pwc-archive/papers-with-abstracts huggingface dataset:
Physical limit to concentration sensing in a changing environment
Authors: Thierry Mora, Ilya Nemenman
Date: 2019-08-12 00:00:00

Cells adapt to changing environments by sensing ligand concentrations using specific receptors. The accuracy of sensing is ultimately limited by the finite number of ligand molecules bound by receptors. Previously derived physical limits to sensing accuracy have assumed that the concentration was constant and ignored its temporal fluctuations. We formulate the problem of concentration sensing in a strongly fluctuating environment as a non-linear field-theoretic problem, for which we find an excellent approximate Gaussian solution. We derive a new physical bound on the relative error in concentration $c$ which scales as $\delta c/c \sim (Dac\tau)^{-1/4}$ with ligand diffusivity $D$, receptor cross-section $a$, and characteristic fluctuation time scale $\tau$, in stark contrast with the usual Berg and Purcell bound $\delta c/c \sim (DacT)^{-1/2}$ for a perfect receptor sensing concentration during time $T$. We show how the bound can be achieved by a simple biochemical network downstream the receptor that adapts the kinetics of signaling as a function of the square root of the sensed concentration.
Maximum Approximated Likelihood Estimation
Date: 2019-08-12 00:00:00

Empirical economic research frequently applies maximum likelihood estimation
in cases where the likelihood function is analytically intractable. Most of the
theoretical literature focuses on maximum simulated likelihood (MSL)
estimators, while empirical and simulation analyzes often find that alternative
approximation methods such as quasi-Monte Carlo simulation, Gaussian
quadrature, and integration on sparse grids behave considerably better
numerically. This paper generalizes the theoretical results widely known for
MSL estimators to a general set of maximum approximated likelihood (MAL)
estimators. We provide general conditions for both the model and the
approximation approach to ensure consistency and asymptotic normality. We also
show specific examples and finite-sample simulation results.
Channel Estimation for MIMO Hybrid Architectures with Low Resolution ADCs for mmWave Communication
Date: 2019-10-30 00:00:00
Tasks: Quantization

Hybrid analog/digital architectures and receivers with low-resolution
analog-to-digital converters (ADCs) are two low power solutions for wireless
systems with large antenna arrays. Most prior work represents two extreme cases
in which either a small number of radio frequency (RF) chains with
full-resolution ADCs, or low-resolution ADC with a number of RF chains equal to
the number of antennas is assumed. In this paper, a generalized hybrid
architecture with a small number of RF chains and a finite number of ADC bits
is proposed. In such a system, the received signal suffers from two compression
stages. One is due to the analog processing stage and the reduced number of RF
chains, while the other is a result of signal quantization by the ADC
converters. The compression steps both complicate the estimation of the MIMO
channel. In this paper, we propose an off grid compressive channel estimator
that exploits the sparse structure of the millimeter wave channel to compensate
for the loss of information inheren to the proposed architecture.
Sparse Annotations with Random Walks for U-Net Segmentation of Biodegradable Bone Implants in Synchrotron Microtomograms
Date: 2019-08-12 00:00:00

Currently, most bone implants used in orthopedics and traumatology are
non-degradable and may need to be surgically removed later on e.g. in the case
of children. This removal is associated with health risks which could be
minimized by using biodegradable implants. Therefore, research on
magnesium-based implants is ongoing, which can be objectively quantified
through synchrotron radiation microtomography and subsequent image analysis. In
order to evaluate the suitability of these materials, e.g. their stability over
time, accurate pixelwise segmentations of these high-resolution scans are
necessary. The fully-convolutional U-Net architecture achieves a Dice
coefficient of 0.750 +/- 0.102 when trained with a small dataset with dense
expert annotations. However, extending the learning to larger databases would
require prohibitive annotation efforts. Hence, in this work we implemented and
compared new training methods that require only a small fraction of manually
annotated pixels. While directly training on these scribble annotation
deteriorates the segmentation quality by 26.8 percentage points, our new random
walk-based semi-automatic target achieves the same Dice overlap as a dense
supervision, and thus offers a more promising approach for sparse annotations.
Sampling Distributions of Optimal Portfolio Weights and Characteristics in Low and Large Dimensions
Authors: Taras Bodnar, Holger Dette, Nestor Parolya, Erik Thorsén
Date: 2019-08-12 00:00:00

