pwc-archive/papers-with-abstracts huggingface dataset:
Model of a Data Mining System for Personalized Therapy of Speech Disorders
Authors: Danubianu Mirela, Pentiuc Stefan Gheorghe, Tobolcea Iolanda, Socaciu Tiberiu
Date: 2018-12-12 00:00:00

Lately, the children with speech disorder have more and more become object of
specialists attention and investment in speech disorder therapy are increasing
The development and use of information technology in order to assist and follow
speech disorder therapy allowed researchers to collect a considerable volume of
data. The aim of this paper is to present a data mining system designed to be
associated with TERAPERS system in order to provide information based on which
one could improve the process of personalized therapy of speech disorders.
Long-range depth imaging using a single-photon detector array and non-local data fusion
Date: 2018-12-11 00:00:00

The ability to measure and record high-resolution depth images at long
stand-off distances is important for a wide range of applications, including
connected and automotive vehicles, defense and security, and agriculture and
mining. In LIDAR (light detection and ranging) applications, single-photon
sensitive detection is an emerging approach, offering high sensitivity to light
and picosecond temporal resolution, and consequently excellent
surface-to-surface resolution. The use of large format CMOS single-photon
detector arrays provides high spatial resolution and allows the timing
information to be acquired simultaneously across many pixels. In this work, we
combine state-of-the-art single-photon detector array technology with non-local
data fusion to generate high resolution three-dimensional depth information of
long-range targets. The system is based on a visible pulsed illumination system
at 670~nm and a 240~$\times$ 320 pixel array sensor, achieving sub-centimeter
precision in all three spatial dimensions at a distance of 150 meters. The
non-local data fusion combines information from an optical image with sparse
sampling of the single-photon array data, providing accurate depth information
at low signature regions of the target.
User-Centric Distributed Antenna Transmission: Secure Precoding and Antenna Selection with Interference Exploitation
Date: 2018-12-12 00:00:00

We address physical layer security in distributed antenna (DA) systems, where
eavesdroppers (Eves) can intercept the information transmitted for the intended
receiver (IR). To realize a user-centric, power-efficient and physical layer
security-addressing system, we aim at minimizing total power consumption by
jointly designing DA selection and secure precoding. Different from the
conventional artificial noise (AN)-aided secure transmission, where AN is
treated as an undesired element for the IR, we design AN such that it is
constructive to the IR while keeping destructive to the Eves. Importantly, we
investigate two practical scenarios, where the IR and Eves' channel state
information (CSI) is imperfectly obtained or the Eves' CSI is completely
unknown. To handle the CSI uncertainties, we solve the problems in
probabilistic and deterministic robust optimization respectively, both
satisfying the IR' signal-to-interference-and-ratio (SINR) requirement by use
of constructive AN and addressing security against the Eves. Simulation results
demonstrate our algorithms consume much less power compared to the centralized
antenna (CA) systems with/without antenna selection, as well as the DA systems
with conventional AN processing. Last but not least, by the proposed
algorithms, the activation of DAs closely relates to users' locations and
quality-of-service (QoS) requirements, featuring a user-centric and on-demand
structure.
A theoretical framework to consider energy transfers within growth theory
Date: 2018-12-12 00:00:00

Growth theory has rarely considered energy despite its invisible hand in all
physical systems. We develop a theoretical framework that places energy
transfers at centerstage of growth theory based on two principles: (1) goods
are material rearrangements and (2) such rearrangements are done by energy
transferred by prime movers (e.g. workers, engines). We derive the implications
of these principles for an autarkic agent that maximizes utility subject to an
energy budget constraint and maximizes energy surplus to relax such constraint.
The solution to these problems shows that growth is driven by positive marginal
energy surplus of energy goods (e.g. rice, oil), yet materializes through prime
mover accumulation. This perspective brings under one framework several results
from previous attempts to insert energy within growth theory, reconciles
economics with natural sciences, and provides a basis for a general
reinterpretation of economics and growth as the interplay between human desires
and thermodynamic processes.
Apropiaci\'on privada de renta de recursos naturales? El caso del cobre en Chile
Date: 2018-12-12 00:00:00

