pwc-archive/papers-with-abstracts huggingface dataset:
Cross-Lingual Citations in English Papers: A Large-Scale Analysis of Prevalence, Usage, and Impact
Authors: Tarek Saier, Michael Färber, Tornike Tsereteli
Date: 2021-11-07 00:00:00
Tasks: Citation Intent Classification, Cross-Lingual Entity Linking, Data Visualization

Citation information in scholarly data is an important source of insight into the reception of publications and the scholarly discourse. Outcomes of citation analyses and the applicability of citation based machine learning approaches heavily depend on the completeness of such data. One particular shortcoming of scholarly data nowadays is that non-English publications are often not included in data sets, or that language metadata is not available. Because of this, citations between publications of differing languages (cross-lingual citations) have only been studied to a very limited degree. In this paper, we present an analysis of cross-lingual citations based on over one million English papers, spanning three scientific disciplines and a time span of three decades. Our investigation covers differences between cited languages and disciplines, trends over time, and the usage characteristics as well as impact of cross-lingual citations. Among our findings are an increasing rate of citations to publications written in Chinese, citations being primarily to local non-English languages, and consistency in citation intent between cross- and monolingual citations. To facilitate further research, we make our collected data and source code publicly available.
Emotional Prosody Control for Speech Generation
Authors: Sarath Sivaprasad, Saiteja Kosgi, Vineet Gandhi
Date: 2021-11-07 00:00:00
Tasks: text-to-speech, Text to Speech

Machine-generated speech is characterized by its limited or unnatural emotional variation. Current text to speech systems generates speech with either a flat emotion, emotion selected from a predefined set, average variation learned from prosody sequences in training data or transferred from a source style. We propose a text to speech(TTS) system, where a user can choose the emotion of generated speech from a continuous and meaningful emotion space (Arousal-Valence space). The proposed TTS system can generate speech from the text in any speaker's style, with fine control of emotion. We show that the system works on emotion unseen during training and can scale to previously unseen speakers given his/her speech sample. Our work expands the horizon of the state-of-the-art FastSpeech2 backbone to a multi-speaker setting and gives it much-coveted continuous (and interpretable) affective control, without any observable degradation in the quality of the synthesized speech.
High-order joint embedding for multi-level link prediction
Authors: Yubai Yuan, Annie Qu
Date: 2021-11-07 00:00:00
Tasks: Link Prediction, Network Embedding, Prediction, Vocal Bursts Intensity Prediction

Link prediction infers potential links from observed networks, and is one of the essential problems in network analyses. In contrast to traditional graph representation modeling which only predicts two-way pairwise relations, we propose a novel tensor-based joint network embedding approach on simultaneously encoding pairwise links and hyperlinks onto a latent space, which captures the dependency between pairwise and multi-way links in inferring potential unobserved hyperlinks. The major advantage of the proposed embedding procedure is that it incorporates both the pairwise relationships and subgroup-wise structure among nodes to capture richer network information. In addition, the proposed method introduces a hierarchical dependency among links to infer potential hyperlinks, and leads to better link prediction. In theory we establish the estimation consistency for the proposed embedding approach, and provide a faster convergence rate compared to link prediction utilizing pairwise links or hyperlinks only. Numerical studies on both simulation settings and Facebook ego-networks indicate that the proposed method improves both hyperlink and pairwise link prediction accuracy compared to existing link prediction algorithms.
A Survey of NLP-Related Crowdsourcing HITs: what works and what does not
Authors: Jessica Huynh, Jeffrey Bigham, Maxine Eskenazi
Date: 2021-11-09 00:00:00

Crowdsourcing requesters on Amazon Mechanical Turk (AMT) have raised questions about the reliability of the workers. The AMT workforce is very diverse and it is not possible to make blanket assumptions about them as a group. Some requesters now reject work en mass when they do not get the results they expect. This has the effect of giving each worker (good or bad) a lower Human Intelligence Task (HIT) approval score, which is unfair to the good workers. It also has the effect of giving the requester a bad reputation on the workers' forums. Some of the issues causing the mass rejections stem from the requesters not taking the time to create a well-formed task with complete instructions and/or not paying a fair wage. To explore this assumption, this paper describes a study that looks at the crowdsourcing HITs on AMT that were available over a given span of time and records information about those HITs. This study also records information from a crowdsourcing forum on the worker perspective on both those HITs and on their corresponding requesters. Results reveal issues in worker payment and presentation issues such as missing instructions or HITs that are not doable.
NATURE: Natural Auxiliary Text Utterances for Realistic Spoken Language Evaluation
Authors: David Alfonso-Hermelo, Ahmad Rashid, Abbas Ghaddar, Philippe Langlais, Mehdi Rezagholizadeh
Date: 2021-11-09 00:00:00
Tasks: Intent Detection, slot-filling, Slot Filling

