Paper Group NAWR 2
Unsupervised Face Normalization With Extreme Pose and Expression in the Wild. Enhancing Topic-to-Essay Generation with External Commonsense Knowledge. A Primal Dual Formulation For Deep Learning With Constraints. BioRelEx 1.0: Biological Relation Extraction Benchmark. An Empirical Investigation of Structured Output Modeling for Graph-based Neural D …
Unsupervised Face Normalization With Extreme Pose and Expression in the Wild
| Title | Unsupervised Face Normalization With Extreme Pose and Expression in the Wild |
| Authors | Yichen Qian, Weihong Deng, Jiani Hu |
| Abstract | Face recognition achieves great success thanks to the emergence of deep learning. However, many contemporary face recognition models still have limited invariance to strong intra-personal variations such as large pose changes. Face normalization provides an effective and cheap way to distil face identity and dispel face variances for recognition. We focus on face generation in the wild with unpaired data. To this end, we propose a Face Normalization Model (FNM) to generate a frontal, neutral expression, photorealistic face image for face recognition. FNM is a well-designed Generative Adversarial Network (GAN) with three distinct novelties. First, a face expert network is introduced to construct generator and provide the ability of retaining face identity. Second, with the reconstruction of normal face, pixel-wise loss is applied to stabilize optimization process. Third, we present a series of face attention discriminators to refine local textures. FNM could recover canonical-view, expression-free image and directly improve the performance of face recognition model. Extensive qualitative and quantitative experiments on both controlled and in-the-wild databases demonstrate the superiority of our face normalization method. |
| Tasks | Face Generation, Face Recognition |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Qian_Unsupervised_Face_Normalization_With_Extreme_Pose_and_Expression_in_the_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Qian_Unsupervised_Face_Normalization_With_Extreme_Pose_and_Expression_in_the_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-face-normalization-with-extreme |
| Repo | https://github.com/mx54039q/fnm |
| Framework | tf |
Enhancing Topic-to-Essay Generation with External Commonsense Knowledge
| Title | Enhancing Topic-to-Essay Generation with External Commonsense Knowledge |
| Authors | Pengcheng Yang, Lei Li, Fuli Luo, Tianyu Liu, Xu Sun |
| Abstract | Automatic topic-to-essay generation is a challenging task since it requires generating novel, diverse, and topic-consistent paragraph-level text with a set of topics as input. Previous work tends to perform essay generation based solely on the given topics while ignoring massive commonsense knowledge. However, this commonsense knowledge provides additional background information, which can help to generate essays that are more novel and diverse. Towards filling this gap, we propose to integrate commonsense from the external knowledge base into the generator through dynamic memory mechanism. Besides, the adversarial training based on a multi-label discriminator is employed to further improve topic-consistency. We also develop a series of automatic evaluation metrics to comprehensively assess the quality of the generated essay. Experiments show that with external commonsense knowledge and adversarial training, the generated essays are more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation. |
| Tasks | Concept-To-Text Generation |
| Published | 2019-07-01 |
| URL | https://www.aclweb.org/anthology/P19-1193/ |
| https://www.aclweb.org/anthology/P19-1193 | |
| PWC | https://paperswithcode.com/paper/enhancing-topic-to-essay-generation-with |
| Repo | https://github.com/TobiasLee/CTEG |
| Framework | tf |
A Primal Dual Formulation For Deep Learning With Constraints
| Title | A Primal Dual Formulation For Deep Learning With Constraints |
| Authors | Yatin Nandwani, Abhishek Pathak, Mausam, Parag Singla |
| Abstract | For several problems of interest, there are natural constraints which exist over the output label space. For example, for the joint task of NER and POS labeling, these constraints might specify that the NER label ‘organization’ is consistent only with the POS labels ‘noun’ and ‘preposition’. These constraints can be a great way of injecting prior knowledge into a deep learning model, thereby improving overall performance. In this paper, we present a constrained optimization formulation for training a deep network with a given set of hard constraints on output labels. Our novel approach first converts the label constraints into soft logic constraints over probability distributions outputted by the network. It then converts the constrained optimization problem into an alternating min-max optimization with Lagrangian variables defined for each constraint. Since the constraints are independent of the target labels, our framework easily generalizes to semi-supervised setting. We experiment on the tasks of Semantic Role Labeling (SRL), Named Entity Recognition (NER) tagging, and fine-grained entity typing and show that our constraints not only significantly reduce the number of constraint violations, but can also result in state-of-the-art performance |
