pwc-archive/papers-with-abstracts huggingface dataset:
Inequality, mobility and the financial accumulation process: A computational economic analysis
Date: 2019-01-13 00:00:00

Our computational economic analysis investigates the relationship between
inequality, mobility and the financial accumulation process. Extending the
baseline model by Levy et al., we characterise the economic process through
stylised return structures generating alternative evolutions of income and
wealth through time. First, we explore the limited heuristic contribution of
one and two factors models comprising one single stock (capital wealth) and one
single flow factor (labour) as pure drivers of income and wealth generation and
allocation over time. Second, we introduce heuristic modes of taxation in line
with the baseline approach. Our computational economic analysis corroborates
that the financial accumulation process featuring compound returns plays a
significant role as source of inequality, while institutional arrangements
including taxation play a significant role in framing and shaping the aggregate
economic process that evolves over socioeconomic space and time.
Formation of three-dimensional auditory space
Date: 2019-01-13 00:00:00

Human listeners need to permanently interact with their three-dimensional
(3-D) environment. To this end, they require efficient perceptual mechanisms to
form a sufficiently accurate 3-D auditory space. In this chapter, we discuss
the formation of the 3-D auditory space from various perspectives. The aim is
to show the link between cognition, acoustics, neurophysiology, and
psychophysics, when it comes to spatial hearing. First, we present recent
cognitive concepts for creating internal models of the complex auditory
environment. Second, we describe the acoustic signals available at our ears and
discuss the spatial information they convey. Third, we look into
neurophysiology, seeking for the neural substrates of the 3-D auditory space.
Finally, we elaborate on psychophysical spatial tasks and percepts that are
possible just because of the formation of the auditory space.
Moonshots for aging
Authors: Sandeep Kumar, Timothy R. Peterson
Date: 2019-01-13 00:00:00
Tasks: Philosophy

As the global population ages, there is increased interest in living longer and improving one's quality of life in later years. However, studying aging - the decline in body function - is expensive and time-consuming. And despite research success to make model organisms live longer, there still aren't really any feasible solutions for delaying aging in humans. With space travel, scientists couldn't know what it would take to get to the moon. They had to extrapolate from theory and shorter-range tests. Perhaps with aging, we need a similar moonshot philosophy. And though "shot" might imply medicine, perhaps we need to think beyond biological interventions. Like the moon, we seem a long way away from provable therapies to increase human healthspan (the healthy period of one's life) or lifespan (how long one lives). This review therefore focuses on radical proposals. We hope it might stimulate discussion on what we might consider doing significantly differently than ongoing aging research.
Multi-Cell Interference Exploitation: A New Dimension in Cell Coordination
Date: 2019-01-13 00:00:00

In this paper, we propose a series of novel coordination schemes for
multi-cell downlink communication. Starting from full base station (BS)
coordination, we first propose a fully-coordinated scheme to exploit beneficial
effects of both inter-cell and intra-cell interference, based on sharing both
channel state information (CSI) and data among the BSs. To reduce the
coordination overhead, we then propose a partially-coordinated scheme where
only intra-cell interference is designed to be constructive while inter-cell is
jointly suppressed by the coordinated BSs. Accordingly, the coordination only
involves CSI exchange and the need for sharing data is eliminated. To further
reduce the coordination overhead, a third scheme is proposed, which only
requires the knowledge of statistical inter-cell channels, at the cost of a
slight increase on the transmission power. For all the proposed schemes,
imperfect CSI is considered. We minimize the total transmission power in terms
of probabilistic and deterministic optimizations. Explicitly, the former
statistically satisfies the users' signal-to-interference-plus-noise ratio
(SINR) while the latter guarantees the SINR requirements in the worst case CSI
uncertainties. Simulation verifies that our schemes consume much lower power
compared to the existing benchmarks, i.e., coordinated multi-point (CoMP) and
coordinated-beamforming (CBF) systems, opening a new dimension on multi-cell
coordination.
Optimal decoding of dynamic stimuli encoded by heterogeneous populations of spiking neurons - a closed form approximation
Date: 2019-01-14 00:00:00
Tasks: Form, State Estimation

