pwc-archive/papers-with-abstracts huggingface dataset:
Contaminants removal and bacterial activity enhancement along the flow path of constructed wetland microbial fuel cells
Date: 2020-03-19 00:00:00

Microbial fuel cells implemented in constructed wetlands (CW-MFCs), albeit a
relatively new technology still under study, have shown to improve treatment
efficiency of urban wastewater. So far the vast majority of CW-MFC systems
investigated were designed as lab-scale systems working under rather
unrealistic hydraulic conditions using synthetic wastewater. The main objective
of this work was to quantify CW-MFCs performance operated under different
conditions in a more realistic setup using meso-scale systems with horizontal
flow fed with real urban wastewater. Operational conditions tested were organic
loading rate (4.9+-1.6, 6.7+-1.4 and 13.6+-3.2 g COD/m2.day) and hydraulic
regime (continuous vs intermittent feeding) as well as different electrical
connections: CW control (conventional CW without electrodes), open-circuit
CW-MFC (external circuit between anode and cathode not connected) and
closed-circuit CW-MFC (external circuit connected). Eight horizontal subsurface
flow CWs were operated for about four months. Each wetland consisted of a PVC
reservoir of 0.193 m2 filled with 4/8 mm granitic riverine gravel. All wetlands
had intermediate sampling points for gravel and interstitial liquid sampling.
The CW-MFCs were designed as three MFCs incorporated one after the other along
the flow path of the CWs. Results showed no significant differences between
tested organic loading rates, hydraulic regimes or electrical connections,
however, on average, systems operated in closed-circuit CW-MFC mode under
continuous flow outperformed the other experimental conditions. Closed-circuit
CW-MFC compared to conventional CW control systems showed around 5% and 22%
higher COD and ammonium removal, respectively. Correspondingly, overall
bacteria activity, as measured by the fluorescein diacetate technique, was
higher (4% to 34%) in closed-circuit systems when compared to CW control
systems.
Step-wise target controllability identifies dysregulated pathways of macrophage networks in multiple sclerosis
Authors: Giulia Bassignana, Jennifer Fransson, Vincent Henry, Olivier Colliot, Violetta Zujovic, Fabrizio De Vico Fallani
Date: 2020-03-19 00:00:00

Identifying the nodes that have the potential to influence the state of a network is a relevant question for many complex systems. In many applications it is often essential to test the ability of an individual node to control a specific target subset of the network. In biological networks, this might provide precious information on how single genes regulate the expression of specific groups of molecules in the cell. Taking into account these constraints, we propose an optimized heuristic based on the Kalman rank condition to quantify the centrality of a node as the number of target nodes it can control. By introducing a hierarchy among the nodes in the target set, and performing a step-wise research, we ensure for sparse and directed networks the identification of a controllable driver-target configuration in a significantly reduced space and time complexity. We show how the method works for simple network configurations, then we use it to characterize the inflammatory pathways in molecular gene networks associated with macrophage dysfunction in patients with multiple sclerosis. Results indicate that the targeted secreted molecules can in general be controlled by a large number of driver nodes (51%) involved in different cell functions, i.e. sensing, signaling and transcription. However, during the inflammatory response only a moderate fraction of all the possible driver-target pairs are significantly coactivated, as measured by gene expression data obtained from human blood samples. Notably, they differ between multiple sclerosis patients and healthy controls, and we find that this is related to the presence of dysregulated genes along the controllable walks. Our method, that we name step-wise target controllability, represents a practical solution to identify controllable driver-target configurations in directed complex networks and test their relevance from a functional perspective.
The generalized Price equation: forces that change population statistics
Date: 2020-03-19 00:00:00
Tasks: Diversity

