pwc-archive/papers-with-abstracts huggingface dataset:
A multiple attribute model resolves a conflict between additive and multiplicative models of incentive salience
Date: 2018-12-20 00:00:00
Tasks: Attribute

A model of incentive salience as a function of stimulus value and
interoceptive state has been previously proposed. In that model, the function
differs depending on whether the stimulus is appetitive or aversive; it is
multiplicative for appetitive stimuli and additive for aversive stimuli. The
authors argued it was necessary to capture data on how extreme changes in salt
appetite could move evaluation of an extreme salt solution from negative to
positive. We demonstrate that arbitrarily varying this function is unnecessary,
and that a multiplicative function is sufficient if one assumes the incentive
salience function for an incentive (such as salt) is comprised of multiple
stimulus features and multiple interoceptive signals. We show that it is also
unnecessary considering the dual-structure approach-aversive nature of the
reward system, which results in separate weighting of appetitive and aversive
stimulus features.
Algebraic graph learning of protein-ligand binding affinity
Date: 2018-12-20 00:00:00
Tasks: Graph Learning

Although algebraic graph theory based models have been widely applied in
physical modeling and molecular studies, they are typically incompetent in the
analysis and prediction of biomolecular properties when compared with other
quantitative approaches. There is a need to explore the capability and
limitation of algebraic graph theory for molecular and biomolecular modeling,
analysis, and prediction. In this work, we propose novel algebraic graph
learning (AGL) models that encode high-dimensional physical and biological
information into intrinsically low-dimensional representations. The proposed
AGL model introduces multiscale weighted colored subgraphs to describe crucial
molecular and biomolecular interactions via graph invariants associated with
the graph Laplacian, its pseudo-inverse, and adjacent matrix. Additionally, the
AGL models are incorporated with an advanced machine learning algorithm to
connect the low-dimensional graph representation of biomolecular structures
with their macroscopic properties. Three popular protein-ligand binding
affinity benchmarks, namely CASF-2007, CASF-2013, and CASF-2016, are employed
to validate the accuracy, robustness, and reliability of the present AGL model.
Numerical results indicate that the proposed AGL method outperforms the other
state-of-the-art methods in the binding affinity predictions of the
protein-ligand complexes.
Stochastic comparisons of the largest claim amounts from two sets of interdependent heterogeneous portfolios
Date: 2018-12-14 00:00:00

Let $ X_{\lambda_1},\ldots,X_{\lambda_n}$ be dependent non-negative random
variables and $Y_i=I_{p_i} X_{\lambda_i}$, $i=1,\ldots,n$, where
$I_{p_1},\ldots,I_{p_n}$ are independent Bernoulli random variables independent
of $X_{\lambda_i}$'s, with ${\rm E}[I_{p_i}]=p_i$, $i=1,\ldots,n$. In actuarial
sciences, $Y_i$ corresponds to the claim amount in a portfolio of risks. In
this paper, we compare the largest claim amounts of two sets of interdependent
portfolios, in the sense of usual stochastic order, when the variables in one
set have the parameters $\lambda_1,\ldots,\lambda_n$ and $p_1,\ldots,p_n$ and
the variables in the other set have the parameters
$\lambda^{*}_1,\ldots,\lambda^{*}_n$ and $p^*_1,\ldots,p^*_n$. For
illustration, we apply the results to some important models in actuary.
An Improved Decentralized Control of Grid-Connected Cascaded Inverters with Different Power Capacities
Date: 2018-12-20 00:00:00

The existing decentralized control for cascaded inverters is based on the
assumption that all modules have same capacities, and a local
fixed-amplitude-varied-phase voltage control is adopted for each inverter
module. However, available source power capacities of cascaded inverters may be
different in some practical applications. To address this issue, this letter
proposes an improved decentralized control scheme, in which the voltage
amplitudes are varied according to their individual available powers. Moreover,
a power factor consistency control is proposed to achieve autonomous voltage
phase synchronization. The steady-state analysis and synchronization mechanism
of cascaded inverters are illustrated. In addition, the proposed strategy has
other advantages, such as adjustable grid power factor and immune to the grid
voltage fault. The effectiveness of the proposed control is tested by
experiments.
On the variance of internode distance under the multispecies coalescent
Date: 2018-12-20 00:00:00

