Symmetry Reduction and Boundary Modes for Fe-Chains on an s-wave Superconductor

We investigate the superconducting phase diagram and boundary modes for a quasi-1D system formed by three Fe-Chains on an s-wave superconductor, motivated by the recent Princeton experiment. The $\vec l\cdot\vec s$ onsite spin-orbit term, inter-chain diagonal hopping couplings, and magnetic disorders in the Fe-chains are shown to be crucial for the superconducting phases, which can be topologically trivial or nontrivial in different parameter regimes. For the topological regime a single Majorana and multiple Andreew bound modes are obtained in the ends of the chain, while for the trivial phase only low-energy Andreev bound states survive. Nontrivial symmetry reduction mechanism induced by the $\vec l\cdot\vec s$ term, diagonal hopping couplings, and magnetic disorder is uncovered to interpret the present results. Our study also implies that the zero-bias peak observed in the recent experiment may or may not reflect the Majorana zero modes in the end of the Fe-chains.Comment: 5 pages, 4 figures; some minor errors are correcte

Tunable stacking fault energies by tailoring local chemical order in CrCoNi medium-entropy alloys

High-entropy alloys (HEAs) are an intriguing new class of metallic materials due to their unique mechanical behavior. Achieving a detailed understanding of structure-property relationships in these materials has been challenged by the compositional disorder that underlies their unique mechanical behavior. Accordingly, in this work, we employ first-principles calculations to investigate the nature of local chemical order and establish its relationship to the intrinsic and extrinsic stacking fault energy (SFE) in CrCoNi medium-entropy solid-solution alloys, whose combination of strength, ductility and toughness properties approach the best on record. We find that the average intrinsic and extrinsic SFE are both highly tunable, with values ranging from -43 mJ.m-2 to 30 mJ.m-2 and from -28 mJ.m-2 to 66 mJ.m-2, respectively, as the degree of local chemical order increases. The state of local ordering also strongly correlates with the energy difference between the face-centered cubic (fcc) and hexagonal-close packed (hcp) phases, which affects the occurrence of transformation-induced plasticity. This theoretical study demonstrates that chemical short-range order is thermodynamically favored in HEAs and can be tuned to affect the mechanical behavior of these alloys. It thus addresses the pressing need to establish robust processing-structure-property relationships to guide the science-based design of new HEAs with targeted mechanical behavior.Comment: 23 pages, 5 figure

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of backgrounds and become hard examples during training. Compared with those proposal-based ones, real-time detectors are in far more serious trouble since they renounce the use of region-proposing stage which is used to filter a majority of backgrounds for achieving real-time rates. Though foregrounds as hard examples are in urgent need of being mined from tons of backgrounds, a considerable number of state-of-the-art real-time detectors, like YOLO series, have yet to profit from existing hard example mining methods, as using these methods need detectors fit series of prerequisites. In this paper, we propose a general hard example mining method named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples. By using LRM, some elements representing easy examples in final feature map are filtered and detectors are forced to concentrate on hard examples during training. Extensive experiments validate the effectiveness of our method. With our method, the improvements of YOLOv2 detector on auto-driving related dataset KITTI and more general dataset PASCAL VOC are over 5% and 2% mAP, respectively. In addition, LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.Comment: 8 pages, 6 figure

Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection

Anomaly detection (AD) is a fundamental task in computer vision. It aims to identify incorrect image data patterns which deviate from the normal ones. Conventional methods generally address AD by preparing augmented negative samples to enforce self-supervised learning. However, these techniques typically do not consider semantics during augmentation, leading to the generation of unrealistic or invalid negative samples. Consequently, the feature extraction network can be hindered from embedding critical features. In this study, inspired by visual attention learning approaches, we propose CutSwap, which leverages saliency guidance to incorporate semantic cues for augmentation. Specifically, we first employ LayerCAM to extract multilevel image features as saliency maps and then perform clustering to obtain multiple centroids. To fully exploit saliency guidance, on each map, we select a pixel pair from the cluster with the highest centroid saliency to form a patch pair. Such a patch pair includes highly similar context information with dense semantic correlations. The resulting negative sample is created by swapping the locations of the patch pair. Compared to prior augmentation methods, CutSwap generates more subtle yet realistic negative samples to facilitate quality feature learning. Extensive experimental and ablative evaluations demonstrate that our method achieves state-of-the-art AD performance on two mainstream AD benchmark datasets

