pwc-archive/papers-with-abstracts huggingface dataset:
Deep-learning-based coupled flow-geomechanics surrogate model for CO$_2$ sequestration
Authors: Meng Tang, Xin Ju, Louis J. Durlofsky
Date: 2021-05-04 00:00:00

A deep-learning-based surrogate model capable of predicting flow and geomechanical responses in CO2 storage operations is presented and applied. The 3D recurrent R-U-Net model combines deep convolutional and recurrent neural networks to capture the spatial distribution and temporal evolution of saturation, pressure and surface displacement fields. The method is trained using high-fidelity simulation results for 2000 storage-aquifer realizations characterized by multi-Gaussian porosity and log-permeability fields. These numerical solutions are expensive because the domain that must be considered for the coupled problem includes not only the storage aquifer but also a surrounding region, overburden and bedrock. The surrogate model is trained to predict the 3D CO2 saturation and pressure fields in the storage aquifer, and 2D displacement maps at the Earth's surface. Detailed comparisons between surrogate model and full-order simulation results for new (test-case) storage-aquifer realizations are presented. The saturation, pressure and surface displacement fields provided by the surrogate model display a high degree of accuracy, both for individual test-case realizations and for ensemble statistics. Finally, the the recurrent R-U-Net surrogate model is applied with a rejection sampling procedure for data assimilation. Although the observations consist of only a small number of surface displacement measurements, significant uncertainty reduction in pressure buildup at the top of the storage aquifer (caprock) is achieved.
BLM-17m: A Large-Scale Dataset for Black Lives Matter Topic Detection on Twitter
Authors: Hasan Kemik, Nusret Özateş, Meysam Asgari-Chenaghlu, Yang Li, Erik Cambria
Date: 2021-05-04 00:00:00

Protection of human rights is one of the most important problems of our world. In this paper, our aim is to provide a dataset which covers one of the most significant human rights contradiction in recent months affected the whole world, George Floyd incident. We propose a labeled dataset for topic detection that contains 17 million tweets. These Tweets are collected from 25 May 2020 to 21 August 2020 that covers 89 days from start of this incident. We labeled the dataset by monitoring most trending news topics from global and local newspapers. Apart from that, we present two baselines, TF-IDF and LDA. We evaluated the results of these two methods with three different k values for metrics of precision, recall and f1-score. The collected dataset is available at https://github.com/MeysamAsgariC/BLMT.
Semantic Extractor-Paraphraser based Abstractive Summarization
Authors: Anubhav Jangra, Raghav Jain, Vaibhav Mavi, Sriparna Saha, Pushpak Bhattacharyya
Date: 2021-05-04 00:00:00
Proceeding: ICON 2020 12
Tasks: Abstractive Text Summarization

The anthology of spoken languages today is inundated with textual information, necessitating the development of automatic summarization models. In this manuscript, we propose an extractor-paraphraser based abstractive summarization system that exploits semantic overlap as opposed to its predecessors that focus more on syntactic information overlap. Our model outperforms the state-of-the-art baselines in terms of ROUGE, METEOR and word mover similarity (WMS), establishing the superiority of the proposed system via extensive ablation experiments. We have also challenged the summarization capabilities of the state of the art Pointer Generator Network (PGN), and through thorough experimentation, shown that PGN is more of a paraphraser, contrary to the prevailing notion of a summarizer; illustrating it's incapability to accumulate information across multiple sentences.
Winter wheat yield prediction using convolutional neural networks from environmental and phenological data
Authors: Amit Kumar Srivastava, Nima Safaei, Saeed Khaki, Gina Lopez, Wenzhi Zeng, Frank Ewert, Thomas Gaiser, Jaber Rahimi
Date: 2021-05-04 00:00:00
Tasks: BIG-bench Machine Learning, Management, Prediction

Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019. We proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional convolution operation to capture the time dependencies of environmental variables. We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results. Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural Network (DNN), and XGBoost were more effective in understanding the relationship between the crop yield and input data compared to the linear models. Our proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher correlation coefficient than the best performing baseline across test data). We aggregated soil moisture and meteorological features at the weekly resolution to address the seasonality of the data. We also moved beyond prediction and interpreted the outputs of our proposed CNN model using SHAP and force plots which provided key insights in explaining the yield prediction results (importance of variables by time). We found DUL, wind speed at week ten, and radiation amount at week seven as the most critical features in winter wheat yield prediction.
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX
Authors: Chengliang Zhang, Junzhe Xia, Baichen Yang, Huancheng Puyang, Wei Wang, Ruichuan Chen, Istemi Ekin Akkus, Paarijaat Aditya, Feng Yan
Date: 2021-05-04 00:00:00
Tasks: Federated Learning

