Improved machine learning models for interpreting CDT results.
problem Improving accuracy and interpretability of CDT results.
method Analysis of pen stroke data using machine learning techniques.
result Machine learning models outperform existing scoring systems.
PENs learn summary statistics for ABC using invariant neural architectures.
problem Learning summary statistics for approximate Bayesian computation (ABC).
method Partially exchangeable networks (PENs) that are invariant to block-switch transformations.
result PENs provide more reliable posterior samples with less training data.
Pen-and-paper exercises cover various machine learning topics.
problem None explicitly stated, focuses on learning through exercises.
method Pen-and-paper exercises on machine learning topics.
result Comprehensive coverage of machine learning concepts through exercises.
Study uses machine learning to predict stroke risk in China with improved accuracy.
problem Predicting stroke risk in China using machine learning.
method Combines VAR and GNN for causal inference, compares multiple classification algorithms, uses SMOTE undersampling.
result Gradient Boosting model shows highest performance and stability in predicting stroke risk.
Paper introduces tractographic feature to predict stroke outcomes.
problem Predicting stroke outcomes using lesion volume alone is limited.
method Tractographic feature combining lesion and connectome data.
result Tractographic feature outperforms stroke volume in predicting mRS grades.
A novel RNN uses stigmergy for temporal input encoding.
problem Temporal input encoding in RNNs.
method Stigmergy-based computational memory in RNN design.
result Stigmergy enables coherent temporal input encoding.
Bayesian CNN improves MRI stroke diagnosis accuracy and uncertainty quantification.
problem Uncertainty quantification in automated image analysis for medical decision-making.
method Bayesian Convolutional Neural Network (CNN) with aggregation methods for patient-level diagnoses.
result Bayesian CNN achieved 95.33% accuracy on 511 patients, 2% higher than non-Bayesian.
New model predicts stroke risk with high accuracy and interpretability.
problem Developing accurate and understandable models for stroke risk prediction.
method Bayesian Rule Lists: a generative model that produces interpretable decision lists.
result Bayesian Rule Lists achieves predictive accuracy comparable to top machine learning algorithms and is more accurate than existing models.
CoSE models complex drawings by treating strokes as a collection that can be composed.
problem Modeling complex free-form structures like diagrams.
method Generative model using autoencoder to project strokes into a fixed latent space, relational model operates in latent space.
result Model captures both individual strokes and their compositional structure.
Improved ML approach for quantifying stroke rehabilitation.
problem No tool to measure functional training after stroke.
method Refined machine learning algorithm and sensor configurations.
result Linear Discriminant Analysis (LDA) showed highest accuracy.
We show that the equivalence problem for three-dimensional Lorentzian manifolds requires at most the fifth covariant derivative of the curvature tensor. We prove that this bound is sharp by exhibiting a class of 3D Lorentzian manifolds which realize this bound. The analysis is based on a three-dimensional analogue of t…
A stroke-based method reconstructs characters from noisy images.
problem Challenges in reconstructing characters from noisy images.
method Uses a weighted quadratic Bezier curve (WQBC) to represent strokes.
result Achieves excellent reconstruction in real scenes.
DeepWriterID uses deep CNN and DropSegment for efficient writer identification.
problem Insufficient data and difficulty in designing good features for writer identification.
method End-to-end system with deep CNN and DropSegment for data augmentation and improved feature representation.
result Achieved new state-of-the-art identification rates of 95.72% for Chinese text and 98.51% for English text.
Paper develops a model to identify LVO in stroke patients.
problem Early identification of LVO in stroke patients to prevent severe outcomes.
method Used demographic, clinical, and CT scan data to build three hierarchical models.
result Level-3 model with clinical and imaging features achieved best performance.
Current studies about motor imagery based rehabilitation training systems for stroke subjects lack an appropriate analytic method, which can achieve a considerable classification accuracy, at the same time detects gradual changes of imagery patterns during rehabilitation process and disinters potential mechanisms about…
StrokeSave app diagnoses stroke via mobile sensors and AI.
problem Inadequate stroke diagnosis in areas without quality medical facilities.
method Mobile app with sensors, facial and retinal images, deep learning model.
result Deep learning model achieves 95% accuracy in diagnosing stroke.
