pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning Prediction of COVID-19 Severity Levels From Salivaomics Data
Authors: Aaron Wang, Feng Li, Samantha Chiang, Jennifer Fulcher, Otto Yang, David Wong, Fang Wei
Date: 2022-07-15 00:00:00
Tasks: BIG-bench Machine Learning

The clinical spectrum of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the strain of coronavirus that caused the COVID-19 pandemic, is broad, extending from asymptomatic infection to severe immunopulmonary reactions that, if not categorized properly, may be life-threatening. Researchers rate COVID-19 patients on a scale from 1 to 8 according to the severity level of COVID-19, 1 being healthy and 8 being extremely sick, based on a multitude of factors including number of clinic visits, days since the first sign of symptoms, and more. However, there are two issues with the current state of severity level designation. Firstly, there exists variation among researchers in determining these patient scores, which may lead to improper treatment. Secondly, researchers use a variety of metrics to determine patient severity level, including metrics involving plasma collection that require invasive procedures. This project aims to remedy both issues by introducing a machine learning framework that unifies severity level designations based on noninvasive saliva biomarkers. Our results show that we can successfully use machine learning on salivaomics data to predict the severity level of COVID-19 patients, indicating the presence of viral load using saliva biomarkers.
Effects of different tumors on the steady-state heat distribution in the human eye using the 3D finite element method
Authors: Aboozar Moradi, Mohammad Reza Yousefi
Date: 2022-07-19 00:00:00

In this paper, a three-dimensional finite element method is developed to simulate the heat distribution in the human eye with different types of tumors to understand the effect of tumors on heat distribution in the human eye. The human eye is modeled as a composition of several homogeneous regions and the physical and thermal properties of each region used in this study are more accurate than the models used in previous studies. By considering the exact and complicated geometry of all parts, the finite element method is a proper solution for solving the heat equation inside the human eye. There are two kinds of boundary conditions called the radiation condition and the Robin condition. The radiation boundary condition is modeled as a Robin boundary condition. For modeling eye tumors and their effect on heat distribution, we need information about eye tumor properties such as heat conductivity, density, specific heat, and so on. Thanks to no accurate reported information about eye tumor properties, the properties of other types of tumors such as skin, and bowel tumors are used. Simulation results with different parameters of eye tumors show the effect of eye tumors on heat distribution in the human eye.
Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning
Authors: Bhaskarjit Sarmah, Nayana Nair, Dhagash Mehta, Stefano Pasquali
Date: 2022-07-14 00:00:00
Tasks: BIG-bench Machine Learning, Management

Understanding non-linear relationships among financial instruments has various applications in investment processes ranging from risk management, portfolio construction and trading strategies. Here, we focus on interconnectedness among stocks based on their correlation matrix which we represent as a network with the nodes representing individual stocks and the weighted links between pairs of nodes representing the corresponding pair-wise correlation coefficients. The traditional network science techniques, which are extensively utilized in financial literature, require handcrafted features such as centrality measures to understand such correlation networks. However, manually enlisting all such handcrafted features may quickly turn out to be a daunting task. Instead, we propose a new approach for studying nuances and relationships within the correlation network in an algorithmic way using a graph machine learning algorithm called Node2Vec. In particular, the algorithm compresses the network into a lower dimensional continuous space, called an embedding, where pairs of nodes that are identified as similar by the algorithm are placed closer to each other. By using log returns of S&P 500 stock data, we show that our proposed algorithm can learn such an embedding from its correlation network. We define various domain specific quantitative (and objective) and qualitative metrics that are inspired by metrics used in the field of Natural Language Processing (NLP) to evaluate the embeddings in order to identify the optimal one. Further, we discuss various applications of the embeddings in investment management.
Schooling and Labor Market Consequences of School Construction in Indonesia: Comment
Authors: David Roodman
Date: 2022-07-19 00:00:00
Tasks: Attribute, Sensitivity

I revisit Duflo (2001), which exploits a 1970s schooling expansion in Indonesia to estimate the returns to schooling. I correct data errors, check sensitivity to two specification choices not discussed in the original text, adjust for potential sources of bias (error correlation within the geographic units of treatment, endogenous weighting, non-parallel trends, weak instruments), and follow up on affected cohorts later in life. The reduced-form impact estimate for wages is robust, while that for years spent in school is not, making the former hard to attribute to the latter.
A Study on Impact of Dividend Policy on Initial Public Offering Price Performance
Authors: S. Meghna, N. Suresh, J. C. Usha
Date: 2022-07-14 00:00:00