We consider the problem of estimating species trees from unrooted gene tree
topologies in the presence of incomplete lineage sorting, a common phenomenon
that creates gene tree heterogeneity in multilocus datasets. One popular class
of reconstruction methods in this setting is based on internode distances, i.e.
the average graph distance between pairs of species across gene trees. While
statistical consistency in the limit of large numbers of loci has been
established in some cases, little is known about the sample complexity of such
methods. Here we make progress on this question by deriving a lower bound on
the worst-case variance of internode distance which depends linearly on the
corresponding graph distance in the species tree. We also discuss some
algorithmic implications.
Model Based Iterative Reconstruction With Spatially Adaptive Sinogram Weights for Wide-Cone Cardiac CT
Date: 2018-12-20 00:00:00
Tasks: Image Reconstruction

With the recent introduction of CT scanners with large cone angles, wide
coverage detectors now provide a desirable scanning platform for cardiac CT
that allows whole heart imaging in a single rotation. On these scanners, while
half-scan data is strictly sufficient to produce images with the best temporal
resolution, acquiring a full 360 degree rotation worth of data is beneficial
for wide-cone image reconstruction at negligible additional radiation dose.
Applying Model-Based Iterative Reconstruction (MBIR) algorithm to the heart has
shown to yield significant enhancement in image quality for cardiac CT. But
imaging the heart in large cone angle geometry leads to apparently conflicting
data usage considerations. On the one hand, in addition to using the fastest
available scanner rotation speed, a minimal complete data set of 180 degrees
plus the fan angle is typically used to minimize both cardiac and respiratory
motion. On the other hand, a full 360 degree acquisition helps better handle
the challenges of missing frequencies and incomplete projections associated
with wide-cone half-scan data acquisition. In this paper, we develop a
Spatially Adaptive sinogram Weights MBIR algorithm (SAW-MBIR) that is designed
to achieve the benefits of both half and full-scan reconstructions in order to
maximize temporal resolution over the heart region while providing stable
results over the whole volume covered with the wide-area detector.
Spatially-adaptive sinogram weights applied to each projection measurement in
SAW-MBIR are designed to selectively perform backprojection from the full and
half-scan portion of the sinogram based on both projection angle and
reconstructed voxel location. We demonstrate with experimental results of
SAW-MBIR applied to whole-heart cardiac CT clinical data that overall temporal
resolution matches half-scan while full volume image quality is on par with
full-scan MBIR.
2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation
Date: 2018-12-20 00:00:00
Tasks: Denoising, GPU, Image Reconstruction

While Model Based Iterative Reconstruction (MBIR) of CT scans has been shown
to have better image quality than Filtered Back Projection (FBP), its use has
been limited by its high computational cost. More recently, deep convolutional
neural networks (CNN) have shown great promise in both denoising and
reconstruction applications. In this research, we propose a fast reconstruction
algorithm, which we call Deep Learning MBIR (DL-MBIR), for approximating MBIR
using a deep residual neural network. The DL-MBIR method is trained to produce
reconstructions that approximate true MBIR images using a 16 layer residual
convolutional neural network implemented on multiple GPUs using Google
Tensorflow. In addition, we propose 2D, 2.5D and 3D variations on the DL-MBIR
method and show that the 2.5D method achieves similar quality to the fully 3D
method, but with reduced computational cost.
An optimization approach to adaptive multi-dimensional capital management
Date: 2018-12-20 00:00:00
Tasks: Management

