pwc-archive/papers-with-abstracts huggingface dataset:
Residual Neural Networks for Digital Predistortion
Authors: Yibo Wu, Ulf Gustavsson, Alexandre Graell i Amat, Henk Wymeersch
Date: 2020-05-12 00:00:00

Tracking the nonlinear behavior of an RF power amplifier (PA) is challenging. To tackle this problem, we build a connection between residual learning and the PA nonlinearity, and propose a novel residual neural network structure, referred to as the residual real-valued time-delay neural network (R2TDNN). Instead of learning the whole behavior of the PA, the R2TDNN focuses on learning its nonlinear behavior by adding identity shortcut connections between the input and output layer. In particular, we apply the R2TDNN to digital predistortion and measure experimental results on a real PA. Compared with neural networks recently proposed by Liu et al. and Wang et al., the R2TDNN achieves the best linearization performance in terms of normalized mean square error and adjacent channel power ratio with less or similar computational complexity. Furthermore, the R2TDNN exhibits significantly faster training speed and lower training error.
Life's mechanism
Authors: Simon Pierce
Date: 2020-05-12 00:00:00

The multifarious internal workings of organisms are difficult to reconcile with a single feature defining a state of being alive. Indeed, definitions of life rely on emergent properties (growth, capacity to evolve, agency) only symptomatic of intrinsic functioning. Empirical studies demonstrate that biomolecules including ratcheting or rotating enzymes and ribozymes undergo repetitive conformation state changes driven either directly or indirectly by thermodynamic gradients. They exhibit disparate structures, but govern processes relying on directional physical motion (DNA transcription, translation, cytoskeleton transport) and share the principle of repetitive uniplanar conformation changes driven by thermodynamic gradients, producing dependable unidirectional motion: heat engines exploiting thermodynamic disequilibria to perform work. Recognition that disparate biological molecules demonstrate conformation state changes involving directional motion, working in self-regulating networks, allows a mechanistic definition: life is a self-regulating process whereby matter undergoes cyclic, uniplanar conformation state changes that convert thermodynamic disequilibria into directed motion, performing work that locally reduces entropy. Living things are structures including an autonomous network of units exploiting thermodynamic gradients to drive uniplanar conformation state changes that perform work. These principles are independent of any specific chemical environment, and can be applied to other biospheres.
Communication, Renegotiation and Coordination with Private Values
Authors: Yuval Heller, Christoph Kuzmics
Date: 2020-05-12 00:00:00

An equilibrium is communication-proof if it is unaffected by new opportunities to communicate and renegotiate. We characterize the set of equilibria of coordination games with pre-play communication in which players have private preferences over the coordinated outcomes. The set of communication-proof equilibria is a small and relatively homogeneous subset of the set of qualitatively diverse Bayesian Nash equilibria. Under a communication-proof equilibrium, players never miscoordinate, play their jointly preferred outcome whenever there is one, and communicate only the ordinal part of their preferences. Moreover, such equilibria are robust to changes in players' beliefs and interim Pareto efficient
MPC-based Vibration Control and Energy Harvesting Using an Electromagnetic Vibration Absorber With Inertia Nonlinearity
Date: 2020-05-12 00:00:00

Simultaneous vibration control and energy harvesting of vehicle suspensions
has attracted great research interests over the past decades. However, existing
frameworks tradeoff suspension performance for energy recovery and are only
responsive to narrow-bandwidth vibrations. In this paper, a new
energy-regenerative vibration absorber (ERVA) using a ball-screw mechanism is
investigated. The ERVA system is based on a rotary electromagnetic generator
with adjustable nonlinear rotational inertia which passively increases the
moment of inertia as the vibration amplitude increases. This structure is
effective for energy harvesting and vibration control without increasing the
suspension size. Furthermore, a nonlinear model predictive controller (NMPC) is
applied to the system for further performance enhancement where we exploit road
profile information as a preview. The performance of NMPC-based ERVA is
demonstrated in a number of simulations and superior performance is
demonstrated.
Non-parametric Estimation of Quadratic Hawkes Processes for Order Book Events
Authors: Antoine Fosset, Jean-Philippe Bouchaud, Michael Benzaquen
Date: 2020-05-12 00:00:00