Identification of a Potentially Functional microRNA-mRNA Regulatory Network in Lung Adenocarcinoma Using a Bioinformatics Analysis

Background Lung adenocarcinoma (LUAD) is a common lung cancer with a high mortality, for which microRNAs (miRNAs) play a vital role in its regulation. Multiple messenger RNAs (mRNAs) may be regulated by miRNAs, involved in LUAD tumorigenesis and progression. However, the miRNA-mRNA regulatory network involved in LUAD has not been fully elucidated. Methods Differentially expressed miRNAs and mRNA were derived from the Cancer Genome Atlas (TCGA) dataset in tissue samples and from our microarray data in plasma (GSE151963). Then, common differentially expressed (Co-DE) miRNAs were obtained through intersected analyses between the above two datasets. An overlap was applied to confirm the Co-DEmRNAs identified both in targeted mRNAs and DEmRNAs in TCGA. A miRNA-mRNA regulatory network was constructed using Cytoscape. The top five miRNA were identified as hub miRNA by degrees in the network. The functions and signaling pathways associated with the hub miRNA-targeted genes were revealed through Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The key mRNAs in the protein-protein interaction (PPI) network were identified using the STRING database and CytoHubba. Survival analyses were performed using Gene Expression Profiling Interactive Analysis (GEPIA). Results The miRNA-mRNA regulatory network consists of 19 Co-DEmiRNAs and 760 Co-DEmRNAs. The five miRNAs (miR-539-5p, miR-656-3p, miR-2110, let-7b-5p, and miR-92b-3p) in the network were identified as hub miRNAs by degrees (>100). The 677 Co-DEmRNAs were targeted mRNAs from the five hub miRNAs, showing the roles in the functional analyses of the GO analysis and KEGG pathways (inclusion criteria: 836 and 48, respectively). The PPI network and Cytoscape analyses revealed that the top ten key mRNAs were NOTCH1, MMP2, IGF1, KDR, SPP1, FLT1, HGF, TEK, ANGPT1, and PDGFB. SPP1 and HGF emerged as hub genes through survival analysis. A high SPP1 expression indicated a poor survival, whereas HGF positively associated with survival outcomes in LUAD. Conclusion This study investigated a miRNA-mRNA regulatory network associated with LUAD, exploring the hub miRNAs and potential functions of mRNA in the network. These findings contribute to identify new prognostic markers and therapeutic targets for LUAD patients in clinical settings.Peer reviewe

Cerebral hemodynamic characteristics of acute mountain sickness upon acute high-altitude exposure at 3,700 m in young Chinese men.

PURPOSE: We aimed at identifying the cerebral hemodynamic characteristics of acute mountain sickness (AMS). METHODS: Transcranial Doppler (TCD) sonography examinations were performed between 18 and 24 h after arrival at 3,700 m via plane from 500 m (n = 454). A subgroup of 151 subjects received TCD examinations at both altitudes. RESULTS: The velocities of the middle cerebral artery, vertebral artery (VA) and basilar artery (BA) increased while the pulsatility indexes (PIs) and resistance indexes (RIs) decreased significantly (all p < 0.05). Velocities of BA were higher in AMS (AMS+) individuals when compared with non-AMS (AMS-) subjects (systolic velocity: 66 ± 12 vs. 69 ± 15 cm/s, diastolic velocity: 29 ± 7 vs. 31 ± 8 cm/s and mean velocity, 42 ± 9 vs. 44 ± 10 cm/s). AMS was characterized by higher diastolic velocity [V d_VA (26 ± 4 vs. 25 ± 4, p = 0.013)] with lower PI and RI (both p = 0.004) in VA. Furthermore, the asymmetry index (AI) of VAs was significantly lower in the AMS + group [-5.7 % (21.0 %) vs. -2.5 % (17.8 %), p = 0.016]. The AMS score was closely correlated with the hemodynamic parameters of BA and the V d_VA, PI, RI and AI of VA. CONCLUSION: AMS is associated with alterations in cerebral hemodynamics in the posterior circulation rather than the anterior one, and is characterized by higher blood velocity with lower resistance. In addition, the asymmetry of VAs may be involved in AMS