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Book ; Online: Learning Quintuplet Loss for Large-scale Visual Geo-Localization

Zhai, Qiang

2019

Abstract: With the maturity of Artificial Intelligence (AI) technology, Large Scale Visual Geo-Localization (LSVGL) is increasingly important in urban computing, where the task is to accurately and efficiently recognize the geo-location of a given query image. The ...

Abstract	With the maturity of Artificial Intelligence (AI) technology, Large Scale Visual Geo-Localization (LSVGL) is increasingly important in urban computing, where the task is to accurately and efficiently recognize the geo-location of a given query image. The main challenge of LSVGL faced by many experiments due to the appearance of real-word places may differ in various ways. While perspective deviation almost inevitably exists between training images and query images because of the arbitrary perspective. To cope with this situation, in this paper, we in-depth analyze the limitation of triplet loss which is the most commonly used metric learning loss in state-of-the-art LSVGL framework, and propose a new QUInTuplet Loss (QUITLoss) by embedding all the potential positive samples to the primitive triplet loss. Extensive experiments have been conducted to verify the effectiveness of the proposed approach and the results demonstrate that our new loss can enhance various LSVGL methods.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2019-07-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: MGL: Mutual Graph Learning for Camouflaged Object Detection.

Zhai, Qiang / Li, Xin / Yang, Fan / Jiao, Zhicheng / Luo, Ping / Cheng, Hong / Liu, Zicheng

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

2023 Volume 32, Page(s) 1897–1910

Abstract: Camouflaged object detection, which aims to detect/segment the object(s) that blend in with their surrounding, remains challenging for deep models due to the intrinsic similarities between foreground objects and background surroundings. Ideally, an ... ...

Abstract	Camouflaged object detection, which aims to detect/segment the object(s) that blend in with their surrounding, remains challenging for deep models due to the intrinsic similarities between foreground objects and background surroundings. Ideally, an effective model should be capable of finding valuable clues from the given scene and integrating them into a joint learning framework to co-enhance the representation. Inspired by this observation, we propose a novel Mutual Graph Learning (MGL) model by shifting the conventional perspective of mutual learning from regular grids to graph domain. Specifically, an image is decoupled by MGL into two task-specific feature maps - one for finding the rough location of the target and the other for capturing its accurate boundary details. Then, the mutual benefits can be fully exploited by reasoning their high-order relations through graphs recurrently. It should be noted that our method is different from most mutual learning models that model all between-task interactions with the use of a shared function. To increase information interactions, MGL is built with typed functions for dealing with different complementary relations. To overcome the accuracy loss caused by interpolation to higher resolution and the computational redundancy resulting from recurrent learning, the S-MGL is equipped with a multi-source attention contextual recovery module, called R-MGL_v2, which uses the pixel feature information iteratively. Experiments on challenging datasets, including CHAMELEON, CAMO, COD10K, and NC4K demonstrate the effectiveness of our MGL with superior performance to existing state-of-the-art methods. The code can be found at https://github.com/fanyang587/MGL.
Language	English
Publishing date	2023-03-21
Publishing country	United States
Document type	Journal Article
ISSN	1941-0042
ISSN (online)	1941-0042
DOI	10.1109/TIP.2022.3223216
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Life Cycle Impact Assessment of Garbage-Classification Based Municipal Solid Waste Management Systems: A Comparative Case Study in China.

Yuan, Yujun / Li, Tong / Zhai, Qiang

International journal of environmental research and public health

2020 Volume 17, Issue 15

Abstract: Confronted with a series of problems caused by surging generation of municipal solid waste (MSW), the Chinese central and local governments have promulgated and implemented policies to deal with them, including promotions of the classification of MSW. ... ...

