pwc-archive/papers-with-abstracts huggingface dataset:
Nanobody interaction unveils structure, dynamics and proteotoxicity of the Finnish-type amyloidogenic gelsolin variant
Date: 2019-03-18 00:00:00

AGel amyloidosis, formerly known as familial amyloidosis of the Finnish-type,
is caused by pathological aggregation of proteolytic fragments of plasma
gelsolin. So far, four mutations in the gelsolin gene have been reported as
responsible for the disease. Although D187N is the first identified variant and
the best characterized, its structure has been hitherto elusive. Exploiting a
recently-developed nanobody targeting gelsolin, we were able to stabilize the
G2 domain of the D187N protein and obtained, for the first time, its
high-resolution crystal structure. In the nanobody-stabilized conformation, the
main effect of the D187N substitution is the impairment of the calcium binding
capability, leading to a destabilization of the C-terminal tail of G2. However,
molecular dynamics simulations show that in the absence of the nanobody,
D187N-mutated G2 further misfolds, ultimately exposing its hydrophobic core and
the furin cleavage site. The nanobody's protective effect is based on the
enhancement of the thermodynamic stability of different G2 mutants (D187N,
G167R and N184K). In particular, the nanobody reduces the flexibility of
dynamic stretches, and most notably decreases the conformational entropy of the
C-terminal tail, otherwise stabilized by the presence of the Ca2+ ion. A
Caenorhabditis elegans-based assay was also applied to quantify the proteotoxic
potential of the mutants and determine whether nanobody stabilization
translates into a biologically relevant effect. Successful protection from G2
toxicity in vivo points to the use of C. elegans as a tool for investigating
the mechanisms underlying AGel amyloidosis and rapidly screen new therapeutics.
Investigating the Impacts of Stochastic Load Fluctuation on Dynamic Voltage Stability Margin Using Bifurcation Theory
Date: 2019-03-18 00:00:00

This paper studies the impacts of stochastic load fluctuations, namely the
fluctuation intensity and the changing speed of load power, on the size of the
voltage stability margin. 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 changing speed may lead to
a smaller voltage stability margin. Particularly, this work may represent the
first attempt to reveal the influence of the time evolution property of the
driving parameters on the voltage stability margin in power systems.
A fast method for pricing American options under the variance gamma model
Date: 2019-03-18 00:00:00

We investigate methods for pricing American options under the variance gamma
model. The variance gamma process is a pure jump process which is constructed
by replacing the calendar time by the gamma time in a Brownian motion with
drift, which makes it a time-changed Brownian motion. In general, the finite
difference method and the simulation method can be used for pricing under this
model, but their speed is not satisfactory. So there is a need for fast but
accurate approximation methods. In the case of Black-Merton-Scholes model,
there are fast approximation methods, but they cannot be utilized for the
variance gamma model. We develop a new fast method inspired by the quadratic
approximation method, while reducing the error by making use of a machine
learning technique on pre-calculated quantities. We compare the performance of
our proposed method with those of the existing methods and show that this
method is efficient and accurate for practical use.
Comparison of Tumor and Normal Cells Protein-Protein Interaction Network Parameters
Date: 2019-03-15 00:00:00

In this paper, we compared cancerous and normal cell according to their
protein-protein interaction network. Cancer is one of the complicated diseases
and experimental investigations have been showed that protein interactions have
an important role in the growth of cancer. We calculated some graph related
parameters such as Number of Vertices, Number of Edges, Closeness, Graph
Diameter, Graph Radius, Index of Aggregation, Connectivity, Number of Edges
divided by the Number of Vertices, Degree, Cluster Coefficient, Subgraph
Centrality, and Betweenness. Furthermore, the number of motifs and hubs in
these networks have been measured. In this paper bone, breast, colon, kidney
and liver benchmark datasets have been used for experiments. The experimental
results show that Graph Degree Mean, Subgraph Centrality, Betweenness, and Hubs
have higher values in the cancer cells and can be used as a measure to
distinguish between normal and cancerous networks. The cancerous tissues of the
five studied samples are denser in the interaction networks.
Joint Frame Synchronization and Channel Estimation: Sparse Recovery Approach and USRP Implementation
Date: 2019-03-18 00:00:00

