Graphs represent gene segment organization, revealing complex interrelationships in a scrambled genome.
problem Understanding gene segment organization and interrelationships in a scrambled genome.
method Directed graphs representing gene segments and their relationships, with graph properties mapped to higher-dimensional space for analysis.
result Emerging star-like structures indicate complex interrelationships, including segments from multiple genes interleaving or overlapping.
Method learns graph-structured data segments using deep learning.
problem Segmenting data structured by an adjacency graph.
method Graph-structured contrastive loss for deep learning.
result Achieved state-of-the-art performance on 3D point cloud segmentation.
Simplified interactive image segmentation using kNN graphs.
problem Interactive image segmentation with user-provided labels.
method Undirected kNN graphs for label propagation.
result Effective interactive segmentation with significant accuracy.
SEG-BERT improves graph instance learning by adapting GRAPH-BERT.
problem Graph instance representation learning challenges due to diverse sizes and node order.
method Adapted GRAPH-BERT with a segmented architecture to handle graph node orderlessness and diverse sizes.
result SEG-BERT outperforms comparison methods on six out of seven benchmark datasets.
Method segments graphs to estimate network models using power graph fused lasso.
problem Estimating non-parametric network models from noisy data.
method Power graph fused lasso (PGFL) for graph segmentation.
result PGFL achieves optimal error rate for graphon estimation under subGaussian noise.
This study proposes a graph partitioning method to improve spatial prediction models.
problem Improving interpretability of spatial prediction models in industries.
method Graph partitioning problem to minimize within-segment variances, formulated as mixed-integer quadratic programming.
result Approximation scheme efficiently identifies spatial segments, improving computational efficiency.
Graph convolutional networks refine organ segmentation using uncertainty analysis.
problem Challenges in organ segmentation due to variability and tissue similarity.
method Uncertainty analysis of graph convolutional networks for semi-supervised learning.
result Improved segmentation accuracy (1% for pancreas, 2% for spleen) compared to state-of-the-art methods.
Novel graph-based approach segments financial integration eras.
problem Assessing worldwide financial integration using data patterns.
method 3-step approach combining graph-based representations and optimization.
result Endogenous stable eras of world-wide financial integration found.
New method segments power grids in real time for easier control.
problem Segmenting large-scale power grids for real-time control.
method Guided machine learning approach with Influence Graph and community detection.
result Promising and original interpretable results on various grids.
We introduce a new numerical knot invariant, termed the \textit{segment number}, which is derived from partitioned knot diagrams subject to specific over/under-crossing constraints. We prove that a knot is non-trivial if and only if its segment number is at least 3. Furthermore, we investigate the structural properties…
Novel framework provides statistical significance for image segmentation results.
problem Evaluating the reliability of individual image segmentation results.
method Selective inference to account for segmentation bias in p-value computation.
result Valid p-values for segmentation results, accounting for bias.
DGE learns event representations from image sequences without manual annotations.
problem Data hunger and domain adaptation issues in self-supervised learning for temporal segmentation.
method Dynamic Graph Embedding (DGE) learns event representations by iteratively updating a graph and its embedding.
result DGE achieves robust temporal segmentation on benchmark datasets, outperforming state-of-the-art methods.
TokenCut detects and segments objects in images and videos without supervision.
problem Detecting and segmenting salient objects in images and videos without labeled data.
method Graph-based approach using self-supervised transformer features and Normalized Cut algorithm.
result Achieves state-of-the-art results on various detection and segmentation tasks.
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.
New Karger-like algorithms solve graph cuts, useful for image segmentation.
problem Finding minimum cuts in graphs and graph-based semi-supervised learning.
method Extensions of Karger's contraction algorithm for s-t-mincut and normalized cut problems. result Simple new algorithm based on Karger's original, yields linear runtime and interpretable potential.
EvoNet predicts events in time-series data by evolving state graphs.
problem Predicting events in time-series data with interpretable patterns.
method Evolutionary State Graph (ESG) and EvoNet model.
result EvoNet outperforms baselines and provides insights into event predictions.