Abstract	Confronted with a series of problems caused by surging generation of municipal solid waste (MSW), the Chinese central and local governments have promulgated and implemented policies to deal with them, including promotions of the classification of MSW. However, to date, practical knowledge and understanding about benefits for garbage classification from its environmental performance perspective is still limited. The present study is purposed to comprehensively investigate the environmental effects of garbage classification on municipal solid waste management (MSWM) systems based on three proposed garbage classification scenarios in China, via a comparative life cycle impact assessment (LCIA). Taking advantage of Impact Assessment of Chemical Toxics (IMPACT) 2002+ method, this comparative LCIA study can quantitatively evaluate midpoint, endpoint, and single scored life cycle impacts for the studied MSWM systems. A Monte Carlo uncertainty analysis is carried out to test the effectiveness and reliabilities of the LCIA results. The LCIA and uncertainty analysis results show that MSWM systems based on various garbage classification scenarios have significant variations in the studied midpoint, endpoint, and single scored environmental impacts. Different garbage classification scenarios have their individual environmental-friendly superiority for specific impact categories. Overall, results of this study demonstrate that MSW treatment systems integrated with garbage classification are more environmentally friendly by comparison with non-classification; and that the more elaborate the level of MSW classification, the smaller its impacts on the environment.
MeSH term(s)	China ; Garbage ; Refuse Disposal ; Solid Waste ; Waste Management
Chemical Substances	Solid Waste
Language	English
Publishing date	2020-07-23
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1660-4601
ISSN (online)	1660-4601
DOI	10.3390/ijerph17155310
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Article ; Online: Life Cycle Assessment on Wave and Tidal Energy Systems: A Review of Current Methodological Practice.

Zhang, Xizhuo / Zhang, Longfei / Yuan, Yujun / Zhai, Qiang

International journal of environmental research and public health

2020 Volume 17, Issue 5

Abstract: Recent decades have witnessed wave and tidal energy technology receiving considerable attention because of their low carbon emissions during electricity production. However, indirect emissions from their entire life cycle should not be ignored. Therefore, ...

Abstract	Recent decades have witnessed wave and tidal energy technology receiving considerable attention because of their low carbon emissions during electricity production. However, indirect emissions from their entire life cycle should not be ignored. Therefore, life cycle assessment (LCA) has been widely applied as a useful approach to systematically evaluate the environmental performance of wave and tidal energy technologies. This study reviews recent LCA studies on wave and tidal energy systems for stakeholders to understand current status of methodological practice and associated inherent limitations and reveal future research needs for application of LCA on wave and tidal technologies. The conformance of the selected LCAs to ISO 14040 (2006) and 14044 (2006) are critically analyzed in strict accordance with the ISO stepwise methodologies, namely, goal and scope definition, life cycle inventory (LCI) analysis, as well as life cycle impact assessment (LCIA). Our systematically screening of these studies indicates that few of the selected studies are of strict conformance with ISO 14040 and 14044 standards, which makes the results unreliable and thus further reduces the confidence of interested stakeholders. Further, our review indicates that current LCA practice on wave and tidal energies is lacking consideration of temporal variations, which should be addressed in future research, as it causes inaccuracy and uncertainties.
MeSH term(s)	Carbon ; Electricity ; Energy-Generating Resources ; Time
Chemical Substances	Carbon (7440-44-0)
Language	English
Publishing date	2020-03-02
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ISSN	1660-4601
ISSN (online)	1660-4601
DOI	10.3390/ijerph17051604
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Toxic epidermal necrolysis with systemic lupus erythematosus: case report and review of the literature.

Fan, Wen-Yang / Zhai, Qiang-Rong / Ma, Qing-Bian / Ge, Hong-Xia

Annals of palliative medicine

2021 Volume 11, Issue 6, Page(s) 2144–2151

Abstract: Toxic epidermal necrolysis (TEN) and Stevens-Johnson syndrome (SJS) are potentially fatal mucocutaneous diseases characterized by extensive necrosis and exfoliation of the epidermis. TEN and SJS are most often caused by various kinds of drugs. Other risk ...

