pwc-archive/papers-with-abstracts huggingface dataset:
The role of self-touch experience in the formation of the self
Date: 2017-12-21 00:00:00

The human self has many facets: there is the physical body and then there are
different concepts or representations supported by processes in the brain such
as the ecological, social, temporal, conceptual, and experiential self. The
mechanisms of operation and formation of the self are, however, largely
unknown. The basis is constituted by the ecological or sensorimotor self that
deals with the configuration of the body in space and its action possibilities.
This self is prereflective, prelinguistic, and initially perhaps even largely
independent of visual inputs. Instead, somatosensory (tactile and
proprioceptive) information both before and after birth may play a key part. In
this paper, we propose that self-touch experience may be a fundamental
mechanisms to bootstrap the formation of the sensorimotor self and perhaps even
beyond. We will investigate this from the perspectives of phenomenology,
developmental psychology, and neuroscience. In light of the evidence from fetus
and infant development, we will speculate about the possible mechanisms that
may drive the formation of first body representations drawing on self-touch
experience.
Reciprocal Nucleopeptides as the Ancestral Darwinian Self-Replicator
Date: 2017-12-21 00:00:00

Even the simplest organisms are too complex to have spontaneously arisen
fully-formed, yet precursors to first life must have emerged ab initio from
their environment. A watershed event was the appearance of the first entity
capable of evolution: the Initial Darwinian Ancestor. Here we suggest that
nucleopeptide reciprocal replicators could have carried out this important role
and contend that this is the simplest way to explain extant replication systems
in a mathematically consistent way. We propose short nucleic-acid templates on
which amino-acylated adapters assembled. Spatial localization drives peptide
ligation from activated precursors to generate phosphodiester-bond-catalytic
peptides. Comprising autocatalytic protein and nucleic acid sequences, this
dynamical system links and unifies several previous hypotheses and provides a
plausible model for the emergence of DNA and the operational code.
Haematopietic stem cells -- entropic landscapes of differentiation
Date: 2017-12-21 00:00:00

The metaphor of a potential epigenetic differentiation landscape broadly
suggests that during differentiation a stem cell follows the steepest
descending gradient toward a stable equilibrium state which represents the
final cell type. It has been conjectured that there is an analogy to the
concept of entropy in statistical mechanics. In this context, in the
undifferentiated state the entropy would be large since fewer constraints exist
on the gene expression programs of the cell. As differentiation progresses,
gene expression programs become more and more constrained and thus the entropy
would be expected to decrease. Such an entropy decrease would, in analogy to
statistical mechanics, require some form of free energy to decrease
accordingly. In order to assess these predictions, we compute the Shannon
entropy for time-resolved single-cell gene expression data in two different
experimental setups of haematopoietic differentiation. We find that the
behaviour of this entropy measure is in contrast to these predictions. In
particular, we find that the Shannon entropy is not a decreasing function of
developmental pseudo-time but instead it increases toward the point of
commitment before decreasing again. This behaviour is consistent with an
increase in gene expression disorder observed in populations sampled at the
point of commitment. Single cells in these populations exhibit different
combinations of regulator activity that suggest the presence of multiple
configurations of a potential differentiation network as a result of multiple
entry points into the committed state.
Survivability of a metapopulation under local extinctions
Date: 2017-12-21 00:00:00

A metapopulation structure in landscape ecology comprises a group of
interacting spatially separated subpopulations or patches of the same species
that may experience several local extinctions. This makes the investigation of
survivability (in the form of global oscillation) of a metapopulation on top of
diverse dispersal topologies extremely crucial. However, among various
dispersal topologies in ecological networks, which one can provide higher
metapopulation survivability under local extinction is still not well explored.
In this article, we scrutinize the robustness of an ecological network
consisting of prey-predator patches having Holling type I functional response,
against progressively extinct population patches. We present a comprehensive
study on this while considering global, small-world and scale-free dispersal of
the subpopulations. Furthermore, we extend our work in enhancing survivability
in the form of sustained global oscillation by introducing asymmetries in the
dispersal rates of the considered species. Our findings affirm that the
asynchrony among the patches plays an important role in the survivability of a
metapopulation. In order to demonstrate the model independence of the observed
phenomenon, we perform a similar analysis for patches exhibiting Holling type
II functional response. On the grounds of the obtained results, our work is
expected to provide a better perception of the influence of dispersal
arrangements on the global survivability of ecological networks.
A probabilistic interpretation of replicator-mutator dynamics
Date: 2017-12-21 00:00:00