With the advancement of machine learning (ML) and its growing awareness, many organizations who own data but not ML expertise (data owner) would like to pool their data and collaborate with those who have expertise but need data from diverse sources to train truly generalizable models (model owner). In such collaborative ML, the data owner wants to protect the privacy of its training data, while the model owner desires the confidentiality of the model and the training method which may contain intellectual properties. However, existing private ML solutions, such as federated learning and split learning, cannot meet the privacy requirements of both data and model owners at the same time. This paper presents Citadel, a scalable collaborative ML system that protects the privacy of both data owner and model owner in untrusted infrastructures with the help of Intel SGX. Citadel performs distributed training across multiple training enclaves running on behalf of data owners and an aggregator enclave on behalf of the model owner. Citadel further establishes a strong information barrier between these enclaves by means of zero-sum masking and hierarchical aggregation to prevent data/model leakage during collaborative training. Compared with the existing SGX-protected training systems, Citadel enables better scalability and stronger privacy guarantees for collaborative ML. Cloud deployment with various ML models shows that Citadel scales to a large number of enclaves with less than 1.73X slowdown caused by SGX.
WiFi Fingerprint Clustering for Urban Mobility Analysis
Authors: Sumudu HasalaMarakkalage, Billy Pik Lik Lau, Yuren Zhou, Ran Liu, Chau Yuen, Wei Quin Yow, Keng Hua Chong
Date: 2021-05-04 00:00:00
Tasks: Clustering

In this paper, we present an unsupervised learning approach to identify the user points of interest (POI) by exploiting WiFi measurements from smartphone application data. Due to the lack of GPS positioning accuracy in indoor, sheltered, and high rise building environments, we rely on widely available WiFi access points (AP) in contemporary urban areas to accurately identify POI and mobility patterns, by comparing the similarity in the WiFi measurements. We propose a system architecture to scan the surrounding WiFi AP, and perform unsupervised learning to demonstrate that it is possible to identify three major insights, namely the indoor POI within a building, neighbourhood activity, and micro-mobility of the users. Our results show that it is possible to identify the aforementioned insights, with the fusion of WiFi and GPS, which are not possible to identify by only using GPS.
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems
Authors: Xiaocong Du, Bhargav Bhushanam, Jiecao Yu, Dhruv Choudhary, Tianxiang Gao, Sherman Wong, Louis Feng, Jongsoo Park, Yu Cao, Arun Kejariwal
Date: 2021-05-04 00:00:00
Tasks: Recommendation Systems

Deep learning recommendation systems at scale have provided remarkable gains through increasing model capacity (i.e. wider and deeper neural networks), but it comes at significant training cost and infrastructure cost. Model pruning is an effective technique to reduce computation overhead for deep neural networks by removing redundant parameters. However, modern recommendation systems are still thirsty for model capacity due to the demand for handling big data. Thus, pruning a recommendation model at scale results in a smaller model capacity and consequently lower accuracy. To reduce computation cost without sacrificing model capacity, we propose a dynamic training scheme, namely alternate model growth and pruning, to alternatively construct and prune weights in the course of training. Our method leverages structured sparsification to reduce computational cost without hurting the model capacity at the end of offline training so that a full-size model is available in the recurring training stage to learn new data in real-time. To the best of our knowledge, this is the first work to provide in-depth experiments and discussion of applying structural dynamics to recommendation systems at scale to reduce training cost. The proposed method is validated with an open-source deep-learning recommendation model (DLRM) and state-of-the-art industrial-scale production models.
End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels
Authors: Songyan Xue, Yi Ma, Na Yi
Date: 2021-05-04 00:00:00

In this paper, a novel end-to-end learning approach, namely JTRD-Net, is proposed for uplink multiuser single-input multiple-output (MU-SIMO) joint transmitter and non-coherent receiver design (JTRD) in fading channels. The basic idea lies in the use of artificial neural networks (ANNs) to replace traditional communication modules at both transmitter and receiver sides. More specifically, the transmitter side is modeled as a group of parallel linear layers, which are responsible for multiuser waveform design; and the non-coherent receiver is formed by a deep feed-forward neural network (DFNN) so as to provide multiuser detection (MUD) capabilities. The entire JTRD-Net can be trained from end to end to adapt to channel statistics through deep learning. After training, JTRD-Net can work efficiently in a non-coherent manner without requiring any levels of channel state information (CSI). In addition to the network architecture, a novel weight-initialization method, namely symmetrical-interval initialization, is proposed for JTRD-Net. It is shown that the symmetrical-interval initialization outperforms the conventional method (e.g. Xavier initialization) in terms of well-balanced convergence-rate among users. Simulation results show that the proposed JTRD-Net approach takes significant advantages in terms of reliability and scalability over baseline schemes on both i.i.d. complex Gaussian channels and spatially-correlated channels.
Streaming end-to-end speech recognition with jointly trained neural feature enhancement
Authors: Chanwoo Kim, Abhinav Garg, Dhananjaya Gowda, Seongkyu Mun, Changwoo Han
Date: 2021-05-04 00:00:00
Tasks: speech-recognition, Speech Recognition

