pwc-archive/papers-with-abstracts huggingface dataset:
New ECCM Techniques Against Noise-like and/or Coherent Interferers
Date: 2019-06-27 00:00:00
Tasks: Compressive Sensing

Multiple-stage adaptive architectures are conceived to face with the problem
of target detection buried in noise, clutter, and intentional interference.
First, a scenario where the radar system is under the electronic attack of
noise-like interferers is considered. In this context, two sets of training
samples are jointly exploited to devise a novel two-step estimation procedure
of the interference covariance matrix. Then, this estimate is plugged in the
adaptive matched filter to mitigate the deleterious effects of the noise-like
jammers on radar sensitivity. Besides, a second scenario, which also includes
the presence of coherent jammers, is addressed. Specifically, the sparse nature
of data is brought to light and the compressive sensing paradigm is applied to
estimate target response and coherent jammers amplitudes. The likelihood ratio
test, where the unknown parameters are replaced by previous estimates, is
designed and assessed. Remarkably, the sparse approach allows for echo
classification and estimation of both angles of arrival and number of the
interfering sources. The performance analysis, conducted resorting to simulated
data, highlights the effectiveness of the newly proposed architectures also in
comparison with suitable competing architectures (when they exist).
Deep Learning Assisted Antenna Selection in Untrusted Relay Networks
Date: 2019-01-10 00:00:00
Tasks: Deep Learning

This letter mainly studies the transmit antenna selection(TAS) based on deep
learning (DL) scheme in untrusted relay networks. In previous work, we discover
that machine learning (ML)-based antenna selection schemes have small
performance degradation caused by complicated coupling relationship between
achievable secrecy rate and the channel gains. To solve the issue, we here
introduce deep neural network (DNN) to decouple the complicated relationship.
The simulation results show the DNN scheme can achieve better decoupling and
thus perform almost the same performance with conventional exhausted searching
scheme.
Forecasting interest rates through Vasicek and CIR models: a partitioning approach
Date: 2019-01-15 00:00:00

The aim of this paper is to propose a new methodology that allows
forecasting, through Vasicek and CIR models, of future expected interest rates
(for each maturity) based on rolling windows from observed financial market
data. The novelty, apart from the use of those models not for pricing but for
forecasting the expected rates at a given maturity, consists in an appropriate
partitioning of the data sample. This allows capturing all the statistically
significant time changes in volatility of interest rates, thus giving an
account of jumps in market dynamics. The performance of the new approach is
carried out for different term structures and is tested for both models. It is
shown how the proposed methodology overcomes both the usual challenges (e.g.
simulating regime switching, volatility clustering, skewed tails, etc.) as well
as the new ones added by the current market environment characterized by low to
negative interest rates.
Neural-network simulations of memory consolidation and reconsolidation
Date: 2019-01-18 00:00:00
Tasks: Hippocampus

In the mammalian brain newly acquired memories depend on the hippocampus for
maintenance and recall, but over time these functions are taken over by the
neocortex through a process called systems consolidation. However, reactivation
of a consolidated memory can induce a brief period of temporary
hippocampus-dependence, followed by return to hippocampus-independence. Here we
present a computational model that uses simulation of recently described
mechanisms of synaptic plasticity to account for findings from the systems
consolidation/reconsolidation literature and to make predictions for future
research.
A mathematical model of contact inhibition of locomotion: coupling contractility and focal adhesions
Date: 2019-03-22 00:00:00

Cell migration is often accompanied by collisions with other cells, which can
lead to cessation of movement, repolarization, and migration away from the
contact site - a process termed contact inhibition of locomotion (CIL). During
CIL, the coupling between actomyosin contractilityand cell-substrate adhesions
is modified. However, mathematical models describing stochastic cell migration
and collision outcomes as a result of the coupling remain elusive. Here, we
extend our previously developed stochastic model of single cell migration to
include CIL. Our simulation results explain, in terms of the modified
contractility and adhesion dynamics, several experimentally observed findings
regarding CIL. These include response modulation in the presence of an external
cue and alterations of group migration in the absence of CIL. Together with our
previous findings, our work is able to explain a wide range of observations
about single and collective cell migration.
Data-oriented Wireless Transmission for Effective QoS Provision in Future Wireless Systems
Date: 2019-04-16 00:00:00

