pwc-archive/papers-with-abstracts huggingface dataset:
Observations and perspectives on the diversification of genomes
Authors: Dirson Jian Li
Date: 2018-07-10 00:00:00
Tasks: Triplet

Rich information on the prebiotic evolution is still stored in contemporary genomic data. The statistical mechanism at the sequence level may play a significant role in the prebiotic evolution. Based on statistical analysis of genome sequences, it has been observed that there is a close relationship between the evolution of the genetic code and the organisation of genomes. A biodiversity space for species is constructed based on comparing the distributions of codons in genomes for different species according to recruitment order of codons in the prebiotic evolution, by which a closely relationship between the evolution of the genetic code and the tree of life has been confirmed. On one hand, the three domain tree of life can be reconstructed according to the distance matrix of species in this biodiversity space, which supports the three-domain tree rather than the eocyte tree. On the other hand, an evolutionary tree of codons can be obtained by comparing the distributions of the 64 codons in genomes, which agrees with the recruitment order of codons on the roadmap. This is a simple phylogenomic method to study the origins of metazoan, the evolution of primates, etc. This study should be regarded as an exploratory attempt to explain the diversification of the three domains of life by statistical mechanism in prebiotic sequence evolution. It is indicated that the number of bases in the triplet codons might be explained statistically by the number of strands in the triplex DNAs. The adaptation of life to the changing environment might be due to assembly of redundant genomes at the sequence level.
Nash equilibrium for risk-averse investors in a market impact game with transient price impact
Date: 2019-06-17 00:00:00

We consider a market impact game for $n$ risk-averse agents that are
competing in a market model with linear transient price impact and additional
transaction costs. For both finite and infinite time horizons, the agents aim
to minimize a mean-variance functional of their costs or to maximize the
expected exponential utility of their revenues. We give explicit
representations for corresponding Nash equilibria and prove uniqueness in the
case of mean-variance optimization. A qualitative analysis of these Nash
equilibria is conducted by means of numerical analysis.
Entrainment of Lymphatic Contraction to Oscillatory Flow
Date: 2018-07-10 00:00:00
Tasks: Sensitivity

Lymphedema, a disfiguring condition characterized by the asymmetrical
swelling of the limbs, is suspected to be caused by dysfunctions in the
lymphatic system. Lymphangions, the spontaneously contracting units of the
lymphatic system, are sensitive to luminal wall shear stress. In this study,
the response of the lymphangions to dynamically varying wall shear stress is
characterized in isolated rat thoracic ducts in relation to their shear
sensitivity. The critical shear stress above which the thoracic duct shows
substantial inhibition of contraction was found to be significantly negatively
correlated to the diameter of the lymphangion. The entrainment of the
lymphangion to an applied oscillatory shear stress was found to be
significantly dependent on the difference between the applied frequency and
intrinsic frequency of contraction of the lymphangion. The strength of
entrainment was also positively correlated to the applied shear stress when
this shear was below the critical shear stress. The results suggest an
adaptation of the lymphangion contractility to the existing oscillatory
mechanical shear stress as a function of its intrinsic contractility and shear
sensitivity. These adaptations might be crucial to ensure synchronized
contraction of adjacent lymphangions through mechanosensitive means and might
help explain the lymphatic dysfunctions that result from impaired
mechanosensitivity.
European Option Pricing with Stochastic Volatility models under Parameter Uncertainty
Date: 2018-07-10 00:00:00

We consider stochastic volatility models under parameter uncertainty and
investigate how model derived prices of European options are affected. We let
the pricing parameters evolve dynamically in time within a specified region,
and formalise the problem as a control problem where the control acts on the
parameters to maximise/minimise the option value. Through a dual representation
with backward stochastic differential equations, we obtain explicit equations
for Heston's model and investigate several numerical solutions thereof. In an
empirical study, we apply our results to market data from the S&P 500 index
where the model is estimated to historical asset prices. We find that the
conservative model-prices cover 98% of the considered market-prices for a set
of European call options.
A Practical Reconstruction Method for Three-Dimensional Phase Contrast Atomic Electron Tomography
Date: 2018-07-10 00:00:00
Tasks: Electron Tomography

