High predictive performance and ease of use and interpretability are important requirements for the applicability of a computer-aided diagnosis (CAD) to human reading studies. We propose a CAD system specifically designed to be more comprehensible to the radiologist reviewing screening breast MRI studies. Multiparametr…
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48 results for “computer aided study”
New method for calculating crosscap numbers of knots.
problem Calculating crosscap numbers of knots efficiently.
method Deformation of plumbing and computer aid.
result Efficient calculation of crosscap numbers.
The paper develops a causal machine learning framework to optimize aid allocation.
problem Optimizing aid allocation to reduce new HIV infections in poor countries.
method The framework uses a balancing autoencoder, counterfactual generator, and inference model to predict heterogeneous treatment effects.
result The framework predicts a reduction of up to 3.3% in new HIV infections, saving 50,000 lives.
A new method studies symplectic configurations in rational 4-manifolds using computer-aided techniques.
problem Understanding symplectic configurations in rational 4-manifolds.
method Computer-aided approach combining Cremona transformations and pseudoholomorphic curves.
result Nonexistence of Fano planes in the symplectic category.
I-AID categorizes disaster tweets into useful information types.
problem Filtering useful information from social media during disasters.
method Multimodel approach using BERT, GAT, and Relation Network.
result I-AID outperforms state-of-the-art approaches in F1 scores.
New graph-based method for compact n-manifolds.
problem Representing compact n-manifolds efficiently.
method Using (n+1)-colored graphs to represent compact n-manifolds.
result Established results on topology of represented manifolds.
TinyLSTMs reduces speech enhancement model size and latency for hearing aids.
problem Large RNNs limit practical deployment in hearing aid hardware.
method Model compression techniques (pruning, integer quantization, state update skipping) for RNN speech enhancement.
result Reduction in model size and operations by 11.9 and 2.9, respectively, without perceptual degradation.
Deep learning aids ADMM-based decoding for binary linear codes.
problem Improving decoding efficiency for binary linear codes.
method Designing a decoding network based on ADMM and deep learning.
result Numerical results show improved performance compared to original ADMM.
Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiationcs.LG
Paper proposes AggITD for efficient federated hypergradient computation.
problem Computing hypergradient in federated settings is challenging due to distributed and nonlinear construction of global Hessian matrices.
method AggITD: a novel communication-efficient federated hypergradient estimator via aggregated iterative differentiation.
result AggITD achieves the same sample complexity as AID-based approaches but with fewer communication rounds, especially in heterogeneous data environments.
Paper explores generalization of AID-based bi-level optimization methods.
problem Uncertainty in generalization properties of AID-based bi-level optimization methods.
method Uniform stability analysis and convergence study of AID-based methods.
result AID-based methods can achieve similar generalization as single-level nonconvex problems.
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysiscs.CV
Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by the superior performance of deep learning methods on various computer vision problems, there has been increasing work applying deep learning t…
Equation discovery method reconstructs model structure and parameters from data.
problem Nonlinear system identification challenges.
method Two interlaced parts: model structure identification and parameter estimation.
result Equation discovery method successfully reconstructs model structure and parameters from data.
With the aid of a computer, we provide a motion picture of the twist-spun trefoil which exhibits the periodicity well.
Study classifies mammographic breast density using residual learning.
problem Classifying mammographic breast density for breast cancer risk.
method Radiomics approach based on residual learning.
result Outstanding classification results with high accuracy.
This paper analyzes the impact of loops on bilevel optimization efficiency.
problem The impact of loops on the efficiency of bilevel optimization algorithms.
method Unified convergence analysis and computational complexity characterization for AID-BiO and ITD-BiO with and without loops.
result Loops in bilevel optimization can improve overall efficiency but increase per-step complexity.
Study efficient derivative computation for nondifferentiable maps in machine learning.
problem Efficiently compute derivatives of fixed-point of nondifferentiable contractions.
method Iterative Differentiation (ITD), Approximate Implicit Differentiation (AID), and New Stochastic Implicit Differentiation (NSID).
result Established convergence rates for ITD, AID, and NSID, matching or improving smooth setting rates.
