pwc-archive/papers-with-abstracts huggingface dataset:
Parisian excursion with capital injection for draw-down reflected Levy insurance risk process
Date: 2020-05-19 00:00:00

This paper discusses Parisian ruin problem with capital injection for Levy
insurance risk process. Capital injection takes place at the draw-down time of
the surplus process when it drops below a pre-specified function of its last
record maximum. The capital is continuously paid to keep the surplus above the
draw-down level until either the surplus process goes above the record high or
a Parisian type ruin occurs, which is announced at the first instance the
surplus process has undergone an excursion below the record for an independent
exponential period of time consecutively since the time the capital was first
injected. Some distributional identities concerning the excursion are
presented. Firstly, we give the Parisian ruin probability and the joint Laplace
transform (possibly killed at the first passage time above a fixed level of the
surplus process) of the ruin time, surplus position at ruin, and the total
capital injection at ruin. Secondly, we obtain the $q$-potential measure of the
surplus process killed at Parisian ruin. Finally, we give expected present
value of the total discounted capital payments up to the Parisian ruin time.
The results are derived using recent developments in fluctuation and excursion
theory of spectrally negative Levy process and are presented semi explicitly in
terms of the scale function of the Levy process. Some numerical examples are
given to facilitate the analysis of the impact of initial surplus and frequency
of observation period to the ruin probability and to the expected total capital
injection.
Pareto-optimal energy sharing between battery-equipped renewable generators
Date: 2020-05-19 00:00:00

The inherent intermittency of renewable sources like wind and solar has
resulted in a bundling of renewable generators with storage resources
(batteries) for increased reliability. In this paper, we consider the problem
of energy sharing between two such bundles, each associated with their own
demand profiles. The demand profiles might, for example, correspond to
commitments made by the bundle to the grid. With each bundle seeking to
minimize its loss of load rate, we explore the possibility that one bundle can
supply energy to the other from its battery at times of deficit, in return for
a reciprocal supply from the other when it faces a deficit itself. We show that
there always exist \emph{mutually beneficial} energy sharing arrangements
between the two bundles. Moreover, we show that Pareto-optimal arrangements
involve at least one bundle transferring energy to the other at the maximum
feasible rate at times of deficit. We illustrate the potential gains from such
dynamic energy sharing via an extensive case study.
Fast cross-validation for multi-penalty ridge regression
Authors: Mark A. van de Wiel, Mirrelijn M. van Nee, Armin Rauschenberger
Date: 2020-05-19 00:00:00
Tasks: regression, Survival Prediction

High-dimensional prediction with multiple data types needs to account for potentially strong differences in predictive signal. Ridge regression is a simple model for high-dimensional data that has challenged the predictive performance of many more complex models and learners, and that allows inclusion of data type specific penalties. The largest challenge for multi-penalty ridge is to optimize these penalties efficiently in a cross-validation (CV) setting, in particular for GLM and Cox ridge regression, which require an additional estimation loop by iterative weighted least squares (IWLS). Our main contribution is a computationally very efficient formula for the multi-penalty, sample-weighted hat-matrix, as used in the IWLS algorithm. As a result, nearly all computations are in low-dimensional space, rendering a speed-up of several orders of magnitude. We developed a flexible framework that facilitates multiple types of response, unpenalized covariates, several performance criteria and repeated CV. Extensions to paired and preferential data types are included and illustrated on several cancer genomics survival prediction problems. Moreover, we present similar computational shortcuts for maximum marginal likelihood and Bayesian probit regression. The corresponding R-package, multiridge, serves as a versatile standalone tool, but also as a fast benchmark for other more complex models and multi-view learners.
Fractional Top Trading Cycle on the Full Preference Domain
Date: 2020-05-19 00:00:00
Tasks: Fairness

Efficiency and fairness are two desiderata in market design. Fairness
requires randomization in many environments. Observing the inadequacy of Top
Trading Cycle (TTC) to incorporate randomization, Yu and Zhang (2020) propose
the class of Fractional TTC mechanisms to solve random allocation problems
efficiently and fairly. The assumption of strict preferences in the paper
restricts the application scope. This paper extends Fractional TTC to the full
preference domain in which agents can be indifferent between objects.
Efficiency and fairness of Fractional TTC are preserved. As a corollary, we
obtain an extension of the probabilistic serial mechanism in the house
allocation model to the full preference domain. Our extension does not require
any knowledge beyond elementary computation.
Cores in discrete exchange economies with complex endowments
Authors: Jun Zhang
Date: 2020-05-19 00:00:00
Tasks: Blocking

