Absence of a transport signature of spin-orbit coupling in graphene with indium adatoms

Enhancement of the spin-orbit coupling in graphene may lead to various topological phenomena and also find applications in spintronics. Adatom absorption has been proposed as an effective way to achieve the goal. In particular, great hope has been held for indium in strengthening the spin-orbit coupling and realizing the quantum spin Hall effect. To search for evidence of the spin-orbit coupling in graphene absorbed with indium adatoms, we carry out extensive transport measurements, i.e., weak localization magnetoresistance, quantum Hall effect and non-local spin Hall effect. No signature of the spin-orbit coupling is found. Possible explanations are discussed.Comment: 5 pages, 4 figures, with supplementary material

Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations

This paper explores a new natural language processing task, review-driven multi-label music style classification. This task requires the system to identify multiple styles of music based on its reviews on websites. The biggest challenge lies in the complicated relations of music styles. It has brought failure to many multi-label classification methods. To tackle this problem, we propose a novel deep learning approach to automatically learn and exploit style correlations. The proposed method consists of two parts: a label-graph based neural network, and a soft training mechanism with correlation-based continuous label representation. Experimental results show that our approach achieves large improvements over the baselines on the proposed dataset. Especially, the micro F1 is improved from 53.9 to 64.5, and the one-error is reduced from 30.5 to 22.6. Furthermore, the visualized analysis shows that our approach performs well in capturing style correlations

Further analysis of natural antibodies against ischemic stroke

BackgroundOur previous study revealed that circulating levels of IgG natural antibodies (NAbs) for vascular endothelial growth factor receptor 1 (VEGFR1) were significantly decreased in patients with arteriosclerosis compared with control subjects. To enhance the sensitivity of an enzyme-linked immunosorbent assay (ELISA) developed in-house for antibody testing, the present work was designed to investigate additive signals in the in-house ELISA developed with the combination of two or more linear peptide antigens derived from target proteins of interest, including VEGFR1, oxidized low-density lipoprotein receptor 1 (LOX-1), interleukins 6 (IL6) and 8 (IL8).MethodsA total of 218 patients with ischemic stroke and 198 healthy controls were enrolled and an in-house ELISA was developed with linear peptides derived from VEGFR1, LOX-1, IL6, and IL8 to detect their IgG levels in plasma.ResultsCompared with control subjects, plasma levels of IgG NAbs for the IL6-IL8 combination were significantly lower in female patients (Z = −3.149, P = 0.002), whereas male patients showed significantly lower levels of plasma anti-VEGFR IgG (Z = −3.895, P < 0.001) and anti-LOX1a IgG (Z = −4.329, P < 0.001). Because plasma levels of IgG NAbs for both the IL6-IL8-LOX1a-LOX1b combination and the VEGFR1a-VEGFR1b-LOX1a-LOX1b combination were significantly lower in the patient group than the control group, receiver operating characteristic (ROC) analysis was performed and the results showed that the VEGFR1a-VEGFR1b-LOX1a-LOX1b combination had an area under the ROC curve (AUC) of 0.70 for its IgG assay with a sensitivity of 27.1% against the specificity of 95.5% and that the IL6-IL8-LOX1a-LOX1b combination had an AUC of 0.67 for its IgG assay with a sensitivity of 21.1% against the specificity of 95.5%. Spearman correlation analysis showed that plasma IgG NAbs against the IL6-IL8 combination were positively correlated with carotid plaque size only in male patients (r = 0.270, p = 0.002).ConclusionsCirculating IgG NAbs for the target molecules studied may be potential biomarkers for a subgroup of ischemic stroke and also contribute to the gender differences in clinical presentation of the disease

Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications

Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a doubt, the emergence of SAM will yield significant benefits for a wide array of practical image segmentation applications. In this study, we conduct a series of intriguing investigations into the performance of SAM across various applications, particularly in the fields of natural images, agriculture, manufacturing, remote sensing, and healthcare. We analyze and discuss the benefits and limitations of SAM and provide an outlook on future development of segmentation tasks. Note that our work does not intend to propose new algorithms or theories, but rather provide a comprehensive view of SAM in practice. This work is expected to provide insights that facilitate future research activities toward generic segmentation.Comment: Tech Repor