pwc-archive/papers-with-abstracts huggingface dataset:
Spatio-Temporal Correlation Analysis of Online Monitoring Data for Anomaly Detection and Location in Distribution Networks
Date: 2019-07-21 00:00:00
Tasks: Anomaly Detection

The online monitoring data in distribution networks contain rich information
on the running states of the networks. By leveraging the data, this paper
proposes a spatio-temporal correlation analysis approach for anomaly detection
and location in distribution networks. First, spatio-temporal matrix for each
feeder line in a distribution network is formulated and the spectrum of its
covariance matrix is analyzed. The spectrum is complex and exhibits two
aspects: 1) bulk, which arises from random noise or fluctuations and 2) spikes,
which represents factors caused by anomaly signals or fault disturbances. Then,
by connecting the estimation of the number of factors to the limiting empirical
spectral density of covariance matrices of residuals, the spatio-temporal
parameters are accurately estimated, during which free random variable
techniques are used. Based on the estimators, anomaly indicators are designed
to detect and locate the anomalies by exploring the variations of
spatio-temporal correlations in the data. The proposed approach is sensitive to
the anomalies and robust to random fluctuations, which makes it possible for
detecting early anomalies and reducing false alarming rate. Case studies on
both synthetic data and real-world online monitoring data verify the
effectiveness and advantages of the proposed approach.
Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models
Date: 2018-10-22 00:00:00

We focus on two particular aspects of model risk: the inability of a chosen
model to fit observed market prices at a given point in time (calibration
error) and the model risk due to recalibration of model parameters (in
contradiction to the model assumptions). In this context, we follow the
approach of Glasserman and Xu (2014) and use relative entropy as a pre-metric
in order to quantify these two sources of model risk in a common framework, and
consider the trade-offs between them when choosing a model and the frequency
with which to recalibrate to the market. We illustrate this approach applied to
the models of Black and Scholes (1973) and Heston (1993), using option data for
Apple (AAPL) and Google (GOOG). We find that recalibrating a model more
frequently simply shifts model risk from one type to another, without any
substantial reduction of aggregate model risk. Furthermore, moving to a more
complicated stochastic model is seen to be counterproductive if one requires a
high degree of robustness, for example as quantified by a 99 percent quantile
of aggregate model risk.
A Parametric Time Frequency-Conditional Granger Causality Method Using Ultra-regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs
Date: 2018-10-22 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Motor Imagery

Objective: This study proposes a new parametric TF (time frequency) CGC
(conditional Granger causality) method for high precision connectivity analysis
over time and frequency in multivariate coupling nonstationary systems, and
applies it to scalp and source EEG signals to reveal dynamic interaction
patterns in oscillatory neocortical sensorimotor networks. Methods: The Geweke
spectral measure is combined with the TVARX (time varying autoregressive with
exogenous input) modelling approach, which uses multiwavelets and ultra
regularized orthogonal least squares (UROLS) algorithm aided by APRESS
(adjustable prediction error sum of squares), to obtain high resolution time
varying CGC representations. The UROLS APRESS algorithm, which adopts both the
regularization technique and the ultra least squares criterion to measure not
only the signal data themselves but also the weak derivatives of them, is a
novel powerful method in constructing time varying models with good
generalization performance, and can accurately track smooth and fast changing
causalities. The generalized measurement based on CGC decomposition is able to
eliminate indirect influences in multivariate systems. Results: The proposed
method is validated on two simulations and then applied to multichannel motor
imagery (MI) EEG signals at scalp and source level, where the predicted
distributions are well recovered with high TF precision, and the detected
connectivity patterns of MI EEG data are physiologically and anatomically
interpretable and yield new insights into the dynamical organization of
oscillatory cortical networks. Conclusion: Experimental results confirm the
effectiveness of the proposed TF CGC method in tracking rapidly varying
causalities of EEG based oscillatory networks. Significance: The novel TF CGC
method is expected to provide important information of neural mechanisms of
perception and cognition.
Mathematical Modelling of Auxin Transport in Plant Tissues: Flux meets Signalling and Growth
Date: 2019-11-13 00:00:00