Firms should keep capital to offer sufficient protection against the risks
they are facing. In the insurance context methods have been developed to
determine the minimum capital level required, but less so in the context of
firms with multiple business lines including allocation. The individual capital
reserve of each line can be represented by means of classical models, such as
the conventional Cram\'{e}r-Lundberg model, but the challenge lies in soundly
modelling the correlations between the business lines. We propose a simple yet
versatile approach that allows for dependence by introducing a common
environmental factor. We present a novel Bayesian approach to calibrate the
latent environmental state distribution based on observations concerning the
claim processes. The calibration approach is adjusted for an environmental
factor that changes over time. The convergence of the calibration procedure
towards the true environmental state is deduced. We then point out how to
determine the optimal initial capital of the different business lines under
specific constraints on the ruin probability of subsets of business lines. Upon
combining the above findings, we have developed an easy-to-implement approach
to capital risk management in a multi-dimensional insurance risk model.
Affine Rough Models
Date: 2018-12-20 00:00:00
Tasks: Survey

The goal of this survey article is to explain and elucidate the affine
structure of recent models appearing in the rough volatility literature, and
show how it leads to exponential-affine transform formulas.
Hierarchical adaptive sparse grids and quasi Monte Carlo for option pricing under the rough Bergomi model
Date: 2020-01-16 00:00:00

The rough Bergomi (rBergomi) model, introduced recently in [5], is a
promising rough volatility model in quantitative finance. It is a parsimonious
model depending on only three parameters, and yet remarkably fits with
empirical implied volatility surfaces. In the absence of analytical European
option pricing methods for the model, and due to the non-Markovian nature of
the fractional driver, the prevalent option is to use the Monte Carlo (MC)
simulation for pricing. Despite recent advances in the MC method in this
context, pricing under the rBergomi model is still a time-consuming task. To
overcome this issue, we have designed a novel, hierarchical approach, based on
i) adaptive sparse grids quadrature (ASGQ), and ii) quasi-Monte Carlo (QMC).
Both techniques are coupled with a Brownian bridge construction and a
Richardson extrapolation on the weak error. By uncovering the available
regularity, our hierarchical methods demonstrate substantial computational
gains with respect to the standard MC method, when reaching a sufficiently
small relative error tolerance in the price estimates across different
parameter constellations, even for very small values of the Hurst parameter.
Our work opens a new research direction in this field, i.e., to investigate the
performance of methods other than Monte Carlo for pricing and calibrating under
the rBergomi model.
Multifractal cross-correlations between the World Oil and other Financial Markets in 2012-2017
Date: 2019-06-21 00:00:00
Tasks: Time Series Analysis

Statistical and multiscaling characteristics of WTI Crude Oil prices
expressed in US dollar in relation to the most traded currencies as well as to
gold futures and to the E-mini S$\&$P500 futures prices on 5 min intra-day
recordings in the period January 2012 - December 2017 are studied. It is shown
that in most of the cases the tails of return distributions of the considered
financial instruments follow the inverse cubic power law. The only exception is
the Russian ruble for which the distribution tail is heavier and scales with
the exponent close to 2. From the perspective of multiscaling the analysed time
series reveal the multifractal organization with the left-sided asymmetry of
the corresponding singularity spectra. Even more, all the considered financial
instruments appear to be multifractally cross-correlated with oil, especially
on the level of medium-size fluctuations, as the multifractal cross-correlation
analysis carried out by means of the multifractal cross-correlation analysis
(MFCCA) and detrended cross-correlation coefficient $\rho_q$ show. The degree
of such cross-correlations is however varying among the financial instruments.
The strongest ties to the oil characterize currencies of the oil extracting
countries. Strength of this multifractal coupling appears to depend also on the
oil market trend. In the analysed time period the level of cross-correlations
systematically increases during the bear phase on the oil market and it
saturates after the trend reversal in 1st half of 2016. The same methodology is
also applied to identify possible causal relations between considered
observables. Searching for some related asymmetry in the information flow
mediating cross-correlations indicates that it was the oil price that led the
Russian ruble over the time period here considered rather than vice versa.
Interaction Design for Explainable AI: Workshop Proceedings
Authors: Madumal Prashan, Singh Ronal, Newn Joshua, Vetere Frank
Date: 2018-12-13 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

