pwc-archive/papers-with-abstracts huggingface dataset:
Symmetries of systems of first order ODEs: Symbolic symmetry computations, mechanistic model construction and applications in biology
Authors: Johannes Borgqvist, Fredrik Ohlsson, Ruth E. Baker
Date: 2022-02-10 00:00:00

We discuss the role and merits of symmetry methods for the analysis of biological systems. In particular, we consider systems of first order ordinary differential equations and provide a comprehensive review of the geometrical foundations pertinent to symmetries of such systems. Subsequently, we present an algorithm for finding infinitesimal generators of symmetries for systems with rational reaction terms, and an open-source implementation of the algorithm using symbolic computations. We discuss two complementary perspectives on symmetries in mechanistic modelling; as tools for the analysis of a given model or as a geometrical principle for incorporating biological properties in the construction of new models. Through numerous examples of relevance to modelling in biology we demonstrate the different uses of symmetry methods, and also discuss how to infer symmetries from experimental data.
von Mises-Fisher distributions and their statistical divergence
Authors: Toru Kitagawa, Jeff Rowley
Date: 2022-02-10 00:00:00

The von Mises-Fisher family is a parametric family of distributions on the surface of the unit ball, summarised by a concentration parameter and a mean direction. As a quasi-Bayesian prior, the von Mises-Fisher distribution is a convenient and parsimonious choice when parameter spaces are isomorphic to the hypersphere (e.g., maximum score estimation in semi-parametric discrete choice, estimation of single-index treatment assignment rules via empirical welfare maximisation, under-identifying linear simultaneous equation models). Despite a long history of application, measures of statistical divergence have not been analytically characterised for von Mises-Fisher distributions. This paper provides analytical expressions for the $f$-divergence of a von Mises-Fisher distribution from another, distinct, von Mises-Fisher distribution in $\mathbb{R}^p$ and the uniform distribution over the hypersphere. This paper also collect several other results pertaining to the von Mises-Fisher family of distributions, and characterises the limiting behaviour of the measures of divergence that we consider.
The Transfer Performance of Economic Models
Authors: Isaiah Andrews, Drew Fudenberg, Lihua Lei, Annie Liang, Chaofeng Wu
Date: 2022-02-10 00:00:00

Economists often estimate models using data from a particular domain, e.g. estimating risk preferences in a particular subject pool or for a specific class of lotteries. Whether a model's predictions extrapolate well across domains depends on whether the estimated model has captured generalizable structure. We provide a tractable formulation for this "out-of-domain" prediction problem and define the transfer error of a model based on how well it performs on data from a new domain. We derive finite-sample forecast intervals that are guaranteed to cover realized transfer errors with a user-selected probability when domains are iid, and use these intervals to compare the transferability of economic models and black box algorithms for predicting certainty equivalents. We find that in this application, the black box algorithms we consider outperform standard economic models when estimated and tested on data from the same domain, but the economic models generalize across domains better than the black-box algorithms do.
Privacy Protection, Measurement Error, and the Integration of Remote Sensing and Socioeconomic Survey Data
Authors: Jeffrey D. Michler, Anna Josephson, Talip Kilic, Siobhan Murray
Date: 2022-02-10 00:00:00
Tasks: Survey

When publishing socioeconomic survey data, survey programs implement a variety of statistical methods designed to preserve privacy but which come at the cost of distorting the data. We explore the extent to which spatial anonymization methods to preserve privacy in the large-scale surveys supported by the World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) introduce measurement error in econometric estimates when that survey data is integrated with remote sensing weather data. Guided by a pre-analysis plan, we produce 90 linked weather-household datasets that vary by the spatial anonymization method and the remote sensing weather product. By varying the data along with the econometric model we quantify the magnitude and significance of measurement error coming from the loss of accuracy that results from protect privacy measures. We find that spatial anonymization techniques currently in general use have, on average, limited to no impact on estimates of the relationship between weather and agricultural productivity. However, the degree to which spatial anonymization introduces mismeasurement is a function of which remote sensing weather product is used in the analysis. We conclude that care must be taken in choosing a remote sensing weather product when looking to integrate it with publicly available survey data.
Vaccination nudges: A study of pre-booked COVID-19 vaccinations in Sweden
Authors: Carl Bonander, Mats Ekman, Niklas Jakobsson
Date: 2022-02-10 00:00:00

