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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.

168,694 papers · 148 categories

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4897145193 · Jun 202019922001200920172026
48 results for balanced embedding

Let X be a smooth subvariety of CP^N. We study a flow, called balancing flow, on the space of projectively equivalent embeddings of X, which attempts to deform the given embedding into a balanced one. If L->X is an ample line bundle, considering embeddings via H^0(L^k) gives a sequence of balancing flows. We prove that…

2008-11-03abs ↗pdf ↗

Existence of balanced embedding proved for complex manifold into infinite-dimensional space.

problem Balanced embedding of non-compact complex manifolds into infinite-dimensional projective space.
method Gradient flow in a Hilbert space, long-time existence established by perturbation, convergence depends on a priori bounds.
result Existence of balanced embedding proved in a model case.

For a polarized Kähler manifold (X,L)(X, L), we show the equivalence between relative balanced embeddings introduced by Mabuchi and σσ-balanced embeddings introduced by Sano, answering a question of Hashimoto. We give a GIT characterization of the existence of a σσ-balanced embedding, and relate the optimal weight σσ t…

2017-10-06abs ↗pdf ↗

The article studies embeddings of edge-colored graphs related to balanced 3- and 4-manifolds.

problem Investigating embeddings of edge-colored dual graphs of balanced 3- and 4-manifolds.
method Introducing the concept of balanced genus and proving lower bounds for the genus of 3- and 4-manifolds.
result Established lower bounds for the balanced genus of 3- and 4-manifolds, and conditions for homeomorphism to spheres.

Unified model for signed networks separates balance and anomaly effects.

problem Ignoring sign information in signed networks leads to inaccurate analysis.
method Low rank plus sparse matrix decomposition with regularized formulation.
result The model accurately detects communities and anomalies in signed networks.

For a certain maximal unipotent family of Abelian varieties over the punctured disc, we show that after a base change, one can complete the family over a disc such that the whole degeneration can be simultaneously balanced embedded into a projective space by the theta functions. Then we study the relationship between t…

2016-05-06abs ↗pdf ↗

We study the intrinsic geometrical structure of hypersurfaces in 6-manifolds carrying a balanced Hermitian SU(3)-structure, which we call {\em balanced} SU(2)-{\em structures}. We provide conditions which imply that such a 5-manifold can be isometrically embedded as a hypersurface in a manifold with a balanced SU(3)-st…

2008-08-08abs ↗pdf ↗

Recent successes in word embedding and document embedding have motivated researchers to explore similar representations for networks and to use such representations for tasks such as edge prediction, node label prediction, and community detection. Such network embedding methods are largely focused on finding distribute…

2017-02-22abs ↗pdf ↗

Neighbor embeddings balance attraction and repulsion to visualize data.

problem Visualizing high-dimensional datasets with trade-offs between continuous and discrete structures.
method Neighbor embeddings combine attractive and repulsive forces to visualize data.
result Changing the exaggeration parameter in t-SNE yields a spectrum of embeddings with a trade-off between continuous and discrete structures.

The study extends Tutte's conflict graph concept to nonplanar graphs.

problem Understanding the structure of nonplanar graphs through conflict graphs.
method Defining a signed conflict graph for maximally planar subgraphs and analyzing their balance.
result For graphs with a flat embedding, every maximal planar subgraph has unbalanced conflict graphs if and only if the graph is intrinsically linked.

Consider EE a holomorphic vector bundle over a projective manifold XX polarized by an ample line bundle LL. Fix kk large enough, the holomorphic sections H0(ELk)H^0(E\otimes L^k) provide embeddings of XX in a Grassmanian space. We define the \textit{balancing flow for bundles} as a flow on the space of projectively equ…

2014-11-11abs ↗pdf ↗

NodeSig efficiently computes binary node embeddings for scalable graph analysis.

problem Scalability issues in graph representation learning models.
method NodeSig uses random walk diffusion probabilities and stable random projections to compute binary node embeddings efficiently.
result NodeSig achieves a good balance between accuracy and efficiency on node classification and link prediction tasks.

Let EME\rightarrow M be a holomorphic vector bundle over a compact Kaehler manifold (M,ω)(M, ω) and let E=E1...EmME=E_1\oplus... \oplus E_m\rightarrow M be its decomposition into irreducible factors. Suppose that each EjE_j admits a ωω-balanced metric in Donaldson-Wang terminology. In this paper we prove that EE admits a unique…

2011-01-16abs ↗pdf ↗

Lickorish has constructed large families of contractible 4--manifolds that have knotted embeddings in the 4--sphere and has also shown that every finitely presented perfect group with balanced presentation occurs as the fundamental group of the complement of a knotted contractible manifold. Here we make a few observati…

2001-11-06abs ↗pdf ↗

Let L be an ample bundle over a compact complex manifold X. Fix a Hermitian metric in L whose curvature defines a Kähler metric on X. The Hessian of Mabuchi energy is a fourth-order elliptic operator D on functions which arises in the study of scalar curvature. We quantise D by the Hessian E(k) of balancing energy, a f…

2010-09-23abs ↗pdf ↗

Detects data drift in deep learning models using neural embeddings.

problem Detecting changes in data distribution in deep learning models.
method Formulates drift detection in a sequential decision framework and introduces a loss function to balance false alarms and quick detection.
result Demonstrates improved ability to balance false alarms and quick detection in change detection.

