pwc-archive/papers-with-abstracts huggingface dataset:
Braitenberg Vehicles as Developmental Neurosimulation
Authors: Stefan Dvoretskii, Ziyi Gong, Ankit Gupta, Jesse Parent, Bradly Alicea
Date: 2020-02-28 00:00:00
Tasks: Developmental Learning

Connecting brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. However, this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We introduce three alternate approaches to demonstrate how developmental embodied agents can be implemented. The resulting developmental BVs (dBVs) will generate behaviors ranging from stimulus responses to group behavior that resembles collective motion. We will situate this work in the domain of artificial brain networks along with broader themes such as embodied cognition, feedback, and emergence. Our perspective is exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we consider future applications of the developmental neurosimulation approach.
$N-1$ Reliability Makes It Difficult for False Data Injection Attacks to Cause Physical Consequences
Date: 2020-03-14 00:00:00

This paper demonstrates that false data injection (FDI) attacks are extremely
limited in their ability to cause physical consequences on $N-1$ reliable power
systems operating with real-time contingency analysis (RTCA) and security
constrained economic dispatch (SCED). Prior work has shown that FDI attacks can
be designed via an attacker-defender bi-level linear program (ADBLP) to cause
physical overflows after re-dispatch using DCOPF. In this paper, it is shown
that attacks designed using DCOPF fail to cause overflows on $N-1$ reliable
systems because the system response modeled is inaccurate. An ADBLP that
accurately models the system response is proposed to find the worst-case
physical consequences, thereby modeling a strong attacker with system level
knowledge. Simulation results on the synthetic Texas system with 2000 buses
show that even with the new enhanced attacks, for systems operated
conservatively due to $N-1$ constraints, the designed attacks only lead to
post-contingency overflows. Moreover, the attacker must control a large portion
of measurements and physically create a contingency in the system to cause
consequences. Therefore, it is conceivable but requires an extremely
sophisticated attacker to cause physical consequences on $N-1$ reliable power
systems operated with RTCA and SCED.
Polynomial Superlevel Set Representation of the Multistationarity Region of Chemical Reaction Networks
Authors: AmirHosein Sadeghimanesh, Matthew England
Date: 2020-03-17 00:00:00

In this paper we introduce a new representation for the multistationarity region of a reaction network, using polynomial superlevel sets. The advantages of using this polynomial superlevel set representation over the already existing representations (cylindrical algebraic decompositions, numeric sampling, rectangular divisions) is discussed, and algorithms to compute this new representation are provided. The results are given for the general mathematical formalism of a parametric system of equations and so may be applied to other application domains.
Day Level Forecasting for Coronavirus Disease (COVID-19) Spread: Analysis, Modeling and Recommendations
Date: 2020-03-15 00:00:00
Tasks: Time Series, Time Series Analysis

In mid of March 2020, Coronaviruses such as COVID-19 is declared as an
international epidemic. More than 125000 confirmed cases and 4,607 death cases
have been recorded around more than 118 countries. Unfortunately, a coronavirus
vaccine is expected to take at least 18 months if it works at all. Moreover,
COVID -19 epidemics can mutate into a more aggressive form. Day level
information about the COVID -19 spread is crucial to measure the behavior of
this new virus globally. Therefore, this study presents a comparison of day
level forecasting models on COVID-19 affected cases using time series models
and mathematical formulation. The forecasting models and data strongly suggest
that the number of coronavirus cases grows exponentially in countries that do
not mandate quarantines, restrictions on travel and public gatherings, and
closing of schools, universities, and workplaces (Social Distancing).
On the gene expression landscape of cancer
Authors: Augusto Gonzalez, Yasser Perera, Rolando Perez
Date: 2020-03-17 00:00:00

A principal component analysis of the TCGA data for 15 cancer localizations unveils the following qualitative facts about tumors: 1) The state of a tissue in gene expression space may be described by a few variables. In particular, there is a single variable describing the progression from a normal tissue to a tumor. 2) Each cancer localization is characterized by a gene expression profile, in which genes have specific weights in the definition of the cancer state. There are no less than 2500 differentially-expressed genes, which lead to power-like tails in the expression distribution functions. 3) Tumors in different localizations share hundreds or even thousands of differentially expressed genes. There are 6 genes common to the 15 studied tumor localizations. 4) The tumor region is a kind of attractor. Tumors in advanced stages converge to this region independently of patient age or genetic variability. 5) There is a landscape of cancer in gene expression space with an approximate border separating normal tissues from tumors.
NISE Estimation of an Economic Model of Crime
Date: 2020-03-17 00:00:00
Tasks: model, valid

