pwc-archive/papers-with-abstracts huggingface dataset:
ANDOR and beyond: dynamically switchable logic gates as modules for flexible information processing in biochemical regulatory networks
Authors: Mohammadreza Bahadorian, Carl D. Modes
Date: 2021-08-02 00:00:00

An information processing circuit must be flexible to perform multiple tasks: biochemical information processing circuits in living systems can have multiple functions, requiring them to perform distinct computational tasks in different scenarios. Here, we describe a tunable dynamical system that performs different logical operations (such as AND, OR, and XOR) depending on which basin of attraction the dynamics resides in. We analyze the robustness of these dynamically switchable logic gates, and characterize the trade-off between reliability and efficiency in their function.
Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale
Authors: Georgia Papacharalampous, Hristos Tyralis, Ilias G. Pechlivanidis, Salvatore Grimaldi, Elena Volpi
Date: 2021-07-25 00:00:00
Tasks: Descriptive, Time Series, Time Series Analysis, Time Series Clustering, Time Series Forecasting

Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability. Despite the scientific interest suggested by such assumptions, the relationships between descriptive time series features (e.g., temporal dependence, entropy, seasonality, trend and linearity features) and actual time series forecastability (quantified by issuing and assessing forecasts for the past) are scarcely studied and quantified in the literature. In this work, we aim to fill in this gap by investigating such relationships, and the way that they can be exploited for understanding hydroclimatic forecastability and its patterns. To this end, we follow a systematic framework bringing together a variety of -- mostly new for hydrology -- concepts and methods, including 57 descriptive features and nine seasonal time series forecasting methods (i.e., one simple, five exponential smoothing, two state space and one automated autoregressive fractionally integrated moving average methods). We apply this framework to three global datasets originating from the larger Global Historical Climatology Network (GHCN) and Global Streamflow Indices and Metadata (GSIM) archives. As these datasets comprise over 13 000 monthly temperature, precipitation and river flow time series from several continents and hydroclimatic regimes, they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global scale...
Information-theoretic symmetry classifications of crystal patterns in the presence of noise and strong Fedorov type pseudosymmetries for an optimal subsequent crystallographic processing of these patterns
Authors: Peter Moeck
Date: 2021-07-22 00:00:00
Tasks: Classification

Statistically sound crystallographic symmetry classifications are obtained with information theory based methods in the presence of approximately Gaussian distributed noise. A set of three synthetic images with very strong Fedorov type pseudosymmetries and varying amounts of noise serve as examples. The correct distinctions between genuine symmetries and their Fedorov type pseudosymmetry counterparts failed only for the noisiest image of the series where an inconsistent combination of plane symmetry group and projected Laue class was obtained. Contrary to traditional crystallographic symmetry classifications with an image processing program such as CRISP, the classification process does not need to be supervised by a human being. This enables crystallographic symmetry classification of digital images that are more or less periodic in two dimensions (2D) as recorded with sufficient spatial resolution from a wide range of samples with different types of scanning probe microscopes. Alternatives to the employed objective classification methods as proposed by members of the computational symmetry community and machine learning proponents are briefly discussed in an appendix and are found to be wanting because they ignore Fedorov type pseudosymmetries completely. The information theory based methods are more accurate than visual classifications at first sight by most human experts.
Validity and reliability of oral temperature compared to ingestible core temperature pill in free-living conditions
Authors: Paurakh Rajbhandary, Gabriel Nallathambi
Date: 2021-08-01 00:00:00

Complex thermodynamics of the human body and external factors may cause differences between oral and core body temperatures. We present a 19-subject study where continuous measurements of ingested core temperature pills are compared against spot measurements of oral temperature, both FDA-cleared devices, in free-living conditions. Based on measurements from 419 samples across 19 subjects, MAE of 0.30 +/- 0.26 oC and 95% limits of agreement of -0.60 and 0.90 oC was observed between ingestible pill temperature and oral temperature. These results demonstrate that statistically significant differences (p<0.001) between oral and core temperature may arise in free-living conditions. Such clinically relevant differences in temperature measurements due to complex thermodynamics of heat exchange in the human body, measurement noise, and other external factors show the intricacies of interpreting body temperatures measured at different locations.
Real-Time ECG Interval Monitoring Using a Fully Disposable Wireless Patch Sensor
Authors: Gabriel Nallathambi, Paurakh Rajbhandary, Thang Tran, Olivier Colliou
Date: 2021-08-01 00:00:00

