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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.

168,695 papers · 148 categories

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48 results for Global Structure

The paper constructs Levi flat structures using structure sheaves and differential complexes.

problem Global solvability and regularity of Levi flat structures.
method Employing formal integrability and differential complexes, the paper constructs a resolution for the structure sheaf.
result Global exactness and Sobolev regularity of the differential complex for Levi flat structures.

Global invariant for path structures and differential equations defined on torus.

problem Global invariant for path structures and differential equations.
method Computed as a secondary invariant from a Cartan connection on a canonical bundle.
result Formula for global invariant of second order differential equations on torus.

Paper proves a quantitative estimate for transforming almost complex structures into standard ones.

problem Transforming almost complex structures into standard ones on bounded domains.
method Proves existence of global diffeomorphisms under Hölder-Zygmund conditions.
result Existence of a global diffeomorphism in a specified Hölder-Zygmund class.

It is observed that on many 4-manifolds there is a unique smooth structure underlying a globally hyperbolic Lorentz metric. For instance, every contractible smooth 4-manifold admitting a globally hyperbolic Lorentz metric is diffeomorphic to the standard R4\R^4. Similarly, a smooth 4-manifold homeomorphic to the produc…

2012-01-29abs ↗pdf ↗

New DR algorithm preserves both local and global structure.

problem Trade-off between preserving local and global structure in DR methods.
method Analysis of existing DR methods and design principles for loss functions.
result Design of PaCMAP algorithm that preserves both local and global structure.

Global fixed income returns span across multiple maturities and economies, that is, they naturally reside on multi-dimensional data structures referred to as tensors. In contrast to standard "flat-view" multivariate models that are agnostic to data structure and only describe linear pairwise relationships, we introduce…

2019-08-06abs ↗pdf ↗

DM2L tackles missing labels in multi-label learning by modeling local and global rank structures.

problem Missing labels in multi-label learning.
method DM2L imposes local low-rank structures and global high-rank structures on predictions of instances from the same and different labels, respectively.
result DM2L outperforms state-of-the-art methods in multi-label learning with missing labels.

Study causal structure of warped spacetimes using novel pre-length spaces.

problem Understanding the causal structure of warped spacetimes.
method Novel notion of Lorentzian pre-length spaces and proof of causal completion as globally hyperbolic pre-length space.
result Causal completion of GRW spacetime is a globally hyperbolic pre-length space under Hausdorff chronological topology.

We examine how the structure of the world trade network has been shaped by globalization and recessions over the last 40 years. We show that by treating the world trade network as an evolving system, theory predicts the trade network is more sensitive to evolutionary shocks and recovers more slowly from them now than i…

2010-10-03abs ↗pdf ↗

Clarifies definitions of global hyperbolicity in various spaces.

problem Clarifying terminology in recent literature on global hyperbolicity.
method Comparing definitions in Lorentzian length spaces, optimal transport, and topological preordered spaces.
result The causal relation is a closed order and preserves compactness in all cases.

Study well-posedness of Faraday tensor problem on specific spacetime manifolds.

problem Well-posedness of the Cauchy problem for the Faraday tensor on globally hyperbolic manifolds with timelike boundary.
method Existence of Green operators for the operator d+δ\mathrm{d}+\delta and a suitable pre-symplectic structure on the space of solutions.
result Existence of Green operators and pre-symplectic structure for the operator d+δ\mathrm{d}+\delta.

This work explores gradient flows and Riemannian structure in Gromov-Wasserstein geometry for data with global structure.

problem Suitable geometry for tasks requiring preservation of global data structure.
method Study of gradient flows and Riemannian structure in Gromov-Wasserstein geometry for distributions on \(\mathbb{R}^d\).
result Established a Benamou-Brenier-like formula for IGW and derived the IGW gradient.

The abstract introduces golden Finsler structures and explores their local and global properties.

problem Investigating geometric properties of golden Finsler structures.
method Local and global analysis of golden Finsler structures, including explicit computations and transformations.
result Proved that golden Finsler structures cannot be projectively related.

TADA detects anomalies in time series using topological data analysis.

problem Detecting global changes in dependency structure between channels in multivariate time series.
method Topological Data Analysis for detecting anomalies in multivariate time series.
result The approach is more suitable for detecting global changes of correlation structures than existing methods.

Smooth actions on manifolds can be globally defined under certain conditions.

problem Globalizing partial smooth actions of Lie groupoids on smooth manifolds.
method Providing necessary and sufficient conditions for globalizability and analyzing orbit and stabilizer spaces.
result There exists a unique differentiable structure on the quotient space for free and proper actions.

