pwc-archive/papers-with-abstracts huggingface dataset:
Consistent Approximation of Epidemic Dynamics on Degree-heterogeneous Clustered Networks
Date: 2018-10-14 00:00:00
Tasks: Clustering

Realistic human contact networks capable of spreading infectious disease, for
example studied in social contact surveys, exhibit both significant degree
heterogeneity and clustering, both of which greatly affect epidemic dynamics.
To understand the joint effects of these two network properties on epidemic
dynamics, the effective degree model of Lindquist et al. is reformulated with a
new moment closure to apply to highly clustered networks. A simulation study
comparing alternative ODE models and stochastic simulations is performed for
SIR (Susceptible-Infected-Removed) epidemic dynamics, including a test for the
conjectured error behaviour in Pellis et al., providing evidence that this
novel model can be a more accurate approximation to epidemic dynamics on
complex networks than existing approaches.
Mapping Web Pages by Internet Protocol (IP) addresses: Analyzing Spatial and Temporal Characteristics of Web Search Engine Results
Authors: Tsou Ming-Hsiang, Lusher Daniel
Date: 2018-10-15 00:00:00

Internet Protocol (IP) addresses are frequently used as a method of locating
web users by researchers in several different fields. However, there are
competing reports concerning the accuracy of those locations, and little
research has been done in manually comparing the IP geolocation databases and
web page geographic information. This paper categorized web page from the Yahoo
search engine into twelve categories, ranging from 'Blog' and 'News' to
'Education' and 'Governmental'. Then we manually compared the mailing or street
address of the web page's content creator with the geolocation results by the
given IP address. We introduced a cartographic design method by creating kernel
density maps for visualizing the information landscape of web pages associated
with specific keywords.
Joint bi-modal image reconstruction of DOT and XCT with an extended Mumford-Shah functional
Date: 2018-10-15 00:00:00
Tasks: Image Reconstruction, SSIM

Feature similarity measures are indispensable for joint image reconstruction
in multi-modality medical imaging, which enable joint multi-modal image
reconstruction (JmmIR) by communication of feature information from one
modality to another, and vice versa. In this work, we establish an image
similarity measure in terms of image edges from Tversky's theory of feature
similarity in psychology. For joint bi-modal image reconstruction (JbmIR), it
is found that this image similarity measure is an extended Mumford-Shah
functional with a-priori edge information proposed previously from the
perspective of regularization approach. This image similarity measure consists
of Hausdorff measures of the common and different parts of image edges from
both modalities. By construction, it posits that two images are more similar if
they have more common edges and fewer unique/distinctive features, and will not
force the nonexistent structures to be reconstructed when applied to JbmIR.
With the Gamma-approximation of the JbmIR functional, an alternating
minimization method is proposed for the JbmIR of diffuse optical tomography and
x-ray computed tomography. The performance of the proposed method is evaluated
by three numerical phantoms. It is found that the proposed method improves the
reconstructed image quality by more than 10% compared to single modality image
reconstruction (SmIR) in terms of the structural similarity index measure
(SSIM)
Specific low frequency electromagnetic fields induce epigenetic and functional changes in U937 cells
Date: 2018-10-15 00:00:00
Tasks: Time Series, Time Series Analysis

In this study, we investigated the effects of specific low frequency
electromagnetic fields sequences on U937 cells, an in vitro model of human
monocyte/macrophage differentiation. U937 cells were exposed to electromagnetic
stimulation by means of the SyntheXer system using two similar sequences,
XR-BC31 and XR-BC31/F. Each sequence was a time series of twenty-nine wave
segments. Here, we report that exposure (4 days, once a day) of U937 cells to
the XR-BC31 setting, but not to the XR-BC31/F, resulted in increased expression
of the histone demethylase KDM6B along with a global reduction in histone H3
lysine 27 (H3K27) tri-methylation. Furthermore, exposure to the XR-BC31
sequence induced differentiation of U937 cells towards a macrophage-like
phenotype displaying a KDM6B dependent increase in expression and secretion of
the anti-inflammatory interleukins (ILs), IL-10 and IL-4. Importantly, all the
observed changes were highly dependent on the sequence's nature. Our results
open a new way of interpretation for the effects of low frequency
electromagnetic fields observed in vivo. Indeed, it is conceivable that a
specific low frequency electromagnetic fields treatment may cause changes in
chromatin accessibility and consequently in the expression of anti-inflammatory
mediators and in cell differentiation.
An Agent Based Modeling of Spatially Inhomogeneous Host-Vector Disease Transmission
Date: 2018-10-15 00:00:00

