Extract coherent structures from Jupiter's clouds and snow events without flow models.
problem Finding coherent structures in fluidic systems from image data.
method Image processing with anisotropic, directed diffusion operator from a directed affinity matrix.
result Demonstrated coherence in Jupiter's clouds and snow events without flow modeling.
Deep learning detects snow avalanches in SAR images with high accuracy.
problem Accurate detection of snow avalanches in remote areas using satellite imagery.
method Fully Convolutional Neural Network trained on manually labeled Sentinel-1 radar images.
result Deep learning model achieves F1 score above 66% compared to 38% for state-of-the-art methods.
Method evolves spatial features from satellite imagery for regional modeling.
problem Regional summaries from high-resolution satellite data for geospatial phenomena.
method Induces spatial aggregations using Genetic Programming to optimize model performance.
result Genetic Programming synthesizes effective spatial aggregations and improves model predictions.
Study characterizes cryospheric spectral feature space using joint PC+t-SNE approach.
problem Characterize cryospheric spectral feature space for remote sensing applications.
method Compare and contrast two approaches for identifying feature space basis vectors via dimensionality reduction (PCA and t-SNE).
result Joint characterization reveals distinct continua and clusters of ice reflectance properties.
bnlearn is an R package which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package to improve their performance via parallel c…
Neuro-symbolic system tackles conversational AI's need for natural, broad-ranging dialogue.
problem Understanding unstated presumptions in conversational AI commands.
method Neuro-symbolic theorem prover for multi-hop reasoning.
result Extracts multi-hop reasoning chains from natural language commands.
Simple noise training improves image recognition robustness.
problem Modern neural networks struggle with unseen image corruptions.
method Additive Gaussian and Speckle noise training, adversarial training against worst-case noise.
result Significant improvement in robustness on ImageNet-C and MNIST-C benchmarks.
Unified machine learning framework for deep learning and web services.
problem Deep learning and web service challenges.
method MMLSpark expands Spark to deep learning, micro-service orchestration, etc.
result Unified API for deep learning and web services.
NeuralHydrology uses LSTMs to forecast rainfall-runoff, revealing interpretable patterns.
problem Difficulty in interpreting LSTMs in environmental sciences.
method Application of LSTMs for rainfall-runoff forecasting in hydrology, analyzing patterns internally.
result Trained LSTMs reveal patterns consistent with hydrological system understanding.
Develops a neural model to predict event occurrence and timing.
problem Standard event time models ignore the distinction between event occurrence probability and predicted time.
method Introduces a conditional event time model using a neural network with a binary stochastic layer.
result Shows superior event occurrence and timing predictions on various datasets.
Paper proposes PP-GCN for fine-grained social event categorization.
problem Challenges in mining social events due to heterogeneous event elements and social network structures.
method Design an event meta-schema, build an HIN, propose PP-GCN, and use KIES.
result PP-GCN outperforms other techniques in social event detection and clustering.
Paper proposes a new trading strategy using corporate event detection from news articles.
problem Predicting stock movements based on corporate events from news articles.
method Bi-level event detection model: low-level for token-level event identification, high-level for article-level event identification.
result The proposed strategy outperforms existing models in stock prediction metrics.
Deep learning detects sleep events in EEG, reducing expert dependency.
problem Manual EEG event annotation by sleep experts is time-consuming and variable.
method Convolutional neural network for joint event detection in EEG signals.
result Deep learning method outperforms existing event-specific algorithms.
Proposes a method to predict stock movements using fine-grained events from finance news.
problem Lack of specific semantic information in coarse-grained events for stock movement prediction.
method Built a finance event dictionary, extracted fine-grained events, combined with stock trade data, and used distant supervision for training.
result Method outperforms all baselines and shows good generalizability.
ProxiModel extracts high-quality news events from news corpora.
problem Mining high-quality structured event knowledge from noisy news data.
method ProxiModel uses a proximity-network to model event correlation within and across news corpora.
result ProxiModel efficiently and effectively extracts high-quality event descriptors and attributes.
AUC is unreliable in rare event settings but stable with moderate numbers of events.
problem Misleading performance metrics in rare event settings.
method Simulation study varying dataset sizes and event rates.
result AUC is unreliable in rare event settings but stable with moderate numbers of events.
