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101 results on '"Peng LIANG"'

1. Review of Technical and Vocational Institution Educators’ Critical Thinking Competencies and the Moderating Effect of Leadership Support

Author

Xue Wei Hong, Gary Tan Peng Liang, Xue Wei Hong, and Gary Tan Peng Liang

Abstract

This review article highlights the increasing focus on developing critical thinking skills in technical and vocational education, underscoring the crucial role of educators' competencies and leadership support in this process. Drawing on research from nursing and higher education, the review presents a wide array of strategies for enhancing critical thinking, such as project-based learning and technology-enhanced tools, that can address existing gaps in vocational curricula. Supporting leadership is essential for successfully implementing these strategies, with effective leadership being linked to improved teaching confidence, innovative curriculum design, and better student engagement. Including insights from nursing and higher education enriches the understanding of critical thinking's universal significance and offers a diverse set of adaptable strategies for vocational education. The review concludes by emphasizing the importance of critical thinking competencies for educators and the significant impact of leadership support. It calls for continued research into effective development strategies and their long-term effects on student success. This multidisciplinary approach highlights the need to adapt educational practices to meet the modern workforce's demands, positioning critical thinking as a pivotal element in preparing students for future challenges and opportunities.

Published
2024

2. Review of Technical and Vocational Institution Educators’ Critical Thinking Competencies and the Moderating Effect of Leadership Support

Author

Xue Wei Hong, Gary Tan Peng Liang, Xue Wei Hong, and Gary Tan Peng Liang

Abstract

This review article highlights the increasing focus on developing critical thinking skills in technical and vocational education, underscoring the crucial role of educators' competencies and leadership support in this process. Drawing on research from nursing and higher education, the review presents a wide array of strategies for enhancing critical thinking, such as project-based learning and technology-enhanced tools, that can address existing gaps in vocational curricula. Supporting leadership is essential for successfully implementing these strategies, with effective leadership being linked to improved teaching confidence, innovative curriculum design, and better student engagement. Including insights from nursing and higher education enriches the understanding of critical thinking's universal significance and offers a diverse set of adaptable strategies for vocational education. The review concludes by emphasizing the importance of critical thinking competencies for educators and the significant impact of leadership support. It calls for continued research into effective development strategies and their long-term effects on student success. This multidisciplinary approach highlights the need to adapt educational practices to meet the modern workforce's demands, positioning critical thinking as a pivotal element in preparing students for future challenges and opportunities.

Published
2024

3. Evaluating the Influence of Organizational Learning on E-Commerce Acceptance Among Small and Medium Enterprise Practitioners: A Pilot Test

Author

Li Kai Feng, Gary Tan Peng Liang, Li Kai Feng, and Gary Tan Peng Liang

Abstract

This study investigates the factors influencing the adoption of e-commerce technologies among Small and Medium-sized Enterprises (SMEs) in Xi'an, China, emphasizing the role of organizational learning, information culture, and advancements in digital technologies such as blockchain and artificial intelligence (AI). Through a comprehensive literature review and quantitative analysis, the research highlights the critical impact of technological readiness, organizational culture, and strategic digital integration on e-commerce adoption. The COVID-19 pandemic's challenges and opportunities for digital marketing and e-commerce are also examined, revealing their essential role in sustaining SME performance during such unprecedented times. Employing Structural Equation Modeling (SEM) for data analysis, the study conducts a detailed psychometric assessment to ensure the reliability and validity of the constructs involved. This multi-phase methodology includes pilot testing to refine measurement instruments, followed by an in-depth examination of internal consistency, discriminant validity, and model fit. The findings reveal high construct reliability, satisfactory Average Variance Extracted (AVE) levels, and good discriminant validity, affirming the theoretical distinctions between constructs. Despite minor deviations from ideal benchmarks, the model demonstrates an acceptable fit, suggesting its adequacy in representing the complexities of e-commerce adoption among SMEs. By providing a holistic view of the e-commerce adoption landscape, this study offers valuable insights for SMEs to assess their readiness, understand the cultural and technological dynamics, and leverage advanced technologies for strategic planning and implementation. This research contributes significantly to the literature on e-commerce adoption in SMEs and presents practical implications for enhancing their competitive edge in the global marketplace.

Published
2024

4. Evaluating the Influence of Organizational Learning on E-Commerce Acceptance Among Small and Medium Enterprise Practitioners: A Pilot Test

Author

Li Kai Feng, Gary Tan Peng Liang, Li Kai Feng, and Gary Tan Peng Liang

Abstract

This study investigates the factors influencing the adoption of e-commerce technologies among Small and Medium-sized Enterprises (SMEs) in Xi'an, China, emphasizing the role of organizational learning, information culture, and advancements in digital technologies such as blockchain and artificial intelligence (AI). Through a comprehensive literature review and quantitative analysis, the research highlights the critical impact of technological readiness, organizational culture, and strategic digital integration on e-commerce adoption. The COVID-19 pandemic's challenges and opportunities for digital marketing and e-commerce are also examined, revealing their essential role in sustaining SME performance during such unprecedented times. Employing Structural Equation Modeling (SEM) for data analysis, the study conducts a detailed psychometric assessment to ensure the reliability and validity of the constructs involved. This multi-phase methodology includes pilot testing to refine measurement instruments, followed by an in-depth examination of internal consistency, discriminant validity, and model fit. The findings reveal high construct reliability, satisfactory Average Variance Extracted (AVE) levels, and good discriminant validity, affirming the theoretical distinctions between constructs. Despite minor deviations from ideal benchmarks, the model demonstrates an acceptable fit, suggesting its adequacy in representing the complexities of e-commerce adoption among SMEs. By providing a holistic view of the e-commerce adoption landscape, this study offers valuable insights for SMEs to assess their readiness, understand the cultural and technological dynamics, and leverage advanced technologies for strategic planning and implementation. This research contributes significantly to the literature on e-commerce adoption in SMEs and presents practical implications for enhancing their competitive edge in the global marketplace.

Published
2024

5. Cutaneous Mycobacterium Abscessus Infection Following Plastic Surgery: Three Case Reports

Author

Shen,Hongwei, Zhang,Qiaomin, Peng,Liang, Ma,Wen, Guo,Jingdong, Shen,Hongwei, Zhang,Qiaomin, Peng,Liang, Ma,Wen, and Guo,Jingdong

Abstract

Hongwei Shen,1,* Qiaomin Zhang,1,* Liang Peng,2 Wen Ma,1 Jingdong Guo2 1Clinical Laboratory, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, People’s Republic of China; 2Department of Burns and Plastic Surgery, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jingdong Guo; Hongwei Shen, Email guojingdong1996@163.com; hongweishen@smu.edu.cnAim: Mycobacterium abscessus is ubiquitous in the environment and seldom causes infections in immunocompetent individuals. However, skin and soft tissue infections caused by M. abscessus have been reported in recent years. Additionally, the cutaneous infections or outbreaks post cosmetic surgery caused by M. abscessus have been increasing due to the popularity of plastic surgery. The main modes of transmission are through contaminated saline, disinfectants, or surgery equipment, as well as close contact between patients. This article describes three patients who were admitted to our hospital between November 2019 and October 2020. They presented with long-term non-healing wounds caused by M. abscessus infection after undergoing plastic surgery. Symptoms presented by the three patients included swelling, ulceration, secretion, and pain. After identification of M. abscessus with Ziehl-Neelsen staining and MALDI-TOF MS system, the patients were treated with surgical debridement and clarithromycin.Conclusion: It is important to note that a long-term wound that does not heal, especially after plastic surgery, should raise suspicion for M. abscessus infection. The infection mechanism in these three patients may have been due to exposure to surgical equipment that was not properly sterilized or due to poor sterile technique by the plastic surgeon. To prevent such infections, it is important to ensure proper sterilization of surgical equipment and saline.Keywords: Mycobacterium ab

Published
2024

6. LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models

Author

Yang, Yang, Wang, Wen, Peng, Liang, Song, Chaotian, Chen, Yao, Li, Hengjia, Yang, Xiaolong, Lu, Qinglin, Cai, Deng, Wu, Boxi, Liu, Wei, Yang, Yang, Wang, Wen, Peng, Liang, Song, Chaotian, Chen, Yao, Li, Hengjia, Yang, Xiaolong, Lu, Qinglin, Cai, Deng, Wu, Boxi, and Liu, Wei

Abstract

Customization generation techniques have significantly advanced the synthesis of specific concepts across varied contexts. Multi-concept customization emerges as the challenging task within this domain. Existing approaches often rely on training a Low-Rank Adaptations (LoRA) fusion matrix of multiple LoRA to merge various concepts into a single image. However, we identify this straightforward method faces two major challenges: 1) concept confusion, which occurs when the model cannot preserve distinct individual characteristics, and 2) concept vanishing, where the model fails to generate the intended subjects. To address these issues, we introduce LoRA-Composer, a training-free framework designed for seamlessly integrating multiple LoRAs, thereby enhancing the harmony among different concepts within generated images. LoRA-Composer addresses concept vanishing through Concept Injection Constraints, enhancing concept visibility via an expanded cross-attention mechanism. To combat concept confusion, Concept Isolation Constraints are introduced, refining the self-attention computation. Furthermore, Latent Re-initialization is proposed to effectively stimulate concept-specific latent within designated regions. Our extensive testing showcases a notable enhancement in LoRA-Composer's performance compared to standard baselines, especially when eliminating the image-based conditions like canny edge or pose estimations. Code is released at https://github.com/Young98CN/LoRA\_Composer.

Published
2024

7. The Role of Organizational Learning Culture in E-Commerce Adoption among SMEs: A Systematic Review

Author

Li Kai Feng, Gary Tan Peng Liang, Li Kai Feng, and Gary Tan Peng Liang

Abstract

This systematic review aims to explore the influence of organizational learning culture on the adoption of e-commerce technologies among Small and Medium-sized Enterprises (SMEs). The rapid evolution of e-commerce has become a critical aspect of business success, especially for SMEs striving to compete in the global market. However, the adoption of such technologies is not merely a technical decision but is deeply rooted in the organizational culture. This review synthesizes research findings from a range of academic databases, employing rigorous selection criteria to ensure the relevance and quality of the sources. The methodology involves a comprehensive search and analysis of peer-reviewed articles, focusing on studies that explicitly link organizational learning culture with e-commerce adoption in the context of SMEs. The findings of this review highlight that a strong organizational learning culture significantly enhances the willingness and ability of SMEs to adopt e-commerce, facilitating a more agile and adaptive approach to technological change. Key themes identified include the role of leadership, knowledge sharing practices, and the openness to change within the organization. The review also discusses challenges faced by SMEs in cultivating such a culture and the implications for future research and practice.

Published
2024

8. VastTrack: Vast Category Visual Object Tracking

Author

Peng, Liang, Gao, Junyuan, Liu, Xinran, Li, Weihong, Dong, Shaohua, Zhang, Zhipeng, Fan, Heng, Zhang, Libo, Peng, Liang, Gao, Junyuan, Liu, Xinran, Li, Weihong, Dong, Shaohua, Zhang, Zhipeng, Fan, Heng, and Zhang, Libo

Abstract

In this paper, we introduce a novel benchmark, dubbed VastTrack, towards facilitating the development of more general visual tracking via encompassing abundant classes and videos. VastTrack possesses several attractive properties: (1) Vast Object Category. In particular, it covers target objects from 2,115 classes, largely surpassing object categories of existing popular benchmarks (e.g., GOT-10k with 563 classes and LaSOT with 70 categories). With such vast object classes, we expect to learn more general object tracking. (2) Larger scale. Compared with current benchmarks, VastTrack offers 50,610 sequences with 4.2 million frames, which makes it to date the largest benchmark regarding the number of videos, and thus could benefit training even more powerful visual trackers in the deep learning era. (3) Rich Annotation. Besides conventional bounding box annotations, VastTrack also provides linguistic descriptions for the videos. The rich annotations of VastTrack enables development of both the vision-only and the vision-language tracking. To ensure precise annotation, all videos are manually labeled with multiple rounds of careful inspection and refinement. To understand performance of existing trackers and to provide baselines for future comparison, we extensively assess 25 representative trackers. The results, not surprisingly, show significant drops compared to those on current datasets due to lack of abundant categories and videos from diverse scenarios for training, and more efforts are required to improve general tracking. Our VastTrack and all the evaluation results will be made publicly available https://github.com/HengLan/VastTrack., Comment: Tech. report

Published
2024

9. The Role of Organizational Learning Culture in E-Commerce Adoption among SMEs: A Systematic Review

Author

Li Kai Feng, Gary Tan Peng Liang, Li Kai Feng, and Gary Tan Peng Liang

Abstract

This systematic review aims to explore the influence of organizational learning culture on the adoption of e-commerce technologies among Small and Medium-sized Enterprises (SMEs). The rapid evolution of e-commerce has become a critical aspect of business success, especially for SMEs striving to compete in the global market. However, the adoption of such technologies is not merely a technical decision but is deeply rooted in the organizational culture. This review synthesizes research findings from a range of academic databases, employing rigorous selection criteria to ensure the relevance and quality of the sources. The methodology involves a comprehensive search and analysis of peer-reviewed articles, focusing on studies that explicitly link organizational learning culture with e-commerce adoption in the context of SMEs. The findings of this review highlight that a strong organizational learning culture significantly enhances the willingness and ability of SMEs to adopt e-commerce, facilitating a more agile and adaptive approach to technological change. Key themes identified include the role of leadership, knowledge sharing practices, and the openness to change within the organization. The review also discusses challenges faced by SMEs in cultivating such a culture and the implications for future research and practice.

Published
2024

10. Searching Priors Makes Text-to-Video Synthesis Better

Author

Cheng, Haoran, Peng, Liang, Xia, Linxuan, Hu, Yuepeng, Li, Hengjia, Lu, Qinglin, He, Xiaofei, Wu, Boxi, Cheng, Haoran, Peng, Liang, Xia, Linxuan, Hu, Yuepeng, Li, Hengjia, Lu, Qinglin, He, Xiaofei, and Wu, Boxi

Abstract

Significant advancements in video diffusion models have brought substantial progress to the field of text-to-video (T2V) synthesis. However, existing T2V synthesis model struggle to accurately generate complex motion dynamics, leading to a reduction in video realism. One possible solution is to collect massive data and train the model on it, but this would be extremely expensive. To alleviate this problem, in this paper, we reformulate the typical T2V generation process as a search-based generation pipeline. Instead of scaling up the model training, we employ existing videos as the motion prior database. Specifically, we divide T2V generation process into two steps: (i) For a given prompt input, we search existing text-video datasets to find videos with text labels that closely match the prompt motions. We propose a tailored search algorithm that emphasizes object motion features. (ii) Retrieved videos are processed and distilled into motion priors to fine-tune a pre-trained base T2V model, followed by generating desired videos using input prompt. By utilizing the priors gleaned from the searched videos, we enhance the realism of the generated videos' motion. All operations can be finished on a single NVIDIA RTX 4090 GPU. We validate our method against state-of-the-art T2V models across diverse prompt inputs. The code will be public.

Published
2024

11. Modeling and simulation of a mechanism for suppressing the flipping problem of a jumping robot

Author

Li, Qi, Peng, Liang, Wu, Zhiyuan, Ye, Pengda, Zhang, Weitao, Xu, Yi, Shi, Qing, Li, Qi, Peng, Liang, Wu, Zhiyuan, Ye, Pengda, Zhang, Weitao, Xu, Yi, and Shi, Qing

Abstract

In order to solve the problem of stable jumping of micro robot, we design a special mechanism: elastic passive joint (EPJ). EPJ can assist in achieving smooth jumping through the opening-closing process when the robot jumps. First, we introduce the composition and operation principle of EPJ, and perform a dynamic modeling of the robot's jumping process. Then, in order to verify the effectiveness of EPJ in controlling the robot's smooth jump, we design a simulation experiment based on MATLAB. Through comparative experiments, it was proved that EPJ can greatly adjust the angular velocity of the robot and increase the jump distance of the robot. Finally, we analyze each parameter in EPJ and performs parameter optimization. After optimization, EPJ achieves a completely flip-free jump of the robot, laying an important foundation for improving the mobility of micro-robot.

Published
2024

12. Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection

Author

Zhang, Zhanwei, Chen, Minghao, Xiao, Shuai, Peng, Liang, Li, Hengjia, Lin, Binbin, Li, Ping, Wang, Wenxiao, Wu, Boxi, Cai, Deng, Zhang, Zhanwei, Chen, Minghao, Xiao, Shuai, Peng, Liang, Li, Hengjia, Lin, Binbin, Li, Ping, Wang, Wenxiao, Wu, Boxi, and Cai, Deng

Abstract

Recent self-training techniques have shown notable improvements in unsupervised domain adaptation for 3D object detection (3D UDA). These techniques typically select pseudo labels, i.e., 3D boxes, to supervise models for the target domain. However, this selection process inevitably introduces unreliable 3D boxes, in which 3D points cannot be definitively assigned as foreground or background. Previous techniques mitigate this by reweighting these boxes as pseudo labels, but these boxes can still poison the training process. To resolve this problem, in this paper, we propose a novel pseudo label refinery framework. Specifically, in the selection process, to improve the reliability of pseudo boxes, we propose a complementary augmentation strategy. This strategy involves either removing all points within an unreliable box or replacing it with a high-confidence box. Moreover, the point numbers of instances in high-beam datasets are considerably higher than those in low-beam datasets, also degrading the quality of pseudo labels during the training process. We alleviate this issue by generating additional proposals and aligning RoI features across different domains. Experimental results demonstrate that our method effectively enhances the quality of pseudo labels and consistently surpasses the state-of-the-art methods on six autonomous driving benchmarks. Code will be available at https://github.com/Zhanwei-Z/PERE., Comment: Accepted by CVPR2024

Published
2024

13. Identification and Clinical Correlation Analysis of IFI44 in Systemic Lupus Erythematosus Combined with Bioinformatics and Immune Infiltration Analysis

Author

Wang,Yuan, Ma,Chengfeng, Ma,Zhanbing, Yang,Mengyi, Pu,Jing, Ma,Xiuhui, Wu,Xi, Peng,Liang, Huo,Zhenghao, Dang,Jie, Wang,Yuan, Ma,Chengfeng, Ma,Zhanbing, Yang,Mengyi, Pu,Jing, Ma,Xiuhui, Wu,Xi, Peng,Liang, Huo,Zhenghao, and Dang,Jie

Abstract

Yuan Wang,1– 3 Chengfeng Ma,1,2 Zhanbing Ma,1,2 Mengyi Yang,1,2 Jing Pu,1,2 Xiuhui Ma,1,2 Xi Wu,1,2 Liang Peng,1,2 Zhenghao Huo,1,2,* Jie Dang1,2,* 1School of Basic Medicine, Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China; 2Key Laboratory of Fertility Preservation and Maintenance of the Ministry of Education, Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China; 3Department of Dermatology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jie Dang; Zhenghao Huo, Department of Medical Genetics and Cell Biology, School of Basic Medicine, Ningxia Medical University, 1160 Shengli Street, Yinchuan, Ningxia, 750001, People’s Republic of China, Tel/Fax +86-951-6980049, Email djjune1978@163.comPurpose: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that can cause systemic damage to multiple organs. This study aims to analyze the value and function of IFI44 in the diagnosis and pathology of SLE by bioinformatics and immune infiltration analysis.Patients and Methods: GSE49454 and GSE65391 of SLE were obtained from the GEO dataset, and R software was employed to identify DEGs and investigate their functions. The PPI network was utilized to identify hub genes associated with SLE. CIBERSORT was used to assess differences in immune cell infiltration in SLE patients and controls. ROC curve analysis was performed to evaluate the diagnostic value of IFI44 in SLE. The expression of IFI44 in PBMCs was detected by RT-qPCR, and the correlation between IFI44 expression and SLE-related clinical indicators was analyzed.Results: A total of 65 DEGs were identified from the GSE49454 and GSE65391 databases. Through PPI analysis, IFI44 and RSAD2 were identified as significantly aberrantly expressed in SLE patients. SLE patients and controls showed a significant difference in the proportion

Published
2023

14. Failure Detection for Motion Prediction of Autonomous Driving: An Uncertainty Perspective

Author

Shao, Wenbo, Xu, Yanchao, Peng, Liang, Li, Jun, Wang, Hong, Shao, Wenbo, Xu, Yanchao, Peng, Liang, Li, Jun, and Wang, Hong

Abstract

Motion prediction is essential for safe and efficient autonomous driving. However, the inexplicability and uncertainty of complex artificial intelligence models may lead to unpredictable failures of the motion prediction module, which may mislead the system to make unsafe decisions. Therefore, it is necessary to develop methods to guarantee reliable autonomous driving, where failure detection is a potential direction. Uncertainty estimates can be used to quantify the degree of confidence a model has in its predictions and may be valuable for failure detection. We propose a framework of failure detection for motion prediction from the uncertainty perspective, considering both motion uncertainty and model uncertainty, and formulate various uncertainty scores according to different prediction stages. The proposed approach is evaluated based on different motion prediction algorithms, uncertainty estimation methods, uncertainty scores, etc., and the results show that uncertainty is promising for failure detection for motion prediction but should be used with caution., Comment: Accepted by 2023 IEEE International Conference on Robotics and Automation (ICRA 2023)

Published
2023

15. Learning Occupancy for Monocular 3D Object Detection

Author

Peng, Liang, Xu, Junkai, Cheng, Haoran, Yang, Zheng, Wu, Xiaopei, Qian, Wei, Wang, Wenxiao, Wu, Boxi, Cai, Deng, Peng, Liang, Xu, Junkai, Cheng, Haoran, Yang, Zheng, Wu, Xiaopei, Qian, Wei, Wang, Wenxiao, Wu, Boxi, and Cai, Deng

Abstract

Monocular 3D detection is a challenging task due to the lack of accurate 3D information. Existing approaches typically rely on geometry constraints and dense depth estimates to facilitate the learning, but often fail to fully exploit the benefits of three-dimensional feature extraction in frustum and 3D space. In this paper, we propose \textbf{OccupancyM3D}, a method of learning occupancy for monocular 3D detection. It directly learns occupancy in frustum and 3D space, leading to more discriminative and informative 3D features and representations. Specifically, by using synchronized raw sparse LiDAR point clouds, we define the space status and generate voxel-based occupancy labels. We formulate occupancy prediction as a simple classification problem and design associated occupancy losses. Resulting occupancy estimates are employed to enhance original frustum/3D features. As a result, experiments on KITTI and Waymo open datasets demonstrate that the proposed method achieves a new state of the art and surpasses other methods by a significant margin. Codes and pre-trained models will be available at \url{https://github.com/SPengLiang/OccupancyM3D}.

