pwc-archive/papers-with-abstracts huggingface dataset:
Nonparametric Quantile Regressions for Panel Data Models with Large T
Date: 2020-09-29 00:00:00

This paper considers panel data models where the conditional quantiles of the
dependent variables are additively separable as unknown functions of the
regressors and the individual effects. We propose two estimators of the
quantile partial effects while controlling for the individual heterogeneity.
The first estimator is based on local linear quantile regressions, and the
second is based on local linear smoothed quantile regressions, both of which
are easy to compute in practice. Within the large T framework, we provide
sufficient conditions under which the two estimators are shown to be
asymptotically normally distributed. In particular, for the first estimator, it
is shown that $N<<T^{2/(d+4)}$ is needed to ignore the incidental parameter
biases, where $d$ is the dimension of the regressors. For the second estimator,
we are able to derive the analytical expression of the asymptotic biases under
the assumption that $N\approx Th^{d}$, where $h$ is the bandwidth parameter in
local linear approximations. Our theoretical results provide the basis of using
split-panel jackknife for bias corrections. A Monte Carlo simulation shows that
the proposed estimators and the bias-correction method perform well in finite
samples.
Impedance-Based Whole-System Modeling for a Composite Grid via Frame-Dynamics Embedding
Date: 2020-06-20 00:00:00

The paper establishes a methodology to overcome the difficulty of dynamic
frame alignment and system separation in impedance modeling of ac grids, and
thereby enables impedance-based whole-system modeling of generator-converter
composite power systems. The methodology is based on a frame-dynamics-embedding
transformation via an intermediary steady frame between local and global
frames, which yields a locally defined impedance model for each generator or
converter that does not rely on a global frame but retains all frame dynamics.
The individual impedance model can then be readily combined into a whole-system
model even for meshed networks via the proposed closed-loop formulation without
network separation. Compared to start-of-the-art impedance-based models, the
proposed method retains both frame dynamics and scalability, and is generally
applicable to various network topologies (meshed, radial, etc) and combinations
of machines (generators, motors, converters, etc). The methodology is used to
analyze the dynamic interaction between generators and converters in a
composite grid, which yields important findings and potential solutions for
unstable oscillation caused by PLL-swing coupling in low-inertia grids.
Quantile Factor Models
Date: 2020-09-23 00:00:00
Tasks: Model Selection, quantile regression, valid

Quantile Factor Models (QFM) represent a new class of factor models for
high-dimensional panel data. Unlike Approximate Factor Models (AFM), where only
location-shifting factors can be extracted, QFM also allow to recover
unobserved factors shifting other relevant parts of the distributions of
observed variables. A quantile regression approach, labeled Quantile Factor
Analysis (QFA), is proposed to consistently estimate all the quantile-dependent
factors and loadings. Their asymptotic distribution is then derived using a
kernel-smoothed version of the QFA estimators. Two consistent model selection
criteria, based on information criteria and rank minimization, are developed to
determine the number of factors at each quantile. Moreover, in contrast to the
conditions required for the use of Principal Components Analysis in AFM, QFA
estimation remains valid even when the idiosyncratic errors have heavy-tailed
distributions. Three empirical applications (regarding macroeconomic, climate
and finance panel data) provide evidence that extra factors shifting the
quantiles other than the means could be relevant in practice.
Collectivised Pension Investment with Exponential Kihlstrom--Mirman Preferences
Authors: John Armstrong, Cristin Buescu
Date: 2019-11-06 00:00:00

In a collectivised pension fund, investors agree that any money remaining in the fund when they die can be shared among the survivors. We give a numerical algorithm to compute the optimal investment-consumption strategy for an infinite collective of identical investors with exponential Kihlstrom--Mirman preferences, investing in the Black--Scholes market in continuous time but consuming in discrete time. Our algorithm can also be applied to an individual investor. We derive an analytic formula for the optimal consumption in the special case of an individual who chooses not to invest in the financial markets. We prove that our problem formulation for a fund with an infinite number of members is a good approximation to a fund with a large, but finite number of members.
Scaling in Income Inequalities and its Dynamical Origin
Authors: Zoltan Neda, Istvan Gere, Tamas S. Biro, Geza Toth, Noemi Derzsy
Date: 2019-11-06 00:00:00

