pwc-archive/papers-with-abstracts huggingface dataset:
Identification of Regression Models with a Misclassified and Endogenous Binary Regressor
Authors: Hiroyuki Kasahara, Katsumi Shimotsu
Date: 2019-04-25 00:00:00
Tasks: regression

We study identification in nonparametric regression models with a misclassified and endogenous binary regressor when an instrument is correlated with misclassification error. We show that the regression function is nonparametrically identified if one binary instrument variable and one binary covariate satisfy the following conditions. The instrumental variable corrects endogeneity; the instrumental variable must be correlated with the unobserved true underlying binary variable, must be uncorrelated with the error term in the outcome equation, but is allowed to be correlated with the misclassification error. The covariate corrects misclassification; this variable can be one of the regressors in the outcome equation, must be correlated with the unobserved true underlying binary variable, and must be uncorrelated with the misclassification error. We also propose a mixture-based framework for modeling unobserved heterogeneous treatment effects with a misclassified and endogenous binary regressor and show that treatment effects can be identified if the true treatment effect is related to an observed regressor and another observable variable.
"Favoring my playmate seems fair": Inhibitory control and theory of mind in preschoolers' self-disadvantaging behaviors
Date: 2019-04-25 00:00:00
Tasks: Fairness

The purpose of this study was to investigate the relationship between
preschoolers' cognitive abilities and their fairness-related allocation
behaviors in a dilemma of equity-efficiency conflict. Four- to 6-year-olds in
Experiment 1 (N = 99) decided how to allocate 5 reward bells. In the
first-party condition, preschoolers were asked to choose among giving more to
self (self-advantageous inequity), wasting one bell (equity) or giving more to
other (self-disadvantageous inequity); while in the third-party condition, they
chose to allocate the extra bell to one of two equally deserving recipients or
to waste it. Results showed that compared to the pattern of decision in the
third-party condition, preschoolers in the first-party condition were more
likely to give the extra bell to other (self-disadvantaging behaviors), and
age, inhibitory control (IC) and theory of mind (ToM) were positively
correlated with their self-disadvantaging choices, but only IC mediated the
relationship between age and self-disadvantaging behaviors. Experiment 2 (N =
41) showed that IC still predicted preschoolers' self-disadvantaging behaviors
when they could choose only between equity and disadvantageous inequity. These
results suggested that IC played a critical role in the implementation of
self-disadvantaging behaviors when this required the control over selfishness
and envy.
Equalization Enhanced Phase Noise in Coherent Receivers: DSP-Aware Analysis and Shaped Constellations
Date: 2019-04-25 00:00:00

We revisit the analysis of equalization-enhanced phase noise (EEPN) arising
in coherent receivers from the interaction between the chromatic dispersion
compensation by an electronic equalizer and the phase noise of the local
oscillator. Through numerical simulations we highlight EEPN characteristics and
investigate its impact on the behavior of the carrier phase recovery algorithm.
We show that the blind phase search, which is usually used in practice to
recover the carrier phase, partially mitigates the EEPN. We detail a numerical
approach to predict the system performance including the phase recovery
algorithm, and show that taking into account EEPN characteristics relaxes the
constraint on the system laser phase noise given by previous pessimistic
analytical models. We present experimental validations of our claims, and
address future advanced transoceanic systems using 98 GBd probabilistically
shaped QAM formats.
A color-difference formula for evaluating color pairs with no separation -- $\Delta E_{NS}$
Date: 2019-04-25 00:00:00