Bayesian analysis shows testimonies for common events increase belief in uncommon events.
problem Analyzing the reliability of testimonies for uncommon events based on common events.
method Full Bayesian solution considering large testimonies for common events and one for an uncommon event.
result The probability of an uncommon event increases when there are many testimonies for common events, making the event more plausible.
Non-spanning identification of scheduled event risk in option pricing.
problem Separating continuous surface from scheduled jump in option pricing.
method Modeling FOMC decisions, CPI releases, and NFP reports as deterministic-time jumps in risk-neutral option pricing.
result Improves held-out event-spanning pricing with Gaussian and two-component mixture jumps.
New approach predicts event probabilities for better event detection.
problem Class imbalance and inaccurate event detection in time series analysis.
method Regression-based approach to predict probability densities at event locations.
result Regression-based approaches outperform segmentation-based methods.
Study examines HTE estimation from time-to-event data with competing events.
problem Estimating HTEs from time-to-event data with competing events.
method Outcome modeling approach using plug-in estimators for potential outcomes.
result Competing events introduce new challenges for HTE estimation.
CAUSE learns Granger causality from event sequences, outperforming existing methods.
problem Learning Granger causality from complex, interdependent event sequences.
method CAUSE uses a neural point process to capture interdependency and an attribution method to extract Granger causality.
result CAUSE outperforms state-of-the-art methods in inferring inter-type Granger causality.
New method detects events with keywords, adapting to new types.
problem Adapting event detection to new types with predefined sets.
method Feature-based attention mechanism for CNNs.
result Benefits for new type extension and attention mechanism.
Adversarial model estimates event-time distributions from health data.
problem Nonparametric estimation of event-time distributions in time-to-event analysis.
method Adversarial learning approach with a principled cost function for censored events.
result Proposed model yields significant performance gains over parametric alternatives.
Model predicts event sequences using neural networks.
problem Predicting future events based on past patterns.
method Neural Hawkes Process with LSTM for evolving event intensities.
result Generative model achieves competitive performance.
REST framework predicts stock trends by considering stock-specific and related-stock events.
problem Predicting stock trends using event information from news, social media, and discussion boards.
method REST framework addresses two main shortcomings of existing event-driven methods: stock-specific event influence and related-stock event influence.
result REST framework achieves higher investment returns compared to baselines.
GANs improve event generation in physics experiments.
problem Improving statistical precision in event generation.
method Used generative adversarial networks (GANs) to generate events.
result GANs amplify the statistical precision of the training sample.
Extracts biological context from biomedical texts to associate with events.
problem Identifying biological context and associating it with biochemical events in texts.
method Analyzed an annotated corpus and developed classifiers using syntactic, distance, and frequency features.
result Developed and evaluated classifiers for context-event association.
New deep learning method handles rare and imbalanced events in time series.
problem Challenges in event detection in time series data, especially rare and imbalanced events.
method Supervised regression-based deep learning approach that handles various types of events.
result Superior performance across diverse domains, particularly for rare events and imbalanced datasets.
Combining multiple collider events improves machine learning performance.
problem Improving machine learning for collider physics.
method Study of single-event vs multi-event classifiers under IID assumption.
result Training single-event classifiers is more effective than multi-event classifiers.
The study uses financial events to predict stock market movements.
problem Predicting stock market movements using financial events.
method Combined event extraction method, BERT/ALBERT enhanced event representation, and extended hierarchical attention network.
result Significantly better accuracies and higher simulated returns compared to state-of-the-art models.
Proposes a model for predicting events from event streams.
problem Predicting events like part replacement and failure in manufacturing and teleservice systems.
method Non-parametric prognostic framework using MGCP modulated Poisson processes.
result MGCP prior facilitates sharing of information and analysis of flexible event patterns.
LOBDIF predicts limit order book events using a diffusion model.
problem Predicting the timing and type of events in a dynamic market system.
method LOBDIF uses a diffusion model to learn the complex time-event distribution in limit order book streams.
result LOBDIF significantly outperforms existing methods in real-world data experiments.
