fastMRI dataset helps machine learning for MRI faster, cheaper.
problem Accelerating MRI to reduce costs and patient stress.
method Open dataset and benchmarks for machine learning.
result Machine learning can reconstruct MRI images from fewer data.
On-device research index
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 “patient stress”
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learningcs.LG
Detects physiological patterns to hemodynamic stress using unsupervised deep learning.
problem Identify and characterize physiological responses to hemorrhage in raw vital sign data.
method Transform vital sign time series into latent space using unsupervised deep learning, identify clusters, and evaluate latent embeddings.
result Clusters in latent embeddings correspond to physiological response patterns matching physicians' intuition.
Model predicts wound and episode-level readmission risk and time to re-admit.
problem Identify patients at high risk of re-admission to prevent wound recurrences and reduce healthcare costs.
method Data-driven analysis of wound care and episode-level patient data.
result Model achieves high recall and precision for predicting re-admission risk and time.
Stressing Dynamic Loss Modelsq-fin.RM
Proposes a reverse stress testing framework for dynamic models.
problem Finding plausible models under adverse stresses.
method Compound Poisson process, Kullback-Leibler divergence, optimization problem.
result Intensity and severity of process depend on time and state.
Study reviews machine learning techniques for stress monitoring.
problem Improving accuracy of stress monitoring devices.
method Reviewed machine learning techniques for various stress indicators.
result Choosing the right classifier depends on multiple factors.
Method generates plausible financial stress scenarios using large deviations.
problem Misleading risk management by overlooking or overemphasizing implausible scenarios.
method Exploits large-deviations principle to concentrate risk factors near most likely stress configurations.
result Can generate informative stress scenarios even with limited historical data.
Derives stress-energy tensor for polyharmonic maps.
problem Characterizing polyharmonic maps between Riemannian manifolds.
method Derives stress-energy tensor and uses it to characterize polyharmonic maps.
result Characterizes polyharmonic maps, focusing on triharmonic maps.
This note improves correlation stress tests using geodesic distance.
problem Improving financial risk management through better covariance stress tests.
method Proposes a new geometrically invariant definition of correlation stress tests.
result Demonstrates a submanifold approach to stress testing covariance matrices.
Study classifies long-term stress using EEG and expert labeling.
problem Classifying long-term stress using EEG signals.
method Baseline EEG recordings, perceived stress scale scores, expert evaluation, frequency domain features, alpha asymmetry, t-test, support vector machine.
result Expert evaluation improves classification accuracy to 85.20%.
Improved algorithm for multidimensional scaling reduces stress.
problem Stress in multidimensional scaling.
method Proposed modifications of the smacof algorithm.
result Convergent majorization algorithm for Kruskal's stress formula two.
The MSPI predicts market stress with machine learning.
problem Estimating the probability of high market stress.
method L1-regularized logistic regression on stock fragility signals.
result MSPI tracks major stress episodes and improves accuracy.
End-to-end transformer model improves lexical stress detection accuracy.
problem Inaccurate phoneme boundaries and limited features for stress classification.
method End-to-end sequence to sequence model using transformer trained on feature sequences and phoneme sequences with stress marks.
result End-to-end model achieves better performance and lower phoneme error rate (6.36%) compared to syllable segmentation methods.
Reverse Stress Testing for Multivariate Scenarios: A Conditional Framework for Stressed Time Seriesq-fin.RM
Develops a method for reverse stress testing in multivariate scenarios.
problem Reconstructing a multivariate stress scenario from a single exogenous shock.
method Maximizing conditional density under three distributional assumptions.
result Simulated scenarios are economically coherent and reproduce risk-reward asymmetry.
A hybrid model combines diffusion and neural operator methods for stress prediction in hyperelastic materials.
problem Challenges in predicting stress fields in hyperelastic materials with complex microstructures.
method A hybrid surrogate framework combining a conditional denoising diffusion probabilistic model (cDDPM) and a modified DeepONet.
result The hybrid model consistently outperforms traditional methods by one to two orders of magnitude.
Deep Learning-based Stress Determinator for Mouse Psychiatric Analysis using Hippocampus Activitycs.LG
Deep learning predicts stress levels from mouse hippocampus activity.
problem Stress level in mice under different environments.
method Deep learning combined with neuron decoding.
result Deep learning model accurately predicts stress levels.
Paper presents a stress prediction model for students using wearable data.
problem Predicting students' stress levels from wearable data is challenging.
method Used Auto-encoders and Multitask learning to predict stress from sensor data and covariates.
result Model improved stress prediction by 45.6% on StudentLife dataset.
Develops a method for stress testing correlations of financial portfolios.
problem Stress testing correlations in financial asset portfolios.
method Parametric representation of correlations, Bayesian variable selection, joint distribution of stress scenarios.
result Inference of worst-case correlation scenarios using stress tests.
