Author: "Wang, Jinglu" / Search Limiters: Full Text - Searchworks@Jio Institute Digital Library Search Results

175 results on '"Wang, Jinglu"'

1. Efficient Autoregressive Audio Modeling via Next-Scale Prediction

Author: Qiu, Kai, Li, Xiang, Chen, Hao, Sun, Jie, Wang, Jinglu, Lin, Zhe, Savvides, Marios, and Raj, Bhiksha
Subjects: Computer Science - Sound, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Audio generation has achieved remarkable progress with the advance of sophisticated generative models, such as diffusion models (DMs) and autoregressive (AR) models. However, due to the naturally significant sequence length of audio, the efficiency of audio generation remains an essential issue to be addressed, especially for AR models that are incorporated in large language models (LLMs). In this paper, we analyze the token length of audio tokenization and propose a novel \textbf{S}cale-level \textbf{A}udio \textbf{T}okenizer (SAT), with improved residual quantization. Based on SAT, a scale-level \textbf{A}coustic \textbf{A}uto\textbf{R}egressive (AAR) modeling framework is further proposed, which shifts the next-token AR prediction to next-scale AR prediction, significantly reducing the training cost and inference time. To validate the effectiveness of the proposed approach, we comprehensively analyze design choices and demonstrate the proposed AAR framework achieves a remarkable \textbf{35}$\times$ faster inference speed and +\textbf{1.33} Fr\'echet Audio Distance (FAD) against baselines on the AudioSet benchmark. Code: \url{https://github.com/qiuk2/AAR}., Comment: 7 pages, 6 figures, 7 tables
Published: 2024

2. $\text{R}^2$-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations

Author: Li, Xiang, Qiu, Kai, Wang, Jinglu, Xu, Xiaohao, Singh, Rita, Yamazak, Kashu, Chen, Hao, Huang, Xiaonan, and Raj, Bhiksha
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Referring perception, which aims at grounding visual objects with multimodal referring guidance, is essential for bridging the gap between humans, who provide instructions, and the environment where intelligent systems perceive. Despite progress in this field, the robustness of referring perception models (RPMs) against disruptive perturbations is not well explored. This work thoroughly assesses the resilience of RPMs against various perturbations in both general and specific contexts. Recognizing the complex nature of referring perception tasks, we present a comprehensive taxonomy of perturbations, and then develop a versatile toolbox for synthesizing and evaluating the effects of composite disturbances. Employing this toolbox, we construct $\text{R}^2$-Bench, a benchmark for assessing the Robustness of Referring perception models under noisy conditions across five key tasks. Moreover, we propose the $\text{R}^2$-Agent, an LLM-based agent that simplifies and automates model evaluation via natural language instructions. Our investigation uncovers the vulnerabilities of current RPMs to various perturbations and provides tools for assessing model robustness, potentially promoting the safe and resilient integration of intelligent systems into complex real-world scenarios., Comment: Code and dataset will be released at https://github.com/lxa9867/r2bench
Published: 2024

3. QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition

Author: Li, Xiang, Wang, Jinglu, Xu, Xiaohao, Peng, Xiulian, Singh, Rita, Lu, Yan, and Raj, Bhiksha
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Audiovisual segmentation (AVS) is a challenging task that aims to segment visual objects in videos according to their associated acoustic cues. With multiple sound sources and background disturbances involved, establishing robust correspondences between audio and visual contents poses unique challenges due to (1) complex entanglement across sound sources and (2) frequent changes in the occurrence of distinct sound events. Assuming sound events occur independently, the multi-source semantic space can be represented as the Cartesian product of single-source sub-spaces. We are motivated to decompose the multi-source audio semantics into single-source semantics for more effective interactions with visual content. We propose a semantic decomposition method based on product quantization, where the multi-source semantics can be decomposed and represented by several disentangled and noise-suppressed single-source semantics. Furthermore, we introduce a global-to-local quantization mechanism, which distills knowledge from stable global (clip-level) features into local (frame-level) ones, to handle frequent changes in audio semantics. Extensive experiments demonstrate that our semantically decomposed audio representation significantly improves AVS performance, e.g., +21.2% mIoU on the challenging AVS-Semantic benchmark with ResNet50 backbone. https://github.com/lxa9867/QSD.
Published: 2023

4. Efficient View Synthesis with Neural Radiance Distribution Field

Author: Wu, Yushuang, Li, Xiao, Wang, Jinglu, Han, Xiaoguang, Cui, Shuguang, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Recent work on Neural Radiance Fields (NeRF) has demonstrated significant advances in high-quality view synthesis. A major limitation of NeRF is its low rendering efficiency due to the need for multiple network forwardings to render a single pixel. Existing methods to improve NeRF either reduce the number of required samples or optimize the implementation to accelerate the network forwarding. Despite these efforts, the problem of multiple sampling persists due to the intrinsic representation of radiance fields. In contrast, Neural Light Fields (NeLF) reduce the computation cost of NeRF by querying only one single network forwarding per pixel. To achieve a close visual quality to NeRF, existing NeLF methods require significantly larger network capacities which limits their rendering efficiency in practice. In this work, we propose a new representation called Neural Radiance Distribution Field (NeRDF) that targets efficient view synthesis in real-time. Specifically, we use a small network similar to NeRF while preserving the rendering speed with a single network forwarding per pixel as in NeLF. The key is to model the radiance distribution along each ray with frequency basis and predict frequency weights using the network. Pixel values are then computed via volume rendering on radiance distributions. Experiments show that our proposed method offers a better trade-off among speed, quality, and network size than existing methods: we achieve a ~254x speed-up over NeRF with similar network size, with only a marginal performance decline. Our project page is at yushuang-wu.github.io/NeRDF., Comment: Accepted by ICCV2023
Published: 2023

5. Rethinking Voice-Face Correlation: A Geometry View

Author: Li, Xiang, Wen, Yandong, Yang, Muqiao, Wang, Jinglu, Singh, Rita, and Raj, Bhiksha
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Previous works on voice-face matching and voice-guided face synthesis demonstrate strong correlations between voice and face, but mainly rely on coarse semantic cues such as gender, age, and emotion. In this paper, we aim to investigate the capability of reconstructing the 3D facial shape from voice from a geometry perspective without any semantic information. We propose a voice-anthropometric measurement (AM)-face paradigm, which identifies predictable facial AMs from the voice and uses them to guide 3D face reconstruction. By leveraging AMs as a proxy to link the voice and face geometry, we can eliminate the influence of unpredictable AMs and make the face geometry tractable. Our approach is evaluated on our proposed dataset with ground-truth 3D face scans and corresponding voice recordings, and we find significant correlations between voice and specific parts of the face geometry, such as the nasal cavity and cranium. Our work offers a new perspective on voice-face correlation and can serve as a good empirical study for anthropometry science., Comment: ACM Multimedia 2023
Published: 2023

6. PaintSeg: Training-free Segmentation via Painting

Author: Li, Xiang, Lin, Chung-Ching, Chen, Yinpeng, Liu, Zicheng, Wang, Jinglu, and Raj, Bhiksha
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: The paper introduces PaintSeg, a new unsupervised method for segmenting objects without any training. We propose an adversarial masked contrastive painting (AMCP) process, which creates a contrast between the original image and a painted image in which a masked area is painted using off-the-shelf generative models. During the painting process, inpainting and outpainting are alternated, with the former masking the foreground and filling in the background, and the latter masking the background while recovering the missing part of the foreground object. Inpainting and outpainting, also referred to as I-step and O-step, allow our method to gradually advance the target segmentation mask toward the ground truth without supervision or training. PaintSeg can be configured to work with a variety of prompts, e.g. coarse masks, boxes, scribbles, and points. Our experimental results demonstrate that PaintSeg outperforms existing approaches in coarse mask-prompt, box-prompt, and point-prompt segmentation tasks, providing a training-free solution suitable for unsupervised segmentation.
Published: 2023

7. High-Fidelity and Freely Controllable Talking Head Video Generation

Author: Gao, Yue, Zhou, Yuan, Wang, Jinglu, Li, Xiao, Ming, Xiang, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Talking head generation is to generate video based on a given source identity and target motion. However, current methods face several challenges that limit the quality and controllability of the generated videos. First, the generated face often has unexpected deformation and severe distortions. Second, the driving image does not explicitly disentangle movement-relevant information, such as poses and expressions, which restricts the manipulation of different attributes during generation. Third, the generated videos tend to have flickering artifacts due to the inconsistency of the extracted landmarks between adjacent frames. In this paper, we propose a novel model that produces high-fidelity talking head videos with free control over head pose and expression. Our method leverages both self-supervised learned landmarks and 3D face model-based landmarks to model the motion. We also introduce a novel motion-aware multi-scale feature alignment module to effectively transfer the motion without face distortion. Furthermore, we enhance the smoothness of the synthesized talking head videos with a feature context adaptation and propagation module. We evaluate our model on challenging datasets and demonstrate its state-of-the-art performance., Comment: CVPR 2023
Published: 2023

8. Two-shot Video Object Segmentation

Author: Yan, Kun, Li, Xiao, Wei, Fangyun, Wang, Jinglu, Zhang, Chenbin, Wang, Ping, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Previous works on video object segmentation (VOS) are trained on densely annotated videos. Nevertheless, acquiring annotations in pixel level is expensive and time-consuming. In this work, we demonstrate the feasibility of training a satisfactory VOS model on sparsely annotated videos-we merely require two labeled frames per training video while the performance is sustained. We term this novel training paradigm as two-shot video object segmentation, or two-shot VOS for short. The underlying idea is to generate pseudo labels for unlabeled frames during training and to optimize the model on the combination of labeled and pseudo-labeled data. Our approach is extremely simple and can be applied to a majority of existing frameworks. We first pre-train a VOS model on sparsely annotated videos in a semi-supervised manner, with the first frame always being a labeled one. Then, we adopt the pre-trained VOS model to generate pseudo labels for all unlabeled frames, which are subsequently stored in a pseudo-label bank. Finally, we retrain a VOS model on both labeled and pseudo-labeled data without any restrictions on the first frame. For the first time, we present a general way to train VOS models on two-shot VOS datasets. By using 7.3% and 2.9% labeled data of YouTube-VOS and DAVIS benchmarks, our approach achieves comparable results in contrast to the counterparts trained on fully labeled set. Code and models are available at https://github.com/yk-pku/Two-shot-Video-Object-Segmentation., Comment: Accepted by CVPR 2023. Code and models are available at https://github.com/yk-pku/Two-shot-Video-Object-Segmentation
Published: 2023

9. Structural Multiplane Image: Bridging Neural View Synthesis and 3D Reconstruction

Author: Zhang, Mingfang, Wang, Jinglu, Li, Xiao, Huang, Yifei, Sato, Yoichi, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: The Multiplane Image (MPI), containing a set of fronto-parallel RGBA layers, is an effective and efficient representation for view synthesis from sparse inputs. Yet, its fixed structure limits the performance, especially for surfaces imaged at oblique angles. We introduce the Structural MPI (S-MPI), where the plane structure approximates 3D scenes concisely. Conveying RGBA contexts with geometrically-faithful structures, the S-MPI directly bridges view synthesis and 3D reconstruction. It can not only overcome the critical limitations of MPI, i.e., discretization artifacts from sloped surfaces and abuse of redundant layers, and can also acquire planar 3D reconstruction. Despite the intuition and demand of applying S-MPI, great challenges are introduced, e.g., high-fidelity approximation for both RGBA layers and plane poses, multi-view consistency, non-planar regions modeling, and efficient rendering with intersected planes. Accordingly, we propose a transformer-based network based on a segmentation model. It predicts compact and expressive S-MPI layers with their corresponding masks, poses, and RGBA contexts. Non-planar regions are inclusively handled as a special case in our unified framework. Multi-view consistency is ensured by sharing global proxy embeddings, which encode plane-level features covering the complete 3D scenes with aligned coordinates. Intensive experiments show that our method outperforms both previous state-of-the-art MPI-based view synthesis methods and planar reconstruction methods., Comment: Accepted to CVPR2023
Published: 2023

10. Neural Capture of Animatable 3D Human from Monocular Video

Author: Te, Gusi, Li, Xiu, Li, Xiao, Wang, Jinglu, Hu, Wei, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We present a novel paradigm of building an animatable 3D human representation from a monocular video input, such that it can be rendered in any unseen poses and views. Our method is based on a dynamic Neural Radiance Field (NeRF) rigged by a mesh-based parametric 3D human model serving as a geometry proxy. Previous methods usually rely on multi-view videos or accurate 3D geometry information as additional inputs; besides, most methods suffer from degraded quality when generalized to unseen poses. We identify that the key to generalization is a good input embedding for querying dynamic NeRF: A good input embedding should define an injective mapping in the full volumetric space, guided by surface mesh deformation under pose variation. Based on this observation, we propose to embed the input query with its relationship to local surface regions spanned by a set of geodesic nearest neighbors on mesh vertices. By including both position and relative distance information, our embedding defines a distance-preserved deformation mapping and generalizes well to unseen poses. To reduce the dependency on additional inputs, we first initialize per-frame 3D meshes using off-the-shelf tools and then propose a pipeline to jointly optimize NeRF and refine the initial mesh. Extensive experiments show our method can synthesize plausible human rendering results under unseen poses and views., Comment: ECCV 2022
Published: 2022

11. Online Video Instance Segmentation via Robust Context Fusion

Author: Li, Xiang, Wang, Jinglu, Xu, Xiaohao, Raj, Bhiksha, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Video instance segmentation (VIS) aims at classifying, segmenting and tracking object instances in video sequences. Recent transformer-based neural networks have demonstrated their powerful capability of modeling spatio-temporal correlations for the VIS task. Relying on video- or clip-level input, they suffer from high latency and computational cost. We propose a robust context fusion network to tackle VIS in an online fashion, which predicts instance segmentation frame-by-frame with a few preceding frames. To acquire the precise and temporal-consistent prediction for each frame efficiently, the key idea is to fuse effective and compact context from reference frames into the target frame. Considering the different effects of reference and target frames on the target prediction, we first summarize contextual features through importance-aware compression. A transformer encoder is adopted to fuse the compressed context. Then, we leverage an order-preserving instance embedding to convey the identity-aware information and correspond the identities to predicted instance masks. We demonstrate that our robust fusion network achieves the best performance among existing online VIS methods and is even better than previously published clip-level methods on the Youtube-VIS 2019 and 2021 benchmarks. In addition, visual objects often have acoustic signatures that are naturally synchronized with them in audio-bearing video recordings. By leveraging the flexibility of our context fusion network on multi-modal data, we further investigate the influence of audios on the video-dense prediction task, which has never been discussed in existing works. We build up an Audio-Visual Instance Segmentation dataset, and demonstrate that acoustic signals in the wild scenarios could benefit the VIS task.
Published: 2022

12. Towards Robust Referring Video Object Segmentation with Cyclic Relational Consensus

Author: Li, Xiang, Wang, Jinglu, Xu, Xiaohao, Li, Xiao, Raj, Bhiksha, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Referring Video Object Segmentation (R-VOS) is a challenging task that aims to segment an object in a video based on a linguistic expression. Most existing R-VOS methods have a critical assumption: the object referred to must appear in the video. This assumption, which we refer to as semantic consensus, is often violated in real-world scenarios, where the expression may be queried against false videos. In this work, we highlight the need for a robust R-VOS model that can handle semantic mismatches. Accordingly, we propose an extended task called Robust R-VOS, which accepts unpaired video-text inputs. We tackle this problem by jointly modeling the primary R-VOS problem and its dual (text reconstruction). A structural text-to-text cycle constraint is introduced to discriminate semantic consensus between video-text pairs and impose it in positive pairs, thereby achieving multi-modal alignment from both positive and negative pairs. Our structural constraint effectively addresses the challenge posed by linguistic diversity, overcoming the limitations of previous methods that relied on the point-wise constraint. A new evaluation dataset, R\textsuperscript{2}-Youtube-VOSis constructed to measure the model robustness. Our model achieves state-of-the-art performance on R-VOS benchmarks, Ref-DAVIS17 and Ref-Youtube-VOS, and also our R\textsuperscript{2}-Youtube-VOS~dataset., Comment: iccv 2023, https://github.com/lxa9867/R2VOS
Published: 2022

13. Towards Robust Video Object Segmentation with Adaptive Object Calibration

Author: Xu, Xiaohao, Wang, Jinglu, Ming, Xiang, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: In the booming video era, video segmentation attracts increasing research attention in the multimedia community. Semi-supervised video object segmentation (VOS) aims at segmenting objects in all target frames of a video, given annotated object masks of reference frames. Most existing methods build pixel-wise reference-target correlations and then perform pixel-wise tracking to obtain target masks. Due to neglecting object-level cues, pixel-level approaches make the tracking vulnerable to perturbations, and even indiscriminate among similar objects. Towards robust VOS, the key insight is to calibrate the representation and mask of each specific object to be expressive and discriminative. Accordingly, we propose a new deep network, which can adaptively construct object representations and calibrate object masks to achieve stronger robustness. First, we construct the object representations by applying an adaptive object proxy (AOP) aggregation method, where the proxies represent arbitrary-shaped segments at multi-levels for reference. Then, prototype masks are initially generated from the reference-target correlations based on AOP. Afterwards, such proto-masks are further calibrated through network modulation, conditioning on the object proxy representations. We consolidate this conditional mask calibration process in a progressive manner, where the object representations and proto-masks evolve to be discriminative iteratively. Extensive experiments are conducted on the standard VOS benchmarks, YouTube-VOS-18/19 and DAVIS-17. Our model achieves the state-of-the-art performance among existing published works, and also exhibits superior robustness against perturbations. Our project repo is at https://github.com/JerryX1110/Robust-Video-Object-Segmentation, Comment: 19 pages, 17 figures, ACM Multimedia 2022 Accepted
Published: 2022

14. Reliable Propagation-Correction Modulation for Video Object Segmentation

Author: Xu, Xiaohao, Wang, Jinglu, Li, Xiao, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Error propagation is a general but crucial problem in online semi-supervised video object segmentation. We aim to suppress error propagation through a correction mechanism with high reliability. The key insight is to disentangle the correction from the conventional mask propagation process with reliable cues. We introduce two modulators, propagation and correction modulators, to separately perform channel-wise re-calibration on the target frame embeddings according to local temporal correlations and reliable references respectively. Specifically, we assemble the modulators with a cascaded propagation-correction scheme. This avoids overriding the effects of the reliable correction modulator by the propagation modulator. Although the reference frame with the ground truth label provides reliable cues, it could be very different from the target frame and introduce uncertain or incomplete correlations. We augment the reference cues by supplementing reliable feature patches to a maintained pool, thus offering more comprehensive and expressive object representations to the modulators. In addition, a reliability filter is designed to retrieve reliable patches and pass them in subsequent frames. Our model achieves state-of-the-art performance on YouTube-VOS18/19 and DAVIS17-Val/Test benchmarks. Extensive experiments demonstrate that the correction mechanism provides considerable performance gain by fully utilizing reliable guidance. Code is available at: https://github.com/JerryX1110/RPCMVOS., Comment: 13 pages, 8 figures, AAAI 2022 Accepted
Published: 2021

