pwc-archive/papers-with-abstracts huggingface dataset:
What Explains Gender Gap in Unpaid Household and Care Work in India?
Authors: Athary Janiso, Prakash Kumar Shukla, Bheemeshwar Reddy A
Date: 2021-06-29 00:00:00

Due to the unavailability of nationally representative data on time use, a systematic analysis of the gender gap in unpaid household and care work has not been undertaken in the context of India. The present paper, using the recent Time Use Survey (2019) data, examines the socioeconomic and demographic factors associated with variation in time spent on unpaid household and care work among men and women. It analyses how much of the gender gap in the time allocated to unpaid work can be explained by differences in these factors. The findings show that women spend much higher time compared to men in unpaid household and care work. The decomposition results reveal that differences in socioeconomic and demographic factors between men and women do not explain most of the gender gap in unpaid household work. Our results indicate that unobserved gender norms and practices most crucially govern the allocation of unpaid work within Indian households.
Exploring the trilemma of cost-efficient, equitable and publicly acceptable onshore wind expansion planning
Authors: Jann Michael Weinand, Russell McKenna, Heidi Heinrichs, Michael Roth, Detlef Stolten, Wolf Fichtner
Date: 2021-06-29 00:00:00

Onshore wind development has historically focused on cost-efficiency, which may lead to inequitable turbine distributions and public resistance due to landscape impacts. Using a multi-criteria planning approach, we show how onshore wind capacity targets can be achieved by 2050 in a cost-efficient, equitable and publicly acceptable way. For the case study of Germany, we build on the existing turbine stock and use open data on technically feasible turbine locations and scenicness of landscapes to plan the optimal expansion. The analysis shows that while the trade-off between cost-efficiency and public acceptance is rather weak with about 15% higher costs or scenicness, an equitable distribution has a large impact on these criteria. Although the onshore wind capacity per inhabitant could be distributed about 220% more equitably through the expansion, equity would severely limit planning flexibility by 2050. Our analysis assists stakeholders in resolving the onshore wind expansion trilemma.
Empirical Framework for Cournot Oligopoly with Private Information
Authors: Gaurab Aryal, Federico Zincenko
Date: 2021-06-29 00:00:00

We propose an empirical framework for asymmetric Cournot oligopoly with private information about variable costs. First, considering a linear demand for a homogenous product with a random intercept, we characterize the Bayesian Cournot-Nash equilibrium. Then we establish the identification of the joint distribution of demand and firm-specific cost distributions. Following the identification steps, we propose a likelihood-based estimation method and apply it to the global market for crude-oil and quantify the welfare effect of private information. We also consider extensions of the model to include either product differentiation, conduct parameters, nonlinear demand, or selective entry.
The Ecological System of Innovation: A New Architectural Framework for a Functional Evidence-Based Platform for Science and Innovation Policy
Authors: Robert M Yawson
Date: 2021-06-24 00:00:00

Models on innovation, for the most part, do not include a comprehensive and end-to-end view. Most innovation policy attention seems to be focused on the capacity to innovate and on input factors such as R&D investment, scientific institutions, human resources and capital. Such inputs frequently serve as proxies for innovativeness and are correlated with intermediate outputs such as patent counts and outcomes such as GDP per capita. While this kind of analysis is generally indicative of innovative behaviour, it is less useful in terms of discriminating causality and what drives successful strategy or public policy interventions. This situation has led to the developing of new frameworks for the innovation system led by National Science and Technology Policy Centres across the globe. These new models of innovation are variously referred to as the National Innovation Ecosystem. There is, however, a fundamental question that needs to be answered: what elements should an innovation policy include, and how should such policies be implemented? This paper attempts to answer this question.
Hierarchical Context-Aware Transformers for Non-Autoregressive Text to Speech
Authors: Jae-Sung Bae, Tae-Jun Bak, Young-Sun Joo, Hoon-Young Cho
Date: 2021-06-29 00:00:00
Tasks: Decoder, Sentence, text-to-speech, Text to Speech

