pwc-archive/papers-with-abstracts huggingface dataset:
The Economics of Interstellar Flight
Authors: Philip Lubin, Alexander N. Cohen
Date: 2021-12-27 00:00:00

Large scale directed energy offers the possibility of radical transformation in a variety of areas, including the ability to achieve relativistic flight that will enable the first interstellar missions, as well as rapid interplanetary transit. In addition, the same technology will allow for long-range beamed power for ion, ablation, and thermal engines, as well as long-range recharging of distant spacecraft, long-range and ultra high bandwidth laser communications, and many additional applications that include remote composition analysis, manipulation of asteroids, and full planetary defense. Directed energy relies on photonics which, like electronics, is an exponentially expanding growth area driven by diverse economic interests that allows transformational advances in space exploration and capability. We have made enormous technological progress in the last few years to enable this long-term vision. In addition to the technological challenges, we must face the economic challenges to bring the vision to reality. The path ahead requires a fundamental change in the system designs to allow for the radical cost reductions required. To afford the full-scale realization of this vision we will need to bring to fore integrated photonics and mass production as a path forward. Fortunately, integrated photonics is a technology driven by vast consumer need for high speed data delivery. We outline the fundamental physics that drive the economics and derive an analytic cost model that allows us to logically plan the path ahead.
Systematic dispersion compensation for spectral domain optical coherence tomography using time-frequency analysis and iterative optimization for iridocorneal angle imaging
Authors: Shangbang Luo, Guy Holland, Eric Mikula, Samantha Bradford, Reza Khazaeinezhad, James V Jester, Tibor Juhasz
Date: 2021-12-31 00:00:00

Dispersion is a common phenomenon in optics due to the frequency dependence of the refractive index in polychromatic light. This issue, if left untreated in optical coherence tomography (OCT) imaging, leads to signal broadening of the coherence length and deterioration of the axial resolution. We report a new numeric method for the systematic dispersion compensation in a spectral-domain (SD) OCT for imaging the iridocorneal angle of human cadaver eyes. The dispersion compensation for our OCT system is calculated by an automated iterative process that minimizes the wavenumber-dependent variance of the ridge extracted from the energy distribution of a mirror's spectral interferogram using Short-Time Fourier Transform (STFT) Time-Frequency Analysis (TFA). The average axial resolution of 2.7 um in air was achieved at a range of depths up to 2 mm. Compensated OCT images of the iridocorneal angle in human cadaver eyes were much clearer than non-compensated images. We demonstrate the feasibility, effectiveness, and robustness of the proposed method for dispersion compensation in an SD-OCT by evaluating both the mirror and human cadaver eye measurements. We also verified that our imaging system is able to visualize the iridocorneal angle details, such as trabecular meshwork (TM), Schlemm's canal (SC), and collector channels (CCs), which are important ocular outflow structures and play a crucial role in glaucoma managements.
A Unifying Approach to Inverse Problems of Ultrasound Beamforming and Deconvolution
Authors: Sobhan Goudarzi, Adrian Basarab, Hassan Rivaz
Date: 2021-12-28 00:00:00
Tasks: Image Reconstruction

Beamforming is an essential step in the ultrasound image formation pipeline and has recently attracted growing interest. An important goal of beamforming is to increase the image spatial resolution, or in other words to narrow down the system point spread function. In parallel to beamforming approaches, deconvolution methods have also been explored in ultrasound imaging to mitigate the adverse effects of PSF. Unfortunately, these two steps have only been considered separately in a sequential approach. Herein, a novel framework for unifying beamforming and deconvolution in ultrasound image reconstruction is introduced. More specifically, the proposed formulation is a regularized inverse problem including two linear models for beamforming and deconvolution plus additional sparsity constraint. We take advantage of the alternating direction method of multipliers algorithm to find the solution of the joint optimization problem. The performance evaluation is presented on a set of publicly available simulations, real phantoms, and in vivo data. Furthermore, the superiority of the proposed approach in comparison with the sequential approach as well as each of the other beamforming and deconvolution approaches alone is also shown. Results demonstrate that our approach combines the advantages of both methods and offers ultrasound images with superior resolution and contrast.
Supervised laser-speckle image sampling of skin tissue to detect very early stage of diabetes by its effects on skin subcellular properties
Authors: Ahmet Orun, Luke Vella Critien, Jennifer Carter, Martin Stacey
Date: 2021-12-18 00:00:00