In this paper, we present a streaming end-to-end speech recognition model based on Monotonic Chunkwise Attention (MoCha) jointly trained with enhancement layers. Even though the MoCha attention enables streaming speech recognition with recognition accuracy comparable to a full attention-based approach, training this model is sensitive to various factors such as the difficulty of training examples, hyper-parameters, and so on. Because of these issues, speech recognition accuracy of a MoCha-based model for clean speech drops significantly when a multi-style training approach is applied. Inspired by Curriculum Learning [1], we introduce two training strategies: Gradual Application of Enhanced Features (GAEF) and Gradual Reduction of Enhanced Loss (GREL). With GAEF, the model is initially trained using clean features. Subsequently, the portion of outputs from the enhancement layers gradually increases. With GREL, the portion of the Mean Squared Error (MSE) loss for the enhanced output gradually reduces as training proceeds. In experimental results on the LibriSpeech corpus and noisy far-field test sets, the proposed model with GAEF-GREL training strategies shows significantly better results than the conventional multi-style training approach.
Regret-Optimal LQR Control
Authors: Oron Sabag, Gautam Goel, Sahin Lale, Babak Hassibi
Date: 2021-05-04 00:00:00
Tasks: Learning Theory

We consider the infinite-horizon LQR control problem. Motivated by competitive analysis in online learning, as a criterion for controller design we introduce the dynamic regret, defined as the difference between the LQR cost of a causal controller (that has only access to past disturbances) and the LQR cost of the \emph{unique} clairvoyant one (that has also access to future disturbances) that is known to dominate all other controllers. The regret itself is a function of the disturbances, and we propose to find a causal controller that minimizes the worst-case regret over all bounded energy disturbances. The resulting controller has the interpretation of guaranteeing the smallest regret compared to the best non-causal controller that can see the future. We derive explicit formulas for the optimal regret and for the regret-optimal controller for the state-space setting. These explicit solutions are obtained by showing that the regret-optimal control problem can be reduced to a Nehari extension problem that can be solved explicitly. The regret-optimal controller is shown to be linear and can be expressed as the sum of the classical $H_2$ state-feedback law and an $n$-th order controller ($n$ is the state dimension), and its construction simply requires a solution to the standard LQR Riccati equation and two Lyapunov equations. Simulations over a range of plants demonstrate that the regret-optimal controller interpolates nicely between the $H_2$ and the $H_\infty$ optimal controllers, and generally has $H_2$ and $H_\infty$ costs that are simultaneously close to their optimal values. The regret-optimal controller thus presents itself as a viable option for control systems design.
Supervised multi-specialist topic model with applications on large-scale electronic health record data
Authors: Ziyang Song, Xavier Sumba Toral, Yixin Xu, Aihua Liu, Liming Guo, Guido Powell, Aman Verma, David Buckeridge, Ariane Marelli, Yue Li
Date: 2021-05-04 00:00:00
Tasks: Diagnostic, Prediction, Variational Inference

Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision medicine. To fully exploit its potential, a realistic data generative process of the EHR data needs to be modelled. We present MixEHR-S to jointly infer specialist-disease topics from the EHR data. As the key contribution, we model the specialist assignments and ICD-coded diagnoses as the latent topics based on patient's underlying disease topic mixture in a novel unified supervised hierarchical Bayesian topic model. For efficient inference, we developed a closed-form collapsed variational inference algorithm to learn the model distributions of MixEHR-S. We applied MixEHR-S to two independent large-scale EHR databases in Quebec with three targeted applications: (1) Congenital Heart Disease (CHD) diagnostic prediction among 154,775 patients; (2) Chronic obstructive pulmonary disease (COPD) diagnostic prediction among 73,791 patients; (3) future insulin treatment prediction among 78,712 patients diagnosed with diabetes as a mean to assess the disease exacerbation. In all three applications, MixEHR-S conferred clinically meaningful latent topics among the most predictive latent topics and achieved superior target prediction accuracy compared to the existing methods, providing opportunities for prioritizing high-risk patients for healthcare services. MixEHR-S source code and scripts of the experiments are freely available at https://github.com/li-lab-mcgill/mixehrS
GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning
Authors: Xin Zhang, Jia Liu, Zhengyuan Zhu, Elizabeth S. Bentley
Date: 2021-05-04 00:00:00

Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementation simplicity. However, three fundamental challenges in designing decentralized optimization algorithms are how to reduce their sample, communication, and memory complexities. In this paper, we propose a \underline{g}radient-\underline{t}racking-based \underline{sto}chastic \underline{r}ecursive \underline{m}omentum (GT-STORM) algorithm for efficiently solving nonconvex optimization problems. We show that to reach an $\epsilon^2$-stationary solution, the total number of sample evaluations of our algorithm is $\tilde{O}(m^{1/2}\epsilon^{-3})$ and the number of communication rounds is $\tilde{O}(m^{-1/2}\epsilon^{-3})$, which improve the $O(\epsilon^{-4})$ costs of sample evaluations and communications for the existing decentralized stochastic gradient algorithms. We conduct extensive experiments with a variety of learning models, including non-convex logistical regression and convolutional neural networks, to verify our theoretical findings. Collectively, our results contribute to the state of the art of theories and algorithms for decentralized network optimization.
LaboRecommender: A crazy-easy to use Python-based recommender system for laboratory tests
Authors: Fabián Villena
Date: 2021-05-03 00:00:00
Tasks: Collaborative Filtering, Decision Making, Recommendation Systems

Laboratory tests play a major role in clinical decision making because they are essential for the confirmation of diagnostics suspicions and influence medical decisions. The number of different laboratory tests available to physicians in our age has been expanding very rapidly due to the rapid advances in laboratory technology. To find the correct desired tests within this expanding plethora of elements, the Health Information System must provide a powerful search engine and the practitioner need to remember the exact name of the laboratory test to correctly select the bag of tests to order. Recommender systems are platforms which suggest appropriate items to a user after learning the users' behaviour. A neighbourhood-based collaborative filtering method was used to model the recommender system, where similar bags, clustered using nearest neighbours algorithm, are used to make recommendations of tests for each other similar bag of laboratory tests. The recommender system developed in this paper achieved 95.54 % in the mean average precision metric. A fully documented Python package named LaboRecommender was developed to implement the algorithm proposed in this paper
All-Clear Flare Prediction Using Interval-based Time Series Classifiers
Authors: Anli Ji, Berkay Aydin, Manolis K. Georgoulis, Rafal Angryk
Date: 2021-05-03 00:00:00
Tasks: All, Prediction, Time Series, Time Series Analysis, Time Series Classification

An all-clear flare prediction is a type of solar flare forecasting that puts more emphasis on predicting non-flaring instances (often relatively small flares and flare quiet regions) with high precision while still maintaining valuable predictive results. While many flare prediction studies do not address this problem directly, all-clear predictions can be useful in operational context. However, in all-clear predictions, finding the right balance between avoiding false negatives (misses) and reducing the false positives (false alarms) is often challenging. Our study focuses on training and testing a set of interval-based time series classifiers named Time Series Forest (TSF). These classifiers will be used towards building an all-clear flare prediction system by utilizing multivariate time series data. Throughout this paper, we demonstrate our data collection, predictive model building and evaluation processes, and compare our time series classification models with baselines using our benchmark datasets. Our results show that time series classifiers provide better forecasting results in terms of skill scores, precision and recall metrics, and they can be further improved for more precise all-clear forecasts by tuning model hyperparameters.
Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding
Authors: Zhengling Qi, Rui Miao, Xiaoke Zhang
Date: 2021-05-03 00:00:00
Tasks: Causal Inference, Decision Making

Data-driven individualized decision making has recently received increasing research interests. Most existing methods rely on the assumption of no unmeasured confounding, which unfortunately cannot be ensured in practice especially in observational studies. Motivated by the recent proposed proximal causal inference, we develop several proximal learning approaches to estimating optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. In particular, we establish several identification results for different classes of ITRs, exhibiting the trade-off between the risk of making untestable assumptions and the value function improvement in decision making. Based on these results, we propose several classification-based approaches to finding a variety of restricted in-class optimal ITRs and develop their theoretical properties. The appealing numerical performance of our proposed methods is demonstrated via an extensive simulation study and one real data application.
Machine Learning Applications for Therapeutic Tasks with Genomics Data
Authors: Kexin Huang, Cao Xiao, Lucas M. Glass, Cathy W. Critchlow, Greg Gibson, Jimeng Sun
Date: 2021-05-03 00:00:00
Tasks: BIG-bench Machine Learning, Survey

Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records (EHR), cellular images, and clinical texts. We identify twenty-two machine learning in genomics applications across the entire therapeutics pipeline, from discovering novel targets, personalized medicine, developing gene-editing tools all the way to clinical trials and post-market studies. We also pinpoint seven important challenges in this field with opportunities for expansion and impact. This survey overviews recent research at the intersection of machine learning, genomics, and therapeutic development.