In this paper, we propose methods for improving the modeling performance of a Transformer-based non-autoregressive text-to-speech (TNA-TTS) model. Although the text encoder and audio decoder handle different types and lengths of data (i.e., text and audio), the TNA-TTS models are not designed considering these variations. Therefore, to improve the modeling performance of the TNA-TTS model we propose a hierarchical Transformer structure-based text encoder and audio decoder that are designed to accommodate the characteristics of each module. For the text encoder, we constrain each self-attention layer so the encoder focuses on a text sequence from the local to the global scope. Conversely, the audio decoder constrains its self-attention layers to focus in the reverse direction, i.e., from global to local scope. Additionally, we further improve the pitch modeling accuracy of the audio decoder by providing sentence and word-level pitch as conditions. Various objective and subjective evaluations verified that the proposed method outperformed the baseline TNA-TTS.
Random Access Procedure over Non-Terrestrial Networks: From Theory to Practice
Authors: O. Kodheli, A. Astro, J. Querol, M. Gholamian, S. Kumar, N. Maturo, S. Chatzinotas
Date: 2021-06-29 00:00:00

Non-terrestrial Networks (NTNs) have become an appealing concept over the last few years and they are foreseen as a cornerstone for the next generations of mobile communication systems. Despite opening up new market opportunities and use cases for the future, the novel impairments caused by the signal propagation over the NTN channel compromises several procedures of the current cellular standards. One of the first and most important procedures impacted is the random access (RA) procedure, which is mainly utilized for achieving uplink synchronization among users in several standards, such as the fourth and fifth generation of mobile communication (4 & 5G) and narrowband internet of things (NB-IoT). In this work, we analyse the challenges imposed by the considerably increased delay in the communication link on the RA procedure and propose new solutions to overcome those challenges. A trade-off analysis of various solutions is provided taking into account also the already existing ones in the literature. In order to broaden the scope of applicability, we keep the analysis general targeting 4G, 5G and NB-IoT systems since the RA procedure is quasi-identical among these technologies. Last but not least, we go one step further and validate our techniques in an experimental setup, consisting of a user and a base station implemented in open air interface (OAI), and an NTN channel implemented in hardware that emulates the signal propagation delay. The laboratory test-bed built in this work, not only enables us to validate various solutions, but also plays a crucial role in identifying novel challenges not previously treated in the literature. Finally, an important key performance indicator (KPI) of the RA procedure over NTN is shown, which is the time that a single user requires to establish a connection with the base station.
Exploiting a Supervised Index for High-accuracy Parameter Estimation in Low SNR
Authors: Kaijie Xu
Date: 2021-06-29 00:00:00
Tasks: parameter estimation

Performance of parameter estimation is one of the most important issues in array signal processing. The root mean square error, probability of success, resolution probabilities, and computational complexity are frequently used indexes for evaluating the performance of the parameter estimation methods. However, a common characteristic of these indexes is that they are unsupervised indexes, and are passively used for evaluating the estimation results. In other words, these indexes cannot participate in the design of estimation methods. It seems that exploiting a validity supervised index for the parameter estimation that can guide the design of the methods will be an interesting and meaningful work. In this study, we exploit an index to build a supervised learning model of the parameter estimation. With the developed model we refine the signal subspace so as to enhance the performance of the parameter estimation method. The main characteristic of the proposed model is a circularly applied feedback of the estimated parameter for refining the estimated subspace. It is a closed loop and supervised method not reported before. This research opens a specific way for improving the performance of the parameter estimation by a supervised index. However, the proposed method is still unsatisfying in some scopes of signal-to-noise ratio (SNR). We believe that exploiting a validity index for the parameter estimation in array signal processing is still a general and interesting problem.
Non-Point Visible Light Transmitter Localization based on Monocular Camera
Authors: Hongxiu Zhao, Xun Zhang, Faouzi Bader, Yue Zhang
Date: 2021-06-29 00:00:00

