CNNs improve ECG noise detection for better heart condition monitoring.
problem Signal noise in mobile ECGs hinders accurate diagnosis.
method Developed and trained a 16-layer CNN on a noise-annotated dataset.
result 16-layer CNN achieves 0.977 AUC on ECG noise detection.
This paper generalizes property (QT) to a broader class of groups.
problem Proving property (QT) for a wider range of groups.
method Using projection complex machinery and hierarchical hyperbolic groups.
result Established sufficient conditions for groups to have property (QT).
3-manifold groups have a property that allows them to act on quasi-trees.
problem Proving property (QT) for 3-manifold groups.
method Analyzing the geometry of 3-manifolds and using quasi-trees.
result Compact, orientable 3-manifold groups have property (QT) if they don't support Sol or Nil geometry.
Machine learning improves detection of Brugada Syndrome from ECGs.
problem Detecting Brugada Syndrome (BrS) from ECGs is challenging due to limited diagnostic criteria.
method Pipeline that reads and processes scanned ECG images, uses LSTM classifier to diagnose.
result The proposed pipeline distinguishes between ECG types and diagnoses BrS with high accuracy.
Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse…
This paper surveys RL methods for quantitative trading.
problem Challenges in sequential decision making for financial markets.
method Taxonomy of RL-based QT models and state of the art summary.
result RL can solve complex QT tasks.
DE-QT detects optimal Q-learning stopping points.
problem Information loss in Q-learning during prolonged training.
method Introducing DE-QT to detect entropy changes in Q-tables.
result DE-QT identifies the best stopping point for Q-learning.
CNN learns diabetic syndromes from patient records.
problem Syndrome differentiation in TCM is complex and lacks standardization.
method Multi-instance multi-task learning with CNN.
result Outperforms other methods on diabetes dataset.
AlphaStock uses deep learning to select winning stocks and avoid losers, balancing risk and return.
problem Balancing risk and return in stock selection, avoiding extreme losses, and making strategies interpretable.
method Integrates deep attention networks with a Sharpe ratio-oriented reinforcement learning framework, modeling asset interrelationships.
result AlphaStock selects stocks with high long-term growth, low volatility, and undervaluation, outperforming other strategies.
In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a longstanding challenge in robotic manipulation. In contrast to static learning behaviors that choose a grasp point and then execu…
The paper studies groups with proper actions on finite products of hyperbolic spaces.
problem Characterizing groups with proper actions on finite products of hyperbolic spaces.
method Lineal actions and central extensions of groups.
result Groups with property (PH) or (PH') have specific properties related to their actions on hyperbolic spaces.
QTNet uses deep reinforcement learning to automate trading strategies.
problem Handling noisy and high-frequency financial data, balancing exploration and exploitation.
method QTNet employs deep reinforcement learning (DRL) with imitative learning to autonomously formulate trading strategies.
result QTNet demonstrates proficiency in extracting robust market features and adaptability to diverse conditions.
Data mining revealed a cluster of economic, psychological, social and cultural indicators that in combination predicted corruption and wealth of European nations. This prosperity syndrome of self-reliant citizens, efficient division of labor, a sophisticated scientific community, and respect for the law, was clearly di…
Bayesian deep learning predicts satellite collisions.
problem Space debris poses planetary risk.
method Bayesian deep learning with LSTM networks.
result Predicts conjunction event evolution with uncertainties.
QT improves inference in complex PGMs with hidden variables.
problem Intractable learning and prediction errors in undirected PGMs with hidden variables.
method Query training (QT) learns a worse model to improve marginal inference.
result QT produces better marginals for a given inference algorithm than the original model.
Clusters of ACS patients identified for better therapeutic stratification.
problem Data-driven classification and subtyping of ACS patients for improved treatment.
method Outcome-driven clustering using a multi-task neural network with attention.
result Seven patient clusters with distinct characteristics and risk profiles identified.
Improved logistic regression for multi-omics data improves prediction and variable selection.
problem Predicting binary class labels from multi-omics datasets with varying characteristics.
method Two-step penalized logistic regression with separate variable selection for each data layer.
result Our approach selects more relevant predictors and achieves comparable prediction performance.
