pwc-archive/papers-with-abstracts huggingface dataset:
Document Classification for COVID-19 Literature
Authors: Bernal Jiménez Gutiérrez, Juncheng Zeng, Dong-dong Zhang, Ping Zhang, Yu Su
Date: 2020-06-15 00:00:00
Proceeding: NLP-COVID19 (ACL) 2020 7
Tasks: Articles, Classification, Document Classification, General Classification

The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields. We provide an analysis of several multi-label document classification models on the LitCovid dataset, a growing collection of 23,000 research papers regarding the novel 2019 coronavirus. We find that pre-trained language models fine-tuned on this dataset outperform all other baselines and that BioBERT surpasses the others by a small margin with micro-F1 and accuracy scores of around 86% and 75% respectively on the test set. We evaluate the data efficiency and generalizability of these models as essential features of any system prepared to deal with an urgent situation like the current health crisis. Finally, we explore 50 errors made by the best performing models on LitCovid documents and find that they often (1) correlate certain labels too closely together and (2) fail to focus on discriminative sections of the articles; both of which are important issues to address in future work. Both data and code are available on GitHub.
Community-Based Data Integration of Course and Job Data in Support of Personalized Career-Education Recommendations
Authors: Guoqing Zhu, Naga Anjaneyulu Kopalle, Yongzhen Wang, Xiaozhong Liu, Kemi Jona, Katy Börner
Date: 2020-06-24 00:00:00
Tasks: Community Detection, Data Integration, Lifelong learning

How does your education impact your professional career? Ideally, the courses you take help you identify, get hired for, and perform the job you always wanted. However, not all courses provide skills that transfer to existing and future jobs; skill terms used in course descriptions might be different from those listed in job advertisements; and there might exist a considerable skill gap between what is taught in courses and what is needed for a job. In this study, we propose a novel method to integrate extensive course description and job advertisement data by leveraging heterogeneous data integration and community detection. The innovative heterogeneous graph approach along with identified skill communities enables cross-domain information recommendation, e.g., given an educational profile, job recommendations can be provided together with suggestions on education opportunities for re- and upskilling in support of lifelong learning.
Movie Box office Prediction via Joint Actor Representations and Social Media Sentiment
Authors: Dezhou Shen
Date: 2020-06-24 00:00:00
Tasks: Binary Classification

In recent years, driven by the Asian film industry, such as China and India, the global box office has maintained a steady growth trend. Previous studies have rarely used long-term, full-sample film data in analysis, lack of research on actors' social networks. Existing film box office prediction algorithms only use film meta-data, lack of using social network characteristics and the model is less interpretable. I propose a FC-GRU-CNN binary classification model in of box office prediction task, combining five characteristics, including the film meta-data, Sina Weibo text sentiment, actors' social network measurement, all pairs shortest path and actors' art contribution. Exploiting long-term memory ability of GRU layer in long sequences and the mapping ability of CNN layer in retrieving all pairs shortest path matrix features, proposed model is 14% higher in accuracy than the current best C-LSTM model.
Diagnosis Prevalence vs. Efficacy in Machine-learning Based Diagnostic Decision Support
Authors: Gil Alon, Elizabeth Chen, Guergana Savova, Carsten Eickhoff
Date: 2020-06-24 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic

Many recent studies use machine learning to predict a small number of ICD-9-CM codes. In practice, on the other hand, physicians have to consider a broader range of diagnoses. This study aims to put these previously incongruent evaluation settings on a more equal footing by predicting ICD-9-CM codes based on electronic health record properties and demonstrating the relationship between diagnosis prevalence and system performance. We extracted patient features from the MIMIC-III dataset for each admission. We trained and evaluated 43 different machine learning classifiers. Among this pool, the most successful classifier was a Multi-Layer Perceptron. In accordance with general machine learning expectation, we observed all classifiers' F1 scores to drop as disease prevalence decreased. Scores fell from 0.28 for the 50 most prevalent ICD-9-CM codes to 0.03 for the 1000 most prevalent ICD-9-CM codes. Statistical analyses showed a moderate positive correlation between disease prevalence and efficacy (0.5866).
Crossmodal Language Grounding in an Embodied Neurocognitive Model
Authors: Stefan Heinrich, Yuan YAO, Tobias Hinz, Zhiyuan Liu, Thomas Hummel, Matthias Kerzel, Cornelius Weber, Stefan Wermter
Date: 2020-06-24 00:00:00
Tasks: model

