Combined ML and JVC-SENSE enable high-resolution imaging with fewer shots and less distortion.
problem Severe distortion artifacts and blurring in high-resolution imaging due to shot-to-shot variations in msEPI.
method Employed deep learning to obtain an interim image with minimal artifacts, which was then used in a Joint Virtual Coil Sensitivity Encoding (JVC-SENSE) reconstruction.
result Enabled navigator-free, highly accelerated multishot EPI with fewer shots and improved geometric fidelity.
Jointly correct bias fields and reconstruct undersampled MRI images.
problem Recovering fully sampled MRI images from undersampled data while accounting for bias field differences.
method An unsupervised learning-based reconstruction algorithm combined with a N4-based bias field estimation method in a joint optimization scheme.
result The proposed method improves reconstruction quality, both visually and in terms of RMSE.
PC-RNN reconstructs MRI images from undersampled data with more details.
problem Recovering fine details from undersampled MRI data.
method Pyramid Convolutional RNN (PC-RNN) with three ConvRNN modules for multi-scale reconstruction.
result PC-RNN outperforms other methods in recovering more details from MRI images.
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstructi…
Ensembled neural networks improve MRI image quality.
problem Accelerated parallel MR imaging with high SSIM scores.
method Ensembled Σ-net combining parallel coil and sensitivity networks, trained with supervised and semi-supervised methods. result Ensembling models achieves visually sharp and textured images with robust SSIM scores.
New Bayesian method for joint sparse parameter inference.
problem Inference of jointly sparse parameter vectors from multiple measurements.
method Hierarchical Bayesian learning with joint sparsity-promoting priors.
result New algorithms consistently outperform existing methods in numerical experiments.
This study uses CNN-IOs to estimate MRI image reconstruction performance bounds.
problem Estimating task-based performance limits for MRI image reconstruction methods.
method Utilized stylized multi-coil SENSE MRI systems and deep-generated stochastic models to estimate IO performance.
result Estimation of IO performance provides guidance for designing under-sampled MRI systems.
Predicting MRI coil failures using image features and ensemble learning.
problem Ensuring trouble-free operation of MRI systems by predicting hardware failures.
method Two-level data analysis with neural networks and Random Forest ensemble learning.
result Improved prediction results with an F-score of 94.14% and an accuracy of 99.09%.
Nyquist ghost artifacts in EPI are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field MRI due to the non-linear and time-varying local magnetic field changes. Recently, it was shown that …
Predicting MRI coil failures using time series classification.
problem Early detection of MRI hardware failures to prevent downtime.
method Training a statistical model on sequential image data features over time.
result LSTMs achieved an F-score of 86.43% and 98.33% accuracy in predicting coil damage.
A classical link in 3-space can be represented by a Gauss paragraph encoding a link diagram in a combinatorial way. A Gauss paragraph may code not a classical link diagram, but a diagram with virtual crossings. We present a criterion and a linear algorithm detecting whether a Gauss paragraph encodes a classical link. W…
Unified model learns from proteins and ligands for drug design.
problem Disjoint data sources and modeling assumptions limit joint use of structure- and ligand-based drug design.
method Contrastive Geometric Learning for Unified Computational Drug Design (ConGLUDe)
result Unified model achieves competitive zero-shot virtual screening performance and state-of-the-art ligand-conditioned pocket selection.
Study on helix curves and their Möbius energy asymptotics.
problem Understanding the asymptotic behavior of Möbius energy for helix curves.
method Investigation of helix curves with fixed radius, focusing on energy decay and blow-up.
result Proven asymptotics for both uncoiling and coiling helix curves, revealing distinct strategies for each.
The paper studies Möbius energy gradient of helix pairs and finds limiting behavior as coiling ratio increases.
problem Characterizing the limiting behavior of Möbius energy gradient for symmetric helix pairs.
method Complex asymptotics
result The gradient diverges in opposing directions based on radius, approaching 1/2 as coiling ratio increases.
A virtual n-string is a chord diagram with n core circles and a collection of arrows between core circles. We consider virtual n-strings up to virtual homotopy, compositions of flat virtual Reidemeister moves on chord diagrams. Given a virtual 1-string α, Turaev associated a based matrix that encodes invariants…
Invariant detects sliceness of virtual knots with specific chord indices.
problem Detecting sliceness in virtual knots with chord indices.
method Constructs an invariant using sliceness obstruction and sensitivity to Δ-move.
result Invariant detects sliceness and is sensitive to chord indices.