In this note, we investigate the relationship between probabilistic updating
mechanisms and discrete-time replicator-mutator dynamics. We consider the
recently shown connection between Bayesian updating and replicator dynamics and
extend it to the replicator-mutator dynamics by considering prediction and
filtering recursions in hidden Markov models (HMM). We show that it is possible
to understand the evolution of the frequency vector of a population under the
replicator-mutator equation as a posterior predictive inference procedure in an
HMM. This view enables us to derive a natural dual version of the
replicator-mutator equation, which corresponds to updating the filtering
distribution. Finally, we conclude with the implications of the interpretation
and with some comments related to the recent discussions about evolution and
learning.
Migrations, vaccinations and epidemic control
Date: 2017-12-21 00:00:00

We consider three regions with different public health conditions. In the
absence of migration among these regions, the first two have good health
conditions and the disease free state is stable; for the third region, on the
other hand, the only stable state is the endemic one. When migration is
included in the model, we assume that the second region has a disease risk that
makes its inhabitants prone to accept to be vaccinated, while the population in
the first region tends to reject the vaccination, considered riskier that the
disease. Therefore, the second region is a "buffer zone" between the two
extremal regions. We study the basic reproductive ratio as a function of the
vaccination in all regions and migration among them. This problem is studied
numerically, showing explicit situations in which migration will have an
overall positive effect in the disease dynamics, with and without vaccinations.
We also find explicit formula in the limit of small ("closed borders") and high
migration ("open borders").
Eco-Evolutionary Dynamics of a Population with Randomly Switching Carrying Capacity
Date: 2018-07-26 00:00:00

Environmental variability greatly influences the eco-evolutionary dynamics of
a population, i.e. it affects how its size and composition evolve. Here, we
study a well-mixed population of finite and fluctuating size whose growth is
limited by a randomly switching carrying capacity. This models the
environmental fluctuations between states of resources abundance and scarcity.
The population consists of two strains, one growing slightly faster than the
other, competing under two scenarios: one in which competition is solely for
resources, and one in which the slow ("cooperating") strain produces a public
good that benefits also the fast ("freeriding") strain. We investigate how the
coupling of demographic and environmental (external) noise affects the
population's eco-evolutionary dynamics. By analytical and computational means,
we study the correlations between the population size and its composition, and
discuss the social-dilemma-like "eco-evolutionary game" characterizing the
public good production. We determine in what conditions it is best to produce a
public good; when cooperating is beneficial but outcompeted by freeriding, and
when the public good production is detrimental for cooperators. Within a linear
noise approximation to populations of varying size, we also accurately analyze
the coupled effects of demographic and environmental noise on the size
distribution.
Predicting physiological developments from human gait using smartphone sensor data
Date: 2017-12-13 00:00:00
Tasks: Recommendation Systems