Published
2023

16. MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning

Author

Peng, Liang, Cai, Songyue, Wu, Zongqian, Shang, Huifang, Zhu, Xiaofeng, Li, Xiaoxiao, Peng, Liang, Cai, Songyue, Wu, Zongqian, Shang, Huifang, Zhu, Xiaofeng, and Li, Xiaoxiao

Abstract

Prompt learning has demonstrated impressive efficacy in the fine-tuning of multimodal large models to a wide range of downstream tasks. Nonetheless, applying existing prompt learning methods for the diagnosis of neurological disorder still suffers from two issues: (i) existing methods typically treat all patches equally, despite the fact that only a small number of patches in neuroimaging are relevant to the disease, and (ii) they ignore the structural information inherent in the brain connection network which is crucial for understanding and diagnosing neurological disorders. To tackle these issues, we introduce a novel prompt learning model by learning graph prompts during the fine-tuning process of multimodal large models for diagnosing neurological disorders. Specifically, we first leverage GPT-4 to obtain relevant disease concepts and compute semantic similarity between these concepts and all patches. Secondly, we reduce the weight of irrelevant patches according to the semantic similarity between each patch and disease-related concepts. Moreover, we construct a graph among tokens based on these concepts and employ a graph convolutional network layer to extract the structural information of the graph, which is used to prompt the pre-trained multimodal large models for diagnosing neurological disorders. Extensive experiments demonstrate that our method achieves superior performance for neurological disorder diagnosis compared with state-of-the-art methods and validated by clinicians.

Published
2023

17. Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving

Author

Xu, Junkai, Peng, Liang, Cheng, Haoran, Xia, Linxuan, Zhou, Qi, Deng, Dan, Qian, Wei, Wang, Wenxiao, Cai, Deng, Xu, Junkai, Peng, Liang, Cheng, Haoran, Xia, Linxuan, Zhou, Qi, Deng, Dan, Qian, Wei, Wang, Wenxiao, and Cai, Deng

Abstract

Multi-camera perception tasks have gained significant attention in the field of autonomous driving. However, existing frameworks based on Lift-Splat-Shoot (LSS) in the multi-camera setting cannot produce suitable dense 3D features due to the projection nature and uncontrollable densification process. To resolve this problem, we propose to regulate intermediate dense 3D features with the help of volume rendering. Specifically, we employ volume rendering to process the dense 3D features to obtain corresponding 2D features (e.g., depth maps, semantic maps), which are supervised by associated labels in the training. This manner regulates the generation of dense 3D features on the feature level, providing appropriate dense and unified features for multiple perception tasks. Therefore, our approach is termed Vampire, stands for "Volume rendering As Multi-camera Perception Intermediate feature REgulator". Experimental results on the Occ3D and nuScenes datasets demonstrate that Vampire facilitates fine-grained and appropriate extraction of dense 3D features, and is competitive with existing SOTA methods across diverse downstream perception tasks like 3D occupancy prediction, LiDAR segmentation and 3D objection detection, while utilizing moderate GPU resources. We provide a video demonstration in the supplementary materials and Codes are available at github.com/cskkxjk/Vampire., Comment: Accepted by AAAI 2024

Published
2023

18. Local Conditional Controlling for Text-to-Image Diffusion Models

Author

Zhao, Yibo, Peng, Liang, Yang, Yang, Luo, Zekai, Li, Hengjia, Chen, Yao, Zhao, Wei, lu, qinglin, Wu, Boxi, Liu, Wei, Zhao, Yibo, Peng, Liang, Yang, Yang, Luo, Zekai, Li, Hengjia, Chen, Yao, Zhao, Wei, lu, qinglin, Wu, Boxi, and Liu, Wei

Abstract

Diffusion models have exhibited impressive prowess in the text-to-image task. Recent methods add image-level controls, e.g., edge and depth maps, to manipulate the generation process together with text prompts to obtain desired images. This controlling process is globally operated on the entire image, which limits the flexibility of control regions. In this paper, we introduce a new simple yet practical task setting: local control. It focuses on controlling specific local areas according to user-defined image conditions, where the rest areas are only conditioned by the original text prompt. This manner allows the users to flexibly control the image generation in a fine-grained way. However, it is non-trivial to achieve this goal. The naive manner of directly adding local conditions may lead to the local control dominance problem. To mitigate this problem, we propose a training-free method that leverages the updates of noised latents and parameters in the cross-attention map during the denosing process to promote concept generation in non-control areas. Moreover, we use feature mask constraints to mitigate the degradation of synthesized image quality caused by information differences inside and outside the local control area. Extensive experiments demonstrate that our method can synthesize high-quality images to the prompt under local control conditions. Code is available at https://github.com/YibooZhao/Local-Control.

Published
2023

19. SmoothVideo: Smooth Video Synthesis with Noise Constraints on Diffusion Models for One-shot Video Tuning

Author

Peng, Liang, Cheng, Haoran, Yang, Zheng, Zhao, Ruisi, Xia, Linxuan, Song, Chaotian, Lu, Qinglin, Wu, Boxi, Liu, Wei, Peng, Liang, Cheng, Haoran, Yang, Zheng, Zhao, Ruisi, Xia, Linxuan, Song, Chaotian, Lu, Qinglin, Wu, Boxi, and Liu, Wei

Abstract

Recent one-shot video tuning methods, which fine-tune the network on a specific video based on pre-trained text-to-image models (e.g., Stable Diffusion), are popular in the community because of the flexibility. However, these methods often produce videos marred by incoherence and inconsistency. To address these limitations, this paper introduces a simple yet effective noise constraint across video frames. This constraint aims to regulate noise predictions across their temporal neighbors, resulting in smooth latents. It can be simply included as a loss term during the training phase. By applying the loss to existing one-shot video tuning methods, we significantly improve the overall consistency and smoothness of the generated videos. Furthermore, we argue that current video evaluation metrics inadequately capture smoothness. To address this, we introduce a novel metric that considers detailed features and their temporal dynamics. Experimental results validate the effectiveness of our approach in producing smoother videos on various one-shot video tuning baselines. The source codes and video demos are available at \href{https://github.com/SPengLiang/SmoothVideo}{https://github.com/SPengLiang/SmoothVideo}.

Published
2023

20. MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection

Author

Xu, Junkai, Peng, Liang, Cheng, Haoran, Li, Hao, Qian, Wei, Li, Ke, Wang, Wenxiao, Cai, Deng, Xu, Junkai, Peng, Liang, Cheng, Haoran, Li, Hao, Qian, Wei, Li, Ke, Wang, Wenxiao, and Cai, Deng

Abstract

In the field of monocular 3D detection, it is common practice to utilize scene geometric clues to enhance the detector's performance. However, many existing works adopt these clues explicitly such as estimating a depth map and back-projecting it into 3D space. This explicit methodology induces sparsity in 3D representations due to the increased dimensionality from 2D to 3D, and leads to substantial information loss, especially for distant and occluded objects. To alleviate this issue, we propose MonoNeRD, a novel detection framework that can infer dense 3D geometry and occupancy. Specifically, we model scenes with Signed Distance Functions (SDF), facilitating the production of dense 3D representations. We treat these representations as Neural Radiance Fields (NeRF) and then employ volume rendering to recover RGB images and depth maps. To the best of our knowledge, this work is the first to introduce volume rendering for M3D, and demonstrates the potential of implicit reconstruction for image-based 3D perception. Extensive experiments conducted on the KITTI-3D benchmark and Waymo Open Dataset demonstrate the effectiveness of MonoNeRD. Codes are available at https://github.com/cskkxjk/MonoNeRD., Comment: Accepted by ICCV 2023

Published
2023

21. Unsupervised Multiplex Graph Learning with Complementary and Consistent Information

Author

Peng, Liang, Wang, Xin, Zhu, Xiaofeng, Peng, Liang, Wang, Xin, and Zhu, Xiaofeng

Abstract

Unsupervised multiplex graph learning (UMGL) has been shown to achieve significant effectiveness for different downstream tasks by exploring both complementary information and consistent information among multiple graphs. However, previous methods usually overlook the issues in practical applications, i.e., the out-of-sample issue and the noise issue. To address the above issues, in this paper, we propose an effective and efficient UMGL method to explore both complementary and consistent information. To do this, our method employs multiple MLP encoders rather than graph convolutional network (GCN) to conduct representation learning with two constraints, i.e., preserving the local graph structure among nodes to handle the out-of-sample issue, and maximizing the correlation of multiple node representations to handle the noise issue. Comprehensive experiments demonstrate that our proposed method achieves superior effectiveness and efficiency over the comparison methods and effectively tackles those two issues. Code is available at https://github.com/LarryUESTC/CoCoMG.

Published
2023
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22. The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation

Author

Kong, Lingdong, Niu, Yaru, Xie, Shaoyuan, Hu, Hanjiang, Ng, Lai Xing, Cottereau, Benoit R., Zhao, Ding, Zhang, Liangjun, Wang, Hesheng, Ooi, Wei Tsang, Zhu, Ruijie, Song, Ziyang, Liu, Li, Zhang, Tianzhu, Yu, Jun, Jing, Mohan, Li, Pengwei, Qi, Xiaohua, Jin, Cheng, Chen, Yingfeng, Hou, Jie, Zhang, Jie, Kan, Zhen, Ling, Qiang, Peng, Liang, Li, Minglei, Xu, Di, Yang, Changpeng, Yao, Yuanqi, Wu, Gang, Kuai, Jian, Liu, Xianming, Jiang, Junjun, Huang, Jiamian, Li, Baojun, Chen, Jiale, Zhang, Shuang, Ao, Sun, Li, Zhenyu, Chen, Runze, Luo, Haiyong, Zhao, Fang, Yu, Jingze, Kong, Lingdong, Niu, Yaru, Xie, Shaoyuan, Hu, Hanjiang, Ng, Lai Xing, Cottereau, Benoit R., Zhao, Ding, Zhang, Liangjun, Wang, Hesheng, Ooi, Wei Tsang, Zhu, Ruijie, Song, Ziyang, Liu, Li, Zhang, Tianzhu, Yu, Jun, Jing, Mohan, Li, Pengwei, Qi, Xiaohua, Jin, Cheng, Chen, Yingfeng, Hou, Jie, Zhang, Jie, Kan, Zhen, Ling, Qiang, Peng, Liang, Li, Minglei, Xu, Di, Yang, Changpeng, Yao, Yuanqi, Wu, Gang, Kuai, Jian, Liu, Xianming, Jiang, Junjun, Huang, Jiamian, Li, Baojun, Chen, Jiale, Zhang, Shuang, Ao, Sun, Li, Zhenyu, Chen, Runze, Luo, Haiyong, Zhao, Fang, and Yu, Jingze

Abstract

Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical applications. Existing depth estimation systems, however, suffer inevitably from real-world corruptions and perturbations and are struggled to provide reliable depth predictions under such cases. In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation. This challenge was developed based on the newly established KITTI-C and NYUDepth2-C benchmarks. We hosted two stand-alone tracks, with an emphasis on robust self-supervised and robust fully-supervised depth estimation, respectively. Out of more than two hundred participants, nine unique and top-performing solutions have appeared, with novel designs ranging from the following aspects: spatial- and frequency-domain augmentations, masked image modeling, image restoration and super-resolution, adversarial training, diffusion-based noise suppression, vision-language pre-training, learned model ensembling, and hierarchical feature enhancement. Extensive experimental analyses along with insightful observations are drawn to better understand the rationale behind each design. We hope this challenge could lay a solid foundation for future research on robust and reliable depth estimation and beyond. The datasets, competition toolkit, workshop recordings, and source code from the winning teams are publicly available on the challenge website., Comment: Technical Report; 65 pages, 34 figures, 24 tables; Code at https://github.com/ldkong1205/RoboDepth

Published
2023

23. Envisioning a Next Generation Extended Reality Conferencing System with Efficient Photorealistic Human Rendering

Author

Shen, Chuanyue, Zhang, Letian, Yang, Zhangsihao, Mortazavi, Masood, Song, Xiyun, Peng, Liang, Yu, Heather, Shen, Chuanyue, Zhang, Letian, Yang, Zhangsihao, Mortazavi, Masood, Song, Xiyun, Peng, Liang, and Yu, Heather

Abstract

Meeting online is becoming the new normal. Creating an immersive experience for online meetings is a necessity towards more diverse and seamless environments. Efficient photorealistic rendering of human 3D dynamics is the core of immersive meetings. Current popular applications achieve real-time conferencing but fall short in delivering photorealistic human dynamics, either due to limited 2D space or the use of avatars that lack realistic interactions between participants. Recent advances in neural rendering, such as the Neural Radiance Field (NeRF), offer the potential for greater realism in metaverse meetings. However, the slow rendering speed of NeRF poses challenges for real-time conferencing. We envision a pipeline for a future extended reality metaverse conferencing system that leverages monocular video acquisition and free-viewpoint synthesis to enhance data and hardware efficiency. Towards an immersive conferencing experience, we explore an accelerated NeRF-based free-viewpoint synthesis algorithm for rendering photorealistic human dynamics more efficiently. We show that our algorithm achieves comparable rendering quality while performing training and inference 44.5% and 213% faster than state-of-the-art methods, respectively. Our exploration provides a design basis for constructing metaverse conferencing systems that can handle complex application scenarios, including dynamic scene relighting with customized themes and multi-user conferencing that harmonizes real-world people into an extended world., Comment: Accepted to CVPR 2023 ECV Workshop

Published
2023

24. Focusing on Relevant Responses for Multi-modal Rumor Detection

Author

Li, Jun, Bin, Yi, Peng, Liang, Yang, Yang, Li, Yangyang, Jin, Hao, Huang, Zi, Li, Jun, Bin, Yi, Peng, Liang, Yang, Yang, Li, Yangyang, Jin, Hao, and Huang, Zi

Abstract

In the absence of an authoritative statement about a rumor, people may expose the truth behind such rumor through their responses on social media. Most rumor detection methods aggregate the information of all the responses and have made great progress. However, due to the different backgrounds of users, the responses have different relevance for discovering th suspicious points hidden in a rumor claim. The methods that focus on all the responding tweets would dilute the effect of the critical ones. Moreover, for a multi-modal rumor claim, the focus of a user may be on several words in the text or an object in the image, so the different modalities should be considered to select the relevant responses and verify the claim. In this paper, we propose a novel multi-modal rumor detection model, termed Focal Reasoning Model (FoRM), to filter out the irrelevant responses and further conduct fine-grained reasoning with the multi-modal claim and corresponding responses. Concretely, there are two main components in our FoRM: the coarse-grained selection and the fine-grained reasoning. The coarse-grained selection component leverages the post-level features of the responses to verify the claim and learns a relevant score of each response. Based on the relevant scores, the most relevant responses are reserved as the critical ones to the further reasoning. In the fine-grained reasoning component, we design a relation attention module to explore the fine-grained relations, i.e., token-to-token and token-to-object relations, between the reserved responses and the multi-modal claim for finding out the valuable clues. Extensive experiments have been conducted on two real-world datasets, and the results demonstrate that our proposed model outperforms all the baselines., Comment: Submitted to TKDE

Published
2023

25. Boosting Semi-Supervised 3D Object Detection with Semi-Sampling

Author

Wu, Xiaopei, Zhao, Yang, Peng, Liang, Chen, Hua, Huang, Xiaoshui, Lin, Binbin, Liu, Haifeng, Cai, Deng, Ouyang, Wanli, Wu, Xiaopei, Zhao, Yang, Peng, Liang, Chen, Hua, Huang, Xiaoshui, Lin, Binbin, Liu, Haifeng, Cai, Deng, and Ouyang, Wanli

Abstract

Current 3D object detection methods heavily rely on an enormous amount of annotations. Semi-supervised learning can be used to alleviate this issue. Previous semi-supervised 3D object detection methods directly follow the practice of fully-supervised methods to augment labeled and unlabeled data, which is sub-optimal. In this paper, we design a data augmentation method for semi-supervised learning, which we call Semi-Sampling. Specifically, we use ground truth labels and pseudo labels to crop gt samples and pseudo samples on labeled frames and unlabeled frames, respectively. Then we can generate a gt sample database and a pseudo sample database. When training a teacher-student semi-supervised framework, we randomly select gt samples and pseudo samples to both labeled frames and unlabeled frames, making a strong data augmentation for them. Our semi-sampling can be regarded as an extension of gt-sampling to semi-supervised learning. Our method is simple but effective. We consistently improve state-of-the-art methods on ScanNet, SUN-RGBD, and KITTI benchmarks by large margins. For example, when training using only 10% labeled data on ScanNet, we achieve 3.1 mAP and 6.4 mAP improvement upon 3DIoUMatch in terms of mAP@0.25 and mAP@0.5. When training using only 1% labeled data on KITTI, we boost 3DIoUMatch by 3.5 mAP, 6.7 mAP and 14.1 mAP on car, pedestrian and cyclist classes. Codes will be made publicly available at https://github.com/LittlePey/Semi-Sampling.

