DL-FUMI learns heartbeat patterns from BCG signals for precise heart rate estimation.
problem Estimating precise heart rates from ballistocardiogram signals with uncertainty.
method Multiple instance dictionary learning to learn heartbeat concepts from BCG signals.
result DL-FUMI's heartbeat concept achieves superior performance over comparison algorithms.
New model for time series classification from single example.
problem Classifying time series patterns from limited data.
method Developed a Hidden semi-Markov Model with variable state duration.
result Different representations of state duration have distinct strengths and weaknesses.
Deep neural networks classify five ECG arrhythmias with high accuracy.
problem Accurate classification of five ECG arrhythmias.
method Deep convolutional neural networks for transferable knowledge.
result Average accuracies of 93.4% for arrhythmia classification and 95.9% for MI classification.
Bayesian deep learning predicts emotion from heartbeat data.
problem Uncertainty in emotion prediction from physiological data.
method End-to-end deep learning model with Bayesian uncertainty estimation.
result Peak classification accuracy of 90% on benchmark datasets.
Reservoir computing models classify ECG signals for patient-adaptive monitoring.
problem Classifying ECG signals for patients with imbalanced heartbeat classes.
method Reservoir computing paradigm applied to recurrent neural networks (RNNs).
result Accurate patient-adaptive ECG classifier that handles imbalanced classes.
Bayesian model predicts emotion from fitness tracker heartbeat data.
problem Predicting emotional valence from consumer fitness tracker heartbeat data.
method End-to-end Bayesian deep learning model using PPG data.
result Peak F1 score of 0.7 for emotional valence classification.
Bayesian nonparametric method segments multi-sequence time series data.
problem Temporal segmentation of multi-sequence time series data into stationary segments.
method Gaussian process priors and nonparametric distribution for segment partitioning.
result Model effectively segments synthetic and real-time series data.
Paper proposes a deep learning model for real-time ECG signal segmentation.
problem Real-time analysis of large ECG datasets for tele-health monitoring.
method Combines CNN and LSTM for detecting heartbeats' waveforms.
result Achieved high sensitivity and precision in QRS detection.
Paper proposes Adversarial Oversampling for heart disease detection.
problem Imbalanced classes in heartbeats images classification.
method 2D Convolutional Neural Network with InfoGAN for synthetic oversampling.
result Proposed Adversarial Oversampling improves classifier performance for minority classes.
We present algorithms for the detection of a class of heart arrhythmias with the goal of eventual adoption by practicing cardiologists. In clinical practice, detection is based on a small number of meaningful features extracted from the heartbeat cycle. However, techniques proposed in the literature use high dimensiona…
System detects and predicts cardiac anomalies from ECG data.
problem Early detection of cardiac anomalies to reduce healthcare costs and mortality.
method Discrete Wavelet Transform (DWT) and Undecimated Wavelet Transform (UWT) for feature extraction; Bayesian Network Classifier for anomaly prediction.
result Average accuracy of 96.6% for PAC, 92.8% for MI, and 87% for PVC on real ECG datasets.
Paper presents DL models for ECG signal denoising.
problem Efficient denoising of ECG signals for wearable devices.
method CNNs, LSTM, RBM, filtering methods, wavelet-based technique.
result CNN model performs well for offline denoising.
Deep learning model detects and classifies arrhythmia from ECG signals.
problem Detecting and classifying abnormal heartbeats (arrhythmia) from ECG signals.
method Use of topological data analysis in a modular neural network architecture for generalization.
result Model achieves state-of-the-art performance in arrhythmia detection and classification.
This paper uses fuzzy C-Means clustering and sonification to analyze heart rate variability.
problem Identifying suitable features from HRV analysis for sonification.
method Unsupervised machine learning (fuzzy C-Means clustering) and sonification techniques.
result Improves sonification interpretability by selecting appropriate HRV features.
Efficient classifier with uncertainty bounds for safety-critical applications.
problem Lack of uncertainty bounds in high-accuracy classifiers for safety-critical tasks.
method Nadaraya-Watson estimator with frequentist bounds.
result Competitive accuracy and uncertainty bounds at reduced computational cost.