15. Hybrid Instance-aware Temporal Fusion for Online Video Instance Segmentation

Author: Li, Xiang, Wang, Jinglu, Li, Xiao, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Recently, transformer-based image segmentation methods have achieved notable success against previous solutions. While for video domains, how to effectively model temporal context with the attention of object instances across frames remains an open problem. In this paper, we propose an online video instance segmentation framework with a novel instance-aware temporal fusion method. We first leverages the representation, i.e., a latent code in the global context (instance code) and CNN feature maps to represent instance- and pixel-level features. Based on this representation, we introduce a cropping-free temporal fusion approach to model the temporal consistency between video frames. Specifically, we encode global instance-specific information in the instance code and build up inter-frame contextual fusion with hybrid attentions between the instance codes and CNN feature maps. Inter-frame consistency between the instance codes are further enforced with order constraints. By leveraging the learned hybrid temporal consistency, we are able to directly retrieve and maintain instance identities across frames, eliminating the complicated frame-wise instance matching in prior methods. Extensive experiments have been conducted on popular VIS datasets, i.e. Youtube-VIS-19/21. Our model achieves the best performance among all online VIS methods. Notably, our model also eclipses all offline methods when using the ResNet-50 backbone., Comment: AAAI 2022
Published: 2021

16. Video Instance Segmentation by Instance Flow Assembly

Author: Li, Xiang, Wang, Jinglu, Li, Xiao, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Instance segmentation is a challenging task aiming at classifying and segmenting all object instances of specific classes. While two-stage box-based methods achieve top performances in the image domain, they cannot easily extend their superiority into the video domain. This is because they usually deal with features or images cropped from the detected bounding boxes without alignment, failing to capture pixel-level temporal consistency. We embrace the observation that bottom-up methods dealing with box-free features could offer accurate spacial correlations across frames, which can be fully utilized for object and pixel level tracking. We first propose our bottom-up framework equipped with a temporal context fusion module to better encode inter-frame correlations. Intra-frame cues for semantic segmentation and object localization are simultaneously extracted and reconstructed by corresponding decoders after a shared backbone. For efficient and robust tracking among instances, we introduce an instance-level correspondence across adjacent frames, which is represented by a center-to-center flow, termed as instance flow, to assemble messy dense temporal correspondences. Experiments demonstrate that the proposed method outperforms the state-of-the-art online methods (taking image-level input) on the challenging Youtube-VIS dataset.
Published: 2021

17. MonoGRNet: A General Framework for Monocular 3D Object Detection

Author: Qin, Zengyi, Wang, Jinglu, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a monocular image due to the geometric information loss during imagery projection. We propose MonoGRNet for the amodal 3D object detection from a monocular image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension. MonoGRNet decomposes the monocular 3D object detection task into four sub-tasks including 2D object detection, instance-level depth estimation, projected 3D center estimation and local corner regression. The task decomposition significantly facilitates the monocular 3D object detection, allowing the target 3D bounding boxes to be efficiently predicted in a single forward pass, without using object proposals, post-processing or the computationally expensive pixel-level depth estimation utilized by previous methods. In addition, MonoGRNet flexibly adapts to both fully and weakly supervised learning, which improves the feasibility of our framework in diverse settings. Experiments are conducted on KITTI, Cityscapes and MS COCO datasets. Results demonstrate the promising performance of our framework in various scenarios., Comment: The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Published: 2021

18. Weakly Supervised 3D Object Detection from Point Clouds

Author: Qin, Zengyi, Wang, Jinglu, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: A crucial task in scene understanding is 3D object detection, which aims to detect and localize the 3D bounding boxes of objects belonging to specific classes. Existing 3D object detectors heavily rely on annotated 3D bounding boxes during training, while these annotations could be expensive to obtain and only accessible in limited scenarios. Weakly supervised learning is a promising approach to reducing the annotation requirement, but existing weakly supervised object detectors are mostly for 2D detection rather than 3D. In this work, we propose VS3D, a framework for weakly supervised 3D object detection from point clouds without using any ground truth 3D bounding box for training. First, we introduce an unsupervised 3D proposal module that generates object proposals by leveraging normalized point cloud densities. Second, we present a cross-modal knowledge distillation strategy, where a convolutional neural network learns to predict the final results from the 3D object proposals by querying a teacher network pretrained on image datasets. Comprehensive experiments on the challenging KITTI dataset demonstrate the superior performance of our VS3D in diverse evaluation settings. The source code and pretrained models are publicly available at https://github.com/Zengyi-Qin/Weakly-Supervised-3D-Object-Detection., Comment: Accepted by ACM MM 2020
Published: 2020

19. Weakly-supervised Temporal Action Localization by Uncertainty Modeling

Author: Lee, Pilhyeon, Wang, Jinglu, Lu, Yan, and Byun, Hyeran
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Weakly-supervised temporal action localization aims to learn detecting temporal intervals of action classes with only video-level labels. To this end, it is crucial to separate frames of action classes from the background frames (i.e., frames not belonging to any action classes). In this paper, we present a new perspective on background frames where they are modeled as out-of-distribution samples regarding their inconsistency. Then, background frames can be detected by estimating the probability of each frame being out-of-distribution, known as uncertainty, but it is infeasible to directly learn uncertainty without frame-level labels. To realize the uncertainty learning in the weakly-supervised setting, we leverage the multiple instance learning formulation. Moreover, we further introduce a background entropy loss to better discriminate background frames by encouraging their in-distribution (action) probabilities to be uniformly distributed over all action classes. Experimental results show that our uncertainty modeling is effective at alleviating the interference of background frames and brings a large performance gain without bells and whistles. We demonstrate that our model significantly outperforms state-of-the-art methods on the benchmarks, THUMOS'14 and ActivityNet (1.2 & 1.3). Our code is available at https://github.com/Pilhyeon/WTAL-Uncertainty-Modeling., Comment: Accepted by the 35th AAAI Conference on Artificial Intelligence (AAAI 2021)
Published: 2020

20. Joint Semantic Segmentation and Boundary Detection using Iterative Pyramid Contexts

Author: Zhen, Mingmin, Wang, Jinglu, Zhou, Lei, Li, Shiwei, Shen, Tianwei, Shang, Jiaxiang, Fang, Tian, and Long, Quan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: In this paper, we present a joint multi-task learning framework for semantic segmentation and boundary detection. The critical component in the framework is the iterative pyramid context module (PCM), which couples two tasks and stores the shared latent semantics to interact between the two tasks. For semantic boundary detection, we propose the novel spatial gradient fusion to suppress nonsemantic edges. As semantic boundary detection is the dual task of semantic segmentation, we introduce a loss function with boundary consistency constraint to improve the boundary pixel accuracy for semantic segmentation. Our extensive experiments demonstrate superior performance over state-of-the-art works, not only in semantic segmentation but also in semantic boundary detection. In particular, a mean IoU score of 81:8% on Cityscapes test set is achieved without using coarse data or any external data for semantic segmentation. For semantic boundary detection, we improve over previous state-of-the-art works by 9.9% in terms of AP and 6:8% in terms of MF(ODS).
Published: 2020

21. Detection of Intracranial Aneurysms Using Multiphase CT Angiography with a Deep Learning Model

Author: Wang, Jinglu, Sun, Jie, Xu, Jingxu, Lu, Shiyu, Wang, Hao, Huang, Chencui, Zhang, Fandong, Yu, Yizhou, Gao, Xiang, Wang, Ming, Wang, Yu, Ruan, Xinzhong, and Pan, Yuning
Published: 2023
Full Text: View/download PDF

22. Triangulation Learning Network: from Monocular to Stereo 3D Object Detection

Author: Qin, Zengyi, Wang, Jinglu, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: In this paper, we study the problem of 3D object detection from stereo images, in which the key challenge is how to effectively utilize stereo information. Different from previous methods using pixel-level depth maps, we propose employing 3D anchors to explicitly construct object-level correspondences between the regions of interest in stereo images, from which the deep neural network learns to detect and triangulate the targeted object in 3D space. We also introduce a cost-efficient channel reweighting strategy that enhances representational features and weakens noisy signals to facilitate the learning process. All of these are flexibly integrated into a solid baseline detector that uses monocular images. We demonstrate that both the monocular baseline and the stereo triangulation learning network outperform the prior state-of-the-arts in 3D object detection and localization on the challenging KITTI dataset., Comment: 9 pages, accepted by CVPR 2019
Published: 2019

23. Learning Fully Dense Neural Networks for Image Semantic Segmentation

Author: Zhen, Mingmin, Wang, Jinglu, Zhou, Lei, Fang, Tian, and Quan, Long
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Semantic segmentation is pixel-wise classification which retains critical spatial information. The "feature map reuse" has been commonly adopted in CNN based approaches to take advantage of feature maps in the early layers for the later spatial reconstruction. Along this direction, we go a step further by proposing a fully dense neural network with an encoder-decoder structure that we abbreviate as FDNet. For each stage in the decoder module, feature maps of all the previous blocks are adaptively aggregated to feed-forward as input. On the one hand, it reconstructs the spatial boundaries accurately. On the other hand, it learns more efficiently with the more efficient gradient backpropagation. In addition, we propose the boundary-aware loss function to focus more attention on the pixels near the boundary, which boosts the "hard examples" labeling. We have demonstrated the best performance of the FDNet on the two benchmark datasets: PASCAL VOC 2012, NYUDv2 over previous works when not considering training on other datasets.
Published: 2019

24. MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization

Author: Qin, Zengyi, Wang, Jinglu, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a single RGB image due to the geometric information loss during imagery projection. We propose MonoGRNet for the amodal 3D object detection from a monocular RGB image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension. MonoGRNet is a single, unified network composed of four task-specific subnetworks, responsible for 2D object detection, instance depth estimation (IDE), 3D localization and local corner regression. Unlike the pixel-level depth estimation that needs per-pixel annotations, we propose a novel IDE method that directly predicts the depth of the targeting 3D bounding box's center using sparse supervision. The 3D localization is further achieved by estimating the position in the horizontal and vertical dimensions. Finally, MonoGRNet is jointly learned by optimizing the locations and poses of the 3D bounding boxes in the global context. We demonstrate that MonoGRNet achieves state-of-the-art performance on challenging datasets., Comment: 8 pages, accepted by AAAI 2019, oral
Published: 2018

25. MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image

Author: Wang, Jinglu, Sun, Bo, and Lu, Yan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: In this paper, we address the problem of reconstructing an object's surface from a single image using generative networks. First, we represent a 3D surface with an aggregation of dense point clouds from multiple views. Each point cloud is embedded in a regular 2D grid aligned on an image plane of a viewpoint, making the point cloud convolution-favored and ordered so as to fit into deep network architectures. The point clouds can be easily triangulated by exploiting connectivities of the 2D grids to form mesh-based surfaces. Second, we propose an encoder-decoder network that generates such kind of multiple view-dependent point clouds from a single image by regressing their 3D coordinates and visibilities. We also introduce a novel geometric loss that is able to interpret discrepancy over 3D surfaces as opposed to 2D projective planes, resorting to the surface discretization on the constructed meshes. We demonstrate that the multi-view point regression network outperforms state-of-the-art methods with a significant improvement on challenging datasets., Comment: 8 pages; accepted by AAAI 2019
Published: 2018

26. Cost and effectiveness of HPV vaccine delivery strategies: A systematic review

Author: Akumbom, Alvine M., Lee, Jennifer J., Reynolds, Nancy R., Thayer, Winter, Wang, Jinglu, and Slade, Eric
Published: 2022
Full Text: View/download PDF

27. Parallel Structure from Motion from Local Increment to Global Averaging

Author: Zhu, Siyu, Shen, Tianwei, Zhou, Lei, Zhang, Runze, Wang, Jinglu, Fang, Tian, and Quan, Long
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: In this paper, we tackle the accurate and consistent Structure from Motion (SfM) problem, in particular camera registration, far exceeding the memory of a single computer in parallel. Different from the previous methods which drastically simplify the parameters of SfM and sacrifice the accuracy of the final reconstruction, we try to preserve the connectivities among cameras by proposing a camera clustering algorithm to divide a large SfM problem into smaller sub-problems in terms of camera clusters with overlapping. We then exploit a hybrid formulation that applies the relative poses from local incremental SfM into a global motion averaging framework and produce accurate and consistent global camera poses. Our scalable formulation in terms of camera clusters is highly applicable to the whole SfM pipeline including track generation, local SfM, 3D point triangulation and bundle adjustment. We are even able to reconstruct the camera poses of a city-scale data-set containing more than one million high-resolution images with superior accuracy and robustness evaluated on benchmark, Internet, and sequential data-sets., Comment: Under review at the International Conference on Computer Vision (ICCV) 2017
Published: 2017

28. Plant microphenotype: from innovative imaging to computational analysis

Author: Zhang, Ying, primary, Gu, Shenghao, additional, Du, Jianjun, additional, Huang, Guanmin, additional, Shi, Jiawei, additional, Lu, Xianju, additional, Wang, Jinglu, additional, Yang, Wanneng, additional, Guo, Xinyu, additional, and Zhao, Chunjiang, additional
Published: 2024
Full Text: View/download PDF

29. Study on the Micro-Phenotype of Different Types of Maize Kernels Based on Micro-CT

Author: ZHAO Huan, WANG Jinglu, LIAO Shengjin, ZHANG Ying, LU Xianju, GUO Xinyu, and ZHAO Chunjiang
Subjects: plant phenomics, micro-phenotype, micro-ct, maize kernel, phenotype identification, Agriculture (General), S1-972, Technology (General), T1-995
Abstract: Plant micro-phenotype mainly refers to the phenotypic information at the tissue, cell, and subcellular levels, which is an important part of plant phenomics research. In view of the problems of low efficiency, large error, and few traits of traditional methods for detecting kernel microscopic traits, Micro-CT scanning technology was used to carry out precise identification of micro-phenotype on 11 varieties of maize kernels. A total of 34 microscopic traits were obtained based on CT sequence images of 7 tissues, including seed, embryo, endosperm, cavity, subcutaneous cavity, endosperm cavity and embryo cavity. Among the 34 microscopic traits, 4 traits, including endosperm cavity surface area, kernel volume, endosperm volume ratio and endosperm cavity specific surface area, were significantly different among maize types (P-value
Published: 2021
Full Text: View/download PDF

30. Transcriptome analysis of the transdifferentiation of canine BMSCs into insulin producing cells

Author: Wang, Jinglu, Dai, Pengxiu, Zou, Tong, Lv, Yangou, Zhao, Wen, Zhang, Xinke, and Zhang, Yihua
Published: 2021
Full Text: View/download PDF

31. Using High-Throughput Phenotyping Analysis to Decipher the Phenotypic Components and Genetic Architecture of Maize Seedling Salt Tolerance

Author: Guo, Shangjing, primary, Lv, Lujia, additional, Zhao, Yanxin, additional, Wang, Jinglu, additional, Lu, Xianju, additional, Zhang, Minggang, additional, Wang, Ronghuan, additional, Zhang, Ying, additional, and Guo, Xinyu, additional
Published: 2023
Full Text: View/download PDF

32. Investigating the genetic basis of maize ear characteristics: a comprehensive genome-wide study utilizing high-throughput phenotypic measurement method and system

Author: Wang, Jinglu, primary, Zhao, Shuaihao, additional, Zhang, Ying, additional, Lu, Xianju, additional, Du, Jianjun, additional, Wang, Chuanyu, additional, Wen, Weiliang, additional, Guo, Xinyu, additional, and Zhao, Chunjiang, additional
Published: 2023
Full Text: View/download PDF

33. Flexible High-Performance and Screen-Printed Symmetric Supercapacitor Using Hierarchical Rodlike V3O7 Inks

Author: Lin, Baoying, primary, Zheng, Yinyin, additional, Wang, Jinglu, additional, Tu, Qian, additional, Tang, Wentao, additional, and Chen, Liangzhe, additional
Published: 2023
Full Text: View/download PDF

34. Differential analysis and genome-wide association analysis of stomata density of maize inbred lines leaves at ear position

Author: Jin, Yu, primary, Wang, Jinglu, additional, Zhang, Ying, additional, Zhao, Yanxin, additional, Lu, Xianju, additional, Wen, Weiliang, additional, Liu, Xiang, additional, Guo, Xinyu, additional, and Zhao, Chunjiang, additional
Published: 2023
Full Text: View/download PDF

35. CT-Based Phenotyping and Genome-Wide Association Analysis of the Internal Structure and Components of Maize Kernels

Author: Li, Dazhuang, primary, Wang, Jinglu, additional, Zhang, Ying, additional, Lu, Xianju, additional, Du, Jianjun, additional, and Guo, Xinyu, additional
Published: 2023
Full Text: View/download PDF

36. Abstract 5010: Inhibition of discoidin domain receptor 1 (DDR1) as a new therapeutic strategy for osteosarcoma

Author: Wang, Jinglu, primary, Walker, Robert, additional, Hornicek, Francis, additional, and Duan, Zhenfeng, additional
Published: 2023
Full Text: View/download PDF

37. Phenotyping analysis of maize stem using micro-computed tomography at the elongation and tasseling stages

Author: Zhang, Ying, Ma, Liming, Wang, Jinglu, Wang, Xiaodong, Guo, Xinyu, and Du, Jianjun
Published: 2020
Full Text: View/download PDF