We provide an analytically treatable model that describes in a unified manner income distribution for all income categories. The approach is based on a master equation with growth and reset terms. The model assumptions on the growth and reset rates are tested on an exhaustive database with incomes on individual level spanning a nine year period in the Cluj county (Romania). In agreement with our theoretical predictions we find that income distributions computed for several years collapse on a master-curve when a properly normalised income is considered. The Beta Prime distribution is appropriate to fit the collapsed data and it is shown that distributions derived for other countries are following similar trends with different fit parameters. The non-universal feature of the fit parameters suggests that for a more realistic modelling the model parameters have to be linked with specific socio-economic regulations.
Electronic Bottleneck Suppression in Next-generation Networks with Integrated Photonic Digital-to-analog Converters
Date: 2019-12-22 00:00:00

Digital-to-analog converters (DAC) are indispensable functional units in
signal processing instrumentation and wide-band telecommunication links for
both civil and military applications. Since photonic systems are capable of
high data throughput and low latency, an increasingly found system limitation
stems from the required domain-crossing such as digital-to-analog, and
electronic-to-optical. A photonic DAC implementation, in contrast, enables a
seamless signal conversion with respect to both energy efficiency and short
signal delay, often require bulky discrete optical components and
electric-optic transformation hence introducing inefficiencies. Here, we
introduce a novel coherent parallel photonic DAC concept along with an
experimental demonstration capable of performing this digital-to-analog
conversion without optic-electric-optic domain crossing. This design hence
guarantees a linear intensity weighting among bits operating at high sampling
rates, yet at a reduced footprint and power consumption compared to other
photonic alternatives. Importantly, this photonic DAC could create seamless
interfaces of next-generation data processing hardware for data-centers,
task-specific compute accelerators such as neuromorphic engines, and network
edge processing applications.
Optimal Placement of Limited PMUs for Transmission Line Outage Detection and Identification
Date: 2020-05-14 00:00:00

Phasor Measurement Unit (PMU) technology is increasingly used for real-time
monitoring applications, especially line outage detection and identification
(D&I) in the power system. Current outage D&I schemes either assume a full PMU
deployment or a partial deployment with fixed PMU placement. However, the
placement of the PMUs has a fundamental impact on the effectiveness of the D&I
scheme. Building on a dynamic relationship between the substation voltage phase
angle and active power, we formulated the optimal PMU placement problem for
outage D&I as an optimization problem readily solvable by any heuristic
algorithm. We tested the formulation using a genetic algorithm and simulated
outages of IEEE 39 bus system. The optimal placement found produces a better
D&I result of single-line outages than a randomly scattered, tree-like, and
degree-based placements.
Towards Optimal System Scheduling with Synthetic Inertia Provision from Wind Turbines
Date: 2020-05-11 00:00:00
Tasks: Scheduling

The undergoing transition from conventional to converter-interfaced renewable
generation leads to significant challenges in maintaining frequency stability
due to declining system inertia. In this paper, a novel control framework for
Synthetic Inertia (SI) provision from Wind Turbines (WTs) is proposed, which
eliminates the secondary frequency dip and allows the dynamics of SI from WTs
to be analytically integrated into the system frequency dynamics. Furthermore,
analytical system frequency constraints with SI provision from WTs are
developed and incorporated into a stochastic system scheduling model, which
enables the provision of SI from WTs to be dynamically optimized on a system
level. Several case studies are carried out on a Great Britain 2030 power
system with different penetration levels of wind generation and inclusion of
frequency response requirements in order to assess the performance of the
proposed model and analyze the influence of the improved SI control scheme on
the potential secondary frequency dip. The results demonstrate that the
inclusion of SI provision from WTs into UC can drastically impact the overall
system costs.
Multiple yield curve modelling with CBI processes
Authors: Claudio Fontana, Alessandro Gnoatto, Guillaume Szulda
Date: 2019-11-07 00:00:00
Tasks: Quantization