This study examines the impact of dividend policy on the performance of initial public offerings in India. The period of study is from the year 2011-2014. Monthly returns of the IPOs issued in the considered period and the Indian Stock Market Index (Nifty 50) were considered for the long-run performance study. The methodological tools used are long-run performance statistics and the GARCH model. The Dummy variable was used to measure the effect of dividends on the IPOs. The study reveals that the dividend policy has no significant effect on the stock prices of IPO.
Star-Shaped deviations
Authors: Marcelo Brutti Righi, Marlon Ruoso Moresco
Date: 2022-07-18 00:00:00

We propose the Star-Shaped deviation measures in the same vein as Star-Shaped risk measures and Star-Shaped acceptability indexes. We characterize Star-Shaped deviation measures through Star-Shaped acceptance sets and as the minimum of a family of Convex deviation measures. We also expose an interplay between Star-Shaped risk measures and deviation measures.
Systemic-risk and evolutionary stable strategies in a financial network
Authors: Indrajit Saha, Veeraruna Kavitha
Date: 2022-06-13 00:00:00

We consider a financial network represented at any time instance by a random liability graph which evolves over time. The agents connect through credit instruments borrowed from each other or through direct lending, and these create the liability edges. These random edges are modified (locally) by the agents over time, as they learn from their experiences and (possibly imperfect) observations. The settlement of the liabilities of various agents at the end of the contract period (at any time instance) can be expressed as solutions of random fixed point equations. Our first step is to derive the solutions of these equations (asymptotically and one for each time instance), using a recent result on random fixed point equations. The agents, at any time instance, adapt one of the two available strategies, risky or less risky investments, with an aim to maximize their returns. We aim to study the emerging strategies of such replicator dynamics that drives the financial network. We theoretically reduce the analysis of the complex system to that of an appropriate ordinary differential equation (ODE). Using the attractors of the resulting ODE we show that the replicator dynamics converges to one of the two pure evolutionary stable strategies (all risky or all less risky agents); one can have mixed limit only when the observations are imperfect. We verify our theoretical findings using exhaustive Monte Carlo simulations. The dynamics avoid the emergence of the systemic-risk regime (where majority default). However, if all the agents blindly adapt risky strategy it can lead to systemic risk regime.
Do uHear? Validation of uHear App for Preliminary Screening of Hearing Ability in Soundscape Studies
Authors: Zhen-Ting Ong, Bhan Lam, Kenneth Ooi, Karn N. Watcharasupat, Trevor Wong, Woon-Seng Gan
Date: 2022-07-16 00:00:00
Tasks: Medical Diagnosis

Studies involving soundscape perception often exclude participants with hearing loss to prevent impaired perception from affecting experimental results. Participants are typically screened with pure tone audiometry, the "gold standard" for identifying and quantifying hearing loss at specific frequencies, and excluded if a study-dependent threshold is not met. However, procuring professional audiometric equipment for soundscape studies may be cost-ineffective, and manually performing audiometric tests is labour-intensive. Moreover, testing requirements for soundscape studies may not require sensitivities and specificities as high as that in a medical diagnosis setting. Hence, in this study, we investigate the effectiveness of the uHear app, an iOS application, as an affordable and automatic alternative to a conventional audiometer in screening participants for hearing loss for the purpose of soundscape studies or listening tests in general. Based on audiometric comparisons with the audiometer of 163 participants, the uHear app was found to have high precision (98.04%) when using the World Health Organization (WHO) grading scheme for assessing normal hearing. Precision is further improved (98.69%) when all frequencies assessed with the uHear app is considered in the grading, which lends further support to this cost-effective, automated alternative to screen for normal hearing.
Deeply-Learned Generalized Linear Models with Missing Data
Authors: David K Lim, Naim U Rashid, Junier B Oliva, Joseph G Ibrahim
Date: 2022-07-18 00:00:00
Tasks: Marketing

Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to supervised learning problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in modern biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of deeply learned generalized linear models, a supervised DL architecture for regression and classification problems. We propose a new architecture, \textit{dlglm}, that is one of the first to be able to flexibly account for both ignorable and non-ignorable patterns of missingness in input features and response at training time. We demonstrate through statistical simulation that our method outperforms existing approaches for supervised learning tasks in the presence of missing not at random (MNAR) missingness. We conclude with a case study of a Bank Marketing dataset from the UCI Machine Learning Repository, in which we predict whether clients subscribed to a product based on phone survey data. Supplementary materials for this article are available online.
Probabilistic Reconciliation of Count Time Series
Authors: Giorgio Corani, Dario Azzimonti, Nicolò Rubattu
Date: 2022-07-19 00:00:00
Tasks: Time Series, Time Series Analysis

