pwc-archive/papers-with-abstracts huggingface dataset:
Deep unfolding-based output feedback control design for linear systems with input saturation
Authors: Koki Kobayashi, Masaki Ogura, Taisuke Kobayashi, Kenji Sugimoto
Date: 2020-11-20 00:00:00

In this paper, we propose a deep unfolding-based framework for the output feedback control of systems with input saturation. Although saturation commonly arises in several practical control systems, there is still a scarce of effective design methodologies that can directly deal with the severe non-linearity of the saturation operator. In this paper, we aim to design an anti-windup controller for enlarging the region of stability of the closed-loop system by learning from the numerical simulations of the closed-loop system. The data-driven framework we propose in this paper is based on a deep-learning technique called Neural Ordinary Differential Equations. Within our framework, we first obtain a candidate controller by using the deep-learning technique, which is then tested by the existing theoretical results already established in the literature, thereby avoiding the computational challenge in the conventional design methodologies as well as theoretically guaranteeing the performance of the system. Our numerical simulation shows that the proposed framework can significantly outperform a conventional design methodology based on linear matrix inequalities.
TSQA: Tabular Scenario Based Question Answering
Authors: Xiao Li, Yawei Sun, Gong Cheng
Date: 2021-01-14 00:00:00
Tasks: Machine Reading Comprehension, Multiple-choice, Question Answering, Reading Comprehension, Sentence

Scenario-based question answering (SQA) has attracted an increasing research interest. Compared with the well-studied machine reading comprehension (MRC), SQA is a more challenging task: a scenario may contain not only a textual passage to read but also structured data like tables, i.e., tabular scenario based question answering (TSQA). AI applications of TSQA such as answering multiple-choice questions in high-school exams require synthesizing data in multiple cells and combining tables with texts and domain knowledge to infer answers. To support the study of this task, we construct GeoTSQA. This dataset contains 1k real questions contextualized by tabular scenarios in the geography domain. To solve the task, we extend state-of-the-art MRC methods with TTGen, a novel table-to-text generator. It generates sentences from variously synthesized tabular data and feeds the downstream MRC method with the most useful sentences. Its sentence ranking model fuses the information in the scenario, question, and domain knowledge. Our approach outperforms a variety of strong baseline methods on GeoTSQA.
Cisco at AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides using Contextualized Embeddings
Authors: Sreyan Ghosh, Sonal Kumar, Harsh Jalan, Hemant Yadav, Rajiv Ratn Shah
Date: 2021-01-10 00:00:00

This paper describes our proposed system for the AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides. In this specific task, given the contents of a slide we are asked to predict the degree of emphasis to be laid on each word in the slide. We propose 2 approaches to this problem including a BiLSTM-ELMo approach and a transformers based approach based on RoBERTa and XLNet architectures. We achieve a score of 0.518 on the evaluation leaderboard which ranks us 3rd and 0.543 on the post-evaluation leaderboard which ranks us 1st at the time of writing the paper.
A More Efficient Chinese Named Entity Recognition base on BERT and Syntactic Analysis
Authors: Xiao Fu, Guijun Zhang
Date: 2021-01-11 00:00:00
Tasks: Chinese Named Entity Recognition, Chinese Word Segmentation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging

We propose a new Named entity recognition (NER) method to effectively make use of the results of Part-of-speech (POS) tagging, Chinese word segmentation (CWS) and parsing while avoiding NER error caused by POS tagging error. This paper first uses Stanford natural language process (NLP) tool to annotate large-scale untagged data so as to reduce the dependence on the tagged data; then a new NLP model, g-BERT model, is designed to compress Bidirectional Encoder Representations from Transformers (BERT) model in order to reduce calculation quantity; finally, the model is evaluated based on Chinese NER dataset. The experimental results show that the calculation quantity in g-BERT model is reduced by 60% and performance improves by 2% with Test F1 to 96.5 compared with that in BERT model.
geoGAT: Graph Model Based on Attention Mechanism for Geographic Text Classification
Authors: WeiPeng Jing, Xianyang Song, Donglin Di, Houbing Song
Date: 2021-01-13 00:00:00
Tasks: Classification, General Classification, Graph Attention, text-classification, Text Classification