We use virtual knot theory to detect the non-invertibility of some classical links in S3. These links appear in the study of virtual covers. Briefly, a virtual cover associates a virtual knot υ to a knot K in a 3-manifold N, under certain hypotheses on K and N. Virtual covers of links in …
We describe a method of encoding various types of link diagrams, including those with classical, flat, rigid, welded, and virtual crossings. We show that this method may be used to encode link diagrams, up to equivalence, in a notation whose length is a cubic function of the number of 'riser marks'. For classical knots…
It is an open question whether there are Vassiliev invariants that can distinguish an oriented knot from its inverse, i.e., the knot with the opposite orientation. In this article, an example is given for a first order Vassiliev invariant that takes different values on a virtual knot and its inverse. The Vassiliev inva…
We study a probabilistic numerical method for the solution of both boundary and initial value problems that returns a joint Gaussian process posterior over the solution. Such methods have concrete value in the statistics on Riemannian manifolds, where non-analytic ordinary differential equations are involved in virtual…
This paper studies cobordism and concordance for virtual knots. We define the affine index polynomial, prove that it is a concordance invariant for knots and links (explaining when it is defined for links), show that it is also invariant under certain forms of labeled cobordism and study a number of examples in relatio…
MIM learns joint distributions with mutual information and low divergence.
problem Learning joint distributions over observations and latent variables.
method Probabilistic auto-encoder with three design principles: low divergence, high mutual information, and low marginal entropy.
result MIM learns representations with high mutual information, consistent encoding and decoding distributions, effective latent clustering, and comparable data log likelihood to VAE.
Forbidden moves categorify fused links into quivers.
problem Categorify fused links to invariant of links.
method Use forbidden moves to categorify fused links.
result Obtain three polynomial invariants of links.
Jointly learns neural networks across datasets to improve network quality.
problem Jointly learning neural networks across diverse datasets to extract correlated information.
method Formulates joint learning as sharing network weights across multiple networks, solves an optimization problem to determine shared layers.
result Our approach outperforms baselines in image classification, auto-encoders, and image generation tasks.
DNM learns efficient representations of input data using deep neural maps.
problem Learning efficient representations of input data.
method Unsupervised representation learning and visualization using deep convolutional networks and self-organizing maps.
result DNM can learn efficient representations of the input data, reflecting class characteristics.
Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing. The methodology has been demonstrated to improve the performance of MLC algorithms whe…
UWM-JEPA predicts future scenarios in belief space, improving accuracy in partially observed environments.
problem Predicting future scenarios in partially observed environments with uncertainty.
method Introduces UWM-JEPA, a JEPA world model with a density-matrix latent and learned unitary predictor.
result UWM-JEPA achieves 0.77 accuracy on a hidden-velocity indicator task, outperforming LSTM-JEPA.
Novel model predicts anticancer compound sensitivity with high accuracy and interpretability.
problem Predicting anticancer compound sensitivity with high accuracy and interpretability.
method Multimodal attention-based convolutional encoder using SMILES, gene expression profiles, and protein-protein interaction networks.
result The model significantly outperforms baseline models and demonstrates high interpretability.
Study on the Lp behavior of helix curves' energy density.
problem Analyzing the Lp asymptotics of helix curves' Möbius energy density. method Contour integration and Laurent expansion near poles.
result Established the precise Lp blowup rate of the Möbius energy density. BriarPatches obscure sensitive attributes to achieve demographic parity.
problem Achieving demographic parity in model predictions.
method Pixel-space interventions that obscure sensitive attributes from classifier representations.
result BriarPatches push downstream predictors towards demographic parity.
Efficiently combines autoregressive and set-based models for joint distributions.
problem Joint distributions over multiple predictions from set-based models.
method Causal autoregressive buffer that caches context and captures dependencies.
result Up to 20x faster joint sampling and density evaluation, up to 7x lower memory usage.
Proposes on-the-fly joint feature selection and classification for time-sensitive decisions.
problem Online feature selection and classification for time-sensitive decision making.
method Proposes a framework for joint feature selection and classification on-the-fly, optimizing feature usage and classification accuracy.
result Demonstrates superior performance over state-of-the-art methods on various datasets.
