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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.

169,051 papers · 148 categories

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48 results for MAP refinement

Improved anti-cancer drug sensitivity prediction using REFINED CNN ensemble learning.

problem Challenges in predicting anti-cancer drug sensitivity for individual cell lines.
method Using REFINED CNN, which represents high-dimensional vectors as compact 2D images with spatial correlations, and building ensembles of these models.
result Ensemble approaches significantly improve drug sensitivity prediction performance compared to single models.

In a previous paper we constructed a spectrum-level refinement of Khovanov homology. This refinement induces stable cohomology operations on Khovanov homology. In this paper we show that these cohomology operations commute with cobordism maps on Khovanov homology. As a consequence we obtain a refinement of Rasmussen's …

2012-06-15abs ↗pdf ↗

Reshetikhin-Turaev (a.k.a. Chern-Simons) TQFT is a functor that associates vector spaces to two-dimensional genus g surfaces and linear operators to automorphisms of surfaces. The purpose of this paper is to demonstrate that there exists a Macdonald q,t-deformation -- refinement -- of these operators that preserves the…

2015-04-10abs ↗pdf ↗

The monopole map defines an element in an equivariant stable cohomotopy group refining the Seiberg-Witten invariant. This first of two articles presents the details of the definition of the stable cohomotopy invariant and discusses its relation to the integer valued Seiberg-Witten invariant.

2002-04-29abs ↗pdf ↗

This is a survey article on the stable cohomotopy refinement of Seiberg-Witten invariants containing also new results, for example: - Stable cohomotopy groups describe path components of certain mapping spaces. - Relation of stable cohomotopy invariants to Seiberg-Witten invariants without restriction on Betti numbers.…

2003-12-31abs ↗pdf ↗

A method improves Cryo-EM 3D map refinement by regularizing rotation estimation.

problem Noise-robustness vs. data-consistency in Cryo-EM 3D map reconstruction.
method Ellipsoidal support lifting (ESL) for regularizing and approximating the global minimizer over Riemannian manifolds.
result The induced bias due to regularizing effect of ESL estimates better rotations than global optimisation.

Unsupervised speech recognition without labeled data using novel cost function and MAP refinement.

problem Training speech recognition systems without labeled data.
method Alternates between phoneme classifier learning and boundary refinement using Segmental Empirical Output Distribution Matching and MAP approach.
result Achieves phone error rate (PER) of 41.6% on TIMIT dataset.

We examine overlapping clustering schemes with functorial constraints, in the spirit of Carlsson--Memoli. This avoids issues arising from the chaining required by partition-based methods. Our principal result shows that any clustering functor is naturally constrained to refine single-linkage clusters and be refined by …

2016-08-15abs ↗pdf ↗

We refine the construction of quasi-homomorphisms on mapping class groups. It is useful to know that there are unbounded quasi-homomorphisms which are bounded when restricted to particular subgroups since then one deduces that the mapping class group is not boundedly generated by these subgroups. In this note we enlarg…

2007-02-09abs ↗pdf ↗

Analyzes complex structure deformations using cohomology contraction methods.

problem Deforming complex structures and identifying obstructions.
method Refined power series method for (p,q)(p,q)-forms and complex structures, using Frölicher spectral sequence.
result All obstruction classes lie in the kernel of contraction maps under natural vanishing conditions.

Overlays were introduced by R. H. Fox [6] as a subclass of covering maps. We offer a different view of overlays: it resembles the definition of paracompact spaces via star refinements of open covers. One introduces covering structures for covering maps and p:XYp:X\to Y is an overlay if it has a covering structure that ha…

2013-01-03abs ↗pdf ↗

Method retrieves similar fashion items from images and text, enabling style refinement.

problem Lack of intuitive, interactive refinement in search engines for fashion items.
method Joint visual-textual embedding training, Mini-Batch Match Retrieval, attribute extraction.
result Improved performance in multimodal style search, demonstrated through benchmark.

VarDeepPCA refines medical image segmentation from small datasets, improving anatomical plausibility and reducing errors.

problem Medical image segmentation fails on out-of-distribution data due to variations in scanners and protocols.
method VarDeepPCA learns valid anatomical geometries using only small in-distribution datasets, providing uncertainty estimates.
result VarDeepPCA restores segmentation maps to OOD data, improving anatomical plausibility and reducing errors.

In this paper, we study the existence of various harmonic maps from Hermitian manifolds to Kaehler, Hermitian and Riemannian manifolds respectively. By using refined Bochner formulas on Hermitian (possibly non-Kaehler) manifolds, we derive new rigidity results on Hermitian harmonic maps from compact Hermitian manifolds…

2014-02-15abs ↗pdf ↗

Study on harmonic maps from surfaces to homogeneous spaces, focusing on bubble formation and geometric constraints.

problem Understanding the behavior of harmonic maps from surfaces to homogeneous spaces, especially in the presence of bubbles.
method Refined asymptotic expansions and obstruction relations for sequences developing a single bubble, geometric constraints for weakly conformal maps.
result New geometric constraints on the tangent planes of the limit map and bubble, depending on the dimensionality.

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.

