pwc-archive/papers-with-abstracts huggingface dataset:
Automated triage of COVID-19 from various lung abnormalities using chest CT features
Authors: Dor Amran, Maayan Frid-Adar, Nimrod Sagie, Jannette Nassar, Asher Kabakovitch, Hayit Greenspan
Date: 2020-10-24 00:00:00
Tasks: Computed Tomography (CT), Descriptive, Specificity

The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.
Road Accident Proneness Indicator Based On Time, Weather And Location Specificity Using Graph Neural Networks
Authors: Srikanth Chandar, Anish Reddy, Muvazima Mansoor, Suresh Jamadagni
Date: 2020-10-24 00:00:00
Tasks: Graph Neural Network, Specificity

In this paper, we present a novel approach to identify the Spatio-temporal and environmental features that influence the safety of a road and predict its accident proneness based on these features. A total of 14 features were compiled based on Time, Weather, and Location (TWL) specificity along a road. To determine the influence each of the 14 features carries, a sensitivity study was performed using Principal Component Analysis. Using the locations of accident warnings, a Safety Index was developed to quantify how accident-prone a particular road is. We implement a novel approach to predict the Safety Index of a road-based on its TWL specificity by using a Graph Neural Network (GNN) architecture. The proposed architecture is uniquely suited for this application due to its ability to capture the complexities of the inherent nonlinear interlinking in a vast feature space. We employed a GNN to emulate the TWL feature vectors as individual nodes which were interlinked vis-\`a-vis edges of a graph. This model was verified to perform better than Logistic Regression, simple Feed-Forward Neural Networks, and even Long Short Term Memory (LSTM) Neural Networks. We validated our approach on a data set containing the alert locations along the routes of inter-state buses. The results achieved through this GNN architecture, using a TWL input feature space proved to be more feasible than the other predictive models, having reached a peak accuracy of 65%.
Advancing Non-Contact Vital Sign Measurement using Synthetic Avatars
Authors: Daniel McDuff, Javier Hernandez, Erroll Wood, Xin Liu, Tadas Baltrusaitis
Date: 2020-10-24 00:00:00
Tasks: Diversity

Non-contact physiological measurement has the potential to provide low-cost, non-invasive health monitoring. However, machine vision approaches are often limited by the availability and diversity of annotated video datasets resulting in poor generalization to complex real-life conditions. To address these challenges, this work proposes the use of synthetic avatars that display facial blood flow changes and allow for systematic generation of samples under a wide variety of conditions. Our results show that training on both simulated and real video data can lead to performance gains under challenging conditions. We show state-of-the-art performance on three large benchmark datasets and improved robustness to skin type and motion.
DeepAtrophy: Teaching a Neural Network to Differentiate Progressive Changes from Noise on Longitudinal MRI in Alzheimer's Disease
Authors: Mengjin Dong, Long Xie, Sandhitsu R. Das, Jiancong Wang, Laura E. M. Wisse, Robin deFlores, David A. Wolk, Paul Yushkevich
Date: 2020-10-24 00:00:00
Tasks: Hippocampus, Image Registration

Volume change measures derived from longitudinal MRI (e.g. hippocampal atrophy) are a well-studied biomarker of disease progression in Alzheimer's Disease (AD) and are used in clinical trials to track the therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures can be confounded by non-biological factors, such as different degrees of head motion and susceptibility artifact between pairs of MRI scans. We hypothesize that deep learning methods applied directly to pairs of longitudinal MRI scans can be trained to differentiate between biological changes and non-biological factors better than conventional approaches based on deformable image registration. To achieve this, we make a simplifying assumption that biological factors are associated with time (i.e. the hippocampus shrinks overtime in the aging population) whereas non-biological factors are independent of time. We then formulate deep learning networks to infer the temporal order of same-subject MRI scans input to the network in arbitrary order; as well as to infer ratios between interscan intervals for two pairs of same-subject MRI scans. In the test dataset, these networks perform better in tasks of temporal ordering (89.3%) and interscan interval inference (86.1%) than a state-of-the-art deformation-based morphometry method ALOHA (76.6% and 76.1% respectively) (Das et al., 2012). Furthermore, we derive a disease progression score from the network that is able to detect a group difference between 58 preclinical AD and 75 beta-amyloid-negative cognitively normal individuals within one year, compared to two years for ALOHA. This suggests that deep learning can be trained to differentiate MRI changes due to biological factors (tissue loss) from changes due to non-biological factors, leading to novel biomarkers that are more sensitive to longitudinal changes at the earliest stages of AD.
Neural Compound-Word (Sandhi) Generation and Splitting in Sanskrit Language
Authors: Sushant Dave, Arun Kumar Singh, Dr. Prathosh A. P., Prof. Brejesh Lall
Date: 2020-10-24 00:00:00
Tasks: Morphological Analysis

