pwc-archive/papers-with-abstracts huggingface dataset:
Fast Nonconvex SDP Solvers for Large-scale Power System State Estimation
Date: 2019-06-19 00:00:00
Tasks: Computational Efficiency, Decision Making, State Estimation

Fast power system state estimation (SE) solution is of paramount importance
for achieving real-time decision making in power grid operations. Semidefinite
programming (SDP) reformulation has been shown effective to obtain the global
optimum for the nonlinear SE problem, while suffering from high computational
complexity. Thus, we leverage the recent advances in nonconvex SDP approach
that allows for the simple first-order gradient-descent (GD) updates. Using the
power system model, we can verify that the SE objective function enjoys nice
properties (strongly convex, smoothness) which in turn guarantee a linear
convergence rate of the proposed GD-based SE method. To further accelerate the
convergence speed, we consider the accelerated gradient descent (AGD)
extension, as well as their robust versions under outlier data and a hybrid
GD-based SE approach with additional synchrophasor measurements. Numerical
tests on the IEEE 118-bus, 300-bus and the synthetic ACTIVSg2000-bus systems
have demonstrated that FGD-SE and AGD-SE, can approach the near-optimal
performance of the SDP-SE solution at significantly improved computational
efficiency, especially so for AGD-SE.
Exploiting Weak Head Movements for Camera-based Respiration Detection
Date: 2019-06-19 00:00:00

In recent years, considerable progress has been made in the non-contact based
detection of the respiration rate from video sequences. Common techniques
either directly assess the movement of the chest due to breathing or are based
on analyzing subtle color changes that occur as a result of hemodynamic
properties of the skin tissue by means of remote photoplethysmography (rPPG).
However, extracting hemodynamic parameters from rPPG is often difficult
especially if the skin is not visible to the camera. In contrast, extracting
respiratory signals from chest movements turned out to be a robust method.
However, the detectability of chest regions cannot be guaranteed in any
application scenario, for instance if the camera setting is optimized to
provide close-up images of the head. In such a case an alternative method for
respiration detection is required.
  It is reasonable to assume that the mechanical coupling between chest and
head induces minor movements of the head which, like in rPPG, can be detected
from subtle color changes as well. Although the strength of these movements is
expected to be much smaller in scale, sensing these intensity variations could
provide a reasonably suited respiration signal for subsequent respiratory rate
analysis.
  In order to investigate this coupling we conducted an experimental study with
12 subjects and applied motion- and rPPG-based methods to estimate the
respiratory frequency from both head regions and chest. Our results show that
it is possible to derive signals correlated to chest movement from facial
regions. The method is a feasible alternative to rPPG-based respiratory rate
estimations when rPPG-signals cannot be derived reliably and chest movement
detection cannot be applied as well.
Optical Wireless cabin communication system
Date: 2019-10-15 00:00:00
Tasks: Diversity

Offering a high data rate to users inside the cabin is attractive for the
aircraft building companies. This paper presents an optical wireless system
that uses visible light for transmitting data. The reading light for each seat
is used as a transmitter in this work to offer high data rates. Red, yellow,
green, and blue (RYGB) laser diode (LD) are utilised in each reading light to
obtain a high modulation bandwidth. Two types of reading lights based on angle
diversity transmitter (ADT) units are utilised in this paper for illumination
and communication. Additionally, two kinds of optical receivers are used: four
branches angle diversity receiver (ADR) and 25 pixels imaging receiver (ImR).
The delay spread and the signal to interference and noise ratio (SINR) are
evaluated. The proposed system can offer high data rates up to 22.8 Gbps for
each user by using simple on-off-keying (OOK) modulation.
Co-existence of Micro, Pico and Atto Cells in Optical Wireless Communication
Date: 2019-10-15 00:00:00
Tasks: Diversity, PICO

Interference between cells or users can have a significant impact on the
quality of optical wireless communication (OWC) links. This paper studies the
co-existence of infrared based Micro cells with Visible light communication
based Pico and Atto Cells for downlink communication. The signal to noise ratio
(SNR) of each cell and the signal to interference and noise ratio (SINR)
between cells are evaluated when an angle diversity receiver and an imaging
receiver are used. The results show that Atto cell systems provide higher SNR
and data rates compared to the Pico cell systems which provide higher SNR and
data rates compared to Micro cell systems. The Micro cell systems however
provide mobility as they provide a larger coverage area, followed by the Pico
cell systems.
Anionic nanoparticle-lipid membrane interactions: the protonation of anionic ligands at the membrane surface reduces membrane disruption
Date: 2019-05-30 00:00:00