Forecast reconciliation is an important research topic. Yet, there is currently neither formal framework nor practical method for the probabilistic reconciliation of count time series. In this paper we propose a definition of coherency and reconciled probabilistic forecast which applies to both real-valued and count variables and a novel method for probabilistic reconciliation. It is based on a generalization of Bayes' rule and it can reconcile both real-value and count variables. When applied to count variables, it yields a reconciled probability mass function. Our experiments with the temporal reconciliation of count variables show a major forecast improvement compared to the probabilistic Gaussian reconciliation.
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
Authors: Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao
Date: 2022-07-19 00:00:00
Tasks: Causal Discovery, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As a pivotal component to attaining generalizable solutions in human intelligence, reasoning provides great potential for reinforcement learning (RL) agents' generalization towards varied goals by summarizing part-to-whole arguments and discovering cause-and-effect relations. However, how to discover and represent causalities remains a huge gap that hinders the development of causal RL. In this paper, we augment Goal-Conditioned RL (GCRL) with Causal Graph (CG), a structure built upon the relation between objects and events. We novelly formulate the GCRL problem into variational likelihood maximization with CG as latent variables. To optimize the derived objective, we propose a framework with theoretical performance guarantees that alternates between two steps: using interventional data to estimate the posterior of CG; using CG to learn generalizable models and interpretable policies. Due to the lack of public benchmarks that verify generalization capability under reasoning, we design nine tasks and then empirically show the effectiveness of the proposed method against five baselines on these tasks. Further theoretical analysis shows that our performance improvement is attributed to the virtuous cycle of causal discovery, transition modeling, and policy training, which aligns with the experimental evidence in extensive ablation studies.
Towards a Low-SWaP 1024-beam Digital Array: A 32-beam Sub-system at 5.8 GHz
Authors: Arjuna Madanayake, Viduneth Ariyarathna, Suresh Madishetty, Sravan Pulipati, R. J. Cintra, Diego Coelho, Raíza Oliveira, Fábio M. Bayer, Leonid Belostotski, Soumyajit Mandal, Theodore S. Rappaport
Date: 2022-07-19 00:00:00

Millimeter wave communications require multibeam beamforming in order to utilize wireless channels that suffer from obstructions, path loss, and multi-path effects. Digital multibeam beamforming has maximum degrees of freedom compared to analog phased arrays. However, circuit complexity and power consumption are important constraints for digital multibeam systems. A low-complexity digital computing architecture is proposed for a multiplication-free 32-point linear transform that approximates multiple simultaneous RF beams similar to a discrete Fourier transform (DFT). Arithmetic complexity due to multiplication is reduced from the FFT complexity of $\mathcal{O}(N\: \log N)$ for DFT realizations, down to zero, thus yielding a 46% and 55% reduction in chip area and dynamic power consumption, respectively, for the $N=32$ case considered. The paper describes the proposed 32-point DFT approximation targeting a 1024-beams using a 2D array, and shows the multiplierless approximation and its mapping to a 32-beam sub-system consisting of 5.8 GHz antennas that can be used for generating 1024 digital beams without multiplications. Real-time beam computation is achieved using a Xilinx FPGA at 120 MHz bandwidth per beam. Theoretical beam performance is compared with measured RF patterns from both a fixed-point FFT as well as the proposed multiplier-free algorithm and are in good agreement.
The role of the geometric mean in case-control studies
Authors: Amanda Coston, Edward H. Kennedy
Date: 2022-07-19 00:00:00

Historically used in settings where the outcome is rare or data collection is expensive, outcome-dependent sampling is relevant to many modern settings where data is readily available for a biased sample of the target population, such as public administrative data. Under outcome-dependent sampling, common effect measures such as the average risk difference and the average risk ratio are not identified, but the conditional odds ratio is. Aggregation of the conditional odds ratio is challenging since summary measures are generally not identified. Furthermore, the marginal odds ratio can be larger (or smaller) than all conditional odds ratios. This so-called non-collapsibility of the odds ratio is avoidable if we use an alternative aggregation to the standard arithmetic mean. We provide a new definition of collapsibility that makes this choice of aggregation method explicit, and we demonstrate that the odds ratio is collapsible under geometric aggregation. We describe how to partially identify, estimate, and do inference on the geometric odds ratio under outcome-dependent sampling. Our proposed estimator is based on the efficient influence function and therefore has doubly robust-style properties.
The m-connecting imset and factorization for ADMG models
Authors: Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson, Peter Spirtes
Date: 2022-07-18 00:00:00

