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Institution

Hefei University of Technology

EducationHefei, China
About: Hefei University of Technology is a education organization based out in Hefei, China. It is known for research contribution in the topics: Computer science & Microstructure. The organization has 28093 authors who have published 24935 publications receiving 324989 citations.


Papers
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Journal ArticleDOI
TL;DR: Aroma volatiles and PLSR analysis suggested MRPF3 had the best sensory characteristics with higher contents of umami amino acids and lower of bitter amino acids, therefore it could be deduced that the umami and meaty characteristics were correlated with the peptides of 1-3;kDa.

145 citations

Journal ArticleDOI
TL;DR: This paper proposes an MIL method with discriminative feature mapping and feature selection, aiming at solving this problem of multiple-instance learning, and is able to explore both the positive and negative concept correlations.
Abstract: Multiple-instance learning (MIL) has been widely investigated in image annotation for its capability of exploring region-level visual information of images. Recent studies show that, by performing feature mapping, MIL can be cast to a single-instance learning problem and, thus, can be solved by traditional supervised learning methods. However, the approaches for feature mapping usually overlook the discriminative ability and the noises of the generated features. In this paper, we propose an MIL method with discriminative feature mapping and feature selection, aiming at solving this problem. Our method is able to explore both the positive and negative concept correlations. It can also select the effective features from a large and diverse set of low-level features for each concept under MIL settings. Experimental results and comparison with other methods demonstrate the effectiveness of our approach.

145 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: Zhang et al. as mentioned in this paper proposed to recursively shrink the search area from the whole image till achieving precise localization of the target person, by fully exploiting information from the query and contextual cues in every recursive search step.
Abstract: We investigate the problem of person search in the wild in this work. Instead of comparing the query against all candidate regions generated in a query-blind manner, we propose to recursively shrink the search area from the whole image till achieving precise localization of the target person, by fully exploiting information from the query and contextual cues in every recursive search step. We develop the Neural Person Search Machines (NPSM) to implement such recursive localization for person search. Benefiting from its neural search mechanism, NPSM is able to selectively shrink its focus from a loose region to a tighter one containing the target automatically. In this process, NPSM employs an internal primitive memory component to memorize the query representation which modulates the attention and augments its robustness to other distracting regions. Evaluations on two benchmark datasets, CUHK-SYSU Person Search dataset and PRW dataset, have demonstrated that our method can outperform current state-of-the-arts in both mAP and top-1 evaluation protocols.

144 citations

Journal ArticleDOI
TL;DR: In this article, the effects of assembly and Zn/Co ratios on the oxygen reduction reaction (ORR) performances of the electrospun fibers derivatives were systematically studied and compared to non-electrospun samples, such doped porous carbon nanofibers exhibited excellent electrocatalytic performances without any etching or other activating processes, and the sample with the molar ratio of Zn: Co = 5:1 even showed comparable ORR performance with the commercial Pt/C catalyst under the same conditions.

144 citations

Journal ArticleDOI
TL;DR: The Tan-Lu Fault Zone (TLFZ) as mentioned in this paper is the largest fault zone in East China, and a typical representative for the circum-Pacific tectonics, and can be used for indication to the subduction history.
Abstract: The NE- to NNE-striking Tan-Lu Fault Zone (TLFZ) is the largest fault zone in East China, and a typical representative for the circum-Pacific tectonics. Its late Mesozoic evolution resulted from subduction of the Paleo-Pacific Plate, and can be used for indication to the subduction history. The TLFZ reactivated at the end of Middle Jurassic since its origination in Middle Triassic. This phase of sinistral motion can only be recognized along the eastern edge of the Dabie-Sulu orogenis, and indicates initiation of the Paleo-Pacific (Izanagi) Plate subduction beneath the East China continent. After the Late Jurassic standstill, the fault zone experienced intense sinistral faulting again at the beginning of Early Cretaceous under N-S compression that resulted from the NNW-ward, low-angle, high-speed subduction of the Izanagi Plate. It turned into normal faulting in the rest of Early Cretaceous, which was simultaneous with the peak destruction of the North China Craton caused by backarc extension that resulted from rollback of the subducting Izanagi Plate. The TLFZ was subjected to sinistral, transpressive displacement again at the end of Early Cretaceous. This shortening event led to termination of the North China Craton destruction. The fault zone suffered local normal faulting in Late Cretaceous due to the far-field, weak backarc extension. The late Mesozoic evolution of the TLFZ show repeated alternation between the transpressive strike-slip motion and normal faulting. Each of the sinistral faulting event took place in a relatively short period whereas every normal faulting event lasted in a longer period, which are related to the subduction way and history of the Paleo-Pacific Plates.

144 citations


Authors

Showing all 28292 results

NameH-indexPapersCitations
Yi Chen2174342293080
Xiang Zhang1541733117576
Jun Chen136185677368
Shuicheng Yan12381066192
Yang Li117131963111
Jian Liu117209073156
Han-Qing Yu10571839735
Jianqiao Ye10196242647
Wei Liu96153842459
Wei Zhou93164039772
Panos M. Pardalos87120739512
Zhong Chen80100028171
Yong Zhang7866536388
Rong Cao7656821747
Qian Zhang7689125517
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023106
2022490
20213,120
20202,931
20192,666
20182,151