Optimal portfolio selection problems are determined by the (unknown) parameters of the data generating process. If an investor wants to realise the position suggested by the optimal portfolios, he/she needs to estimate the unknown parameters and to account for the parameter uncertainty in the decision process. Most often, the parameters of interest are the population mean vector and the population covariance matrix of the asset return distribution. In this paper, we characterise the exact sampling distribution of the estimated optimal portfolio weights and their characteristics. This is done by deriving their sampling distribution by its stochastic representation. This approach possesses several advantages, {e.g.} (i) it determines the sampling distribution of the estimated optimal portfolio weights by expressions, which could be used to draw samples from this distribution efficiently; (ii) the application of the derived stochastic representation provides an easy way to obtain the asymptotic approximation of the sampling distribution. The later property is used to show that the high-dimensional asymptotic distribution of optimal portfolio weights is a multivariate normal and to determine its parameters. Moreover, a consistent estimator of optimal portfolio weights and their characteristics is derived under the high-dimensional settings. Via an extensive simulation study, we investigate the finite-sample performance of the derived asymptotic approximation and study its robustness to the violation of the model assumptions used in the derivation of the theoretical results.
Signaling gradients in surface dynamics as basis for planarian regeneration
Date: 2019-08-13 00:00:00

We introduce and analyze a mathematical model for the regeneration of
planarian flatworms. This system of differential equations incorporates
dynamics of head and tail cells which express positional control genes that in
turn translate into localized signals that guide stem cell differentiation.
Orientation and positional information is encoded in the dynamics of a long
range wnt-related signaling gradient. We motivate our model in relation to
experimental data and demonstrate how it correctly reproduces cut and graft
experiments. In particular, our system improves on previous models by
preserving polarity in regeneration, over orders of magnitude in body size
during cutting experiments and growth phases. Our model relies on tristability
in cell density dynamics, between head, trunk, and tail. In addition, key to
polarity preservation in regeneration, our system includes sensitivity of cell
differentiation to gradients of wnt-related signals %measured relative to the
tissue surface. This process is particularly relevant in a small tissue layer
close to wounds during their healing, and modeled here in a robust fashion
through dynamic boundary conditions.
A Frequency Domain Channel Estimation Based on Atomic Norm Minimization for Frequency Selective MmWave MIMO Systems
Date: 2019-08-12 00:00:00

In this study, a channel estimator for millimeter wave (mmWave) systems is
proposed. By considering the sparse nature of channels in millimeter wave band,
the channel estimation problem formulated as an atomic norm minimization
problem. Previous studies on mmWave channel estimation have focused on
frequency flat channels. However, in this study the work is based on a
frequency selective channel and adopted atomic norm minimization and reweighed
atomic norm minimization technique for channel estimation in the frequency
domain. Simulation results verify the accuracy of the atomic and reweighted
atomic norm minimization techniques.
Interactive coin offerings
Date: 2019-08-12 00:00:00

Ethereum has emerged as a dynamic platform for exchanging cryptocurrency
tokens. While token crowdsales cannot simultaneously guarantee buyers both
certainty of valuation and certainty of participation, we show that if each
token buyer specifies a desired purchase quantity at each valuation then
everyone can successfully participate. Our implementation introduces smart
contract techniques which recruit outside participants in order to circumvent
computational complexity barriers.
Random walk model from the point of view of algorithmic trading
Authors: Oleh Danyliv, Bruce Bland, Alexandre Argenson
Date: 2019-08-12 00:00:00
Tasks: Algorithmic Trading