Efficient superpixel method for real-time segmentation.
problem Real-time superpixel generation for computer vision tasks.
method Two-stage graph-based framework with Deep Affinity Learning and Hierarchical Entropy Rate Segmentation.
result HERS produces superpixels in near real-time.
Efficient method for video segmentation using spatio-temporal graph inference.
problem Efficient video segmentation with deep learning.
method VideoGCRF method that couples neuron decisions across space and time, using deep Gaussian Conditional Random Fields.
result Efficient and end-to-end trainable inference on spatio-temporal graphs for video segmentation.
This paper introduces a novel technique to track structures in time evolving graphs. The method is based on a parameter free approach for three-dimensional co-clustering of the source vertices, the target vertices and the time. All these features are simultaneously segmented in order to build time segments and clusters…
We present two graph-based algorithms for multiclass segmentation of high-dimensional data. The algorithms use a diffuse interface model based on the Ginzburg-Landau functional, related to total variation compressed sensing and image processing. A multiclass extension is introduced using the Gibbs simplex, with the fun…
Innovative neural networks reduce memory usage for efficient, accurate segmentation.
problem Efficiently segmenting large graphs with limited memory.
method Iterative neural networks with loops and multiple outputs.
result State-of-the-art semantic segmentation results on demanding datasets.
This paper introduces a novel technique to track structures in time varying graphs. The method uses a maximum a posteriori approach for adjusting a three-dimensional co-clustering of the source vertices, the destination vertices and the time, to the data under study, in a way that does not require any hyper-parameter t…
Graph neural network improves SOH estimation of lithium-ion batteries.
problem Accurate SOH estimation requires alignment of statistical distributions between training and testing datasets.
method Graph convolutional networks (GCNs) with anomaly detection for selecting discharge voltage segments.
result Achieves precise SOH estimation with a root mean squared error of less than 1%.
GCNs adapted for road networks improve performance on edge prediction tasks.
problem Improving machine learning on road networks for edge prediction tasks.
method Introducing Relational Fusion Network (RFN) for road networks.
result RFN outperforms state-of-the-art GCNs on road segment regression and classification tasks.
Algorithm selects optimal segment for physiological signal analysis.
problem Physiological signals are often corrupted by noise, requiring selective analysis.
method Combines deep neural networks for signal analysis and combinatorial optimization for segment selection.
result Significant improvement in system performance, e.g. 2.4% increase in sensitivity for heart sound segmentation.
A framework uses complex networks for image segmentation.
problem Over-segmentation in image segmentation.
method Initial segmentation, adaptive network construction, community detection.
result The proposed framework improves segmentation performance.
Generative model extracts road networks from images.
problem Extracting road networks from image data.
method Developed Generative Graph Transformer (GGT) for image-conditioned graph generation.
result Demonstrated effective road network generation from semantic segmentation data.
Novel graph-based method detects R-peaks in noisy ECG signals without preprocessing.
problem Detecting R-peaks in noisy ECG signals for real-time analysis.
method Graph-constrained Changepoint Detection (GCCD) approach.
result GCCD achieves high sensitivity, positive predictivity, and low detection error rate.
We describe a new optimization scheme for finding high-quality correlation clusterings in planar graphs that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on the energy of the optimal correlation clustering that are typically fast to compute and tight in practice. We demonstrate our a…
Algorithms based on spectral graph cut objectives such as normalized cuts, ratio cuts and ratio association have become popular in recent years because they are widely applicable and simple to implement via standard eigenvector computations. Despite strong performance for a number of clustering tasks, spectral graph cu…
New bounds for causal effect identification in time series graphs with latent confounders.
problem Identifying causal effects in time series graphs with latent confounders over unbounded time intervals.
method Applying the Causal Identification algorithm to a constant-size segment of the time series graph.
result A bound on the number of past time steps needed for causal effect identification.
We propose and analyze a constrained level-set method for semi-automatic image segmentation. Our level-set model with constraints on the level-set function enables us to specify which parts of the image lie inside respectively outside the segmented objects. Such a-priori information can be expressed in terms of upper a…
Improved graph-based multiclass classification for multilayer data.
problem Efficient classification of multilayer data with limited labeled examples.
method Generalized diffuse interface methods applied to multilayer graphs, using spectral decomposition and fast matrix-vector products.
result Highly scalable and efficient classification for large, high-dimensional data sets.