In this article we consider a microscopic model for host-vector disease
transmission based on configuration space analysis. Using Vlasov scaling we
obtain the corresponding mesoscopic (kinetic) equations, describing the density
of susceptible and infected compartments in space. The resulting system of
equations can be seen as a generalization to a spatial SISUV model.
On the sensitivity analysis of energy quanto options
Date: 2018-10-12 00:00:00
Tasks: Sensitivity

In recent years there has been an advent of quanto options in energy markets.
The structure of the payoff is rather a different type from other markets since
it is written as a product of an underlying energy index and a measure of
temperature. In the HJM framework, by adopting the futures energy dynamics, we
use the Malliavin calculus to derive the delta and the cross-gamma expectation
formulas. This work can be viewed as an extension of the work done, for example
by Benth et al. [1].
Translocatome: a novel resource for the analysis of protein translocation between cellular organelles
Authors: Peter Mendik, Levente Dobronyi, Ferenc Hari, Csaba Kerepesi, Leonardo Maia-Moco, Donat Buszlai, Peter Csermely, Daniel V. Veres
Date: 2018-10-15 00:00:00

Here we present Translocatome, the first dedicated database of human translocating proteins. The core of the Translocatome database is the manually curated data set of 213 human translocating proteins listing the source of their experimental validation, several details of their translocation mechanism, their local compartmentalized interactome, as well as their involvement in signalling pathways and disease development. In addition, using the well-established and widely used gradient boosting machine learning tool, XGBoost, Translocatome provides translocation probability values for 13,066 human proteins identifying 1133 and 3268 high- and low-confidence translocating proteins, respectively. The database has user-friendly search options with a UniProt autocomplete quick search and advanced search for proteins filtered by their localization, UniProt identifiers, translocation likelihood or data complexity. Download options of search results, manually curated and predicted translocating protein sets are available on its website. The update of the database is helped by its manual curation framework and connection to the previously published ComPPI compartmentalized protein-protein interaction database. As shown by the application examples of merlin (NF2) and tumor protein 63 (TP63) Translocatome allows a better comprehension of protein translocation as a systems biology phenomenon and can be used as a discovery-tool in the protein translocation field. The database is available here: http://translocatome.linkgroup.hu
Replica Analysis for Maximization of Net Present Value
Date: 2018-10-15 00:00:00

In this paper, we use replica analysis to determine the investment strategy
that can maximize the net present value for portfolios containing multiple
development projects. Replica analysis was developed in statistical mechanical
informatics and econophysics to evaluate disordered systems, and here we use it
to formulate the maximization of the net present value as an optimization
problem under budget and investment concentration constraints. Furthermore, we
confirm that a common approach from operations research underestimates the true
maximal net present value as the maximal expected net present value by
comparing our results with the maximal expected net present value as derived in
operations research. Moreover, it is shown that the conventional method for
estimating the net present value does not consider variance in the cash flow.
Implementation Of Digital Image Processing And Computation Technology On Measurement And Testing Of Various Knit Fabric Parameters
Date: 2018-09-28 00:00:00

