pwc-archive/papers-with-abstracts huggingface dataset:
Embedding information in physically generated random bit sequences while maintaining certified randomness
Date: 2019-10-31 00:00:00

Ultrafast physical random bit generation at hundreds of Gb/s rates, with
verified randomness, is a crucial ingredient in secure communication and have
recently emerged using optics based physical systems. Here we examine the
inverse problem and measure the ratio of information bits that can be
systematically embedded in a random bit sequence without degrading its
certified randomness. These ratios exceed 0.01 in experimentally obtained long
random bit sequences. Based on these findings we propose a high-capacity
private-key cryptosystem with a finite key length, where the existence as well
as the content of the communication is concealed in the random sequence. Our
results call for a rethinking of the current quantitative definition of
practical classical randomness as well as the measure of randomness generated
by quantum methods, which have to include bounds using the proposed inverse
information embedding method.
Integration into \'economie-monde and regionalisation of the Central Eastern European space since 1989
Date: 2019-10-31 00:00:00

The fall of the Berlin Wall in 1989, modified the relations between cities of
the former communist bloc. The European and worldwide reorientation of
interactions that followed raises the question of the actual state of
historical relationships between Central Eastern European cities, but also with
ex-USSR and ex-Yugoslavian ones. Do Central and Eastern European cities
reproduce trajectories from the past in a new economic context? This paper will
examine their evolution in terms of trade exchanges and air traffic connexions
since 1989. They are confronted with transnational firm networks for the recent
years. The main contribution is to show a progressive formation of several
economic regions in Central and Eastern Europe as a result of integration into
Braudel's \'economie-monde.
Optimal Control Approach for Implementation of Sterile Insect Techniques
Date: 2019-10-31 00:00:00

Vector or pest control is essential to reduce the risk of vector-borne
diseases or crop losses. Among the available biological control tools, the
Sterile Insect Technique (SIT) is one of the most promising. However,
SIT-control campaigns must be carefully planned in advance in order to render
desirable outcomes. In this paper, we design SIT-control intervention programs
that can avoid the real-time monitoring of the wild population and require to
mass-rear a minimal overall number of sterile insects, in order to induce a
local elimination of the wild population in the shortest time. Continuous-time
release programs are obtained by applying an optimal control approach, and then
laying the groundwork of more practical SIT-control programs consisting of
periodic impulsive releases.
Improving positioning accuracy of the mobile laser scanning in GPS-denied environments: An experimental case study
Date: 2019-10-31 00:00:00

The positioning accuracy of the mobile laser scanning (MLS) system can reach
the level of centimeter under the conditions where GPS works normally. However,
in GPS-denied environments this accuracy can be reduced to the decimeter or
even the meter level because the observation mode errors and the boresight
alignment errors of MLS cannot be calibrated or corrected by the GPS signal. To
bridge this research gap, this paper proposes a novel technique that
appropriately incorporates the robust weight total least squares (RWTLS) and
the full information maximum likelihood optimal estimation (FIMLOE) to improve
the positioning accuracy of the MLS system under GPS-denied environment. First
of all, the coordinate transformation relationship and the observation
parameters vector of MLS system are established. Secondly, the RWTLS algorithm
is used to correct the 3D point observation model; then the uncertainty
propagation parameter vector and the boresight alignment errors between the
laser scanner frame and the IMU frame are calibrated by FIMLOE. Lastly,
experimental investigation in indoor scenarios was performed to evaluate the
effectiveness of the proposed method. The experimental results demonstrate that
the proposed method is able to significantly improve the positioning accuracy
of an MLS system in GPS-denied environments.
Variance and Error in One-Step Phase-Retrieval
Date: 2019-10-31 00:00:00
Tasks: Retrieval, SSIM