The -character variety of math.GT
Authors determine the SL(2,C) character variety of a specific knot without computer aid.
problem Determine the SL(2,C) character variety of a specific knot without computational assistance.
method Develop an efficient method for working with conjugacy classes of four elements of SL(2,C).
result Determine the character variety of the knot 8_18 efficiently and software-free.
Adversarial attacks can manipulate ML-aided visualizations, tricking analysts.
problem Adversarial attacks on ML-aided visualizations.
method Identifying attack surface and exemplifying five adversarial attacks.
result Adversaries can induce various attacks, like creating arbitrary and deceptive visualizations.
Anomaly detection scores from VAE gradients improve tumor detection.
problem Improving anomaly detection in medical imaging.
method Using Variational Autoencoders to approximate anomaly ratings.
result Variance Autoencoder gradient-based ratings outperform other methods in tumor detection.
This paper reviews deep learning and knowledge-based methods for molecular design.
problem Optimizing molecular properties for scientific advances and process performance.
method Survey of deep learning and knowledge-based methods for molecular design.
result Deep learning models show promise in overcoming computational challenges.
No standard compact Clifford-Klein forms found for exceptional Lie groups.
problem Proving the non-existence of standard compact Clifford-Klein forms for exceptional Lie groups.
method Computer-aided approach, algorithmic methods for classifying semisimple subalgebras, and invariant calculations.
result Proves the non-existence of standard compact Clifford-Klein forms for homogeneous spaces of exceptional Lie groups.
Framelet Pooling Aided Deep Learning Network : The Method to Process High Dimensional Medical Datacs.LG
Paper introduces a method to process medical images efficiently.
problem High computational cost in processing large medical image data.
method Framelet-pooling aided deep learning method to reduce complexity.
result Significant reduction in computational costs with comparable performance.
SketchGraphs dataset aids in modeling CAD designs.
problem Training models to reason about CAD designs efficiently.
method Collection of 15 million sketches with geometric constraint graphs.
result Demonstrated use cases for generative modeling and conditional generation.
Optimal model diagnoses funduscopic images for ocular diseases.
problem Binary classification of funduscopic images for ocular diseases.
method Transfer learning using Xception base architecture, Adam optimizer, mean squared error loss function, and custom heuristic equation.
result 90% accuracy, 94% sensitivity, and 86% specificity achieved.
The paper analyzes convergence rates of bilevel optimization algorithms and introduces a new stochastic algorithm.
problem Nonconvex-strongly-convex bilevel optimization problems in machine learning.
method Comprehensive convergence rate analysis for deterministic bilevel optimization using AID and ITD, and a novel stochastic algorithm stocBiO.
result Theoretical convergence rates for AID and ITD methods, and stocBiO's superior performance.
Homological stability aids in computing group homology.
problem Computing homology of families of groups.
method Proving homological stability theorems and computing stable homology.
result Computation of Higman-Thompson groups' homology.
AI improves OFDM receivers for robust real-world communication.
problem Performance gap between simulation and real-world OFDM systems.
method Comparison of AI-aided OFDM receivers and development of SwitchNet receiver.
result SwitchNet receiver adapts to real channels online, improving robustness.
A PDE approach to the existence and regularity of surfaces of minimum mean curvature variationmath.DG
Analyzes surfaces minimizing mean curvature variation using PDEs.
problem Finding surfaces of minimum mean curvature variation.
method Develops an analytic theory using partial differential equations.
result Establishes existence and regularity of minimizers.
Multiple modalities of biomarkers have been proved to be very sensitive in assessing the progression of Alzheimer's disease (AD), and using these modalities and machine learning algorithms, several approaches have been proposed to assist in the early diagnosis of AD. Among the recent investigated state-of-the-art appro…
Constructs stable bundles on K3 surfaces using monad construction.
problem Stability of bundles on K3 surfaces.
method Monad construction, Generalised Hoppe Criterion, computer aid.
result Examples of real stable bundles constructed on K3 surfaces.
Graph neural networks improve molecular property prediction.
problem Efficiently predicting molecular properties with high accuracy and scalability.
method Gated Graph Recursive Neural Networks (GGNN) with skip connections.
result GGNN achieves state-of-the-art performance on molecular property prediction benchmarks.
