pwc-archive/papers-with-abstracts huggingface dataset:
A hybrid stochastic differential reinsurance and investment game with bounded memory
Date: 2019-10-22 00:00:00
Tasks: Decision Making

This paper investigates a hybrid stochastic differential reinsurance and
investment game between one reinsurer and two insurers, including a stochastic
Stackelberg differential subgame and a non-zero-sum stochastic differential
subgame. The reinsurer, as the leader of the Stackelberg game, can price
reinsurance premium and invest its wealth in a financial market that contains a
risk-free asset and a risky asset. The two insurers, as the followers of the
Stackelberg game, can purchase proportional reinsurance from the reinsurer and
invest in the same financial market. The competitive relationship between two
insurers is modeled by the non-zero-sum game, and their decision making will
consider the relative performance measured by the difference in their terminal
wealth. We consider wealth processes with delay to characterize the bounded
memory feature. This paper aims to find the equilibrium strategy for the
reinsurer and insurers by maximizing the expected utility of the reinsurer's
terminal wealth with delay and maximizing the expected utility of the
combination of insurers' terminal wealth and the relative performance with
delay. By using the idea of backward induction and the dynamic programming
approach, we derive the equilibrium strategy and value functions explicitly.
Then, we provide the corresponding verification theorem. Finally, some
numerical examples and sensitivity analysis are presented to demonstrate the
effects of model parameters on the equilibrium strategy. We find the delay
factor discourages or stimulates investment depending on the length of delay.
Moreover, competitive factors between two insurers make their optimal
reinsurance-investment strategy interact, and reduce reinsurance demand and
reinsurance premium price.
The Value of Insider Information for Super--Replication with Quadratic Transaction Costs
Authors: Yan Dolinsky, Jonathan Zouari
Date: 2019-10-22 00:00:00

We study super--replication of European contingent claims in an illiquid market with insider information. Illiquidity is captured by quadratic transaction costs and insider information is modeled by an investor who can peek into the future. Our main result describes the scaling limit of the super--replication prices when the number of trading periods increases to infinity. Moreover, the scaling limit gives us the asymptotic value of being an insider.
How spatial frequencies and color drive object search in real-world scenes: A new eye-movement corpus
Date: 2020-03-20 00:00:00
Tasks: Object, Object Localization

When studying how people search for objects in scenes, the inhomogeneity of
the visual field is often ignored. Due to physiological limitations peripheral
vision is blurred and mainly uses coarse-grained information (i.e., low spatial
frequencies) for selecting saccade targets, whereas high-acuity central vision
uses fine-grained information (i.e., high spatial frequencies) for analysis of
details. Here we investigated how spatial frequencies and color affect object
search in real-world scenes. Using gaze-contingent filters we attenuated high
or low frequencies in central or peripheral vision while viewers searched color
or grayscale scenes. Results showed that peripheral filters and central
high-pass filters hardly affected search accuracy, whereas accuracy dropped
drastically with central low-pass filters. Peripheral filtering increased the
time to localize the target by decreasing saccade amplitudes and increasing
number and duration of fixations. The use of coarse-grained information in the
periphery was limited to color scenes. Central filtering increased the time to
verify target identity instead, especially with low-pass filters. We conclude
that peripheral vision is critical for object localization and central vision
is critical for object identification. Visual guidance during peripheral object
localization is dominated by low-frequency color information, whereas
high-frequency information, relatively independent of color, is most important
for object identification in central vision.
Adaptive-Aggressive Traders Don't Dominate
Authors: Daniel Snashall, Dave Cliff
Date: 2019-10-19 00:00:00
Tasks: Cloud Computing