| Tasks | Entity Typing, Named Entity Recognition, Semantic Role Labeling |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/9385-a-primal-dual-formulation-for-deep-learning-with-constraints |
| http://papers.nips.cc/paper/9385-a-primal-dual-formulation-for-deep-learning-with-constraints.pdf | |
| PWC | https://paperswithcode.com/paper/a-primal-dual-formulation-for-deep-learning |
| Repo | https://github.com/dair-iitd/dl-with-constraints |
| Framework | pytorch |
BioRelEx 1.0: Biological Relation Extraction Benchmark
| Title | BioRelEx 1.0: Biological Relation Extraction Benchmark |
| Authors | Hrant Khachatrian, Lilit Nersisyan, Karen Hambardzumyan, Tigran Galstyan, Anna Hakobyan, Arsen Arakelyan, Andrey Rzhetsky, Aram Galstyan |
| Abstract | Automatic extraction of relations and interactions between biological entities from scientific literature remains an extremely challenging problem in biomedical information extraction and natural language processing in general. One of the reasons for slow progress is the relative scarcity of standardized and publicly available benchmarks. In this paper we introduce BioRelEx, a new dataset of fully annotated sentences from biomedical literature that capture \textit{binding} interactions between proteins and/or biomolecules. To foster reproducible research on the interaction extraction task, we define a precise and transparent evaluation process, tools for error analysis and significance tests. Finally, we conduct extensive experiments to evaluate several baselines, including SciIE, a recently introduced neural multi-task architecture that has demonstrated state-of-the-art performance on several tasks. |
| Tasks | Relation Extraction |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-5019/ |
| https://www.aclweb.org/anthology/W19-5019 | |
| PWC | https://paperswithcode.com/paper/biorelex-10-biological-relation-extraction |
| Repo | https://github.com/YerevaNN/BioRelEx |
| Framework | none |
An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing
| Title | An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing |
| Authors | Zhisong Zhang, Xuezhe Ma, Eduard Hovy |
| Abstract | In this paper, we investigate the aspect of structured output modeling for the state-of-the-art graph-based neural dependency parser (Dozat and Manning, 2017). With evaluations on 14 treebanks, we empirically show that global output-structured models can generally obtain better performance, especially on the metric of sentence-level Complete Match. However, probably because neural models already learn good global views of the inputs, the improvement brought by structured output modeling is modest. |
| Tasks | Dependency Parsing |
| Published | 2019-07-01 |
| URL | https://www.aclweb.org/anthology/P19-1562/ |
| https://www.aclweb.org/anthology/P19-1562 | |
| PWC | https://paperswithcode.com/paper/an-empirical-investigation-of-structured |
| Repo | https://github.com/zzsfornlp/zmsp |
| Framework | none |
SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration Without Correspondences
| Title | SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration Without Correspondences |
| Authors | Huu M. Le, Thanh-Toan Do, Tuan Hoang, Ngai-Man Cheung |
| Abstract | This paper presents a novel randomized algorithm for robust point cloud registration without correspondences. Most existing registration approaches require a set of putative correspondences obtained by extracting invariant descriptors. However, such descriptors could become unreliable in noisy and contaminated settings. In these settings, methods that directly handle input point sets are preferable. Without correspondences, however, conventional randomized techniques require a very large number of samples in order to reach satisfactory solutions. In this paper, we propose a novel approach to address this problem. In particular, our work enables the use of randomized methods for point cloud registration without the need of putative correspondences. By considering point cloud alignment as a special instance of graph matching and employing an efficient semi-definite relaxation, we propose a novel sampling mechanism, in which the size of the sampled subsets can be larger-than-minimal. Our tight relaxation scheme enables fast rejection of the outliers in the sampled sets, resulting in high quality hypotheses. We conduct extensive experiments to demonstrate that our approach outperforms other state-of-the-art methods. Importantly, our proposed method serves as a generic framework which can be extended to problems with known correspondences. |
| Tasks | Graph Matching, Point Cloud Registration |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Le_SDRSAC_Semidefinite-Based_Randomized_Approach_for_Robust_Point_Cloud_Registration_Without_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Le_SDRSAC_Semidefinite-Based_Randomized_Approach_for_Robust_Point_Cloud_Registration_Without_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/sdrsac-semidefinite-based-randomized-approach-1 |
| Repo | https://github.com/intellhave/SDRSAC |
| Framework | none |
Sampling Sketches for Concave Sublinear Functions of Frequencies