The Price equation partitions the change in the expected value of a
population measure. The first component describes the partial change caused by
altered frequencies. The second component describes the partial change caused
by altered measurements. In biology, frequency changes often associate with the
direct effect of natural selection. Measure changes reflect processes during
transmission that alter trait values. More broadly, the two components describe
the direct forces that change population composition and the altered frame of
reference that changes measured values. The classic Price equation is limited
to population statistics that can expressed as the expected value of a measure.
Many statistics cannot be expressed as expected values, such as the harmonic
mean and the family of rescaled diversity measures. We generalize the Price
equation to any population statistic that can be expressed as a function of
frequencies and measurements. We obtain the generalized partition between the
direct forces that cause frequency change and the altered frame of reference
that changes measurements.
Pilot Pattern Design for Deep Learning-Based Channel Estimation in OFDM Systems
Date: 2020-03-19 00:00:00
Tasks: Decoder, feature selection

In this paper, we present a downlink pilot design scheme for Deep Learning
(DL) based channel estimation (ChannelNet) in orthogonal frequency-division
multiplexing (OFDM) systems. Specifically, in the proposed scheme, a feature
selection method named Concrete Autoencoder (ConcreteAE) is used to find the
most informative locations for pilot transmission. This autoencoder consists of
a concrete layer as the encoder and a multilayer perceptron (MLP) as the
decoder. During the training, the concrete layer selects the most informative
pilot locations, and the decoder reconstructs an approximate estimation of the
channel. Eventually, the ChannelNet is trained on the output of the ConcreteAE
aiming to reconstruct the ideal channel response. The estimation error results
show that this approach outperforms the previously presented ChannelNet with a
uniformly distributed pilot pattern, and its performance is comparable to the
minimum mean square error (MMSE).
Trend analysis of the COVID-19 pandemic in China and the rest of the world
Date: 2020-03-19 00:00:00

The recent epidemic of Coronavirus (COVID-19) that started in China has
already been "exported" to more than 140 countries in all the continents,
evolving in most of them by local spreading. In this contribution we analyze
the trends of the cases reported in all the Chinese provinces, as well as in
some countries that, until March 15th, 2020, have more than 500 cases reported.
Notably and differently from other epidemics, the provinces did not show an
exponential phase. The data available at the Johns Hopkins University site seem
to fit well an algebraic sub-exponential growing behavior as was pointed out
recently. All the provinces show a clear and consistent pattern of slowing down
with growing exponent going nearly zero, so it can be said that the epidemic
was contained in China. On the other side, the more recent spread in countries
like, Italy, Iran, and Spain show a clear exponential growth, as well as other
European countries. Even more recently, US -which was one of the first
countries to have an individual infected outside China (Jan 21st, 2020)- seems
to follow the same path. We calculate the exponential growth of the most
affected countries, showing the evolution along time after the first local
case. We identify clearly different patterns in the analyzed data and we give
interpretations and possible explanations for them. The analysis and
conclusions of our study can help countries that, after importing some cases,
are not yet in the local spreading phase, or have just started.
Uniformly accurate effective equations for disease transmission mediated by pair formation dynamics
Date: 2020-03-20 00:00:00
Tasks: valid

We derive and asymptotically analyze mass-action models for disease spread
that include transient pair formation and dissociation. Populations of unpaired
susceptibles and infecteds are distinguished from the population of three types
of pairs of individuals; both susceptible, one susceptible and one infected,
and both infected. Disease transmission can occur only within a pair consisting
of one susceptible individual and one infected individual. By considering the
fast pair formation and fast pair dissociation limits, we use a perturbation
expansion to formally derive a uniformly valid approximation for the dynamics
of the total infected and susceptible populations. Under different parameter
regimes, we derive uniformly valid effective equations for the total infected
population and compare their results to those of the full mass-action model.
Our results are derived from the fundamental mass-action system without
implicitly imposing transmission mechanisms such as that used in
frequency-dependent models. They provide a new formulation for effective
pairing models and are compared with previous models.
Acoustic Scene Classification using Audio Tagging
Date: 2020-04-19 00:00:00
Tasks: Acoustic Scene Classification, Audio Tagging, Classification, Scene Classification, TAG

