Study compares feature selection methods for stress hotspot classification.
problem Optimizing feature selection for stress hotspot classification in materials.
method Applied various feature selection methods to microstructural data.
result Demonstrated some feature selection techniques are biased, highlighting a preferred method.
Paper proposes active learning for hotspot detection in VLSI design.
problem Hotspot detection in VLSI design is computationally expensive and relies on costly reference libraries.
method Active learning-based layout pattern sampling and hotspot detection flow.
result Significantly reduces lithography simulation overhead with satisfactory detection accuracy.
New method enhances hotspot prediction in IC designs.
problem Design hotspots vary between designs and are hard to predict.
method Synthetic pattern generation based on DOEs.
result Significantly reduces false alarms in hotspot prediction.
Defense against DL-based lithographic hotspot detectors backdooring attacks reduces success rate from 84% to ~0%
problem DL-based lithographic hotspot detectors are vulnerable to backdoor attacks that can misclassify hotspots.
method Training data augmentation to eliminate intentional biases introduced during training.
result Significant reduction in attack success rate (from 84% to ~0%) using the proposed defense.
This work evaluates machine learning-based hotspot detectors on synthesized layout patterns.
problem Evaluating model robustness and generality of machine learning-based hotspot detectors.
method Developed an automatic layout generation tool to synthesize various layout patterns and tested machine learning-based detectors on these synthesized layouts.
result Machine learning-based detectors need continuous study for robustness and generality in DFM flows.
Paper presents a spatio-temporal Bayesian model for early detection of COVID-19 hotspots.
problem Understanding spatio-temporal dynamics of COVID-19 hotspots to prevent outbreaks.
method Spatio-temporal Bayesian framework with a zero-mean Gaussian process and non-stationary kernel function enhanced by deep neural networks.
result Model demonstrates superior hotspot-detection performance compared to baseline methods.
Adversarial perturbations can fool CNN-based lithographic hotspot detectors, but retraining can improve robustness.
problem Adversarial perturbations can mislead ML-based lithographic hotspot detectors.
method Proposed adversarial retraining strategy to improve robustness of CNN-based detectors.
result Adversarial retraining significantly improves robustness of CNN-based hotspot detection against perturbations.
A new clustering algorithm tracks satellite hotspot data for bushfire tracking.
problem Tracking bushfires using satellite data.
method Spatiotemporal clustering algorithm with adjustable parameters.
result The algorithm effectively clusters satellite hotspot data for tracking bushfires.
Modeling infection hotspots to quantify effects of contact tracing and testing.
problem Capturing the role of infection hotspots in disease transmission.
method Temporal point process modeling framework to represent visits and disease transmission.
result Estimation of transmission rates at sites and households using Bayesian optimization.
Survey of robust clustering methods for hotspot detection.
problem Detecting false positives in spatial hotspot mapping.
method Statistically rigorous clustering techniques.
result Survey of models and algorithms for robust clustering.
Neural network ensembles predict design rule violations from early stages of IC design.
problem Predicting design rule violations from placement and global routing stages in IC design.
method Proposes a framework using neural network ensembles with soft voting and PCA-based subset selection.
result Significant improvement in model performance compared to baseline, including better performance than random forest.
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.
Model predicts drug overdose hotspots using EMS and toxicology data.
problem Predicting drug overdose hotspots to focus limited services.
method Spatial-temporal point process model integrating EMS and toxicology data.
result Model improves prediction accuracy by integrating heterogeneous data.
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.
Model predicts unseen climate extremes to inform risk planning.
problem Missing unseen climate extremes in historical records.
method DeepX-GAN model capturing spatial dependence.
result Unseen heat extremes disproportionately threaten vulnerable regions.
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.
Examines stress tests in European banking supervision.
problem Ensuring financial stability in European banks.
method Reviews existing financial stability institutions and stress testing.
result Stress tests are crucial for European banking supervision.
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.
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.
New method uses neural networks for accurate population genetic inference.
problem Inference for complex, population-scale genetic data.
method Exchangeable neural networks for likelihood-free inference.
result Outperforms state-of-the-art methods on recombination hotspot testing.
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 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.
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.
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.
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…
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.
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.
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.
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.