Neural network model predicts alternating event-free periods.
problem Dynamic prediction of alternating recurrent events with statistical nuance.
method Developed an online dynamic prediction framework using neural network theory.
result Outstanding performance in predicting alternating recurrent event-free time.
New STH distance finds patterns in event timeseries without resampling.
problem Lack of efficient analysis methods for event and state timeseries.
method Define STE-ts, propose STH, leveraging both time and state duration.
result Improved precision and computation time compared to resampled metrics.
Paper tackles event stream outliers using a novel weight function.
problem Learning event streams with unexpected absences or occurrences.
method Temporal point process framework with a novel weight function.
result The proposed method effectively handles both commission and omission outliers.
Adaptive social media event detection improves accuracy by 350%.
problem Concept drift in event signals over time.
method Continuous concept drift adaptation using machine learning classifiers.
result 350% improvement in landslide detection accuracy.
A fast geometric regularizer improves event camera performance.
problem Event collapse in contrast maximization framework.
method Geometric regularizer to mitigate overfitting.
result State-of-the-art accuracy with reduced computational complexity.
Bayesian model improves categorization of explosions from sparse data.
problem Challenges in categorizing explosions from limited data.
method Bayesian update to Event Categorization Matrix model with Bayesian Decision Theory.
result Consistent gains in overall accuracy and lower false negative rates.
Proposes OC4Seq for detecting anomalies in discrete event sequences.
problem Challenges in detecting anomalies in discrete event sequences, including data imbalance, discrete events, and sequential nature.
method Integrates anomaly detection with recurrent neural networks (RNNs) to embed sequences into latent spaces and designs a multi-scale RNN framework to capture multi-scale sequential patterns.
result OC4Seq consistently outperforms various baselines on three benchmark datasets.
Proposes a deep neural network for event intensity estimation.
problem Modeling irregular event sequences with historical dependencies.
method Non-parametric deep neural network with multi-channel RNN and fake event epochs.
result Outperforms state-of-the-art baselines on model fitting tasks.
New method corrects bias in event study analysis using machine learning.
problem Bias in conventional event study analysis mechanisms.
method Topological machine-learning approach with self-organizing map (SOM).
result Identifies factors of abnormal stock returns and depicts event clusters.
Study shows pre-event L2 liquidity state predicts crypto futures liquidity better than event labels.
problem Understanding how crypto futures liquidity changes over time.
method Combining L2 order book data, trade-flow records, and macro-event windows to define discrete liquidity-state transitions and evaluate models.
result Pre-event L2 liquidity state predicts post-event liquidity regimes better than event labels, and order flow adds value only when layered on top of the state model.
CyPhERS provides real-time event info for CPSs, avoiding downtime.
problem Real-time event identification in CPSs is challenging due to complex interdependencies and rare events.
method CyPhERS integrates cyber and physical components, generating event signatures for known and unknown events.
result Event signatures provide relevant and inferable information on both known and unknown event types.
Theory reconstructs network connectivity from event timings.
problem Reconstructing network connectivity from incomplete continuous-time data.
method Linearizes event space mapping to reveal direct influences.
result Reveals synapse presence and inhibitory/activating nature.
Modeling solar ramping events with spatio-temporal point processes.
problem Predicting solar ramping events influenced by weather conditions.
method Novel spatio-temporal categorical point process model.
result Effective modeling of spatio-temporal correlations in solar ramping events.
Neural model uses deductive database to predict events from past patterns.
problem Difficulty in predicting future events from past patterns when event types are large.
method Temporal deductive database with rules to prove facts from other facts and past events. Neural nets model fact states and probabilities.
result Neural models derived from concise Datalog programs improve prediction by encoding domain knowledge.
NESA learns user preferences and calendar contexts for efficient event scheduling.
problem Challenges in understanding user preferences and complex calendar contexts for automated event scheduling.
method Leverages deep neural networks to learn user preferences and calendar context from raw online calendars.
result Significantly outperforms previous models in personal and multi-attendee event scheduling tasks.
UNHaP removes noise from physiological events using Hawkes processes.
problem Challenges in identifying true events from spurious ones in physiological signal analysis.
method UNHaP uses marked Hawkes processes to distinguish and unmix true events from noise.
result UNHaP significantly reduces false detection rates and enhances event understanding.