A new algorithm efficiently partitions images without seeds or thresholds.
problem Efficiently partitioning images without explicit seeds or thresholds.
method Mutex Watershed algorithm that incorporates both attractive and repulsive cues.
result Determines optimal segments without seeds or thresholds, solving NP-hard problem.
Paper studies gradient fields from discrete Morse functions for watershed-cut computation.
problem Computing watershed-cuts from discrete Morse functions.
method Discrete Morse Theory and simplicial stacks.
result Minimum Spanning Forest of dual graph is induced by gradient vector field.
New method clusters hydrological and sediment data for storm event analysis.
problem Analyzing storm events for water quality constituents like turbidity.
method Multivariate time series clustering of river discharge and sediment data.
result Clusters differ from 2-D hysteresis loop classifications.
CNN automates vitiligo lesion segmentation quickly and accurately.
problem Manual segmentation of vitiligo lesions is time-consuming and inconsistent.
method U-Net architecture with modified contracting path, followed by watershed algorithm refinement.
result CNN achieves 73.6% Jaccard Index, significantly outperforming state-of-the-art methods.
LSTM model predicts climate impacts on floods and droughts.
problem Predicting climate impacts on individual watersheds is challenging.
method Large-scale LSTM training on extensive data sets.
result LSTM model outperforms state-of-the-art models in predicting extreme flows.
Study compares different superpixel methods for explaining CNNs in biological images.
problem Transparency of deep learning models in image classification.
method Compared Felzenszwalb, SLIC, and Compact-Watershed superpixel methods on explaining CNNs.
result Compact-Watershed method most closely matches human-referenced relevance areas.
Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have …
In the study of the curve shortening flow on general closed curves, Abresch and Langer posed a conjecture that the homothetic curves can be regarded as saddle points between multi-folded circles and some singular curves. In other words, these homothetic curves are the watershed between curves with a nonsingular future …
Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated by assessing the potential environmental threat of coal mining through increased sulfate concentrations in river networks, which do not belong to any simple …
CauSTream forecasts streamflow by integrating causal graphs for better interpretability.
problem Streamflow forecasting lacks interpretability and generalization due to fixed causal models.
method CauSTream learns causal graphs for meteorological forcings and routing dependencies.
result CauSTream outperforms existing methods, especially at longer forecast windows.
AGENTICAITA uses AI agents to autonomously trade markets without human intervention.
problem Inability of traditional trading systems to adapt to market complexity.
method Introduces an agentic AI framework with specialized LLM agents reasoning, negotiating, and acting.
result Demonstrated operational correctness and non-trivial inter-agent negotiation in live market conditions.
LSTM model predicts rainfall runoff with high temporal resolution.
problem Accurate and efficient rainfall runoff simulations for flood risk management.
method Data-driven rainfall runoff model using Long-short-Term-Memory (LSTM) networks.
result LSTM model achieves high-resolution discharge predictions with improved performance.