Study of Calabi-Yau manifold degenerations near complex structure limits.
problem Understanding polarized degenerations of Calabi-Yau manifolds.
method Improvement of metric convergence results on generic regions.
result Metric convergence for collapsing Ricci-flat Kähler metrics on generic regions.
Given a sequence of complete(compact or noncompact) Kähler manifolds Min with bisectional curvature lower bound and noncollapsed volume, we prove that the pointed Gromov-Hausdorff limit is homeomorphic to a normal complex analytic space. The complex analytic structure is the natural "limit" of complex structure of …
The study connects K-stability and large complex structure limits in mirror symmetry.
problem Understanding K-stability and its relation to large complex structure limits in mirror symmetry.
method Analyzing Kähler test configurations and their mirror Landau-Ginzburg models, studying scaling behavior, and focusing on specific limiting cases.
result New formulae for the Donaldson-Futaki invariant are derived in terms of theta functions on the mirror in certain limiting cases.
We study the limits of holonomy representations of complex projective structures on a compact Riemann surface in the Morgan-Shalen compactification of the character variety. We show that the dual R-trees of the quadratic differentials associated to a divergent sequence of projective structures determine the Morgan-Shal…
This research connects Higgs bundles to projective structures via conformal limits.
problem Mapping Higgs bundles to projective structures.
method Using non-abelian Hodge correspondence and conformal limit.
result The family of connections in the conformal limit can be understood as complex projective structures.
Study transverse Dolbeault cohomology for almost complex structures.
problem Understanding cohomology of transverse structures on manifolds.
method Define transverse Dolbeault cohomology, extend transverse complex structure, introduce involutive limit distribution.
result Cohomology spaces of (p,0) for almost complex structures coincide with transverse Dolbeault cohomology.
Motivated by the classical statements of Mirror Symmetry, we study certain Kahler metrics on the complexified Kahler cone of a Calabi-Yau threefold, conjecturally corresponding to approximations to the Weil-Petersson metric near large complex structure limit for the mirror. In particular, the naturally defined Riemanni…
The paper proves mirror symmetry for del Pezzo surfaces and computes related structures.
problem Understanding mirror symmetry for del Pezzo surfaces and related geometric structures.
method Using hyperKähler rotation and Floer theory, the paper constructs and compares Landau-Ginzburg mirrors and complex affine structures.
result The limit of the complex affine structure of special Lagrangian fibrations agrees with integral affine structures.
A holomorphic Poisson structure induces a deformation of the complex structure as Hitchin's generalized geometry. Its associated cohomology naturally appears as the limit of a spectral sequence of a double complex. The first sheet of this spectral sequence is the Dolbeault cohomology with coefficients in the exterior a…
Gravitational instantons collapse to a punctured plane with a special Kahler metric.
problem The collapse of gravitational instantons from a complex structure limit.
method Analysis of a sequence of ALH*-gravitational instantons and their collapse to a punctured plane.
result The moduli space of pointed ALH*-gravitational instantons collapses to a punctured plane with a special Kahler metric.
Based on our recent adaptation of the adiabatic limit construction to the case of complex structures, we prove the fact that the deformation limiting manifold of any holomorphic family of Moishezon manifolds is Moishezon. Two new ingredients, hopefully of independent interest, are introduced. The first one associates w…
In this paper, we explore the limit structure of a sequence of Riemannian manifolds with Bakry-Émery Ricci curvature bounded below in the Gromov-Hausdorff topology. By extending the techniques established by Cheeger-Cloding for Riemannian manifolds with Ricci curvature bounded below, we prove that each tangent space at…
We use a generalization of the Gibbons-Hawking ansatz to study the behavior of certain non-compact Calabi-Yau manifolds in the large complex structure limit. This analysis provides an intermediate step toward proving the metric collapse conjecture for toric hypersurfaces and complete intersections.
The twistor space of a Riemannian 4-manifold carries two almost complex structures, J+ and J−, and a natural closed 2-form ω. This article studies limits of manifolds for which ω tames either J+ or J−. This amounts to a curvature inequality involving self-dual Weyl curvature and Ricci curvature, and whi…
Study compact Willmore surfaces without complex structure convergence, computing energy loss and geodesic lengths.
problem Compactness of Willmore surfaces without complex structure convergence.
method Compute energy loss in neck and geodesic lengths in Grassmannian G(2,n). result Limit of Gauss map image is a geodesic in G(2,n) with computable length. In this paper, we show the spectral convergence result of ∂-Laplacians when (X,ω) is a compact toric symplectic manifold equipped with the natural prequantum line bundle L. We consider a family {Js}s of ω-compatible complex structures tending to the large complex structure limit, and ob…
Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model …
The paper proves vanishing theorems for complex line bundles using a new adiabatic limit approach.