Unexpected increases of natural resource prices can generate rents, value
that should be recovered by the State to minimize inefficiencies, avoid
arbitrary discrimination between citizens and keep a sustainable trajectory. As
a case study about private appropriation of natural resource rent, this work
explores the case of copper in Chile since 1990, empirically analyzing if the
12 main private mining companies have recovered in present value more than
their investment during their life cycle. The results of this exercise,
applicable to other natural resources, indicate that some actually have,
capturing about US$ 40 billion up to 2012. Elaborating an adequate
institutional framework for future deposits remain important challenges for
Chile to plentifully take advantage of its mining potential, as well as for any
country with an abundant resource base to better enjoy its natural wealth. For
that purpose, a concession known as Least Present Value Revenue (LPVR) is
proposed.
Chaotic Dynamics Enhance the Sensitivity of Inner Ear Hair Cells
Date: 2019-05-21 00:00:00
Tasks: Sensitivity

Hair cells of the auditory and vestibular systems are capable of detecting
sounds that induce sub-nanometer vibrations of the hair bundle, below the
stochastic noise levels of the surrounding fluid. Hair bundles of certain
species are also known to oscillate without external stimulation, indicating
the presence of an underlying active mechanism. We propose that chaotic
dynamics enhance the sensitivity and temporal resolution of the hair bundle
response, and provide experimental and theoretical evidence for this effect. By
varying the viscosity and ionic composition of the surrounding fluid, we are
able to modulate the degree of chaos observed in the hair bundle dynamics in
vitro. We consistently find that the hair bundle is most sensitive to a
stimulus of small amplitude when it is poised in the weakly chaotic regime.
Further, we show that the response time to a force step decreases with
increasing levels of chaos. These results agree well with our numerical
simulations of a chaotic Hopf oscillator and suggest that chaos may be
responsible for the sensitivity and temporal resolution of hair cells.
Estimating Position Bias without Intrusive Interventions
Authors: Agarwal Aman, Zaitsev Ivan, Wang Xuanhui, Li Cheng, Najork Marc, Joachims Thorsten
Date: 2018-12-12 00:00:00
Tasks: counterfactual, Learning-To-Rank, Position

Presentation bias is one of the key challenges when learning from implicit
feedback in search engines, as it confounds the relevance signal. While it was
recently shown how counterfactual learning-to-rank (LTR) approaches
\cite{Joachims/etal/17a} can provably overcome presentation bias when
observation propensities are known, it remains to show how to effectively
estimate these propensities. In this paper, we propose the first method for
producing consistent propensity estimates without manual relevance judgments,
disruptive interventions, or restrictive relevance modeling assumptions. First,
we show how to harvest a specific type of intervention data from historic
feedback logs of multiple different ranking functions, and show that this data
is sufficient for consistent propensity estimation in the position-based model.
Second, we propose a new extremum estimator that makes effective use of this
data. In an empirical evaluation, we find that the new estimator provides
superior propensity estimates in two real-world systems -- Arxiv Full-text
Search and Google Drive Search. Beyond these two points, we find that the
method is robust to a wide range of settings in simulation studies.
Searching for Relevant Lessons Learned Using Hybrid Information Retrieval Classifiers: A Case Study in Software Engineering
Authors: Abdellatif Tamer Mohamed, Capretz Luiz Fernando
Date: 2018-12-12 00:00:00
Tasks: Information Retrieval, Management, Retrieval

The lessons learned (LL) repository is one of the most valuable sources of
knowledge for a software organization. It can provide distinctive guidance
regarding previous working solutions for historical software management
problems, or former success stories to be followed. However, the unstructured
format of the LL repository makes it difficult to search using general queries,
which are manually inputted by project managers (PMs). For this reason, this
repository may often be overlooked despite the valuable information it
provides. Since the LL repository targets PMs, the search method should be
domain specific rather than generic as in the case of general web searching. In
previous work, we provided an automatic information retrieval based LL
classifier solution. In our solution, we relied on existing project management
artifacts in constructing the search query on-the-fly. In this paper, we extend
our previous work by examining the impact of the hybridization of multiple LL
classifiers, from our previous study, on performance. We employ two of the
hybridization techniques from the literature to construct the hybrid
classifiers. An industrial dataset of 212 LL records is used for validation.
The results show the superiority of the hybrid classifier over the top
achieving individual classifier, which reached 25%.
The transition distribution of a sample from a Wright-Fisher diffusion with general small mutation rates
Date: 2019-09-11 00:00:00