Acoustic scene classification systems using deep neural networks classify
given recordings into pre-defined classes. In this study, we propose a novel
scheme for acoustic scene classification which adopts an audio tagging system
inspired by the human perception mechanism. When humans identify an acoustic
scene, the existence of different sound events provides discriminative
information which affects the judgement. The proposed framework mimics this
mechanism using various approaches. Firstly, we employ three methods to
concatenate tag vectors extracted using an audio tagging system with an
intermediate hidden layer of an acoustic scene classification system. We also
explore the multi-head attention on the feature map of an acoustic scene
classification system using tag vectors. Experiments conducted on the detection
and classification of acoustic scenes and events 2019 task 1-a dataset
demonstrate the effectiveness of the proposed scheme. Concatenation and
multi-head attention show a classification accuracy of 75.66 % and 75.58 %,
respectively, compared to 73.63 % accuracy of the baseline. The system with the
proposed two approaches combined demonstrates an accuracy of 76.75 %.
Complex risk statistics with scenario analysis
Authors: Fei Sun, Yichuan Dong
Date: 2020-03-19 00:00:00
Tasks: Management

Complex risk is a critical factor for both intelligent systems and risk management. In this paper, we consider a special class of risk statistics, named complex risk statistics. Our result provides a new approach for addressing complex risk, especially in deep neural networks. By further developing the properties related to complex risk statistics, we are able to derive dual representation for such risk.
Power Assisted Trend Following
Authors: Andreas A. Aigner, Walter Schrabmair
Date: 2020-03-20 00:00:00

'The trend is your friend' is a common saying, the difficulty lies in determining if and when you are in a trend. Is the trend strong enough to trade? When does the trend reverse and how are you going to determine this? We will try and answer at least some of these questions here. We are deriving a novel indicator to measure the power of a trend using digital signal processing techniques, separating the Signal from the Noise. We apply these to examples as well as real data and evaluate the accuracy of these and the relation to PNL performance of the 'Volatility Index' trend following algorithm devised by J. Welles Wilder Jr. in 1978.
Graham's Formula for Valuing Growth Stocks
Authors: Andreas A. Aigner, Walter Schrabmair
Date: 2020-03-20 00:00:00

Benjamin Graham introduced a very simple formula for valuing a growth stock in 1962. How does it work and why? What is a sensible way to calculate this across many stocks and provide a scoring system to compare stocks amongst each other? We are presenting a methodology here which is put into practice.
The early phase of the COVID-19 outbreak in Lombardy, Italy
Date: 2020-03-20 00:00:00

In the night of February 20, 2020, the first case of novel coronavirus
disease (COVID-19) was confirmed in the Lombardy Region, Italy. In the week
that followed, Lombardy experienced a very rapid increase in the number of
cases. We analyzed the first 5,830 laboratory-confirmed cases to provide the
first epidemiological characterization of a COVID-19 outbreak in a Western
Country. Epidemiological data were collected through standardized interviews of
confirmed cases and their close contacts. We collected demographic backgrounds,
dates of symptom onset, clinical features, respiratory tract specimen results,
hospitalization, contact tracing. We provide estimates of the reproduction
number and serial interval. The epidemic in Italy started much earlier than
February 20, 2020. At the time of detection of the first COVID-19 case, the
epidemic had already spread in most municipalities of Southern-Lombardy. The
median age for of cases is 69 years (range, 1 month to 101 years). 47% of
positive subjects were hospitalized. Among these, 18% required intensive care.
The mean serial interval is estimated to be 6.6 days (95% CI, 0.7 to 19). We
estimate the basic reproduction number at 3.1 (95% CI, 2.9 to 3.2). We
estimated a decreasing trend in the net reproduction number starting around
February 20, 2020. We did not observe significantly different viral loads in
nasal swabs between symptomatic and asymptomatic. The transmission potential of
COVID-19 is very high and the number of critical cases may become largely
unsustainable for the healthcare system in a very short-time horizon. We
observed a slight decrease of the reproduction number, possibly connected with
an increased population awareness and early effect of interventions. Aggressive
containment strategies are required to control COVID-19 spread and catastrophic
outcomes for the healthcare system.
Traffic Abstractions of Nonlinear Homogeneous Event-Triggered Control Systems
Date: 2020-03-20 00:00:00
Tasks: Scheduling