A nudge changes people's actions without removing their options or altering their incentives. During the COVID-19 vaccine rollout, the Swedish Region of Uppsala sent letters with pre-booked appointments to inhabitants aged 16-17 instead of opening up manual appointment booking. Using regional and municipal vaccination data, we document a higher vaccine uptake among 16- to 17-year-olds in Uppsala compared to untreated control regions (constructed using the synthetic control method as well as neighboring municipalities). The results highlight pre-booked appointments as a strategy for increasing vaccination rates in populations with low perceived risk.
Creating an institutional ecosystem for cash transfer programming: Lessons from post-disaster governance in Indonesia
Authors: Jonatan A. Lassa, Gisela Emanuela Nappoe, Susilo Budhi Sulistyo
Date: 2022-02-10 00:00:00
Tasks: Humanitarian, Management

Humanitarian and disaster management actors have increasingly adopted cash transfer to reduce the sufferings and vulnerability of the survivors. Case transfers have also been used as a critical instrument in the current COVID-19 pandemic. Unfortunately, academic work on humanitarian and disaster-cash transfer related issues remains limited. This article explores how NGOs and governments implement humanitarian cash transfer in a post-disaster setting using an exploratory research strategy. It asks What are institutional constraints and opportunities faced by humanitarian emergency responders in ensuring an effective humanitarian cash transfer and how humanitarian actors address such institutional conditions. We introduced a new conceptual framework, namely humanitarian and disaster management ecosystem for cash transfer. This framework allows non-governmental actors to restore complex relations between the state, disaster survivors or citizen, local market economy and civil society. Mixed methods and multistage research strategy were used to collect and analyze primary and secondary data. The findings suggest that implementing cash transfers in the context of post tsunamigenic earthquakes and liquefaction hazards, NGOs must co-create an ecosystem of response that not only aimed at restoring peoples access to cash and basic needs but first they must restore relations between the states and their citizen while linking the at-risk communities with the private sectors to jump-starting local livelihoods and market economy.
Buying Opinions
Authors: Mark Whitmeyer, Kun Zhang
Date: 2022-02-10 00:00:00

A principal hires an agent to acquire soft information about an unknown state. Even though neither how the agent learns nor what the agent discovers are contractible, we show the principal is unconstrained as to what information the agent can be induced to acquire and report honestly. When the agent is risk neutral, and a) is not asked to learn too much, b) can acquire information sufficiently cheaply, or c) can face sufficiently large penalties, the principal can attain the first-best outcome. We discuss the effect of risk aversion (on the part of the agent) and characterize the second-best contracts.
How rare are the properties of binary relations?
Authors: Ram Sewak Dubey, Giorgio Laguzzi
Date: 2022-02-10 00:00:00

Knoblauch (2014) and Knoblauch (2015) investigate the relative size of the collection of binary relations with desirable features as compared to the set of all binary relations using symmetric difference metric (Cantor) topology and Hausdorff metric topology. We consider Ellentuck and doughnut topologies to further this line of investigation. We report the differences among the size of the useful binary relations in Cantor, Ellentuck and doughnut topologies. It turns out that the doughnut topology admits binary relations with more general properties in contrast to the other two. We further prove that among the induced Cantor and Ellentuck topologies, the latter captures the relative size of partial orders among the collection of all quasi-orders. Finally we show that the class of ethical binary relations is small in Ellentuck (and therefore in Cantor) topology but is not small in doughnut topology. In essence, the Ellentuck topology fares better compared to Cantor topology in capturing the relative size of collections of binary relations.
Rationalizable Implementation of Social Choice Functions: Complete Characterization
Authors: Siyang Xiong
Date: 2022-02-10 00:00:00

We provide a necessary and sufficient condition for rationalizable implementation of social choice functions, i.e., we offer a complete answer regarding what social choice functions can be rationalizably implemented.
Stable allocations in discrete exchange economies
Authors: Federico Echenique, Sumit Goel, SangMok Lee
Date: 2022-02-09 00:00:00