We propose an actionable calibration procedure for general Quadratic Hawkes models of order book events (market orders, limit orders, cancellations). One of the main features of such models is to encode not only the influence of past events on future events but also, crucially, the influence of past price changes on such events. We show that the empirically calibrated quadratic kernel is well described by a diagonal contribution (that captures past realised volatility), plus a rank-one "Zumbach" contribution (that captures the effect of past trends). We find that the Zumbach kernel is a power-law of time, as are all other feedback kernels. As in many previous studies, the rate of truly exogenous events is found to be a small fraction of the total event rate. These two features suggest that the system is close to a critical point -- in the sense that stronger feedback kernels would lead to instabilities.
Management of Electric Vehicles as Flexibility Resource for Optimized Integration of Renewable Energy with Large Buildings
Date: 2020-05-12 00:00:00
Tasks: Management

The increasing penetration of renewable generation requires new tools to
achieve a high matching between demand and renewable generation at building and
community levels. Large commercial and public buildings with parking lots have
a considerable potential to provide energy flexibility by controlling the
charging of Electric Vehicles (EVs) and injecting part of the stored energy
into the building, using Building-to-Vehicle (B2V) and Vehicle-to-Building
(V2B) systems. However, EVs and buildings do not often belong to the same
entity and in Portugal the existing regulation does not allow financial
transactions between buildings and EVs as separate entities. Addressing this
regulation hurdle requires innovative optimization methods for the
implementation of B2V/V2B systems. Moreover, the new legislation regarding the
self-consumption of renewable generation in Portugal enables the trade of
renewable generation surplus between buildings and the establishment of
renewable energy communities. This paper intends to address this issue and
proposes a formulation to aggregate and manage the sharing of generation
surplus between buildings, using EVs as a flexibility resource. The simulation
results showcase the achieved increase in renewable self-consumption at
building and community levels, as well as the reduction in electricity costs.
Instability of Defection in the Prisoner's Dilemma Under Best Experienced Payoff Dynamics
Authors: Srinivas Arigapudi, Yuval Heller, Igal Milchtaich
Date: 2020-05-12 00:00:00

We study population dynamics under which each revising agent tests each strategy k times, with each trial being against a newly drawn opponent, and chooses the strategy whose mean payoff was highest. When k = 1, defection is globally stable in the prisoner`s dilemma. By contrast, when k > 1 we show that there exists a globally stable state in which agents cooperate with probability between 28% and 50%. Next, we characterize stability of strict equilibria in general games. Our results demonstrate that the empirically plausible case of k > 1 can yield qualitatively different predictions than the case of k = 1 that is commonly studied in the literature.
Stability analysis of discrete-time LPV switched systems
Date: 2020-05-12 00:00:00
Tasks: Form

This paper addresses the stability problem for discrete-time switched systems
under autonomous switching. Each mode of the switched system is modeled as a
Linear Parameter Varying (LPV) system, the time-varying parameters can vary
arbitrarily fast and are represented in a polytopic form. The Lyapunov theory
is employed to get new conditions in the form of parameter-dependent LMIs. The
constructed Lyapunov function takes advantage of using an augmented state
vector with shifted states in its construction. In this sense, the Lyapunov
function employed in this paper can be viewed as a discrete-time LPV switched
Lyapunov function. Numerical experiments illustrate the efficacy of the
technique in providing stability certificates.
Understanding the principles of pattern formation driven by Notch signaling by integrating experiments and theoretical models
Authors: Federico Bocci, Jose Nelson Onuchic, Mohit Kumar Jolly
Date: 2020-05-12 00:00:00

