Nanjing University of Information Science and Technology

About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.

Real-Time Probabilistic Covariance Tracking With Efficient Model Update

Abstract: The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.

Insights into characteristics, sources, and evolution of submicron aerosols during harvest seasons in the Yangtze River delta region, China

Abstract: . Atmospheric submicron particulate matter (PM1) is one of the most significant pollution components in China. Despite its current popularity in the studies of aerosol chemistry, the characteristics, sources and evolution of atmospheric PM1 species are still poorly understood in China, particularly for the two harvest seasons, namely, the summer wheat harvest and autumn rice harvest. An Aerodyne Aerosol Chemical Speciation Monitor (ACSM) was deployed for online monitoring of PM1 components during summer and autumn harvest seasons in urban Nanjing, in the Yangtze River delta (YRD) region of China. PM1 components were shown to be dominated by organic aerosol (OA, 39 and 41%) and nitrate (23 and 20%) during the harvest seasons (the summer and autumn harvest). Positive matrix factorization (PMF) analysis of the ACSM OA mass spectra resolved four OA factors: hydrocarbon-like mixed with cooking-related OA (HOA + COA), fresh biomass-burning OA (BBOA), oxidized biomass-burning-influenced OA (OOA-BB), and highly oxidized OA (OOA); in particular the oxidized BBOA contributes ~80% of the total BBOA loadings. Both fresh and oxidized BBOA exhibited apparent diurnal cycles with peak concentration at night, when the high ambient relative humidity and low temperature facilitated the partitioning of semi-volatile organic species into the particle phase. The fresh BBOA concentrations for the harvests are estimated as BBOA = 15.1 × (m/z 60–0.26% × OA), where m/z (mass-to-charge ratio) 60 is a marker for levoglucosan-like species. The (BBOA + OOA-BB)/ΔCO, (ΔCO is the CO minus background CO), decreases as a function of f44 (fraction of m/z 44 in OA signal), which might indicate that BBOA was oxidized to less volatile OOA, e.g., more aged and low volatility OOA (LV-OOA) during the aging process. Analysis of air mass back trajectories indicates that the high BB pollutant concentrations are linked to the air masses from the western (summer harvest) and southern (autumn harvest) areas.

Dual-regularized matrix factorization with deep neural networks for recommender systems

Abstract: In recommender systems, many efforts have been made on utilizing textual information in matrix factorization to alleviate the problem of data sparsity. Recently, some of the works have explored neural networks to do an in-depth understanding of textual item content and achieved impressive effectiveness by generating more accurate item latent models. Nevertheless, there remains an open issue as how to effectively exploit description documents of both users and items in matrix factorization. In this paper, we proposed dual-regularized matrix factorization with deep neural networks (DRMF) to deal with this issue. DRMF adopts a multilayered neural network model by stacking convolutional neural network and gated recurrent neural network, to generate independent distributed representations of contents of users and items. Then, representations serve to regularize the generation of latent models both for users and items in matrix factorization. We propose the corresponding algorithm for learning all parameters in DRMF. Experimental results proved that the dual-way regularization strategy significantly improves the matrix factorization methods on the accuracy of rating prediction and the recall of top-n recommendations. Also, as the components of DRMF, the new neural network model works better than the single convolutional neural network model.

Changes in Annual Extremes of Daily Temperature and Precipitation in CMIP6 Models

Abstract: Author(s): Li, C; Zwiers, F; Zhang, X; Li, G; Sun, Y; Wehner, M | Abstract: This study presents an analysis of daily temperature and precipitation extremes with return periods ranging from 2 to 50 years in phase 6 of the Coupled Model Intercomparison Project (CMIP6) multimodel ensemble of simulations. Judged by similarity with reanalyses, the new-generation models simulate the present-day temperature and precipitation extremes reasonably well. In line with previous CMIP simulations, the new simulations continue to project a large-scale picture of more frequent and more intense hot temperature extremes and precipitation extremes and vanishing cold extremes under continued global warming. Changes in temperature extremes outpace changes in global annual mean surface air temperature (GSAT) over most landmasses, while changes in precipitation extremes follow changes in GSAT globally at roughly the Clausius-Clapeyron rate of;7% 8C21. Changes in temperature and precipitation extremes normalized with respect to GSAT do not depend strongly on the choice of forcing scenario or model climate sensitivity, and do not vary strongly over time, but with notable regional variations. Over the majority of land regions, the projected intensity increases and relative frequency increases tend to be larger for more extreme hot temperature and precipitation events than for weaker events. To obtain robust estimates of these changes at local scales, large initial-condition ensemble simulations are needed. Appropriate spatial pooling of data from neighboring grid cells within individual simulations can, to some extent, reduce the needed ensemble size.