This study develops a NURBS-based method for conformal surface flattening without singularities.
problem Flatten surfaces conformally without singularities.
method NURBS-based approach with iterative refinement of input and flattening surfaces, leveraging nonlinear extension of VarPro.
result Developed a singularity-free NURBS-based method for conformal surface flattening.
The paper proposes a method to create density-equalizing maps for open surfaces.
problem Creating flattening maps of simply-connected open surfaces with prescribed density distributions.
method Density diffusion principle and algorithm for computing flattening maps.
result Area-preserving parameterizations of simply-connected open surfaces can be easily computed.
Method flattens complex surfaces with consistent density and shape.
problem Shape deformations and local geometric distortions in density-equalizing maps for multiply-connected surfaces.
method Formulates density diffusion as a quasiconformal flow, solving an energy minimization problem involving the Beltrami coefficient to ensure bijectivity and control distortion.
result Achieves optimal parameterization of multiply-connected surfaces with bijective and controlled geometric distortions.
Paper studies spaces of flattenings of simplicial spheres and their homotopy type.
problem Existence and uniqueness of differentiable structures on simplicial spheres.
method Analyzes spaces of flattenings of simplicial spheres and their homotopy type.
result Spaces of flattenings have the homotopy type of the orthogonal group.
Flattenings of knotted surfaces help define new invariants.
problem Understanding and quantifying knotted surfaces in 4-sphere.
method Using hyperbolic decompositions and projections onto 2-sphere.
result Introduced layering, trunk, and partition number invariants.
Study FRS deformations of space curves with focus on flattenings, vertices, and twistings.
problem Geometric analysis of space curves with specific deformations.
method Investigation of FRS-deformations of space curves with emphasis on geometric properties. result Detailed study of flattenings, vertices, and twistings points on space curves.
New neural networks flatten and reconstruct manifolds from samples.
problem Learning from high-dimensional data embedded in submanifolds.
method Flattening Networks (FlatNet) that linearize and reconstruct embedded submanifolds.
result FlatNet achieves balance of interpretability, feasibility, and generalization.
The paper flattens a non-degenerate CR singular point in complex space.
problem Flattening a non-degenerate CR singular point of real codimension two.
method Geometric approach and formal theory approach.
result Provides a general flattening theorem for non-degenerate CR singular points.
We discuss Ghys' theorem on 4 zeroes of the Schwarzian derivative and its relation with flattening points of Legendrian curves and Sturm theory.
The paper explores centroaffine geometry of polygons and their duals.
problem Understanding centroaffine dual pairs of spatial polygons.
method Defining centroaffine dual pairs and proving properties of polygon duals.
result Constant curvature polygons are dual to planar polygons.
A primary goal in this paper is to study the question that asks when a real analytic submanifold M in Cn+1 bounds a real analytic (up to M) Levi-flat hypersurface M^ near p∈M such that M^ is foliated by a family of complex hypersurfaces moving along the normal direction of M at …
Proposes methods to improve hierarchical classification accuracy by flattening inconsistent nodes.
problem Error propagation in top-down hierarchical classification due to inconsistent nodes.
method Data-driven approaches for identifying and flattening inconsistent nodes.
result Improves classification performance by up to 7% in Macro-F1 score.
Any smooth surface in R^3 may be flattened along the z-axis, and the flattened surface becomes close to a billiard table in R^2 . We show that, under some hypotheses, the geodesic flow of this surface converges locally uniformly to the billiard flow. Moreover, if the billiard is dispersive and has finite horizon, then …
Let K be an algebraically closed field endowed with a complete non-archimedean norm with valuation ring R. Let f:Y -> X be a map of K-affinoid varieties. In this paper we study the analytic structure of the image f(Y) in X; such an image is a typical example of a subanalytic set. We show that the subanalytic sets are p…
B. Fedosov has given a simple and very natural construction of a deformation quantization for any symplectic manifold, using a flat connection on the bundle of formal Weyl algebras associated to the tangent bundle of a symplectic manifold. The connection is obtained by affinizing, nonlinearizing, and iteratively flatte…
AWP improves robustness by flattening weight loss landscape.
problem Improving robustness of deep neural networks against adversarial examples.
method Explicitly regularizes the flatness of weight loss landscape through adversarial weight perturbation.
result AWP forms a double-perturbation mechanism in adversarial training, leading to flatter weight loss landscape.