All color-difference formulas are developed to evaluate color differences for
pairs of stimuli with hair-line separation. In printing applications, however,
color differences are frequently judged between a pair of samples with
no-separation because they are printed adjacent on the same piece of paper. A
new formula, dENS has been developed for pairs of stimuli with no-separation
(NS). An experiment was conducted to investigate the effect of different
color-difference magnitudes using sample pairs with NS. 1,012 printed pairs
with NS were prepared around 11 CIE recommended color centers. The pairs,
representing four color-difference magnitudes of 1, 2, 4 and 8 CIELAB units
were visually evaluated by a panel of 19 observers using the gray-scale method.
Comparison of the present data based on pairs with NS, and previously generated
data using pairs with hair-line separation, showed a clear separation effect. A
new color-difference equation for the NS viewing condition (dENS) is proposed
by modifying the CIEDE2000 formula. The separation effect can be well described
by the new formula. For a sample pair with NS, when the CIEDE2000 color
difference is less than 9.1, a larger color difference leads to a larger
lightness difference, and thus the total color difference increases. When the
CIEDE2000 color difference is greater than 9.1, the effect is opposite, i.e.
the lightness difference decreases, and thus the total color difference also
decreases. The new formula is recommended for future research to evaluate its
performance in appropriate applications.
A Multi-agent approach for $\textit{in silico}$ simulations of micro-biological systems
Authors: Daniele Proverbio, Luca Gallo, Barbara Passalacqua, Jacopo Pellegrino, Marco Maggiora
Date: 2019-04-16 00:00:00

Using a Multi-agent systems paradigm, the present project develops, validates and exploits a computational $\textit{testbed}$ that simulates micro-biological complex systems, namely the aggregation patterns of the social amoeba $\textit{Dyctiostelium discoideum}$. We propose a new design and implementation for managing discrete simulations with autonomous agents on a microscopic scale, thus focusing on their social behavior and mutual interactions. Then, the dependence on the main physical variables is tested, namely density and number of amoebas; in addition, we analyze the robustness of the dynamics against various noise sources. Along with these results, we suggest a methodology for further studies that make use of our validated model.
Shared factory: a new production node for social manufacturing in the context of sharing economy
Date: 2019-04-12 00:00:00

Manufacturing industry is heading towards socialization, interconnection, and
platformization. Motivated by the infiltration of sharing economy usage in
manufacturing, this paper addresses a new factory model -- shared factory --
and provides a theoretical architecture and some actual cases for manufacturing
sharing. Concepts related to three kinds of shared factories which deal
respectively with sharing production-orders, manufacturing-resources and
manufacturing-capabilities, are defined accordingly. These three kinds of
shared factory modes can be used for building correspondent sharing
manufacturing ecosystems. On the basis of sharing economic analysis, we
identify feasible key enabled technologies for configuring and running a shared
factory. At the same time, opportunities and challenges of enabling the shared
factory are also analyzed in detail. In fact, shared factory, as a new
production node, enhances the sharing nature of social manufacturing paradigm,
fits the needs of light assets and gives us a new chance to use socialized
manufacturing resources. It can be drawn that implementing a shared factory
would reach a win-win way through production value-added transformation and
social innovation.
Multiple Benefits through Smart Home Energy Management Solutions -- A Simulation-Based Case Study of a Single-Family House in Algeria and Germany
Date: 2019-04-25 00:00:00
Tasks: energy management, Management

From both global and local perspectives, there are strong reasons to promote
energy efficiency. These reasons have prompted leaders in the European Union
(EU) and countries of the Middle East and North Africa (MENA) to adopt policies
to move their citizenry toward more efficient energy consumption. Energy
efficiency policy is typically framed at the national, or transnational level.
Policy makers then aim to incentivize microeconomic actors to align their
decisions with macroeconomic policy. We suggest another path towards greater
energy efficiency: Highlighting individual benefits at microeconomic level. By
simulating lighting, heating and cooling operations in a model single-family
home equipped with modest automation, we show that individual actors can be led
to pursue energy efficiency out of enlightened self-interest. We apply
simple-to-use, easily, scalable impact indicators that can be made available to
homeowners and serve as intrinsic economic, environmental and social motivators
for pursuing energy efficiency. The indicators reveal tangible homeowner
benefits realizable under both the market-based pricing structure for energy in
Germany and the state-subsidized pricing structure in Algeria. Benefits accrue
under both the continental climate regime of Germany and the Mediterranean
regime of Algeria, notably in the case that cooling energy needs are
considered. Our findings show that smart home technology provides an attractive
path for advancing energy efficiency goals. The indicators we assemble can help
policy makers both to promote tangible benefits of energy efficiency to
individual homeowners, and to identify those investments of public funds that
best support individual pursuit of national and transnational energy goals.
Event Driven Fusion
Date: 2019-04-25 00:00:00