Correlation-based techniques used for frame synchronization can suffer
significant performance degradation over multi-path frequency-selective
channels. In this paper, we propose a joint frame synchronization and channel
estimation (JFSCE) framework as a remedy to this problem. This framework,
however, increases the size of the resulting combined channel vector which
should capture both the channel impulse response (CIR) vector and the frame
boundary offset and, therefore, its estimation becomes more challenging. On the
other hand, because the combined channel vector is sparse, sparse channel
estimation methods can be applied. We propose several JFSCE methods using
popular sparse signal recovery (SSR) algorithms which exploit the sparsity of
the combined channel vector. Subsequently, the sparse channel vector estimate
is used to design a sparse equalizer. Our simulation results and experimental
measurements using software defined radios (SDRs) show that in some scenarios
our proposed method improves the overall system performance significantly, in
terms of the mean square error (MSE) between the transmitted and the equalized
symbols compared to the conventional method.
The Prosumer Economy -- Being Like a Forest
Date: 2019-03-16 00:00:00

Planetary life support systems are collapsing due to climate change and the
biodiversity crisis. The root cause is the existing consumer economy, coupled
with profit maximisation based on ecological and social externalities. Trends
can be reversed, civilisation may be saved by transforming the profit
maximising consumer economy into an ecologically and socially just economy,
which we call the prosumer economy. Prosumer economy is a macro scale circular
economy with minimum negative or positive ecological and social impact, an
ecosystem of producers and prosumers, who have synergistic and circular
relationships with deepened circular supply chains, networks, where leakage of
wealth out of the system is minimised. In a prosumer economy there is no waste,
no lasting negative impacts on the ecology and no social exploitation. The
prosumer economy is like a lake or a forest, an economic ecosystem that is
productive and supportive of the planet. We are already planting this forest
through Good4Trust.org, started in Turkey. Good4Trust is a community platform
bringing together ecologically and socially just producers and prosumers.
Prosumers come together around a basic ethical tenet the golden rule and share
on the platform their good deeds. The relationship are already deepening and
circularity is forming to create a prosumer economy. The platforms software to
structure the economy is open source, and is available to be licenced to start
Good4Trust anywhere on the planet. Complexity theory tells us that if enough
agents in a given system adopt simple rules which they all follow, the system
may shift. The shift from a consumer economy to a prosumer economy has already
started, the future is either ecologically and socially just or bust.
Cyclic Weighted Centroid Algorithm for Transmitter Localization in the Presence of Interference
Date: 2019-03-18 00:00:00

This paper addresses the problem of localizing a non-cooperative transmitter
in the presence of a spectrally overlapped interferer in a cognitive receiver
(CR) network. It has been observed that the performance of non-cooperative
weighted centroid localization (WCL) algorithm degrades in the presence of a
spectrally overlapped interferer. We propose cyclic WCL algorithm that uses
cyclic autocorrelation (CAC) of received signals at CRs in the network to
estimate the location coordinates of the target transmitter. Performance of the
proposed algorithm is further improved by eliminating CRs in the vicinity of
the interferer from the localization process. In order to identify and
eliminate CRs in the vicinity of the interferer, the ratio of the variance and
the mean of the square of absolute value of the CAC, referred to as feature
variation coefficient, is used. Theoretical analysis of the cyclic WCL
algorithm is presented in order to compute the root mean square error in the
location estimates. We further study the impacts of the interferer's power and
location, CR density, and fading environment on the performance of cyclic WCL.
The comparison between cyclic WCL and traditional WCL is also presented.
Cyclic weighted centroid localization for spectrally overlapped sources in cognitive radio networks
Date: 2019-03-18 00:00:00

