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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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326395126 · May 202619922001200920172026
48 results for neighborhood equivalence

Linearizability of singular foliations is preserved under a specific equivalence relation.

problem Preserving properties of singular foliations under equivalence relations.
method Characterization of tubular neighborhood embeddings using Euler-like vector fields.
result Linearizability along a leaf is a Morita invariant.

Paper proves equivalence of two Floer theories using pearly trees and Hamiltonian flows.

problem Proving equivalence between two Floer theories.
method Using pearly tree discs and local Hamiltonian flows.
result Equivalence relation between immersed Lagrangian Floer theory and Hamiltonian immersed Lagrangian Floer theory.

In this paper, we introduce a partial order on neighborhood equivalence classes of maximally spread essential multibranched surfaces embedded in a 3-manifold. We show that if a maximally spread essential multibranched surface is atoroidal and acylindrical, then its equivalence class is minimal with respect to the parti…

2019-05-03abs ↗pdf ↗

We study torsion properties of the twisted Alexander modules of the affine complement MM of a complex essential hyperplane arrangement, as well as those of punctured stratified tubular neighborhoods of complex essential hyperplane arrangements. We investigate divisibility properties between the twisted Alexander polyn…

2017-10-18abs ↗pdf ↗

This article is one of three highly influential articles on the topology of manifolds written by Robert D. Edwards in the 1970's but never published. Organizers of the Workshops in Geometric Topology (http://www.uwm.edu/~craigg/workshopgtt.htm) with the support of the National Science Foundation have facilitated the pr…

2009-04-29abs ↗pdf ↗

Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing popularity in a variety of graph analysis tasks, including node classification and link prediction. Existing representation learning methods …

2019-10-04abs ↗pdf ↗

Develops GNNs for incomplete graphs, improving learning from missing node attributes.

problem Learning from incomplete graphs with missing node attributes.
method Introduces PaGNNs with novel partial aggregation functions for incomplete graph data.
result Demonstrates effectiveness and efficiency of PaGNNs on various datasets.

Generalized complex structures on certain torus bundles are explored.

problem Exploring generalized complex structures on specific torus bundles.
method Analyzing principal torus bundles over complex manifolds with even dimensional fibers and characteristic class of type (1,1).
result Generalized complex structures on these bundles are equivalent to products of complex and symplectic structures in tubular neighborhoods of fibers.

Survey on minimal rational curves and their geometric structures.

problem Germ-equivalence problem of minimal rational curves on uniruled projective manifolds.
method Analysis of isotrivial families of projective varieties and G-structures.
result Natural G-structure on Zariski-open subset of uniruled projective manifolds.

The paper studies graded manifolds and their functorial relationship.

problem Understanding the functor between two categories of graded manifolds.
method Examines polynomial filtrations and homogeneity structures, applying the Batchelor-Gawedzki theorem and Borel-Whitney theorem.
result The functor is full and surjective on objects between the categories of graded vector bundles and manifolds.

Let NN be a hyperbolic 3-manifold and BB a component of the interior of AH(π1(N))AH(π_1(N)), the space of marked hyperbolic 3-manifolds homotopy equivalent to NN. We will give topological conditions on NN sufficient to give ρBˉρ\in \bar{B} such that for every small neighborhood VV of ρρ, VBV \cap B is disconnected. This …

2000-09-15abs ↗pdf ↗

Consider an analytic map of a neighborhood of 0 in a vector space to a Euclidean space. Suppose that this map takes all germs of lines passing through 0 to germs of circles. Such a map is called rounding. We introduce a natural equivalence relation on roundings and prove that any rounding, whose differential at 0 has r…

2002-12-06abs ↗pdf ↗

PushNet efficiently and adaptively pushes messages in neural networks, improving performance.

problem Inefficient and inflexible synchronous message passing in neural networks.
method Asynchronous message passing with adaptive receptive fields and multiple scale correlations.
result PushNet outperforms competitors in semi-supervised node classification on multiple datasets.

Unified approach combines prediction-powered inference and variance reduction for semi-supervised optimization.

problem Scarcity of labeled data in semi-supervised optimization.
method PPI-SVRG, combining PPI and SVRG methods.
result Unified convergence bound with improved performance under label scarcity.