| Title | Sampling Sketches for Concave Sublinear Functions of Frequencies |
| Authors | Edith Cohen, Ofir Geri |
| Abstract | We consider massive distributed datasets that consist of elements modeled as key-value pairs and the task of computing statistics or aggregates where the contribution of each key is weighted by a function of its frequency (sum of values of its elements). This fundamental problem has a wealth of applications in data analytics and machine learning, in particular, with concave sublinear functions of the frequencies that mitigate the disproportionate effect of keys with high frequency. The family of concave sublinear functions includes low frequency moments ($p \leq 1$), capping, logarithms, and their compositions. A common approach is to sample keys, ideally, proportionally to their contributions and estimate statistics from the sample. A simple but costly way to do this is by aggregating the data to produce a table of keys and their frequencies, apply our function to the frequency values, and then apply a weighted sampling scheme. Our main contribution is the design of composable sampling sketches that can be tailored to any concave sublinear function of the frequencies. Our sketch structure size is very close to the desired sample size and our samples provide statistical guarantees on the estimation quality that are very close to that of an ideal sample of the same size computed over aggregated data. Finally, we demonstrate experimentally the simplicity and effectiveness of our methods. |
| Tasks | |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/8417-sampling-sketches-for-concave-sublinear-functions-of-frequencies |
| http://papers.nips.cc/paper/8417-sampling-sketches-for-concave-sublinear-functions-of-frequencies.pdf | |
| PWC | https://paperswithcode.com/paper/sampling-sketches-for-concave-sublinear |
| Repo | https://github.com/ofirgeri/concave-sublinear-sampling |
| Framework | none |
Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis
| Title | Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis |
| Authors | Weiyu Cheng, Yanyan Shen, Linpeng Huang, Yanmin Zhu |
| Abstract | Latent factor models (LFMs) such as matrix factorization have achieved the state-of-the-art performance among various collaborative filtering approaches for recommendation. Despite the high recommendation accuracy of LFMs, a critical issue to be resolved is their lack of interpretability. Extensive efforts have been devoted to interpreting the prediction results of LFMs. However, they either rely on auxiliary information which may not be available in practice, or sacrifice recommendation accuracy for interpretability. Influence functions, stemming from robust statistics, have been developed to understand the effect of training points on the predictions of black-box models. Inspired by this, we propose a novel explanation method named FIA (Fast Influence Analysis) to understand the prediction of trained LFMs by tracing back to the training data with influence functions. We present how to employ influence functions to measure the impact of historical user-item interactions on the prediction results of LFMs and provide intuitive neighbor-style explanations based on the most influential interactions. Our proposed FIA exploits the characteristics of two important LFMs, matrix factorization and neural collaborative filtering, and is capable of accelerating the overall influence analysis process. We provide a detailed complexity analysis for FIA over LFMs and conduct extensive experiments to evaluate its performance using real-world datasets. The results demonstrate the effectiveness and efficiency of FIA, and the usefulness of the generated explanations for the recommendation results. |
| Tasks | |
| Published | 2019-08-04 |
| URL | https://doi.org/10.1145/3292500.3330857 |
| https://weiyucheng.github.io/Files/kdd19-sigconf.pdf | |
| PWC | https://paperswithcode.com/paper/incorporating-interpretability-into-latent |
| Repo | https://github.com/WeiyuCheng/FIA-KDD-19 |
| Framework | tf |
Accuracy Evaluation of Overlapping and Multi-resolution Clustering Algorithms on Large Datasets
| Title | Accuracy Evaluation of Overlapping and Multi-resolution Clustering Algorithms on Large Datasets |
| Authors | Artem Lutov, Mourad Khayati, Philippe Cudré-Mauroux |
| Abstract | Performance of clustering algorithms is evaluated with the help of accuracy metrics. There is a great diversity of clustering algorithms, which are key components of many data analysis and exploration systems. However, there exist only few metrics for the accuracy measurement of overlapping and multi-resolution clustering algorithms on large datasets. In this paper, we first discuss existing metrics, how they satisfy a set of formal constraints, and how they can be applied to specific cases. Then, we propose several optimizations and extensions of these metrics. More specifically, we introduce a new indexing technique to reduce both the runtime and the memory complexity of the Mean F1 score evaluation. Our technique can be applied on large datasets and it is faster on a single CPU than state-of-the-art implementations running on high-performance servers. In addition, we propose several extensions of the discussed metrics to improve their effectiveness and satisfaction to formal constraints without affecting their efficiency. All the metrics discussed in this paper are implemented in C++ and are available for free as open-source packages that can be used either as stand-alone tools or as part of a benchmarking system to compare various clustering algorithms. |