38. Combined analysis of genome-wide expression profiling of maize (Zea mays L.) leaves infected with Ustilago maydis

Author: Wang, Jinglu, Zhang, Ying, Du, Jianjun, Pan, Xiaodi, Ma, Liming, Shao, Meng, and Guo, Xinyu
Subjects: Corn -- Genetic aspects -- Diseases and pests, Phytopathogenic fungi -- Environmental aspects -- Genetic aspects, Genome-wide association studies, Biological sciences
Abstract: Although many gene expression profiling studies of maize leaves infected with Ustilago maydis have been published, heterogeneity of the results, caused by various data processing methods and pathogenic strains in different data sets, remains strong. Hence, we conducted a combined analysis of six genome-wide expression data sets of maize leaves infected with five different U. maydis strains by using the same pre-processing and quality control procedures. Six data sets were regrouped into five groups according to pathogenic strain used. Subsequently, each group of data set was processed by Multi-array Average for pre-processing and by pair-wise Pearson correlation for quality control. The differentially expressed genes were calculated by a standard linear mixed-effect model and then validated by various sensitivity analysis and multiple evidences. Finally, 44 unique differentially expressed genes were identified. Pathway enrichment analysis indicated that these genes related to response to fungus, oxidation-reduction, transferase activity, and several carbohydrate metabolic and catabolic processes. In addition, the hub genes within protein--protein interaction networks showed high relevance with the basic pathogenesis. We report a highly credible differentially expressed list, and the genes with multiple validations may denote a common signature of U. maydis in maize, which provides a new window for disease-resistant protection of maize plants. Key words: maize, gene expression, Ustilago maydis, differentially expressed genes. Bien que de nombreuses études transcriptomiques sur les feuilles de maïs infectées par l'Ustilago maydis aient été publiées, l'hétérogénéité des résultats, occasionnée par diverses méthodes de traitement des données et l'emploi de souches différentes de l'agent pathogène, demeure considérable. Ici, les auteurs ont réalisé une analyse combinée de six jeux de données transcriptomiques dérivés de feuilles de maïs infectées par cinq souches de l'U. maydis en faisant appel aux mêmes procédures pour le pré-traitement et le contrôle de qualité des données. Les six jeux de données ont été groupés en cinq groupes sur la base de la souche employée. Ensuite, chaque groupe de données a été soumis à un pré-traitement par l'approche << Multi-array Average >> et un contrôle de qualité a été réalisé en calculant une corrélation Pearson pour ces jeux deux à deux. Les gènes exprimés de manière différentielle ont été identifiés à l'aide d'un modèle linéaire standard à effets mixtes et ceux-ci ont ensuite été validés via des analyses de sensibilité et d'autres évidences. Enfin, 44 gènes uniques exprimés de manière différentielle ont été identifiés. Une analyse d'enrichissement des sentiers a révélé que ces gènes étaient impliqués dans la réponse aux champignons, l'oxydoréduction, les activités transférase ainsi que plusieurs processus métaboliques et cataboliques des hydrates de carbone. De plus, les gènes centraux au sein de réseaux d'interactions protéine-protéine étaient très significatifs en rapport avec la pathogénie de base. Les auteurs rapportent ainsi une liste de grande qualité des gènes différentiellement exprimés, et les gènes validés via diverses approches peuvent constituer une signature commune de l'U. maydis chez le maïs, laquelle fournit de nouvelles avenues pour le développement de maïs résistants. [Traduit par la Rédaction] Mots-clés : maïs, expression génique, Ustilago maydis, gènes exprimés de manière différentielle., Introduction Maize (Zea mays L.) is the highest yielding cereal crop, grown worldwide for grain yield or silage. Inrecent years, except for food crops, maize has also been used to [...]
Published: 2018
Full Text: View/download PDF

39. Reliable Propagation-Correction Modulation for Video Object Segmentation

Author: Xu, Xiaohao, Wang, Jinglu, Li, Xiao, and Lu, Yan
Subjects: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, General Medicine
Abstract: Error propagation is a general but crucial problem in online semi-supervised video object segmentation. We aim to suppress error propagation through a correction mechanism with high reliability. The key insight is to disentangle the correction from the conventional mask propagation process with reliable cues. We introduce two modulators, propagation and correction modulators, to separately perform channel-wise re-calibration on the target frame embeddings according to local temporal correlations and reliable references respectively. Specifically, we assemble the modulators with a cascaded propagation-correction scheme. This avoids overriding the effects of the reliable correction modulator by the propagation modulator. Although the reference frame with the ground truth label provides reliable cues, it could be very different from the target frame and introduce uncertain or incomplete correlations. We augment the reference cues by supplementing reliable feature patches to a maintained pool, thus offering more comprehensive and expressive object representations to the modulators. In addition, a reliability filter is designed to retrieve reliable patches and pass them in subsequent frames. Our model achieves state-of-the-art performance on YouTube-VOS18/19 and DAVIS17-Val/Test benchmarks. Extensive experiments demonstrate that the correction mechanism provides considerable performance gain by fully utilizing reliable guidance. Code is available at: https://github.com/JerryX1110/RPCMVOS., 13 pages, 8 figures, AAAI 2022 Accepted
Published: 2022

40. Accurate Phenotypic Identification and Genetic Analysis of the Ear Leaf Veins in Maize (Zea mays L.)

Author: Guo, Shangjing, primary, Zhu, Mingyi, additional, Du, Jianjun, additional, Wang, Jinglu, additional, Lu, Xianju, additional, Jin, Yu, additional, Zhang, Minggang, additional, Guo, Xinyu, additional, and Zhang, Ying, additional
Published: 2023
Full Text: View/download PDF

41. Selenium regulates Nrf2 signaling to prevent hepatotoxicity induced by hexavalent chromium in broilers

Author: Wang, Jingqiu, primary, Liu, Ci, additional, Zhao, Yanbing, additional, Wang, Jinglu, additional, Li, Jianhui, additional, and Zheng, Mingxue, additional
Published: 2023
Full Text: View/download PDF

42. Unsupervised Temporal Correspondence Learning for Unified Video Object Removal

Author: Wang, Zhongdao, primary, Wang, Jinglu, additional, Li, Xiao, additional, Li, Ya-Li, additional, Lu, Yan, additional, and Wang, Shengjin, additional
Published: 2023
Full Text: View/download PDF