Many algorithms for visible light positioning (VLP) localization do not consider the shapes of the transmitters, which leads to the impracticality of the algorithm and the low localization accuracy. Therefore, this paper proposes a novel VLP algorithm and addresses the problems in terms of practicality and complexity by using one non-point transmitter based on a monocular. Because the shape of the transmitter is considered, the proposed algorithm is easy for practice and has wide applicability. Besides, it decreases the computation for simple geometric model and expands the coverage for there is a greater chance of receiving signals from one light than that of receiving signals from multiple lights.
Cascaded Composite Turbulence and Misalignment: Statistical Characterization and Applications to Reconfigurable Intelligent Surface-Empowered Wireless Systems
Authors: Alexandros-Apostolos A. Boulogeorgos, Nestor Chatzidiamantis, Harilaos G. Sandalidis, Angeliki Alexiou, Marco Di Renzo
Date: 2021-06-29 00:00:00

Reconfigurable intelligent surfaces (RISs) empowered high-frequency (HF) wireless systems are expected to become the supporting pillar for several reliability and data rate hungry applications. Such systems are, however, sensitive to misalignment and atmospheric phenomena including turbulence. Most of the existing studies on the performance assessment of RIS-empowered wireless systems ignore the impact of the aforementioned phenomena. Motivated by this, the current contribution presents a theoretical framework for analyzing the performance of multi-RIS empowered HF wireless systems. More specifically, we statistically characterize the cascaded composite turbulence and misalignment channels in terms of probability density and cumulative distribution functions. Building upon the derived analytical expressions, we present novel closed-form formulas that quantify the joint impact of turbulence and misalignment on the outage performance for two scenarios of high interest namely cascaded multi-RIS-empowered free space optics (FSO) and terahertz (THz) wireless systems. In addition, we provide an insightful outage probability upper-bound for a third scenario that considers parallel multi-RIS-empowered FSO systems. Our results highlight the importance of accurately modeling both turbulence and misalignment when assessing the performance of such systems.
Partially Coherent Radar Unties Range Resolution from Bandwidth Limitations
Authors: Rony Komissarov, Vitali Kozlov, Dmitry Filonov, Pavel Ginzburg
Date: 2021-06-28 00:00:00
Tasks: Astronomy

It is widely believed that range resolution, the ability to distinguish between two closely situated targets, depends inversely on the bandwidth of the transmitted radar signal. Here we demonstrate a different type of ranging system, which possesses superior range resolution that is almost completely free of bandwidth limitations. By sweeping over the coherence length of the transmitted signal, the partially coherent radar experimentally demonstrates an improvement of over an order of magnitude in resolving targets, compared to standard coherent radars with the same bandwidth.. A theoretical framework is developed to show that the resolution could be further improved without a bound, revealing a tradeoff between bandwidth and sweep time. This concept offers solutions to problems which require high range resolution and accuracy but available bandwidth is limited, as is the case for the autonomous car industry, optical imaging, and astronomy to name just few.
PDL Impact on Linearly Coded Digital Phase Conjugation Techniques in CO-OFDM Systems
Authors: O. S. Sunish Kumar, A. Amari, O. A. Dobre, R. Venkatesan
Date: 2021-06-27 00:00:00

We investigate the impact of polarization-dependent loss (PDL) on the linearly coded digital phase conjugation (DPC) techniques in coherent optical orthogonal frequency division multiplexing (CO-OFDM) superchannel systems. We consider two DPC approaches: one uses orthogonal polarizations to transmit the linearly coded signal and its phase conjugate, while the other uses two orthogonal time slots of the same polarization. We compare the performances of these DPC approaches by considering both aligned- and statistical-PDL models. The investigation with aligned-PDL model indicates that the latter approach is more tolerant to PDL-induced distortions when compared to the former. Furthermore, the study using statistical-PDL model shows that the outage probability of the latter approach tends to zero at a root mean square PDL value of 3.6 dB. On the other hand, the former shows an outage probability of 0.63 for the same PDL value.
Patient-independent Schizophrenia Relapse Prediction Using Mobile Sensor based Daily Behavioral Rhythm Changes
Authors: Bishal Lamichhane, Dror Ben-Zeev, Andrew Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Mikio Obuchi, Emily Scherer, Megan Walsh, Rui Wang, Weichen Wang, Akane Sano
Date: 2021-06-25 00:00:00
Tasks: Feature Engineering, Rhythm

