pwc-archive/papers-with-abstracts huggingface dataset:
Influence of the vessel wall geometry on the wall-induced migration of red blood cells
Authors: Ying Zhang, Thomas G. Fai
Date: 2022-03-23 00:00:00

The geometry of the blood vessel wall plays a regulatory role on the motion of red blood cells (RBCs). The overall topography of the vessel wall depends on many features, among which the endothelial lining of the endothelial surface layer (ESL) is an important one. The endothelial lining of vessel walls presents a large surface area for exchanging materials between blood and tissues. The ESL plays a critical role in regulating vascular permeability, hindering leukocyte adhesion as well as inhibiting coagulation during inflammation. Changes in the ESL structure are believed to cause vascular hyperpermeability and entrap immune cells during sepsis, which could significantly alter the vessel wall geometry and disturb interactions between RBCs and the vessel wall, including the wall-induced migration of RBCs and the thickening of a cell-free layer. To investigate the influence of the vessel wall geometry particularly changed by the ESL under various pathological conditions, such as sepsis, on the motion of RBCs, we developed two models to represent the ESL using the immersed boundary method in two dimensions. In particular, we used simulations to study how the lift force and drag force on a RBC near the vessel wall vary with different wall thickness, spatial variation, and permeability associated with changes in the vessel wall geometry. We find that the spatial variation of the wall has a significant effect on the wall-induced migration of the RBC for a high permeability, and that the wall-induced migration is significantly inhibited as the vessel diameter is increased.
A homogenized constrained mixture model of cardiac growth and remodeling: Analyzing mechanobiological stability and reversal
Authors: Amadeus M. Gebauer, Martin R. Pfaller, Fabian A. Braeu, Christian J. Cyron, Wolfgang A. Wall
Date: 2022-03-23 00:00:00

Cardiac growth and remodeling (G&R) patterns change ventricular size, shape, and function both globally and locally. Biomechanical, neurohormonal, and genetic stimuli drive these patterns through changes in myocyte dimension and fibrosis. We propose a novel microstructure-motivated model that predicts organ-scale G&R in the heart based on the homogenized constrained mixture theory. Previous models, based on the kinematic growth theory, reproduced consequences of G&R in bulk myocardial tissue by prescribing the direction and extent of growth but neglected underlying cellular mechanisms. In our model, the direction and extent of G&R emerge naturally from intra- and extra cellular turnover processes in myocardial tissue constituents and their preferred homeostatic stretch state. We additionally propose a method to obtain a mechanobiologically equilibrated reference configuration. We test our model on an idealized 3D left ventricular geometry and demonstrate that our model aims to maintain tensional homeostasis in hypertension conditions. In a stability map, we identify regions of stable and unstable G&R from an identical parameter set with varying systolic pressures and growth factors. Furthermore, we show the extent of G&R reversal after returning the systolic pressure to baseline following stage 1 and 2 hypertension. A realistic model of organ-scale cardiac G&R has the potential to identify patients at risk of heart failure, enable personalized cardiac therapies, and facilitate the optimal design of medical devices.
Scatter Ptychography
Authors: Qian Huang, Zhipeng Dong, Yuzuru Takashima, Timothy J. Schulz, David J. Brady
Date: 2022-03-23 00:00:00
Tasks: Retrieval

Coherent illumination reflected by a remote target may be secondarily scattered by intermediate objects or materials. Here we show that phase retrieval on remotely observed images of such scattered fields enables imaging of the illuminated object at resolution proportional to $\lambda R_s/A_s$, where $R_s$ is the range between the scatterer and the target and $A_s$ is the diameter of the observed scatter. This resolution may exceed the resolution of directly viewing the target by the factor $R_cA_s/R_sA_c$, where $R_c$ is the range between the observer and the target and $A_c$ is the observing aperture. Here we use this technique to demonstrate $\approx 32\times$ resolution improvement relative to direct imaging.
Bounds for Bias-Adjusted Treatment Effect in Linear Econometric Models
Authors: Deepankar Basu
Date: 2022-03-23 00:00:00
Tasks: regression