Notch signaling is an evolutionary conserved cell-cell communication pathway. Besides regulating cell-fate decisions at an individual cell level, Notch signaling coordinates the emergent spatiotemporal patterning in a tissue through ligand-receptor interactions among transmembrane molecules of neighboring cells, as seen in embryonic development, angiogenesis, or wound healing. Due to its ubiquitous nature, Notch signaling is also implicated in several aspects of cancer progression, including tumor angiogenesis, stemness of cancer cells and cellular invasion. Here, we review experimental and computational models to help understand the operating principles of cell patterning driven by Notch signaling. First, we discuss the basic mechanisms of spatial patterning via canonical lateral inhibition and lateral induction mechanisms, including examples from angiogenesis, inner ear development and cancer metastasis. Next, we analyze additional layers of complexity in the Notch pathway such as the effects of varying cell sizes and shapes, ligand-receptor binding within the same cell, variable binding affinity of different ligand/receptor subtypes, and filopodia. Finally, we discuss some recent evidence of mechanosensitivity in the Notch pathway in driving collective epithelial cell migration and cardiovascular morphogenesis.
Projected gradient descent for non-convex sparse spike estimation
Authors: Yann Traonmilin, Jean-François Aujol, Arthur Leclaire
Date: 2020-05-12 00:00:00

We propose a new algorithm for sparse spike estimation from Fourier measurements. Based on theoretical results on non-convex optimization techniques for off-the-grid sparse spike estimation, we present a projected gradient descent algorithm coupled with a spectral initialization procedure. Our algorithm permits to estimate the positions of large numbers of Diracs in 2d from random Fourier measurements. We present, along with the algorithm, theoretical qualitative insights explaining the success of our algorithm. This opens a new direction for practical off-the-grid spike estimation with theoretical guarantees in imaging applications.
Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors
Authors: C. Rajashekar Reddy, T. Mukku, A. Dwivedi, A. Rout, S. Chaudhari, K. Vemuri, K. S. Rajan, A. M. Hussain
Date: 2020-05-12 00:00:00
Tasks: Spatial Interpolation

Current air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment. In addition, the data from these monitoring stations may not be easily accessible. This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes. For this, total nine low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are deployed in a small educational campus in Indian city of Hyderabad. Out of these, eight IoT nodes were developed at IIIT-H while one was bought off the shelf. A web based dashboard website is developed to easily monitor the real-time PM values. The data is collected from these nodes for more than five months. Different analyses such as correlation and spatial interpolation are done on the data to understand efficacy of dense deployment in better understanding the spatial variability and time-dependent changes to the local pollution indicators.
Moment Conditions for Dynamic Panel Logit Models with Fixed Effects
Authors: Bo E. Honoré, Martin Weidner
Date: 2020-05-12 00:00:00

This paper investigates the construction of moment conditions in discrete choice panel data with individual specific fixed effects. We describe how to systematically explore the existence of moment conditions that do not depend on the fixed effects, and we demonstrate how to construct them when they exist. Our approach is closely related to the numerical "functional differencing" construction in Bonhomme (2012), but our emphasis is to find explicit analytic expressions for the moment functions. We first explain the construction and give examples of such moment conditions in various models. Then, we focus on the dynamic binary choice logit model and explore the implications of the moment conditions for identification and estimation of the model parameters that are common to all individuals.
Socio-Economic Impacts of COVID-19 on Household Consumption and Poverty
Date: 2020-05-12 00:00:00
Tasks: Decision Making