Neural decoding may be formulated as dynamic state estimation (filtering)
based on point process observations, a generally intractable problem. Numerical
sampling techniques are often practically useful for the decoding of real
neural data. However, they are less useful as theoretical tools for modeling
and understanding sensory neural systems, since they lead to limited conceptual
insight about optimal encoding and decoding strategies. We consider sensory
neural populations characterized by a distribution over neuron parameters. We
develop an analytically tractable Bayesian approximation to optimal filtering
based on the observation of spiking activity, that greatly facilitates the
analysis of optimal encoding in situations deviating from common assumptions of
uniform coding. Continuous distributions are used to approximate large
populations with few parameters, resulting in a filter whose complexity does
not grow with the population size, and allowing optimization of population
parameters rather than individual tuning functions. Numerical comparison with
particle filtering demonstrates the quality of the approximation. The analytic
framework leads to insights which are difficult to obtain from numerical
algorithms, and is consistent with biological observations about the
distribution of sensory cells' preferred stimuli.
Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries
Date: 2019-04-23 00:00:00
Tasks: Diagnostic, Image Segmentation, Segmentation, Semantic Segmentation, Uncertainty Quantification

Computational fluid dynamics (CFD) models are emerging as tools for assisting
in diagnostic assessment of cardiovascular disease. Recent advances in image
segmentation has made subject-specific modelling of the cardiovascular system a
feasible task, which is particularly important in the case of pulmonary
hypertension (PH), which requires a combination of invasive and non-invasive
procedures for diagnosis. Uncertainty in image segmentation can easily
propagate to CFD model predictions, making uncertainty quantification crucial
for subject-specific models. This study quantifies the variability of
one-dimensional (1D) CFD predictions by propagating the uncertainty of network
geometry and connectivity to blood pressure and flow predictions. We analyse
multiple segmentations of an image of an excised mouse lung using different
pre-segmentation parameters. A custom algorithm extracts vessel length, vessel
radii, and network connectivity for each segmented pulmonary network. We
quantify uncertainty in geometric features by constructing probability
densities for vessel radius and length, and then sample from these
distributions and propagate uncertainties of haemodynamic predictions using a
1D CFD model. Results show that variation in network connectivity is a larger
contributor to haemodynamic uncertainty than vessel radius and length.
Game theoretic modelling of infectious disease dynamics and intervention methods: a mini-review
Date: 2019-01-14 00:00:00
Tasks: Decision Making, Vocal Bursts Type Prediction

We review research papers which use game theory to model the decision making
of individuals during an epidemic, attempting to classify the literature and
identify the emerging trends in this field. We show that the literature can be
classified based on (i) type of population modelling (compartmental or
network-based), (ii) frequency of the game (non-iterative or iterative), and
(iii) type of strategy adoption (self-evaluation or imitation). We highlight
that the choice of model depends on many factors such as the type of immunity
the disease confers, the type of immunity the vaccine confers, and size of
population and level of mixing therein. We show that while early studies used
compartmental modelling with self-evaluation based strategy adoption, the
recent trend is to use network-based modelling with imitation-based strategy
adoption. Our review indicates that game theory continues to be an effective
tool to model intervention (vaccination or social distancing) decision-making
by individuals.
A Spatio-temporal Spreading Communication Technique and its Applications
Date: 2019-01-14 00:00:00

In this paper we address a fundamental question in communication, that is, in
the presence of various noise scenarios such as white/colored Gaussian noise
and impulsive -type noises, how to efficiently and accurately transmit a set of
various signals either they are speeches or images from one side into another
via a communication channel? We need to manipulate the various signals in a way
so that they are more robust to different types of noises while at the same
time the set of signals can be efficiently and accurately transmitted into
another side via a communication channel. In this paper we propose a
spatial-temporal spreading method for a set of signals and show its
applications in communications.
Reducing measles risk in Turkey through social integration of Syrian refugees
Date: 2019-01-14 00:00:00
Tasks: Humanitarian