Electron tomography is a technique used in both materials science and
structural biology to image features well below optical resolution limit. In
this work, we present a new algorithm for reconstructing the
three-dimensional(3D) electrostatic potential of a sample at atomic resolution
from phase contrast imaging using high-resolution transmission electron
microscopy. Our method accounts for dynamical and strong phase scattering,
providing more accurate results with much lower electron doses than those
current atomic electron tomography experiments. We test our algorithm using
simulated images of a synthetic needle geometry dataset composed of an
amorphous silicon dioxide shell around a silicon core. Our results show that,
for a wide range of experimental parameters, we can accurately determine both
atomic positions and species, and also identify vacancies even for light
elements such as silicon and disordered materials such as amorphous silicon
dioxide and also identify vacancies.
Limits to Surprise in Recommender Systems
Authors: de Lima Andre Paulino, Peres Sarajane Marques
Date: 2018-07-10 00:00:00
Tasks: Recommendation Systems

In this study, we address the challenge of measuring the ability of a
recommender system to make surprising recommendations. Although current
evaluation methods make it possible to determine if two algorithms can make
recommendations with a significant difference in their average surprise
measure, it could be of interest to our community to know how competent an
algorithm is at embedding surprise in its recommendations, without having to
resort to making a direct comparison with another algorithm. We argue that a)
surprise is a finite resource in a recommender system, b) there is a limit to
how much surprise any algorithm can embed in a recommendation, and c) this
limit can provide us with a scale against which the performance of any
algorithm can be measured. By exploring these ideas, it is possible to define
the concepts of maximum and minimum potential surprise and design a surprise
metric called "normalised surprise" that employs these limits to potential
surprise. Two experiments were conducted to test the proposed metric. The aim
of the first was to validate the quality of the estimates of minimum and
maximum potential surprise produced by a greedy algorithm. The purpose of the
second experiment was to analyse the behaviour of the proposed metric using the
MovieLens dataset. The results confirmed the behaviour that was expected, and
showed that the proposed surprise metric is both effective and consistent for
differing choices of recommendation algorithms, data representations and
distance functions.
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture
Date: 2018-07-11 00:00:00
Tasks: Autonomous Vehicles, Bayesian Inference, Gaussian Processes

Heterogeneous nature of the vehicular networks, which results from the
co-existence of human-driven, semi-automated, and fully autonomous vehicles, is
a challenging phenomenon toward the realization of the intelligent
transportation systems with an acceptable level of safety, comfort, and
efficiency. Safety applications highly suffer from communication resource
limitations, specifically in dense and congested vehicular networks. The idea
of model-based communication (MBC) has been recently proposed to address this
issue. In this work, we propose Gaussian Process-based Stochastic Hybrid System
with Cumulative Relevant History (CRH-GP-SHS) framework, which is a
hierarchical stochastic hybrid modeling structure, built upon a non-parametric
Bayesian inference method, i.e. Gaussian processes. This framework is proposed
in order to be employed within the MBC context to jointly model driver/vehicle
behavior as a stochastic object. Non-parametric Bayesian methods relieve the
limitations imposed by non-evolutionary model structures and enable the
proposed framework to properly capture different stochastic behaviors. The
performance of the proposed CRH-GP-SHS framework at the inter-mode level has
been evaluated over a set of realistic lane change maneuvers from NGSIM-US101
dataset. The results show a noticeable performance improvement for GP in
comparison to the baseline constant speed model, specifically in critical
situations such as highly congested networks. Moreover, an augmented model has
also been proposed which is a composition of GP and constant speed models and
capable of capturing the driver behavior under various network reliability
conditions.
Robust Beamforming Design in a NOMA Cognitive Radio Network Relying on SWIPT
Date: 2018-07-11 00:00:00

