on manifolds.

The paper sets lower bounds for Laplacian eigenvalues and clamped plate problem eigenvalues.

Smooth compactness theorem for elasticae, except straight segments.

Paper proves rigidity estimates for hyperbolic shells and applies them to \(Γ\)-limit theory.

Affirm Lord Rayleigh's conjecture on curved spaces for clamped plates.

In this paper, we study eigenvalues of a clamped plate problem. We obtain a lower bound for eigenvalues, which gives an important improvement of results due to Levine and Protter.

We examine the effect of clamping variables for approximate inference in undirected graphical models with pairwise relationships and discrete variables. For any number of variable labels, we demonstrate that clamping and summing approximate sub-partition functions can lead only to a decrease in the partition function e…

In this paper, we study estimates for eigenvalues of the clamped plate problem. A sharp upper bound for eigenvalues is given and the lower bound for eigenvalues in [10] is improved.

For a bounded domain $Ω$ in a complete Riemannian manifold $M^n$, we study estimates for lower order eigenvalues of a clamped plate problem. We obtain universal inequalities for lower order eigenvalues. We would like to remark that our results are sharp.

New isoperimetric inequality for clamped plates in RCD(0,N) spaces, sharp and stable.

This paper studies eigenvalues of the clamped plate problem on a bounded domain in an $n$-dimensional Euclidean space. We give an estimate for the gap between $\sqrt {Γ_{k+1}-Γ_{1}}$ and $\sqrt {Γ_{k}-Γ_{1}}$, for any positive integer $k$. According to the asymptotic formula of Agmon and Pleijel, we know, the gap betwe…

CLAMP uses neural manifold packing to improve self-supervised learning.

The paper studies eigenvalue inequalities for a clamped plate problem involving a generalized elliptic differential operator.

The paper connects eigenvalue problems for various operators and establishes inequalities and asymptotic formulas for heat traces.

We study Lord Rayleigh's problem for clamped plates on an arbitrary $n$-dimensional $(n\geq 2)$ Cartan-Hadamard manifold $(M,g)$ with sectional curvature $\textbf{K}\leq -κ^2$ for some $κ\geq 0.$ We first prove a McKean-type spectral gap estimate, i.e. the fundamental tone of any domain in $(M,g)$ is universally bounde…

The Poisson problem consists in finding an immersed surface $Σ\subset\mathbb{R}^m$ minimising Germain's elastic energy (known as Willmore energy in geometry) with prescribed boundary, boundary Gauss map and area which constitutes a non-linear model for the equilibrium state of thin, clamped elastic plates originating f…

Paper studies eigenvalues of a specific operator on Riemannian manifolds.

Analyzes Willmore flow for graphs with boundary data, proving existence and convergence.

Neural networks are commonly trained to make predictions through learning algorithms. Contrastive Hebbian learning, which is a powerful rule inspired by gradient backpropagation, is based on Hebb's rule and the contrastive divergence algorithm. It operates in two phases, the forward (or free) phase, where the data are …

New method improves model reconstruction using counterfactuals and polytope theory.

Sharp spectral gap estimates for higher-order operators on hyperbolic spaces.

Recent work (Pennington et al, 2017) suggests that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning. Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs). We find th…

Sharp bounds derived for eigenvalues on specific geometric spaces.

Study on hyperbolic elastic flow, proving convergence and quantifying singularities.

In this paper, we establish sharp inequalities for four kinds of classical eigenvalues on a bounded domain of a Riemannian manifold. We also establish asymptotic formulas for the eigenvalues of the buckling and clamped plate problems. In addition, we give a negative answer to the Payne conjecture for the one-dimensiona…

In this paper, we establish universal inequalities for eigenvalues of the clamped plate problem on compact submanifolds of Euclidean spaces, of spheres and of real, complex and quaternionic projective spaces. We also prove similar results for the biharmonic operator on domains of Riemannian manifolds admitting spherica…

The paper bridges stochastic control and deep hedging for European call options with transaction costs.

The paper explores inequalities between eigenvalues on Riemannian manifolds.

Study proves inequalities for eigenvalues of fourth-order elliptic operators on Riemannian manifolds.

Probabilistic graphical models are traditionally known for their successes in generative modeling. In this work, we advocate layered graphical models (LGMs) for probabilistic discriminative learning. To this end, we design LGMs in close analogy to neural networks (NNs), that is, they have deep hierarchical structures a…

We used machine learning methods to predict NaV1.7 inhibitors and found the model RF-CDK that performed best on the imbalanced dataset. Using the RF-CDK model for screening drugs, we got effective compounds K1. We use the cell patch clamp method to verify K1. However, because the model evaluation method in this article…

In this paper, we obtain a new abstract formula relating eigenvalues of a self-adjoint operator to two families of symmetric and skew-symmetric operators and their commutators. This formula generalizes earlier ones obtained by Harrell, Stubbe, Hook, Ashbaugh, Hermi, Levitin and Parnovski. We also show how one can use t…

Improved lower bounds for poly-Laplacian eigenvalues in arbitrary dimensions.

We develop a stochastic whole-brain and body simulator of the nematode roundworm Caenorhabditis elegans (C. elegans) and show that it is sufficiently regularizing to allow imputation of latent membrane potentials from partial calcium fluorescence imaging observations. This is the first attempt we know of to "complete t…

Near-optimal private tests for simple and MLR hypotheses developed under Gaussian differential privacy.

Learning attribute applicability of products in the Amazon catalog (e.g., predicting that a shoe should have a value for size, but not for battery-type at scale is a challenge. The need for an interpretable model is contingent on (1) the lack of ground truth training data, (2) the need to utilise prior information abou…

We introduce the hierarchical compositional network (HCN), a directed generative model able to discover and disentangle, without supervision, the building blocks of a set of binary images. The building blocks are binary features defined hierarchically as a composition of some of the features in the layer immediately be…

Hypothesis testing plays a central role in statistical inference, and is used in many settings where privacy concerns are paramount. This work answers a basic question about privately testing simple hypotheses: given two distributions $P$ and $Q$, and a privacy level $\varepsilon$, how many i.i.d. samples are needed to…

Enhances drug discovery models by understanding human language.

RAGuard improves safety in LLMs for offshore wind maintenance.

IGSD separates task-specific content channels in transformer components by comparing activation replacement with zero ablation.

We propose to meta-learn causal structures based on how fast a learner adapts to new distributions arising from sparse distributional changes, e.g. due to interventions, actions of agents and other sources of non-stationarities. We show that under this assumption, the correct causal structural choices lead to faster ad…

Proposes a new method to learn meta-priors from data.

Proves equivalence of two types of boundaries in metric spaces.

Proves well-posedness for Einstein equations with specific boundary conditions.