pwc-archive/papers-with-abstracts huggingface dataset:
Partially Observed Discrete-Time Risk-Sensitive Mean Field Games
Authors: Naci Saldi, Tamer Basar, Maxim Raginsky
Date: 2020-03-24 00:00:00

In this paper, we consider discrete-time partially observed mean-field games with the risk-sensitive optimality criterion. We introduce risk-sensitivity behaviour for each agent via an exponential utility function. In the game model, each agent is weakly coupled with the rest of the population through its individual cost and state dynamics via the empirical distribution of states. We establish the mean-field equilibrium in the infinite-population limit using the technique of converting the underlying original partially observed stochastic control problem to a fully observed one on the belief space and the dynamic programming principle. Then, we show that the mean-field equilibrium policy, when adopted by each agent, forms an approximate Nash equilibrium for games with sufficiently many agents. We first consider finite-horizon cost function, and then, discuss extension of the result to infinite-horizon cost in the next-to-last section of the paper.
Experimental evaluation of beamforming on UAVs in cellular systems
Date: 2020-03-24 00:00:00

The usage of beamforming in Unmanned Aerial Vehicles (UAVs) has the potential
of significantly improving the air-to-ground link quality. This paper presents
the outcome of experimental trial of such a UAV-based beamforming system over
live cellular networks. A testbed with directional antennas has been built for
the experiments. It is shown that beamforming can extend the signal coverage
due to antenna gain, as well as spatially reduce interference leading to higher
signal quality. Moreover, it has a positive impact on the mobility performance
of a flying UAV by reducing handover occurrences. It is also discussed, in
which situations beamforming should translate into the uplink throughput gain.
Effects of number of digits in large-scale multilateration
Date: 2020-03-20 00:00:00

Since many years ago, multilateration has been used in precision engineering
notably in machine tool and coordinate measuring machine calibration. This
technique needs, first, the use of laser trackers or tracking interferometers,
and second, the use of nonlinear optimization algorithms to determine point
coordinates. Research works have shown the influence of the experimental
configuration on measure precision in multilateration. However, the impact of
floating-point precision in computations on large-scale multilateration
precision has not been addressed. In this work, the effects of numerical errors
(rounding and cancellation effects) due to floating-point precision (number of
digits) were studied. In order to evaluate these effects in large-scale
multilateration, a multilateration measurement system was simulated. This
protocol is illustrated with a case study where large distances ($\le$20 m)
between pairs of target points were simulated. The results show that the use of
multi-precision libraries is recommended to control the propagation of
uncertainties during the multilateration computation.
Photonic convolutional neural networks using integrated diffractive optics
Date: 2020-02-17 00:00:00
Tasks: Deep Learning, image-classification, Image Classification

With recent rapid advances in photonic integrated circuits, it has been
demonstrated that programmable photonic chips can be used to implement
artificial neural networks. Convolutional neural networks (CNN) are a class of
deep learning methods that have been highly successful in applications such as
image classification and speech processing. We present an architecture to
implement a photonic CNN using the Fourier transform property of integrated
star couplers. We show, in computer simulation, high accuracy image
classification using the MNIST dataset. We also model component imperfections
in photonic CNN and show that the performance degradation can be recovered in a
programmable chip. Our proposed architecture provides a large reduction in
physical footprint compared to current implementations as it utilizes the
natural advantages of optics and hence offers a scalable pathway towards
integrated photonic deep learning processors.
Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis
Date: 2020-03-26 00:00:00
Tasks: Dynamic Time Warping, Time Series Analysis

