Reviewerly/exHarmony huggingface dataset:
Autonomous Driving Motion Planning With Constrained Iterative LQR

Motion planning is a core technique for autonomous driving. Nowadays, there still exists a lot of challenges in motion planning for autonomous driving in complicated environments due to: 1) the need of both spatial and temporal planning in highly dynamic environments; 2) nonlinear vehicle dynamic models and non-convex collision avoidance constraints; and 3) the need of high computation efficiency for real-time implementation. Iterative linear quadratic regulator (ILQR) is an algorithm to solve the optimal control problem with nonlinear system very efficiently. However, it can not deal with constraints. In this paper, the constrained iterative LQR (CILQR) is proposed to efficiently solve the optimal control problem with nonlinear system dynamics and general form of constraints. An autonomous driving motion planning problem is then formulated and solved using CILQR. Simulation case studies show the capability of the CILQR algorithm to solve these kind of problems and the computation efficiency of CILQR is shown to be much higher than the standard SQP solver.
Remote sensing images destriping using unidirectional hybrid total variation and nonconvex low-rank regularization

In this paper, we propose a novel model for remote sensing images destriping, which includes the Schatten 1∕2-norm and the unidirectional first-order and high-order total variation regularization. The main idea is that the stripe layer is low-rank, and the desired image possesses smoothness across stripes. Therefore, we use the Schatten 1∕2-norm regularization to depict the low-rankness of stripes, and use the unidirectional total variation and the unidirectional high-order total variation to guarantee the smoothness of the underlying image. We develop the alternating direction method of multipliers algorithm to solve the proposed model. Extensive experiments on synthetic and real data are reported to show the superiority of the proposed method over state-of-the-art methods in terms of both quantitative and qualitative assessments.
SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics.Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress.We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish.33 teams submitted 186 systems, which were evaluated on two subtasks. OverviewRecent years have seen an exponentially rising interest in computational Lexical Semantic Change (LSC) detection (Tahmasebi et al., 2018;Kutuzov et al., 2018).However, the field is lacking standard evaluation tasks and data.Almost all papers differ in how the evaluation is performed and what factors are considered in the evaluation.Very few are evaluated on a manually annotated diachronic corpus (McGillivray et al., 2019;Perrone et al., 2019; Schlechtweg et al., 2019, e.g.).This puts a damper on the development of computational models for LSC, and is a barrier for high-quality, comparable results that can be used in follow-up tasks.We report the results of the first SemEval shared task on Unsupervised LSC detection. 1We introduce two related subtasks for computational LSC detection, which aim to identify the change in meaning of words over time using corpus data.We provide a high-quality multilingual (English, German, Latin, Swedish) LSC gold standard relying on approximately 100,000 instances of human judgment.For the first time, it is possible to compare the variety of proposed models on relatively solid grounds and across languages, and to put previously reached conclusions on trial.We may now provide answers to questions concerning the performance of different types of semantic representations (such as token embeddings vs. type embeddings, and topic models vs. vector space models), alignment methods and change measures.We provide a thorough analysis of the submitted results uncovering trends for models and opening perspectives for further improvements.In addition to this, the CodaLab website will remain open to allow any reader to directly and easily compare their results to the participating systems.We expect the long-term impact of the task to be significant, and hope to encourage the study of LSC in more languages than are currently studied, in particular less-resourced languages. SubtasksFor the proposed tasks we rely on the comparison of two time-specific corpora C 1 and C 2 .While this simplifies the LSC detection problem, it has two main advantages: (i) it reduces the number of time periods for which data has to be annotated, so we can annotate larger corpus samples and hence more reliably represent the sense distributions of target words; (ii) it reduces the task complexity, allowing
Multilingual Neural Machine Translation with Knowledge Distillation.

Multilingual machine translation, which translates multiple languages with a single model, has attracted much attention due to its efficiency of offline training and online serving. However, traditional multilingual translation usually yields inferior accuracy compared with the counterpart using individual models for each language pair, due to language diversity and model capacity limitations. In this paper, we propose a distillation-based approach to boost the accuracy of multilingual machine translation. Specifically, individual models are first trained and regarded as teachers, and then the multilingual model is trained to fit the training data and match the outputs of individual models simultaneously through knowledge distillation. Experiments on IWSLT, WMT and Ted talk translation datasets demonstrate the effectiveness of our method. Particularly, we show that one model is enough to handle multiple languages (up to 44 languages in our experiment), with comparable or even better accuracy than individual models.
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting

J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Distinguishing two types of labels for multi-label feature selection

