Evolving center-vortex loops

We consider coarse-graining applied to nonselfintersecting planar center-vortex loops as they emerge in the confining phase of an SU(2) Yang-Mills theory. Well-established properties of planar curve-shrinking predict that a suitably defined, geometric effective action exhibits (mean-field) critical behavior when the conformal limit of circular points is reached. This suggests the existence of an asymptotic mass gap. We demonstrate that the initially sharp mean center-of-mass position in a given ensemble of curves develops a variance under the flow as is the case for a position eigenstate in free-particle quantum mechanics under unitary time evolution. A possible application of these concepts is an approach to high-$T_c$ superconductivity based (a) on the nonlocal nature of the electron (1-fold selfintersecting center-vortex loop) and (b) on planar curve-shrinking flow representing the decrease in thermal noise in a cooling cuprate.Comment: 15 pages, 8 figure

TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers

This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO. It aims to enable object detection on microcontrollers in the power domain of milliwatts, with less than 0.5MB memory available for storing convolutional neural network (CNN) weights. The proposed quantized network architecture with 422k parameters, enables real-time object detection on embedded microcontrollers, and it has been evaluated to exploit CNN accelerators. In particular, the proposed network has been deployed on the MAX78000 microcontroller achieving high frame-rate of up to 180fps and an ultra-low energy consumption of only 196{\mu}J per inference with an inference efficiency of more than 106 MAC/Cycle. TinyissimoYOLO can be trained for any multi-object detection. However, considering the small network size, adding object detection classes will increase the size and memory consumption of the network, thus object detection with up to 3 classes is demonstrated. Furthermore, the network is trained using quantization-aware training and deployed with 8-bit quantization on different microcontrollers, such as STM32H7A3, STM32L4R9, Apollo4b and on the MAX78000's CNN accelerator. Performance evaluations are presented in this paper

3D characterisation of hydrogen environmentally assisted cracking during static loading of AA7449-T7651

In this investigation, synchrotron X-ray microtomography was used to perform 3D in situ observations of crack initiation and growth during hydrogen environmentally assisted cracking (HEAC) in tensile samples of AA7449-T7651. Two smooth tensile samples with a 1 mm diameter gauge section were held at a fixed displacement (≈30% of yield stress) in warm, moist air (≈76∘C, 73% relative humidity). The samples were then imaged repeatedly using X-ray tomography until they fractured completely. The tomograms showing the nucleation and evolution of intergranular cracks were correlated with electron microscopy fractographs. This enabled the identification of crack initiation sites and the characterisation of the crack growth behaviour relative to the microstructure. The samples were found to fracture within an environmental exposure time of 240 min. Some cracks in both samples nucleated within an exposure time of 80 min (33–40% of the total lifetime). Many cracks were found to nucleate both internally and at the sample surface. However, only superficial cracks contributed to the final fracture surface as they grew faster owing to the direct environmental exposure and the larger crack opening. HEAC occurred prominently via brittle intergranular cracking, and cracks were found to slow down when approaching grain boundary triple junctions. Additionally, crack shielding from nearby cracks and the presence of coarse Al–Cu–Fe particles at the grain boundaries were also found to temporarily reduce the crack growth rates. After prolonged crack growth, the HEAC cracks displayed ductile striations and transgranular fracture, revealing a change in the crack growth mechanism at higher stress intensity factors

Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO

Smart glasses are rapidly gaining advanced functionality thanks to cutting-edge computing technologies, accelerated hardware architectures, and tiny AI algorithms. Integrating AI into smart glasses featuring a small form factor and limited battery capacity is still challenging when targeting full-day usage for a satisfactory user experience. This paper illustrates the design and implementation of tiny machine-learning algorithms exploiting novel low-power processors to enable prolonged continuous operation in smart glasses. We explore the energy- and latency-efficient of smart glasses in the case of real-time object detection. To this goal, we designed a smart glasses prototype as a research platform featuring two microcontrollers, including a novel milliwatt-power RISC-V parallel processor with a hardware accelerator for visual AI, and a Bluetooth low-power module for communication. The smart glasses integrate power cycling mechanisms, including image and audio sensing interfaces. Furthermore, we developed a family of novel tiny deep-learning models based on YOLO with sub-million parameters customized for microcontroller-based inference dubbed TinyissimoYOLO v1.3, v5, and v8, aiming at benchmarking object detection with smart glasses for energy and latency. Evaluations on the prototype of the smart glasses demonstrate TinyissimoYOLO's 17ms inference latency and 1.59mJ energy consumption per inference while ensuring acceptable detection accuracy. Further evaluation reveals an end-to-end latency from image capturing to the algorithm's prediction of 56ms or equivalently 18 fps, with a total power consumption of 62.9mW, equivalent to a 9.3 hours of continuous run time on a 154mAh battery. These results outperform MCUNet (TinyNAS+TinyEngine), which runs a simpler task (image classification) at just 7.3 fps per second