Time multiplexed approaches for high frame-rate holographic displays have
been around since the invention of One-Step Phase-Retrieval (OSPR) in the early
2000s. When discovered, formulations were created for variance reduction but
other image quality metrics were ignored.
  This work sets out statistical models for the mean squared error (MSE) and
structural similarity index (SSIM) behaviour of OSPR for a range of image types
in order to better understand the effect of time multiplexing on visible
images. This finds that while observed variances converges to zero as the
number of frames per second increases, MSE converges to a non-zero value while
SSIM converges quadratically to a non-unitary value.
Short-term and spike-timing-dependent plasticities facilitate the formation of modular neural networks
Date: 2019-10-31 00:00:00

The brain has the phenomenal ability to reorganize itself by forming new
connections among neurons and by pruning others. The so-called neural or brain
plasticity facilitates the modification of brain structure and function over
different time scales. Plasticity might occur due to external stimuli received
from the environment, during recovery from brain injury, or due to
modifications within the body and brain itself. In this paper, we study the
combined effect of short-term (STP) and spike-timing-dependent plasticities
(STDP) on the synaptic strength of excitatory coupled Hodgkin-Huxley neurons
and show that plasticity can facilitate the formation of modular neural
networks with complex topologies that resemble those of networks with
preferential attachment properties. In particular, we use an STDP rule that
alters the synaptic coupling intensity based on time intervals between spikes
of postsynaptic and presynaptic neurons. Previous works have shown that STDP
may induce the appearance of directed connections from high to low-frequency
spiking neurons. On the other hand, STP is attributed to the release of
neurotransmitters in the synaptic cleft of neurons that alter its synaptic
efficiency. Our results suggest that the combined effect of STP and STDP with
high recovery time facilitates the formation of connections among neurons with
similar spike frequencies only, a kind of preferential attachment. We then
pursue this further and show that, when starting with all-to-all neural
configurations, depending on the STP recovery time and distribution of neural
frequencies, modular neural networks can emerge as a direct result of the
combined effect of STP and STDP.
Spontaneous back-pain alters randomness in functional connections in large scale brain networks: A random matrix perspective
Date: 2019-10-31 00:00:00
Tasks: Functional Connectivity

We use randomness as a measure to assess the impact of evoked pain on brain
networks. Randomness is defined here as the intrinsic correlations that exist
between different brain regions when the brain is in a task-free state. We use
fMRI data of three brain states in a set of back pain patients monitored over a
period of 6 months. We find that randomness in the task-free state closely
follows the predictions of Gaussian orthogonal ensemble of random matrices.
However, the randomness decreases when the brain is engaged in attending to
painful inputs in patients suffering with early stages of back pain. A
persistence of this pattern is observed in the patients that develop chronic
back pain, while the patients who recover from pain after six months, the
randomness no longer varies with the pain task. The study demonstrates the
effectiveness of random matrix theory in differentiating between resting state
and two distinct task states within the same patient. Further, it demonstrates
that random matrix theory is effective in measuring systematic changes
occurring in functional connectivity and offers new insights on how acute and
chronic pain are processed in the brain at a network level.
Regularized Quantile Regression with Interactive Fixed Effects
Authors: Junlong Feng
Date: 2019-11-01 00:00:00
Tasks: quantile regression, regression

This paper studies large $N$ and large $T$ conditional quantile panel data models with interactive fixed effects. We propose a nuclear norm penalized estimator of the coefficients on the covariates and the low-rank matrix formed by the fixed effects. The estimator solves a convex minimization problem, not requiring pre-estimation of the (number of the) fixed effects. It also allows the number of covariates to grow slowly with $N$ and $T$. We derive an error bound on the estimator that holds uniformly in quantile level. The order of the bound implies uniform consistency of the estimator and is nearly optimal for the low-rank component. Given the error bound, we also propose a consistent estimator of the number of fixed effects at any quantile level. To derive the error bound, we develop new theoretical arguments under primitive assumptions and new results on random matrices that may be of independent interest. We demonstrate the performance of the estimator via Monte Carlo simulations.
Asset Prices with Investor Protection and Past Information
Authors: Jia Yue, Ben-Zhang Yang, Ming-Hui Wang, Nan-Jing Huang
Date: 2019-11-01 00:00:00