Despite the fact that an intraday market price distribution is not normal, the random walk model of price behaviour is as important for the understanding of basic principles of the market as the pendulum model is a starting point of many fundamental theories in physics. This model is a good zero order approximation for liquid fast moving markets where the queue position is less important than the price action. In this paper we present an exact solution for the cost of the static passive slice execution. It is shown, that if a price has a random walk behaviour, there is no optimal limit level for an order execution: all levels have the same execution cost as an immediate aggressive execution at the beginning of the slice. Additionally the estimations for the risk of a limit order as well as the probability of a limit order execution as functions of the slice time and standard deviation of the price are derived.
Uncertainty Model Estimation in an Augmented Data Space for Robust State Estimation
Date: 2019-08-12 00:00:00
Tasks: State Estimation

The requirement to generate robust robotic platforms is a critical enabling
step to allow such platforms to permeate safety-critical applications (i.e.,
the localization of autonomous platforms in urban environments). One of the
primary components of such a robotic platform is the state estimation engine,
which enables the platform to reason about itself and the environment based
upon sensor readings. When such sensor readings are degraded traditional state
estimation approaches are known to breakdown. To overcome this issue, several
robust state estimation frameworks have been proposed. One such method is the
batch covariance estimation (BCE) framework. The BCE approach enables robust
state estimation by iteratively updating the measurement error uncertainty
model through the fitting of a Gaussian mixture model (GMM) to the measurement
residuals. This paper extends upon the BCE approach by arguing that the
uncertainty estimation process should be augmented to include metadata (e.g.,
the signal strength of the associated GNSS observation). The modification of
the uncertainty estimation process to an augmented data space is significant
because it increases the likelihood of a unique partitioning in the measurement
residual domain and thus provides the ability to more accurately characterize
the measurement uncertainty model. The proposed batch covariance estimation
over an augmented data-space (BCE-AD) is experimentally validated on collected
data where it is shown that a significant increase in state estimation accuracy
can be granted compared to previously proposed robust estimation techniques.
Zero Black-Derman-Toy interest rate model
Authors: Grzegorz Krzyżanowski, Ernesto Mordecki, Andrés Sosa
Date: 2019-08-12 00:00:00
Tasks: model

We propose a modification of the classical Black-Derman-Toy (BDT) interest rate tree model, which includes the possibility of a jump with small probability at each step to a practically zero interest rate. The corresponding BDT algorithms are consequently modified to calibrate the tree containing the zero interest rate scenarios. This modification is motivated by the recent 2008-2009 crisis in the United States and it quantifies the risk of a future crises in bond prices and derivatives. The proposed model is useful to price derivatives. This exercise also provides a tool to calibrate the probability of this event. A comparison of option prices and implied volatilities on US Treasury bonds computed with both the proposed and the classical tree model is provided, in six different scenarios along the different periods comprising the years 2002-2017.
Active Damping of Power Oscillations Following Frequency Changes in Low Inertia Power Systems
Date: 2019-08-12 00:00:00

The absolute requirement to increase the amount of energy generation from
renewable sources e.g. predominantly asynchronously connected wind turbines and
photovoltaic installations, may in practice during transient events (where
frequency changes are examined) excite oscillatory response of the power output
of large grid connected synchronous-generators. The response of such generators
must be controlled either by varying the applied torque of a turbine or by
altering the electromagnetic torque in the airgap. Choosing the latter, the
adequacy of a voltage regulator, particularly that of the embedded Power System
Stabilizer (PSS) circuit, is investigated using the IEEE PSS1A model for the
automatic voltage regulator of a synchronous generator driven by a gas turbine.
The response is obtained via closed form analytic solutions for both small
(linear) and large (nonlinear) scale transient events in the energy grid
system. In tandem with the analytical study, the behavior simulated with a
computer model from MatLab-SimPowerSystems is reviewed.
Comparison of coupled nonlinear oscillator models for the transient response of power generating stations connected to low inertia systems
Date: 2019-08-12 00:00:00

