DeepSource uses deep learning to detect sources in radio interferometry images.
problem Challenging point source detection at low signal-to-noise in radio interferometry images.
method Convolutional neural networks to enhance SNR, dynamic blob detection.
result DeepSource achieves essentially perfect purity and completeness down to SNR = 4, outperforming PyBDSF.
Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical models for point pattern data have not received much attention. This paper proposes the modelling of point pattern…
New method detects change points in quasi-periodic signals without supervision.
problem Detecting change points in complex, non-harmonic signals.
method Optimal transport theory, topological analysis, bootstrap procedure.
result Successfully detects abnormal cardiac cycles in various arrhythmias.
ABACUS detects changes in multivariate data without labels.
problem Challenging to detect changes in correlated multivariate data.
method Bayesian source separation with multi-level sparsity.
result Competitive or superior performance in simulations and real-world applications.
A new compressed sensing system speeds up PSTE detection.
problem Accurately detecting fast, small optical events (PSTEs) is challenging.
method Developed a novel compressed sensing algorithm for rolling shutter systems.
result Accurately recovered PSTEs with spatial undersampling, showing speed and quality advantages.
Method detects if text is generated by a language model with watermarks.
problem Detecting if text is generated by a language model with watermarks.
method Randomization tests and change point detection techniques.
result Method ensures Type I and Type II error control and accurately identifies watermarked sub-strings.
Detecting a change point is a crucial task in statistics that has been recently extended to the quantum realm. A source state generator that emits a series of single photons in a default state suffers an alteration at some point and starts to emit photons in a mutated state. The problem consists in identifying the poin…
Deep learning detects software vulnerabilities from source code.
problem Automated detection of software vulnerabilities in source code.
method Deep feature representation learning on lexed source code.
result Deep learning can effectively detect software vulnerabilities.
A registration-free framework monitors shape and color in 4D point clouds.
problem Monitoring shape and color changes in complex parts without registration.
method Laplace-Beltrami operator spectral properties for geometric and color feature capture; combined monitoring scheme for shape and color anomalies.
result Effective detection of shape deformations and color anomalies without registration or mesh reconstruction.
DMD separates mixed time series with uncorrelated components.
problem Separating mixed time series with uncorrelated components.
method Dynamic Mode Decomposition (DMD) applied to a data matrix of mixed time series.
result DMD can approximate the mixing matrix of uncorrelated time series.
Unified model detects transferable variables and source data in high-dimensional linear regression.
problem Scarcity of target data and heterogeneity of source and target data distributions.
method UTrans model, estimation error bounds, hypothesis testing for source detection.
result UTrans achieves lower estimation and prediction errors than existing methods.
Paper proposes a technique to detect and predict sources of contaminants in complex systems.
problem Difficulty in identifying sources of contaminants in coupled natural and human systems.
method Developed a technique for simultaneous source detection and prediction.
result Outperforms other approaches in detecting potential groundwater contamination.
Study improves model fit by transferring info from related datasets.
problem Improving model fit on target data using source data.
method Proposes a transfer learning algorithm for GLMs, derives error bounds, and introduces detection of informative sources.
result Theoretical and practical improvements over classical methods in high-dimensional GLM settings.
Study shows latent space OOD detection isn't a reliable proxy for model performance.
problem Evaluating and interpreting deep learning systems on real-world data.
method Empirical investigation of latent space OOD detection and classification accuracy using SAR datasets.
result OOD detection cannot be used as a proxy measure for model performance.
The paper presents a method for sound event localization and detection using CRNN models.
problem Sound event localization and detection in complex environments.
method Consecutive ensemble of CRNN models for estimating event onset, offset, direction of arrival, and classification.
result The proposed method outperforms other participants in the DCASE2019 task3.
Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle, emerging irregularities. Additionally, recent dete…
Study efficient resource allocation for detecting extreme values.
problem Efficiently allocate limited resources to detect extreme values in various fields.
method Proposes ExtremeHunter algorithm for sequential resource allocation under limited feedback.
result Demonstrates ExtremeHunter outperforms oracle policy in detecting extreme values.
