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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.

168,932 papers · 148 categories

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48 results for knee replacement

Crowdsourced reinforcement learning optimizes knee replacement pathway, reducing costs.

problem Optimizing the sequential decision process for knee replacement surgery.
method Reinforcement learning, value iteration, state compression, kernel representation, cross validation.
result Optimized policy reduces overall cost by 7% and excessive cost premium by 33%.

Improved assessment of knee osteoarthritis using geodesic B-score.

problem Need for automatic, reader-independent measures of osteoarthritis clinical outcomes.
method Derive a geodesic B-score for Riemannian shape spaces, develop efficient algorithm for large shape populations.
result Geodesic B-score exhibits improved discrimination ability over Euclidean B-score.

Convolutional neural networks improve KL grade prediction from Indian knee radiographs.

problem Improving accuracy of knee osteoarthritis grading from Indian radiographs.
method Two-stage approach: object detection followed by regression.
result Fine-tuning model on private hospital data reduces mean absolute error from 1.09 to 0.28.

In this paper we construct some natural geometrical objects on the 1-jet space J^1(R,R^3), like a nonlinear connection, a Cartan linear connection (together with its d-torsions and d-curvatures), a jet "electromagnetic" d-field and its geometric "electromagnetic" Yang-Mills energy, starting from a given dynamical syste…

2011-11-17abs ↗pdf ↗

Expands statistical background for knee osteoarthritis treatment models.

problem Developing optimal exercise and weight loss treatments for knee osteoarthritis.
method Precision medicine models and jackknife cross-validation method.
result Jackknife estimator provides consistent value function estimation.

New model selects more promising patients for knee osteoarthritis trials.

problem Selecting patients likely to benefit from osteoarthritis treatments.
method Multi-classifier prediction from longitudinal data, cost-sensitive learning, feature selection.
result Model reduces by 20-25% the number of patients showing no progression.

MosaicMRI expands public datasets for musculoskeletal MRI, revealing cross-anatomical correlations.

problem Limited diversity in public MRI datasets hinders model evaluation across different anatomical settings.
method Developed a large, diverse dataset (MosaicMRI) and conducted experiments on a baseline model (VarNet).
result Models trained on combined anatomies outperform anatomy-specific models in low-sample regimes.

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.

We apply L0 regularization to reduce neural network complexity and interpretability.

problem Over-parameterization in neural networks leads to susceptibility to attacks, loss of interpretability, and increased SWaP-C.
method We use L0 regularization to reduce complexity and evaluate the trade-off between complexity and desired metrics.
result L0 regularization captures saliency in the input space and reduces complexity of neural networks.

New algorithm trains neural nets on simple skills to learn complex tasks faster.

problem Learning complex tasks through simple imitation.
method Train neural networks on simple, easy-to-learn skills to accelerate learning of complex, hard-to-learn tasks.
result Consistently outperforms state-of-the-art baseline in training speed and performance.

Efficiently samples sequences without replacement for machine learning models.

problem Generating diverse outputs from sequential models without duplicates.
method Incremental sampling procedure for randomized programs, including neural models.
result Efficacy and flexibility of incremental sampling for large output spaces.

This paper addresses credit valuation adjustment with a new closeout convention.

problem Accurate estimation of financial claim value considering counterparty credit risk.
method Theoretical and computational analysis of a nonlinear valuation system using neural networks.
result A neural network-based algorithm effectively solves the high-dimensional nonlinear valuation system.

SGD without replacement decouples into curvature-following and flatness-regularizing steps.

problem Theoretical analysis of SGD without replacement for large-scale neural networks.
method Analysis of SGD without replacement in a realistic regime, considering high curvature and flatness.
result Optimizing with SGD without replacement is locally equivalent to an additional regularizer step.

AIB method improves robustness against adversarial perturbations.

problem Optimizing the IB principle for better robustness and understanding compression effects.
method Proposes adversarial information bottleneck (AIB) method to optimize IB principle without explicit distribution assumptions.
result Demonstrates effectiveness in learning more invariant representations and mitigating adversarial perturbations.