Study uses reinforcement learning to optimize metachronal paddling at low Reynolds number.
problem Optimizing metachronal paddling strategies for efficient swimming at low Reynolds numbers.
method Applied reinforcement learning to a swimmer model with varying paddle spacings.
result The reinforcement learning algorithm selects a back-to-front metachronal wave-like stroke as the most efficient, regardless of the number of paddles.
Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. Major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically find such strokes, we propose to mode…
Deep learning identifies functional primitives in stroke rehabilitation.
problem Measuring the necessary dose of rehabilitation training for stroke recovery.
method Automatic identification of functional primitives using IMUs and deep learning.
result Convolutional neural network achieved 70% accuracy in primitive classification.
Paper proposes a faster DNN pruning method.
problem Efficiently prune DNNs without sacrificing accuracy.
method Uses PCA to automatically optimize pruning.
result Prunes DNNs in one shot, no retraining needed.
Automated GMA using accelerometers detects abnormal infant movements with human-level accuracy.
problem Undetected perinatal stroke leads to lifelong disability.
method Wearable accelerometers and Discriminative Pattern Discovery (DPD) for automated GMA.
result Automated method correctly identifies abnormal movements with human-level accuracy.
The method learns to partition event time space for better prediction.
problem Improving event time prediction in clinical settings with limited data.
method Develops a method to learn cut points for partitioning event time space.
result Improved prediction performance on real-world datasets.
Neural network estimates rigid motion in stroke imaging to improve image quality.
problem Rigid patient motion during C-arm CBCT imaging reduces image quality.
method Neural network trained to regress reprojection error based on image information.
result Neural network outperforms entropy-based method in motion estimation.
This paper uses CNNs to automatically segment ischaemic stroke lesions from MRI sequences.
problem Automatically segmenting ischaemic stroke lesions from MRI sequences is challenging.
method Adversarial training of CNNs on multi-sequence MRI data.
result The method achieves high Dice scores for core and penumbra segmentation.
RATE metrics evaluate treatment prioritization rules, subsuming existing methods.
problem Comparing and testing the quality of treatment prioritization rules.
method Rank-weighted average treatment effect (RATE) metrics.
result RATE metrics enable asymptotically exact inference in various study settings.
This paper presents a study in task-oriented approach to stroke rehabilitation by controlling a haptic device via near-infrared spectroscopy-based brain-computer interface (BCI). The task is to command the haptic device to move in opposing directions of leftward and rightward movement. Our study consists of data acquis…
New method predicts aphasia severity with narrower uncertainty intervals.
problem Predicting aphasia severity in stroke patients using neuroimages.
method Sparse heteroscedastic Bayesian high-dimensional regression with H-PROBE algorithm.
result H-PROBE provides narrower prediction intervals for aphasia severity.
Automatically identifies geometric flat outputs for robotic systems.
problem Lack of systematic and practical means to identify flat outputs for arbitrary robotic systems.
method Casts the search for a globally valid, equivariant flat output as an optimization problem using Riemannian geometry, Lie group theory, and differential forms.
result Approximate transcription of continuum formulation to a quadratic program achieves precise agreement with known closed-form flat outputs.
Deep neural networks map brain lesions to deficits for better brain function understanding.
problem Mapping the functional brain organization from pathological lesions.
method Deep generative neural network architectures, specifically variational convolutional volumetric auto-encoders.
result Our model outperforms established methods in lesion-deficit inference across various scenarios.
Report on formalizing differential geometry in Lean.
problem Formalizing differential geometry in a proof assistant.
method Lean's type theory approach to formalization.
result Surprising differences between formal and informal proofs.
SparseVM registers clinical 3D scans faster and more accurately.
problem Inaccurate and slow registration of sparse clinical 3D scans.
method Learning-based registration method tailored for clinical sparse MRI.
result Orders of magnitude faster and more accurate than existing methods.
Causal ML methods failed to validate their personalized treatment effects in two large trials.
problem Validating causal machine learning methods for personalized treatment effects in precision medicine.
method Assessed 17 mainstream causal heterogeneity ML methods using two large randomized controlled trials.
result None of the ML methods reliably validated their performance, internal or external, showing significant discrepancies between training and test data.
Sketch-rnn generates coherent drawings from crude sketches.
problem Creating detailed drawings from simple sketches.
method Recurrent Neural Network (RNN) trained on human-drawn images.
result Generates coherent vector drawings from crude sketches.
