pwc-archive/papers-with-abstracts huggingface dataset:
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps
Authors: Manuel López-Radcenco, Jean-Marc Delouis, Laurent Vibert
Date: 2020-12-17 00:00:00
Tasks: Data Augmentation, Dimensionality Reduction, Transfer Learning

In the present work, we propose a neural network based data inversion approach to reduce structured contamination sources, with a particular focus on the mapmaking for Planck High Frequency Instrument (Planck-HFI) data and the removal of large-scale systematic effects within the produced sky maps. The removal of contamination sources is rendered possible by the structured nature of these sources, which is characterized by local spatiotemporal interactions producing couplings between different spatiotemporal scales. We focus on exploring neural networks as a means of exploiting these couplings to learn optimal low-dimensional representations, optimized with respect to the contamination source removal and mapmaking objectives, to achieve robust and effective data inversion. We develop multiple variants of the proposed approach, and consider the inclusion of physics informed constraints and transfer learning techniques. Additionally, we focus on exploiting data augmentation techniques to integrate expert knowledge into an otherwise unsupervised network training approach. We validate the proposed method on Planck-HFI 545 GHz Far Side Lobe simulation data, considering ideal and non-ideal cases involving partial, gap-filled and inconsistent datasets, and demonstrate the potential of the neural network based dimensionality reduction to accurately model and remove large-scale systematic effects. We also present an application to real Planck-HFI 857 GHz data, which illustrates the relevance of the proposed method to accurately model and capture structured contamination sources, with reported gains of up to one order of magnitude in terms of contamination removal performance. Importantly, the methods developed in this work are to be integrated in a new version of the SRoll algorithm (SRoll3), and we describe here SRoll3 857 GHz detector maps that will be released to the community.
Neural Radiance Flow for 4D View Synthesis and Video Processing
Authors: Yilun Du, Yinan Zhang, Hong-Xing Yu, Joshua B. Tenenbaum, Jiajun Wu
Date: 2020-12-17 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Super-Resolution, Super-Resolution, Temporal View Synthesis

We present a method, Neural Radiance Flow (NeRFlow),to learn a 4D spatial-temporal representation of a dynamic scene from a set of RGB images. Key to our approach is the use of a neural implicit representation that learns to capture the 3D occupancy, radiance, and dynamics of the scene. By enforcing consistency across different modalities, our representation enables multi-view rendering in diverse dynamic scenes, including water pouring, robotic interaction, and real images, outperforming state-of-the-art methods for spatial-temporal view synthesis. Our approach works even when inputs images are captured with only one camera. We further demonstrate that the learned representation can serve as an implicit scene prior, enabling video processing tasks such as image super-resolution and de-noising without any additional supervision.
Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency
Authors: Qiang Zhang, Tete Xiao, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang
Date: 2020-12-17 00:00:00
Proceeding: ICLR 2021 1
Tasks: Friction, Imitation Learning, Transfer Learning

At the heart of many robotics problems is the challenge of learning correspondences across domains. For instance, imitation learning requires obtaining correspondence between humans and robots; sim-to-real requires correspondence between physics simulators and the real world; transfer learning requires correspondences between different robotics environments. This paper aims to learn correspondence across domains differing in representation (vision vs. internal state), physics parameters (mass and friction), and morphology (number of limbs). Importantly, correspondences are learned using unpaired and randomly collected data from the two domains. We propose \textit{dynamics cycles} that align dynamic robot behavior across two domains using a cycle-consistency constraint. Once this correspondence is found, we can directly transfer the policy trained on one domain to the other, without needing any additional fine-tuning on the second domain. We perform experiments across a variety of problem domains, both in simulation and on real robot. Our framework is able to align uncalibrated monocular video of a real robot arm to dynamic state-action trajectories of a simulated arm without paired data. Video demonstrations of our results are available at: https://sjtuzq.github.io/cycle_dynamics.html .
Tag-based regulation of modules in genetic programming improves context-dependent problem solving
Authors: Alexander Lalejini, Matthew Andres Moreno, Charles Ofria
Date: 2020-12-16 00:00:00
Tasks: Program Synthesis, TAG