ECG signal learning predicts cardiovascular death risk.
problem Scarce positive ECG event examples and class imbalance.
method Multiple instance learning framework for raw ECG signals.
result Learned risk score outperforms existing metrics.
AI agents on social networks rarely engage in extended conversations.
problem Understanding the persistence of interactions in AI-agent social networks.
method Analysis of Moltbook, a social network of AI agents, using interaction half-life and spectral tests.
result Most comments on Moltbook receive a direct reply within seconds, indicating a ``fast response or silence'' regime.
Paper uses deep learning to suppress bones on chest X-rays.
problem Improving pathologies classification by suppressing bones on chest X-rays.
method Conditional Generative Adversarial Network (GAN) and Haar 2D wavelet decomposition.
result Achieves state-of-the-art performance on bone suppression.
TEASER improves early time series classification accuracy and speed.
problem Early and accurate classification of time series data.
method TEASER models eTSC as a two-tier classification problem, using a first-tier classifier to assess class probabilities and a second-tier to decide reliability.
result TEASER is two to three times faster at predictions than competitors while maintaining or improving accuracy.
Improved KAN model explains brain dynamics through edge learning and synaptic strength.
problem Explaining brain dynamics and frequencies in different brain regions.
method ELKAN (Edge Learning KNN) model with edge learning and trimming, inspired by brain science.
result ELKAN model outperforms KAN in explaining brain frequencies and dynamics.
Characterizes toroidally alternating knots topologically.
problem Defining and characterizing toroidally alternating knots.
method Extending Howie's characterization to toroidally alternating knots and providing necessary and sufficient conditions.
result Necessary and sufficient conditions for a knot to be toroidally alternating.
Paper characterizes generic transversality, improving on Mather's result.
problem Understanding and defining generic transversality.
method Characterization of transversality based on Mather's work.
result Improves on Mather's transversality result.
Study characterizes Einstein metrics in warped product spaces.
problem Characterizing Einstein metrics in warped product spaces.
method Local characterizations and global restatements of known results.
result Restated global characterizations of Einstein manifolds.
The study provides homological characterizations for Q-manifolds and l2-manifolds.
problem Density of maps in characterizing Q-manifolds and l2-manifolds. method Investigates weakening the density of Zn-maps and Z-maps to homological maps. result Obtains homological characterizations for Q-manifolds and l2-manifolds. The paper characterizes Alexander quandles of finite groups.
problem Characterizing Alexander quandles of finite groups.
method Using group theory and automorphism groups, the paper provides characterizations of Alexander quandles.
result Generalized Alexander quandles of finite groups are characterized in terms of automorphism groups and underlying groups.
No single parameter characterizes the learnability of probability distributions.
problem Finding a parameter to characterize the learnability of probability distributions.
method Analyzing various notions of learnability and showing impossibility results.
result No such parameter exists for characterizing learnability of probability distributions.
Generalizing Howie and Greene's characterization of alternating knots, we give a topological characterization of almost alternating knots.
Characterizes the OU matrix for up to 5 strands in braids.
problem Understanding the structure of braid diagrams through their matrices.
method Characterization of the OU matrix for up to 5 strands in braids.
result Standard form of the OU matrix for general braids of up to 5 strands is given and characterized.
Study provides concrete examples of knot slopes.
problem Finding explicit characterizing slopes for knots.
method Concrete examples for the (-2,3,7)-pretzel knot.
result Explicit characterizing slopes for the knot 12n242. Characterizes the sample complexity of list regression tasks.
problem Understanding the sample complexity of list learning tasks in regression.
method Introducing two combinatorial dimensions: k-OIG dimension and k-fat-shattering dimension.
result These dimensions characterize realizable and agnostic k-list regression.
Characterizes geodesic laminations on surfaces.
problem Understanding geodesic laminations on surfaces.
method Topological characterization of geodesic laminations.
result Proved a topological characterization of geodesic laminations.
Study characterizes geodesic ray transform on surfaces, isolating and separating sub-ranges.
problem Characterizing the range of the attenuated geodesic ray transform on surfaces.
method Isolating and separating sub-ranges of sums of functions and one-forms, deriving new inversion formulas.
result Range characterizations and new inversion formulas for geodesic ray transform.
