arXiv research

This research uses machine learning to approximate ideal and hotelling observer performance for binary signal detection.

Deep learning improves GW signal detection efficiency and robustness.

New method approximates Ideal Observer using GANs and MCMC.

Machine learning helps create accurate models of neutron star postmerger signals.

Bayesian Neural Networks detect gravitational wave events with high accuracy and real-time potential.

Detects corruption in agentic models during execution.

Paper explores supervised learning methods to approximate ideal observer for joint signal detection and localization.

New method learns signals from binary measurements, surpassing existing techniques.

Multipath is among the major sources of errors in precise positioning using GPS and continues to be extensively studied. Two Fast Fourier Transform (FFT)-based detectors are presented in this paper as GPS multipath detection techniques. The detectors are formulated as binary hypothesis tests under the assumption that t…

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidime…

QNNs can't distinguish binary signals from their negations, revealing a new symmetry.

Machine learning for entropy calculation from binary signals.

The paper introduces a method to learn local maxima from unlabeled data.

Convolutional neural networks show promise for flagging potential gravitational-wave signals.

The paper proposes a least squares method for binary compressive sampling with low intrinsic dimension signals.

Paper extends FOFC algorithm to work with mixed data types.

The classic N p chart gives a signal if the number of successes in a sequence of inde- pendent binary variables exceeds a control limit. Motivated by engineering applications in industrial image processing and, to some extent, financial statistics, we study a simple modification of this chart, which uses only the most …

EagleEye detects localized density anomalies in multivariate data.

Binary encoding enables neural networks to extrapolate periodic functions.

We present a second iteration of a machine learning approach to static code analysis and fingerprinting for weaknesses related to security, software engineering, and others using the open-source MARF framework and the MARFCAT application based on it for the NIST's SATE IV static analysis tool exposition workshop's data…

Novel SCUSUM detects weak spatial signals more efficiently.

Optimizes signal detection in particle physics by decorrelating classifiers.

Study robust learning of Lipschitz functions under corrupted binary signals.

New ML-based detection improves PMH signal detection in load-modulated MIMO systems.

We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace. This is a matched subspace detection problem. Tensor matched subspace detection problem is more challenging because of the intertwined signal dimensions. We s…

Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is responsible for sudden cardiac arrests. Thus, various algorithms have been developed to predict VF from Electrocardiogram (ECG), which is a binary classification problem. In the literature, we find a number of algorithms based on signal processing…

Paper uses machine learning to detect dark matter subhalos in simulated Gaia DR2 data.

BIND removes background noise from binary matrices, improving detection accuracy and fairness.

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…

Neural networks can detect weak patterns hidden in noise.

Detects graph topology changes from noisy signals using prior spectral information.

ABC uses autoencoders for better anomaly detection.

Study detects signal in financial stock correlations using phase-ordering kinetics.

We show linear XOR classification is possible and propose equality separation for anomaly detection.

Traditionally, community detection in graphs can be solved using spectral methods or posterior inference under probabilistic graphical models. Focusing on random graph families such as the stochastic block model, recent research has unified both approaches and identified both statistical and computational detection thr…

Improves signal detection in non-Gaussian noise using transformed data.

Conformal prediction improves signal detection accuracy in railway images.

Generalized change-point detection using various binary models.

Deep learning models detect nanopore translocation events with high accuracy.

Optimal test for detecting signal in noisy matrix model.

Paper studies signal detection in noisy environments with limited communication.

Better signal detection in undersampled data using joint and cross covariances.

Matrix factorization is a key tool in data analysis; its applications include recommender systems, correlation analysis, signal processing, among others. Binary matrices are a particular case which has received significant attention for over thirty years, especially within the field of data mining. Dictionary learning …

Paper proposes a method to detect adversarial examples that can resist norm-constrained attacks.

Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the source of the problem that produced the anomaly is also essential. This is particularly the case in aircraft engine health monitoring where detecting early signs of failure (anomalies) and helping the engine …

The paper detects entrance positions using Wi-Fi and GPS signals.

Constructs Gabor frames for curved manifolds to detect boundaries.

The paper detects changes in graph signal means offline.