Video content is responsible for more than 70% of the global IP traffic.
Consequently, it is important for content delivery infrastructures to rapidly
detect and respond to changes in content popularity dynamics. In this paper, we
propose the employment of on-line change point (CP) analysis to implement
real-time, autonomous and low-complexity video content popularity detection.
Our proposal, denoted as real-time change point detector (RCPD), estimates the
existence, the number and the direction of changes on the average number of
video visits by combining: (i) off-line and on-line CP detection algorithms;
(ii) an improved time-series segmentation heuristic for the reliable detection
of multiple CPs; and (iii) two algorithms for the identification of the
direction of changes. The proposed detector is validated against synthetic
data, as well as a large database of real YouTube video visits. It is
demonstrated that the RCPD can accurately identify changes in the average
content popularity and the direction of change. In particular, the success rate
of the RCPD over synthetic data is shown to exceed 94% for medium and large
changes in content popularity. Additionally,the dynamic time warping distance,
between the actual and the estimated changes, has been found to range
between20sampleson average, over synthetic data, to52samples, in real data.The
rapid responsiveness of the RCPD is instrumental in the deployment of
real-time, lightweight load balancing solutions, as shown in a real example.
Intelligent Reflecting Surface Assisted Beam Index-Modulation for Millimeter Wave Communication
Authors: Sarath Gopi, Sheetal Kalyani, Lajos Hanzo
Date: 2020-03-26 00:00:00
Tasks: compressed sensing

Millimeter wave communication is eminently suitable for high-rate wireless systems, which may be beneficially amalgamated with intelligent reflecting surfaces (IRS), relying on beam-index modulation. Explicitly, we propose three different architectures based on IRSs for beam-index modulation in millimeter wave communication, which circumvent the line-of-sight blockage of millimeter wave frequencies. We conceive both the optimal maximum likelihood detector and a low-complexity compressed sensing detector for the proposed schemes. Finally, the schemes conceived are evaluated through extensive simulations, which are compared to our analytically obtained bounds.
Modelling Recovered Cases and Death Probabilities for the COVID-19 Outbreak
Date: 2020-03-26 00:00:00

From March 23rd, the data for the recovered cases of COVID-19 are missing
from the standard repository maintained by the Johns Hopkins University in
collaboration with the WHO. But since data concerning recovered patients are
extremely important for modelling the COVID-19 outbreak, a method for
estimating the missing data is provided and tested. As a byproduct, it produces
estimates for the probabilities to die $k$ days after confirmation, or to
survive after $d$ days.
A Review of Multi-Objective Deep Learning Speech Denoising Methods
Date: 2020-03-26 00:00:00
Tasks: Deep Learning, Denoising, Speech Denoising

This paper presents a review of multi-objective deep learning methods that
have been introduced in the literature for speech denoising. After stating an
overview of conventional, single objective deep learning, and hybrid or
combined conventional and deep learning methods, a review of the mathematical
framework of the multi-objective deep learning methods for speech denoising is
provided. A representative method from each speech denoising category, whose
codes are publicly available, is selected and a comparison is carried out by
considering the same public domain dataset and four widely used objective
metrics. The comparison results indicate the effectiveness of the
multi-objective method compared with the other methods, in particular when the
signal-to-noise ratio is low. Possible future improvements that can be achieved
are also mentioned.
Moving horizon-based optimal scheduling of EV charging: A power system-cognizant approach
Date: 2020-03-27 00:00:00
Tasks: Scheduling

The rapid escalation in plug-in electric vehicles (PEVs) and their
uncoordinated charging patterns pose several challenges in distribution system
operation. Some of the undesirable effects include overloading of transformers,
rapid voltage fluctuations, and over/under voltages. While this compromises the
consumer power quality, it also puts on extra stress on the local voltage
control devices. These challenges demand for a well-coordinated and power
network-aware charging approach for PEVs in a community. This paper formulates
a real-time electric vehicle charging scheduling problem as an mixed-integer
linear program (MILP). The problem is to be solved by an aggregator, that
provides charging service in a residential community. The proposed formulation
maximizes the profit of the aggregator, enhancing the utilization of available
infrastructure. With a prior knowledge of load demand and hourly electricity
prices, the algorithm uses a moving time horizon optimization approach,
allowing the number of vehicles arriving unknown. In this realistic setting,
the proposed framework ensures that power system constraints are satisfied and
guarantees desired PEV charging level within stipulated time. Numerical tests
on a IEEE 13-node feeder system demonstrate the computational and performance
superiority of the proposed MILP technique.
PDE models of adder mechanisms in cellular proliferation
Authors: Mingtao Xia, Chris D. Greenman, Tom Chou
Date: 2020-03-27 00:00:00