An economic model of crime is used to explore the consistent estimation of a
simultaneous linear equation without recourse to instrumental variables. A
maximum-likelihood procedure (NISE) is introduced, and its results are compared
to ordinary least squares and two-stage least squares. The paper is motivated
by previous research on the crime model and by the well-known practical problem
that valid instruments are frequently unavailable.
Longitudinal Control of Vehicles in Traffic Microsimulation
Date: 2020-03-17 00:00:00
Tasks: Autonomous Vehicles

Current state-of-art traffic microsimulation tools cannot accurately estimate
safety, efficiency, and mobility benefits of automated driving systems and
vehicle connectivity because of not considering physical and powertrain
characteristics of vehicles and resistance forces. This paper proposes
realistic longitudinal control functions for autonomous vehicles with and
without vehicle-to-vehicle communications and a realistic vehicle-following
model for human-driven vehicles, considering driver characteristics and vehicle
dynamics. Conventional longitudinal control functions apply a constant time gap
policy and use empirical constant controller coefficients, potentially
sacrificing safety or reducing throughput. Proposed longitudinal control
functions calculate minimum safe time gaps at each simulation time step and
tune controller coefficients at each simulation time step during acceleration
and deceleration to maximize throughput without compromising safety.
Pricing with Variance Gamma Information
Authors: Lane P. Hughston, Leandro Sánchez-Betancourt
Date: 2020-03-17 00:00:00

In the information-based pricing framework of Brody, Hughston and Macrina, the market filtration $\{ \mathcal F_t\}_{t\geq 0}$ is generated by an information process $\{ \xi_t\}_{t\geq0}$ defined in such a way that at some fixed time $T$ an $\mathcal F_T$-measurable random variable $X_T$ is "revealed". A cash flow $H_T$ is taken to depend on the market factor $X_T$, and one considers the valuation of a financial asset that delivers $H_T$ at $T$. The value $S_t$ of the asset at any time $t\in[0,T)$ is the discounted conditional expectation of $H_T$ with respect to $\mathcal F_t$, where the expectation is under the risk neutral measure and the interest rate is constant. Then $S_{T^-} = H_T$, and $S_t = 0$ for $t\geq T$. In the general situation one has a countable number of cash flows, and each cash flow can depend on a vector of market factors, each associated with an information process. In the present work, we construct a new class of models for the market filtration based on the variance-gamma process. The information process is obtained by subordinating a particular type of Brownian random bridge with a gamma process. The filtration is taken to be generated by the information process together with the gamma bridge associated with the gamma subordinator. We show that the resulting extended information process has the Markov property and hence can be used to price a variety of different financial assets, several examples of which are discussed in detail.
State of Charge Estimation of Parallel Connected Battery Cells via Descriptor System Theory
Date: 2020-03-17 00:00:00

This manuscript presents an algorithm for individual Lithium-ion (Li-ion)
battery cell state of charge (SOC) estimation when multiple cells are connected
in parallel, using only terminal voltage and total current measurements. For
battery packs consisting of thousands of cells, it is desirable to estimate
individual SOCs by only monitoring the total current in order to reduce sensing
cost. Mathematically, series connected cells yield dynamics given by ordinary
differential equations under classical full voltage sensing. In contrast,
parallel connected cells are evidently more challenging because the dynamics
are governed by a nonlinear descriptor system, including differential equations
and algebraic equations arising from voltage and current balance across cells.
An observer with linear output error injection is formulated, where the
individual cell SOCs and local currents are locally observable from the total
current and voltage measurements. The asymptotic convergence of differential
and algebraic states is established by considering local Lipschitz continuity
property of system nonlinearities. Simulation results on LiNiMnCoO$_2$/Graphite
(NMC) cells illustrate convergence for SOCs, local currents, and terminal
voltage.
Options on infectious diseases
Authors: Andrew Lesniewski, Nicholas Lesniewski
Date: 2020-03-18 00:00:00
Tasks: Epidemiology

We present a parsimonious stochastic model for valuation of options on the fraction of infected individuals during an epidemic. The underlying stochastic dynamical system is a stochastic differential version of the SIR model of mathematical epidemiology.
A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety
Date: 2020-03-18 00:00:00

