Anomalies of upper critical field in the spinel superconductor LiTi2_2O4−δ_{4-\delta}

High-field electrical transport and point-contact tunneling spectroscopy were used to investigate superconducting properties of the unique spinel oxide, LiTi$_2$O$_{4-\delta}$ films with various oxygen content. We find that the upper critical field $B_\mathrm{c2}$ gradually increases as more oxygen impurities are brought into the samples by carefully tuning the deposition atmosphere. It is striking that although the superconducting transition temperature and energy gap are almost unchanged, an astonishing isotropic $B_\mathrm{c2}$ up to $\sim$ 26 Tesla is observed in oxygen-rich sample, which is doubled compared to the anoxic sample and breaks the Pauli limit. Such anomalies of $B_\mathrm{c2}$ were rarely reported in other three dimensional superconductors. Combined with all the anomalies, three dimensional spin-orbit interaction induced by tiny oxygen impurities is naturally proposed to account for the remarkable enhancement of $B_\mathrm{c2}$ in oxygen-rich LiTi$_2$O$_{4-\delta}$ films. Such mechanism could be general and therefore provides ideas for optimizing practical superconductors with higher $B_\mathrm{c2}$

Anomalies of upper critical field in the spinel superconductor LiTi2 O4-δ

© 2019 American Physical Society. High-field electrical transport and point-contact tunneling spectroscopy are used to investigate superconducting properties of spinel oxide LiTi2O4-δ films with various oxygen contents. It is striking that although the superconducting transition temperature and energy gap are almost unchanged, an isotropic upper critical field Bc2 up to 26.0 T is observed in the oxygen-rich sample, which is more than twice the Bc2 of 11.3 T in the anoxic one. The change of the dominating pair-breaking mechanism from the orbital effect to the spin flip at Bc2 is achieved by tuning oxygen contents, which can be explained by the appearance of small Fermi pockets due to extra oxygen. Our paper provides deep understanding of the intrinsic relation between Bc2 and the complex Fermi surface, and contributes a promising way to enhance Bc2 for practical superconductors

Robust Cross-Scene Foreground Segmentation in Surveillance Video

Training only one deep model for large-scale cross-scenevideo foreground segmentation is challenging due to the off-the-shelf deep learning based segmentor relies on scene-specific structural information. This results in deep mod-els that are scene-biased and evaluations that are scene-influenced.In this paper, we integrate dual modalities(foregrounds’ motion and appearance), and then eliminat-ing features without representativeness of foreground throughattention-module-guided selective-connection structures. It isin an end-to-end training manner and to achieve scene adap-tation in the plug and play style. Experiments indicate theproposed method significantly outperforms the state-of-the-art deep models and background subtraction methods in un-trained scenes – LIMU and LASIESTA. (Codes and datasetwill be available after the anonymous stage.

Learning Calibrated-Guidance for Object Detection in Aerial Images

Object detection is one of the most fundamental yet challenging research topics in the domain of computer vision. Recently, the study on this topic in aerial images has made tremendous progress. However, complex background and worse imaging quality are obvious problems in aerial object detection. Most state-of-the-art approaches tend to develop elaborate attention mechanisms for the space-time feature calibrations with arduous computational complexity, while surprisingly ignoring the importance of feature calibrations in channel-wise. In this work, we propose a simple yet effective Calibrated-Guidance(CG) scheme to enhance channel communications in a feature transformer fashion, which can adaptively determine the calibration weights for each channel based on the global feature affinity correlations. Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance. Then, re-representing each channel by aggregating all the channels weighted together via the guidance operation. Our CG is a general module that can be plugged into any deep neural networks, which is named as CG-Net. To demonstrate its effectiveness and efficiency, extensive experiments are carried out on both oriented object detection task and horizontal object detection task in aerial images. Experimental results on two challenging benchmarks(i.e., DOTA and HRSC2016) demonstrate that our CG-Net can achieve the new state-of-the-art performance in accuracy with a fair computational overhead. The source code has been open sourced at https://github.com/WeiZongqi/CG-Ne

Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images

Object detection has made tremendous strides in computer vision. Small object detection with appearance degradation is a prominent challenge, especially for aerial observations. To collect sufficient positive/negative samples for heuristic training, most object detectors preset region anchors in order to calculate Intersection-over-Union (IoU) against the ground-truthed data. In this case, small objects are frequently abandoned or mislabeled. In this paper, we present an effective Dynamic Enhancement Anchor (DEA) network to construct a novel training sample generator. Different from the other state-of-the-art techniques, the proposed network leverages a sample discriminator to realize interactive sample screening between an anchor-based unit and an anchor-free unit to generate eligible samples. Besides, multi-task joint training with a conservative anchor-based inference scheme enhances the performance of the proposed model while reducing computational complexity. The proposed scheme supports both oriented and horizontal object detection tasks. Extensive experiments on two challenging aerial benchmarks (i.e., DOTA and HRSC2016) indicate that our method achieves state-of-the-art performance in accuracy with moderate inference speed and computational overhead for training. On DOTA, DEA-Net surpasses the other state-of-the-art by 0.40% mean-Average-Precision (mAP) for oriented object detection with a weaker backbone network (ResNet-101vsResNet-152) and 3.08% mean-Average-Precision (mAP)for horizontal object detection with the same backbone. OnHRSC2016, it surpasses the previous best model by 1.1% using only 3 horizontal anchors

Environmental Knowledge-Driven Over-the-Horizon Propagation Loss Prediction Based on Short- and Long- Parallel Double-Flow TrellisNets

Accurate perceptual knowledge of atmospheric characteristics in the propagation path is of great significance for the design of communication systems. However, the atmosphere above the ocean is inhomogeneous, which brings challenges to accurate prediction of propagation loss. Moreover, the atmospheric refractive index distribution model calculated from atmospheric data requires at least two stations with near-sea meteorological data. In real maritime over-the-horizon communication or detection, only one station transmits and receives meteorological data, and the received meteorological data and propagation loss have large temporal noise. To address these issues, first, a denoising model based on the one-dimensional convolution autoencoder (1DCAE) is constructed to filter out the temporal noise of input environmental meteorological data and propagation loss. Second, to accurately predict the influence of environmental factors on the prediction of propagation loss, a deep-learning framework called short- and long- term parallel double-flow TrellisNets (SL-TrellisNets) is proposed to predict the loss. Finally, the extensive experiments demonstrated that the root mean square and mean absolute errors of the proposed 1DCAE are dramatically reduced. Our proposed SL-TrellisNets outperforms the other state-of-the-art techniques in terms of propagation loss prediction. In addition, we analyzed the impact of the environmental factors on the accuracy of over-the-horizon propagation loss prediction.</p