Published
2022

26. SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving

Author

Peng, Liang, Li, Boqi, Yu, Wenhao, Yang, Kai, Shao, Wenbo, Wang, Hong, Peng, Liang, Li, Boqi, Yu, Wenhao, Yang, Kai, Shao, Wenbo, and Wang, Hong

Abstract

Autonomous driving confronts great challenges in complex traffic scenarios, where the risk of Safety of the Intended Functionality (SOTIF) can be triggered by the dynamic operational environment and system insufficiencies. The SOTIF risk is reflected not only intuitively in the collision risk with objects outside the autonomous vehicles (AVs), but also inherently in the performance limitation risk of the implemented algorithms themselves. How to minimize the SOTIF risk for autonomous driving is currently a critical, difficult, and unresolved issue. Therefore, this paper proposes the "Self-Surveillance and Self-Adaption System" as a systematic approach to online minimize the SOTIF risk, which aims to provide a systematic solution for monitoring, quantification, and mitigation of inherent and external risks. The core of this system is the risk monitoring of the implemented artificial intelligence algorithms within the AV. As a demonstration of the Self-Surveillance and Self-Adaption System, the risk monitoring of the perception algorithm, i.e., YOLOv5 is highlighted. Moreover, the inherent perception algorithm risk and external collision risk are jointly quantified via SOTIF entropy, which is then propagated downstream to the decision-making module and mitigated. Finally, several challenging scenarios are demonstrated, and the Hardware-in-the-Loop experiments are conducted to verify the efficiency and effectiveness of the system. The results demonstrate that the Self-Surveillance and Self-Adaption System enables dependable online monitoring, quantification, and mitigation of SOTIF risk in real-time critical traffic environments., Comment: 16 pages, 10 figures, 2 tables, submitted to IEEE TITS

Published
2022

27. PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in Long-tail Traffic Scenarios

Author

Peng, Liang, Li, Jun, Shao, Wenbo, Wang, Hong, Peng, Liang, Li, Jun, Shao, Wenbo, and Wang, Hong

Abstract

Perception algorithms in autonomous driving systems confront great challenges in long-tail traffic scenarios, where the problems of Safety of the Intended Functionality (SOTIF) could be triggered by the algorithm performance insufficiencies and dynamic operational environment. However, such scenarios are not systematically included in current open-source datasets, and this paper fills the gap accordingly. Based on the analysis and enumeration of trigger conditions, a high-quality diverse dataset is released, including various long-tail traffic scenarios collected from multiple resources. Considering the development of probabilistic object detection (POD), this dataset marks trigger sources that may cause perception SOTIF problems in the scenarios as key objects. In addition, an evaluation protocol is suggested to verify the effectiveness of POD algorithms in identifying the key objects via uncertainty. The dataset never stops expanding, and the first batch of open-source data includes 1126 frames with an average of 2.27 key objects and 2.47 normal objects in each frame. To demonstrate how to use this dataset for SOTIF research, this paper further quantifies the perception SOTIF entropy to confirm whether a scenario is unknown and unsafe for a perception system. The experimental results show that the quantified entropy can effectively and efficiently reflect the failure of the perception algorithm., Comment: 7 pages, 5 figures, 4 tables, submitted to 2023 ICRA

Published
2022

28. DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection

Author

Peng, Liang, Wu, Xiaopei, Yang, Zheng, Liu, Haifeng, Cai, Deng, Peng, Liang, Wu, Xiaopei, Yang, Zheng, Liu, Haifeng, and Cai, Deng

Abstract

Monocular 3D detection has drawn much attention from the community due to its low cost and setup simplicity. It takes an RGB image as input and predicts 3D boxes in the 3D space. The most challenging sub-task lies in the instance depth estimation. Previous works usually use a direct estimation method. However, in this paper we point out that the instance depth on the RGB image is non-intuitive. It is coupled by visual depth clues and instance attribute clues, making it hard to be directly learned in the network. Therefore, we propose to reformulate the instance depth to the combination of the instance visual surface depth (visual depth) and the instance attribute depth (attribute depth). The visual depth is related to objects' appearances and positions on the image. By contrast, the attribute depth relies on objects' inherent attributes, which are invariant to the object affine transformation on the image. Correspondingly, we decouple the 3D location uncertainty into visual depth uncertainty and attribute depth uncertainty. By combining different types of depths and associated uncertainties, we can obtain the final instance depth. Furthermore, data augmentation in monocular 3D detection is usually limited due to the physical nature, hindering the boost of performance. Based on the proposed instance depth disentanglement strategy, we can alleviate this problem. Evaluated on KITTI, our method achieves new state-of-the-art results, and extensive ablation studies validate the effectiveness of each component in our method. The codes are released at https://github.com/SPengLiang/DID-M3D., Comment: ECCV 2022

Published
2022

29. Quasifree photoionization under the reaction microscope

Author

Grundmann, Sven Eric, Trinter, Florian, Fang, Yong-Kang, Fehre, Kilian, Strenger, Nico, Pier, Andreas, Kaiser, Leon, Kircher, Max, Peng, Liang-You, Jahnke, Till, Dörner, Reinhard, Schöffler, Markus S., Grundmann, Sven Eric, Trinter, Florian, Fang, Yong-Kang, Fehre, Kilian, Strenger, Nico, Pier, Andreas, Kaiser, Leon, Kircher, Max, Peng, Liang-You, Jahnke, Till, Dörner, Reinhard, and Schöffler, Markus S.

Abstract

We experimentally investigated the quasifree mechanism (QFM) in one-photon double ionization of He and H2 at 800 eV photon energy and circular polarization with a COLTRIMS reaction microscope. Our work provides new insight into this elusive photoionization mechanism that was predicted by Miron Amusia more than four decades ago. We found the distinct four-fold symmetry in the angular emission pattern of QFM electrons from H2 double ionization that has previously only been observed for He. Furthermore, we provide experimental evidence that the photon momentum is not imparted onto the center of mass in quasifree photoionization, which is in contrast to the situation in single ionization and in double ionization mediated by the shake-off and knock-out mechanisms. This finding is substantiated by numerical results obtained by solving the system’s full-dimensional time-dependent Schrödinger equation beyond the dipole approximation.

Published
2022

30. Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion

Author

Wu, Xiaopei, Peng, Liang, Yang, Honghui, Xie, Liang, Huang, Chenxi, Deng, Chengqi, Liu, Haifeng, Cai, Deng, Wu, Xiaopei, Peng, Liang, Yang, Honghui, Xie, Liang, Huang, Chenxi, Deng, Chengqi, Liu, Haifeng, and Cai, Deng

Abstract

Current LiDAR-only 3D detection methods inevitably suffer from the sparsity of point clouds. Many multi-modal methods are proposed to alleviate this issue, while different representations of images and point clouds make it difficult to fuse them, resulting in suboptimal performance. In this paper, we present a novel multi-modal framework SFD (Sparse Fuse Dense), which utilizes pseudo point clouds generated from depth completion to tackle the issues mentioned above. Different from prior works, we propose a new RoI fusion strategy 3D-GAF (3D Grid-wise Attentive Fusion) to make fuller use of information from different types of point clouds. Specifically, 3D-GAF fuses 3D RoI features from the couple of point clouds in a grid-wise attentive way, which is more fine-grained and more precise. In addition, we propose a SynAugment (Synchronized Augmentation) to enable our multi-modal framework to utilize all data augmentation approaches tailored to LiDAR-only methods. Lastly, we customize an effective and efficient feature extractor CPConv (Color Point Convolution) for pseudo point clouds. It can explore 2D image features and 3D geometric features of pseudo point clouds simultaneously. Our method holds the highest entry on the KITTI car 3D object detection leaderboard, demonstrating the effectiveness of our SFD. Codes are available at https://github.com/LittlePey/SFD., Comment: Accepted by CVPR 2022 (Oral)

Published
2022

31. GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis

Author

Peng, Liang, Wang, Nan, Xu, Jie, Zhu, Xiaofeng, Li, Xiaoxiao, Peng, Liang, Wang, Nan, Xu, Jie, Zhu, Xiaofeng, and Li, Xiaoxiao

Abstract

In this work, we focus on the challenging task, neuro-disease classification, using functional magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph convolutional neural networks (GCNs) have achieved remarkable success. However, these achievements are inseparable from abundant labeled data and sensitive to spurious signals. To improve fMRI representation learning and classification under a label-efficient setting, we propose a novel and theory-driven self-supervised learning (SSL) framework on GCNs, namely Graph CCA for Temporal self-supervised learning on fMRI analysis GATE. Concretely, it is demanding to design a suitable and effective SSL strategy to extract formation and robust features for fMRI. To this end, we investigate several new graph augmentation strategies from fMRI dynamic functional connectives (FC) for SSL training. Further, we leverage canonical-correlation analysis (CCA) on different temporal embeddings and present the theoretical implications. Consequently, this yields a novel two-step GCN learning procedure comprised of (i) SSL on an unlabeled fMRI population graph and (ii) fine-tuning on a small labeled fMRI dataset for a classification task. Our method is tested on two independent fMRI datasets, demonstrating superior performance on autism and dementia diagnosis.

Published
2022
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32. WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection

Author

Peng, Liang, Yan, Senbo, Wu, Boxi, Yang, Zheng, He, Xiaofei, Cai, Deng, Peng, Liang, Yan, Senbo, Wu, Boxi, Yang, Zheng, He, Xiaofei, and Cai, Deng

Abstract

Monocular 3D object detection is one of the most challenging tasks in 3D scene understanding. Due to the ill-posed nature of monocular imagery, existing monocular 3D detection methods highly rely on training with the manually annotated 3D box labels on the LiDAR point clouds. This annotation process is very laborious and expensive. To dispense with the reliance on 3D box labels, in this paper we explore the weakly supervised monocular 3D detection. Specifically, we first detect 2D boxes on the image. Then, we adopt the generated 2D boxes to select corresponding RoI LiDAR points as the weak supervision. Eventually, we adopt a network to predict 3D boxes which can tightly align with associated RoI LiDAR points. This network is learned by minimizing our newly-proposed 3D alignment loss between the 3D box estimates and the corresponding RoI LiDAR points. We will illustrate the potential challenges of the above learning problem and resolve these challenges by introducing several effective designs into our method. Codes will be available at https://github.com/SPengLiang/WeakM3D., Comment: Accepted by ICLR 2022

Published
2022

33. FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction

Author

Peng, Liang, Wang, Nan, Dvornek, Nicha, Zhu, Xiaofeng, Li, Xiaoxiao, Peng, Liang, Wang, Nan, Dvornek, Nicha, Zhu, Xiaofeng, and Li, Xiaoxiao

Abstract

Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis. Specifically, in medical applications, GCNs can be used for disease prediction on a population graph, where graph nodes represent individuals and edges represent individual similarities. However, GCNs rely on a vast amount of data, which is challenging to collect for a single medical institution. In addition, a critical challenge that most medical institutions continue to face is addressing disease prediction in isolation with incomplete data information. To address these issues, Federated Learning (FL) allows isolated local institutions to collaboratively train a global model without data sharing. In this work, we propose a framework, FedNI, to leverage network inpainting and inter-institutional data via FL. Specifically, we first federatively train missing node and edge predictor using a graph generative adversarial network (GAN) to complete the missing information of local networks. Then we train a global GCN node classifier across institutions using a federated graph learning platform. The novel design enables us to build more accurate machine learning models by leveraging federated learning and also graph learning approaches. We demonstrate that our federated model outperforms local and baseline FL methods with significant margins on two public neuroimaging datasets.

Published
2021
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34. Improved regression inference using a second overlapping regression model

Author

Peng, Liang, Einmahl, John, Peng, Liang, and Einmahl, John

Abstract

Two time series of financial losses may be observed in different overlapping windows, serially dependent, heteroscedastic, and cross-sectionally dependent. Fitting a regression model to each of the two time series, we construct an improved least squares estimator in one series exploiting the cross-sectional dependence with the other series. We employ a random weight bootstrap method to define the new estimator and to establish its asymptotic normality. The developed inference is robust against heteroscedasticity as we do not estimate the GARCH errors. Simulations confirm the efficiency improvement through substantial variance reduction, especially when the cross-sectional dependence is strong and the second series is longer. We illustrate the usefulness of the method by analyzing mutual funds' returns.

Published
2021

35. Multi-level Feature Learning for Contrastive Multi-view Clustering

Author

Xu, Jie, Tang, Huayi, Ren, Yazhou, Peng, Liang, Zhu, Xiaofeng, He, Lifang, Xu, Jie, Tang, Huayi, Ren, Yazhou, Peng, Liang, Zhu, Xiaofeng, and He, Lifang

Abstract

Multi-view clustering can explore common semantics from multiple views and has attracted increasing attention. However, existing works punish multiple objectives in the same feature space, where they ignore the conflict between learning consistent common semantics and reconstructing inconsistent view-private information. In this paper, we propose a new framework of multi-level feature learning for contrastive multi-view clustering to address the aforementioned issue. Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces. Specifically, the reconstruction objective is conducted on the low-level features. Two consistency objectives based on contrastive learning are conducted on the high-level features and the semantic labels, respectively. They make the high-level features effectively explore the common semantics and the semantic labels achieve the multi-view clustering. As a result, the proposed framework can reduce the adverse influence of view-private information. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art clustering effectiveness.

Published
2021

36. Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG

Author

Fan, Chen-Chen, Xie, Haiqun, Peng, Liang, Yang, Hongjun, Ni, Zhen-Liang, Wang, Guan'an, Zhou, Yan-Jie, Chen, Sheng, Fang, Zhijie, Huang, Shuyun, Hou, Zeng-Guang, Fan, Chen-Chen, Xie, Haiqun, Peng, Liang, Yang, Hongjun, Ni, Zhen-Liang, Wang, Guan'an, Zhou, Yan-Jie, Chen, Sheng, Fang, Zhijie, Huang, Shuyun, and Hou, Zeng-Guang

Abstract

Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group-level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system., Comment: This paper has been accepted by 2021 International Conference on Robotics and Automation (ICRA 2021)

Published
2021

37. Lidar Point Cloud Guided Monocular 3D Object Detection

Author

Peng, Liang, Liu, Fei, Yu, Zhengxu, Yan, Senbo, Deng, Dan, Yang, Zheng, Liu, Haifeng, Cai, Deng, Peng, Liang, Liu, Fei, Yu, Zhengxu, Yan, Senbo, Deng, Dan, Yang, Zheng, Liu, Haifeng, and Cai, Deng

Abstract

Monocular 3D object detection is a challenging task in the self-driving and computer vision community. As a common practice, most previous works use manually annotated 3D box labels, where the annotating process is expensive. In this paper, we find that the precisely and carefully annotated labels may be unnecessary in monocular 3D detection, which is an interesting and counterintuitive finding. Using rough labels that are randomly disturbed, the detector can achieve very close accuracy compared to the one using the ground-truth labels. We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels. Motivated by the conclusions above and considering the precise LiDAR 3D measurement, we propose a simple and effective framework, dubbed LiDAR point cloud guided monocular 3D object detection (LPCG). This framework is capable of either reducing the annotation costs or considerably boosting the detection accuracy without introducing extra annotation costs. Specifically, It generates pseudo labels from unlabeled LiDAR point clouds. Thanks to accurate LiDAR 3D measurements in 3D space, such pseudo labels can replace manually annotated labels in the training of monocular 3D detectors, since their 3D location information is precise. LPCG can be applied into any monocular 3D detector to fully use massive unlabeled data in a self-driving system. As a result, in KITTI benchmark, we take the first place on both monocular 3D and BEV (bird's-eye-view) detection with a significant margin. In Waymo benchmark, our method using 10% labeled data achieves comparable accuracy to the baseline detector using 100% labeled data. The codes are released at https://github.com/SPengLiang/LPCG., Comment: ECCV 2022

Published
2021

38. OCM3D: Object-Centric Monocular 3D Object Detection

Author

Peng, Liang, Liu, Fei, Yan, Senbo, He, Xiaofei, Cai, Deng, Peng, Liang, Liu, Fei, Yan, Senbo, He, Xiaofei, and Cai, Deng

Abstract

Image-only and pseudo-LiDAR representations are commonly used for monocular 3D object detection. However, methods based on them have shortcomings of either not well capturing the spatial relationships in neighbored image pixels or being hard to handle the noisy nature of the monocular pseudo-LiDAR point cloud. To overcome these issues, in this paper we propose a novel object-centric voxel representation tailored for monocular 3D object detection. Specifically, voxels are built on each object proposal, and their sizes are adaptively determined by the 3D spatial distribution of the points, allowing the noisy point cloud to be organized effectively within a voxel grid. This representation is proved to be able to locate the object in 3D space accurately. Furthermore, prior works would like to estimate the orientation via deep features extracted from an entire image or a noisy point cloud. By contrast, we argue that the local RoI information from the object image patch alone with a proper resizing scheme is a better input as it provides complete semantic clues meanwhile excludes irrelevant interferences. Besides, we decompose the confidence mechanism in monocular 3D object detection by considering the relationship between 3D objects and the associated 2D boxes. Evaluated on KITTI, our method outperforms state-of-the-art methods by a large margin. The code will be made publicly available soon.

Published
2021

39. Experimental Study on the Bearing Capacity of Glass Deck under the Condition of Vehicle Traffic

Author

Peng Tang*, Sai Gong, Peng Liang, Gao Cheng, Peng Tang*, Sai Gong, Peng Liang, and Gao Cheng

Abstract

In order to study the mechanical properties of the glass plate structure applied to the automobile bridge deck, the bearing capacity test of the glass bridge deck under the wheel load is carried out, and the failure mode, load displacement curve and safety function of the glass plate under the boundary, position and number of layers of the wheel load are analyzed. The results show that the ultimate bearing capacity of laminated glass under the condition of simply supported boundary on both sides is about four sides supported 71.8%. The ultimate bearing capacity of single-layer glass under the boundary condition of simple support on both sides is about four sides 51.4% from the point of view of meeting the structural strength requirements. The loading test is carried out by applying different multiple wheel loads at the plate angle and the center of the plate. The test results can provide reference for the application of the glass bridge deck in engineering.

Published
2021

40. Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving

Author

Peng, Liang, Deng, Dan, Cai, Deng, Peng, Liang, Deng, Dan, and Cai, Deng

Abstract

Recently, there are emerging many stereo matching methods for autonomous driving based on unsupervised learning. Most of them take advantage of reconstruction losses to remove dependency on disparity groundtruth. Occlusion handling is a challenging problem in stereo matching, especially for unsupervised methods. Previous unsupervised methods failed to take full advantage of geometry properties in occlusion handling. In this paper, we introduce an effective way to detect occlusion regions and propose a novel unsupervised training strategy to deal with occlusion that only uses the predicted left disparity map, by making use of its geometry features in an iterative way. In the training process, we regard the predicted left disparity map as pseudo groundtruth and infer occluded regions using geometry features. The resulting occlusion mask is then used in either training, post-processing, or both of them as guidance. Experiments show that our method could deal with the occlusion problem effectively and significantly outperforms the other unsupervised methods for stereo matching. Moreover, our occlusion-aware strategies can be extended to the other stereo methods conveniently and improve their performances.

Published
2020

41. Inference for Heavy-Tailed Data : Applications in Insurance and Finance

Author

Peng, Liang, Qi, Yongcheng, Peng, Liang, Peng, Liang, Qi, Yongcheng, and Peng, Liang

Abstract

Heavy tailed data appears frequently in social science, internet traffic, insurance and finance. Statistical inference has been studied for many years, which includes recent bias-reduction estimation for tail index and high quantiles with applications in risk management, empirical likelihood based interval estimation for tail index and high quantiles, hypothesis tests for heavy tails, the choice of sample fraction in tail index and high quantile inference. These results for independent data, dependent data, linear time series and nonlinear time series are scattered in different statistics journals. Inference for Heavy-Tailed Data Analysis puts these methods into a single place with a clear picture on learning and using these techniques.Contains comprehensive coverage of new techniques of heavy tailed data analysisProvides examples of heavy tailed data and its usesBrings together, in a single place, a clear picture on learning and using these techniques

Published
2017

42. Inference for Heavy-Tailed Data : Applications in Insurance and Finance

Author

Peng, Liang, Qi, Yongcheng, Peng, Liang, Peng, Liang, Qi, Yongcheng, and Peng, Liang

Abstract

Heavy tailed data appears frequently in social science, internet traffic, insurance and finance. Statistical inference has been studied for many years, which includes recent bias-reduction estimation for tail index and high quantiles with applications in risk management, empirical likelihood based interval estimation for tail index and high quantiles, hypothesis tests for heavy tails, the choice of sample fraction in tail index and high quantile inference. These results for independent data, dependent data, linear time series and nonlinear time series are scattered in different statistics journals. Inference for Heavy-Tailed Data Analysis puts these methods into a single place with a clear picture on learning and using these techniques.Contains comprehensive coverage of new techniques of heavy tailed data analysisProvides examples of heavy tailed data and its usesBrings together, in a single place, a clear picture on learning and using these techniques

Published
2017

43. Inference for Heavy-Tailed Data : Applications in Insurance and Finance

Author

Peng, Liang, Qi, Yongcheng, Peng, Liang, Peng, Liang, Qi, Yongcheng, and Peng, Liang

Abstract

Heavy tailed data appears frequently in social science, internet traffic, insurance and finance. Statistical inference has been studied for many years, which includes recent bias-reduction estimation for tail index and high quantiles with applications in risk management, empirical likelihood based interval estimation for tail index and high quantiles, hypothesis tests for heavy tails, the choice of sample fraction in tail index and high quantile inference. These results for independent data, dependent data, linear time series and nonlinear time series are scattered in different statistics journals. Inference for Heavy-Tailed Data Analysis puts these methods into a single place with a clear picture on learning and using these techniques.Contains comprehensive coverage of new techniques of heavy tailed data analysisProvides examples of heavy tailed data and its usesBrings together, in a single place, a clear picture on learning and using these techniques

Published
2017

44. Inference for Heavy-Tailed Data : Applications in Insurance and Finance

Author

Peng, Liang, Qi, Yongcheng, Peng, Liang, Peng, Liang, Qi, Yongcheng, and Peng, Liang

Abstract

Heavy tailed data appears frequently in social science, internet traffic, insurance and finance. Statistical inference has been studied for many years, which includes recent bias-reduction estimation for tail index and high quantiles with applications in risk management, empirical likelihood based interval estimation for tail index and high quantiles, hypothesis tests for heavy tails, the choice of sample fraction in tail index and high quantile inference. These results for independent data, dependent data, linear time series and nonlinear time series are scattered in different statistics journals. Inference for Heavy-Tailed Data Analysis puts these methods into a single place with a clear picture on learning and using these techniques.Contains comprehensive coverage of new techniques of heavy tailed data analysisProvides examples of heavy tailed data and its usesBrings together, in a single place, a clear picture on learning and using these techniques

Published
2017

45. Analysis of temporal and spatial variations in hydrometeorological elements in the Yarkant River Basin, China

Author

Wei, Ren-juan, Peng, Liang, Liang, Chuan, Haemmig, Christoph, Huss, Matthias, Mu, Zhen-xia, He, Ying, Wei, Ren-juan, Peng, Liang, Liang, Chuan, Haemmig, Christoph, Huss, Matthias, Mu, Zhen-xia, and He, Ying

Abstract

Yarkant River is a tributary of Tarim River in China, and the basin lacks observational data. To investigate past climatic variations and predict future climate changes, precipitation, air temperature and runoff data from Kaqun hydrological station are analysed at monthly and seasonal scales using detrended fluctuation analysis (DFA). Results show that DFA scaling exponents of monthly precipitation, air temperature and runoff are 0.535, 0.662 and 0.582, respectively. These three factors all show long- range correlations. Their increasing trends will continue in the near future as the climate shifts towards warmer and more humid. Spring and winter precipitation exhibit long-range correlations and will increase in the future. In contrast, summer and autumn precipitation exhibits random fluctuations and does not show stable trends. Air temperature during all seasons exhibits long-range correlations and will continue to increase in the future. Runoff during the spring and summer exhibits weak anti- persistence, but autumn and winter runoff show long-range correlations and increasing trends. The vertical distribution of precipitation was first analysed using observed data and climate reanalysis data. It indicates that precipitation increases with elevations below 2,000 m a.s.l. at a rate of 26 mm per 100 m and decreases with elevations above 2,000 m a.s.l.