Plant hormone auxin has critical roles in plant growth, dependent on its
heterogeneous distribution in plant tissues. Exactly how auxin transport and
developmental processes such as growth coordinate to achieve the precise
patterns of auxin observed experimentally is not well understood. Here we use
mathematical modelling to examine the interplay between auxin dynamics and
growth and their contribution to formation of patterns in auxin distribution in
plant tissues. Mathematical models describing the auxin-related signalling
pathway, PIN and AUX1 dynamics, auxin transport, and cell growth in plant
tissues are derived. A key assumption of our models is the regulation of PIN
proteins by the auxin-responsive ARF-Aux/IAA signalling pathway, with
upregulation of PIN biosynthesis by ARFs. Models are analysed and solved
numerically to examine the long-time behaviour and auxin distribution. Changes
in auxin-related signalling processes are shown to be able to trigger
transition between passage and spot type patterns in auxin distribution. The
model was also shown to be able to generate isolated cells with oscillatory
dynamics in levels of components of the auxin signalling pathway which could
explain oscillations in levels of ARF targets that have been observed
experimentally. Cell growth was shown to have influence on PIN polarisation and
determination of auxin distribution patterns. Numerical simulation results
indicate that auxin-related signalling processes can explain the different
patterns in auxin distributions observed in plant tissues, whereas the
interplay between auxin transport and growth can explain the `reverse-fountain'
pattern in auxin distribution observed at plant root tips.
Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes
Date: 2019-10-16 00:00:00
Tasks: Survey

We examine the household-specific effects of the introduction of Time-of-Use
(TOU) electricity pricing schemes. Using a causal forest (Athey and Imbens,
2016; Wager and Athey, 2018; Athey et al., 2019), we consider the association
between past consumption and survey variables, and the effect of TOU pricing on
household electricity demand. We describe the heterogeneity in household
variables across quartiles of estimated demand response and utilise variable
importance measures.
  Household-specific estimates produced by a causal forest exhibit reasonable
associations with covariates. For example, households that are younger, more
educated, and that consume more electricity, are predicted to respond more to a
new pricing scheme. In addition, variable importance measures suggest that some
aspects of past consumption information may be more useful than survey
information in producing these estimates.
Scalable NoC-based Neuromorphic Hardware Learning and Inference
Authors: Fang Haowem, Shrestha Amar, Ma De, Qiu Qinru
Date: 2018-09-18 00:00:00

Bio-inspired neuromorphic hardware is a research direction to approach
brain's computational power and energy efficiency. Spiking neural networks
(SNN) encode information as sparsely distributed spike trains and employ
spike-timing-dependent plasticity (STDP) mechanism for learning. Existing
hardware implementations of SNN are limited in scale or do not have in-hardware
learning capability. In this work, we propose a low-cost scalable
Network-on-Chip (NoC) based SNN hardware architecture with fully distributed
in-hardware STDP learning capability. All hardware neurons work in parallel and
communicate through the NoC. This enables chip-level interconnection,
scalability and reconfigurability necessary for deploying different
applications. The hardware is applied to learn MNIST digits as an evaluation of
its learning capability. We explore the design space to study the trade-offs
between speed, area and energy. How to use this procedure to find optimal
architecture configuration is also discussed.
Nonlinear methods to quantify Movement Variability in Human-Humanoid Interaction Activities
Authors: Miguel Xochicale, Chirs Baber
Date: 2018-10-17 00:00:00
Tasks: Diagnostic, Time Series, Time Series Analysis

Human movement variability arises from the process of mastering redundant (bio)mechanical degrees of freedom to successfully accomplish any given motor task where flexibility and stability of many possible joint combinations helps to adapt to environment conditions. While the analysis of movement of variability is becoming increasingly popular as a diagnostic tool or skill performance evaluation, there are remain challenges on applying the most appropriate methods. We therefore investigate nonlinear methods such as reconstructed state space (RSSs), uniform time-delay embedding, recurrence plots (RPs) and recurrence quantification analysis (RQAs) with real-world time-series data of wearable inertial sensors. That said, twenty healthy participants imitated vertical and horizontal arm movements in normal and faster velocity from an humanoid robot. We applied nonlinear methods to the collected data to found visual differences in the patterns of RSSs and RPs and statistical differences with RQAs. We conclude that Shannon Entropy with RQA is a robust method that helps to quantify activities, types of sensors, windows lengths and level of smoothness. Hence this work might enhance the development of better diagnostic tools for applications in rehabilitation and sport science for skill performance or new forms of human-humanoid interaction for quantification of movement adaptations and motor pathologies.
The Price equation program: simple invariances unify population dynamics, thermodynamics, probability, information and inference
Date: 2018-12-14 00:00:00

The fundamental equations of various disciplines often seem to share the same
basic structure. Natural selection increases information in the same way that
Bayesian updating increases information. Thermodynamics and the forms of common
probability distributions express maximum increase in entropy, which appears
mathematically as loss of information. Physical mechanics follows paths of
change that maximize Fisher information. The information expressions typically
have analogous interpretations as the Newtonian balance between force and
acceleration, representing a partition between direct causes of change and
opposing changes in the frame of reference. This web of vague analogies hints
at a deeper common mathematical structure. I suggest that the Price equation
expresses that underlying universal structure. The abstract Price equation
describes dynamics as the change between two sets. One component of dynamics
expresses the change in the frequency of things, holding constant the values
associated with things. The other component of dynamics expresses the change in
the values of things, holding constant the frequency of things. The separation
of frequency from value generalizes Shannon's separation of the frequency of
symbols from the meaning of symbols in information theory. The Price equation's
generalized separation of frequency and value reveals a few simple invariances
that define universal geometric aspects of change. For example, the
conservation of total frequency, although a trivial invariance by itself,
creates a powerful constraint on the geometry of change. That constraint plus a
few others seem to explain the common structural forms of the equations in
different disciplines. From that abstract perspective, interpretations such as
selection, information, entropy, force, acceleration, and physical work arise
from the same underlying geometry expressed by the Price equation.
Description of Incomplete Financial Markets for the Discrete Time Evolution of Risk Assets
Date: 2018-10-22 00:00:00

