The global decline of water quality in rivers and streams has resulted in a pressing need to
design new watershed management strategies. Water quality can be affected by multiple …

The advancement of deep convolutional neural networks (DCNNs) has driven significant
improvement in the accuracy of recognition systems for many computer vision tasks …

An efficient strategy for weakly-supervised segmentation is to impose constraints or
regularization priors on target regions. Recent efforts have focused on incorporating such …

The simplicity of gradient descent (GD) made it the default method for training ever-deeper
and complex neural networks. Both loss functions and architectures are often explicitly tuned …

We study the energy minimization problem in low-level vision tasks from a novel
perspective. We replace the heuristic regularization term with a data-driven learnable …

Paucity of large curated hand labeled training data forms a major bottleneck in the
deployment of machine learning models in computer vision and other fields. Recent work …

This paper exploits the potential of edge intelligence empowered satellite-terrestrial
networks, where users' computation tasks are offloaded to the satellites or terrestrial base …

This thesis considers two complementary approaches for decoupling in composite
optimization problems by lower relaxations. The first approach is based on component-wise …

Medical image segmentation is an important pre-processing step in computer-aided
diagnosis systems. Methods based on neural networks have demonstrated state-of-the-art …

What a year 2018 was, full to the brim with events celebrating our university's 150th
anniversary! Established on Easter Sunday (!) of 1868 by decree of the 23-year-old King …