One of the big challenges of the industry today is how to produce quality
products, one of which is in the knit fabric industry. The improvement of the
evaluation and quality control processes of non woven production has been
widely developed to support the improvement of the quality of production. The
use of information and computational technology is now widely applied to the
quality control process of textile material production, one of which is the use
of image processing technology in the evaluation process of knit fabric. This
chapter will explain various methods of applying image processing technology in
the field of evaluation and quality control of textile production.
Implementation Of Image Analysis Techniques For Various Textile Identification
Date: 2018-09-28 00:00:00

Computer image analysis techniques used to identify textile products are
presented in this chapter, together with a brief review of the historical
development of the method. Automatic and semi-automatic image correction
methods are described, which are often used for identification of textile yarn
products, and can also be used to identify various textile indentification.
  Keywords: digital image acquirement, image modeling, image quality
improvement, image correction methods, median filtration, Laplace filter,
threshold function, autocorrelation, Fourier transform, erosion, dilatation,
digitalisation algorithm
Smart Load Node for Nonsmart Load Under Smart Grid Paradigm: A New Home Energy Management System
Date: 2019-05-26 00:00:00
Tasks: energy management, Management

This article presents a novel approach for efficient operation of non-smart
household appliances under smart grid environment using the proposed smart load
node (SLN). In real world scenario, there are so many non-smart loads currently
in use and embedding appliance specific intelligence into them to make them as
smart loads will be more expensive compared to the proposed SLN, which is a
common solution for all types of non-smart loads. This makes the proposed
low-cost SLN, which neither requires any infrastructural change in the
electrical wiring of a house nor any constructional change in home appliances
at the manufacturing stage and at the consumer end, as a feasible solution for
intelligent operation of non-smart home appliances under smart grid
environment. The SLNs, which are placed in a home like distributed wireless
sensor nodes, form a home area network (HAN). The HAN includes a load
management unit (LMU) which acts as master for all distributed SLNs. Wi-Fi is
chosen as a medium of communication in the HAN. The LMU incorporates load
management algorithm which is written in Python script.
Regression Based Approach for Measurement of Current in Single-Phase Smart Energy Meter
Date: 2018-10-12 00:00:00
Tasks: regression

In the present scenario rate of utilization of electrical energy is
continuously increasing, to preserve the non-renewable energy resources,
efficient consumption of electrical energy is one of the most important needs
of this era. Smart Energy Meter (SEM) plays a vital role in efficient use of
electrical energy in consumer premises. Accuracy in the measurement of current
is one of the key factors to be considered while measuring energy consumption.
This paper introduces Arduino based low cost single-phase SEM and proposes a
linear regression based technique for accurate measurement of current. The
proposed current measurement approach gives accurate measurement for linear as
well as highly non-linear loads. The SEM built in the laboratory has data
transfer capability and also embedded with Demand Response (DR) feature. Block
Rate Tariff structure used by Tamil Nadu Generation and Distribution Company is
considered to incorporate DR that warns the consumers whenever their energy
consumption reaches the boundary of a block.
Simulation Framework for Cooperative Adaptive Cruise Control with Empirical DSRC Module
Date: 2018-10-15 00:00:00

Wireless communication plays a vital role in the promising performance of
connected and automated vehicle (CAV) technology. This paper proposes a
Vissim-based microscopic traffic simulation framework with an analytical
dedicated short-range communication (DSRC) module for packet reception. Being
derived from ns-2, a packet-level network simulator, the DSRC probability
module takes into account the imperfect wireless communication that occurs in
real-world deployment. Four managed lane deployment strategies are evaluated
using the proposed framework. While the average packet reception rate is above
93\% among all tested scenarios, the results reveal that the reliability of the
vehicle-to-vehicle (V2V) communication can be influenced by the deployment
strategies. Additionally, the proposed framework exhibits desirable scalability
for traffic simulation and it is able to evaluate transportation-network-level
deployment strategies in the near future for CAV technologies.
A generalized GN-model closed-form formula
Date: 2018-11-21 00:00:00
Tasks: Form, Management, model