We introduce and experimentally demonstrate the utility of tag-based genetic regulation, a new genetic programming (GP) technique that allows programs to dynamically adjust which code modules to express. Tags are evolvable labels that provide a flexible mechanism for referencing code modules. Tag-based genetic regulation extends existing tag-based naming schemes to allow programs to "promote" and "repress" code modules in order to alter expression patterns. This extension allows evolution to structure a program as a gene regulatory network where modules are regulated based on instruction executions. We demonstrate the functionality of tag-based regulation on a range of program synthesis problems. We find that tag-based regulation improves problem-solving performance on context-dependent problems; that is, problems where programs must adjust how they respond to current inputs based on prior inputs. Indeed, the system could not evolve solutions to some context-dependent problems until regulation was added. Our implementation of tag-based genetic regulation is not universally beneficial, however. We identify scenarios where the correct response to a particular input never changes, rendering tag-based regulation an unneeded functionality that can sometimes impede adaptive evolution. Tag-based genetic regulation broadens our repertoire of techniques for evolving more dynamic genetic programs and can easily be incorporated into existing tag-enabled GP systems.
Optimizing the Parameters of A Physical Exercise Dose-Response Model: An Algorithmic Comparison
Authors: Mark Connor, Michael O'Neill
Date: 2020-12-16 00:00:00
Tasks: global-optimization

The purpose of this research was to compare the robustness and performance of a local and global optimization algorithm when given the task of fitting the parameters of a common non-linear dose-response model utilized in the field of exercise physiology. Traditionally the parameters of dose-response models have been fit using a non-linear least-squares procedure in combination with local optimization algorithms. However, these algorithms have demonstrated limitations in their ability to converge on a globally optimal solution. This research purposes the use of an evolutionary computation based algorithm as an alternative method to fit a nonlinear dose-response model. The results of our comparison over 1000 experimental runs demonstrate the superior performance of the evolutionary computation based algorithm to consistently achieve a stronger model fit and holdout performance in comparison to the local search algorithm. This initial research would suggest that global evolutionary computation based optimization algorithms may present a fast and robust alternative to local algorithms when fitting the parameters of non-linear dose-response models.
Deep Learning Techniques for Super-Resolution in Video Games
Authors: Alexander Watson
Date: 2020-12-17 00:00:00
Tasks: Deep Learning, Super-Resolution, Video Super-Resolution

The computational cost of video game graphics is increasing and hardware for processing graphics is struggling to keep up. This means that computer scientists need to develop creative new ways to improve the performance of graphical processing hardware. Deep learning techniques for video super-resolution can enable video games to have high quality graphics whilst offsetting much of the computational cost. These emerging technologies allow consumers to have improved performance and enjoyment from video games and have the potential to become standard within the game development industry.
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning
Authors: Ying Bi, Bing Xue, Mengjie Zhang
Date: 2020-12-17 00:00:00
Tasks: General Classification, image-classification, Image Classification

Using evolutionary computation algorithms to solve multiple tasks with knowledge sharing is a promising approach. Image feature learning can be considered as a multitask problem because different tasks may have a similar feature space. Genetic programming (GP) has been successfully applied to image feature learning for classification. However, most of the existing GP methods solve one task, independently, using sufficient training data. No multitask GP method has been developed for image feature learning. Therefore, this paper develops a multitask GP approach to image feature learning for classification with limited training data. Owing to the flexible representation of GP, a new knowledge sharing mechanism based on a new individual representation is developed to allow GP to automatically learn what to share across two tasks and to improve its learning performance. The shared knowledge is encoded as a common tree, which can represent the common/general features of two tasks. With the new individual representation, each task is solved using the features extracted from a common tree and a task-specific tree representing task-specific features. To learn the best common and task-specific trees, a new evolutionary process and new fitness functions are developed. The performance of the proposed approach is examined on six multitask problems of 12 image classification datasets with limited training data and compared with three GP and 14 non-GP-based competitive methods. Experimental results show that the new approach outperforms these compared methods in almost all the comparisons. Further analysis reveals that the new approach learns simple yet effective common trees with high effectiveness and transferability.
MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification
Authors: Te-Lin Wu, Shikhar Singh, Sayan Paul, Gully Burns, Nanyun Peng
Date: 2020-12-16 00:00:00
Tasks: Classification, General Classification

We introduce a new dataset, MELINDA, for Multimodal biomEdicaL experImeNt methoD clAssification. The dataset is collected in a fully automated distant supervision manner, where the labels are obtained from an existing curated database, and the actual contents are extracted from papers associated with each of the records in the database. We benchmark various state-of-the-art NLP and computer vision models, including unimodal models which only take either caption texts or images as inputs, and multimodal models. Extensive experiments and analysis show that multimodal models, despite outperforming unimodal ones, still need improvements especially on a less-supervised way of grounding visual concepts with languages, and better transferability to low resource domains. We release our dataset and the benchmarks to facilitate future research in multimodal learning, especially to motivate targeted improvements for applications in scientific domains.
InSRL: A Multi-view Learning Framework Fusing Multiple Information Sources for Distantly-supervised Relation Extraction
Authors: Zhendong Chu, Haiyun Jiang, Yanghua Xiao, Wei Wang
Date: 2020-12-17 00:00:00
Tasks: MULTI-VIEW LEARNING, Relation, Relation Extraction, Representation Learning

