We present a conditional generative model to learn variation in cell and nuclear morphology and the location of subcellular structures from microscopy images. Our model generalizes to a wide range of subcellular localization and allows for a probabilistic interpretation of cell and nuclear morphology and structure loca…
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48 results for “nuclear structure”
Minimizing the nuclear norm of a matrix has been shown to be very efficient in reconstructing a low-rank sampled matrix. Furthermore, minimizing the sum of nuclear norms of matricizations of a tensor has been shown to be very efficient in recovering a low-Tucker-rank sampled tensor. In this paper, we propose to recover…
Exact partitioning of high-order planted models achieved through convex optimization.
problem Efficiently partitioning hypergraphs generated by high-order planted models.
method Solving a computationally efficient convex optimization problem with a tensor nuclear norm constraint.
result Exact recovery of true underlying cluster structures with high probability.
Joint Nuclear and Regularization for Logistic Matrix Regression with Applications to Brain Imagingstat.ME
New method uses nuclear and ℓ1 penalties for matrix regression, improving brain disorder detection.
problem Modeling high-dimensional matrix predictors with binary responses.
method Convex optimization with ADMM for low-rank and sparse structures.
result Effective in identifying brain disorder-related connectivity patterns.
The study uses statistical methods to analyze nuclear mass models.
problem Understanding the information content of nuclear masses from models.
method Bayesian calibration, Bayesian model averaging, chi-square correlation analysis, principal component analysis.
result A dramatic parameter reduction can be achieved in both 4-parameter and 14-parameter models.
We propose the nuclear norm penalty as an alternative to the ridge penalty for regularized multinomial regression. This convex relaxation of reduced-rank multinomial regression has the advantage of leveraging underlying structure among the response categories to make better predictions. We apply our method, nuclear pen…
Matrix completion works well for smooth non-linear structures, even without low-rank assumptions.
problem Matrix completion for smooth non-linear structures.
method Nuclear-norm penalization for matrices lying in a low-dimensional non-linear manifold.
result Nuclear-norm penalization is minimax rate optimal for recovering smooth non-linear matrices with missing data.
Selective sampling improves matrix completion with known structure.
problem Reconstructing a low-rank matrix with incomplete data.
method Designing observation sets based on matrix structure and selective sampling.
result Improved reconstruction accuracy with selective sampling.
Bayesian model predicts drip-line locations in heavy calcium isotopes.
problem Determining the neutron drip line in the Ca region of heavy nuclei.
method Bayesian model averaging with Gaussian-process-based extrapolations.
result Predicted posterior probabilities for drip-line locations in heavy calcium isotopes.
NukeBERT improves performance on nuclear domain Q&A with less training data.
problem Lack of annotated data for nuclear domain Q&A.
method Developed NQuAD dataset and NukeBERT model incorporating novel BERT vocabulary technique.
result NukeBERT outperformed BERT significantly on NQuAD.
Proposes tensor Q-rank for better tensor rank recovery in complex data.
problem Improving tensor rank recovery for complex data with low sampling rate.
method Introduces tensor Q-rank and two selection methods for , proposing VMTQN and MOTQN models.
result Demonstrates superior performance in tensor completion problems compared to TNN-based methods.
Recovering a low-rank tensor from incomplete information is a recurring problem in signal processing and machine learning. The most popular convex relaxation of this problem minimizes the sum of the nuclear norms of the unfoldings of the tensor. We show that this approach can be substantially suboptimal: reliably recov…
Bayesian analysis predicts properties of proton-emitting nuclei beyond the proton drip line.
problem Predicting properties of unstable nuclei in the proton-rich region.
method Bayesian Gaussian processes and mass models corrected with statistical emulators.
result Quantified predictions for separation energies and probabilities of proton emission.
For the problems of low-rank matrix completion, the efficiency of the widely-used nuclear norm technique may be challenged under many circumstances, especially when certain basis coefficients are fixed, for example, the low-rank correlation matrix completion in various fields such as the financial market and the low-ra…
Quantified limits of nuclear stability beyond drip lines.
problem Predicting nuclear stability beyond known isotopes.
method Microscopic nuclear mass models, Bayesian methodology, Gaussian processes.
result Quantified predictions of one- and two-nucleon separation energies.
Study on tensor nuclear norm's decomposability and subdifferential.
problem Understanding tensor nuclear norm in higher-order tensors.
method Showed decomposability over specific subspaces, derived subdifferential inclusions, and studied subgradients.
result Established the statistical performance of tensor robust principal component analysis.