Multi-label feature selection plays an important role in pattern recognition, which can improve multi-label classification performance. In traditional multi-label feature selection methods based on information theory, feature relevance is evaluated by the accumulated mutual information between a candidate feature and each label. However, to the best of our knowledge, traditional methods ignore the effect of label redundancy on the evaluation of feature relevance. To address this issue, we propose a new multi-label feature selection method named multi-label Feature Selection based on Label Redundancy (LRFS). First, we categorize labels into two groups: independent labels and dependent labels. Second, by analyzing the differences between independent labels and dependent labels, we propose a new feature relevance term, that is, the conditional mutual information between candidate features and each label given other labels. Finally, we combine the new feature relevance term with the feature redundancy term to design our feature selection method. To evaluate the classification performance of our method, LRFS is compared to three information-theoretical-based multi-label feature selection methods on an artificial data set. Furthermore, LRFS is compared to five algorithm adaption feature selection methods and two problem transformation feature selection methods on 12 real-world multi-label data sets. The experimental results demonstrate that LRFS outperforms the other compared methods in terms of four evaluation metrics.
Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging

Since the advent of deep convolutional neural networks (DNNs), computer vision has seen an extremely rapid progress that has led to huge advances in medical imaging. Every year, many new methods are reported at conferences such as the International Conference on Medical Image Computing and Computer-Assisted Intervention and Machine Learning for Medical Image Reconstruction, or published online at the preprint server arXiv. There is a plethora of surveys on applications of neural networks in medical imaging (see [1] for a relatively recent comprehensive survey). This article does not aim to cover all aspects of the field, but focuses on a particular topic, image-to-image translation. Although the topic may not sound familiar, it turns out that many seemingly irrelevant applications can be understood as instances of image-to-image translation. Such applications include (1) noise reduction, (2) super-resolution, (3) image synthesis, and (4) reconstruction. The same underlying principles and algorithms work for various tasks. Our aim is to introduce some of the key ideas on this topic from a uniform viewpoint. We introduce core ideas and jargon that are specific to image processing by use of DNNs. Having an intuitive grasp of the core ideas of applications of neural networks in medical imaging and a knowledge of technical terms would be of great help to the reader for understanding the existing and future applications. Most of the recent applications which build on image-to-image translation are based on one of two fundamental architectures, called pix2pix and CycleGAN, depending on whether the available training data are paired or unpaired (see Sect. 1.3). We provide codes ([2, 3]) which implement these two architectures with various enhancements. Our codes are available online with use of the very permissive MIT license. We provide a hands-on tutorial for training a model for denoising based on our codes (see Sect. 6). We hope that this article, together with the codes, will provide both an overview and the details of the key algorithms and that it will serve as a basis for the development of new applications.
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation

Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio (Lebret et al., 2016), often contain reference texts that diverge from the information in the corresponding semistructured data.We show that metrics which rely solely on the reference texts, such as BLEU and ROUGE, show poor correlation with human judgments when those references diverge.We propose a new metric, PAR-ENT, which aligns n-grams from the reference and generated texts to the semi-structured data before computing their precision and recall.Through a large scale human evaluation study of table-to-text models for WikiBio, we show that PARENT correlates with human judgments better than existing text generation metrics.We also adapt and evaluate the information extraction based evaluation proposed in Wiseman et al. (2017), and show that PAR-ENT has comparable correlation to it, while being easier to use.We show that PARENT is also applicable when the reference texts are elicited from humans using the data from the WebNLG challenge.
IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning

Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate.
Deep Image Deblurring: A Survey

Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. This paper presents a comprehensive and timely survey of recently published deep-learning based image deblurring approaches, aiming to serve the community as a useful literature review. We start by discussing common causes of image blur, introduce benchmark datasets and performance metrics, and summarize different problem formulations. Next, we present a taxonomy of methods using convolutional neural networks (CNN) based on architecture, loss function, and application, offering a detailed review and comparison. In addition, we discuss some domain-specific deblurring applications including face images, text, and stereo image pairs. We conclude by discussing key challenges and future research directions.
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing

Linguistic typology aims to capture structural and semantic variation across the world’s languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-utilization of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such an approach could be facilitated by recent developments in data-driven induction of typological knowledge.
Phase unwrapping in optical metrology via denoised and convolutional segmentation networks

The interferometry technique is commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system.
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input

Non-autoregressive translation (NAT) models, which remove the dependence on previous target tokens from the inputs of the decoder, achieve significantly inference speedup but at the cost of inferior accuracy compared to autoregressive translation (AT) models. Previous work shows that the quality of the inputs of the decoder is important and largely impacts the model accuracy. In this paper, we propose two methods to enhance the decoder inputs so as to improve NAT models. The first one directly leverages a phrase table generated by conventional SMT approaches to translate source tokens to target tokens, which are then fed into the decoder as inputs. The second one transforms source-side word embeddings to target-side word embeddings through sentence-level alignment and word-level adversary learning, and then feeds the transformed word embeddings into the decoder as inputs. Experimental results show our method largely outperforms the NAT baseline (Gu et al. 2017) by 5.11 BLEU scores on WMT14 English-German task and 4.72 BLEU scores on WMT16 English-Romanian task.
ToTTo: A Controlled Table-To-Text Generation Dataset