Monolayer-protected gold nanoparticles (Au NPs) are promising biomedical
tools with applications to diagnosis and therapy, thanks to their
biocompatibility and versatility. Here we show how the NP surface
functionalization can drive the mechanism of interaction with lipid membranes.
In particular, we show that the spontaneous protonation of anionic carboxylic
groups on the NP surface can make the NP-membrane interaction faster and less
disruptive.
Resilient Leader-Follower Consensus with Time-Varying Leaders in Discrete-Time Systems
Date: 2019-06-19 00:00:00

The problem of consensus in the presence of adversarially behaving agents has
been studied extensively in the literature. The proposed algorithms typically
guarantee that the consensus value lies within the convex hull of initial
normal agents' states. In leader-follower consensus problems however, the
objective for normally behaving agents is to track a time-varying reference
state that may take on values outside of this convex hull. In this paper we
present a method for agents with discrete-time dynamics to resiliently track a
set of leaders' common time-varying reference state despite a bounded subset of
the leaders and followers behaving adversarially. The efficacy of our results
are demonstrated through simulations.
Hunchback promoters can readout morphogenetic positional information in less than a minute
Date: 2019-06-21 00:00:00

The first cell fate decisions in the developing fly embryo are made very
rapidly : hunchback genes decide in a few minutes whether a given nucleus
follows the anterior or the posterior developmental blueprint by reading out
the positional information encoded in the Bicoid morphogen. This developmental
system constitutes a prototypical instance of the broad spectrum of regulatory
decision processes that combine speed and accuracy. Traditional arguments based
on fixed-time sampling of Bicoid concentration indicate that an accurate
readout is not possible within the short times observed experimentally. This
raises the general issue of how speed-accuracy tradeoffs are achieved. Here, we
compare fixed-time sampling strategies to decisions made on-the-fly, which are
based on updating and comparing the likelihoods of being at an anterior or a
posterior location. We found that these more efficient schemes can complete
reliable cell fate decisions even within the very short embryological
timescales. We discuss the influence of promoter architectures on the mean
decision time and decision error rate and present concrete promoter
architectures that allow for the fast readout of the morphogen. Lastly, we
formulate explicit predictions for new experiments involving Bicoid mutants.
On Distributed Estimation in Hierarchical Power Constrained Wireless Sensor Networks
Date: 2020-04-30 00:00:00

We consider distributed estimation of a random source in a hierarchical power
constrained wireless sensor network. Sensors within each cluster send their
measurements to a cluster head (CH). CHs optimally fuse the received signals
and transmit to the fusion center (FC) over orthogonal fading channels. To
enable channel estimation at the FC, CHs send pilots, prior to data
transmission. We derive the mean square error (MSE) corresponding to the linear
minimum mean square error (LMMSE) estimator of the source at the FC, and derive
the Bayesian Cramer-Rao bound (CRB). Our goal is to find (i) the optimal
training power, (ii) the optimal power that sensors in a cluster spend to
transmit their amplified measurements to their CH, and (iii) the optimal weight
vector employed by each CH for its linear signal fusion, such that the MSE is
minimized, subject to a network power constraint. To untangle the performance
gain that optimizing each set of these variables provide, we also analyze three
special cases of the original problem, where in each special case, only two
sets of variables are optimized across clusters. We define three factors that
allow us to quantify the effectiveness of each power allocation scheme in
achieving an MSE-power tradeoff that is close to that of the Bayesian CRB.
Combining the information gained from the factors and Bayesian CRB with our
computational complexity analysis provides the system designer with
quantitative complexity-versus-MSE improvement tradeoffs offered by different
power allocation schemes.
A novel linear battery energy storage system (BESS) life loss calculation model for BESS-integrated wind farm in scheduled power tracking
Date: 2019-10-27 00:00:00
Tasks: Relation

Recently, rapid development of battery technology makes it feasible to
integrate renewable generations with battery energy storage system (BESS). The
consideration of BESS life loss for different BESS application scenarios is
economic imperative. In this paper, a novel linear BESS life loss calculation
model for BESS-integrated wind farm in scheduled power tracking is proposed.
Firstly, based on the life cycle times-depth of discharge (DOD) relation-curve,
the BESS life loss coefficient for unit throughput energy with different state
of charge (SOC) can be determined from the life cycle times-DOD relation-curve
fitting function directly. Secondly, as unidirectional variation of SOC in a
single time step, the BESS life loss can be calculated through integration of
the life loss coefficient-SOC relation function. A linear BESS life loss
calculation model is established through self-optimal piecewise linearization
of the primitive function of the life loss coefficient-SOC relation function.
Thirdly, the proposed life loss calculation model is incorporated in the
BESS-integrated wind farm scheduled power tracking optimization. Case studies
demonstrate that with the proposed method, the BESS life loss item can be
incorporated in the optimization model effectively, and the scheduled power
tracking cost of the BESS-integrated wind farm can be determined and optimized
more comprehensively.
UAV-Enabled Covert Wireless Data Collection
Date: 2019-06-20 00:00:00
Tasks: Scheduling

