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Found 13 papers, 3 papers with code
Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2
We propose a new approach for non-Cartesian magnetic resonance image reconstruction.
Balancing reaction-diffusion network for cell polarization pattern with stability and asymmetry
Once again, a generic set of kinetic parameters moves the interface towards either the anterior or posterior end, yet a polarized pattern can be stabilized through spatial tuning of one or more parameters coupled to intracellular or extracellular cues.
Plug-and-play imaging with model uncertainty quantification in radio astronomy
In a previous work, we introduced a class of convergent PnP algorithms, dubbed AIRI, relying on a forward-backward algorithm, with a differentiable data-fidelity term and dynamic range-specific denoisers trained on highly pre-processed unrelated optical astronomy images.
HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment
We embed the supply chain network corresponding to individual goods in the supply chain using the graph embedding module, resulting in a directed homogeneous graph with just product nodes.
Stochastic gradient descent introduces an effective landscape-dependent regularization favoring flat solutions
Empirical studies showed a strong correlation between flatness of the loss landscape at a solution and its generalizability, and stochastic gradient descent (SGD) is crucial in finding the flat solutions.
Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers
The approach consists in learning a prior image model by training a deep neural network (DNN) as a denoiser, and substituting it for the handcrafted proximal regularization operator of an optimization algorithm.
Spontaneous mechanical and energetic state transitions during Caenorhabditis elegans gastrulation
Gastrulation, namely cell internalization, is a significant milestone during the development of metazoans from worm to human, which generates multiple embryonic layers with distinct cell fates and spatial organizations.
Speed and fate diversity tradeoff in nematode's early embryogenesis
Nematode species are well-known for their invariant cell lineage pattern during development.
An Adaptive View of Adversarial Robustness from Test-time Smoothing Defense
The safety and robustness of learning-based decision-making systems are under threats from adversarial examples, as imperceptible perturbations can mislead neural networks to completely different outputs.
Recognizing Object Affordances to Support Scene Reasoning for Manipulation Tasks
Unfortunately, the top performing affordance recognition methods use object category priors to boost the accuracy of affordance detection and segmentation.
Using Synthetic Data and Deep Networks to Recognize Primitive Shapes for Object Grasping
Each primitive shape is designed with parametrized grasp families, permitting the pipeline to identify multiple grasp candidates per shape primitive region.
Deciphering gene regulation from gene expression dynamics using deep neural network
Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments.
30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine
Heretofore, global burned area (BA) products are only available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution.
