pwc-archive/papers-with-abstracts huggingface dataset:
Technical Report: A New Decision-Theory-Based Framework for Echo Canceler Control
Date: 2017-11-29 00:00:00
Tasks: Classification

A control logic has a central role in many echo cancellation systems for
optimizing the performance of adaptive filters while estimating the echo path.
For reliable control, accurate double-talk (DT) and channel change (CC)
detectors are usually incorporated to the echo canceler. This work expands the
usual detection strategy to define a classification problem characterizing four
possible states of the echo canceler operation. The new formulation allow the
use of decision theory to continuously control the transitions among the
different modes of operation. The classification rule reduces to a low cost
statistics for which it is possible to determine the probability of error under
all hypotheses, allowing the classification performance to be accessed
analytically. Monte Carlo simulations using synthetic and real data illustrate
the reliability of the proposed method.
Numerical Simulation of Fecal Coliform Dynamics in Luruaco Lake, Colombia
Date: 2017-11-30 00:00:00

The Luruaco Lake located in the Department of Atl\'antico, Colombia, is
damaged by the discharge of untreated sewage, bringing risks to the health of
all who use its waters. The present study aims to perform the numerical
simulation of the concentration dynamics of fecal coliforms in the lake. The
simulation of the hydrodynamic flow is carried out by means of a
two-dimensional horizontal (2DH) model, given by a Navier-Stokes system. The
simulation of fecal coliform transport is described by a
convective-dispersive-reactive equation. These equations are solved numerically
by the Finite Difference Method (FDM) and the Mark and Cell (MAC) method, in
generalized coordinates. Regarding the construction of the computational mesh
of the Luruaco Lake, the cubic spline and multiblock methods were used. The
results obtained in the simulations allow a better understanding of the
dynamics of fecal coliforms in the Luruaco Lake, showing the more polluted
regions. They can also advise public agencies on identifying the emitters of
pollutants in the lake and on developing an optimal treatment for the recovery
of the polluted environment.
Fluctuation identities with continuous monitoring and their application to price barrier options
Date: 2017-11-30 00:00:00

We present a numerical scheme to calculate fluctuation identities for
exponential L\'evy processes in the continuous monitoring case. This includes
the Spitzer identities for touching a single upper or lower barrier, and the
more difficult case of the two-barriers exit problem. These identities are
given in the Fourier-Laplace domain and require numerical inverse transforms.
Thus we cover a gap in the literature that has mainly studied the discrete
monitoring case; indeed, there are no existing numerical methods that deal with
the continuous case. As a motivating application we price continuously
monitored barrier options with the underlying asset modelled by an exponential
L\'evy process. We perform a detailed error analysis of the method and develop
error bounds to show how the performance is limited by the truncation error of
the sinc-based fast Hilbert transform used for the Wiener-Hopf factorisation.
By comparing the results for our new technique with those for the discretely
monitored case (which is in the Fourier-$z$ domain) as the monitoring time step
approaches zero, we show that the error convergence with continuous monitoring
represents a limit for the discretely monitored scheme.
On the Combined Effect of Directional Antennas and Imperfect Spectrum Sensing upon Ergodic Capacity of Cognitive Radio Systems
Date: 2017-11-30 00:00:00

We consider a cognitive radio system, consisting of a primary transmitter
(PUtx), a primary receiver (PUrx), a secondary transmitter (SUtx), and a
secondary receiver (SUrx). The secondary users (SUs) are equipped with
steerable directional antennas. We assume the SUs and primary users (PUs)
coexist and the SUtx knows the geometry of network. We find the ergodic
capacity of the channel between SUtx and SUrx , and study how spectrum sensing
errors affect the capacity. In our system, the SUtx first senses the spectrum
and then transmits data at two power levels, according to the result of
sensing. The optimal SUtx transmit power levels and the optimal directions of
SUtx transmit antenna and SUrx receive antenna are obtained by maximizing the
ergodic capacity, subject to average transmit power and average interference
power constraints. To study the effect of fading channel, we considered three
scenarios: 1) when SUtx knows fading channels between SUtx and PUrx, PUtx and
SUrx, SUtx and SUrx, 2) when SUtx knows only the channel between SUtx and SUrx,
and statistics of the other two channels, and, 3) when SUtx only knows the
statistics of these three fading channels. For each scenario, we explore the
optimal SUtx transmit power levels and the optimal directions of SUtx and SUrx
antennas, such that the ergodic capacity is maximized, while the power
constraints are satisfied.
Hint of a Universal Law for the Financial Gains of Competitive Sport Teams. The case of Tour de France cycle race
Date: 2017-11-30 00:00:00