Distant supervision makes it possible to automatically label bags of sentences for relation extraction by leveraging knowledge bases, but suffers from the sparse and noisy bag issues. Additional information sources are urgently needed to supplement the training data and overcome these issues. In this paper, we introduce two widely-existing sources in knowledge bases, namely entity descriptions, and multi-grained entity types to enrich the distantly supervised data. We see information sources as multiple views and fusing them to construct an intact space with sufficient information. An end-to-end multi-view learning framework is proposed for relation extraction via Intact Space Representation Learning (InSRL), and the representations of single views are jointly learned simultaneously. Moreover, inner-view and cross-view attention mechanisms are used to highlight important information on different levels on an entity-pair basis. The experimental results on a popular benchmark dataset demonstrate the necessity of additional information sources and the effectiveness of our framework. We will release the implementation of our model and dataset with multiple information sources after the anonymized review phase.
Interactive Question Clarification in Dialogue via Reinforcement Learning
Authors: Xiang Hu, Zujie Wen, Yafang Wang, Xiaolong Li, Gerard de Melo
Date: 2020-12-17 00:00:00
Proceeding: COLING 2020 8
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Coping with ambiguous questions has been a perennial problem in real-world dialogue systems. Although clarification by asking questions is a common form of human interaction, it is hard to define appropriate questions to elicit more specific intents from a user. In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query. We first formulate a collection partitioning problem to select a set of labels enabling us to distinguish potential unambiguous intents. We list the chosen labels as intent phrases to the user for further confirmation. The selected label along with the original user query then serves as a refined query, for which a suitable response can more easily be identified. The model is trained using reinforcement learning with a deep policy network. We evaluate our model based on real-world user clicks and demonstrate significant improvements across several different experiments.
Ultra-Fast, Low-Storage, Highly Effective Coarse-grained Selection in Retrieval-based Chatbot by Using Deep Semantic Hashing
Authors: Tian Lan, Xian-Ling Mao, Xiaoyan Gao, Wei Wei, Heyan Huang
Date: 2020-12-17 00:00:00
Tasks: Chatbot, Open-Ended Question Answering, Retrieval

We study the coarse-grained selection module in retrieval-based chatbot. Coarse-grained selection is a basic module in a retrieval-based chatbot, which constructs a rough candidate set from the whole database to speed up the interaction with customers. So far, there are two kinds of approaches for coarse-grained selection module: (1) sparse representation; (2) dense representation. To the best of our knowledge, there is no systematic comparison between these two approaches in retrieval-based chatbots, and which kind of method is better in real scenarios is still an open question. In this paper, we first systematically compare these two methods from four aspects: (1) effectiveness; (2) index stoarge; (3) search time cost; (4) human evaluation. Extensive experiment results demonstrate that dense representation method significantly outperforms the sparse representation, but costs more time and storage occupation. In order to overcome these fatal weaknesses of dense representation method, we propose an ultra-fast, low-storage, and highly effective Deep Semantic Hashing Coarse-grained selection method, called DSHC model. Specifically, in our proposed DSHC model, a hashing optimizing module that consists of two autoencoder models is stacked on a trained dense representation model, and three loss functions are designed to optimize it. The hash codes provided by hashing optimizing module effectively preserve the rich semantic and similarity information in dense vectors. Extensive experiment results prove that, our proposed DSHC model can achieve much faster speed and lower storage than sparse representation, with limited performance loss compared with dense representation. Besides, our source codes have been publicly released for future research.
Hate Speech detection in the Bengali language: A dataset and its baseline evaluation
Authors: Nauros Romim, Mosahed Ahmed, Hriteshwar Talukder, Md Saiful Islam
Date: 2020-12-17 00:00:00
Tasks: Hate Speech Detection