For more than a decade Vytelingum's Adaptive-Aggressive (AA) algorithm has been recognized as the best-performing automated auction-market trading-agent strategy currently known in the AI/Agents literature; in this paper, we demonstrate that it is in fact routinely outperformed by another algorithm when exhaustively tested across a sufficiently wide range of market scenarios. The novel step taken here is to use large-scale compute facilities to brute-force exhaustively evaluate AA in a variety of market environments based on those used for testing it in the original publications. Our results show that even in these simple environments AA is consistently out-performed by IBM's GDX algorithm, first published in 2002. We summarize here results from more than one million market simulation experiments, orders of magnitude more testing than was reported in the original publications that first introduced AA. A 2019 ICAART paper by Cliff claimed that AA's failings were revealed by testing it in more realistic experiments, with conditions closer to those found in real financial markets, but here we demonstrate that even in the simple experiment conditions that were used in the original AA papers, exhaustive testing shows AA to be outperformed by GDX. We close this paper with a discussion of the methodological implications of our work: any results from previous papers where any one trading algorithm is claimed to be superior to others on the basis of only a few thousand trials are probably best treated with some suspicion now. The rise of cloud computing means that the compute-power necessary to subject trading algorithms to millions of trials over a wide range of conditions is readily available at reasonable cost: we should make use of this; exhaustive testing such as is shown here should be the norm in future evaluations and comparisons of new trading algorithms.
Order patterns, their variation and change points in financial time series and Brownian motion
Date: 2019-10-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

Order patterns and permutation entropy have become useful tools for studying
biomedical, geophysical or climate time series. Here we study day-to-day market
data, and Brownian motion which is a good model for their order patterns. A
crucial point is that for small lags (1 up to 6 days), pattern frequencies in
financial data remain essentially constant. The two most important order
parameters of a time series are turning rate and up-down balance. For change
points in EEG brain data, turning rate is excellent while for financial data,
up-down balance seems the best. The fit of Brownian motion with respect to
these parameters is tested, providing a new version of a forgotten test by
Bienaym'e.
The CMMV Pricing Model in Practice
Authors: Bernard De Meyer, Moussa Dabo
Date: 2019-10-22 00:00:00
Tasks: Econometrics, model

Mainstream financial econometrics methods are based on models well tuned to replicate price dynamics, but with little to no economic justification. In particular, the randomness in these models is assumed to result from a combination of exogenous factors. In this paper, we present a model originating from game theory, whose corresponding price dynamics are a direct consequence of the information asymmetry between private and institutional investors. This model, namely the CMMV pricing model, is therefore rooted in market microstructure. The pricing methods derived from it also appear to fit very well historical price data. Indeed, as evidenced in the last section of the paper, the CMMV model does a very good job predicting option prices from readily available data. It also enables to recover the dynamic of the volatility surface.
Power-Efficient Beam Pattern Synthesis via Dual Polarization Beamforming
Date: 2019-10-22 00:00:00

Beamforming is traditionally associated with coherent summation of signals
from antenna elements of the same polarization, here referred to as single
polarization beamforming (SPBF). In this paper we focus on a new method, called
dual polarization beamforming (DPBF), to design beam patterns. Instead of using
only a single element-polarization, as for SPBF, a desired beam pattern is
designed as the sum of powers for two orthogonal element-polarizations. So,
with DPBF the focus is on total radiated power beam patterns. The DPBF
technique provides additional degrees of freedom to form a desired beam pattern
such that amplitude variations in the beamforming vector can often be
significantly reduced, potentially to uniform amplitude. Phase-only
beamforming, possibly using only minor amplitude variations, is a very
interesting property, especially for active antennas, since it offers the
potential of full power amplifier utilization. In this paper we apply DPBF to
uniform linear arrays (ULAs) as well as uniform rectangular arrays (URAs). In
communication systems, it is often desired to have a second beam with identical
beam pattern but orthogonal polarization in all directions. We show how such
beams are designed with DPBF, both for ULAs and URAs.
Resolving issues of scaling for gramian based input-output pairing methods
Date: 2019-10-22 00:00:00