Study builds machine learning models to predict ICU patients' one-year mortality.
problem Improving mortality prediction accuracy in ICU patients with heart disease.
method Developed and evaluated various machine learning models using MIMIC-III database.
result Best models achieved 85.12% prediction accuracy and AUC of 0.901.
Generative AI decodes quantum codes without labeled data.
problem Efficient decoding of quantum error-correcting codes.
method Generative Transformers learn logical operators from unsupervised syndromes.
result Significantly better decoding accuracy than traditional methods.
Quantum time evolution exhibits rich physics, attributable to the interplay between the density and phase of a wave function. However, unlike classical heat diffusion, the wave nature of quantum mechanics has not yet been extensively explored in modern data analysis. We propose that the Laplace transform of quantum tra…
Olympic Games consistently exceed budgets, leading to unpredictable costs.
problem High costs and unpredictability of the Olympic Games.
method Statistical analysis of historical data to explain cost risks.
result Olympic costs follow a power-law distribution with infinite mean and variance.
Study uses LCA to identify ARDS sub-phenotypes improving predictive models.
problem Complex and heterogeneous nature of ARDS makes early recognition difficult.
method Applied latent class analysis to identify sub-groups, then built predictive models.
result Significantly improved prediction performance for two sub-phenotypes of ARDS.
The paper prices a new life insurance policy for couples, considering various contingent benefits.
problem Valuation of life insurance policies for dependent coupled lives.
method Employed the QP-rule combining real-world and risk-neutral measures for pricing. result Explicit pricing expressions for the new insurance policy were derived and analyzed.
Every torus knot can be represented as a Fourier-(1,1,2) knot which is the simplest possible Fourier representation for such a knot. This answers a question of Kauffman and confirms the conjecture made by Boocher, Daigle, Hoste and Zheng. In particular, the torus knot T(p,q) can be parameterized as x(t)=cos(pt), y(t)=c…
Safe-FinRL uses DRL for high-frequency stock trading, reducing bias and variance.
problem Challenges in applying DRL to high-frequency stock trading, especially bias and variance issues.
method Safe-FinRL separates financial time series into near-stationary short environments and uses Trace-SAC with a general retrace operator.
result Safe-FinRL reduces bias and variance significantly in near-stationary financial environments.
High-performance quantum codes decoded with minimal data.
problem Efficient decoding of linear-rate LDPC quantum codes.
method Tessellations of hyperbolic manifolds, Coxeter groups, and Galois fields.
result Achieved encoding rate of 13/72 with high performance.
Enhanced mortality prediction model using EHR and free-text data.
problem Predicting one-year mortality in AMI patients.
method Used deep learning models with word embedding features from free-text discharge summaries.
result Significant improvement in model performance (92.89% accuracy, 0.928 F-measure).
This paper automates mining of COVID-19 scholarly articles using machine learning.
problem Time-consuming and impractical manual extraction of relevant COVID-19 research articles.
method Used machine learning approaches, specifically clustering and parallel one-class support vector machines (OCSVMs), on the CORD-19 dataset.
result Parallel OCSVMs outperform other methods for both original and reduced feature space.
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.
DSM improves visual quality of fetoscopic videos by mosaicking.
problem Challenges in identifying all vascular connections during fetoscopy due to low visual quality and drift.
method Proposes a Deep Sequential Mosaicking (DSM) framework using deep learning techniques to handle visual variations and reduce drift.
result DSM framework reduces accumulated drift and improves visual quality of fetoscopic videos.
Q2-Opt improves robot grasping success and efficiency.
problem Improving robot grasping success and efficiency in vision-based tasks.
method Quantile QT-Opt, a distributional variant of Q-learning for continuous domains.
result Q2-Opt achieves superior grasping success and is more sample efficient.
BCIQT model improves ML prediction effectiveness using quantum theory.
problem Improving prediction effectiveness in machine learning models.
method Proposes Binary Classifier Inspired by Quantum Theory (BCIQT) model.
result BCIQT model outperforms state-of-the-art models in recall.
Develops a novel approach for estimating optimal DTRs with multicategory treatments and censored data.
problem Estimating optimal treatment regimes for chronic diseases with censored data.
method Angle-based multicategory classification algorithm for maximizing conditional survival function.
result The proposed method outperforms existing approaches in maximizing conditional survival function.