Future wireless systems need to support diverse big data and Internet of
Things (IoT) applications with dramatically different quality of service (QoS)
requirements. Novel designs across the protocol stack are required to achieve
effective and efficient QoS provision. In this article, we introduce a novel
data oriented approach for the design and analysis of advanced wireless
transmission technologies. Unlike conventional channel-oriented approach, we
propose to optimally design transmission strategies for individual data
transmission sessions, considering both the QoS requirement and instantaneous
operating environment. The resulting design can effectively satisfy highly
stringent performance and efficiency requirements of future applications.
Mean-variance portfolio selection under partial information with drift uncertainty
Authors: Jie Xiong, Zuo Quan Xu, Jiayu Zheng
Date: 2019-01-10 00:00:00

In this paper, we study the mean-variance portfolio selection problem under partial information with drift uncertainty. First we show that the market model is complete even in this case while the information is not complete and the drift is uncertain. Then, the optimal strategy based on partial information is derived, which reduces to solving a related backward stochastic differential equation (BSDE). Finally, we propose an efficient numerical scheme to approximate the optimal portfolio that is the solution of the BSDE mentioned above. Malliavin calculus and the particle representation play important roles in this scheme.
Self-similar shapes under Errera division rule on the cone
Date: 2019-01-10 00:00:00

This study provides a general construction method of cell shape invariant by
the Errera rule of division on a cone and provides analytical bounds for the
apical angle of the cone on which these cells are connected and thus
biologically meaningful. This idealized model highlights how the curvature of
the tissue can influence Errera rule.
Automatic detection of passable roads after floods in remote sensed and social media data
Authors: Ahmad Kashif, Pogorelov Konstantin, Riegler Michael, Ostroukhova Olga, Halvorsen Paal, Conci Nicola, Dahyot Rozenn
Date: 2019-01-10 00:00:00
Tasks: Benchmarking, Transfer Learning

This paper addresses the problem of floods classification and floods
aftermath detection utilizing both social media and satellite imagery.
Automatic detection of disasters such as floods is still a very challenging
task. The focus lies on identifying passable routes or roads during floods. Two
novel solutions are presented, which were developed for two corresponding tasks
at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification
of images providing evidence for road passability and (ii) differentiation and
detection of passable and non-passable roads in images from two complementary
sources of information. For the first challenge, we mainly rely on object and
scene-level features extracted through multiple deep models pre-trained on the
ImageNet and Places datasets. The object and scene-level features are then
combined using early, late and double fusion techniques. To identify whether or
not it is possible for a vehicle to pass a road in satellite images, we rely on
Convolutional Neural Networks and a transfer learning-based classification
approach. The evaluation of the proposed methods are carried out on the
large-scale datasets provided for the benchmark competition. The results
demonstrate significant improvement in the performance over the recent
state-of-art approaches.
When does privatization spur entrepreneurial performance? The moderating effect of institutional quality in an emerging market
Date: 2019-01-10 00:00:00

We explore how institutional quality moderates the effectiveness of
privatization on entrepreneurs sales performance. To do this, we blend agency
theory and entrepreneurial cognition theory with insights from institutional
economics to develop a model of emerging market venture performance. Using data
from the World Banks Enterprise Survey of entrepreneurs in China, our results
suggest that private-owned enterprises (POEs) outperform state-owned
enterprises (SOEs) but only in environments with high-quality market
institutions. In environments with low-quality market institutions, SOEs
outperform POEs. These findings suggest that the effectiveness of privatization
on entrepreneurial performance is context-specific, which reveals more nuance
than previously has been attributed.
Community Matters: Heterogeneous Impacts of a Sanitation Intervention
Date: 2020-02-10 00:00:00