The core is a traditional and useful solution concept in economic theory. But in discrete exchange economies without transfers, when endowments are complex, the core may be empty. This motivates Balbuzanov and Kotowski (2019) to interpret endowments as exclusion rights and propose a new concept called exclusion core. Our contribution is twofold. First, we propose a rectification of the core to solve its problem under complex endowments. Second, we propose a refinement of Balbuzanov and Kotowski's exclusion core to improve its performance. Our two core concepts share a common idea of correcting the misused altruism of unaffected agents in blocking coalitions. We propose a mechanism to find allocations in the two cores.
Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets
Authors: Nino Antulov-Fantulin, Tian Guo, Fabrizio Lillo
Date: 2020-05-19 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the outperformance of our model by comparing its outcomes with those obtained with different time series and machine learning methods. Finally, we discuss the predictions conditional to volume and we find that also in this case machine learning methods outperform econometric models.
Diffusivity Estimation for Activator-Inhibitor Models: Theory and Application to Intracellular Dynamics of the Actin Cytoskeleton
Authors: Gregor Pasemann, Sven Flemming, Sergio Alonso, Carsten Beta, Wilhelm Stannat
Date: 2020-05-15 00:00:00
Tasks: parameter estimation

A theory for diffusivity estimation for spatially extended activator-inhibitor dynamics modelling the evolution of intracellular signaling networks is developed in the mathematical framework of stochastic reaction-diffusion systems. In order to account for model uncertainties, we extend the results for parameter estimation for semilinear stochastic partial differential equations, as developed in [PS20], to the problem of joint estimation of diffusivity and parametrized reaction terms. Our theoretical findings are applied to the estimation of effective diffusivity of signaling components contributing to intracellular dynamics of the actin cytoskeleton in the model organism Dictyostelium discoideum.
Mathematical model of COVID-19 intervention scenarios for Sao Paulo- Brazil
Date: 2020-05-18 00:00:00

An epidemiological compartmental model was used to simulate social distancing
strategies to contain the COVID-19 pandemic and prevent a second wave in Sao
Paulo, Brazil. Optimization using genetic algorithm was used to determine the
optimal solutions. Our results suggest the best-case strategy for Sao Paulo is
to maintain or increase the current magnitude of social distancing for at least
60 more days and increase the current levels of personal protection behaviors
by a minimum of 10% (e.g., wearing facemasks, proper hand hygiene and avoid
agglomeration). Followed by a long-term oscillatory level of social distancing
with a stepping-down approach every 80 days over a period of two years with
continued protective behavior.
An Empirical Investigation of Cash Conversion Cycle of Manufacturing Firms and its Association with Firm Size and Profitability
Authors: Nusrat Jahan
Date: 2020-05-15 00:00:00
Tasks: Management, Miscellaneous

The purpose of this empirical study is to investigate Cash Conversion Cycle of thirty manufacturing firms listed in Dhaka Stock Exchanges under six different categories, which are, Food and allied, Pharmaceuticals and chemical, Cement, Textile, Engineering and Miscellaneous. This paper sets industry average Cash Conversion Cycle for these six industries and examines the relationship of Cash Conversion Cycle with firm size and profitability. This study did not find statistically significant differences among the Cash Conversion Cycle of varying manufacturing industries. The result of this study indicates a statistically significant negative relationship between the Cash Conversion Cycle and profitability, especially in terms of Return on Equity. The result also shows that the Cash Conversion Cycle of manufacturing firm also has significant negative relationship with firm size, when measured in terms of net sales. The present study contributes to the literature on working capital management written in the context of Bangladesh.
An Investigation into the Equivalency of Three Performance Dimensions: Evidence from Commercial Banks in Bangladesh
Authors: Nusrat Jahan
Date: 2020-05-15 00:00:00