This paper presents a technique which exploits the occurrence of certain events as observed by different sensors, to detect and classify objects. This technique explores the extent of dependence between features being observed by the sensors, and generates more informed probability distributions over the events. Provided some additional information about the features of the object, this fusion technique can outperform other existing decision level fusion approaches that may not take into account the relationship between different features. Furthermore, this paper addresses the issue of coping with damaged sensors when using the model, by learning a hidden space between sensor modalities which can be exploited to safeguard detection performance.
The Black-Scholes Equation in Presence of Arbitrage
Authors: Simone Farinelli, Hideyuki Takada
Date: 2019-04-17 00:00:00

We apply Geometric Arbitrage Theory to obtain results in Mathematical Finance, which do not need stochastic differential geometry in their formulation. First, for a generic market dynamics given by a multidimensional It\^o's process we specify and prove the equivalence between (NFLVR) and expected utility maximization. As a by-product we provide a geometric characterization of the (NUPBR) condition given by the zero curvature (ZC) condition. Finally, we extend the Black-Scholes PDE to markets allowing arbitrage.
Parameter regions that give rise to 2[n/2]+1 positive steady states in the n-site phosphorylation system
Date: 2019-04-26 00:00:00

The distributive sequential n-site phosphorylation/dephosphorylation system
is an important building block in networks of chemical reactions arising in
molecular biology, which has been intensively studied. In the nice paper of
Wang and Sontag (2008) it is shown that for certain choices of the reaction
rate constants and total conservation constants, the system can have 2[n/2]+1
positive steady states (that is, n+1 positive steady states for n even and n
positive steady states for n odd). In this paper we give open parameter regions
in the space of reaction rate constants and total conservation constants that
ensure these number of positive steady states, while assuming in the modeling
that roughly only 1/4 of the intermediates occur in the reaction mechanism.
This result is based on the general framework developed by Bihan, Dickenstein,
and Giaroli (2018), which can be applied to other networks. We also describe
how to implement these tools to search for multistationarity regions in a
computer algebra system and present some computer aided results.
A Hilbert Space Theory of Generalized Graph Signal Processing
Date: 2019-09-16 00:00:00

Graph signal processing (GSP) has become an important tool in many areas such
as image processing, networking learning and analysis of social network data.
In this paper, we propose a broader framework that not only encompasses
traditional GSP as a special case, but also includes a hybrid framework of
graph and classical signal processing over a continuous domain. Our framework
relies extensively on concepts and tools from functional analysis to generalize
traditional GSP to graph signals in a separable Hilbert space with infinite
dimensions. We develop a concept analogous to Fourier transform for generalized
GSP and the theory of filtering and sampling such signals.
Video coding technique with parametric modeling of noise
Date: 2019-04-26 00:00:00

This paper presents a video encoding method in which noise is encoded using a
novel parametric model representing spectral envelope and spatial distribution
of energy. The proposed method has been experimentally assessed using video
test sequences in a practical setup consisting of a simple, real-time noise
reduction technique and High Efficiency Video Codec (HEVC). The attained
results show that the use of the proposed parametric modeling of noise can
improve the subjective quality of reconstructed video by approximately 1.8 Mean
Opinion Scope (MOS) points (in 11-point scale) related to the classical video
coding. Moreover, the present work confirms results attained in the previous
works that the usage of even sole noise reduction prior to the encoding
provides quality increase.
A Multicriteria Decision Making Approach to Study the Barriers to the Adoption of Autonomous Vehicles
Date: 2019-12-27 00:00:00
Tasks: Autonomous Vehicles, Decision Making

