Separates Dirac equation on 2D product spaces and black hole horizons.
problem Separating Dirac equation on complex spacetime backgrounds.
method Used separation of variables in 2D product spaces to solve Dirac equation.
result Dirac equation separable in specific static black hole solutions.
New curvature K(x) measures manifold properties without integrals.
problem Understanding curvature on compact Riemannian manifolds.
method Developed index expectation curvature K(x) for 2D manifolds, constructed as a product of sectional index expectation curvatures.
result For small 2D manifolds with boundary, definite sign index expectation curvature K(x) exists and satisfies Gauss-Bonnet relation.
Study on a specific type of Riemannian manifolds constructed from 2D space-forms.
problem Characterizing and understanding new types of Riemannian manifolds.
method Constructed as a product of a real line and a 2-dimensional Riemannian space-form, with metrics derived from cone and hyperbolic extensions.
result Characterized and studied in terms of their curvature properties.
A parallel algorithm learns efficient Kronecker product dictionaries.
problem Sparse representation of 2D signals like images and hyperspectral data.
method Highly parallelizable algorithm for learning separable dictionaries.
result Competitive sparse representations at lower computational cost.
Computes a new metric quantity Y(M) for Riemannian 2d-manifolds.
problem No simple metric quantity exists for Riemannian manifolds.
method Defines Y(M) and Y_disc(M) involving sectional curvatures and computes them for specific manifolds.
result Y(M) and Y_disc(M) differ from the Euler characteristic and can be positive or negative.
Quantization on even-dimensional compact manifolds using cell decomposition.
problem Quantization of compact even-dimensional manifolds.
method Cell decomposition and embedding in CP^d, inducing local Poisson structure and star product.
result Achieved Berezin-type quantization on compact even-dimensional manifolds.
The paper develops a theory linking Hamiltonian and quasi-Hamiltonian manifolds.
problem Understanding the deformation of Hamiltonian quasi-Poisson manifolds to Hamiltonian Poisson manifolds.
method Introduces a generalized Hamiltonian deformation theory and constructs a topological quantum field theory.
result Shows that the imploded cross section of the double $D(G)_\imp$ deforms to the implosion of the cotangent bundle $T^*G_\imp$.
The study constructs AdS manifolds from Gromov-Thurston manifolds.
problem Creating hyperbolic and anti-de Sitter structures from Gromov-Thurston manifolds.
method Explicit correspondence between quasifuchsian AdS manifolds and compact quotients of Ø(2d,2)/U(d,1).
result Existence of quasifuchsian AdS manifolds and hyperbolic ends with specified boundary.
New 2D maps improve image captioning models.
problem Captions generated by RNNs are often poor.
method Used 2D maps instead of vectors to represent latent states.
result 2D maps lead to better captioning performance.
New method reconstructs 3D shapes from 2D images using Kendall's shape space.
problem Reconstruct 3D shapes from 2D images, especially for rare specimens.
method Kendall's shape space approach with prior information.
result More robust and plausible shapes compared to previous methods.
Following the previous authors works (joint with I.A.Dynnikov) we develop a theory of the discrete analogs of the differential-geometrical (DG) connections in the triangulated manifolds. We study a nonstandard discretization based on the interpretation of DG Connection as linear first order (''triangle'') difference eq…
Study classifies equidistant decompositions in 2D spaces.
problem Classifying equidistant decompositions in 2D spaces.
method Full classification of decompositions in Euclidean plane and sphere.
result Complete classification of equidistant decompositions in 2D spaces.
Curvature of 2D subsets preserved in their space.
problem Understanding curvature of subsets in 2D spaces.
method Analyzing subsets with vanishing first homology.
result Closed subsets inherit curvature bounds from ambient spaces.
Constructs new topological theories in 2D not fitting standard axioms.
problem Developing new topological theories in 2D that don't conform to traditional axioms.
method Universal construction by Blanchet et al., Kronecker's characterization, field extension, Hankel matrices, Schur polynomials, and foam evaluation.
result Introduction of non-multiplicative theories and classification over finite-dimensional state spaces.
Algorithms compute the topology of hyperelliptic curves in 2D and 3D.
problem Computing the topology of hyperelliptic curves in higher dimensions.
method Birational mapping of the plane or space to compute the topology of the curve.
result Algorithms implemented in { t Maple} for computing the topology of hyperelliptic curves.