Cell division is a process that involves many biochemical steps and complex biophysical mechanisms. To simplify the understanding of what triggers cell division, three basic models that subsume more microscopic cellular processes associated with cell division have been proposed. Cells can divide based on the time elapsed since their birth, their size, and/or the volume added since their birth -- the timer, sizer, and adder models, respectively. Here, we propose unified adder-sizer models and investigate some of the properties of different adder processes arising in cellular proliferation. Although the adder-sizer model provides a direct way to model cell population structure, we illustrate how it is mathematically related to the well-known model in which cell division depends on age and size. Existence and uniqueness of weak solutions to our 2+1-dimensional PDE model are proved, leading to the convergence of the discretized numerical solutions and allowing us to numerically compute the dynamics of cell population densities. We then generalize our PDE model to incorporate recent experimental findings of a system exhibiting mother-daughter correlations in cellular growth rates. Numerical experiments illustrating possible average cell volume blowup and the dynamical behavior of cell populations with mother-daughter correlated growth rates are carried out. Finally, motivated by new experimental findings, we extend our adder model cases where the controlling variable is the added size between DNA replication initiation points in the cell cycle.
On-Line Synthesis of Permissive Supervisors for Partially Observed Discrete Event Systems under scLTL Constraints
Date: 2020-03-27 00:00:00

We consider a supervisory control problem of a discrete event system (DES)
under partial observation, where a control specification is given by a fragment
of linear temporal logic. We design an on-line supervisor that dynamically
computes its control action with the complete information of the product
automaton of the DES and an acceptor for the specification. The concepts of
controllability and observability are defined by means of a ranking function
defined on the product automaton, which decreases its value if an accepting
state of the product automaton is being approached. The proposed on-line
control scheme leverages the ranking function and a permissiveness function,
which represents a time-varying permissiveness level. As a result, the on-line
supervisor achieves the specification, being aware of the tradeoff between its
permissiveness and acceptance of the specification, if the product automaton is
controllable and observable.
A Network Decoupling Method for Voltage Stability Analysis Based on Holomorphic Embedding
Date: 2020-03-27 00:00:00

This paper proposes a network decoupling method based on Holomorphic
Embedding (HE) for voltage stability analysis. Using the proposed HE method
with a physical load scaling factor s, it develops a set of decoupled two-bus
circuit channels between a target bus and the swing bus. Accordingly, a
complex-valued virtual index, named {\sigma}(s), is introduced in each channel
to assess the voltage stability of prospective operating conditions with
ensured accuracy. Then the stability margin at each bus can be analyzed by
visualizing its respective {\sigma}(s) index on the {\sigma} plane with a
unified boundary of voltage collapse. Moreover, benefitted by the embedding
with physical loading, the trajectory of {\sigma}(s) for each bus with the
variation of operating conditions can be analytically given about the scaling
factor s. The effectiveness of proposed network decoupling method is verified
on the IEEE 14-bus power system.
Light Field Image Coding Using Dual Discriminator Generative Adversarial Network and VVC Temporal Scalability
Date: 2020-03-27 00:00:00
Tasks: Decoder, Generative Adversarial Network, Image Compression

Light field technology represents a viable path for providing a high-quality
VR content. However, such an imaging system generates a high amount of data
leading to an urgent need for LF image compression solution. In this paper, we
propose an efficient LF image coding scheme based on view synthesis. Instead of
transmitting all the LF views, only some of them are coded and transmitted,
while the remaining views are dropped. The transmitted views are coded using
Versatile Video Coding (VVC) and used as reference views to synthesize the
missing views at decoder side. The dropped views are generated using the
efficient dual discriminator GAN model. The selection of reference/dropped
views is performed using a rate distortion optimization based on the VVC
temporal scalability. Experimental results show that the proposed method
provides high coding performance and overcomes the state-of-the-art LF image
compression solutions.
Tuning an H-Infinity Controller with a Given Order and a Structure for Interconnected Systems with Delays
Date: 2020-03-26 00:00:00
Tasks: Sensitivity