In the area of geographic information processing. There are few researches on geographic text classification. However, the application of this task in Chinese is relatively rare. In our work, we intend to implement a method to extract text containing geographical entities from a large number of network text. The geographic information in these texts is of great practical significance to transportation, urban and rural planning, disaster relief and other fields. We use the method of graph convolutional neural network with attention mechanism to achieve this function. Graph attention networks is an improvement of graph convolutional neural networks. Compared with GCN, the advantage of GAT is that the attention mechanism is proposed to weight the sum of the characteristics of adjacent nodes. In addition, We construct a Chinese dataset containing geographical classification from multiple datasets of Chinese text classification. The Macro-F Score of the geoGAT we used reached 95\% on the new Chinese dataset.
Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task
Authors: Akansha Gautam, Venktesh V, Sarah Masud
Date: 2021-01-12 00:00:00
Tasks: Fake News Detection

With the ease of access to information, and its rapid dissemination over the internet (both velocity and volume), it has become challenging to filter out truthful information from fake ones. The research community is now faced with the task of automatic detection of fake news, which carries real-world socio-political impact. One such research contribution came in the form of the Constraint@AAA12021 Shared Task on COVID19 Fake News Detection in English. In this paper, we shed light on a novel method we proposed as a part of this shared task. Our team introduced an approach to combine topical distributions from Latent Dirichlet Allocation (LDA) with contextualized representations from XLNet. We also compared our method with existing baselines to show that XLNet + Topic Distributions outperforms other approaches by attaining an F1-score of 0.967.
An Explainable CNN Approach for Medical Codes Prediction from Clinical Text
Authors: Shu Yuan Hu, Fei Teng
Date: 2021-01-14 00:00:00
Tasks: Attribute, Document Classification, Medical Code Prediction, Medical Codes Prediction

Method: We develop CNN-based methods for automatic ICD coding based on clinical text from intensive care unit (ICU) stays. We come up with the Shallow and Wide Attention convolutional Mechanism (SWAM), which allows our model to learn local and low-level features for each label. The key idea behind our model design is to look for the presence of informative snippets in the clinical text that correlated with each code, and we infer that there exists a correspondence between "informative snippet" and convolution filter. Results: We evaluate our approach on MIMIC-III, an open-access dataset of ICU medical records. Our approach substantially outperforms previous results on top-50 medical code prediction on MIMIC-III dataset. We attribute this improvement to SWAM, by which the wide architecture gives the model ability to more extensively learn the unique features of different codes, and we prove it by ablation experiment. Besides, we perform manual analysis of the performance imbalance between different codes, and preliminary conclude the characteristics that determine the difficulty of learning specific codes. Conclusions: We present SWAM, an explainable CNN approach for multi-label document classification, which employs a wide convolution layer to learn local and low-level features for each label, yields strong improvements over previous metrics on the ICD-9 code prediction task, while providing satisfactory explanations for its internal mechanics.
Transformer-Based Models for Question Answering on COVID19
Authors: Hillary Ngai, Yoona Park, John Chen, Mahboobeh Parsapoor
Date: 2021-01-16 00:00:00
Tasks: Question Answering

In response to the Kaggle's COVID-19 Open Research Dataset (CORD-19) challenge, we have proposed three transformer-based question-answering systems using BERT, ALBERT, and T5 models. Since the CORD-19 dataset is unlabeled, we have evaluated the question-answering models' performance on two labeled questions answers datasets \textemdash CovidQA and CovidGQA. The BERT-based QA system achieved the highest F1 score (26.32), while the ALBERT-based QA system achieved the highest Exact Match (13.04). However, numerous challenges are associated with developing high-performance question-answering systems for the ongoing COVID-19 pandemic and future pandemics. At the end of this paper, we discuss these challenges and suggest potential solutions to address them.
Machine learning with limited data
Authors: Fupin Yao
Date: 2021-01-18 00:00:00
Tasks: BIG-bench Machine Learning, Cross-Domain Few-Shot, cross-domain few-shot learning, Face Recognition, Few-Shot Image Classification, Few-Shot Learning, image-classification, Image Classification, object-detection, Object Detection, Object Recognition, Pose Estimation

Thanks to the availability of powerful computing resources, big data and deep learning algorithms, we have made great progress on computer vision in the last few years. Computer vision systems begin to surpass humans in some tasks, such as object recognition, object detection, face recognition and pose estimation. Lots of computer vision algorithms have been deployed to real world applications and started to improve our life quality. However, big data and labels are not always available. Sometimes we only have very limited labeled data, such as medical images which requires experts to label them. In this paper, we study few shot image classification, in which we only have very few labeled data. Machine learning with little data is a big challenge. To tackle this challenge, we propose two methods and test their effectiveness thoroughly. One method is to augment image features by mixing the style of these images. The second method is applying spatial attention to explore the relations between patches of images. We also find that domain shift is a critical issue in few shot learning when the training domain and testing domain are different. So we propose a more realistic cross-domain few-shot learning with unlabeled data setting, in which some unlabeled data is available in the target domain. We propose two methods in this setting. Our first method transfers the style information of the unlabeled target dataset to the samples in the source dataset and trains a model with stylized images and original images. Our second method proposes a unified framework to fully utilize all the data. Both of our methods surpass the baseline method by a large margin.
Khmer Word Segmentation Using Conditional Random Fields
Authors: Vichet Chea, Ye Kyaw Thu, Chenchen Ding, Masao Utiyama, Andrew Finch, Eiichiro Sumita
Date: 2015-10-15 00:00:00
Tasks: Segmentation, Text Segmentation, Translation