This paper investigates the effectiveness of an expert system based on K-nearest neighbors algorithm for laser speckle image sampling applied to the early detection of diabetes. With the latest developments in artificial intelligent guided laser speckle imaging technologies, it may be possible to optimise laser parameters, such as wavelength, energy level and image texture measures in association with a suitable AI technique to interact effectively with the subcellular properties of a skin tissue to detect early signs of diabetes. The new approach is potentially more effective than the classical skin glucose level observation because of its optimised combination of laser physics and AI techniques, and additionally, it allows non-expert individuals to perform more frequent skin tissue tests for an early detection of diabetes.
Generative adversarial network for super-resolution imaging through a fiber
Authors: Wei Li, Ksenia Abrashitova, Gerwin Osnabrugge, Lyubov V. Amitonova
Date: 2022-01-03 00:00:00
Tasks: Compressive Sensing, Generative Adversarial Network, Image Reconstruction, Super-Resolution

A multimode fiber represents the ultimate limit in miniaturization of imaging endoscopes. Here we propose a fiber imaging approach employing compressive sensing with a data-driven machine learning framework. We implement a generative adversarial network for image reconstruction without relying on a sample sparsity constraint. The proposed method outperforms the conventional compressive imaging algorithms in terms of image quality and noise robustness. We experimentally demonstrate speckle-based imaging below the diffraction limit at a sub-Nyquist speed through a multimode fiber.
Deep-learned speckle pattern and its application to ghost imaging
Authors: Xiaoyu Nie, Haotian Song, Wenhan Ren, Xingchen Zhao, Zhedong Zhang, Tao Peng, Marlan O. Scully
Date: 2021-12-25 00:00:00

In this paper, we present a method for speckle pattern design using deep learning. The speckle patterns possess unique features after experiencing convolutions in Speckle-Net, our well-designed framework for speckle pattern generation. We then apply our method to the computational ghost imaging system. The standard deep learning-assisted ghost imaging methods use the network to recognize the reconstructed objects or imaging algorithms. In contrast, this innovative application optimizes the illuminating speckle patterns via Speckle-Net with specific sampling ratios. Our method, therefore, outperforms the other techniques for ghost imaging, particularly its ability to retrieve high-quality images with extremely low sampling ratios. It opens a new route towards nontrivial speckle generation by referring to a standard loss function on specified objectives with the modified deep neural network. It also has great potential for applications in the fields of dynamic speckle illumination microscopy, structured illumination microscopy, x-ray imaging, photo-acoustic imaging, and optical lattices.
Extreme Beam-forming with Metagrating-assisted Planar Antennas
Authors: Gengyu Xu, Sean V. Hum, George V. Eleftheriades
Date: 2021-10-06 00:00:00

We present a highly efficient metagrating-assisted antenna (MGA) architecture with a simple integrated feed. Power from a localized line source is distributed throughout the arbitrarily large antenna aperture with the help of a passive and lossless electromagnetic metagrating (MG). With appropriately designed meta-wire loading, the omnidirectional source field can be efficiently transformed into directive radiation. To aid the design process, a 2-dimensional volume-surface integral equation framework which accurately predicts the radiation pattern of the MGA is developed. Through constrained optimization, the directivity of the MGA in the desired direction is maximized. In this way, extreme-angle beam steering is demonstrated.
Ghost Image Processing
Authors: Harry Penketh, William L Barnes, Jacopo Bertolotti
Date: 2021-12-14 00:00:00
Tasks: Edge Detection

