Solves the Cauchy problem for linearised Einstein equation on globally hyperbolic spacetimes.
problem Initial value problem for gravitational waves on globally hyperbolic vacuum spacetimes.
method Proves the solution map is an isomorphism of locally convex topological vector spaces and solves linearised constraint equations on closed manifolds.
result Well-posedness of the Cauchy problem for gravitational waves on globally hyperbolic spacetimes.
Improves state space models' resistance to noise.
problem State space models' initialization assumes noise-free data, which is often violated.
method Uncertainty-aware initialization for state space models, reformulating HiPPO with measurement noise.
result Improves model resistance to noise at training and inference time.
Survey on preserving curvature bounds for non-smooth Ricci flow.
problem Preserving curvature bounds for non-smooth initial data in Ricci flow.
method Survey of various weak initial data and preservation of curvature bounds.
result Various curvature lower bounds preserved up to a constant for non-smooth initial data.
This article, written to appear as a chapter in "The Springer Handbook of Spacetime", is a review of the initial value problem for Einstein's gravitational field theory in general relativity. Designed to be accessible to graduate students who have taken a first course in general relativity, the article first discusses …
Proves rigidity for specific initial data sets under the dominant energy condition.
problem Rigidity of initial data sets with boundary and convex polytopes.
method Solution of boundary value problems for Dirac operators and approximations by manifolds with smooth boundary.
result Proves rigidity for compact smooth spin manifolds and convex polytopes under the dominant energy condition.
Conditions for conformal Killing vectors in vacuum spacetimes.
problem Finding conditions for conformal Killing vectors in vacuum spacetimes.
method Classical argument to identify a suitable propagation identity and check well-posedness of the initial value problem.
result Necessary and sufficient conditions for conformal Killing initial data (CKID) are found, extending known Killing initial data (KID).
New initialization methods speed up Sinkhorn algorithm for OT problems.
problem Improving runtime of the Sinkhorn algorithm for optimal transport problems.
method Data-dependent initializers for Sinkhorn algorithm, based on closed-form solutions for specific settings.
result Data-dependent initializers result in dramatic speed-ups without affecting differentiability.
We study the problem of inviscid slightly compressible fluids in a bounded domain. We find a unique solution to the initial-boundary value problem and show that it is near the analogous solution for an incompressible fluid provided the initial conditions for the two problems are close. In particular, the divergence of …
Proves spacetime positive mass theorem for spin initial data sets with arbitrary ends.
problem Proving the spacetime positive mass theorem for specific spacetime configurations.
method Solving a mixed boundary value problem for the Dirac-Witten operator with a Callias potential.
result Established spacetime positive mass theorem for asymptotically flat spin initial data sets with arbitrary ends.
We show existence and uniqueness of very weak solutions of the Cauchy problem for the porous medium equation on Cartan-Hadamard manifolds satisfying suitable lower bounds on Ricci curvature, with initial data that can grow at infinity at a prescribed rate, that depends crucially on the curvature bounds. The curvature c…
The Degasperis-Procesi equation's solutions define pseudospherical metrics and can lead to surface collapse.
problem Understanding the breakdown of manifolds determined by Cauchy problems of the Degasperis-Procesi equation.
method Analyzing the pseudospherical nature of local and non-local formulations of the Degasperis-Procesi equation.
result Solutions to Cauchy problems with non-trivial initial data define an orthonormal coframe for pseudospherical metrics.
New method for better initial centers in clustering with improved accuracy and privacy.
problem Improving the quality of clustering centers in metric spaces.
method HST initialization based on metric embedding tree structure, combined with efficient search algorithm and DP extension.
result HST initialization produces better initial centers than k-median++ with comparable efficiency and improved privacy. The paper explores conditions for homothetic Killing vectors on spacetime hypersurfaces.
problem Conditions for the existence of homothetic Killing vectors on spacetime hypersurfaces.
method General identities relating deformation tensor and tensor on hypersurfaces, applied to specific settings.
result Necessary and sufficient conditions for homothetic Killing vectors on spacetime hypersurfaces.
New approach removes obstructions in gluing spacelike and null hypersurfaces in Einstein equations.
problem Gluing two solutions of the Einstein equations along a hypersurface.
method Active utilization of nonlinearity, low-frequency linear analysis, high-frequency nonlinear control.
result Removes 10-dimensional obstructions in null and spacelike gluing problems.
Proposes a new pretraining strategy for RNNs to improve classification performance.
problem Poor generalization of RNNs due to initial parameter assignment.
method Data-aware layer-wise pretraining strategy to initialize RNN parameters.
result Data-aware strategies positively support the initialization of RNN-based classification models.
