pwc-archive/papers-with-abstracts huggingface dataset:
Growth Dynamics of Value and Cost Trade-off in Temporal Networks
Authors: Sheida Hasani, Razieh Masoomi, Jamshid Ardalankia, Mohammadbashir Sedighi, Hamid Jafari
Date: 2019-08-29 00:00:00

The question is: What does happen to the real-world networks which cause them not to grow permanently? The idea here is that real-world networks have to pay the cost of growth. We investigate the growth and trade-off between value and cost in the networks with cost and preferential attachment together. Since the preferential attachment in the BA model does not consider any stop against the infinite growth of networks, we introduce a modified version of preferential attachment of the BA model. This idea makes sense because the growth of real networks may be finite. In the present study, by combining preferential attachment in the science of temporal networks (interval graphs), and, the first-order differential equations of value and cost of making links, the future equilibrium of an evolving network is illustrated. During the process of achieving a winning position, the variables against growth such as the competition cost, besides the internally structural cost may emerge. In the end, by applying this modified model, we found the circumstances in which a trade-off between value and cost emerges.
Three errors and two problems in a recent paper: gazenet: End-to-end eye-movement event detection with deep neural networks (Zemblys, Niehorster, and Holmqvist, 2019)
Date: 2019-12-20 00:00:00
Tasks: Event Detection

Zemblys et al. \cite{gazeNet} reported on a method for the classification of
eye-movements ("gazeNet"). I have found 3 errors and two problems with that
paper that are explained herein. \underline{\textit{\textbf{Error 1}}} The
gazeNet classification method was built assuming that a hand-scored dataset
from Lund University was all collected at 500 Hz, but in fact, six of the 34
recording files were actually collected at 200Hz. Of the six datasets that were
used as the training set for the gazeNet algorithm, 2 were actually collected
at 200Hz. \underline{\textit{\textbf{Problem 1}}} has to do with the fact that
even among the 500Hz data, the inter-timestamp intervals varied widely.
\underline{\textit{\textbf{Problem 2}}} is that there are many unusual
discontinuities in the saccade trajectories from the Lund University dataset
that make it a very poor choice for the construction of an automatic
classification method. \underline{\textit{\textbf{Error 2}}} The gazeNet
algorithm was trained on the Lund dataset, and then compared to other methods,
not trained on this dataset, in terms of performance on this dataset. This is
an inherently unfair comparison, and yet no where in the gazeNet paper is this
unfairness mentioned. \underline{\textit{\textbf{Error 3}}} arises out of the
novel event-related agreement analysis employed by the gazeNet authors.
Although the authors intended to classify unmatched events as either false
positives or false negatives, many are actually being classified as true
negatives. True negatives are not errors, and any unmatched event misclassified
as a true negative is actually driving kappa higher, whereas unmatched events
should be driving kappa lower.
A Worst-Case Performance Optimization Based Design Approach to Robust Symbol-Level Precoding for Downlink MU-MIMO
Date: 2019-08-29 00:00:00
Tasks: Robust Design

This paper addresses the optimization problem of symbol-level precoding (SLP)
in the downlink of a multiuser multiple-input multiple-output (MU-MIMO)
wireless system while the precoder's output is subject to partially-known
distortions. In particular, we assume a linear distortion model with bounded
additive noise. The original signal-to-interference-plus-noise ratio (SINR)
-constrained SLP problem minimizing the total transmit power is first
reformulated as a penalized unconstrained problem, which is referred to as the
relaxed robust formulation. We then adopt a worst-case design approach to
protect the users' intended symbols and the targeted constructive interference
with a desired level of confidence. Due to the non-convexity of the relaxed
robust formulation, we propose an iterative algorithm based on the block
coordinate ascent-descent method. We show through simulation results that the
proposed robust design is flexible in the sense that the CI constraints can be
relaxed so as to keep a desirable balance between achievable rate and power
consumption. Remarkably, the new formulation yields more energy-efficient
solutions for appropriate choices of the penalty parameter, compared to the
original problem.
A Soft Range Limited K-Nearest Neighbours Algorithm for Indoor Localization Enhancement
Date: 2019-08-29 00:00:00
Tasks: Indoor Localization