43. The ATLAS Experiment at the CERN Large Hadron Collider: A Description of the Detector Configuration for Run 3

Author: Aad, Georges, Abbott, Braden Keim, Abbott, Dale, Abdallah, Jalal, Abeling, Kira, Abidi, Haider, Aboulhorma, Asmaa, Abovyan, Sergey, Abramowicz, Halina, Abreu, Henso, Abulaiti, Yiming, Abusleme, Angel, Acharya, Bobby Samir, Adam Bourdarios, Claire, Adamczyk, Leszek, Adamek, Lukas, Addepalli, Sagar, Adelman, Jahred, Adersberger, Michael, Adiguzel, Aytul, Adorni Braccesi Chiassi, Sofia, Adye, Tim, Affolder, Tony, Afik, Yoav, Agaras, Merve Nazlim, Agarwala, Jinky, Aggarwal, Anamika, Agheorghiesei, Catalin, Aguilar Saavedra, Juan Antonio, Ahmad, Ammara, Ahmadov, Faig, Ahmed, Waleed Syed, Ahuja, Sudha, Ai, Xiaocong, Aielli, Giulio, Ait Tamlihat, Malak, Aitbenchikh, Brahim, Aizenberg, Iakov, Akbiyik, Melike, Akesson, Torsten, Akhperjanyan, Gevorg, Akimov, Andrei, Al Khoury, Konie, Alberghi, Gian Luigi, Albert, Justin, Albicocco, Pietro, Alderweireldt, Sara, Aleksa, Martin, Alexandrov, Igor, Alexa, Calin, Alexopoulos, Theodoros, Alfonsi, Alice, Alfonsi, Fabrizio, Alhroob, Muhammad, Ali, Babar, Ali, Shahzad, Aliev, Malik, Alimonti, Gianluca, Alkakhi, Wael, Allaire, Corentin, Allard, Jérôme, Allbrooke, Benedict, Allendes Flores, Cristian Andres, Allport, Philip Patrick, Aloisio, Alberto, Alonso, Francisco, Alpigiani, Cristiano, Alvarez Estevez, Manuel, Alvarez Gonzalez, Barbara, Alviggi, Mariagrazia, Aly, Mohamed, Do Amaral Coutinho, Yara, Ambler, Alessandro, Amelung, Christoph, Amerl, Maximilian, Ames, Christoph, Amidei, Dante, Amor dos Santos, Susana Patricia, Amos, Kieran Robert, Ananiev, Viktor, Anastopoulos, Christos, Andari, Nansi, Andeen, Timothy Robert, Anders, John Kenneth, Andrean, Stefio Yosse, Andreazza, Attilio, Anelli, Christopher Ryan, Angelidakis, Stylianos, Angerami, Aaron, Anisenkov, Alexey, Annovi, Alberto, Antel, Claire, Anthony, Matthew Thomas, Antipov, Egor, Antonelli, Mario, Antonescu, Mihai, Antrim, Daniel Joseph, Anulli, Fabio, Aoki, Masato, Aoki, Takumi, Aparisi Pozo, Javier Alberto, Aparo, Marco, Aperio Bella, Ludovica, Appelt, Christian, Aranzabal Barrio, Nordin, Araujo Ferraz, Victor, Arcangeletti, Chiara, Arce, Ayana Tamu, Arena, Eloisa, Arguin, Jean-Francois, Argyris, Anastasios, Argyropoulos, Spyros, Arling, Jan-Hendrik, Armbruster, Aaron James, Armijo, Charles Edward, Arnaez, Olivier, Arnold, Hannah, Arrubarrena Tame, Zulit Paola, Artoni, Giacomo, Asada, Haruka, Asai, Kanae, Asai, Shoji, Asbah, Nedaa Alexandra, Assahsah, Jihad, Assamagan, Ketevi Adikle, Astalos, Robert, Atkin, Ryan Justin, Atkinson, Markus Julian, Atlay, Naim Bora, Atmani, Hicham, Atmasiddha, Prachi, Aubernon, Erwann, Augsten, Kamil, Aune, Aune, Auricchio, Silvia, Auriol, Adrien, Aust, Florian, Austrup, Volker Andreas, Avner, Gal, Avolio, Giuseppe, Avoni, Giulio, Axen, David, Axiotis, Konstantinos, Aydiner, Pelin, Ayoub, Mohamad Kassem, Azaryan, Tatiana, Azuelos, Georges, Babal, Dominik, Bachacou, Henri, Bachas, Konstantinos, Bachiu, Alexander, Backman, Karl Filip, Badea, Anthony, Bagnaia, Paolo, Bahmani, Marzieh, Bailey, Adam, Bailey, Virginia, Baines, John, Bakalis, Christos, Baker, Keith, Bakker, Pepijn Johannes, Bakos, Evelin, Bakshi Gupta, Debottam, Balaji, Shyam, Balasubramanian, Rahul, Balbi, Gabriele, Baldin, Evgenii, Balek, Petr, Ball, Robert, Ballabene, Eric, Ballansat, Jacques, Balli, Fabrice, Baltes, Lisa Marie, Balunas, William Keaton, Balz, Johannes, Ban, Jaroslav, Banas, Elzbieta, Bandieramonte, Marilena, Bandyopadhyay, Anjishnu, Bansal, Shubham, Barak, Liron, Barberio, Elisabetta, Barberis, Dario, Barbero, Marlon Benoit, Barbier, Gerard, Barbour, Gregory, Bardo, Laetitia, Barends, Kevin Nicholas, Barfusser, Anja, Barillari, Teresa, Barisits, Martin, Barklow, Tim, Barnett, Michael, Baron, Petr, Baron, Diego, Baroncelli, Toni, Barone, Gaetano, Barr, Alan, Barranco Navarro, Laura, Barreiro Alonso, Fernando, Barreiro Guimaraes da Costa, Joao, Barron, Uriel, Barros, Maura, Barsov, Sergey, Bartels, Falk, Bartoldus, Rainer, Barton, Adam Edward, Bartos, Pavol, Basalaev, Artem, Basan, Alexander, Baselga Bacardit, Marta, Bashta, Inna, Bassalat, Ahmed, Basso, Matthew Joseph, Basson, Candice Ruth, Bates, Richard, Batlamous, Souad, Batley, Richard, Batool, Binish, Battaglia, Marco, Battulga, Daariimaa, Bauce, Matteo, Bauer, Patrick, Bayirli, Arif, Beacham, James, Beau, Tristan, Belhesan, Fredy, Beltramelli, Beltramelli, Beauchamp, Blake Christopher, Beauchemin, Pierre-Hugues, Beccherle, Roberto, Becherer, Fabian, Bechtle, Philip, Beck, Hans Peter, Becker, Kathrin, Beddall, Andrew, Bednyakov, Vadim, Bee, Chris, Beemster, Lars, Beermann, Thomas, Begalli, Marcia, Begel, Michael, Behera, Arabinda, Behr, Janna Katharina, Beirao da Cruz E Silva, Cristovao, Beirer, Joshua Falco, Beisiegel, Florian, Belanger-Champagne, Camille, Belfkir, Mohamed, Bella, Gideon, Bellachia, Fatih, Bellagamba, Lorenzo, Bellerive, Alain, Bellos, Panagiotis, Beloborodov, Konstantin, Belotskiy, Konstantin, Belyaev, Nikita, Raviv Moshe, Meny, Benchekroun, Driss, Bendebba, Fatima, Bendotti, Jerome, Benhammou, Yan, Benjamin, Doug, Benoit, Mathieu, Benoit, Theophile Arthur, Bensinger, Jim, Bentvelsen, Stan, Beresford, Lydia Audrey, Beretta, Matteo Mario, Bergeaas Kuutmann, Elin, Berger, Nicolas, Bergmann, Benedikt Ludwig, Beringer, Juerg, Berlendis, Simon, Bernardi, Gregorio, Bernius, Catrin, Bernlochner, Florian Urs, Bernon, Florent, Berry, Tracey, Berta, Peter, Berthold, Anne-Sophie, Bertram, Iain, Bervas, Hervé, Besin, Dominique, Bessudo, Ilan, Bethke, Siegfried, Betti, Alessandra, Bevan, Adrian, Bey, Bey, Bhamjee, Muaaz, Bhatta, Somadutta, Bhattacharya, Deb Sankar, Bhattarai, Prajita, Bhopatkar, Vallary Shashikant, Bi, Ran, Bianchi, Riccardo Maria, Bianga, Yves, Biaut, Mathieu, Biebel, Otmar, Bielski, Rafal, Biglietti, Michela, Billoud, Thomas, Bindi, Marcello, Bingul, Ahmet, Bini, Cesare, Biondini, Alessandro, Bira, Calin, Birch-Sykes, Callum Jacob, Bird, Gareth Adam, Birman, Mattias, Birney, Paul, Biros, Marek, Bisanz, Tobias, Bisceglie, Emanuele, Biswas, Diptaparna, Bita, Daniel, Bitadze, Alexander, Bjoerke, Kristian, Blaszczyk, Tomasz Piotr, Bloch, Ingo, Blocker, Craig, Blue, Andrew James, Blumenschein, Ulla, Blumenthal, Julian, Bobbink, Gerjan, Bobrovnikov, Viktor, Boehler, Michael, Bohm, Burkhard, Bogavac, Danijela, Bogdanchikov, Alexander, Bohm, Christian, Boisvert, Veronique, Bokan, Petar, Bold, Tomasz, Boline, Daniel Dooley, Bomben, Marco, Bona, Marcella, Bonini, Filiberto, Boonekamp, Maarten, Booth, Callum Dale, Borbely, Albert Gyorgy, Borecka-Bielska, Hanna Maria, Borgna, Lucas Santiago, Borissov, Guennadi, Bortfeldt, Jona, Bortoletto, Daniela, Bortolin, Claudio, Boscherini, Davide, Fernandez-Bosman, Martine, Bossio, Jonathan, Botte, James, Bouaouda, Khalil, Bouaziz, Saïd, Bouchhar, Naseem, Boudreau, Joseph, Bouedo, Thierry, Bouhova-Thacker, Eva, Boumediene, Djamel Eddine, Bouquet, Romain, Boveia, Antonio, Boyd, Jamie, Boye, Diallo, Boyko, Igor, Braam, Nick, Bracinik, Juraj, Braga Lisboa, Pedro Henrique, Brahimi, Nihal, Brandt, Gerhard Immanuel, Brandt, Oleg, Braren, Frued Erik, Brau, Benjamin Paul, Brau, Jim, Brawn, Ian, Brendlinger, Kurt, Schimmel Brener, Roy, Brenner, Lydia, Brenner, Richard, Bressler, Shikma, Breugnon, Patrick, Britton, David, Britzger, Daniel Andreas, Brock, Ian, Brooijmans, Gustaaf, Brooks, William King, Brost, Elizabeth, Brown, Leesa Marea, Bruce, Laura Elaine, Bruckler, Tim Lukas, Bruckman de Renstrom, Pawel, Bruers, Ben, Bruncko, Dusan, Bruni, Alessia, Bruni, Graziano, Brunner, Kathrin Michaela, Bruschi, Marco, Bruscino, Nello, Buanes, Trygve, Buat, Quentin, Buchholz, Peter, Buckley, Andy, Buda, Stelian, Budagov, Ioulian, Bugge, Magnar Kopangen, Bulekov, Oleg, Bullard, Brendon, Burdin, Sergey, Burgard, Carsten, Burger, Angela Maria, Burghgrave, Blake Oliver, Burr, Jon, Burton, Charles, Burzynski, Jackson Carl, Busch, Elena Laura, Buescher, Volker, Bussey, Peter John, Butler, John Mark, Buttar, Craig Macleod, Butterworth, Jonathan, Buttinger, Will, Buxo Vazquez, Carlos Josue, Buzykaev, Alexey, Cabras, Grazia, Cabrera Urban, Susana, Cadoux, Frank Raphael, Caforio, Davide, Cai, Huacheng, Cai, Yuchen, Cairo, Valentina, Cakir, Orhan, Calabro, Domenico, Calace, Noemi, Calafiura, Paolo, Calderini, Giovanni, Calfayan, Philippe, Callea, Giuseppe, Caloba, Luiz, Calvet, David, Calvet, Samuel, Calvet, Thomas Philippe, Calvetti, Milene, Camacho Toro, Reina Coromoto, Camarda, Stefano, Camarero Munoz, Daniel, Camarri, Paolo, Camerlingo, Maria Teresa, Cameron, David, Camincher, Clement, Campanelli, Mario, Camplani, Alessandra, Canale, Vincenzo, Canesse, Auriane, Bret Cano, Marc, Cantero Garcia, Josu, Cao, Nina Yuan Yuan, Cao, Yumeng, Cap, Sebastien, Capitolo, Emilio, Capocasa, Francesca, Capradossi, Giulio, Capua, Marcella, Cara, Gael Benjamin, Carbone, Antonio, Cardarelli, Roberto, Cardenas, Juan Carlos, Cardillo, Fabio, Cardot, Charles Andre, Carli, Tancredi, Carlino, Giampaolo, Carlotta, Mathilde Doriane, Carlotto, Juan Ignacio, Carlson, Ben, Carlson, Evan Michael, Carlson, Katelyn Joyce, Carminati, Leonardo, Carnesale, Maria, Caron, Sascha, Carquin Lopez, Edson, Carra, Sonia, Carratta, Giuseppe, Carrio Argos, Fernando, Carter, Joseph, Carter, Thomas Michael, Casado Lechuga, Pilar, Caserio, Alessandro, Casha, Albert Francis, Cassese, Ciro, Castiglia, Emma Grace, Castillo, Florencia Luciana, Castillo Garcia, Lucia, Castillo Gimenez, Victoria, Castro, Nuno, Catinaccio, Andrea, Catmore, James, Cavaliere, Viviana, Cavalli, Noemi, Cavasinni, Vincenzo, Celebi, Emre, Celli, Federico, Centonze, Martino Salomone, Ceradini, Filippo, Cerny, Karel, Santiago Cerqueira, Augusto, Cerri, Alex, Cerrito, Lucio, Cerutti, Fabio, Cervelli, Alberto, Cetin, Serkant, Chadi, Zakaria, Chakraborty, Dhiman, Chala, Mikael, Chaleil, Thierry, Chan, Jay, Chan, Stephen Kam-Wah, Chan, Wai Yuen, Chapman, John Derek, Chapman, Jay, Chargeishvili, Bakar, Charlton, Dave, Charman, Thomas Paul, Chatterjee, Meghranjana, Chau, Chav Chhiv, Chekanov, Sergei, Chekulaev, Sergey, Shelkov, G, Chen, Andy, Chen, Boping, Chen, Charlie, Chen, Huirun, Chen, Hucheng, Chen, Jing, Chen, Jiayi, Chen, Kai, Chen, Olay, Chen, Shion, Chen, Shenjian, Chen, Xiang, Chen, Xin, Chen, Ye, Cheng, Alkaid, Cheng, Hok Chuen Tom, Cheong, Sanha, Cheplakov, Alexander, Cheremushkina, Evgeniya, Cherepanova, Elizaveta, Cherkaoui El Moursli, Rajaa, Cheu, Elliott, Cheung, Kingman, Chevalier, Laurent, Chevillot, Nicolas, Chiarella, Vitaliano, Chiarelli, Giorgio, Chiedde, Nemer, Chiodini, Gabriele, Chisholm, Andrew Stephen, Chitan, Adrian, Chitishvili, Mariam, Chiu, Justin, Chizhov, Mihail, Choi, Kyungeon, Chomont, Arthur, Chou, Yuan-Tang, Chow, Edwin, Chowdhury, Tasnuva, Christopher, Lawrence Davou, Chrul, Anna, Chu, Michael Kwok Lam, Chu, Ming Chung, Chu, Xiaotong, Chudoba, Jiri, Chwastowski, Janusz, Ciapetti, Guido, Ciapetti, Marco, Constable, Miles, Corbaz, Florian, Ciecko, Robert Piotr, Cieri, Davide, Ciesla, Krzysztof, Cindro, Vladimir, Ciocio, Alessandra, Cirotto, Francesco, Citron, Zvi, Citterio, Mauro, Ciubotaru, Dan Andrei, Ciungu, Bianca Monica, Clark, Allan, Clark, Brian Lee, Clark, Philip, Clavijo Columbie, Jose Manuel, Clawson, Savannah, Cleland, Bill, Clemens, Jcc, Clement, Christophe, Clercx, Joshua, Clissa, Luca, Coadou, Yann, Cobal, Marina, Coccaro, Andrea, Barrue, Ricardo, Coelho Lopes de Sa, Rafael, Coelli, Simone, Cohen, Gil, Cohen, Hadar, Coimbra, Artur, Cole, Brian, Coliban, Radu Mihai, Collot, Johann, Conde Muino, Patricia, Connell, Matt, Connell, Simon, Connelly, Ian Allan, Conroy, Eimear Isobel, Conventi, Francesco, Cooke, Harry, Sarkar, Amanda, Cormier, Felix, Corpe, Louie Dartmoor, Corradi, Massimo, Corrigan, Eric Edward, Corriveau, Francois, Corsetti, Sabrina, Cortes Gonzalez, Arely, Costa Mezquita, Maria Jose, Costa de Paiva, Thiago, Costanza, Francesco, Costanzo, Davide, Cote, Benjamin, Cowan, Glen, Cowley, James William, Cranmer, Kyle Stuart, Crepe-Renaudin, Sabine, Crescioli, Francesco, Crespo-Lopez, Olivier, Cristinziani, Markus, Cristoforetti, Marco, Croft, Vincent Alexander, Crosetti, Nanni, Cueto Gomez, Ana Rosario, Cuhadar Donszelmann, Tulay, Cui, Han, Cui, Zhaoyuan, Cunningham, Liam, Curcio, Francesco, Czodrowski, Patrick Karl, Czurylo, Marta, Sousa, Mario Jose, da Fonseca Pinto, Joao Victor, da Via, Cinzia, Dabrowski, Wladyslaw, Dado, Tomas, Daguin, Jerome, Dahbi, Salah-Eddine, Dai, Tiesheng, Dallapiccola, Carlo, Dam, Mogens, d'Amen, Gabriele, d'Amico, Valerio, Damp, Johannes Frederic, Dandoy, Jeff, Daneri, Maria Florencia, Danielsson, Hans, Danielyan, Varuzhan, Danilevich, Evgueni, Danninger, Matthias, Dao, Valerio, Darbo, Nanni, Darmora, Smita, Das, Sruthy Jyothi, d'Auria, Saverio, David, Claire, David, Pierre-Yves, Davidek, Tomas, Davis, Douglas Raymond, Davis, Paul Matthew, Davis-Purcell, Benjamin Richard, Davoine, Loic, Dawson, Ian, De, Kaushik, de Asmundis, Riccardo, de Beurs, Marcus, de Biase, Nicola, de Castro, Stefano, de Cecco, Sandro, de Fazio, Benedetto, de Geronimo, Gianluigi, de Groot, Nicolo, de Jong, Paul, de Jong, Samuel Rudy, de la Torre Perez, Hector, de Maria, Antonio, de Salvo, Alessandro, de Sanctis, Umberto, de Santo, Antonella, de Vivie de Regie, Jean-Baptiste, de Olivaira, Rui, Decock, Gilles, Dedovich, Dmitri, Degens, Jordy, Degrange, Jordan, Deiana, Allison Mccarn, del Corso, Francesca, del Peso, Jose, del Rio, Fer, Delebecque, Pierre, Deliot, Frederic, Delitzsch, Chris Malena, Della Pietra, Massimo, Della Volpe, Domenico, Dell'Acqua, Andrea, Dell'Asta, Lidia, Delmastro, Marco, Delsart, Pierre Antoine, Demers Konezny, Sarah Marie, Demichev, Mikhail, Deng, Binwei, Denisov, Serguei, d'Eramo, Louis, Derendarz, Dominik Karol, Derue, Frederic, Dervan, Paul, Desch, Klaus, Deschamps, Herve, Desforge, Daniel, Dette, Karola, Deutsch, Christopher, Di Bello, Francesco Armando, Di Ciaccio, Anna, Di Ciaccio, Lucia, Di Domenico, Antonio, Di Donato, Camilla, Di Girolamo, Alessandro, Di Gregorio, Giulia, Di Luca, Andrea, Di Micco, Biagio, Di Nardo, Roberto, Di Petrillo, Karri Folan, Di Stante, Luigi, Diaconu, Cristinel, de Almeida Dias, Flavia, Vale, Tiago, Diaz Gutierrez, Marco Aurelio, Diaz Capriles, Federico Guillermo, Didenko, Mariia, Diehl, Edward, Diehl, Leena, Dietsche, Wolfgang Albert, Diez Cornell, Sergio, Diez Pardos, Carmen, Dik, Alexey, Dikic, Nikola, Dima, Katerina, Dimitriadi, Christina, Dimitrievska, Aleksandra, Ding, Wenxiang, Dingfelder, Jochen Christian, Dinkespiler, Bernard, Dinu, Ioan-Mihail, Disset, Gaël, Dittmeier, Sebastian, Dittus, Fido, Djama, Fares, Djobava, Tamar, Djuvsland, Julia Isabell, Dobos, Daniel, Doglioni, Caterina, Dolejsi, Jiri, Dolezal, Zdenek, Donadelli, Marisilvia, Dondero, Paolo, Dong, Binbin, Donini, Julien Noce, d'Onofrio, Adelina, d'Onofrio, Monica, Dopke, Jens, Dorholt, Ole, Doria, Alessandra, Doubek, Martin, Dova, Maria Teresa, Doyle, Tony, Draguet, Maxence, Drechsler, Eric, Dreyer, Etienne, Drivas-Koulouris, Ioannis, Drobac, Alec Swenson, Drozdova, Mariia, Du, Dongshuo, Du, Yanyan, Du Pree, Tristan Arnoldus, Duan, Yanyun, Dubinin, Filipp, Dubovsky, Michal, Duchovni, Ehud, Duckeck, Guenter, Ducu, Otilia Anamaria, Duda, Dominik, Dudarev, Alexey, Dudder, Andreas Christian, d'Uffizi, Daniele, d'Uffizi, Matteo, Duflot, Laurent, Duehrssen-Debling, Michael, Duim, Rowan M, Dulsen, Carsten, Dumitriu, Ana Elena, Dumont Dayot, Nicolas, Dunford, Monica, Dunford, Matthew Gordon, Dungs, Sascha, Dunne, Katherine Elaine, Duperrin, Arnaud, Yildiz, Hatice, Dueren, Michael Johannes, Durglishvili, Archil, Dury, Bryan, Dushkin, Andrey, Dwyer, Brianna, Dyckes, Ian, Dyndal, Mateusz, Dysch, Samuel Dezso, Dziedzic, Bartosz Sebastian, Dziurdzia, Piotr, Earnshaw, Zoe Olivia, Eckerova, Barbora, Eggebrecht, Stephen, Eggleston, Michael Glenn, Purcino de Souza, Edmar Egidio, Ehrke, Lukas, Eigen, Gerald, Einsweiler, Kevin Frank, Ekelof, Tord Johan Carl, Ekman, Per Alexander, El Ghazali, Yassine, El Jarrari, Hassnae, El Moussaouy, Ali, Ellajosyula, Venugopal, Ellert, Mattias, Elles, Sabine, Ellinghaus, Frank, Elliot, Alison, Ellis, Nick, Elmsheuser, Johannes, Elsing, Markus, Emeliyanov, Dmitry, Emerman, Alex, Enari, Yuji, Ene, Irina, Epari, Shalini, Erdmann, Johannes, Ereditato, Antonio, Erland, Paula Agnieszka, Errenst, Martin, Escalier, Marc, Escobar Ibanez, Carlos, Etzion, Erez, Gaspar de Andrade Evans, Guiomar, Evans, Hal, Evans, Meirin Oan, Eyring, Andreas, Ezhilov, Aleksei, Ezzarqtouni, Sanae, Fabbri, Federica, Fabbri, Laura, Facini, Gabriel, Fadeyev, Vitaliy, Fakhrutdinov, Rinat, Falchieri, Davide, Falciano, Speranza, Falda Coelho, Luis, Falke, Peter Johannes, Falke, Saskia, Falou, Aboud, Falsetti, Gregorio, Faltova, Jana, Fan, Yunyun, Fang, Yaquan, Fanourakis, Georgios, Fanti, Marcello, Faraj, Mohammed, Farazpay, Zahra, Farbin, Amir, Farilla, Ada, Farina, Edoardo Maria, Farooque, Trisha, Farrell, Jason, Farrington, Sinead, Fassi, Farida, Fassouliotis, Dimitris, Faszer, Wayne, Faucci