A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Hidden Markov Models detect hand gestures from wearable sEMG signals.
ANODE uses neural density estimation for anomaly detection in physics.
Method detects new physics signals without prior knowledge.
Paper develops SKPD framework for signal region detection in image regression.
Detecting weak clustered signal in spatial data is important but challenging in applications such as medical image and epidemiology. A more efficient detection algorithm can provide more precise early warning, and effectively reduce the decision risk and cost. To date, many methods have been developed to detect signals…
A new VAD method uses respiration patterns from video to detect speech.
We introduce a novel approach for parallelizing MCMC inference in models with spatially determined conditional independence relationships, for which existing techniques exploiting graphical model structure are not applicable. Our approach is motivated by a model of seismic events and signals, where events detected in d…
Autonomous systems can be used to search for sparse signals in a large space; e.g., aerial robots can be deployed to localize threats, detect gas leaks, or respond to distress calls. Intuitively, search algorithms may increase efficiency by collecting aggregate measurements summarizing large contiguous regions. However…
Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthq…
Sleep disorders are implicated in a growing number of health problems. In this paper, we present a signal-processing/machine learning approach to detecting arousals in the multi-channel polysomnographic recordings of the Physionet/CinC Challenge2018 dataset. Methods: Our network architecture consists of two components.…
Improved fMRI activation detection for single-subject studies.
The paper analyzes convergence in SGD with momentum and proposes a diagnostic test.
New method combines simulations and data for anomaly detection.
Challenge uses unsupervised learning to detect new physics signals at LHC.
Understanding the connectivity in the brain is an important prerequisite for understanding how the brain processes information. In the Brain/MINDS project, a connectivity study on marmoset brains uses two-photon microscopy fluorescence images of axonal projections to collect the neuron connectivity from defined brain r…
This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. We assume that only one fault occurs at any one time and model the signal by two separate non-parametric Gaussian process model…
Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality and low signal-to-noise ratio of fMRI, embedding informative and robust brain regional fMRI representations for both graph-level classificati…
Objective The electrical characteristics of the EEG signals can be used for seizure detection. Statistical independence between different brain regions is measured by functional brain connectivity (FBC). Specific directional effects can't consider by FBC and thus effective brain connectivity (EBC) is used to measure ca…
Biodiversity monitoring using audio recordings is achievable at a truly global scale via large-scale deployment of inexpensive, unattended recording stations or by large-scale crowdsourcing using recording and species recognition on mobile devices. The ability, however, to reliably identify vocalising animal species is…
Proposes a method to quantify the reliability of salient regions in deep learning models using p-values.
Machine-learned anomaly detection in new-physics searches needs calibration and look-elsewhere correction
New method detects dynamical system changes in time series data.
OneFlow detects anomalies by finding a minimal volume region, outperforming other methods.
Optimizes signal detection in particle physics by decorrelating classifiers.
New method detects and locates changes in spatio-temporal point processes.
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…
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…
We introduce a new multi-dimensional nonlinear embedding -- Piecewise Flat Embedding (PFE) -- for image segmentation. Based on the theory of sparse signal recovery, piecewise flat embedding with diverse channels attempts to recover a piecewise constant image representation with sparse region boundaries and sparse clust…
Detects graph topology changes from noisy signals using prior spectral information.
Study detects signal in financial stock correlations using phase-ordering kinetics.
Paper presents a unique method to recover signals from their bispectrum.
With the recent advances in complex networks theory, graph-based techniques for image segmentation has attracted great attention recently. In order to segment the image into meaningful connected components, this paper proposes an image segmentation general framework using complex networks based community detection algo…
TIER uses extended strain data to improve gravitational wave detection sensitivity.
Improves signal detection in non-Gaussian noise using transformed data.
Paper uses machine learning to detect dark matter subhalos in simulated Gaia DR2 data.
Conformal prediction improves signal detection accuracy in railway images.
Deep learning models detect nanopore translocation events with high accuracy.
Phase Modulation on the Hypersphere (PMH) is a power efficient modulation scheme for the \textit{load-modulated} multiple-input multiple-output (MIMO) transmitters with central power amplifiers (CPA). However, it is difficult to obtain the precise channel state information (CSI), and the traditional optimal maximum lik…
A new method detects interactions in machine learning models.
Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e.g., fraud detection, climate analysis, or healthcare monitoring. We present an algorithm for detecting anomalous regions in multivariate spatio-temporal time-series, which allows for spotting the interesti…
Optimal test for detecting signal in noisy matrix model.
Paper studies signal detection in noisy environments with limited communication.
DI-sNMF uses data imputation to separate circumstellar signals in high contrast imaging.
Better signal detection in undersampled data using joint and cross covariances.
AUCRSS detects change points in partially observed multivariate autocorrelated data.
Constructs Gabor frames for curved manifolds to detect boundaries.
The paper detects changes in graph signal means offline.
Representation and classification of Electroencephalography (EEG) brain signals are critical processes for their analysis in cognitive tasks. Particularly, extraction of discriminative features from raw EEG signals, without any pre-processing, is a challenging task. Motivated by nuclear norm, we observed that there is …