This paper studies a multiple-input single-output non-orthogonal multiple
access cognitive radio network relying on simultaneous wireless information and
power transfer. A realistic non-linear energy harvesting model is applied and a
power splitting architecture is adopted at each secondary user (SU). Since it
is difficult to obtain perfect channel state information (CSI) in practice,
instead either a bounded or gaussian CSI error model is considered. Our robust
beamforming and power splitting ratio are jointly designed for two problems
with different objectives, namely that of minimizing the transmission power of
the cognitive base station and that of maximizing the total harvested energy of
the SUs, respectively. The optimization problems are challenging to solve,
mainly because of the non-linear structure of the energy harvesting and CSI
errors models. We converted them into convex forms by using semi-definite
relaxation. For the minimum transmission power problem, we obtain the rank-2
solution under the bounded CSI error model, while for the maximum energy
harvesting problem, a two-loop procedure using a one-dimensional search is
proposed. Our simulation results show that the proposed scheme significantly
outperforms its traditional orthogonal multiple access counterpart.
Furthermore, the performance using the gaussian CSI error model is generally
better than that using the bounded CSI error model.
Maximum entropy and population heterogeneity in continuous cell cultures
Date: 2018-12-18 00:00:00

Continuous cultures of mammalian cells are complex systems displaying
hallmark phenomena of nonlinear dynamics, such as multi-stability, hysteresis,
as well as sharp transitions between different metabolic states. In this
context mathematical models may suggest control strategies to steer the system
towards desired states. Although even clonal populations are known to exhibit
cell-to-cell variability, most of the currently studied models assume that the
population is homogeneous. To overcome this limitation, we use the maximum
entropy principle to model the phenotypic distribution of cells in a chemostat
as a function of the dilution rate. We consider the coupling between cell
metabolism and extracellular variables describing the state of the bioreactor
and take into account the impact of toxic byproduct accumulation on cell
viability. We present a formal solution for the stationary state of the
chemostat and show how to apply it in two examples. First, a simplified model
of cell metabolism where the exact solution is tractable, and then a
genome-scale metabolic network of the Chinese hamster ovary (CHO) cell line.
Along the way we discuss several consequences of heterogeneity, such as:
qualitative changes in the dynamical landscape of the system, increasing
concentrations of byproducts that vanish in the homogeneous case, and larger
population sizes.
A knowledge based system approach in securing distributed wireless sensor networks
Date: 2018-07-11 00:00:00

Sensor networks technologies had proved their great practicability in the
real world, being just a matter of time until this kind of networks will be
standardized and used in the field. This paper presents a new approach to
secure the transmission of information in sensor networks and is based on a
combined hardware-software architecture using three components: a) a mechanism
to provide sensor authentication and secret key distribution; b) AES
symmetrical encryption algorithm with predistributed keys; and c) an attack
detection stratagem using an expert system based on the prediction of the
values provided by the sensors, followed by reducing the sensor trust
coefficient.
Prevention of Metro Rail Accidents and Incidents in Stations Using RFID Technology
Date: 2018-07-11 00:00:00
Tasks: TAG

Today, metro is one of the urban infrastructure and plays an important role
in urban transport. The safety and health of people in a city are always
important, and transport in the metro should also be safe. When subway trains
operate, it is possible to occur various accidents such as an exit from the
rails or collision with a possible obstacle on the rails (human or another
train). In this paper, a model is proposed based on RFID technology in which
the train is equipped with a RFID reader, and a control circuit with a
microcontroller as well as placing RFID active tags at specific points of the
path. When the train approaches the tagged points of the route, the train tag
reader scans the tag number, and then the microcontroller identifies the status
of the environment by retrieving the information from the internal database.
Then, the control circuit adjusts the rotational speed of the electric motor
and the train speed consequently based on that data. In this way, the speed of
the train is adapted automatically and promptly to the conditions and the
probability of an accident in unforeseen circumstances is reduced.
Interfering Channel Estimation in Radar-Cellular Coexistence: How Much Information Do We Need?
Date: 2018-07-18 00:00:00

