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

Small Sample Image Segmentation By Coupling Convolutions and Transformers

Compared with natural image segmentation, small sample image segmentation tasks, such as medical image segmentation and defect detection, have been less studied. Recent studies made efforts on bringing together Convolutional Neural Networks (CNNs) and Transformers in a serial or interleaved architecture in order to incorporate long-range dependencies into the features extracted using CNNs. In this study, we argue that these architectures limit the capability of the combination of CNNs and Transformers. To this end, we propose a dual-stream small sample image segmentation network, namely, the Interactive Coupling of Convolutions and Transformers Based UNet (ICCT-UNet) 1 , motivated by the success achieved using the UNet in the scenario of small sample image segmentation. Within this network, a CNN stream is paralleled with a Transformer stream while maintaining feature exchange inside each block through the proposed Window-Based Multi-head Cross-Attention (W-MHCA) mechanism. To derive an overall segmentation, the features learned by both the streams are further fused using a Residual Fusion Module (RFM). Experimental results show that the ICCT-UNet outperforms, or at least performs comparably to, its counterparts on eight sets of medical and defective images. These promising results should be attributed to the effective combination of the local and global features fulfilled by the proposed interactive coupling method.</p

Contrastive Hashing with Vision Transformer for Image Retrieval

Hashing techniques have attracted considerable attention owing to their advantages of efficient computation and economical storage. However, it is still a challenging problem to generate more compact binary codes for promising performance. In this paper, we propose a novel contrastive vision transformer hashing method, which seamlessly integrates contrastive learning and vision transformers (ViTs) with hash technology into a well-designed model to learn informative features and compact binary codes simultaneously. First, we modify the basic contrastive learning framework by designing several hash layers to meet the specific requirement of hash learning. In our hash network, ViTs are applied as backbones for feature learning, which is rarely performed in existing hash learning methods. Then, we design a multiobjective loss function, in which contrastive loss explores discriminative features by maximizing agreement between different augmented views from the same image, similarity preservation loss performs pairwise semantic preservation to enhance the representative capabilities of hash codes, and quantization loss controls the quantitative error. Hence, we can facilitate end-to-end joint training to improve the retrieval performance. The encouraging experimental results on three widely used benchmark databases demonstrate the superiority of our algorithm compared with several state-of-the-art hashing algorithms.</p

WRD-Net: Water Reflection Detection Using A Parallel Attention Transformer

In contrast to symmetry detection, Water Reflection Detection (WRD) is less studied. We treat this topic as a Symmetry Axis Point Prediction task which outputs a set of points by implicitly learning Gaussian heat maps and explicitly learning numerical coordinates. We first collect a new data set, namely, the Water Reflection Scene Data Set (WRSD). Then, we introduce a novel Water Reflection Detection Network, i.e., WRD-Net. This network is built on top of a series of Parallel Attention Vision Transformer blocks with the Atrous Spatial Pyramid (ASP-PAViT) that we deliberately design. Each block captures both the local and global features at multiple scales. To our knowledge, neither the WRSD nor the WRD-Net has been used for water reflection detection before. To derive the axis of symmetry, we perform Principal Component Analysis (PCA) on the points predicted. Experimental results show that the WRD-Net outperforms its counterparts and achieves the true positive rate of 0.823 compared with the human annotation.</p