Develops algorithms to optimize machine replacement schedules using operational data.

problem Optimizing machine replacement intervals when the lifetime distribution is unknown.
method Formulates as a stochastic multi-armed bandit problem and proposes Hoeffding- and Bernstein-based algorithms.
result Achieves optimal or near-optimal replacement intervals with minimal regret.

The present study provides a comparative assessment of non-invasive sensors as means of estimating the microbial contamination and time-on-shelf (i.e. storage time) of leafy green vegetables, using a novel unified spectra analysis workflow. Two fresh ready-to-eat green salads were used in the context of this study for …

2019-03-21abs ↗pdf ↗

If M and N are equivariantly homotopy equivalent G-manifolds, then the fixed sets M^G and N^G are also homotopy equivalent. The replacement problem asks the converse question: If F is homotopy equivalent to the fixed set M^G, is F = N^G for a G-manifold equivariantly homotopy equivalent to M? We prove that for locally …

2009-09-28abs ↗pdf ↗

Sampling without replacement speeds up optimization in minimax problems.

problem Optimizing minimax problems with faster convergence rates.
method Analysis of gradient descent ascent and proximal point method with two sampling strategies.
result Sampling without replacement leads to faster convergence rates in minimax optimization.

New research disproves a key conjecture in optimization.

problem Comparison of sampling methods in stochastic optimization.
method Reduction to noncommutative arithmetic-geometric mean inequality and application of noncommutative Positivstellensatz.
result The Recht-Ré conjecture is false for general n.

New sketches for weighted p\ell_p sampling without replacement improve accuracy and efficiency.

problem Efficiently sampling weighted data with high accuracy and minimal redundancy.
method Design of novel composable sketches for WOR p\ell_p sampling, based on CountSketch.
result First to provide WOR sampling for p>1p>1 and signed updates.

We develop an analog of harmonic replacement in the gauge theory context. The idea behind harmonic replacement dates back to Schwarz and Perron. The technique, as introduced by Jost and further developed by Colding and Minicozzi, involves taking a map v ⁣:ΣMv\colonΣ\to M defined on a surface ΣΣ and replacing its values on…

2016-08-24abs ↗pdf ↗

Boring is an operation which converts a knot or two-component link in a 3--manifold into another knot or two-component link. It generalizes rational tangle replacement and can be described as a type of 2--handle attachment. Sutured manifold theory is used to study the existence of essential spheres and planar surfaces …

2007-09-26abs ↗pdf ↗

This paper reformulates FβF_β for better model performance and interpretation.

problem Optimizing model performance and interpretation using FβF_β metric.
method Reformulate FβF_β metric to facilitate statistical distributions and dynamic penalty weights.
result Better and interpretable results with a 14% boost in F1F_1 score for IMDB data.

Improved convergence for VIPs with SEG-RR, a variant of SEG with random reshuffling.

problem Solving variational inequality problems (VIPs) in machine learning.
method Stochastic Extragradient with Random Reshuffling (SEG-RR).
result SEG-RR achieves faster convergence rates than with-replacement variants for certain VIP classes.

Skeleta and other pure subsets of manifold stratified spaces are shown to have neighborhoods which are teardrops of stratified approximate fibrations (under dimension and compactness assumptions). In general, the stratified approximate fibrations cannot be replaced by bundles, and the teardrops cannot be replaced by ma…

2005-01-07abs ↗pdf ↗

The paper introduces methods to quantify uncertainty in sampling without replacement.

problem Accurately estimating parameters from finite populations sampled without replacement.
method Develops confidence sequences using Bayesian and empirical methods.
result Improved confidence intervals and sequences for sampling without replacement.

A frame independent formulation of analytical mechanics in the Newtonian space-time is presented. The differential geometry of affine values i.e., the differential geometry in which affine bundles replace vector bundles and sections of one dimensional affine bundles replace functions on manifolds, is used. Lagrangian a…

2004-04-29abs ↗pdf ↗