We present TOTTO, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.To obtain generated targets that are natural but also faithful to the source table, we introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia.We present systematic analyses of our dataset and annotation process as well as results achieved by several state-of-the-art baselines.While usually fluent, existing methods often hallucinate phrases that are not supported by the table, suggesting that this dataset can serve as a useful research benchmark for high-precision conditional text generation.
Two-Level Attention Network With Multi-Grain Ranking Loss for Vehicle Re-Identification

Vehicle re-identification (re-ID) aims to identify the same vehicle across multiple non-overlapping cameras, which is rather a challenging task. On the one hand, subtle changes in viewpoint and illumination condition can make the same vehicle look much different. On the other hand, different vehicles, even different vehicle models, may look quite similar. In this paper, we propose a novel Two-level Attention network supervised by a Multi-grain Ranking loss (TAMR) to learn an efficient feature embedding for the vehicle re-ID task. The two-level attention network consisting of hard part-level attention and soft pixel-level attention can adaptively extract discriminative features from the visual appearance of vehicles. The former one is designed to localize the salient vehicle parts, such as windscreen and car head. The latter one gives an additional attention refinement at pixel level to focus on the distinctive characteristics within each part. In addition, we present a multi-grain ranking loss to further enhance the discriminative ability of learned features. We creatively take the multi-grain relationship between vehicles into consideration. Thus, not only the discrimination between different vehicles but also the distinction between different vehicle models is constrained. Finally, the proposed network can learn a feature space, where both intra-class compactness and inter-class discrimination are well guaranteed. Extensive experiments demonstrate the effectiveness of our approach and we achieve state-of-the-art results on two challenging datasets, including VehicleID and Vehicle-1M.
Pano

Streaming 360° videos requires more bandwidth than non-360° videos. This is because current solutions assume that users perceive the quality of 360° videos in the same way they perceive the quality of non-360° videos. This means the bandwidth demand must be proportional to the size of the user's field of view. However, we found several quality-determining factors unique to 360° videos, which can help reduce the bandwidth demand. They include the moving speed of a user's viewpoint (center of the user's field of view), the recent change of video luminance, and the difference in depth-of-fields of visual objects around the viewpoint.
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios

Deep neural networks and huge language models are becoming omnipresent in natural language applications.As they are known for requiring large amounts of training data, there is a growing body of work to improve the performance in low-resource settings.Motivated by the recent fundamental changes towards neural models and the popular pre-train and fine-tune paradigm, we survey promising approaches for low-resource natural language processing.After a discussion about the different dimensions of data availability, we give a structured overview of methods that enable learning when training data is sparse.This includes mechanisms to create additional labeled data like data augmentation and distant supervision as well as transfer learning settings that reduce the need for target supervision.A goal of our survey is to explain how these methods differ in their requirements as understanding them is essential for choosing a technique suited for a specific low-resource setting.Further key aspects of this work are to highlight open issues and to outline promising directions for future research.
Incremental Structure-Mapping

Many cognitive tasks involving analogy, such as understanding metaphors, problem-solving, and learning, require the ability to extend mappings as new information is found. This paper describes a new version of SME, called I-SME, that operates incrementally. I-SME is inspired by Keane’s IAM model and the use of incremental mapping in Falkenhainer’s PHINEAS learning system. We describe the I-SME algorithm and discuss tradeoffs introduced by incremental mapping, including parallel versus serial processing and pragmatic influences. The utility of I-SME is illustrated by two examples. First, we show that I-SME can account for the psychological results found by Keane on a serial version of the Holyoak & Thagard attribute mapping task. Second, we describe how I-SME is used in the Minimal Analogical Reasoning System (MARS), which uses analogy to solve engineering thermodynamics problems.
Efficient Online Multi-Person 2D Pose Tracking With Recurrent Spatio-Temporal Affinity Fields

We present an online approach to efficiently and simultaneously detect and track 2D poses of multiple people in a video sequence. We build upon Part Affinity Field (PAF) representation designed for static images, and propose an architecture that can encode and predict Spatio-Temporal Affinity Fields (STAF) across a video sequence. In particular, we propose a novel temporal topology cross-linked across limbs which can consistently handle body motions of a wide range of magnitudes. Additionally, we make the overall approach recurrent in nature, where the network ingests STAF heatmaps from previous frames and estimates those for the current frame. Our approach uses only online inference and tracking, and is currently the fastest and the most accurate bottom-up approach that is runtime-invariant to the number of people in the scene and accuracy-invariant to input frame rate of camera. Running at ~30 fps on a single GPU at single scale, it achieves highly competitive results on the PoseTrack benchmarks.