This paper describes neural network based approaches to the process of the formation and splitting of word-compounding, respectively known as the Sandhi and Vichchhed, in Sanskrit language. Sandhi is an important idea essential to morphological analysis of Sanskrit texts. Sandhi leads to word transformations at word boundaries. The rules of Sandhi formation are well defined but complex, sometimes optional and in some cases, require knowledge about the nature of the words being compounded. Sandhi split or Vichchhed is an even more difficult task given its non uniqueness and context dependence. In this work, we propose the route of formulating the problem as a sequence to sequence prediction task, using modern deep learning techniques. Being the first fully data driven technique, we demonstrate that our model has an accuracy better than the existing methods on multiple standard datasets, despite not using any additional lexical or morphological resources. The code is being made available at https://github.com/IITD-DataScience/Sandhi_Prakarana
A Benchmark Corpus and Neural Approach for Sanskrit Derivative Nouns Analysis
Authors: Arun Kumar Singh, Sushant Dave, Dr. Prathosh A. P., Prof. Brejesh Lall, Shresth Mehta
Date: 2020-10-24 00:00:00
Tasks: Morphological Analysis

This paper presents first benchmark corpus of Sanskrit Pratyaya (suffix) and inflectional words (padas) formed due to suffixes along with neural network based approaches to process the formation and splitting of inflectional words. Inflectional words spans the primary and secondary derivative nouns as the scope of current work. Pratyayas are an important dimension of morphological analysis of Sanskrit texts. There have been Sanskrit Computational Linguistics tools for processing and analyzing Sanskrit texts. Unfortunately there has not been any work to standardize & validate these tools specifically for derivative nouns analysis. In this work, we prepared a Sanskrit suffix benchmark corpus called Pratyaya-Kosh to evaluate the performance of tools. We also present our own neural approach for derivative nouns analysis while evaluating the same on most prominent Sanskrit Morphological Analysis tools. This benchmark will be freely dedicated and available to researchers worldwide and we hope it will motivate all to improve morphological analysis in Sanskrit Language.
Recurrent Neural Based Electricity Load Forecasting of G-20 Members
Authors: Jaymin Suhagiya, Deep Raval, Siddhi Vinayak Pandey, Jeet Patel, Ayushi Gupta, Akshay Srivastava
Date: 2020-10-24 00:00:00
Tasks: Load Forecasting

Forecasting the actual amount of electricity with respect to the need/demand of the load is always been a challenging task for each power plants based generating stations. Due to uncertain demand of electricity at receiving end of station causes several challenges such as: reduction in performance parameters of generating and receiving end stations, minimization in revenue, increases the jeopardize for the utility to predict the future energy need for a company etc. With this issues, the precise forecasting of load at the receiving end station is very consequential parameter to establish the impeccable balance between supply and demand chain. In this paper, the load forecasting of G-20 members have been performed utilizing the Recurrent Neural Network coupled with sliding window approach for data generation. During the experimentation we have achieved Mean Absolute Test Error of 16.2193 TWh using LSTM.
Triclustering in Big Data Setting
Authors: Dmitry Egurnov, Dmitry I. Ignatov, Dmitry Tochilkin
Date: 2020-10-24 00:00:00

In this paper, we describe versions of triclustering algorithms adapted for efficient calculations in distributed environments with MapReduce model or parallelisation mechanism provided by modern programming languages. OAC-family of triclustering algorithms shows good parallelisation capabilities due to the independent processing of triples of a triadic formal context. We provide the time and space complexity of the algorithms and justify their relevance. We also compare performance gain from using a distributed system and scalability.
LagNetViP: A Lagrangian Neural Network for Video Prediction
Authors: Christine Allen-Blanchette, Sushant Veer, Anirudha Majumdar, Naomi Ehrich Leonard
Date: 2020-10-24 00:00:00
Tasks: Acrobot, OpenAI Gym, Prediction, Video Prediction

The dominant paradigms for video prediction rely on opaque transition models where neither the equations of motion nor the underlying physical quantities of the system are easily inferred. The equations of motion, as defined by Newton's second law, describe the time evolution of a physical system state and can therefore be applied toward the determination of future system states. In this paper, we introduce a video prediction model where the equations of motion are explicitly constructed from learned representations of the underlying physical quantities. To achieve this, we simultaneously learn a low-dimensional state representation and system Lagrangian. The kinetic and potential energy terms of the Lagrangian are distinctly modelled and the low-dimensional equations of motion are explicitly constructed using the Euler-Lagrange equations. We demonstrate the efficacy of this approach for video prediction on image sequences rendered in modified OpenAI gym Pendulum-v0 and Acrobot environments.
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
Authors: Arnaud Delaunoy, Antoine Wehenkel, Tanja Hinderer, Samaya Nissanke, Christoph Weniger, Andrew R. Williamson, Gilles Louppe
Date: 2020-10-24 00:00:00