The study proves a discrete version of Segre's theorem for polygonal curves.
problem Proving a discrete analog of a four-vertex theorem for spherical curves.
method Using the concept of discrete tangent indicatrix of a polygon.
result A polygon with at least four vertices and a non-self-intersecting discrete tangent indicatrix has at least four flattenings.
Theory explains power-law distributions without complex models.
problem Understanding power-law distributions in geometrically growing systems.
method Developed a theory of geometrically growing systems and applied it to explain various distributions.
result The geometrically growing system's distribution flattens over time, increasing relative size ratios.
A mathematical model describes deforming manifolds with precise vectors and fields.
problem Modeling and describing the deformation of complex manifolds in practical applications.
method Proposes a modified differential dynamic model with constraints on spatial and temporal continuity, presenting deforming vector and field.
result Demonstrates the effectiveness of an autonomous deforming field in data dimension reduction tasks.
A method to automatically and symbolically detect and resolve degenerate parameter combinations from parameter-data pairs.
problem Identifying degenerate parameter combinations in physical models or real-world datasets.
method The degeneracy distillery method detects and resolves degenerate parameter combinations from parameter-data pairs.
result The method reduces the simulation budget required for downstream neural posterior estimation.
Analyzes how BatchNorm flattens the loss landscape in neural networks.
problem Understanding BatchNorm's impact on neural network optimization.
method Mean-field theory applied to quantify BatchNorm's effect on loss landscape.
result BatchNorm flattens the loss landscape, allowing for larger learning rates.
A single-vertex origami is a piece of paper with straight-line rays called creases emanating from a fold vertex placed in its interior or on its boundary. The Single-Vertex Origami Flattening problem asks whether it is always possible to reconfigure the creased paper from any configuration compatible with the metric, t…
A new algorithm flattens multi-modal distributions for better deep learning.
problem Bayesian learning in big data with multi-modal distributions.
method Contour Stochastic Gradient Langevin Dynamics (CSGLD) algorithm.
result The CSGLD algorithm avoids local traps in deep neural networks.
Deep learning boosts cable capacity by 19%.
problem Maximizing cable capacity under power constraints.
method Optimized launch powers using deep neural networks.
result 19% increase in capacity per Watt.
AutoGraph uses transformers to efficiently generate graphs as sequences.
problem Efficiently generating large, sparse graphs without expensive node features.
method Flattening graphs into sequences and using decoder-only transformers.
result AutoGraph achieves state-of-the-art performance on synthetic and molecular benchmarks.
Standard bubbles and partitions are stable in various model spaces.
problem Stability of standard bubbles and partitions in different model spaces.
method New conjugated Brascamp-Lieb inequality and conformally flattening boundary potential.
result Stability of standard bubbles and partitions in Rn, Sn, and Hn. New Holder bounds improve variational inference by flattening thermodynamic curves.
problem Improving variational inference by addressing performance gaps between theory and practice.
method Generalizing thermodynamic integration to weighted Holder mean, introducing Holder bounds.
result Holder bounds promise a one-step approximation of exact marginal log-likelihood.
Graph neural networks detect anomalies in object-centric business processes.
problem Detecting anomalies in graph-like business processes.
method Graph convolutional autoencoder architecture for anomaly detection.
result Promising performance in detecting anomalies at the activity type and attributes level.
Study identifies cancer genes through graph anomaly analysis of protein interactions.
problem Insufficient modeling of biological information in protein interaction networks for cancer gene identification.
method Proposes HIerarchical-Perspective Graph Neural Network (HIPGNN) to detect weight heterogeneity and spectral flattening in cancer gene nodes.
result HIPGNN detects weight heterogeneity and spectral flattening, leading to improved cancer gene identification.
The paper concerns discrete versions of the three well-known results of projective differential geometry: the four vertex theorem, the six affine vertex theorem and the Ghys theorem on four zeroes of the Schwarzian derivative. We study geometry of closed polygonal lines in $\bbRP^d$ and prove that polygons satisfying a…
Graph neural networks help AI agents learn more complex language.
problem Understanding how AI agents learn and use language.
method Developed graph referential games to compare different AI models.
result Graph neural networks enable AI to learn more complex, compositional language.
New method flattens decision boundary by targeting shortcut-aligned axes in disentangled latent space.
problem Shortcut learning in neural networks, leading to poor out-of-distribution generalization.
method Injects targeted anisotropic noise to regularize classifier sensitivity along shortcut-aligned axes.
result Achieves state-of-the-art OOD performance without shortcut labels or conflicting samples.