Coupled nonlinear oscillators, e.g., Kuramoto models, are commonly used to
analyze electrical power systems. The cage model from statistical mechanics has
also been used to describe the dynamics of synchronously connected generation
stations. Whereas the Kuramoto model is good for describing high inertia grid
systems, the cage one allows both high and low inertia grids to be modelled.
This is illustrated by comparing both the synchronization time and relaxation
towards synchronization of each model by treating their equations of motion in
a common framework rooted in the dynamics of many coupled phase oscillators. A
solution of these equations via matrix continued fractions is implemented
rendering the characteristic relaxation times of a grid-generator system over a
wide range of inertia and damping. Following an abrupt change in the dynamical
system, the power output and both generator and grid frequencies all exhibit
damped oscillations now depending on the (finite) grid inertia. In practical
applications, it appears that for a small inertia system the cage model is
preferable.
Stability Analysis of Reservoir Computers Dynamics via Lyapunov Functions
Date: 2019-09-27 00:00:00

A Lyapunov design method is used to analyze the nonlinear stability of a
generic reservoir computer for both the cases of continuous-time and
discrete-time dynamics. Using this method, for a given nonlinear reservoir
computer, a radial region of stability around a fixed point is analytically
determined. We see that the training error of the reservoir computer is lower
in the region where the analysis predicts global stability but is also affected
by the particular choice of the individual dynamics for the reservoir systems.
For the case that the dynamics is polynomial, it appears to be important for
the polynomial to have nonzero coefficients corresponding to at least one odd
power (e.g., linear term) and one even power (e.g., quadratic term).
Low-cost low-power in-vehicle occupant detection with mm-wave FMCW radar
Date: 2019-08-12 00:00:00

In this paper, we use a low-cost low-power mm-wave frequency modulated
continuous wave (FMCW) radar for the in-vehicle occupant detection. We propose
an algorithm using Capon filter for the joint range-azimuth estimation. Then,
the minimum necessary features are extracted to train machine learning
classifiers to have reasonable computational complexity while achieving high
accuracy. In addition, experiments were carried out in a minivan to detect
occupancy of each row using support vector machine (SVM). Finally, our proposed
system achieved 97.8% accuracy on average in finding the defined scenarios.
Moreover, the system can correctly identify if the vehicle is occupied or not
with 100% accuracy.
Dynamic Contract Design for Systemic Cyber Risk Management of Interdependent Enterprise Networks
Date: 2019-08-12 00:00:00
Tasks: Management

The interconnectivity of cyber and physical systems and Internet of things
has created ubiquitous concerns of cyber threats for enterprise system
managers. It is common that the asset owners and enterprise network operators
need to work with cybersecurity professionals to manage the risk by
remunerating them for their efforts that are not directly observable. In this
paper, we use a principal-agent framework to capture the service relationships
between the two parties, i.e., the asset owner (principal) and the cyber risk
manager (agent). Specifically, we consider a dynamic systemic risk management
problem with asymmetric information where the principal can only observe cyber
risk outcomes of the enterprise network rather than directly the efforts that
the manager expends on protecting the resources. Under this information
pattern, the principal aims to minimize the systemic cyber risks by designing a
dynamic contract that specifies the compensation flows and the anticipated
efforts of the manager by taking into account his incentives and rational
behaviors. We formulate a bi-level mechanism design problem for dynamic
contract design within the framework of a class of stochastic differential
games. We show that the principal has rational controllability of the systemic
risk by designing an incentive compatible estimator of the agent's hidden
efforts. We characterize the optimal solution by reformulating the problem as a
stochastic optimal control program which can be solved using dynamic
programming. We further investigate a benchmark scenario with complete
information and identify conditions that yield zero information rent and lead
to a new certainty equivalence principle for principal-agent problems. Finally,
case studies over networked systems are carried out to illustrate the
theoretical results obtained.
Random Pilot and Data Access for Massive MIMO Spatially Correlated Rayleigh Fading Channels
Date: 2019-08-15 00:00:00