| Tasks | Accuracy Metrics |
| Published | 2019-02-27 |
| URL | https://arxiv.org/abs/1902.01691 |
| https://arxiv.org/pdf/1902.01691 | |
| PWC | https://paperswithcode.com/paper/accuracy-evaluation-of-overlapping-and-multi |
| Repo | https://github.com/eXascaleInfolab/xmeasures |
| Framework | none |
Learning towards Abstractive Timeline Summarization
| Title | Learning towards Abstractive Timeline Summarization |
| Authors | Xiuying Chen, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao, Rui Yan |
| Abstract | Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods.In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events.Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order.To tackle this challenge, we propose a memory-based timeline summarization model (MTS).Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process.Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events.Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations. |
| Tasks | Abstractive Text Summarization, Document Summarization, Timeline Summarization, Time Series |
| Published | 2019-08-11 |
| URL | https://www.ijcai.org/proceedings/2019/686 |
| https://www.ijcai.org/proceedings/2019/0686.pdf | |
| PWC | https://paperswithcode.com/paper/learning-towards-abstractive-timeline |
| Repo | https://github.com/yingtaomj/Learning-towards-Abstractive-Timeline-Summarization |
| Framework | none |
On the Over-Smoothing Problem of CNN Based Disparity Estimation
| Title | On the Over-Smoothing Problem of CNN Based Disparity Estimation |
| Authors | Chuangrong Chen, Xiaozhi Chen, Hui Cheng |
| Abstract | Currently, most deep learning based disparity estimation methods have the problem of over-smoothing at boundaries, which is unfavorable for some applications such as point cloud segmentation, mapping, etc. To address this problem, we first analyze the potential causes and observe that the estimated disparity at edge boundary pixels usually follows multimodal distributions, causing over-smoothing estimation. Based on this observation, we propose a single-modal weighted average operation on the probability distribution during inference, which can alleviate the problem effectively. To integrate the constraint of this inference method into training stage, we further analyze the characteristics of different loss functions and found that using cross entropy with gaussian distribution consistently further improves the performance. For quantitative evaluation, we propose a novel metric that measures the disparity error in the local structure of edge boundaries. Experiments on various datasets using various networks show our method’s effectiveness and general applicability. Code will be available at https://github.com/chenchr/otosp. |
| Tasks | Disparity Estimation |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Chen_On_the_Over-Smoothing_Problem_of_CNN_Based_Disparity_Estimation_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_On_the_Over-Smoothing_Problem_of_CNN_Based_Disparity_Estimation_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-over-smoothing-problem-of-cnn-based |
| Repo | https://github.com/chenchr/otosp |
| Framework | none |
Elastic Boundary Projection for 3D Medical Image Segmentation
| Title | Elastic Boundary Projection for 3D Medical Image Segmentation |
| Authors | Tianwei Ni, Lingxi Xie, Huangjie Zheng, Elliot K. Fishman, Alan L. Yuille |
| Abstract | We focus on an important yet challenging problem: using a 2D deep network to deal with 3D segmentation for medical image analysis. Existing approaches either applied multi-view planar (2D) networks or directly used volumetric (3D) networks for this purpose, but both of them are not ideal: 2D networks cannot capture 3D contexts effectively, and 3D networks are both memory-consuming and less stable arguably due to the lack of pre-trained models. In this paper, we bridge the gap between 2D and 3D using a novel approach named Elastic Boundary Projection (EBP). The key observation is that, although the object is a 3D volume, what we really need in segmentation is to find its boundary which is a 2D surface. Therefore, we place a number of pivot points in the 3D space, and for each pivot, we determine its distance to the object boundary along a dense set of directions. This creates an elastic shell around each pivot which is initialized as a perfect sphere. We train a 2D deep network to determine whether each ending point falls within the object, and gradually adjust the shell so that it gradually converges to the actual shape of the boundary and thus achieves the goal of segmentation. EBP allows boundary-based segmentation without cutting a 3D volume into slices or patches, which stands out from conventional 2D and 3D approaches. EBP achieves promising accuracy in abdominal organ segmentation. Our code will be released on https://github.com/twni2016/Elastic-Boundary-Projection . |