This paper proposes a soft range limited K nearest neighbours (SRL-KNN)
localization fingerprinting algorithm. The conventional KNN determines the
neighbours of a user by calculating and ranking the fingerprint distance
measured at the unknown user location and the reference locations in the
database. Different from that method, SRL-KNN scales the fingerprint distance
by a range factor related to the physical distance between the user's previous
position and the reference location in the database to reduce the spatial
ambiguity in localization. Although utilizing the prior locations, SRL-KNN does
not require knowledge of the exact moving speed and direction of the user.
Moreover, to take into account of the temporal fluctuations of the received
signal strength indicator (RSSI), RSSI histogram is incorporated into the
distance calculation. Actual on-site experiments demonstrate that the new
algorithm achieves an average localization error of $0.66$ m with $80\%$ of the
errors under $0.89$ m, which outperforms conventional KNN algorithms by $45\%$
under the same test environment.
The economics of minority language use: theory and empirical evidence for a language game model
Date: 2019-08-30 00:00:00
Tasks: Diversity

Language and cultural diversity is a fundamental aspect of the present world.
We study three modern multilingual societies -- the Basque Country, Ireland and
Wales -- which are endowed with two, linguistically distant, official
languages: $A$, spoken by all individuals, and $B$, spoken by a bilingual
minority. In the three cases it is observed a decay in the use of minoritarian
$B$, a sign of diversity loss. However, for the "Council of Europe" the key
factor to avoid the shift of $B$ is its use in all domains. Thus, we
investigate the language choices of the bilinguals by means of an evolutionary
game theoretic model. We show that the language population dynamics has reached
an evolutionary stable equilibrium where a fraction of bilinguals have shifted
to speak $A$. Thus, this equilibrium captures the decline in the use of $B$. To
test the theory we build empirical models that predict the use of $B$ for each
proportion of bilinguals. We show that model-based predictions fit very well
the observed use of Basque, Irish, and Welsh.
Sign-sensitivities for reaction networks: an algebraic approach
Date: 2019-08-30 00:00:00

This paper presents an algebraic framework to study sign-sensitivities for
reaction networks modeled by means of systems of ordinary differential
equations. Specifically, we study the sign of the derivative of the
concentrations of the species in the network at steady state with respect to a
small perturbation on the parameter vector. We provide a closed formula for the
derivatives that accommodates common perturbations, and illustrate its form
with numerous examples. We argue that, mathematically, the study of the
response to the system with respect to changes in total amounts is not well
posed, and that one should rather consider perturbations with respect to the
initial conditions. We find a sign-based criterion to determine, without
computing the sensitivities, whether the sign depends on the steady state and
parameters of the system. This is based on earlier results of so-called
injective networks. Finally, we address systems with multiple steady states and
the restriction to stable steady states.
Torus Breakdown in a Uni Junction Memristor
Date: 2019-07-29 00:00:00

Experimental study of a uni junction transistor (UJT) has enabled to show
that this electronic component has the same features as the so-called
"memristor". So, we have used the memristor's direct current (DC) vM--iM
characteristic for modeling the UJT's DC current--voltage characteristic. This
has led us to confirm on the one hand, that the UJT is a memristor and, on the
other hand, to propose a new four-dimensional autonomous dynamical system
allowing to describe experimentally observed phenomena such as the transition
from a limit cycle to torus breakdown.
Identification of Switched Autoregressive and Switched Autoregressive Exogenous Systems from Large Noisy Data Sets
Date: 2019-08-29 00:00:00

The paper introduces novel methodologies for the identification of
coefficients of switched autoregressive and switched autoregressive exogenous
linear models. We consider cases which system's outputs are contaminated by
possibly large values of noise for the both case of measurement noise in
switched autoregressive models and process noise in switched autoregressive
exogenous models. It is assumed that only partial information on the
probability distribution of the noise is available. Given input-output data, we
aim at identifying switched system coefficients and parameters of the
distribution of the noise, which are compatible with the collected data. We
demonstrate the efficiency of the proposed approach with several academic
examples. The method is shown to be extremely effective in the situations where
a large number of measurements is available; cases in which previous approaches
based on polynomial or mixed-integer optimization cannot be applied due to very
large computational burden.
A Continuous Effective Model of the Protein Dynamics
Date: 2019-08-30 00:00:00
Tasks: model

