pwc-archive/papers-with-abstracts huggingface dataset:
Templates of generic geographic information for answering where-questions
Authors: Ehsan Hamzei, Stephan Winter, Martin Tomko
Date: 2021-01-21 00:00:00
Tasks: Reading Comprehension

In everyday communication, where-questions are answered by place descriptions. To answer where-questions automatically, computers should be able to generate relevant place descriptions that satisfy inquirers' information needs. Human-generated answers to where-questions constructed based on a few anchor places that characterize the location of inquired places. The challenge for automatically generating such relevant responses stems from selecting relevant anchor places. In this paper, we present templates that allow to characterize the human-generated answers and to imitate their structure. These templates are patterns of generic geographic information derived and encoded from the largest available machine comprehension dataset, MS MARCO v2.1. In our approach, the toponyms in the questions and answers of the dataset are encoded into sequences of generic information. Next, sequence prediction methods are used to model the relation between the generic information in the questions and their answers. Finally, we evaluate the performance of predicting templates for answers to where-questions.
What is all this new MeSH about? Exploring the semantic provenance of new descriptors in the MeSH thesaurus
Authors: Anastasios Nentidis, Anastasia Krithara, Grigorios Tsoumakas, Georgios Paliouras
Date: 2021-01-20 00:00:00
Tasks: All, Articles

The Medical Subject Headings (MeSH) thesaurus is a controlled vocabulary widely used in biomedical knowledge systems, particularly for semantic indexing of scientific literature. As the MeSH hierarchy evolves through annual version updates, some new descriptors are introduced that were not previously available. This paper explores the conceptual provenance of these new descriptors. In particular, we investigate whether such new descriptors have been previously covered by older descriptors and what is their current relation to them. To this end, we propose a framework to categorize new descriptors based on their current relation to older descriptors. Based on the proposed classification scheme, we quantify, analyse and present the different types of new descriptors introduced in MeSH during the last fifteen years. The results show that only about 25% of new MeSH descriptors correspond to new emerging concepts, whereas the rest were previously covered by one or more existing descriptors, either implicitly or explicitly. Most of them were covered by a single existing descriptor and they usually end up as descendants of it in the current hierarchy, gradually leading towards a more fine-grained MeSH vocabulary. These insights about the dynamics of the thesaurus are useful for the retrospective study of scientific articles annotated with MeSH, but could also be used to inform the policy of updating the thesaurus in the future.
Latent Dirichlet Allocation
Authors: David M. Blei, Andrew Y. Ng, Michael I. Jordan
Date: 2003-01-01 00:00:00
Tasks: Collaborative Filtering, parameter estimation, Text Categorization, text-classification, Text Classification, Topic Models

We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of
discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each
item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in
turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of
text modeling, the topic probabilities provide an explicit representation of a document. We present
efficient approximate inference techniques based on variational methods and an EM algorithm for
empirical Bayes parameter estimation. We report results in document modeling, text classification,
and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI
model.
Multiple Instance Learning with Center Embeddings for Histopathology Classification
Authors: Philip Chikontwe, Meejeong Kim, Soo Jeong Nam, Heounjeong Go, Sang Hyun Park
Date: 2020-09-29 00:00:00
Tasks: Classification, General Classification, Histopathological Image Classification, Multiple Instance Learning, Representation Learning, whole slide images

Histopathology image analysis plays an important role in the treatment and diagnosis of cancer. However, analysis of whole slide images (WSI) with deep learning is challenging given that the curation of pixel-level annotations is laborious and time consuming. To address this, recent methods have considered WSI classification as a Multiple Instance Learning (MIL) problem often with a multi-stage process for learning instance and slide level features. Currently, most methods focus on either instance-selection or instance prediction-aggregation that often fails to generalize and ignores instance relations. In this work, we propose a MIL-based method to jointly learn both instance- and bag-level embeddings in a single framework. In addition, we propose a center loss that maps embeddings of instances from the same bag to a single centroid and reduces intra-class variations. Consequently, our model can accurately predict instance labels and leverages robust hierarchical pooling of features to obtain bag-level features without sacrificing accuracy. Experimental results on curated colon datasets show the effectiveness of the proposed methods against recent state-of-the-art methods.
Object Shape Error Response Using Bayesian 3-D Convolutional Neural Networks for Assembly Systems With Compliant Parts
Authors: Sumit Sinha, Pasquale Franciosa and Dariusz Ceglarek
Date: 2021-12-08 00:00:00
Tasks: 3D Shape Modeling, Benchmarking, Object, object-detection, Object Detection, Uncertainty Quantification