The automation technology is emerging, but the adoption rate of autonomous
vehicles (AV) will largely depend upon how policymakers and the government
address various challenges such as public acceptance and infrastructure
development. This study proposes a five-step method to understand these
barriers to AV adoption. First, based on a literature review followed by
discussions with experts, ten barriers are identified. Second, the opinions of
eighteen experts from industry and academia regarding inter-relations between
these barriers are recorded. Third, a multicriteria decision making (MCDM)
technique, the grey-based Decision-making Trial and Evaluation Laboratory
(Grey-DEMATEL), is applied to characterize the structure of relationships
between the barriers. Fourth, robustness of the results is tested using
sensitivity analysis. Fifth, the key results are depicted in a causal loop
diagram (CLD), a systems thinking approach, to comprehend cause-and-effect
relationships between the barriers. The results indicate that the lack of
customer acceptance (LCA) is the most prominent barrier, the one which should
be addressed at the highest priority. The CLD suggests that LCA can be rather
mitigated by addressing two other prominent, yet more tangible, barriers --
lack of industry standards and the absence of regulations and certifications.
The study's overarching contribution thus lies in bringing to fore multiple
barriers to AV adoption and their potential influences on each other. Moreover,
the insights from this study can help associations related to AVs prioritize
their endeavors to expedite AV adoption. From the methodological perspective,
this is the first study in transportation literature that integrates
Grey-DEMATEL with systems thinking.
The Category of Node-and-Choice Forms, with Subcategories for Choice-Sequence Forms and Choice-Set Forms
Date: 2019-04-27 00:00:00
Tasks: Form

The literature specifies extensive-form games in many styles, and eventually
I hope to formally translate games across those styles. Toward that end, this
paper defines $\mathbf{NCF}$, the category of node-and-choice forms. The
category's objects are extensive forms in essentially any style, and the
category's isomorphisms are made to accord with the literature's small handful
of ad hoc style equivalences.
  Further, this paper develops two full subcategories: $\mathbf{CsqF}$ for
forms whose nodes are choice-sequences, and $\mathbf{CsetF}$ for forms whose
nodes are choice-sets. I show that $\mathbf{NCF}$ is "isomorphically enclosed"
in $\mathbf{CsqF}$ in the sense that each $\mathbf{NCF}$ form is isomorphic to
a $\mathbf{CsqF}$ form. Similarly, I show that $\mathbf{CsqF_{\tilde a}}$ is
isomorphically enclosed in $\mathbf{CsetF}$ in the sense that each
$\mathbf{CsqF}$ form with no-absentmindedness is isomorphic to a
$\mathbf{CsetF}$ form. The converses are found to be almost immediate, and the
resulting equivalences unify and simplify two ad hoc style equivalences in
Kline and Luckraz 2016 and Streufert 2019.
  Aside from the larger agenda, this paper already makes three practical
contributions. Style equivalences are made easier to derive by [1] a natural
concept of isomorphic invariance and [2] the composability of isomorphic
enclosures. In addition, [3] some new consequences of equivalence are
systematically deduced.
Geological modeling using a recursive convolutional neural networks approach
Date: 2019-04-27 00:00:00

Resource models are constrained by the extent of geological units that often
depend on the lithology, alteration and mineralization. A three dimensional
model of these geological units must be built from scarce information coming
from drillholes and limited understanding about the geological setting in which
the ore deposit is places. In this work, we present a new technique for
multiple-point geostatistical simulation based on a recursive convolutional
neural network approach (RCNN). The method requires conditioning data and a
training image that depicts the type of geological structures expected to be
found in the deposit. This training image is used to learn the patterns of
categories found and these are imposed in the final simulated model conditioned
by the categories found during logging at the actual drill-hole samples. A
lithological modeling process is carried out in a copper deposit in Chile to
demonstrate the method. Comparison with current techniques and spatial metrics
are used to clarify concepts and RCNN properties. Also, strengths and
weaknesses of the methodology are discussed by briefly reviewing the
theoretical perspective and looking into some of its practical aspects.
A Block Sparsity Based Estimator for mmWave Massive MIMO Channels with Beam Squint
Date: 2019-04-28 00:00:00
Tasks: Compressive Sensing