The theory of elastic rods can be used to describe certain geometric and
topological properties of the DNA molecules. A similar effective field theory
approach was previously suggested to describe the conformations and dynamics of
proteins. In this letter we report a detailed study of the basic features of a
version of the proposed model, which assumes proteins to be very long
continuous curves. In the most appealing case, the model is based on a
potential with a pair of minima corresponding to helical and strand-like
configurations of the curves. It allows to derive several predictions about the
geometric features of the molecules, and we show that the predictions are
compatible with the phenomenology. While the helices represent the ground state
configurations, the abundance of beta strands is controlled by a parameter,
which can either completely suppress their presence in a molecule, or make them
abundant. The few-parameter model investigated in the letter rather represents
a universality class of protein molecules. Generalizations accounting for the
discrete nature and inhomogeneity of the molecules presumably allow to model
realistic cases.
bcd mRNA active transport: 3D agent-based modeling
Authors: Marat A. Sabirov, Victoria Yu. Samuta, Alexander V. Spirov
Date: 2019-08-30 00:00:00

The article presents a 3D agent-based model to simulate the dynamics of redistribution of bicoid mRNA. The model is developed on the basis of the Skeledyne software [Odell and Foe, 2008]. The model reproduces active transport by molecular motors over an undirected network of short microtubules. The model is intended to simulate the data presented in [Spirov et al., 2009; Little et al., 2011; Fahmy et al., 2014; Ali-Murthy and Kornberg, 2016; Cai, Spirov, Akber et al., 2017].
Implicitly Modeling Frequency Control within Power Flow
Date: 2019-08-30 00:00:00

In this paper, we extend a circuit-based, current-voltage power flow
formulation to include frequency deviations and implicitly model generator
primary and secondary control actions as a function of their temporal
dependence. This includes extending the slack bus generator model(s) to better
represents its true behavior with frequency controls. These implicit models
obviate the need for outer iteration loops and improve the robustness of the
simulation convergence when frequency deviations are considered. The simulation
framework is highly scalable and is demonstrated on 85k+ bus systems.
Network Mechanism for Insect Olfaction
Authors: Pamela B Pyzza, Katherine A Newhall, Douglas Zhou, Gregor Kovacic, David Cai
Date: 2019-08-30 00:00:00

Early olfactory pathway responses to the presentation of an odor exhibit remarkably similar dynamical behavior across phyla from insects to mammals, and frequently involve transitions among quiescence, collective network oscillations, and asynchronous firing. We hypothesize that the time scales of fast excitation and fast and slow inhibition present in these networks may be the essential element underlying this similar behavior, and design an idealized, conductance-based integrate-and-fire (I&F) model to verify this hypothesis via numerical simulations. To better understand the mathematical structure underlying the common dynamical behavior across species, we derive a firing-rate (FR) model and use it to extract a slow passage through a saddle-node-on-an-invariant-circle (SNIC) bifurcation structure. We expect this bifurcation structure to provide new insights into the understanding of the dynamical behavior of neuronal assemblies and that a similar structure can be found in other sensory systems.
Brain Energetics, Mitochondria, and Traumatic Brain Injury
Authors: Haym Benaroya
Date: 2019-08-30 00:00:00

We review current thinking about, and draw connections between, brain energetics and metabolism, mitochondria and traumatic brain injury. In addition to summarizing current thinking in these disciplines, our goal is to suggest a framework for mechanisms and pathways based on optimal energetic decisions.
Racial Disparities in Voting Wait Times: Evidence from Smartphone Data
Date: 2020-10-31 00:00:00

