pwc-archive/papers-with-abstracts huggingface dataset:
Domain-aware Neural Language Models for Speech Recognition
Authors: Linda Liu, Yile Gu, Aditya Gourav, Ankur Gandhe, Shashank Kalmane, Denis Filimonov, Ariya Rastrow, Ivan Bulyko
Date: 2021-01-05 00:00:00
Tasks: Domain Adaptation, domain classification, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

As voice assistants become more ubiquitous, they are increasingly expected to support and perform well on a wide variety of use-cases across different domains. We present a domain-aware rescoring framework suitable for achieving domain-adaptation during second-pass rescoring in production settings. In our framework, we fine-tune a domain-general neural language model on several domains, and use an LSTM-based domain classification model to select the appropriate domain-adapted model to use for second-pass rescoring. This domain-aware rescoring improves the word error rate by up to 2.4% and slot word error rate by up to 4.1% on three individual domains -- shopping, navigation, and music -- compared to domain general rescoring. These improvements are obtained while maintaining accuracy for the general use case.
Statistical Methods for cis-Mendelian Randomization with Two-sample Summary-level Data
Authors: Apostolos Gkatzionis, Stephen Burgess, Paul J. Newcombe
Date: 2021-01-11 00:00:00
Tasks: Variable Selection

Mendelian randomization is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data Mendelian randomization analyses with many correlated variants from a single gene region, and particularly on cis-Mendelian randomization studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-Mendelian randomization with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis and Bayesian variable selection. In a simulation study, we show that the various methods have a comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of LDL-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions respectively.
Frequency Response Assessment of U.S. Power Grids in High PV Penetration
Authors: Shutang You
Date: 2021-01-11 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Nonsynchronous generations such as photovoltaics (PVs) are expected to undermine bulk power systems (BPSs) frequency response at high penetration levels. Though the underlying mechanism has been relatively well understood, the accurate assessment and effective enhancement of the U.S. interconnections frequency response under extra-high PV penetration conditions remains an issue. In this paper, the industry-provided full-detail interconnection models were further validated by synchrophasor frequency measurements and realistically-projected PV geographic distribution information were used to develop extra-high PV penetration scenarios and dynamic models for the three main U.S. interconnections, including Eastern Interconnection (EI), Western Electricity Coordinating Council (WECC), and Electric Reliability Council of Texas (ERCOT). Up to 65% instantaneous PV and 15% wind penetration were simulated and the frequency response change trend of each U.S. interconnection due to the increasing PV penetration level were examined. Most importantly, the practical solutions to address the declining frequency response were discussed. This paper will provide valuable guidance for policy makers, utility operators and academic researchers not only in the U.S. but also other countries in the world.
Optimize the Co-expansion of Generation and Transmission Considering Wind Power in the US Eastern Interconnection
Authors: Shutang You
Date: 2021-01-11 00:00:00
Tasks: Paper generation

This paper studies the generation and transmission expansion co-optimization problem with a high wind power penetration rate in large-scale power grids. In this paper, generation and transmission expansion co-optimization is modeled as a mixed-integer programming (MIP) problem. A scenario creation method is proposed to capture the variation and correlation of both load and wind power across regions for large-scale power grids. Obtained scenarios that represent load and wind uncertainties can be easily introduced into the MIP problem and then solved to obtain the co-optimized generation and transmission expansion plan. Simulation results show that the proposed planning model and the scenario creation method can improve the expansion result significantly through modeling more detailed information of wind and load variation among regions in the US EI system. The improved expansion plan that combines generation and transmission will aid system planners and policy makers to maximize the social welfare in large-scale power grids.
Covariance Estimation from Compressive Data Partitions using a Projected Gradient-based Algorithm
Authors: Jonathan Monsalve, Juan Ramirez, Iñaki Esnaola, Henry Arguello
Date: 2021-01-11 00:00:00
Tasks: Compressive Sensing

