Study predicts adolescents' intention to smoke cigarettes using ML models.
problem Early identification of adolescents' smoking intentions.
method Five machine learning algorithms tested for predicting intention to smoke cigarettes.
result Gradient Boosting Classifier showed highest accuracy.
Paper proposes real-time health monitoring using wearable sensors.
problem Real-time health monitoring using wearable sensors.
method Learning shallow detection cascades for real-time wearable-phone or wearable-phone-cloud systems.
result Cigarette smoking detection from actigraphy and respiration data.
Network Elastic Net identifies smoking-specific gene expression for lung cancer prognosis.
problem Identifying smoking-specific gene expression biomarkers in lung cancer prognosis.
method Introduces Network Elastic Net, a method that clusters and regresses on graphs based on smoking behavior.
result Shows efficacy of clusters in identifying cancer stages using gene expression and smoking behavior.
Model predicts smoking events using a Hawkes process.
problem Predict smoking events to improve cessation interventions.
method Time-varying semi-parametric Hawkes process model.
result TV-SPHP achieves superior prediction performance.
Kernel methods identify treatment effects with unobserved confounding using negative controls.
problem Learning causal relationships with unmeasured confounding.
method Kernel ridge regression algorithms for nonparametric treatment effects.
result Uniform consistency and finite sample rates of convergence proved.
Smart watches can identify smoking gestures with high accuracy.
problem Identifying smoking gestures to prevent relapses.
method Used accelerometer sensors in smart watches and Artificial Neural Networks (ANNs).
result 85%-95% success rates in identifying smoking gestures.
Machine learning helps predict smoke types for safer forest burns.
problem Determining which fuels to burn safely to reduce wildfire risk and minimize smoke.
method Machine learning techniques like spectral clustering and manifold learning.
result Interpretable representations and tools for differentiating smoke types.
New approach to learning cascaded classifiers for heterogeneous embedded devices.
problem Real-time monitoring and adaptive interventions in mobile health.
method Generalized tree-structured cascades, joint learning of classifiers, novel output combination during training.
result Joint learning of all detectors for tree-structured cascades in resource-constrained environments.
Unsupervised fire and smoke segmentation from IR videos.
problem Early detection of fire from infrared videos.
method Spatial, temporal, and motion information fusion using optical flow, divergence, and intensity values.
result Markov Random Field outperforms other clustering algorithms in fire and smoke segmentation.
Paper addresses measurement error in observational data, estimating effects of maternal smoking and opioid use on childhood obesity.
problem Systematic bias in inferences from observational datasets due to measurement error.
method Missing data view of measurement error problem; marginalizes latent true exposure; identifies outcome distribution under specific conditions.
result Method estimates effects of maternal smoking and opioid use on childhood obesity using only subject-reported data, refining estimates and consistency with existing literature.
Proposes a robust EM algorithm for analyzing incomplete panel count data.
problem Missing reports in panel count data.
method Functional EM algorithm for non-parametric counting process mean function estimation.
result Robust to misspecification of Poisson process assumption and missing completely at random.
Study identifies biomarkers for lung cancer in female non-smokers.
problem Identifying prognostic biomarkers for stage III NSCLC in non-smoking females.
method Gene expression profiling and XGBoost machine learning algorithm.
result Top biomarkers validated in literature, with AUC score of 0.835.
Alternative approach to model selection using transformation analysis.
problem Over-simplistic models lead to erroneous interpretations.
method Step-wise complexity reduction to identify simpler, better-interpretable models.
result Transformation models improve model fit and interpretability.
Improved functional data modeling with modern imputation methods.
problem Estimating complex non-linear models with sparsely and irregularly sampled functional data.
method Modified multiple imputation methods combining MissForest and Local Linear Forest.
result New imputation methods produce better estimates than existing methods.
Bayesian method estimates QTEs from observational data.
problem Estimating nuanced characteristics of counterfactual distributions.
method Bayesian semiparametric conditional distribution regression model with double balancing score.
result Proposed method provides more accurate QTE estimates than other methods.