The Dirichlet eigenvalues of the Laplace-Beltrami operator are larger on an annulus than on any other surface of revolution in R3 with the same boundary. This is established by defining a sequence of shrinking cylinders about the axis of symmetry and proving that flattening a surface outside of each cylinde…
We find flat band Hamiltonians and Ginsparg-Wilson relations for symmetry classes.
problem Finding flat band Hamiltonians and Ginsparg-Wilson relations for symmetry classes.
method Integrating out the additional bulk direction to obtain effective Dirac operators and then deriving flat and overlap Dirac operators.
result Established Ginsparg-Wilson relations and mod-two index theorems for each symmetry class.
We develop a tractable model of realization utility that studies the role of reference-dependent S-shaped preferences in a dynamic investment setting with reinvestment. Our model generates both voluntarily realized gains and losses. It makes specific predictions about the volume of gains and losses, the holding periods…
A new activation function FTS improves deep learning performance.
problem Hindered propagation of negative values in ReLU.
method Proposed Flatten-T Swish (FTS) activation function, evaluated on MNIST dataset.
result FTS with T=-0.20 improves MNIST classification accuracy by 1.15% on 8-layer DFNN.
The paper proposes a data-driven method for optimal power flow and voltage regulation in distribution grids.
problem Optimal power flow and voltage regulation in decentralized power grids.
method The approach uses a network model, historic data, and regression to find functions approximating optimal reactive power injections for inverters.
result The method achieves near-optimal results in voltage- and capacity-constrained loss minimization and voltage flattening.
This paper studies geometrical structure of the manifold of escort probability distributions and shows its new applicability to information science. In order to realize escort probabilities we use a conformal transformation that flattens so-called alpha-geometry of the space of discrete probability distributions, which…
We study the topology of the space $\d\K^n$ of complete convex hypersurfaces of Rn which are homeomorphic to Rn−1. In particular, using Minkowski sums, we construct a deformation retraction of $\d\K^n$ onto the Grassmannian space of hyperplanes. So every hypersurface in $\d \K^n$ may be flattened in a canonic…
We study the basic properties of Higgs sheaves over compact Kähler manifolds and we establish some results concerning the notion of semistability; in particular, we show that any extension of semistable Higgs sheaves with equal slopes is semistable. Then, we use the flattening theorem to construct a regularization of a…
Analyzes Gerstner's trochoidal waves and their geometric properties.
problem Understanding the geometry and kinematics of trochoidal waves.
method Derives velocity and arc length conditions for cycloidal, curtate, and prolate trochoids using Galilean transformations.
result Conditions for arc lengths of prolate and curtate trochoids to coincide over a wave cycle.
Tensorial Neural Networks improve neural network compression and performance.
problem Efficiently compressing neural networks while maintaining or improving performance.
method Introducing tensor operations on high-order operands to solve hierarchical nonlinear tensor decomposition using stochastic gradient descent.
result TNNs achieve up to 5% test accuracy improvement on CIFAR10 compared to state-of-the-art compression methods.
New tensor encodings improve retrieval accuracy in deep learning.
problem Deep learning classifiers flatten feature tensors, losing multi-linear structure.
method Proposes structured tensor factorization schemes for improved retrieval.
result Structured tensor encodings achieve retrieval performance similar to Fisher vectors.
Decomposing tensors into orthogonal factors is a well-known task in statistics, machine learning, and signal processing. We study orthogonal outer product decompositions where the factors in the summands in the decomposition are required to be orthogonal across summands, by relating this orthogonal decomposition to the…
TRNN combines tensor geometry with neural network nonlinearity for HD data.
problem Modeling high-dimensional data with preserved tensor geometry and nonlinear interactions.
method Introduces TRNN that integrates tensor geometry and neural network nonlinearity.
result TRNN preserves tensor geometry while offering nonlinearity.
Dropout technique is analyzed using information geometry.
problem Understanding the regularization performance of dropout in neural networks.
method Unified analysis from information geometry viewpoint.
result Dropout flattens the model manifold and its performance depends on curvature.
A flat Klein bottle is visualized using origami.
problem Visualizing a Klein bottle's flatness and topology.
method Curved-crease origami with inelastic film.
result The sculpture illustrates both flatness and non-orientability.
The paper diagnoses factor models using characteristic axes and zero-curve restrictions.
problem Tackles systematic sign reversals and overcorrections in factor model pricing errors.
method Extends cap-axis integral diagnostic to general characteristic axes, measuring pricing errors as bridge-alpha curves.
result Axis-level pricing errors are nearly orthogonal to maximum-Sharpe gains, showing systematic sign reversals and overcorrections.