Gravitational waves from compact binaries measured by the LIGO and Virgo detectors are routinely analyzed using Markov Chain Monte Carlo sampling algorithms. Because the evaluation of the likelihood function requires evaluating millions of waveform models that link between signal shapes and the source parameters, running Markov chains until convergence is typically expensive and requires days of computation. In this extended abstract, we provide a proof of concept that demonstrates how the latest advances in neural simulation-based inference can speed up the inference time by up to three orders of magnitude -- from days to minutes -- without impairing the performance. Our approach is based on a convolutional neural network modeling the likelihood-to-evidence ratio and entirely amortizes the computation of the posterior. We find that our model correctly estimates credible intervals for the parameters of simulated gravitational waves.
Disease Normalization with Graph Embeddings
Authors: Dhruba Pujary, Camilo Thorne, Wilker Aziz
Date: 2020-10-24 00:00:00
Tasks: Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

The detection and normalization of diseases in biomedical texts are key biomedical natural language processing tasks. Disease names need not only be identified, but also normalized or linked to clinical taxonomies describing diseases such as MeSH. In this paper we describe deep learning methods that tackle both tasks. We train and test our methods on the known NCBI disease benchmark corpus. We propose to represent disease names by leveraging MeSH's graphical structure together with the lexical information available in the taxonomy using graph embeddings. We also show that combining neural named entity recognition models with our graph-based entity linking methods via multitask learning leads to improved disease recognition in the NCBI corpus.
Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration
Authors: wei he, Quanming Yao, Chao Li, Naoto Yokoya, Qibin Zhao, Hongyan zhang, Liangpei Zhang
Date: 2020-10-24 00:00:00
Tasks: Denoising, Image Restoration

Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) restoration, which includes the tasks of denoising, compressed HSI reconstruction and inpainting. Unfortunately, while its restoration performance benefits from more spectral bands, its runtime also substantially increases. In this paper, we claim that the HSI lies in a global spectral low-rank subspace, and the spectral subspaces of each full band patch group should lie in this global low-rank subspace. This motivates us to propose a unified paradigm combining the spatial and spectral properties for HSI restoration. The proposed paradigm enjoys performance superiority from the non-local spatial denoising and light computation complexity from the low-rank orthogonal basis exploration. An efficient alternating minimization algorithm with rank adaptation is developed. It is done by first solving a fidelity term-related problem for the update of a latent input image, and then learning a low-dimensional orthogonal basis and the related reduced image from the latent input image. Subsequently, non-local low-rank denoising is developed to refine the reduced image and orthogonal basis iteratively. Finally, the experiments on HSI denoising, compressed reconstruction, and inpainting tasks, with both simulated and real datasets, demonstrate its superiority with respect to state-of-the-art HSI restoration methods.
Causal Effects of Linguistic Properties
Authors: Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar
Date: 2020-10-24 00:00:00
Proceeding: NAACL 2021 4
Tasks: Language Modelling

We consider the problem of using observational data to estimate the causal effects of linguistic properties. For example, does writing a complaint politely lead to a faster response time? How much will a positive product review increase sales? This paper addresses two technical challenges related to the problem before developing a practical method. First, we formalize the causal quantity of interest as the effect of a writer's intent, and establish the assumptions necessary to identify this from observational data. Second, in practice, we only have access to noisy proxies for the linguistic properties of interest -- e.g., predictions from classifiers and lexicons. We propose an estimator for this setting and prove that its bias is bounded when we perform an adjustment for the text. Based on these results, we introduce TextCause, an algorithm for estimating causal effects of linguistic properties. The method leverages (1) distant supervision to improve the quality of noisy proxies, and (2) a pre-trained language model (BERT) to adjust for the text. We show that the proposed method outperforms related approaches when estimating the effect of Amazon review sentiment on semi-simulated sales figures. Finally, we present an applied case study investigating the effects of complaint politeness on bureaucratic response times.
RUArt: A Novel Text-Centered Solution for Text-Based Visual Question Answering
Authors: Zan-Xia Jin, Heran Wu, Chun Yang, Fang Zhou, Jingyan Qin, Lei Xiao, Xu-Cheng Yin
Date: 2020-10-24 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Question Answering, TextVQA, Visual Question Answering, Visual Question Answering (VQA)