A key problem in process control is to decide which inputs should control
which outputs. There are multiple ways to solve this problem, among them using
gramian based measures, which include the Hankel interaction index array, the
participation matrix and the $\Sigma_{2}$ method. The gramian based measures
however have issues with input and output scaling. Generally, this is resolved
by scaling all inputs and outputs to have equal range. However, we demonstrate
how this can result in an incorrect pairing and examine alternative methods of
scaling the gramian based measures, using either row or column sums, or by
utilizing the Sinkhorn-Knopp algorithm. The benefits of these scaling
strategies are first illustrated by applying them to the control structure
selection for a heat exchanger network. Then, to more systematically analyze
the benefits of the scaling schemes, a multiple input multiple output model
generator is used to test the different schemes on a large number of systems.
This, along with implementation of automatic controller tuning, allows for a
statistical comparison of the scaling methods. This assessment shows
considerable benefits to be gained from the alternative scaling of the gramian
based measures, especially when using the Sinkhorn-Knopp algorithm. The use of
this method also has the advantage that the results are completely independent
of the original scaling of the inputs and outputs.
Hybrid data-driven physics model-based framework for enhance cyber-physical smart grid security
Date: 2019-11-15 00:00:00
Tasks: Anomaly Detection, State Estimation

This paper presents a hybrid data-driven physics model-based framework for
real time monitoring in smart grids. As the power grid transitions to the use
of smart grid technology, it's real time monitoring becomes more vulnerable to
cyber attacks like false data injections (FDI). Although smart grids
cyber-physical security has an extensive scope, this paper focuses on FDI
attacks, which are modeled as bad data. State of the art strategies for FDI
detection in real time monitoring rely on physics model-based weighted least
squares state estimation solution and statistical tests. This strategy is
inherently vulnerable by the linear approximation and the companion statistical
modeling error, which means it can be exploited by a coordinated FDI attack. In
order to enhance the robustness of FDI detection, this paper presents a
framework which explores the use of data-driven anomaly detection methods in
conjunction with physics model-based bad data detection via data fusion.
Multiple anomaly detection methods working at both the system level and
distributed local detection level are fused. The fusion takes into
consideration the confidence of the various anomaly detection methods to
provide the best overall detection results. Validation considers tests on the
IEEE 118 bus system.
Analytical Study of the Impacts of Stochastic Load Fluctuation on the Dynamic Voltage Stability Margin Using Bifurcation Theory
Date: 2019-10-22 00:00:00

This paper studies the impacts of stochastic load fluctuations, namely the
fluctuation intensity and the load power variation speed, on power system
dynamic voltage stability. Additionally, the trade-off relationship between the
two parameters is revealed, which provides important insights regarding the
potential of using energy storage to maintain voltage stability under high
uncertainty. To this end, Stochastic Differential-Algebraic Equations (SDAEs)
are used to model the stochastic load variation; bifurcation analysis is
carried out to explain the influence of stochasticity. Numerical study and
Monte Carlo simulations on the IEEE 14-bus system demonstrate that a larger
fluctuation intensity or a slower load power variation speed may lead to a
smaller voltage stability margin. To the best of authors' knowledge, this work
uncovers the impacts of the time evolution property of the driving parameters,
i.e., the load power variation speed and its trade off effect with the
fluctuation intensity on the size of the dynamic voltage stability margin.
From Random to Regular: Variation in the Patterning of Retinal Mosaics
Date: 2019-10-22 00:00:00
Tasks: Descriptive, Diagnostic, Misconceptions