Multiple-input multiple-output (MIMO) millimeter wave (mmWave) communication
is a key technology for next generation wireless networks. One of the
consequences of utilizing a large number of antennas with an increased
bandwidth is that array steering vectors vary among different subcarriers. Due
to this effect, known as beam squint, the conventional channel model is no
longer applicable for mmWave massive MIMO systems. In this paper, we study
channel estimation under the resulting non-standard model. To that aim, we
first analyze the beam squint effect from an array signal processing
perspective, resulting in a model which sheds light on the angle-delay sparsity
of mmWave transmission. We next design a compressive sensing based channel
estimation algorithm which utilizes the shift-invariant block-sparsity of this
channel model. The proposed algorithm jointly computes the off-grid angles, the
off-grid delays, and the complex gains of the multi-path channel. We show that
the newly proposed scheme reflects the mmWave channel more accurately and
results in improved performance compared to traditional approaches. We then
demonstrate how this approach can be applied to recover both the uplink as well
as the downlink channel in frequency division duplex (FDD) systems, by
exploiting the angle-delay reciprocity of mmWave channels.
A Fast Converging Distributed Solver for Linear Systems with Generalised Diagonal Dominance
Date: 2019-04-28 00:00:00

This paper proposes a new distributed algorithm for solving linear systems
associated with a sparse graph under a generalised diagonal dominance
assumption. The algorithm runs iteratively on each node of the graph, with low
complexities on local information exchange between neighbouring nodes, local
computation and local storage. For an acyclic graph under the condition of
diagonal dominance, the algorithm is shown to converge to the correct solution
in a finite number of iterations, equalling the diameter of the graph. For a
loopy graph, the algorithm is shown to converge to the correct solution
asymptotically. Simulations verify that the proposed algorithm significantly
outperforms the classical Jacobi method and a recent distributed linear system
solver based on average consensus and orthogonal projection.
Rough volatility of Bitcoin
Date: 2019-04-28 00:00:00
Tasks: Time Series, Time Series Analysis

Recent studies have found that the log-volatility of asset returns exhibit
roughness. This study investigates roughness or the anti-persistence of Bitcoin
volatility. Using the multifractal detrended fluctuation analysis, we obtain
the generalized Hurst exponent of the log-volatility increments and find that
the generalized Hurst exponent is less than $1/2$, which indicates
log-volatility increments that are rough. Furthermore, we find that the
generalized Hurst exponent is not constant. This observation indicates that the
log-volatility has multifractal property. Using shuffled time series of the
log-volatility increments, we infer that the source of multifractality partly
comes from the distributional property.
Identification of Key Companies for International Profit Shifting in the Global Ownership Network
Date: 2019-04-28 00:00:00

In the global economy, the intermediate companies owned by multinational
corporations are becoming an important policy issue as they are likely to cause
international profit shifting and diversion of foreign direct investments. The
purpose of this analysis is to call the intermediate companies with high risk
of international profit shifting as key firms and to identifying and clarify
them. For this aim, we propose a model that focuses on each affiliate's
position on the ownership structure of each multinational corporation. Based on
the information contained in the Orbis database, we constructed the Global
Ownership Network, reflecting the relationship that can give significant
influence to a firm, and analyzed for large multinational corporations listed
in Fortune Global 500. In this analysis, first, we confirmed the validity of
this model by identifying affiliates playing an important role in international
tax avoidance at a certain degree. Secondly, intermediate companies are mainly
found in the Netherlands and the United Kingdom, etc., and tended to be located
in jurisdictions favorable to treaty shopping. And it was found that such key
firms are concentrated on the IN component of the bow-tie structure that the
giant weakly connected component of the Global Ownership Network consist of.
Therefore, it clarifies that the key firms are geographically located in
specific jurisdictions, and concentrates on specific components in the Global
Ownership Network. The location of key firms are related with the ease of
treaty shopping, and there is a difference in the jurisdiction where key firms
are located depending on the location of the multinational corporations.