pwc-archive/papers-with-abstracts huggingface dataset:
Quantifying the Effects of COVID-19 on Mental Health Support Forums
Authors: Laura Biester, Katie Matton, Janarthanan Rajendran, Emily Mower Provost, Rada Mihalcea
Date: 2020-09-08 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12

The COVID-19 pandemic, like many of the disease outbreaks that have preceded it, is likely to have a profound effect on mental health. Understanding its impact can inform strategies for mitigating negative consequences. In this work, we seek to better understand the effects of COVID-19 on mental health by examining discussions within mental health support communities on Reddit. First, we quantify the rate at which COVID-19 is discussed in each community, or subreddit, in order to understand levels of preoccupation with the pandemic. Next, we examine the volume of activity in order to determine whether the quantity of people seeking online mental health support has risen. Finally, we analyze how COVID-19 has influenced language use and topics of discussion within each subreddit.
Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function
Authors: Devendra Singh Sachan, Manzil Zaheer, Ruslan Salakhutdinov
Date: 2020-09-08 00:00:00
Proceeding: AAAI 2019 2019 2
Tasks: Classification, General Classification, Language Modeling, Language Modelling, Relation Extraction, Semi-Supervised Text Classification, Sentiment Analysis, Text Classification, Topic Classification

In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semi-supervised approaches. Several prior works have suggested that either complex pretraining schemes using unsupervised methods such as language modeling (Dai and Le 2015; Miyato, Dai, and Goodfellow 2016) or complicated models (Johnson and Zhang 2017) are necessary to achieve a high classification accuracy. However, we develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results compared with more complex approaches. Furthermore, in addition to cross-entropy loss, by using a combination of entropy minimization, adversarial, and virtual adversarial losses for both labeled and unlabeled data, we report state-of-the-art results for text classification task on several benchmark datasets. In particular, on the ACL-IMDB sentiment analysis and AG-News topic classification datasets, our method outperforms current approaches by a substantial margin. We also show the generality of the mixed objective function by improving the performance on relation extraction task.
Fuzzy Unique Image Transformation: Defense Against Adversarial Attacks On Deep COVID-19 Models
Authors: Achyut Mani Tripathi, Ashish Mishra
Date: 2020-09-08 00:00:00

Early identification of COVID-19 using a deep model trained on Chest X-Ray and CT images has gained considerable attention from researchers to speed up the process of identification of active COVID-19 cases. These deep models act as an aid to hospitals that suffer from the unavailability of specialists or radiologists, specifically in remote areas. Various deep models have been proposed to detect the COVID-19 cases, but few works have been performed to prevent the deep models against adversarial attacks capable of fooling the deep model by using a small perturbation in image pixels. This paper presents an evaluation of the performance of deep COVID-19 models against adversarial attacks. Also, it proposes an efficient yet effective Fuzzy Unique Image Transformation (FUIT) technique that downsamples the image pixels into an interval. The images obtained after the FUIT transformation are further utilized for training the secure deep model that preserves high accuracy of the diagnosis of COVID-19 cases and provides reliable defense against the adversarial attacks. The experiments and results show the proposed model prevents the deep model against the six adversarial attacks and maintains high accuracy to classify the COVID-19 cases from the Chest X-Ray image and CT image Datasets. The results also recommend that a careful inspection is required before practically applying the deep models to diagnose the COVID-19 cases.
Bayesian Inverse Reinforcement Learning for Collective Animal Movement
Authors: Toryn L. J. Schafer, Christopher K. Wikle, Mevin B. Hooten
Date: 2020-09-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the self propelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.
Conditional Uncorrelation and Efficient Non-approximate Subset Selection in Sparse Regression
Authors: Jianji Wang, Qi Liu, Shupei Zhang, Nanning Zheng, Fei-Yue Wang
Date: 2020-09-08 00:00:00
Tasks: regression

Given $m$ $d$-dimensional responsors and $n$ $d$-dimensional predictors, sparse regression finds at most $k$ predictors for each responsor for linear approximation, $1\leq k \leq d-1$. The key problem in sparse regression is subset selection, which usually suffers from high computational cost. Recent years, many improved approximate methods of subset selection have been published. However, less attention has been paid on the non-approximate method of subset selection, which is very necessary for many questions in data analysis. Here we consider sparse regression from the view of correlation, and propose the formula of conditional uncorrelation. Then an efficient non-approximate method of subset selection is proposed in which we do not need to calculate any coefficients in regression equation for candidate predictors. By the proposed method, the computational complexity is reduced from $O(\frac{1}{6}{k^3}+mk^2+mkd)$ to $O(\frac{1}{6}{k^3}+\frac{1}{2}mk^2)$ for each candidate subset in sparse regression. Because the dimension $d$ is generally the number of observations or experiments and large enough, the proposed method can greatly improve the efficiency of non-approximate subset selection.
A Distance-preserving Matrix Sketch
Authors: Leland Wilkinson, Hengrui Luo
Date: 2020-09-08 00:00:00
Tasks: Clustering, feature selection, Visual Reasoning