Study shows stress affects emotion recognition models, improving generalizability.
problem Stress affects emotion recognition models, reducing their generalizability.
method Used adversarial networks to control for stress effects on emotion recognition.
result Emotion recognition models that control for stress during training have better generalizability.
Credit risk stress tests can misrepresent default probabilities due to inconsistent parameterization.
problem Misleading default probability projections in credit risk stress tests.
method Analysis of credit risk stress testing models and their parameterization.
result Current portfolios tend to align with through-the-cycle portfolios, leading to spurious default rate projections.
Study classifies human stress using EEG, GSR, and PPG signals.
problem Classifying perceived human stress using physiological signals.
method Data acquisition, feature extraction (time domain), classification using multiple classifiers (SVM, Naive Bayes, MLP).
result Best classification accuracy of 75% achieved by MLP classifier.
Machine learning improves financial stress testing in Indian markets.
problem Conventional stress testing limitations in Indian financial markets.
method Dimensionality reduction, latent factor modeling, Variational Autoencoders, Monte Carlo simulation.
result Improved flexibility, robustness, and realism in financial stress testing.
Classification of Crop Tolerance to Heat and Drought: A Deep Convolutional Neural Networks Approachcs.LG
Deep learning classifies corn hybrids' tolerance to drought and heat.
problem Classifying corn hybrids' tolerance to drought and heat stress.
method Unsupervised deep convolutional neural networks approach.
result 121 hybrids labeled drought tolerant, 193 heat tolerant, 29 both.
Paper proposes MAST to identify stress conditions in forecasting models.
problem Improving reliability and transparency of univariate forecasting models under stress.
method Meta-learning and data augmentation approach to predict stress conditions.
result MAST identifies conditions leading to large errors in forecasting models.
Paper defines p-biharmonic submanifolds and stress tensors in space forms.
problem Characterizing p-biharmonic submanifolds in space forms.
method Provided necessary and sufficient conditions for p-biharmonic submanifolds and properties of stress p-bienergy tensors.
result New properties of stress p-bienergy tensors for p-biharmonic submanifolds.
Using Hilbert's criterion, we consider the stress-energy tensor associated to the bienergy functional. We show that it derives from a variational problem on metrics and exhibit the peculiarity of dimension four. First, we use this tensor to construct new examples of biharmonic maps, then classify maps with vanishing or…
Study identifies mental stress in firefighters using heart rate variability data.
problem Unsupervised identification of mental stress in firefighters from heart rate variability data.
method Exploration and comparison of three unsupervised methods: K-Means, convolutional autoencoders, and LSTM autoencoders.
result Convolutional and LSTM autoencoders successfully stratify stressed versus normal samples using HRV markers.
Reverse sensitivity analysis for risk models under various stresses.
problem Understanding model changes under output stress.
method Deriving the closest stressed distribution and model parameters.
result Numerically efficient method for calculating stressed model.
Two new methods score stress test scenarios for risk managers.
problem Comparing and evaluating stress test scenarios for risk managers.
method Inspired by Archer-Mouy-Selmi, two methodologies for scoring stress test scenarios.
result New methods can compare and evaluate stress test scenarios.
This paper uses multivariate probability models to assess financial system risks.
problem Assessing systemic risk in financial systems.
method Computes multivariate conditional probability distributions for elliptical distributions, focusing on Student-t and Normal models.
result Proposes measures of stress impact and systemic risk.
Research shows ESG signals lower exposure to market fragility during stress periods.
problem Market fragility often occurs together, and ESG is associated with reduced exposure.
method Monthly data on S&P 500 constituents from 2014 to 2025, analyzing downside returns, volatility, illiquidity, and cofragility states.
result A one-standard-deviation increase in ESG lowers the probability of severe cofragility by 0.92 percentage points during stress periods.
Bayesian Monte-Carlo method assesses uncertainty in shear stress entropy models.
problem Uncertainty in evaluating shear stress entropy models remains an open question.
method Bayesian Monte-Carlo (BMC) uncertainty method to evaluate four entropy models.
result FOCB statistic index determines certainty of entropy models in shear stress estimation.
Develops a robust hedging valuation adjustment measure for dynamic hedging under liquidity-demand stress.
problem Dynamic hedging under liquidity-demand stress
method Define robust HVA as the worst-case expected loss over a relative-entropy neighborhood of the loss distribution generated by simulated rebalancing and maturity-unwind trades.
result Distinguishes fixed-radius convention from fixed benchmark-stress convention and shows wider no-trade bands lower rebalancing costs but raise hedge-error risk.
