pwc-archive/papers-with-abstracts huggingface dataset:
Child Care Provider Survival Analysis
Authors: Phillip Sherlock, Herman T. Knopf, Robert Chapman, Maya Schreiber, Courtney K. Blackwell
Date: 2022-08-03 00:00:00
Tasks: Survival Analysis

The aggregate ability of child care providers to meet local demand for child care is linked to employment rates in many sectors of the economy. Amid growing concern regarding child care provider sustainability due to the COVID-19 pandemic, state and local governments have received large amounts of new funding to better support provider stability. In response to this new funding aimed at bolstering the child care market in Florida, this study was devised as an exploratory investigation into features of child care providers that lead to business longevity. In this study we used optimal survival trees, a machine learning technique designed to better understand which providers are expected to remain operational for longer periods of time, supporting stabilization of the child care market. This tree-based survival analysis detects and describes complex interactions between provider characteristics that lead to differences in expected business survival rates. Results show that small providers who are religiously affiliated, and all providers who are serving children in Florida's universal Prekindergarten program and/or children using child care subsidy, are likely to have the longest expected survival rates.
Coherence without Rationality at the Zero Lower Bound
Authors: Guido Ascari, Sophocles Mavroeidis, Nigel McClung
Date: 2022-08-03 00:00:00
Tasks: Friction

Standard rational expectations models with an occasionally binding zero lower bound constraint either admit no solutions (incoherence) or multiple solutions (incompleteness). This paper shows that deviations from full-information rational expectations mitigate concerns about incoherence and incompleteness. Models with no rational expectations equilibria admit self-confirming equilibria involving the use of simple mis-specified forecasting models. Completeness and coherence is restored if expectations are adaptive or if agents are less forward-looking due to some information or behavioral friction. In the case of incompleteness, the E-stability criterion selects an equilibrium.
Regulation and Frontier Housing Supply
Authors: Dan Ben-Moshe, David Genesove
Date: 2022-08-03 00:00:00

Regulation is a major driver of housing supply, yet often difficult to observe directly. This paper estimates frontier cost, the non-land cost of producing housing absent regulation, and regulatory tax, which quantifies regulation in money terms. Working within an urban environment of multi-floor, multi-family housing and using only apartment prices and building heights, we show that the frontier is identified from the support of supply and demand shocks without recourse to instrumental variables. In an application to new Israeli residential construction, and accounting for random housing quality, the estimated mean regulatory tax is 48% of housing prices, with significant variation across locations. Higher regulation is associated with proximity to city center, higher density, and higher prices. We construct a lower bound for the regulatory tax that allows quality to differ systematically over location and time, by assuming (weak) complementarity between quality and demand. The bound is most useful after prices have increased, so that at the end of our sample period, with prices at their highest, we bound the regulatory tax between 40% (using a 2km radius) and 53%.
From Workplace to Residence: The Spillover Effects of Minimum Wage Policies on Local Housing Markets
Authors: Gabriele Borg, Diego Gentile Passaro, Santiago Hermo
Date: 2022-08-03 00:00:00
Tasks: counterfactual

The recent rise of sub-national minimum wage (MW) policies in the US has resulted in significant dispersion of MW levels within urban areas. In this paper, we study the spillover effects of these policies on local rental markets through commuting. To do so, for each USPS ZIP code we construct a "workplace" MW measure based on the location of its resident's jobs, and use it to estimate the effect of MW policies on rents. We use a novel identification strategy that exploits the fine timing of differential changes in the workplace MW across ZIP codes that share the same "residence" MW, defined as the same location's MW. Our baseline results imply that a 10 percent increase in the workplace MW increases rents at residence ZIP codes by 0.69 percent. To illustrate the importance of commuting patterns, we use our estimates and a simple model to simulate the impact of federal and city counterfactual MW policies. The simulations suggest that landlords pocket approximately 10 cents of each dollar generated by the MW across directly and indirectly affected areas, though the incidence on landlords varies systematically across space.
Agglomeration and welfare of the Krugman model in a continuous space
Authors: Kensuke Ohtake
Date: 2022-08-03 00:00:00

Two spatial equilibria, agglomeration and dispersion, in a continuous space core-periphery model are examined to discuss which equilibrium is socially preferred. It is shown that when transport cost is lower than a critical value, the agglomeration equilibrium is preferable in the sense of Scitovszky, while when the transport cost is above the critical value, the two equilibria can not be ordered in the sense of Scitovszky.
A look back at the core of games in characteristic function form: some new axiomatization results
Authors: Anindya Bhattacharya
Date: 2022-08-02 00:00:00
Tasks: Form