In computational ghost imaging the object is illuminated with a sequence of known patterns, and the scattered light is collected using a detector that has no spatial resolution. Using those patterns and the total intensity measurement from the detector, one can reconstruct the desired image. Here we study how the reconstructed image is modified if the patterns used for the reconstruction are not the same as the illumination patterns, and show that one can choose how to illuminate the object, such that the reconstruction process behaves like a spatial filtering operation on the image. The ability to measure directly a processed image, allows one to bypass the post-processing steps, and thus avoid any noise amplification they imply. As a simple example we show the case of an edge-detection filter.
Spread of variants of epidemic disease based on the microscopic numerical simulations on networks
Authors: Yutaka Okabe, Akira Shudo
Date: 2021-12-28 00:00:00
Tasks: Epidemiology

Viruses constantly undergo mutations with genomic changes. The propagation of variants of viruses is an interesting problem. We perform numerical simulations of the microscopic epidemic model based on network theory for the spread of variants. Assume that a small number of individuals infected with the variant are added to widespread infection with the original virus. When a highly infectious variant that is more transmissible than the original lineage is added, the variant spreads quickly to the wide space. On the other hand, if the infectivity is about the same as that of the original virus, the infection will not spread. The rate of spread is not linear as a function of the infection strength but increases non-linearly. This cannot be explained by the compartmental model of epidemiology but can be understood in terms of the dynamic absorbing state known from the contact process.
Activity-based and agent-based Transport model of Melbourne (AToM): an open multi-modal transport simulation model for Greater Melbourne
Authors: Afshin Jafari, Dhirendra Singh, Alan Both, Mahsa Abdollahyar, Lucy Gunn, Steve Pemberton, Billie Giles-Corti
Date: 2021-12-16 00:00:00

Agent-based and activity-based models for simulating transportation systems have attracted significant attention in recent years. Few studies, however, include a detailed representation of active modes of transportation - such as walking and cycling - at a city-wide level, where dominating motorised modes are often of primary concern. This paper presents an open workflow for creating a multi-modal agent-based and activity-based transport simulation model, focusing on Greater Melbourne, and including the process of mode choice calibration for the four main travel modes of driving, public transport, cycling and walking. The synthetic population generated and used as an input for the simulation model represented Melbourne's population based on Census 2016, with daily activities and trips based on the Victoria's 2016-18 travel survey data. The road network used in the simulation model includes all public roads accessible via the included travel modes. We compared the output of the simulation model with observations from the real world in terms of mode share, road volume, travel time, and travel distance. Through these comparisons, we showed that our model is suitable for studying mode choice and road usage behaviour of travellers.
The Quantum Version of Prediction for Binary Classification Problem by Ensemble Methods
Authors: Kamil Khadiev, Liliia Safina
Date: 2021-12-26 00:00:00
Tasks: Binary Classification

In this work, we consider the performance of using a quantum algorithm to predict a result for a binary classification problem if a machine learning model is an ensemble from any simple classifiers. Such an approach is faster than classical prediction and uses quantum and classical computing, but it is based on a probabilistic algorithm. Let $N$ be a number of classifiers from an ensemble model and $O(T)$ be the running time of prediction on one classifier. In classical case, an ensemble model gets answers from each classifier and "averages" the result. The running time in classical case is $O\left( N \cdot T \right)$. We propose an algorithm which works in $O\left(\sqrt{N} \cdot T\right)$.
Auction Throttling and Causal Inference of Online Advertising Effects
Authors: George Gui, Harikesh Nair, Fengshi Niu
Date: 2021-12-30 00:00:00
Tasks: Causal Inference