Change detection (CD) in time series data is a critical problem as it reveal changes in the underlying generative processes driving the time series. Despite having received significant attention, one important unexplored aspect is how to efficiently utilize additional correlated information to improve the detection and…
Much information available on the web is copied, reused or rephrased. The phenomenon that multiple web sources pick up certain information is often called trend. A central problem in the context of web data mining is to detect those web sources that are first to publish information which will give rise to a trend. We p…
TransNet improves community detection on target networks using privacy-preserved source networks.
problem Community detection on sensitive network data with privacy constraints.
method Spectral clustering framework leveraging locally distributed privacy-preserved auxiliary networks via randomized response and adaptive weighting.
result TransNet delivers strong gains in community detection across various privacy levels and heterogeneity patterns.
HMN detects fake faces using memory networks and visual cues.
problem Detecting fake faces from unseen manipulation types.
method Hierarchical Memory Network (HMN) architecture.
result Superior performance in fake and fraudulent face detection.
Framework detects fake news using weak social signals from multiple sources.
problem Lack of annotated data for early fake news detection.
method Jointly uses weak social signals and clean data to train deep neural networks in a meta-learning framework.
result Framework outperforms state-of-the-art baselines for early fake news detection.
Adaptive object detection method synthesizes target domain images from source domain images.
problem Large domain gap between source and target domains in object detection.
method Cross-domain CutMix with adversarial learning.
result Higher accuracy in different domain settings compared to conventional methods.
WISER detects watermarked segments in text via epidemic change-point analysis.
problem Identifying watermarked segments in mixed-source texts.
method Epidemic change-point perspective, WISER algorithm.
result WISER outperforms state-of-the-art methods in accuracy and speed.
We propose an algorithm for detecting patterns exhibited by anomalous clusters in high dimensional discrete data. Unlike most anomaly detection (AD) methods, which detect individual anomalies, our proposed method detects groups (clusters) of anomalies; i.e. sets of points which collectively exhibit abnormal patterns. I…
New mechanism detects overlap density for weak-to-strong generalization.
problem Understanding what aspects of data enable weak-to-strong generalization.
method Data-centric mechanism and overlap detection algorithm.
result Overlap density is a key factor in weak-to-strong generalization.
BLISS detects and separates astronomical sources quickly and accurately.
problem Detecting and separating overlapping astronomical sources in large images.
method Bayesian Light Source Separator (BLISS) using deep generative models and variational inference.
result BLISS can process megapixel images in seconds and produce highly accurate catalogs.
PyODDS automates outlier detection for new data sources.
problem Manual outlier detection is inefficient and domain-specific.
method Automated end-to-end system with Apache Spark and database support.
result PyODDS optimizes outlier detection pipelines automatically.
This work tackles robust multi-source domain adaptation under label shift.
problem Label shift and data contamination in multi-source domain adaptation.
method Domain-weighted empirical risk minimization framework with refinement procedure.
result The proposed method achieves superior performance in multi-category classification problems.
A framework for flagging content with limited data.
problem Content flagging with scarce target-language data.
method Nearest-neighbor architecture using Transformer representations.
result Significant performance improvements over prior work.
New PCA method handles multiple datasets and detects sparse patterns robustly.
problem Handling multi-source data with sparse and outlier-robust PCA.
method Developed a regularization problem with a penalty for structured sparsity and outlier resistance.
result The method detects global and local patterns across multiple data sources robustly.
Backdoors can be implanted in neural models of source code, and we detect and remove them.
problem Vulnerability of neural models to backdoors in source code.
method Defined and implemented various backdoor classes, adapted robust statistics algorithms, and detected poisoned data through spectral signatures.
result Demonstrated the ease of injecting and removing backdoors in neural models of source code.