In previous work, linear time-invariant event-triggered control (ETC) systems
were abstracted to finite-state systems that capture the original systems'
sampling behaviour. It was shown that these abstractions can be employed for
scheduling of communication traffic in networks of ETC loops. In this paper, we
extend this framework to the class of nonlinear homogeneous systems, however
adopting a different approach in a number of steps. Finally, we discuss how the
proposed methodology could be extended to general nonlinear systems.
A Correlated Random Coefficient Panel Model with Time-Varying Endogeneity
Authors: Louise Laage
Date: 2020-03-20 00:00:00

This paper studies a class of linear panel models with random coefficients. We do not restrict the joint distribution of the time-invariant unobserved heterogeneity and the covariates. We investigate identification of the average partial effect (APE) when fixed-effect techniques cannot be used to control for the correlation between the regressors and the time-varying disturbances. Relying on control variables, we develop a constructive two-step identification argument. The first step identifies nonparametrically the conditional expectation of the disturbances given the regressors and the control variables, and the second step uses ``between-group'' variations, correcting for endogeneity, to identify the APE. We propose a natural semiparametric estimator of the APE, show its $\sqrt{n}$ asymptotic normality and compute its asymptotic variance. The estimator is computationally easy to implement, and Monte Carlo simulations show favorable finite sample properties. Control variables arise in various economic and econometric models, and we propose applications of our argument in several models. As an empirical illustration, we estimate the average elasticity of intertemporal substitution in a labor supply model with random coefficients.
Excitation of Orthogonal Radiation States
Authors: Michal Masek, Lukas Jelinek, Miloslav Capek
Date: 2020-03-18 00:00:00

A technique of designing antenna excitation realizing orthogonal states is presented. It is shown that a symmetric antenna geometry is required in order to achieve orthogonality with respect to all physical quantities. A maximal number of achievable orthogonal states and a minimal number of ports required to excite them are rigorously determined from the knowledge of an antenna's symmetries. The number of states and number of ports are summarized for commonly used point groups (a rectangle, a square, etc.). The theory is applied to an example of a rectangular rim where the positions of ports providing the best total active reflection coefficient, an important metric in multi-port systems, are determined. The described technique can easily be implemented in existing solvers based on integral equations.
Neural Network aided quarantine control model estimation of COVID spread in Wuhan, China
Date: 2020-03-18 00:00:00

In a move described as unprecedented in public health history, starting 24
January 2020, China imposed quarantine and isolation restrictions in Wuhan, a
city of more than 10 million people. This raised the question: is mass
quarantine and isolation effective as a social tool in addition to its
scientific use as a medical tool? In an effort to address this question, using
a epidemiological model driven approach augmented by machine learning, we show
that the quarantine and isolation measures implemented in Wuhan brought down
the effective reproduction number R(t) of the CoVID-19 spread from R(t) > 1 to
R(t) <1 within a month after the imposition of quarantine control measures in
Wuhan, China. This ultimately resulted in a stagnation phase in the infected
case count in Wuhan. Our results indicate that the strict public health
policies implemented in Wuhan may have played a crucial role in halting down
the spread of infection and such measures should potentially be implemented in
other highly affected countries such as South Korea, Italy and Iran to curtail
spread of the disease. Finally, our forecasting results predict a stagnation in
the quarantine control measures implemented in Wuhan towards the end of March
2020; this would lead to a subsequent stagnation in the effective reproduction
number at R(t) <1. We warn that immediate relaxation of the quarantine measures
in Wuhan may lead to a relapse in the infection spread and a subsequent
increase in the effective reproduction number to R(t) >1. Thus, it may be wise
to relax quarantine measures after sufficient time has elapsed, during which
maximum of the quarantined/isolated individuals are recovered.
Engine and Aftertreatment Co-Optimization of Connected HEVs via Multi-Range Vehicle Speed Planning and Prediction
Date: 2020-03-20 00:00:00
Tasks: Management, Prediction, Vehicle Speed Estimation