Study Ricci vector fields on 2D space with diagonal metrics.
problem Understanding Ricci vector fields on 2D space with specific metrics.
method Examined Ricci vector fields on R2 with a diagonal metric. result Characterized Ricci vector fields on R2 with a diagonal metric. New model preserves symmetry in multivariate time series, improving performance.
problem Implicit ordering in MTS models violates inherent exchangeability.
method Permutation-equivariant 2D state space model with canonical architecture.
result Eliminates sequential dependency chains and simplifies stability analysis.
This paper analyzes shallow ReLU networks in L^p and Sobolev spaces, focusing on approximation and generalization.
problem Approximation and generalization of shallow ReLU networks in L^p and Sobolev spaces.
method Spherical harmonic analysis and embeddings into spectral Barron spaces for L^p spaces, path-norm control for Sobolev spaces.
result Minimax-optimal rates for nonparametric regression with shallow ReLU networks under path-norm control.
Proposes a model to generate 3D-aware images from 2D images.
problem Generating 3D-aware images from 2D images.
method Likelihood-based top-down model using Neural Radiance Fields and energy-based latent variables.
result Model can infer 3D object structures from 2D images and generate novel views.
The paper lists all self-similar solutions for a flow in 2D space.
problem Finding solutions to the inverse mean curvature flow in 2D.
method Obtained a complete list of self-similar solutions.
result Completely enumerated all self-similar solutions for the flow.
For integers d≥2 and ε=0 or 1, let S1,d−1(ε) denote the sphere product S1×Sd−1 if ε=0 and the twisted Sd−1 bundle over S1 if ε=1. The main results of this paper are: (a) if d≡ε (mod 2) then S1,d−1(ε) has a unique minimal triangulation using 2d+3 …
Study 2D spaces with curvature, finding a graph structure.
problem Understanding the geometry of 2D spaces with curvature constraints.
method Analyzing spaces as unions of disks, identifying singular points.
result Obtained a graph structure of topological singular points.
Optimizes master faces for 2D and 3D face verification using evolutionary algorithms and neural networks.
problem Impersonation attacks using master faces for face-based identity authentication.
method Evolutionary algorithm in latent space of StyleGAN, neural network to direct search, 2D and 3D face reconstruction.
result Obtains high impersonation rates with fewer master faces for 2D and 3D face verification.
A novel method compresses point cloud attributes by folding them onto a 2D grid.
problem Efficiently compressing point cloud attributes for storage and transmission.
method Interpreting point clouds as 2D manifolds, folding onto a grid, and mapping attributes to the grid using optimized methods.
result The proposed folding-based approach achieves performance comparable to state-of-the-art codecs.
Study of symmetries in a 2D space with specific metric properties.
problem Understanding symmetries in a 2D space with diagonal metrics.
method Analyzing Killing vector fields under specific restrictions on Lamé coefficients.
result Concretely described symmetries of the metric under given conditions.
Study bends 2D surfaces in 3D space using special equations.
problem Investigate infinitesimal bendings of 2D surfaces in 3D space.
method Use Bers-Vekua type equations and systems of differential equations with periodic coefficients.
result Construct bending fields for specific classes of 2D surfaces.
A parameter-free statistical model is used to study multiplicity signatures for coherent production of charged-pairs of parabosons of order p=2 in comparison with those arising in the case of ordinary bosons, p=1. Two non-negative real parameters arise because "ab" and "ba" are fundamentally distinct pair operators of …
Paper studies isoperimetric problem in 2D Finsler space with k=0.
problem Isoperimetric problem in 2D Finsler space with k=0.
method Holmes-Thompson area used to investigate.
result Circle centered at origin achieves maximum area.
New method for optimizing risk in financial models using Fourier transforms.
problem Optimizing risk in financial models with multi-period mean-CVaR.
method Strictly monotone 2D integration scheme via Fourier-trained transition kernels.
result Established robust and accurate optimization method for financial models.
Many 2D Artin groups are residually finite.
problem Residual finiteness of 2D Artin groups.
method Showed these groups split as free products with amalgamation or HNN extensions of finite rank free groups.
result Many 2D Artin groups are residually finite.
Study 2D spaces with curvature, focusing on structure and approximations.
problem Characterize and understand 2D metric spaces with curvature constraints.
method Lipschitz homotopy approximations, curvature measures, convergence analysis.
result Established Gauss-Bonnet Theorem and characterized spaces.
Projective connection explains gravity dynamics in 2D.
problem Understanding dynamics in 2D gravity with projective connection.
method Using projective connection over affine connections, defining action with curvature invariants.
result Projective connection naturally describes metric interaction in 2D gravity.