We study the effectiveness of a community-level information intervention
aimed at improving sanitation using a cluster-randomized controlled trial (RCT)
in Nigerian communities. The intervention, Community-Led Total Sanitation
(CLTS), is currently part of national sanitation policy in more than 25
countries. While average impacts are exiguous almost three years after
implementation at scale, the results hide important heterogeneity: the
intervention has strong and lasting effects on sanitation practices in poorer
communities. These are realized through increased sanitation investments. We
show that community wealth, widely available in secondary data, is a key
statistic for effective intervention targeting. Using data from five other
similar randomized interventions in various contexts, we find that
community-level wealth heterogeneity can rationalize the wide range of impact
estimates in the literature. This exercise provides plausible external validity
to our findings, with implications for intervention scale-up. JEL Codes: O12,
I12, I15, I18.
Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks
Date: 2019-01-11 00:00:00
Tasks: Compressive Sensing, Image Reconstruction

This tutorial covers biomedical image reconstruction, from the foundational concepts of system modeling and direct reconstruction to modern sparsity and learning-based approaches. Imaging is a critical tool in biological research and medicine, and most imaging systems necessarily use an image-reconstruction algorithm to create an image; the design of these algorithms has been a topic of research since at least the 1960's. In the last few years, machine learning-based approaches have shown impressive performance on image reconstruction problems, triggering a wave of enthusiasm and creativity around the paradigm of learning. Our goal is to unify this body of research, identifying common principles and reusable building blocks across decades and among diverse imaging modalities. We first describe system modeling, emphasizing how a few building blocks can be used to describe a broad range of imaging modalities. We then discuss reconstruction algorithms, grouping them into three broad generations. The first are the classical direct methods, including Tikhonov regularization; the second are the variational methods based on sparsity and the theory of compressive sensing; and the third are the learning-based (also called data-driven) methods, especially those using deep convolutional neural networks. There are strong links between these generations: classical (first-generation) methods appear as modules inside the latter two, and the former two are used to inspire new designs for learning-based (third-generation) methods. As a result, a solid understanding of all of three generations is necessary for the design of state-of-the-art algorithms.
Motion, fixation probability and the choice of an evolutionary process
Date: 2019-01-11 00:00:00

Different evolutionary models are known to make disparate predictions for the
success of an invading mutant in some situations. For example, some
evolutionary mechanics lead to amplification of selection in structured
populations, while others suppress it. Here, we use computer simulations to
study evolutionary populations moved by flows, and show how the speed of this
motion impacts the fixation probability of an invading mutant. Flows of
different speeds interpolate between evolutionary dynamics on fixed
heterogeneous graphs and in well-stirred populations. We find that the motion
has an active role in amplifying or suppressing selection, accomplished by
fragmenting and reconnecting the interaction graph. While increasing flow
speeds suppress selection for most evolutionary models, we identify
characteristic responses to flow for the different update rules we test. We
suggest these responses as a potential aid for choosing the most suitable
update rule for a given biological system.
Performance Analysis of Receive Antenna Selection in Multi-Antenna Systems under Finite Constellation Size
Date: 2019-07-16 00:00:00

Antenna selection (AS) is regarded as one of the most prospective
technologies to reduce hardware cost but keep relatively high spectral
efficiency in multi-antenna systems. By selecting a subset of antennas to
transceive messages, AS greatly alleviates the requirement on RF chains. This
paper studies receive antenna selection in single-input multiple-output (SIMO)
systems, namely the antenna-selection SIMO (AS-SIMO) systems, from the
perspective of digital modulation. The receiver, equipped with multiple
antennas, selects an optimal antenna subset to receive messages from the
single-antenna transmitter. By assuming independent and identical distributed
(i.i.d) flat fading Rayleigh channels, we first analyze the input-output mutual
information, also referred as symmetric capacity, of AS-SIMO systems when the
modulation style is BPSK/QPSK/16QAM. To reduce the computation complexity of
the capacity, closed-form approximated expressions of the symmetric capacity
based on asymptotic theory are given for the first time to approach the exact
results. Compared with the conventional derivations, our approximation holds
much lower computation complexity with the guarantee of high precision. Next,
this asymptotic approximation technique is extended to estimate the symbol
error rate (SER) of antenna-selection SIMO systems and approximated expressions
for SER are proposed which indicates much lower complexity. Finally, a special
scenario of single-antenna-selection is detailedly investigated and series
expressions of the symmetric capacity are formulated for the first time. Beside
analytical derivations, simulation results are provided to demonstrate the
approximation precision of the derived results. Experiment results show that
the asymptotic theory has a remarkable approximation effect.
Report for the Commission of Inquiry Respecting the Muskrat Falls Project
Date: 2019-01-10 00:00:00