The COVID-19 pandemic has caused a massive economic shock across the world
due to business interruptions and shutdowns from social-distancing measures. To
evaluate the socio-economic impact of COVID-19 on individuals, a micro-economic
model is developed to estimate the direct impact of distancing on household
income, savings, consumption, and poverty. The model assumes two periods: a
crisis period during which some individuals experience a drop in income and can
use their precautionary savings to maintain consumption; and a recovery period,
when households save to replenish their depleted savings to pre-crisis level.
The San Francisco Bay Area is used as a case study, and the impacts of a
lockdown are quantified, accounting for the effects of unemployment insurance
(UI) and the CARES Act federal stimulus. Assuming a shelter-in-place period of
three months, the poverty rate would temporarily increase from 17.1% to 25.9%
in the Bay Area in the absence of social protection, and the lowest income
earners would suffer the most in relative terms. If fully implemented, the
combination of UI and CARES could keep the increase in poverty close to zero,
and reduce the average recovery time, for individuals who suffer an income
loss, from 11.8 to 6.7 months. However, the severity of the economic impact is
spatially heterogeneous, and certain communities are more affected than the
average and could take more than a year to recover. Overall, this model is a
first step in quantifying the household-level impacts of COVID-19 at a regional
scale. This study can be extended to explore the impact of indirect
macroeconomic effects, the role of uncertainty in households' decision-making
and the potential effect of simultaneous exogenous shocks (e.g., natural
disasters).
Quadratic Hedging for Sequential Claims with Random Weights in Discrete Time
Date: 2020-05-12 00:00:00

We study a quadratic hedging problem for a sequence of contingent claims with random weights in discrete time. We obtain the optimal hedging strategy explicitly in a recursive representation, without imposing the non-degeneracy (ND) condition on the model and square integrability on hedging strategies. We relate the general results to hedging under random horizon and fair pricing in the quadratic sense. We illustrate the significance of our results in an example in which the ND condition fails.
Detection of cellular micromotion by advanced signal processing
Authors: Stephan Rinner, Alberto Trentino, Heike Url, M. Florian G. Burger, Julian von Lautz, Bernhard Wolfrum, Friedemann Reinhard
Date: 2020-05-12 00:00:00

Cellular micromotion - a tiny movement of cell membranes on the nanometer-micrometer scale - has been proposed as a pathway for inter-cellular signal transduction and as a label-free proxy signal to neural activity. Here we harness several recent approaches of signal processing to detect such micromotion in video recordings of unlabeled cells. Our survey includes spectral filtering of the video signal, matched filtering, as well as 1D and 3D convolutional neural networks acting on pixel-wise time-domain data and a whole recording respectively.
Kaplan-Meier type survival curves for COVID-19: a health data based decision-making tool
Date: 2020-09-28 00:00:00
Tasks: Decision Making

Countries are recording health information on the global spread of COVID-19
using different methods, sometimes changing the rules after a few days. They
are all publishing the number of new individuals infected, cured and dead,
along with some supplementary data. These figures are often recorded in a
non-uniform manner and do not match the standard definitions of these
variables. However, in this paper we show that the Kaplan-Meier curves
calculated with them could provide useful information about the dynamics of the
disease in different countries. Our aim is to present a robust and simple model
to show certain characteristics of the evolution of the dynamic process,
showing that the differences of evolution among the countries is reflected in
the corresponding Kaplan-Meier-type curves. We compare the curves obtained for
the most affected countries so far, proposing possible interpretations of the
properties that distinguish them.
A Cyber Physical System Framework for UAV Communications
Authors: Haijun Wang, Haitao Zhao, Dongtang Ma, Jibo Wei
Date: 2020-05-13 00:00:00

Diverse applications have witnessed the prevalence of unmanned aerial vehicles (UAVs) due to their agility and versatility. Compared with computation and control, the communication tends to be the bottleneck of the whole UAV system. Cyber physical system (CPS), which achieves the integration of the cyber and physical domains, can inspire us to deal with the communication problems through a cross-disciplinary method. To this end, we first expound the coupling effects of computation and control to communication. Then, we propose a novel CPS framework for UAV communications. By extending the dimension of communication decisions to computation and control, the framework can precisely orient and settle the communication issues. Further, a quantitative energy optimization model is established to guide the protocol and algorithm design for UAV communications. Case simulation results validate the CPS framework in terms of the energy consumption and communication delay.
Inequality, a scourge of the XXI century
Authors: José Roberto Iglesias, Ben-Hur Francisco Cardoso, Sebastián Gonçalves
Date: 2020-05-13 00:00:00

