We consider the change-point detection problem of deciding, based on noisy measurements, whether an unknown signal over a given graph is constant or is instead piecewise constant over two connected induced subgraphs of relatively low cut size. We analyze the corresponding generalized likelihood ratio (GLR) statistics a…
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48 results for “Graph scan statistic”
Novel method detects group differences in temporal data using scan statistics.
problem Detecting group differences in temporally evolving data with poor effect sizes.
method Parametric model for SPD matrix trends, generalized scan statistics for graph structures.
result Identifies scientifically interesting group differences not seen in full graph models.
The detection of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity monitoring in social networks. Beyond its wide applicability, graph structured anomaly detection serves as a case study in the difficulty of bal…
Polynomial-time test for detecting dense subgraphs in heterogeneous networks.
problem Detecting a planted community in heterogeneous networks.
method Proposes a polynomial-time test with a standard normal distribution null limiting distribution.
result The test is efficient and performs well in both simulations and real data.
A new algorithm optimizes graph-structured sparsity for nonlinear functions.
problem Optimizing sparsity-constrained optimization with graph-structured constraints.
method Graph-Structured Matching Pursuit (Graph-Mp) algorithm.
result Graph-Mp algorithm achieves strong convergence rate and approximation accuracy.
Proposes a continuous scan statistic to detect anomalies more accurately.
problem Binary region definitions limit the detection of smooth anomalies.
method Kernel Spatial Scan Statistic (KSS) that allows continuous point contributions.
result Efficient computation and high statistical power for detecting anomalous regions.
Paper reproduces a kernel-based scan B-statistic for online change-point detection.
problem Continuous detection of distribution changes in online data streams.
method Efficient kernel-based scan B-statistic for online change-point detection.
result Scan B-statistic outperforms parametric methods in challenging scenarios.
Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions. There are two common scan orders for the variables: random scan and systematic scan. Due to the benefits of locality in hardware, systematic scan is commonly used, even though most st…
New algorithm detects changes in high-dimensional data with mean and variance.
problem Challenges in detecting changes in high-dimensional data with mean and variance.
method Complete graph-based approach to detect changes of mean and variance from low to high-dimensional online data.
result The proposed method outperforms existing methods in terms of detection power.
A CNN on semi-regular meshes classifies brain diseases from MRI scans.
problem Classifying brain diseases from MRI scans.
method Developed a vertex-based graph CNN for semi-regular triangulated meshes.
result Vertex-based graph CNN outperformed spectral graph CNN in classifying MCI and AD.
Study reveals limits of detecting local geometry in random graphs.
problem Detecting local geometry in random graphs with hidden communities.
method Introduced model and used information-theoretic and computational limits to investigate detection.
result Detection threshold determined at for fixed .
Novel online graph-based method detects changes in high-dimensional data.
problem Challenges in detecting changes in high-dimensional data.
method Graph-based similarity measure derived from graph-spanning ratio.
result High detection power and controlled false alarm rate for high-dimensional data.
Detects events from unlabeled data using graph structure learning.
problem Detecting future events from unlabeled data.
method Proposes a novel framework using constrained and unconstrained subset scans, mean normalized log-likelihood ratio score, and efficient graph structure search.
result Shows faster and more accurate detection of events.
Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinementcs.CV
Automated labeling of intracranial arteries improves accuracy and efficiency.
problem Challenges in accurately labeling intracranial arteries due to variations and limited datasets.
method Graph Neural Network (GNN) combined with hierarchical refinement for improved accuracy.
result Achieved 97.5% node labeling accuracy on a testing set of 105 scans.
Detects anomalous patterns in non-independent data streams.
problem Low detection power for subtle, emerging irregularities in non-iid data.
method Combines Gaussian processes with subset scanning techniques.
result Powerful, interpretable methods for anomalous pattern detection.
A new method for choosing thresholds in data sequences without assuming distribution.
problem Choosing thresholds for random sequences without distributional assumptions.
method Data-driven threshold machine (DTM) that estimates three parameters of extreme value distributions and extremal index.
result DTM provides a reliable estimate of thresholds with robustness and computational efficiency.
Detects anomalous inputs in neural networks using subset scanning.
problem Detecting adversarial noise and out-of-distribution samples in neural networks.
method Subset scanning applied to neural network activations using non-parametric scan statistics.
result Identifies the most anomalous subset of node activations in neural networks.
