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Found 9 papers, 1 papers with code
Deep Convolutional Neural Networks Meet Variational Shape Compactness Priors for Image Segmentation
Shape compactness is a key geometrical property to describe interesting regions in many image segmentation tasks.
BP-DeepONet: A new method for cuffless blood pressure estimation using the physcis-informed DeepONet
Unlike previous methods, our approach requires the predicted ABP waveforms to satisfy the Navier-Stokes equation with a time-periodic condition and a Windkessel boundary condition.
Structure Regularized Attentive Network for Automatic Femoral Head Necrosis Diagnosis and Localization
However, due to the tissue overlap, X-ray images are difficult to provide fine-grained features for early diagnosis.
Generalization Error Analysis of Neural networks with Gradient Based Regularization
We mainly focus on two regularization methods: the total variation and the Tikhonov regularization.
Axi-Higgs Cosmology
If the electroweak Higgs vacuum expectation value $v$ in early universe is $\sim 1 \%$ higher than its present value $v_0=246$ GeV, the $^7$Li puzzle in BBN and the CMB/$\Lambda$CDM tension with late-universe measurements on Hubble parameter are mitigated.
High Energy Physics - Phenomenology Cosmology and Nongalactic Astrophysics High Energy Physics - Theory
A level set representation method for N-dimensional convex shape and applications
We apply this new method to two applications: object segmentation with convexity prior and convex hull problem (especially with outliers).
Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging
Annual low dose computed tomography (CT) lung screening is currently advised for individuals at high risk of lung cancer (e. g., heavy smokers between 55 and 80 years old).
Convex hull algorithms based on some variational models
In this model, the convex hull is characterized by the zero sublevel-set of a convex level set function, which is non-positive at every given point.