The paper proposes a novel Object Shape Error Response (OSER) approach to estimate the dimensional and geometric variation of assembled products and then, relate, these to process parameters, which can be interpreted as root causes (RC) of the object shape defects. The OSER approach leverages Bayesian 3D-Convolutional Neural Networks integrated with Computer-Aided Engineering (CAE) simulations for RC isolation. Compared with the existing methods, the proposed approach (i) addresses a novel problem of applying deep learning for object shape error identification instead of object detection; (ii) overcomes fundamental performance limitations of current linear approaches for Root Cause Analysis (RCA) of assembly systems that cannot be used on point cloud data; and, (iii) provides capabilities for unsolved challenges such as ill-conditioning, fault-multiplicity, RC prediction with uncertainty quantification and learning at design phase when no measurement data is available. Comprehensive benchmarking with existing machine learning models demonstrates superior performance with "R" ^"2"  "=0.98"  and "MAE=0.05 mm" , thus improving RCA capabilities by 29%.
From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts fromResting-state Functional Connectivity
Authors: Gia H. Ngo, Meenakshi Khosla, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
Date: 2020-08-07 00:00:00
Tasks: BIG-bench Machine Learning, Functional Connectivity, Specificity

Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals. Connectomic fingerprints have proven useful in many machine learning tasks, such as predicting subject-specific behavioral traits or task-evoked activity. In this work, we propose a surface-based convolutional neural network (BrainSurfCNN) model to predict individual task contrasts from their resting-state fingerprints. We introduce a reconstructive-contrastive loss that enforces subject-specificity of model outputs while minimizing predictive error. The proposed approach significantly improves the accuracy of predicted contrasts over a well-established baseline. Furthermore, BrainSurfCNN’s prediction also surpasses test-retest benchmark in a subject identification task.
Asymmetric Semi-Nonnegative Matrix Factorization for Directed Graph Clustering
Authors: Reyhaneh Abdollahi; Seyed Amjad Seyedi; Mohamad Reza Noorimehr
Date: 2020-12-31 00:00:00
Tasks: Clustering, Graph Clustering

Graph clustering is a fundamental task in the network analysis, which is essential for many modern applications. In recent years, Nonnegative Matrix Factorization (NMF) has been effectively used to discover cluster structures due to its powerful interpretability property. In this paper, we introduce a clustering algorithm based on Semi-Nonnegative Matrix Factorization that is one of the well-known extensions of NMF. This factorization allows algorithms to capture more accurate (positive and negative) relationships among clusters and, thereby, to derive a latent factor that is even proper for clustering and also has much more responsibility in the regularization. Moreover, to improve the clustering, we define an asymmetric graph regularization to penalize the asymmetric similarity of nodes denoted by cluster memberships. Experimental results on four real-world datasets validate the effectiveness of the proposed method.
Asymmetric Semi-Nonnegative Matrix Factorization for Directed Graph Clustering
Authors: Reyhaneh Abdollahi, Seyed Amjad Seyedi, Mohamad Reza Noorimehr
Date: 2020-12-31 00:00:00
Tasks: Clustering, Graph Clustering

Graph clustering is a fundamental task in the network analysis, which is essential for many modern applications. In recent years, Nonnegative Matrix Factorization (NMF) has been effectively used to discover cluster structures due to its powerful interpretability property. In this paper, we introduce a clustering algorithm based on Semi-Nonnegative Matrix Factorization that is one of the well-known extensions of NMF. This factorization allows algorithms to capture more accurate (positive and negative) relationships among clusters and, thereby, to derive a latent factor that is even proper for clustering and also has much more responsibility in the regularization. Moreover, to improve the clustering, we define an asymmetric graph regularization to penalize the asymmetric similarity of nodes denoted by cluster memberships. Experimental results on four real-world datasets validate the effectiveness of the proposed method.
Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs
Authors: Bjørn-Jostein Singstad, Christian Tronstad
Date: 2020-12-31 00:00:00
Tasks: ECG Classification

The objective of this study was to classify 27 cardiac abnormalities based on a data set of 43 101 ECG recordings. A hybrid model combining a rule-based algorithm with different deep learning architectures was developed. 

We compared two different Convolutional Neural Networks; a Fully Convolutional Neural Network and an Encoder Network, a combination of both, and with the addition of another neural network using age and gender as input. Two of these combinations were finally combined with a rule-based model using derived ECG features. The performance of the models was evaluated on validation data during model development using hold-out validation. Finally, the models were deployed to a Docker image, trained
on the provided development data, and tested on the Challenge validation set. The model that performed best on the Challenge validation set was then deployed and tested on the full Challenge test set. The performance was evaluated based on a particular Challenge score.