We study stable allocations in an exchange economy with indivisible goods. The problem is well-known to be challenging, and rich enough to encode fundamentally unstable economies, such as the roommate problem. Our approach stems from generalizing the original study of an exchange economy with unit demand and unit endowments, the \emph{housing model}. Our first approach uses Scarf's theorem, and proposes sufficient conditions under which a ``convexify then round'' technique ensures that the core is nonempty. The upshot is that a core allocation exists in categorical economies with dichotomous preferences. Our second approach uses a generalization of the TTC: it works under general conditions, and finds a solution that is a version of the stable set.
AI-based Robust Resource Allocation in End-to-End Network Slicing under Demand and CSI Uncertainties
Authors: Amir Gharehgoli, Ali Nouruzi, Nader Mokari, Paeiz Azmi, Mohamad Reza Javan, Eduard A. Jorswieck
Date: 2022-02-10 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Network slicing (NwS) is one of the main technologies in the fifth-generation of mobile communication and beyond (5G+). One of the important challenges in the NwS is information uncertainty which mainly involves demand and channel state information (CSI). Demand uncertainty is divided into three types: number of users requests, amount of bandwidth, and requested virtual network functions workloads. Moreover, the CSI uncertainty is modeled by three methods: worst-case, probabilistic, and hybrid. In this paper, our goal is to maximize the utility of the infrastructure provider by exploiting deep reinforcement learning algorithms in end-to-end NwS resource allocation under demand and CSI uncertainties. The proposed formulation is a nonconvex mixed-integer non-linear programming problem. To perform robust resource allocation in problems that involve uncertainty, we need a history of previous information. To this end, we use a recurrent deterministic policy gradient (RDPG) algorithm, a recurrent and memory-based approach in deep reinforcement learning. Then, we compare the RDPG method in different scenarios with soft actor-critic (SAC), deep deterministic policy gradient (DDPG), distributed, and greedy algorithms. The simulation results show that the SAC method is better than the DDPG, distributed, and greedy methods, respectively. Moreover, the RDPG method out performs the SAC approach on average by 70%.
Advanced sleep spindle identification with neural networks
Authors: Lars Kaulen, Justus T. C. Schwabedal, Jules Schneider, Philipp Ritter, Stephan Bialonski
Date: 2022-02-06 00:00:00
Proceeding: Scientific Reports 2022 5
Tasks: Diagnostic, EEG, Electroencephalogram (EEG), Sleep Micro-event detection, Sleep Quality, Sleep spindles detection, Spindle Detection, Time Series Analysis

Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model's performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance.
Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network
Authors: Prateek Singh, Ambalika Sharma, Shreesha Maiya
Date: 2022-01-26 00:00:00
Tasks: Denoising, Specificity

The incidences of atrial fibrillation (AFib) are increasing at a daunting rate worldwide. For the early detection of the risk of AFib, we have developed an automatic detection system based on deep neural networks. For achieving better classification, it is mandatory to have good pre-processing of physiological signals. Keeping this in mind, we have proposed a two-fold study. First, an end-to-end model is proposed to denoise the electrocardiogram signals using denoising autoencoders (DAE). To achieve denoising, we have used three networks including, convolutional neural network (CNN), dense neural network (DNN), and recurrent neural networks (RNN). Compared the three models and CNN based DAE performance is found to be better than the other two. Therefore, the signals denoised by the CNN based DAE were used to train the deep neural networks for classification. Three neural networks' performance has been evaluated using accuracy, specificity, sensitivity, and signal to noise ratio (SNR) as the evaluation criteria. The proposed end-to-end deep learning model for detecting atrial fibrillation in this study has achieved an accuracy rate of 99.20%, a specificity of 99.50%, a sensitivity of 99.50%, and a true positive rate of 99.00%. The average accuracy of the algorithms we compared is 96.26%, and our algorithm's accuracy is 3.2% higher than this average of the other algorithms. The CNN classification network performed better as compared to the other two. Additionally, the model is computationally efficient for real-time applications, and it takes approx 1.3 seconds to process 24 hours ECG signal. The proposed model was also tested on unseen dataset with different proportions of arrhythmias to examine the model's robustness, which resulted in 99.10% of recall and 98.50% of precision.
A note on a prey-predator model with constant-effort harvesting
Authors: Márcia Lemos-Silva, Delfim F. M. Torres
Date: 2022-02-10 00:00:00

We study a prey-predator model based on the classical Lotka-Volterra system with Leslie-Gower and Holling IV schemes and a constant-effort harvesting. Our goal is twofold: to present the model proposed by Cheng and Zhang in 2021, pointing out some inconsistencies; to analyse the number and type of equilibrium points of the model. We end by proving the stability of the meaningful equilibrium point, according to the distribution of the eigenvalues.
Omicron BA.2 (B.1.1.529.2): high potential to becoming the next dominating variant
Authors: Jiahui Chen, Guo-Wei Wei
Date: 2022-02-10 00:00:00