Slot-filling and intent detection are the backbone of conversational agents such as voice assistants, and are active areas of research. Even though state-of-the-art techniques on publicly available benchmarks show impressive performance, their ability to generalize to realistic scenarios is yet to be demonstrated. In this work, we present NATURE, a set of simple spoken-language oriented transformations, applied to the evaluation set of datasets, to introduce human spoken language variations while preserving the semantics of an utterance. We apply NATURE to common slot-filling and intent detection benchmarks and demonstrate that simple perturbations from the standard evaluation set by NATURE can deteriorate model performance significantly. Through our experiments we demonstrate that when NATURE operators are applied to evaluation set of popular benchmarks the model accuracy can drop by up to 40%.
Tackling Morphological Analogies Using Deep Learning -- Extended Version
Authors: Safa Alsaidi, Amandine Decker, Esteban Marquer, Pierre-Alexandre Murena, Miguel Couceiro
Date: 2021-11-09 00:00:00
Tasks: Deep Learning

Analogical proportions are statements of the form "A is to B as C is to D". They constitute an inference tool that provides a logical framework to address learning, transfer, and explainability concerns and that finds useful applications in artificial intelligence and natural language processing. In this paper, we address two problems, namely, analogy detection and resolution in morphology. Multiple symbolic approaches tackle the problem of analogies in morphology and achieve competitive performance. We show that it is possible to use a data-driven strategy to outperform those models. We propose an approach using deep learning to detect and solve morphological analogies. It encodes structural properties of analogical proportions and relies on a specifically designed embedding model capturing morphological characteristics of words. We demonstrate our model's competitive performance on analogy detection and resolution over multiple languages. We provide an empirical study to analyze the impact of balancing training data and evaluate the robustness of our approach to input perturbation.
Multimodal intelligibility of scholarly hypertext: the documentalist's contribution. A required collaboration for serial documentisation in the scientific editorial process
Authors: Gérald Kembellec
Date: 2021-11-09 00:00:00

This article shows that the boundaries between the editing and online publishingprofessions are losing their strength. In this context it would only make sense that the wayhypertexts are documented be renewed, especially facing of the Web's evolution. We arethinking in particular of the trickier scholar hypertexts documentation process - specifically inscientific or cultural contexts. The purpose of this article is to demonstrate that, consideringthe numerous branches of the Web, the hypertext enhance of a document of quality can onlybe done through a proper dialogue between authors, editors, and broadcasters. It would satisfythe readership as they could reach the appropriate information. It will also be shown that eachactor in this auctorial-editorial process would be a gainer. Indeed, a qualitative formalizationwork would be coupled with a strong broadcasting scope. Finally, we will point out that thiswork of mediating must be led by an actor of information-communication, to make the textunderstandable to both humans and machines. This meditative act is designated here under theterm of serial documentarisation.
Robust Estimation for Random Graphs
Authors: Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang
Date: 2021-11-09 00:00:00

We study the problem of robustly estimating the parameter $p$ of an Erd\H{o}s-R\'enyi random graph on $n$ nodes, where a $\gamma$ fraction of nodes may be adversarially corrupted. After showing the deficiencies of canonical estimators, we design a computationally-efficient spectral algorithm which estimates $p$ up to accuracy $\tilde O(\sqrt{p(1-p)}/n + \gamma\sqrt{p(1-p)} /\sqrt{n}+ \gamma/n)$ for $\gamma < 1/60$. Furthermore, we give an inefficient algorithm with similar accuracy for all $\gamma <1/2$, the information-theoretic limit. Finally, we prove a nearly-matching statistical lower bound, showing that the error of our algorithms is optimal up to logarithmic factors.
Exploratory Factor Analysis of Data on a Sphere
Authors: Fan Dai, Karin S. Dorman, Somak Dutta, Ranjan Maitra
Date: 2021-11-09 00:00:00

Data on high-dimensional spheres arise frequently in many disciplines either naturally or as a consequence of preliminary processing and can have intricate dependence structure that needs to be understood. We develop exploratory factor analysis of the projected normal distribution to explain the variability in such data using a few easily interpreted latent factors. Our methodology provides maximum likelihood estimates through a novel fast alternating expectation profile conditional maximization algorithm. Results on simulation experiments on a wide range of settings are uniformly excellent. Our methodology provides interpretable and insightful results when applied to tweets with the $\#MeToo$ hashtag in early December 2018, to time-course functional Magnetic Resonance Images of the average pre-teen brain at rest, to characterize handwritten digits, and to gene expression data from cancerous cells in the Cancer Genome Atlas.
The World in a Grain of Sand: Condensing the String Vacuum Degeneracy
Authors: Yang-Hui He, Shailesh Lal, M. Zaid Zaz
Date: 2021-11-08 00:00:00
Tasks: Sand