Giannelli, Michele, Fausten, Camille, Favareto, Andrea, Fawcett, William James, Fayard, Louis, Febvre, Damien Romaric, Federicova, Pavla, Fedin, Oleg, Fedotov, Gleb, Feickert, Matthew, Feligioni, Lorenzo, Fell, Alix, Fellers, Deion Elgin, Felt, Nathan, Feng, Cunfeng, Feng, Minyu, Feng, Zhuoran, Fenton, Michael James, Fenyuk, Alexandre, Ferencz, Lars, Ferguson, Ruby Alice Molly, Fernandez Luengo, Sergio Ivan, Ferrando, James, Ferrari, Arnaud, Ferrari, Pamela, Ferrari, Roberto, Dias, Ualison, Ferrer Ribas, Esther, Ferrere, Didier, Ferretti, Claudio, Fiedler, Frank, Fielitz, William, Filippov, Yury, Filipcic, Andrej, Filmer, Emily, Filthaut, Frank, Castro Nunes Fiolhais, Miguel, Fiore, Giuseppe, Fiorini, Luca, Fischer, Alexander Peter, Leitgeb, Florian Christoph, Fisher, Wade Cameron, Fitschen, Tobias, Fleck, Ivor, Fleischmann, Philipp, Flick, Tobias, Flierl, Bernhard Matthias, Flores, Carlos, Flores, Lucas Macrorie, Flores, Marvin, Flores Castillo, Luis Roberto, Follega, Francesco Maria, Fomichev, Ivan, Fomin, Nikolai, Fontaine, Michel, Foo, Joel Hengwei, Forland, Blake Christopher, Formenti, Lia, Formica, Andrea, Forti, Alessandra, Fortin, Etienne Marie, Fortman, Anne Winifred, Foti, Maria Giovanna, Fougeron, Denis, Fountas, Leonidas, Fournier, Daniel, Fox, Harald, Fragnaud, Jasmin Corentin, Francavilla, Paolo, Francescato, Simone, Franchellucci, Stefano, Franchini, Matteo, Franchino, Silvia, Francis, David, Franco, Luca, Franconi, Laura, Frank, Norbert, Franklin, Melissa, Fras, Markus, Frattari, Guglielmo, Freddi, Angelo, Freegard, Arran Charles, Freeman, Patrick Moriishi, Spolidoro Freund, Werner, Fritzsche, Nick, Froch, Alexander, Froidevaux, Daniel, Frost, James, Fu, Yao, Fujimoto, Minori, Fullana Torregrosa, Esteban, Furukawa, Marin, Fuster Verdu, Juan, Gabrielli, Alessandro, Gabrielli, Andrea, Gadow, Philipp, Gagliardi, Guido, Gagnon, Louis-Guillaume, Gallardo, Gabriel, Gallas, Elizabeth, Galleguillos Silva, Renato Bruno, Gallop, Bruce Joseph, Galuszka, Szymon Jan, Gamboa Goni, Rodrigo, Gan, Kock Kiam, Ganguly, Sanmay, Gantel, Laurent Mathieu, Gao, Jun, Gao, Wei, Gao, Yanyan, Garay Walls, Francisca, Garcia, Berenice, Garcia, Carmen, Garcia Navarro, Jose Enrique, Garcia-Sciveres, Maurice, Gardner, Robert William, Gareau, Matthew, Garelli, Nicoletta, Garg, Diksha, Garg, Rocky Bala, Gariano, Giuseppe, Garner, Christopher Andrew, Garonne, Vincent, Gasiorowski, Sean Joseph, Do Nascimento Gaspar, Philipp, Gaudiello, Andrea, Gaudio, Gabriella, Gautam, Viveka, Gauzzi, Paolo, Gavrilenko, Igor, Gavriliuk, Alexander, Gay, Colin Warren, Gaycken, Goetz, Gazis, Evangelos, Geanta, Andrei Alexandru, Gebyehu, Mesfin, Gee, Carolyn, Gee, Norman, Geisen, Jannik, Gemme, Claudia, Genest, Marie-Helene, Gentile, Simonetta, George, Matthias Alexander, George, Simon, George, William Frederick, Georgiev, Vjaceslav, Geralis, Theodoros, Gerlach, Lino Oscar, Gessinger-Befurt, Paul, Ghasemi Bostanabad, Meisam, Ghneimat, Mazuza, Ghorbanian, Keanu, Ghosal, Arpan, Ghosh, Aishik, Ghosh, Anindya, Giacobbe, Benedetto, Giagu, Stefano, Giannetti, Paola, Giannini, Antonio, Gibson, Stephen, Giganon, Giganon, Gigliotti, Kade, Gignac, Matthew, Gil, Damian Tomasz, Gilbert, Alexander Kevin, Gilbert, Benjamin Jacob, Gillberg, Dag, Gilles, Geoffrey, Gillwald, Nils, Ginabat, Louis, Gingrich, Doug, Giokaris, Athanasios, Giordani, Mapo, Giraud, Julien, Giraud, Pierre-Francois, Giromini, Paolo, Giugliarelli, Gilberto, Giugni, Danilo, Giuli, Francesco, Gjersdal, Havard Ervik, Gkialas, Ioannis, Gkougkousis, Vagelis, Gkountoumis, Panagiotis, Gladilin, Leonid, Glasman, Claudia, Gledhill, Galen Rhodes, Glisic, Marija, Glitza, Karl-Walter, Glonti, George, Gnesi, Ivan, Go, Yeonju, Goblin, Cyril, Goblirsch-Kolb, Maximilian Emanuel, Gocke, Benedikt, Godin, Dominique, Godiot, Stephanie, Gokturk, Berare, Goldfarb, Steven, Golling, Tobias, Gololo, Mpho Gift Doctor, Golubkov, Dmitry, Golyzniak, Dominik, Gombas, Jason Peter, da Silva Gomes, Agostinho, Gomes, Gabriel, Gomez Delegido, Antonio Jesus, Goncalves Gama, Rafael, Morais Silva Goncalo, Ricardo Jose, Gonella, Giulia, Gonella, Laura, Gong, Datao, Gongadze, Alexi, Gonnella, Francesco, Gonski, Julia Lynne, Gonzalez, Richards, Gonzalez de la Hoz, Santiago, Gonzalez Fernandez, Sergio, Gonzalez Lopez, Ricardo, Gonzalez Renteria, Cesar, Gonzalez Suarez, Rebeca, Gonzalez Sevilla, Sergio, Gonzalvo Rodriguez, Galo Rafael, Goossens, Luc, Gorasia, Nandish Arjan, Gorbounov, Petr, Gorini, Benedetto, Gorini, Edoardo, Gorisek, Andrej, Goshaw, Al, Goessling, Claus, Gostkin, Mikhail, Goswami, Soumyananda, Gottardo, Carlo Alberto, Gotz, Stefanie Andrea, Gouighri, Mohamed, Goumarre, Vincent, Goussiou, Anna, Govender, Nicolin, Goy, Corinne, Grabas, Aude Marie, Grabowska-Bold, Iwona, Graham, Kevin Robert, Gramstad, Eirik, Grancagnolo, Sergio, Grandi, Mario, Grachev, Vadim, Gravelle, Philippe, Gravila, Paul, Gravili, Francesco Giuseppe, Gray, Heather, Grayzman, Israel, Greco, Matteo, Grefe, Christian, Gregor, Ingrid, Grenier, Philippe, Grieco, Chiara, Grillo, Alex, Grimm, Kathryn, Grinstein, Sebastian, Grivaz, Jean-Francois, Grohs, Johannes Philipp, Gross, Eilam, Grosse-Knetter, Joern, Grud, Christopher Ryan, Grummer, Aidan, Grundy, James Cameron, Guan, Liang, Guan, Wen, Gubbels, Chris, Guelfo Gigli, Samuel, Guerrero Rojas, Jesus, Guerrieri, Giovanni, Guescini, Francesco, Guettouche, Nour El Houda, Gugel, Ralf, Guhit, Jem Aizen Mendiola, Guida, Alessandro, Guido, Elisa, Guillard, Jean-Christophe, Guillemin, Thibault, Guilloton, Eva, Guindon, Stefan, Guo, Di, Guo, Fangyi, Guo, Jun, Guo, Linghua, Guo, Yuxiang, Gupta, Ruchi, Gurbuz, Saime, Gurdasani, Simran Sunil, Gustavino, Giuliano, Guth, Manuel, Gutierrez, Phillip, Gutierrez Zagazeta, Luis Felipe, Gutschow, Christian, Guyot, Claude, Gwenlan, Claire, Gwilliam, Carl, Haaland, Even Simonsen, Haas, Andrew, Haas, Stefan Ludwig, Habedank, Martin, Haber, Carl, Habring, Jorg, Hadavand, Haleh, Hadef, Asma, Hadzic, Sejla, Haines, Emil, Haleem, Mahsana, Haley, Joseph, Hall, Jack Joseph, Hallewell, Gregory, Halser, Lea, Hamano, Kenji, Hamdaoui, Hassane, Hamer, Matthias, Hamity, Guillermo Nicolas, Han, Jingyi, Han, Kunlin, Han, Liangliang, Han, Liang, Han, Shuo, Han, Yi Fei, Hanagaki, Kazunori, Hance, Mike, Hangal, Dhanush Anil, Hanif, Hamza, Hank, Michael Donald, Hankache, Robert, Hansen, Jorgen Beck, Hansen, Dines, Hansen, Peter, Hara, Kazuhiko, Harada, Daigo, Harenberg, Torsten, Harkusha, Siarhei, Harris, Ynyr, Harrison, Natalie, Harrison, Paul Fraser, Hartman, Nicole Michelle, Hartmann, Nikolai, Hartung, Peter, Hasegawa, Yoji, Hashemi, Kevan, Hasib, Ahmed, Hasley, Lloyd Alan, Haubold, Thomas, Haug, Sigve, Hauser, Reiner, Havranek, Miroslav, Hawkes, Chris, Hawkings, Richard, Hayashi, Yuichiro, Hayashida, Shota, Hayden, Daniel, Hayes, Christopher Robyn, Hayes, Robin, Hays, Chris, Hays, Jonathan, Hayward, Helen, He, Fudong, He, Yunjian, He, Yajun, Heath, Matthew Peter, Hedberg, Vincent, Heggelund, Andreas Lokken, Hehir, Natasha, Heidegger, Constantin, Heidegger, Kim Katrin, Heidorn, William Dale, Heilman, Jesse, Heim, Sarah, Heim, Timon, Heinlein, James Geddy, Heinrich, Jochen Jens, Heinrich, Lukas Alexander, Hejbal, Jiri, Helary, Louis, Held, Alexander, Hellesund, Simen, Helling, Cole Michael, Hellman, Sten, Helsens, Clement, Hemperek, Tomasz, Henderson, Robert, Henkelmann, Lars, Henriques Correia, Ana Maria, Hentges, Rainer, Herde, Hannah Elizabeth, Hernandez Jimenez, Yesenia, Herrmann, Lena Maria, Herrmann, Maximilian Georg, Herrmann, Tim, Herten, Gregor, Hertenberger, Ralf, Hervas, Luis, Hessey, Nigel, Hibi, Hiroaki, Higon-Rodriguez, Emilio, Hillier, Stephen, Hils, Maximilian, Hinchliffe, Ian, Hinterkeuser, Florian, Hirose, Minoru, Hirose, Shigeki, Hirschbuehl, Dominic, Hitchings, Thomas Glyn, Hiti, Bojan, Hobbs, John David, Hobincu, Radu, Tal Hod, Noam, Hodgkinson, Mark, Hodkinson, Benjamin Haslum, Hoecker, Andreas, Hoeferkamp, Martin, Hofer, Judith, Hoffmann, August Edward, Hoffmann, Dirk, Hohn, David, Hohov, Dmytro, Holm, Tanja, Holzbock, Michael, Hommels, Bart, Honan, Benjamin Paul, Hong, Jiangliu, Hong, Tae Min, Honig, Jan Cedric, Honle, Andreas, Hooberman, Benjamin Henry, Hopkins, Walter, Horii, Yasuyuki, Horn, Philipp, Hou, Suen, Howard, Alissa Shirley-Ann, Howarth, James William, Hoya, Joaquin, Hrabovsky, Miroslav, Hrynevich, Aliaksei, Hrynova, Tetiana, Hsu, Pai-Hsien, Hsu, Shih-Chieh, Hu, Kun, Hu, Qipeng, Hu, Xueye, Hu, Yifan, Huang, Danping, Huang, Fred, Huang, Shuhui, Huang, Xiaozhong, Huang, Yicong, Huang, Yanping, Huang, Zuchen, Hubacek, Zdenek, Hubner, Michael, Huegging, Fabian, Huffman, Todd Brian, Huhtinen, Mika, Huiberts, Simon Kristian, Hulek, Wojciech Krzysztof, Hulsken, Raphael Alain, Huseynov, Nazim, Huston, Joey, Huth, John, Hyneman, Rachel Jordan, Hyrych, Sofiia, Iacobucci, Giuseppe, Iakovidis, George, Iakovidis, Kostas, Iankovskaia, Vera, Yankovsky, Boris, Ibragimov, Iskander, Iconomidou-Fayard, Lydia, Iengo, Paolo, Iguchi, Ryunosuke, Iizawa, Tomoya, Ikegami, Yoichi, Ikeno, Masahiro, Ilg, Armin, Ilic, Nikolina, Ilyashenko, Igor, Imam, Hajar, Ingebretsen Carlson, Tom, Innocente, Angelo, Introzzi, Gianluca, Iodice, Mauro, Ippolito, Valerio, Ishino, Masaya, Islam, Wasikul, Issever, Cigdem, Issinski, Sergey, Istin, Serhat, Ito, Hiroki, Iturbe, Julia, Iuppa, Roberto, Iurikovskii, Oleg, Ivanovici, Mihail, Ivina, Anna, Izen, Joseph Michael, Izzo, Vincenzo, Jacka, Petr, Jackson, Paul, Jacobs, Ruth Magdalena, Jaeger, Benjamin Paul, Jagfeld, Christoph, Jain, Prasham, Jakel, Gunnar, Jakobs, Karl, Jakoubek, Tomas, Jamieson, Jonathan, Janas, Krzysztof, Janssen, Jens, Jarlskog, Goran, Jaspan, Adam Elliott, Javello, Sandrine, Javurkova, Martina, Jeanneau, Fabien, Jeanty, Laura, Jejelava, Juansher, Jenni, Peter, Mertens, Jennifer, Jessiman, Callan, Jezequel, Stephane, Jia, Chen, Jia, Jiangyong, Jia, Xiaohe, Jia, Xuewei, Jia, Zihang, Jiang, Yi, Jiggins, Stephen, Jimenez Pena, Javier, Jin, Ge, Jin, Shan, Jinaru, Adam, Jinnouchi, Osamu, Johansson, Per Daniel Conny, Johns, Kenneth, Johnson, Jacob Wayne, Jones, Dominic, Jones, Eleanor, Jones, Paul, Jones, Roger, Jones, Tim, Joos, Markus, Joseph, John Matthew, Joshi, Roshan, Jourde, Didier, Jovicevic, Jelena, Ju, Xiangyang, Junggeburth, Johannes, Junkermann, Thomas, Juste Rozas, Aurelio, Kabana, Sonia, Kaczmarska, Anna, Kado, Marumi, Kagan, Harris, Kagan, Michael, Kahn, Alan Mathew, Kahn, Abraham, Kahra, Christian, Kaji, Toshiaki, Kajomovitz Must, Enrique, Kakati, Nilotpal, Kalderon, Will, Kallitsopoulou, Alexandra, Kamenshchikov, Andrey, Kanayama, Shuhei, Kanellos, Nikolaos, Kang, Nathan Jihoon, Kapusciak, Kacper Kamil, Kar, Deepak, Karava, Kla, Kareem, Mohammad, Karentzos, Efstathios, Karkanias, Ioannis, Karpov, Sergey, Karpova, Zoya, Kartvelishvili, Vato, Karyukhin, Andrey, Kasimi, Eirini, Kato, Chikuma, Katunin, Serge, Katzy, Judith, Kaur, Sandeep, Kawade, Kentaro, Kawagoe, Kiyotomo, Kawamoto, Tatsuo, Kawamura, Gen, Kay, Ellis, Kaya, Colette, Kazakos, Stergios, Kazanin, Vassili, Kazarov, Andrei, Ke, Yan, Keaveney, James Michael, Keberri, Mariam, Keeler, Richard, Kehris, Gustavs, Keller, John Stakely, Kelly, Aidan Sean, Kelsey, Daniel Christopher, Kempster, Jacob Julian, Kennedy, Kiley Elizabeth, Kennedy, Philip David, Kepka, Oldrich, Kerridge, Benjamin Philip, Kersten, Susanne, Kersevan, Borut Paul, Keshri, Sumit, Keszeghova, Lucia, Ketabchi, Sana, Khandoga, Mykola, Khanov, Alexander, Kharlamov, Alexey, Kharlamova, Tatyana, Khoda, Elham E, Khoo, Teng Jian, Khoriauli, Gia, Khubua, Djemal, Khwaira, Yahya A.R, Kiehn, Moritz, Kilgallon, Aaron Joseph, Kim, Dongwon, Kim, Eunchong, Kim, Young-Kee, Kimura, Naoki, Kind, Peter, Kinget, Peter, Kirchhoff, Andreas, Kirchmeier, David, Kirfel, Christian, Kirk, Julie, Kiryunin, Andrei, Kishimoto, Tomoe, Kiskiras, Ioannis, Kisliuk, Dylan Perry, Kitsaki, Chara, Kivernyk, Oleh, Klapdor-Kleingrothaus, Thorwald, Klassen, Martin, Klein, Christoph Thomas, Klein, Lucas, Klein, Matthew Henry, Klein, Max, Klein, Samuel Byrne, Klein, Uta, Klemm, Peter Rudolf, Klimek, Pawel Jan, Klimentov, Alexei, Klimpel, Fabian, Klioutchnikova, Tatiana, Klitzner, Felix Fidelio, Kluit, Peter, Kluth, Stefan, Kneringer, Emmerich, Knight, Timothy Michael, Knoops, Edith, Knue, Andrea Helen, Kobayashi, Dai, Kobayashi, Ren, Kocian, Martin, Kodys, Peter, Kock, Daniela, Konig, Philipp, Koffas, Thomas, Kolb, Mathis, Kolbasin, Andrey, Koletsou, Iro, Kolitsi, Foteini, Komarek, Tomas, Kompogiannis, Spyridon, Koeneke, Karsten, Kong, Albert, Kongsore, Marius, Kono, Takanori, Konstantinidis, Nikolaos, Konya, Balazs, Kopeliansky, Revital, Koperny, Stefan Zenon, Korcyl, Krzysztof Marian, Kordas, Kostas, Koren, Guy, Korn, Andreas, Korn, Steffen, Korolkov, Ilya, Korotkova, Natalia, Kortman, Bryan, Kortner, Oliver, Kortner, Sandra, Kostecka, Will, Kostyukhin, Vadim, Kotsokechagia, Anastasia, Kotwal, Ashutosh, Koulouris, Aimilianos, Kourkoumeli-Charalampidi, Athina, Kourkoumelis, Christine, Kourlitis, Vangelis, Koutelieris, Georgios, Koutsosimos, Theodoros, Kouyoumdjian Argueso, David Felipe, Kovalenko, Serguei, Kovanda, Ondrej, Koveshnikov, Ilya, Kowalewski, Bob, Kozanecki, Witold, Kozhin, Anatoli, Kramarenko, Viktor, Kramberger, Gregor, Kramer, Peter, Krasny, Mieczyslaw Witold, Krasznahorkay, Attila, Kremer, Jakub, Kresse, Tom, Kretzschmar, Jan, Kreul, Ken Matthias, Krieger, Nina, Krieger, Peter, Krishnamurthy, Samyukta, Krivos, Martin, Krizka, Karol, Kroeninger, Kevin Alexander, Kroha, Hubert, Kroll, Jiri, Kroll, Ira Joseph, Krowpman, Kyle Stuart, Kruchonak, Uladzimir, Krueger, Hans, Krumnack, Nils Erik, Kruse, Mark, Krzysiak, Janina Anna, Kubik, Zdenek, Kuchinskaia, Olesia, Kuday, Sinan, Kuchler, Daniela, Kuechler, Jan, Kuehn, Susanne, Kusters, Roman, Kuger, Fabian, Kuhl, Thorsten, Kukhtin, Victor, Koultchitski, Yuri, Kuleshov, Serguei, Kulinska, Anna Malgorzata, Kulka, Peter, Kumar, Mukesh, Kumari, Neelam, Kunkler, Brandon Michael, Kupco, Alexander, Kupfer, Tobias, Kupich, Andrey, Kuppambatti, Jayanth, Kuprash, Oleg, Kurashige, Hisaya, Kurchaninov, Leonid, Kurochkin, Yurii, Kurova, Anastasia, Kuze, Masahiro, Kvam, Audrey Katherine, Kvita, Jiri, Kwan, Tony, Kwok, King Wai, Kyriacou, Nicholas, Kyriakis-Bitzaros, Efstathios, Laatu, Lauri Antti Olavi, Lacasta Llacer, Carlos, Lacava, Francesco, Lacker, Heiko Markus, Lacour, Didier, Lad, Nisha, Ladygin, Evgueni, Laforge, Bertrand, Lafrasse, Sylvain, Lagouri, Theodota, Lai, Stan, Lakomiec, Inga Katarzyna, Lalloue, Nathan, Lam, Hon Piu, Lambert, Joseph Earl, Lammers, Sabine Wedam, Lampardaki, Eleni, Lampl, Walter, Lampoudis, Christos, Lancaster, Alec, Lancon, Eric Christian, Landgraf, Ulrich, Landon, Murrough, Landraud, Cedric, Lang, Valerie, Langenberg, Robert Johannes, Langstaff, Roy, Lankford, Andrew James, Lanni, Francesco, Lantzsch, Kerstin, Lanza, Agostino, Lapertosa, Alessandro, Laporte, Didier, Laporte, Jean-Francois, Lari, Tommaso, Lasagni Manghi, Federico, Lassnig, Mario, Latonova, Vera, Latorre, Stefano, Lau, Tak Shun, Laudrain, Antoine, Laugier, Daniele, Laurier, Alexandre, Lawlor, Sean Dean, Lawrence, Zak, Lax, Ignazio, Lazzaroni, Massimo, Le, Brian, Le Goff, Fabrice, Le, Xiao, Le Bourlout, Pascal, Leban, Blaz, Lebedev, Alexandre, Leblanc, Matt, Leboeuf, Didier, Leboube, Christian Georges, Le Compte, Tom, Ledroit, Fabienne, Lee, Ava Chloe Audrey, Lee, Claire, Lee, Graham Richard, Lee, Lawrence, Lee, Shih-Chang, Lee, Suhyun, Lee, Ting Fung, Leeuw, Lerothodi Leonard, Lefebvre, Benoit, Lefebvre, Helena, Lefebvre, Michel, Lefevre, Marc, Leggett, Charles, Lehmann, Konstantin, Lehmann Miotto, Giovanna, Leigh, Matthew, Leight, William Axel, Leis, Ulrich, Leisos, Antonios, Lisboa Leite, Marco, Leitgeb, Clara Elisabeth, Leitner, Rupert, Lenckowski, Mark, Leney, Katharine, Lenz, Tatjana, Leone, Sandra, Leonidopoulos, Christos, Leopold, Alexander, Lepota, Thabo James, Leroy, Claude, Les, Robert, Lester, Christopher, Leung, Hin Kwong, Levchenko, Mikhail, Leveque, Jessica, Levin, Dan, Levinson, Lorne, Lewicki, Maciej Piotr, Lewis, Daniel, Li, Ang, Li, Bing, Li, Chihao, Li, Changyu, Li, Changqiao, Li, Feng, Li, Heng, Li, Han, Li, Huanguo, Li, Haifeng, Li, Jing, Li, Ke, Li, Liang, Li, Mengran, Li, Quanyin, Li, Shuqi, Li, Shu, Li, Tong, Li, Xingguo, Li, Xiaoting, Li, Zhi, Li, Zhiying, Li, Zhelun, Li, Zhiyuan, Li, Zhan, Liang, Futian, Liang, Zhijun, Liaqat, Shawaiz, Liberatore, Marianna, Liberti, Barbara, Lie, Ki, Lieber Marin, Juan, Lien, Hsuan-Chu, Lim, Chapman, Lin, Kuan-Yu, Lin, Tai-Hua, Linck, Rebecca, Lindley, Rachel Elizabeth, Lindon, Jack, Linss, Arthur, Lipeles, Elliot, Lipniacka, Anna, Lippert, Markus, Lister, Alison, Little, Jared, Liu, Bo, Liu, Bingxuan, Liu, Chonghan, Liu, Danning, Liu, Hongbin, Liu, Jianbei, Liu, Jesse, Liu, Kun, Liu, Minghui, Liu, Mingyi, Liu, Peilian, Liu, Qibin, Liu, Shengquan, Liu, Tiankuan, Liu, Xiaotian, Liu, Yang, Liu, Yanlin, Liu, Yanwen, Liubimtcev, Dmitrii, Livan, Michele, Liz Vargas, Matias