Turkey hosts almost 3.5M refugees and has to face a humanitarian emergency of
unprecedented levels. We use mobile phone data to map the mobility patterns of
both Turkish and Syrian refugees, and use these patterns to build data-driven
computational models for quantifying the risk of epidemics spreading for
measles -- a disease having a satisfactory immunization coverage in Turkey but
not in Syria, due to the recent civil war -- while accounting for hypothetical
policies to integrate the refugees with the Turkish population. Our results
provide quantitative evidence that policies to enhance social integration
between refugees and the hosting population would reduce the transmission
potential of measles by almost 50%, preventing the onset of widespread large
epidemics in the country. Our results suggest that social segregation does not
hamper but rather boosts potential outbreaks of measles to a greater extent in
Syrian refugees but also in Turkish citizens, although to a lesser extent. This
is due to the fact that the high immunization coverage of Turkish citizens can
shield Syrian refugees from getting exposed to the infection and this in turn
reduces potential sources of infection and spillover of cases among Turkish
citizens as well, in a virtuous cycle reminiscent of herd immunity.
Asymmetric RF/FSO Relaying with HPA non-Linearities and Feedback Delay Constraints
Date: 2019-01-18 00:00:00

In this work, we investigate the performance of a dual-hop multiple relays
system consisting of mixed Radio-Frequency (RF)/Free Space Optical (FSO)
channels. The RF channels are subject to Rayleigh fading while the optical
links experience the Double Generalized Gamma including atmospheric turbulence,
path loss and the misalignment between the transmitter and the receiver
aperture (also known as the pointing error). The FSO model also takes into
account the receiver detection technique which could be either heterodyne or
intensity modulation and direct detection. Partial Relay Selection with
outdated Channel State Information is assumed based on the RF channels to
select a relay and we also consider fixed and variable Amplify-and-Forward
relaying schemes. In addition, we assume that the relays are affected by the
high power amplifier non-linearities and herein we discuss two power amplifiers
called Soft Envelope Limiter and Traveling Wave Tube Amplifier. Furthermore,
novel closed-forms and tight upper bounds of the outage probability, the bit
error probability, and the ergodic capacity are derived. Capitalizing on these
performance, we derive the high SNR asymptotic to get engineering insights
about the system gains such as the diversity and the coding gains. Finally, the
mathematical expressions are validated using the Monte Carlo simulation.
Designing An Industrial Policy For Developing Countries: A New Approach
Date: 2019-01-14 00:00:00

In this study, the prevalent methodology for design of the industrial policy
in developing countries was critically assessed, and it was shown that the
mechanism and content of classical method is fundamentally contradictory to the
goals and components of the endogenous growth theories. This study, by
proposing a new approach, along settling Schumpeter's economic growth theory as
a policy framework, designed the process of entering, analyzing and processing
data as the mechanism of the industrial policy in order to provide "theoretical
consistency" and "technical and Statistical requirements" for targeting the
growth stimulant factor effectively.
Natural Disasters Detection in Social Media and Satellite imagery: a survey
Authors: Said Naina, Ahmad Kashif, Regular Michael, Pogorelov Konstantin, Hassan Laiq, Ahmad Nasir, Conci Nicola
Date: 2019-01-14 00:00:00
Tasks: Benchmarking

The analysis of natural disaster-related multimedia content got great
attention in recent years. Being one of the most important sources of
information, social media have been crawled over the years to collect and
analyze disaster-related multimedia content. Satellite imagery has also been
widely explored for disasters analysis. In this paper, we survey the existing
literature on disaster detection and analysis of the retrieved information from
social media and satellites. Literature on disaster detection and analysis of
related multimedia content on the basis of the nature of the content can be
categorized into three groups, namely (i) disaster detection in text; (ii)
analysis of disaster-related visual content from social media; and (iii)
disaster detection in satellite imagery. We extensively review different
approaches proposed in these three domains. Furthermore, we also review
benchmarking datasets available for the evaluation of disaster detection
frameworks. Moreover, we provide a detailed discussion on the insights obtained
from the literature review, and identify future trends and challenges, which
will provide an important starting point for the researchers in the field.
Characterizing and Predicting Email Deferral Behavior
Authors: Sarrafzadeh Bahareh, Awadallah Ahmed Hassan, Lin Christopher H., Lee Chia-Jung, Shokouhi Milad, Dumais Susan T.
Date: 2019-01-14 00:00:00