ECG interval monitoring provides key insights into the diagnosis of cardiac diseases. The standard 12-lead ECG is generally used, however, because of the current COVID-19 pandemic there is a strong need for a remote monitoring solution which will reduce exposure of health care providers to coronavirus. This article presents a disposable wireless patch biosensor (VitalPatch) and associated platform functionalities for real-time continuous measurement of clinically relevant ECG intervals including PR interval, QRS duration, QT interval, corrected QT interval by Bazett (QTb), and corrected QT interval by Fridericia (QTf). The performance of the VitalPatch is validated by comparing its automated algorithm interval measurements to the manually annotated global intervals of the 12-lead ECG device in 30 subjects. The accuracy of interval monitoring (in terms of mean timing error calculated by subtracting the VitalPatch measurements from the global intervals) is 2.7+/-15.94 ms, -1.97+/-12.29 ms, -14.6+/-12.97 ms, - 15.33+/-14.11 ms, and -15.08+/-13.69 ms for PR interval, QRS duration, QT interval, QTb, and QTf, respectively. These results demonstrate that the VitalPatch is a viable solution for measuring ECG intervals while taking advantage of its remote monitoring feature during the pandemic.
Extreme outbreak dynamics in epidemic models
Authors: Jason Hindes, Michael Assaf, Ira B. Schwartz
Date: 2021-08-02 00:00:00

Motivated by recent epidemic outbreaks, including those of COVID-19, we solve the canonical problem of calculating the dynamics and likelihood of extensive outbreaks in a population within a large class of stochastic epidemic models with demographic noise, including the Susceptible-Infected-Recovered (SIR) model and its general extensions. In the limit of large populations, we compute the probability distribution for all extensive outbreaks, including those that entail unusually large or small (extreme) proportions of the population infected. Our approach reveals that, unlike other well-known examples of rare events occurring in discrete-state stochastic systems, the statistics of extreme outbreaks emanate from a full continuum of Hamiltonian paths, each satisfying unique boundary conditions with a conserved probability flux.
Partial Identification and Inference for Conditional Distributions of Treatment Effects
Authors: SungWon Lee
Date: 2021-08-02 00:00:00
Tasks: valid

This paper considers identification and inference for the distribution of treatment effects conditional on observable covariates. Since the conditional distribution of treatment effects is not point identified without strong assumptions, we obtain bounds on the conditional distribution of treatment effects by using the Makarov bounds. We also consider the case where the treatment is endogenous and propose two stochastic dominance assumptions to tighten the bounds. We develop a nonparametric framework to estimate the bounds and establish the asymptotic theory that is uniformly valid over the support of treatment effects. An empirical example illustrates the usefulness of the methods.
Implementing an Improved Test of Matrix Rank in Stata
Authors: Qihui Chen, Zheng Fang, Xun Huang
Date: 2021-08-01 00:00:00
Tasks: regression, valid

We develop a Stata command, bootranktest, for implementing the matrix rank test of Chen and Fang (2019) in linear instrumental variable regression models. Existing rank tests employ critical values that may be too small, and hence may not even be first order valid in the sense that they may fail to control the Type I error. By appealing to the bootstrap, they devise a test that overcomes the deficiency of existing tests. The command bootranktest implements the two-step version of their test, and also the analytic version if chosen. The command also accommodates data with temporal and cluster dependence.
US Spillovers of US Monetary Policy: Information effects & Financial Flows
Authors: Santiago Camara
Date: 2021-08-02 00:00:00

This paper quantifies the international spillovers of US interest rates by explicitly controlling for the "Fed Information Effect". I use multiple identification strategies that identify two components of monetary policy surprises around FOMC meetings: a pure US monetary policy shock component and a "Fed Information Effect" component. On the one hand, a US tightening caused by a pure US monetary policy component leads to an economic recession, an exchange rate depreciation and tighter financial conditions. On the other hand, a tightening of US monetary policy caused by the "Fed Information Effect" leads to an economic expansion, an exchange rate appreciation and looser financial conditions. Ignoring the "Fed Information Effect" biases the impact of US interest rates and may explain recent atypical findings which suggest an expansionary impact of US monetary policy shocks on the rest of the world.
Nighttime Light Intensity and Child Health Outcomes in Bangladesh
Authors: Mohammad Rafiqul Islam, Masud Alam, Munshi Naser İbne Afzal, Sakila Alam
Date: 2021-08-02 00:00:00
Tasks: Additive models

This study examines the impact of nighttime light intensity on child health outcomes in Bangladesh. We use nighttime light intensity as a proxy measure of urbanization and argue that the higher intensity of nighttime light, the higher is the degree of urbanization, which positively affects child health outcomes. In econometric estimation, we employ a methodology that combines parametric and non-parametric approaches using the Gradient Boosting Machine (GBM), K-Nearest Neighbors (KNN), and Bootstrap Aggregating that originate from machine learning algorithms. Based on our benchmark estimates, findings show that one standard deviation increase of nighttime light intensity is associated with a 1.515 rise of Z-score of weight for age after controlling for several control variables. The maximum increase of weight for height and height for age score range from 5.35 to 7.18 units. To further understand our benchmark estimates, generalized additive models also provide a robust positive relationship between nighttime light intensity and children's health outcomes. Finally, we develop an economic model that supports the empirical findings of this study that the marginal effect of urbanization on children's nutritional outcomes is strictly positive.
What do Firms Gain from Patenting? The Case of the Global ICT Industry
Authors: Dimitrios Exadaktylos, Mahdi Ghodsi, Armando Rungi
Date: 2021-08-02 00:00:00