Crochet creates precise 2D shapes from 1D material.
problem Creating precise 2D shapes from 1D material.
method Using crochet to generate constant flat, spherical, or hyperbolic shapes.
result Crochet is the most flexible and precise method for building dynamical systems with high curvature precision.
This paper proposes grid cells encode position via a conformal isometric embedding of 2D physical space.
problem Hexagonal grid firing patterns in grid cells.
method Learning a distance-preserving position embedding in neural space using a recurrent neural network.
result The conformal isometric embedding of 2D physical space into neural space explains hexagonal grid firing patterns.
iSTFTNet2 improves iSTFTNet's speed and lightness with 1D-2D CNN.
problem Efficiently synthesizing high-fidelity speech.
method Improved iSTFTNet using 1D-2D CNNs for temporal and spectrogram structures.
result iSTFTNet2 is faster and more lightweight with comparable speech quality.
Method generates multiple 3D poses from 2D joint detections, addressing ambiguity and uncertainty.
problem Ambiguity and uncertainty in 3D human pose estimation from 2D joint detections.
method Generative model, compositional, anatomical constraints, removing model bias.
result Generates multiple valid 3D poses consistent with 2D joint detections.
Tensor networks improve medical image classification performance.
problem Improving medical image classification accuracy.
method Extending tensor networks to medical image analysis, focusing on 2D images.
result Tensor networks achieve comparable performance to deep learning methods with fewer hyperparameters and resources.
Study provides explicit formula for complex 2D Kähler manifold quantization.
problem Quantization of complex 2D locally symmetric Kähler manifolds.
method Deformation quantization with separation of variables, solving recurrence relations.
result Explicit formula for star product on complex 2D locally symmetric Kähler manifolds.
A new matrix concentration inequality for random products of matrices.
problem Understanding the behavior of random matrix products under bounded independent positive semidefinite matrices.
method Developed a non-asymptotic concentration inequality for the product of matrices.
result The inequality provides a bound on the deviation of the matrix product from its expected value.
Transformer-M learns molecular data in 2D or 3D formats.
problem Learning models for molecules are limited to specific data formats.
method Developed a Transformer-based model that can handle 2D and 3D molecular data.
result Transformer-M achieves strong performance on both 2D and 3D molecular tasks.
Sharp inequalities for star bodies in 2D space.
problem Understanding star bodies in 2D space.
method Sharp inequalities for star bodies in R2. result New inequalities and proofs for star bodies.
Tensor networks and RNNs are equivalent, improving wave function encoding.
problem Efficiently encoding quantum states in neural networks.
method Generalized RNN architecture for tensor networks, supporting polynomial time wave function evaluation.
result Tensorial RNNs can encode quantum states with lower bond dimensions and higher accuracy.
New neural model processes 2D data with long-range dependencies efficiently.
problem Limited receptive field of convolutions for complex 2D tasks.
method Proposes Matrix Shuffle-Exchange network with O(logn) layers and O(n2logn) complexity. result Exceeds convolutional and graph neural network baselines in long-range dependency modeling.
A new knot invariant using tangle-valued 1-cocycles.
problem Creating a strong and calculable knot invariant.
method Constructing a non-trivial combinatorial 1-cocycle L that takes values in H0(Θ;Z) with the scan-property. result The Alexander tree is an isotopy invariant of knots, demonstrating the non-invertibility of specific knots.
Open 2D TFTs extend to closed theories with circle value as Hochschild homology.
problem Extending open 2D TFTs to closed theories.
method Using symmetric monoidal ∞-categories and Hochschild homology.
result Open 2D TFTs admit initial open-closed extensions.
Novel boundary integral equations for Dirac operators in 3D Lipschitz domains.
problem Developing equations for Dirac operators in complex 3D domains.
method First-kind boundary integral equations, generalized Garding inequalities, Fredholm operators, finite dimensional kernels, Betti numbers.
result Finite dimensional kernels equal to the sum of Betti numbers, explaining the bilinear forms.
Improved 2D cardiac MRI with less data using deep learning.
problem Reducing artefacts in undersampled 2D radial cine MRI.
method Modified U-net trained on spatio-temporal slices.
result Outperforms existing methods in image quality and training efficiency.
Study geodesic curvature in 2D Alexandrov spaces, generalizing results from spaces with curvature above.
problem Geodesic curvature in Alexandrov spaces with curvature below.
method Comparison and rigidity theorems for geodesic curvatures.
result Generalized known results for geodesic curvature in spaces with curvature above.