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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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371013 · Feb 202019922001200920172026
48 results for MRI QC

A quaternionic contact (qc) heat equation and the corresponding qc energy functional are introduced. It is shown that the qc energy functional is monotone non-increasing along the qc heat equation on a compact qc manifold provided certain positivity conditions are satisfied.

2016-08-01abs ↗pdf ↗

We describe explicitly all quaternionic contact hypersurfaces (qc-hypersurfaces) in the flat quaternion space $\Hnn$ and the quaternion projective space. We show that up to a quaternionic affine transformation a qc-hypersurface in $\Hnn$ is contained in one of the three qc-hyperquadrics in $\Hnn$. Moreover, we show tha…

2014-06-17abs ↗pdf ↗

We study positive definite quaternionic contact (4n+3)(4n+3)-manifolds (qcqc-manifold for short). Just like the CRCR-structure contains the class of Sasaki manifolds, the qcqc-structure admits a class of 33-Sasaki manifolds with integrable distribution isomorphic to su(2)\mathfrak{su}(2). A big difference concerning the inte…

2019-02-23abs ↗pdf ↗

The paper establishes sub-gradient estimates and entropy formulas for quaternionic contact geometry heat equations.

problem Developing sub-gradient estimates and entropy formulas for quaternionic contact geometry.
method Establishing sub-gradient estimates and entropy formulas for the quaternionic contact heat equation.
result Two Perelman-type entropy formulas and sub-gradient estimates for the quaternionic contact heat equation.

The main result is that the qc-scalar curvature of a seven dimensional quaternionic contact Einstein manifold is a constant. In addition, we characterize qc-Einstein structures with certain flat vertical connection and develop their local structure equations. Finally, regular qc-Ricci flat structures are shown to fibre…

2013-06-03abs ↗pdf ↗

Quasi-conformal (QC) theory is an important topic in complex analysis, which studies geometric patterns of deformations between shapes. Recently, computational QC geometry has been developed and has made significant contributions to medical imaging, computer graphics and computer vision. Existing computational QC theor…

2015-10-17abs ↗pdf ↗

The 'anholonomic frame' method (see gr-qc/0005025, gr-qc/0001060 and hep-th/0110250) is applied for constructing new classes of exact solutions of vacuum Einstein equations with off-diagonal metrics in 4D and 5D gravity. We examine several black tori solutions generated by anholonomic transforms with non-trivial topolo…

2001-10-30abs ↗pdf ↗

We investigate quaternionic contact (qc) manifolds from the point of view of intrinsic torsion. We argue that the natural structure group for this geometry is a non-compact Lie group K containing Sp(n)H^*, and show that any qc structure gives rise to a canonical K-structure with constant intrinsic torsion, except in se…

2013-06-04abs ↗pdf ↗

Quaternion Conformer GAN (QC-GAN) is a parameter-efficient speech enhancement framework that combines a Quaternion Conformer generator with MetricGAN-based training.

problem Speech Enhancement
method Quaternion Conformer GAN
result Achieved a PESQ score of 3.48 with 0.89M parameters, comparable to state-of-the-art models at less than half their size.

Quantum computing improves fault diagnosis in industrial processes.

problem Fault detection and diagnosis in industrial process systems.
method Integrates quantum computing and deep learning to extract features and diagnose faults.
result Quantum-assisted deep learning achieves high fault detection rates (79.2% and 99.39%).

Unified AI system for data quality control and governance in regulated environments.

problem Isolated data quality control steps in existing systems.
method AI-driven framework integrating rule-based, statistical, and AI methods.
result Empirical gains in anomaly detection, reduced manual remediation, improved auditability.

We investigate the Fefferman spaces of conformal type which are induced, via parabolic geometry, by the quaternionic contact (qc) manifolds introduced by O.Biquard. Equivalent characterizations of these spaces are proved: as conformal manifolds with symplectic conformal holonomy of the appropriate signature; as pseudo-…

2010-03-09abs ↗pdf ↗

This paper analyzes convergence of DP-SGD with adaptive quantile clipping.

problem Empirical success of adaptive clipping methods lacks theoretical understanding.
method Comprehensive convergence analysis of SGD with quantile clipping (QC-SGD).
result Establishes theoretical guarantees for DP-QC-SGD, revealing relationships between quantile selection, step size, and convergence.