Social and economic inequality is a plague of the XXI Century. It is continuously widening, as the wealth of a relatively small group increases and, therefore, the rest of the world shares a shrinking fraction of resources. This situation has been predicted and denounced by economists and econophysicists. The latter ones have widely used models of market dynamics which consider that wealth distribution is the result of wealth exchanges among economic agents. A simple analogy relates the wealth in a society with the kinetic energy of the molecules in a gas, and the trade between agents to the energy exchange between the molecules during collisions. However, while in physical systems, thanks to the equipartition of energy, the gas eventually arrives at an equilibrium state, in many exchange models the economic system never equilibrates. Instead, it moves toward a "condensed" state, where one or a few agents concentrate all the wealth of the society and the rest of agents shares zero or a very small fraction of the total wealth. Here we discuss two ways of avoiding the "condensed" state. On one hand, we consider a regulatory policy that favors the poorest agent in the exchanges, thus increasing the probability that the wealth goes from the richest to the poorest agent. On the other hand, we study a tax system and its effects on wealth distribution. We compare the redistribution processes and conclude that complete control of the inequalities can be attained with simple regulations or interventions.
Metapopulation network models for understanding, predicting and managing the coronavirus disease COVID-19
Date: 2020-06-16 00:00:00

Mathematical models of SARS-CoV-2 spread are used for guiding the design of
mitigation steps aimed at containing and decelerating the contagion, and at
identifying impending breaches of health care system surge capacity. The
challenges of having only lacunary information about daily new infections are
compounded by the geographic heterogeneity of the population. To address this
problem, we propose to account for the differences between rural and urban
settings using network-based, distributed models where the spread of the
pandemic is described in distinct local cohorts with nested SEIR models. The
setting of the model parameters takes into account the fact that SARS-CoV-2
transmission occurs mostly via human-to-human contact, and that the frequency
of contact among individuals differs between urban and rural areas, and may
change over time. Moreover, the probability that the virus spreads into an
uninfected community is associated with influx of individuals from other
communities where the infection is present. To account for these important
aspects, each node of the network is characterized by the frequency of contact
between its members and by its level of connectivity with other nodes. Census
and cell phone data can be used to set up the adjacency matrix of the network,
which can, in turn, be modified to account for different levels of mitigation
measures. In order to make the network SEIR model that we propose easy to
customize, it is formulated in terms of easily interpretable parameters that
can be estimated from available community level data. The models parameters are
estimated with Bayesian techniques using COVID-19 data for the states of Ohio
and Michigan. The network model also gives rise to a geographically distributed
computational model that explains the geographic dynamics of the contagion,
e.g., in larger cities surrounded by suburban and rural areas.
Parallel Learning of Koopman Eigenfunctions and Invariant Subspaces For Accurate Long-Term Prediction
Authors: Masih Haseli, Jorge Cortés
Date: 2020-05-13 00:00:00

We present a parallel data-driven strategy to identify finite-dimensional functional spaces invariant under the Koopman operator associated to an unknown dynamical system. We build on the Symmetric Subspace Decomposition (SSD) algorithm, a centralized method that under mild conditions on data sampling provably finds the maximal Koopman-invariant subspace and all Koopman eigenfunctions in an arbitrary finite-dimensional functional space. A network of processors, each aware of a common dictionary of functions and equipped with a local set of data snapshots, repeatedly interact over a directed communication graph. Each processor receives its neighbors' estimates of the invariant dictionary and refines its estimate by applying SSD with its local data on the intersection of the subspaces spanned by its own dictionary and the neighbors' dictionaries. We identify conditions on the network topology to ensure the algorithm identifies the maximal Koopman-invariant subspace in the span of the original dictionary, characterize its time, computational, and communication complexity, and establish its robustness against communication failures.