Measurements of the amplitude-dependent microwave surface resistance of an Au/Nb bilayer

Surface properties are critical to the capabilities of superconducting microwave devices. The native oxide of niobium-based devices is thought to consist of a thin normal conducting layer. To improve understanding on the importance of this layer, an attempt was made to replace it with a more easily controlled gold film. A niobium sample host microwave cavity was used to measure the surface resistance in continuous wave operation at 4.0 GHz and 5.2 GHz. Sample conditions studied include temperatures ranging from 1.6 K to 4.2 K with RF magnetic fields on the sample surface ranging from 1 mT to the maximum field before the superconducting properties were lost (quench field). The nominal film thickness of the gold layer was increased from 0.1 nm to 2.0 nm in five steps to study the impact of the normal layer thickness on surface resistance on a single niobium substrate. The 0.1 nm film was found to reduce the surface resistance of the sample and to enhance the quench field. With the exception of the final step from a 1.5 nm gold film to 2.0 nm, the magnitude of the surface resistance increased substantially with gold film thickness. The nature of the surface resistance field-dependence appeared to be roughly independent from the gold layer thickness. This initial study provides new perspectives and suggests avenues for optimizing and designing surfaces for resonant cavities in particle accelerators and quantum information applications.Comment: Submitted to: Superconductor Science and Technolog

MegDet: A Large Mini-Batch Object Detector

The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network, new framework, or novel loss design. But mini-batch size, a key factor in the training, has not been well studied. In this paper, we propose a Large MiniBatch Object Detector (MegDet) to enable the training with much larger mini-batch size than before (e.g. from 16 to 256), so that we can effectively utilize multiple GPUs (up to 128 in our experiments) to significantly shorten the training time. Technically, we suggest a learning rate policy and Cross-GPU Batch Normalization, which together allow us to successfully train a large mini-batch detector in much less time (e.g., from 33 hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st place of Detection task

EqCo: Equivalent Rules for Self-supervised Contrastive Learning

In this paper, we propose a method, named EqCo (Equivalent Rules for Contrastive Learning), to make self-supervised learning irrelevant to the number of negative samples in InfoNCE-based contrastive learning frameworks. Inspired by the InfoMax principle, we point that the margin term in contrastive loss needs to be adaptively scaled according to the number of negative pairs in order to keep steady mutual information bound and gradient magnitude. EqCo bridges the performance gap among a wide range of negative sample sizes, so that we can use only a few negative pairs (e.g. 16 per query) to perform self-supervised contrastive training on large-scale vision datasets like ImageNet, while with almost no accuracy drop. This is quite a contrast to the widely used large batch training or memory bank mechanism in current practices. Equipped with EqCo, our simplified MoCo (SiMo) achieves comparable accuracy with MoCo v2 on ImageNet (linear evaluation protocol) while only involves 4 negative pairs per query instead of 65536, suggesting that large quantities of negative samples might not be a critical factor in InfoNCE loss