Word Segmentation is a critical task that
is the foundation of much natural language processing
research. This paper is a study of Khmer word segmentation using an approach based on conditional random
fields (CRFs). A large manually-segmented corpus was
developed to train the segmenter, and we provide details
of a set of word segmentation strategies that were used
by the human annotators during the manual annotation.
The trained CRF segmenter was compared empirically to
a baseline approach based on maximum matching that
used a dictionary extracted from the manually segmented
corpus. The CRF segmenter outperformed the baseline in
terms of precision, recall and f-score by a wide margin.
The segmenter was also evaluated as a pre-processing step
in a statistical machine translation system. It gave rise to
substantial increases in BLEU score of up to 7.7 points,
relative to a maximum matching baseline.
Construction of gene causal regulatory networks using microarray data with the coefficient of intrinsic dependence
Authors: Li‑yu Daisy Liu1*, Ya‑Chun Hsiao1, Hung‑Chi Chen2, Yun‑Wei Yang1 and Men‑Chi Chang1
Date: 2019-09-11 00:00:00

In the past two decades, biologists have been able to identify the gene signatures associated with
various phenotypes through the monitoring of gene expressions with high-throughput biotechnologies. These gene
signatures have in turn been successfully applied to drug development, disease prevention, crop improvement, etc.
However, ignoring the interactions among genes has weakened the predictive power of gene signatures in practical
applications. Gene regulatory networks, in which genes are represented by nodes and the associations between
genes are represented by edges, are typically constructed to analyze and visualize such gene interactions. More specifically,
the present study sought to measure gene–gene associations by using the coefficient of intrinsic dependence
(CID) to capture more nonlinear as well as cause-effect gene relationships.
Cooperative success in epithelial public goods games
Authors: Jessie Renton, Karen M. Page
Date: 2021-01-27 00:00:00

Cancer cells obtain mutations which rely on the production of diffusible growth factors to confer a fitness benefit. These mutations can be considered cooperative, and studied as public goods games within the framework of evolutionary game theory. The population structure, benefit function and update rule all influence the evolutionary success of cooperators. We model the evolution of cooperation in epithelial cells using the Voronoi tessellation model. Unlike traditional evolutionary graph theory, this allows us to implement global updating, for which birth and death events are spatially decoupled. We compare, for a sigmoid benefit function, the conditions for cooperation to be favoured and/or beneficial for well mixed and structured populations. We find that when population structure is combined with global updating, cooperation is more successful than if there were local updating or the population were well-mixed. Interestingly, the qualitative behaviour for the well-mixed population and the Voronoi tessellation model is remarkably similar, but the latter case requires significantly lower incentives to ensure cooperation.
Imitation Learning with Stability and Safety Guarantees
Authors: He Yin, Peter Seiler, Ming Jin, Murat Arcak
Date: 2020-12-16 00:00:00
Tasks: Imitation Learning

A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL). Convex stability and safety conditions are derived for linear time-invariant plant dynamics with NN controllers by merging Lyapunov theory with local quadratic constraints to bound the nonlinear activation functions in the NN. These conditions are incorporated in the IL process, which minimizes the IL loss, and maximizes the volume of the region of attraction associated with the NN controller simultaneously. An alternating direction method of multipliers based algorithm is proposed to solve the IL problem. The method is illustrated on an inverted pendulum system, aircraft longitudinal dynamics, and vehicle lateral dynamics.
Predicting Participation in Cancer Screening Programs with Machine Learning
Authors: Donghyun Kim
Date: 2021-01-27 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we present machine learning models based on random forest classifiers, support vector machines, gradient boosted decision trees, and artificial neural networks to predict participation in cancer screening programs in South Korea. The top performing model was based on gradient boosted decision trees and achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.8706 and average precision of 0.8776. The results of this study are encouraging and suggest that with further research, these models can be directly applied to Korea's healthcare system, thus increasing participation in Korea's National Cancer Screening Program.
A transformer based approach for fighting COVID-19 fake news
Authors: S. M. Sadiq-Ur-Rahman Shifath, Mohammad Faiyaz Khan, Md. Saiful Islam
Date: 2021-01-28 00:00:00
Tasks: Fake News Detection, Misinformation