Patricio, Llorente Merino, Javier, Lloyd, Steve, Lobodzinska, Ewelina Maria, Loch, Peter, Loffredo, Salvatore, Lohse, Thomas, Lohwasser, Kristin, Loiseau, Carl Arthur, Lokajicek, Milos, Long, Jonathan, Longarini, Iacopo, Longo, Luigi, Longo, Riccardo, Lopez Mateos, David, Lopez Paz, Ivan, Lopez Solis, Alvaro, Lorenz, Jeanette Miriam, Lorenzo Martinez, Narei, Lory, Alexander, Losel, Philipp Jonathan, Lou, Xuanhong, Lou, Xinchou, Lounis, Abdenour, Love, Jeremy, Love, Peter, Lozano Bahilo, Julio, Lu, Gangcheng, Lu, Miaoran, Lu, Sicong, Lu, Yun-Ju, Lubatti, Henry, Luci, Claudio, Lucio Alves, Fabio Lucio, Lucotte, Arnaud, Luehring, Fred, Luise, Ilaria, Lukianchuk, Oleksii, Luminari, Lamberto, Lunadei, Riccardo, Lundberg, Olof, Lund-Jensen, Bengt, Luongo, Nicholas, Lupu, Nachman, Lutz, Margaret Susan, Luz, Ricardo, Lynn, David, Lyons, Harry John, Lysak, Roman, Lytken, Else, Lyu, Feng, Lyubushkin, Vladimir, Lyubushkina, Tatiana, Lyukova, Mars, Ma, Hong, Ma, Lianliang, Ma, Yanhui, Macdonell, Danika Marina, Maccarrone, Giovanni, Macdonald, Jack, Madar, Romain, Mader, Wolfgang, Madhoun, Khaled, Maeda, Junpei, Maeno, Tadashi, Maerker, Max, Maguire, Helen Ruth, Mahon, Devin, Maier, Ronald, Maio, Amelia, Maj, Klaudia, Majersky, Oliver, Majewski, Stephanie, Makovec, Nikola, Maksimovic, Veljko, Malaescu, Bogdan, Malaquin, Joffrey Alex, Malecki, Pawel, Maleev, Victor, Malek, Fairouz, Malito, Davide, Mallik, Usha, Malone, Claire, Maltezos, Stavros, Maly, Pavel, Malyukov, Sergey, Mamuzic, Judita, Manca, Fabio Oscar, Mancini, Giada, Manco, Giulia, Mandalia, Jesal, Mandic, Igor, Mandjavidze, Irakli, Manhaes de Andrade Filho, Luciano, Maniatis, Ioannis Michail, Manisha, Manisha, Manjarres, Joany, Mankad, Dvij, Mann, Alexander, Manousos, Thanos, Manson, Sebastian, Mansoulie, Bruno, Manthos, Ioannis, Manzoni, Stefano, Maragkou Didi, Evaggelia, Marantis, Alexandros, Marchiori, Giovanni, Marcisovsky, Michal, Marcon, Caterina, Marinescu, Mihaela, Marjanovic, Marija, Marshall, Emma, Marshall, Zach, Marti I Garcia, Salvador, Martin, Tim, Martin, Victoria, Martin Dit Latour, Bertrand, Martinelli, Luca, Martinez-Perez, Mario, Martinez Agullo, Pablo, Martinez Outschoorn, Verena Ingrid, Martinez Suarez, Paula, Martin-Haugh, Stewart, Martoiu, Sorin, Martyniuk, Alex Christopher, Marzin, Antoine, Mas, Philippe, Maschek, Stefan Raimund, Mascione, Daniela, Masetti, Lucia, Mashimo, Tetsuro, Masik, Jiri, Maslennikov, Alexei, Massa, Lorenzo, Massarotti, Paolo, Massol, Nicolas, Mastrandrea, Paolo, Mastroberardino, Anna, Masubuchi, Tatsuya, Matakias, Dimitrios, Mathisen, Thomas, Matsuzawa, Nobuo, Mattig, Peter, Maurer, Julien, Macek, Bostjan, Maximov, Dmitriy, Mazini, Rachid, Maznas, Ioannis, Mazza, Maria, Mazza, Simone Michele, Mc Ginn, Christopher, Mc Gowan, John Patrick, Mc Kee, Shawn, Mccracken, Callum, Mcdonald, Millie, Mcdougall, Ashley Ellen, Mcfayden, Josh, Mc Govern, Bobby, Mchedlidze, Gvantsa, Mckenzie, Ryan Peter, Mclachlan, Thomas Christopher, Mclaughlin, Donal Joseph, Mclean, Kayla, Mcmahon, Steve, Mcnamara, Peter Charles, Mcpartland, Conor Michael, Mcpherson, Rob, Megy, Theo, Mehalev, Ifat, Mehlhase, Sascha, Mehta, Andrew, Meirose, Bernhard, Melini, Davide, Mellado Garcia, Bruce, Melo, Andres Hugo, Meloni, Federico, Gouveia, Emanuel, Mendes Jacques da Costa, Antonio Manuel, Meneghini, Stefano, Meng, Huan Yu, Meng, Lingxin, Menke, Sven, Menouni, Mohsine, Mentink, Matthias, Meoni, Evelin, Merlassino, Claudia, Merola, Leonardo, Meroni, Chiara, Merz, Garrett William, Meshkov, Oleg, Mesolongitis, Ioannis, Metcalfe, Jessica, Mete, Alaettin Serhan, Meyer, Chris, Meyer, Jean-Pierre, Miao, Peng, Miccoli, Alessandro, Michal, Sebastien, Michetti, Michele, Middleton, Robin, Miglioranzi, Silvia, Migne, Julien, Mijovic, Liza, Mikenberg, George, Mikestikova, Marcela, Mikuz, Marko, Mildner, Hannes, Milic, Adriana, Milke, Chris, Miller, David, Miller, Laura Stephanie, Milov, Alexander, Milovanovic, Marko, Milstead, David Anthony, Min, Tianjue, Minaenko, Andrei, Minami, Yuto, Minano Moya, Mercedes, Minashvili, Irakli, Mince, Laurynas, Mincer, Allen Irving, Mindur, Bartosz, Mineev, Mikhail, Mino, Yuya, Mir Martinez, Lluisa Maria, Miralles Lopez, Marcos, Mironova, Maria, Missio, Marion, Mitani, Takashi, Mitra, Ankush, Mitsou, Vasiliki, Miu, Ovidiu, Miyagawa, Paul, Miyazaki, Yuta, Mizukami, Atsushi, Mjoernmark, Jan-Ulf, Mkrtchyan, Tigran, Mladenovic, Goran, Mlinarevic, Toni, Mlynarikova, Michaela, Moa, Torbjorn, Mobius, Silke, Mochizuki, Kazuya, Moder, Paul, Mogg, Philipp, Fadol Mohammed, Abdualazem, Mohapatra, Soumya, Mokgatitswane, Gaogalalwe, Mokrenko, Sergei, Moleri, Luca, Molina Gonzalez, Emmanuel Josue, Monay E Silva, Alessa, Mondal, Buddhadeb, Mondal, Santu, Monig, Klaus, Monnier, Emmanuel, Monsonis Romero, Luis, Montejo Berlingen, Javier, Montella, Marco, Monti, Mauro, Monticelli, Fernando, Moraga Jimenez, Juan Pablo Andres, Morange, Nicolas, Carvalho, Luisa, Moreno Llacer, Maria, Moreno Martinez, Carlos, Morettini, Paolo, Morgenstern, Stefanie, Morii, Masahiro, Morinaga, Masahiro, Morley, Anthony, Morodei, Federico, Morvaj, Ljiljana, Moschovakos, Paris, Moser, Brian, Mosidze, Maia, Moskalets, Tetiana, Moskvitina, Polina, Moss, Joshua, Moszkowicz, Piotr, Moyse, Edward, Mtintsilana, Onesimo, Muanza, Steve, Mueller, James Alfred, Muller, Ralph, Muenstermann, Daniel, Mueller, Roman, Mullier, Geoffrey, Mullin, Joseph, Mulski, Alexis Elizabeth, Mungo, Davide Pietro, Munoz Martinez, Jose Luis, Munoz Perez, David, Munoz Sanchez, Francisca, Mur, Michel Jean Yves, Murin, Martin, Murray, Bill, Murrone, Alessia, Muse, Joseph M, Muskinja, Miha, Mwewa, Chilufya, Myagkov, Alexei, Myers, Andrew Joel, Myers, Ava Anne, Myers, Greg, Myska, Miroslav, Nachman, Ben, Nackenhorst, Olaf, Naeem, Muhammad, Nag, Abhishek, Nagai, Koichi, Nagano, Kunihiro, Nagle, James Lawrence, Nagy, Elemer, Nairz, Armin, Nakahama Higuchi, Yu, Nakamura, Koji, Nanjo, Hajime, Narayan, Rohin Thampilali, Narayanan, Easwar Anand, Narevicius, Julia, Narvaez Paredes, Lautaro Leon, Naryshkin, Iurii, Naseri, Mohsen, Nass, Christian, Natsios, Marios Dimitrios, Navarro, Gabriela Alejandra, Navarro Gonzalez, Josep, Nayak, Ranjit, Nayaz, Ab, Nechaeva, Polina, Nechansky, Filip, Nedic, Luka, Neep, Tom, Negri, Andrea, Negrini, Matteo, Nellist, Clara, Nelson, Christina, Nelson, Kevin Michael, Nemecek, Stanislav, Nessi, Marzio, Neubauer, Mark, Neuhaus, Friedemann, Neundorf, Jonas, Newhouse, Robin, Newman, Paul Richard, Ng, Chi Wing, Ng, Sam Yanwing, Ng, Ying Wun Yvonne, Ngair, Badr-Eddine, Nguyen, Hoang Dai Nghia, Nickerson, Richard, Nikolaidou, Rosy, Nielsen, Jason, Niemeyer, Marcel, Nikiforou, Nikiforos, Nikolaenko, Vladimir, Nikolic, Irena, Nikolopoulos, Konstantinos, Nila, Michael Lee, Nilsson, Paul, Ninca, Ilona-Stefana, Nindhito, Herjuno Rah, Nisati, Aleandro, Nishu, Nishu, Nisius, Richard, Nitschke, Jan-Eric, Nkadimeng, Edward Khomotso, Noacco Rosende, Santiago, Nobe, Takuya, Noel, Daniel Louis, Noel, Jerome, Noguchi, Yohei, Nommensen, Thomas, Nomura, Marcelo Ayumu, Norfolk, Mitchell Bradley, Bin Norisam, Raif Rafideen, Norman, Bryce John, Novak, Jakob, Novak, Tadej, Novgorodova, Olga, Novotny, Lukas, Novotny, Radek, Nozka, Libor, Ntekas, Kostas, Nunes de Moura, Natanael, Nurse, Emily Laura, Oakham, Gerald, Ocariz, Jose Humberto, Ochi, Atsuhiko, Ochoa, Ines, Ockenfels, Walter, Oehm, Rolf, Ordek, Serhat, Offermann, Jan Tuzlic, Ogrodnik, Agnieszka Ewa, Oh, Alexander, Ohm, Christian, Oide, Hideyuki, Oikonomou, Konstantinos, Oishi, Reiyo, Ojeda, Martina Laura, Okazaki, Yuta, O'Keefe, Michael William, Okumura, Yasuyuki, Olariu, Albert, Seabra, Luis, Olivares, Sebastian, Oliveira Damazio, Denis, Oliveira Goncalves, Dayane, Oliver, Jason, Olsson, Mats Joakim Robert, Olszewski, Andrzej, Olszowska, Jolanta, Oncel, Ogul, O'Neil, Dugan, O'Neill, Aaron Paul, Onofre, Antonio, Onyisi, Peter, Openshaw, Robert, Oreglia, Mark, Orellana, Gonzalo Enrique, Orestano, Domizia, Orlando, Nicola, Orr, Robert, O'Shea, Val, Ospanov, Rustem, Ostrega, Maciej Stanislaw, Otero y Garzon, Gustavo, Otono, Hidetoshi, Ott, Philipp Sebastian, Ottino, Gregory James, Ouchrif, Mohamed, Ouellette, Jeff, Ould-Saada, Farid, Owen, Mark Andrew, Owen, Rhys, Oyulmaz, Kaan Yuksel, Ozbey, Aydin, Ozcan, Erkcan, Ozturk, Nurcan, Ozturk, Sertac, Pacalt, Josef, Pacey, Holly, Pachal, Katherine, Pacheco Pages, Andreu, Padilla Aranda, Cristobal, Padovano, Giovanni, Pagan Griso, Simone, Palacino, Gabriel, Palazzo, Alessandra, Palestini, Sandro, Palka, Marek, Pan, Jingjing, Pan, Tong, Pancake, Charles, Panchal, Dev, Pandini, Carlo Enrico, Panduro Vazquez, William, Pang, Hao, Pangaud, Patrick, Pani, Priscilla, Panico, Lorenzo, Panizzo, Giancarlo, Paolozzi, Lorenzo, Papadatos, Constantine, Parajuli, Santosh, Paramonov, Alexander, Paraskevopoulos, Christos, Paredes Hernandez, Daniela Katherinne, Park, Tae Hyoun, Parker, Andy, Parodi, Fabrizio, Parrish, Elliot, Parrish, Victoria Alexis, Parsons, John, Paruzza, Gianfranco, Parzefall, Ulrich, Paschalias, Panagiotis, Pascual Dias, Bruna, Pascual Dominguez, Luis, Pascuzzi, Vincent, Pasmantirer, Binyamin, Pasquali, Federica, Pasqualucci, Enrico, Passaggio, Stefano, Pastore, Francesca, Pastori, Enrico, Pasuwan, Patrawan, Patel, Pragati, Pater, Jo, Pauly, Thilo, Pazos, Camila, Pearkes, Jannicke Andree, Pedersen, Maiken, Costa Batalha Pedro, Rute, Peleganchuk, Sergey, Pelosi, Alessandro, Penc, Ondrej, Pender, Emily Alexandra, Peng, Chen, Peng, Haiping, Penski, Katrin Elisabeth, Penzin, Maksim, Pepe, Marco, Sotto-Maior Peralva, Bernardo, Pereira, Mackenzie, Peixoto, Ana, Pereira Sanchez, Laura, Perepelitsa, Dennis, Perez Codina, Estel, Perez Gomez, Francisco, Perganti, Maria, Perini, Laura, Pernegger, Heinz, Perrella, Sabrina, Perrevoort, Ann-Kathrin, Perrin, Oceane, Perrot, Guy, Peters, Krisztian, Peters, Reinhild, Petersen, Brian, Petersen, Troels, Petit, Elisabeth, Petousis, Vlasios, Petridou, Chariclia, Petruccetti, Marco, Petrucci, Fabrizio, Petrukhin, Alexey, Pettee, Mariel, Pettersson, Nora Emilia, Petukhov, Aleksandr, Mihule, Kristina, Peyaud, Alan, Pezoa Rivera, Raquel, Pezzotti, Lorenzo, Pezzullo, Gianantonio, Pfeifer, Bernhard, Pham, Minh Tuan, Pham, Joni, Phillips, Peter, Phipps, Michael William, Piacquadio, Giacinto, Pianori, Elisabetta, Piazza, Federica, Piegaia, Ricardo, Pietreanu, Dorel, Pileggi, Giuseppe, Pilkington, Andrew, Pinamonti, Michele, Pinfold, James, Pereira, Beatriz, Pinnell, Jamie Thomas, Pinto Bustos, Roberto Andres, Pirea, Radu Nicolae, Piret, Yves, Pirola, Michele, Piscitelli, Francesco, Pitman Donaldson, Charlie Bruno, Pizzi, Dylan, Pizzimento, Luca, Pizzini, Alessio, Pleier, Marc-Andre, Plesanovs, Vladislavs, Pleskot, Vojtech, Plotnikova, Elena, Pluzhnikov, Andrey, Poddar, Gitanjali, Podkladkin, Serguei, Pottgen, Ruth, Poffenberger, Paul, Poggioli, Luc, Pogrebnyak, Ivan, Pohl, David-Leon, Pokharel, Ishan, Polacek, Stanislav, Polesello, Giacomo, Poley, Anne-Luise, Polifka, Richard, Polini, Alessandro, Politis, Epaminondas, Pollard, Chris, Pollock, Zachary, Polychronakos, Venetios, Pompa Pacchi, Elena, Ponomarenko, Daniil, Ponsot, Patrick, Pontecorvo, Ludovico, Pontoriere, Giuseppe, Ponzio, Bruno, Popa, Stefan, Popeneciu, Gabriel, Porter, Ryan Douglas, Portillo Quintero, Dilia Maria, Pospisil, Stanislav, Postolache, Petronel, Potamianos, Karolos, Potrap, Igor, Potter, Tina, Potti, Harish, Poulsen, Trine, Poveda Torres, Ximo, Pozo Astigarraga, Eukeni, Prades Ibanez, Alberto, Pranav Bhagawath Prasad, Pranav Bhagawath, Prapa, Maria Myrto, Pretel, Jose, Price, Darren, Primavera, Margherita, Principe Martin, Miguel Angel, Privara, Radek, Proffitt, Mason, Proklova, Nadezhda, Prokofiev, Kirill, Prono, Gilles, Proto, Giorgia, Protopopescu, Serban, Proudfoot, James, Przybycien, Mariusz, Puddefoot, Joshua Eldon, Pudzha, Dennis, Puzo, Patrick Michel, Pyatiizbyantseva, Diana, Qian, Jianming, Qian, Weiming, Dong, Qichen, Qin, Quake, Qiu, Tong, Quadt, Arnulf, Queitsch-Maitland, Michaela, Quercia, Lorenzo, Quetant, Guillaume, Rabanal Bolanos, Gabriel, Rabel, Joseph, Rafanoharana, Dimbiniaina, Ragusa, Francesco, Rainbolt, Lacey, Raine, Johnny, Rajagopalan, Srini, Ramakoti, Ekaterina, Ramirez-Berend, Ian Alejandro, Ran, Kunlin, Rapheeha, Phuti, Rashid, Tasneem, Raskina, Valentina, Rassloff, Damir Fabrice, Rave, Stefan, Ravina, Baptiste, Ravinovich, Ilia, Raymond, Michel, Read, Alexander Lincoln, Readioff, Nathan Peter, Rebuzzi, Daniela, Redlinger, George, Reeves, Kendall, Reidelsturz, Joshua Aaron, Reikher, David, Rej, Amartya, Rembser, Christoph, Renardi, Alessia, Renda, Michele, Rendel, Marian Benedikt, Renklioglu, Ahmet, Renner, Frederic, Rennie, Adam, Resconi, Silvia, Ressegotti, Martina, Resseguie, Elodie Deborah, Rettie, Sebastien, Reyes Rivera, Jose Gabriel, Reynolds, Bryan, Reynolds, Elliot, Rezaei Estabragh, Mohsen, Rezanova, Olga, Reznicek, Pavel, Riallot, Marc, Ribaric, Neza, Ricci, Ester, Richert, William, Richter, Robert, Richter, Stefan, Richter-Was, Elzbieta, Ridel, Melissa, Ridouani, Selaiman, Rieck, Patrick, Riedler, Petra, Riegel, Christian, Rijssenbeek, Michael, Rimoldi, Adele, Rimoldi, Marco, Rinaldi, Lorenzo, Rinn, Timothy Thomas, Rinnagel, Maximilian Paul, Ripellino, Giulia, Riu, Imma, Rivadeneira Bracho, Pablo Andres, Rivera Vergara, Juan Cristobal, Rizatdinova, Flera, Rizvi, Eram Syed, Rizzi, Chiara, Roberts, Bryn Arthur, Roberts, Ryan, Robertson, Steven, Robichaud-Veronneau, Andree, Robin, Matthieu, Robinson, Dave, Robles Gajardo, Carolina Michel, Robles Manzano, Marisol, Robson, Aidan, Rocchi, Alessandro, Roda, Chiara, Rodriguez Bosca, Sergi, Rodriguez Garcia, Yohany, Rodriguez Rodriguez, Arturo, Rodriguez Vera, Ana Maria, Roe, Shaun, Roemer, Jonas Till, Roepe-Gier, Amber, Roggel, Jens, Rohne, Ole, Roich, Alexander, Rojas Caballero, Rimsky Alejandro, Roland, Benoit, Roland, Christophe Pol A, Roloff, Jennifer, Romaniouk, Anatoli, Romano, Emanuele, Romano, Marino, Romero, Anabel, Rompotis, Nikolaos, Roos, Lydia, Rosati, Stefano, Roscilli, Lorenzo, Rose-Dulcina, Louis, Rößl, Stefan, Rosser, Benjamin John, Rossi, Cecilia, Rossi, Eleonora, Rossi, Elvira, Rossi, Fabrizio, Rossi, Leonardo, Rossini, Lorenzo, Rosten, Rachel Christine, Rotaru, Marina, Rottler, Benjamin, Rougier, Charline, Rousseau, David, Rousso, David, Rovani, Alessandro, Rovelli, Giulia, Roy, Avik, Rozanov, Alexandre, Rozen, Yoram, Ruan, Ruanxf, Rubio Jimenez, Adrian, Ruby, Adam James, Ruelas Rivera, Victor Hugo, Ruggeri, Tristan Andrew, Ruggieri, Alessandro, Ruggieri, Daniele, Ruehr, Frederik, Ruiz Martinez, Arantxa, Rummler, Andre, Rurikova, Zuzana, Rusakovich, Nikolai, Ruscino, Ettore, Russell, Heather, Rutherfoord, John P, Růžička, Ondřej, Rybacki, Katherine Amy, Rybar, Martin, Rye, Eli Baverfjord, Ryjov, Vladimir, Ryzhov, Andrey, Sabater Iglesias, Jorge Andres, Sabatini, Fabrizio, Sabatini, Paolo, Sabetta, Luigi, Sadrozinski, Hartmut, Safai Tehrani, Francesco, Safarzadeh Samani, Batool, Safdari, Murtaza, Saha, Shreya, Sahinsoy, Merve, Saimpert, Matthias, Saito, Masahiko, Saito, Tomoyuki, Sajid, Muhammad, Salamani, Dalila, Salamanna, Giuseppe, Salnikov, Andy, Salomon, Franck, Salt, Jose, Salvador Salas, Adrian, Salvatore, Daniela, Salvatore, Fabrizio, Salzburger, Andreas, Samarati, Jerome, Sammel, Dirk, Sampsonidis, Dimos, Sampsonidou, Despoina, Sanchez Martinez, Francisco Javier, Sanchez Pineda, Arturos, Sanchez Sebastian, Victoria, Sandaker, Heidi, Sander, Christian, Sandesara, Jay Ajitbhai, Sandhoff, Marisa, Sandoval Usme, Carlos, Sankey, David, Sanny, Bernd, Sano, Takane, Sansoni, Andrea, Santi, Lorenzo, Santoni, Claudio, Santos, Helena, Santpur, Sai Neha, Santra, Arka, Saoucha, Kamal, Mendes Saraiva, Joao Gentil, Sardain, Jad Mathieu, Sasaki, Osamu, Sato, Koji, Satterthwaite, Toby, Satzkowski, Reinhard, Sauer, Christof, Sauerburger, Frank, Sauvan, Emmanuel, Sauve, Ryan Matthew, Savard, Pierre, Sawada, Ryu, Sawyer, Craig, Sawyer, Lee, Sayago Galvan, Ivan, Sbarra, Carla, Sbrizzi, Antonio, Scagliotti, Claudio, Scanlon, Timothy Paul, Schaarschmidt, Jana, Schacht, Peter, Schaefer, Douglas Michael, Schaefer, Uli, Schaffer, R.D, Schaile, Dorothee, Schaile, Otto, Schamberger, Robert Dean, Schanet, Eric, Scharf, Christian, Schefer, Meinrad Moritz, Chtcheguelski, Valery, Scheirich, Daniel, Schenck, Ferdinand, Scherino, Lorenzo, Schernau, Michael, Scheulen, Chris, Schiavi, Carlo, Schillaci, Zachary Michael, Schioppa, Enrico, Schioppa, Marco, Schlag, Bastian, Schleicher, Katharina, Schlenker, Stefan, Schmeing, Jonas, Schmidt, Mustafa Andre, Schmieden, Kristof, Schmitt, Christian, Schmitt, Stefan, Schnarr, Rodney Micheal, Schoeffel, Laurent Olivier, Schoening, Andre, Scholer, Patrick, Schopf, Elisabeth, Schorlemmer, Andre Lukas, Schott, Matthias, Schovancova, Jaroslava, Schramm, Steven, Schroeder, Frederic, Schultz-Coulon, Hans-Christian, Schumacher, Jorn, Schumacher, Markus, Schumm, Bruce Andrew, Schune, Philippe, Schwartz, Hava Rhian, Schwartzman, Ariel Gustavo, Schwarz, Thomas