Email triage involves going through unhandled emails and deciding what to do
with them. This familiar process can become increasingly challenging as the
number of unhandled email grows. During a triage session, users commonly defer
handling emails that they cannot immediately deal with to later. These deferred
emails, are often related to tasks that are postponed until the user has more
time or the right information to deal with them. In this paper, through
qualitative interviews and a large-scale log analysis, we study when and what
enterprise email users tend to defer. We found that users are more likely to
defer emails when handling them involves replying, reading carefully, or
clicking on links and attachments. We also learned that the decision to defer
emails depends on many factors such as user's workload and the importance of
the sender. Our qualitative results suggested that deferring is very common,
and our quantitative log analysis confirms that 12% of triage sessions and 16%
of daily active users had at least one deferred email on weekdays. We also
discuss several deferral strategies such as marking emails as unread and
flagging that are reported by our interviewees, and illustrate how such
patterns can be also observed in user logs. Inspired by the characteristics of
deferred emails and contextual factors involved in deciding if an email should
be deferred, we train a classifier for predicting whether a recently triaged
email is actually deferred. Our experimental results suggests that deferral can
be classified with modest effectiveness. Overall, our work provides novel
insights about how users handle their emails and how deferral can be modeled.
How to correctly quantify neuronal phase-response curves from noisy recordings
Date: 2019-01-14 00:00:00

At the level of individual neurons, various coding properties can be inferred
from the input-output relationship of a cell. For small inputs, this relation
is captured by the phase-response curve (PRC), which measures the effect of a
small perturbation on the timing of the subsequent spike. Experimentally,
however, an accurate experimental estimation of PRCs is challenging. Despite
elaborate measurement efforts, experimental PRC estimates often cannot be
related to those from modeling studies. In particular, experimental PRCs rarely
resemble the generic PRC expected close to spike initiation, which is
indicative of the underlying spike-onset bifurcation. Here, we show for
conductance-based model neurons that the correspondence between theoretical and
measured phase-response curve is lost when the stimuli used for the estimation
are too large. In this case, the derived phase-response curve is distorted
beyond recognition and takes on a generic shape that reflects the measurement
protocol, but not the real neuronal dynamics. We discuss how to identify
appropriate stimulus strengths for perturbation and noise-stimulation methods,
which permit to estimate PRCs that reliably reflect the spike-onset bifurcation
-- a task that is particularly difficult if a lower bound for the stimulus
amplitude is dictated by prominent intrinsic neuronal noise.
Roadblocked monotonic paths and the enumeration of coalescent histories for non-matching caterpillar gene trees and species trees
Date: 2019-01-14 00:00:00

Given a gene tree topology and a species tree topology, a coalescent history
represents a possible mapping of the list of gene tree coalescences to
associated branches of a species tree on which those coalescences take place.
Enumerative properties of coalescent histories have been of interest in the
analysis of relationships between gene trees and species trees. The simplest
enumerative result identifies a bijection between coalescent histories for a
matching caterpillar gene tree and species tree with monotonic paths that do
not cross the diagonal of a square lattice, establishing that the associated
number of coalescent histories for $n$-taxon matching caterpillar trees ($n
\geqslant 2$) is the Catalan number $C_{n-1} = \frac{1}{n} {2n-2 \choose n-1}$.
Here, we show that a similar bijection applies for \emph{non-matching}
caterpillars, connecting coalescent histories for a non-matching caterpillar
gene tree and species tree to a class of \emph{roadblocked} monotonic paths.
The result provides a simplified algorithm for enumerating coalescent histories
in the non-matching caterpillar case. It enables a rapid proof of a known
result that given a caterpillar species tree, no non-matching caterpillar gene
tree has a number of coalescent histories exceeding that of the matching gene
tree. Additional results on coalescent histories can be obtained by a bijection
between permissible roadblocked monotonic paths and Dyck paths. We study the
number of coalescent histories for non-matching caterpillar gene trees that
differ from the species tree by nearest-neighbor-interchange and
subtree-prune-and-regraft moves, characterizing the non-matching caterpillar
with the largest number of coalescent histories. We discuss the implications of
the results for the study of the combinatorics of gene trees and species trees.
Systemic Risk: Conditional Distortion Risk Measures
Date: 2019-01-28 00:00:00
Tasks: Attribute, Triplet