A schizophrenia relapse has severe consequences for a patient's health, work, and sometimes even life safety. If an oncoming relapse can be predicted on time, for example by detecting early behavioral changes in patients, then interventions could be provided to prevent the relapse. In this work, we investigated a machine learning based schizophrenia relapse prediction model using mobile sensing data to characterize behavioral features. A patient-independent model providing sequential predictions, closely representing the clinical deployment scenario for relapse prediction, was evaluated. The model uses the mobile sensing data from the recent four weeks to predict an oncoming relapse in the next week. We used the behavioral rhythm features extracted from daily templates of mobile sensing data, self-reported symptoms collected via EMA (Ecological Momentary Assessment), and demographics to compare different classifiers for the relapse prediction. Naive Bayes based model gave the best results with an F2 score of 0.083 when evaluated in a dataset consisting of 63 schizophrenia patients, each monitored for up to a year. The obtained F2 score, though low, is better than the baseline performance of random classification (F2 score of 0.02 $\pm$ 0.024). Thus, mobile sensing has predictive value for detecting an oncoming relapse and needs further investigation to improve the current performance. Towards that end, further feature engineering and model personalization based on the behavioral idiosyncrasies of a patient could be helpful.
Multiphase modelling of glioma pseudopalisading under acidosis
Authors: Pawan Kumar, Christina Surulescu, Anna Zhigun
Date: 2021-06-29 00:00:00

We propose a multiphase modeling approach to describe glioma pseudopalisade patterning under the influence of acidosis. The phases considered at the model onset are glioma, normal tissue, necrotic matter, and interstitial fluid in a void-free volume with acidity represented by proton concentration. We start from mass and momentum balance to characterize the respective volume fractions and deduce reaction-cross diffusion equations for the space-time evolution of glioma, normal tissue, and necrosis. These are supplemented with a reaction-diffusion equation for the acidity dynamics and lead to formation of patterns which are typical for high grade gliomas. Unlike previous works, our deduction also works in higher dimensions and involves less restrictions. We also investigate the existence of weak solutions to the obtained system of equations and perform numerical simulations to illustrate the solution behavior and the pattern occurrence.
The Variance Gamma++ Process and Applications to Energy Markets
Authors: Gardini, M., Sabino, P., Sasso, e
Date: 2021-06-29 00:00:00
Tasks: parameter estimation, Triplet

The purpose of this article is to introduce a new L\'evy process, termed Variance Gamma++ process, to model the dynamic of assets in illiquid markets. Such a process has the mathematical tractability of the Variance Gamma process and is obtained applying the self-decomposability of the gamma law. Compared to the Variance Gamma model, it has an additional parameter representing the measure of the trading activity. We give a full characterization of the Variance Gamma++ process in terms of its characteristic triplet, characteristic function and transition density. In addition, we provide efficient path simulation algorithms, both forward and backward in time. We also obtain an efficient "integral-free" explicit pricing formula for European options. These results are instrumental to apply Fourier-based option pricing and maximum likelihood techniques for the parameter estimation. Finally, we apply our model to illiquid markets, namely to the calibration of European power future market data. We accordingly evaluate exotic derivatives using the Monte Carlo method and compare these values to those obtained using the Variance Gamma process and give an economic interpretation of the obtained results. Finally, we illustrate an extension to the multivariate framework.
Bubble lifetimes in DNA gene promoters and their mutations affecting transcription
Authors: M Hillebrand, G Kalosakas, A R Bishop, Ch Skokos
Date: 2021-06-29 00:00:00
Tasks: Position