The transition distribution of a sample taken from a Wright-Fisher diffusion
with general small mutation rates is found using a coalescent approach. The
approximation is equivalent to having at most one mutation in the coalescent
tree of the sample up to the most recent common ancestor with additional
mutations occurring on the lineage from the most recent common ancestor to the
time origin if complete coalescence occurs before the origin. The sampling
distribution leads to an approximation for the transition density in the
diffusion with small mutation rates. This new solution has interest because the
transition density in a Wright-Fisher diffusion with general mutation rates is
not known.
Calibrating rough volatility models: a convolutional neural network approach
Date: 2019-07-28 00:00:00

In this paper we use convolutional neural networks to find the H\"older
exponent of simulated sample paths of the rBergomi model, a recently proposed
stock price model used in mathematical finance. We contextualise this as a
calibration problem, thereby providing a very practical and useful application.
Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments
Authors: Salman Tara, Bhamare Deval, Erbad Aiman, Jain Raj, Samaka Mohammed
Date: 2018-10-23 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Intrusion Detection

Recently, advances in machine learning techniques have attracted the
attention of the research community to build intrusion detection systems (IDS)
that can detect anomalies in the network traffic. Most of the research works,
however, do not differentiate among different types of attacks. This is, in
fact, necessary for appropriate countermeasures and defense against attacks. In
this paper, we investigate both detecting and categorizing anomalies rather
than just detecting, which is a common trend in the contemporary research
works. We have used a popular publicly available dataset to build and test
learning models for both detection and categorization of different attacks. To
be precise, we have used two supervised machine learning techniques, namely
linear regression (LR) and random forest (RF). We show that even if detection
is perfect, categorization can be less accurate due to similarities between
attacks. Our results demonstrate more than 99% detection accuracy and
categorization accuracy of 93.6%, with the inability to categorize some
attacks. Further, we argue that such categorization can be applied to
multi-cloud environments using the same machine learning techniques.
Optimising observing strategies for monitoring animals using drone-mounted thermal infrared cameras
Date: 2018-11-12 00:00:00

The proliferation of relatively affordable off-the-shelf drones offers great
opportunities for wildlife monitoring and conservation. Similarly the recent
reduction in cost of thermal infrared cameras also offers new promise in this
field, as they have the advantage over conventional RGB cameras of being able
to distinguish animals based on their body heat and being able to detect
animals at night. However, the use of drone-mounted thermal infrared cameras
comes with several technical challenges. In this paper we address some of these
issues, namely thermal contrast problems due to heat from the ground,
absorption and emission of thermal infrared radiation by the atmosphere,
obscuration by vegetation, and optimizing the flying height of drones for a
best balance between covering a large area and being able to accurately image
and identify animals of interest. We demonstrate the application of these
methods with a case study using field data, and make the first ever detection
of the critically endangered riverine rabbit (Bunolagus monticularis) in
thermal infrared data. We provide a web-tool so that the community can easily
apply these techniques to other studies
(http://www.astro.ljmu.ac.uk/~aricburk/uav_calc/).
Graph Signal Representation with Wasserstein Barycenters
Date: 2018-12-13 00:00:00
Tasks: Dictionary Learning, Dimensionality Reduction

In many applications signals reside on the vertices of weighted graphs. Thus,
there is the need to learn low dimensional representations for graph signals
that will allow for data analysis and interpretation. Existing unsupervised
dimensionality reduction methods for graph signals have focused on dictionary
learning. In these works the graph is taken into consideration by imposing a
structure or a parametrization on the dictionary and the signals are
represented as linear combinations of the atoms in the dictionary. However, the
assumption that graph signals can be represented using linear combinations of
atoms is not always appropriate. In this paper we propose a novel
representation framework based on non-linear and geometry-aware combinations of
graph signals by leveraging the mathematical theory of Optimal Transport. We
represent graph signals as Wasserstein barycenters and demonstrate through our
experiments the potential of our proposed framework for low-dimensional graph
signal representation.
Fiber-induced nonlinearity compensation in coherent optical systems by affinity propagation soft-clustering
Date: 2018-12-21 00:00:00
Tasks: Clustering