This study evaluated the three dimensions of performance of commercial banks in Bangladesh by analyzing the trend of the Malmquist Productivity Index (MPI) of the Total Factor Productivity (TFP), Return on Asset (ROA) and Total Stock Return (TSR) over the period 2011 to 2015. The study developed an empirical framework with the intention to examine the equivalency of three dimensions of performance. Since, the measures of performance are different, they cannot be tested in their original form; hence, the growth rate of each category of performance measures were estimated and tested to examine the comparability among them. Evaluation of profitability revealed a decreasing trend and evaluation of stock performance suggests that investors are incurring losses on their investment over the selected period. Evaluation of productivity indicates that productivity regress was recorded initially but at the end of the studied period a modest productivity growth was recorded. Finally, this study was able to ascertain the anticipated equivalency of outcome of the three dimensions of performance.
Simulation of laser induced retinal thermal injuries for non-uniform irradiance profiles and their evaluation according to the laser safety standard
Authors: Sebastian Kotzur, Siegfried Wahl, Annette Frederiksen
Date: 2020-05-19 00:00:00

Laser systems emitting radiation in the visible and near infrared region are potentially hazardous for the retina of the human eye. This can result in irreparable injuries due to photomechanical, photothermal or photochemical light-tissue interactions. This investigation focuses on the photothermal interaction for which a computer model is used to simulate the thermal behavior of the retina and to predict the injury threshold values. The most important factors are the wavelength of the radiation, the exposure time and the irradiance profile on the retina. For performing safety evaluations and classifications the laser safety standard IEC 60825-1:2014 has to be considered. These evaluations are based on emission limits which depend on the same above mentioned factors. According to the IEC 60825-1:2014, non-uniform retinal images are treated by an image analysis where an averaged spot size is used. This averaged size is calculated by the extent of the irradiance profile along two orthogonal directions. Unlike the laser safety standard, the computer model predicts the injury thresholds for an irradiance profile on the retina without averaging the spot size. In this investigation, a broad variety of non-uniform retinal images is investigated with regard to the injury thresholds predicted by the computer model and to the classifications according to the laser safety standard.
Instrumental Variables with Treatment-Induced Selection: Exact Bias Results
Date: 2020-05-19 00:00:00
Tasks: Selection bias

Instrumental variables (IV) estimation suffers selection bias when the
analysis conditions on the treatment. Judea Pearl's early graphical definition
of instrumental variables explicitly prohibited conditioning on the treatment.
Nonetheless, the practice remains common. In this paper, we derive exact
analytic expressions for IV selection bias across a range of data-generating
models, and for various selection-inducing procedures. We present four sets of
results for linear models. First, IV selection bias depends on the conditioning
procedure (covariate adjustment vs. sample truncation). Second, IV selection
bias due to covariate adjustment is the limiting case of IV selection bias due
to sample truncation. Third, in certain models, the IV and OLS estimators under
selection bound the true causal effect in large samples. Fourth, we
characterize situations where IV remains preferred to OLS despite selection on
the treatment. These results broaden the notion of IV selection bias beyond
sample truncation, replace prior simulation findings with exact analytic
formulas, and enable formal sensitivity analyses.
Minimal models of invasion and clonal selection in cancer
Authors: Chay Paterson
Date: 2020-05-19 00:00:00

In this thesis we develop minimal models of the relationship between motility, growth, and evolution of cancer cells. We utilise simple simulations of a population of individual cells in space to examine how changes in mechanical properties of invasive cells and their surroundings can affect the speed of cell migration. We also find that the growth rate of large lesions depends weakly on the migration speed of escaping cells, and has stronger and more complex dependencies on the rates of other stochastic processes in the model, namely the rate at which cells transition to being motile and the reverse rate at which cells cease to be motile. To examine how the rates of growth and evolution of an ensemble of cancerous lesions depends on their geometry and underlying fitness landscape, we develop an analytical framework in which the spatial structure is coarse grained and the cancer treated as a continuously growing system with stochastic migration events. Both approaches conclude that the whole ensemble can undergo migration-driven exponential growth regardless of the dependence of size on time of individual lesions, and that the relationship between growth rate and rate of migration is determined by the geometrical constraints of individual lesions. We also find that linear fitness landscapes result in faster-than-exponential growth of the ensemble, and we can determine the expected number of driver mutations present in several important cases of the model. Finally, we study data from a clinical study of the effectiveness of a new low-dose combined chemotherapy. This enables us to test some important hypotheses about the growth rate of pancreatic cancers and the speed with which evolution occurs in reality. Despite this, we find that the frequency of resistant mutants is far too high to be explained without resorting to novel mechanisms of cross-resistance to multiple drugs.
Animal social networks: an introduction for complex systems scientists
Date: 2020-05-19 00:00:00