It is proved that the Heisenberg group Nil3\operatorname*{Nil}\nolimits_{3} with a balanced metric, the sum of the left and right invariant metrics, splits as a Riemannian product T×Z\mathbb{T\times Z}, where T\mathbb{T} is a totally geodesic surface and Z\mathbb{Z} the center of Nil\operatorname*{Nil}% \nolimits_{3}. It…

2019-08-12abs ↗pdf ↗

We generalize the construction of the Heegaard Floer homology for a singular knot to that for a balanced bipartite graph. For a given graph, we provide a combinatorial description of the Euler characteristic of its Heegaard Floer homology by using the "Kauffman states" on a graph diagram.

2014-01-26abs ↗pdf ↗

Examples are given to show that some compact contractible 4-manifolds can be knotted in the 4-sphere. It is then proved that any finitely presented perfect group with a balanced presentation is a knot group for an embedding of some contractible 4-manifold in the 4-sphere.

2001-07-31abs ↗pdf ↗

Let L^\hat{L} be the projective completion of an ample line bundle LL over DD, a smooth projective manifold. Hwang-Singer \cite{HwangS} have constructed complete CSCK metric on L^\D\hat{L}\backslash D. When the corresponding \kahler form is in the cohomology class of a rational divisor AA and when LL has negative CSC…

2017-06-11abs ↗pdf ↗

Given a smooth polarized Riemann surface (X, L) endowed with a hyperbolic metric ωω with cusp singularities along a divisor D, we show the L^2 projective embedding of (X, D) defined by L^k is asymptotically almost balanced in a weighted sense. The proof depends on sufficiently precise understanding of the behavior of …

2016-05-03abs ↗pdf ↗

DTE uses tree leaf means to embed data, balancing accuracy and speed.

problem High variance in decision tree splits and computational inefficiency of ensembles.
method DTE constructs an interpretable feature representation using leaf means of a trained tree.
result DTE strikes a balance between accuracy and computational efficiency, outperforming ensembles.

Protein Thoughts interprets protein interactions with clear reasoning, improving prediction accuracy.

problem Lack of mechanistic justification in protein-protein interaction predictions.
method Interpretable search problem reformulation, hypothesis-guided entropy-regularized Tree-of-Thoughts search, embedding-space flow matching.
result Improves mean best-binder rank from 47.7 to 11.2 on SHS148k benchmark.

CSNE embeds signed networks by separating structural and fine-grained information.

problem Improving sign prediction in signed networks using inaccurate or incomplete balance theories.
method Conditional Signed Network Embedding (CSNE) models structural and fine-grained information separately, integrating them rigorously.
result CSNE outperforms state-of-the-art on sign prediction tasks, and MaxEnt priors are competitive in resource-constrained settings.

A new HP model balances interpretability and flexibility for EHR event sequences.

problem Balancing interpretability and flexibility in modeling diagnostic event sequences.
method Proposes a neural network-based HP with flexible impact kernel and transformer layers.
result Accurately recovers impact functions, competitive performance, and clinically meaningful interpretation.

Proposes a text perturbation method using a Mahalanobis metric to balance privacy and utility.

problem Low utility of text analysis when using spherical noise for privacy-preserving text embedding.
method Regularized Mahalanobis metric to add elliptical noise, accounting for embedding space density.
result Improves privacy statistics while maintaining utility, outperforming Laplace mechanism.

Despite recent advances in architectures for mobile devices, deep learning computational requirements remains prohibitive for most embedded devices. To address that issue, we envision sharing the computational costs of inference between local devices and the cloud, taking advantage of the compression performed by the f…

2019-11-23abs ↗pdf ↗

New algorithm improves interpretability in sequence classification.

problem Lack of human-independent interpretability metrics in sequence classification.
method Combines linear classifiers with background knowledge embeddings to create a new feature space.
result Preserves predictive power while delivering more interpretable models.

In this paper, we develop a novel weighted Laplacian method, which is partially inspired by the theory of graph Laplacian, to study recent popular graph problems, such as multilevel graph partitioning and balanced minimum cut problem, in a more convenient manner. Since the weighted Laplacian strategy inherits the virtu…

2019-11-23abs ↗pdf ↗

The stable Andrews-Curtis conjecture in combinatorial group theory is the statement that every balanced presentation of the trivial group can be simplified to the trivial form by elementary moves corresponding to "handle-slides" together with "stabilization" moves. Schoenflies conjecture is the statement that the compl…

2014-06-26abs ↗pdf ↗

Study shows neural collapse is invariant to class imbalances under certain conditions.

problem Neural collapse properties are only valid for balanced data.
method Adopted UFM and introduced SELI for invariant characterization.
result Embeddings and classifiers always interpolate a simplex-encoded label matrix regardless of class imbalances.

SessionPath improves category suggestions in type-ahead search.

problem Improving precision and recall in eCommerce type-ahead suggestions.
method SessionPath uses session embeddings and a probability distribution model to predict facets.
result SessionPath outperforms count-based and neural models in eCommerce shops.

In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters. Optimal transport metrics, such as the Wasserstein distance, allow incorporating semantic side information such as word embeddings. Using W. barycenters to find the consensus between…

2019-02-13abs ↗pdf ↗

Efficient method for constrained optimization under partial observations with provable convergence.

problem Optimizing under partial and constrained data.
method Improved acquisition functions and Gaussian process embedding for partially observable constraints.
result Empirically validated method outperforms traditional approaches.