Random access is necessary in crowded scenarios due to the limitation of
pilot sequences and the intermittent pattern of device activity. Nowadays, most
of the related works are based on independent and identically distributed
(i.i.d.) channels. However, massive multiple-input multiple-output (MIMO)
channels are not always i.i.d. in realistic outdoor wireless propagation
environments. In this paper, a device grouping and pilot set allocation
algorithm is proposed for the uplink massive MIMO systems over spatially
correlated Rayleigh fading channels. Firstly, devices are divided into multiple
groups, and the channel covariance matrixes of devices within the same group
are approximately orthogonal. In each group, a dedicated pilot set is assigned.
Then active devices perform random pilot and data access process. The mean
square error of channel estimation (MSE-CE) and the spectral efficiency of this
scheme are derived, and the MSE-CE can be minimized when collision devices have
non-overlapping angle of arrival (AoA) intervals. Simulation results indicate
that the MSE-CE and spectral efficiency of this protocol are improved compared
with the traditional scheme. The MSE-CE of the proposed scheme is close to the
theoretical lower bound over a wide signal-to-noise ratio (SNR) region
especially for long pilot sequence. Furthermore, the MSE-CE performance gains
are significant in high SNR and strongly correlated scenarios.
Forecast Encompassing Tests for the Expected Shortfall
Authors: Timo Dimitriadis, Julie Schnaitmann
Date: 2019-08-13 00:00:00

We introduce new forecast encompassing tests for the risk measure Expected Shortfall (ES). The ES currently receives much attention through its introduction into the Basel III Accords, which stipulate its use as the primary market risk measure for the international banking regulation. We utilize joint loss functions for the pair ES and Value at Risk to set up three ES encompassing test variants. The tests are built on misspecification robust asymptotic theory and we investigate the finite sample properties of the tests in an extensive simulation study. We use the encompassing tests to illustrate the potential of forecast combination methods for different financial assets.
A Simulative Study on Active Disturbance Rejection Control (ADRC) as a Control Tool for Practitioners
Authors: Gernot Herbst
Date: 2019-08-13 00:00:00

As an alternative to both classical PID-type and modern model-based approaches to solving control problems, active disturbance rejection control (ADRC) has gained significant traction in recent years. With its simple tuning method and robustness against process parameter variations, it puts itself forward as a valuable addition to the toolbox of control engineering practitioners. This article aims at providing a single-source introduction and reference to linear ADRC with this audience in mind. A simulative study is carried out using generic first- and second-order plants to enable a quick visual assessment of the abilities of ADRC. Finally, a modified form of the discrete-time case is introduced to speed up real-time implementations as necessary in applications with high dynamic requirements.
A general model for vegetation patterns including rhizome growth
Date: 2020-06-18 00:00:00

Vegetation patterns, a natural phenomenon observed worldwide, are typically
driven by spatially distributed feedback. However, the spatial colonization
mechanisms of clonal plants, driven by the growth of a rhizome, are usually not
considered in prototypical models. Here we propose a general equation for the
vegetation density that includes all main clonal-growth features as well as the
essential ingredients leading to spatial self-organization. This generic model
reproduces the phase diagram of a fully detailed model of clonal growth. The
relation of each term of the model with the mechanisms of clonal growth is
discussed.
Practical Active Disturbance Rejection Control: Bumpless Transfer, Rate Limitation and Incremental Algorithm
Date: 2019-08-13 00:00:00

Practical applications of controllers often impose further requirements on
the implementation beyond the actual control performance, such as the ability
to switch between manual and automatic control or between different control
laws or controller parameter settings, known as bumpless transfer. Another
common requirement is to limit the control signal in magnitude and/or rate.
This article examines and extends several discrete-time variants of active
disturbance rejection control (ADRC), which is increasingly being applied
especially in the field of power electronics and drives, in this regard.
Detailed guidelines for practical ADRC implementations with these abilities are
presented, and all features are being demonstrated with the help of simulation
examples.