pwc-archive/papers-with-abstracts huggingface dataset:
The Experimenters' Dilemma: Inferential Preferences over Populations
Authors: Neeraja Gupta, Luca Rigotti, Alistair Wilson
Date: 2021-07-11 00:00:00

We compare three populations commonly used in experiments by economists and other social scientists: undergraduate students at a physical location (lab), Amazon's Mechanical Turk (MTurk), and Prolific. The comparison is made along three dimensions: the noise in the data due to inattention, the cost per observation, and the elasticity of response. We draw samples from each population, examining decisions in four one-shot games with varying tensions between the individual and socially efficient choices. When there is no tension, where individual and pro-social incentives coincide, noisy behavior accounts for 60% of the observations on MTurk, 19% on Prolific, and 14% for the lab. Taking costs into account, if noisy data is the only concern Prolific dominates from an inferential power point of view, combining relatively low noise with a cost per observation one fifth of the lab's. However, because the lab population is more sensitive to treatment, across our main PD game comparison the lab still outperforms both Prolific and MTurk.
E-Learning and its Socioeconomics
Authors: Avni Singh
Date: 2021-07-11 00:00:00

While controversial, e-learning has become an essential tool for all kinds of education: especially within the kindergarten-to-twelfth sector. However, pockets of this sector lack access, mainly economically underserved students. This paper explores the options available to underserved and aptly resourced members of the kindergarten-to-twelfth educational sector: a 250-million-person market, with only 9 million students enrolled in online education. The paper also provides a brief overview of the options and challenges of making e-learning available to everyone in the kindergarten-to-twelfth educational sector. To establish whether e-learning is beneficial, it also discusses the results of a survey conducted on students and educators who have experienced e-learning, with the results showing that it is beneficial, with a general trend of teachers showing more comfort with online learning than students. The paper utilizes primary and secondary resources for this purpose, with information both from the internet, and from surveys conducted within people from the system: parents, students, and teachers.
Sustained cost declines in solar PV and battery storage needed to eliminate coal generation in India
Authors: Aniruddh Mohan, Shayak Sengupta, Parth Vaishnav, Rahul Tongia, Asim Ahmed, Ines L. Azevedo
Date: 2021-07-10 00:00:00

Unabated coal power in India must be phased out by mid-century to achieve global climate targets under the Paris Agreement. Here we estimate the costs of hybrid power plants - lithium-ion battery storage with wind and solar PV - to replace coal generation. We design least cost mixes of these technologies to supply stylized baseload and load-following generation profiles in three Indian states - Karnataka, Gujarat, and Tamil Nadu. Our analysis shows that availability of low cost capital, solar PV capital costs of at least $250/kW, and battery storage capacity costs at least 50% cheaper than current levels will be required to phase out existing coal power plants. Phaseout by 2040 requires a 6% annual decline in the cost of hybrid systems over the next two decades. We find that replacing coal generation with hybrid systems 99% of the hours over multiple decades is roughly 40% cheaper than 100% replacement, indicating a key role for other low cost grid flexibility mechanisms to help hasten coal phaseout. Solar PV is more suited to pairing with short duration storage than wind power. Overall, our results describe the challenging technological and policy advances needed to achieve the temperature goals of the Paris Agreement.
Geographic Spillover Effects of Prescription Drug Monitoring Programs (PDMPs)
Authors: Daniel Guth, Shiyu Zhang
Date: 2021-07-10 00:00:00

Prescription Drug Monitoring Programs (PDMPs) seek to potentially reduce opioid misuse by restricting the sale of opioids in a state. We examine discontinuities along state borders, where one side may have a PDMP and the other side may not. We find that electronic PDMP implementation, whereby doctors and pharmacists can observe a patient's opioid purchase history, reduces a state's opioid sales but increases opioid sales in neighboring counties on the other side of the state border. We also find systematic differences in opioid sales and mortality between border counties and interior counties. These differences decrease when neighboring states both have ePDMPs, which is consistent with the hypothesis that individuals cross state lines to purchase opioids. Our work highlights the importance of understanding the opioid market as connected across counties or states, as we show that states are affected by the opioid policies of their neighbors.
The unreasonable effectiveness of optimal transport in economics
Authors: Alfred Galichon
Date: 2021-07-09 00:00:00
Tasks: Discrete Choice Models, quantile regression, regression