Detects bias in classifiers using subset scan and parametric bootstrap.
problem Identifying significant predictive bias in classifiers.
method Subset scan method and parametric bootstrap.
result Detects subgroups with significant bias or poor fit.
Prototype for adaptive electron microscopy scans reduces dose and time.
problem Reduce electron microscopy scan time and dose with minimal loss.
method Adaptive partial scanning with reinforcement learning.
result Reinforcement learning trained neural network optimizes scan paths.
We consider the problem of detecting a tight community in a sparse random network. This is formalized as testing for the existence of a dense random subgraph in a random graph. Under the null hypothesis, the graph is a realization of an Erdös-Rényi graph on vertices and with connection probability ; under the …
Deep learning predicts SAH patient mortality from initial CT scans.
problem High mortality rates in SAH patients.
method CNN-based algorithm using transfer learning on CT scans.
result Model accurately predicts mortality (74% accuracy, 82% AUC).
Survey of robust clustering methods for hotspot detection.
problem Detecting false positives in spatial hotspot mapping.
method Statistically rigorous clustering techniques.
result Survey of models and algorithms for robust clustering.
Machine learning speeds BSM data interpretation at LHC.
problem Challenging to scan high-dimensional BSM theories due to expensive simulations.
method Machine learning to predict BSM theory parameters from data.
result Predicts natural SUSY events up to 4 orders of magnitude faster.
TAGCN improves graph CNN performance without approximation.
problem Performance loss in spectral graph convolutional neural networks.
method Topology adaptive graph convolutional network (TAGCN) with adaptive filters.
result TAGCN outperforms existing spectral CNNs on various datasets.
Compressed Sensing of Scanning Transmission Electron Microscopy (STEM) on Non-Rectangular Scansphysics.ins-det
New method for real-time reconstruction of sparse STEM images from non-rectangular scans.
problem Sparse sampling and non-rectangular scanning in STEM.
method General method for real-time reconstruction of sparsely sampled images from high-speed, non-invasive and diverse scanning pathways.
result Demonstrated on synthetic and experimental STEM data, achieving real-time reconstruction.
This study assesses the reproducibility of 1H-MRS scans across different vendors and sessions.
problem Lack of harmonization in magnetic resonance spectroscopy protocols among vendors.
method Analysis of CV and ICC for within- and between-sessions, and correlation coefficients for across machines.
result Metabolite concentrations are highly reproducible across different vendors and sessions.
New method learns community properties robustly against adversaries.
problem Learning community properties in noisy networks.
method Nonparametric, unsupervised, scalable graph scan procedure.
result Asymptotically correct estimation of normal user activity.
Statistical image reconstruction (SIR) methods are studied extensively for X-ray computed tomography (CT) due to the potential of acquiring CT scans with reduced X-ray dose while maintaining image quality. However, the longer reconstruction time of SIR methods hinders their use in X-ray CT in practice. To accelerate st…
BrainCast predicts whole-brain fMRI time series from short scans.
problem Short scans reduce fMRI data quality and statistical power.
method Spatio-temporal forecasting framework for fMRI time series.
result BrainCast improves fMRI time series quality and prediction.
SparseVM registers clinical 3D scans faster and more accurately.
problem Inaccurate and slow registration of sparse clinical 3D scans.
method Learning-based registration method tailored for clinical sparse MRI.
result Orders of magnitude faster and more accurate than existing methods.
Random scan CAVI converges linearly under log-concave assumptions.
problem Analyzing the convergence rate of random scan Coordinate Ascent Variational Inference (CAVI) under log-concave conditions.
method Building on previous work, we analyze the random scan version of CAVI using optimal transport geometry.
result We obtain tight linear convergence rates for the random scan version of CAVI.
New technique halves scan time for multi-echo MR images.
problem Slow acquisition of multi-echo magnetic resonance images.
method Structured deep dictionary learning for adaptive reconstruction.
result Scan time reduced by half compared to state-of-the-art.
Adapts scanning algorithm for odd Khovanov homology.
problem Computing odd Khovanov homology efficiently.
method Uses mapping cone construction instead of tensor product.
result Determines odd Khovanov homology of 3-strand torus links.