Published
2019

46. State of the climate in 2017

Author

Abernethy, R., Ackerman, Steven A., Adler, R., Albanil Encarnación, Adelina, Aldeco, Laura S., Alfaro, Eric J., Aliaga-Nestares, Vannia, Allan, Richard P., Allan, Rob, Alves, Lincoln M., Amador, Jorge A., Anderson, John, Andreassen, L. M., Argüez, Anthony, Armitage, C., Arndt, Derek S., Avalos, Grinia, Azorin-Molina, César, Báez, Julián, Bardin, M. Yu, Barichivich, Jonathan, Baringer, Molly O., Barreira, Sandra, Baxter, Stephen, Beck, H. E., Becker, Andreas, Bedka, Kristopher M., Behe, Carolina, Bell, Gerald D., Bellouin, Nicolas, Belmont, M., Benedetti, Angela, Bernhard, G. H., Berrisford, Paul, Berry, David I., Bhatt, U. S., Bissolli, Peter, Bjerke, J., Blake, Eric S., Blenkinsop, Stephen, Blunden, Jessica, Bolmgren, K., Bosilovich, Michael G., Boucher, Olivier, Bouchon, Marilú, Box, J. E., Boyer, Tim, Braathen, Geir O., Bromwich, David H., Brown, R., Buehler, S., Bulygina, Olga N., Burgess, D., Calderón, Blanca, Camargo, Suzana J., Campbell, Ethan C., Campbell, Jayaka D., Cappelen, J., Carrea, Laura, Carter, Brendan R., Castro, Anabel, Chambers, Don P., Cheng, Lijing, Christiansen, Hanne H., Christy, John R., Chung, E. S., Clem, Kyle R., Coelho, Caio A.S., Coldewey-Egbers, Melanie, Colwell, Steve, Cooper, Owen R., Copland, L., Costanza, Carol, Covey, Curt, Coy, Lawrence, Cronin, T., Crouch, Jake, Cruzado, Luis, Daniel, Raychelle, Davis, Sean M., Davletshin, S. G., De Eyto, Elvira, De Jeu, Richard A.M., De La Cour, Jacqueline L., De Laat, Jos, De Gasperi, Curtis L., Degenstein, Doug, Deline, P., Demircan, Mesut, Derksen, C., Dewitte, Boris, Dhurmea, R., Di Girolamo, Larry, Diamond, Howard J., Dickerson, C., Dlugokencky, Ed J., Dohan, Kathleen, Dokulil, Martin T., Dolman, A. Johannes, Domingues, Catia M., Domingues, Ricardo, Donat, Markus G., Dong, Shenfu, Dorigo, Wouter A., Drozdov, D. S., Dunn, Robert J.H., Durre, Imke, Dutton, Geoff S., Eakin, C. Mark, El Kharrim, M., Elkins, James W., Epstein, H. E., Espinoza, Jhan C., Famiglietti, James S., Farmer, J., Farrell, S., Fauchald, P., Fausto, R. S., Feely, Richard A., Feng, Z., Fenimore, Chris, Fettweis, X., Fioletov, Vitali E., Flemming, Johannes, Fogt, Ryan L., Folland, Chris, Forbes, B. C., Foster, Michael J., Francis, S. D., Franz, Bryan A., Frey, Richard A., Frith, Stacey M., Froidevaux, Lucien, Ganter, Catherine, Geiger, Erick F., Gerland, S., Gilson, John, Gobron, Nadine, Goldenberg, Stanley B., Gomez, Andrea M., Goni, Gustavo, Grooß, Jens Uwe, Gruber, Alexander, Guard, Charles P., Gugliemin, Mario, Gupta, S. K., Gutiérrez, Dimitri, Haas, C., Hagos, S., Hahn, Sebastian, Haimberger, Leo, Hall, Brad D., Halpert, Michael S., Hamlington, Benjamin D., Hanna, E., Hansen, K., Hanssen-Bauer, L., Harris, Ian, Hartfield, Gail, Heidinger, Andrew K., Heim, Richard R., Helfrich, S., Hemming, D. L., Hendricks, S., Hernández, Rafael, Hernández, Sosa Marieta, Heron, Scott F., Heuzé, C., Hidalgo, Hugo G., Ho, Shu Peng, Hobbs, William R., Horstkotte, T., Huang, Boyin, Hubert, Daan, Hueuzé, Céline, Hurst, Dale F., Ialongo, Iolanda, Ibrahim, M. M., Ijampy, J. A., Inness, Antje, Isaac, Victor, Isaksen, K., Ishii, Masayoshi, Jacobs, Stephanie J., Jeffries, Martin O., Jevrejeva, Svetlana, Jiménez, C., Jin, Xiangze, John, Viju, Johns, William E., Johnsen, Bjørn, Johnson, Bryan, Johnson, Gregory C., Johnson, Kenneth S., Jones, Philip D., Jumaux, Guillaume, Kabidi, Khadija, Kaiser, J. W., Karaköylü, Erdem M., Kato, Seiji, Kazemi, A., Keller, Linda M., Kennedy, John, Kerr, Kenneth, Khan, M. S., Kholodov, A. L., Khoshkam, Mahbobeh, Killick, Rachel, Kim, Hyungjun, Kim, S. J., Klotzbach, Philip J., Knaff, John A., Kohler, J., Korhonen, Johanna, Korshunova, Natalia N., Kramarova, Natalya, Kratz, D. P., Kruger, Andries, Kruk, Michael C., Krumpen, T., Ladd, C., Lakatos, Mónika, Lakkala, Kaisa, Lander, Mark A., Landschützer, Peter, Landsea, Chris W., Lankhorst, Matthias, Lavado-Casimiro, Waldo, Lazzara, Matthew A., Lee, S. E., Lee, T. C., Leuliette, Eric, L'Heureux, Michelle, Li, Tim, Lieser, Jan L., Lin, I. I., Mears, Carl A., Liu, Gang, Li, Bailing, Liu, Hongxing, Locarnini, Ricardo, Loeb, Norman G., Long, Craig S., López, Luis A., Lorrey, Andrew M., Loyola, Diego, Lumpkin, Rick, Luo, Jing Jia, Luojus, K., Luthcke, S., Macias-Fauria, M., Malkova, G. V., Manney, Gloria L., Marcellin, Vernie, Marchenko, S. S., Marengo, José A., Marín, Dora, Marra, John J., Marszelewski, Wlodzimierz, Martens, B., Martin, A., Martínez, Alejandra G., Martínez-Güingla, Rodney, Martínez-Sánchez, Odalys, Marsh, Benjamin L., Lyman, John M., Massom, Robert A., May, Linda, Mayer, Michael, Mazloff, Matthew, McBride, Charlotte, McCabe, M. F., McCarthy, Mark, Meier, W., Meijers, Andrew J.S., Mekonnen, Ademe, Mengistu Tsidu, G., Menzel, W. Paul, Merchant, Christopher J., Meredith, Michael P., Merrifield, Mark A., Miller, Ben, Miralles, Diego G., Mitchum, Gary T., Mitro, Sukarni, Moat, Ben, Mochizuki, Y., Monselesan, Didier, Montzka, Stephen A., Mora, Natalie, Morice, Colin, Mosquera-Vásquez, Kobi, Mostafa, Awatif E., Mote, T., Mudryk, L., Mühle, Jens, Mullan, A. Brett, Müller, Rolf, Myneni, R., Nash, Eric R., Nerem, R. Steven, Newman, L., Newman, Paul A., Nielsen-Gammon, John W., Nieto, Juan José, Noetzli, Jeannette, Noll, Ben E., O'Neel, S., Osborn, Tim J., Osborne, Emily, Overland, J., Oyunjargal, Lamjav, Park, T., Pasch, Richard J., Pascual-Ramírez, Reynaldo, Pastor Saavedra, Maria Asuncion, Paterson, Andrew M., Paulik, Christoph, Pearce, Petra R., Peltier, Alexandre, Pelto, Mauri S., Peng, Liang, Perkins-Kirkpatrick, Sarah E., Perovich, Don, Petropavlovskikh, Irina, Pezza, Alexandre B., Phillips, C., Phillips, David, Phoenix, G., Pinty, Bernard, Pinzon, J., Po-Chedley, S., Polashenski, C., Purkey, Sarah G., Quispe, Nelson, Rajeevan, Madhavan, Rakotoarimalala, C., Rayner, Darren, Raynolds, M. K., Reagan, James, Reid, Phillip, Reimer, Christoph, Rémy, Samuel, Revadekar, Jayashree V., Richardson, A. D., Richter-Menge, Jacqueline, Ricker, R., Rimmer, Alon, Robinson, David A., Rodell, Matthew, Rodriguez Camino, Ernesto, Romanovsky, Vladimir E., Ronchail, Josyane, Rosenlof, Karen H., Rösner, Benjamin, Roth, Chris, Roth, David Mark, Rusak, James A., Rutishäuser, T., Sallée, Jean Bapiste, Sánchez-Lugo, Ahira, Santee, Michelle L., Sasgen, L., Sawaengphokhai, P., Sayad, T. A., Sayouri, Amal, Scambos, Ted A., Scanlon, T., Schenzinger, Verena, Schladow, S. Geoffrey, Schmid, Claudia, Schmid, Martin, Schreck, Carl J., Selkirk, H. B., Send, Uwe, Sensoy, Serhat, Sharp, M., Shi, Lei, Shiklomanov, Nikolai I., Shimaraeva, Svetlana V., Siegel, David A., Silow, Eugene, Sima, Fatou, Simmons, Adrian J., Skirving, William J., Smeed, David A., Smeets, C. J.P.P., Smith, Adam, Smith, Sharon L., Soden, B., Sofieva, Viktoria, Sparks, T. H., Spence, Jacqueline M., Spillane, Sandra, Srivastava, A. K., Stackhouse, Paul W., Stammerjohn, Sharon, Stanitski, Diane M., Steinbrecht, Wolfgang, Stella, José L., Stengel, M., Stephenson, Kimberly, Stephenson, Tannecia S., Strahan, Susan, Streletskiy, Dimitri A., Strong, Alan E., Sun-Mack, Sunny, Sutton, Adrienne J., Swart, Sebastiaan, Sweet, William, Takahashi, Kenneth S., Tamar, Gerard, Taylor, Michael A., Tedesco, M., Thackeray, S. J., Thoman, R. L., Thompson, Philip, Thomson, L., Thorsteinsson, T., Timbal, Bertrand, Timmermans, M. L., TImofeyev, Maxim A., Tirak, Kyle V., Tobin, Skie, Togawa, H., Tømmervik, H., Tourpali, Kleareti, Trachte, Katja, Trewin, Blair C., Triñanes, Joaquin A., Trotman, Adrian R., Tschudi, M., Tucker, C. J., Tye, Mari R., Van As, D., Van De Wal, R. S.W., Van Der Ronald, J. A., Van Der Schalie, Robin, Van Der Schrier, Gerard, Van Der Werf, Guido R., Van Meerbeeck, Cedric J., Velden, Christopher S., Velicogna, I., Verburg, Piet, Vickers, H., Vincent, Lucie A., Vömel, Holger, Vose, Russell S., Wagner, Wolfgang, Walker, D. A., Walsh, J., Wang, Bin, Wang, Junhong, Wang, Lei, Wang, M., Wang, Ray, Wang, Sheng Hung, Wanninkhof, Rik, Watanabe, Shohei, Weber, Mark, Webster, M., Weller, Robert A., Westberry, Toby K., Weyhenmeyer, Gesa A., Whitewood, Robert, Widlansky, Matthew J., Wiese, David N., Wijffels, Susan E., Wilber, Anne C., Wild, Jeanette D., Willett, Kate M., Willis, Josh K., Wolken, G., Wong, Takmeng, Wood, E. F., Wood, K., Woolway, R. Iestyn, Wouters, B., Xue, Yan, Yin, Xungang, Yoon, Huang, York, A., Yu, Lisan, Zambrano, Eduardo, Zhang, Huai Min, Zhang, Peiqun, Zhao, Guanguo, Zhao, Lin, Zhu, Zhiwei, Ziel, R., Ziemke, Jerry R., Ziese, Markus G., Griffin, Jessicca, Hammer, Gregory, Love-Brotak, S. Elizabeth, Misch, Deborah J., Riddle, Deborah B., Slagle, Mary, Sprain, Mara, Veasey, Sara W., McVicar, Tim R., Abernethy, R., Ackerman, Steven A., Adler, R., Albanil Encarnación, Adelina, Aldeco, Laura S., Alfaro, Eric J., Aliaga-Nestares, Vannia, Allan, Richard P., Allan, Rob, Alves, Lincoln M., Amador, Jorge A., Anderson, John, Andreassen, L. M., Argüez, Anthony, Armitage, C., Arndt, Derek S., Avalos, Grinia, Azorin-Molina, César, Báez, Julián, Bardin, M. Yu, Barichivich, Jonathan, Baringer, Molly O., Barreira, Sandra, Baxter, Stephen, Beck, H. E., Becker, Andreas, Bedka, Kristopher M., Behe, Carolina, Bell, Gerald D., Bellouin, Nicolas, Belmont, M., Benedetti, Angela, Bernhard, G. H., Berrisford, Paul, Berry, David I., Bhatt, U. S., Bissolli, Peter, Bjerke, J., Blake, Eric S., Blenkinsop, Stephen, Blunden, Jessica, Bolmgren, K., Bosilovich, Michael G., Boucher, Olivier, Bouchon, Marilú, Box, J. E., Boyer, Tim, Braathen, Geir O., Bromwich, David H., Brown, R., Buehler, S., Bulygina, Olga N., Burgess, D., Calderón, Blanca, Camargo, Suzana J., Campbell, Ethan C., Campbell, Jayaka D., Cappelen, J., Carrea, Laura, Carter, Brendan R., Castro, Anabel, Chambers, Don P., Cheng, Lijing, Christiansen, Hanne H., Christy, John R., Chung, E. S., Clem, Kyle R., Coelho, Caio A.S., Coldewey-Egbers, Melanie, Colwell, Steve, Cooper, Owen R., Copland, L., Costanza, Carol, Covey, Curt, Coy, Lawrence, Cronin, T., Crouch, Jake, Cruzado, Luis, Daniel, Raychelle, Davis, Sean M., Davletshin, S. G., De Eyto, Elvira, De Jeu, Richard A.M., De La Cour, Jacqueline L., De Laat, Jos, De Gasperi, Curtis L., Degenstein, Doug, Deline, P., Demircan, Mesut, Derksen, C., Dewitte, Boris, Dhurmea, R., Di Girolamo, Larry, Diamond, Howard J., Dickerson, C., Dlugokencky, Ed J., Dohan, Kathleen, Dokulil, Martin T., Dolman, A. Johannes, Domingues, Catia M., Domingues, Ricardo, Donat, Markus G., Dong, Shenfu, Dorigo, Wouter A., Drozdov, D. S., Dunn, Robert J.H., Durre, Imke, Dutton, Geoff S., Eakin, C. Mark, El Kharrim, M., Elkins, James W., Epstein, H. E., Espinoza, Jhan C., Famiglietti, James S., Farmer, J., Farrell, S., Fauchald, P., Fausto, R. S., Feely, Richard A., Feng, Z., Fenimore, Chris, Fettweis, X., Fioletov, Vitali E., Flemming, Johannes, Fogt, Ryan L., Folland, Chris, Forbes, B. C., Foster, Michael J., Francis, S. D., Franz, Bryan A., Frey, Richard A., Frith, Stacey M., Froidevaux, Lucien, Ganter, Catherine, Geiger, Erick F., Gerland, S., Gilson, John, Gobron, Nadine, Goldenberg, Stanley B., Gomez, Andrea M., Goni, Gustavo, Grooß, Jens Uwe, Gruber, Alexander, Guard, Charles P., Gugliemin, Mario, Gupta, S. K., Gutiérrez, Dimitri, Haas, C., Hagos, S., Hahn, Sebastian, Haimberger, Leo, Hall, Brad D., Halpert, Michael S., Hamlington, Benjamin D., Hanna, E., Hansen, K., Hanssen-Bauer, L., Harris, Ian, Hartfield, Gail, Heidinger, Andrew K., Heim, Richard R., Helfrich, S., Hemming, D. L., Hendricks, S., Hernández, Rafael, Hernández, Sosa Marieta, Heron, Scott F., Heuzé, C., Hidalgo, Hugo G., Ho, Shu Peng, Hobbs, William R., Horstkotte, T., Huang, Boyin, Hubert, Daan, Hueuzé, Céline, Hurst, Dale F., Ialongo, Iolanda, Ibrahim, M. M., Ijampy, J. A., Inness, Antje, Isaac, Victor, Isaksen, K., Ishii, Masayoshi, Jacobs, Stephanie J., Jeffries, Martin O., Jevrejeva, Svetlana, Jiménez, C., Jin, Xiangze, John, Viju, Johns, William E., Johnsen, Bjørn, Johnson, Bryan, Johnson, Gregory C., Johnson, Kenneth S., Jones, Philip D., Jumaux, Guillaume, Kabidi, Khadija, Kaiser, J. W., Karaköylü, Erdem M., Kato, Seiji, Kazemi, A., Keller, Linda M., Kennedy, John, Kerr, Kenneth, Khan, M. S., Kholodov, A. L., Khoshkam, Mahbobeh, Killick, Rachel, Kim, Hyungjun, Kim, S. J., Klotzbach, Philip J., Knaff, John A., Kohler, J., Korhonen, Johanna, Korshunova, Natalia N., Kramarova, Natalya, Kratz, D. P., Kruger, Andries, Kruk, Michael C., Krumpen, T., Ladd, C., Lakatos, Mónika, Lakkala, Kaisa, Lander, Mark A., Landschützer, Peter, Landsea, Chris W., Lankhorst, Matthias, Lavado-Casimiro, Waldo, Lazzara, Matthew A., Lee, S. E., Lee, T. C., Leuliette, Eric, L'Heureux, Michelle, Li, Tim, Lieser, Jan L., Lin, I. I., Mears, Carl A., Liu, Gang, Li, Bailing, Liu, Hongxing, Locarnini, Ricardo, Loeb, Norman G., Long, Craig S., López, Luis A., Lorrey, Andrew M., Loyola, Diego, Lumpkin, Rick, Luo, Jing Jia, Luojus, K., Luthcke, S., Macias-Fauria, M., Malkova, G. V., Manney, Gloria L., Marcellin, Vernie, Marchenko, S. S., Marengo, José A., Marín, Dora, Marra, John J., Marszelewski, Wlodzimierz, Martens, B., Martin, A., Martínez, Alejandra G., Martínez-Güingla, Rodney, Martínez-Sánchez, Odalys, Marsh, Benjamin L., Lyman, John M., Massom, Robert A., May, Linda, Mayer, Michael, Mazloff, Matthew, McBride, Charlotte, McCabe, M. F., McCarthy, Mark, Meier, W., Meijers, Andrew J.S., Mekonnen, Ademe, Mengistu Tsidu, G., Menzel, W. Paul, Merchant, Christopher J., Meredith, Michael P., Merrifield, Mark A., Miller, Ben, Miralles, Diego G., Mitchum, Gary T., Mitro, Sukarni, Moat, Ben, Mochizuki, Y., Monselesan, Didier, Montzka, Stephen A., Mora, Natalie, Morice, Colin, Mosquera-Vásquez, Kobi, Mostafa, Awatif E., Mote, T., Mudryk, L., Mühle, Jens, Mullan, A. Brett, Müller, Rolf, Myneni, R., Nash, Eric R., Nerem, R. Steven, Newman, L., Newman, Paul A., Nielsen-Gammon, John W., Nieto, Juan José, Noetzli, Jeannette, Noll, Ben E., O'Neel, S., Osborn, Tim J., Osborne, Emily, Overland, J., Oyunjargal, Lamjav, Park, T., Pasch, Richard J., Pascual-Ramírez, Reynaldo, Pastor Saavedra, Maria Asuncion, Paterson, Andrew M., Paulik, Christoph, Pearce, Petra R., Peltier, Alexandre, Pelto, Mauri S., Peng, Liang, Perkins-Kirkpatrick, Sarah E., Perovich, Don, Petropavlovskikh, Irina, Pezza, Alexandre B., Phillips, C., Phillips, David, Phoenix, G., Pinty, Bernard, Pinzon, J., Po-Chedley, S., Polashenski, C., Purkey, Sarah G., Quispe, Nelson, Rajeevan, Madhavan, Rakotoarimalala, C., Rayner, Darren, Raynolds, M. K., Reagan, James, Reid, Phillip, Reimer, Christoph, Rémy, Samuel, Revadekar, Jayashree V., Richardson, A. D., Richter-Menge, Jacqueline, Ricker, R., Rimmer, Alon, Robinson, David A., Rodell, Matthew, Rodriguez Camino, Ernesto, Romanovsky, Vladimir E., Ronchail, Josyane, Rosenlof, Karen H., Rösner, Benjamin, Roth, Chris, Roth, David Mark, Rusak, James A., Rutishäuser, T., Sallée, Jean Bapiste, Sánchez-Lugo, Ahira, Santee, Michelle L., Sasgen, L., Sawaengphokhai, P., Sayad, T. A., Sayouri, Amal, Scambos, Ted A., Scanlon, T., Schenzinger, Verena, Schladow, S. Geoffrey, Schmid, Claudia, Schmid, Martin, Schreck, Carl J., Selkirk, H. B., Send, Uwe, Sensoy, Serhat, Sharp, M., Shi, Lei, Shiklomanov, Nikolai I., Shimaraeva, Svetlana V., Siegel, David A., Silow, Eugene, Sima, Fatou, Simmons, Adrian J., Skirving, William J., Smeed, David A., Smeets, C. J.P.P., Smith, Adam, Smith, Sharon L., Soden, B., Sofieva, Viktoria, Sparks, T. H., Spence, Jacqueline M., Spillane, Sandra, Srivastava, A. K., Stackhouse, Paul W., Stammerjohn, Sharon, Stanitski, Diane M., Steinbrecht, Wolfgang, Stella, José L., Stengel, M., Stephenson, Kimberly, Stephenson, Tannecia S., Strahan, Susan, Streletskiy, Dimitri A., Strong, Alan E., Sun-Mack, Sunny, Sutton, Adrienne J., Swart, Sebastiaan, Sweet, William, Takahashi, Kenneth S., Tamar, Gerard, Taylor, Michael A., Tedesco, M., Thackeray, S. J., Thoman, R. L., Thompson, Philip, Thomson, L., Thorsteinsson, T., Timbal, Bertrand, Timmermans, M. L., TImofeyev, Maxim A., Tirak, Kyle V., Tobin, Skie, Togawa, H., Tømmervik, H., Tourpali, Kleareti, Trachte, Katja, Trewin, Blair C., Triñanes, Joaquin A., Trotman, Adrian R., Tschudi, M., Tucker, C. J., Tye, Mari R., Van As, D., Van De Wal, R. S.W., Van Der Ronald, J. A., Van Der Schalie, Robin, Van Der Schrier, Gerard, Van Der Werf, Guido R., Van Meerbeeck, Cedric J., Velden, Christopher S., Velicogna, I., Verburg, Piet, Vickers, H., Vincent, Lucie A., Vömel, Holger, Vose, Russell S., Wagner, Wolfgang, Walker, D. A., Walsh, J., Wang, Bin, Wang, Junhong, Wang, Lei, Wang, M., Wang, Ray, Wang, Sheng Hung, Wanninkhof, Rik, Watanabe, Shohei, Weber, Mark, Webster, M., Weller, Robert A., Westberry, Toby K., Weyhenmeyer, Gesa A., Whitewood, Robert, Widlansky, Matthew J., Wiese, David N., Wijffels, Susan E., Wilber, Anne C., Wild, Jeanette D., Willett, Kate M., Willis, Josh K., Wolken, G., Wong, Takmeng, Wood, E. F., Wood, K., Woolway, R. Iestyn, Wouters, B., Xue, Yan, Yin, Xungang, Yoon, Huang, York, A., Yu, Lisan, Zambrano, Eduardo, Zhang, Huai Min, Zhang, Peiqun, Zhao, Guanguo, Zhao, Lin, Zhu, Zhiwei, Ziel, R., Ziemke, Jerry R., Ziese, Markus G., Griffin, Jessicca, Hammer, Gregory, Love-Brotak, S. Elizabeth, Misch, Deborah J., Riddle, Deborah B., Slagle, Mary, Sprain, Mara, Veasey, Sara W., and McVicar, Tim R.