Equal access to voting is a core feature of democratic government. Using data
from millions of smartphone users, we quantify a racial disparity in voting
wait times across a nationwide sample of polling places during the 2016 U.S.
presidential election. Relative to entirely-white neighborhoods, residents of
entirely-black neighborhoods waited 29% longer to vote and were 74% more likely
to spend more than 30 minutes at their polling place. This disparity holds when
comparing predominantly white and black polling places within the same states
and counties, and survives numerous robustness and placebo tests. We shed light
on the mechanism for these results and discuss how geospatial data can be an
effective tool to both measure and monitor these disparities going forward.
Single-cell stochastic gene expression kinetics with coupled positive-plus-negative feedback
Date: 2019-10-25 00:00:00

Here we investigate single-cell stochastic gene expression kinetics in a
minimal coupled gene circuit with positive-plus-negative feedback. A triphasic
stochastic bifurcation upon the increasing ratio of the positive and negative
feedback strengths is observed, which reveals a strong synergistic interaction
between positive and negative feedback loops. We discover that coupled
positive-plus-negative feedback amplifies gene expression mean but reduces gene
expression noise over a wide range of feedback strengths when promoter
switching is relatively slow, stabilizing gene expression around a relatively
high level. In addition, we study two types of macroscopic limits of the
discrete chemical master equation model: the Kurtz limit applies to proteins
with large burst frequencies and the L\'{e}vy limit applies to proteins with
large burst sizes. We derive the analytic steady-state distributions of the
protein abundance in a coupled gene circuit for both the discrete model and its
two macroscopic limits, generalizing the results obtained in [Chaos 26:043108,
2016]. We also obtain the analytic time-dependent protein distribution for the
classical Friedman-Cai-Xie random bursting model proposed in [Phys. Rev. Lett.
97:168302, 2006]. Our analytic results are further applied to study the
structure of gene expression noise in a coupled gene circuit and a complete
decomposition of noise in terms of five different biophysical origins is
provided.
Distributed finite-time termination for consensus algorithm in switching topologies
Date: 2019-08-30 00:00:00

In this article, we present a finite time stopping criterion for consensus
algorithms in networks with dynamic communication topology. Recent results
provide asymptotic convergence to the consensus algorithm. However, the
asymptotic convergence of these algorithms pose a challenge in the practical
settings where the response from agents is required in finite time. To this
end, we propose a Maximum-Minimum protocol which propagates the global maximum
and minimum values of agent states (while running consensus algorithm) in the
network. We establish that global maximum and minimum values are strictly
monotonic even for a dynamic topology and can be utilized to distributively
ascertain the closeness to convergence in finite time. We show that each node
can have access to the global maximum and minimum by running the proposed
Maximum-Minimum protocol and use it as a finite time stopping criterion for the
otherwise asymptotic consensus algorithm. The practical utility of the
algorithm is illustrated through experiments where each agent is instantiated
by a NodeJS socket.io server.
Parallel Unbalanced Optimal Transport Regularization for Large Scale Imaging Problems
Authors: John Lee, Nicholas P. Bertrand, Christopher J. Rozell
Date: 2019-08-31 00:00:00
Tasks: Optical Flow Estimation

The modeling of phenomenological structure is a crucial aspect in inverse imaging problems. One emerging modeling tool in computational imaging is the optimal transport framework. Its ability to model geometric displacements across an image's support gives it attractive qualities similar to those of optical flow methods which are effective at capturing visual motion, but are restricted to operate in significantly smaller state-spaces. Despite this advantage, two major drawbacks make it unsuitable for general deployment: (i) it suffers from exorbitant computational costs due to a quadratic optimization-variable complexity, and (ii) it has a mass-balancing assumption that limits applications with natural images. We tackle these issues simultaneously by proposing a novel formulation for an unbalanced optimal transport regularizer that has linear optimization-variable complexity. In addition, we present a general parallelizable proximal method for this regularizer, and demonstrate superior empirical performance on novel dynamical tracking applications in synthetic and real video.
Mapping Firms' Locations in Technological Space: A Topological Analysis of Patent Statistics
Authors: Emerson G. Escolar, Yasuaki Hiraoka, Mitsuru Igami, Yasin Ozcan
Date: 2019-08-31 00:00:00
Tasks: Clustering, Data Visualization, Topological Data Analysis