There is a tremendous need to maximize signal capacity without sacrificing
energy consumption and complexity in optical fiber communications due to the
rise of network traffic demand and critical latency-aware services such as
telemedicine and the Internet-of-Things. In this work, the first
system-agnostic and training-data-free fiber nonlinearity compensator (NLC)
using affinity propagation (AP) clustering is experimentally demonstrated. It
is shown that compared to linear equalization, AP can increase the signal
quality-factor up to about 5 dB in 16-quadrature-amplitude-modulated coherent
optical systems by effectively tackling deterministic and stochastic
nonlinearities. AP outperforms benchmark clustering algorithms and complex
deterministic schemes such as K-means, fuzzy-logic, digital-back propagation
and Volterra-based NLC, offering a power margin extension of up to 4 dB.
Experimental evolution of diverse Escherichia coli metabolic mutants identifies genetic loci for convergent adaptation of growth rate
Date: 2018-12-13 00:00:00

Cell growth is determined by substrate availability and the cell's metabolic
capacity to assimilate substrates into building blocks. Metabolic genes that
determine growth rate may interact synergistically or antagonistically, and can
accelerate or slow growth, depending on the genetic background and
environmental conditions. We evolved a diverse set of Escherichia coli
single-gene deletion mutants with a spectrum of growth rates and identified
mutations that generally increase growth rate. Despite the metabolic
differences between parent strains, mutations that enhanced growth largely
mapped to the core transcription machinery, including the $\beta$ and $\beta'$
subunits of RNA polymerase (RNAP) and the transcription elongation factor,
NusA. The structural segments of RNAP that determine enhanced growth have been
previously implicated in antibiotic resistance and in the control of
transcription elongation and pausing. We further developed a computational
framework to characterize how the transcriptional changes that occur upon
acquisition of these mutations affect growth rate across strains. Our
experimental and computational results provide evidence for cases in which RNAP
mutations shift the competitive balance between active transcription and gene
silencing. This study demonstrates that mutations in specific regions of RNAP
are a convergent adaptive solution that can enhance the growth rate of cells
from distinct metabolic states.
Fitness dependence of the fixation-time distribution for evolutionary dynamics on graphs
Date: 2019-05-31 00:00:00

Evolutionary graph theory models the effects of natural selection and random
drift on structured populations of competing mutant and non-mutant individuals.
Recent studies have found that fixation times in such systems often have
right-skewed distributions. Little is known, however, about how these
distributions and their skew depend on mutant fitness. Here we calculate the
fitness dependence of the fixation-time distribution for the Moran Birth-death
process in populations modeled by two extreme networks: the complete graph and
the one-dimensional ring lattice, obtaining exact solutions in the limit of
large network size. We find that with non-neutral fitness, the Moran process on
the ring has normally distributed fixation times, independent of the relative
fitness of mutants and non-mutants. In contrast, on the complete graph, the
fixation-time distribution is a fitness-weighted convolution of two Gumbel
distributions. When fitness is neutral the fixation-time distribution jumps
discontinuously and becomes highly skewed on both the complete graph and the
ring. Even on these simple networks, the fixation-time distribution exhibits
rich fitness dependence, with discontinuities and regions of universality.
Extensions of our results to two-fitness Moran models, times to partial
fixation, and evolution on random networks are discussed.
Forgetting in order to Remember Better
Date: 2018-12-12 00:00:00