In linear econometric models with proportional selection on unobservables, omitted variable bias in estimated treatment effects are real roots of a cubic equation involving estimated parameters from a short and intermediate regression. The roots of the cubic are functions of $\delta$, the degree of selection on unobservables, and $R_{max}$, the R-squared in a hypothetical long regression that includes the unobservable confounder and all observable controls. In this paper I propose and implement a novel algorithm to compute roots of the cubic equation over relevant regions of the $\delta$-$R_{max}$ plane and use the roots to construct bounding sets for the true treatment effect. The algorithm is based on two well-known mathematical results: (a) the discriminant of the cubic equation can be used to demarcate regions of unique real roots from regions of three real roots, and (b) a small change in the coefficients of a polynomial equation will lead to small change in its roots because the latter are continuous functions of the former. I illustrate my method by applying it to the analysis of maternal behavior on child outcomes.
Submission to the Commission on Taxation and Welfare on introducing a site value tax
Authors: Eóin Flaherty, Constantin Gurdgiev, Ronan Lyons, Emer Ó Siochrú, James Pike
Date: 2022-03-23 00:00:00

This submission to the Irish Commission on Taxation and Welfare advocates the introduction of a site value tax in Ireland. Ireland has high and volatile property prices, constraining social and economic development. Site values are the main driver of these phenomena. Taxing site values would reduce both the level and volatility of property prices, and thus help to alleviate these problems. Site value tax has many other beneficial features. For example, it captures price gains due to the community and government rather than owners' efforts and thus diminishes the incentive to buy land for speculative reasons. Site value tax can be used to finance infrastructural investments, help facilitate site assembly for development and as a support for the maintenance of protected structures. Site value tax is also a tax on wealth.
School-age Vaccination, School Openings and Covid-19 diffusion
Authors: Emanuele Amodio, Michele Battisti, Antonio Francesco Gravina, Andrea Mario Lavezzi, Giuseppe Maggio
Date: 2022-03-23 00:00:00
Tasks: counterfactual

Do school openings trigger Covid-19 diffusion when school-age vaccination is available? We investigate this question using a unique geo-referenced high frequency database on school openings, vaccinations, and Covid-19 cases from the Italian region of Sicily. The analysis focuses on the change of Covid-19 diffusion after school opening in a homogeneous geographical territory. The identification of causal effects derives from a comparison of the change in cases before and after school opening in 2020/21, when vaccination was not available, and in 2021/22, when the vaccination campaign targeted individuals of age 12-19 and above 19. The results indicate that, while school opening determined an increase in the growth rate of Covid-19 cases in 2020/2021, this effect has been substantially reduced by school-age vaccination in 2021/2022. In particular, we find that an increase of approximately 10% in the vaccination rate of school-age population reduces the growth rate of Covid-19 cases after school opening by approximately 1.4%. In addition, a counterfactual simulation suggests that a permanent no vaccination scenario would have implied an increase of 19% in ICU beds occupancy.
The Impact of Geopolitical Conflicts on Trade, Growth, and Innovation
Authors: Carlos Góes, Eddy Bekkers
Date: 2022-03-23 00:00:00

Geopolitical conflicts have increasingly been a driver of trade policy. We study the potential effects of global and persistent geopolitical conflicts on trade, technological innovation, and economic growth. In conventional trade models the welfare costs of such conflicts are modest. We build a multi-sector multi-region general equilibrium model with dynamic sector-specific knowledge diffusion, which magnifies welfare losses of trade conflicts. Idea diffusion is mediated by the input-output structure of production, such that both sector cost shares and import trade shares characterize the source distribution of ideas. Using this framework, we explore the potential impact of a "decoupling of the global economy," a hypothetical scenario under which technology systems would diverge in the global economy. We divide the global economy into two geopolitical blocs -- East and West -- based on foreign policy similarity and model decoupling through an increase in iceberg trade costs (full decoupling) or tariffs (tariff decoupling). Results yield three main insights. First, the projected welfare losses for the global economy of a decoupling scenario can be drastic, as large as 15% in some regions and are largest in the lower income regions as they would benefit less from technology spillovers from richer areas. Second, the described size and pattern of welfare effects are specific to the model with diffusion of ideas. Without diffusion of ideas the size and variation across regions of the welfare losses would be substantially smaller. Third, a multi-sector framework exacerbates diffusion inefficiencies induced by trade costs relative to a single-sector one.
Economic Networks: Theory and Computation
Authors: Thomas J. Sargent, John Stachurski
Date: 2022-03-22 00:00:00