| Tasks | Medical Image Segmentation, Semantic Segmentation |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Ni_Elastic_Boundary_Projection_for_3D_Medical_Image_Segmentation_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Ni_Elastic_Boundary_Projection_for_3D_Medical_Image_Segmentation_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/elastic-boundary-projection-for-3d-medical-1 |
| Repo | https://github.com/twni2016/EBP |
| Framework | pytorch |
Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning
| Title | Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning |
| Authors | Xiaopeng Yan, Ziliang Chen, Anni Xu, Xiaoxi Wang, Xiaodan Liang, Liang Lin |
| Abstract | Resembling the rapid learning capability of human, low-shot learning empowers vision systems to understand new concepts by training with few samples. Leading approaches derived from meta-learning on images with a single visual object. Obfuscated by a complex background and multiple objects in one image, they are hard to promote the research of low-shot object detection/segmentation. In this work, we present a flexible and general methodology to achieve these tasks. Our work extends Faster /Mask R-CNN by proposing meta-learning over RoI (Region-of-Interest) features instead of a full image feature. This simple spirit disentangles multi-object information merged with the background, without bells and whistles, enabling Faster /Mask R-CNN turn into a meta-learner to achieve the tasks. Specifically, we introduce a Predictor-head Remodeling Network (PRN) that shares its main backbone with Faster /Mask R-CNN. PRN receives images containing low-shot objects with their bounding boxes or masks to infer their class attentive vectors. The vectors take channel-wise soft-attention on RoI features, remodeling those R-CNN predictor heads to detect or segment the objects consistent with the classes these vectors represent. In our experiments, Meta R-CNN yields the new state of the art in low-shot object detection and improves low-shot object segmentation by Mask R-CNN. Code: https://yanxp.github.io/metarcnn.html. |
| Tasks | Few-Shot Object Detection, Meta-Learning, Object Detection, Semantic Segmentation |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Yan_Meta_R-CNN_Towards_General_Solver_for_Instance-Level_Low-Shot_Learning_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Yan_Meta_R-CNN_Towards_General_Solver_for_Instance-Level_Low-Shot_Learning_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/meta-r-cnn-towards-general-solver-for-1 |
| Repo | https://github.com/yanxp/MetaR-CNN |
| Framework | pytorch |
Deep Meta Metric Learning
| Title | Deep Meta Metric Learning |
| Authors | Guangyi Chen, Tianren Zhang, Jiwen Lu, Jie Zhou |
| Abstract | In this paper, we present a deep meta metric learning (DMML) approach for visual recognition. Unlike most existing deep metric learning methods formulating the learning process by an overall objective, our DMML formulates the metric learning in a meta way, and proves that softmax and triplet loss are consistent in the meta space. Specifically, we sample some subsets from the original training set and learn metrics across different subsets. In each sampled sub-task, we split the training data into a support set as well as a query set, and learn the set-based distance, instead of sample-based one, to verify the query cell from multiple support cells. In addition, we introduce hard sample mining for set-based distance to encourage the intra-class compactness. Experimental results on three visual recognition applications including person re-identification, vehicle re-identification and face verification show that the proposed DMML method outperforms most existing approaches. |
| Tasks | Face Verification, Metric Learning, Person Re-Identification, Vehicle Re-Identification |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Chen_Deep_Meta_Metric_Learning_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Deep_Meta_Metric_Learning_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/deep-meta-metric-learning |
| Repo | https://github.com/CHENGY12/DMML |
| Framework | pytorch |
PAC-Bayes under potentially heavy tails
| Title | PAC-Bayes under potentially heavy tails |
| Authors | Matthew Holland |
| Abstract | We derive PAC-Bayesian learning guarantees for heavy-tailed losses, and obtain a novel optimal Gibbs posterior which enjoys finite-sample excess risk bounds at logarithmic confidence. Our core technique itself makes use of PAC-Bayesian inequalities in order to derive a robust risk estimator, which by design is easy to compute. In particular, only assuming that the first three moments of the loss distribution are bounded, the learning algorithm derived from this estimator achieves nearly sub-Gaussian statistical error, up to the quality of the prior. |
| Tasks | |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/8539-pac-bayes-under-potentially-heavy-tails |
| http://papers.nips.cc/paper/8539-pac-bayes-under-potentially-heavy-tails.pdf | |
| PWC | https://paperswithcode.com/paper/pac-bayes-under-potentially-heavy-tails-1 |
| Repo | https://github.com/feedbackward/1dim |
| Framework | none |