In this paper we provide three new results axiomatizing the core of games in characteristic function form (not necessarily having transferable utility) obeying an innocuous condition (that the set of individually rational pay-off vectors is bounded). One novelty of this exercise is that our domain is the {\em entire} class of such games: i.e., restrictions like "non-levelness" or "balancedness" are not required.
Photonics-assisted analog wideband self-interference cancellation for in-band full-duplex MIMO systems with adaptive digital amplitude and delay pre-matching
Authors: Taixia Shi, Yang Chen
Date: 2022-08-03 00:00:00

A photonics-assisted analog wideband RF self-interference (SI) cancellation and frequency downconversion approach for in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems with adaptive digital amplitude and delay pre-matching is proposed based on a dual-parallel Mach-Zehnder modulator (DP-MZM). In each MIMO receiving antenna, the received signal, including different SI signals from different transmitting antennas and the signal of interest, is applied to one arm of the upper dual-drive Mach-ehnder modulator (DD-MZM) of the DP-MZM, the reference signal is applied to the other arm of the upper DD-MZM, and the local oscillator signal is applied to the lower DD-MZM. The SI signals are canceled in the optical domain in the upper DD-MZM and the frequency downconversion is achieved after photodetection. To cancel the SI signals, the reference signal is constructed in the digital domain, while the amplitude and delay of the constructed reference are adjusted digitally by upsampling with high accuracy. Experiments are performed when two different SI signals are employed. The genetic algorithm and least-squares algorithm are combined with segmented searching respectively for the SI signal reconstruction and amplitude and delay pre-matching. A cancellation depth of around 20 dB is achieved for the 1-Gbaud 16 quadrature-amplitude modulation orthogonal frequency-division multiplexing signal.
Digitally-assisted photonic analog domain self-interference cancellation for in-band full-duplex MIMO systems via LS algorithm with adaptive order
Authors: Moxuan Han, Yang Chen
Date: 2022-08-03 00:00:00

A digitally-assisted photonic analog domain self-interference cancellation (SIC) and frequency downconversion method is proposed for in-band full-duplex multiple-input multiple-output (MIMO) systems using the least square (LS) algorithm with adaptive order. The SIC and frequency downconversion are achieved in the optical domain via a dual-parallel Mach-Zehnder modulator (DP-MZM), while the downconverted signal is processed by the LS algorithm with adaptive order that is used to track the response of the multipath self-interference (SI) channel and reconstruct the reference signal for SIC. The proposed method can overcome the reconstruction difficulty of the multipath analog reference signal for SIC with high complexity in the MIMO scenario and can also solve the problem that the order of the reference reconstruction algorithm is not optimized when the wireless environment changes. An experiment is carried out to verify the concept. 30.2, 26.9, 23.5, 19.5, and 15.8 dB SIC depths are achieved when the SI signal has a carrier frequency of 10 GHz and baud rates of 0.1, 0.25, 0.5, 1, and 2 Gbaud, respectively. The convergence of the LS algorithm with adaptive order is also verified for different MIMO multipath SI signals.
Joint Beamforming Design for RIS-Assisted Integrated Sensing and Communication Systems
Authors: Honghao Luo, Rang Liu, Ming Li, Yang Liu, Qian Liu
Date: 2022-08-03 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has been envisioned as a promising technology to tackle the spectrum congestion problem for future networks. In this correspondence, we investigate to deploy a reconfigurable intelligent surface (RIS) in an ISAC system for achieving better performance. In particular, a multi-antenna base station (BS) simultaneously serves multiple single-antenna users with the assistance of a RIS and detects potential targets. The active beamforming of the BS and the passive beamforming of the RIS are jointly optimized to maximize the achievable sum-rate of the communication users while satisfying the constraint of beampattern similarity for radar sensing, the restriction of the RIS, and the transmit power budget. An efficient alternating algorithm based on the fractional programming (FP), majorization-minimization (MM), and manifold optimization methods is developed to convert the resulting non-convex optimization problem into two solvable sub-problems and iteratively solve them. Simulation studies illustrate the advancement of deploying RIS in ISAC systems and the effectiveness of the proposed algorithm.
Segmented Learning for Class-of-Service Network Traffic Classification
Authors: Yoga Suhas Kuruba Manjunath, Sihao Zhao, Hatem Abou-zeid, Akram Bin Sediq, Ramy Atawia, Xiao-Ping Zhang
Date: 2022-08-03 00:00:00
Tasks: Classification, Scheduling, Traffic Classification