Proves stability of Minkowski space for specific initial data.
problem Stability of Minkowski space under spacelike-characteristic initial data.
method Vectorfield method and bootstrapping argument, with new geometric constructions.
result Global nonlinear stability of Minkowski space proved for the spacelike-characteristic Cauchy problem.
The paper analyzes how good initial guesses affect the amount of data needed for low-rank matrix recovery.
problem Theoretical guarantee of local optimization algorithms requires excessive data to prevent spurious local minima.
method Quantifies the relationship between initial guess quality and sample complexity using restricted isometry constant.
result A linear improvement in initial guess quality leads to a constant factor improvement in sample complexity.
This paper studies how gradient descent in control systems can perform well on unseen data.
problem The extent of a learned controller's ability to extrapolate to unseen initial states.
method Theoretical study of policy gradient in Linear Quadratic Regulator (LQR) problems, focusing on the role of exploration.
result The performance of a learned controller on unseen initial states depends on the degree of exploration induced by the system.
Paper tackles estimating initial conditions of spatio-temporal processes from sparse data.
problem Estimating initial conditions of spatio-temporal advection-diffusion processes from sparse data.
method Regularized convex optimization problem with Alternating Direction Method of Multipliers.
result Efficient solutions for non-uniform and shifted uniform sampling schemes.
Solves Ricci flow on Riemann surfaces with measure initial data.
problem Existence and smoothness of Ricci flow on Riemann surfaces.
method Formulation and solution of existence problem using Ricci flow.
result New examples of nongradient expanding Ricci solitons.
New method optimizes deep neural network weights using data statistics.
problem Vanishing/exploding gradient problem and non-convex objective function in DNNs.
method Data-dependent initialization of network weights.
result Our algorithm achieves superior classification accuracy on practical datasets.
New method improves BO's AF maximizer initialization for high-dimensional problems.
problem Challenges in maximizing acquisition functions in high-dimensional Bayesian optimization.
method Proposes a heuristic optimizer-based initialization approach to improve AF maximizer performance.
result Our approach significantly enhances BO performance in most test cases.
Chameleon learns model initializations across tasks with different schemas.
problem Meta-learning parameter initialization across tasks with varying predictor schemas.
method Chameleon aligns different predictor schemas to a common representation.
result Chameleon successfully learns parameter initializations across tasks with different schemas.
The Einstein equations in wave map gauge are a geometric second order system for a Lorentzian metric. To study existence of solutions of this hyperbolic quasi diagonal system with initial data on a characteristic cone which are not zero in a neighbourhood of the vertex one can appeal to theorems due to Cagnac and Dossa…
We solve Bartnik's stationary extension problem near Schwarzschild spheres.
problem Existence and uniqueness of asymptotically flat stationary vacuum spacetimes.
method Developed a double geodesic gauge, reducing equations to elliptic and transport-type problems.
result Local well-posedness for Bartnik stationary metric extension problem near Schwarzschild spheres.
Initial data for pp-wave spacetimes constructed in 4D.
problem Characterizing initial data for pp-wave spacetimes. method Constructs a vacuum initial data set with extra conditions related to CKID.
result Data development is a subset of a vacuum pp-wave. Proves initial data on big bang singularities for Einstein-nonlinear scalar field equations lead to unique solutions.
problem Initial data on big bang singularities for Einstein equations.
method Geometric formulation of initial data, proving existence and uniqueness of solutions.
result Initial data on the singularity for the Einstein-nonlinear scalar field equations in 4 spacetime dimensions lead to a unique development of the data.
Paper presents robust clustering methods for general mixture models.
problem Clustering with sub-Gaussian error assumptions often invalid in practice.
method Hybrid clustering with robust centroid estimate and data-driven initialization.
result Provably near-optimal mislabeling guarantees for general error distributions.
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 learns good initialization for gradient descent from past solutions.
problem Challenges in solving non-convex optimization problems.
method Learning good initialization rules from previous solutions.
result Our approach performs better than random initialization in various non-convex problems.
Two new scalable K-means initialization methods proposed for large-scale clustering.
problem Efficient initialization for large-scale clustering problems.
method Divide-and-conquer approach and random projection method for multiple lower-dimensional subspaces.
result The proposed methods outperform state-of-the-art in large-scale clustering tasks.
Gradient descent with random initialization solves phase retrieval problems efficiently.
problem Solving systems of quadratic equations for phase retrieval.
method Gradient descent with random initialization for nonconvex least squares problem.
result Gradient descent achieves near-optimal computational and sample complexities for phase retrieval.