We develop a modelling framework for multiple yield curves driven by continuous-state branching processes with immigration (CBI processes). Exploiting the self-exciting behavior of CBI jump processes, this approach can reproduce the relevant empirical features of spreads between different interbank rates. In particular, we introduce multi-curve models driven by a flow of tempered alpha-stable CBI processes. Such models are especially parsimonious and tractable, and can generate contagion effects among different spreads. We provide a complete analytical framework, including a detailed study of discounted exponential moments of CBI processes. The proposed approach allows for explicit valuation formulae for all linear interest rate derivatives and semi-closed formulae for non-linear derivatives via Fourier techniques and quantization. We show that a simple specification of the model can be successfully calibrated to market data.
Stability and performance in transient average constrained economic MPC without terminal constraints
Date: 2020-10-20 00:00:00
Tasks: Model Predictive Control

In this paper, we investigate system theoretic properties of transient
average constrained economic model predictive control (MPC) without terminal
constraints. We show that the optimal open-loop solution passes by the optimal
steady-state for consecutive time instants. Using this turnpike property and
suitable controllability conditions, we provide closed-loop performance bounds.
Furthermore, stability is proved by combining the rotated value function with
an input-to-state (ISS) Lyapunov function of an extended state related to the
transient average constraints. The results are illustrated with a numerical
example.
A nonlinear tracking model predictive control scheme for dynamic target signals
Date: 2020-10-20 00:00:00
Tasks: Model Predictive Control, Trajectory Planning

We present a nonlinear model predictive control (MPC) scheme for tracking of
dynamic target signals. The scheme combines stabilization and dynamic
trajectory planning in one layer, thus ensuring constraint satisfaction
irrespective of changes in the dynamic target signal. For periodic target
signals we ensure exponential stability of the optimal reachable periodic
trajectory using suitable terminal ingredients and a convexity condition for
the underlying periodic optimal control problem. Furthermore, we introduce an
online optimization of the terminal set size to automate the trade-off between
fast convergence and operation close to the constraints. In addition, we show
how stabilization and dynamic trajectory planning can be formulated as
partially decoupled optimization problems, which reduces the computational
demand while ensuring recursive feasibility and convergence. The main tool to
enable the proposed design is a novel reference generic offline computation
that provides suitable terminal ingredients for tracking of dynamic reference
trajectories. The practicality of this approach is demonstrated on benchmark
examples, which demonstrates superior performance compared to state of the art
approaches.
An analysis of Uniswap markets
Authors: Guillermo Angeris, Hsien-Tang Kao, Rei Chiang, Charlie Noyes, Tarun Chitra
Date: 2019-11-08 00:00:00

Uniswap -- and other constant product markets -- appear to work well in practice despite their simplicity. In this paper, we give a simple formal analysis of constant product markets and their generalizations, showing that, under some common conditions, these markets must closely track the reference market price. We also show that Uniswap satisfies many other desirable properties and numerically demonstrate, via a large-scale agent-based simulation, that Uniswap is stable under a wide range of market conditions.
Optimal Torque Control of Permanent Magnet Synchronous Motors Using Adaptive Dynamic Programming
Date: 2020-08-17 00:00:00

In this study, a new approach based on adaptive dynamic programming (ADP) is
proposed to control permanent magnet synchronous motors (PMSMs). The objective
of this paper is to control the torque and consequently the speed of a PMSM
when an unknown load torque is applied to it. The proposed controller achieves
a fast transient response, low ripples and small steady-state error. The
control algorithm uses two neural networks, called critic and actor. The former
is utilized to evaluate the cost and the latter is used to generate control
signals. The training is done once offline and the calculated optimal weights
of actor network are used in online control to achieve fast and accurate torque
control of PMSMs. This algorithm is compared with field oriented control (FOC)
and direct torque control based on space vector modulation (DTC-SVM).
Simulations and experimental results show that the proposed algorithm provides
desirable results under both accurate and uncertain modeled dynamics. Although
the performance of FOC method is comparable with ADP under nominal conditions,
the torque and speed response of ADP is better than FOC under realistic
scenarios, that is, when parameter uncertainties exist.
OFDM With Hybrid Number and Index Modulation
Date: 2019-11-20 00:00:00

A novel transmission scheme is introduced for efficient data transmission by
conveying additional information bits through jointly changing the index and
number of active subcarriers within each orthogonal frequency division
multiplexing (OFDM) subblock. The proposed scheme is different from the
conventional OFDM-subcarrier number modulation (OFDM-SNM) and OFDM-index
modulation (OFDM-IM), in which data bits are transmitted using either number or
index of active subcarriers. The proposed modulation technique offers superior
spectral and energy efficiency compared to its counterparts OFDM-SNM and
OFDM-IM, especially at low modulation orders such as binary phase shift keying
(BPSK) that can provide high reliability and low complexity, making it suitable
for Internet of Things (IoT) applications that require better spectral and
energy efficiency while enjoying high reliability and low complexity. Bit error
rate (BER) performance analysis is provided for the proposed scheme, and Monte
Carlo simulations are presented to prove the consistency of simulated BER with
the analyzed one.
Real Signal Equalization for OQAM
Date: 2019-11-10 00:00:00

This correspondence proposes the use of a real-only equalizer (ROE), which
acts on real signals derived from the received offset quadrature amplitude
modulation (OQAM) symbols. For the same fading channel, we prove that both ROE
and the widely linear equalizer (WLE) yield equivalent outputs. Hence, these
exhibit the same performance. Our complexity analysis finds that depending on
the frame length, ROE can be computationally less complex, and save significant
signal processing time over WLE. In the adaptive normalized least mean square
implementation, ROE performs better with lower complexity than its counterpart,
for a given number of pilot bits.
Compressed Underwater Acoustic Communications for Dynamic Interaction with Underwater Vehicles
Date: 2019-11-11 00:00:00
Tasks: Data Compression

Underwater vehicles are utilized in various applications including underwater
data-collection missions. The tethered connection constrains the mission both
in distance traveled and number of vehicles that can run in the same area,
while the addition of acoustic communications onto the vehicles grants them
several functionalities. However, due to the low bandwidth of the underwater
acoustic channel-which leads to low data rates-and the time overhead imposed by
both the channel propagation delay and the processing delay by the acoustic
modems, efficient protocols are required. In this paper, an implicit
data-compression and transmission protocol is proposed to carry out
environmental monitoring missions such as adaptive sampling of physical and
chemical parameters in the water. In a semi-autonomous manner between the
vehicle and the control center, both sides keep silent in data transmission as
long as they can estimate and predict the actions of the other side, unless
environmental data and/or kinematic data are found to be unpredictable. Our
design puts the human in the loop to send high-level control commands.
Experiments were conducted using an autonomous vehicle with WHOI micro-modems
in the Raritan River, Somerset, Carnegie Lake in Princeton, and in the Marine
Park in Red Bank, all in New Jersey.
Variable Gain Gradient Descent-based Reinforcement Learning for Robust Optimal Tracking Control of Uncertain Nonlinear System with Input-Constraints
Date: 2020-06-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent times, a variety of Reinforcement Learning (RL) algorithms have
been proposed for optimal tracking problem of continuous time nonlinear systems
with input constraints. Most of these algorithms are based on the notion of
uniform ultimate boundedness (UUB) stability, in which normally higher learning
rates are avoided in order to restrict oscillations in state error to smaller
values. However, this comes at the cost of higher convergence time of critic
neural network weights. This paper addresses that problem by proposing a novel
tuning law containing a variable gain gradient descent for critic neural
network that can adjust the learning rate based on Hamilton-Jacobi-Bellman
(HJB) error. By allowing high learning rate the proposed variable gain gradient
descent tuning law could improve the convergence time of critic neural network
weights. Simultaneously, it also results in tighter residual set, on which
trajectories of augmented system converge to, leading to smaller oscillations
in state error. A tighter bound for UUB stability of the proposed update
mechanism is proved. Numerical studies are then presented to validate the
robust Reinforcement Learning control scheme in controlling a continuous time
nonlinear system.
Unsupervised Training for Deep Speech Source Separation with Kullback-Leibler Divergence Based Probabilistic Loss Function
Date: 2019-11-11 00:00:00