Class-of-service (CoS) network traffic classification (NTC) classifies a group of similar traffic applications. The CoS classification is advantageous in resource scheduling for Internet service providers and avoids the necessity of remodelling. Our goal is to find a robust, lightweight, and fast-converging CoS classifier that uses fewer data in modelling and does not require specialized tools in feature extraction. The commonality of statistical features among the network flow segments motivates us to propose novel segmented learning that includes essential vector representation and a simple-segment method of classification. We represent the segmented traffic in the vector form using the EVR. Then, the segmented traffic is modelled for classification using random forest. Our solution's success relies on finding the optimal segment size and a minimum number of segments required in modelling. The solution is validated on multiple datasets for various CoS services, including virtual reality (VR). Significant findings of the research work are i) Synchronous services that require acknowledgment and request to continue communication are classified with 99% accuracy, ii) Initial 1,000 packets in any session are good enough to model a CoS traffic for promising results, and we therefore can quickly deploy a CoS classifier, and iii) Test results remain consistent even when trained on one dataset and tested on a different dataset. In summary, our solution is the first to propose segmentation learning NTC that uses fewer features to classify most CoS traffic with an accuracy of 99%. The implementation of our solution is available on GitHub.
Streaming Reconstruction from Non-uniform Samples
Authors: Justin Romberg
Date: 2022-08-02 00:00:00

We present an online algorithm for reconstructing a signal from a set of non-uniform samples. By representing the signal using compactly supported basis functions, we show how estimating the expansion coefficients using least-squares can be implemented in a streaming manner: as batches of samples over subsequent time intervals are presented, the algorithm forms an initial estimate of the signal over the sampling interval then updates its estimates over previous intervals. We give conditions under which this reconstruction procedure is stable and show that the least-squares estimates in each interval converge exponentially, meaning that the updates can be performed with finite memory with almost no loss in accuracy. We also discuss how our framework extends to more general types of measurements including time-varying convolution with a compactly supported kernel.
Prevalence Estimation and Optimal Classification Methods to Account for Time Dependence in Antibody Levels
Authors: Prajakta Bedekar, Anthony J. Kearsley, Paul N. Patrone
Date: 2022-08-03 00:00:00

Serology testing can identify past infection by quantifying the immune response of an infected individual providing important public health guidance. Individual immune responses are time-dependent, which is reflected in antibody measurements. Moreover, the probability of obtaining a particular measurement changes due to prevalence as the disease progresses. Taking into account these personal and population-level effects, we develop a mathematical model that suggests a natural adaptive scheme for estimating prevalence as a function of time. We then combine the estimated prevalence with optimal decision theory to develop a time-dependent probabilistic classification scheme that minimizes error. We validate this analysis by using a combination of real-world and synthetic SARS-CoV-2 data and discuss the type of longitudinal studies needed to execute this scheme in real-world settings.
Sequential mutations in exponentially growing populations
Authors: Michael D. Nicholson, David Cheek, Tibor Antal
Date: 2022-08-03 00:00:00

Stochastic models of sequential mutation acquisition are widely used to quantify cancer and bacterial evolution. Across manifold scenarios, recurrent research questions are: how many cells are there with $n$ alterations, and how long will it take for these cells to appear. For exponentially growing populations, these questions have been tackled only in special cases so far. Here, within a multitype branching process framework, we consider a general mutational path where mutations may be advantageous, neutral or deleterious. In the biologically relevant limiting regimes of large times and small mutation rates, we derive probability distributions for the number, and arrival time, of cells with n mutations. Surprisingly, the two quantities respectively follow Mittag-Leffler and logistic distributions regardless of $n$ or the mutations' selective effects. Our results provide a rapid method to assess how altering the fundamental division, death, and mutation rates impacts the arrival time, and number, of mutant cells. We highlight consequences for mutation rate inference in fluctuation assays.
Impact of imperfect vaccine, vaccine trade-off and population turnover on infectious disease dynamics
Authors: Hetsron L. Nyandjo-Bamen, Jean Marie Ntaganda, Aurelien Tellier, Olivier Menoukeu-Pamen
Date: 2022-08-03 00:00:00
Tasks: Management

Vaccination is essential for the management of infectious diseases, many of which continue to pose devastating public health and economic challenges across the world. However, many vaccines are imperfect having only a partial protective effect in decreasing disease transmission and/or favouring recovery of infected individuals, and possibly exhibiting trade-off between these two properties. Furthermore, population turnover, that is the rate at which individuals enter and exit the population, is another key factor determining the epidemiological dynamics. While these factors have yet been studied separately, we investigate the interplay between the efficiency and property of an imperfect vaccine and population turnover. We build a mathematical model with frequency incidence rate, a recovered compartment, and an heterogeneous host population with respect to vaccination. We first compute the basic reproduction number $\mathcal{R}_0$ and study the global stability of the equilibrium points. Using a sensitivity analysis, we then assess the most influential parameters determining the total number of infected and $\mathcal{R}_0$ over time. We derive analytically and numerically conditions for the vaccination coverage and efficiency to achieve disease eradication ($\mathcal{R}_0 < 1$) assuming different intensity of the population turnover (weak and strong), vaccine properties (transmission and/or recovery) and trade-off between the latter. We show that the minimum vaccination coverage increases with lower population turnover, decreases with higher vaccine efficiency (transmission or recovery), and is increased/decreased by up to 15\% depending on the trade-off between the vaccine properties. We conclude that the coverage target for vaccination campaigns should be evaluated based on the interplay between these factors.
Geometric analysis enables biological insight from complex non-identifiable models using simple surrogates
Authors: Alexander P Browning, Matthew J Simpson
Date: 2022-08-03 00:00:00