This work considers unmanned aerial vehicle (UAV) networks for collecting
data covertly from ground users. The full-duplex UAV intends to gather critical
information from a scheduled user (SU) through wireless communication and
generate artificial noise (AN) with random transmit power in order to ensure a
negligible probability of the SU's transmission being detected by the
unscheduled users (USUs). To enhance the system performance, we jointly design
the UAV's trajectory and its maximum AN transmit power together with the user
scheduling strategy subject to practical constraints, e.g., a covertness
constraint, which is explicitly determined by analyzing each USU's detection
performance, and a binary constraint induced by user scheduling. The formulated
design problem is a mixed-integer non-convex optimization problem, which is
challenging to solve directly, but tackled by our developed penalty successive
convex approximation (P-SCA) scheme. An efficient UAV trajectory initialization
is also presented based on the Successive Hover-and-Fly (SHAF) trajectory,
which also serves as a benchmark scheme. Our examination shows the developed
P-SCA scheme significantly outperforms the benchmark scheme in terms of
achieving a higher max-min average transmission rate from all the SUs to the
UAV.
Detecting long-range attraction between migrating cells based on p-value distributions
Authors: Claus Metzner
Date: 2019-06-20 00:00:00

Immune cells have evolved to recognize and eliminate pathogens, and the efficiency of this process can be measured in a Petri dish. Yet, even if the cells are time-lapse recorded and tracked with high resolution, it is difficult to judge whether the immune cells find their targets by mere chance, or if they approach them in a goal-directed way, perhaps using remote sensing mechanisms such as chemotaxis. To answer this question, we assign to each step of an immune cell a 'p-value', the probability that a move, at least as target-directed as observed, can be explained with target-independent migration behavior. The resulting distribution of p-values is compared to the distribution of a reference system with randomized target positions. By using simulated data, based on various chemotactic search mechanisms, we demonstrate that our method can reliably distinguish between blind migration and target-directed 'hunting' behavior.
Dual-control based approach to batch process operation under uncertainty based on optimality-conditions parameterization
Date: 2019-06-20 00:00:00
Tasks: Computational Efficiency

This paper presents a scheme for dual robust control of batch processes under
parametric uncertainty. The dual-control paradigm arises in the context of
adaptive control. A trade-off should be decided between the control actions
that (robustly) optimize the plant performance and between those that excite
the plant such that unknown plant model parameters can be learned precisely
enough to increase the robust performance of the plant. Some recently proposed
approaches can be used to tackle this problem, however, this will be done at
the price of conservativeness or significant computational burden. In order to
increase computational efficiency, we propose a scheme that uses parameterized
conditions of optimality in the adaptive predictive-control fashion. The dual
features of the controller are incorporated through scenario-based
(multi-stage) approach, which allows for modeling of the adaptive robust
decision problem and for projecting this decision into predictions of the
controller. The proposed approach is illustrated on a case study from batch
membrane filtration.
The dichotomy of distributed and centralized control: METRO-HAUL, when control planes collide for 5G networks
Date: 2019-06-20 00:00:00
Tasks: Management

Automating the provisioning of 5G services, deployed over a heterogeneous
infrastructure (in terms of domains, technologies, and management platforms),
remains a complex task, yet driven by the constant need to provide end-to-end
connections at network slices at reducing costs and service deployment time. At
the same time, such services are increasingly conceived around interconnected
functions and require allocation of computing, storage, and networking
resources.
  The METRO-HAUL 5G research initiative acknowledges the need for automation
and strives to develop an orchestration platform for services and resources
that extends, integrates, and builds on top of existing approaches,
macroscopically adopting Transport Software Defined Networking principles, and
leveraging the programmability and open control of Transport SDN.
Quantifying impacts of the drought 2018 on European ecosystems in comparison to 2003
Date: 2019-07-15 00:00:00