Where do firms innovate? Mapping their locations and directions in technological space is challenging due to its high dimensionality. We propose a new method to characterize firms' inventive activities via topological data analysis (TDA) that represents high-dimensional data in a shape graph. Applying this method to 333 major firms' patents in 1976--2005 reveals substantial heterogeneity: some firms remain undifferentiated; others develop unique portfolios. Firms with unique trajectories, which we define and measure graph-theoretically as "flares" in the Mapper graph, perform better. This association is statistically and economically significant, and continues to hold after we control for portfolio size, firm survivorship, industry classification, and firm fixed effects. By contrast, existing techniques -- such as principal component analysis (PCA) and Jaffe's (1989) clustering method -- struggle to track these firm-level dynamics.
Fixed-k Inference for Conditional Extremal Quantiles
Date: 2020-07-19 00:00:00
Tasks: valid

We develop a new extreme value theory for repeated cross-sectional and panel
data to construct asymptotically valid confidence intervals (CIs) for
conditional extremal quantiles from a fixed number $k$ of nearest-neighbor tail
observations. As a by-product, we also construct CIs for extremal quantiles of
coefficients in linear random coefficient models. For any fixed $k$, the CIs
are uniformly valid without parametric assumptions over a set of nonparametric
data generating processes associated with various tail indices. Simulation
studies show that our CIs exhibit superior small-sample coverage and length
properties than alternative nonparametric methods based on asymptotic
normality. Applying the proposed method to Natality Vital Statistics, we study
factors of extremely low birth weights. We find that signs of major effects are
the same as those found in preceding studies based on parametric models, but
with different magnitudes.
A Unified Statistical Model for Atmospheric Turbulence-Induced Fading in Orbital Angular Momentum Multiplexed FSO Systems
Date: 2019-10-23 00:00:00
Tasks: Diversity

This paper proposes a unified statistical channel model to characterize the
atmospheric turbulence induced distortions faced by orbital angular momentum
(OAM) in free space optical (FSO) communication systems. In this channel model,
the self-channel irradiance of OAM modes as well as crosstalk irradiances
between different OAM modes are characterized by a Generalized Gamma
distribution (GGD). The latter distribution is shown to provide an excellent
match with simulated data for all regimes of atmospheric turbulence. Therefore,
it can be used to overcome the computationally complex numerical simulations to
model the propagation of OAM modes through atmospheric turbulent FSO channels.
The GGD allows obtaining very simple tractable closed-form expressions for a
variety of performance metrics. Indeed, the average capacity, the bit-error
rate, and the outage probability are derived for FSO systems using single OAM
mode transmission with direct detection. Furthermore, we extend our study to
FSO systems using OAM mode diversity to improve the performance. By using a
maximum ratio combining (MRC) at the receiver, the GGD is also shown to fit the
simulated combined received optical powers. Finally, space-time (ST) coding is
proposed to provide diversity and multiplexing gains, and the error probability
is theoretically derived under the newly proposed generic model.
Economic model predictive control for snake robot locomotion
Date: 2020-05-22 00:00:00
Tasks: model, Model Predictive Control

In this work, the control of snake robot locomotion via economic model
predictive control (MPC) is studied. Only very few examples of applications of
MPC to snake robots exist and rigorous proofs for recursive feasibility and
convergence are missing. We propose an economic MPC algorithm that maximizes
the robot's forward velocity and integrates the choice of the gait pattern into
the closed loop. We show recursive feasibility of the MPC optimization problem,
where some of the developed techniques are also applicable for the analysis of
a more general class of system. Besides, we provide performance results and
illustrate the achieved performance by numerical simulations. We thereby show
that the economic MPC algorithm outperforms a standard lateral undulation
controller and achieves constraint satisfaction. Surprisingly, a gait pattern
different to lateral undulation results from the optimization.
Rational Inattention and Perceptual Distance
Date: 2019-12-26 00:00:00
Tasks: counterfactual, Perceptual Distance

This paper uses an axiomatic foundation to create a new measure for the cost
of learning that allows for multiple perceptual distances in a single choice
environment so that some events can be harder to differentiate between than
others. The new measure maintains the tractability of Shannon's classic measure
but produces richer choice predictions and identifies a new form of
informational bias significant for welfare and counterfactual analysis.