Single-layer behavior and slow carrier density dynamic of twisted graphene bilayer

We report scanning tunneling microscopy (STM) and spectroscopy (STS) of twisted graphene bilayer on SiC substrate. For twist angle ~ 4.5o the Dirac point ED is located about 0.40 eV below the Fermi level EF due to the electron doping at the graphene/SiC interface. We observed an unexpected result that the local Dirac point around a nanoscaled defect shifts towards the Fermi energy during the STS measurements (with a time scale about 100 seconds). This behavior was attributed to the decoupling between the twisted graphene and the substrate during the measurements, which lowers the carrier density of graphene simultaneously

Strain Induced One-Dimensional Landau-Level Quantization in Corrugated Graphene

Theoretical research has predicted that ripples of graphene generates effective gauge field on its low energy electronic structure and could lead to zero-energy flat bands, which are the analog of Landau levels in real magnetic fields. Here we demonstrate, using a combination of scanning tunneling microscopy and tight-binding approximation, that the zero-energy Landau levels with vanishing Fermi velocities will form when the effective pseudomagnetic flux per ripple is larger than the flux quantum. Our analysis indicates that the effective gauge field of the ripples results in zero-energy flat bands in one direction but not in another. The Fermi velocities in the perpendicular direction of the ripples are not renormalized at all. The condition to generate the ripples is also discussed according to classical thin-film elasticity theory.Comment: 4 figures, Phys. Rev.

Transfer Learning and Deep Domain Adaptation

Transfer learning is an emerging technique in machine learning, by which we can solve a new task with the knowledge obtained from an old task in order to address the lack of labeled data. In particular deep domain adaptation (a branch of transfer learning) gets the most attention in recently published articles. The intuition behind this is that deep neural networks usually have a large capacity to learn representation from one dataset and part of the information can be further used for a new task. In this research, we firstly present the complete scenarios of transfer learning according to the domains and tasks. Secondly, we conduct a comprehensive survey related to deep domain adaptation and categorize the recent advances into three types based on implementing approaches: fine-tuning networks, adversarial domain adaptation, and sample-reconstruction approaches. Thirdly, we discuss the details of these methods and introduce some typical real-world applications. Finally, we conclude our work and explore some potential issues to be further addressed

Electronic Structures of Graphene Layers on Metal Foil: Effect of Point Defects

Here we report a facile method to generate a high density of point defects in graphene on metal foil and show how the point defects affect the electronic structures of graphene layers. Our scanning tunneling microscopy (STM) measurements, complemented by first principle calculations, reveal that the point defects result in both the intervalley and intravalley scattering of graphene. The Fermi velocity is reduced in the vicinity area of the defect due to the enhanced scattering. Additionally, our analysis further points out that periodic point defects can tailor the electronic properties of graphene by introducing a significant bandgap, which opens an avenue towards all-graphene electronics.Comment: 4 figure

Advances in Convolutional Neural Networks

Deep Learning, also known as deep representation learning, has dramatically improved the performances on a variety of learning tasks and achieved tremendous successes in the past few years. Specifically, artificial neural networks are mainly studied, which mainly include Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Among these networks, CNNs got the most attention due to the kernel methods with the weight sharing mechanism, and achieved state-of-the-art in many domains, especially computer vision. In this research, we conduct a comprehensive survey related to the recent improvements in CNNs, and we demonstrate these advances from the low level to the high level, including the convolution operations, convolutional layers, architecture design, loss functions, and advanced applications

Long intergenic non-coding RNA expression signature in human breast cancer

Breast cancer is a complex disease, characterized by gene deregulation. There is less systematic investigation of the capacity of long intergenic non-coding RNAs (lincRNAs) as biomarkers associated with breast cancer pathogenesis or several clinicopathological variables including receptor status and patient survival. We designed a two-stage study, including 1,000 breast tumor RNA-seq data from The Cancer Genome Atlas (TCGA) as the discovery stage, and RNA-seq data of matched tumor and adjacent normal tissue from 50 breast cancer patients as well as 23 normal breast tissue from healthy women as the replication stage. We identified 83 lincRNAs showing the significant expression changes in breast tumors with a false discovery rate (FDR) < 1% in the discovery dataset. Thirty-seven out of the 83 were validated in the replication dataset. Integrative genomic analyses suggested that the aberrant expression of these 37 lincRNAs was probably related with the expression alteration of several transcription factors (TFs). We observed a differential co-expression pattern between lincRNAs and their neighboring genes. We found that the expression levels of one lincRNA (RP5-1198O20 with Ensembl ID ENSG00000230615) were associated with breast cancer survival with P < 0.05. Our study identifies a set of aberrantly expressed lincRNAs in breast cancer

Hand‐Held Gamma‐Ray Imaging Sensors Using Room‐Temperature 3‐Dimensional Position‐Sensitive Semiconductor Spectrometers

This paper demonstrates the capability of compact gamma‐ray imaging devices using 3‐dimensional position sensitive CdZnTe semiconductor gamma‐ray spectrometers, developed at the University of Michigan. A prototype imager was constructed and tested using two 1 cm cube 3‐dimensional position sensitive CdZnTe detectors. Energy resolutions of 1.5% FWHM for single pixel events at 662 keV gamma‐ray energy were obtained on both detectors, and an angular resolution of about 5° FWHM was demonstrated. The capabilities of proposed devices, which can cover a wider energy range up to 2.6 MeV, are discussed. © 2002 American Institute of PhysicsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87581/2/209_1.pd

Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation

This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS), which learns to segment objects of arbitrary classes using mere image-text pairs. Existing works turn to enhance the vanilla vision transformer by introducing explicit grouping recognition, i.e., employing several group tokens/centroids to cluster the image tokens and perform the group-text alignment. Nevertheless, these methods suffer from a granularity inconsistency regarding the usage of group tokens, which are aligned in the all-to-one v.s. one-to-one manners during the training and inference phases, respectively. We argue that this discrepancy arises from the lack of elaborate supervision for each group token. To bridge this granularity gap, this paper explores explicit supervision for the group tokens from the prototypical knowledge. To this end, this paper proposes the non-learnable prototypical regularization (NPR) where non-learnable prototypes are estimated from source features to serve as supervision and enable contrastive matching of the group tokens. This regularization encourages the group tokens to segment objects with less redundancy and capture more comprehensive semantic regions, leading to increased compactness and richness. Based on NPR, we propose the prototypical guidance segmentation network (PGSeg) that incorporates multi-modal regularization by leveraging prototypical sources from both images and texts at different levels, progressively enhancing the segmentation capability with diverse prototypical patterns. Experimental results show that our proposed method achieves state-of-the-art performance on several benchmark datasets. The source code is available at https://github.com/Ferenas/PGSeg.Comment: 14 pages, Accept in NeurIPS 202

Enhanced Intervalley Scattering of Twisted Bilayer Graphene by Periodic AB Stacked Atoms

The electronic properties of twisted bilayer graphene on SiC substrate were studied via combination of transport measurements and scanning tunneling microscopy. We report the observation of enhanced intervalley scattering from one Dirac cone to the other, which contributes to weak localization, of the twisted bilayer graphene by increasing the interlayer coupling strength. Our experiment and analysis demonstrate that the enhanced intervalley scattering is closely related to the periodic AB stacked atoms (the A atom of layer 1 and the B atom of layer 2 that have the same horizontal positions) that break the sublattice degeneracy of graphene locally. We further show that these periodic AB stacked atoms affect intervalley but not intravalley scattering. The result reported here provides an effective way to atomically manipulate the intervalley scattering of graphene.Comment: 4figure