Causally identifying the effect of digital advertising is challenging, because experimentation is expensive, and observational data lacks random variation. This paper identifies a pervasive source of naturally occurring, quasi-experimental variation in user-level ad-exposure in digital advertising campaigns. It shows how this variation can be utilized by ad-publishers to identify the causal effect of advertising campaigns. The variation pertains to auction throttling, a probabilistic method of budget pacing that is widely used to spread an ad-campaign`s budget over its deployed duration, so that the campaign`s budget is not exceeded or overly concentrated in any one period. The throttling mechanism is implemented by computing a participation probability based on the campaign`s budget spending rate and then including the campaign in a random subset of available ad-auctions each period according to this probability. We show that access to logged-participation probabilities enables identifying the local average treatment effect (LATE) in the ad-campaign. We present a new estimator that leverages this identification strategy and outline a bootstrap procedure for quantifying its variability. We apply our method to real-world ad-campaign data from an e-commerce advertising platform, which uses such throttling for budget pacing. We show our estimate is statistically different from estimates derived using other standard observational methods such as OLS and two-stage least squares estimators. Our estimated conversion lift is 110%, a more plausible number than 600%, the conversion lifts estimated using naive observational methods.
Estimating a Continuous Treatment Model with Spillovers: A Control Function Approach
Authors: Tadao Hoshino
Date: 2021-12-30 00:00:00

We study a continuous treatment effect model in the presence of treatment spillovers through social networks. We assume that one's outcome is affected not only by his/her own treatment but also by a (weighted) average of his/her neighbors' treatments, both of which are treated as endogenous variables. Using a control function approach with appropriate instrumental variables, we show that the conditional mean potential outcome can be nonparametrically identified. We also consider a more empirically tractable semiparametric model and develop a three-step estimation procedure for this model. As an empirical illustration, we investigate the causal effect of the regional unemployment rate on the crime rate.
Modeling and Forecasting Intraday Market Returns: a Machine Learning Approach
Authors: Iuri H. Ferreira, Marcelo C. Medeiros
Date: 2021-12-30 00:00:00
Tasks: BIG-bench Machine Learning

In this paper we examine the relation between market returns and volatility measures through machine learning methods in a high-frequency environment. We implement a minute-by-minute rolling window intraday estimation method using two nonlinear models: Long-Short-Term Memory (LSTM) neural networks and Random Forests (RF). Our estimations show that the CBOE Volatility Index (VIX) is the strongest candidate predictor for intraday market returns in our analysis, specially when implemented through the LSTM model. This model also improves significantly the performance of the lagged market return as predictive variable. Finally, intraday RF estimation outputs indicate that there is no performance improvement with this method, and it may even worsen the results in some cases.
An Analysis of an Alternative Pythagorean Expected Win Percentage Model: Applications Using Major League Baseball Team Quality Simulations
Authors: Justin Ehrlich, Christopher Boudreaux, James Boudreau, Shane Sanders
Date: 2021-12-29 00:00:00
Tasks: Time Series Analysis

We ask if there are alternative contest models that minimize error or information loss from misspecification and outperform the Pythagorean model. This article aims to use simulated data to select the optimal expected win percentage model among the choice of relevant alternatives. The choices include the traditional Pythagorean model and the difference-form contest success function (CSF). Method. We simulate 1,000 iterations of the 2014 MLB season for the purpose of estimating and analyzing alternative models of expected win percentage (team quality). We use the open-source, Strategic Baseball Simulator and develop an AutoHotKey script that programmatically executes the SBS application, chooses the correct settings for the 2014 season, enters a unique ID for the simulation data file, and iterates these steps 1,000 times. We estimate expected win percentage using the traditional Pythagorean model, as well as the difference-form CSF model that is used in game theory and public choice economics. Each model is estimated while accounting for fixed (team) effects. We find that the difference-form CSF model outperforms the traditional Pythagorean model in terms of explanatory power and in terms of misspecification-based information loss as estimated by the Akaike Information Criterion. Through parametric estimation, we further confirm that the simulator yields realistic statistical outcomes. The simulation methodology offers the advantage of greatly improved sample size. As the season is held constant, our simulation-based statistical inference also allows for estimation and model comparison without the (time series) issue of non-stationarity. The results suggest that improved win (productivity) estimation can be achieved through alternative CSF specifications.
Random Rank-Dependent Expected Utility
Authors: Nail Kashaev, Victor Aguiar
Date: 2021-12-27 00:00:00