We propose a novel approach toward the vacuum degeneracy problem of the string landscape, by finding an efficient measure of similarity amongst compactification scenarios. Using a class of some one million Calabi-Yau manifolds as concrete examples, the paradigm of few-shot machine-learning and Siamese Neural Networks represents them as points in R(3) where the similarity score between two manifolds is the Euclidean distance between their R(3) representatives. Using these methods, we can compress the search space for exceedingly rare manifolds to within one percent of the original data by training on only a few hundred data points. We also demonstrate how these methods may be applied to characterize `typicality' for vacuum representatives.
Community detection using low-dimensional network embedding algorithms
Authors: Aman Barot, Shankar Bhamidi, Souvik Dhara
Date: 2021-11-04 00:00:00
Tasks: Community Detection, Network Embedding, Representation Learning

With the increasing relevance of large networks in important areas such as the study of contact networks for spread of disease, or social networks for their impact on geopolitics, it has become necessary to study machine learning tools that are scalable to very large networks, often containing millions of nodes. One major class of such scalable algorithms is known as network representation learning or network embedding. These algorithms try to learn representations of network functionals (e.g.~nodes) by first running multiple random walks and then using the number of co-occurrences of each pair of nodes in observed random walk segments to obtain a low-dimensional representation of nodes on some Euclidean space. The aim of this paper is to rigorously understand the performance of two major algorithms, DeepWalk and node2vec, in recovering communities for canonical network models with ground truth communities. Depending on the sparsity of the graph, we find the length of the random walk segments required such that the corresponding observed co-occurrence window is able to perform almost exact recovery of the underlying community assignments. We prove that, given some fixed co-occurrence window, node2vec using random walks with a low non-backtracking probability can succeed for much sparser networks compared to DeepWalk using simple random walks. Moreover, if the sparsity parameter is low, we provide evidence that these algorithms might not succeed in almost exact recovery. The analysis requires developing general tools for path counting on random networks having an underlying low-rank structure, which are of independent interest.
A quantitative and qualitative open citation analysis of retracted articles in the humanities
Authors: Ivan Heibi, Silvio Peroni
Date: 2021-11-09 00:00:00
Tasks: Articles, Descriptive, Philosophy

In this article, we show and discuss the results of a quantitative and qualitative analysis of open citations to retracted publications in the humanities domain. Our study was conducted by selecting retracted papers in the humanities domain and marking their main characteristics (e.g., retraction reason). Then, we gathered the citing entities and annotated their basic metadata (e.g., title, venue, subject, etc.) and the characteristics of their in-text citations (e.g., intent, sentiment, etc.). Using these data, we performed a quantitative and qualitative study of retractions in the humanities, presenting descriptive statistics and a topic modeling analysis of the citing entities' abstracts and the in-text citation contexts. As part of our main findings, we noticed that there was no drop in the overall number of citations after the year of retraction, with few entities which have either mentioned the retraction or expressed a negative sentiment toward the cited publication. In addition, on several occasions, we noticed a higher concern/awareness when it was about citing a retracted publication, by the citing entities belonging to the health sciences domain, if compared to the humanities and the social science domains. Philosophy, arts, and history are the humanities areas that showed the higher concern toward the retraction.
Aggregated Feasible Region of Heterogeneous Demand-Side Flexible Resources---Part I: Theoretical Derivation of the Exact Model
Authors: Yilin Wen, Zechun Hu, Shi You, Xiaoyu Duan
Date: 2021-11-09 00:00:00

In the first part of the two-part series, the model to describe the exact aggregated feasible region (AFR) of multiple types of demand-side resources is derived. Based on a discrete-time unified individual model of heterogeneous resources, the calculation of AFR is, in fact, a feasible region projection problem. Therefore, the Fourier-Motzkin Elimination (FME) method is used for derivation. By analyzing the redundancy of all possible constraints in the FME process, the mathematical expression and calculation method for the exact AFR is proposed. The number of constraints is linear with the number of resources and is exponential with the number of time intervals, respectively. The computational complexity has been dramatically simplified compared with the original FME. However, the number of constraints in the model is still exponential and cannot be simplified anymore. Hence, In Part II of this paper, several approximation methods are proposed and analyzed in detail.
unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata
Authors: Tarek Saier, Michael Färber
Date: 2020-03-02 00:00:00
Proceeding: Scientometrics 2020 3
Tasks: Citation Recommendation, Document Summarization, Scientific Concept Extraction, Scientific Results Extraction, Semantic Text Matching