The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international …

2011-07-28abs ↗pdf ↗

We investigate the community structure of the global ownership network of transnational corporations. We find a pronounced organization in communities that cannot be explained by randomness. Despite the global character of this network, communities reflect first of all the geographical location of firms, while the indu…

2013-01-11abs ↗pdf ↗

A Poisson structure is represented by a bivector whose Schouten bracket vanishes. We study a global Poisson structure on S4S^4 associated with a holomorphic Poisson structure on CP3\mathbb{CP}^3. The space of the Poisson structures on S4S^4 is a real algebraic variety in the space of holomorphic Poisson structures on $\…

2015-10-02abs ↗pdf ↗

Study global geometry of dynamical systems with entire vector fields.

problem Understanding the global structure of equilibria and their basins.
method Step-by-step analysis of basins of centers, nodes, and foci; introduction of global elliptic sectors.
result Characterization of heteroclinic regions connecting equilibria.

Paper proposes G-CRD to improve GNNs by preserving global graph topology.

problem Improving lightweight GNNs for robust performance on large-scale real-world graphs.
method Introduces Graph Contrastive Representation Distillation (G-CRD) using contrastive learning.
result G-CRD consistently boosts GNN performance and robustness, outperforming existing methods.

Study the boundaries of ε-neighborhoods of planar sets, showing their structure and curvature.

problem Understanding the structure and smoothness of boundaries of ε-neighborhoods of planar sets.
method Analyzing the global topological structure and smoothness of boundaries of ε-neighborhoods of compact planar sets.
result The boundary of ε-neighborhoods can be expressed as a disjoint union of Jordan curves and singularities.

iREPA shows spatial structure, not global semantic, drives generation performance in REPA.

problem Understanding what aspect of the target representation matters for generation.
method Empirical analysis of 27 vision encoders, two modifications to REPA.
result Spatial structure, not global semantic, drives generation performance.

This thesis explores GNNs, categorizing them into local and global approaches.

problem Understanding the convergence of global GNNs and connecting local and global approaches.
method Categorization of GNNs into local and global, study of Invariant Graph Networks, connecting local and global approaches, and using local MPNN for graph coarsening.
result Established a connection between local and global GNN approaches.

Trade is a fundamental pillar of economy and a form of social organization. Its empirical characterization at the worldwide scale is represented by the World Trade Web (WTW), the network built upon the trade relationships between the different countries. Several scientific studies have focused on the structural charact…

2012-01-10abs ↗pdf ↗

It is common for CCTV operators to overlook inter- esting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveil…

2014-10-14abs ↗pdf ↗

We describe the flat surfaces with flat normal bundle and regular Gauss map immersed in R^4 using spinors and Lorentz numbers. We obtain a new proof of the local structure of these surfaces. We also study the flat tori in the sphere S^3 and obtain a new representation formula. We then deduce new proofs of their global …

2013-10-12abs ↗pdf ↗

This study analyzes the correlation structure of global agricultural futures markets using RMT.

problem Understanding the complex correlation structure of global agricultural futures markets.
method Random Matrix Theory (RMT) applied to analyze correlation coefficients and eigenvalues.
result The correlation structure is asymmetric and right skewed, with significant eigenvalues indicating market effects and commodity groups.

GAMLA learns manifold structures with auto-encoding for global insights.

problem Limited global insight and lack of interpretable analytical descriptions in manifold learning.
method Two-round auto-encoding process to derive character and complementary representations.
result GAMLA provides global and analytical descriptions of smooth manifolds.

With the random matrix theory, we study the spatial structure of the Chinese stock market, American stock market and global market indices. After taking into account the signs of the components in the eigenvectors of the cross-correlation matrix, we detect the subsector structure of the financial systems. The positive …

2012-01-31abs ↗pdf ↗

Significant strides have been made toward designing better generative models in recent years. Despite this progress, however, state-of-the-art approaches are still largely unable to capture complex global structure in data. For example, images of buildings typically contain spatial patterns such as windows repeating at…

2019-01-24abs ↗pdf ↗

PerCDL learns personalized dictionaries for physiological signals combining global and local structures.

problem Representing datasets with both global and local structures in human physiological signals.
method Personalized Convolutional Dictionary Learning (PerCDL) that combines a global and personalized local dictionary.
result PerCDL effectively learns interpretable representations for human locomotion data.

Paper solves globally optimal k-means for low dimensional data.

problem Finding globally optimal k-means solutions for low dimensional data.
method Formulates as a concave assignment problem, iteratively solving small concave and large linear programming problems.
result Solves k-means to global optimality for large data sets with several clusters.

New discrete conformal structures on surfaces with boundary, proving global rigidity and constructing hyperbolic metrics.

problem Creating new discrete conformal structures on surfaces with boundary.
method Introducing new discrete conformal structures, proving global rigidity using variational principles, and introducing combinatorial curvature flows.
result Global rigidity of new discrete conformal structures and effective algorithms for constructing hyperbolic metrics.

Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence. Autoregressive models, such as WaveNet, model local structure at the expense of global latent structure and slow iterative sampling, while Generative A…

2019-02-23abs ↗pdf ↗

Combines global and local features for better social circle prediction in ego-networks.

problem Efficiently analyzing ego-networks with hidden local structures.
method Evolved deep learning techniques to capture both global and local network features.
result Social circle prediction benefits from a combination of global and local features.