Text-based visual question answering (VQA) requires to read and understand text in an image to correctly answer a given question. However, most current methods simply add optical character recognition (OCR) tokens extracted from the image into the VQA model without considering contextual information of OCR tokens and mining the relationships between OCR tokens and scene objects. In this paper, we propose a novel text-centered method called RUArt (Reading, Understanding and Answering the Related Text) for text-based VQA. Taking an image and a question as input, RUArt first reads the image and obtains text and scene objects. Then, it understands the question, OCRed text and objects in the context of the scene, and further mines the relationships among them. Finally, it answers the related text for the given question through text semantic matching and reasoning. We evaluate our RUArt on two text-based VQA benchmarks (ST-VQA and TextVQA) and conduct extensive ablation studies for exploring the reasons behind RUArt's effectiveness. Experimental results demonstrate that our method can effectively explore the contextual information of the text and mine the stable relationships between the text and objects.
Modeling and Optimization Trade-off in Meta-learning
Authors: Katelyn Gao, Ozan Sener
Date: 2020-10-24 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Bilevel Optimization, Meta-Learning, Meta Reinforcement Learning

By searching for shared inductive biases across tasks, meta-learning promises to accelerate learning on novel tasks, but with the cost of solving a complex bilevel optimization problem. We introduce and rigorously define the trade-off between accurate modeling and optimization ease in meta-learning. At one end, classic meta-learning algorithms account for the structure of meta-learning but solve a complex optimization problem, while at the other end domain randomized search (otherwise known as joint training) ignores the structure of meta-learning and solves a single level optimization problem. Taking MAML as the representative meta-learning algorithm, we theoretically characterize the trade-off for general non-convex risk functions as well as linear regression, for which we are able to provide explicit bounds on the errors associated with modeling and optimization. We also empirically study this trade-off for meta-reinforcement learning benchmarks.
Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning
Authors: Xiyao Wang, Junge Zhang, Wenzhen Huang, Qiyue Yin
Date: 2020-10-24 00:00:00
Tasks: continuous-control, Continuous Control, Decision Making, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-based reinforcement learning (MBRL) is believed to have higher sample efficiency compared with model-free reinforcement learning (MFRL). However, MBRL is plagued by dynamics bottleneck dilemma. Dynamics bottleneck dilemma is the phenomenon that the performance of the algorithm falls into the local optimum instead of increasing when the interaction step with the environment increases, which means more data can not bring better performance. In this paper, we find that the trajectory reward estimation error is the main reason that causes dynamics bottleneck dilemma through theoretical analysis. We give an upper bound of the trajectory reward estimation error and point out that increasing the agent's exploration ability is the key to reduce trajectory reward estimation error, thereby alleviating dynamics bottleneck dilemma. Motivated by this, a model-based control method combined with exploration named MOdel-based Progressive Entropy-based Exploration (MOPE2) is proposed. We conduct experiments on several complex continuous control benchmark tasks. The results verify that MOPE2 can effectively alleviate dynamics bottleneck dilemma and have higher sample efficiency than previous MBRL and MFRL algorithms.
Classifying Eye-Tracking Data Using Saliency Maps
Authors: Shafin Rahman, Sejuti Rahman, Omar Shahid, Md. Tahmeed Abdullah, Jubair Ahmed Sourov
Date: 2020-10-24 00:00:00
Tasks: General Classification

A plethora of research in the literature shows how human eye fixation pattern varies depending on different factors, including genetics, age, social functioning, cognitive functioning, and so on. Analysis of these variations in visual attention has already elicited two potential research avenues: 1) determining the physiological or psychological state of the subject and 2) predicting the tasks associated with the act of viewing from the recorded eye-fixation data. To this end, this paper proposes a visual saliency based novel feature extraction method for automatic and quantitative classification of eye-tracking data, which is applicable to both of the research directions. Instead of directly extracting features from the fixation data, this method employs several well-known computational models of visual attention to predict eye fixation locations as saliency maps. Comparing the saliency amplitudes, similarity and dissimilarity of saliency maps with the corresponding eye fixations maps gives an extra dimension of information which is effectively utilized to generate discriminative features to classify the eye-tracking data. Extensive experimentation using Saliency4ASD, Age Prediction, and Visual Perceptual Task dataset show that our saliency-based feature can achieve superior performance, outperforming the previous state-of-the-art methods by a considerable margin. Moreover, unlike the existing application-specific solutions, our method demonstrates performance improvement across three distinct problems from the real-life domain: Autism Spectrum Disorder screening, toddler age prediction, and human visual perceptual task classification, providing a general paradigm that utilizes the extra-information inherent in saliency maps for a more accurate classification.
Word Embeddings for Chemical Patent Natural Language Processing
Authors: Camilo Thorne, Saber Akhondi
Date: 2020-10-24 00:00:00
Tasks: Word Embeddings

We evaluate chemical patent word embeddings against known biomedical embeddings and show that they outperform the latter extrinsically and intrinsically. We also show that using contextualized embeddings can induce predictive models of reasonable performance for this domain over a relatively small gold standard.