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.

Spatio-Temporal Laplacian Pyramid Coding for Action Recognition

Abstract: We present a novel descriptor, called spatio-temporal Laplacian pyramid coding (STLPC), for holistic representation of human actions. In contrast to sparse representations based on detected local interest points, STLPC regards a video sequence as a whole with spatio-temporal features directly extracted from it, which prevents the loss of information in sparse representations. Through decomposing each sequence into a set of band-pass-filtered components, the proposed pyramid model localizes features residing at different scales, and therefore is able to effectively encode the motion information of actions. To make features further invariant and resistant to distortions as well as noise, a bank of 3-D Gabor filters is applied to each level of the Laplacian pyramid, followed by max pooling within filter bands and over spatio-temporal neighborhoods. Since the convolving and pooling are performed spatio-temporally, the coding model can capture structural and motion information simultaneously and provide an informative representation of actions. The proposed method achieves superb recognition rates on the KTH, the multiview IXMAS, the challenging UCF Sports, and the newly released HMDB51 datasets. It outperforms state of the art methods showing its great potential on action recognition.

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

Abstract: The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly available1.

Radical chemistry at a rural site (Wangdu) in the North China Plain: observation and model calculations of OH, HO 2 and RO 2 radicals

Abstract: . A comprehensive field campaign was carried out in summer 2014 in Wangdu, located in the North China Plain. A month of continuous OH, HO2 and RO2 measurements was achieved. Observations of radicals by the laser-induced fluorescence (LIF) technique revealed daily maximum concentrations between (5–15) × 106 cm−3, (3–14) × 108 cm−3 and (3–15) × 108 cm−3 for OH, HO2 and RO2, respectively. Measured OH reactivities (inverse OH lifetime) were 10 to 20 s−1 during daytime. The chemical box model RACM 2, including the Leuven isoprene mechanism (LIM), was used to interpret the observed radical concentrations. As in previous field campaigns in China, modeled and measured OH concentrations agree for NO mixing ratios higher than 1 ppbv, but systematic discrepancies are observed in the afternoon for NO mixing ratios of less than 300 pptv (the model–measurement ratio is between 1.4 and 2 in this case). If additional OH recycling equivalent to 100 pptv NO is assumed, the model is capable of reproducing the observed OH, HO2 and RO2 concentrations for conditions of high volatile organic compound (VOC) and low NOx concentrations. For HO2, good agreement is found between modeled and observed concentrations during day and night. In the case of RO2, the agreement between model calculations and measurements is good in the late afternoon when NO concentrations are below 0.3 ppbv. A significant model underprediction of RO2 by a factor of 3 to 5 is found in the morning at NO concentrations higher than 1 ppbv, which can be explained by a missing RO2 source of 2 ppbv h−1. As a consequence, the model underpredicts the photochemical net ozone production by 20 ppbv per day, which is a significant portion of the daily integrated ozone production (110 ppbv) derived from the measured HO2 and RO2. The additional RO2 production from the photolysis of ClNO2 and missing reactivity can explain about 10 % and 20 % of the discrepancy, respectively. The underprediction of the photochemical ozone production at high NOx found in this study is consistent with the results from other field campaigns in urban environments, which underlines the need for better understanding of the peroxy radical chemistry for high NOx conditions.

Lake eutrophication and its ecosystem response

Abstract: China is a country with many lakes, about one-third of which are freshwater mainly distributed in the middle and lower reaches of the Yangtze River. Currently most of the lakes are mesotrophic or eutrophic. Lake eutrophication has become one of the major ecological and environmental problems faced by lakes in China and can lead to a series of abnormal ecosystem responses, including extinction of submerged plants, frequent occurrence of cyanobacterial blooms, increased microbial biomass and productivity, decreased biodiversity, accelerated cycles, and a change in the efficient use of nutrients. With development of eutrophication, the whole lake ecosystem suffers decreased biodiversity, simplification of biotic community structure, instability of the ecosystem, and ultimately the clear-water, macrophyte-dominated ecosystem gradually shifts to a turbid-water, algae-dominated ecosystem. This ecosystem succession mechanism is speculated to be caused by different nutrient utilization efficiencies of macrophytes and phytoplankton. The ultimate ecosystem succession trend of seriously eutrophic lakes is that a phytoplankton-dominated autotrophic lake shifts to a heterotrophic lake dominated by micro-organisms, protozoans.