In this paper, we focus on the coexistence between a MIMO radar and cellular
base stations. We study the interfering channel estimation, where the radar is
operated in the "search and track" mode, and the BS receives the interference
from the radar. Unlike the conventional methods where the radar and the
cellular systems fully cooperate with each other, in this work we consider that
they are uncoordinated and the BS needs to acquire the interfering channel
state information (ICSI) by exploiting the radar probing waveforms. For
completeness, both the line-of-sight (LoS) and Non-LoS (NLoS) channels are
considered in the coexistence scenario. By further assuming that the BS has
limited a priori knowledge about the radar waveforms, we propose several
hypothesis testing methods to identify the working mode of the radar, and then
obtain the ICSI through a variety of channel estimation schemes. Based on the
statistical theory, we analyze the theoretical performance of both the
hypothesis testing and the channel estimation methods. Finally, simulation
results verify the effectiveness of our theoretical analysis and demonstrate
that the BS can effectively estimate the interfering channel even with limited
information from the radar.
Clustering Macroeconomic Time Series
Date: 2018-07-18 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis, Time Series Clustering

The data mining technique of time series clustering is well established in
many fields. However, as an unsupervised learning method, it requires making
choices that are nontrivially influenced by the nature of the data involved.
The aim of this paper is to verify usefulness of the time series clustering
method for macroeconomics research, and to develop the most suitable
methodology.
  By extensively testing various possibilities, we arrive at a choice of a
dissimilarity measure (compression-based dissimilarity measure, or CDM) which
is particularly suitable for clustering macroeconomic variables. We check that
the results are stable in time and reflect large-scale phenomena such as
crises. We also successfully apply our findings to analysis of national
economies, specifically to identifying their structural relations.
A Robust Nonlinear RLS Type Adaptive Filter for Second-Order-Intermodulation Distortion Cancellation in FDD LTE and 5G Direct Conversion Transceivers
Date: 2018-07-11 00:00:00

Transceivers operating in frequency division duplex experience a transmitter
leakage (TxL) signal into the receiver due to the limited duplexer stop-band
isolation. This TxL signal in combination with the second-order nonlinearity of
the receive mixer may lead to a baseband (BB) second-order intermodulation
distortion (IMD2) with twice the transmit signal bandwidth. In direct
conversion receivers, this nonlinear IMD2 interference may cause a severe
signal-to-interference-plus-noise ratio degradation of the wanted receive
signal. This contribution presents a nonlinear Wiener model recursive
least-squares (RLS) type adaptive filter for the cancellation of the IMD2
interference in the digital BB. The included channel-select-, and DC-notch
filter at the output of the proposed adaptive filter ensure that the provided
IMD2 replica includes the receiver front-end filtering. A second, robust
version of the nonlinear RLS algorithm is derived which provides numerical
stability for highly correlated input signals which arise in e.g. LTE-A
intra-band multi-cluster transmission scenarios. The performance of the
proposed algorithms is evaluated by numerical simulations and by measurement
data.
Self-Estimation of Path-Loss Exponent in Wireless Networks and Applications
Date: 2018-07-11 00:00:00

The path-loss exponent (PLE) is one of the most crucial parameters in
wireless communications to characterize the propagation of fading channels. It
is currently adopted for many different kinds of wireless network problems such
as power consumption issues, modelling the communication environment, and
received signal strength (RSS)-based localization. PLE estimation is thus of
great use to assist in wireless networking. However, a majority of methods to
estimate the PLE require either some particular information of the wireless
network, which might be unknown or some external auxiliary devices, such as
anchor nodes or the Global Positioning System. Moreover, this external
information might sometimes be unreliable, spoofed, or difficult to obtain.
Therefore, a self-estimator for the PLE, which is able to work independently,
becomes an urgent demand to robustly and securely get a grip on the PLE for
various wireless network applications. This paper is the first to introduce two
methods that can solely and locally estimate the PLE. To start, a new linear
regression model for the PLE is presented. Based on this model, a closed-form
total least squares (TLS) method to estimate the PLE is first proposed, in
which, with no other assistance or external information, each node can estimate
the PLE merely by collecting RSSs. Second, to suppress the estimation errors, a
closed-form weighted TLS method is further developed, having a better
performance. Due to their simplicity and independence of any auxiliary system,
our two proposed methods can be easily incorporated into any kind of wireless
communication stack. Simulation results show that our estimators are reliable,
even in harsh environments, where the PLE is high. Many potential applications
are also explicitly illustrated in this paper, such as secure RSS-based
localization, kth nearest neighbour routing, etc.
Recognising Cardiac Abnormalities in Wearable Device Photoplethysmography (PPG) with Deep Learning
Date: 2018-07-11 00:00:00
Tasks: Photoplethysmography (PPG), Rhythm