The rapid outbreak of COVID-19 has caused humanity to come to a stand-still and brought with it a plethora of other problems. COVID-19 is the first pandemic in history when humanity is the most technologically advanced and relies heavily on social media platforms for connectivity and other benefits. Unfortunately, fake news and misinformation regarding this virus is also available to people and causing some massive problems. So, fighting this infodemic has become a significant challenge. We present our solution for the "Constraint@AAAI2021 - COVID19 Fake News Detection in English" challenge in this work. After extensive experimentation with numerous architectures and techniques, we use eight different transformer-based pre-trained models with additional layers to construct a stacking ensemble classifier and fine-tuned them for our purpose. We achieved 0.979906542 accuracy, 0.979913119 precision, 0.979906542 recall, and 0.979907901 f1-score on the test dataset of the competition.
B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRI
Authors: Guanhua Wang, Tianrui Luo, Jon-Fredrik Nielsen, Douglas C. Noll, Jeffrey A. Fessler
Date: 2021-01-27 00:00:00
Tasks: compressed sensing, Image Reconstruction

Optimizing k-space sampling trajectories is a promising yet challenging topic for fast magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method and sampling trajectories jointly concerning image reconstruction quality in a supervised learning manner. We parameterize trajectories with quadratic B-spline kernels to reduce the number of parameters and apply multi-scale optimization, which may help to avoid sub-optimal local minima. The algorithm includes an efficient non-Cartesian unrolled neural network-based reconstruction and an accurate approximation for backpropagation through the non-uniform fast Fourier transform (NUFFT) operator to accurately reconstruct and back-propagate multi-coil non-Cartesian data. Penalties on slew rate and gradient amplitude enforce hardware constraints. Sampling and reconstruction are trained jointly using large public datasets. To correct for possible eddy-current effects introduced by the curved trajectory, we use a pencil-beam trajectory mapping technique. In both simulations and in-vivo experiments, the learned trajectory demonstrates significantly improved image quality compared to previous model-based and learning-based trajectory optimization methods for 10 times acceleration factors. Though trained with neural network-based reconstruction, the proposed trajectory also leads to improved image quality with compressed sensing-based reconstruction.
The Impact of Privacy Laws on Online User Behavior
Authors: Julia Schmitt, Klaus M. Miller, Bernd Skiera
Date: 2021-01-27 00:00:00

Policymakers worldwide draft privacy laws that require trading-off between safeguarding consumer privacy and preventing economic loss to companies that use consumer data. However, little empirical knowledge exists as to how privacy laws affect companies' performance. Accordingly, this paper empirically quantifies the effects of the enforcement of the EU's General Data Protection Regulation (GDPR) on online user behavior over time, analyzing data from 6,286 websites spanning 24 industries during the 10 months before and 18 months after the GDPR's enforcement in 2018. A panel differences estimator, with a synthetic control group approach, isolates the short- and long-term effects of the GDPR on user behavior. The results show that, on average, the GDPR's effects on user quantity and usage intensity are negative; e.g., the numbers of total visits to a website decrease by 4.9% and 10% due to GDPR in respectively the short- and long-term. These effects could translate into average revenue losses of $7 million for e-commerce websites and almost $2.5 million for ad-based websites 18 months after GDPR. The GDPR's effects vary across websites, with some industries even benefiting from it; moreover, more-popular websites suffer less, suggesting that the GDPR increased market concentration.
Low-Complexity Joint CFO and Channel Estimation for RIS-aided OFDM Systems
Authors: Sumin Jeong, Arman Farhang, Nemanja Stefan Perović, Mark Flanagan
Date: 2021-01-27 00:00:00

Accurate channel estimation is essential for achieving the performance gains offered by reconfigurable intelligent surface (RIS)-aided wireless communications. A variety of channel estimation methods have been proposed for such systems; however, none of the existing methods takes into account the effect of synchronization errors such as carrier frequency offset (CFO). In general, CFO can significantly degrade channel estimation performance of orthogonal frequency division multiplexing (OFDM) systems. Motivated by this, we investigate the effect of CFO on channel estimation for RIS-aided OFDM systems. Furthermore, we propose a joint CFO and channel impulse response (CIR) estimation method for these systems. Simulation results demonstrate the effectiveness of our proposed method, and also demonstrate that the use of time-domain rather than frequency-domain estimation in this context results in an improvement in the mean-squared error (MSE) performance of channel estimation as well as a significantly lower overall computational complexity.