We construct an abelian quotient of the symplectic derivation Lie algebra of the free Lie algebra generated by the fundamental representation of . More specifically, we show that the weight part of the abelianization of is -dimensional for $g…
Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspectivecs.LG
A model learns symptom-drug relations for PD patients.
problem Automatic prescription recommendation for Parkinson's Disease patients.
method Builds a dataset of PD symptoms and prescriptions, learns latent symptom space, uses alternating optimization.
result Effective in recommending suitable prescription drugs for new PD patients.
Novel approach localizes optic disc and fovea centers efficiently.
problem Localizing optic disc and fovea centers in retinal images.
method Simultaneously process optic disc and fovea, modeling their relative geometry and appearance.
result Improves localization and recognition by incorporating object-object relations.
Computational method finds examples of extremal hyperbolic surfaces.
problem Constructing explicit examples of extremal hyperbolic surfaces is complicated.
method Brute force computational procedure.
result Examples of extremal hyperbolic surfaces constructed in all cases.
Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. Keeping this as our main objective we develop in this paper, a new compu…
We study isospectrality for manifolds with mixed Dirichlet-Neumann boundary conditions and express the well-known transplantation method in graph- and representation-theoretic terms. This leads to a characterization of transplantability in terms of monomial relations in finite groups and allows for the generating of ne…
Proof of existence of a complex structure on the six-sphere, followed by an explicit computation of its underlying integrable almost complex tensor by the aid of inner automorphisms of the octonions, is exhibited. Both are elementary and self-contained however the size and complexity of the emerging almost complex tens…
We study the geometry of complete generic Ricci solitons with the aid of some geometric-analytical tools extending techniques of the usual Riemannian setting.
Computational topology is a vibrant contemporary subfield and this article integrates knot theory and mathematical visualization. Previous work on computer graphics developed a sequence of smooth knots that were shown to converge point wise to a piecewise linear (PL) approximant. This is extended to isotopic convergenc…
Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics. A key problem faced by practitioners is measuring the accuracy of an approximate infe…
CLCNet improves noise reduction in hearing aids with deep learning.
problem Noise reduction in hearing aids is challenging due to real-time and frequency resolution constraints.
method Proposes CLCNet, a deep learning framework based on complex linear coding.
result CLCNet outperforms traditional methods in noisy environments.
This paper gives a combinatorial description of spin and spin^c-structures on triangulated PL-manifolds of arbitrary dimension. These formulations of spin and spin^c-structures are established primarily for the purpose of aiding in computations. The novelty of the approach is we rely heavily on the naturality of binary…
PhIK uses physics models to improve Gaussian process regression.
problem Improving Gaussian process regression for complex systems.
method Constructs non-stationary Gaussian processes from physics models, avoiding hyperparameter optimization.
result Guaranteed physical constraints in predictions and error estimates.
We consider the braid groups on finite simplicial complexes , which are generalizations of those on both manifolds and graphs that have been studied already by many authors. We figure out the relationships between geometric decompositions for and their effects on braid groups, and provide an al…
A new DVAE architecture improves channel estimation by incorporating temporal correlations.
problem Improving the estimation of time-varying channels.
method Introducing k-MemoryMarkovVAE (k-MMVAE) architecture to learn temporal correlations.
result The k-MMVAE aided channel estimator outperforms other ML aided estimators.
A new method tackles bilevel optimization using Lanczos process for efficient hyper-gradient computation.
problem Efficiently solving large-scale bilevel optimization problems with gradient-based methods.
method Constructing low-dimensional approximate Krylov subspaces with the Lanczos process to approximate the Hessian inverse vector product.
result Demonstrates a convergence rate and efficiency in synthetic and deep learning tasks.
Deep learning predicts patient trajectories in open Mimic-III dataset.
problem Predicting future medical conditions from patient history.
method Two parallel bi-directional Minimal Gated Recurrent Unit networks trained on Mimic-III dataset.
result Significant improvements in automated medical prognosis measured by Recall@k.