This report was commissioned by the Commission of Inquiry Respecting the
Muskrat Falls Project to provide the national and international context in
which the Muskrat Falls Project took place. The Commission asked for the report
to cover three specific topics of questions: (1) What is the national and
international context of the Muskrat Falls Project with regards to cost overrun
and schedule overrun? (What are the typical cost and schedule overruns of
hydro-electric dam projects? How do hydro-electric dams compare to other
capital investment projects? How do Canadian projects compare to other
countries?), (2) What are the causes and root causes of cost and schedule
overruns? (3) What are recommendations, based on international experience and
research into capital investment projects, to prevent cost and schedule
overruns in hydro-electric dam projects and other capital investment projects?
  Keywords: Hydroelectric Dams, Megaprojects, Cost Overrun, Schedule Overrun,
Optimism Bias, Strategic Misrepresentation, Infrastructure, Capital Investment
Projects, Canada, Muskrat Falls
Reverse Engineering the Raspberry Pi Camera V2: A study of Pixel Non-Uniformity using a Scanning Electron Microscope
Date: 2019-01-12 00:00:00

In this paper we reverse engineer the Sony IMX219PQ image sensor, otherwise
known as the Raspberry Pi Camera v2.0. We provide a visual reference for pixel
non-uniformity by analysing variations in transistor length, microlens optic
system and in the photodiode. We use these measurements to demonstrate
irregularities at the microscopic level and link this to the signal variation
measured as pixel non-uniformity used for unique identification of discrete
image sensors.
Mastering Panel 'Metrics: Causal Impact of Democracy on Growth
Date: 2019-01-12 00:00:00
Tasks: valid

The relationship between democracy and economic growth is of long-standing
interest. We revisit the panel data analysis of this relationship by Acemoglu,
Naidu, Restrepo and Robinson (forthcoming) using state of the art econometric
methods. We argue that this and lots of other panel data settings in economics
are in fact high-dimensional, resulting in principal estimators -- the fixed
effects (FE) and Arellano-Bond (AB) estimators -- to be biased to the degree
that invalidates statistical inference. We can however remove these biases by
using simple analytical and sample-splitting methods, and thereby restore valid
statistical inference. We find that the debiased FE and AB estimators produce
substantially higher estimates of the long-run effect of democracy on growth,
providing even stronger support for the key hypothesis in Acemoglu, Naidu,
Restrepo and Robinson (forthcoming). Given the ubiquitous nature of panel data,
we conclude that the use of debiased panel data estimators should substantially
improve the quality of empirical inference in economics.
Fuzzy Profit Shifting: A Model for Optimal Tax-induced Transfer Pricing with Fuzzy Arm's Length Parameter
Date: 2019-01-12 00:00:00

This paper proposes a model of optimal tax-induced transfer pricing with a
fuzzy arm's length parameter. Fuzzy numbers provide a suitable structure for
modelling the ambiguity that is intrinsic to the arm's length parameter. For
the usual conditions regarding the anti-shifting mechanisms, the optimal
transfer price becomes a maximising $\alpha$-cut of the fuzzy arm's length
parameter. Nonetheless, we show that it is profitable for firms to choose any
maximising transfer price if the probability of tax audit is sufficiently low,
even if the chosen price is considered a completely non-arm's length price by
tax authorities. In this case, we derive the necessary and sufficient
conditions to prevent this extreme shifting strategy
Multiplexing More Streams in the MU-MISO Downlink by Interference Exploitation Precoding
Date: 2019-01-12 00:00:00