An eigenvalue based framework is developed for the H-infinity norm analysis
and its norm minimization of coupled systems with time-delays, which are
naturally described by delay differential algebraic equations (DDAEs). For
these equations H-infinity norms are analyzed and their sensitivity with
respect to small delay perturbations is studied. Subsequently, numerical
methods for the H-infinity norm computation and for designing controllers
minimizing the H-infinity norm with a prescribed structure or order, based on a
direct optimization approach, are briefly addressed. The effectiveness of the
approach is illustrated with a software demo. The chapter concludes by pointing
out the similarities with the computation and optimization of characteristic
roots of DDAEs.
Deriving a representative variant for the functional safety development according to ISO 26262
Date: 2020-03-26 00:00:00

The emerging mass individualization in series produced road vehicles,
superseding mass customization, entails an increase in variants. The question
"How may the functional safety development effort, corresponding to the variant
numbers, be tackled while complying to the standard ISO 26262:2018?" motives
this work. The lever on containing the effort is most effective when applied in
the development life-cycle as early as possible. The initiating functional
safety artifact is the item definition. Strategic clustering of variants into a
few or even a single item-definition(s) is feasible by exploiting platform
development. Yet, the functional safety development effort for each defined
item may only be limited when basing all subsequent phases on one single
representative variant. In this work we particularly elaborate on answering the
question "Under what conditions can a representative variant be defined?",
accompanied by the notion of "How is it defined?". Therefore, we assess the ISO
26262:2018 for deriving prerequisites on a representative variant. Furthermore,
we propose a structured and efficient method to determine representative
variants given vehicle parameters and a framing context. The method is
exemplified for two easily comprehensible use-case situations in drivetrain
development. The characteristic equations may be used for future drivetrain
functional safety development.
Metabolic graphs, LIFE method and the modeling of drug action on Mycobacterium tuberculosis
Authors: Sean T. McQuade, Nathaniel J. Merrill, Benedetto Piccoli
Date: 2020-03-26 00:00:00

This paper serves as a framework for designing advanced models for drug action on metabolism. Drug treatment may affect metabolism by either enhancing or inhibiting metabolic reactions comprising a metabolic network. We introduce the concept of \textit{metabolic graphs}, a generalization of hypergraphs having specialized features common to metabolic networks. Linear-in-flux-expression (briefly LIFE) is a methodology for analyzing metabolic networks and simulating virtual patients. We extend LIFE dynamics to be compatible with metabolic graphs, including the more complex interactions of enhancer and inhibitor molecules that affect biochemical reactions. We discuss results considering network structure required for existence and uniqueness of equilibria on metabolic graphs and show simulations of drug action on \textit{Mycobacterium tuberculosis} (briefly MTB)
Bayesian Sequential Joint Detection and Estimation under Multiple Hypotheses
Authors: Dominik Reinhard, Michael Fauß, Abdelhak M. Zoubir
Date: 2020-03-27 00:00:00

We consider the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution. This problem is investigated in a sequential setup under mild assumptions on the underlying random process. The optimal method minimizes the expected number of samples while ensuring that the average detection/estimation errors do not exceed a certain level. After converting the constrained problem to an unconstrained one, we characterize the general solution by a non-linear Bellman equation, which is parametrized by a set of cost coefficients. A strong connection between the derivatives of the cost function with respect to the coefficients and the detection/estimation errors of the sequential procedure is derived. Based on this fundamental property, we further show that for suitably chosen cost coefficients the solutions of the constrained and the unconstrained problem coincide. We present two approaches to finding the optimal coefficients. For the first approach, the final optimization problem is converted into a linear program, whereas the second approach solves it with a projected gradient ascent. To illustrate the theoretical results, we consider two problems for which the optimal schemes are designed numerically. Using Monte Carlo simulations, it is validated that the numerical results agree with the theory.
Temperature dependence of COVID-19 transmission
Date: 2020-04-22 00:00:00