Abstract

In 2017, the dominant greenhouse gases released into Earth's atmosphere-carbon dioxide, methane, and nitrous oxide-reached new record highs. The annual global average carbon dioxide concentration at Earth's surface for 2017 was 405.0 ± 0.1 ppm, 2.2 ppm greater than for 2016 and the highest in the modern atmospheric measurement record and in ice core records dating back as far as 800 000 years. The global growth rate of CO2 has nearly quadrupled since the early 1960s. With ENSO-neutral conditions present in the central and eastern equatorial Pacific Ocean during most of the year and weak La Niña conditions notable at the start and end, the global temperature across land and ocean surfaces ranked as the second or third highest, depending on the dataset, since records began in the mid-to-late 1800s. Notably, it was the warmest non-El Niño year in the instrumental record. Above Earth's surface, the annual lower tropospheric temperature was also either second or third highest according to all datasets analyzed. The lower stratospheric temperature was about 0.2°C higher than the record cold temperature of 2016 according to most of the in situ and satellite datasets. Several countries, including Argentina, Uruguay, Spain, and Bulgaria, reported record high annual temperatures. Mexico broke its annual record for the fourth consecutive year. On 27 January, the temperature reached 43.4°C at Puerto Madryn, Argentina-the highest temperature recorded so far south (43°S) anywhere in the world. On 28 May in Turbat, western Pakistan, the high of 53.5°C tied Pakistan's all-time highest temperature and became the world-record highest temperature for May. In the Arctic, the 2017 land surface temperature was 1.6°C above the 1981-2010 average, the second highest since the record began in 1900, behind only 2016. The five highest annual Arctic temperatures have all occurred since 2007. Exceptionally high temperatures were observed in the permafrost across the Arctic, with re

Published
2018

47. State of the climate in 2017

Author

Sub Dynamics Meteorology, Sub Soft Condensed Matter, LS Religiewetenschap, Sub Atmospheric physics and chemistry, Zonder bezoldiging NED, LS Taalverwerving, Leerstoel Tubergen, Afd Chemical Biology and Drug Discovery, Hafd Faculteitsbureau GW, Afd Pharmacology, Dep IRAS, Marine and Atmospheric Research, OFR - Religious Studies, Abernethy, R., Ackerman, Steven A., Adler, R., Albanil Encarnación, Adelina, Aldeco, Laura S., Alfaro, Eric J., Aliaga-Nestares, Vannia, Allan, Richard P., Allan, Rob, Alves, Lincoln M., Amador, Jorge A., Anderson, John, Andreassen, L. M., Argüez, Anthony, Armitage, C., Arndt, Derek S., Avalos, Grinia, Azorin-Molina, César, Báez, Julián, Bardin, M. Yu, Barichivich, Jonathan, Baringer, Molly O., Barreira, Sandra, Baxter, Stephen, Beck, H. E., Becker, Andreas, Bedka, Kristopher M., Behe, Carolina, Bell, Gerald D., Bellouin, Nicolas, Belmont, M., Benedetti, Angela, Bernhard, G. H., Berrisford, Paul, Berry, David I., Bhatt, U. S., Bissolli, Peter, Bjerke, J., Blake, Eric S., Blenkinsop, Stephen, Blunden, Jessica, Bolmgren, K., Bosilovich, Michael G., Boucher, Olivier, Bouchon, Marilú, Box, J. E., Boyer, Tim, Braathen, Geir O., Bromwich, David H., Brown, R., Buehler, S., Bulygina, Olga N., Burgess, D., Calderón, Blanca, Camargo, Suzana J., Campbell, Ethan C., Campbell, Jayaka D., Cappelen, J., Carrea, Laura, Carter, Brendan R., Castro, Anabel, Chambers, Don P., Cheng, Lijing, Christiansen, Hanne H., Christy, John R., Chung, E. S., Clem, Kyle R., Coelho, Caio A.S., Coldewey-Egbers, Melanie, Colwell, Steve, Cooper, Owen R., Copland, L., Costanza, Carol, Covey, Curt, Coy, Lawrence, Cronin, T., Crouch, Jake, Cruzado, Luis, Daniel, Raychelle, Davis, Sean M., Davletshin, S. G., De Eyto, Elvira, De Jeu, Richard A.M., De La Cour, Jacqueline L., De Laat, Jos, De Gasperi, Curtis L., Degenstein, Doug, Deline, P., Demircan, Mesut, Derksen, C., Dewitte, Boris, Dhurmea, R., Di Girolamo, Larry, Diamond, Howard J., Dickerson, C., Dlugokencky, Ed J., Dohan, Kathleen, Dokulil, Martin T., Dolman, A. Johannes, Domingues, Catia M., Domingues, Ricardo, Donat, Markus G., Dong, Shenfu, Dorigo, Wouter A., Drozdov, D. S., Dunn, Robert J.H., Durre, Imke, Dutton, Geoff S., Eakin, C. Mark, El Kharrim, M., Elkins, James W., Epstein, H. E., Espinoza, Jhan C., Famiglietti, James S., Farmer, J., Farrell, S., Fauchald, P., Fausto, R. S., Feely, Richard A., Feng, Z., Fenimore, Chris, Fettweis, X., Fioletov, Vitali E., Flemming, Johannes, Fogt, Ryan L., Folland, Chris, Forbes, B. C., Foster, Michael J., Francis, S. D., Franz, Bryan A., Frey, Richard A., Frith, Stacey M., Froidevaux, Lucien, Ganter, Catherine, Geiger, Erick F., Gerland, S., Gilson, John, Gobron, Nadine, Goldenberg, Stanley B., Gomez, Andrea M., Goni, Gustavo, Grooß, Jens Uwe, Gruber, Alexander, Guard, Charles P., Gugliemin, Mario, Gupta, S. K., Gutiérrez, Dimitri, Haas, C., Hagos, S., Hahn, Sebastian, Haimberger, Leo, Hall, Brad D., Halpert, Michael S., Hamlington, Benjamin D., Hanna, E., Hansen, K., Hanssen-Bauer, L., Harris, Ian, Hartfield, Gail, Heidinger, Andrew K., Heim, Richard R., Helfrich, S., Hemming, D. L., Hendricks, S., Hernández, Rafael, Hernández, Sosa Marieta, Heron, Scott F., Heuzé, C., Hidalgo, Hugo G., Ho, Shu Peng, Hobbs, William R., Horstkotte, T., Huang, Boyin, Hubert, Daan, Hueuzé, Céline, Hurst, Dale F., Ialongo, Iolanda, Ibrahim, M. M., Ijampy, J. A., Inness, Antje, Isaac, Victor, Isaksen, K., Ishii, Masayoshi, Jacobs, Stephanie J., Jeffries, Martin O., Jevrejeva, Svetlana, Jiménez, C., Jin, Xiangze, John, Viju, Johns, William E., Johnsen, Bjørn, Johnson, Bryan, Johnson, Gregory C., Johnson, Kenneth S., Jones, Philip D., Jumaux, Guillaume, Kabidi, Khadija, Kaiser, J. W., Karaköylü, Erdem M., Kato, Seiji, Kazemi, A., Keller, Linda M., Kennedy, John, Kerr, Kenneth, Khan, M. S., Kholodov, A. L., Khoshkam, Mahbobeh, Killick, Rachel, Kim, Hyungjun, Kim, S. J., Klotzbach, Philip J., Knaff, John A., Kohler, J., Korhonen, Johanna, Korshunova, Natalia N., Kramarova, Natalya, Kratz, D. P., Kruger, Andries, Kruk, Michael C., Krumpen, T., Ladd, C., Lakatos, Mónika, Lakkala, Kaisa, Lander, Mark A., Landschützer, Peter, Landsea, Chris W., Lankhorst, Matthias, Lavado-Casimiro, Waldo, Lazzara, Matthew A., Lee, S. E., Lee, T. C., Leuliette, Eric, L'Heureux, Michelle, Li, Tim, Lieser, Jan L., Lin, I. I., Mears, Carl A., Liu, Gang, Li, Bailing, Liu, Hongxing, Locarnini, Ricardo, Loeb, Norman G., Long, Craig S., López, Luis A., Lorrey, Andrew M., Loyola, Diego, Lumpkin, Rick, Luo, Jing Jia, Luojus, K., Luthcke, S., Macias-Fauria, M., Malkova, G. V., Manney, Gloria L., Marcellin, Vernie, Marchenko, S. S., Marengo, José A., Marín, Dora, Marra, John J., Marszelewski, Wlodzimierz, Martens, B., Martin, A., Martínez, Alejandra G., Martínez-Güingla, Rodney, Martínez-Sánchez, Odalys, Marsh, Benjamin L., Lyman, John M., Massom, Robert A., May, Linda, Mayer, Michael, Mazloff, Matthew, McBride, Charlotte, McCabe, M. F., McCarthy, Mark, Meier, W., Meijers, Andrew J.S., Mekonnen, Ademe, Mengistu Tsidu, G., Menzel, W. Paul, Merchant, Christopher J., Meredith, Michael P., Merrifield, Mark A., Miller, Ben, Miralles, Diego G., Mitchum, Gary T., Mitro, Sukarni, Moat, Ben, Mochizuki, Y., Monselesan, Didier, Montzka, Stephen A., Mora, Natalie, Morice, Colin, Mosquera-Vásquez, Kobi, Mostafa, Awatif E., Mote, T., Mudryk, L., Mühle, Jens, Mullan, A. Brett, Müller, Rolf, Myneni, R., Nash, Eric R., Nerem, R. Steven, Newman, L., Newman, Paul A., Nielsen-Gammon, John W., Nieto, Juan José, Noetzli, Jeannette, Noll, Ben E., O'Neel, S., Osborn, Tim J., Osborne, Emily, Overland, J., Oyunjargal, Lamjav, Park, T., Pasch, Richard J., Pascual-Ramírez, Reynaldo, Pastor Saavedra, Maria Asuncion, Paterson, Andrew M., Paulik, Christoph, Pearce, Petra R., Peltier, Alexandre, Pelto, Mauri S., Peng, Liang, Perkins-Kirkpatrick, Sarah E., Perovich, Don, Petropavlovskikh, Irina, Pezza, Alexandre B., Phillips, C., Phillips, David, Phoenix, G., Pinty, Bernard, Pinzon, J., Po-Chedley, S., Polashenski, C., Purkey, Sarah G., Quispe, Nelson, Rajeevan, Madhavan, Rakotoarimalala, C., Rayner, Darren, Raynolds, M. K., Reagan, James, Reid, Phillip, Reimer, Christoph, Rémy, Samuel, Revadekar, Jayashree V., Richardson, A. D., Richter-Menge, Jacqueline, Ricker, R., Rimmer, Alon, Robinson, David A., Rodell, Matthew, Rodriguez Camino, Ernesto, Romanovsky, Vladimir E., Ronchail, Josyane, Rosenlof, Karen H., Rösner, Benjamin, Roth, Chris, Roth, David Mark, Rusak, James A., Rutishäuser, T., Sallée, Jean Bapiste, Sánchez-Lugo, Ahira, Santee, Michelle L., Sasgen, L., Sawaengphokhai, P., Sayad, T. A., Sayouri, Amal, Scambos, Ted A., Scanlon, T., Schenzinger, Verena, Schladow, S. Geoffrey, Schmid, Claudia, Schmid, Martin, Schreck, Carl J., Selkirk, H. B., Send, Uwe, Sensoy, Serhat, Sharp, M., Shi, Lei, Shiklomanov, Nikolai I., Shimaraeva, Svetlana V., Siegel, David A., Silow, Eugene, Sima, Fatou, Simmons, Adrian J., Skirving, William J., Smeed, David A., Smeets, C. J.P.P., Smith, Adam, Smith, Sharon L., Soden, B., Sofieva, Viktoria, Sparks, T. H., Spence, Jacqueline M., Spillane, Sandra, Srivastava, A. K., Stackhouse, Paul W., Stammerjohn, Sharon, Stanitski, Diane M., Steinbrecht, Wolfgang, Stella, José L., Stengel, M., Stephenson, Kimberly, Stephenson, Tannecia S., Strahan, Susan, Streletskiy, Dimitri A., Strong, Alan E., Sun-Mack, Sunny, Sutton, Adrienne J., Swart, Sebastiaan, Sweet, William, Takahashi, Kenneth S., Tamar, Gerard, Taylor, Michael A., Tedesco, M., Thackeray, S. J., Thoman, R. L., Thompson, Philip, Thomson, L., Thorsteinsson, T., Timbal, Bertrand, Timmermans, M. L., TImofeyev, Maxim A., Tirak, Kyle V., Tobin, Skie, Togawa, H., Tømmervik, H., Tourpali, Kleareti, Trachte, Katja, Trewin, Blair C., Triñanes, Joaquin A., Trotman, Adrian R., Tschudi, M., Tucker, C. J., Tye, Mari R., Van As, D., Van De Wal, R. S.W., Van Der Ronald, J. A., Van Der Schalie, Robin, Van