Solves initial value problem for harmonic maps on specific manifolds.
problem Initial value problem for harmonic maps on cohomogeneity one manifolds.
method Setup and solve the initial value problem using equivariant harmonic maps and regular-singular systems.
result Local existence of harmonic maps in a neighborhood of singular orbits.
New method for blind over-the-air computation without CSI.
problem Over-the-air computation without channel information.
method Wirtinger flow solution with random initialization.
result Statistical optimality and global convergence of the method.
Study proves convergence of quantized geodesics to Mabuchi geodesics.
problem Convergence of quantized geodesics to Mabuchi geodesics in short time.
method Real-analytic initial data and convergence proof.
result Proves convergence of quantized Bergman geodesics to Mabuchi geodesics.
Solves initial boundary value problem for vacuum Einstein equations and proves geometric uniqueness.
problem Initial boundary value problem for vacuum Einstein equations.
method Formulated IBVP, solved simultaneously in local harmonic coordinates, constructed unique maximal globally hyperbolic solution.
result Vacuum spacetimes satisfying fixed initial-boundary conditions and corner conditions are geometrically unique near the initial surface.
Butterfly-Net improves CNN performance with structured connections and initialization.
problem Improving the performance of convolutional neural networks (CNNs).
method Butterfly-Net introduces structured and sparse cross-channel connections, and Butterfly initialization strategy.
result Butterfly-Net approximates Fourier representations with exponentially decaying error as depth increases.
Given a collection of N solutions of the (3+1) vacuum Einstein constraint equations which are asymptotically Euclidean, we show how to construct a new solution of the constraints which is itself asymptotically Euclidean, and which contains specified sub-regions of each of the N given solutions. This generalizes earlier…
The paper solves a flow problem on surfaces with boundary to converge to the hyperbolic metric.
problem Solving a normalized Ricci flow on surfaces with boundary to converge to the complete hyperbolic metric.
method Introduced a unique solution for the normalized Ricci flow with prescribed geodesic curvature on the boundary, using a Cauchy-Dirichlet problem.
result The flow converges locally uniformly to the complete hyperbolic metric.
Sharp conditions found for solving heat equation on Riemannian manifolds.
problem Solving semilinear heat equation on Riemannian manifolds.
method Sharp conditions derived for local-in-time solvability.
result Sharp conditions on solvability given for complete and connected manifolds.
New network learns non-parametric invariances from data.
problem Modeling non-parametric invariances in data.
method Introduces PRC-NPTN networks with permanent random connectomes.
result Improves generalization and outperforms existing methods.
Efficiently trains GMMs for streaming data with SGD, addressing local optima and numerical instabilities.
problem Local optima and numerical instabilities in training GMMs for high-dimensional streaming data.
method Stochastic Gradient Descent (SGD) with adaptive annealing and exponential-free approximation.
result SGD approach trains GMMs without k-means initialization and outperforms sEM for high-dimensional data.
New principle for optimal control with higher order differential constraints.
problem Optimal control problems with higher order differential constraints.
method Derivation of the Principle of Minimal Labour and generalization of Pontryagin Maximum Principle.
result Generalized Pontryagin Maximum Principle for higher order constraints.
Paper proves rigidity of initial data sets with boundary and capillary MOTS.
problem Rigidity of initial data sets with boundary and capillary MOTS.
method Estimates area of MOTS, proves rigidity for 3D, extends to high dimensions using Yamabe constant.
result Rigidity results for initial data sets with boundary and capillary MOTS.
Proves density and mass theorems for specific initial data sets.
problem Initial data sets with boundary in spacetime.
method Harmonic asymptotics and dominant energy condition.
result Spacetime positive mass theorem for initial data sets with apparent horizon boundary.
We study the global theory of linear wave equations for sections of vector bundles over globally hyperbolic Lorentz manifolds. We introduce spaces of finite energy sections and show well-posedness of the Cauchy problem in those spaces. These spaces depend in general on the choice of a time function but it turns out tha…
Algorithm recovers factors of rank-1 matrices from noisy measurements.
problem Estimating factors of a rank-1 matrix from nonlinearly transformed and noisy measurements.
method Alternating minimization with random initialization and analysis of empirical error recursion.
result Algorithm converges geometrically fast from random initialization, with sharp guarantees.
When working with asymptotically hyperbolic initial data sets for general relativity it is convenient to assume certain simplifying properties. We prove that the subset of initial data sets with such properties is dense in the set of physically reasonable asymptotically hyperbolic initial data sets. More specifically, …