Given a group endowed with a Z/2-valued morphism we associate a Gauss diagram theory, and show that for a particular choice of the group these diagrams encode faithfully virtual knots on a given arbitrary surface. This theory contains all of the earlier attempts to decorate Gauss diagrams, in a way that is made precise…
Develops a deep model for joint image-text learning.
problem Bidirectional joint image-text modeling.
method Variational hetero-encoder randomized GAN (VHE-GAN).
result Achieves state-of-the-art performance in image-text learning and generation.
Paper proposes a novel auto-encoder for latent density estimation.
problem Challenges of learning generative probabilistic models due to curse of dimensionality.
method Joint dimensionality reduction and non-parametric density estimation framework using a novel estimator.
result Proposed model achieves promising results on various datasets.
The derived group of a permutation representation, introduced by R.H. Crowell, unites many notions of knot theory. We survey Crowell's construction, and offer new applications. The twisted Alexander group of a knot is defined. Using it, we obtain twisted Alexander modules and polynomials. Also, we extend a well-known t…
Improved OOD detection across various shifts using multi-encoder fusion of RDMs.
problem Out-of-distribution detection across multiple types of distribution shifts.
method Statistical identification of encoder sensitivity, EncMin2L fusion, and Tippett minimum combination.
result Achieves AUROC ≥ 0.94 across four shift types, outperforming state-of-the-art detectors.
In recent years, several families of hyperbolic knots have been shown to have both volume and λ1 (first eigenvalue of the Laplacian) bounded in terms of the twist number of a diagram, while other families of knots have volume bounded by a generalized twist number. We show that for general knots, neither the twist nu…
Fisher auto-encoders use Fisher divergence for more robust generative modeling.
problem Model uncertainty in generative models.
method Minimizing Fisher divergence between true and modeled joint distributions.
result Fisher auto-encoders can more accurately quantify model uncertainty.
New technique reduces bias in DNN models without sensitive attribute annotations.
problem Existing bias mitigation methods require instance-level annotations and do not guarantee removal of all sensitive information.
method Representation Neutralization for Fairness (RNF) debiases only the classification head of DNN models using neutralized representations.
result RNF effectively reduces discrimination of DNN models with minimal performance degradation.
A combinatorial framework classifies genus-one knots and links.
problem Classifying knots and links on a torus.
method Maps on surfaces, permutation pairs, bit data, Reidemeister II reductions, state sums.
result Completely enumerated and reproducible genus-one knot and link diagrams.
This paper presents an automatic approach for selecting optimal meta-models for sensitivity analysis in complex systems.
problem Efficient surrogate models for high-dimensional problems in virtual prototyping.
method Automatic selection of meta-models, variable space reduction, and advanced sensitivity measures.
result Optimal meta-models and subspace identification for accurate probabilistic analysis.
Proposes MC-AE for better unsupervised clustering of unlabeled data.
problem Lack of consideration for multi-local collaborative relationships in autoencoders.
method Integrates LSH for multi-local cross blocks, mcrRBM and mcrGRBM models.
result MC-AE improves unsupervised clustering performance.
Study uses GPLFM to create Digital Twin for ferry quay health monitoring.
problem Deterioration of ferry quays due to harsh maritime environments and impacts.
method Gaussian Process Latent Force Model (GPLFM) integrating physics-based model and machine learning.
result GPLFM provides accurate acceleration response estimates, even under simplifying assumptions.
A joint model predicts IT operations and detects anomalies.
problem Predicting IT operations and detecting anomalies in noisy data.
method A joint model combining variational auto-encoder and LSTM, with spectral residual analysis integration.
result The joint model outperforms models trained separately on prediction and anomaly detection tasks.
New method certifies joint adversarial robustness of model ensembles.
problem Ensuring robustness of model ensembles against adversarial attacks.
method Proposes a novel technique to certify joint robustness, building on prior work on single-model robustness certification.
result Demonstrates the effectiveness of certifying joint robustness of ensembles, improving understanding of ensemble defenses.
DeepcomplexMRI uses deep residual networks for faster MRI imaging.
problem Accelerating parallel MR imaging with high accuracy.
method Deep complex convolutional neural network with residual connections and k-space consistency.
result The method can accurately reconstruct multi-channel MRI images.
Enhances sensitivity analysis for correlated inputs.
problem Estimating sensitivity indices in models with correlated inputs.
method Proposes an extension of Sobol' estimator using a linear correlation model.
result Improves accuracy in variance-based sensitivity analysis.