This short note is intended as a "Letter to the Editor" Perspective in order
that it serves as a contribution, in view of reaching the physics community
caring about rare events and scaling laws and unexpected findings, on a domain
of wide interest: sport and money. It is apparent from the data reported and
discussed below that the scarcity of such data does not allow to recommend a
complex elaboration of an agent based model, - at this time. In some sense,
this also means that much data on sport activities is not necessarily given in
terms of physics prone materials, but it could be, and would then attract much
attention. Nevertheless the findings tie the data to well known scaling laws
and physics processes. It is found that a simple scaling law describes the
gains of teams in recent bicycle races, like the Tour de France. An analogous
case, ranking teams in Formula 1 races, is shown in an Appendix
Fundamental Principles in Bacterial Physiology - History, Recent progress, and the Future with Focus on Cell Size Control: A Review
Date: 2018-03-14 00:00:00

Bacterial physiology is a branch of biology that aims to understand
overarching principles of cellular reproduction. Many important issues in
bacterial physiology are inherently quantitative, and major contributors to the
field have often brought together tools and ways of thinking from multiple
disciplines. This article presents a comprehensive overview of major ideas and
approaches developed since the early 20th century for anyone who is interested
in the fundamental problems in bacterial physiology. This article is divided
into two parts. In the first part (Sections 1 to 3), we review the first
`golden era' of bacterial physiology from the 1940s to early 1970s and provide
a complete list of major references from that period. In the second part
(Sections 4 to 7), we explain how the pioneering work from the first golden era
has influenced various rediscoveries of general quantitative principles and
significant further development in modern bacterial physiology. Specifically,
Section 4 presents the history and current progress of the `adder' principle of
cell size homeostasis. Section 5 discusses the implications of coarse-graining
the cellular protein composition, and how the coarse-grained proteome `sectors'
re-balance under different growth conditions. Section 6 focuses on
physiological invariants, and explains how they are the key to understanding
the coordination between growth and the cell cycle underlying cell size control
in steady-state growth. Section 7 overviews how the temporal organization of
all the internal processes enables balanced growth. In the final Section 8, we
conclude by discussing the remaining challenges for the future in the field.
Interplay of activation kinetics and the derivative conductance determines resonance properties of neurons
Date: 2018-04-12 00:00:00

In a neuron with hyperpolarization activated current ($I_h$), the correct
input frequency leads to an enhancement of the output response. This behavior
is known as resonance and is well described by the neuronal impedance. In a
simple neuron model we derive equations for the neuron's resonance and we link
its frequency and existence with the biophysical properties of $I_h$. For a
small voltage change, the component of the ratio of current change to voltage
change ($dI/dV$) due to the voltage-dependent conductance change ($dg/dV$) is
known as derivative conductance ($G_h^{Der}$). We show that both $G_h^{Der}$
and the current activation kinetics (characterized by the activation time
constant $\tau_h$) are mainly responsible for controlling the frequency and
existence of resonance. The increment of both factors ($G_h^{Der}$ and
$\tau_h$) greatly contributes to the appearance of resonance. We also
demonstrate that resonance is voltage dependent due to the voltage dependence
of $G_h^{Der}$. Our results have important implications and can be used to
predict and explain resonance properties of neurons with the $I_h$ current.
Multi-currency reserving for coherent risk measures
Date: 2017-12-15 00:00:00

We examine the problem of dynamic reserving for risk in multiple currencies
under a general coherent risk measure. The reserver requires to hedge risk in a
time-consistent manner by trading in baskets of currencies. We show that
reserving portfolios in multiple currencies $\mathbf{V}$ are time-consistent
when (and only when) a generalisation of Delbaen's m-stability condition
\cite{D06}, termed optional $\V$-m-stability, holds. We prove a version of the
Fundamental Theorem of Asset Pricing in this context. We show that this problem
is equivalent to dynamic trading across baskets of currencies (rather than just
pairwise trades) in a market with proportional transaction costs and with a
frictionless final period.
The use of multilayer network analysis in animal behaviour
Date: 2018-11-21 00:00:00

Network analysis has driven key developments in research on animal behaviour
by providing quantitative methods to study the social structures of animal
groups and populations. A recent formalism, known as \emph{multilayer network
analysis}, has advanced the study of multifaceted networked systems in many
disciplines. It offers novel ways to study and quantify animal behaviour as
connected 'layers' of interactions. In this article, we review common questions
in animal behaviour that can be studied using a multilayer approach, and we
link these questions to specific analyses. We outline the types of behavioural
data and questions that may be suitable to study using multilayer network
analysis. We detail several multilayer methods, which can provide new insights
into questions about animal sociality at individual, group, population, and
evolutionary levels of organisation. We give examples for how to implement
multilayer methods to demonstrate how taking a multilayer approach can alter
inferences about social structure and the positions of individuals within such
a structure. Finally, we discuss caveats to undertaking multilayer network
analysis in the study of animal social networks, and we call attention to
methodological challenges for the application of these approaches. Our aim is
to instigate the study of new questions about animal sociality using the new
toolbox of multilayer network analysis.
Assessing Chronic Kidney Disease from Office Visit Records Using Hierarchical Meta-Classification of an Imbalanced Dataset
Date: 2017-11-17 00:00:00

