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…
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…
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.
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.
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.
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.
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…
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.
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.
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.
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.
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.
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.
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…
New algorithms for topic modeling using conic geometry.
problem Unknown number of topics in nonparametric topic modeling.
method Analysis of topic simplex's concentration and angular geometry.
result Accuracy comparable to Gibbs sampler, fast compared to state-of-the-art techniques.
Automated brain CT image retrieval from traumatic brain injury cohorts using deep neural networks.
problem Manual image retrieval of whole brain CT scans from large clinical cohorts is time-consuming and resource-intensive.
method Proposes a deep convolutional neural network (dMIR) for automated classification of 2D montage images.
result Achieved high accuracy (f1=1.0) for validation and testing data sets.
LOUPE optimizes MRI under-sampling patterns for faster scans.
problem Accelerating MRI scans while maintaining image quality.
method End-to-end learning framework that trains on full-resolution scans.
result LOUPE-optimized masks yield superior reconstructions with 8x faster scans.
Deep learning method for brain CT scan anomaly labeling using nearest neighbors.
problem Automated anatomical labeling of brain CT scan anomalies.
method Combines local and global context, uses Relation Networks (RNs) for prediction, and employs nearest neighbors for training.
result Improved performance of Relation Networks (RNs) through nearest neighbors training strategy.
Radiological imaging of the prostate is becoming more popular among researchers and clinicians in searching for diseases, primarily cancer. Scans might be acquired with different equipment or at different times for prognosis monitoring, with patient movement between scans, resulting in multiple datasets that need to be…
A new knot invariant using tangle-valued 1-cocycles.
problem Creating a strong and calculable knot invariant.
method Constructing a non-trivial combinatorial 1-cocycle L that takes values in H0(Θ;Z) with the scan-property. result The Alexander tree is an isotopy invariant of knots, demonstrating the non-invertibility of specific knots.
Researchers use clustering to differentiate COVID-19 lung scans.
problem Identifying infected individuals with COVID-19.
method Applied unsupervised clustering techniques using PCA, K-Means++, and RCC.
result KM++ and RCC algorithms improved in clustering COVID-19 lung scans.
Six AI solutions accurately detect growth plate planes in mice bone scans.
problem Manual, time-consuming, and variable bone growth plate detection in micro-CT scans.
method Prepared and annotated a dataset of 3D μCT scans, organized a challenge, and developed six computer vision solutions.
result Achieved mean absolute error of 1.91±0.87 planes from ground truth.
Optimizes ASL-MRF scan design for precise brain hemodynamics quantification.
problem Fixing model parameters in ASL introduces bias, and multiparametric estimation degrades precision.
method Optimizes ASL labeling durations using Cramer-Rao Lower Bound (CRLB) and proposes a neural network regression framework.
result Improved precision in estimating multiple hemodynamic parameters from a single scan.
FlowScan models exchangeable data sets with flexible flow transformations.
problem Density estimation for exchangeable, non-i.i.d. data.
method Combines invertible flow transformations with a sorted scan.
result Achieves new state-of-the-art performance on point cloud and image set modeling.