Shear Stress Distribution Prediction in Symmetric Compound Channels Using Data Mining and Machine Learning Modelsphysics.flu-dyn
Study predicts shear stress in compound channels using data mining and machine learning.
problem Predicting shear stress distribution in symmetric compound channels.
method Conducted experiments to measure shear stress. Used data mining and machine learning models (RF, M5P, RC, KStar, AR) to predict.
result Random Forest (RF) model showed highest accuracy with R2=0.9.
Prediction of flow and elastic stresses in a viscoelastic turbulent channel flow using convolutional neural networksphysics.flu-dyn
Neural networks predict flow and elastic stresses in viscoelastic turbulence.
problem Predicting flow and elastic stresses in viscoelastic turbulent flows using limited experimental data.
method Convolutional neural networks trained on wall-normal velocity and pressure data.
result Neural networks accurately predict flow and elastic stresses, especially during low-drag events.
Paper develops a robust HVA measure for dynamic hedging under liquidity stress.
problem Valuation of dynamic hedging under liquidity stress.
method Defines robust HVA as worst-case expected loss over a relative-entropy neighborhood of loss distributions for no-trade bands.
result Wider no-trade bands lower rebalancing costs but increase hedge-error risk.
This work extends elasticity theory to curved spaces, solving stress potentials.
problem Addressing elasticity in curved spaces with boundary.
method Using double forms and bilaplacian operator regularity, solving biharmonic equations.
result Stress potentials can be used in non-Euclidean geometries.
Paper improves SVaR estimation for stress testing under macro scenarios using a hybrid GPR-HS framework.
problem Numerical instability in traditional SVaR estimation under extreme shocks.
method Extends GPR-HS framework to forward-looking stress scenarios with SACS for stable covariance.
result Stable SVaR ranges from -2.1020% to -2.2231%, preserving coherence property.
An extendable, integrated, and dynamic approach to forecasting and stress-testing credit riskq-fin.CP
An integrated and extendable approach for stress-testing loan portfolios
problem Stress-testing loan portfolios
method Simulate completed portfolios, generate uncertain cash flow history, compute credit risk metrics
result Enhanced stress-testing practices within any bank
The paper analyzes stress shocks and their impact on historical data.
problem Determining the appropriate size of stress shocks for historical data analysis.
method Investigates the number of standard deviations required to exceed any historical observation, considering kurtosis.
result Provides tighter bounds for stress shocks than existing inequalities.
One has not any conventional energy-momentum conservation law in Lagrangian field theory, but relations involving different stress-energy-momentum tensors associated with different connections. It is not obvious how to choose the true energy-momentum tensor. This problem is solved in the framework of the multimomentum …
SPX optimizes multiple graph drawing metrics for better readability.
problem Graph drawing algorithms often optimize one metric at a time, leading to suboptimal layouts.
method Introduces Stress-Plus-X (SPX) framework that optimizes stress, crossings, angles, and upwardness simultaneously.
result SPX achieves results close to state-of-the-art algorithms that optimize metrics individually.
This paper proposes non-stationary factor models for financial stress in the UK.
problem Managing financial vulnerabilities in the UK's complex financial system.
method Creation of non-stationary factor models to capture financial stress.
result Non-stationary factor models can better capture financial stress, especially tail events.
New deep learning method improves financial stress testing accuracy.
problem Traditional stress testing methods are criticized for unrealistic assumptions and estimation errors.
method Proposes a novel Deep Learning approach for Dynamic Balance Sheet Stress Testing.
result Empirical results show significant improvement in accuracy over traditional methods.
Adaptive Stress Testing detects financial fraud by simulating potential failures.
problem Detecting and mitigating vulnerabilities in financial systems.
method Developed a simplified model using historical data and reinforcement learning.
result Identified the most likely path to system failure and improved fraud detection.
A quick review of European financial stability institutions and the role of stress tests in the current juridical system.
EM-GAN: Fast Stress Analysis for Multi-Segment Interconnect Using Generative Adversarial Networkscs.LG
EM-GAN uses GANs for fast stress analysis of multi-segment interconnects.
problem Fast and accurate stress analysis for EM failure assessment in multi-segment interconnects.
method Conditional GAN model trained on images of multi-segment wires and current densities.
result EM-GAN provides accurate stress distribution with 6.6% error and 8.3X speedup.
Analyzes how uncertainty in financial networks affects stability.
problem Understanding how uncertainty in financial networks impacts stability.
method Introduced a minimal stochastic dynamical model of the interbank network with linear interactions. Derived the interaction correction to the stress expectation and studied it on the short-medium timescale.
result Interactions increase the stress expectation on average, highlighting the importance of disclosure.
LLM generates coherent macroeconomic stress scenarios for portfolio risk assessment.
problem Macro-financial stress testing and portfolio risk assessment using traditional methods.
method Hybrid prompt-RAG pipeline combining structured prompting and retrieval of country fundamentals and news.
result LLM-generated scenarios yield stable tail-risk amplification with limited sensitivity to retrieval choices.