Let XX be an infinite hyperbolic surface endowed with an upper bounded geodesic pants decomposition. Alessandrini, Liu, Papadopoulos, Su and Sun \cite{ALPSS}, \cite{ALPS} parametrized the quasiconformal Teichmüller space Tqc(X)T_{qc}(X) and the length spectrum Teichmüller space Tls(X)T_{ls}(X) using the Fenchel-Nielsen coordi…

2015-07-21abs ↗pdf ↗

Study heat kernel on quaternionic contact manifolds, finding linear dependence of coefficients on curvature.

problem Analyzing heat kernel on quaternionic contact manifolds.
method Explicit computation of heat kernel coefficients and dependence on curvature.
result Second coefficient of heat kernel's small time asymptotics depends linearly on the qc scalar curvature.

Generative adversarial networks reconstruct MRI images without full data.

problem Lack of fully-sampled ground truth data for supervised MRI reconstruction.
method Generative adversarial networks for unsupervised MRI reconstruction.
result Reconstructed images show more anatomical structure than conventional methods.

QC methods improve reliability of machine learning-based image segmentation.

problem Inaccuracies in machine learning algorithms limit their clinical applicability.
method Analysis and validation of QC approaches for automatic segmentation.
result Aggregation of uncertainty and Dice prediction methods improved segmentation reliability.

A novel method automates quality control of fMRI scans, improving accuracy and generalizability.

problem Lack of automated QC for fMRI scans limits clinical neuroscience research.
method Train machine learning classifiers using runtime log features to predict scan quality.
result Classifiers trained on FLAG-QC features outperform previous methods (AUC=0.79 vs AUC=0.56).

New MRI method maps tissue parameters more accurately by ignoring voxel independence.

problem Voxel independence assumption limits model fitting reliability and repeatability.
method Self-supervised deep variational approach with Gaussian mixture prior.
result Our method outperforms current techniques in dMRI simulations and real data.

We explore the consequences of curvature and torsion on the topology of quaternionic contact manifolds with integrable vertical distribution. We prove a general Myers theorem and establish a Cartan-Hadamard result for almost qc-Einstein manifolds.

2014-02-07abs ↗pdf ↗

StaPLR improves Alzheimer's disease classification by identifying important MRI scan types and measures.

problem Classifying Alzheimer's disease using multi-source MRI data.
method Stacked penalized logistic regression (StaPLR) with hierarchical multi-view structure and new view importance measure.
result StaPLR identifies the most important MRI scan types and measures for Alzheimer's disease classification.

QC-SPHARM detects Alzheimer's Disease early using hippocampal surface geometry.

problem Early detection of Alzheimer's Disease (AD) using hippocampal surface geometry.
method Spherical harmonics registration, conformality and curvature distortions quantification, t-test feature selection, SVM classification.
result 85.2% testing accuracy on ADNI data, 81.2% on aMCI progression data.

Deep learning improves MRI image quality from down-sampled data.

problem Improving MRI image quality from accelerated, down-sampled k-space data.
method Deep Residual Dense U-Net architecture with Residual Dense Block and new loss function.
result The proposed method achieves better performance in reconstructing high-quality images from down-sampled k-space data.

CSGM framework applied to clinical MRI data for robust reconstructions.

problem Applying deep generative priors to clinical MRI data for high-quality reconstructions.
method Training a generative prior on brain scans from the fastMRI dataset and using Langevin dynamics for posterior sampling.
result Posterior sampling via Langevin dynamics achieves high quality reconstructions in clinical MRI data.

This study improves lung tumor segmentation in mice MRI scans with nnU-Net, reducing annotation needs.

problem Accurate lung tumor segmentation in mice MRI scans for drug discovery.
method Optimized nnU-Net 3D model for lung tumor segmentation with minimal annotations.
result nnU-Net 3D models outperform 2D models in MRI mice scans, requiring fewer annotations.

In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to achieve accelerated scan times. CS-MRI presents two fundamental problems: (1) where to sample and (2) how to reconstruct an under-sampled scan. In this paper, we tackle both problems simultaneously for the specific case of 2D Cartesian sampli…

2019-07-26abs ↗pdf ↗