In this paper we seek to quantify the ability of learning to improve safety
guarantees endowed by Control Barrier Functions (CBFs). In particular, we
investigate how model uncertainty in the time derivative of a CBF can be
reduced via learning, and how this leads to stronger statements on the safe
behavior of a system. To this end, we build upon the idea of Input-to-State
Safety (ISSf) to define Projection-to-State Safety (PSSf), which characterizes
degradation in safety in terms of a projected disturbance. This enables the
direct quantification of both how learning can improve safety guarantees, and
how bounds on learning error translate to bounds on degradation in safety. We
demonstrate that a practical episodic learning approach can use PSSf to reduce
uncertainty and improve safety guarantees in simulation and experimentally.
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles
Date: 2020-03-18 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning methods have been widely used in recent years for
autonomous vehicle's decision-making. A key issue is that deep neural networks
can be fragile to adversarial attacks or other unseen inputs. In this paper, we
address the latter issue: we focus on generating socially acceptable
perturbations (SAP), so that the autonomous vehicle (AV agent), instead of the
challenging vehicle (attacker), is primarily responsible for the crash. In our
process, one attacker is added to the environment and trained by deep
reinforcement learning to generate the desired perturbation. The reward is
designed so that the attacker aims to fail the AV agent in a socially
acceptable way. After training the attacker, the agent policy is evaluated in
both the original naturalistic environment and the environment with one
attacker. The results show that the agent policy which is safe in the
naturalistic environment has many crashes in the perturbed environment.
Ethnic Groups' Access to State Power and Group Size
Date: 2020-03-18 00:00:00

Many countries are ethnically diverse. However, despite the benefits of
ethnic heterogeneity, ethnic-based political inequality and discrimination are
pervasive. Why is this? This study suggests that part of the variation in
ethnic-based political inequality depends on the relative size of ethnic groups
within each country. Using group-level data for 569 ethnic groups in 175
countries from 1946 to 2017, I find evidence of an inverted-U-shaped
relationship between an ethnic group's relative size and its access to power.
This single-peaked relationship is robust to many alternative specifications,
and a battery of robustness checks suggests that relative size influences
access to power. Through a very simple model, I propose an explanation based on
an initial high level of political inequality, and on the incentives that more
powerful groups have to continue limiting other groups' access to power. This
explanation incorporates essential elements of several existing theories on the
relationship between group size and discrimination, and suggests a new
empirical prediction: the single-peaked pattern should be weaker in countries
where political institutions have historically been less open. This additional
prediction is supported by the data.
Dynamic Regret Analysis for Online Tracking of Time-varying Structural Equation Model Topologies
Date: 2020-03-18 00:00:00

Identifying dependencies among variables in a complex system is an important
problem in network science. Structural equation models (SEM) have been used
widely in many fields for topology inference, because they are tractable and
incorporate exogenous influences in the model. Topology identification based on
static SEM is useful in stationary environments; however, in many applications
a time-varying underlying topology is sought. This paper presents an online
algorithm to track sparse time-varying topologies in dynamic environments and
most importantly, performs a detailed analysis on the performance guarantees.
The tracking capability is characterized in terms of a bound on the dynamic
regret of the proposed algorithm. Numerical tests show that the proposed
algorithm can track changes under different models of time-varying topologies.
Short-term predictions and prevention strategies for COVID-19: A model based study
Date: 2020-07-22 00:00:00
Tasks: Management

An outbreak of respiratory disease caused by a novel coronavirus is ongoing
from December 2019. As of July 22, 2020, it has caused an epidemic outbreak
with more than 15 million confirmed infections and above 6 hundred thousand
reported deaths worldwide. During this period of an epidemic when
human-to-human transmission is established and reported cases of coronavirus
disease 2019 (COVID-19) are rising worldwide, investigation of control
strategies and forecasting are necessary for health care planning. In this
study, we propose and analyze a compartmental epidemic model of COVID-19 to
predict and control the outbreak. The basic reproduction number and control
reproduction number are calculated analytically. A detailed stability analysis
of the model is performed to observe the dynamics of the system. We calibrated
the proposed model to fit daily data from the United Kingdom (UK) where the
situation is still alarming. Our findings suggest that independent
self-sustaining human-to-human spread ($R_0>1$, $R_c>1$) is already present.
Short-term predictions show that the decreasing trend of new COVID-19 cases is
well captured by the model. Further, we found that effective management of
quarantined individuals is more effective than management of isolated
individuals to reduce the disease burden. Thus, if limited resources are
available, then investing on the quarantined individuals will be more fruitful
in terms of reduction of cases.
Optimization of Rate Fairness in Multi-Pair Wireless-Powered Relaying Systems
Date: 2020-03-18 00:00:00
Tasks: Fairness

This letter considers a multi-pair decode-and-forward relay network where a
power-splitting (PS) protocol is adopted at the energy-constrained relay to
provide simultaneous wireless information and energy harvesting (EH). To
achieve higher efficiency of EH, we propose a new PS-based EH architecture at
the relay by incorporating an alternating current (AC) computing logic, which
is employed to directly use the wirelessly harvested AC energy for
computational blocks. Under a nonlinear EH circuit, our goal is to maximize the
fairness of end-to-end rate among user pairs subject to power constraints,
resulting in a nonconvex problem. We propose an iterative algorithm to achieve
a suboptimal and efficient solution to this challenging problem by leveraging
the inner approximation framework. Numerical results demonstrate that the
proposed algorithm outperforms the traditional direct current computing and
other baseline schemes.
Distributed Control of Multi-zone HVAC Systems Considering Indoor Air Quality
Date: 2020-03-17 00:00:00
Tasks: Management