Abstract	Toxic epidermal necrolysis (TEN) and Stevens-Johnson syndrome (SJS) are potentially fatal mucocutaneous diseases characterized by extensive necrosis and exfoliation of the epidermis. TEN and SJS are most often caused by various kinds of drugs. Other risk factors for SJS/TEN include pneumonia infection, HIV infection, genetic factors, underlying immune diseases, and tumors. SJS and TEN were first identified in 1922, but at present, a widely recognized view is that SJS and TEN represent phases in the continuous progress of the same disease. SJS/TEN has a very high mortality, but is rare, and cases of SJS/TEN combined with systemic lupus erythematosus (SLE) are even less common. Occasionally, acute cutaneous manifestations of SLE and SJS/TEN can be phenotypically similar, both causing extensive epidermal necrosis. In this paper, we present a recent case of a 32-year-old female SLE patient with a drug-induced (the health product, astaxanthin) TEN/SJS. To provide context to this case, we have reviewed relevant case studies published in English, accessed via PubMed databases. The search covers all published case studies from 1988 to 2019. We collected a total of 30 cases in the literature, and analyzed their characteristics from the aspects of gender, suspicious medication history, and treatment in order to expand clinicians' approach to diagnosis and treatment.
MeSH term(s)	Adult ; Female ; HIV Infections/complications ; Humans ; Lupus Erythematosus, Systemic/complications ; Necrosis ; Stevens-Johnson Syndrome/diagnosis ; Stevens-Johnson Syndrome/etiology ; Stevens-Johnson Syndrome/pathology
Language	English
Publishing date	2021-08-16
Publishing country	China
Document type	Case Reports ; Review
ZDB-ID	2828544-X
ISSN	2224-5839 ; 2224-5839
ISSN (online)	2224-5839
ISSN	2224-5839
DOI	10.21037/apm-21-341
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Impact Measurement of COVID-19 Lockdown on China's Electricity-Carbon Nexus.

Zhao, Mingyue / Niu, Yuqing / Tian, Lei / Liu, Yizhi / Zhai, Qiang

International journal of environmental research and public health

2021 Volume 18, Issue 18

Abstract: Lockdown measures to prevent the spread of coronavirus disease 2019 (COVID-19) resulted in the plummeting of China's overall electric-power demand and production. To date, power generation remains one of the largest carbon dioxide ( ... ...

Abstract	Lockdown measures to prevent the spread of coronavirus disease 2019 (COVID-19) resulted in the plummeting of China's overall electric-power demand and production. To date, power generation remains one of the largest carbon dioxide (CO
MeSH term(s)	COVID-19 ; Carbon Dioxide/analysis ; China ; Coal ; Communicable Disease Control ; Electricity ; Humans ; SARS-CoV-2
Chemical Substances	Coal ; Carbon Dioxide (142M471B3J)
Language	English
Publishing date	2021-09-15
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph18189736
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Article ; Online: Rice nitrogen nutrition monitoring classification method based on the convolution neural network model: Direct detection of rice nitrogen nutritional status.

Zhai, Qiang / Ye, Chun / Li, Shuang / Liu, Jizhong / Guo, Zhiming / Chang, Ruzhi / Hua, Jing

PloS one

2022 Volume 17, Issue 11, Page(s) e0273360

Abstract: The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer ... ...

Abstract	The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer according to the results. However, this method is labor- and time-consuming. To realize automatic rice nitrogen nutrition monitoring, we constructed the Jiangxi rice nitrogen nutrition monitoring model based on a convolution neural network (CNN) using the same region rice canopy image in different generation periods. Our CNN model was evaluated using multiple evaluation criteria (Accuracy, Recall, Precision, and F1 score). The results show that the same CNN model could distinguish the rice nitrogen nutrition status in different periods, which can completely realize the automatic discrimination of nitrogen nutrition status so as to guide the scientific nitrogen application of rice in this area. This will greatly improve the discrimination efficiency of the nitrogen nutrition status and reduce the time and labor cost. The application of the proposed method also proved that the CNN model can be applied in the discrimination of the nitrogen nutrition status. Among CNN models, GoogleNet model proposed a CNN architecture named Inception which can improve the depth of the network and extract higher-level features without changing the amount of calculation of the model. The GoogleNet model achieved the highest accuracy, 95.7%.
MeSH term(s)	Oryza ; Nitrogen ; Nutritional Status ; Fertilizers ; Neural Networks, Computer
Chemical Substances	Nitrogen (N762921K75) ; Fertilizers
Language	English
Publishing date	2022-11-22
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0273360
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Co-visual pattern augmented generative transformer learning for automobile geo-localization

Zhao, Jianwei / Zhai, Qiang / Zhao, Pengbo / Huang, Rui / Cheng, Hong

2022

Abstract: Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions. Cross-view geo-localization (CVGL), which aims to estimate the geographical location of ... ...