Visualizing very large matrices involves many formidable problems. Various popular solutions to these problems involve sampling, clustering, projection, or feature selection to reduce the size and complexity of the original task. An important aspect of these methods is how to preserve relative distances between points in the higher-dimensional space after reducing rows and columns to fit in a lower dimensional space. This aspect is important because conclusions based on faulty visual reasoning can be harmful. Judging dissimilar points as similar or similar points as dissimilar on the basis of a visualization can lead to false conclusions. To ameliorate this bias and to make visualizations of very large datasets feasible, we introduce two new algorithms that respectively select a subset of rows and columns of a rectangular matrix. This selection is designed to preserve relative distances as closely as possible. We compare our matrix sketch to more traditional alternatives on a variety of artificial and real datasets.
Modeling Wildfire Perimeter Evolution using Deep Neural Networks
Authors: Maxfield E. Green, Karl Kaiser, Nat Shenton
Date: 2020-09-08 00:00:00
Tasks: Management

With the increased size and frequency of wildfire eventsworldwide, accurate real-time prediction of evolving wildfirefronts is a crucial component of firefighting efforts and for-est management practices. We propose a wildfire spreadingmodel that predicts the evolution of the wildfire perimeter in24 hour periods. The fire spreading simulation is based ona deep convolutional neural network (CNN) that is trainedon remotely sensed atmospheric and environmental time se-ries data. We show that the model is able to learn wildfirespreading dynamics from real historic data sets from a seriesof wildfires in the Western Sierra Nevada Mountains in Cal-ifornia. We validate the model on a previously unseen wild-fire and produce realistic results that significantly outperformhistoric alternatives with validation accuracies ranging from78% - 98%
Convergence Rates of Empirical Bayes Posterior Distributions: A Variational Perspective
Authors: Fengshuo Zhang, Chao GAO
Date: 2020-09-08 00:00:00
Tasks: Density Estimation

We study the convergence rates of empirical Bayes posterior distributions for nonparametric and high-dimensional inference. We show that as long as the hyperparameter set is discrete, the empirical Bayes posterior distribution induced by the maximum marginal likelihood estimator can be regarded as a variational approximation to a hierarchical Bayes posterior distribution. This connection between empirical Bayes and variational Bayes allows us to leverage the recent results in the variational Bayes literature, and directly obtains the convergence rates of empirical Bayes posterior distributions from a variational perspective. For a more general hyperparameter set that is not necessarily discrete, we introduce a new technique called "prior decomposition" to deal with prior distributions that can be written as convex combinations of probability measures whose supports are low-dimensional subspaces. This leads to generalized versions of the classical "prior mass and testing" conditions for the convergence rates of empirical Bayes. Our theory is applied to a number of statistical estimation problems including nonparametric density estimation and sparse linear regression.
Health-behaviors associated with the growing risk of adolescent suicide attempts: A data-driven cross-sectional study
Authors: Zhiyuan Wei, Sayanti Mukherjee
Date: 2020-09-08 00:00:00

Purpose: Identify and examine the associations between health behaviors and increased risk of adolescent suicide attempts, while controlling for socioeconomic and demographic differences. Design: A data-driven analysis using cross-sectional data. Setting: Communities in the state of Montana from 1999 to 2017. Subjects: Selected 22,447 adolescents of whom 1,631 adolescents attempted suicide at least once. Measures: Overall 29 variables (predictors) accounting for psychological behaviors, illegal substances consumption, daily activities at schools and demographic backgrounds, were considered. Analysis: A library of machine learning algorithms along with the traditionally-used logistic regression were used to model and predict suicide attempt risk. Model performances (goodness-of-fit and predictive accuracy) were measured using accuracy, precision, recall and F-score metrics. Results: The non-parametric Bayesian tree ensemble model outperformed all other models, with 80.0% accuracy in goodness-of-fit (F-score:0.802) and 78.2% in predictive accuracy (F-score:0.785). Key health-behaviors identified include: being sad/hopeless, followed by safety concerns at school, physical fighting, inhalant usage, illegal drugs consumption at school, current cigarette usage, and having first sex at an early age (below 15 years of age). Additionally, the minority groups (American Indian/Alaska Natives, Hispanics/Latinos), and females are also found to be highly vulnerable to attempting suicides. Conclusion: Significant contribution of this work is understanding the key health-behaviors and health disparities that lead to higher frequency of suicide attempts among adolescents, while accounting for the non-linearity and complex interactions among the outcome and the exposure variables.
Joint Pose and Shape Estimation of Vehicles from LiDAR Data
Authors: Hunter Goforth, Xiaoyan Hu, Michael Happold, Simon Lucey
Date: 2020-09-08 00:00:00