Chronic Kidney Disease (CKD) is an increasingly prevalent condition affecting
13% of the US population. The disease is often a silent condition, making its
diagnosis challenging. Identifying CKD stages from standard office visit
records can help in early detection of the disease and lead to timely
intervention. The dataset we use is highly imbalanced. We propose a
hierarchical meta-classification method, aiming to stratify CKD by severity
levels, employing simple quantitative non-text features gathered from office
visit records, while addressing data imbalance. Our method effectively
stratifies CKD severity levels obtaining high average sensitivity, precision
and F-measure (~93%). We also conduct experiments in which the dimensionality
of the data is significantly reduced to include only the most salient features.
Our results show that the good performance of our system is retained even when
using the reduced feature sets, as well as under much reduced training sets,
indicating that our method is stable and generalizable.
Universal fluctuations in growth dynamics of economic systems
Date: 2018-05-21 00:00:00
Tasks: Open-Ended Question Answering

The growth of business firms is an example of a system of complex interacting
units that resembles complex interacting systems in nature such as earthquakes.
Remarkably, work in econophysics has provided evidence that the statistical
properties of the growth of business firms follow the same sorts of power laws
that characterize physical systems near their critical points. Given how
economies change over time, whether these statistical properties are
persistent, robust, and universal like those of physical systems remains an
open question. Here, we show that the scaling properties of firm growth
previously demonstrated for publicly-traded U.S. manufacturing firms from 1974
to 1993 apply to the same sorts of firms from 1993 to 2015, to firms in other
broad sectors (such as materials), and to firms in new sectors (such as
Internet services). We measure virtually the same scaling exponent for
manufacturing for the 1993 to 2015 period as for the 1974 to 1993 period and
virtually the same scaling exponent for other sectors as for manufacturing.
Furthermore, we show that fluctuations of the growth rate for new industries
self-organize into a power law over relatively short time scales.
Sequence Covering for Efficient Host-Based Intrusion Detection
Authors: Marteau Pierre-François  EXPRESSION
Date: 2018-08-24 00:00:00
Tasks: Intrusion Detection

This paper introduces a new similarity measure, the covering similarity, that
we formally define for evaluating the similarity between a symbolic sequence
and a set of symbolic sequences. A pair-wise similarity can also be directly
derived from the covering similarity to compare two symbolic sequences. An
efficient implementation to compute the covering similarity is proposed that
uses a suffix tree data-structure, but other implementations, based on suffix
array for instance, are possible and possibly necessary for handling large
scale problems. We have used this similarity to isolate attack sequences from
normal sequences in the scope of Host-based Intrusion Detection. We have
assessed the covering similarity on two well-known benchmarks in the field. In
view of the results reported on these two datasets for the state of the art
methods, and according to the comparative study we have carried out based on
three challenging similarity measures commonly used for string processing or in
bioinformatics, we show that the covering similarity is particularly relevant
to address the detection of anomalies in sequences of system calls
A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering
Date: 2018-05-10 00:00:00
Tasks: Clustering, Dimensionality Reduction

We introduce a new factor model for log volatilities that performs
dimensionality reduction and considers contributions globally through the
market, and locally through cluster structure and their interactions. We do not
assume a-priori the number of clusters in the data, instead using the Directed
Bubble Hierarchical Tree (DBHT) algorithm to fix the number of factors. We use
the factor model and a new integrated non parametric proxy to study how
volatilities contribute to volatility clustering. Globally, only the market
contributes to the volatility clustering. Locally for some clusters, the
cluster itself contributes statistically to volatility clustering. This is
significantly advantageous over other factor models, since the factors can be
chosen statistically, whilst also keeping economically relevant factors.
Finally, we show that the log volatility factor model explains a similar amount
of memory to a Principal Components Analysis (PCA) factor model and an
exploratory factor model.
A Kalman Filter Approach for Biomolecular Systems with Noise Covariance Updating
Date: 2018-11-12 00:00:00

