U-net is a convolutional neural network (CNN) model and not transformer model however it have encoder decoder structure that that make it confusing and correlate it with transformers, it is specifically designed for image segmentation. Structure Encoder (Contracting Path) Function: The encoder part.
In the scorched aftermath of World War III, the Earth is a nuclear wasteland, and humanity’s last hope lies in autonomous war machines called \.
Anduril Industries is planning to construct a new factory to scale weapons production for the United States defense base. Anduril Industries Co-Founder and Executive Chairman Trae Stephens joins Market Domination Overtime to discuss this initiative.
Stephens explains that the legacy defense industry has traditionally focused on \.
Cognitive flexibility, the ability to rapidly switch between different thoughts and mental concepts, is a highly advantageous human capability. This salient capability supports multi-tasking, the rapid acquisition of new skills and the adaptation to new situations.
While artificial intelligence (AI) systems have become increasingly advanced over the past few decades, they currently do not exhibit the same flexibility as humans in learning new skills and switching between tasks. A better understanding of how biological neural circuits support cognitive flexibility, particularly how they support multi-tasking, could inform future efforts aimed at developing more flexible AI.
Recently, some computer scientists and neuroscientists have been studying neural computations using artificial neural networks. Most of these networks, however, were generally trained to tackle specific tasks individually as opposed to multiple tasks.
Researchers from North Carolina State University have demonstrated miniature soft hydraulic actuators that can be used to control the deformation and motion of soft robots that are less than a millimeter thick. The researchers have also demonstrated that this technique works with shape memory materials, allowing users to repeatedly lock the soft robots into a desired shape and return to the original shape as needed.
“Soft robotics holds promise for many applications, but it is challenging to design the actuators that drive the motion of soft robots on a small scale,” says Jie Yin, corresponding author of a paper on the work (Advanced Materials, “Fully 3D-Printed Miniature Soft Hydraulic Actuators with Shape Memory Effect for Morphing and Manipulation”) and an associate professor of mechanical and aerospace engineering at NC State. “Our approach makes use of commercially available multi-material 3D printing technologies and shape memory polymers to create soft actuators on a microscale that allow us to control very small soft robots, which allows for exceptional control and delicacy.”
The new technique relies on creating soft robots that consist of two layers. The first layer is a flexible polymer that is created using 3D printing technologies and incorporates a pattern of microfluidic channels – essentially very small tubes running through the material. The second layer is a flexible shape memory polymer. Altogether, the soft robot is only 0.8 millimeters thick.
A team of researchers at Delft University of Technology has developed a drone that flies autonomously using neuromorphic image processing and control based on the workings of animal brains. Animal brains use less data and energy compared to current deep neural networks running on GPUs (graphic chips). Neuromorphic processors are therefore very suitable for small drones because they don’t need heavy and large hardware and batteries.
The results are extraordinary: during flight the drone’s deep neural network processes data up to 64 times faster and consumes three times less energy than when running on a GPU. Further developments of this technology may enable the leap for drones to become as small, agile, and smart as flying insects or birds.
Photo of the “neuromorphic drone” flying over a flower pattern. It illustrates the visual inputs the drone receives from the neuromorphic camera in the corners. Red indicates pixels getting darker, green indicates pixels getting brighter. (Image: TU Delft)
The services are necessary to maintain a domestic trusted source for strategic radiation-hardened microelectronics to meet the U.S. Department of Defense (DOD) certification to Congress, as stipulated by the fiscal 2018 National Defense Authorization Act Section 1,670, DOD officials say.
Radiation-hardened microelectronics components are necessary for manned and unmanned spacecraft operating on long-duration orbital missions in high-radiation space environments like geosynchronous orbits.