As artificial intelligence (AI) systems become increasingly complex and
ubiquitous, these systems will be responsible for making decisions that
directly affect individuals and society as a whole. Such decisions will need to
be justified due to ethical concerns as well as trust, but achieving this has
become difficult due to the `black-box' nature many AI models have adopted.
Explainable AI (XAI) can potentially address this problem by explaining its
actions, decisions and behaviours of the system to users. However, much
research in XAI is done in a vacuum using only the researchers' intuition of
what constitutes a `good' explanation while ignoring the interaction and the
human aspect. This workshop invites researchers in the HCI community and
related fields to have a discourse about human-centred approaches to XAI rooted
in interaction and to shed light and spark discussion on interaction design
challenges in XAI.
A topological study of protein folding kinetics
Date: 2019-09-28 00:00:00
Tasks: Protein Folding

Focusing on a small set of proteins that i) fold in a concerted, all-or-none
fashion and ii) do not contain knots or slipknots, we show that the Gauss
linking integral, the torsion and the number of sequence-distant contacts
provide information regarding the folding rate. Our results suggest that the
global topology/geometry of the proteins shifts from right-handed to
left-handed with decreasing folding rate, and that this topological change is
associated with an increase in the number of more sequence-distant contacts.
Iterative Relevance Feedback for Answer Passage Retrieval with Passage-level Semantic Match
Authors: Bi Keping, Ai Qingyao, Croft W. Bruce
Date: 2018-12-20 00:00:00
Tasks: Passage Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity

Relevance feedback techniques assume that users provide relevance judgments
for the top k (usually 10) documents and then re-rank using a new query model
based on those judgments. Even though this is effective, there has been little
research recently on this topic because requiring users to provide substantial
feedback on a result list is impractical in a typical web search scenario. In
new environments such as voice-based search with smart home devices, however,
feedback about result quality can potentially be obtained during users'
interactions with the system. Since there are severe limitations on the length
and number of results that can be presented in a single interaction in this
environment, the focus should move from browsing result lists to iterative
retrieval and from retrieving documents to retrieving answers. In this paper,
we study iterative relevance feedback techniques with a focus on retrieving
answer passages. We first show that iterative feedback is more effective than
the top-k approach for answer retrieval. Then we propose an iterative feedback
model based on passage-level semantic match and show that it can produce
significant improvements compared to both word-based iterative feedback models
and those based on term-level semantic similarity.
Internal migration and education: A cross-national comparison
Date: 2018-12-21 00:00:00

Migration the main process shaping patterns of human settlement within and
between countries. It is widely acknowledged to be integral to the process of
human development as it plays a significant role in enhancing educational
outcomes. At regional and national levels, internal migration underpins the
efficient functioning of the economy by bringing knowledge and skills to the
locations where they are needed. It is the multi-dimensional nature of
migration that underlines its significance in the process of human development.
Human mobility extends in the spatial domain from local travel to international
migration, and in the temporal dimension from short-term stays to permanent
relocations. Classification and measurement of such phenomena is inevitably
complex, which has severely hindered progress in comparative research, with
very few large-scale cross-national comparisons of migration. The linkages
between migration and education have been explored in a separate line of
inquiry that has predominantly focused on country-specific analyses as to the
ways in which migration affects educational outcomes and how educational
attainment affects migration behaviour. A recurrent theme has been the
educational selectivity of migrants, which in turn leads to an increase of
human capital in some regions, primarily cities, at the expense of others.
Questions have long been raised as to the links between education and migration
in response to educational expansion, but have not yet been fully answered
because of the absence, until recently, of adequate data for comparative
analysis of migration. In this paper, we bring these two separate strands of
research together to systematically explore links between internal migration
and education across a global sample of 57 countries at various stages of
development, using data drawn from the IPUMS database.
Inequality in resource allocation and population dynamics models
Date: 2018-12-21 00:00:00