Coronary artery disease, heart failure, angina pectoris and diabetes are
among the leading causes of morbidity and mortality over the globe.
Susceptibility to such disorders is compounded by changing lifestyles, poor
dietary routines, aging and obesity. Besides, conventional diagnostics are
limited in their capability to detect such pathologies at an early stage. This
generates demand for automatic recommender systems that could effectively
monitor and predict pathogenic behaviors in the body. To this end, we propose
human gait analysis for predicting two important physiological parameters
associated with different diseases, body mass index and age. Predicting age and
body mass index by actively profiling the gait samples, could be further used
for providing suitable healthcare recommendations. Existing strategies for
predicting age and body mass index, however, necessitate stringent experimental
settings for achieving appropriate performance. For instance, precisely
recorded speech signals were recently used for predicting body mass indices of
different subjects. Similarly, age groups were predicted by recording gait
samples from on-body and wearable sensors. Such specialized methods limit
active and convenient profiling of human age and body mass indices. We address
these issues, by introducing smartphone sensors as a means for recording gait
signals. Using on-board accelerometer and gyroscope helps in developing
easy-to-use and accessible systems for predicting body mass index and age. To
empirically show the effectiveness of our proposed methodology, we collected
gait samples from sixty-three different subjects that were classified in body
mass index and age groups using six well-known machine learning classifiers. We
evaluated our system using two different windowing operations for feature
extraction, namely Gaussian and Square.
Analyzing transient-evoked otoacoustic emissions by concentration of frequency and time
Date: 2018-07-10 00:00:00

The linear part of transient evoked (TE) otoacoustic emission (OAE) is
thought to be generated via coherent reflection near the characteristic place
of constituent wave components. Because of the tonotopic organization of the
cochlea, high frequency emissions return earlier than low frequencies; however,
due to the random nature of coherent reflection, the instantaneous frequency
(IF) and amplitude envelope of TEOAEs both fluctuate. Multiple reflection
components and synchronized spontaneous emissions can further make it difficult
to extract the IF by linear transforms. In this paper, we propose to model
TEOAEs as a sum of {\em intrinsic mode-type functions} and analyze it by a
{nonlinear-type time-frequency analysis} technique called concentration of
frequency and time (ConceFT). When tested with synthetic OAE signals {with
possibly multiple oscillatory components}, the present method is able to
produce clearly visualized traces of individual components on the
time-frequency plane. Further, when the signal is noisy, the proposed method is
compared with existing linear and bilinear methods in its accuracy for
estimating the fluctuating IF. Results suggest that ConceFT outperforms the
best of these methods in terms of optimal transport distance, reducing the
error by 10 to {21\%} when the signal to noise ratio is 10 dB or below.
On the Use of a Spectral Glottal Model for the Source-filter Separation of Speech
Date: 2017-12-21 00:00:00

The estimation of glottal flow from a speech waveform is a key method for
speech analysis and parameterization. Significant research effort has been made
to dissociate the first vocal tract resonance from the glottal formant (the
low-frequency resonance describing the open-phase of the vocal fold vibration).
However few methods cope with estimation of high-frequency spectral tilt to
describe the return-phase of the vocal fold vibration, which is crucial to the
perception of vocal effort. This paper proposes an improved version of the
well-known Iterative Adaptive Inverse Filtering (IAIF) called GFM-IAIF.
GFM-IAIF includes a full spectral model of the glottis that incorporates both
glottal formant and spectral tilt features. Comparisons with the standard IAIF
method show that while GFM-IAIF maintains good performance on vocal tract
removal, it significantly improves the perceptive timbral variations associated
to vocal effort.
On Long Memory Origins and Forecast Horizons
Date: 2017-12-21 00:00:00
Tasks: Econometrics

Most long memory forecasting studies assume that the memory is generated by
the fractional difference operator. We argue that the most cited theoretical
arguments for the presence of long memory do not imply the fractional
difference operator, and assess the performance of the autoregressive
fractionally integrated moving average $(ARFIMA)$ model when forecasting series
with long memory generated by nonfractional processes. We find that high-order
autoregressive $(AR)$ models produce similar or superior forecast performance
than $ARFIMA$ models at short horizons. Nonetheless, as the forecast horizon
increases, the $ARFIMA$ models tend to dominate in forecast performance. Hence,
$ARFIMA$ models are well suited for forecasts of long memory processes
regardless of the long memory generating mechanism, particularly for medium and
long forecast horizons. Additionally, we analyse the forecasting performance of
the heterogeneous autoregressive ($HAR$) model which imposes restrictions on
high-order $AR$ models. We find that the structure imposed by the $HAR$ model
produces better long horizon forecasts than $AR$ models of the same order, at
the price of inferior short horizon forecasts in some cases. Our results have
implications for, among others, Climate Econometrics and Financial Econometrics
models dealing with long memory series at different forecast horizons. We show
in an example that while a short memory autoregressive moving average $(ARMA)$
model gives the best performance when forecasting the Realized Variance of the
S\&P 500 up to a month ahead, the $ARFIMA$ model gives the best performance for
longer forecast horizons.
Overview of the Triple Scoring Task at the WSDM Cup 2017
Authors: Bast Hannah, Buchhold Björn, Haussmann Elmar
Date: 2017-12-21 00:00:00
Tasks: valid