Connected vehicles (CVs) have situational awareness that can be exploited for
control and optimization of the powertrain system. While extensive studies have
been carried out for energy efficiency improvement of CVs via eco-driving and
planning, the implication of such technologies on the thermal responses of CVs
has not been fully investigated. One of the key challenges in leveraging
connectivity for optimization-based thermal management of CVs is the relatively
slow thermal dynamics, which necessitate the use of a long prediction horizon
to achieve the best performance. Long-term prediction of the CV speed, unlike
the V2V/V2I-based short-range prediction, is difficult and error-prone. The
multiple timescales inherent to power and thermal systems call for a variable
timescale optimization framework with access to short- and long-term vehicle
speed preview. To this end, a model predictive controller (MPC) with a
multi-range speed preview for integrated power and thermal management (iPTM) of
connected hybrid electric vehicles (HEVs) is presented in this paper. The MPC
is formulated to manage the power-split between the engine and the battery
while enforcing the power and thermal (engine coolant and catalytic converter
temperatures) constraints. The MPC exploits prediction and optimization over a
shorter receding horizon and longer shrinking horizon. Over the longer
shrinking horizon, the vehicle speed estimation is based on the data collected
from the connected vehicles traveling on the same route as the ego-vehicle.
Simulation results of applying the MPC over real-world urban driving cycles in
Ann Arbor, MI are presented to demonstrate the effectiveness and fuel-saving
potentials of the proposed iPTM strategy under the uncertainty associated with
long-term predictions of the CV's speed.
On a quarantine model of coronavirus infection and data analysis
Date: 2020-03-20 00:00:00

Attempts to curb the spread of coronavirus by introducing strict quarantine
measures apparently have different effect in different countries: while the
number of new cases has reportedly decreased in China and South Korea, it still
exhibit significant growth in Italy and other countries across Europe. In this
brief note, we endeavour to assess the efficiency of quarantine measures by
means of mathematical modelling. Instead of the classical SIR model, we
introduce a new model of infection progression under the assumption that all
infected individual are isolated after the incubation period in such a way that
they cannot infect other people. Disease progression in this model is
determined by the basic reproduction number $\mathcal{R}_0$ (the number of
newly infected individuals during the incubation period), which is different
compared to that for the standard SIR model. If $\mathcal{R}_0 >1$, then the
number of latently infected individuals exponentially grows. However, if
$\mathcal{R}_0 <1$ (e.g.~due to quarantine measures and contact restrictions
imposed by public authorities), then the number of infected decays
exponentially. We then consider the available data on the disease development
in different countries to show that there are three possible patterns: growth
dynamics, growth-decays dynamics, and patchy dynamics (growth-decay-growth).
Analysis of the data in China and Korea shows that the peak of infection
(maximum of daily cases) is reached about 10 days after the restricting
measures are introduced. During this period of time, the growth rate of the
total number of infected was gradually decreasing. However, the growth rate
remains exponential in Italy. Arguably, it suggests that the introduced
quarantine is not sufficient and stricter measures are needed.
A simple mathematical model for the evolution of the corona virus
Date: 2020-03-20 00:00:00

The goal of this note is to present a simple mathematical model with two
parameters for the number of deaths due to the corona (COVID-19) virus. The
model only requires basic knowledge in differential calculus, and can also be
understood by pupils attending secondary school. The model can easily be
implemented on a computer, and we will illustrate it on the basis of case
studies for different countries.