Paper proposes MSSDDPG for better financial trading strategies.
problem Extracting accurate features from noisy, non-stationary financial time series.
method Multi-scale stroke deep deterministic policy gradient reinforcement learning model (MSSDDPG).
result MSSDDPG outperforms other strategies in China's CSI 300 and SSE Composite.
Model uses unsupervised learning to classify medical reports with less labeled data.
problem Lack of labeled data for fine-grained disease classification in medical reports.
method Developed a pipeline combining an unsupervised encoder-language model and a supervised classifier model.
result Improved classification accuracy with less labeled data compared to previous methods.
We suggest an original physical approach to describe the mechanism of market pricing. The core of our approach is to consider pricing at different time scales separately, using independent equations of motion. Such an approach leads to a pricing model that not only allows estimating the volatility of future market pric…
We introduce a novel class of rotation invariants of two dimensional curves based on iterated integrals. The invariants we present are in some sense complete and we describe an algorithm to calculate them, giving explicit computations up to order six. We present an application to online (stroke-trajectory based) charac…
Framework tests CATE homogeneity across trials and evaluates confounding.
problem Assessing treatment effect consistency across randomized and observational studies.
method Leverages multiple randomized trials to test CATE homogeneity and compares with observational data.
result Identifies potential confounding and effect heterogeneity in treatment effects.
Generative Neuro-Symbolic model learns from raw data with rich conceptual representations.
problem Learning rich, general-purpose conceptual representations from raw perceptual inputs.
method Generative Neuro-Symbolic (GNS) model combining symbolic and neural network approaches.
result Model learns from raw data and generalizes to 4 unique tasks.
Bayesian meta-learning improves health prediction models across similar diseases.
problem Inter- and intra-task variability in healthcare predictions due to disease heterogeneity and patient differences.
method Bayesian meta-learning approach that models task similarity to mitigate negative transfer and improve generalizability.
result Significant generalizability improvements in stroke prediction tasks using electronic health record data.
RNN model predicts handwritten characters from accelerometer and gyroscope data.
problem Online handwritten character recognition using sensor data.
method RNN-based neural network trained on gyroscope and accelerometer data.
result High accuracy on test data, achieving character prediction.
The paper proposes a new algorithm to select subsets of training data for better accuracy and explainability.
problem Tackles the challenge of balancing accuracy and explainability in pattern recognition.
method Identifies multiple subsets with simple local patterns by clustering similar instances.
result The sub-setting algorithm outperformed traditional decision trees by 15% on the international stroke dataset.
Machine learning detects and characterizes whistler radio waves for real-time monitoring of the plasmasphere.
problem Detect and characterise whistler radio waves generated by lightning strokes for real-time monitoring of the plasmasphere.
method Developed a machine learning model using image classification and localisation on spectrogram data to identify and localise whistlers.
result The proposed detectors achieve a misdetection and false alarm rate of less than 15% on Marion's dataset.
Bayesian Supervised Causal Clustering identifies patient subgroups for personalized decision-making.
problem Finding patient subgroups with similar characteristics for personalized decision-making.
method Bayesian Supervised Causal Clustering (BSCC) that identifies homogenous subgroups based on treatment effects.
result BSCC identifies subgroups with similar covariate profiles and treatment effects.
Robotic table tennis learns efficient policies to return balls at 100Hz.
problem Developing efficient robotic table tennis strategies.
method Model-free reinforcement learning using evolutionary search on CNN-based policies.
result Robots can develop multi-modal styles (forehand and backhand) with 80% return rate.
The paper is motivated by a problem concerning the monotonicity of insurance premiums with respect to their loading parameter: the larger the parameter, the larger the insurance premium is expected to be. This property, usually called loading monotonicity, is satisfied by premiums that appear in the literature. The inc…
Method condenses brain signal components into clusters based on within-trial dynamics.
problem Variability in optimized spatial filters due to temporal dynamics and hyperparameters.
method Condensing oscillatory brain signal components into clusters based on within-trial envelope dynamics.
result Subject-specific distinct temporal envelope dynamics of components.
The paper identifies collaborations in codebases using commit activity and language usage.
problem Identifying organic team interactions and collaborations in large codebases.
method Embedding and clustering commit activity, language usage, and code identifier topics.
result Restores engineering organization and reveals hidden collaborations.