Our team, TeamUIO, achieved a Challenge validation score of 0.377, and a full test score of 0.206 for our best model. The score on the full test set placed us at 20th out of 41 teams in the official ranking.
Learning Illumination Patterns for Coded Diffraction Phase Retrieval
Date: 2020-06-07 00:00:00
Tasks: Retrieval

Signal recovery from nonlinear measurements involves solving an iterative optimization problem. In this paper, we present a framework to optimize the sensing parameters to improve the quality of the signal recovered by the given iterative method. In particular, we learn illumination patterns to recover signals from coded diffraction patterns using a fixed-cost alternating minimization-based phase retrieval method. Coded diffraction phase retrieval is a physically realistic system in which the signal is first modulated by a sequence of codes before the sensor records its Fourier amplitude. We represent the phase retrieval method as an unrolled network with a fixed number of layers and minimize the recovery error by optimizing over the measurement parameters. Since the number of iterations/layers are fixed, the recovery incurs a fixed cost. We present extensive simulation results on a variety of datasets under different conditions and a comparison with existing methods. Our results demonstrate that the proposed method provides near-perfect reconstruction using patterns learned with a small number of training images. Our proposed method provides significant improvements over existing methods both in terms of accuracy and speed.
Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis
Authors: Kasra Rafiezadeh Shahi, Mahdi Khodadadzadeh, Laura Tusa, Pedram Ghamisi, Raimon Tolosana-Delgado, and Richard Gloaguen
Date: 2020-07-28 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Hyperspectral image analysis, Sparse subspace-based clustering

Hyperspectral imaging techniques are becoming one of the most important tools to remotely acquire fine spectral information on different objects. However, hyperspectral images (HSIs) require dedicated processing for most applications. Therefore, several machine learning techniques were proposed in the last decades. Among the proposed machine learning techniques, unsupervised learning techniques have become popular as they do not need any prior knowledge. Specifically, sparse subspace-based clustering algorithms have drawn special attention to cluster the HSI into meaningful groups since such algorithms are able to handle high dimensional and highly mixed data, as is the case in real-world applications. Nonetheless, sparse subspace-based clustering algorithms usually tend to demand high computational power and can be time-consuming. In addition, the number of clusters is usually predefined. In this paper, we propose a new hierarchical sparse subspace-based clustering algorithm (HESSC), which handles the aforementioned problems in a robust and fast manner and estimates the number of clusters automatically. In the experiment, HESSC is applied to three real drill-core samples and one well-known rural benchmark (i.e., Trento) HSI datasets. In order to evaluate the performance of HESSC, the performance of the new proposed algorithm is quantitatively and qualitatively compared to the state-of-the-art sparse subspace-based algorithms. In addition, in order to have a comparison with conventional clustering algorithms, HESSC’s performance is compared with K-means and FCM. The obtained clustering results demonstrate that HESSC performs well when clustering HSIs compared to the other applied clustering algorithms.
Kidney Level Lupus Nephritis Classification using Uncertainty Guided Bayesian Convolutional Neural Networks
Authors: Pietro A. Cicalese, Aryan Mobiny, Zahed Shahmoradi, Xiongfeng Yi, Chandra Mohan, Hien Van Nguyen
Date: 2020-11-18 00:00:00
Tasks: Classification, General Classification

The kidney biopsy based diagnosis of Lupus Nephritis (LN) is characterized by low inter-observer agreement, with misdiagnosis being associated with increased patient morbidity and mortality. Although various Computer Aided Diagnosis (CAD) systems have been developed for other nephrohistopathological applications, little has been done to accurately classify kidneys based on their kidney level Lupus Glomerulonephritis (LGN) scores. The successful implementation of CAD systems has also been hindered by the diagnosing physician's perceived classifier strengths and weaknesses, which has been shown to have a negative effect on patient outcomes. We propose an Uncertainty-Guided Bayesian Classification (UGBC) scheme that is designed to accurately classify control, class I/II, and class III/IV LGN (3 class) at both the glomerular-level classification task (26,634 segmented glomerulus images) and the kidney-level classification task (87 MRL/lpr mouse kidney sections). Data annotation was performed using a high throughput, bulk labeling scheme that is designed to take advantage of Deep Neural Network's (or DNNs) resistance to label noise. Our augmented UGBC scheme achieved a 94.5% weighted glomerular-level accuracy while achieving a weighted kidney-level accuracy of 96.6%, improving upon the standard Convolutional Neural Network (CNN) architecture by 11.8% and 3.5% respectively.
Long-term effects of female teacher on her pupils' smoking behaviour later in life
Authors: Eiji Yamamura
Date: 2021-01-21 00:00:00