In this paper, we propose a multi-channel speech source separation with a
deep neural network (DNN) which is trained under the condition that no clean
signal is available. As an alternative to a clean signal, the proposed method
adopts an estimated speech signal by an unsupervised speech source separation
with a statistical model. As a statistical model of microphone input signal, we
adopts a time-varying spatial covariance matrix (SCM) model which includes
reverberation and background noise submodels so as to achieve robustness
against reverberation and background noise. The DNN infers intermediate
variables which are needed for constructing the time-varying SCM. Speech source
separation is performed in a probabilistic manner so as to avoid overfitting to
separation error. Since there are multiple intermediate variables, a loss
function which evaluates a single intermediate variable is not applicable.
Instead, the proposed method adopts a loss function which evaluates the output
probabilistic signal directly based on Kullback-Leibler Divergence (KLD).
Gradient of the loss function can be back-propagated into the DNN through all
the intermediate variables. Experimental results under reverberant conditions
show that the proposed method can train the DNN efficiently even when the
number of training utterances is small, i.e., 1K.
Tumor stabilization induced by T-cell recruitment fluctuations
Authors: Irina Bashkirtseva, Lev Ryashko, Álvaro G. López, Jesus M. Seoane, Miguel A. F. Sanjuán
Date: 2019-11-07 00:00:00

The influence of random fluctuations on the recruitment of effector cells towards a tumor is studied by means of a stochastic mathematical model. Aggressively growing tumors are confronted against varying intensities of the cell-mediated immune response for which chaotic and periodic oscillations coexist together with stable tumor dynamics. A thorough parametric analysis of the noise-induced transition from this oscillatory regime to complete tumor dominance is carried out. A hysteresis phenomenon is uncovered, which stabilizes the tumor at its carrying capacity and drives the healthy and the immune cell populations to their extinction. Furthermore, it is shown that near a crisis bifurcation such transitions occur under weak noise intensities. Finally, the corresponding noise-induced chaos-order transformation is analyzed and discussed in detail.
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development
Date: 2019-11-12 00:00:00
Tasks: Bayesian Inference, Drug Discovery, Gaussian Processes, Model Selection

Biomedical data, particularly in the field of genomics, has characteristics
which make it challenging for machine learning applications - it can be sparse,
high dimensional and noisy. Biomedical applications also present challenges to
model selection - whilst powerful, accurate predictions are necessary, they
alone are not sufficient for a model to be deemed useful. Due to the nature of
the predictions, a model must also be trustworthy and transparent, empowering a
practitioner with confidence that its use is appropriate and reliable. In this
paper, we propose that this can be achieved through the use of judiciously
built feature sets coupled with Bayesian models, specifically Gaussian
processes. We apply Gaussian processes to drug discovery, using inexpensive
transcriptomic profiles from human cell lines to predict animal kidney and
liver toxicity after treatment with specific chemical compounds. This approach
has the potential to reduce invasive and expensive animal testing during
clinical trials if in vitro human cell line analysis can accurately predict
model animal phenotypes. We compare results across a range of feature sets and
models, to highlight model importance for medical applications.
Transcranial direct current stimulation to remediate myasthenia gravis symptoms
Date: 2019-11-11 00:00:00

Our findings indicated that brain stimulation exerted no effect on the
patients cognitive functions. The study outcome suggest that tDCS over primary
motor cortex may be considered as a potential nonpharmochological treatment
add-on in MG. Larger-sized studies need to evaluate the significance of this
approach is real-life practice.