Many animals live in societies where individuals frequently interact socially with each other. The social structures of these systems can be studied in depth by means of network analysis. A large number of studies on animal social networks in many species have in recent years been carried out in the biological research field of animal behaviour and have provided new insights into behaviour, ecology, and social evolution. This line of research is currently not so well connected to the field of complex systems as could be expected. The purpose of this paper is to provide an introduction to animal social networks for complex systems scientists and highlight areas of synergy. We believe that an increased integration of animal social networks with the interdisciplinary field of complex systems and networks would be beneficial for various reasons. Increased collaboration between researchers in this field and biologists studying animal social systems could be valuable in solving challenges that are of importance to animal social network research. Furthermore, animal social networks provide the opportunity to investigate hypotheses about complex systems across a range of natural real-world social systems. In this paper, we describe what animal social networks are and main research themes where they are studied; we give an overview of the methods commonly used to study animal social networks; we highlight challenges in the study of animal social networks where complex systems expertise may be particularly valuable; and we consider aspects of animal social networks that may be of particular interest to complex systems researchers. We hope that this will help to facilitate further interdisciplinary collaborations involving animal social networks, and further integration of these networks into the field of complex systems.
Learning Equations from Biological Data with Limited Time Samples
Date: 2020-05-19 00:00:00
Tasks: Denoising, Model Discovery, Model Selection

Equation learning methods present a promising tool to aid scientists in the
modeling process for biological data. Previous equation learning studies have
demonstrated that these methods can infer models from rich datasets, however,
the performance of these methods in the presence of common challenges from
biological data has not been thoroughly explored. We present an equation
learning methodology comprised of data denoising, equation learning, model
selection and post-processing steps that infers a dynamical systems model from
noisy spatiotemporal data. The performance of this methodology is thoroughly
investigated in the face of several common challenges presented by biological
data, namely, sparse data sampling, large noise levels, and heterogeneity
between datasets. We find that this methodology can accurately infer the
correct underlying equation and predict unobserved system dynamics from a small
number of time samples when the data is sampled over a time interval exhibiting
both linear and nonlinear dynamics. Our findings suggest that equation learning
methods can be used for model discovery and selection in many areas of biology
when an informative dataset is used. We focus on glioblastoma multiforme
modeling as a case study in this work to highlight how these results are
informative for data-driven modeling-based tumor invasion predictions.
A systems biology approach to COVID-19 progression in a population
Authors: Magdalena Djordjevic, Andjela Rodic, Igor Salom, Dusan Zigic, Ognjen Milicevic, Bojana Ilic, Marko Djordjevic
Date: 2020-05-19 00:00:00
Tasks: Epidemiology

A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.
Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
Authors: Jinxiang Song, Christian Häger, Jochen Schröder, Tim O'Shea, Henk Wymeersch
Date: 2020-05-19 00:00:00
Tasks: Benchmarking

End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO) systems has been shown to have the potential of exceeding the performance of engineered MIMO transceivers, without any a priori knowledge of communication-theoretic principles. In this work, we aim to understand to what extent and for which scenarios this claim holds true when comparing with fair benchmarks. We study closed-loop MIMO, open-loop MIMO, and multi-user MIMO and show that the gains of ML-based communication in the former two cases can be to a large extent ascribed to implicitly learned geometric shaping and bit and power allocation, not to learning new spatial encoders. For MU-MIMO, we demonstrate the feasibility of a novel method with centralized learning and decentralized executing, outperforming conventional zero-forcing. For each scenario, we provide explicit descriptions as well as open-source implementations of the selected neural-network architectures.