The recent coronavirus pandemic follows in its early stages an almost
exponential growth, with the number of cases quite well fit in time by
$N(t)\propto e^{\alpha t}$, in many countries. We analyze the rate $\alpha$ for
each country, starting from a threshold of 30 total cases and using the next 12
days, capturing thus the early growth homogeneously. We look for a link between
$\alpha$ and the average temperature $T$ of each country, in the month of the
epidemic growth. We analyze a {\it base} set of 42 countries, which developed
the epidemic earlier, an {\it intermediate} set of 88 countries and an {\it
extended} set of 125 countries, which developed the epidemic more recently.
Applying a linear fit $\alpha(T)$, we find increasing evidence for a decreasing
$\alpha$ as a function of $T$, at $99.66\%$C.L., $99.86\%$C.L. and $99.99995
\%$ C.L. ($p$-value $5 \cdot 10^{-7}$, or 5$\sigma$ detection) in the {\it
base}, {\it intermediate} and {\it extended} dataset, respectively. The
doubling time is expected to increase by $40\%\sim 50\%$, going from $5^\circ$
C to $25^\circ$ C. In the {\it base} set, going beyond a linear model, a peak
at $(7.7\pm 3.6)^\circ C$ seems to be present, but its evidence disappears for
the larger datasets. We also analyzed a possible bias: poor countries, often
located in warm regions, might have less intense testing. By excluding
countries below a given GDP per capita, we find that our conclusions are only
slightly affected and only for the {\it extended} dataset. The significance
remains high, with a $p$-value of $10^{-3}-10^{-4}$ or less. Our findings give
hope that, for northern hemisphere countries, the growth rate should
significantly decrease as a result of both warmer weather and lockdown
policies. In general the propagation should be hopefully stopped by strong
lockdown, testing and tracking policies, before the arrival of the cold season.
Synchronized Pivots
Authors: J. C. Phillips
Date: 2020-03-22 00:00:00

Thermodynamic profiles, based on bioinformatic hydropathicity scales and membrane-fitted sliding windows, exhibit accurately level sets of hydrophobic peaks or valleys in the constant fragment Fc of monoclonal antibodies. These sets provide a roadmap for selected short regions where a few directed amino acid mutations could produce either accelerated antibody kinetics or increased stability. These profiles may reveal dramatic differences in antibody activity
How macroscopic laws describe complex dynamics: asymptomatic population and CoviD-19 spreading
Date: 2020-04-19 00:00:00
Tasks: Time Series Analysis

Macroscopic growth laws, solutions of mean field equations, describe in an
effective way an underlying complex dynamics. They are applied to study the
spreading of infections, as in the case of CoviD-19, where the counting of the
cumulated number $N(t)$ of detected infected individuals is a generally
accepted, coarse-grain, variable to understand the epidemic phase. However
$N(t)$ does not take into account the unknown number of asymptomatic, not
detected, cases $A(t)$. Therefore, the question arises if the observed time
series of data of $N(t)$ is a reliable tool for monitoring the evolution of the
infectious disease. We study a system of coupled differential equations which
includes the dynamics of the spreading among symptomatic and asymptomatic
individuals and the strong containment effects due to the social isolation. The
solution is therefore compared with a macroscopic law for the population $N(t)$
coming from a single, non-linear, differential equation with no explicit
reference to $A(t)$, showing the equivalence of the two methods. Indeed, $N(t)$
takes into account a more complex and detailed population dynamics which
permits the evaluation of the number of asymptomatic individuals also. The
model is then applied to Covid-19 spreading in Italy where a transition from an
exponential behavior to a Gompertz growth for $N(t)$ has been observed in more
recent data. Then the information contained in the data analysis of $N(t)$ is
reliable to understand the epidemic phase, although it does not describe the
total infected population. The asymptomatic population is larger than the
symptomatic one in the fast growth phase of the spreading.