Cardiac abnormalities affecting heart rate and rhythm are commonly observed
in both healthy and acutely unwell people. Although many of these are benign,
they can sometimes indicate a serious health risk. ECG monitors are typically
used to detect these events in electrical heart activity, however they are
impractical for continuous long-term use. In contrast, current-generation
wearables with optical photoplethysmography (PPG) have gained popularity with
their low-cost, lack of wires and tiny size. Many cardiac abnormalities such as
ectopic beats and AF can manifest as both obvious and subtle anomalies in a PPG
waveform as they disrupt blood flow. We propose an automatic method for
recognising these anomalies in PPG signal alone, without the need for ECG. We
train an LSTM deep neural network on 400,000 clean PPG samples to learn typical
PPG morphology and rhythm, and flag PPG signal diverging from this as cardiac
abnormalities. We compare the cardiac abnormalities our approach recognises
with the ectopic beats recorded by a bedside ECG monitor for 29 patients over
47.6 hours of gold standard observations. Our proposed cardiac abnormality
recognition approach recognises 60%+ of ECG-detected PVCs in PPG signal, with a
false positive rate of 23% - demonstrating the compelling power and value of
this novel approach. Finally we examine how cardiac abnormalities manifest in
PPG signal for in- and out-of-hospital patient populations using a wearable
device during standard care.
RTF-Based Binaural MVDR Beamformer Exploiting an External Microphone in a Diffuse Noise Field
Date: 2018-07-12 00:00:00

Besides suppressing all undesired sound sources, an important objective of a
binaural noise reduction algorithm for hearing devices is the preservation of
the binaural cues, aiming at preserving the spatial perception of the acoustic
scene. A well-known binaural noise reduction algorithm is the binaural minimum
variance distortionless response beamformer, which can be steered using the
relative transfer function (RTF) vector of the desired source, relating the
acoustic transfer functions between the desired source and all microphones to a
reference microphone. In this paper, we propose a computationally efficient
method to estimate the RTF vector in a diffuse noise field, requiring an
additional microphone that is spatially separated from the head-mounted
microphones. Assuming that the spatial coherence between the noise components
in the head-mounted microphone signals and the additional microphone signal is
zero, we show that an unbiased estimate of the RTF vector can be obtained.
Based on real-world recordings, experimental results for several reverberation
times show that the proposed RTF estimator outperforms the widely used RTF
estimator based on covariance whitening and a simple biased RTF estimator in
terms of noise reduction and binaural cue preservation performance.
Heterogeneous Effects of Unconventional Monetary Policy on Loan Demand and Supply. Insights from the Bank Lending Survey
Date: 2018-07-11 00:00:00

This paper analyzes the bank lending channel and the heterogeneous effects on
the euro area, providing evidence that the channel is indeed working. The
analysis of the transmission mechanism is based on structural impulse responses
to an unconventional monetary policy shock on bank loans. The Bank Lending
Survey (BLS) is exploited in order to get insights on developments of loan
demand and supply. The contribution of this paper is to use country-specific
data to analyze the consequences of unconventional monetary policy, instead of
taking an aggregate stance by using euro area data. This approach provides a
deeper understanding of the bank lending channel and its effects. That is, an
expansionary monetary policy shock leads to an increase in loan demand, supply
and output growth. A small north-south disparity between the countries can be
observed.