This textbook is an introduction to economic networks, intended for students and researchers in the fields of economics and applied mathematics. The textbook emphasizes quantitative modeling, with the main underlying tools being graph theory, linear algebra, fixed point theory and programming. The text is suitable for a one-semester course, taught either to advanced undergraduate students who are comfortable with linear algebra or to beginning graduate students.
PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic Information for Language Identification
Authors: Hexin Liu, Leibny Paola Garcia Perera, Andy W. H. Khong, Suzy J. Styles, Sanjeev Khudanpur
Date: 2022-03-23 00:00:00
Tasks: Language Identification

We propose a novel model to hierarchically incorporate phoneme and phonotactic information for language identification (LID) without requiring phoneme annotations for training. In this model, named PHO-LID, a self-supervised phoneme segmentation task and a LID task share a convolutional neural network (CNN) module, which encodes both language identity and sequential phonemic information in the input speech to generate an intermediate sequence of phonotactic embeddings. These embeddings are then fed into transformer encoder layers for utterance-level LID. We call this architecture CNN-Trans. We evaluate it on AP17-OLR data and the MLS14 set of NIST LRE 2017, and show that the PHO-LID model with multi-task optimization exhibits the highest LID performance among all models, achieving over 40% relative improvement in terms of average cost on AP17-OLR data compared to a CNN-Trans model optimized only for LID. The visualized confusion matrices imply that our proposed method achieves higher performance on languages of the same cluster in NIST LRE 2017 data than the CNN-Trans model. A comparison between predicted phoneme boundaries and corresponding audio spectrograms illustrates the leveraging of phoneme information for LID.
Design of Customized Adaptive Radar Detectors in the CFAR Feature Plane
Authors: Angelo Coluccia, Alessio Fascista, Giuseppe Ricci
Date: 2022-03-23 00:00:00

The paper addresses the design of adaptive radar detectors having desired behavior, in Gaussian disturbance with unknown statistics. Specifically, given detection probability specifications for chosen signal-to-noise ratios and steering vector mismatch levels, a methodology for the optimal design of customized CFAR detectors is devised in a suitable feature plane based on maximal invariant statistics. To overcome the analytical and numerical intractability of the resulting optimization problem, a novel general reduced-complexity algorithm is developed, which is shown to be effective in providing a close approximation of the desired detector. The proposed approach solves the open problem of ensuring a prefixed false alarm probability while controlling the behavior under both matched and mismatched conditions, so enabling the design of fully customized adaptive CFAR detectors.
Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory
Authors: Khouloud Abdelli, Helmut Griesser, Carsten Tropschug, Stephan Pachnicke
Date: 2022-03-19 00:00:00
Tasks: Denoising, Diagnostic, Event Detection, Fault Detection

Optical time-domain reflectometry (OTDR) has been widely used for characterizing fiber optical links and for detecting and locating fiber faults. OTDR traces are prone to be distorted by different kinds of noise, causing blurring of the backscattered signals, and thereby leading to a misleading interpretation and a more cumbersome event detection task. To address this problem, a novel method combining a denoising convolutional autoencoder (DCAE) and a bidirectional long short-term memory (BiLSTM) is proposed, whereby the former is used for noise removal of OTDR signals and the latter for fault detection, localization, and diagnosis with the denoised signal as input. The proposed approach is applied to noisy OTDR signals of different levels of input SNR ranging from -5 dB to 15 dB. The experimental results demonstrate that: (i) the DCAE is efficient in denoising the OTDR traces and it outperforms other deep learning techniques and the conventional denoising methods; and (ii) the BiLSTM achieves a high detection and diagnostic accuracy of 96.7% with an improvement of 13.74% compared to the performance of the same model trained with noisy OTDR signals.
Solar Term Anomaly in China Stock Market: Evidence from Shanghai Index
Authors: Zhou Tianbao, Li Xinghao, Zhao Junguang
Date: 2022-03-04 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