A well known result of Da Rios and Levi-Civita says that a closed planar curve is elastic if and only if it is stationary under the localized induction (or smoke ring) equation, where stationary means that the evolution under the localized induction equation is by rigid motions. We prove an analogous result for surface…
A cheap CRC screening method combining FIT, BMI, smoking, and diabetes.
problem Inadequate sensitivity and specificity of current CRC screening methods.
method Ensemble-based classification algorithm considering FIT, BMI, smoking, and diabetes.
result 92% specificity, 89% sensitivity, 90% precision, AUC of .95.
Bäcklund transformations for smooth and ``space discrete'' Hashimoto surfaces are discussed and a geometric interpretation is given. It is shown that the complex curvature of a discrete space curve evolves with the discrete nonlinear Schrödinger equation (NLSE) of Ablowitz and Ladik, when the curve evolves with the Has…
The paper classifies cervical cancer using various techniques and feature selection.
problem Classifying cervical cancer from a dataset with missing values and imbalance.
method Feature selection, over-sampling, under-sampling, dimensionality reduction, and classification techniques.
result Age, first sexual intercourse, number of pregnancies, smokes, hormonal contraceptives, and STDs: genital herpes are the main predictive features with high accuracy.
A new algorithm estimates causal effects in studies with multiple causes.
problem Assuming ignorability for causal inference when confounders are unobserved.
method Deconfounder combines machine learning and predictive model checking.
result The deconfounder requires weaker assumptions than classical methods.
A method for low-dimensional MDP representation using deep neural networks.
problem Constructing a low-dimensional Markov decision process representation.
method Use a deep neural network to define a class of potential process representations and estimate the process of lowest dimension.
result A decision strategy that maximizes mean utility for the low-dimensional representation also maximizes mean utility for the original process.
Study predicts colorectal polyp recurrence using medical records and statistical models.
problem Identifying patient characteristics influencing colorectal polyp recurrence.
method Natural language processing for extracting polyp characteristics, Kaplan-Meier curves, Cox proportional hazards modeling, random survival forest models.
result Polyp size, number, location, and patient smoking status significantly influence recurrence risk.
Pyrocast predicts pyrocumulonimbus clouds six hours before they form.
problem Predicting pyrocumulonimbus clouds to adapt to climate change.
method Database of 148 pyroCb events, Random Forests, CNNs, and Auto-Encoders.
result Best model predicts pyroCb with 90% accuracy.
Study identifies key health behaviors linked to adolescent suicide attempts.
problem Understanding health behaviors associated with increased risk of adolescent suicide attempts.
method Cross-sectional data analysis using machine learning algorithms and logistic regression.
result Non-parametric Bayesian tree ensemble model outperforms other models, achieving 80.0% accuracy in goodness-of-fit and 78.2% in predictive accuracy.
New statistics improve feature importance detection with false discovery guarantees.
problem Identifying truly correlated features from observational data.
method Developed efficient knockoff generation from Bayesian Networks and new statistics.
result Improved power and efficiency of feature importance detection.
The paper introduces vortex cycles and nerves, inspired by Thomson's vortex atoms.
problem Understanding vortex structures and their homology.
method Introducing and analyzing non-concentric, nesting vortex cycles and nerves.
result Whitehead CW topology and Leader uniform topology outcomes of vortex cycles.
Valid causal inference with unobserved confounding in high-dimensional settings.
problem Estimating causal effects with unobserved confounders in high-dimensional data.
method Proposes methods to estimate causal effects with valid confidence intervals in the presence of unobserved confounders and high-dimensional nuisance models.
result Valid semiparametric inference can be obtained with unobserved confounding, and uncertainty intervals are proposed.
Team aims to predict particulate matter levels on ISS using Bi-GRU.
problem Early warning system for particulate matter on ISS.
method Bi-GRU algorithm analyzing past 90 minutes of data.
result Bi-GRU predicts particulate matter levels up to 1 minute in advance.
Stable long-term predictions for fluid flows using neural networks.
problem Predicting complex dynamics of fluid flows with high temporal stability.
method End-to-end trained neural network architecture combining CNN for spatial compression and LSTM for temporal prediction.
result Novel latent space subdivision (LSS) allows stable and controllable long-term predictions.