This study investigates the causal relationship between patent grants and firms' dynamics in the Information and Communication Technology (ICT) industry, as the latter is a peculiar sector of modern economies, often under the lens of antitrust authorities. For our purpose, we exploit matched information about financial accounts and patenting activity in 2009-2017 by 179,660 companies operating in 39 countries. Preliminarily, we show how bigger companies are less than 2% of the sample, although they concentrate about 89% of the grants obtained in the period of analyses. Thus, we test that patent grants in the ICT industry have a significant and large impact on market shares and firm size of smaller companies (31.5% and 30.7%, respectively) in the first year after the grants, while we have no evidence of an impact for bigger companies. After a novel instrumental variable strategy that exploits information at the level of patent offices, we confirm that most of the effects on smaller companies are due to the protection of property rights and not to the innovative content of inventions. Finally, we never observe a significant impact on either profitability or productivity for any firm size category. Eventually, we discuss how our findings support the idea that the ICT industry is a case of endogenous R&D sunk costs, which prevent profit margins from rising in the presence of a relatively high market concentration.
Knowing When to Splurge: Precautionary Saving and Chinese-Canadians
Authors: Mark S. Manger, J. Scott Matthews
Date: 2021-08-01 00:00:00

Why do household saving rates differ so much across countries? This micro-level question has global implications: countries that systematically "oversave" export capital by running current account surpluses. In the recipient countries, interest rates are thus too low and financial stability is put at risk. Existing theories argue that saving is precautionary, but tests are limited to cross-country comparisons and are not always supportive. We report the findings of an original survey experiment. Using a simulated financial saving task implemented online, we compare the saving preferences of a large and diverse sample of Chinese-Canadians with other Canadians. This comparison is instructive given that Chinese-Canadians migrated from, or descend from those who migrated from, a high-saving environment to a low-savings, high-debt environment. We also compare behavior in the presence and absence of a simulated "welfare state," which we represent in the form of mandatory insurance. Our respondents exhibit behavior in the saving task that corresponds to standard economic assumptions about lifecycle savings and risk aversion. We find strong evidence that precautionary saving is reduced when a mandatory insurance is present, but no sign that Chinese cultural influences - represented in linguistic or ethnic terms - have any effect on saving behavior.
The Inelastic Market Hypothesis: A Microstructural Interpretation
Authors: Jean-Philippe Bouchaud
Date: 2021-07-31 00:00:00
Tasks: Unity

We attempt to reconcile Gabaix and Koijen's (GK) recent Inelastic Market Hypothesis (IMH) with the order-driven view of markets that emerged within the microstructure literature in the past 20 years. We review the most salient empirical facts and arguments that give credence to the idea that market price fluctuations are mostly due to order flow, whether informed or non-informed. We show that the Latent Liquidity Theory of price impact makes a precise prediction for GK's multiplier $M$, which measures by how many dollars, on average, the market value of a company goes up if one buys one dollar worth of its stocks. Our central result is that $M$ is of order unity, as found by GK, and increases with the volatility of the stock and decreases with the fraction of the market cap. traded daily. We discuss several empirical results suggesting that the lion's share of volatility is due to trading activity. We argue that the IMH holds for all asset classes, beyond the case of stock markets considered by GK.
A generative model for age and income distribution
Authors: Fatih Ozhamaratli, Oleg Kitov, Paolo Barucca
Date: 2021-07-26 00:00:00
Tasks: model

Each individual in society experiences an evolution of their income during their lifetime. Macroscopically, this dynamics creates a statistical relationship between age and income for each society. In this study, we investigate income distribution and its relationship with age and identify a stable joint distribution function for age and income within the United Kingdom and the United States. We demonstrate a flexible calibration methodology using panel and population surveys and capture the characteristic differences between the UK and the US populations. The model here presented can be utilised for forecasting income and planning pensions.
On the probability of the Condorcet Jury Theorem or the Miracle of Aggregation
Authors: Álvaro Romaniega
Date: 2021-08-02 00:00:00
Tasks: Decision Making