FUNSD dataset tackles noisy scanned forms, offering comprehensive annotations.
problem Extracting and structuring textual content from noisy scanned documents.
method Comprehensive dataset with real, fully annotated forms, including text detection, OCR, layout analysis, and entity linking.
result First publicly available dataset for form understanding, addressing challenges in noisy scanned documents.
New method tests weighted networks without thresholding, improving accuracy.
problem Testing and anomaly detection on weighted network data.
method Hierarchical Bayesian hypothesis testing framework for weighted networks.
result Method shows lower Type I error and higher statistical power compared to alternatives.
A novel multi-resolution cluster detection (MCD) method is proposed to identify irregularly shaped clusters in space. Multi-scale test statistic on a single cell is derived based on likelihood ratio statistic for Bernoulli sequence, Poisson sequence and Normal sequence. A neighborhood variability measure is defined to …
SCAN learns hierarchical visual concepts from unsupervised data.
problem Discovering coherent rules in natural world visual diversity.
method SCAN learns concepts through fast symbol association and disentangled visual primitives.
result SCAN generates diverse images from symbolic descriptions and manipulates visual concepts hierarchically.
Develops a new criterion for subgroup fairness in algorithmic decision support.
problem Identifying fair recommendations in algorithms despite group-level differences.
method IJDI criterion and IJDI-Scan approach to detect and mitigate disparities.
result Identifies significant disparities in recommendations across subpopulations.
Paper proposes a fast stability scanning method for future grid scenarios.
problem Capturing inter-seasonal variations in renewable generation.
method Novel feature selection algorithm and self-adaptive PSO-k-means clustering.
result Reduced computational burden up to ten times with acceptable accuracy.
Paper explores vulnerabilities in image authenticity detection methods, especially printing and scanning attacks.
problem Vulnerability of image authenticity detection models to printing and scanning attacks.
method Demonstrates and proposes a new machine learning model to counter these attacks.
result Proposed model outperforms state-of-the-art models when trained on images from a single printer.
Early detection and precise characterization of emerging topics in text streams can be highly useful in applications such as timely and targeted public health interventions and discovering evolving regional business trends. Many methods have been proposed for detecting emerging events in text streams using topic modeli…
Adaptive scan Gibbs sampler improves large-scale inference performance.
problem Efficiently updating large-scale online inference problems.
method Derives an adaptive scan Gibbs sampler that optimizes mini-batch size selection.
result Demonstrates superior performance compared to collapsed Gibbs sampler.
Using a Generative Adversarial Network for CT Normalization and its Impact on Radiomic Featureseess.IV
GAN normalizes CT scans for consistent radiomic feature values.
problem Variations in dose levels and slice thickness affect radiomic features sensitivity.
method Used a 3D generative adversarial network (GAN) to normalize reduced dose, thick slice images to normal dose, thinner slice images.
result GAN-based approach led to significantly smaller error in radiomic features.
Noise2Noise learns to restore images from noisy data alone.
problem Learning to restore images without clean data.
method Applying statistical reasoning to machine learning for image restoration using corrupted observations.
result It is possible to learn image restoration from corrupted data alone, achieving performance comparable to using clean data.
New Gibbs sampling method improves MCMC efficiency.
problem Improving efficiency of Gibbs sampling.
method Non-uniform random scan with selection probability optimization.
result Non-uniform scan improves mixing time of Markov chain.
SCAN divides deep neural networks into shallow classifiers for efficient deployment.
problem Explosive growth in storage and computation limits deep neural networks on edge devices.
method SCAN divides networks into shallow classifiers, uses attention modules and knowledge distillation, and employs a threshold-controlled scalable inference mechanism.
result SCAN achieves significant performance gain on CIFAR100 and ImageNet without hyper-parameter adjustments.
RADNET achieves radiologist-level accuracy in CT scan hemorrhage detection.
problem Automated detection of brain hemorrhages in CT scans.
method RADNET uses a 3D context-aware deep learning model with attention mechanisms.
result RADNET achieves 81.82% accuracy in hemorrhage prediction, comparable to radiologists.
Motivated by a range of applications in engineering and genomics, we consider in this paper detection of very short signal segments in three settings: signals with known shape, arbitrary signals, and smooth signals. Optimal rates of detection are established for the three cases and rate-optimal detectors are constructe…