In human memory, forgetting occur rapidly after the remembering and the rate
of forgetting slowed down as time went. This is so-called the Ebbinghaus
forgetting curve. There are many explanations of how this curve occur based on
the properties of the brains. In this article, we use a simple mathematical
model to explain the mechanism of forgetting based on rearrangement inequality
and get a general formalism for short-term and long-term memory and use it to
fit the Ebbinghaus forgetting curve. We also find out that forgetting is not a
flaw, instead it is help to improve the efficiency of remembering when human
confront different situations by reducing the interference of information and
reducing the number of retrievals. Furthurmove, we find that the interference
of informations limits the capacity of human memory, which is the "magic number
seven".
Crosstalk Impacts on Homogeneous Weakly-Coupled Multicore Fiber Based IM/DD System
Date: 2018-11-13 00:00:00

We numerically discussed crosstalk impacts on homogeneous weakly-coupled
multicore fiber based intensity modulation/direct-detection (IM/DD) systems
taking into account mean crosstalk power fluctuation, walk-off between cores,
laser frequency offset, and laser linewidth.
A Polarization Multiplexed Carrier based Coherent Link with Adaptive Polarization Control
Date: 2018-12-14 00:00:00

Transmitting polarization multiplexed carrier makes the receiver of a
coherent system local oscillator-less and frequency offset-free. A polarization
multiplexed carrier based self-homodyne (PMC-SH) system with an adaptive
polarization control (PC) can replace pulse amplitude modulation (PAM- 4) data
center interconnects. An adaptive PC technique is practically implemented by
using an electrically controlled PC along-with control circuitry for PMC-SH
systems. The de-multiplexing of the carrier and the modulated signal by using
this technique is validated through simulations for a 50 Gbaud PMC-SH
quadrature phase shift keying (QPSK) system with 20km standard single mode
fiber (SSMF). We successfully demonstrate 16 Gbaud PMC-SH systems with adaptive
PC for 10km SSMF channel. A bit error rate (BER) of 5.9 x 10^(-5) is achieved
with 32 Gb/s PMC-SHQPSK system without any signal processing while a BER of 8.7
x 10^(-3) is achieved with a 64 Gb/s PMC-SH quadrature amplitude modulation
(16QAM) system after equalization.
Can a flux-based mechanism explain positioning of protein clusters in a three-dimensional cell geometry?
Authors: Matthias Kober, Silke Bergeler, Erwin Frey
Date: 2018-12-14 00:00:00

The plane of bacterial cell division must be precisely positioned. In the bacterium Myxococcus xanthus, the proteins PomX and PomY form a large cluster, which is tethered to the nucleoid by the ATPase PomZ and moves in a stochastic, but biased manner towards midcell, where it initiates cell division. Previously, a positioning mechanism based on the fluxes of PomZ on the nucleoid was proposed. However, the cluster dynamics was analyzed in a reduced, one-dimensional geometry. Here we introduce a mathematical model that accounts for the three-dimensional shape of the nucleoid, such that nucleoid-bound PomZ dimers can diffuse past the cluster without interacting with it. Using stochastic simulations, we find that the cluster still moves to and localizes at midcell. Redistribution of PomZ by diffusion in the cytosol is essential for this cluster dynamics. Our mechanism also positions two clusters equidistantly on the nucleoid. We conclude that a flux-based mechanism allows for cluster positioning in a biologically realistic three-dimensional cell geometry.
Consistent Time-Homogeneous Modeling of SPX and VIX Derivatives
Authors: Andrew Papanicolaou
Date: 2018-12-14 00:00:00

This paper shows how to recover a stochastic volatility model (SVM) from a market model of the VIX futures term structure. Market models have more flexibility for fitting of curves than do SVMs, and therefore are better suited for pricing VIX futures and VIX derivatives. But the VIX itself is a derivative of the S&P500 (SPX) and it is common practice to price SPX derivatives using an SVM. Therefore, consistent modeling for both SPX and VIX should involve an SVM that can be obtained by inverting the market model. This paper's main result is a method for the recovery of a stochastic volatility function by solving an inverse problem where the input is the VIX function given by a market model. Analysis will show conditions necessary for there to be a unique solution to this inverse problem. The models are consistent if the recovered volatility function is non-negative. Examples are presented to illustrate the theory, to highlight the issue of negativity in solutions, and to show the potential for inconsistency in non-Markov settings.