Enhanced framework predicts transportation modes from GPS data.
problem Predicting transportation modes from GPS records.
method Extended Etemad et al. framework, investigated wrapper search and information retrieval methods for feature selection, compared with deep learning methods.
result Framework achieved better performance than related papers.
This research optimizes feature selection for predicting transportation modes in smart cities.
problem Finding the best subset of features for predicting transportation modes.
method Wrapper and information retrieval methods were used to find the best feature subset.
result The proposed framework achieved better performance compared to related studies.
Study improves cross-modal bike-share and transit demand prediction.
problem Cross-modal ripple effects in urban transportation demand.
method Transfer learning and stacked LSTM models for cross-modal demand prediction.
result Transfer learning models outperform unimodal models in cross-modal demand prediction.
Study develops a semi-supervised deep ResNet for Wi-Fi mode detection.
problem Utilizing Wi-Fi signals for multimodal transportation mode detection with limited labeled data.
method Semi-supervised deep residual network (ResNet) framework.
result Framework achieves high prediction accuracy (81.8% for walking, 82.5% for biking, 86.0% for driving).
This work explains GAN mode collapse and convergence issues via optimal transportation theory.
problem GANs struggle with convergence and mode collapse due to discontinuous optimal transportation mappings.
method The study connects GANs to optimal transportation theory, testing hypotheses about discontinuity and proposing a new method to approximate continuous Brenier potentials.
result The supports of real data distributions are often non-convex, leading to discontinuous optimal transportation mappings and mode collapse in GANs.
New model generates data without mode collapse or mixture.
problem Mode collapse and mode mixture in generative models.
method Inspired by AE-OT, improved sample generation algorithm.
result Free from mode collapse and mixture issues.
This work proposes a new feature for transportation mode classification using GPS trajectories.
problem Classifying transportation modes from GPS trajectories to optimize urban mobility.
method The Ordinal Pattern Transition Graph and its self-transition probability are used for classification.
result The proposed feature outperforms existing methods in transportation mode classification.
Identifying the distribution of users' transportation modes is an essential part of travel demand analysis and transportation planning. With the advent of ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach for inferring commuters' mobility mode(s) is to leverage their GPS trajectories. A maj…
Study provides guidelines for smartphone-based transportation mode detection.
problem Developing a reliable transportation mode detection system using smartphone sensors.
method Detailed dataset construction, sensor relevance analysis, and unknown user detection.
result Demonstrated the feasibility and effectiveness of smartphone-based TMD.
k-GANs uses an ensemble of GANs with semi-discrete OT for better modeling.
problem Mode collapse in GANs.
method Semi-discrete optimal transport for training an ensemble of GANs.
result k-GANs consistently outperforms baseline GANs in experiments.
A two-layer classifier improves smartphone transportation mode recognition.
problem Improving accuracy of transportation mode classification.
method Two-layer hierarchical classifier combining time and frequency domain features.
result Maximum classification accuracy of 97.02%.
This study aims to predict vessel stay and delay times at ports to optimize logistics.
problem Uncertainties in maritime logistics, including weather, cargo diversity, and port dynamics, lead to increased costs and inefficiencies.
method Developed predictive analytics to address shortcomings in previous works, using feature analysis and SHAP explanations.
result Predictive analytics can assist in efficient planning and scheduling of port operations, reducing costs and improving logistics.
A new method ranks uncertainty vectors from multiple measures for robust prediction.
problem Single scalar measures of model reliability are insufficient for comprehensive uncertainty quantification.
method Optimal transport ranks vectors of uncertainty measures, supporting flexible fusion of aleatoric and epistemic uncertainties.
result The method provides a robust ranking of uncertainty that supports various downstream tasks.
Paper improves predictive distributions for rare events using a simple framework.
problem Local miscalibration of predictive distributions for rare events.
method Semiparametric diagnostic transport maps to correct tail probabilities.
result Semiparametric maps improve predictions for severe weather hazards.
The paper introduces diagnostic transport maps to improve the reliability of rare event predictions.
problem Improper calibration of predictive distributions, especially for rare events.
method Diagnostic transport maps to adjust base model's probabilities for better calibration.
result Diagnostic transport maps improve predictive performance for rare events, including 24-hour rapid intensity change.
Predicts traffic flow using reinforcement learning and sensor data.
problem Accurately predict expanding and evolving long-term streaming traffic networks.
method Formulates the problem as a continuous reinforcement learning task, where the agent predicts future traffic based on sensor data.
result The approach improves accuracy in predicting traffic flow by updating the agent's state representation over time.
Ensemble CNNs improve mode classification in smartphone travel surveys.
problem Classifying transportation modes from smartphone travel survey data.
method Developed an ensemble of CNN models with different architectures and hyper-parameters, combined using average voting, majority voting, optimal weights, and a Random Forest meta-learner.
result The ensemble method with Random Forest as meta-learner achieved 91.8% accuracy, surpassing other methods.