The GN-model of fiber non-linearity has had quite substantial success in
modern optical telecommunications networks as a design and management tool. A
version of it, capable of handling arbitrary WDM combs and link structures in
closed form, was proposed in 2014. Here we upgrade that formula, to make it
capable of handling frequency-dependent dispersion, frequency-dependent loss
and frequency-dependent gain/loss due to Stimulated Raman Scattering (SRS)
among channels. This way, more challenging and complex network scenarios, like
the ones that are being deployed right now, can be dealt with in real-time, to
the great advantage of management, control and optimization of such networks.
Design and Control of a Photonic Neural Network Applied to High-Bandwidth Classification
Date: 2019-09-16 00:00:00

Neural networks can very effectively perform multidimensional nonlinear
classification. However, electronic networks suffer from significant bandwidth
limitations due to carrier lifetimes and capacitive coupling. This project
investigates photonic neural networks that can get around these limitations by
performing both the activation function and weighted addition in the optical
domain using microring resonators. These optical microring resonators provide
both nonlinearity and superior fan-in without compromising bandwidth. The
ability to thermally calibrate networks of cascaded axons and dendrites and
train such a network to solve nonlinear classification problems are
demonstrated using theory and simulations. The former is also demonstrated
experimentally on a two-channel axon cascaded into a two-channel dendrite,
showing good agreement between simulation and experiment. In addition, the use
of transverse modes to increase the size of each photonic layer is examined.
Simulations that determined the optimal waveguide geometry for using these
modes were experimentally validated.
Effects of migration on vector-borne diseases with forward and backward stage progression
Date: 2018-10-16 00:00:00

Is it possible to break the host-vector chain of transmission when there is
an influx of infectious hosts into a na\"{i}ve population and competent vector?
To address this question, a class of vector-borne disease models with an
arbitrary number of infectious stages that account for immigration of infective
individuals is formulated. The proposed model accounts for forward and backward
progression, capturing the mitigation and aggravation to and from any stages of
the infection, respectively. The model has a rich dynamic, which depends on the
patterns of infected immigrant influx into the host population and connectivity
of the transfer between infectious classes. We provide conditions under which
the answer of the initial question is positive.
Essentiality landscape of metabolic networks
Date: 2018-10-16 00:00:00

Local perturbations of individual metabolic reactions may result in different
levels of lethality, depending on their roles in metabolism and the size of
subsequent cascades induced by their failure. Moreover, essentiality of
individual metabolic reactions may show large variations within and across
species. Here we quantify their essentialities in hundreds of species by
computing the growth rate after removal of individual and pairs of reactions by
flux balance analysis. We find that about 10% of reactions are essential, i.e.,
growth stops without them, and most of the remaining reactions are redundant in
the metabolic network of each species. This large-scale and cross-species study
allows us to determine ad hoc ages of each reaction and species. We find that
when a reaction is older and contained in younger species, the reaction is more
likely to be essential. Such correlations of essentiality with the ages of
reactions and species may be attributable to the evolution of cellular
metabolism, in which alternative pathways are recruited to ensure the stability
of important reactions to various degrees across species.
Lattice consensus: A partial order on phylogenetic trees that induces an associatively stable consensus method
Date: 2018-10-19 00:00:00

There is a long tradition of the axiomatic study of consensus methods in
phylogenetics that satisfy certain desirable properties. One
recently-introduced property is associative stability, which is desirable
because it confers a computational advantage, in that the consensus method only
needs to be computed "pairwise". In this paper, we introduce a phylogenetic
consensus method that satisfies this property, in addition to being "regular".
The method is based on the introduction of a partial order on the set of rooted
phylogenetic trees, itself based on the notion of a hierarchy-preserving map
between trees. This partial order may be of independent interest. We call the
method "lattice consensus", because it takes the unique maximal element in a
lattice of trees defined by the partial order. Aside from being associatively
stable, lattice consensus also satisfies the property of being Pareto on rooted
triples, answering in the affirmative a question of Bryant et al (2017). We
conclude the paper with an answer to another question of Bryant et al, showing
that there is no regular extension stable consensus method for binary trees.
Classes of treebased networks
Date: 2019-11-27 00:00:00