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly replaced the Delta variant as a dominating SARS-CoV-2 variant because of natural selection, which favors the variant with higher infectivity and stronger vaccine breakthrough ability. Omicron has three lineages or subvariants, BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), and BA.3 (B.1.1.529.3). Among them, BA.1 is the currently prevailing subvariant. BA.2 shares 32 mutations with BA.1 but has 28 distinct ones. BA.3 shares most of its mutations with BA.1 and BA.2 except for one. BA.2 is found to be able to alarmingly reinfect patients originally infected by Omicron BA.1. An important question is whether BA.2 or BA.3 will become a new dominating "variant of concern". Currently, no experimental data has been reported about BA.2 and BA.3. We construct a novel algebraic topology-based deep learning model trained with tens of thousands of mutational and deep mutational data to systematically evaluate BA.2's and BA.3's infectivity, vaccine breakthrough capability, and antibody resistance. Our comparative analysis of all main variants namely, Alpha, Beta, Gamma, Delta, Lambda, Mu, BA.1, BA.2, and BA.3, unveils that BA.2 is about 1.5 and 4.2 times as contagious as BA.1 and Delta, respectively. It is also 30% and 17-fold more capable than BA.1 and Delta, respectively, to escape current vaccines. Therefore, we project that Omicron BA.2 is on its path to becoming the next dominating variant. We forecast that like Omicron BA.1, BA.2 will also seriously compromise most existing mAbs, except for sotrovimab developed by GlaxoSmithKline.
Protection gaps in Amazon floodplains will increase with climate change: Insight from the world's largest scaled freshwater fish
Authors: Nicolas Dubos, Maxime Lenormand, Leandro Castello, Thierry Oberdorff, Antoine Guisan, Sandra Luque
Date: 2022-02-10 00:00:00

The Amazon floodplains represent important surfaces of highly valuable ecosystems, yet they remain neglected from protected areas. While the efficiency of the protected area network of the Amazon basin may be jeopardised by climate change, floodplains are exposed to important consequences of climate change but are omitted from species distribution models and protection gap analyses. We modelled the current and future (2070) distribution of the giant bony-tongue fish Arapaima sp. accounting for climate and habitat requirements, with consideration of dam presence (already existing and planned constructions) and hydroperiod (high- and low-water stages). We further quantified the amount of suitable environment which falls inside and outside the current network of protected areas to identify spatial conservation gaps. We predict climate change to cause the decline of environmental suitability by 16.6% during the high-water stage, and by 19.4% during the low-water stage. We found that about 70% of the suitable environments of Arapaima sp. remain currently unprotected, which is likely to increase by 5% with future climate change effects. Both current and projected dam constructions may hamper population flows between the central and the Bolivian and Peruvian parts of the basin. We highlight protection gaps mostly in the southwestern part of the basin and recommend the extension of the current network of protected areas in the floodplains of the upper Ucayali, Juru\`a and Purus Rivers and their tributaries. This study showed the importance of taking into account hydroperiods and dispersal barriers in forecasting the distribution of freshwater fish species, and stresses the urgent need to integrate floodplains to the protected area networks.
Evaluating the impact of quarantine measures on COVID-19 spread
Authors: Renquan Zhang, Yu Wang, Zheng Lv, Sen Pei
Date: 2022-02-09 00:00:00
Tasks: counterfactual, Decision Making

During the early stage of the COVID-19 pandemic, many countries implemented non-pharmaceutical interventions (NPIs) to control the transmission of SARS-CoV-2, the causative pathogen of COVID-19. Among those NPIs, quarantine measures were widely adopted and enforced through stay-at-home and shelter-in-place orders. Understanding the effectiveness of quarantine measures can inform decision-making and control planning during the ongoing COVID-19 pandemic and for future disease outbreaks. In this study, we use mathematical models to evaluate the impact of quarantine measures on COVID-19 spread in four cities that experienced large-scale outbreaks in the spring of 2020: Wuhan, New York, Milan, and London. We develop a susceptible-exposed-infected-removed (SEIR)-type model with a component of quarantine and couple this disease transmission model with a data assimilation method. By calibrating the model to case data, we estimate key epidemiological parameters before lockdown in each city. We further examine the impact of quarantine rates on COVID-19 spread after lockdown using model simulations. Results indicate that quarantine of susceptible and exposed individuals and undetected infections is necessary to contain the outbreak; however, the quarantine rates for these populations can be reduced through faster isolation of confirmed cases. We generate counterfactual simulations to estimate effectiveness of quarantine measures. Without quarantine measures, the cumulative confirmed cases could be 73, 22, 43 and 93 times higher than reported numbers within 40 days after lockdown in Wuhan, New York, Milan, and London. Our findings underscore the essential role of quarantine during the early phase of the pandemic.
Machine Learning and Data Science: Foundations, Concepts, Algorithms, and Tools
Authors: Milad Vazan
Date: 2022-02-03 00:00:00
Tasks: BIG-bench Machine Learning

Today, data is a fuel for businesses to gain important insights and improve their performance. There is no industry in the world today that does not use data. But who will get this insight? Who processes all the raw data? Everything is done by a data analyst or a data scientist.