We consider the problem of localizing spectrally overlapped sources in
cognitive radio networks. A new weighted centroid localization algorithm (WCL)
called Cyclic WCL is proposed, which exploits the cyclostationary feature of
the target signal to estimate its location coordinates. In order to analyze the
algorithm in terms of root-mean-square error (RMSE), we model the location
estimates as the ratios of quadratic forms in a Gaussian random vector. With
analysis and simulation, we show the impact of the interferer location and its
modulation scheme on the RMSE. We also study the RMSE performance of the
algorithm for different power levels of the target and the interference.
Further, the comparison between Cyclic WCL and WCL w/o cyclostationarity is
presented. It is observed that the Cyclic WCL provides significant performance
gain over WCL.
Unsupervised frequency clustering algorithm for null space estimation in wideband spectrum sharing networks
Date: 2019-03-18 00:00:00
Tasks: Clustering

In spectrum sharing networks, a base station (BS) needs to mitigate the
interference to users associated with other coexisting network in the same
band. The BS can achieve this by transmitting its downlink signal in the null
space of channels to such users. However, under a wideband scenario, the BS
needs to estimate null space matrices using the received signal from such
non-cooperative users in each frequency bin where the users are active. To
reduce the computational complexity of this operation, we propose a frequency
clustering algorithm that exploits the channel correlations among adjacent
frequency bins. The proposed algorithm forms clusters of frequency bins with
correlated channel vectors without prior knowledge of the channels and obtains
a single null space matrix for each cluster. We show that the number of
matrices and the number of eigenvalue decompositions required to obtain the
null space significantly reduce using the proposed clustering algorithm.
Kuiper test based modulation level classification under unknown frequency selective channels
Date: 2019-03-18 00:00:00

In this paper, we address the problem of Identifying the modulation level of
the received signal under an unknown frequency selective channel. The
modulation level classification is performed using reduced-complexity Kuiper
(rcK) test which utilizes the distribution of signal features such as magnitude
of the received samples or phase difference in consecutive received samples.
However, in frequency selective channels, these features are severely distorted
resulting in a poor classification performance. We propose to use constant
modulus algorithm (CMA) to mitigate the impact of the frequency selective
channel on the signal feature. Simulation and analytical results show that the
proposed CMA-rcK technique outperforms state-of-the-art cumulant-based
technique as well as blind equalizer-based technique that uses Alphabet Matched
Algorithm.
Risk and Return models for Equity Markets and Implied Equity Risk Premium
Authors: Enzo Busseti
Date: 2019-03-18 00:00:00
Tasks: ERP

Equity risk premium is a central component of every risk and return model in finance and a key input to estimate costs of equity and capital in both corporate finance and valuation. An article by Damodaran examines three broad approaches for estimating the equity risk premium. The first is survey based, it consists in asking common investors or big players like pension fund managers what they require as a premium to invest in equity. The second is to look at the premia earned historically by investing in stocks, as opposed to risk-free investments. The third method tries to extrapolate a market-consensus on equity risk premium (Implied Equity Risk Premium) by analysing equity prices on the market today. After having introduced some basic concepts and models, I'll briefly explain the pluses and minuses of the first two methods, and analyse more deeply the third. In the end I'll show the results of my estimation of ERP on real data, using variants of the Implied ERP (third) method.
J. S. Mill's Liberal Principle and Unanimity
Date: 2019-03-18 00:00:00

The broad concept of an individual's welfare is actually a cluster of related
specific concepts that bear a "family resemblance" to one another. One might
care about how a policy will affect people both in terms of their subjective
preferences and also in terms of some notion of their objective interests. This
paper provides a framework for evaluation of policies in terms of welfare
criteria that combine these two considerations. Sufficient conditions are
provided for such a criterion to imply the same ranking of social states as
does Pareto's unanimity criterion. Sufficiency is proved via study of a
community of agents with interdependent ordinal preferences.
Vulnerability Assessment of N-1 Reliable Power Systems to False Data Injection Attacks
Date: 2019-03-19 00:00:00