Recently, so-called treebased phylogenetic networks have gained considerable
interest in the literature, where a treebased network is a network that can be
constructed from a phylogenetic tree, called the base tree, by adding
additional edges. The main aim of this manuscript is to provide some sufficient
criteria for treebasedness by reducing phylogenetic networks to related graph
structures. While it is generally known that deciding whether a network is
treebased is NP-complete, one of these criteria, namely edgebasedness, can be
verified in linear time. Surprisingly, the class of edgebased networks is
closely related to a well-known family of graphs, namely the class of
generalized series parallel graphs, and we will explore this relationship in
full detail. Additionally, we introduce further classes of treebased networks
and analyze their relationships.
Millimeter-Wave for Unmanned Aerial Vehicles Networks: Enabling Multi-Beam Multi-Stream Communications
Date: 2018-10-16 00:00:00

With the fifth-generation (5G) mobile networks being actively standardized
and deployed, many new vehicular communications technologies are developed to
support and enrich various application scenarios. Unmanned aerial vehicle (UAV)
enabled communications emerges as one of many promising solutions of
constructing the next-generation highly reconfigurable and mobile networks. In
this article, we first investigate and envision the challenges of future UAV
applications from the net-work, system, and hardware design perspectives, and
then pre-sent a UAV aerial base station (ABS) prototype which works at
millimeter-wave (mmWave) bands and enable multi-beam mul-ti-stream
communications. In terms of the field trial tests of the first UAV-ABS of its
kind in the world, multi-giga-bit-per-second data rate of uplink and downlink
is verified with good stability and reliability against mildly challenging
weather conditions.
Constructing energy accounts for WIOD 2016 release
Date: 2018-10-16 00:00:00

Most of today's products and services are made in global supply chains. As a
result, a consumption of goods and services in one country is associated with
various environmental pressures all over the world due to international trade.
Advances in global multi-region input-output models have allowed researchers to
draw detailed, international supply-chain connections between production and
consumptions activities and associated environmental impacts. Due to a limited
data availability there is little evidence about the more recent trends in
global energy footprint. In order to expand the analytical potential of the
existing WIOD 2016 dataset to a wider range of research themes, this paper
develops energy accounts and presents the global energy footprint trends for
the period 2000-2014.
A Path Integral Approach to Business Cycle Models with Large Number of Agents
Date: 2018-10-16 00:00:00

This paper presents an analytical treatment of economic systems with an
arbitrary number of agents that keeps track of the systems' interactions and
agents' complexity. This formalism does not seek to aggregate agents. It rather
replaces the standard optimization approach by a probabilistic description of
both the entire system and agents'behaviors. This is done in two distinct
steps. A first step considers an interacting system involving an arbitrary
number of agents, where each agent's utility function is subject to
unpredictable shocks. In such a setting, individual optimization problems need
not be resolved. Each agent is described by a time-dependent probability
distribution centered around his utility optimum. The entire system of agents
is thus defined by a composite probability depending on time, agents'
interactions and forward-looking behaviors. This dynamic system is described by
a path integral formalism in an abstract space-the space of the agents'
actions-and is very similar to a statistical physics or quantum mechanics
system. We show that this description, applied to the space of agents'actions,
reduces to the usual optimization results in simple cases. Compared to a
standard optimization, such a description markedly eases the treatment of
systems with small number of agents. It becomes however useless for a large
number of agents. In a second step therefore, we show that for a large number
of agents, the previous description is equivalent to a more compact description
in terms of field theory. This yields an analytical though approximate
treatment of the system. This field theory does not model the aggregation of a
microeconomic system in the usual sense. It rather describes an environment of
a large number of interacting agents. From this description, various phases or
equilibria may be retrieved, along with individual agents' behaviors and their
interactions with the environment. For illustrative purposes, this paper
studies a Business Cycle model with a large number of agents.