Compressive covariance estimation has arisen as a class of techniques whose aim is to obtain second-order statistics of stochastic processes from compressive measurements. Recently, these methods have been used in various image processing and communications applications, including denoising, spectrum sensing, and compression. Notice that estimating the covariance matrix from compressive samples leads to ill-posed minimizations with severe performance loss at high compression rates. In this regard, a regularization term is typically aggregated to the cost function to consider prior information about a particular property of the covariance matrix. Hence, this paper proposes an algorithm based on the projected gradient method to recover low-rank or Toeplitz approximations of the covariance matrix from compressive measurements. The algorithm divides the compressive measurements into data subsets projected onto different subspaces and accurately estimates the covariance matrix by solving a single optimization problem assuming that each data subset contains an approximation of the signal statistics. Furthermore, gradient filtering is included at every iteration of the proposed algorithm to minimize the estimation error. The error induced by the proposed splitting approach is analytically derived along with the convergence guarantees of the proposed method. The algorithm estimates the covariance matrix of hyperspectral images from synthetic and real compressive samples. Extensive simulations show that the proposed algorithm can effectively recover the covariance matrix of hyperspectral images from compressive measurements (8-15% approx). Moreover, simulations and theoretical results show that the filtering step reduces the recovery error up to twice the number of eigenvectors. Finally, an optical implementation is proposed, and real measurements are used to validate the theoretical findings.
Species Area Relationship (SAR): Pattern Description with Geometrical Approach
Authors: Saeid Alirezazadeh, Khadijeh Alibabaei, Stephen P. Hubbell
Date: 2020-07-02 00:00:00
Tasks: Translation

Several formulations are describing the pattern of species-area relationship, log-log linear, semi-log linear, among others. These patterns mainly explain the species-area relationship for large areas, and for the small area, they provide significant differences from real data. We consider the geometric position of individuals of species, and base on that, we find the probability of observing at least one individual of the species. We apply a translation of the well-studied problem of mixed salt-water in a tank to describe the formula of SAR. For a rectangular sample area the species-area relationship follows the pattern, with some simplification, $S=c|A^{\beta}+a|^z$, where $S$ is the number of species in the area of size $A$ and $a,c,z$, and $\beta$ are constants with $z<1$ and $\beta\leq1$. We also show how the constant $z$ relates to some macroecological patterns, namely spatial aggregation, percentage of area coverage, and the core-satellite model. We exemplify our method using data on tropical tree species from a 50ha plot in Barro Colorado Island (BCI), Panama, using all individuals.
Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019
Authors: Archontis Politis, Annamaria Mesaros, Sharath Adavanne, Toni Heittola, Tuomas Virtanen
Date: 2020-09-06 00:00:00
Tasks: Data Augmentation, Sound Event Localization and Detection

Sound event localization and detection is a novel area of research that emerged from the combined interest of analyzing the acoustic scene in terms of the spatial and temporal activity of sounds of interest. This paper presents an overview of the first international evaluation on sound event localization and detection, organized as a task of the DCASE 2019 Challenge. A large-scale realistic dataset of spatialized sound events was generated for the challenge, to be used for training of learning-based approaches, and for evaluation of the submissions in an unlabeled subset. The overview presents in detail how the systems were evaluated and ranked and the characteristics of the best-performing systems. Common strategies in terms of input features, model architectures, training approaches, exploitation of prior knowledge, and data augmentation are discussed. Since ranking in the challenge was based on individually evaluating localization and event classification performance, part of the overview focuses on presenting metrics for the joint measurement of the two, together with a reevaluation of submissions using these new metrics. The new analysis reveals submissions that performed better on the joint task of detecting the correct type of event close to its original location than some of the submissions that were ranked higher in the challenge. Consequently, ranking of submissions which performed strongly when evaluated separately on detection or localization, but not jointly on both, was affected negatively.
Gene targeting in disease networks
Authors: Deborah Weighill, Marouen Ben Guebila, Kimberly Glass, John Platig, Jen Jen Yeh, John Quackenbush
Date: 2021-01-11 00:00:00

Profiling of whole transcriptomes has become a cornerstone of molecular biology and an invaluable tool for the characterization of clinical phenotypes and the identification of disease subtypes. Analyses of these data are becoming ever more sophisticated as we move beyond simple comparisons to consider networks of higher-order interactions and associations. Gene regulatory networks model the regulatory relationships of transcription factors and genes and have allowed the identification of differentially regulated processes in disease systems. In this perspective we discuss gene targeting scores, which measure changes in inferred regulatory network interactions, and their use in identifying disease-relevant processes. In addition, we present an example analysis or pancreatic ductal adenocarcinoma demonstrating the power of gene targeting scores to identify differential processes between complex phenotypes; processes which would have been missed by only performing differential expression analysis. This example demonstrates that gene targeting scores are an invaluable addition to gene expression analysis in the characterization of diseases and other complex phenotypes.
Key Phrase Extraction & Applause Prediction
Authors: Krishna Yadav, Lakshya Choudhary
Date: 2021-01-01 00:00:00
Tasks: BIG-bench Machine Learning, Prediction, Word Embeddings