In Japan, teacher and student is randomly matched in the first year of elementary school. Under the quasi-natural experimental setting, we examine how learning in female teacher homeroom class in the elementary school influence pupils' smoking behavior after they become adult. We found that pupils are unlikely to smoke later in life if they belonged to female teacher homeroom class in pupil's first year of school.
Female teachers effect on male pupils' voting behavior and preference formation
Authors: Eiji Yamamura
Date: 2021-01-21 00:00:00

This study examines the influence of learning in a female teacher homeroom class in elementary school on pupils' voting behavior later in life, using independently collected individual-level data. Further, we evaluate its effect on preference for women's participation in the workplace in adulthood. Our study found that having a female teacher in the first year of school makes individuals more likely to vote for female candidates, and to prefer policy for female labor participation in adulthood. However, the effect is only observed among males, and not female pupils. These findings offer new evidence for the female socialization hypothesis.
Changing views about remote working during the COVID-19 pandemic: Evidence using panel data from Japan
Authors: Eiji Yamamura, Yoshiro Tsutsui
Date: 2021-01-21 00:00:00

COVID-19 has led to school closures in Japan to cope with the pandemic. Under the state of emergency, in addition to school closure, after-school care has not been sufficiently supplied. We independently collected individual level data through internet surveys to construct short panel data from mid-March to mid-June 2020, which covered before and after the state of emergency. We analyze how the presence of school-aged children influences their parents views about working from home. After controlling for various factors using a fixed effects model, we find that in cases where parents were workers, and the children are (1) in primary school, parents are willing to promote working from home. If children are (2) in junior high school, the parents view is hardly affected. (3) Surprisingly, workers whose children are primary school pupils are most likely to support promotion of working from home after schools reopen. Due to school closure and a lack of after-school care, parents need to work from home, and this experience motivated workers with small children to continue doing so to improve work-life balance even after schools reopen.
A Study of F0 Modification for X-Vector Based Speech Pseudonymization Across Gender
Authors: Pierre Champion, Denis Jouvet, Anthony Larcher
Date: 2021-01-21 00:00:00

Speech pseudonymization aims at altering a speech signal to map the identifiable personal characteristics of a given speaker to another identity. In other words, it aims to hide the source speaker identity while preserving the intelligibility of the spoken content. This study takes place in the VoicePrivacy 2020 challenge framework, where the baseline system performs pseudonymization by modifying x-vector information to match a target speaker while keeping the fundamental frequency (F0) unchanged. We propose to alter other paralin-guistic features, here F0, and analyze the impact of this modification across gender. We found that the proposed F0 modification always improves pseudonymization We observed that both source and target speaker genders affect the performance gain when modifying the F0.
Impact of closing schools on mental health during the COVID-19 pandemic: Evidence using panel data from Japan
Authors: Eiji Yamamura, Yoshiro Tsutsui
Date: 2021-01-21 00:00:00

The spread of the novel coronavirus disease caused schools in Japan to close to cope with the pandemic. In response to this, parents of students were obliged to care for their children during the daytime when they were usually at school. Does the increase in burden of childcare influence parents mental health? Based on short panel data from mid-March to mid-April 2020, we explored how school closures influenced the mental health of parents with school-aged children. Using the fixed effects model, we found that school closures lead to students mothers suffering from worse mental health than other females, while the fathers mental health did not differ from other males. This tendency was only observed for less educated mothers who had children attending primary school, but not those attending junior high school. The contribution of this paper is to show that school closures increased the inequality of mental health between genders and the educational background of parents.
Targeted Data-driven Regularization for Out-of-Distribution Generalization
Authors: Mohammad Mahdi Kamani, Sadegh Farhang, Mehrdad Mahdavi, James Z. Wang
Date: 2020-08-01 00:00:00
Tasks: Bilevel Optimization, Meta-Learning, Out-of-Distribution Generalization

Due to biases introduced by large real-world datasets, deviations of deep learning models from their expected behavior on out-of-distribution test data are worrisome. Especially when data come from imbalanced or heavy-tailed label distributions or minority groups of a sensitive feature. Classical approaches to address these biases are mostly data- or application-dependent, hence are burdensome to tune. Some meta-learning approaches, on the other hand, aim to learn hyperparameters in the learning process using different objective functions on training and validation data. However, these methods suffer from high computational complexity and are not scalable to large datasets. In this paper, we propose a unified data-driven regularization approach to learn a generalizable model from biased data. The proposed framework, named targeted data-driven regularization (TDR), is model- and dataset-agnostic and employs a target dataset that resembles the desired nature of test data in order to guide the learning process in a coupled manner. We cast the problem as a bilevel optimization and propose an efficient stochastic gradient descent based method to solve it. The framework can be utilized to alleviate various types of biases in real-world applications. We empirically show, on both synthetic and real-world datasets, the superior performance of TDR for resolving issues stem from these biases.