New method uses kernel Stein discrepancy for measure transport without strict continuity constraints.
problem Minimizing Kullback-Leibler divergence for posterior approximation.
method Proposes minimizing kernel Stein discrepancy instead of Kullback-Leibler divergence.
result Demonstrates consistency and competitiveness of the new method.
Valid prediction sets for dynamic graphs using conformal prediction.
problem Uncertainty quantification in dynamic graph neural networks.
method Dynamic graph unfolding for input to GNNs, combined with conformal prediction.
result Valid prediction sets with minimal assumptions, even in challenging scenarios.
This paper presents a bus travel time prediction system using deep neural networks.
problem Accurate travel time predictions for urban buses to compete with other modes of transport.
method Multi-output, multi-time-step deep neural network combining convolutional and LSTM layers.
result The proposed model significantly outperforms other methods and detects small irregular peaks quickly.
Unified framework for sampling from complex densities using PDEs and neural networks.
problem Sampling from complicated probability densities.
method Dynamical measure transport via PDEs and physics-informed neural networks (PINNs).
result Significantly better mode coverage and high accuracy in sampling.
Accelerates optimal transport computation by 10x with spectral insights.
problem Exponential slow-down of convergence in Entropic Optimal Transport as regularization weakens.
method Spectral insights and spectral warm-start strategy to mitigate convergence issues.
result Faster convergence compared to the reference method Sinkhorn algorithm.
A JAX toolbox solves optimal transport problems for point clouds and histograms.
problem Optimal transport problems between point clouds and histograms.
method Automatic and custom reverse mode differentiation, vectorization, just-in-time compilation, and accelerators support.
result Solves a wide range of optimal transport problems including regularized OT, barycenters, Gromov-Wasserstein, and low-rank solvers.
POTNet uses penalized optimal transport to generate data without mode collapse.
problem Mode collapse in WGANs leading to poor synthetic data generation.
method POTNet employs marginally-penalized Wasserstein distance for deep generative modeling.
result POTNet effectively captures underlying data structures, including tail behaviors and minor modalities.
Unified mathematical theory for analyzing biomolecular geometry and flexibility.
problem Lack of a unified mathematical theory for analyzing biomolecular geometry and flexibility.
method Introducing de Rham-Hodge theory, Helmholtz-Hodge decomposition, and discrete exterior calculus.
result Unified framework for predicting macromolecular flexibility and natural modes.
Unified model predicts multi-mode failure with multi-sensor data.
problem Independent failure mode and RUL prediction ignores inherent relationship.
method Hierarchical Bayesian framework with Cox model, Gaussian process, and multinomial distributions.
result Robust uncertainty quantification and accurate prediction of multi-mode failure.
A new dynamical formulation of log-PCA captures local principal modes of geodesic variations.
problem Learning principal variations of random probability measures under Wasserstein geometry.
method Introducing a new dynamical formulation of log-PCA as a variational approach.
result Deriving a general statistical convergence rate for empirical WT-PCA.
CAST predicts distribution-valued time series by stabilizing and transporting simplex-supported successors.
problem Forecasting distribution-valued time series with structural failure modes.
method CAST (Causal Anchored Simplex Transport) uses successors retrieved from causal context, stabilized with a persistence anchor, and locally transported on ordered supports.
result CAST outperforms baselines on eleven public and simulated benchmarks, achieving best average rank on both one-step KL and autoregressive rollout JSD.
GMM uses mmWave radar to classify traffic modes in poor lighting.
problem Classifying traffic modes in poor lighting conditions.
method GMM on mmWave radar point clouds.
result Good segmentation performance in pedestrian and car classification.
Deep neural networks predict walking, biking, and driving from Wi-Fi signals.
problem Predicting human mobility modes using Wi-Fi signals.
method Deployed Wi-Fi sensors at four locations, developed and tested multiple classifiers (MLP, Decision Tree, Bagged Decision Tree, Random Forest).
result Multilayer Perceptron achieved 86.52% correct predictions of mobility modes.
Framework predicts remaining useful life of DSH subsystems under unknown failure modes.
problem Predicting remaining useful life of DSH subsystems with unknown failure modes.
method Unsupervised framework using mixture of Gaussian regressions and Expectation-Maximization algorithm.
result Improved prediction accuracy and interpretability of RUL.
Language models fail to process hallucinated responses, and this study diagnoses the failure.
problem Language models fail to process hallucinated responses, leading to over-concentration or diffuse attention.
method The study uses forced scoring of benchmark-labeled responses to compute attention shapes and analyze the symmetric component of the degree-normalized attention operator.
result The study proves that every transpose-invariant spectral diagnostic of the attention operator is orientation-blind and bounds the sensitivity of any Lipschitz diagnostic by the asymmetry coefficient \(G\).