problem Vanishing theorems for complex line bundles under specific conditions.
method Generalizes adiabatic limit construction to connections on complex line bundles, proving vanishing theorems.
result Proves vanishing theorems for D′′-cohomology groups under certain conditions. Algorithmic fairness is a field of study that addresses the systematic disadvantage of marginalized groups in machine learning systems.
problem Modern machine learning systems increasingly determine access to economic and social opportunities, leading to structural inequalities and prejudices.
method Statistical and structural approaches to algorithmic fairness.
result The field of algorithmic fairness emerged to address the systematic disadvantage of marginalized groups in machine learning systems.
New AMP algorithms for rotationally invariant models with reduced complexity.
problem Signal estimation in generalized linear models with arbitrary spectral design matrices.
method Rotationally invariant approximate message passing (AMP) algorithms.
result Performance close to Vector AMP with significantly lower complexity.
Motivated by the picture of mirror symmetry suggested by Strominger, Yau and Zaslow, we made a conjecture concerning the Gromov-Hausdorff limits of Calabi-Yau n-folds (with Ricci-flat Kähler metric) as one approaches a large complex structure limit point in moduli; a similar conjecture was made independently by Kontsev…
We determine the 6-dimensional solvmanifolds admitting an invariant complex structure with holomorphically trivial canonical bundle. Such complex structures are classified up to isomorphism, and the existence of strong Kähler with torsion (SKT), generalized Gauduchon, balanced and strongly Gauduchon metrics is studied.…
Study develops advanced models to forecast complex LOB data.
problem Forecasting high-frequency data in a limit order book (LOB).
method Advanced multidimensional sequence-to-sequence models with compound multivariate embedding.
result Method outperforms other multivariate forecasting methods, achieving lowest forecasting error.
This research tackles sample complexity in causal graph recovery with temporal heterogeneity.
problem Recovering a unique causal graph from observational data with temporal heterogeneity.
method Integrates time-series dynamics and multi-environment heterogeneity to constrain the problem, enabling a rigorous analysis of statistical limits.
result Unified necessary identifiability conditions and explicit information-theoretic bounds quantify the sample complexity under different noise distributions.
Study the limit of Calabi-Yau metrics with degenerate skeletons.
problem Understanding the behavior of Calabi-Yau metrics with degenerate skeletons.
method Using polarised degenerations and optimal transport problems.
result Describe the limiting behaviour of the Calabi-Yau potential.
Kaehler-Einstein metrics on orbifolds derived from Einstein sequences.
problem Desingularizing Einstein orbifolds with Kaehler-Einstein metrics.
method Analyzing sequences of smooth compact Einstein 4-manifolds converging to orbifolds.
result The limit orbifold is Kaehler-Einstein and one of the classified orbifold limits.
Can multilayer neural networks -- typically constructed as highly complex structures with many nonlinearly activated neurons across layers -- behave in a non-trivial way that yet simplifies away a major part of their complexities? In this work, we uncover a phenomenon in which the behavior of these complex networks -- …
We study adiabatic limits of Ricci-flat Kahler metrics on a Calabi-Yau manifold which is the total space of a holomorphic fibration when the volume of the fibers goes to zero. By establishing some new a priori estimates for the relevant complex Monge-Ampere equation, we show that the Ricci-flat metrics collapse (away f…
We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show that low-comp…
This paper develops a nonparametric model for complex network data.
problem Capturing conditional independence structure in multivariate data with heterogeneous graph structures.
method Integrates network embedding with nonparametric graphical model estimation, solving a linear equation system.
result The proposed method effectively recovers heterogeneous graph structures without distributional assumptions.
New Markov chains defined on simplicial complexes for understanding their topology.
problem Understanding the topology of simplicial complexes and hypergraphs.
method Defining new Markov chains on simplicial complexes and studying their properties.
result The generator of the new Markov chain is the upper Laplacian, and the Markov chain is positive recurrent.
Active learning methods, like uncertainty sampling, combined with probabilistic prediction techniques have achieved success in various problems like image classification and text classification. For more complex multivariate prediction tasks, the relationships between labels play an important role in designing structur…
Paper describes integrable structure of Hitchin moduli spaces.
problem Integrable structure of Hitchin moduli spaces.
method Explicit parameterizations and Separation of Variables method.
result Clear analogy with Drinfeld's geometric Langlands correspondence.