In recent years, scholarly data sets have been used for various purposes, such as paper recommendation, citation recommendation, citation context analysis, and citation context-based document summarization. The evaluation of approaches to such tasks and their applicability in real-world scenarios heavily depend on the used data set. However, existing scholarly data sets are limited in several regards. In this paper, we propose a new data set based on all publications from all scientific disciplines available on arXiv.org. Apart from providing the papers' plain text, in-text citations were annotated via global identifiers. Furthermore, citing and cited publications were linked to the Microsoft Academic Graph, providing access to rich metadata. Our data set consists of over one million documents and 29.2 million citation contexts. The data set, which is made freely available for research purposes, not only can enhance the future evaluation of research paper-based and citation context-based approaches, but also serve as a basis for new ways to analyze in-text citations, as we show prototypically in this article.
Electrostimulation of Brain Deep Structures in Parkinson's Disease
Authors: Elcin Huseyn
Date: 2021-09-18 00:00:00

The study involved 56 patients with advanced and late stages of Parkinsons disease, which could be considered as potentially requiring neurosurgical treatment-electrical stimulation of deep brain structures. An algorithm has been developed for selecting patients with advanced and late stages of Parkinsons disease for neurological treatment-implantation of a system for electrical stimulation of deep brain structures in distant neurosurgical centers, which includes two stages for patients with limited mobility - outpatient and inpatient. The development of an algorithm for referral to neurological treatment has shortened the path of a patient with limited mobility from a polyclinic to a neurological center. Electro stimulation of deep brain structures in Parkinsons disease significantly improved the condition of patients-to increase functional activity by 55%, reduce the severity of motor disorders by 55%, and reduce the dose of levodopa drugs by half.
Model-assisted deep learning of rare extreme events from partial observations
Authors: Anna Asch, Ethan Brady, Hugo Gallardo, John Hood, Bryan Chu, Mohammad Farazmand
Date: 2021-11-04 00:00:00

To predict rare extreme events using deep neural networks, one encounters the so-called small data problem because even long-term observations often contain few extreme events. Here, we investigate a model-assisted framework where the training data is obtained from numerical simulations, as opposed to observations, with adequate samples from extreme events. However, to ensure the trained networks are applicable in practice, the training is not performed on the full simulation data; instead we only use a small subset of observable quantities which can be measured in practice. We investigate the feasibility of this model-assisted framework on three different dynamical systems (Rossler attractor, FitzHugh-Nagumo model, and a turbulent fluid flow) and three different deep neural network architectures (feedforward, long short-term memory, and reservoir computing). In each case, we study the prediction accuracy, robustness to noise, reproducibility under repeated training, and sensitivity to the type of input data. In particular, we find long short-term memory networks to be most robust to noise and to yield relatively accurate predictions, while requiring minimal fine-tuning of the hyperparameters.
Stress field prediction in fiber-reinforced composite materials using a deep learning approach
Authors: Anindya Bhaduri, Ashwini Gupta, Lori Graham-Brady
Date: 2021-11-01 00:00:00

Computational stress analysis is an important step in the design of material systems. Finite element method (FEM) is a standard approach of performing stress analysis of complex material systems. A way to accelerate stress analysis is to replace FEM with a data-driven machine learning based stress analysis approach. In this study, we consider a fiber-reinforced matrix composite material system and we use deep learning tools to find an alternative to the FEM approach for stress field prediction. We first try to predict stress field maps for composite material systems of fixed number of fibers with varying spatial configurations. Specifically, we try to find a mapping between the spatial arrangement of the fibers in the composite material and the corresponding von Mises stress field. This is achieved by using a convolutional neural network (CNN), specifically a U-Net architecture, using true stress maps of systems with same number of fibers as training data. U-Net is a encoder-decoder network which in this study takes in the composite material image as an input and outputs the stress field image which is of the same size as the input image. We perform a robustness analysis by taking different initializations of the training samples to find the sensitivity of the prediction accuracy to the small number of training samples. When the number of fibers in the composite material system is increased for the same volume fraction, a finer finite element mesh discretization is required to represent the geometry accurately. This leads to an increase in the computational cost. Thus, the secondary goal here is to predict the stress field for systems with larger number of fibers with varying spatial configurations using information from the true stress maps of relatively cheaper systems of smaller fiber number.
Learning General Event Schemas with Episodic Logic
Authors: Lane Lawley, Benjamin Kuehnert, Lenhart Schubert
Date: 2021-06-01 00:00:00
Proceeding: ACL (NALOMA, IWCS) 2021 6
Tasks: World Knowledge

We present a system for learning generalized, stereotypical patterns of events—or “schemas”—from natural language stories, and applying them to make predictions about other stories. Our schemas are represented with Episodic Logic, a logical form that closely mirrors natural language. By beginning with a “head start” set of protoschemas— schemas that a 1- or 2-year-old child would likely know—we can obtain useful, general world knowledge with very few story examples—often only one or two. Learned schemas can be combined into more complex, composite schemas, and used to make predictions in other stories where only partial information is available.