Optimal transport has become part of the standard quantitative economics toolbox. It is the framework of choice to describe models of matching with transfers, but beyond that, it allows to: extend quantile regression; identify discrete choice models; provide new algorithms for computing the random coefficient logit model; and generalize the gravity model in trade. This paper offer a brief review of the basics of the theory, its applications to economics, and some extensions.
Waiting to Borrow From a 457(b) Plan
Authors: Alex Garivaltis
Date: 2021-07-09 00:00:00

This paper formulates and solves the optimal stopping problem for a loan made to one's self from a tax-advantaged retirement account such as a 401(k), 403(b), or 457(b) plan. If the plan participant has access to an external asset with a higher expected rate of return than the investment funds and indices that are available within the retirement account, then he must decide how long to wait before exercising the loan option. On the one hand, taking the loan quickly will result in many years of exponential capital growth at the higher (external) rate; on the other hand, if we wait to accumulate more funds in the 457(b), then we can make a larger deposit into the external asset (albeit for a shorter period of time). I derive a variety of cutoff rules for optimal loan control; in general, the investor must wait until he accumulates a certain amount of money (measured in contribution-years) that depends on the disparate yields, the loan parameters, and the date certain at which he will liquidate the retirement account. Letting the horizon tend to infinity, the optimal (horizon-free) policy gains in elegance, simplicity, and practical robustness to different life outcomes. When asset prices and returns are stochastic, the (continuous time) cutoff rule turns into a "wait region," whereby the mean of terminal wealth is rising and the variance of terminal wealth is falling. After his sojourn through the wait region is over, the participant finds himself on the mean-variance frontier, at which point his subsequent behavior is a matter of personal risk preference.
Seasonal and Secular Periodicities Identified in the Dynamics of US FDA Medical Devices (1976 2020) Portends Intrinsic Industrial Transformation and Independence of Certain Crises
Authors: Iraj Daizadeh
Date: 2021-07-09 00:00:00
Tasks: Econometrics, Time Series Analysis

Background: The US Food and Drug Administration (FDA) regulates medical devices (MD), which are predicated on a concoction of economic and policy forces (e.g., supply/demand, crises, patents). Assuming that the number of FDA MD (Premarketing Notifications (PMN), Approvals (PMAs), and their sum) Applications behaves similarly to those of other econometrics, this work explores the hypothesis of the existence (and, if so, the length scale(s)) of economic cycles (periodicities). Methods: Beyond summary statistics, the monthly (May, 1976 to December, 2020) number of observed FDA MD Applications are investigated via an assortment of time series techniques (including: Discrete Wavelet Transform, Running Moving Average Filter (RMAF), Complete Ensemble Empirical Mode with Adaptive Noise decomposition (CEEMDAN), and Seasonal Trend Loess (STL) decomposition) to exhaustively search and characterize such periodicities. Results: The data were found to be non-normal, non-stationary (fractional order of integration < 1), non-linear, and strongly persistent (Hurst > 0.5). Importantly, periodicities exist and follow seasonal, 1 year short-term, 5-6 year (Juglar), and a single 24-year medium-term (Kuznets) period (when considering the total number of MD Applications). Economic crises (e.g., COVID-19) do not seem to affect the evolution of the periodicities. Conclusions: This work concludes that (1) PMA and PMN data may be viewed as a proxy measure of the MD industry; (2) periodicities exists in the data with time lengths associated with seasonal/1-year, Juglar and Kuznets affects; (4) these metrics do not seem affected by specific crises (such as COVID-19) (similarly with other econometrics used in periodicity assessments); (5) PMNs and PMAs evolve inversely and suggest a structural industrial transformation; (6) Total MDs are predicted to continue their decline into the mid-2020s prior to recovery.
Characterization of the probability and information entropy of a process with an increasing sample space by different functional forms of expansion, with an application to hyperinflation
Authors: Laurence Francis Lacey
Date: 2021-05-27 00:00:00