Study shows how leveraging hierarchical similarity graphs improves matrix completion in recommender systems.
problem Improving matrix completion in recommender systems using hierarchical similarity graphs.
method Characterizes the optimal sample complexity using hierarchical stochastic block models and low-rank rating matrices.
result Exploiting hierarchical structure of social graphs significantly reduces the number of observed entries needed for accurate matrix completion.
Study complex structures with perturbed differential operators to compute curvature-like operators and obtain vanishing results.
problem Analyzing complex structures with perturbed differential operators.
method Perturbing the standard differential operator to a first-order operator Dη and computing Bochner-Kodaira-Nakano-type formulae. result Obtained vanishing results for certain harmonic spaces and Dolbeault cohomology.
Study geometric quantization on K3 surfaces, showing spectral convergence.
problem Quantization of K3 surfaces from spectral perspective.
method Special Lagrangian fibrations and hyper-Kähler structures.
result Spectral convergence of ∂ˉ-Laplacians on prequantum line bundles. We characterize a certain neck-pinching degeneration of (marked) CP1- structures on a closed oriented surface S of genus at least two. Namely, we consider a path Ct of CP1-structures on S leaving every compact subset in the deformation space of (marked) CP1-structures on S, such that its holonomy converges …
Paper tackles robust estimation of tree-structured Ising models without side information.
problem Learning tree-structured Ising models with flipped signs of variables.
method Proves unidentifiability, proposes an algorithm with logarithmic sample complexity and polynomial run-time complexity.
result Empirically demonstrates robustness of proposed algorithm in the flipped signs setting.
Proves Kähler-Ricci shrinkers are complex analytic varieties.
problem Characterizing singular Kähler-Ricci shrinkers.
method Analyzes limits of Kähler-Ricci flows and applies algebraic geometry.
result Singular Kähler-Ricci shrinkers are locally algebraic complex-analytic varieties.
The contact graph of a CAT(0) cubical complex has unbounded structure and a Gaussian CLT for random walks.
problem Understanding the structure and behavior of random walks on CAT(0) cubical complexes.
method Proved the contact graph is unbounded and homeomorphic to the boundary. Reformulated Caprace-Sageev's theorem. Proved a Central Limit Theorem for random walks.
result A Central Limit Theorem for random walks on CAT(0) cubical complexes, with a non-degenerate Gaussian distribution.
HLOB predicts mid-price changes in L.O.Bs using deep learning.
problem Forecasting mid-price changes in Limit Order Books.
method HLOB uses a deep learning model with an Information Filtering Network and Homological Convolutional Neural Networks.
result HLOB outperforms state-of-the-art models in real-world datasets.
Energy trees handle complex data structures with multiple variable types.
problem Handling intricate data structures with various types of covariates.
method Energy trees, a regression and classification model, use energy statistics to accommodate structured covariates of different types.
result Energy trees maintain statistical foundations, interpretability, and robustness to overfitting.
Smooth knots in complex hyperbolic plane limit sets to chains or R-circles.
problem Characterizing limit sets of knots in complex hyperbolic geometry.
method Analyzing embeddings of knots as limit sets of discrete subgroups of PU(2, 1).
result Knots are either chains or R-circles as limit sets.
New algorithm improves understanding of decentralized SBO transient iteration complexity.
problem Limited understanding of how network topology, data heterogeneity, and nested structures affect SBO.
method D-SOBA framework with two variants: D-SOBA-SO and D-SOBA-FO, providing non-asymptotic convergence analysis and transient iteration complexity.
result First theoretical understanding of how network topology, data heterogeneity, and nested structures influence decentralized SBO.
The paper examines the limit of harmonic flow on flat vector bundles.
problem Understanding the limiting behavior of harmonic flow on flat complex vector bundles.
method Analyzes the harmonic flow and proves the limit is isomorphic to a graded flat complex vector bundle.
result The limit of the harmonic flow on flat complex vector bundles is isomorphic to a graded flat complex vector bundle.
Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution. It enables analyzing the native structures of macromolecular complexes and their spatial organization inside single cells. However, due to the high degree …
Proposes a differentiable structure learning framework for general binary data.
problem Limitations of existing methods in discrete data structure learning.
method Formulates a differentiable optimization task for arbitrary dependencies in general discrete models.
result Establishes identifiability of complete set of compatible parameters and structures under mild assumptions.
We produce special Lagrangian Tn-fibrations on the generic regions of some Calabi-Yau hypersurfaces in the Fermat family Xs={Z0…Zn+1+e−s(Z0n+2+…Zn+1n+2)=0}⊂CPn+1 near the large complex structure limit s→+∞.