This paper provides an overview of the triple scoring task at the WSDM Cup
2017, including a description of the task and the dataset, an overview of the
participating teams and their results, and a brief account of the methods
employed. In a nutshell, the task was to compute relevance scores for
knowledge-base triples from relations, where such scores make sense. Due to the
way the ground truth was constructed, scores were required to be integers from
the range 0..7. For example, reasonable scores for the triples "Tim Burton
profession Director" and "Tim Burton profession Actor" would be 7 and 2,
respectively, because Tim Burton is well-known as a director, but he acted only
in a few lesser known movies.
  The triple scoring task attracted considerable interest, with 52 initial
registrations and 21 teams who submitted a valid run before the deadline. The
winning team achieved an accuracy of 87%, that is, for that fraction of the
triples from the test set (which was revealed only after the deadline) the
difference to the score from the ground truth was at most 2. The best result
for the average difference from the test set scores was 1.50.
Simultaneous Confidence Intervals for High-dimensional Linear Models with Many Endogenous Variables
Date: 2019-08-28 00:00:00
Tasks: valid

High-dimensional linear models with endogenous variables play an increasingly
important role in recent econometric literature. In this work we allow for
models with many endogenous variables and many instrument variables to achieve
identification. Because of the high-dimensionality in the second stage,
constructing honest confidence regions with asymptotically correct coverage is
non-trivial. Our main contribution is to propose estimators and confidence
regions that would achieve that. The approach relies on moment conditions that
have an additional orthogonal property with respect to nuisance parameters.
Moreover, estimation of high-dimension nuisance parameters is carried out via
new pivotal procedures. In order to achieve simultaneously valid confidence
regions we use a multiplier bootstrap procedure to compute critical values and
establish its validity.
Efficient European and American option pricing under a jump-diffusion process
Date: 2017-12-21 00:00:00

When the underlying asset displays oscillations, spikes or heavy-tailed
distributions, the lognormal diffusion process (for which Black and Scholes
developed their momentous option pricing formula) is inadequate: in order to
overcome these real world difficulties many models have been developed. Merton
proposed a jump-diffusion model, where the dynamics of the price of the
underlying are subject to variations due to a Brownian process and also to
possible jumps, driven by a compound Poisson process. Merton's model admits a
series solution for the European option price, and there have been a lot of
attempts to obtain a discretisation of the Merton model with tree methods in
order to price American or more complex options, e. g. Amin, the $O(n^3)$
procedure by Hilliard and Schwartz and the $O(n^{2.5})$ procedure by Dai et al.
Here, starting from the implementation of the seven-nodes procedure by Hilliard
and Schwartz, we prove theoretically that it is possible to reduce the
complexity to $O(n \ln n)$ in the European case and $O(n^2 \ln n)$ in the
American put case. These theoretical results can be obtained through suitable
truncation of the lattice structure and the proofs provide closed formulas for
the truncation limitations.
Pricing double barrier options on homogeneous diffusions: a Neumann series of Bessel functions representation
Date: 2017-12-21 00:00:00