An enduring challenge in computational biology is to balance data quality and quantity with model complexity. Tools such as identifiability analysis and information criterion have been developed to harmonise this juxtaposition, yet cannot always resolve the mismatch between available data and the granularity required in mathematical models to answer important biological questions. Often, it is only simple phenomenological models, such as the logistic and Gompertz growth models, that are identifiable from standard experimental measurements. To draw insights from the complex, non-identifiable models that incorporate key biological mechanisms of interest, we study the geometry of a map in parameter space from the complex model to a simple, identifiable, surrogate model. By studying how non-identifiable parameters in the complex model quantitatively relate to identifiable parameters in surrogate, we introduce and exploit a layer of interpretation between the set of non-identifiable parameters and the goodness-of-fit metric or likelihood studied in typical identifiability analysis. We demonstrate our approach by analysing a hierarchy of mathematical models for multicellular tumour spheroid growth. Typical data from tumour spheroid experiments are limited and noisy, and corresponding mathematical models are very often made arbitrarily complex. Our geometric approach is able to predict non-identifiabilities, subset non-identifiable parameter spaces into identifiable parameter combinations that relate to individual data features, and overall provide additional biological insight from complex non-identifiable models.
Kasabach-Merritt syndrome arising from an Enteroatmospheric Fistula
Authors: Kunal Shah, Krishan Rajaratnam
Date: 2022-08-03 00:00:00
Tasks: Management

Kasabach-Merritt syndrome (KMS) is a rare, life-threatening condition that is characterized by profound thrombocytopenia, hypofibrinogenemia, elevated partial thromboplastin time, and may also be associated with microangiopathic hemolytic anemia. It is well established that this phenomenon is notably associated with the vascular tumors kaposiform hemangioendothelioma and tufted angioma; however, recent literature has suggested its presence in the settings of various vascular malformations (i.e. without neoplastic proliferation of endothelial cells). This report focuses on a patient in the first year of life, who experienced a chronic, consumptive coagulopathy in the setting of a highly vascular enteroatmospheric fistula. Sharing many features with the aforementioned syndrome, this anomaly suggests a novel association of the Kasabach-Merritt phenomenon with a unique vascular malformation. Although potentially lethal, Kasabach-Merritt syndrome can resolve with appropriate diagnosis and management; uncovering new associations can help to improve recognition and treatment in future cases.
Merton's Default Risk Model for Private Company
Authors: Battulga Gankhuu
Date: 2022-08-03 00:00:00
Tasks: model

Because the asset value of a private company does not observable except in quarterly reports, the structural model has not been developed for a private company. For this reason, this paper attempt to develop the Merton's structural model for the private company by using the dividend discount model (DDM). In this paper, we obtain closed--form formulas of risk--neutral equity and liability values and default probability for the private company. Also, the paper provides ML estimators and the EM algorithm of our model's parameters.
A Roadmap for Greater Public Use of Privacy-Sensitive Government Data: Workshop Report
Authors: Chris Clifton, Bradley Malin, Anna Oganian, Ramesh Raskar, Vivek Sharma
Date: 2022-06-17 00:00:00

Government agencies collect and manage a wide range of ever-growing datasets. While such data has the potential to support research and evidence-based policy making, there are concerns that the dissemination of such data could infringe upon the privacy of the individuals (or organizations) from whom such data was collected. To appraise the current state of data sharing, as well as learn about opportunities for stimulating such sharing at a faster pace, a virtual workshop was held on May 21st and 26th, 2021, sponsored by the National Science Foundation and National Institute of Standards and Technologies, where a multinational collection of researchers and practitioners were brought together to discuss their experiences and learn about recently developed technologies for managing privacy while sharing data. The workshop specifically focused on challenges and successes in government data sharing at various levels. The first day focused on successful examples of new technology applied to sharing of public data, including formal privacy techniques, synthetic data, and cryptographic approaches. Day two emphasized brainstorming sessions on some of the challenges and directions to address them.