Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks

Deep clustering has recently attracted significant attention. Despite the remarkable progress, most of the previous deep clustering works still suffer from two limitations. First, many of them focus on some distribution-based clustering loss, lacking the ability to exploit sample-wise (or augmentation-wise) relationships via contrastive learning. Second, they often neglect the indirect sample-wise structure information, overlooking the rich possibilities of multi-scale neighborhood structure learning. In view of this, this paper presents a new deep clustering approach termed Image clustering with contrastive learning and multi-scale Graph Convolutional Networks (IcicleGCN), which bridges the gap between convolutional neural network (CNN) and graph convolutional network (GCN) as well as the gap between contrastive learning and multi-scale neighborhood structure learning for the image clustering task. The proposed IcicleGCN framework consists of four main modules, namely, the CNN-based backbone, the Instance Similarity Module (ISM), the Joint Cluster Structure Learning and Instance reconstruction Module (JC-SLIM), and the Multi-scale GCN module (M-GCN). Specifically, with two random augmentations performed on each image, the backbone network with two weight-sharing views is utilized to learn the representations for the augmented samples, which are then fed to ISM and JC-SLIM for instance-level and cluster-level contrastive learning, respectively. Further, to enforce multi-scale neighborhood structure learning, two streams of GCNs and an auto-encoder are simultaneously trained via (i) the layer-wise interaction with representation fusion and (ii) the joint self-adaptive learning that ensures their last-layer output distributions to be consistent. Experiments on multiple image datasets demonstrate the superior clustering performance of IcicleGCN over the state-of-the-art

Impact of template backbone heterogeneity on RNA polymerase II transcription.

Variations in the sugar component (ribose or deoxyribose) and the nature of the phosphodiester linkage (3'-5' or 2'-5' orientation) have been a challenge for genetic information transfer from the very beginning of evolution. RNA polymerase II (pol II) governs the transcription of DNA into precursor mRNA in all eukaryotic cells. How pol II recognizes DNA template backbone (phosphodiester linkage and sugar) and whether it tolerates the backbone heterogeneity remain elusive. Such knowledge is not only important for elucidating the chemical basis of transcriptional fidelity but also provides new insights into molecular evolution. In this study, we systematically and quantitatively investigated pol II transcriptional behaviors through different template backbone variants. We revealed that pol II can well tolerate and bypass sugar heterogeneity sites at the template but stalls at phosphodiester linkage heterogeneity sites. The distinct impacts of these two backbone components on pol II transcription reveal the molecular basis of template recognition during pol II transcription and provide the evolutionary insight from the RNA world to the contemporary 'imperfect' DNA world. In addition, our results also reveal the transcriptional consequences from ribose-containing genomic DNA

Anisotropic Pauli spin-blockade effect and spin-orbit interaction field in an InAs nanowire double quantum dot

We report on experimental detection of the spin-orbit interaction field in an InAs nanowire double quantum dot device. In the spin blockade regime, leakage current through the double quantum dot is measured and is used to extract the effects of spin-orbit interaction and hyperfine interaction on spin state mixing. At finite magnetic fields, the leakage current arising from the hyperfine interaction is suppressed and the spin-orbit interaction dominates spin state mixing. We observe dependence of the leakage current on the applied magnetic field direction and determine the direction of the spin-orbit interaction field. We show that the spin-orbit field lies in a direction perpendicular to the nanowire axis but with a pronounced off-substrate-plane angle. It is for the first time that such an off-substrate-plane spin-orbit field in an InAs nanowire has been detected. The results are expected to have an important implication in employing InAs nanowires to construct spin-orbit qubits and topological quantum devices.Comment: 20 pages, 5 figures, Supporting Informatio

NLO fragmentation functions for a quark into a spin-singlet quarkonium: Same flavor case

In the paper, we calculate the fragmentation functions for $c \to \eta_c$ and $b \to \eta_b$ up to next-to-leading-order (NLO) QCD accuracy. The ultraviolet divergences in the real corrections are removed through operator renormalization under the modified minimal subtraction scheme. We then obtain the fragmentation functions $D_{c \to \eta_c}(z,\mu_F)$ and $D_{b \to \eta_b}(z,\mu_F)$ up to NLO QCD accuracy, which are presented as figures and fitting functions. The numerical results show that the NLO corrections are significant. The sensitives of the fragmentation functions to the renormalization scale and the factorization scale are analyzed explicitly.Comment: 18 pages, 7 figure

Gate defined quantum dot realized in a single crystalline InSb nanosheet

Single crystalline InSb nanosheet is an emerging planar semiconductor material with potential applications in electronics, infrared optoelectronics, spintronics and topological quantum computing. Here we report on realization of a quantum dot device from a single crystalline InSb nanosheet grown by molecular-beam epitaxy. The device is fabricated from the nanosheet on a Si/SiO2 substrate and the quantum dot confinement is achieved by top gate technique. Transport measurements show a series of Coulomb diamonds, demonstrating that the quantum dot is well defined and highly tunable. Tunable, gate-defined, planar InSb quantum dots offer a renewed platform for developing semiconductor-based quantum computation technology.Comment: 12 pages, 4 figure