Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

High Catalytic Activity and Chemoselectivity of Sub-nanometric Pd Clusters on Porous Nanorods of CeO2 for Hydrogenation of Nitroarenes

Abstract: Sub-nanometric Pd clusters on porous nanorods of CeO2 (PN-CeO2) with a high Pd dispersion of 73.6% exhibit the highest catalytic activity and best chemoselectivity for hydrogenation of nitroarenes to date. For hydrogenation of 4-nitrophenol, the catalysts yield a TOF of ∼44059 h–1 and a chemoselectivity to 4-aminophenol of >99.9%. The superior catalytic performance can be attributed to a cooperative effect between the highly dispersed sub-nanometric Pd clusters for hydrogen activation and unique surface sites of PN-CeO2 with a high concentration of oxygen vacancy for an energetically and geometrically preferential adsorption of nitroarenes via nitro group. The high concentration of surface defects of PN-CeO2 and large Pd dispersion contribute to the enhanced catalytic activity for the hydrogenation reactions. The high chemoselectivity is mainly governed by the high Pd dispersion on the support. The catalysts also deliver high catalytic activity and selectivity for nitroaromatics with various reducible sub...

Learning without Human Scores for Blind Image Quality Assessment

Abstract: General purpose blind image quality assessment (BIQA) has been recently attracting significant attention in the fields of image processing, vision and machine learning. State-of-the-art BIQA methods usually learn to evaluate the image quality by regression from human subjective scores of the training samples. However, these methods need a large number of human scored images for training, and lack an explicit explanation of how the image quality is affected by image local features. An interesting question is then: can we learn for effective BIQA without using human scored images? This paper makes a good effort to answer this question. We partition the distorted images into overlapped patches, and use a percentile pooling strategy to estimate the local quality of each patch. Then a quality-aware clustering (QAC) method is proposed to learn a set of centroids on each quality level. These centroids are then used as a codebook to infer the quality of each patch in a given image, and subsequently a perceptual quality score of the whole image can be obtained. The proposed QAC based BIQA method is simple yet effective. It not only has comparable accuracy to those methods using human scored images in learning, but also has merits such as high linearity to human perception of image quality, real-time implementation and availability of image local quality map.

Comparative genetic architectures of schizophrenia in East Asian and European populations

Abstract: Schizophrenia is a debilitating psychiatric disorder with approximately 1% lifetime risk globally. Large-scale schizophrenia genetic studies have reported primarily on European ancestry samples, potentially missing important biological insights. Here, we report the largest study to date of East Asian participants (22,778 schizophrenia cases and 35,362 controls), identifying 21 genome-wide-significant associations in 19 genetic loci. Common genetic variants that confer risk for schizophrenia have highly similar effects between East Asian and European ancestries (genetic correlation = 0.98 ± 0.03), indicating that the genetic basis of schizophrenia and its biology are broadly shared across populations. A fixed-effect meta-analysis including individuals from East Asian and European ancestries identified 208 significant associations in 176 genetic loci (53 novel). Trans-ancestry fine-mapping reduced the sets of candidate causal variants in 44 loci. Polygenic risk scores had reduced performance when transferred across ancestries, highlighting the importance of including sufficient samples of major ancestral groups to ensure their generalizability across populations.