The Hassell model has been widely used as a general discrete-time population
dynamics model that describes both contest and scramble intraspecific
competition through a tunable exponent. Since the two types of competition
generally lead to different degrees of inequality in the resource distribution
among individuals, the exponent is expected to be related to this inequality.
However, among various first-principles derivations of this model, none is
consistent with this expectation. This paper explores whether a Hassell model
with an exponent related to inequality in resource allocation can be derived
from first principles. Indeed, such a Hassell model can be derived by assuming
random competition for resources among the individuals wherein each individual
can obtain only a fixed amount of resources at a time. Changing the size of the
resource unit alters the degree of inequality, and the exponent changes
accordingly. The Beverton-Holt and Ricker models can be regarded as special
cases of the derived Hassell model. Two additional Hassell models are derived
under some modified assumptions.
Computation of the spectrum of $\text{dc}^2$-balanced codes
Date: 2018-12-21 00:00:00

We apply the central limit theorem for deriving approximations to the
auto-correlation function and power density function (spectrum) of second-order
spectral null (dc2-balanced) codes.We show that the auto-correlation function
of dc2-balanced codes can be accurately approximated by a cubic function. We
show that the difference between the approximate and exact spectrum is less
than 0.04 dB for codeword length n = 256.
Parametric Modeling of EEG Signals
Date: 2018-12-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

In this paper, a new signal model is suggested for parametric representation
of the electroencephalogram (EEG) signals. The proposed model which is an
amplitude and frequency modulated sinusoidal signal model, has been found to
capture the non-stationary characteristics of the EEG signal with good
accuracy. When the EEG signal is considered for longer duration of time, the
model parameters have turned to be time-variant.
How spread changes affect the order book: Comparing the price responses of order deletions and placements to trades
Date: 2018-12-21 00:00:00

We observe the effects of the three different events that cause spread
changes in the order book, namely trades, deletions and placement of limit
orders. By looking at the frequencies of the relative amounts of price changing
events, we discover that deletions of orders open the bid-ask spread of a stock
more often than trades do. We see that once the amount of spread changes due to
deletions exceeds the amount of the ones due to trades, other observables in
the order book change as well. We then look at how these spread changing events
affect the prices of stocks, by means of the price response. We not only see
that the self-response of stocks is positive for both spread changing trades
and deletions and negative for order placements, but also cross-response to
other stocks and therefore the market as a whole. In addition, the
self-response function of spread-changing trades is similar to that of all
trades. This leads to the conclusion that spread changing deletions and order
placements have a similar effect on the order book and stock prices over time
as trades.
Econometric modelling and forecasting of intraday electricity prices
Date: 2019-09-23 00:00:00
Tasks: Time Series, Time Series Analysis

In the following paper, we analyse the ID$_3$-Price in the German Intraday
Continuous electricity market using an econometric time series model. A
multivariate approach is conducted for hourly and quarter-hourly products
separately. We estimate the model using lasso and elastic net techniques and
perform an out-of-sample, very short-term forecasting study. The model's
performance is compared with benchmark models and is discussed in detail.
Forecasting results provide new insights to the German Intraday Continuous
electricity market regarding its efficiency and to the ID$_3$-Price behaviour.
Statistical models of neural activity, criticality, and Zipf's law
Date: 2018-12-21 00:00:00

In this overview, we discuss the connections between the observations of
critical dynamics in neuronal networks and the maximum entropy models that are
often used as statistical models of neural activity, focusing in particular on
the relation between "statistical" and "dynamical" criticality. We present
examples of systems that are critical in one way, but not in the other,
exemplifying thus the difference of the two concepts. We then discuss the
emergence of Zipf laws in neural activity, verifying their presence in retinal
activity under a number of different conditions. In the second part of the
chapter we review connections between statistical criticality and the structure
of the parameter space, as described by Fisher information. We note that the
model-based signature of criticality, namely the divergence of specific heat,
emerges independently of the dataset studied; we suggest this is compatible
with previous theoretical findings.