Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Heavy-flavour and quarkonium production in the LHC era: from proton-proton to heavy-ion collisions

Abstract: This report reviews the study of open heavy-flavour and quarkonium production in high-energy hadronic collisions, as tools to investigate fundamental aspects of Quantum Chromodynamics, from the proton and nucleus structure at high energy to deconfinement and the properties of the Quark-Gluon Plasma. Emphasis is given to the lessons learnt from LHC Run 1 results, which are reviewed in a global picture with the results from SPS and RHIC at lower energies, as well as to the questions to be addressed in the future. The report covers heavy flavour and quarkonium production in proton-proton, proton-nucleus and nucleus-nucleus collisions. This includes discussion of the effects of hot and cold strongly interacting matter, quarkonium photo-production in nucleus-nucleus collisions and perspectives on the study of heavy flavour and quarkonium with upgrades of existing experiments and new experiments. The report results from the activity of the SaporeGravis network of the I3 Hadron Physics programme of the European Union 7th Framework Programme.

A Multi-Organ Nucleus Segmentation Challenge

Abstract: Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

Regularized Robust Coding for Face Recognition

Abstract: Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1 -norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes the computational cost of SRC very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding (IR3C) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting, and expression changes, etc.

Reduction of nitrophenols to aminophenols under concerted catalysis by Au/g-C3N4 contact system

Abstract: We report a facile one-step strategy to fabricate an Au/g-C 3 N 4 contact system with different Au contents. Morphology observation shows that Au nanoparticles with an average diameter of 2.6 nm are firmly anchored on the surface of two-dimensional g-C 3 N 4 sheets. It is found that the Au/g-C 3 N 4 contact system exhibits an unusual bi-functionality of catalytic and visible-light-driven photocatalytic activities, thus the hydrogenation reduction of nitrophenol to aminophenol can be rapidly achieved under concerted catalysis by the system. Among the Au/g-C 3 N 4 contact systems studied, the Au/g-C 3 N 4 -6 exhibits the highest rate constant of 5.9362 × 10 −3 s −1 in the dark and 7.9895 × 10 −3 s −1 under visible light irradiation for the reduction of p -nitrophenol to p -aminophenol, which is impressively higher than that pure Au nanoparticles or recently reported Au-based nanocatalysts. Such a concerted catalysis can be attributed to the negative shift in Fermi level of Au caused by the induced charge-transfer effect as a result of the strong interaction between Au nanoparticles and g-C 3 N 4 .