The Condorcet Jury Theorem or the Miracle of Aggregation are frequently invoked to ensure the competence of some aggregate decision-making processes. In this article we explore an estimation of the prior probability of the thesis predicted by the theorem (if there are enough voters, majority rule is a competent decision procedure). We use tools from measure theory to conclude that, prima facie, it will fail almost surely. To update this prior either more evidence in favor of competence would be needed or a modification of the decision rule. Following the latter, we investigate how to obtain an almost sure competent information aggregation mechanism for almost any evidence on voter competence (including the less favorable ones). To do so, we substitute simple majority rule by weighted majority rule based on some weights correlated with epistemic rationality such that every voter is guaranteed a minimal weight equal to one.
Cross-Modal Knowledge Transfer via Inter-Modal Translation and Alignment for Affect Recognition
Authors: Vandana Rajan, Alessio Brutti, Andrea Cavallaro
Date: 2021-08-02 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification, Transfer Learning, Translation

Multi-modal affect recognition models leverage complementary information in different modalities to outperform their uni-modal counterparts. However, due to the unavailability of modality-specific sensors or data, multi-modal models may not be always employable. For this reason, we aim to improve the performance of uni-modal affect recognition models by transferring knowledge from a better-performing (or stronger) modality to a weaker modality during training. Our proposed multi-modal training framework for cross-modal knowledge transfer relies on two main steps. First, an encoder-classifier model creates task-specific representations for the stronger modality. Then, cross-modal translation generates multi-modal intermediate representations, which are also aligned in the latent space with the stronger modality representations. To exploit the contextual information in temporal sequential affect data, we use Bi-GRU and transformer encoder. We validate our approach on two multi-modal affect datasets, namely CMU-MOSI for binary sentiment classification and RECOLA for dimensional emotion regression. The results show that the proposed approach consistently improves the uni-modal test-time performance of the weaker modalities.
Application of Tilt Correlation Statistics to Anisoplanatic Optical Turbulence Modeling and Mitigation
Authors: Russell C. Hardie, Michael A. Rucci, Santasri Bose-Pillai, Richard Van Hook
Date: 2021-08-01 00:00:00
Tasks: Image Registration, parameter estimation

Atmospheric optical turbulence can be a significant source of image degradation, particularly in long range imaging applications. Many turbulence mitigation algorithms rely on an optical transfer function (OTF) model that includes the Fried parameter. We present anisoplanatic tilt statistics for spherical wave propagation. We transform these into 2D autocorrelation functions that can inform turbulence modeling and mitigation algorithms. Using these, we construct an OTF model that accounts for image registration. We also propose a spectral-ratio Fried parameter estimation algorithm that is robust to camera motion and requires no specialized scene content or sources. We employ the Fried parameter estimation and OTF model for turbulence mitigation. A numerical wave-propagation turbulence simulator is used to generate data to quantitatively validate the proposed methods. Results with real camera data are also presented.
Open and free EEG datasets for epilepsy diagnosis
Authors: Palak Handa, Monika Mathur, Nidhi Goel
Date: 2021-08-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Management

The Epilepsies are a common, chronic neurological disorder affecting more than 50 million individuals across the globe. It is characterized by unprovoked, recurring (similar or different type) seizures which are commonly diagnosed through clinical EEGs. Good-quality, open-access and free EEG data can act as a catalyst for on-going state-of-the-art (SOTA) research works for detection, prediction and management of epilepsy and seizures. They can also aid in improving the quality of life (QOL) of these diseased individuals and contribute research in healthcare multimedia, data analytics and Artificial Intelligence (AI) in personalized medicine. This paper presents widely used, available, open and free EEG datasets available for epilepsy and seizure diagnosis. A brief comparison and discussion of open and private datasets has also been done. Such datasets will help in development and evaluation of automatic computer-aided system in healthcare.
Energy-efficient Blood Pressure Monitoring based on Single-site Photoplethysmogram on Wearable Devices
Authors: Wenrui Lin, Berken Utku Demirel, Mohammad Abdullah Al Faruque, G. P. Li
Date: 2021-08-02 00:00:00
Tasks: Edge-computing

The paper proposes accurate Blood Pressure Monitoring (BPM) based on a single-site Photoplethysmographic (PPG) sensor and provides an energy-efficient solution on edge cuffless wearable devices. Continuous PPG signal preprocessed and used as input of the Artificial Neural Network (ANN), and outputs systolic BP (SBP), diastolic BP (DBP), and mean arterial BP (MAP) values for each heartbeat. The improvement of the BPM accuracy is obtained by removing outliers in the preprocessing step and the whole-based inputs compared to parameter-based inputs extracted from the PPG signal. Performance obtained is $3.42 \pm 5.42$ mmHg (MAE $\pm$ RMSD) for SBP, $1.92 \pm 3.29$ mmHg for DBP, and $2.21 \pm 3.50$ mmHg for MAP which is competitive compared to other studies. This is the first BPM solution with edge computing artificial intelligence as we have learned so far. Evaluation experiments on real hardware show that the solution takes 42.2 ms, 18.2 KB RAM, and 2.1 mJ average energy per reading.