Der Schrier, Gerard, Van Der Werf, Guido R., Van Meerbeeck, Cedric J., Velden, Christopher S., Velicogna, I., Verburg, Piet, Vickers, H., Vincent, Lucie A., Vömel, Holger, Vose, Russell S., Wagner, Wolfgang, Walker, D. A., Walsh, J., Wang, Bin, Wang, Junhong, Wang, Lei, Wang, M., Wang, Ray, Wang, Sheng Hung, Wanninkhof, Rik, Watanabe, Shohei, Weber, Mark, Webster, M., Weller, Robert A., Westberry, Toby K., Weyhenmeyer, Gesa A., Whitewood, Robert, Widlansky, Matthew J., Wiese, David N., Wijffels, Susan E., Wilber, Anne C., Wild, Jeanette D., Willett, Kate M., Willis, Josh K., Wolken, G., Wong, Takmeng, Wood, E. F., Wood, K., Woolway, R. Iestyn, Wouters, B., Xue, Yan, Yin, Xungang, Yoon, Huang, York, A., Yu, Lisan, Zambrano, Eduardo, Zhang, Huai Min, Zhang, Peiqun, Zhao, Guanguo, Zhao, Lin, Zhu, Zhiwei, Ziel, R., Ziemke, Jerry R., Ziese, Markus G., Griffin, Jessicca, Hammer, Gregory, Love-Brotak, S. Elizabeth, Misch, Deborah J., Riddle, Deborah B., Slagle, Mary, Sprain, Mara, Veasey, Sara W., McVicar, Tim R., Sub Dynamics Meteorology, Sub Soft Condensed Matter, LS Religiewetenschap, Sub Atmospheric physics and chemistry, Zonder bezoldiging NED, LS Taalverwerving, Leerstoel Tubergen, Afd Chemical Biology and Drug Discovery, Hafd Faculteitsbureau GW, Afd Pharmacology, Dep IRAS, Marine and Atmospheric Research, OFR - Religious Studies, Abernethy, R., Ackerman, Steven A., Adler, R., Albanil Encarnación, Adelina, Aldeco, Laura S., Alfaro, Eric J., Aliaga-Nestares, Vannia, Allan, Richard P., Allan, Rob, Alves, Lincoln M., Amador, Jorge A., Anderson, John, Andreassen, L. M., Argüez, Anthony, Armitage, C., Arndt, Derek S., Avalos, Grinia, Azorin-Molina, César, Báez, Julián, Bardin, M. Yu, Barichivich, Jonathan, Baringer, Molly O., Barreira, Sandra, Baxter, Stephen, Beck, H. E., Becker, Andreas, Bedka, Kristopher M., Behe, Carolina, Bell, Gerald D., Bellouin, Nicolas, Belmont, M., Benedetti, Angela, Bernhard, G. H., Berrisford, Paul, Berry, David I., Bhatt, U. S., Bissolli, Peter, Bjerke, J., Blake, Eric S., Blenkinsop, Stephen, Blunden, Jessica, Bolmgren, K., Bosilovich, Michael G., Boucher, Olivier, Bouchon, Marilú, Box, J. E., Boyer, Tim, Braathen, Geir O., Bromwich, David H., Brown, R., Buehler, S., Bulygina, Olga N., Burgess, D., Calderón, Blanca, Camargo, Suzana J., Campbell, Ethan C., Campbell, Jayaka D., Cappelen, J., Carrea, Laura, Carter, Brendan R., Castro, Anabel, Chambers, Don P., Cheng, Lijing, Christiansen, Hanne H., Christy, John R., Chung, E. S., Clem, Kyle R., Coelho, Caio A.S., Coldewey-Egbers, Melanie, Colwell, Steve, Cooper, Owen R., Copland, L., Costanza, Carol, Covey, Curt, Coy, Lawrence, Cronin, T., Crouch, Jake, Cruzado, Luis, Daniel, Raychelle, Davis, Sean M., Davletshin, S. G., De Eyto, Elvira, De Jeu, Richard A.M., De La Cour, Jacqueline L., De Laat, Jos, De Gasperi, Curtis L., Degenstein, Doug, Deline, P., Demircan, Mesut, Derksen, C., Dewitte, Boris, Dhurmea, R., Di Girolamo, Larry, Diamond, Howard J., Dickerson, C., Dlugokencky, Ed J., Dohan, Kathleen, Dokulil, Martin T., Dolman, A. Johannes, Domingues, Catia M., Domingues, Ricardo, Donat, Markus G., Dong, Shenfu, Dorigo, Wouter A., Drozdov, D. S., Dunn, Robert J.H., Durre, Imke, Dutton, Geoff S., Eakin, C. Mark, El Kharrim, M., Elkins, James W., Epstein, H. E., Espinoza, Jhan C., Famiglietti, James S., Farmer, J., Farrell, S., Fauchald, P., Fausto, R. S., Feely, Richard A., Feng, Z., Fenimore, Chris, Fettweis, X., Fioletov, Vitali E., Flemming, Johannes, Fogt, Ryan L., Folland, Chris, Forbes, B. C., Foster, Michael J., Francis, S. D., Franz, Bryan A., Frey, Richard A., Frith, Stacey M., Froidevaux, Lucien, Ganter, Catherine, Geiger, Erick F., Gerland, S., Gilson, John, Gobron, Nadine, Goldenberg, Stanley B., Gomez, Andrea M., Goni, Gustavo, Grooß, Jens Uwe, Gruber, Alexander, Guard, Charles P., Gugliemin, Mario, Gupta, S. K., Gutiérrez, Dimitri, Haas, C., Hagos, S., Hahn, Sebastian, Haimberger, Leo, Hall, Brad D., Halpert, Michael S., Hamlington, Benjamin D., Hanna, E., Hansen, K., Hanssen-Bauer, L., Harris, Ian, Hartfield, Gail, Heidinger, Andrew K., Heim, Richard R., Helfrich, S., Hemming, D. L., Hendricks, S., Hernández, Rafael, Hernández, Sosa Marieta, Heron, Scott F., Heuzé, C., Hidalgo, Hugo G., Ho, Shu Peng, Hobbs, William R., Horstkotte, T., Huang, Boyin, Hubert, Daan, Hueuzé, Céline, Hurst, Dale F., Ialongo, Iolanda, Ibrahim, M. M., Ijampy, J. A., Inness, Antje, Isaac, Victor, Isaksen, K., Ishii, Masayoshi, Jacobs, Stephanie J., Jeffries, Martin O., Jevrejeva, Svetlana, Jiménez, C., Jin, Xiangze, John, Viju, Johns, William E., Johnsen, Bjørn, Johnson, Bryan, Johnson, Gregory C., Johnson, Kenneth S., Jones, Philip D., Jumaux, Guillaume, Kabidi, Khadija, Kaiser, J. W., Karaköylü, Erdem M., Kato, Seiji, Kazemi, A., Keller, Linda M., Kennedy, John, Kerr, Kenneth, Khan, M. S., Kholodov, A. L., Khoshkam, Mahbobeh, Killick, Rachel, Kim, Hyungjun, Kim, S. J., Klotzbach, Philip J., Knaff, John A., Kohler, J., Korhonen, Johanna, Korshunova, Natalia N., Kramarova, Natalya, Kratz, D. P., Kruger, Andries, Kruk, Michael C., Krumpen, T., Ladd, C., Lakatos, Mónika, Lakkala, Kaisa, Lander, Mark A., Landschützer, Peter, Landsea, Chris W., Lankhorst, Matthias, Lavado-Casimiro, Waldo, Lazzara, Matthew A., Lee, S. E., Lee, T. C., Leuliette, Eric, L'Heureux, Michelle, Li, Tim, Lieser, Jan L., Lin, I. I., Mears, Carl A., Liu, Gang, Li, Bailing, Liu, Hongxing, Locarnini, Ricardo, Loeb, Norman G., Long, Craig S., López, Luis A., Lorrey, Andrew M., Loyola, Diego, Lumpkin, Rick, Luo, Jing Jia, Luojus, K., Luthcke, S., Macias-Fauria, M., Malkova, G. V., Manney, Gloria L., Marcellin, Vernie, Marchenko, S. S., Marengo, José A., Marín, Dora, Marra, John J., Marszelewski, Wlodzimierz, Martens, B., Martin, A., Martínez, Alejandra G., Martínez-Güingla, Rodney, Martínez-Sánchez, Odalys, Marsh, Benjamin L., Lyman, John M., Massom, Robert A., May, Linda, Mayer, Michael, Mazloff, Matthew, McBride, Charlotte, McCabe, M. F., McCarthy, Mark, Meier, W., Meijers, Andrew J.S., Mekonnen, Ademe, Mengistu Tsidu, G., Menzel, W. Paul, Merchant, Christopher J., Meredith, Michael P., Merrifield, Mark A., Miller, Ben, Miralles, Diego G., Mitchum, Gary T., Mitro, Sukarni, Moat, Ben, Mochizuki, Y., Monselesan, Didier, Montzka, Stephen A., Mora, Natalie, Morice, Colin, Mosquera-Vásquez, Kobi, Mostafa, Awatif E., Mote, T., Mudryk, L., Mühle, Jens, Mullan, A. Brett, Müller, Rolf, Myneni, R., Nash, Eric R., Nerem, R. Steven, Newman, L., Newman, Paul A., Nielsen-Gammon, John W., Nieto, Juan José, Noetzli, Jeannette, Noll, Ben E., O'Neel, S., Osborn, Tim J., Osborne, Emily, Overland, J., Oyunjargal, Lamjav, Park, T., Pasch, Richard J., Pascual-Ramírez, Reynaldo, Pastor Saavedra, Maria Asuncion, Paterson, Andrew M., Paulik, Christoph, Pearce, Petra R., Peltier, Alexandre, Pelto, Mauri S., Peng, Liang, Perkins-Kirkpatrick, Sarah E., Perovich, Don, Petropavlovskikh, Irina, Pezza, Alexandre B., Phillips, C., Phillips, David, Phoenix, G., Pinty, Bernard, Pinzon, J., Po-Chedley, S., Polashenski, C., Purkey, Sarah G., Quispe, Nelson, Rajeevan, Madhavan, Rakotoarimalala, C., Rayner, Darren, Raynolds, M. K., Reagan, James, Reid, Phillip, Reimer, Christoph, Rémy, Samuel, Revadekar, Jayashree V., Richardson, A. D., Richter-Menge, Jacqueline, Ricker, R., Rimmer, Alon, Robinson, David A., Rodell, Matthew, Rodriguez Camino, Ernesto, Romanovsky, Vladimir E., Ronchail, Josyane, Rosenlof, Karen H., Rösner, Benjamin, Roth, Chris, Roth, David Mark, Rusak, James A., Rutishäuser, T., Sallée, Jean Bapiste, Sánchez-Lugo, Ahira, Santee, Michelle L., Sasgen, L., Sawaengphokhai, P., Sayad, T. A., Sayouri, Amal, Scambos, Ted A., Scanlon, T., Schenzinger, Verena, Schladow, S. Geoffrey, Schmid, Claudia, Schmid, Martin, Schreck, Carl J., Selkirk, H. B., Send, Uwe, Sensoy, Serhat, Sharp, M., Shi, Lei, Shiklomanov, Nikolai I., Shimaraeva, Svetlana V., Siegel, David A., Silow, Eugene, Sima, Fatou, Simmons, Adrian J., Skirving, William J., Smeed, David A., Smeets, C. J.P.P., Smith, Adam, Smith, Sharon L., Soden, B., Sofieva, Viktoria, Sparks, T. H., Spence, Jacqueline M., Spillane, Sandra, Srivastava, A. K., Stackhouse, Paul W., Stammerjohn, Sharon, Stanitski, Diane M., Steinbrecht, Wolfgang, Stella, José L., Stengel, M., Stephenson, Kimberly, Stephenson, Tannecia S., Strahan, Susan, Streletskiy, Dimitri A., Strong, Alan E., Sun-Mack, Sunny, Sutton, Adrienne J., Swart, Sebastiaan, Sweet, William, Takahashi, Kenneth S., Tamar, Gerard, Taylor, Michael A., Tedesco, M., Thackeray, S. J., Thoman, R. L., Thompson, Philip, Thomson, L., Thorsteinsson, T., Timbal, Bertrand, Timmermans, M. L., TImofeyev, Maxim A., Tirak, Kyle V., Tobin, Skie, Togawa, H., Tømmervik, H., Tourpali, Kleareti, Trachte, Katja, Trewin, Blair C., Triñanes, Joaquin A., Trotman, Adrian R., Tschudi, M., Tucker, C. J., Tye, Mari R., Van As, D., Van De Wal, R. S.W., Van Der Ronald, J. A., Van Der Schalie, Robin, Van Der Schrier, Gerard, Van Der Werf, Guido R., Van Meerbeeck, Cedric J., Velden, Christopher S., Velicogna, I., Verburg, Piet, Vickers, H., Vincent, Lucie A., Vömel, Holger, Vose, Russell S., Wagner, Wolfgang, Walker, D. A., Walsh, J., Wang, Bin, Wang, Junhong, Wang, Lei, Wang, M., Wang, Ray, Wang, Sheng Hung, Wanninkhof, Rik, Watanabe, Shohei, Weber, Mark, Webster, M., Weller, Robert A., Westberry, Toby K., Weyhenmeyer, Gesa A., Whitewood, Robert, Widlansky, Matthew J., Wiese, David N., Wijffels, Susan E., Wilber, Anne C., Wild, Jeanette D., Willett, Kate M., Willis, Josh K., Wolken, G., Wong, Takmeng, Wood, E. F., Wood, K., Woolway, R. Iestyn, Wouters, B., Xue, Yan, Yin, Xungang, Yoon, Huang, York, A., Yu, Lisan, Zambrano, Eduardo, Zhang, Huai Min, Zhang, Peiqun, Zhao, Guanguo, Zhao, Lin, Zhu, Zhiwei, Ziel, R., Ziemke, Jerry R., Ziese, Markus G., Griffin, Jessicca, Hammer, Gregory, Love-Brotak, S. Elizabeth, Misch, Deborah J., Riddle, Deborah B., Slagle, Mary, Sprain, Mara, Veasey, Sara W., and McVicar, Tim R.