This work examines how low-rank weights improve adversarial robustness in neural networks.
problem Improving adversarial robustness in neural networks.
method The study investigates the impact of low-rank structure on adversarial robustness through compression measures.
result Promoting low-rank structure in weight matrices enhances adversarial robustness in neural networks.
We compare correlations and coherent structures in nuclei and financial markets. In the nuclear physics part we review giant resonances which can be interpreted as a coherent structure embedded in chaos. With similar methods we investigate the financial empirical correlation matrix of the DAX and Dow Jones. We will sho…
Many problems can be formulated as recovering a low-rank tensor. Although an increasingly common task, tensor recovery remains a challenging problem because of the delicacy associated with the decomposition of higher order tensors. To overcome these difficulties, existing approaches often proceed by unfolding tensors i…
Efficiently regularizes deep learning models using Jacobian nuclear norm.
problem Regularizing deep learning models to prevent overfitting and improve generalization.
method Proposes a denoising-style approximation to penalize the Jacobian nuclear norm without computing the Jacobian matrix.
result Demonstrates that penalizing the average squared Frobenius norm of and is equivalent to penalizing the Jacobian nuclear norm for function compositions.
Low-rank matrix recovery has found many applications in science and engineering such as machine learning, signal processing, collaborative filtering, system identification, and Euclidean embedding. But the low-rank matrix recovery problem is an NP hard problem and thus challenging. A commonly used heuristic approach is…
Study estimates risks of nuclear waste storage projects.
problem Cost and schedule risks in nuclear waste storage projects.
method Reference class of 216 past projects for cost risk, 200 for schedule risk.
result Cost and schedule risks are substantial for nuclear waste storage projects.
Machine learning predicts nuclear physics parameters with high accuracy.
problem Predicting nuclear physics parameters for superheavy elements.
method Gradient boosted trees algorithm trained on nuclear data.
result Predictions have standard deviation from 0.00035 to 0.73.
Paper tackles robust prediction of nuclear reactor materials under scarce data.
problem Challenges of data scarcity and uncertainty in nuclear reactor design.
method Meta-learning approach informed by uncertainty and prior knowledge.
result Achieves superior performance in rupture life prediction.
Sharp asymptotics reveal how network width controls learnability in quadratic neural networks.
problem Understanding learnability in overparameterized quadratic neural networks.
method Mapping ERM to convex matrix sensing with nuclear norm penalization.
result Characterization of global minima and precise generalization thresholds.
A deep learning framework separates overlapping nuclei in histology images.
problem Challenges in nuclear segmentation due to overlapping nuclei.
method Proposal-free spatially-aware deep learning framework with multi-scale spatial information.
result State-of-the-art performance in nuclear segmentation on a multi-organ data set.
Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learningastro-ph.IM
Deep learning predicts nuclear equation of state from rotating core collapse GW signals.
problem Classifying the nuclear equation of state from rotating core collapse gravitational wave signals.
method Employed deep convolutional neural networks to classify visual and temporal patterns in GW signals.
result Up to 97% correct classifications of nuclear equation of state in the test set.
Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutionsstat.ML
DeepONet accelerates nuclear DT inference with high accuracy and efficiency.
problem Real-time prediction and model evaluation in nuclear systems.
method Deep Neural Operator (DeepONet) for surrogate modeling.
result DeepONet outperforms traditional ML methods in accuracy and speed.
A new tensor p-shrinkage nuclear norm improves low-rank tensor completion.
problem Estimating tensors from partial observations with low rank.
method Proposed tensor p-shrinkage nuclear norm (p-TNN) and an efficient algorithm.
result Upper bound of recovery error provided for the LRTC model.
The R-function theory of Thomas is used to model neutron inelastic scattering and the fine, intermediate, and gross structure observed in the Dow Jones Industrial Average on a typical trading day.
Deep learning detects corrosion in nuclear fuel canisters.
problem Ensuring safety and integrity of used nuclear fuel dry storage canisters.
method Residual neural networks (ResNets) for real-time corrosion detection of canister images.
result Deep learning approach accurately detects corrosion and classifies canisters as corroded or intact.
Bayesian optimization helps find best nuclear interaction parameters.
problem Finding best coupling constants in complex nuclear interaction models.
method Bayesian optimization applied to chiral effective field theory.
result Bayesian optimization performs well in low-dimensional parameter domains.