In the paper, the martingales and super-martingales relative to a regular set
of measures are systematically studied. The notion of local regular
super-martingale relative to a set of equivalent measures is introduced and the
necessary and sufficient conditions of the local regularity of it in the
discrete case are founded. The regular set of measures play fundamental role
for the description of incomplete markets. In the partial case, the description
of the regular set of measures is presented. The notion of completeness of the
regular set of measures have the important significance for the simplification
of the proof of the optional decomposition for super-martingales. Using this
notion, the important inequalities for some random values are obtained. These
inequalities give the simple proof of the optional decomposition of the
majorized super-martingales. The description of all local regular
super-martingales relative to the regular set of measures is presented. It is
proved that every majorized super-martingale relative to the complete set of
measures is a local regular one. In the case, as evolution of a risk asset is
given by the discrete geometric Brownian motion, the financial market is
incomplete and a new formula for the fair price of super-hedge is founded.
Hyperfinite Construction of $G$-expectation
Date: 2018-10-22 00:00:00

The hyperfinite $G$-expectation is a nonstandard discrete analogue of
$G$-expectation (in the sense of Robinsonian nonstandard analysis). A lifting
of a continuous-time $G$-expectation operator is defined as a hyperfinite
$G$-expectation which is infinitely close, in the sense of nonstandard
topology, to the continuous-time $G$-expectation. We develop the basic theory
for hyperfinite $G$-expectations and prove an existence theorem for liftings of
(continuous-time) $G$-expectation. For the proof of the lifting theorem, we use
a new discretization theorem for the $G$-expectation (also established in this
paper, based on the work of Dolinsky et al. [Weak approximation of
$G$-expectations, Stoch. Proc. Appl. 122(2), (2012), pp.664--675]).
Global Closed-form Approximation of Free Boundary for Optimal Investment Stopping Problems
Date: 2018-10-22 00:00:00
Tasks: Form

In this paper we study a utility maximization problem with both optimal
control and optimal stopping in a finite time horizon. The value function can
be characterized by a variational equation that involves a free boundary
problem of a fully nonlinear partial differential equation. Using the dual
control method, we derive the asymptotic properties of the dual value function
and the associated dual free boundary for a class of utility functions,
including power and non-HARA utilities. We construct a global closed-form
approximation to the dual free boundary, which greatly reduces the
computational cost. Using the duality relation, we find the approximate
formulas for the optimal value function, trading strategy, and exercise
boundary for the optimal investment stopping problem. Numerical examples show
the approximation is robust, accurate and fast.
Resonant Inductive Coupling as a Potential Means for Wireless Power Transfer to Printed Spiral Coil
Date: 2018-10-19 00:00:00

This paper proposes an inductive coupled wireless power transfer system that
analyses the relationship between induced voltage and distance of resonating
inductance in a printed circuit spiral coils. The resonant frequency produced
by the circuit model of the proposed receiving and transmitting coils are
analyzed by simulation and laboratory experiment. The outcome of the two
results is compared to verify the validity of the proposed inductive coupling
system. Experimental measurements are consistent with simulations over a range
of frequencies spanning the resonance.
Simulations meet Machine Learning in Structural Biology
Date: 2018-10-19 00:00:00
Tasks: BIG-bench Machine Learning, Drug Discovery

Classical molecular dynamics (MD) simulations will be able to reach sampling
in the second timescale within five years, producing petabytes of simulation
data at current force field accuracy. Notwithstanding this, MD will still be in
the regime of low-throughput, high-latency predictions with average accuracy.
We envisage that machine learning (ML) will be able to solve both the accuracy
and time-to-prediction problem by learning predictive models using expensive
simulation data. The synergies between classical, quantum simulations and ML
methods, such as artificial neural networks, have the potential to drastically
reshape the way we make predictions in computational structural biology and
drug discovery.
A Dynamics and Stability Framework for Avian Jumping Take-off
Date: 2018-10-16 00:00:00