In this paper, we consider a dynamic asset pricing model in an approximate fractional economy to address empirical regularities related to both investor protection and past information. Our newly developed model features not only in terms with a controlling shareholder who diverts a fraction of the output, but also good (or bad) memory in his budget dynamics which can be well-calibrated by a pathwise way from the historical data. We find that poorer investor protection leads to higher stock holdings of controlling holders, lower gross stock returns, lower interest rates, and lower modified stock volatilities if the ownership concentration is sufficiently high. More importantly, by establishing an approximation scheme for good (bad) memory of investors on the historical market information, we conclude that good (bad) memory would increase (decrease) aforementioned dynamics and reveal that good (bad) memory strengthens (weakens) investor protection for minority shareholder when the ownership concentration is sufficiently high, while good (bad) memory inversely weakens (strengthens) investor protection for minority shareholder when the ownership concentration is sufficiently low. Our model's implications are consistent with a number of interesting facts documented in the recent literature.
Evaluation of the efficacy of RUTI and ID93/GLA-SE vaccines in tuberculosis treatment: in silico trial through UISS-TB simulator
Date: 2019-10-26 00:00:00

Tuberculosis (TB) is one of the deadliest diseases worldwide, with 1,5
million fatalities every year along with potential devastating effects on
society, families and individuals. To address this alarming burden, vaccines
can play a fundamental role, even though to date no fully effective TB vaccine
really exists. Current treatments involve several combinations of antibiotics
administered to TB patients for up to two years, leading often to financial
issues and reduced therapy adherence. Along with this, the development and
spread of drug-resistant TB strains is another big complicating matter. Faced
with these challenges, there is an urgent need to explore new vaccination
strategies in order to boost immunity against tuberculosis and shorten the
duration of treatment. Computational modeling represents an extraordinary way
to simulate and predict the outcome of vaccination strategies, speeding up the
arduous process of vaccine pipeline development and relative time to market.
Here, we present EU - funded STriTuVaD project computational platform able to
predict the artificial immunity induced by RUTI and ID93/GLA-SE, two specific
tuberculosis vaccines. Such an in silico trial will be validated through a
phase 2b clinical trial. Moreover, STriTuVaD computational framework is able to
inform of the reasons for failure should the vaccinations strategies against M.
tuberculosis under testing found not efficient, which will suggest possible
improvements.
Energy Efficient UAV Communication with Energy Harvesting
Date: 2019-12-24 00:00:00

This paper investigates an unmanned aerial vehicle (UAV)-enabled wireless
communication system with energy harvesting, where the UAV transfers energy to
the users in half duplex or full duplex, and the users harvest energy for data
transmission to the UAV. We minimize the total energy consumption of the UAV
while accomplishing the minimal data transmission requests of the users. The
original optimization problem is decomposed into two subproblems: path planning
subproblem and energy minimization subproblem with fixed path planning. For
path planning subproblem, the optimal visiting order is obtained by using the
dual method and the trajectory is optimized via the successive convex
approximation technique. For energy minimization subproblem with fixed path
planning, we firstly obtain the optimal portion of data transmission time
within the entire procedure and the optimal transmission power of each user.
Then, the the energy minimization subproblem is greatly simplified and it is
efficiently solved via a one-dimensional search method. Simulation results are
illustrated to verify the theoretical findings.
Time discounting under uncertainty
Date: 2020-03-12 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