Rank minimization (RM) is a wildly investigated task of finding solutions by exploiting low-rank structure of parameter matrices. Recently, solving RM problem by leveraging non-convex relaxations has received significant attention. It has been demonstrated by some theoretical and experimental work that non-convex relax…
Recently theoretical guarantees have been obtained for matrix completion in the non-uniform sampling regime. In particular, if the sampling distribution aligns with the underlying matrix's leverage scores, then with high probability nuclear norm minimization will exactly recover the low rank matrix. In this article, we…
SpINNEr uses matrix regression to analyze brain connectivity, improving accuracy over other methods.
problem Analyzing multi-dimensional data like brain imaging arrays using traditional scalar regression methods.
method SpINNEr applies matrix regression with nuclear norm and lasso norms to encourage low rank and sparse solutions.
result SpINNEr outperforms other methods in estimating brain connectivity, especially in well-connected regions.
New method speeds up nuclear-norm constrained learning over multiple machines.
problem Synchronization slowdown and high communication costs in large-scale learning.
method Asynchronous Stochastic Frank-Wolfe (SFW-asyn) method.
result SFW-asyn achieves the same convergence rate as vanilla SFW but with speed-ups almost linear to the number of machines.
Efficient system classifies EEG signals for cognitive tasks using nuclear features.
problem Classification of raw EEG signals for cognitive tasks is challenging.
method Singular value decomposition for computing dominant variances of EEG signals, using them as nuclear features, and a simple classifier.
result Nuclear features from frontal brain region achieved 100% prediction accuracy.
This research assesses uncertainty quantification and sensitivity analysis for DTs in nuclear fuel performance.
problem Understanding the reliability and performance of advanced nuclear fuels using DTs.
method Introduces ML-based uncertainty quantification and sensitivity analysis methods applied to BISON fuel performance code.
result Demonstrates the effectiveness of DTs in multi-criteria decision-making for nuclear fuel performance.
Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with `Big Data' analytics. However, increasingly noisy, heterogeneous, and incomplete datasets as well as the need for {\em real-time} processing of streaming data pose major challenge…
Efficient solver for nonconvex tensor regularization reduces computational cost.
problem Computational inefficiency in extending nonconvex regularization to tensor learning.
method Proximal average algorithm with adaptive momentum, maintaining sparse plus low-rank structure.
result Shows good statistical performance and accuracy on tensor completion problems.
In this paper, we study the problem of approximately computing the product of two real matrices. In particular, we analyze a dimensionality-reduction-based approximation algorithm due to Sarlos [1], introducing the notion of nuclear rank as the ratio of the nuclear norm over the spectral norm. The presented bound has i…
In this paper we investigate panel regression models with interactive fixed effects. We propose two new estimation methods that are based on minimizing convex objective functions. The first method minimizes the sum of squared residuals with a nuclear (trace) norm regularization. The second method minimizes the nuclear …
Recovering a large matrix from limited measurements is a challenging task arising in many real applications, such as image inpainting, compressive sensing and medical imaging, and this kind of problems are mostly formulated as low-rank matrix approximation problems. Due to the rank operator being non-convex and discont…
Abstract: Extends Drinfeld correspondence to infinite-dimensional Lie groups.
problem Establishing Drinfeld correspondence in infinite dimensions.
method Extending Drinfeld correspondence to Poisson Lie groups and Lie bialgebras in infinite-dimensional settings.
result Extended Drinfeld correspondence to regular Lie groups modeled on nuclear Fréchet and Silva spaces.
New method GSAT improves robustness against structured perturbations.
problem Structured perturbations in biological data.
method Formulates GSAT as a non-convex concave minimax optimization problem and solves it with GDADMM.
result Improves robustness against group-sparse and rank-constrained perturbations.
Characterizes dropout's regularizer in deep linear networks.
problem Understanding dropout's regularization effect in deep learning.
method Formal characterization of dropout's regularizer, showing it is composed of an -path regularizer and the squared nuclear norm.
result For large dropout rates, the global optima of the dropout objective can be characterized.
A new method classifies color images using quaternion algebra.
problem Classifying color images with preserved intrinsic relationships.
method LSQMM model with quaternion nuclear norm regularization and ADMM algorithm.
result LSQMM outperforms state-of-the-art methods in classification accuracy and efficiency.
NuClick uses clicks inside nuclei to improve nuclear segmentation.
problem Lack of efficient tools for nuclear segmentation due to labor-intensive annotation.
method Convolutional neural network framework that uses single point clicks for nuclei segmentation.
result NuClick generates superior segmentation results and facilitates more annotations.