With the increase in content availability over the internet it is very difficult to get noticed. It has become an upmost the priority of the blog writers to get some feedback over their creations to be confident about the impact of their article. We are training a machine learning model to learn popular article styles, in the form of vector space representations using various word embeddings, and their popularity based on claps and tags.
Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models
Authors: Alexandre Duval, Thomas Lamson, Gael de Leseleuc de Kerouara, Matthias Gallé
Date: 2020-12-19 00:00:00
Proceeding: EACL 2021 2
Tasks: Text Generation, Transfer Learning

It is standard procedure these days to solve Information Extraction task by fine-tuning large pre-trained language models. This is not the case for generation task, which relies on a variety of techniques for controlled language generation. In this paper, we describe a system that fine-tunes a natural language generation model for the problem of solving Writer's Block. The fine-tuning changes the conditioning to also include the right context in addition to the left context, as well as an optional list of entities, the size, the genre and a summary of the paragraph that the human author wishes to generate. Our proposed fine-tuning obtains excellent results, even with a small number of epochs and a total cost of USD 150. The system can be accessed as a web-service, and all the code is released. A video showcasing the interface and the model is also available.
SDA: Improving Text Generation with Self Data Augmentation
Authors: Ping Yu, Ruiyi Zhang, Yang Zhao, Yizhe Zhang, Chunyuan Li, Changyou Chen
Date: 2021-01-02 00:00:00
Tasks: Data Augmentation, Imitation Learning, Sentence, Text Generation

Data augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision. However, less work has been done in the context of text, partially due to its discrete nature and the complexity of natural languages. In this paper, we propose to improve the standard maximum likelihood estimation (MLE) paradigm by incorporating a self-imitation-learning phase for automatic data augmentation. Unlike most existing sentence-level augmentation strategies, which are only applied to specific models, our method is more general and could be easily adapted to any MLE-based training procedure. In addition, our framework allows task-specific evaluation metrics to be designed to flexibly control the generated sentences, for example, in terms of controlling vocabulary usage and avoiding nontrivial repetitions. Extensive experimental results demonstrate the superiority of our method on two synthetic and several standard real datasets, significantly improving related baselines.
Learning to Emphasize: Dataset and Shared Task Models for Selecting Emphasis in Presentation Slides
Authors: Amirreza Shirani, Giai Tran, Hieu Trinh, Franck Dernoncourt, Nedim Lipka, Paul Asente, Jose Echevarria, Thamar Solorio
Date: 2021-01-02 00:00:00

Presentation slides have become a common addition to the teaching material. Emphasizing strong leading words in presentation slides can allow the audience to direct the eye to certain focal points instead of reading the entire slide, retaining the attention to the speaker during the presentation. Despite a large volume of studies on automatic slide generation, few studies have addressed the automation of design assistance during the creation process. Motivated by this demand, we study the problem of Emphasis Selection (ES) in presentation slides, i.e., choosing candidates for emphasis, by introducing a new dataset containing presentation slides with a wide variety of topics, each is annotated with emphasis words in a crowdsourced setting. We evaluate a range of state-of-the-art models on this novel dataset by organizing a shared task and inviting multiple researchers to model emphasis in this new domain. We present the main findings and compare the results of these models, and by examining the challenges of the dataset, we provide different analysis components.
Predicting Individual Substance Abuse Vulnerability using Machine Learning Techniques
Authors: Uwaise Ibna Islam, Iqbal H. Sarker, Enamul Haque, Mohammed Moshiul Hoque
Date: 2020-12-09 00:00:00
Tasks: BIG-bench Machine Learning, regression

Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol. Continuous use of these substances can ultimately lead a human to disastrous consequences. As patients display a high rate of relapse, prevention at an early stage can be an effective restraint. We therefore propose a binary classifier to identify any individual's present vulnerability towards substance abuse by analyzing subjects' socio-economic environment. We have collected data by a questionnaire which is created after carefully assessing the commonly involved factors behind substance abuse. Pearson's chi-squared test of independence is used to identify key feature variables influencing substance abuse. Later we build the predictive classifiers using machine learning classification algorithms on those variables. Logistic regression classifier trained with 18 features can predict individual vulnerability with the best accuracy.
Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function
Authors: Zhaowei Zhu, Xiang Lan, Tingting Zhao, Yangming Guo, Pipin Kojodjojo, Zhuoyang Xu, Zhuo Liu, SiQi Liu, Han Wang, Xingzhi Sun, Mengling Feng
Date: 2020-12-12 00:00:00

Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abnormalities. Early and accurate diagnosis will allow early treatment and intervention to prevent severe complications of cardiovascular disease. In the PhysioNet/Computing in Cardiology Challenge 2020, our objective is to develop an algorithm that automatically identifies 27 ECG abnormalities from 12-lead ECG recordings.
DeepFilter: an ECG baseline wander removal filter using deep learning techniques
Date: 2021-01-09 00:00:00
Tasks: Diagnostic, ECG Denoising

According to the World Health Organization, around 36% of the annual deaths are associated with cardiovascular diseases and 90% of heart attacks are preventable. Electrocardiogram signal analysis in ambulatory electrocardiography, during an exercise stress test, and in resting conditions allows cardiovascular disease diagnosis. However, during the acquisition, there is a variety of noises that may damage the signal quality thereby compromising their diagnostic potential. The baseline wander is one of the most undesirable noises. In this work, we propose a novel algorithm for BLW noise filtering using deep learning techniques. The model performance was validated using the QT Database and the MIT-BIH Noise Stress Test Database from Physionet. In addition, several comparative experiments were performed against state-of-the-art methods using traditional filtering procedures as well as deep learning techniques. The proposed approach yields the best results on four similarity metrics: the sum of squared distance, maximum absolute square, percentage of root distance, and cosine similarity with 4.29 (6.35) au, 0.34 (0.25) au, 45.35 (29.69) au and, 91.46 (8.61) au, respectively. The source code of this work, containing our method and related implementations, is freely available on Github.
DENet: a deep architecture for audio surveillance applications
Authors: Antonio Greco, Antonio Roberto, Alessia Saggese, Mario Vento
Date: 2021-01-11 00:00:00
Tasks: Denoising, Sound Event Detection

In the last years, a big interest of both the scientific community and the market has been devoted to the design of audio surveillance systems, able to analyse the audio stream and to identify events of interest; this is particularly true in security applications, in which the audio analytics can be profitably used as an alternative to video analytics systems, but also combined with them. Within this context, in this paper we propose a novel recurrent convolutional neural network architecture, named DENet; it is based on a new layer that we call denoising-enhancement (DE) layer, which performs denoising and enhancement of the original signal by applying an attention map on the components of the band-filtered signal. Differently from state-of-the-art methodologies, DENet takes as input the lossless raw waveform and is able to automatically learn the evolution of the frequencies-of-interest over time, by combining the proposed layer with a bidirectional gated recurrent unit. Using the feedbacks coming from classifications related to consecutive frames (i.e. that belong to the same event), the proposed method is able to drastically reduce the misclassifications. We carried out experiments on the MIVIA Audio Events and MIVIA Road Events public datasets, confirming the effectiveness of our approach with respect to other state-of-the-art methodologies.
A note on the option price and 'Mass at zero in the uncorrelated SABR model and implied volatility asymptotics'
Authors: Jaehyuk Choi, Lixin Wu
Date: 2020-11-01 00:00:00
Tasks: Math, Numerical Integration

Gulisashvili et al. [Quant. Finance, 2018, 18(10), 1753-1765] provide a small-time asymptotics for the mass at zero under the uncorrelated stochastic-alpha-beta-rho (SABR) model by approximating the integrated variance with a moment-matched lognormal distribution. We improve the accuracy of the numerical integration by using the Gauss--Hermite quadrature. We further obtain the option price by integrating the constant elasticity of variance (CEV) option prices in the same manner without resorting to the small-strike volatility smile asymptotics of De Marco et al. [SIAM J. Financ. Math., 2017, 8(1), 709-737]. For the uncorrelated SABR model, the new option pricing method is accurate and arbitrage-free across all strike prices.
Mean-Variance Investment and Risk Control Strategies -- A Time-Consistent Approach via A Forward Auxiliary Process
Authors: Yang shen, Bin Zou
Date: 2021-01-11 00:00:00

We consider an optimal investment and risk control problem for an insurer under the mean-variance (MV) criterion. By introducing a deterministic auxiliary process defined forward in time, we formulate an alternative time-consistent problem related to the original MV problem, and obtain the optimal strategy and the value function to the new problem in closed-form. We compare our formulation and optimal strategy to those under the precommitment and game-theoretic framework. Numerical studies show that, when the financial market is negatively correlated with the risk process, optimal investment may involve short selling the risky asset and, if that happens, a less risk averse insurer short sells more risky asset.