We study intertemporal decision making under uncertainty. We fully
characterize discounted expected utility in a framework \`a la Savage. Despite
the popularity of this model, no characterization is available in this setting.
The concept of stationarity, introduced by Koopmans for deterministic
discounted utility, plays a central role for both attitudes towards time and
towards uncertainty. We show that a strong stationarity axiom characterizes
discounted expected utility. When hedging considerations are taken into
account, a weaker stationarity axiom generalizes discounted expected utility to
Choquet discounted expected utility, allowing for non-neutral attitudes towards
uncertainty.
FootSLAM meets Adaptive Thresholding
Date: 2020-03-26 00:00:00

Calibration of the zero-velocity detection threshold is an essential
prerequisite for zero-velocity-aided inertial navigation. However, the
literature is lacking a self-contained calibration method, suitable for
large-scale use in unprepared environments without map information or
pre-deployed infrastructure. In this paper, the calibration of the
zero-velocity detection threshold is formulated as a maximum likelihood
problem. The likelihood function is approximated using estimation quantities
readily available from the FootSLAM algorithm. Thus, we obtain a method for
adaptive thresholding that does not require map information, measurements from
supplementary sensors, or user input. Experimental evaluations are conducted
using data with different gait speeds, sensor placements, and walking
trajectories. The proposed calibration method is shown to outperform
fixed-threshold zero-velocity detectors and a benchmark using a speed-based
threshold classifier.
Peak detection for MALDI mass spectrometry imaging data using sparse frame multipliers
Date: 2019-10-31 00:00:00

MALDI mass spectrometry imaging (MALDI MSI) is a spatially resolved
analytical tool for biological tissue analysis by measuring mass-to-charge
ratios of ionized molecules. With increasing spatial and mass resolution of
MALDI MSI data, appropriate data analysis and interpretation is getting more
and more challenging. A reliable separation of important peaks from noise (aka
peak detection) is a prerequisite for many subsequent processing steps and
should be as accurate as possible. We propose a novel peak detection algorithm
based on sparse frame multipliers, which can be applied to raw MALDI MSI data
without prior preprocessing. The accuracy is evaluated on a simulated data set
in comparison with a state-of-the-art algorithm. These results also show the
proposed method's robustness to baseline and noise effects. In addition, the
method is evaluated on two real MALDI-TOF data sets, whereby spatial
information can be included in the peak picking process.
Do readers use character information when programming return-sweep saccades?
Authors: Martin R. Vasilev, Victoria I. Adedeji, Calvin Laursen, Marcin Budka, Timothy J. Slattery
Date: 2019-11-02 00:00:00

Reading saccades that occur within a single line of text are guided by the size of letters. However, readers occasionally need to make longer saccades (known as return-sweeps) that take their eyes from the end of one line of text to the beginning of the next. In this study, we tested whether return-sweep saccades are also guided by font size information and whether this guidance depends on visual acuity of the return-sweep target area. To do this, we manipulated the font size of letters (0.29 vs 0.39 deg. per character) and the length of the first line of text (16 vs 26 deg.). The larger font resulted in return-sweeps that landed further to the right of the line start and in a reduction of under-sweeps compared to the smaller font. This suggests that font size information is used when programming return-sweeps. Return-sweeps in the longer line condition landed further to the right of the line start and the proportion of under-sweeps increased compared to the short line condition. This likely reflects an increase in saccadic undershoot error with the increase in intended saccade size. Critically, there was no interaction between font size and line length. This suggests that when programming return-sweeps, the use of font size information does not depend on visual acuity at the saccade target. Instead, it appears that readers rely on global typographic properties of the text in order to maintain an optimal number of characters to the left of their first fixation on a new line.
Stochastic Optimal Control of HVAC system for Energy-efficient Buildings
Date: 2020-07-17 00:00:00