Even though clustering trajectory data attracted considerable attention in the last few years, most of prior work assumed that moving objects can move freely in an euclidean space and did not consider the eventual presence of an underlying road network and its influence on evaluating the similarity between trajectories…
Graph neural network predicts optimal coarse-grained mapping operators.
problem Optimal coarse-grained mapping operators selection for molecular dynamics simulations.
method Graph Neural Network (DSGPM) trained on expert-annotated data.
result DSGPM outperforms state-of-the-art methods in graph segmentation.
Graph neural networks improve charged particle tracking on FPGAs.
problem Charged particle trajectory determination in high interaction density conditions.
method Graph neural networks (GNNs) embedded in tracker data as graphs, classifying edges as track segments.
result GNNs implemented on FPGAs for charged particle tracking, enabling future HL-LHC experiments.
Study of Poincaré-Reeb graphs for algebraic domains.
problem Characterizing geometric shapes of algebraic domains.
method Collapsing vertical segments to form Poincaré-Reeb graphs and analyzing their properties.
result Any transversal graph with specific properties can be realized as a Poincaré-Reeb graph.
In this paper, we study statistical classification accuracy of two different Markov field environments for pixelwise image segmentation, considering the labels of the image as hidden states and solving the estimation of such labels as a solution of the MAP equation. The emission distribution is assumed the same in all …
The paper proposes a model to learn street network representations directly from graphs.
problem Loss of detailed topological data in raster representations of street networks.
method Variational autoencoder with graph convolutional layers and a probabilistic fully-connected graph decoder.
result The model infers good representations directly from street networks, capturing both local structure and spatial distribution.
Connected flip graphs for triangulations on hyperbolic surfaces.
problem Connecting triangulations on hyperbolic surfaces via flips.
method Proving connectedness of flip graphs and giving bounds on edge flips.
result Flip graphs of geometric triangulations are connected.
We obtain a criterion for approximability by embeddings of piecewise linear maps of a circle to the plane, analogous to the one proved by Minc for maps of a segment to the plane. Theorem. Let S be a triangulation of a circle with s vertices. Let f be a simplicial map of the graph S to the plane. The map f is approximab…
New method for deep learning hierarchies like sequences and graphs.
problem Lack of formalism for dealing with hierarchical structures in deep learning.
method Express equivariant maps for hierarchies using combinations of building block symmetries.
result Demonstrates effectiveness on semantic segmentation of point-cloud data.
Study examines persistence diagrams in machine learning, proposing permutation tests.
problem Understanding the power and limitations of persistence diagrams in machine learning.
method Carried out experiments on graph and shape data, proposed permutation tests for persistence diagrams.
result Persistence pairing shows significant improvement in various tasks, but the most critical values are most discriminative.
Method estimates travel times on urban roads using Uber data.
problem Estimating travel times on urban roads where data is scarce.
method Graph representation, trip sampling, least-squares optimization.
result Estimates travel times on arterial roads using aggregated Uber data.
This paper analyzes various graph clustering methods and their applications.
problem Dividing graphs into homogeneous groups for diverse applications.
method Traditional and deep learning-based clustering methods are compared.
result Deep learning techniques improve clustering accuracy.
Diffuse interface methods have recently been introduced for the task of semi-supervised learning. The underlying model is well-known in materials science but was extended to graphs using a Ginzburg--Landau functional and the graph Laplacian. We here generalize the previously proposed model by a non-smooth potential fun…
Novel graph network learns hierarchical network structure.
problem Lack of information in hierarchical network topology.
method Hierarchical clustering for multiscale decomposition, graph convolutional layers.
result Competitive performance on citation network benchmark.
New method clusters directed graphs using Koopman operators.
problem Challenges in clustering directed graphs, especially complex eigenvalues and lack of cluster definition.
method Relate graph Laplacians to transfer operators and metastable sets in stochastic systems, derive clustering algorithms for directed and time-evolving graphs.
result Clusters can be interpreted as coherent sets, useful for analyzing transport and mixing processes.