Andrew, Schwemling, Philippe, Schwienhorst, Reinhard, Sciandra, Andrea, Sciolla, Gabriella, Sciuccati, Augusto, Scott, Garrett Joseph, Scuri, Fabrizio, Scutti, Federico, Sebastiani, Cristiano, Secord, Chris, Sedlaczek, Kevin, Seema, Pienpen, Seidel, Sally, Seiden, Abraham, Seidlitz, Blair Daniel, Seitz, Claudia, Seixas, Jose, Sekhniaidze, Givi, Sekula, Stephen Jacob, Selem, Luka, Seletskiy, Alexandre, Semprini Cesari, Nicola, Sen, Sourav, Sengupta, Debajyoti, Senthilkumar, Varsha, Serin, Laurent, Serkin, Leonid, Serochkin, Mikhail, Sessa, Marco, Severini, Horst, Sexton, Kenneth Alan, Sforza, Federico, Sfyrla, Anna, Shabalina, Elizaveta, Shafto, Gene, Shaheen, Rabia, Shahinian, Jeff, Shaked, Ohad, Shaked Renous, Dan, Shan, Lianyou, Shapiro, Marjorie, Sharma, Abhishek, Sharma, Punit, Sharma, Surabhi, Shatalov, Sppavel, Shaw, Kate, Shaw, Savanna, Shen, Qiuping, Sheppard, Damian Joseph, Sherpa, Pasang Nuri, Sherwood, Peter, Shi, Liaoshan, Shimmin, Chase Owen, Shimogama, Yoshihiro, Shinner, James David, Shipsey, Ian, Shirabe, Shohei, Shiyakova, Mariya, Shlomi, Jonathan, Shoa, Meir, Shochet, Mel, Shojaii, Seyed Ruhollah, Shooltz, Dean Daniel, Shope, David Richard, Shrestha, Suyog, Shrif, Esra Mohammed, Shroff, Maheyer Jamshed, Shutov, Alexander, Sicho, Petr, Sickles, Anne Marie, Sideras Haddad, Elias, Sidiropoulou, Ourania, Sidoti, Antonio, Siegert, Frank, Sijacki, Dorde, Sikora, Rafal, Sili, Francisco, Cardoso Silva, Julia Manuela, Silva Oliveira, Marcos Vinicius, Silverstein, Samuel, Simion, Stefan, Simola, Vesa, Simoniello, Rosa, Simpson, Ethan Lewis, Simpson, Liana, Simpson, Nathan Daniel, Simsek, Sinem, Sindhu, Sreelakshmi, Sinervo, Pekka, Singh, Shuvay, Singh, Sundeep, Singh, Sahibjeet, Sinha, Supriya, Sinha, Sukanya, Sioli, Maximiliano, Sippach, William, Siral, Ismet, Sivoklokov, Serguei, Siyad, Mohamed Jimcale, Sjoelin, Joergen, Skaf, Ali, Skorda, Eleni, Skubic, Patrick, Slawinska, Magdalena, Sliwa, Krzysztof, Smakhtin, Vladimir, Smart, Ben Harry, Smiesko, Juraj, Smirnov, Serge, Smirnov, Yury, Smirnova, Lidia, Smirnova, Oxana, Smith, Andrew Caldon, Smith, Dale Shane, Smith, Emily Ann, Smith, Hayden Alexander, Smith, James, Smith, Rachel Emma Clarke, Smizanska, Maria, Smolek, Karel, Smykiewicz, Andrzej, Snesarev, Andrei, Snoek, Hella, Snyder, Scott, Sobie, Randy, Soffer, Abi, Solans Sanchez, Carlos, Soldatov, Evgeny, Soldevila Serrano, Urmila, Solis, Michelle Ann, Soliveres Riviere, Francoise Simone, Solodkov, Sanya, Solomon, Shalu, Soloshenko, Aleksey, Solovieva, Ksenia, Solovyanov, Oleg, Solovyev, Victor, Soluk, Richard, Sommer, Philip, Sonay, Anil, Song, Wen Yi, Sonneveld, Jory, Sopczak, Andre, Sopio, Alex, Sopkova, Filomena, Sorbe, Jérôme, Sothilingam, Varsiha, Sottocornola, Simone, Soualah, Rachik, Soumaimi, Zainab, South, David, Soyk, Daniel, Spagnolo, Stefania, Spalla, Margherita, Spano, Francesco, Speers, Peter, Sperlich, Dennis, Spigo, Giancarlo, Spina, Mario, Spinali, Sebastiano, Spiteri, Dwayne Patrick, Spiwoks, Ralf, Spousta, Martin, Staats, Ezekiel, Stabile, Alberto, Staley, Richard John, Stamen, Rainer, Stamenkovic, Marko, Stamoulos, Ioannis, Stampekis, Alexios, Standke, Mark, Stanecka, Ewa, Stange, Max Vincent, Stanislaus, Beojan, Stanitzki, Marcel, Stankaityte, Migle, Stapf, Birgit Sylvia, Starchenko, Jenya, Stark, Giordon Holtsberg, Stark, Jan, Starko, Darij Markian, Staroba, Pavel, Starovoitov, Pavel, Staerz, Steffen, Staszewski, Rafal, Stavropoulos, George, Steentoft, Jonas, Steinberg, Peter Alan, Steinhebel, Amanda, Stelzer, Bernd, Stelzer, Joerg, Stelzer-Chilton, Oliver, Stenzel, Hasko, Stevenson, Thomas James, Stewart, Graeme A, Stockton, Mark, Stoicea, Gabriel, Stolarski, Marcin, Stonjek, Stefan, Stouras, Nikos, Straessner, Arno, Strandberg, Jonas, Strandberg, Sara, Strauss, Mike, Strebler, Thomas, Strickland, Vance, Strizenec, Pavol, Strohmer, Raimund, Strom, David, Strom, Lars Rickard, Stroynowski, Ryszard, Strubig, Antonia, Stucci, Stefania Antonia, Stugu, Bjarne, Stupak, John, Sturdy, Jared, Styles, Nicholas, Su, Dong, Su, Shixiang, Su, Wanyun, Su, Xiaowen, Sugizaki, Kaito, Sulin, Vladimir, Sullivan, Matthew James, Sultan, D.M.S, Sultanaliyeva, Laily, Sultanov, Salekh, Sumida, Toshi, Sun, Quan, Sun, Siyuan, Sun, Shaojun, Gudnadottir, Olga, Sutton, Mark, Svatos, Michal, Swiatlowski, Maximilian J, Swirski, Thorben, Sykora, Ivan, Sykora, Martin, Sykora, Tomas, Ta, Duc Bao, Tackmann, Kerstin, Taffard, Anyes, Tafirout, Reda, Tafoya Vargas, Juan Salvador, Taghavirad, Saeed, Taibah, Reem Hani M, Takashima, Ryuichi, Takeda, Kosuke, Takeva, Emily Petrova, Takubo, Yosuke, Talby, Mossadek, Talyshev, Alexei, Tam, Kai Chung, Tamir, Nadav Michael, Tanaka, Aoto, Tanaka, Junichi, Tanaka, Rei, Tanasini, Martino, Tang, Jiannan, Tang, Shaochun, Tao, Zhengcheng, Tapia Araya, Sebastian, Tapprogge, Stefan, Tar, Bora, Tarek, Ahmed, Tarem, Shlomit, Tarem, Zvi, Tariq, Khuram, Tarna, Grigore, Tartarelli, Francesco, Tas, Petr, Tasevsky, Marek, Tasevsky, Mito, Tassi, Enrico, Tate, Aric, Tateno, Gen, Tayalati, Yahya, Taylor, Geoffrey Norman, Taylor, Wendy Jane, Teagle, Hamish Edward, Tee, Amy, Teixeira de Lima, Rafael, Teixeira-Dias, Pedro, Teoh, Jia Jian, Terashi, Koji, Terron Cuadrado, Juan, Terzo, Stefano, Testa, Marianna, Teterin, Peter, Teurnier, Marie-Solene, Teuscher, Richard, Thaler, Alexander, Theiner, Ondrej, Themistokleous, Neofytos, Theveneaux-Pelzer, Timothee, Thielmann, Oliver, Thomas, Chris, Thomas, David William, Thomas, James Oscar, Thomas, Juergen, Thompson, Emily Anne, Thompson, Paul, Thomson, Evelyn Jean, Thorpe, Edward James, Tian, Yusong, Tikhomirov, Vladimir, Tikhonov, Iouri, Timoshenko, Sergei, Ting, Edmund Xiang Lin, Tipton, Paul Louis, Tisserant, Sylvain, Tlou, Humphry, Tnourji, Abdellah, Tobias, Juergen, Todome, Kazuki, Todorov, Teddy, Todorova, Sarka, Todt, Stefanie, Togawa, Manabu, Tojo, Junji, Tokar, Stano, Tokushuku, Katsuo, Toldaiev, Alex, Tombs, Rupert, Tomoto, Makoto, Tompkins, Daniel, Tompkins, Lauren Alexandra, Topolnicki, Kacper Wojciech, Tornambe, Peter, Torrence, Eric, Torres, Heberth, Torro Pastor, Emma, Toscani, Mariana, Tosciri, Cecilia, Tost, Marc, Tovey, Daniel, Traeet, Are Sivertsen, Tranchand, Laure, Trandafir, Iulia-Stefania, Trantou, Foteini, Trattino, Pietro, Travaglini, Riccardo, Trefzger, Thomas, Tricoli, Alessandro, Trigger, Isabel, Trincaz-Duvoid, Sophie, Trischuk, Dominique Anderson, Trocme, Benjamin, Troeglazov, Ivan, Trofymov, Artur, Troncon, Clara, Troska, Georg, Trotta, Danilo, Trovatelli, Monica, Trovato, Marco, Truong, Thi Ngoc Loan, Trzebinski, Maciej, Trzupek, Adam, Tsai, Fang-Ying, Tsai, Meng-Ju, Tse, Wan Ho, Tsiafis, Yoannis, Tsiamis, Angelos, Tsiareshka, Pavel, Tsigaridas, Stergios, Tsirigotis, Apostolos, Tsiskaridze, Vakhtang, Tskhadadze, Edisher, Tsopoulou, Maria-Evanthia, Tsujikawa, Yoshiaki, Tsukerman, Ilia, Tsulaia, Vakhtang, Tsuno, Soshi, Tsur, Omer, Tsybyshev, Dmitry, Tu, Yanjun, Tudorache, Alexandra, Tudorache, Valentina, Tuna, Alexander Naip, Turchikhin, Semen, Turco, Paola, Turk Cakir, Ilkay, Turra, Ruggero, Turtuvshin, Tulgaa, Tusi, Enrico, Tuts, Mike, Tzamarias, Spyros, Tzanis, Polyneikis, Tzanos, Stamatios, Tzovara, Eftychia, Uchida, Kenta, Ukah, Kelechi Rock, Ukegawa, Fumihiko, Ulloa Poblete, Pablo Augusto, Umaka, Ejiro Naomi, Unal, Guillaume, Unal, Mesut, Undrus, Alexander, Unel, Gokhan, Uno, Kenta, Urban, Josef, Urbasek, Vladimir, Urquijo, Phillip, Usai, Giulio, Ushioda, Risa, Usman, Muhammad, Usseglio, Michel, Uysal, Zekeriya, Vacavant, Laurent, Vacek, Vic, Vacher, Thierry, Vachon, Brigitte, Vadla, Knut Oddvar Hoie, Vafeiadis, Theodoros, Vaitkus, Andrius, Valderanis, Chrysostomos, Valdes Santurio, Eduardo, Valente, Marco, Valentinetti, Sara, Valero Biot, Alberto, Vallier, Alexis, Valls Ferrer, Juan, van Arneman, Dylan Remberto, van Daalen, Tal Roelof, van Gemmeren, Peter, van Overbeek, Martijn, van Rijnbach, Milou, van Stroud, Samuel, van Vulpen, Ivo, Vanadia, Marco, Vandelli, Wainer, Vandenbroucke, Maxence, van de Wall, Evan Richard, Vannicola, Damiano, Vannoli, Leonardo, Varga-Rehling, Attila, Vari, Riccardo, Varnes, Erich Ward, Varni, Carlo, Mete, Tulin, Varouchas, Dimitris, Varriale, Lorenzo, Varvell, Kevin, Vasile, Matei, Vaslin, Louis, Vasquez, Gerardo, Vazeille, Francois, Vazquez Schroeder, Tamara, Vdovin, Aleksander, Veatch, Jason Robert, Vecchio, Valentina, Veen, Michiel Jan, Veliscek, Iza, Veloce, Laurelle Maria, Veloso, Filipe, Veneziano, Stefano, Ventura, Andrea, Venturi, Nicola, Verbytskyi, Andrii, Vercellati, Filippo, Verducci, Monica, Vergain, Maurice, Vergis, Christos, Verissimo de Araujo, Micael, Verkerke, Wouter, Verlaat, Bart, Vermeulen, Jos, Vernieri, Caterina, Verschuuren, Pim Jordi, Vessella, Makayla, Vetterli, Michel Joseph, Vgenopoulos, Andreas, Viaux Maira, Nicolas, Vichoudis, Paschalis, Vickey, Trevor, Vickey Boeriu, Oana, Viehhauser, Georg, Vieira de Souza, Julio, Vigani, Luigi, Vigeolas, Eric, Villa, Mauro, Villaplana, Miguel, Villhauer, Elena Michelle, Vilucchi, Elisabetta, Vincter, Manuella, Vinogradov, Mikhail, Virdee, Govindraj Singh, Vishwakarma, Akanksha, Vittori, Camilla, Vivarelli, Iacopo, Vlachos, Sotiris, Vladimirov, Vangelis, Voevodina, Elena, Vogel, Fabian, Vogt, Sven, Vokac, Petr, von Ahnen, Janik, von Torne, Eckhard, Vormwald, Benedikt, Vorobel, Vit, Vorobev, Konstantin, Vos, Marcel, Voss, Katharina, Vossebeld, Joost, Vozak, Matous, Vozdecky, Lubos, Vranjes, Nenad, Vranjes Milosavljevic, Marija, Vreeswijk, Marcel, Vuillemin, Cyrille, Vuillermet, Raphael, Vujinovic, Olivera, Vukotic, Ilija, Wada, Sayaka, Wagner, Cooper, Wagner, Wolfgang, Wahdan, Shayma, Wahlberg, Hernan Pablo, Wakasa, Rena, Wakida, Moe, Walbrecht, Verena Maria, Walder, James William, Walker, Rodney, Walker, Robert Bond, Walkowiak, Wolfgang, Wang, Ann Miao, Wang, Alex Zeng, Wang, Chen, Wang, Chenliang, Wang, Haichen, Wang, Jiawei, Wang, Jinglu, Wang, Jinhong, Wang, Qiang, Wang, Renjie, Wang, Rongkun, Wang, Rui, Wang, Song-Ming, Wang, Shuanggeng, Wang, Tao, Wang, Weitao, Wang, Xu, Wang, Xin, Wang, Xinxin, Wang, Xiaoning, Wang, Xi, Wang, Yufeng, Wang, Yuhao, Wang, Zirui, Wang, Zhen, Wang, Zhichen, Warburton, Andreas, Ward, Robert James, Warrack, Neil, Watson, Alan, Watson, Harriet, Watson, Miriam, Watts, Gordon, Waugh, Benedict Martin, Weaverdyck, Curtis John, Webb, Aaron, Weber, Christian, Weber, Hannsjorg, Weber, Jens, Weber, Maarten, Weber, Michele, Weber, Stephen, Weber, Sebastian Mario, Wei, Chuanshun, Wei, Yingjie, Weidberg, Anthony, Weingarten, Jens, Weirich, Marcel, Weiser, Christian, Welch, Steven, Wells, Craig John, Wells, Pippa, Wenaus, Torre, Wendland, Bjoern, Wengler, Thorsten, Wenke, Nina Stephanie, Wensing, Marius, Wermes, Norbert, Wessels, Martin, Whalen, Kate, Wharton, Andrew Mark, White, Aaron Stephen, White, Andrew, White, Martin John, Whiteson, Daniel, Wickremasinghe, Lakmin, Wiedenmann, Werner, Wiel, Christian, Wielers, Monika, Wiglesworth, Craig, Wiik-Fuchs, Liv, Wilbern, Daniel John, Wilkens, Henric, Williams, Daniel, Williams, Hugh, Williams, Sarah Louise, Willocq, Stephane, Windischhofer, Philipp, Wingerter, Isabelle, Winklmeier, Frank, Winter, Benedict Tobias, Winter, Joshua Krystian, Wittgen, Matthias, Wittig, Tobias, Wobisch, Markus, Woelker, Ricardo, Wollrath, Julian, Wolniewicz, Kevin, Wolter, Marcin, Wolters, Helmut, Wong, Vincent Wai Sum, Wongel, Alicia, Worm, Steven, Wosiek, Barbara Krystyna, Wotschack, Joerg, Woyshville, Aaron, Wozniak, Krzysztof Wieslaw, Wraight, Kenneth Gibb, Wu, Jinfei, Wu, Minlin, Wu, Mengqing, Wu, Sau Lan, Wu, Weihao, Wu, Wenjing, Wu, Xin, Wu, Yusheng, Wu, Zhibo, Wurzinger, Jonas, Wyatt, Terry, Wynne, Benjamin Michael, Xella, Stefania, Xia, Ligang, Xia, Mingming, Xiang, Jianhuan, Xiao, Xiong, Xie, Mingzhe, Xie, Xiangyu, Xin, Shuiting, Xiong, Junwen, Xiotidis, Ioannis, Xu, Da, Xu, Hanlin, Xu, Hao, Xu, Lailin, Xu, Riley, Xu, Rui, Xu, Tairan, Xu, Wenhao, Xu, Yue, Xu, Zhongyukun, Xu, Zijun, Yabsley, Bruce Donald, Yacoob, Sahal, Yamaguchi, Naoki, Yamaguchi, Yohei, Yamamoto, Shimpei, Yamauchi, Hiroki, Yamazaki, Tomohiro, Yamazaki, Yuji, Yan, Jun, Yan, Siyuan, Yan, Zhen, Yandyan, Armen, Yang, Haijun, Yang, Hongtao, Yang, Siqi, Yang, Tianyi, Yang, Xiao, Yang, Xuan, Yang, Yi-Lin, Yang, Zhe, Yao, Lin, Yao, Wei-Ming, Yap, Yee Chinn, Ye, Hanfei, Ye, Hua, Ye, Jingbo, Ye, Shuwei, Ye, Xinmeng, Yeh, Yoran, Yeletskikh, Ivan, Yeo, Beom Ki, Yexley, Melissa, Yildiz, Cenk, Yin, Pengqi, Yin, Weigang, Yorita, Kohei, Younas, Sulman, Young, Christopher, Young, Charlie, Yu, Yi, Yuan, Man, Yuan, Rui, Yue, Luzhan, Yue, Xiaoguang, Yukhimchuk, Sergey, Zaazoua, Mohamed, Zabinski, Bartlomiej Henryk, Zachariadou, Katerina, Zaghia, Hamid, Zahradnik, Vit, Zaid, Estifa'A, Zakareishvili, Tamar, Zakharchuk, Nataliia, Zambito, Stefano, Zamora Saa, Jilberto Antonio, Zang, Jiaqi, Zanzi, Daniele, Zaplatilek, Ota, Zeissner, Sonja Verena, Zeitnitz, Christian, Zeng, Jiancong, Zenger, Todd, Zenin, Oleg, Zenis, Tibor, Zenz, Seth, Zerradi, Soufiane, Zerwas, Dirk, Zhai, Mingjie, Zhang, Bowen, Zhang, Dengfeng, Zhang, Jie, Zhang, Jinlong, Zhang, Kaili, Zhang, Lei, Zhang, Peng, Zhang, Rui, Zhang, Shuzhou, Zhang, Tingyu, Zhang, Xiangke, Zhang, Xueyao, Zhang, Yulei, Zhang, Zhicai, Zhang, Zhiqing Philippe, Zhao, Haoran, Zhao, Pingchuan, Zhao, Tongbin, Zhao, Xiandong, Zhao, Yuzhan, Zhao, Zhengguo, Zhemchugov, Alexey, Zheng, Xiangxuan, Zheng, Zhi, Zhivun, Elena, Zhong, Dewen, Zhou, Bing, Zhou, Chen, Zhou, Hao, Zhou, Ning, Zhou, Shun, Zhou, You, Zhu, Chengguang, Zhu, Chenzheng, Zhu, Heling, Zhu, Hongbo, Zhu, Junjie, Zhu, Yifan, Zhu, Yingchun, Zhuang, Xuai, Zhukov, Konstantin, Zhulanov, Vladimir, Zibell, Andre, Zich, Jan, Zimine, Nikolai, Zimmermann, Jorg, Zimmermann, Stephanie Ulrike, Zinsser, Joachim, Ziolkowski, Michal, Zivkovic, Lidija, Zoccoli, Antonio, Zoch, Knut, Zolkin, Igor, Zonca, Eric, Zorbas, Theodore, Zormpa, Olga, Zou, Wenkai, Zuk, George, Zullo, Antonio, Zwalinski, Lukasz, Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), and ATLAS
Subjects: Physics - Instrumentation and Detectors, CERN Lab, accelerator, data acquisition, diffraction, gap, FOS: Physical sciences, scintillation counter, trigger, electron, energy resolution, High Energy Physics - Experiment, p p, cross section, High Energy Physics - Experiment (hep-ex), pixel, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], muon, trigger, CERN LHC Coll, upgrade, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], forward spectrometer, track data analysis, vertex, background, photon, trigger, electronics, calorimeter, liquid argon, Instrumentation and Detectors (physics.ins-det), ATLAS, crossing, heavy ion, pile-up, trigger, threshold, luminosity, monitoring, Particle Physics - Experiment, signature, Micromegas, performance
Abstract: The ATLAS detector is installed in its experimental cavern at Point 1 of the CERN Large Hadron Collider. During Run 2 of the LHC, a luminosity of $\mathcal{L}=2\times 10^{34}\mathrm{cm}^{-2}\mathrm{s}^{-1}$ was routinely achieved at the start of fills, twice the design luminosity. For Run 3, accelerator improvements, notably luminosity levelling, allow sustained running at an instantaneous luminosity of $\mathcal{L}=2\times 10^{34}\mathrm{cm}^{-2}\mathrm{s}^{-1}$, with an average of up to 60 interactions per bunch crossing. The ATLAS detector has been upgraded to recover Run 1 single-lepton trigger thresholds while operating comfortably under Run 3 sustained pileup conditions. A fourth pixel layer 3.3 cm from the beam axis was added before Run 2 to improve vertex reconstruction and $b$-tagging performance. New Liquid Argon Calorimeter digital trigger electronics, with corresponding upgrades to the Trigger and Data Acquisition system, take advantage of a factor of 10 finer granularity to improve triggering on electrons, photons, taus, and hadronic signatures through increased pileup rejection. The inner muon endcap wheels were replaced by New Small Wheels with Micromegas and small-strip Thin Gap Chamber detectors, providing both precision tracking and Level-1 Muon trigger functionality. Tile Calorimeter scintillation counters were added to improve electron energy resolution and background rejection. Upgrades to Minimum Bias Trigger Scintillators and Forward Detectors improve luminosity monitoring and enable total proton-proton cross section, diffractive physics, and heavy ion measurements. These upgrades are all compatible with operation in the much harsher environment anticipated after the High-Luminosity upgrade of the LHC and are the first steps towards preparing ATLAS for the High-Luminosity upgrade of the LHC. This paper describes the Run 3 configuration of the ATLAS detector., Comment: 233 pages in total, author list starting page 214, 116 figures, 15 tables, submitted to JINST. All figures including auxiliary figures are available at http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/GENR-2019-02/
Published: 2023
Full Text: View/download PDF