Hybrid neural network infers states from black-box systems.
problem Inference of state models in black-box systems without source code instrumentation.
method Convolutional and recurrent layers learn correlations between time series signals.
result Improves state change point detection by up to 102%.
Improved malware detection by adding auxiliary loss terms to a neural network.
problem Malware detection accuracy with a single label.
method Fit deep neural networks to multiple auxiliary prediction targets derived from metadata.
result Significant improvement in detection performance, reducing false negatives by 42.6% at a low false positive rate.
ACE explains security anomaly detection models through feature contributions.
problem Understanding which features contribute to security anomalies.
method Regression framework to locally approximate anomaly scores.
result Identifies correct contributing features in synthetic data and real data.
CNN detects multipath in GNSS signals using correlator output.
problem Multipath contamination errors in GNSS signals.
method Deep Convolutional Neural Networks (CNN) applied to GNSS correlator output.
result CNN accurately detects multipath in GNSS signals.
Study detects and mitigates stealthy DDoS attacks in IoT networks.
problem Stealthy DDoS attacks in IoT networks.
method Anomaly-based IDS for timely detection and mitigation.
result Demonstrated capability of detecting and mitigating small attack size per source.
New scheme detects anomalies in time series data quickly and accurately.
problem Detecting anomalies in time series data with multi-seasonality and unlabeled data.
method Prediction-driven, unsupervised anomaly detection scheme combining decomposition and inference.
result Our scheme outperforms existing algorithms in AUC metric while maintaining efficiency.
FAKTA automates fact checking across media sources.
problem Automating fact checking across diverse media sources.
method Unified framework integrating document retrieval, stance detection, evidence extraction, and linguistic analysis.
result FAKTA predicts factuality and provides evidence for claims.
Deep learning model detects and corrects outliers in crowd-sourced weather data.
problem Data quality issues in crowd-sourced weather data.
method Bayesian deep learning approach with Gaussian-uniform mixture density network.
result Automated outlier detection in spatio-temporal environmental modeling.
Proposes deep learning method for GCI detection from pathological speech.
problem Detecting glottal closure instants (GCI) in pathological acoustic speech.
method Convolutional neural network with fused deep acoustic speech and linear prediction residual features.
result Significantly better than state-of-the-art methods in GCI detection.
New method controls false detections in brain activity localization.
problem Statistical control of false detections in brain activity localization.
method Adapted Lasso estimator for spatio-temporal MEG/EEG data.
result Offers statistical guarantees and adaptive method for thresholding.
Paper tackles cross-modal anomalies in multi-source data.
problem Detect anomalies in multi-modal data where patterns are inconsistent across different sources.
method Proposes a deep structured anomaly detection framework.
result Demonstrates effectiveness on real-world datasets.
GCAN detects fake news on social media with explanations.
problem Detecting fake news on social media with explanations.
method Graph-aware Co-Attention Networks (GCAN).
result GCAN significantly outperforms state-of-the-art methods in accuracy.
Online CPD for weighted and directed graphs using RDPG model.
problem Monitoring and detecting changes in weighted and directed graph data.
method Spectral embeddings of RDPG models for online updates and error-rate control.
result A lightweight online CPD algorithm with improved detection resolution and delay.
Paper tackles open set domain adaptation by detecting unknown classes.
problem Adapting to target domains with unknown classes when label spaces partially overlap.
method Instance-level reweighting strategy combined with Extreme Value Theory for unknown class detection.
result Proposed method outperforms state-of-the-art models on conventional datasets.
Paper proposes MLPCD for protein community detection in large PPI networks.
problem Identifying reliable protein communities from large-scale PPI networks.
method Integrates Gene Expression Data and uses Multi-source Learning with cloud computing.
result Demonstrates superior performance compared to existing methods.
Anomaly detection aids in labeling fast-running processes for machine learning.
problem Manual labeling of fast-running processes for machine learning models.
method Anomaly detection to assist in labeling data, specific metrics for model validation.
result Possibility to manually classify data for training machine learning models.