In recent decades, an increasing persistence of atmospheric circulation
patterns has been observed. In the course of the associated long-lasting
anticyclonic summer circulations, heat waves and drought spells often coincide,
leading to so-called hotter droughts. Previous hotter droughts caused a
decrease in agricultural yields and increase in tree mortality, and thus, had a
remarkable effect on carbon budgets and negative economic impacts.
Consequently, a quantification of ecosystem responses to hotter droughts and a
better understanding of the underlying mechanisms is crucial. In this context,
the European hotter drought of the year 2018 may be considered as a key event.
As a first step towards the quantification of its causes and consequences, we
here assess anomalies of atmospheric circulation patterns, temperature loads,
and climatic water balance as potential drivers of ecosystem responses as
quantified by remote sensing using the MODIS vegetation indices NDVI and EVI.
To place the drought of 2018 within a climatological context, we compare its
climatic features and ecosystem response with the extreme hot drought of 2003.
Our results indicated 2018 to be characterized by a climatic dipole, featuring
extremely hot and dry weather conditions north of the Alps but comparably cool
and moist conditions across large parts of the Mediterranean. Analyzing
ecosystem response of five dominant land-cover classes, we found significant
positive effects of April-July climatic water balance on ecosystem
productivity. Negative drought impacts appeared to affect a larger area in 2018
compared to 2003. We found a significantly higher sensitivity of pastures and
arable land to climatic water balance compared to forests in both years. This
study quantifies the drought of 2018 as a yet unprecedented event and provides
valuable insights into the heterogeneous drought responses of European
ecosystems.
Towards a consensus on rheological models for shear waves in soft tissues
Date: 2019-09-19 00:00:00

A rising wave of technologies and instruments are enabling more labs and
clinics to make a variety of measurements related to tissue viscoelastic
properties. These instruments include elastography imaging scanners,
rheological shear viscometers, and a variety of calibrated stress-strain
analyzers. From these many sources of disparate data, a common step in
analyzing results is to fit the measurements of tissue response to some
viscoelastic model. In the best scenario, this places the measurements within a
theoretical framework and enables meaningful comparisons of the parameters
against other types of tissues. However, there is a large set of established
rheological models, even within the class of linear, causal, viscoelastic solid
models, so which of these should be chosen? Is it simply a matter of best fit
to a minimum mean squared error of the model to several data points? We argue
that the long history of biomechanics, including the concept of the extended
relaxation spectrum, along with data collected from viscoelastic soft tissues
over an extended range of times and frequencies, and the theoretical framework
of multiple relaxation models which model the multi-scale nature of physical
tissues, all lead to the conclusion that fractional derivative models represent
the most succinct and meaningful models of soft tissue viscoelastic behavior.
These arguments are presented with the goal of clarifying some distinctions
between, and consequences of, some of the most commonly used models, and with
the longer term goal of reaching a consensus among different sub-fields in
acoustics, biomechanics, and elastography that have common interests in
comparing tissue measurements.
Kam method for Cryo-EM particle reconstruction
Date: 2019-06-18 00:00:00

The Cryo-EM 3D particle reconstruction is essential for identifying protein
and uncover the biological mechanism of the macro-molecules. In this paper, we
use Kam method for reconstruction. Kam method is \textit{ab-initio}, and it
assumes that the projection angles of the particle are uniformly distributed.
Based on the data covariance matrix, we compute the radial frequency component
of the matrix. The particle density function can be obtained by the radial
frequency component and the angular frequency basis function. In order to
uniquely and accurately identify the radial frequency component, an initial
guess of the structure is applied. Experiment shows that Kam method works for
low resolution particle reconstruction. Further improvement can be made by
including projection angle distribution in covariance matrix, and applying the
fast algorithm to enhance computation speed.
A model for the generalized droop formula
Date: 2019-06-19 00:00:00
Tasks: model, Relation

We provide a new analytical model that fully justifies the recently disclosed
Generalized Droop Formula of the nonlinear signal-to-noise (SNR) ratio in
very-long submarine links with power-mode amplifiers, and show its relation
with the Gaussian-Noise model SNR.
Petri-net modeling of B-cell receptor signaling pathways: A case study in CLL
Authors: Gajendra Pratap Singh, Madhuri Jha
Date: 2019-06-20 00:00:00

Immunology is the emerging research area which deals with the study of the immune system in any living organism. It is modelled through various computational and mathematical models to deal with the problem facing while to boost the immune system of an organism or to fight with the infectious disease at the very initial stage. Such models are very important for a better understanding of the complex behaviour of pathways inside the cells. The signalling pathways between the cells are complex and difficult to visualize in the immune system of human beings. So, it's important to study the function of these cells separately. T-cells and B-cells are an important part of the immune system and both have their own receptors and their different signalling pathways by which they deal with any antigens. In this paper, we discuss the B-cell receptor and its different signalling pathways downstream of the BCR. We designed a Petri-net model of the process of gathering antigens through B-cells independent of T-cell and the effect of that in the immune system of the organism. We will also discuss the contribution of BCR in the selection of the precursor tumour cell in CLL.