Human infants are able to acquire natural language seemingly easily at an early age. Their language learning seems to occur simultaneously with learning other cognitive functions as well as with playful interactions with the environment and caregivers. From a neuroscientific perspective, natural language is embodied, grounded in most, if not all, sensory and sensorimotor modalities, and acquired by means of crossmodal integration. However, characterising the underlying mechanisms in the brain is difficult and explaining the grounding of language in crossmodal perception and action remains challenging. In this paper, we present a neurocognitive model for language grounding which reflects bio-inspired mechanisms such as an implicit adaptation of timescales as well as end-to-end multimodal abstraction. It addresses developmental robotic interaction and extends its learning capabilities using larger-scale knowledge-based data. In our scenario, we utilise the humanoid robot NICO in obtaining the EMIL data collection, in which the cognitive robot interacts with objects in a children's playground environment while receiving linguistic labels from a caregiver. The model analysis shows that crossmodally integrated representations are sufficient for acquiring language merely from sensory input through interaction with objects in an environment. The representations self-organise hierarchically and embed temporal and spatial information through composition and decomposition. This model can also provide the basis for further crossmodal integration of perceptually grounded cognitive representations.
Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression
Authors: Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso, Joseph Salmon
Date: 2020-06-24 00:00:00
Tasks: regression

Owing to their statistical properties, non-convex sparse regularizers have attracted much interest for estimating a sparse linear model from high dimensional data. Given that the solution is sparse, for accelerating convergence, a working set strategy addresses the optimization problem through an iterative algorithm by incre-menting the number of variables to optimize until the identification of the solution support. While those methods have been well-studied and theoretically supported for convex regularizers, this paper proposes a working set algorithm for non-convex sparse regularizers with convergence guarantees. The algorithm, named FireWorks, is based on a non-convex reformulation of a recent primal-dual approach and leverages on the geometry of the residuals. Our theoretical guarantees derive from a lower bound of the objective function decrease between two inner solver iterations and shows the convergence to a stationary point of the full problem. More importantly, we also show that convergence is preserved even when the inner solver is inexact, under sufficient decay of the error across iterations. Our experimental results demonstrate high computational gain when using our working set strategy compared to the full problem solver for both block-coordinate descent or a proximal gradient solver.
OvA-INN: Continual Learning with Invertible Neural Networks
Authors: G. Hocquet, O. Bichler, D. Querlioz
Date: 2020-06-24 00:00:00
Tasks: Continual Learning

In the field of Continual Learning, the objective is to learn several tasks one after the other without access to the data from previous tasks. Several solutions have been proposed to tackle this problem but they usually assume that the user knows which of the tasks to perform at test time on a particular sample, or rely on small samples from previous data and most of them suffer of a substantial drop in accuracy when updated with batches of only one class at a time. In this article, we propose a new method, OvA-INN, which is able to learn one class at a time and without storing any of the previous data. To achieve this, for each class, we train a specific Invertible Neural Network to extract the relevant features to compute the likelihood on this class. At test time, we can predict the class of a sample by identifying the network which predicted the highest likelihood. With this method, we show that we can take advantage of pretrained models by stacking an Invertible Network on top of a feature extractor. This way, we are able to outperform state-of-the-art approaches that rely on features learning for the Continual Learning of MNIST and CIFAR-100 datasets. In our experiments, we reach 72% accuracy on CIFAR-100 after training our model one class at a time.
Time Series Analysis and Forecasting of COVID-19 Cases Using LSTM and ARIMA Models
Authors: Arko Barman
Date: 2020-06-05 00:00:00
Tasks: Time Series, Time Series Analysis

Coronavirus disease 2019 (COVID-19) is a global public health crisis that has been declared a pandemic by World Health Organization. Forecasting country-wise COVID-19 cases is necessary to help policymakers and healthcare providers prepare for the future. This study explores the performance of several Long Short-Term Memory (LSTM) models and Auto-Regressive Integrated Moving Average (ARIMA) model in forecasting the number of confirmed COVID-19 cases. Time series of daily cumulative COVID-19 cases were used for generating 1-day, 3-day, and 5-day forecasts using several LSTM models and ARIMA. Two novel k-period performance metrics - k-day Mean Absolute Percentage Error (kMAPE) and k-day Median Symmetric Accuracy (kMdSA) - were developed for evaluating the performance of the models in forecasting time series values for multiple days. Errors in prediction using kMAPE and kMdSA for LSTM models were both as low as 0.05%, while those for ARIMA were 0.07% and 0.06% respectively. LSTM models slightly underestimated while ARIMA slightly overestimated the numbers in the forecasts. The performance of LSTM models is comparable to ARIMA in forecasting COVID-19 cases. While ARIMA requires longer sequences, LSTMs can perform reasonably well with sequence sizes as small as 3. However, LSTMs require a large number of training samples. Further, the development of k-period performance metrics proposed is likely to be useful for performance evaluation of time series models in predicting multiple periods. Based on the k-period performance metrics proposed, both LSTMs and ARIMA are useful for time series analysis and forecasting for COVID-19.
Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks
Authors: Rohit Lokwani, Ashrika Gaikwad, Viraj Kulkarni, Aniruddha Pant, Amit Kharat
Date: 2020-06-23 00:00:00
Tasks: Sensitivity, Specificity

COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV (2003). Currently, swab samples are being used for its diagnosis. The most common testing method used is the RT-PCR method, which has high specificity but variable sensitivity. AI-based detection has the capability to overcome this drawback. In this paper, we propose a prospective method wherein we use chest CT scans to diagnose the patients for COVID-19 pneumonia. We use a set of open-source images, available as individual CT slices, and full CT scans from a private Indian Hospital to train our model. We build a 2D segmentation model using the U-Net architecture, which gives the output by marking out the region of infection. Our model achieves a sensitivity of 96.428% (95% CI: 88%-100%) and a specificity of 88.39% (95% CI: 82%-94%). Additionally, we derive a logic for converting our slice-level predictions to scan-level, which helps us reduce the false positives.
Certified variational quantum algorithms for eigenstate preparation
Authors: Andrey Kardashin, Alexey Uvarov, Dmitry Yudin, Jacob Biamonte
Date: 2020-06-23 00:00:00
Tasks: Variational Monte Carlo

Solutions to many-body problem instances often involve an intractable number of degrees of freedom and admit no known approximations in general form. In practice, representing quantum-mechanical states of a given Hamiltonian using available numerical methods, in particular those based on variational Monte Carlo simulations, become exponentially more challenging with increasing system size. Recently quantum algorithms implemented as variational models have been proposed to accelerate such simulations. The variational ansatz states are characterized by a polynomial number of parameters devised in a way to minimize the expectation value of a given Hamiltonian, which is emulated by local measurements. In this study, we develop a means to certify the termination of variational algorithms. We demonstrate our approach by applying it to three models: the transverse field Ising model, the model of one-dimensional spinless fermions with competing interactions, and the Schwinger model of quantum electrodynamics. By means of comparison, we observe that our approach shows better performance near critical points in these models. We hence take a further step to improve the applicability and to certify the results of variational quantum simulators.
Rescaling Egocentric Vision
Authors: Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Evangelos Kazakos, Jian Ma, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray
Date: 2020-06-23 00:00:00
Tasks: Action Anticipation, Action Detection, Action Recognition, Cross-Modal Retrieval, Domain Adaptation, Retrieval, Unsupervised Domain Adaptation

This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version, EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection enables new challenges such as action detection and evaluating the "test of time" - i.e. whether models trained on data collected in 2018 can generalise to new footage collected two years later. The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications
Authors: Imon Banerjee, Priyanshu Sinha, Saptarshi Purkayastha, Nazanin Mashhaditafreshi, Amara Tariq, Jiwoong Jeong, Hari Trivedi, Judy W. Gichoya
Date: 2020-06-23 00:00:00
Tasks: COVID-19 Diagnosis

Purpose: Since the recent COVID-19 outbreak, there has been an avalanche of research papers applying deep learning based image processing to chest radiographs for detection of the disease. To test the performance of the two top models for CXR COVID-19 diagnosis on external datasets to assess model generalizability. Methods: In this paper, we present our argument regarding the efficiency and applicability of existing deep learning models for COVID-19 diagnosis. We provide results from two popular models - COVID-Net and CoroNet evaluated on three publicly available datasets and an additional institutional dataset collected from EMORY Hospital between January and May 2020, containing patients tested for COVID-19 infection using RT-PCR. Results: There is a large false positive rate (FPR) for COVID-Net on both ChexPert (55.3%) and MIMIC-CXR (23.4%) dataset. On the EMORY Dataset, COVID-Net has 61.4% sensitivity, 0.54 F1-score and 0.49 precision value. The FPR of the CoroNet model is significantly lower across all the datasets as compared to COVID-Net - EMORY(9.1%), ChexPert (1.3%), ChestX-ray14 (0.02%), MIMIC-CXR (0.06%). Conclusion: The models reported good to excellent performance on their internal datasets, however we observed from our testing that their performance dramatically worsened on external data. This is likely from several causes including overfitting models due to lack of appropriate control patients and ground truth labels. The fourth institutional dataset was labeled using RT-PCR, which could be positive without radiographic findings and vice versa. Therefore, a fusion model of both clinical and radiographic data may have better performance and generalization.
Learning-to-Fly: Learning-based Collision Avoidance for Scalable Urban Air Mobility
Authors: Alëna Rodionova, Yash Vardhan Pant, Kuk Jang, Houssam Abbas, Rahul Mangharam
Date: 2020-06-23 00:00:00
Tasks: Collision Avoidance, Decision Making, Management