We establish a characterization of adequate knots in terms of the degree of their colored Jones polynomial. We show that, assuming the Strong Slope conjecture, our characterization can be reformulated in terms of "Jones slopes" of knots and the essential surfaces that realize the slopes .For alternating knots the refor…
Study characterizes specific almost Kenmotsu metrics meeting Miao-Tam equation.
problem Characterizing almost Kenmotsu metrics.
method Characterization through Miao-Tam equation.
result Characterized specific almost Kenmotsu metrics.
In this paper, we give some characterizations for spacelike helices in Minkowski space-time. We find the differential equations characterizing the spacelike helices and also give the integral characterizations for these curves in Minkowski space-time.
Research characterizes critical points of scalar curvature functionals.
problem Characterizing critical points of scalar curvature functionals.
method Translation and analysis of a previous Russian paper.
result Provides insights into critical points of scalar curvature functionals.
We review several results related to the characterization of polyhedra in hyperbolic 3-space. In particular we present Rivin's theorem that gives a characterization of compact convex hyperbolic polyhedra, and Hodgson's proof of the Adreev's theorem. We also review the analogous characterization of ideal polyhedra, and …
It is of interest to characterize algebraically the dynamical types of isometries of the complex and quaternionic hyperbolic planes. In the complex case, such a characterization is known from the work of Giraud-Goldman. In this paper, we offer an algebraic characterization of the isometries of the two-dimensional quate…
Characterizes learnability of multioutput functions in various settings.
problem Learning multioutput function classes in batch and online settings.
method Characterizes learnability based on single-output restrictions.
result Complete characterization of learnability in multioutput classification and regression.
New research shows that many slopes are characterizing for satellite knots.
problem Characterizing slopes for satellite knots.
method Detailed examination of the JSJ decomposition of a surgery along a knot, combined with other authors' constraints on surgery slopes.
result Many non-integral slopes are characterizing for composite knots.
Characterizes conditions for quotient spaces of decompositions to be manifolds.
problem Conditions for quotient spaces of decompositions to be manifolds.
method Generalized characterizations of upper semi-continuity for decomposition into one for a class decomposition.
result Characterizations of necessary and sufficient conditions for quotient spaces of decompositions to be k-manifolds (k=1,2). Characterizes alternating link exteriors using cubed complexes.
problem Understanding the structure of alternating link exteriors.
method Introducing signed BW cubed-complexes and characterizing their homeomorphism to link exteriors.
result Characterization of alternating link exteriors in terms of cubed complexes.
Characterizes paths minimizing anisotropic lengths in Euclidean space.
problem Finding paths of minimal anisotropic length between points.
method Characterization through geometric connection to anisotropic isoperimetric set.
result Established a connection between minimizing paths and anisotropic isoperimetric geometry.
Characterizes a specific type of spacetime using vector fields.
problem Classifying a specific type of spacetime.
method Using vector fields to characterize 1+n doubly twisted spacetimes.
result Simple classification of 1+n doubly-twisted spacetimes.
The paper characterizes Conway-Coxeter friezes using rational links.
problem Characterizing Conway-Coxeter friezes of zigzag type.
method Characterization via rational links and application to Jones polynomial.
result Jones polynomial can be defined for Conway-Coxeter friezes of zigzag type.
Characterizes Kerr spacetimes using conformal methods.
problem Understanding Kerr spacetimes through conformal transformations.
method Conformally covariant characterization approach.
result Ideal characterization of Kerr spacetimes.
The paper aims to develop new combinatorial dimensions for bounded memory learning.
problem Characterize bounded memory learning using combinatorial dimensions.
method Proposes a candidate solution based on the SQ dimension of neighboring distributions and proves upper and lower bounds.
result Characterizes bounded memory learning in a specific parameter regime, matching equivalence between bounded memory and SQ learning.
Characterizes the range of a tensor field transform in Schwartz space.
problem Range characterization of a tensor field transform in Schwartz space.
method Differential and integral equations for characterizing the range in different dimensions.
result Range characterization for the operator on Schwartz space of rank m tensor fields.