This work explores the generalization properties of diffusion models, providing theoretical and empirical insights.
problem Theoretical understanding of diffusion models' generalization capabilities remains underdeveloped.
method Theoretical exploration and quantitative analysis of generalization gaps in diffusion models.
result Established polynomially small generalization error (O(n−2/5+m−4/5)) for diffusion models, avoiding the curse of dimensionality. New method predicts RUL and failure modes from partial data.
problem Predicting RUL and failure modes from incomplete data.
method Formulated as vector General Value Function (GVF) prediction on an absorbing degradation process, using TD(n,λ) for estimation. result TD improves RUL and failure-mode prediction compared to Monte Carlo methods, especially under scarce complete labels.
Neurons predict future scalar inputs by learning top modes of lag vectors.
problem Predicting future scalar inputs with physiological delays.
method Normal Mode Decomposition to extract independently evolving modes.
result Temporal filters of neurons correspond to left eigenvectors of a generalized eigenvalue problem.
Deep learning model predicts protein-ligand binding modes from docking data.
problem Improving protein-ligand binding mode prediction accuracy.
method Dual-graph architecture with separate sub-networks for ligand topology and protein-ligand interactions.
result Deep learning model outperforms docking programs in binding mode prediction.
CT compares two distributions using Bayes' theorem and chain rule.
problem Measuring the difference between two probability distributions.
method Conditional transport (CT) using chain rule and Bayes' theorem.
result CT strikes a good balance between mode-covering and mode-seeking behaviors.
Enhances flexibility in data reweighting with optimal transport and maximum entropy principles.
problem Adapting empirical distributions to predefined constraints on moments, tail behavior, etc.
method Nonparametric distributional constraints, maximum entropy principle, optimal transport.
result Maximum entropy weight adjusted empirical distribution close to a specified distribution in optimal transport metric.
Unified model predicts disease spread using EMD and ensemble learning.
problem Predicting fluctuating disease spread and individual behavior.
method SEIS-A framework, EMD decomposition, ensemble learning, on-line query data.
result The method outperforms other methods in predicting HFMD consultation rates.
New method clusters travel behavior data from 1990-2017.
problem Challenges in analyzing large-scale travel data.
method Divide and Combine K-means clustering on time series data.
result Activity-travel patterns can be grouped into three clusters.
GyroSwin models plasma turbulence with neural nets, reducing costs and capturing neglected nonlinearities.
problem Understanding plasma turbulence in fusion reactors, which impairs confinement and limits reactor design.
method Introduces GyroSwin, a scalable 5D neural surrogate that approximates 5D nonlinear gyrokinetic simulations.
result GyroSwin outperforms reduced models in heat flux prediction and captures turbulent energy cascade.
Semi-supervised GANs improve travel mode inference from GPS data.
problem Travel mode inference from GPS trajectories.
method Developed semi-supervised GANs and compared them with CNNs on a large-scale smartphone dataset.
result Best semi-supervised GAN model achieved 83.4% prediction accuracy.
Semi-supervised method predicts faults in systems with multiple operation modes.
problem Fault detection in industrial systems with missing target data.
method Semi-supervised soft sensor modeling incorporating mode transition properties.
result Regression coefficients estimated under constraint conditions from mode transitions.
Deep learning improves trip prediction accuracy in transportation planning.
problem Traditional models fail to accurately predict person and vehicle trips due to complexity and dynamics.
method Developed and trained a deep learning model using NHTS data.
result Deep learning model achieved 98% accuracy for person trip prediction and 96% for vehicle trip estimation.
TRACE improves conformal prediction for multi-dimensional outputs.
problem Challenges in constructing valid and informative conformal prediction regions for multi-dimensional outputs.
method TRACE uses transport alignment in diffusion and flow matching models to define nonconformity scores.
result TRACE yields valid and adaptive conformal prediction regions for multimodal and non-convex distributions.
Method predicts how probability distributions evolve over time.
problem Predicting how systems described by probability distributions evolve under different conditions.
method Wasserstein Parallel Transport
result Wasserstein Parallel Transport provides counterfactual comparisons of distributional dynamics.
Paper uses GNN and conformal prediction for accurate edge weight prediction.
problem Predicting edge weights on graphs for various applications.
method Graph Neural Network (GNN) with conformal prediction and error reweighting.
result Our method provides better coverage and efficiency than baselines.
TS-Fault benchmarks TSF models against structural faults.
problem Evaluating the robustness of time series forecasting models against structured events.
method TS-Fault uses parameterized fault scenarios with controllable difficulty.
result Three findings contradict common leaderboard intuition.