There is a random variable (X) with a determined outcome (i.e., X = x0), p(x0) = 1. Consider x0 to have a discrete uniform distribution over the integer interval [1, s], where the size of the sample space (s) = 1, in the initial state, such that p(x0) = 1. What is the probability of x0 and the associated information entropy (H), as s increases by means of different functional forms of expansion? Such a process has been characterised in the case of (1) a mono-exponential expansion of the sample space; (2) a power function expansion; (3) double exponential expansion. The double exponential expansion of the sample space with time (from a natural log relationship between t and n) describes a "hyperinflationary" process. Over the period from the middle of 1920 to the end of 1923, the purchasing power of the Weimar Republic paper Mark to purchase one gold Mark became close to zero (1 paper Mark = 10 to the power of -12 gold Mark). From the purchasing power of the paper Mark to purchase one gold Mark, the information entropy of this hyperinflationary process was determined.
A theoretical look at ordinal classification methods based on reference sets composed of characteristic actions
Authors: Eduardo Fernandez, Jorge Navarro, Efrain Solares
Date: 2021-07-09 00:00:00
Tasks: Ordinal Classification, Relation

From a theoretical view, this paper addresses the general problem of designing ordinal classification methods based on comparing actions with subset of actions, which are representative of their classes (categories). The basic demand of the proposal consists in setting a relational system (D, S), where S is a reflexive relation compatible with the preferential order of the set of classes, and D is a transitive relation such that D is a subset of S. Different ordinal classification methods can be derived from diverse model of preferences fulfilling the basic conditions on S and D. Two complementary assignment procedures compose each method, which correspond through the transposition operation and should be used complementarily. The methods work under relatively slight conditions on the representative actions and satisfy several fundamental properties. ELECTRE TRI-nC, INTERCLASS-nC, and the hierarchical ELECTRE TRI-nC with interacting criteria, can be considered as particular cases of this general framework.
Sound Event Detection: A Tutorial
Authors: Annamaria Mesaros, Toni Heittola, Tuomas Virtanen, Mark D. Plumbley
Date: 2021-07-12 00:00:00
Tasks: BIG-bench Machine Learning, Event Detection, Sound Event Detection

The goal of automatic sound event detection (SED) methods is to recognize what is happening in an audio signal and when it is happening. In practice, the goal is to recognize at what temporal instances different sounds are active within an audio signal. This paper gives a tutorial presentation of sound event detection, including its definition, signal processing and machine learning approaches, evaluation, and future perspectives.
UniSpeech at scale: An Empirical Study of Pre-training Method on Large-Scale Speech Recognition Dataset
Authors: Chengyi Wang, Yu Wu, Shujie Liu, Jinyu Li, Yao Qian, Kenichi Kumatani, Furu Wei
Date: 2021-07-12 00:00:00
Tasks: Self-Supervised Learning, speech-recognition, Speech Recognition

Recently, there has been a vast interest in self-supervised learning (SSL) where the model is pre-trained on large scale unlabeled data and then fine-tuned on a small labeled dataset. The common wisdom is that SSL helps resource-limited tasks in which only a limited amount of labeled data is available. The benefit of SSL keeps diminishing when the labeled training data amount increases. To our best knowledge, at most a few thousand hours of labeled data was used in the study of SSL. In contrast, the industry usually uses tens of thousands of hours of labeled data to build high-accuracy speech recognition (ASR) systems for resource-rich languages. In this study, we take the challenge to investigate whether and how SSL can improve the ASR accuracy of a state-of-the-art production-scale Transformer-Transducer model, which was built with 65 thousand hours of anonymized labeled EN-US data.
Sentinel-1 Additive Noise Removal from Cross-Polarization Extra-Wide TOPSAR with Dynamic Least-Squares
Authors: Peter Q. Lee, Linlin Xu, David A. Clausi
Date: 2021-07-12 00:00:00
Tasks: Denoising