In this paper, we study the interference exploitation precoding for the
scenario where the number of streams simultaneously transmitted by the base
station (BS) is larger than that of transmit antennas at the BS, and derive the
optimal precoding structure by employing the pseudo inverse. We show that the
optimal pre-scaling vector is equal to a linear combination of the right
singular vectors that correspond to zero singular values of the coefficient
matrix. By formulating the dual problem, the optimal precoding matrix can be
expressed as a function of the dual variables in a closed form, and an
equivalent quadratic programming (QP) formulation is further derived for
computational complexity reduction. Numerical results validate our analysis and
demonstrate significant performance improvements for interference exploitation
precoding for the considered scenario.
Anatomy of the vertebral column lymphatic network in mice
Date: 2019-01-12 00:00:00
Tasks: Anatomy, Navigate

Cranial lymphatic vessels (LVs) are involved in transport of fluids,
macromolecules and CNS immune responses. Little information about spinal LVs is
available, because these delicate structures are embedded within vertebral
tissues and difficult to visualize using traditional histology. Here we reveal
an extended vertebral column LV network using three-dimensional imaging of
decalcified iDISCO-clarified spine segments. Spinal LVs are metameric circuits
exiting along spinal nerve roots and connecting to lymph nodes and the thoracic
duct. They navigate in the epidural space and the dura mater around the spinal
cord, and associate with leukocytes, peripheral dorsal root and sympathetic
ganglia. Spinal LVs are VEGF-C-dependent and remodel extensively after spinal
cord injury. They constitute an extension to cranial circuits for meningeal
fluids, but also a route for perineural fluids and a link with peripheral
immune and nervous circuits. Vertebral column LVs may be potential targets to
improve the maintenance and repair of 32 spinal tissues as well as gatekeepers
of CNS immunity.
A Risk-Sharing Framework of Bilateral Contracts
Date: 2019-12-29 00:00:00

We introduce a two-agent problem which is inspired by price asymmetry arising
from funding difference. When two parties have different funding rates, the two
parties deduce different fair prices for derivative contracts even under the
same pricing methodology and parameters. Thus, the two parties should enter the
derivative contracts with a negotiated price, and we call the negotiation a
risk-sharing problem. This framework defines the negotiation as a problem that
maximizes the sum of utilities of the two parties. By the derived optimal
price, we provide a theoretical analysis on how the price is determined between
the two parties. As well as the price, the risk-sharing framework produces an
optimal amount of collateral. The derived optimal collateral can be used for
contracts between financial firms and non-financial firms. However,
inter-dealers markets are governed by regulations. As recommended in Basel III,
it is a convention in inter-dealer contracts to pledge the full amount of a
close-out price as collateral. In this case, using the optimal collateral, we
interpret conditions for the full margin requirement to be indeed optimal.
How many people microwork in France? Estimating the size of a new labor force
Date: 2019-01-12 00:00:00

Microwork platforms allocate fragmented tasks to crowds of providers with
remunerations as low as few cents. Instrumental to the development of today's
artificial intelligence, these micro-tasks push to the extreme the logic of
casualization already observed in "uberized" workers. The present article uses
the results of the DiPLab study to estimate the number of people who microwork
in France. We distinguish three categories of microworkers, corresponding to
different modes of engagement: a group of 14,903 "very active" microworkers,
most of whom are present on these platforms at least once a week; a second
featuring 52,337 "routine" microworkers, more selective and present at least
once a month; a third circle of 266,126 "casual" microworkers, more
heterogeneous and who alternate inactivity and various levels of work practice.
Our results show that microwork is comparable to, and even larger than, the
workforce of ride-sharing and delivery platforms in France. It is therefore not
an anecdotal phenomenon and deserves great attention from researchers, unions
and policy-makers.