Abstract	Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions. Cross-view geo-localization (CVGL), which aims to estimate the geographical location of the ground-level camera by matching against enormous geo-tagged aerial (\emph{e.g.}, satellite) images, has received lots of attention but remains extremely challenging due to the drastic appearance differences across aerial-ground views. In existing methods, global representations of different views are extracted primarily using Siamese-like architectures, but their interactive benefits are seldom taken into account. In this paper, we present a novel approach using cross-view knowledge generative techniques in combination with transformers, namely mutual generative transformer learning (MGTL), for CVGL. Specifically, by taking the initial representations produced by the backbone network, MGTL develops two separate generative sub-modules -- one for aerial-aware knowledge generation from ground-view semantics and vice versa -- and fully exploits the entirely mutual benefits through the attention mechanism. Moreover, to better capture the co-visual relationships between aerial and ground views, we introduce a cascaded attention masking algorithm to further boost accuracy. Extensive experiments on challenging public benchmarks, \emph{i.e.}, {CVACT} and {CVUSA}, demonstrate the effectiveness of the proposed method which sets new records compared with the existing state-of-the-art models. Comment: 21pages
Keywords	Computer Science - Computer Vision and Pattern Recognition
Publishing date	2022-03-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Preparation and Characterisation of Polyurethane Acrylate-Based Titanium Dioxide Pigment for Blue Light-Curable Ink.

Wang, Chenglong / Qiao, Luyang / Zhai, Qiang / Yan, Kai / Wang, Lili / Zheng, Jinhuan

Polymers

2021 Volume 13, Issue 22

Abstract: Herein, a polyurethane acrylate-based ... ...

Abstract	Herein, a polyurethane acrylate-based TiO
Language	English
Publishing date	2021-11-17
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527146-5
ISSN	2073-4360 ; 2073-4360
ISSN (online)	2073-4360
ISSN	2073-4360
DOI	10.3390/polym13223977
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Taxonomic status of Rana nigromaculata mongolia and the validity of Pelophylax tenggerensis (Anura, Ranidae).

Zhou, Shengbo / He, L I / Ma, Siyu / Xu, Shujun / Zhai, Qiang / Guan, Ping / Wang, Hui / Shi, Jingsong

Zootaxa

2022 Volume 5165, Issue 4, Page(s) 486–500

Abstract: The Black-spotted Pond Frog, Pelophylax nigromaculatus, is widely distributed across mainland China, Korean Peninsula, and Japan. The taxonomic relationships among P. n. nigromaculatus, Rana nigromaculata mongolia (sensu P. n. mongolicus), and P. ... ...

Abstract	The Black-spotted Pond Frog, Pelophylax nigromaculatus, is widely distributed across mainland China, Korean Peninsula, and Japan. The taxonomic relationships among P. n. nigromaculatus, Rana nigromaculata mongolia (sensu P. n. mongolicus), and P. tenggerensis have long been ambiguous. Here we examine the topotype specimens of P. tenggerensis and R. n. mongolia, and provide phylogenic analyses based on four mitochondrial DNA sequences. The combined evidences from morphology and molecular phylogeny have shown the distinct specific-level of P. n. mongolicus that distant from P. nigromaculatus, while indicating the homogeneity between P. n. mongolicus and P. tenggerensis. Thus, we suggest elevating P. n. mongolicus as a full species Pelophylax mongolicus comb. nov., and place P. tenggerensis to be a secondary synonym of P. mongolicus comb. nov.
MeSH term(s)	Animals ; DNA, Mitochondrial/genetics ; Mitochondria/genetics ; Mongolia ; Phylogeny ; Ranidae/genetics
Chemical Substances	DNA, Mitochondrial
Language	English
Publishing date	2022-07-18
Publishing country	New Zealand
Document type	Journal Article
ISSN	1175-5334
ISSN (online)	1175-5334
DOI	10.11646/zootaxa.5165.4.2
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