This paper studies the vulnerability of large-scale power systems to false
data injection (FDI) attacks through their physical consequences. Prior work
has shown that an attacker-defender bi-level linear program (ADBLP) can be used
to determine the worst-case consequences of FDI attacks aiming to maximize the
physical power flow on a target line. Understanding the consequences of these
attacks requires consideration of power system operations commonly used in
practice, specifically real-time contingency analysis (RTCA) and security
constrained economic dispatch (SCED). An ADBLP is formulated with detailed
assumptions on attacker's knowledge, and a modified Benders' decomposition
algorithm is introduced to solve such an ADBLP. The vulnerability analysis
results presented for the synthetic Texas system with 2000 buses show that
intelligent FDI attacks can cause post-contingency overflows.
Dynamic Hurst Exponent in Time Series
Date: 2019-03-19 00:00:00
Tasks: Time Series, Time Series Analysis

The market efficiency hypothesis has been proposed to explain the behavior of
time series of stock markets. The Black-Scholes model (B-S) for example, is
based on the assumption that markets are efficient. As a consequence, it is
impossible, at least in principle, to "predict" how a market behaves, whatever
the circumstances. Recently we have found evidence which shows that it is
possible to find self-organized behavior in the prices of assets in financial
markets during deep falls of those prices. Through a kurtosis analysis we have
identified a critical point that separates time series from stock markets in
two different regimes: the mesokurtic segment compatible with a random walk
regime and the leptokurtic one that allegedly follows a power law behavior. In
this paper we provide some evidence, showing that the Hurst exponent is a good
estimator of the regime in which the market is operating. Finally, we propose
that the Hurst exponent can be considered as a critical variable in just the
same way as magnetization, for example, can be used to distinguish the phase of
a magnetic system in physics.
Non-traded call's volatility smiles
Authors: Marek Capinski
Date: 2019-03-19 00:00:00

Real life hedging in the Black-Scholes model must be imperfect and if the stock's drift is higher than the risk free rate, leads to a profit on average. Hence the option price is examined as a fair game agreement between the parties, based on expected payoffs and a simple measure of risk. The resulting prices result in the volatility smile.
Bayesian regression explains how human participants handle parameter uncertainty
Date: 2019-03-19 00:00:00
Tasks: regression

The human brain copes with sensory uncertainty in accordance with Bayes'
rule. However, it is unknown how the brain makes predictions in the presence of
parameter uncertainty. Here, we tested whether and how humans take parameter
uncertainty into account in a regression task. Participants extrapolated a
parabola from a limited number of noisy points, shown on a computer screen. The
quadratic parameter was drawn from a prior distribution, unknown to the
observers. We tested whether human observers take full advantage of the given
information, including the likelihood function of the observed points and the
prior distribution of the quadratic parameter. We compared human performance
with Bayesian regression, which is the (Bayes) optimal solution to this
problem, and three sub-optimal models, namely maximum likelihood regression,
prior regression and maximum a posteriori regression, which are simpler to
compute. Our results clearly show that humans use Bayesian regression. We
further investigated several variants of Bayesian regression models depending
on how the generative noise is treated and found that participants act in line
with the more sophisticated version.
Zero-Velocity Detection - A Bayesian Approach to Adaptive Thresholding
Date: 2019-05-13 00:00:00