This paper investigates the solar term effect in China stock market as a supplementary to the existing literature of calender effect. Based on a regression framework, this paper verifies the existence of solar term effect in Shanghai Index in multiple dimensions: inter-solar-term analysis, full sample analysis at mean level and risk level as well as the turn of solar term effect. Several solar terms have been found to cause significant positive and negative value to the return such as solar term 1,3 and 4. and bring high volatility such as solar term 8, 11 and 14. The result is reliable and robust under the Extreme Bound Analysis and various assumptions of errors distribution in IGARCH model. These findings give readers a new perspective to view calender effect under the influence of traditional Chinese culture that solar terms affect the market through affecting investors mood, expectation, enthusiasm, etc. which is a good evidence to the Culture bonus hypothesis proposed by Chen and Chien and the possible influence by the Chinese culture in other Asian markets.
Grandes fraudes y gobiernos corporativos en la Economía desde mediados del siglo XX
Authors: I Martín-de-Santos
Date: 2022-02-06 00:00:00

The international financial system is currently not yet prepared to face a foreseeable crisis mainly motivated by the dichotomy between the real economy and the virtual economy. Skepticism is widespread even when it comes to investments in sustainable economy. The concentration of capital in a few persons is one of the greatest risks for the possible reiteration of economic crises. The benevolent sentences of the courts to some of the fraudsters do not contribute to dispelling the ghost of fraud nor to the disappearance of tax havens. From the diachronic perspective, it is observed that economic crises are increasingly frequent and incidents always in the financial field; which forces us to rethink an economic model on an international scale in which there is a greater weight of the economic policy of governments over the power of multinational companies in the context of globalization. In the context of Corporate Social Responsibility, Corporate Governance is listed as one of the fundamental levers to curb large business fraud, but its efficiency seems insufficient due to the lack of international regulations and the ignorance of hidden forces in what has been known as fiscal and financial engineering. The application of liberal policies in an unorthodox way is causing real social gaps in the distribution of income and is undermining the current capitalist system. The need to implement corporate governments is recommended as one of the essential formulas for sustaining the international economic system.
Journey of Cryptocurrency in India In View of Financial Budget 2022-23
Authors: Varun Shukla, Manoj Kumar Misra, Atul Chaturvedi
Date: 2022-02-04 00:00:00

Recently, Indian Finance minister Nirmala Sitharaman announced in Union budget 2022-23 that Indian government will put 30% tax (the highest tax slab in India) on income generated from cryptocurrencies. Big financial institutions, experts and academicians have different opinions in this regard. They claim that it would be the end of cryptocurrency market in India or it would be possible that RBI (Reserve Bank of India) may launch its own crypto or digital currency. So in this context, in this article, the journey and future aspects of cryptocurrency in India are discussed and we hope that it will be a reference for further research and discussion in this area.
Transactional Interpretation for the Principle of Minimum Fisher Information
Authors: Marcin Makowski, Edward W. Piotrowski, Piotr Frąckiewicz, Marek Szopa
Date: 2022-01-23 00:00:00