We consider the interplay of point counts, singular cohomology, étale cohomology, eigenvalues of the Frobenius and the Grothendieck ring of varieties for two families of varieties: spaces of rational maps and moduli spaces of marked, degree dd rational curves in Pn\mathbb{P}^n. We deduce as special cases algebro-geome…

2015-06-08abs ↗pdf ↗

The Mabuchi K-energy map is exhibited as a singular metric on the refined CM polarization of any equivariant family XpS\mathbf{X}\overset{p}{\to} S. Consequently we show that the generalized Futaki invariant is the leading term in the asymptotics of the reduced K-energy of the generic fiber of the map pp. Properness of…

2006-06-20abs ↗pdf ↗

Improved Liouville theorems for ancient solutions to V-harmonic map heat flows.

problem Establishing Liouville theorems for ancient solutions to V-harmonic map heat flows.
method Refined gradient estimates and exponential growth conditions.
result Better Liouville theorems for ancient solutions to V-harmonic map heat flows.

We establish a direct map between refined topological vertex and sl(N) homological invariants of the of Hopf link, which include Khovanov-Rozansky homology as a special case. This relation provides an exact answer for homological invariants of the of Hopf link, whose components are colored by arbitrary representations …

2007-05-10abs ↗pdf ↗

We refine Osserman's argument on the exceptional values of the Gauss map of algebraic minimal surfaces. This gives an effective estimate for the number of exceptional values and the totally ramified value number for a wider class of complete minimal surfaces that includes algebraic minimal surfaces. It also provides a …

2005-11-22abs ↗pdf ↗

This work generates synthetic 3D thermal facial data using 2D facial data and deep learning.

problem Creating large datasets for deep learning in computer vision.
method 3D facial modelling techniques and deep learning methodologies.
result Synthetic 3D thermal facial data created for deep learning applications.

The report presents the theory of harmonic maps from Kähler manifolds.

problem Understanding harmonic maps from Kähler manifolds.
method Reviewing and specializing the theory of harmonic maps between Riemannian manifolds, introducing pluriharmonic maps, and proving refined Bochner formulas.
result Strong rigidity results and applications to symmetric spaces of noncompact type.

Paper refines cross-lingual word embeddings using Manhattan norm.

problem Sensitivity of 2\ell_{2} norm loss function to outliers in CLWEs.
method Post-processing step using 1\ell_{1} norm to improve CLWEs.
result The 1\ell_{1} refinement substantially outperforms state-of-the-art baselines.

We construct a functor from the category of manifolds with generalized corners to the category of complexes of toric monoids, and for every `refinement' of the complex associated to a manifold, we show there is a unique `blow-up', i.e., a new manifold mapping to the original one, which satisfies a universal property an…

2015-09-13abs ↗pdf ↗

Co-PLNet combines point and line predictions to improve wireframe parsing accuracy and efficiency.

problem Separate line and point predictions lead to inconsistent wireframes.
method Co-PLNet uses a Point-Line Prompt Encoder to convert early point detections into spatial prompts, which guide line refinement.
result Co-PLNet achieves better accuracy and robustness in wireframe parsing compared to existing methods.

DAGR improves navigation by refining goal representations conditioned on the current state.

problem Goal-conditioned reinforcement learning lacks state awareness, leading to inefficient policy recovery.
method DAGR refines static goal embeddings into state-conditioned ones using gated cross-attention with a state-goal discrepancy map.
result DAGR improves navigation tasks on OGBench, matching or outperforming base methods.

VarDeepPCA: A Sampling-Free Variational DNN Plugin for OOD Segmentation with Uncertainty Estimation

problem Deep neural networks (DNNs) fail to generalize to out-of-distribution (OOD) medical images due to variations in scanners and acquisition protocols.
method VarDeepPCA is a lightweight variational DNN framework that learns a distribution of valid anatomical geometries using small in-distribution datasets.
result VarDeepPCA restores segmentation maps produced by existing methods on OOD data to improve anatomical plausibility and reduce errors.

Study the kernel of surgery map restricted to 1-loop part of homology cylinders.

problem Understanding the kernel of the surgery map restricted to 1-loop parts of homology cylinders.
method Using Jacobi diagrams and clasper surgery, determine the kernel of the surgery map restricted to the 1-loop part.
result Determined the kernel of the surgery map restricted to the 1-loop part of homology cylinders.

3D dust map of the Milky Way improves resolution and accuracy.

problem Reconstructing the 3D dust distribution in the Milky Way.
method Gaussian process regression on spherical coordinates with iterative grid refinement.
result Improved 3D dust map with increased resolution and accuracy.

Homological stability proved for handlebody mapping class groups.

problem Homological stability for handlebody mapping class groups.
method Categorical framework developed by Randal-Williams and Wahl, allowing for any number of marked discs and boundary points.
result Homology of handlebody groups stabilizes with respect to genus and number of marked discs for all finite degree coefficient systems.

A natural generalization of interval exchange maps are linear involutions, first introduced by Danthony and Nogueira. Recurrent train tracks with a single switch provide a subclass of linear involutions. We call such linear involutions non-classical interval exchanges. They are related to measured foliations on orienta…

2009-06-14abs ↗pdf ↗

Study uniformly differentiable graphs in Carnot groups, proving area formulas.

problem Characterize uniformly differentiable intrinsic graphs in Carnot groups.
method Characterize uniform intrinsic differentiability via Hölder properties of projections of vector fields.
result Explicit area formula for uniformly intrinsically differentiable maps in Carnot groups.