This paper investigates robust and efficient DR/RDR estimators for WATEs.
problem Lack of systematic investigation into robustness and efficiency conditions for WATE estimation.
method Proposes three RDR estimators using semiparametric efficient influence function and double/debiased machine learning.
result Demonstrates the practical relevance of the methods in medical and social sciences.
Paper develops an efficient algorithm for experiment design using synthetic controls.
problem Designing optimal experiments for treatment effect estimation.
method Solves a phase synchronization problem via a normalized generalized power method.
result First global optimality guarantee for experiment design with pre-treatment data.
Paper develops methods for evaluating mHealth interventions using historical data.
problem Evaluating the long-term effectiveness of mHealth interventions designed for near-term outcomes.
method Develops off-policy estimation techniques to infer long-term average outcomes from historical data.
result Provides estimators and confidence intervals for evaluating mHealth policies.
New methods estimate causal effects through mediators, handling confounding without strict assumptions.
problem Estimating causal effects through mediators while accounting for unmeasured confounding.
method Developed four nonparametric identification strategies using proximal confounding bridge functions, efficient influence function, and quadruply robust estimator. Proposed proximal debiased machine learning approach for high-dimensional nuisance parameters.
result Achieved n \sqrt{n} n -consistency and asymptotic normality for path-specific effect estimation. RETR improves indoor radar perception with a novel transformer model.
problem Indoor radar perception lacks models tailored for multi-view radar settings.
method RETR extends DETR architecture with depth-prioritized feature similarity, tri-plane loss, and radar-to-camera transformation.
result RETR outperforms state-of-the-art methods by 15.38+ AP for object detection and 11.91+ IoU for instance segmentation.
Systems with long-range persistence and memory are shown to exhibit different precursory as well as recovery patterns in response to shocks of exogeneous versus endogeneous origins. By endogeneous, we envision either fluctuations resulting from an underlying chaotic dynamics or from a stochastic forcing origin which ma…
Paper proposes decision-theoretic approach to combat wildfires.
problem Complexity and uncertainty in wildfire management.
method Partially-observable Markov decision process and data-driven model.
result Forecasting model accurately models wildfire spread.
SVMs improve forest fire detection accuracy on challenging datasets.
problem Rapid and accurate detection of forest fires.
method Training SVMs on labeled fire image datasets, focusing on data preprocessing, feature extraction, and model training.
result SVMs enhance detection accuracy on complex datasets, revealing key parameters affecting performance.
HFM uses deep learning to infer fluid dynamics from visual data.
problem Data assimilation of fluid dynamics from flow visualizations.
method Physics-informed deep learning framework based on Navier-Stokes equations.
result Accurate predictions of velocity and pressure fields in complex flows.
ENN method uses expectile regression for genetic data analysis of complex diseases.
problem Discover additional genetic variants contributing to complex diseases.
method Developed an expectile neural network (ENN) method integrating expectile regression and neural networks.
result ENN method outperforms existing expectile regression in discovering genetic variants predisposing to sub-populations.
Deep neural nets predict vortex-induced vibrations from limited flow data.
problem Predicting lift and drag forces on structures from scattered velocity field data.
method Extended deep neural networks solving coupled Navier-Stokes and structural dynamics equations.
result Deep neural networks can accurately infer structural parameters, pressure field, and velocity field from limited flow data.
New method speeds up uncertainty estimation for large datasets in causal inference.
problem Computational infeasibility of bootstrap-based uncertainty quantification for large datasets.
method Extends cBLB algorithm to kernel methods, combining subsampling and resampling.
result Achieves computational scalability with nominal coverage.
Predicts academic risk in college students using interpretable machine learning.
problem Predicting academic risk from high-dimensional, unbalanced student data.
method Binary classification task using LightGBM model and Shapley value.
result 8 predictors for academic risk identified, including quality of academic partners and dormitory study atmosphere.
This work addresses privacy issues in IoT data sharing by balancing information disclosure and user privacy.
problem Balancing privacy and utility in time-series data sharing from IoT devices.
method Formulated as POMDPs, solved using A2C DRL, evaluated with synthetic and real data.
result Proposed policies achieve a good balance between privacy and utility.