Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models.

Model fairness (a.k.a.

Automation of ultrasonographic optic nerve sheath diameter measurement using convolutional neural networks.

Background And Purpose: Ultrasonographic optic nerve sheath (ONS) diameter is a noninvasive intracranial pressure (ICP) surrogate. ICP is monitored invasively in specialized intensive care units. Noninvasive ICP monitoring is important in less specialized settings.

Distributed contrastive learning for medical image segmentation.

Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective. Federated learning (FL) can learn a shared model from decentralized data.

DAC-SDC Low Power Object Detection Challenge for UAV Applications.

The 55th Design Automation Conference (DAC) held its first System Design Contest (SDC) in 2018. SDC'18 features a lower power object detection challenge (LPODC) on designing and implementing novel algorithms based object detection in images taken from unmanned aerial vehicles (UAV). The dataset includes 95 categories and 150k images, and the hardware platforms include Nvidia's TX2 and Xilinx's PYNQ Z1.

Ring-shaped Racetrack memory based on spin orbit torque driven chiral domain wall motions.

Racetrack memory (RM) has sparked enormous interest thanks to its outstanding potential for low-power, high-density and high-speed data storage. However, since it requires bi-directional domain wall (DW) shifting process for outputting data, the mainstream stripe-shaped concept certainly suffers from the data overflow issue. This geometrical restriction leads to increasing complexity of peripheral circuits or programming as well as undesirable reliability issue.