An important part of system modeling is determining parameter values,
particularly for biomolecular systems, where direct measurements of individual
parameters are typically hard. While Extended Kalman Filters have been used for
this purpose, the choice of the process noise covariance is generally unclear.
In this chapter, we address this issue for biomolecular systems using a
combination of Monte Carlo simulations and experimental data, exploiting the
dependence of the process noise covariance on the states and parameters, as
given in the Langevin framework. We adapt a Hybrid Extended Kalman Filtering
technique by updating the process noise covariance at each time step based on
estimates. We compare the performance of this framework with different fixed
values of process noise covariance in biomolecular system models, including an
oscillator model, as well as in experimentally measured data for a negative
transcriptional feedback circuit. We find that the Extended Kalman Filter with
such process noise covariance update is closer to the optimality condition in
the sense that the innovation sequence becomes white and in achieving a balance
between the mean square estimation error and parameter convergence time. The
results of this chapter may help in the use of Extended Kalman Filters for
systems where process noise covariance depends on states and/or parameters.
On monitoring development indicators using high resolution satellite images
Date: 2018-06-25 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

We develop a machine learning based tool for accurate prediction of
socio-economic indicators from daytime satellite imagery. The diverse set of
indicators are often not intuitively related to observable features in
satellite images, and are not even always well correlated with each other. Our
predictive tool is more accurate than using night light as a proxy, and can be
used to predict missing data, smooth out noise in surveys, monitor development
progress of a region, and flag potential anomalies. Finally, we use predicted
variables to do robustness analysis of a regression study of high rate of
stunting in India.
A Context-Aware User-Item Representation Learning for Item Recommendation
Authors: Wu Libing, Quan Cong, Li Chenliang, Wang Qian, Zheng Bolong
Date: 2017-12-29 00:00:00
Tasks: Prediction, Representation Learning

Both reviews and user-item interactions (i.e., rating scores) have been
widely adopted for user rating prediction. However, these existing techniques
mainly extract the latent representations for users and items in an independent
and static manner. That is, a single static feature vector is derived to encode
her preference without considering the particular characteristics of each
candidate item. We argue that this static encoding scheme is difficult to fully
capture the users' preference. In this paper, we propose a novel context-aware
user-item representation learning model for rating prediction, named CARL.
Namely, CARL derives a joint representation for a given user-item pair based on
their individual latent features and latent feature interactions. Then, CARL
adopts Factorization Machines to further model higher-order feature
interactions on the basis of the user-item pair for rating prediction.
Specifically, two separate learning components are devised in CARL to exploit
review data and interaction data respectively: review-based feature learning
and interaction-based feature learning. In review-based learning component,
with convolution operations and attention mechanism, the relevant features for
a user-item pair are extracted by jointly considering their corresponding
reviews. However, these features are only review-driven and may not be
comprehensive. Hence, interaction-based learning component further extracts
complementary features from interaction data alone, also on the basis of
user-item pairs. The final rating score is then derived with a dynamic linear
fusion mechanism. Experiments on five real-world datasets show that CARL
achieves significantly better rating prediction accuracy than existing
state-of-the-art alternatives. Also, with attention mechanism, we show that the
relevant information in reviews can be highlighted to interpret the rating
prediction.
Deciding the existence of a cherry-picking sequence is hard on two trees
Date: 2019-01-25 00:00:00

Here we show that deciding whether two rooted binary phylogenetic trees on
the same set of taxa permit a cherry-picking sequence, a special type of
elimination order on the taxa, is NP-complete. This improves on an earlier
result which proved hardness for eight or more trees. Via a known equivalence
between cherry-picking sequences and temporal phylogenetic networks, our result
proves that it is NP-complete to determine the existence of a temporal
phylogenetic network that contains topological embeddings of both trees. The
hardness result also greatly strengthens previous inapproximability results for
the minimum temporal-hybridization number problem. This is the optimization
version of the problem where we wish to construct a temporal phylogenetic
network that topologically embeds two given rooted binary phylogenetic trees
and that has a minimum number of indegree-2 nodes, which represent events such
as hybridization and horizontal gene transfer. We end on a positive note,
pointing out that fixed parameter tractability results in this area are likely
to ensure the continued relevance of the temporal phylogenetic network model.
Aggregating Google Trends: Multivariate Testing and Analysis
Date: 2018-03-25 00:00:00

Web search data are a valuable source of business and economic information.
Previous studies have utilized Google Trends web search data for economic
forecasting. We expand this work by providing algorithms to combine and
aggregate search volume data, so that the resulting data is both consistent
over time and consistent between data series. We give a brand equity example,
where Google Trends is used to analyze shopping data for 100 top ranked brands
and these data are used to nowcast economic variables. We describe the
importance of out of sample prediction and show how principal component
analysis (PCA) can be used to improve the signal to noise ratio and prevent
overfitting in nowcasting models. We give a finance example, where exploratory
data analysis and classification is used to analyze the relationship between
Google Trends searches and stock prices.