Published
2018

48. State of the climate in 2017

Author

Sub Dynamics Meteorology, Sub Soft Condensed Matter, LS Religiewetenschap, Sub Atmospheric physics and chemistry, Zonder bezoldiging NED, LS Taalverwerving, Leerstoel Tubergen, Afd Chemical Biology and Drug Discovery, Hafd Faculteitsbureau GW, Afd Pharmacology, Dep IRAS, Marine and Atmospheric Research, OFR - Religious Studies, Abernethy, R., Ackerman, Steven A., Adler, R., Albanil Encarnación, Adelina, Aldeco, Laura S., Alfaro, Eric J., Aliaga-Nestares, Vannia, Allan, Richard P., Allan, Rob, Alves, Lincoln M., Amador, Jorge A., Anderson, John, Andreassen, L. M., Argüez, Anthony, Armitage, C., Arndt, Derek S., Avalos, Grinia, Azorin-Molina, César, Báez, Julián, Bardin, M. Yu, Barichivich, Jonathan, Baringer, Molly O., Barreira, Sandra, Baxter, Stephen, Beck, H. E., Becker, Andreas, Bedka, Kristopher M., Behe, Carolina, Bell, Gerald D., Bellouin, Nicolas, Belmont, M., Benedetti, Angela, Bernhard, G. H., Berrisford, Paul, Berry, David I., Bhatt, U. S., Bissolli, Peter, Bjerke, J., Blake, Eric S., Blenkinsop, Stephen, Blunden, Jessica, Bolmgren, K., Bosilovich, Michael G., Boucher, Olivier, Bouchon, Marilú, Box, J. E., Boyer, Tim, Braathen, Geir O., Bromwich, David H., Brown, R., Buehler, S., Bulygina, Olga N., Burgess, D., Calderón, Blanca, Camargo, Suzana J., Campbell, Ethan C., Campbell, Jayaka D., Cappelen, J., Carrea, Laura, Carter, Brendan R., Castro, Anabel, Chambers, Don P., Cheng, Lijing, Christiansen, Hanne H., Christy, John R., Chung, E. S., Clem, Kyle R., Coelho, Caio A.S., Coldewey-Egbers, Melanie, Colwell, Steve, Cooper, Owen R., Copland, L., Costanza, Carol, Covey, Curt, Coy, Lawrence, Cronin, T., Crouch, Jake, Cruzado, Luis, Daniel, Raychelle, Davis, Sean M., Davletshin, S. G., De Eyto, Elvira, De Jeu, Richard A.M., De La Cour, Jacqueline L., De Laat, Jos, De Gasperi, Curtis L., Degenstein, Doug, Deline, P., Demircan, Mesut, Derksen, C., Dewitte, Boris, Dhurmea, R., Di Girolamo, Larry, Diamond, Howard J., Dickerson, C., Dlugokencky, Ed J., Dohan, Kathleen, Dokulil, Martin T., Dolman, A. Johannes, Domingues, Catia M., Domingues, Ricardo, Donat, Markus G., Dong, Shenfu, Dorigo, Wouter A., Drozdov, D. S., Dunn, Robert J.H., Durre, Imke, Dutton, Geoff S., Eakin, C. Mark, El Kharrim, M., Elkins, James W., Epstein, H. E., Espinoza, Jhan C., Famiglietti, James S., Farmer, J., Farrell, S., Fauchald, P., Fausto, R. S., Feely, Richard A., Feng, Z., Fenimore, Chris, Fettweis, X., Fioletov, Vitali E., Flemming, Johannes, Fogt, Ryan L., Folland, Chris, Forbes, B. C., Foster, Michael J., Francis, S. D., Franz, Bryan A., Frey, Richard A., Frith, Stacey M., Froidevaux, Lucien, Ganter, Catherine, Geiger, Erick F., Gerland, S., Gilson, John, Gobron, Nadine, Goldenberg, Stanley B., Gomez, Andrea M., Goni, Gustavo, Grooß, Jens Uwe, Gruber, Alexander, Guard, Charles P., Gugliemin, Mario, Gupta, S. K., Gutiérrez, Dimitri, Haas, C., Hagos, S., Hahn, Sebastian, Haimberger, Leo, Hall, Brad D., Halpert, Michael S., Hamlington, Benjamin D., Hanna, E., Hansen, K., Hanssen-Bauer, L., Harris, Ian, Hartfield, Gail, Heidinger, Andrew K., Heim, Richard R., Helfrich, S., Hemming, D. L., Hendricks, S., Hernández, Rafael, Hernández, Sosa Marieta, Heron, Scott F., Heuzé, C., Hidalgo, Hugo G., Ho, Shu Peng, Hobbs, William R., Horstkotte, T., Huang, Boyin, Hubert, Daan, Hueuzé, Céline, Hurst, Dale F., Ialongo, Iolanda, Ibrahim, M. M., Ijampy, J. A., Inness, Antje, Isaac, Victor, Isaksen, K., Ishii, Masayoshi, Jacobs, Stephanie J., Jeffries, Martin O., Jevrejeva, Svetlana, Jiménez, C., Jin, Xiangze, John, Viju, Johns, William E., Johnsen, Bjørn, Johnson, Bryan, Johnson, Gregory C., Johnson, Kenneth S., Jones, Philip D., Jumaux, Guillaume, Kabidi, Khadija, Kaiser, J. W., Karaköylü, Erdem M., Kato, Seiji, Kazemi, A., Keller, Linda M., Kennedy, John, Kerr, Kenneth, Khan, M. S., Kholodov, A. L., Khoshkam, Mahbobeh, Killick, Rachel, Kim, Hyungjun, Kim, S. J., Klotzbach, Philip J., Knaff, John A., Kohler, J., Korhonen, Johanna, Korshunova, Natalia N., Kramarova, Natalya, Kratz, D. P., Kruger, Andries, Kruk, Michael C., Krumpen, T., Ladd, C., Lakatos, Mónika, Lakkala, Kaisa, Lander, Mark A., Landschützer, Peter, Landsea, Chris W., Lankhorst, Matthias, Lavado-Casimiro, Waldo, Lazzara, Matthew A., Lee, S. E., Lee, T. C., Leuliette, Eric, L'Heureux, Michelle, Li, Tim, Lieser, Jan L., Lin, I. I., Mears, Carl A., Liu, Gang, Li, Bailing, Liu, Hongxing, Locarnini, Ricardo, Loeb, Norman G., Long, Craig S., López, Luis A., Lorrey, Andrew M., Loyola, Diego, Lumpkin, Rick, Luo, Jing Jia, Luojus, K., Luthcke, S., Macias-Fauria, M., Malkova, G. V., Manney, Gloria L., Marcellin, Vernie, Marchenko, S. S., Marengo, José A., Marín, Dora, Marra, John J., Marszelewski, Wlodzimierz, Martens, B., Martin, A., Martínez, Alejandra G., Martínez-Güingla, Rodney, Martínez-Sánchez, Odalys, Marsh, Benjamin L., Lyman, John M., Massom, Robert A., May, Linda, Mayer, Michael, Mazloff, Matthew, McBride, Charlotte, McCabe, M. F., McCarthy, Mark, Meier, W., Meijers, Andrew J.S., Mekonnen, Ademe, Mengistu Tsidu, G., Menzel, W. Paul, Merchant, Christopher J., Meredith, Michael P., Merrifield, Mark A., Miller, Ben, Miralles, Diego G., Mitchum, Gary T., Mitro, Sukarni, Moat, Ben, Mochizuki, Y., Monselesan, Didier, Montzka, Stephen A., Mora, Natalie, Morice, Colin, Mosquera-Vásquez, Kobi, Mostafa, Awatif E., Mote, T., Mudryk, L., Mühle, Jens, Mullan, A. Brett, Müller, Rolf, Myneni, R., Nash, Eric R., Nerem, R. Steven, Newman, L., Newman, Paul A., Nielsen-Gammon, John W., Nieto, Juan José, Noetzli, Jeannette, Noll, Ben E., O'Neel, S., Osborn, Tim J., Osborne, Emily, Overland, J., Oyunjargal, Lamjav, Park, T., Pasch, Richard J., Pascual-Ramírez, Reynaldo, Pastor Saavedra, Maria Asuncion, Paterson, Andrew M., Paulik, Christoph, Pearce, Petra R., Peltier, Alexandre, Pelto, Mauri S., Peng, Liang, Perkins-Kirkpatrick, Sarah E., Perovich, Don, Petropavlovskikh, Irina, Pezza, Alexandre B., Phillips, C., Phillips, David, Phoenix, G., Pinty, Bernard, Pinzon, J., Po-Chedley, S., Polashenski, C., Purkey, Sarah G., Quispe, Nelson, Rajeevan, Madhavan, Rakotoarimalala, C., Rayner, Darren, Raynolds, M. K., Reagan, James, Reid, Phillip, Reimer, Christoph, Rémy, Samuel, Revadekar, Jayashree V., Richardson, A. D., Richter-Menge, Jacqueline, Ricker, R., Rimmer, Alon, Robinson, David A., Rodell, Matthew, Rodriguez Camino, Ernesto, Romanovsky, Vladimir E., Ronchail, Josyane, Rosenlof, Karen H., Rösner, Benjamin, Roth, Chris, Roth, David Mark, Rusak, James A., Rutishäuser, T., Sallée, Jean Bapiste, Sánchez-Lugo, Ahira, Santee, Michelle L., Sasgen, L., Sawaengphokhai, P., Sayad, T. A., Sayouri, Amal, Scambos, Ted A., Scanlon, T., Schenzinger, Verena, Schladow, S. Geoffrey, Schmid, Claudia, Schmid, Martin, Schreck, Carl J., Selkirk, H. B., Send, Uwe, Sensoy, Serhat, Sharp, M., Shi, Lei, Shiklomanov, Nikolai I., Shimaraeva, Svetlana V., Siegel, David A., Silow, Eugene, Sima, Fatou, Simmons, Adrian J., Skirving, William J., Smeed, David A., Smeets, C. J.P.P., Smith, Adam, Smith, Sharon L., Soden, B., Sofieva, Viktoria, Sparks, T. H., Spence, Jacqueline M., Spillane, Sandra, Srivastava, A. K., Stackhouse, Paul W., Stammerjohn, Sharon, Stanitski, Diane M., Steinbrecht, Wolfgang, Stella, José L., Stengel, M., Stephenson, Kimberly, Stephenson, Tannecia S., Strahan, Susan, Streletskiy, Dimitri A., Strong, Alan E., Sun-Mack, Sunny, Sutton, Adrienne J., Swart, Sebastiaan, Sweet, William, Takahashi, Kenneth S., Tamar, Gerard, Taylor, Michael A., Tedesco, M., Thackeray, S. J., Thoman, R. L., Thompson, Philip, Thomson, L., Thorsteinsson, T., Timbal, Bertrand, Timmermans, M. L., TImofeyev, Maxim A., Tirak, Kyle V., Tobin, Skie, Togawa, H., Tømmervik, H., Tourpali, Kleareti, Trachte, Katja, Trewin, Blair C., Triñanes, Joaquin A., Trotman, Adrian R., Tschudi, M., Tucker, C. J., Tye, Mari R., Van As, D., Van De Wal, R. S.W., Van Der Ronald, J. A., Van Der Schalie, Robin, Van Der Schrier, Gerard, Van Der Werf, Guido R., Van Meerbeeck, Cedric J., Velden, Christopher S., Velicogna, I., Verburg, Piet, Vickers, H., Vincent, Lucie A., Vömel, Holger, Vose, Russell S., Wagner, Wolfgang, Walker, D. A., Walsh, J., Wang, Bin, Wang, Junhong, Wang, Lei, Wang, M., Wang, Ray, Wang, Sheng Hung, Wanninkhof, Rik, Watanabe, Shohei, Weber, Mark, Webster, M., Weller, Robert A., Westberry, Toby K., Weyhenmeyer, Gesa A., Whitewood, Robert, Widlansky, Matthew J., Wiese, David N., Wijffels, Susan E., Wilber, Anne C., Wild, Jeanette D., Willett, Kate M., Willis, Josh K., Wolken, G., Wong, Takmeng, Wood, E. F., Wood, K., Woolway, R. Iestyn, Wouters, B., Xue, Yan, Yin, Xungang, Yoon, Huang, York, A., Yu, Lisan, Zambrano, Eduardo, Zhang, Huai Min, Zhang, Peiqun, Zhao, Guanguo, Zhao, Lin, Zhu, Zhiwei, Ziel, R., Ziemke, Jerry R., Ziese, Markus G., Griffin, Jessicca, Hammer, Gregory, Love-Brotak, S. Elizabeth, Misch, Deborah J., Riddle, Deborah B., Slagle, Mary, Sprain, Mara, Veasey, Sara W., McVicar, Tim R., Sub Dynamics Meteorology, Sub Soft Condensed Matter, LS Religiewetenschap, Sub Atmospheric physics and chemistry, Zonder bezoldiging NED, LS Taalverwerving, Leerstoel Tubergen, Afd Chemical Biology and Drug Discovery, Hafd Faculteitsbureau GW, Afd Pharmacology, Dep IRAS, Marine and Atmospheric Research, OFR - Religious Studies, Abernethy, R., Ackerman, Steven A., Adler, R., Albanil Encarnación, Adelina, Aldeco, Laura S., Alfaro, Eric J., Aliaga-Nestares, Vannia, Allan, Richard P., Allan, Rob, Alves, Lincoln M., Amador, Jorge A., Anderson, John, Andreassen, L. M., Argüez, Anthony, Armitage, C., Arndt, Derek S., Avalos, Grinia, Azorin-Molina, César, Báez, Julián, Bardin, M. Yu, Barichivich, Jonathan, Baringer, Molly O., Barreira, Sandra, Baxter, Stephen, Beck, H. E., Becker, Andreas, Bedka, Kristopher M., Behe, Carolina, Bell, Gerald D., Bellouin, Nicolas, Belmont, M., Benedetti, Angela, Bernhard, G. H., Berrisford, Paul, Berry, David I., Bhatt, U. S., Bissolli, Peter, Bjerke, J., Blake, Eric S., Blenkinsop, Stephen, Blunden, Jessica, Bolmgren, K., Bosilovich, Michael G., Boucher, Olivier, Bouchon, Marilú, Box, J. E., Boyer, Tim, Braathen, Geir O., Bromwich, David H., Brown, R., Buehler, S., Bulygina, Olga N., Burgess, D., Calderón, Blanca, Camargo, Suzana J., Campbell, Ethan C., Campbell, Jayaka D., Cappelen, J., Carrea, Laura, Carter, Brendan R., Castro, Anabel, Chambers, Don P., Cheng, Lijing, Christiansen, Hanne H., Christy, John R., Chung, E. S., Clem, Kyle R., Coelho, Caio A.S., Coldewey-Egbers, Melanie, Colwell, Steve, Cooper, Owen R., Copland, L., Costanza, Carol, Covey, Curt, Coy, Lawrence, Cronin, T., Crouch, Jake, Cruzado, Luis, Daniel, Raychelle, Davis, Sean M., Davletshin, S. G., De Eyto, Elvira, De Jeu, Richard A.M., De La Cour, Jacqueline L., De Laat, Jos, De Gasperi, Curtis L., Degenstein, Doug, Deline, P., Demircan, Mesut, Derksen, C., Dewitte, Boris, Dhurmea, R., Di Girolamo, Larry, Diamond, Howard J., Dickerson, C., Dlugokencky, Ed J., Dohan, Kathleen, Dokulil, Martin T., Dolman, A. Johannes, Domingues, Catia M., Domingues, Ricardo, Donat, Markus G., Dong, Shenfu, Dorigo, Wouter A., Drozdov, D. S., Dunn, Robert J.H., Durre, Imke, Dutton, Geoff S., Eakin, C. Mark, El Kharrim, M., Elkins, James W., Epstein, H. E., Espinoza, Jhan C., Famiglietti, James S., Farmer, J., Farrell, S., Fauchald, P., Fausto, R. S., Feely, Richard A., Feng, Z., Fenimore, Chris, Fettweis, X., Fioletov, Vitali E., Flemming, Johannes, Fogt, Ryan L., Folland, Chris, Forbes, B. C., Foster, Michael J., Francis, S. D., Franz, Bryan A., Frey, Richard A., Frith, Stacey M., Froidevaux, Lucien, Ganter, Catherine, Geiger, Erick F., Gerland, S., Gilson, John, Gobron, Nadine, Goldenberg, Stanley B., Gomez, Andrea M., Goni, Gustavo, Grooß, Jens Uwe, Gruber, Alexander, Guard, Charles P., Gugliemin, Mario, Gupta, S. K., Gutiérrez, Dimitri, Haas, C., Hagos, S., Hahn, Sebastian, Haimberger, Leo, Hall, Brad D., Halpert, Michael S., Hamlington, Benjamin D., Hanna, E., Hansen, K., Hanssen-Bauer, L., Harris, Ian, Hartfield, Gail, Heidinger, Andrew K., Heim, Richard R., Helfrich, S., Hemming, D. L., Hendricks, S., Hernández, Rafael, Hernández, Sosa Marieta, Heron, Scott F., Heuzé, C., Hidalgo, Hugo G., Ho, Shu Peng, Hobbs, William R., Horstkotte, T., Huang, Boyin, Hubert, Daan, Hueuzé, Céline, Hurst, Dale F., Ialongo, Iolanda, Ibrahim, M. M., Ijampy, J. A., Inness, Antje, Isaac, Victor, Isaksen, K., Ishii, Masayoshi, Jacobs, Stephanie J., Jeffries, Martin O., Jevrejeva, Svetlana, Jiménez, C., Jin, Xiangze, John, Viju, Johns, William E., Johnsen, Bjørn, Johnson, Bryan, Johnson, Gregory C., Johnson, Kenneth S., Jones, Philip D., Jumaux, Guillaume, Kabidi, Khadija, Kaiser, J. W., Karaköylü, Erdem M., Kato, Seiji, Kazemi, A., Keller, Linda M., Kennedy, John, Kerr, Kenneth, Khan, M. S., Kholodov, A. L., Khoshkam, Mahbobeh, Killick, Rachel, Kim, Hyungjun, Kim, S. J., Klotzbach, Philip J., Knaff, John A., Kohler, J., Korhonen, Johanna, Korshunova, Natalia N., Kramarova, Natalya, Kratz, D. P., Kruger, Andries, Kruk, Michael C., Krumpen, T., Ladd, C., Lakatos, Mónika, Lakkala, Kaisa, Lander, Mark A., Landschützer, Peter, Landsea, Chris W., Lankhorst, Matthias, Lavado-Casimiro, Waldo, Lazzara, Matthew A., Lee, S. E., Lee, T. C., Leuliette, Eric, L'Heureux, Michelle, Li, Tim, Lieser, Jan L., Lin, I. I., Mears, Carl A., Liu, Gang, Li, Bailing, Liu, Hongxing, Locarnini, Ricardo, Loeb, Norman G., Long, Craig S., López, Luis A., Lorrey, Andrew M., Loyola, Diego, Lumpkin, Rick, Luo, Jing Jia, Luojus, K., Luthcke, S., Macias-Fauria, M., Malkova, G. V., Manney, Gloria L., Marcellin, Vernie, Marchenko, S. S., Marengo, José A., Marín, Dora, Marra, John J., Marszelewski, Wlodzimierz, Martens, B., Martin, A., Martínez, Alejandra G., Martínez-Güingla, Rodney, Martínez-Sánchez, Odalys, Marsh, Benjamin L., Lyman, John M., Massom, Robert A., May, Linda, Mayer, Michael, Mazloff, Matthew, McBride, Charlotte, McCabe, M. F., McCarthy, Mark, Meier, W., Meijers, Andrew J.S., Mekonnen, Ademe, Mengistu Tsidu, G., Menzel, W. Paul, Merchant, Christopher J., Meredith, Michael P., Merrifield, Mark A., Miller, Ben, Miralles, Diego G., Mitchum, Gary T., Mitro, Sukarni, Moat, Ben, Mochizuki, Y., Monselesan, Didier, Montzka, Stephen A., Mora, Natalie, Morice, Colin, Mosquera-Vásquez, Kobi, Mostafa, Awatif E., Mote, T., Mudryk, L., Mühle, Jens, Mullan, A. Brett, Müller, Rolf, Myneni, R., Nash, Eric R., Nerem, R. Steven, Newman, L., Newman, Paul A., Nielsen-Gammon, John W., Nieto, Juan José, Noetzli, Jeannette, Noll, Ben E., O'Neel, S., Osborn, Tim J., Osborne, Emily, Overland, J., Oyunjargal, Lamjav, Park, T., Pasch, Richard J., Pascual-Ramírez, Reynaldo, Pastor Saavedra, Maria Asuncion, Paterson, Andrew M., Paulik, Christoph, Pearce, Petra R., Peltier, Alexandre, Pelto, Mauri S., Peng, Liang, Perkins-Kirkpatrick, Sarah E., Perovich, Don, Petropavlovskikh, Irina, Pezza, Alexandre B., Phillips, C., Phillips, David, Phoenix, G., Pinty, Bernard, Pinzon, J., Po-Chedley, S., Polashenski, C., Purkey, Sarah G., Quispe, Nelson, Rajeevan, Madhavan, Rakotoarimalala, C., Rayner, Darren, Raynolds, M. K., Reagan, James, Reid, Phillip, Reimer, Christoph, Rémy, Samuel, Revadekar, Jayashree V., Richardson, A. D., Richter-Menge, Jacqueline, Ricker, R., Rimmer, Alon, Robinson, David A., Rodell, Matthew, Rodriguez Camino, Ernesto, Romanovsky, Vladimir E., Ronchail, Josyane, Rosenlof, Karen H., Rösner, Benjamin, Roth, Chris, Roth, David Mark, Rusak, James A., Rutishäuser, T., Sallée, Jean Bapiste, Sánchez-Lugo, Ahira, Santee, Michelle L., Sasgen, L., Sawaengphokhai, P., Sayad, T. A., Sayouri, Amal, Scambos, Ted A., Scanlon, T., Schenzinger, Verena, Schladow, S. Geoffrey, Schmid, Claudia, Schmid, Martin, Schreck, Carl J., Selkirk, H. B., Send, Uwe, Sensoy, Serhat, Sharp, M., Shi, Lei, Shiklomanov, Nikolai I., Shimaraeva, Svetlana V., Siegel, David A., Silow, Eugene, Sima, Fatou, Simmons, Adrian J., Skirving, William J., Smeed, David A., Smeets, C. J.P.P., Smith, Adam, Smith, Sharon L., Soden, B., Sofieva, Viktoria, Sparks, T. H., Spence, Jacqueline M., Spillane, Sandra, Srivastava, A. K., Stackhouse, Paul W., Stammerjohn, Sharon, Stanitski, Diane M., Steinbrecht, Wolfgang, Stella, José L., Stengel, M., Stephenson, Kimberly, Stephenson, Tannecia S., Strahan, Susan, Streletskiy, Dimitri A., Strong, Alan E., Sun-Mack, Sunny, Sutton, Adrienne J., Swart, Sebastiaan, Sweet, William, Takahashi, Kenneth S., Tamar, Gerard, Taylor, Michael A., Tedesco, M., Thackeray, S. J., Thoman, R. L., Thompson, Philip, Thomson, L., Thorsteinsson, T., Timbal, Bertrand, Timmermans, M. L., TImofeyev, Maxim A., Tirak, Kyle V., Tobin, Skie, Togawa, H., Tømmervik, H., Tourpali, Kleareti, Trachte, Katja, Trewin, Blair C., Triñanes, Joaquin A., Trotman, Adrian R., Tschudi, M., Tucker, C. J., Tye, Mari R., Van As, D., Van De Wal, R. S.W., Van Der Ronald, J. A., Van Der Schalie, Robin, Van Der Schrier, Gerard, Van Der Werf, Guido R., Van Meerbeeck, Cedric J., Velden, Christopher S., Velicogna, I., Verburg, Piet, Vickers, H., Vincent, Lucie A., Vömel, Holger, Vose, Russell S., Wagner, Wolfgang, Walker, D. A., Walsh, J., Wang, Bin, Wang, Junhong, Wang, Lei, Wang, M., Wang, Ray, Wang, Sheng Hung, Wanninkhof, Rik, Watanabe, Shohei, Weber, Mark, Webster, M., Weller, Robert A., Westberry, Toby K., Weyhenmeyer, Gesa A., Whitewood, Robert, Widlansky, Matthew J., Wiese, David N., Wijffels, Susan E., Wilber, Anne C., Wild, Jeanette D., Willett, Kate M., Willis, Josh K., Wolken, G., Wong, Takmeng, Wood, E. F., Wood, K., Woolway, R. Iestyn, Wouters, B., Xue, Yan, Yin, Xungang, Yoon, Huang, York, A., Yu, Lisan, Zambrano, Eduardo, Zhang, Huai Min, Zhang, Peiqun, Zhao, Guanguo, Zhao, Lin, Zhu, Zhiwei, Ziel, R., Ziemke, Jerry R., Ziese, Markus G., Griffin, Jessicca, Hammer, Gregory, Love-Brotak, S. Elizabeth, Misch, Deborah J., Riddle, Deborah B., Slagle, Mary, Sprain, Mara, Veasey, Sara W., and McVicar, Tim R.