Directed acyclic graph (DAG) models have become widely studied and applied in statistics and machine learning -- indeed, their simplicity facilitates efficient procedures for learning and inference. Unfortunately, these models are not closed under marginalization, making them poorly equipped to handle systems with latent confounding. Acyclic directed mixed graph (ADMG) models characterize margins of DAG models, making them far better suited to handle such systems. However, ADMG models have not seen wide-spread use due to their complexity and a shortage of statistical tools for their analysis. In this paper, we introduce the m-connecting imset which provides an alternative representation for the independence models induced by ADMGs. Furthermore, we define the m-connecting factorization criterion for ADMG models, characterized by a single equation, and prove its equivalence to the global Markov property. The m-connecting imset and factorization criterion provide two new statistical tools for learning and inference with ADMG models. We demonstrate the usefulness of these tools by formulating and evaluating a consistent scoring criterion with a closed form solution.
Isotonic propensity score matching
Authors: Mengshan Xu, Taisuke Otsu
Date: 2022-07-18 00:00:00
Tasks: regression

We propose a one-to-many matching estimator of the average treatment effect based on propensity scores estimated by isotonic regression. This approach is predicated on the assumption of monotonicity in the propensity score function, a condition that can be justified in many economic applications. We show that the nature of the isotonic estimator can help us to fix many problems of existing matching methods, including efficiency, choice of the number of matches, choice of tuning parameters, robustness to propensity score misspecification, and bootstrap validity. As a by-product, a uniformly consistent isotonic estimator is developed for our proposed matching method.
Why do tree-based models still outperform deep learning on tabular data?
Authors: Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux
Date: 2022-07-18 00:00:00
Tasks: Benchmarking

While deep learning has enabled tremendous progress on text and image datasets, its superiority on tabular data is not clear. We contribute extensive benchmarks of standard and novel deep learning methods as well as tree-based models such as XGBoost and Random Forests, across a large number of datasets and hyperparameter combinations. We define a standard set of 45 datasets from varied domains with clear characteristics of tabular data and a benchmarking methodology accounting for both fitting models and finding good hyperparameters. Results show that tree-based models remain state-of-the-art on medium-sized data ($\sim$10K samples) even without accounting for their superior speed. To understand this gap, we conduct an empirical investigation into the differing inductive biases of tree-based models and Neural Networks (NNs). This leads to a series of challenges which should guide researchers aiming to build tabular-specific NNs: 1. be robust to uninformative features, 2. preserve the orientation of the data, and 3. be able to easily learn irregular functions. To stimulate research on tabular architectures, we contribute a standard benchmark and raw data for baselines: every point of a 20 000 compute hours hyperparameter search for each learner.
Complexity of High-Dimensional Identity Testing with Coordinate Conditional Sampling
Authors: Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda
Date: 2022-07-19 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We study the identity testing problem for high-dimensional distributions. Given as input an explicit distribution $\mu$, an $\varepsilon>0$, and access to sampling oracle(s) for a hidden distribution $\pi$, the goal in identity testing is to distinguish whether the two distributions $\mu$ and $\pi$ are identical or are at least $\varepsilon$-far apart. When there is only access to full samples from the hidden distribution $\pi$, it is known that exponentially many samples (in the dimension) may be needed for identity testing, and hence previous works have studied identity testing with additional access to various "conditional" sampling oracles. We consider a significantly weaker conditional sampling oracle, which we call the $\mathsf{Coordinate\ Oracle}$, and provide a computational and statistical characterization of the identity testing problem in this new model. We prove that if an analytic property known as approximate tensorization of entropy holds for an $n$-dimensional visible distribution $\mu$, then there is an efficient identity testing algorithm for any hidden distribution $\pi$ using $\tilde{O}(n/\varepsilon)$ queries to the $\mathsf{Coordinate\ Oracle}$. Approximate tensorization of entropy is a pertinent condition as recent works have established it for a large class of high-dimensional distributions. We also prove a computational phase transition: for a well-studied class of $n$-dimensional distributions, specifically sparse antiferromagnetic Ising models over $\{+1,-1\}^n$, we show that in the regime where approximate tensorization of entropy fails, there is no efficient identity testing algorithm unless $\mathsf{RP}=\mathsf{NP}$. We complement our results with a matching $\Omega(n/\varepsilon)$ statistical lower bound for the sample complexity of identity testing in the $\mathsf{Coordinate\ Oracle}$ model.