Learning the Precise Feature for Cluster Assignment

Clustering is one of the fundamental tasks in com-puter vision and pattern recognition. Recently, deep clusteringmethods (algorithms based on deep learning) have attractedwide attention with their impressive performance. Most ofthese algorithms combine deep unsupervised feature learningand standard clustering together. However, the separation offeature extraction and clustering will lead to suboptimal solutionsbecause the two-stage strategy prevents representation learningfrom adapting to subsequent tasks (e.g., clustering according tospecific cues). To overcome this issue, efforts have been made inthe dynamic adaption of representation and cluster assignment,whereas current state-of-the-art methods suffer from heuristicallyconstructed objectives with representation and cluster assignmentalternatively optimized. To further standardize the clusteringproblem, we formulate the objective of clustering as findinga precise feature as the cue for cluster assignment. Based onthis, we propose a general-purpose deep clustering frameworkwhich radically integrate representation learning and clusteringinto an individual pipeline for the first time. The proposedframework exploits the powerful ability of recently progressedgenerative models for learning intrinsic features, and imposes anentropy minimization on the distribution of cluster assignmentby a variational algorithm. Experimental results show that theperformance of our method is superior, or at least comparable to,the state-of-the-art methods on the handwritten digit recognition,race recognition and object recognition benchmark datasets

BeDCV: Blockchain-Enabled Decentralized Consistency Verification for Cross-Chain Calculation

With the increase of data stored on the blockchain, the efficiency of storage and calculation of blockchain has gradually become a bottleneck restricting the development of blockchain. By storing data on multiple chains, blockchains can request data from other chains for calculation and the storage pressure can be alleviated. But the transfer of a large amount of data between chains suffers from low transfer efficiency and poor security. A reasonable design is to perform the calculation on the data storage chain and only transfer the results across chains. However, since the calculation process is invisible, blockchains cannot judge the consistency of calculation results from other chains. In this paper, we provide a blockchain-enabled decentralized consistency verification scheme for cross-chain calculation (BeDCV). Considering the decentralized characteristic of blockchain, we adopt the blockchain called supervision chain for decentralized auditing. We modify paillier homomorphic encryption to encrypt data involved in the calculation for correctness verification. Then, we aggregate the ciphertexts of data to generate the audit proof for integrity verification. Besides, we verify whether the data involved in the calculation are real-time by leveraging a counting bloom filter. The supervision chain can check the correctness, integrity, and real-time performance of cross-chain data calculation without revealing any original information about the data. The theoretical and experimental analysis demonstrates that BeDCV can verify the consistency of cross-chain data calculation result effectively, realizing secure and reliable expansion of blockchain

Stabilizing mechanism and running behavior of couplers on heavy haul trains

Published studies in regard to coupler systems have been mainly focused on the manufacturing process or coupler strength issues. With the ever increasing of tonnage and length of heavy haul trains, lateral in-train forces generated by longitudinal in-train forces and coupler rotations have become a more and more significant safety issue for heavy haul train operations. Derailments caused by excessive lateral in-train forces are frequently reported. This article studies two typical coupler systems used on heavy haul locomotives. Their structures and stabilizing mechanism are analyzed before the corresponding models are developed. Coupler systems models are featured by two distinct stabilizing mechanism models and draft gear models with hysteresis considered. A model set which consists of four locomotives and three coupler systems is developed to study the rotational behavior of different coupler systems and their implications for locomotive dynamics. Simulated results indicate that when the locomotives are equipped with the type B coupler system, locomotives can meet the dynamics standard on tangent tracks; while the dynamics performance on curved tracks is very poor. The maximum longitudinal in-train force for locomotives equipped with the type B coupler system is 2000 kN. Simulations revealed a distinct trend for the type A coupler system. Locomotive dynamics are poorer for the type A case when locomotives are running on tangent tracks, while the dynamics are better for the type A case when locomotives are running on curved tracks. Theoretical studies and simulations carried out in this article suggest that a combination of the two types of stabilizing mechanism can result in a good design which can significantly decrease the relevant derailments

Longitudinal double-spin asymmetry and cross section for inclusive jet production in polarized proton collisions at square root of s = 200 GeV

We report a measurement of the longitudinal double-spin asymmetry A(LL) and the differential cross section for inclusive midrapidity jet production in polarized proton collisions at s=200 GeV. The cross section data cover transverse momenta 5 < p(T)< 50 GeV/c and agree with next-to-leading order perturbative QCD evaluations. The A(LL) data cover 5 < p(T)< 17 GeV/c and disfavor at 98% C.L. maximal positive gluon polarization in the polarized nucleon