A Bayesian zero-velocity detector for foot-mounted inertial navigation
systems is presented. The detector extends existing zero-velocity detectors
based on the likelihood-ratio test, and allows, possibly time-dependent, prior
information about the two hypotheses - the sensors being stationary or in
motion - to be incorporated into the test. It is also possible to incorporate
information about the cost of a missed detection or a false alarm.
Specifically, we consider an hypothesis prior based on the velocity estimates
provided by the navigation system and an exponential model for how the cost of
a missed detection increases with the time since the last zero-velocity update.
Thereby, we obtain a detection threshold that adapts to the motion
characteristics of the user. Thus, the proposed detection framework efficiently
solves one of the key challenges in current zero-velocity-aided inertial
navigation systems: the tuning of the zero-velocity detection threshold. A
performance evaluation on data with normal and fast gait demonstrates that the
proposed detection framework outperforms any detector that chooses two separate
fixed thresholds for the two gait speeds.
An Integrated Panel Data Approach to Modelling Economic Growth
Date: 2019-03-19 00:00:00
Tasks: Variable Selection

Empirical growth analysis has three major problems --- variable selection,
parameter heterogeneity and cross-sectional dependence --- which are addressed
independently from each other in most studies. The purpose of this study is to
propose an integrated framework that extends the conventional linear growth
regression model to allow for parameter heterogeneity and cross-sectional error
dependence, while simultaneously performing variable selection. We also derive
the asymptotic properties of the estimator under both low and high dimensions,
and further investigate the finite sample performance of the estimator through
Monte Carlo simulations. We apply the framework to a dataset of 89 countries
over the period from 1960 to 2014. Our results reveal some cross-country
patterns not found in previous studies (e.g., "middle income trap hypothesis",
"natural resources curse hypothesis", "religion works via belief, not
practice", etc.).
Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction
Date: 2020-06-11 00:00:00
Tasks: Prediction

We propose a new approach to mixed-frequency regressions in a
high-dimensional environment that resorts to Group Lasso penalization and
Bayesian techniques for estimation and inference. In particular, to improve the
prediction properties of the model and its sparse recovery ability, we consider
a Group Lasso with a spike-and-slab prior. Penalty hyper-parameters governing
the model shrinkage are automatically tuned via an adaptive MCMC algorithm. We
establish good frequentist asymptotic properties of the posterior of the
in-sample and out-of-sample prediction error, we recover the optimal posterior
contraction rate, and we show optimality of the posterior predictive density.
Simulations show that the proposed models have good selection and forecasting
performance in small samples, even when the design matrix presents
cross-correlation. When applied to forecasting U.S. GDP, our penalized
regressions can outperform many strong competitors. Results suggest that
financial variables may have some, although very limited, short-term predictive
content.
Cellular reproduction number, generation time and growth rate differ between human- and avian-adapted influenza strains
Date: 2019-03-19 00:00:00

When analysing in vitro data, growth kinetics of influenza strains are often
compared by computing their growth rates, which are sometimes used as proxies
for fitness. However, analogous to mechanistic epidemic models, the growth rate
can be defined as a function of two parameters: the basic reproduction number
(the average number of cells each infected cell infects) and the mean
generation time (the average length of a replication cycle). Using a
mechanistic model, previously published data from experiments in human lung
cells, and newly generated data, we compared estimates of all three parameters
for six influenza A strains. Using previously published data, we found that the
two human-adapted strains (pre-2009 seasonal H1N1, and pandemic H1N1) had a
lower basic reproduction number, shorter mean generation time and slower growth
rate than the two avian-adapted strains (H5N1 and H7N9). These same differences
were then observed in data from new experiments where two strains were
engineered to have different internal proteins (pandemic H1N1 and H5N1), but
the same surface proteins (PR8), confirming our initial findings and implying
that differences between strains were driven by internal genes. Also, the model
predicted that the human-adapted strains underwent more replication cycles than
the avian-adapted strains by the time of peak viral load, potentially
accumulating mutations more quickly. These results suggest that the in vitro
reproduction number, generation time and growth rate differ between
human-adapted and avian-adapted influenza strains, and thus could be used to
assess host adaptation of internal proteins to inform pandemic risk assessment.