Sentinel-1 is a synthetic aperture radar (SAR) platform with an operational mode called extra wide (EW) that offers large regions of ocean areas to be observed. A major issue with EW images is that the cross-polarized HV and VH channels have prominent additive noise patterns relative to low backscatter intensity, which disrupts tasks that require manual or automated interpretation. The European Space Agency (ESA) provides a method for removing the additive noise pattern by means of lookup tables, but applying them directly produces unsatisfactory results because characteristics of the noise still remain. Furthermore, evidence suggests that the magnitude of the additive noise dynamically depends on factors that are not considered by the ESA estimated noise field. To address these issues we propose a quadratic objective function to model the mis-scale of the provided noise field on an image. We consider a linear denoising model that re-scales the noise field for each subswath, whose parameters are found from a least-squares solution over the objective function. This method greatly reduces the presence of additive noise while not requiring a set of training images, is robust to heterogeneity in images, dynamically estimates parameters for each image, and finds parameters using a closed-form solution. Two experiments were performed to validate the proposed method. The first experiment simulated noise removal on a set of RADARSAT-2 images with noise fields artificially imposed on them. The second experiment conducted noise removal on a set of Sentinel-1 images taken over the five oceans. Afterwards, quality of the noise removal was evaluated based on the appearance of open-water. The two experiments indicate that the proposed method marks an improvement both visually and through numerical measures.
Generative adversarial network based single pixel imaging
Authors: Ming Zhao, Fengqiang Li, Fengyue Huo, Zhiming Tian
Date: 2021-07-11 00:00:00
Tasks: Generative Adversarial Network

Single pixel imaging can reconstruct two-dimensional images of a scene with only a single-pixel detector. It has been widely used for imaging in non-visible bandwidth (e.g., near-infrared and X-ray) where focal-plane array sensors are challenging to be manufactured. In this paper, we propose a generative adversarial network based reconstruction algorithm for single pixel imaging, which demonstrates efficient reconstruction in 10ms and higher quality. We verify the proposed method with both synthetic and real-world experiments, and demonstrate a good quality of reconstruction of a real-world plaster using a 0.05 sampling rate.
Collaborative Filtering-Based Method for Low-Resolution and Details Preserving Image Denoising
Authors: Basit O. Alawode, Mudassir Masood, Tarig Ballal, Tareq Al-Naffouri
Date: 2021-07-10 00:00:00
Tasks: Collaborative Filtering, Denoising, Image Denoising

Over the years, progressive improvements in denoising performance have been achieved by several image denoising algorithms that have been proposed. Despite this, many of these state-of-the-art algorithms tend to smooth out the denoised image resulting in the loss of some image details after denoising. Many also distort images of lower resolution resulting in a partial or complete structural loss. In this paper, we address these shortcomings by proposing a collaborative filtering-based (CoFiB) denoising algorithm. Our proposed algorithm performs weighted sparse domain collaborative denoising by taking advantage of the fact that similar patches tend to have similar sparse representations in the sparse domain. This gives our algorithm the intelligence to strike a balance between image detail preservation and noise removal. Our extensive experiments showed that our proposed CoFiB algorithm does not only preserve the image details but also perform excellently for images of any given resolution where many denoising algorithms tend to struggle, specifically at low resolutions.
Multi-modality Deep Restoration of Extremely Compressed Face Videos
Authors: Xi Zhang, Xiaolin Wu
Date: 2021-07-05 00:00:00
Tasks: Quantization

Arguably the most common and salient object in daily video communications is the talking head, as encountered in social media, virtual classrooms, teleconferences, news broadcasting, talk shows, etc. When communication bandwidth is limited by network congestions or cost effectiveness, compression artifacts in talking head videos are inevitable. The resulting video quality degradation is highly visible and objectionable due to high acuity of human visual system to faces. To solve this problem, we develop a multi-modality deep convolutional neural network method for restoring face videos that are aggressively compressed. The main innovation is a new DCNN architecture that incorporates known priors of multiple modalities: the video-synchronized speech signal and semantic elements of the compression code stream, including motion vectors, code partition map and quantization parameters. These priors strongly correlate with the latent video and hence they are able to enhance the capability of deep learning to remove compression artifacts. Ample empirical evidences are presented to validate the superior performance of the proposed DCNN method on face videos over the existing state-of-the-art methods.
Implementing a LoRa Software-Defined Radio on a General-Purpose ULP Microcontroller
Authors: Mathieu Xhonneux, Jérôme Louveaux, David Bol
Date: 2021-07-12 00:00:00
Tasks: CPU