With increasing urban population, there is global interest in Urban Air Mobility (UAM), where hundreds of autonomous Unmanned Aircraft Systems (UAS) execute missions in the airspace above cities. Unlike traditional human-in-the-loop air traffic management, UAM requires decentralized autonomous approaches that scale for an order of magnitude higher aircraft densities and are applicable to urban settings. We present Learning-to-Fly (L2F), a decentralized on-demand airborne collision avoidance framework for multiple UAS that allows them to independently plan and safely execute missions with spatial, temporal and reactive objectives expressed using Signal Temporal Logic. We formulate the problem of predictively avoiding collisions between two UAS without violating mission objectives as a Mixed Integer Linear Program (MILP).This however is intractable to solve online. Instead, we develop L2F, a two-stage collision avoidance method that consists of: 1) a learning-based decision-making scheme and 2) a distributed, linear programming-based UAS control algorithm. Through extensive simulations, we show the real-time applicability of our method which is $\approx\!6000\times$ faster than the MILP approach and can resolve $100\%$ of collisions when there is ample room to maneuver, and shows graceful degradation in performance otherwise. We also compare L2F to two other methods and demonstrate an implementation on quad-rotor robots.
Automating Text Naturalness Evaluation of NLG Systems
Authors: Erion Çano, Ondřej Bojar
Date: 2020-06-23 00:00:00
Tasks: Text Generation

Automatic methods and metrics that assess various quality criteria of automatically generated texts are important for developing NLG systems because they produce repeatable results and allow for a fast development cycle. We present here an attempt to automate the evaluation of text naturalness which is a very important characteristic of natural language generation methods. Instead of relying on human participants for scoring or labeling the text samples, we propose to automate the process by using a human likeliness metric we define and a discrimination procedure based on large pretrained language models with their probability distributions. We analyze the text probability fractions and observe how they are influenced by the size of the generative and discriminative models involved in the process. Based on our results, bigger generators and larger pretrained discriminators are more appropriate for a better evaluation of text naturalness. A comprehensive validation procedure with human participants is required as follow up to check how well this automatic evaluation scheme correlates with human judgments.
Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images
Authors: Xiaocong Chen, Lina Yao, Tao Zhou, Jinming Dong, Yu Zhang
Date: 2020-06-16 00:00:00
Tasks: Contrastive Learning, COVID-19 Diagnosis, Diagnostic

The current pandemic, caused by the outbreak of a novel coronavirus (COVID-19) in December 2019, has led to a global emergency that has significantly impacted economies, healthcare systems and personal wellbeing all around the world. Controlling the rapidly evolving disease requires highly sensitive and specific diagnostics. While real-time RT-PCR is the most commonly used, these can take up to 8 hours, and require significant effort from healthcare professionals. As such, there is a critical need for a quick and automatic diagnostic system. Diagnosis from chest CT images is a promising direction. However, current studies are limited by the lack of sufficient training samples, as acquiring annotated CT images is time-consuming. To this end, we propose a new deep learning algorithm for the automated diagnosis of COVID-19, which only requires a few samples for training. Specifically, we use contrastive learning to train an encoder which can capture expressive feature representations on large and publicly available lung datasets and adopt the prototypical network for classification. We validate the efficacy of the proposed model in comparison with other competing methods on two publicly available and annotated COVID-19 CT datasets. Our results demonstrate the superior performance of our model for the accurate diagnosis of COVID-19 based on chest CT images.
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads
Authors: Jialin Ding, Vikram Nathan, Mohammad Alizadeh, Tim Kraska
Date: 2020-06-23 00:00:00

Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse. Techniques to accelerate the execution of filter expressions include clustered indexes, specialized sort orders (e.g., Z-order), multi-dimensional indexes, and, for high selectivity queries, secondary indexes. However, these schemes are hard to tune and their performance is inconsistent. Recent work on learned multi-dimensional indexes has introduced the idea of automatically optimizing an index for a particular dataset and workload. However, the performance of that work suffers in the presence of correlated data and skewed query workloads, both of which are common in real applications. In this paper, we introduce Tsunami, which addresses these limitations to achieve up to 6X faster query performance and up to 8X smaller index size than existing learned multi-dimensional indexes, in addition to up to 11X faster query performance and 170X smaller index size than optimally-tuned traditional indexes.