The various types of retinal neurons are each positioned at their respective
depths within the retina where they are believed to be assembled as orderly
mosaics, in which like-type neurons minimize proximity to one another. Two
common statistical analyses for assessing the spatial properties of retinal
mosaics include the nearest neighbor analysis, from which an index of their
"regularity" is commonly calculated, and the density recovery profile derived
from auto-correlation analysis, revealing the presence of an exclusion zone
indicative of anti-clustering. While each of the spatial statistics derived
from these analyses, the regularity index and the effective radius, can be
useful in characterizing such properties of orderly retinal mosaics, they are
rarely sufficient for conveying the natural variation in the self-spacing
behavior of different types of retinal neurons and the extent to which that
behavior generates uniform intercellular spacing across the mosaic. We consider
the strengths and limitations of different spatial statistical analyses for
assessing the patterning in retinal mosaics, highlighting a number of
misconceptions and their frequent misuse. Rather than being diagnostic criteria
for determining simply whether a population is "regular", they should be
treated as descriptive statistics that convey variation in the factors that
influence neuronal positioning. We subsequently apply multiple spatial
statistics to the analysis of eight different mosaics in the mouse retina,
demonstrating conspicuous variability in the degree of patterning present, from
essentially random to notably regular. This variability in patterning has both
a developmental as well as a functional significance, reflecting the rules
governing the positioning of different types of neurons as the architecture of
the retina is assembled (abstract truncated).
Coverage Analysis for Energy-Harvesting UAV-assisted mmWave Cellular Networks
Date: 2019-10-22 00:00:00

In this paper, we jointly consider the downlink simultaneous wireless
information and power transfer (SWIPT) and uplink information transmission in
unmanned aerial vehicle (UAV)-assisted millimeter wave (mmWave) cellular
networks, in which the user equipment (UE) locations are modeled using Poisson
cluster processes (e.g., Thomas cluster processes or Mat\'ern cluster
processes). Distinguishing features of mmWave communications, such as different
path loss models for line-of-sight (LOS) and non-LOS (NLOS) links and
directional transmissions are taken into account. In the downlink phase, the
association probability, and energy coverages of different tier UAVs and ground
base stations (GBSs) are investigated. Moreover, we define a successful
transmission probability to jointly present the energy and
signal-to-interference-plus-noise ratio (SINR) coverages and provide general
expressions. In the uplink phase, we consider the scenario that each UAV
receives information from its own cluster member UEs. We determine the Laplace
transform of the interference components and characterize the uplink SINR
coverage. In addition, we formulate the average uplink throughput, with the
goal to identify the optimal time division multiplexing between the donwlink
and uplink phases. Through numerical results we investigate the impact of key
system parameters on the performance. We show that the network performance is
improved when the cluster size becomes smaller. In addition, we analyze the
optimal height of UAVs, optimal power splitting value and optimal time division
multiplexing that maximizes the network performance.
Algorithm for Independent Vector Extraction Based on Semi-Time-Variant Mixing Model
Authors: Zbyněk Koldovský, Václav Kautský, Tomáš Kounovský, Jaroslav Čmejla
Date: 2019-10-22 00:00:00
Tasks: Speech Extraction

A new algorithm for dynamic independent vector extraction is proposed. It is based on the mixing model where mixing parameters related to the source-of-interest (SOI) are time-variant while the separating parameters are time-invariant. A contrast function based on the quasi-likelihood approach is optimized using the Newton-Raphson approach. The update is computed without imposing the orthogonal constraint, and the orthogonality is enforced afterward. This yields an algorithm that is significantly faster than gradient-based algorithms while different from fixed-point methods, which are even faster. We show advantageous properties of the proposed algorithm compared to the fixed-point methods in an on-line processing regime where stable convergence to the SOI is the important issue. The effectiveness of the method is demonstrated in a speech extraction experiment with a dense microphone array.
Hyperspectral Super-resolution: A Coupled Nonnegative Block-term Tensor Decomposition Approach
Date: 2019-10-22 00:00:00
Tasks: Super-Resolution, Tensor Decomposition