We present a novel characterization of random rank-dependent expected utility for finite datasets and finite prizes. The test lends itself to statistical testing using the tools in Kitamura and Stoye (2018).
Critical Values Robust to P-hacking
Authors: Adam McCloskey, Pascal Michaillat
Date: 2020-05-08 00:00:00
Tasks: Informativeness

P-hacking is prevalent in reality but absent from classical hypothesis testing theory. As a consequence, significant results are much more common than they are supposed to be when the null hypothesis is in fact true. In this paper, we build a model of hypothesis testing with p-hacking. From the model, we construct critical values such that, if the values are used to determine significance, and if scientists' p-hacking behavior adjusts to the new significance standards, significant results occur with the desired frequency. Such robust critical values allow for p-hacking so they are larger than classical critical values. To illustrate the amount of correction that p-hacking might require, we calibrate the model using evidence from the medical sciences. In the calibrated model the robust critical value for any test statistic is the classical critical value for the same test statistic with one fifth of the significance level.
Bayesian Approaches to Shrinkage and Sparse Estimation
Authors: Dimitris Korobilis, Kenichi Shimizu
Date: 2021-12-22 00:00:00
Tasks: Bayesian Inference, quantile regression, regression, Variable Selection

In all areas of human knowledge, datasets are increasing in both size and complexity, creating the need for richer statistical models. This trend is also true for economic data, where high-dimensional and nonlinear/nonparametric inference is the norm in several fields of applied econometric work. The purpose of this paper is to introduce the reader to the world of Bayesian model determination, by surveying modern shrinkage and variable selection algorithms and methodologies. Bayesian inference is a natural probabilistic framework for quantifying uncertainty and learning about model parameters, and this feature is particularly important for inference in modern models of high dimensions and increased complexity. We begin with a linear regression setting in order to introduce various classes of priors that lead to shrinkage/sparse estimators of comparable value to popular penalized likelihood estimators (e.g.\ ridge, lasso). We explore various methods of exact and approximate inference, and discuss their pros and cons. Finally, we explore how priors developed for the simple regression setting can be extended in a straightforward way to various classes of interesting econometric models. In particular, the following case-studies are considered, that demonstrate application of Bayesian shrinkage and variable selection strategies to popular econometric contexts: i) vector autoregressive models; ii) factor models; iii) time-varying parameter regressions; iv) confounder selection in treatment effects models; and v) quantile regression models. A MATLAB package and an accompanying technical manual allow the reader to replicate many of the algorithms described in this review.
Efficient Estimation of State-Space Mixed-Frequency VARs: A Precision-Based Approach
Authors: Joshua C. C. Chan, Aubrey Poon, Dan Zhu
Date: 2021-12-21 00:00:00
Tasks: State Space Models

State-space mixed-frequency vector autoregressions are now widely used for nowcasting. Despite their popularity, estimating such models can be computationally intensive, especially for large systems with stochastic volatility. To tackle the computational challenges, we propose two novel precision-based samplers to draw the missing observations of the low-frequency variables in these models, building on recent advances in the band and sparse matrix algorithms for state-space models. We show via a simulation study that the proposed methods are more numerically accurate and computationally efficient compared to standard Kalman-filter based methods. We demonstrate how the proposed method can be applied in two empirical macroeconomic applications: estimating the monthly output gap and studying the response of GDP to a monetary policy shock at the monthly frequency. Results from these two empirical applications highlight the importance of incorporating high-frequency indicators in macroeconomic models.
Ranking and Selection from Pairwise Comparisons: Empirical Bayes Methods for Citation Analysis
Authors: Jiaying Gu, Roger Koenker
Date: 2021-12-21 00:00:00

We study the Stigler model of citation flows among journals adapting the pairwise comparison model of Bradley and Terry to do ranking and selection of journal influence based on nonparametric empirical Bayes procedures. Comparisons with several other rankings are made.