This paper develops a novel analytically tractable Neumann series of Bessel
functions representation for pricing (and hedging) European-style double
barrier knock-out options, which can be applied to the whole class of
one-dimensional time-homogeneous diffusions even for the cases where the
corresponding transition density is not known. The proposed numerical method is
shown to be efficient and simple to implement. To illustrate the flexibility
and computational power of the algorithm, we develop an extended jump to
default model that is able to capture several empirical regularities commonly
observed in the literature.
COOJA Network Simulator: Exploring the Infinite Possible Ways to Compute the Performance Metrics of IOT Based Smart Devices to Understand the Working of IOT Based Compression & Routing Protocols
Date: 2017-12-22 00:00:00

This paper demonstrates the scheme regarding Internet of Things (IOT) which
is well thought-out the next generation of Internet. IOT explicitly elaborates
the assimilation of human beings and physical systems, as they can cooperate
with each other so leading towards a sort of encroachment in networking by
interconnecting things together while making use of wireless embedded systems,
said to be the building blocks of IOT, that are capable to be given an IP
address and thus making them part of the global internet. Several essential
approaches that entail in IOT and supports this innovation are being argued in
this paper. 6LoWPAN (IPV6 Low Power Personal Area Networks) is a protocol used
to appropriately and efficiently use IPV6 addresses. Control messages of RPL
routing protocol for low power devices are discussed to understand the working
of RPL protocol. In the end Contiki OS based COOJA Network simulator is used to
demonstrate the working of how these routing and compression protocol works in
real time simulation.
A threshold model for local volatility: evidence of leverage and mean reversion effects on historical data
Date: 2019-02-22 00:00:00

In financial markets, low prices are generally associated with high
volatilities and vice-versa, this well known stylized fact usually being
referred to as leverage effect. We propose a local volatility model, given by a
stochastic differential equation with piecewise constant coefficients, which
accounts of leverage and mean-reversion effects in the dynamics of the prices.
This model exhibits a regime switch in the dynamics accordingly to a certain
threshold. It can be seen as a continuous-time version of the Self-Exciting
Threshold Autoregressive (SETAR) model. We propose an estimation procedure for
the volatility and drift coefficients as well as for the threshold level.
Parameters estimated on the daily prices of 348 stocks of NYSE and S\&P 500, on
different time windows, show consistent empirical evidence for leverageeffects.
Mean-reversion effects are also detected, most markedly in crisis periods.
Reconfigurable Digital Channelizer Design Using Factored Markov Decision Processes
Date: 2017-12-22 00:00:00

In this work, a novel digital channelizer design is developed through the use
of a compact, system-level modeling approach. The model efficiently captures
key properties of a digital channelizer system and its time-varying operation.
The model applies powerful Markov Decision Process (MDP) techniques in new ways
for design optimization of reconfigurable channelization processing. The result
is a promising methodology for design and implementation of digital
channelizers that adapt dynamically to changing use cases and stochastic
environments while optimizing simultaneously for multiple conflicting
performance goals. The method is used to employ an MDP to generate a runtime
reconfiguration policy for a time-varying environment. Through extensive
simulations, the robustness of the adaptation is demonstrated in comparison
with the prior state of the art.
Finding People's Professions and Nationalities Using Distant Supervision - The FMI@SU "goosefoot" team at the WSDM Cup 2017 Triple Scoring Task
Authors: Zmiycharov Valentin, Alexandrov Dimitar, Nakov Preslav, Koychev Ivan, Kiprov Yasen
Date: 2017-12-22 00:00:00
Tasks: Word Embeddings

We describe the system that our FMI@SU student's team built for participating
in the Triple Scoring task at the WSDM Cup 2017. Given a triple from a
"type-like" relation, profession or nationality, the goal is to produce a
score, on a scale from 0 to 7, that measures the relevance of the statement
expressed by the triple: e.g., how well does the profession of an Actor fit for
Quentin Tarantino? We propose a distant supervision approach using information
crawled from Wikipedia, DeletionPedia, and DBpedia, together with task-specific
word embeddings, TF-IDF weights, and role occurrence order, which we combine in
a linear regression model. The official evaluation ranked our submission 1st on
Kendall's Tau, 7th on Average score difference, and 9th on Accuracy, out of 21
participating teams.