Social media sites such as YouTube and Facebook have become an integral part of everyone's life and in the last few years, hate speech in the social media comment section has increased rapidly. Detection of hate speech on social media websites faces a variety of challenges including small imbalanced data sets, the findings of an appropriate model and also the choice of feature analysis method. further more, this problem is more severe for the Bengali speaking community due to the lack of gold standard labelled datasets. This paper presents a new dataset of 30,000 user comments tagged by crowd sourcing and varified by experts. All the comments are collected from YouTube and Facebook comment section and classified into seven categories: sports, entertainment, religion, politics, crime, celebrity and TikTok & meme. A total of 50 annotators annotated each comment three times and the majority vote was taken as the final annotation. Nevertheless, we have conducted base line experiments and several deep learning models along with extensive pre-trained Bengali word embedding such as Word2Vec, FastText and BengFastText on this dataset to facilitate future research opportunities. The experiment illustrated that although all deep learning models performed well, SVM achieved the best result with 87.5% accuracy. Our core contribution is to make this benchmark dataset available and accessible to facilitate further research in the field of in the field of Bengali hate speech detection.
Five Psycholinguistic Characteristics for Better Interaction with Users
Authors: Sanja Štajner, Seren Yenikent, Marc Franco-Salvador
Date: 2020-12-17 00:00:00
Tasks: Binary Classification

When two people pay attention to each other and are interested in what the other has to say or write, they almost instantly adapt their writing/speaking style to match the other. For a successful interaction with a user, chatbots and dialogue systems should be able to do the same. We propose a framework consisting of five psycholinguistic textual characteristics for better human-computer interaction. We describe the annotation processes used for collecting the data, and benchmark five binary classification tasks, experimenting with different training sizes and model architectures. The best architectures noticeably outperform several baselines and achieve macro-averaged F$_1$-scores between 72\% and 96\% depending on the language and the task. The proposed framework proved to be fairly easy to model for various languages even with small amount of manually annotated data if right architectures are used.
The voice of COVID-19: Acoustic correlates of infection
Authors: Katrin D. Bartl-Pokorny, Florian B. Pokorny, Anton Batliner, Shahin Amiriparian, Anastasia Semertzidou, Florian Eyben, Elena Kramer, Florian Schmidt, Rainer Schönweiler, Markus Wehler, Björn W. Schuller
Date: 2020-12-17 00:00:00

COVID-19 is a global health crisis that has been affecting many aspects of our daily lives throughout the past year. The symptomatology of COVID-19 is heterogeneous with a severity continuum. A considerable proportion of symptoms are related to pathological changes in the vocal system, leading to the assumption that COVID-19 may also affect voice production. For the very first time, the present study aims to investigate voice acoustic correlates of an infection with COVID-19 on the basis of a comprehensive acoustic parameter set. We compare 88 acoustic features extracted from recordings of the vowels /i:/, /e:/, /o:/, /u:/, and /a:/ produced by 11 symptomatic COVID-19 positive and 11 COVID-19 negative German-speaking participants. We employ the Mann-Whitney U test and calculate effect sizes to identify features with the most prominent group differences. The mean voiced segment length and the number of voiced segments per second yield the most important differences across all vowels indicating discontinuities in the pulmonic airstream during phonation in COVID-19 positive participants. Group differences in the front vowels /i:/ and /e:/ are additionally reflected in the variation of the fundamental frequency and the harmonics-to-noise ratio, group differences in back vowels /o:/ and /u:/ in statistics of the Mel-frequency cepstral coefficients and the spectral slope. Findings of this study can be considered an important proof-of-concept contribution for a potential future voice-based identification of individuals infected with COVID-19.
The effectiveness of unsupervised subword modeling with autoregressive and cross-lingual phone-aware networks
Authors: Siyuan Feng, Odette Scharenborg
Date: 2020-12-17 00:00:00
Tasks: Self-Supervised Learning, Transfer Learning

This study addresses unsupervised subword modeling, i.e., learning acoustic feature representations that can distinguish between subword units of a language. We propose a two-stage learning framework that combines self-supervised learning and cross-lingual knowledge transfer. The framework consists of autoregressive predictive coding (APC) as the front-end and a cross-lingual deep neural network (DNN) as the back-end. Experiments on the ABX subword discriminability task conducted with the Libri-light and ZeroSpeech 2017 databases showed that our approach is competitive or superior to state-of-the-art studies. Comprehensive and systematic analyses at the phoneme- and articulatory feature (AF)-level showed that our approach was better at capturing diphthong than monophthong vowel information, while also differences in the amount of information captured for different types of consonants were observed. Moreover, a positive correlation was found between the effectiveness of the back-end in capturing a phoneme's information and the quality of the cross-lingual phone labels assigned to the phoneme. The AF-level analysis together with t-SNE visualization results showed that the proposed approach is better than MFCC and APC features in capturing manner and place of articulation information, vowel height, and backness information. Taken together, the analyses showed that the two stages in our approach are both effective in capturing phoneme and AF information. Nevertheless, monophthong vowel information is less well captured than consonant information, which suggests that future research should focus on improving capturing monophthong vowel information.