Hyperspectral super-resolution (HSR) aims at fusing a hyperspectral image
(HSI) and a multispectral image (MSI) to produce a super-resolution image
(SRI). Recently, a coupled tensor factorization approach was proposed to handle
this challenging problem, which admits a series of advantages over the classic
matrix factorization-based methods. In particular, modeling the HSI and MSI as
low-rank tensors following the {\it canonical polyadic decomposition} (CPD)
model, the approach is able to provably identify the SRI, under some mild
conditions. However, the latent factors in the CPD model have no physical
meaning, which makes utilizing prior information of spectral images as
constraints or regularizations difficult---but using such information is often
important in practice, especially when the data is noisy. In this work, we
propose an alternative coupled tensor decomposition approach, where the HSI and
MSI are assumed to follow the {\it block-term decomposition (BTD)} model.
Notably, the new method also entails identifiability of the SRI under realistic
conditions. More importantly, when modeling a spectral image as a BTD tensor,
the latent factors have clear physical meaning, and thus prior knowledge about
spectral images can be naturally incorporated. Simulations using real
hyperspectral images are employed to showcase the effectiveness of the proposed
approach with nonnegativity constraints.
Prioritized Inverse Kinematics: Desired Task Trajectories in Nonsingular Task Spaces
Date: 2019-10-23 00:00:00

A prioritized inverse kinematics (PIK) solution can be considered as a
(regulation or output tracking) control law of a dynamical system with
prioritized multiple outputs. We propose a method that guarantees that a joint
trajectory generated from a class of PIK solutions exists uniquely in a
nonsingular configuration space. We start by assuming that desired task
trajectories stay in nonsingular task spaces and find conditions for task
trajectories to stay in a neighborhood of desired task trajectories in which we
can guarantee existence and uniqueness of a joint trajectory in a nonsingular
configuration space. Based on this result, we find a sufficient condition for
task convergence and analyze various stability notions such as stability,
uniform stability, uniform asymptotic stability, and exponential stability in
both continuous and discrete times. We discuss why the number of tasks is
limited in discrete time and show how preconditioning can be used in order to
overcome this limitation.
System Equivalence Transformation: Robust Convergence of Iterative Learning Control with Nonrepetitive Uncertainties
Date: 2019-10-23 00:00:00

For iterative learning control (ILC), one of the basic problems left to
address is how to solve the contradiction between convergence conditions for
the output tracking error and for the input signal (or error). This problem is
considered in the current paper, where the robust convergence analysis is
achieved for ILC systems in the presence of nonrepetitive uncertainties. A
system equivalence transformation (SET) is proposed for ILC such that given any
desired reference trajectories, the output tracking problems for general
nonsquare multi-input, multi-output (MIMO) systems can be equivalently
transformed into those for the specific class of square MIMO systems with the
same input and output channels. As a benefit of SET, a unified condition is
only needed to guarantee both the uniform boundedness of all system signals and
the robust convergence of the output tracking error, which avoids causing the
condition contradiction problem in implementing the double-dynamics analysis
approach to ILC. Simulation examples are included to demonstrate the validity
of our established robust ILC results.
Perceptual-assisted Adversarial Adaptation for Choroid Segmentation in optical coherence tomography
Date: 2019-10-23 00:00:00
Tasks: Segmentation

Accurate choroid segmentation in optical coherence tomography (OCT) image is
vital because the choroid thickness is a major quantitative biomarker of many
ocular diseases. Deep learning has shown its superiority in the segmentation of
the choroid region but subjects to the performance degeneration caused by the
domain discrepancies (e.g., noise level and distribution) among datasets
obtained from the OCT devices of different manufacturers. In this paper, we
present an unsupervised perceptual-assisted adversarial adaptation (PAAA)
framework for efficiently segmenting the choroid area by narrowing the domain
discrepancies between different domains. The adversarial adaptation module in
the proposed framework encourages the prediction structure information of the
target domain to be similar to that of the source domain. Besides, a perceptual
loss is employed for matching their shape information (the curvatures of
Bruch's membrane and choroid-sclera interface) which can result in a fine
boundary prediction. The results of quantitative experiments show that the
proposed PAAA segmentation framework outperforms other state-of-the-art
methods.