ECG-DelNet uses neural networks to accurately delineate ECGs, even with low-quality data.
problem Lack of model explainability and small databases limit deep learning applicability in ECG detection.
method Adapted U-Net architecture for 1D data, used PhysioNet's QT database, applied data augmentation and regularization techniques.
result Best configuration achieved high precision and recall for P, QRS, and T waves.
DDGM generates realistic ECG signals for clinical use.
problem Generating accurate ECG signals from noisy data.
method Bayesian ECG reconstruction using DDGM trained on healthy ECG data.
result DDGM successfully generates realistic ECG signals for clinical applications.
A Convolutional Neural Network was used to predict kidney function in patients with chronic kidney disease from high-resolution digital pathology scans of their kidney biopsies. Kidney biopsies were taken from participants of the NEPTUNE study, a longitudinal cohort study whose goal is to set up infrastructure for obse…
The study explores vector flows on manifolds, focusing on polynomial constraints and equivalence relations.
problem Understanding vector flows on compact manifolds with constraints on tangency patterns.
method Introduces equivalence relations (quasitopy) and computes spaces of polynomials with constrained divisors.
result Quasitopy classes stabilize as the degree of polynomials increases.
Develops a risk score to assist ECMO planning for critically ill patients with viral or unspecified pneumonia.
problem Lack of a risk score to guide ECMO planning for critically ill patients.
method Leverages machine learning to develop the PEER score.
result PEER score predicts mortality and decompensation in patients eligible for ECMO.
Study uses machine learning to identify posturographic features in Parkinson's patients who fall.
problem Identifying patients at risk of falling in Parkinson's syndrome.
method Employed ts-AUC, a non-parametric multivariate two-sample test, to analyze posturographic features.
result Significant differences in antero-posterior movements and posturographic area between fallers and non-fallers identified.
Harmonization schemes limit accuracy due to domain information.
problem Harmonization schemes lead to inaccurate predictions due to domain information.
method Analysis of mutual information and real label value informativeness.
result Accuracy is limited by the domain with least information.
YOLOv3 detects ships in real-time with high accuracy.
problem Real-time target detection in maritime scenarios.
method YOLOv3 model trained on a large dataset of marine vessels.
result Average Precision up to 96% for IoU of 0.5.
A point in the (N,q)-torus knot in R3 goes q times along a vertical circle while this circle rotates N times around the vertical axis. In the Lissajous-toric knot K(N,q,p), the point goes along a vertical Lissajous curve (parametrized by t↦(sin(qt+φ),cos(pt+ψ))) while this curve rotates $N…
Causal modeling has long been an attractive topic for many researchers and in recent decades there has seen a surge in theoretical development and discovery algorithms. Generally discovery algorithms can be divided into two approaches: constraint-based and score-based. The constraint-based approach is able to detect co…
AQL uses amortized inference to handle high-dimensional action spaces in Q-learning.
problem Difficulty in maximizing over large action spaces in Q-learning.
method Replace expensive maximization over all actions with a maximization over a small subset sampled from a learned proposal distribution.
result AQL outperforms existing methods on continuous control tasks with up to 21 dimensional actions.
Deep learning system tracks stool consistency for GI disease assessment.
problem Lack of objective stool consistency measurements in chronic GI disease.
method Computer vision and deep convolutional neural networks (CNN).
result Developed a stool detection and tracking system.
Accounting frameworks follow stipulations of existing Accounting Theories. This exploratory research sets out to trace the evolution of accounting theories of Charge and Discharge Syndrome and the Corollary of Double Entry. Furthermore, it dives into the theories of Income Determination, garnishing it with areas of div…
Study of embeddings avoiding certain tangent patterns using polynomial spaces.
problem Classifying embeddings avoiding specific tangent patterns.
method Introduce equivalence relation (quasitopy) and use spaces of polynomials as Grassmannians.
result Quasitopy classes of Θ-constrained embeddings stabilize as degree increases.
The paper proposes a method to predict the performance of data-driven algorithms using surrogate models.
problem Improving the performance prediction of data-driven knowledge discovery algorithms.
method Surrogate-assisted performance prediction using evolutionary modeling of clinical pathways.
result The proposed approach provides interpretable prediction of algorithm performance and quality.