We address the problem of estimating the pose and shape of vehicles from LiDAR scans, a common problem faced by the autonomous vehicle community. Recent work has tended to address pose and shape estimation separately in isolation, despite the inherent connection between the two. We investigate a method of jointly estimating shape and pose where a single encoding is learned from which shape and pose may be decoded in an efficient yet effective manner. We additionally introduce a novel joint pose and shape loss, and show that this joint training method produces better results than independently-trained pose and shape estimators. We evaluate our method on both synthetic data and real-world data, and show superior performance against a state-of-the-art baseline.
Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling
Authors: Yiding Hao, Simon Mendelsohn, Rachel Sterneck, Randi Martinez, Robert Frank
Date: 2020-09-08 00:00:00
Proceeding: EMNLP (CMCL) 2020 11
Tasks: Language Modeling, Language Modelling

By positing a relationship between naturalistic reading times and information-theoretic surprisal, surprisal theory (Hale, 2001; Levy, 2008) provides a natural interface between language models and psycholinguistic models. This paper re-evaluates a claim due to Goodkind and Bicknell (2018) that a language model's ability to model reading times is a linear function of its perplexity. By extending Goodkind and Bicknell's analysis to modern neural architectures, we show that the proposed relation does not always hold for Long Short-Term Memory networks, Transformers, and pre-trained models. We introduce an alternate measure of language modeling performance called predictability norm correlation based on Cloze probabilities measured from human subjects. Our new metric yields a more robust relationship between language model quality and psycholinguistic modeling performance that allows for comparison between models with different training configurations.
Towards Unique and Informative Captioning of Images
Authors: Zeyu Wang, Berthy Feng, Karthik Narasimhan, Olga Russakovsky
Date: 2020-09-08 00:00:00
Proceeding: ECCV 2020 8
Tasks: Diversity, Image Captioning, Re-Ranking

Despite considerable progress, state of the art image captioning models produce generic captions, leaving out important image details. Furthermore, these systems may even misrepresent the image in order to produce a simpler caption consisting of common concepts. In this paper, we first analyze both modern captioning systems and evaluation metrics through empirical experiments to quantify these phenomena. We find that modern captioning systems return higher likelihoods for incorrect distractor sentences compared to ground truth captions, and that evaluation metrics like SPICE can be 'topped' using simple captioning systems relying on object detectors. Inspired by these observations, we design a new metric (SPICE-U) by introducing a notion of uniqueness over the concepts generated in a caption. We show that SPICE-U is better correlated with human judgements compared to SPICE, and effectively captures notions of diversity and descriptiveness. Finally, we also demonstrate a general technique to improve any existing captioning model -- by using mutual information as a re-ranking objective during decoding. Empirically, this results in more unique and informative captions, and improves three different state-of-the-art models on SPICE-U as well as average score over existing metrics.
Covid-Transformer: Detecting COVID-19 Trending Topics on Twitter Using Universal Sentence Encoder
Authors: Meysam Asgari-Chenaghlu, Narjes Nikzad-Khasmakhi, Shervin Minaee
Date: 2020-09-08 00:00:00
Tasks: Clustering, Sentence, Sentence Similarity, Text Summarization

The novel corona-virus disease (also known as COVID-19) has led to a pandemic, impacting more than 200 countries across the globe. With its global impact, COVID-19 has become a major concern of people almost everywhere, and therefore there are a large number of tweets coming out from every corner of the world, about COVID-19 related topics. In this work, we try to analyze the tweets and detect the trending topics and major concerns of people on Twitter, which can enable us to better understand the situation, and devise better planning. More specifically we propose a model based on the universal sentence encoder to detect the main topics of Tweets in recent months. We used universal sentence encoder in order to derive the semantic representation and the similarity of tweets. We then used the sentence similarity and their embeddings, and feed them to K-means clustering algorithm to group similar tweets (in semantic sense). After that, the cluster summary is obtained using a text summarization algorithm based on deep learning, which can uncover the underlying topics of each cluster. Through experimental results, we show that our model can detect very informative topics, by processing a large number of tweets on sentence level (which can preserve the overall meaning of the tweets). Since this framework has no restriction on specific data distribution, it can be used to detect trending topics from any other social media and any other context rather than COVID-19. Experimental results show superiority of our proposed approach to other baselines, including TF-IDF, and latent Dirichlet allocation (LDA).
Nonparametric Density Estimation from Markov Chains
Authors: Andrea De Simone, Alessandro Morandini
Date: 2020-09-08 00:00:00
Tasks: Density Estimation