Jumping take-off in birds is an explosive behaviour with the goal of
providing a rapid transition from ground to airborne locomotion. An effective
jump is predicated on the need to maintain dynamic stability through the
acceleration phase. The present study concerns understanding how birds retain
control of body attitude and trajectory during take-off. Cursory observation
suggests that stability is achieved with relatively little cost. However,
analysis of the problem shows that the stability margins during jumping are
actually very small and that stability considerations play a significant role
in selection of appropriate jumping kinematics. We use theoretical models to
understand stability in prehensile take-off (from a perch) and also in
non-prehensile take-off (from the ground). The primary instability is tipping,
defined as rotation of the centre of gravity about the ground contact point.
Tipping occurs when the centre of pressure falls outside the functional foot. A
contribution of the paper is the development of graphical tipping stability
margins for both centre of gravity location and acceleration angle. We show
that the nose-up angular acceleration extends stability bounds forward and is
hence helpful in achieving shallow take-offs. The stability margins are used to
interrogate simulated take-offs of real birds using published experimental
kinematic data from a guinea fowl (ground take-off) and a diamond dove (perch
take-off). For the guinea fowl the initial part of the jump is stable, however
simulations exhibit a stuttering instability not observed experimentally that
is probably due to absence of compliance in the idealised joints. The diamond
dove model confirms that the foot provides an active torque reaction during
take-off, extending the range of stable jump angles by around 45{\deg}.
In Vivo Measurement of Blood Clot Mechanics from Computational Fluid Dynamics based on Intravital Microscopy Images
Date: 2018-10-23 00:00:00

Ischemia leading to heart attacks and strokes is the major cause of deaths in
the world. Whether an occlusion occurs or not, depends on the ability of a
growing thrombus to resist forces exerted on its structure. This manuscript
provides the first known in vivo measurement of the stresses that clots can
withstand, before yielding to the surrounding blood flow. Namely,
Lattice-Boltzmann Method flow simulations are performed based on 3D clot
geometries. The latter are estimated from intravital microscopy images of
laser-induced injuries in cremaster microvasculature of live mice. In addition
to reporting the blood clot yield stresses, we also show that the thrombus
'core' does not experience significant deformation, while its 'shell' does.
This indicates that the latter is more prone to embolization. Hence, drugs
should be designed to target the shell selectively, while leaving the core
intact (to minimize excessive bleeding). Finally, we laid down a foundation for
a nondimensionalization procedure, which unraveled a relationship between clot
mechanics and biology. Hence, the proposed framework could ultimately lead to a
unified theory of thrombogenesis, capable of explaining all clotting events.
Thus, the findings presented herein will be beneficial to the understanding and
treatment of heart attacks, strokes and hemophilia.
Cliquet option pricing in a jump-diffusion L\'{e}vy model
Date: 2018-10-23 00:00:00

We investigate the pricing of cliquet options in a jump-diffusion model. The
considered option is of monthly sum cap style while the underlying stock price
model is driven by a drifted L\'{e}vy process entailing a Brownian diffusion
component as well as compound Poisson jumps. We also derive representations for
the density and distribution function of the emerging L\'{e}vy process. In this
setting, we infer semi-analytic expressions for the cliquet option price by two
different approaches. The first one involves the probability distribution
function of the driving L\'{e}vy process whereas the second draws upon Fourier
transform techniques. With view on sensitivity analysis and hedging purposes,
we eventually deduce representations for several Greeks while putting emphasis
on the Vega.
SwitchNet: a neural network model for forward and inverse scattering problems
Authors: Khoo Yuehaw, Ying Lexing
Date: 2018-10-23 00:00:00

We propose a novel neural network architecture, SwitchNet, for solving the
wave equation based inverse scattering problems via providing maps between the
scatterers and the scattered field (and vice versa). The main difficulty of
using a neural network for this problem is that a scatterer has a global impact
on the scattered wave field, rendering typical convolutional neural network
with local connections inapplicable. While it is possible to deal with such a
problem using a fully connected network, the number of parameters grows
quadratically with the size of the input and output data. By leveraging the
inherent low-rank structure of the scattering problems and introducing a novel
switching layer with sparse connections, the SwitchNet architecture uses much
fewer parameters and facilitates the training process. Numerical experiments
show promising accuracy in learning the forward and inverse maps between the
scatterers and the scattered wave field.
The Interpretation of Linear Prediction by Interpolation Framework and Several following Results
Date: 2019-05-18 00:00:00

This paper gives a general interpretation of Linear Prediction (LP) by
interpolation framework different from the perspective of statistics. This
interpretation is proved to be useful by several following results, such as:
The mechanism of widely used least square estimation of LP coefficients can be
explained more intuitively. In data modeling, LP coefficients cannot
distinguish signals spanned by the same interpolation bases. Two new general LP
constructive methods instead of least square estimation are presented with
their upper bounds of approximation error and some properties given; one is
based on DCT-1 and the other is based on difference operator. We also establish
the relationship between LP and Taylor series.