Emerging Internet-of-Things sensing applications rely on ultra low-power (ULP) microcontroller units (MCUs) that wirelessly transmit data to the cloud. Typical MCUs nowadays consist of generic blocks, except for the protocol-specific radios implemented in hardware. Hardware radios however slow down the evolution of wireless protocols due to retrocompatiblity concerns. In this work, we explore a software-defined radio architecture by demonstrating a LoRa transceiver running on custom ULP MCU codenamed SleepRider with an ARM Cortex-M4 CPU. In SleepRider MCU, we offload the generic baseband operations (e.g., low-pass filtering) to a reconfigurable digital front-end block and use the Cortex-M4 CPU to perform the protocol-specific computations. Our software implementation of the LoRa physical layer only uses the native SIMD instructions of the Cortex-M4 to achieve real-time transmission and reception of LoRa packets. SleepRider MCU has been fabricated in a 28nm FDSOI CMOS technology and is used in a testbed to experimentally validate the software implementation. Experimental results show that the proposed software-defined radio requires only a CPU frequency of 20 MHz to correctly receive a LoRa packet, with an ultra-low power consumption of 0.42 mW on average.
A Multi-Lead Fusion Method for the Accurate Delineation of QRS Complex Location in 12 Lead ECG Signal
Authors: Chhaviraj Chauhan, Monika Agrawal, Pooja Sabherwal
Date: 2021-07-12 00:00:00
Tasks: Sensitivity

This paper presents a multi-lead fusion method for the accurate and automated detection of the QRS complex location in 12 lead ECG (Electrocardiogram) signals. The proposed multi-lead fusion method accurately delineates the QRS complex by the fusion of detected QRS complexes of the individual 12 leads. The proposed algorithm consists of two major stages. Firstly, the QRS complex location of each lead is detected by the single lead QRS detection algorithm. Secondly, the multi-lead fusion algorithm combines the information of the QRS complex locations obtained in each of the 12 leads. The performance of the proposed algorithm is improved in terms of Sensitivity and Positive Predictivity by discarding the false positives. The proposed method is validated on the ECG signals with various artifacts, inter and intra subject variations. The performance of the proposed method is validated on the long duration recorded ECG signals of St. Petersburg INCART database with Sensitivity of 99.87% and Positive Predictivity of 99.96% and on the short duration recorded ECG signals of CSE (Common Standards for Electrocardiography) multi-lead database with 100% Sensitivity and 99.13% Positive Predictivity.
A Survey on Non-Geostationary Satellite Systems: The Communication Perspective
Authors: Hayder Al-Hraishawi, Houcine Chougrani, Steven Kisseleff, Eva Lagunas, Symeon Chatzinotas
Date: 2021-07-12 00:00:00
Tasks: Management, Survey

The next phase of satellite technology is being characterized by a new evolution in non-geostationary orbit (NGSO) satellites, which conveys exciting new communication capabilities to provide non-terrestrial connectivity solutions and to support a wide range of digital technologies from various industries. NGSO communication systems are known for a number of key features such as lower propagation delay, smaller size, and lower signal losses in comparison to the conventional geostationary orbit (GSO) satellites, which can potentially enable latency-critical applications to be provided through satellites. NGSO promises a substantial boost in communication speed and energy efficiency, and thus, tackling the main inhibiting factors of commercializing GSO satellites for broader utilization. The promised improvements of NGSO systems have motivated this paper to provide a comprehensive survey of the state-of-the-art NGSO research focusing on the communication prospects, including physical layer and radio access technologies along with the networking aspects and the overall system features and architectures. Beyond this, there are still many NGSO deployment challenges to be addressed to ensure seamless integration not only with GSO systems but also with terrestrial networks. These unprecedented challenges are also discussed in this paper, including coexistence with GSO systems in terms of spectrum access and regulatory issues, satellite constellation and architecture designs, resource management problems, and user equipment requirements. Finally, we outline a set of innovative research directions and new opportunities for future NGSO research.