Making an adaptive prediction based on one's input is an important ability for general artificial intelligence. In this work, we step forward in this direction and propose a semi-parametric method, Meta-Neighborhoods, where predictions are made adaptively to the neighborhood of the input. We show that Meta-Neighborhood…

2019-09-18abs ↗pdf ↗

Study geodesics entering a fixed cusp neighborhood multiple times.

problem Understanding geodesics entering a specific cusp neighborhood multiple times.
method Investigate reciprocal geodesics entering a fixed cusp neighborhood a fixed number of times.
result Characterized the class of reciprocal geodesics entering a fixed cusp neighborhood a fixed number of times.

Urban2Vec combines street view imagery and POIs for better urban neighborhood embeddings.

problem Lack of comprehensive representation of urban neighborhoods using heterogeneous data.
method Unsupervised multi-modal framework using CNN for visual features and bag-of-words for POI data.
result Urban2Vec achieves better performance than baseline models and comparable to fully-supervised methods.

Skeleta and other pure subsets of manifold stratified spaces are shown to have neighborhoods which are teardrops of stratified approximate fibrations (under dimension and compactness assumptions). In general, the stratified approximate fibrations cannot be replaced by bundles, and the teardrops cannot be replaced by ma…

2005-01-07abs ↗pdf ↗

This paper tackles selection bias in recommender systems by considering the neighborhood effect.

problem Selection bias in recommender systems due to filtering and user selection.
method Formalizes neighborhood effect as interference problem, introduces treatment representation, and proposes ideal loss.
result Proposed methods achieve unbiased learning when both selection bias and neighborhood effect are present.

Maximally hyperbolic solutions contain future neighborhoods of intersecting hypersurfaces.

problem Maximally globally hyperbolic solutions of higher-dimensional vacuum Einstein equations.
method Analyzing intersections of characteristic hypersurfaces.
result Contains a future neighborhood of intersecting hypersurfaces.

Let KK be a closed polydisc or ball in $\C^n$, and let YY be a quasi projective algebraic manifold which is Zariski locally equivalent to $\C^p$, or a complement of an algebraic subvariety of codimension 2\ge 2 in such manifold. If rr is an integer satisfying (nr+1)(pr+1)2(n-r+1) (p-r+1)\geq 2 then every holomorphic map from …

2006-10-06abs ↗pdf ↗

Many prediction problems can be phrased as inferences over local neighborhoods of graphs. The graph represents the interaction between entities, and the neighborhood of each entity contains information that allows the inferences or predictions. We present an approach for applying machine learning directly to such graph…

2016-11-21abs ↗pdf ↗

Proposes a new NMF method incorporating neighborhood structure for better anomaly detection.

problem NMF's inability to incorporate neighborhood structure information limits its performance in nonlinear manifold structures.
method Integrates neighborhood structure information using Minimum Spanning Tree (MST) within NMF framework.
result Empirical results show superior performance in anomaly detection using the proposed method.

Study on transverse knots and their neighborhoods, proving unique standard neighborhoods and destabilization results.

problem Understanding the structure and properties of transverse knots and their neighborhoods.
method Proves unique standard neighborhoods and structure theorems for non-loose Legendrian knots through destabilization results.
result Finds a manifold with infinite tight contact structures, up to contactomorphism, without Giroux torsion.

Revises GNN neighborhood aggregation for more accurate node classification.

problem Flaws in benchmark GNN models for node classification.
method Statistical signal processing approach to neighborhood aggregation.
result Novel insights for designing more efficient GNN models.

The study proves a neighborhood theorem for mean curvature flow in higher dimensions.

problem Proving a canonical neighborhood theorem for mean curvature flow in higher dimensions.
method Proved a canonical neighborhood theorem for mean curvature flow of compact submanifolds in RN\mathbb{R}^N with a pinching condition.
result Proved a canonical neighborhood theorem for mean curvature flow in dimensions n5n \geq 5.

GraphAIR improves graph representation learning by capturing non-linear interactions.

problem Challenges in capturing non-linear interactions in graph data.
method Integrates neighborhood aggregation and interaction modeling.
result Demonstrates improved performance on node classification and link prediction tasks.

A new method for efficient structural node embeddings using Von Neumann entropy.

problem Efficiently identifying structurally equivalent nodes in complex networks.
method VNEstruct: a simple approach generating low-dimensional structural node embeddings using Von Neumann entropy.
result VNEstruct achieves robustness on structural role identification and state-of-the-art performance on graph classification tasks.

Neighborhood sampling affects graph neural network training outcomes.

problem Understanding the impact of neighborhood sampling on graph neural network training.
method Theoretical analysis using neural tangent kernels and Gaussian processes.
result Posterior covariance differs for different neighborhood sampling approaches, indicating no dominant approach.