We introduce a new nonparametric density estimator inspired by Markov Chains, and generalizing the well-known Kernel Density Estimator (KDE). Our estimator presents several benefits with respect to the usual ones and can be used straightforwardly as a foundation in all density-based algorithms. We prove the consistency of our estimator and we find it typically outperforms KDE in situations of large sample size and high dimensionality. We also employ our density estimator to build a local outlier detector, showing very promising results when applied to some realistic datasets.
Quantifying the Causal Effects of Conversational Tendencies
Authors: Justine Zhang, Sendhil Mullainathan, Cristian Danescu-Niculescu-Mizil
Date: 2020-09-08 00:00:00
Tasks: Causal Inference

Understanding what leads to effective conversations can aid the design of better computer-mediated communication platforms. In particular, prior observational work has sought to identify behaviors of individuals that correlate to their conversational efficiency. However, translating such correlations to causal interpretations is a necessary step in using them in a prescriptive fashion to guide better designs and policies. In this work, we formally describe the problem of drawing causal links between conversational behaviors and outcomes. We focus on the task of determining a particular type of policy for a text-based crisis counseling platform: how best to allocate counselors based on their behavioral tendencies exhibited in their past conversations. We apply arguments derived from causal inference to underline key challenges that arise in conversational settings where randomized trials are hard to implement. Finally, we show how to circumvent these inference challenges in our particular domain, and illustrate the potential benefits of an allocation policy informed by the resulting prescriptive information.
Binarized Neural Architecture Search for Efficient Object Recognition
Authors: Hanlin Chen, Li'an Zhuo, Baochang Zhang, Xiawu Zheng, Jianzhuang Liu, Rongrong Ji, David Doermann, Guodong Guo
Date: 2020-09-08 00:00:00
Tasks: Edge-computing, Face Recognition, Neural Architecture Search, Object, Object Recognition

Traditional neural architecture search (NAS) has a significant impact in computer vision by automatically designing network architectures for various tasks. In this paper, binarized neural architecture search (BNAS), with a search space of binarized convolutions, is introduced to produce extremely compressed models to reduce huge computational cost on embedded devices for edge computing. The BNAS calculation is more challenging than NAS due to the learning inefficiency caused by optimization requirements and the huge architecture space, and the performance loss when handling the wild data in various computing applications. To address these issues, we introduce operation space reduction and channel sampling into BNAS to significantly reduce the cost of searching. This is accomplished through a performance-based strategy that is robust to wild data, which is further used to abandon less potential operations. Furthermore, we introduce the Upper Confidence Bound (UCB) to solve 1-bit BNAS. Two optimization methods for binarized neural networks are used to validate the effectiveness of our BNAS. Extensive experiments demonstrate that the proposed BNAS achieves a comparable performance to NAS on both CIFAR and ImageNet databases. An accuracy of $96.53\%$ vs. $97.22\%$ is achieved on the CIFAR-10 dataset, but with a significantly compressed model, and a $40\%$ faster search than the state-of-the-art PC-DARTS. On the wild face recognition task, our binarized models achieve a performance similar to their corresponding full-precision models.
Neural-PDE: A RNN based neural network for solving time dependent PDEs
Authors: Yihao Hu, Tong Zhao, Shixin Xu, Zhiliang Xu, Lizhen Lin
Date: 2020-09-08 00:00:00
Tasks: BIG-bench Machine Learning

Partial differential equations (PDEs) play a crucial role in studying a vast number of problems in science and engineering. Numerically solving nonlinear and/or high-dimensional PDEs is often a challenging task. Inspired by the traditional finite difference and finite elements methods and emerging advancements in machine learning, we propose a sequence deep learning framework called Neural-PDE, which allows to automatically learn governing rules of any time-dependent PDE system from existing data by using a bidirectional LSTM encoder, and predict the next n time steps data. One critical feature of our proposed framework is that the Neural-PDE is able to simultaneously learn and simulate the multiscale variables.We test the Neural-PDE by a range of examples from one-dimensional PDEs to a high-dimensional and nonlinear complex fluids model. The results show that the Neural-PDE is capable of learning the initial conditions, boundary conditions and differential operators without the knowledge of the specific form of a PDE system.In our experiments the Neural-PDE can efficiently extract the dynamics within 20 epochs training, and produces accurate predictions. Furthermore, unlike the traditional machine learning approaches in learning PDE such as CNN and MLP which require vast parameters for model precision, Neural-PDE shares parameters across all time steps, thus considerably reduces the computational complexity and leads to a fast learning algorithm.