Relative lifetimes of inherent double stranded DNA openings with lengths up to ten base pairs are presented for different gene promoters and corresponding mutants that either increase or decrease transcriptional activity, in the framework of the Peyrard-Bishop-Dauxois model. Extensive microcanonical simulations are used, with energies corresponding to physiological temperature. The bubble lifetime profiles along the DNA sequences demonstrate a significant reduction of the average lifetime at the mutation sites when the mutated promoter decreases transcription, while a corresponding enhancement of the bubble lifetime is observed in the case of mutations leading to increased transcription. The relative difference of bubble lifetimes between the mutated and the wild type promoters at the position of mutation varies from 20% to more than 30% as the bubble length is decreasing
Modularity maximization as a flexible and generic framework for brain network exploratory analysis
Authors: Farnaz Zamani Esfahlani, Youngheun Jo, Maria Grazia Puxeddu, Haily Merritt, Jacob C. Tanner, Sarah Greenwell, Riya Patel, Joshua Faskowitz, Richard F. Betzel
Date: 2021-06-29 00:00:00

The modular structure of brain networks supports specialized information processing, complex dynamics, and cost-efficient spatial embedding. Inter-individual variation in modular structure has been linked to differences in performance, disease, and development. There exist many data-driven methods for detecting and comparing modular structure, the most popular of which is modularity maximization. Although modularity maximization is a general framework that can be modified and reparamaterized to address domain-specific research questions, its application to neuroscientific datasets has, thus far, been narrow. Here, we highlight several strategies in which the ``out-of-the-box'' version of modularity maximization can be extended to address questions specific to neuroscience. First, we present approaches for detecting ``space-independent'' modules and for applying modularity maximization to signed matrices. Next, we show that the modularity maximization frame is well-suited for detecting task- and condition-specific modules. Finally, we highlight the role of multi-layer models in detecting and tracking modules across time, tasks, subjects, and modalities. In summary, modularity maximization is a flexible and general framework that can be adapted to detect modular structure resulting from a wide range of hypotheses. This article highlights opens multiple frontiers for future research and applications.
Effect of Labour Income on the Optimal Bankruptcy Problem
Authors: Guodong Ding, Daniele Marazzina
Date: 2021-06-29 00:00:00

In this paper we deal with the optimal bankruptcy problem for an agent who can optimally allocate her consumption rate, the amount of capital invested in the risky asset as well as her leisure time. In our framework, the agent is endowed by an initial debt, and she is required to repay her debt continuously. Declaring bankruptcy, the debt repayment is exempted at the cost of a wealth shrinkage. We implement the duality method to solve the problem analytically and conduct a sensitivity analysis to the cost and benefit parameters of bankruptcy. Introducing the flexible leisure/working rate, and therefore the labour income, into the bankruptcy model, we investigate its effect on the optimal strategies.
Environmental, Social, Governance scores and the Missing pillar -- Why does missing information matter?
Authors: Özge Sahin, Karoline Bax, Claudia Czado, Sandra Paterlini
Date: 2021-06-29 00:00:00

Environmental, Social, and Governance (ESG) scores measure companies' performance concerning sustainability and societal impact and are organized on three pillars: Environmental (E), Social (S), and Governance (G). These complementary non-financial ESG scores should provide information about the ESG performance and risks of different companies. However, the extent of not yet published ESG information makes the reliability of ESG scores questionable. To explicitly denote the not yet published information on ESG category scores, a new pillar, the so-called Missing (M) pillar, is formulated. Environmental, Social, Governance, and Missing (ESGM) scores are introduced to consider the potential release of new information in the future. Furthermore, an optimization scheme is proposed to compute ESGM scores, linking them to the companies' riskiness. By relying on the data provided by Refinitiv, we show that the ESGM scores strengthen the companies' risk relationship. These new scores could benefit investors and practitioners as ESG exclusion strategies using only ESG scores might exclude assets with a low score solely because of their missing information and not necessarily because of a low ESG merit.