Published
2018

49. State of the climate in 2017

Author

Sub Dynamics Meteorology, Sub Soft Condensed Matter, LS Religiewetenschap, Sub Atmospheric physics and chemistry, Zonder bezoldiging NED, LS Taalverwerving, Leerstoel Tubergen, Afd Chemical Biology and Drug Discovery, Hafd Faculteitsbureau GW, Afd Pharmacology, Dep IRAS, Marine and Atmospheric Research, OFR - Religious Studies, Abernethy, R., Ackerman, Steven A., Adler, R., Albanil Encarnación, Adelina, Aldeco, Laura S., Alfaro, Eric J., Aliaga-Nestares, Vannia, Allan, Richard P., Allan, Rob, Alves, Lincoln M., Amador, Jorge A., Anderson, John, Andreassen, L. M., Argüez, Anthony, Armitage, C., Arndt, Derek S., Avalos, Grinia, Azorin-Molina, César, Báez, Julián, Bardin, M. Yu, Barichivich, Jonathan, Baringer, Molly O., Barreira, Sandra, Baxter, Stephen, Beck, H. E., Becker, Andreas, Bedka, Kristopher M., Behe, Carolina, Bell, Gerald D., Bellouin, Nicolas, Belmont, M., Benedetti, Angela, Bernhard, G. H., Berrisford, Paul, Berry, David I., Bhatt, U. S., Bissolli, Peter, Bjerke, J., Blake, Eric S., Blenkinsop, Stephen, Blunden, Jessica, Bolmgren, K., Bosilovich, Michael G., Boucher, Olivier, Bouchon, Marilú, Box, J. E., Boyer, Tim, Braathen, Geir O., Bromwich, David H., Brown, R., Buehler, S., Bulygina, Olga N., Burgess, D., Calderón, Blanca, Camargo, Suzana J., Campbell, Ethan C., Campbell, Jayaka D., Cappelen, J., Carrea, Laura, Carter, Brendan R., Castro, Anabel, Chambers, Don P., Cheng, Lijing, Christiansen, Hanne H., Christy, John R., Chung, E. S., Clem, Kyle R., Coelho, Caio A.S., Coldewey-Egbers, Melanie, Colwell, Steve, Cooper, Owen R., Copland, L., Costanza, Carol, Covey, Curt, Coy, Lawrence, Cronin, T., Crouch, Jake, Cruzado, Luis, Daniel, Raychelle, Davis, Sean M., Davletshin, S. G., De Eyto, Elvira, De Jeu, Richard A.M., De La Cour, Jacqueline L., De Laat, Jos, De Gasperi, Curtis L., Degenstein, Doug, Deline, P., Demircan, Mesut, Derksen, C., Dewitte, Boris, Dhurmea, R., Di Girolamo, Larry, Diamond, Howard J., Dickerson, C., Dlugokencky, Ed J., Dohan, Kathleen, Dokulil, Martin T., Dolman, A. Johannes, Domingues, Catia M., Domingues, Ricardo, Donat, Markus G., Dong, Shenfu, Dorigo, Wouter A., Drozdov, D. S., Dunn, Robert J.H., Durre, Imke, Dutton, Geoff S., Eakin, C. Mark, El Kharrim, M., Elkins, James W., Epstein, H. E., Espinoza, Jhan C., Famiglietti, James S., Farmer, J., Farrell, S., Fauchald, P., Fausto, R. S., Feely, Richard A., Feng, Z., Fenimore, Chris, Fettweis, X., Fioletov, Vitali E., Flemming, Johannes, Fogt, Ryan L., Folland, Chris, Forbes, B. C., Foster, Michael J., Francis, S. D., Franz, Bryan A., Frey, Richard A., Frith, Stacey M., Froidevaux, Lucien, Ganter, Catherine, Geiger, Erick F., Gerland, S., Gilson, John, Gobron, Nadine, Goldenberg, Stanley B., Gomez, Andrea M., Goni, Gustavo, Grooß, Jens Uwe, Gruber, Alexander, Guard, Charles P., Gugliemin, Mario, Gupta, S. K., Gutiérrez, Dimitri, Haas, C., Hagos, S., Hahn, Sebastian, Haimberger, Leo, Hall, Brad D., Halpert, Michael S., Hamlington, Benjamin D., Hanna, E., Hansen, K., Hanssen-Bauer, L., Harris, Ian, Hartfield, Gail, Heidinger, Andrew K., Heim, Richard R., Helfrich, S., Hemming, D. L., Hendricks, S., Hernández, Rafael, Hernández, Sosa Marieta, Heron, Scott F., Heuzé, C., Hidalgo, Hugo G., Ho, Shu Peng, Hobbs, William R., Horstkotte, T., Huang, Boyin, Hubert, Daan, Hueuzé, Céline, Hurst, Dale F., Ialongo, Iolanda, Ibrahim, M. M., Ijampy, J. A., Inness, Antje, Isaac, Victor, Isaksen, K., Ishii, Masayoshi, Jacobs, Stephanie J., Jeffries, Martin O., Jevrejeva, Svetlana, Jiménez, C., Jin, Xiangze, John, Viju, Johns, William E., Johnsen, Bjørn, Johnson, Bryan, Johnson, Gregory C., Johnson, Kenneth S., Jones, Philip D., Jumaux, Guillaume, Kabidi, Khadija, Kaiser, J. W., Karaköylü, Erdem M., Kato, Seiji, Kazemi, A., Keller, Linda M., Kennedy, John, Kerr, Kenneth, Khan, M. S., Kholodov, A. L., Khoshkam, Mahbobeh, Killick, Rachel, Kim, Hyungjun, Kim, S. J., Klotzbach, Philip J., Knaff, John A., Kohler, J., Korhonen, Johanna, Korshunova, Natalia N., Kramarova, Natalya, Kratz, D. P., Kruger, Andries, Kruk, Michael C., Krumpen, T., Ladd, C., Lakatos, Mónika, Lakkala, Kaisa, Lander, Mark A., Landschützer, Peter, Landsea, Chris W., Lankhorst, Matthias, Lavado-Casimiro, Waldo, Lazzara, Matthew A., Lee, S. E., Lee, T. C., Leuliette, Eric, L'Heureux, Michelle, Li, Tim, Lieser, Jan L., Lin, I. I., Mears, Carl A., Liu, Gang, Li, Bailing, Liu, Hongxing, Locarnini, Ricardo, Loeb, Norman G., Long, Craig S., López, Luis A., Lorrey, Andrew M., Loyola, Diego, Lumpkin, Rick, Luo, Jing Jia, Luojus, K., Luthcke, S., Macias-Fauria, M., Malkova, G. V., Manney, Gloria L., Marcellin, Vernie, Marchenko, S. S., Marengo, José A., Marín, Dora, Marra, John J., Marszelewski, Wlodzimierz, Martens, B., Martin, A., Martínez, Alejandra G., Martínez-Güingla, Rodney, Martínez-Sánchez, Odalys, Marsh, Benjamin L., Lyman, John M., Massom, Robert A., May, Linda, Mayer, Michael, Mazloff, Matthew, McBride, Charlotte, McCabe, M. F., McCarthy, Mark, Meier, W., Meijers, Andrew J.S., Mekonnen, Ademe, Mengistu Tsidu, G., Menzel, W. Paul, Merchant, Christopher J., Meredith, Michael P., Merrifield, Mark A., Miller, Ben, Miralles, Diego G., Mitchum, Gary T., Mitro, Sukarni, Moat, Ben, Mochizuki, Y., Monselesan, Didier, Montzka, Stephen A., Mora, Natalie, Morice, Colin, Mosquera-Vásquez, Kobi, Mostafa, Awatif E., Mote, T., Mudryk, L., Mühle, Jens, Mullan, A. Brett, Müller, Rolf, Myneni, R., Nash, Eric R., Nerem, R. Steven, Newman, L., Newman, Paul A., Nielsen-Gammon, John W., Nieto, Juan José, Noetzli, Jeannette, Noll, Ben E., O'Neel, S., Osborn, Tim J., Osborne, Emily, Overland, J., Oyunjargal, Lamjav, Park, T., Pasch, Richard J., Pascual-Ramírez, Reynaldo, Pastor Saavedra, Maria Asuncion, Paterson, Andrew M., Paulik, Christoph, Pearce, Petra R., Peltier, Alexandre, Pelto, Mauri S., Peng, Liang, Perkins-Kirkpatrick, Sarah E., Perovich, Don, Petropavlovskikh, Irina, Pezza, Alexandre B., Phillips, C., Phillips, David, Phoenix, G., Pinty, Bernard, Pinzon, J., Po-Chedley, S., Polashenski, C., Purkey, Sarah G., Quispe, Nelson, Rajeevan, Madhavan, Rakotoarimalala, C., Rayner, Darren, Raynolds, M. K., Reagan, James, Reid, Phillip, Reimer, Christoph, Rémy, Samuel, Revadekar, Jayashree V., Richardson, A. D., Richter-Menge, Jacqueline, Ricker, R., Rimmer, Alon, Robinson, David A., Rodell, Matthew, Rodriguez Camino, Ernesto, Romanovsky, Vladimir E., Ronchail, Josyane, Rosenlof, Karen H., Rösner, Benjamin, Roth, Chris, Roth, David Mark, Rusak, James A., Rutishäuser, T., Sallée, Jean Bapiste, Sánchez-Lugo, Ahira, Santee, Michelle L., Sasgen, L., Sawaengphokhai, P., Sayad, T. A., Sayouri, Amal, Scambos, Ted A., Scanlon, T., Schenzinger, Verena, Schladow, S. Geoffrey, Schmid, Claudia, Schmid, Martin, Schreck, Carl J., Selkirk, H. B., Send, Uwe, Sensoy, Serhat, Sharp, M., Shi, Lei, Shiklomanov, Nikolai I., Shimaraeva, Svetlana V., Siegel, David A., Silow, Eugene, Sima, Fatou, Simmons, Adrian J., Skirving, William J., Smeed, David A., Smeets, C. J.P.P., Smith, Adam, Smith, Sharon L., Soden, B., Sofieva, Viktoria, Sparks, T. H., Spence, Jacqueline M., Spillane, Sandra, Srivastava, A. K., Stackhouse, Paul W., Stammerjohn, Sharon, Stanitski, Diane M., Steinbrecht, Wolfgang, Stella, José L., Stengel, M., Stephenson, Kimberly, Stephenson, Tannecia S., Strahan, Susan, Streletskiy, Dimitri A., Strong, Alan E., Sun-Mack, Sunny, Sutton, Adrienne J., Swart, Sebastiaan, Sweet, William, Takahashi, Kenneth S., Tamar, Gerard, Taylor, Michael A., Tedesco, M., Thackeray, S. J., Thoman, R. L., Thompson, Philip, Thomson, L., Thorsteinsson, T., Timbal, Bertrand, Timmermans, M. L., TImofeyev, Maxim A., Tirak, Kyle V., Tobin, Skie, Togawa, H., Tømmervik, H., Tourpali, Kleareti, Trachte, Katja, Trewin, Blair C., Triñanes, Joaquin A., Trotman, Adrian R., Tschudi, M., Tucker, C. J., Tye, Mari R., Van As, D., Van De Wal, R. S.W., Van Der Ronald, J. A., Van Der Schalie, Robin, Van Der Schrier, Gerard, Van Der Werf, Guido R., Van Meerbeeck, Cedric J., Velden, Christopher S., Velicogna, I., Verburg, Piet, Vickers, H., Vincent, Lucie A., Vömel, Holger, Vose, Russell S., Wagner, Wolfgang, Walker, D. A., Walsh, J., Wang, Bin, Wang, Junhong, Wang, Lei, Wang, M., Wang, Ray, Wang, Sheng Hung, Wanninkhof, Rik, Watanabe, Shohei, Weber, Mark, Webster, M., Weller, Robert A., Westberry, Toby K., Weyhenmeyer, Gesa A., Whitewood, Robert, Widlansky, Matthew J., Wiese, David N., Wijffels, Susan E., Wilber, Anne C., Wild, Jeanette D., Willett, Kate M., Willis, Josh K., Wolken, G., Wong, Takmeng, Wood, E. F., Wood, K., Woolway, R. Iestyn, Wouters, B., Xue, Yan, Yin, Xungang, Yoon, Huang, York, A., Yu, Lisan, Zambrano, Eduardo, Zhang, Huai Min, Zhang, Peiqun, Zhao, Guanguo, Zhao, Lin, Zhu, Zhiwei, Ziel, R., Ziemke, Jerry R., Ziese, Markus G., Griffin, Jessicca, Hammer, Gregory, Love-Brotak, S. Elizabeth, Misch, Deborah J., Riddle, Deborah B., Slagle, Mary, Sprain, Mara, Veasey, Sara W., McVicar, Tim R., Sub Dynamics Meteorology, Sub Soft Condensed Matter, LS Religiewetenschap, Sub Atmospheric physics and chemistry, Zonder bezoldiging NED, LS Taalverwerving, Leerstoel Tubergen, Afd Chemical Biology and Drug Discovery, Hafd Faculteitsbureau GW, Afd Pharmacology, Dep IRAS, Marine and Atmospheric Research, OFR - Religious Studies, Abernethy, R., Ackerman, Steven A., Adler, R., Albanil Encarnación, Adelina, Aldeco, Laura S., Alfaro, Eric J., Aliaga-Nestares, Vannia, Allan, Richard P., Allan, Rob, Alves, Lincoln M., Amador, Jorge A., Anderson, John, Andreassen, L. M., Argüez, Anthony, Armitage, C., Arndt, Derek S., Avalos, Grinia, Azorin-Molina, César, Báez, Julián, Bardin, M. Yu, Barichivich, Jonathan, Baringer, Molly O., Barreira, Sandra, Baxter, Stephen, Beck, H. E., Becker, Andreas, Bedka, Kristopher M., Behe, Carolina, Bell, Gerald D., Bellouin, Nicolas, Belmont, M., Benedetti, Angela, Bernhard, G. H., Berrisford, Paul, Berry, David I., Bhatt, U. S., Bissolli, Peter, Bjerke, J., Blake, Eric S., Blenkinsop, Stephen, Blunden, Jessica, Bolmgren, K., Bosilovich, Michael G., Boucher, Olivier, Bouchon, Marilú, Box, J. E., Boyer, Tim, Braathen, Geir O., Bromwich, David H., Brown, R., Buehler, S., Bulygina, Olga N., Burgess, D., Calderón, Blanca, Camargo, Suzana J., Campbell, Ethan C., Campbell, Jayaka D., Cappelen, J., Carrea, Laura, Carter, Brendan R., Castro, Anabel, Chambers, Don P., Cheng, Lijing, Christiansen, Hanne H., Christy, John R., Chung, E. S., Clem, Kyle R., Coelho, Caio A.S., Coldewey-Egbers, Melanie, Colwell, Steve, Cooper, Owen R., Copland, L., Costanza, Carol, Covey, Curt, Coy, Lawrence, Cronin, T., Crouch, Jake, Cruzado, Luis, Daniel, Raychelle, Davis, Sean M., Davletshin, S. G., De Eyto, Elvira, De Jeu, Richard A.M., De La Cour, Jacqueline L., De Laat, Jos, De Gasperi, Curtis L., Degenstein, Doug, Deline, P., Demircan, Mesut, Derksen, C., Dewitte, Boris, Dhurmea, R., Di Girolamo, Larry, Diamond, Howard J., Dickerson, C., Dlugokencky, Ed J., Dohan, Kathleen, Dokulil, Martin T., Dolman, A. Johannes, Domingues, Catia M., Domingues, Ricardo, Donat, Markus G., Dong, Shenfu, Dorigo, Wouter A., Drozdov, D. S., Dunn, Robert J.H., Durre, Imke, Dutton, Geoff S., Eakin, C. Mark, El Kharrim, M., Elkins, James W., Epstein, H. E., Espinoza, Jhan C., Famiglietti, James S., Farmer, J., Farrell, S., Fauchald, P., Fausto, R. S., Feely, Richard A., Feng, Z., Fenimore, Chris, Fettweis, X., Fioletov, Vitali E., Flemming, Johannes, Fogt, Ryan L., Folland, Chris, Forbes, B. C., Foster, Michael J., Francis, S. D., Franz, Bryan A., Frey, Richard A., Frith, Stacey M., Froidevaux, Lucien, Ganter, Catherine, Geiger, Erick F., Gerland, S., Gilson, John, Gobron, Nadine, Goldenberg, Stanley B., Gomez, Andrea M., Goni, Gustavo, Grooß, Jens Uwe, Gruber, Alexander, Guard, Charles P., Gugliemin, Mario, Gupta, S. K., Gutiérrez, Dimitri, Haas, C., Hagos, S., Hahn, Sebastian, Haimberger, Leo, Hall, Brad D., Halpert, Michael S., Hamlington, Benjamin D., Hanna, E., Hansen, K., Hanssen-Bauer, L., Harris, Ian, Hartfield, Gail, Heidinger, Andrew K., Heim, Richard R., Helfrich, S., Hemming, D. L., Hendricks, S., Hernández, Rafael, Hernández, Sosa Marieta, Heron, Scott F., Heuzé, C., Hidalgo, Hugo G., Ho, Shu Peng, Hobbs, William R., Horstkotte, T., Huang, Boyin, Hubert, Daan, Hueuzé, Céline, Hurst, Dale F., Ialongo, Iolanda, Ibrahim, M. M., Ijampy, J. A., Inness, Antje, Isaac, Victor, Isaksen, K., Ishii, Masayoshi, Jacobs, Stephanie J., Jeffries, Martin O., Jevrejeva, Svetlana, Jiménez, C., Jin, Xiangze, John, Viju, Johns, William E., Johnsen, Bjørn, Johnson, Bryan, Johnson, Gregory C., Johnson, Kenneth S., Jones, Philip D., Jumaux, Guillaume, Kabidi, Khadija, Kaiser, J. W., Karaköylü, Erdem M., Kato, Seiji, Kazemi, A., Keller, Linda M., Kennedy, John, Kerr, Kenneth, Khan, M. S., Kholodov, A. L., Khoshkam, Mahbobeh, Killick, Rachel, Kim, Hyungjun, Kim, S. J., Klotzbach, Philip J., Knaff, John A., Kohler, J., Korhonen, Johanna, Korshunova, Natalia N., Kramarova, Natalya, Kratz, D. P., Kruger, Andries, Kruk, Michael C., Krumpen, T., Ladd, C., Lakatos, Mónika, Lakkala, Kaisa, Lander, Mark A., Landschützer, Peter, Landsea, Chris W., Lankhorst, Matthias, Lavado-Casimiro, Waldo, Lazzara, Matthew A., Lee, S. E., Lee, T. C., Leuliette, Eric, L'Heureux, Michelle, Li, Tim, Lieser, Jan L., Lin, I. I., Mears, Carl A., Liu, Gang, Li, Bailing, Liu, Hongxing, Locarnini, Ricardo, Loeb, Norman G., Long, Craig S., López, Luis A., Lorrey, Andrew M., Loyola, Diego, Lumpkin, Rick, Luo, Jing Jia, Luojus, K., Luthcke, S., Macias-Fauria, M., Malkova, G. V., Manney, Gloria L., Marcellin, Vernie, Marchenko, S. S., Marengo, José A., Marín, Dora, Marra, John J., Marszelewski, Wlodzimierz, Martens, B., Martin, A., Martínez, Alejandra G., Martínez-Güingla, Rodney, Martínez-Sánchez, Odalys, Marsh, Benjamin L., Lyman, John M., Massom, Robert A., May, Linda, Mayer, Michael, Mazloff, Matthew, McBride, Charlotte, McCabe, M. F., McCarthy, Mark, Meier, W., Meijers, Andrew J.S., Mekonnen, Ademe, Mengistu Tsidu, G., Menzel, W. Paul, Merchant, Christopher J., Meredith, Michael P., Merrifield, Mark A., Miller, Ben, Miralles, Diego G., Mitchum, Gary T., Mitro, Sukarni, Moat, Ben, Mochizuki, Y., Monselesan, Didier, Montzka, Stephen A., Mora, Natalie, Morice, Colin, Mosquera-Vásquez, Kobi, Mostafa, Awatif E., Mote, T., Mudryk, L., Mühle, Jens, Mullan, A. Brett, Müller, Rolf, Myneni, R., Nash, Eric R., Nerem, R. Steven, Newman, L., Newman, Paul A., Nielsen-Gammon, John W., Nieto, Juan José, Noetzli, Jeannette, Noll, Ben E., O'Neel, S., Osborn, Tim J., Osborne, Emily, Overland, J., Oyunjargal, Lamjav, Park, T., Pasch, Richard J., Pascual-Ramírez, Reynaldo, Pastor Saavedra, Maria Asuncion, Paterson, Andrew M., Paulik, Christoph, Pearce, Petra R., Peltier, Alexandre, Pelto, Mauri S., Peng, Liang, Perkins-Kirkpatrick, Sarah E., Perovich, Don, Petropavlovskikh, Irina, Pezza, Alexandre B., Phillips, C., Phillips, David, Phoenix, G., Pinty, Bernard, Pinzon, J., Po-Chedley, S., Polashenski, C., Purkey, Sarah G., Quispe, Nelson, Rajeevan, Madhavan, Rakotoarimalala, C., Rayner, Darren, Raynolds, M. K., Reagan, James, Reid, Phillip, Reimer, Christoph, Rémy, Samuel, Revadekar, Jayashree V., Richardson, A. D., Richter-Menge, Jacqueline, Ricker, R., Rimmer, Alon, Robinson, David A., Rodell, Matthew, Rodriguez Camino, Ernesto, Romanovsky, Vladimir E., Ronchail, Josyane, Rosenlof, Karen H., Rösner, Benjamin, Roth, Chris, Roth, David Mark, Rusak, James A., Rutishäuser, T., Sallée, Jean Bapiste, Sánchez-Lugo, Ahira, Santee, Michelle L., Sasgen, L., Sawaengphokhai, P., Sayad, T. A., Sayouri, Amal, Scambos, Ted A., Scanlon, T., Schenzinger, Verena, Schladow, S. Geoffrey, Schmid, Claudia, Schmid, Martin, Schreck, Carl J., Selkirk, H. B., Send, Uwe, Sensoy, Serhat, Sharp, M., Shi, Lei, Shiklomanov, Nikolai I., Shimaraeva, Svetlana V., Siegel, David A., Silow, Eugene, Sima, Fatou, Simmons, Adrian J., Skirving, William J., Smeed, David A., Smeets, C. J.P.P., Smith, Adam, Smith, Sharon L., Soden, B., Sofieva, Viktoria, Sparks, T. H., Spence, Jacqueline M., Spillane, Sandra, Srivastava, A. K., Stackhouse, Paul W., Stammerjohn, Sharon, Stanitski, Diane M., Steinbrecht, Wolfgang, Stella, José L., Stengel, M., Stephenson, Kimberly, Stephenson, Tannecia S., Strahan, Susan, Streletskiy, Dimitri A., Strong, Alan E., Sun-Mack, Sunny, Sutton, Adrienne J., Swart, Sebastiaan, Sweet, William, Takahashi, Kenneth S., Tamar, Gerard, Taylor, Michael A., Tedesco, M., Thackeray, S. J., Thoman, R. L., Thompson, Philip, Thomson, L., Thorsteinsson, T., Timbal, Bertrand, Timmermans, M. L., TImofeyev, Maxim A., Tirak, Kyle V., Tobin, Skie, Togawa, H., Tømmervik, H., Tourpali, Kleareti, Trachte, Katja, Trewin, Blair C., Triñanes, Joaquin A., Trotman, Adrian R., Tschudi, M., Tucker, C. J., Tye, Mari R., Van As, D., Van De Wal, R. S.W., Van Der Ronald, J. A., Van Der Schalie, Robin, Van Der Schrier, Gerard, Van Der Werf, Guido R., Van Meerbeeck, Cedric J., Velden, Christopher S., Velicogna, I., Verburg, Piet, Vickers, H., Vincent, Lucie A., Vömel, Holger, Vose, Russell S., Wagner, Wolfgang, Walker, D. A., Walsh, J., Wang, Bin, Wang, Junhong, Wang, Lei, Wang, M., Wang, Ray, Wang, Sheng Hung, Wanninkhof, Rik, Watanabe, Shohei, Weber, Mark, Webster, M., Weller, Robert A., Westberry, Toby K., Weyhenmeyer, Gesa A., Whitewood, Robert, Widlansky, Matthew J., Wiese, David N., Wijffels, Susan E., Wilber, Anne C., Wild, Jeanette D., Willett, Kate M., Willis, Josh K., Wolken, G., Wong, Takmeng, Wood, E. F., Wood, K., Woolway, R. Iestyn, Wouters, B., Xue, Yan, Yin, Xungang, Yoon, Huang, York, A., Yu, Lisan, Zambrano, Eduardo, Zhang, Huai Min, Zhang, Peiqun, Zhao, Guanguo, Zhao, Lin, Zhu, Zhiwei, Ziel, R., Ziemke, Jerry R., Ziese, Markus G., Griffin, Jessicca, Hammer, Gregory, Love-Brotak, S. Elizabeth, Misch, Deborah J., Riddle, Deborah B., Slagle, Mary, Sprain, Mara, Veasey, Sara W., and McVicar, Tim R.

Published
2018

50. Review of testing issues in extremes: in honor of Professor Laurens de Haan

Author

Hüsler, Jürg, Peng, Liang, Hüsler, Jürg, and Peng, Liang

Abstract

As a leading statistician in extreme value theory, Professor Laurens de Haan has made significant contribution in both probability and statistics of extremes. In honor of his 70th birthday, we review testing issues in extremes, which include research done by Professor Laurens de Haan and many others. In comparison with statistical estimation in extremes, research on testing has received less attention. So we also point out some practical questions in this direction

Published
2018

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