This paper aims to develop an agile, adaptive and energy-efficient method for
HVAC control via Markov decision process (MDP). Our main contributions are
outlined First, we formulate the problem as a MDP, which incorporates i) the
multiple uncertainties resulting from the weather and occupancy, ii) the
elaborate Predicted Mean Vote (PMV) thermal comfort model. Second, to cope with
the computational challenges, we propose a gradient-based policy iteration
(GBPI) method to learn the policies based on the performance gradients. Thrid,
we theoretically prove that the method can converge to an optimal policy of the
formulated MDP. The advantages of the proposed method are that: i) it uses
off-line computation to learn control policies thus reducing on-line
computation burden, and ii) it handles the non-convex and nonlinear system
dynamics efficiently and can accommodate the non-analytical thermal comfort
models (e.g., PMV) in the literature. The favorable performance of the policies
yield by the GBPI is demonstrated through comparisons with the optimal solution
obtained by assuming all the information is available before the planning in
several case studies.
iCurrency?
Date: 2019-11-12 00:00:00

We discuss the idea of a purely algorithmic universal world iCurrency set
forth in [Kakushadze and Liew, 2014] (https://ssrn.com/abstract=2542541) and
expanded in [Kakushadze and Liew, 2017] (https://ssrn.com/abstract=3059330) in
light of recent developments, including Libra. Is Libra a contender to become
iCurrency? Among other things, we analyze the Libra proposal, including the
stability and volatility aspects, and discuss various issues that must be
addressed. For instance, one cannot expect a cryptocurrency such as Libra to
trade in a narrow band without a robust monetary policy. The presentation in
the main text of the paper is intentionally nontechnical. It is followed by an
extensive appendix with a mathematical description of the dynamics of
(crypto)currency exchange rates in target zones, mechanisms for keeping the
exchange rate from breaching the band, the role of volatility, etc.
Post-FEC BER Benchmarking for Bit-Interleaved Coded Modulation with Probabilistic Shaping
Date: 2020-04-24 00:00:00
Tasks: Benchmarking

Accurate performance benchmarking after forward error correction (FEC)
decoding is essential for system design in optical fiber communications.
Generalized mutual information (GMI) has been shown to be successful at
benchmarking the bit-error rate (BER) after FEC decoding (post-FEC BER) for
systems with soft-decision (SD) FEC without probabilistic shaping (PS).
However, GMI is not relevant to benchmark post-FEC BER for systems with SD-FEC
and PS. For such systems, normalized GMI (NGMI), asymmetric information (ASI),
and achievable FEC rate have been proposed instead. They are good at
benchmarking post-FEC BER or to give an FEC limit in bit-interleaved coded
modulation (BICM) with PS, but their relation has not been clearly explained so
far. In this paper, we define generalized L-values under mismatched decoding,
which are connected to the GMI and ASI. We then show that NGMI, ASI, and
achievable FEC rate are theoretically equal under matched decoding but not
under mismatched decoding. We also examine BER before FEC decoding (pre-FEC
BER) and ASI over Gaussian and nonlinear fiber-optic channels with
approximately matched decoding. ASI always shows better correlation with
post-FEC BER than pre-FEC BER for BICM with PS. On the other hand, post-FEC BER
can differ at a given ASI when we change the bit mapping, which describes how
each bit in a codeword is assigned to a bit tributary.
A Control Chart Approach to Power System Line Outage Detection Under Transient Dynamics
Date: 2020-05-22 00:00:00
Tasks: Change Detection

Online transmission line outage detection over the entire network enables
timely corrective action to be taken, which prevents a local event from
cascading into a large scale blackout. Line outage detection aims to detect an
outage as soon as possible after it happened. Traditional methods either do not
consider the transient dynamics following an outage or require a full Phasor
Measurement Unit (PMU) deployment. Using voltage phase angle data collected
from a limited number of PMUs, we propose a real-time dynamic outage detection
scheme based on alternating current (AC) power flow model and statistical
change detection theory. The proposed method can capture system dynamics since
it retains the time-variant and nonlinear nature of the power system. The
method is computationally efficient and scales to large and realistic networks.
Extensive simulation studies on IEEE 39-bus and 2383-bus systems demonstrated
the effectiveness of the proposed method.