44. In situ evaluation of stalk lodging resistance for different maize (Zea mays L.) cultivars using a mobile wind machine

Author: Wen, Weiliang, Gu, Shenghao, Xiao, Boxiang, Wang, Chuanyu, Wang, Jinglu, Ma, Liming, Wang, Yongjian, Lu, Xianju, Yu, Zetao, Zhang, Ying, Du, Jianjun, and Guo, Xinyu
Published: 2019
Full Text: View/download PDF

45. Flexible High-Performance and Screen-Printed Symmetric Supercapacitor Using Hierarchical Rodlike V 3 O 7 Inks.

Author: Lin, Baoying, Zheng, Yinyin, Wang, Jinglu, Tu, Qian, Tang, Wentao, and Chen, Liangzhe
Subjects: SUPERCAPACITOR electrodes, SUPERCAPACITORS, CARBON-black, ENERGY density, VALENCE bonds, SCREEN process printing, POWER resources
Abstract: The emergence of the Internet of things stimulates the pursuit of flexible and miniaturized supercapacitors. As an advanced technology, screen printing displays vigor and tremendous potential in fabricating supercapacitors, but the adoption of high-performance ink is a great challenge. Here, hierarchical V3O7 with rodlike texture was prepared via a facile template–solvothermal route; and the morphology, component, and valence bond information are characterized meticulously. Then, the screen-printed inks composed of V3O7, acetylene black, and PVDF are formulated, and the rheological behaviors are studied detailedly. Benefitting from the orderly aligned ink, the optimal screen-printed electrode can exhibit an excellent specific capacitance of 274.5 F/g at 0.3 A/g and capacitance retention of 81.9% after 5000 cycles. In addition, a flexible V3O7 symmetrical supercapacitor (SSC) is screen-printed and assembled on the Ag current collector, exhibiting a decent areal specific capacitance of 322.5 mF/cm2 at 0.5 mA/cm2, outstanding cycling stability of 90.8% even after 5000 cycles, satisfactory maximum energy density of 129.45 μWh/cm2 at a power density of 0.42 mW/cm2, and remarkable flexibility and durability. Furthermore, a single SSC enables the showing of an actual voltage of 1.70 V after charging, and no obvious self-discharge phenomenon is found, revealing the great applied value in supply power. Therefore, this work provides a facile and low-cost reference of screen-printed ink for large-scale fabrication of flexible supercapacitors. [ABSTRACT FROM AUTHOR]
Published: 2023
Full Text: View/download PDF

46. High-throughput micro-phenotyping measurements applied to assess stalk lodging in maize (Zea mays L.)

Author: Zhang, Ying, Du, Jianjun, Wang, Jinglu, Ma, Liming, Lu, Xianju, Pan, Xiaodi, Guo, Xinyu, and Zhao, Chunjiang
Published: 2018
Full Text: View/download PDF

47. Characterization and genetic dissection of maize ear leaf midrib acquired by 3D digital technology

Author: Wu, Sheng, primary, Wang, Jinglu, additional, Zhao, Yanxin, additional, Wen, Weiliang, additional, Zhang, Ying, additional, Lu, Xianju, additional, Wang, Chuanyu, additional, Liu, Kai, additional, Chen, Bo, additional, Guo, Xinyu, additional, and Zhao, Chunjiang, additional
Published: 2022
Full Text: View/download PDF

48. A deep learning-integrated phenotyping pipeline for vascular bundle phenotypes and its application in evaluating sap flow in the maize stem

Author: Du, Jianjun, primary, Zhang, Ying, additional, Lu, Xianju, additional, Zhang, Minggang, additional, Wang, Jinglu, additional, Liao, Shengjin, additional, Guo, Xinyu, additional, and Zhao, Chunjiang, additional
Published: 2022
Full Text: View/download PDF

49. R^2VOS: Robust Referring Video Object Segmentation via Relational Multimodal Cycle Consistency

Author: Li, Xiang, Wang, Jinglu, Xu, Xiaohao, Li, Xiao, Lu, Yan, and Raj, Bhiksha
Subjects: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract: Referring video object segmentation (R-VOS) aims to segment the object masks in a video given a referring linguistic expression to the object. It is a recently introduced task attracting growing research attention. However, all existing works make a strong assumption: The object depicted by the expression must exist in the video, namely, the expression and video must have an object-level semantic consensus. This is often violated in real-world applications where an expression can be queried to false videos, and existing methods always fail in such false queries due to abusing the assumption. In this work, we emphasize that studying semantic consensus is necessary to improve the robustness of R-VOS. Accordingly, we pose an extended task from R-VOS without the semantic consensus assumption, named Robust R-VOS ($\mathrm{R}^2$-VOS). The $\mathrm{R}^2$-VOS task is essentially related to the joint modeling of the primary R-VOS task and its dual problem (text reconstruction). We embrace the observation that the embedding spaces have relational consistency through the cycle of text-video-text transformation, which connects the primary and dual problems. We leverage the cycle consistency to discriminate the semantic consensus, thus advancing the primary task. Parallel optimization of the primary and dual problems are enabled by introducing an early grounding medium. A new evaluation dataset, $\mathrm{R}^2$-Youtube-VOS, is collected to measure the robustness of R-VOS models against unpaired videos and expressions. Extensive experiments demonstrate that our method not only identifies negative pairs of unrelated expressions and videos, but also improves the segmentation accuracy for positive pairs with a superior disambiguating ability. Our model achieves the state-of-the-art performance on Ref-DAVIS17, Ref-Youtube-VOS, and the novel $\mathrm{R}^2$-Youtube-VOS dataset.
Published: 2022

50. Dissecting the Genetic Structure of Maize Leaf Sheaths at Seedling Stage by Image-Based High-Throughput Phenotypic Acquisition and Characterization

Author: Wang, Jinglu, Wang, Chuanyu, Lu, Xianju, Zhang, Ying, Zhao, Yanxin, Wen, Weiliang, Song, Wei, and Guo, Xinyu
Subjects: Plant Science
Abstract: The rapid development of high-throughput phenotypic detection techniques makes it possible to obtain a large number of crop phenotypic information quickly, efficiently, and accurately. Among them, image-based phenotypic acquisition method has been widely used in crop phenotypic identification and characteristic research due to its characteristics of automation, non-invasive, non-destructive and high throughput. In this study, we proposed a method to define and analyze the traits related to leaf sheaths including morphology-related, color-related and biomass-related traits at V6 stage. Next, we analyzed the phenotypic variation of leaf sheaths of 418 maize inbred lines based on 87 leaf sheath-related phenotypic traits. In order to further analyze the mechanism of leaf sheath phenotype formation, 25 key traits (2 biomass-related, 19 morphology-related and 4 color-related traits) with heritability greater than 0.3 were analyzed by genome-wide association studies (GWAS). And 1816 candidate genes of 17 whole plant leaf sheath traits and 1,297 candidate genes of 8 sixth leaf sheath traits were obtained, respectively. Among them, 46 genes with clear functional descriptions were annotated by single nucleotide polymorphism (SNPs) that both Top1 and multi-method validated. Functional enrichment analysis results showed that candidate genes of leaf sheath traits were enriched into multiple pathways related to cellular component assembly and organization, cell proliferation and epidermal cell differentiation, and response to hunger, nutrition and extracellular stimulation. The results presented here are helpful to further understand phenotypic traits of maize leaf sheath and provide a reference for revealing the genetic mechanism of maize leaf sheath phenotype formation.
Published: 2022

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Database

Publisher

175 results on '"Wang, Jinglu"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources