pwc-archive/papers-with-abstracts huggingface dataset:
Lifetime Ruin under High-watermark Fees and Drift Uncertainty
Authors: Junbeom Lee, Xiang Yu, Chao Zhou
Date: 2019-09-03 00:00:00
Tasks: Dimensionality Reduction, Vocal Bursts Intensity Prediction

This paper aims to make a new contribution to the study of lifetime ruin problem by considering investment in two hedge funds with high-watermark fees and drift uncertainty. Due to multi-dimensional performance fees that are charged whenever each fund profit exceeds its historical maximum, the value function is expected to be multi-dimensional. New mathematical challenges arise as the standard dimension reduction cannot be applied, and the convexity of the value function and Isaacs condition may not hold in our ruin probability minimization problem with drift uncertainty. We propose to employ the stochastic Perron's method to characterize the value function as the unique viscosity solution to the associated Hamilton Jacobi Bellman (HJB) equation without resorting to the proof of dynamic programming principle. The required comparison principle is also established in our setting to close the loop of stochastic Perron's method.
GNU-Octave Como Alternativa de Simulaci\'on de Sistemas Din\'amicos No Lineales en la Ense\~nanza de la Ingenier\'ia
Date: 2019-08-29 00:00:00

This paper presents a proposed alternative to simulate non-linear dynamical
systems. This has an application in bachelor programs like: Electrical and
mechanical engineering, Networks and Telecommunications engineering, Mechanical
engineering and more, than they are taught in several public universities in
Guerrero state. Commonly, the computer devices used for simulations require of
high hardware capacity to support the simulation software. Moreover, the
simulation software in the majority of the cases is under license permission.
For these reasons, implementing a simulation lab in a public university is very
high cost. Thus, we show an alternative by using a commercial development board
Raspberry Pi supporting the GNU-Octave software, which is a free software, to
simulate non-linear dynamical systems like a 4 grades of freedom SCARA robot
and a rotational inverted pendulum. The comparision of the simulated dynamical
models in both the specialized software and the proposed free software, exhibit
the viability of the proposed alternative.
Aggregating Privacy-Conscious Distributed Energy Resources for Grid Service Provision
Date: 2020-10-26 00:00:00

The increasing adoption of advanced metering infrastructure has led to
growing concerns regarding privacy risks stemming from the high resolution
measurements. This has given rise to privacy protection techniques that
physically alter the consumer's energy load profile, masking private
information by using localised devices, e.g. batteries or flexible loads.
Meanwhile, there has also been increasing interest in aggregating the
distributed energy resources (DERs) of residential consumers to provide
services to the grid. In this paper, we propose an online distributed algorithm
to aggregate the DERs of privacy-conscious consumers to provide services to the
grid, whilst preserving their privacy. Results show that the optimisation
solution from the distributed method converges to one close to the optimum
computed using an ideal centralised solution method, balancing between grid
service provision, consumer preferences and privacy protection. More
importantly, the distributed method preserves consumer privacy, and does not
require high-bandwidth two-way communications infrastructure.
Reevaluating the performance of the Double Exponential Smoothing filter and its Control Parameters
Date: 2020-02-22 00:00:00

Double Exponential Smoothing (DES) has broad application in various fields
primarily as a forecasting tool. The values of the two smoothing parameters and
, involved in DES, are traditionally chosen by the users which yield minimum
MSE. In this work the authors endeavor to assess the performance of the DES as
a filter and tried to suggest the suitable values of the and for which DES
perform best as a filter. In this regard along with the conventional MSE
method, the dependency of the stability and other aspects associated with the
frequency response of the filter like transfer function, cutoff frequency,
bandwidth and center frequency on the smoothing parameters are also studied.
The values of the parameters close to 0.5 are found to be most appropriate when
DES acts as a filter.
Homogeneous Models of Nonlinear Circuits
Date: 2020-05-30 00:00:00

This paper develops a general approach to nonlinear circuit modelling aimed
at preserving the intrinsic symmetry of electrical circuits when formulating
reduced models. The goal is to provide a framework accommodating such
reductions in a global manner and without any loss of generality in the working
assumptions; that is, we avoid global hypotheses imposing the existence of a
classical circuit variable controlling each device. Classical (voltage/current
but also flux/charge) models are easily obtained as particular cases of a
general homogeneous model. Our approach extends the results introduced for
linear circuits in a previous paper, by means of a systematic use of global
parametrizations of smooth planar curves. This makes it possible to formulate
reduced models in terms of homogeneous variables also in the nonlinear context:
contrary to voltages and currents (and also to fluxes and charges), homogeneous
variables qualify as state variables in reduced models of uncoupled circuits
without any restriction in the characteristics of devices. The inherent
symmetry of this formalism makes it possible to address in broad generality
certain analytical problems in nonlinear circuit theory, such as the
state-space problem and related issues involving impasse phenomena, as well as
index analyses of differential-algebraic models. Our framework applies also to
circuits with memristors, and can be extended to include controlled sources and
coupling effects. Several examples illustrate the results.
Learning Koopman Eigenfunctions and Invariant Subspaces from Data: Symmetric Subspace Decomposition
Date: 2020-02-22 00:00:00

This paper develops data-driven methods to identify eigenfunctions of the
Koopman operator associated to a dynamical system and subspaces that are
invariant under the operator. We build on Extended Dynamic Mode Decomposition
(EDMD), a data-driven method that finds a finite-dimensional approximation of
the Koopman operator on the span of a predefined dictionary of functions. We
propose a necessary and sufficient condition to identify Koopman eigenfunctions
based on the application of EDMD forward and backward in time. Checking this
condition requires the comparison of the eigendecomposition of matrices whose
size grows with the size of the dictionary. To address this, we propose the
Symmetric Subspace Decomposition (SSD) algorithm which provably identifies the
maximal Koopman-invariant subspace and the Koopman eigenfunctions in the span
of the dictionary. We also introduce the Streaming Symmetric Subspace
Decomposition (SSSD) algorithm, an online method that only requires a small,
fixed memory and updates its estimate of the invariant subspace as new data is
received. We prove that, given a data set, SSSD and SSD find the same solution.
Linear robust adaptive model predictive control: Computational complexity and conservatism -- extended version
Date: 2020-03-11 00:00:00
Tasks: Model Predictive Control

In this paper, we present a robust adaptive model predictive control (MPC)
scheme for linear systems subject to parametric uncertainty and additive
disturbances. The proposed approach provides a computationally efficient
formulation with theoretical guarantees (constraint satisfaction and
stability), while allowing for reduced conservatism and improved performance
due to online parameter adaptation. A moving window parameter set
identification is used to compute a fixed complexity parameter set based on
past data. Robust constraint satisfaction is achieved by using a
computationally efficient tube based robust MPC method. The predicted cost
function is based on a least mean squares point estimate, which ensures
finite-gain $\mathcal{L}_2$ stability of the closed loop. The overall algorithm
has a fixed (user specified) computational complexity. We illustrate the
applicability of the approach and the trade-off between conservatism and
computational complexity using a numerical example.
  This paper is an extended version of~[1], and contains additional details
regarding the theoretical proof of Theorem~1, the numerical example, and the
offline computations in Appendix~A--B.
Robust Utility Maximizing Strategies under Model Uncertainty and their Convergence
Authors: Jörn Sass, Dorothee Westphal
Date: 2019-09-04 00:00:00

In this paper we investigate a utility maximization problem with drift uncertainty in a multivariate continuous-time Black-Scholes type financial market which may be incomplete. We impose a constraint on the admissible strategies that prevents a pure bond investment and we include uncertainty by means of ellipsoidal uncertainty sets for the drift. Our main results consist firstly in finding an explicit representation of the optimal strategy and the worst-case parameter, secondly in proving a minimax theorem that connects our robust utility maximization problem with the corresponding dual problem. Thirdly, we show that, as the degree of model uncertainty increases, the optimal strategy converges to a generalized uniform diversification strategy.
The influence of diversity on the measurement of functional impairment: An international validation of the Amsterdam IADL Questionnaire in 8 countries
Date: 2020-01-29 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Diversity

INTRODUCTION: To understand the potential influence of diversity on the
measurement of functional impairment in dementia, we aimed to investigate
possible bias caused by age, gender, education, and cultural differences.
METHODS: 3,571 individuals (67.1 {\pm} 9.5 years old, 44.7% female) from the
Netherlands, Spain, France, United States, United Kingdom, Greece, Serbia and
Finland were included. Functional impairment was measured using the Amsterdam
IADL Questionnaire. Item bias was assessed using differential item functioning
(DIF) analysis. RESULTS: There were some differences in activity endorsement. A
few items showed statistically significant DIF. However, there was no evidence
of meaningful item bias: effect sizes were low ({\Delta}R2 range 0-0.03).
Impact on total scores was minimal. DISCUSSION: The results imply a limited
bias for age, gender, education and culture in the measurement of functional
impairment. This study provides an important step in recognizing the potential
influence of diversity on primary outcomes in dementia research.
Scalable Double Regularization for 3D Nano-CT Reconstruction
Date: 2020-01-14 00:00:00
Tasks: CT Reconstruction

Nano-CT (computerized tomography) has emerged as a non-destructive
high-resolution cross-sectional imaging technique to effectively study the
sub-$\mu$m pore structure of shale, which is of fundamental importance to the
evaluation and development of shale oil and gas. Nano-CT poses unique
challenges to the inverse problem of reconstructing the 3D structure due to the
lower signal-to-noise ratio (than Micro-CT) at the nano-scale, increased
sensitivity to the misaligned geometry caused by the movement of object
manipulator, limited sample size, and a larger volume of data at higher
resolution. In this paper, we propose a scalable double regularization (SDR)
method to utilize the entire dataset for simultaneous 3D structural
reconstruction across slices through total variation regularization within
slices and $L_1$ regularization between adjacent slices. SDR allows information
borrowing both within and between slices, contrasting with the traditional
methods that usually build on slice by slice reconstruction. We develop a
scalable and memory-efficient algorithm by exploiting the systematic sparsity
and consistent geometry induced by such Nano-CT data. We illustrate the
proposed method using synthetic data and two Nano-CT imaging datasets of
Jiulaodong (JLD) shale and Longmaxi (LMX) shale acquired in the Sichuan Basin.
These numerical experiments show that the proposed method substantially
outperforms selected alternatives both visually and quantitatively.
Towards a reliable approach on scaling in data acquisition
Date: 2019-08-29 00:00:00

Data acquisition is an important process in the functioning of any control
system. Usually, the acquired signal is analogic, representing a continuous
physical measure, and it should be processed in a digital system based on an
analog to digital converter (ADC) and a microcontroller. The ADC provides the
converted value in ADC units, but the system and its operator need the value
expressed in physical units. In this paper we propose a novel design solution
for the scaling module, which is a key component of a digital measurement
system. The scaling module refers to fitting the sensor result of a variable
number of bits depending on the ADC resolution into physical units. A general
method for scaling is proposed and a SageMath script is presented for obtaining
easily the scaling function. In the last part of the paper, the proposed method
is validated in a case study, by calculus, and it is implemented on a low-cost
development system in order to create a wireless sensor node.
Online Dissolved Gas Analysis (DGA) Monitoring System
Date: 2019-08-31 00:00:00

Transformers are critical assets in power systems and transformer failures
can cause asset damage, customer outages, and safety concerns. Dominion Energy
has a sophisticated monitoring process for the transformers. One of the most
cost-efficient, convenient and practical transformer monitoring methods in
industry is Dissolved Gas Analysis(DGA). Leveraging new technology, on-line
transformer monitoring equipment is able to measure samples automatically. The
challenges of unstable sampling measurements and contradicted analysis results
for DGA are discussed in this paper. To provide further insight of transformer
health and support a new transformer monitoring process in Dominion Energy, a
DGA monitoring system is proposed. The DGA analysis methods used in the
monitoring system are selected based on laboratory verification results from
Dominion Energy. After derive the thresholds from IEEE standard, the solution
of the proposed monitoring system and test results are presented. In the end, a
historical transformer failure case in Dominion was analyzed and the results
indicate the monitoring system can provide prescient information and sufficient
supplemental report for making operational decisions.
On Epidemic Spreading under Mobility on Networks
Date: 2019-09-26 00:00:00

We study a coupled epidemic-mobility model in which, at each time,
individuals move in a network of spatially-distributed regions
(sub-populations) according to a Continuous Time Markov Chain (CTMC) and
subsequently interact with the local sub-population according to an SIS model.
We derive a deterministic continuum limit model describing these interactions.
We prove the existence of a disease-free equilibrium and an endemic equilibrium
under different parameter regimes and establish their (almost) global
asymptotic stability using Lyapunov techniques. For the stability of
disease-free equilibrium, we also deduce some simple sufficient conditions
which highlight the influence of mobility on the behavior of the SIS dynamics.
Finally, we numerically illustrate that the derived model provides a good
approximation to the stochastic model with a finite population and also
demonstrate the influence of the graph structure on the transient performance.
Spectral analysis for gene communities in cancer cells
Date: 2019-09-24 00:00:00

We investigate gene interaction networks in various cancer cells by spectral
analysis of the adjacency matrices. We observe localization of the networks on
hub genes which have extraordinarily many links. The eigenvector centralities
take finite values only on special nodes when the hub degree exceeds a critical
value $d_c \simeq 40$. The degree correlation function shows the disassortative
behavior in the large degrees, and the nodes whose degrees $d \gtrsim 40$ have
tendencies to link to small degree nodes. The communities of the gene networks
centered at the hub genes are extracted by the amount of node degree
discrepancies between linked nodes. We verify the Wigner-Dyson distribution of
the nearest neighbor eigenvalues spacing distribution $P(s)$ in the small
degree discrepancy communities, and the Poisson $P(s)$ in the communities of
large degree discrepancies including the hubs.
Closing the Loop: Dynamic State Estimation and Feedback Optimization of Power Grids
Authors: Miguel Picallo, Saverio Bolognani, Florian Dörfler
Date: 2019-09-06 00:00:00
Tasks: State Estimation

This paper considers the problem of online feedback optimization to solve the AC Optimal Power Flow in real-time in power grids. This consists in continuously driving the controllable power injections and loads towards the optimal set-points in time-varying conditions based on real-time measurements performed on the grid. However, instead of assuming noise-free full state measurement like recent feedback optimization approaches, we connect a dynamic State Estimation using available measurements, and study its dynamic interaction with the optimization scheme. We certify stability of this interconnection and the convergence in expectation of the state estimate and the control inputs towards the true state values and optimal set-points respectively. Additionally, we bound the resulting stochastic error. Finally, we show the effectiveness of the approach on a test case using high resolution consumption data.
Shrinkage Estimation of Network Spillovers with Factor Structured Errors
Authors: Ayden Higgins, Federico Martellosio
Date: 2019-09-06 00:00:00