In this paper, we introduce the rich classes of conditional distortion (CoD)
risk measures and distortion risk contribution ($\Delta$CoD) measures as
measures of systemic risk and analyze their properties and representations. The
classes include the well-known conditional Value-at-Risk, conditional Expected
Shortfall, and risk contribution measures in terms of the VaR and ES as special
cases. Sufficient conditions are presented for two random vectors to be ordered
by the proposed CoD-risk measures and distortion risk contribution measures.
These conditions are expressed using the conventional stochastic dominance,
increasing convex/concave, dispersive, and excess wealth orders of the
marginals and canonical positive/negative stochastic dependence notions.
Numerical examples are provided to illustrate our theoretical findings. This
paper is the second in a triplet of papers on systemic risk by the same
authors. In \cite{DLZorder2018a}, we introduce and analyze some new stochastic
orders related to systemic risk. In a third (forthcoming) paper, we attribute
systemic risk to the different participants in a given risky environment.
Orthonormal Embedding-based Deep Clustering for Single-channel Speech Separation
Date: 2019-01-15 00:00:00
Tasks: Clustering, Deep Clustering, Speech Separation

Deep clustering is a deep neural network-based speech separation algorithm
that first trains the mixed component of signals with high-dimensional
embeddings, and then uses a clustering algorithm to separate each mixture of
sources. In this paper, we extend the baseline criterion of deep clustering
with an additional regularization term to further improve the overall
performance. This term plays a role in assigning a condition to the embeddings
such that it gives less correlation to each embedding dimension, leading to
better decomposition of the spectral bins. The regularization term helps to
mitigate the unavoidable permutation problem in the conventional deep
clustering method, which enables to bring better clustering through the
formation of optimal embeddings. We evaluate the results by varying embedding
dimension, signal-to-interference ratio (SIR), and gender dependency. The
performance comparison with the source separation measurement metric, i.e.
signal-to-distortion ratio (SDR), confirms that the proposed method outperforms
the conventional deep clustering method.
The generality of transient compartmentalization and its associated error thresholds
Date: 2019-12-15 00:00:00

Can prelife proceed without cell division? A recently proposed mechanism
suggests that transient compartmentalization could have preceded cell division
in prebiotic scenarios. Here, we study transient compartmentalization dynamics
in the presence of mutations and noise in replication, as both can be
detrimental the survival of compartments. Our study comprises situations where
compartments contain uncoupled autocatalytic reactions feeding on a common
resource, and systems based on RNA molecules copied by replicases, following a
recent experimental study.
  Using the theory of branching processes, we show analytically that two
regimes are possible. In the diffusion-limited regime, replication is
asynchronous which leads to a large variability in the composition of
compartments. In contrast, in a replication-limited regime, the growth is
synchronous and thus the compositional variability is low. Typically, simple
autocatalysts are in the former regime, while polymeric replicators can access
the latter.
  For deterministic growth dynamics, we introduce mutations that turn
functional replicators into parasites. We derive the phase boundary separating
coexistence or parasite dominance as a function of relative growth, inoculation
size and mutation rate. We show that transient compartmentalization allows
coexistence beyond the classical error threshold, above which the parasite
dominates. Our findings invite to revisit major prebiotic transitions, notably
the transitions towards cooperation, complex polymers and cell division.