This paper studies a scalable control method for multi-zone heating, ventilation and air-conditioning (HVAC) systems to optimize the energy cost for maintaining thermal comfort and indoor air quality (IAQ) (represented by CO2) simultaneously. This problem is computationally challenging due to the complex system dynamics, various spatial and temporal couplings as well as multiple control variables to be coordinated. To address the challenges, we propose a two-level distributed method (TLDM) with a upper level and lower level control integrated. The upper level computes zone mass flow rates for maintaining zone thermal comfort with minimal energy cost, and then the lower level strategically regulates zone mass flow rates and the ventilation rate to achieve IAQ while preserving the near energy saving performance of upper level. As both the upper and lower level computation are deployed in a distributed manner, the proposed method is scalable and computationally efficient. The near-optimal performance of the method in energy cost saving is demonstrated through comparison with the centralized method. In addition, the comparisons with the existing distributed method show that our method can provide IAQ with only little increase of energy cost while the latter fails. Moreover, we demonstrate our method outperforms the demand controlled ventilation strategies (DCVs) for IAQ management with about 8-10% energy cost reduction.
Controllability Analysis of Functional Brain Networks
Date: 2020-03-18 00:00:00
Tasks: Emotional Intelligence

Network control theory has recently emerged as a promising approach for
understanding brain function and dynamics. By operationalizing notions of
control theory for brain networks, it offers a fundamental explanation for how
brain dynamics may be regulated by structural connectivity. While powerful, the
approach does not currently consider other non-structural explanations of brain
dynamics. Here we extend the analysis of network controllability by formalizing
the evolution of neural signals as a function of effective inter-regional
coupling and pairwise signal covariance. We find that functional
controllability characterizes a region's impact on the capacity for the whole
system to shift between states, and significantly predicts individual
difference in performance on cognitively demanding tasks including those task
working memory, language, and emotional intelligence. When comparing
measurements from functional and structural controllability, we observed
consistent relations between average and modal controllability, supporting
prior work. In the same comparison, we also observed distinct relations between
controllability and synchronizability, reflecting the additional information
obtained from functional signals. Our work suggests that network control theory
can serve as a systematic analysis tool to understand the energetics of brain
state transitions, associated cognitive processes, and subsequent behaviors.
Vision-Based Autonomous Driving: A Model Learning Approach
Date: 2020-03-18 00:00:00
Tasks: Autonomous Driving, model

We present an integrated approach for perception and control for an
autonomous vehicle and demonstrate this approach in a high-fidelity urban
driving simulator. Our approach first builds a model for the environment, then
trains a policy exploiting the learned model to identify the action to take at
each time-step. To build a model for the environment, we leverage several deep
learning algorithms. To that end, first we train a variational autoencoder to
encode the input image into an abstract latent representation. We then utilize
a recurrent neural network to predict the latent representation of the next
frame and handle temporal information. Finally, we utilize an
evolutionary-based reinforcement learning algorithm to train a controller based
on these latent representations to identify the action to take. We evaluate our
approach in CARLA, a high-fidelity urban driving simulator, and conduct an
extensive generalization study. Our results demonstrate that our approach
outperforms several previously reported approaches in terms of the percentage
of successfully completed episodes for a lane keeping task.
Experimental Design under Network Interference
Authors: Davide Viviano
Date: 2020-03-18 00:00:00
Tasks: Experimental Design

This paper studies the design of two-wave experiments in the presence of spillover effects when the researcher aims to conduct precise inference on treatment effects. We consider units connected through a single network, local dependence among individuals, and a general class of estimands encompassing average treatment and average spillover effects. We introduce a statistical framework for designing two-wave experiments with networks, where the researcher optimizes over participants and treatment assignments to minimize the variance of the estimators of interest, using a first-wave (pilot) experiment to estimate the variance. We derive guarantees for inference on treatment effects and regret guarantees on the variance obtained from the proposed design mechanism. Our results illustrate the existence of a trade-off in the choice of the pilot study and formally characterize the pilot's size relative to the main experiment. Simulations using simulated and real-world networks illustrate the advantages of the method.
A Variational Analysis Approach to Solving the Merton Problem
Authors: Ali Al-Aradi, Sebastian Jaimungal
Date: 2020-03-18 00:00:00

We address the Merton problem of maximizing the expected utility of terminal wealth using techniques from variational analysis. Under a general continuous semimartingale market model with stochastic parameters, we obtain a characterization of the optimal portfolio for general utility functions in terms of a forward-backward stochastic differential equation (FBSDE) and derive solutions for a number of well-known utility functions. Our results complement a previous studies conducted on optimal strategies in markets driven by Brownian noise with random drift and volatility parameters.