The principle of minimum Fisher information states that in the set of acceptable probability distributions characterizing the given system, it is best done by the one that minimizes the corresponding Fisher information. This principle can be applied to transaction processes, the dynamics of which can be interpreted as the market tendency to minimize the information revealed about itself. More information involves higher costs (information is physical). The starting point for our considerations is a description of the market derived from the assumption of minimum Fisher information for a strategy with a fixed financial risk. Strategies of this type that minimize Fisher information overlap with the well-known eigenstates of a the quantum harmonic oscillator. The analytical extension of this field of strategy to the complex vector space (traditional for quantum mechanics) suggests the study of the interference of the oscillator eigenstates in terms of their minimization of Fisher information. It is revealed that the minimum value of Fisher information of the superposition of the two strategies being the ground state and the second excited state of the oscillator, has Fisher information less than the ground state of the oscillator. Similarly, less information is obtained for the system of strategies (the oscillator eigenstates) randomized by the Gibbs distribution. We distinguish two different views on the description of Fisher information. One of them, the classical, is based on the value of Fisher information. The second, we call it transactional, expresses Fisher information from the perspective of the constant risk of market strategies. The orders of the market strategies derived from these two descriptions are different. From a market standpoint, minimizing Fisher information is equivalent to minimizing risk.
Favorit: farmers volatility risk treatment
Authors: Dadasaheb G. Godase, P. R. Sheshagiri Rao, Anil Gore
Date: 2022-03-23 00:00:00
Tasks: Management

This paper seeks to develop a strategy based on analytics, for an individual Indian farmer to tackle market price fluctuations. The idea is to select a month (or a week or a day) on which to take the produce to market for a good return on the sale. The choice is based on the history of price data and associated variability. Market-wise price data for the last decade or so are used. These ideas are applied to three vegetable crops, tomato, onion, and coriander for some markets in the state of Maharashtra. It is proposed that similar work should be done crop-wise and market-wise for different parts of India by local academic groups from any of the subjects such as statistics, analytics, data science, agriculture, business management, commerce, and economics. The objective is to mitigate the adverse impact of price fluctuation on farmers.
Reducing overestimating and underestimating volatility via the augmented blending-ARCH model
Authors: Jun Lu, Shao Yi
Date: 2022-03-15 00:00:00
Tasks: Time Series, Time Series Analysis

SVR-GARCH model tends to "backward eavesdrop" when forecasting the financial time series volatility in which case it tends to simply produce the prediction by deviating the previous volatility. Though the SVR-GARCH model has achieved good performance in terms of various performance measurements, trading opportunities, peak or trough behaviors in the time series are all hampered by underestimating or overestimating the volatility. We propose a blending ARCH (BARCH) and an augmented BARCH (aBARCH) model to overcome this kind of problem and make the prediction towards better peak or trough behaviors. The method is illustrated using real data sets including SH300 and S&P500. The empirical results obtained suggest that the augmented and blending models improve the volatility forecasting ability.
Neural Network and Order Flow, Technical Analysis: Predicting short-term direction of futures contract
Authors: Yiyang Zheng
Date: 2022-02-23 00:00:00

Predictions of short-term directional movement of the futures contract can be challenging as its pricing is often based on multiple complex dynamic conditions. This work presents a method for predicting the short-term directional movement of an underlying futures contract. We engineered a set of features from technical analysis, order flow, and order-book data. Then, Tabnet, a deep learning neural network, is trained using these features. We train our model on the Silver Futures Contract listed on Shanghai Futures Exchange and achieve an accuracy of 0.601 on predicting the directional change during the selected period.
Mixing Constant Sum and Constant Product Market Makers
Authors: Alexander Port, Neelesh Tiruviluamala
Date: 2022-03-23 00:00:00

Two popular forms of automated market makers are constant sum and constant product (CSMM and CPMM respectively). Each has its advantages and disadvantages: CSMMs have stable exchange rates but are vulnerable to arbitrage and can sometimes fail to provide liquidity, while a CPMM can have large impermanent loss due to exchange rate changes but are always able to provide liquidity to participants. A significant amount of work has been done in order to get the best of both constant sum and constant product characteristics. Perhaps most the relevant to this paper is Stableswap, which has an "amplification coefficient" parameter controlling the balance between the two types of behavior [Ego19]. Alternative approaches, such as in [AEC21], involve constructing AMMs using portfolio value functions. However, there is still much work to be done on these fronts. This paper presents multiple novel methods for mixing market makers and demonstrates new tools for designing markets with specific features.