This paper explores the estimation of a panel data model with cross-sectional interaction that is flexible both in its approach to specifying the network of connections between cross-sectional units, and in controlling for unobserved heterogeneity. It is assumed that there are different sources of information available on a network, which can be represented in the form of multiple weights matrices. These matrices may reflect observed links, different measures of connectivity, groupings or other network structures, and the number of matrices may be increasing with sample size. A penalised quasi-maximum likelihood estimator is proposed which aims to alleviate the risk of network misspecification by shrinking the coefficients of irrelevant weights matrices to exactly zero. Moreover, controlling for unobserved factors in estimation provides a safeguard against the misspecification that might arise from unobserved heterogeneity. The asymptotic properties of the estimator are derived in a framework where the true value of each parameter remains fixed as the total number of parameters increases. A Monte Carlo simulation is used to assess finite sample performance, and in an empirical application the method is applied to study the prevalence of network spillovers in determining growth rates across countries.
Supervised Learning Based Super-Resolution DoA Estimation Utilizing Antenna Array Extrapolation
Authors: Udaya Sampath K. P. Miriya Thanthrige, Aya Mostafa Ahmed, Aydin Sezgin
Date: 2019-09-06 00:00:00
Tasks: Dictionary Learning, Super-Resolution

In this paper, we introduce a novel algorithm that can dramatically reduce the number of antenna elements needed to accurately predict the direction of arrival (DOA) for multiple input multiple output (MIMO) radar. The new proposed algorithm predicts the received signal of a large antenna setup using reduced number of antenna by using coupled dictionary learning. Hence, this enables the MIMO radar to resolve more paths, which could not be resolved by the fewer antennas. Specifically, we overcome the problem of inaccurate DOA estimation due to a small virtual array setup. For example, we can use dictionary learning to predict 100 virtual array elements using only 25. To evaluate our algorithm, we used multiple signal classification (MUSIC) as a DOA estimation technique to estimate the DOA for non coherent multiple targets. The results show that using the predicted received signal, the proposed algorithm could resolve all the targets in the scene, which could not been resolved using only the received signal from the reduced antenna setup.
Utilizing Neurons for Digital Logic Circuits: A Molecular Communications Analysis
Date: 2019-09-18 00:00:00

With the advancement of synthetic biology, several new tools have been
conceptualized over the years as alternative treatments for current medical
procedures. Most of those applications are applied to various chronic diseases.
This work investigates how synthetically engineered neurons can operate as
digital logic gates that can be used towards bio-computing for the brain. We
quantify the accuracy of logic gates under high firing rates amid a network of
neurons and by how much it can smooth out uncontrolled neuronal firings. To
test the efficacy of our method, simulations composed of computational models
of neurons connected in a structure that represents a logic gate are performed.
The simulations demonstrated the accuracy of performing the correct logic
operation, and how specific properties such as the firing rate can play an
important role in the accuracy. As part of the analysis, the Mean squared error
is used to quantify the quality of our proposed model and predicting the
accurate operation of a gate based on different sampling frequencies. As an
application, the logic gates were used to trap epileptic seizures in a neuronal
network, where the results demonstrated the effectiveness of reducing the
firing rate. Our proposed system has the potential for computing numerous
neurological conditions of the brain.
Cooperative regulation of cellular identity in systems with intercellular communication defects
Authors: Nataliya Stankevich, Aneta Koseska
Date: 2019-08-26 00:00:00

The cooperative dynamics of cellular populations emerging from the underlying interactions determines cellular functions, and thereby their identity in tissues. Global deviations from this dynamics on the other hand reflects pathological conditions. However, how these conditions are stabilized from dis-regulation on the level of the single entities is still unclear. Here we tackle this question using generic Hodgkin-Huxley type of models that describe physiological bursting dynamics of pancreatic beta-cells, and introduce channel dis-function to mimic pathological silent dynamics. The probability for pathological behavior in beta-cell populations is $\sim100\%$ when all cell have these defects, despite the negligible size of the silent state basin of attraction for single cells. In stark contrast, in a more realistic scenario for a heterogeneous population, stabilization of the pathological state depends on the size of the sub-population which acquired the defects. However, the probability to exhibit stable pathological dynamics in this case is less than $\sim10\%$. These results therefore suggest that the physiological bursting dynamics of a population of beta-cells is cooperatively regulated, even under intercellular communication defects induced by dis-functional channels of single-cells.