pwc-archive/papers-with-abstracts huggingface dataset:
iCrawl: Improving the Freshness of Web Collections by Integrating Social Web and Focused Web Crawling
Authors: Gossen Gerhard, Demidova Elena, Risse Thomas
Date: 2016-12-19 00:00:00

Researchers in the Digital Humanities and journalists need to monitor,
collect and analyze fresh online content regarding current events such as the
Ebola outbreak or the Ukraine crisis on demand. However, existing focused
crawling approaches only consider topical aspects while ignoring temporal
aspects and therefore cannot achieve thematically coherent and fresh Web
collections. Especially Social Media provide a rich source of fresh content,
which is not used by state-of-the-art focused crawlers. In this paper we
address the issues of enabling the collection of fresh and relevant Web and
Social Web content for a topic of interest through seamless integration of Web
and Social Media in a novel integrated focused crawler. The crawler collects
Web and Social Media content in a single system and exploits the stream of
fresh Social Media content for guiding the crawler.
The Topology of Inter-industry Relations from the Portuguese National Accounts
Date: 2016-12-19 00:00:00

In last years, the Portuguese economy has gone through a severe adjustment
process, affecting almost all industrial sectors, the building blocks of
economic structures. Research on economic structural changes has made use of
input/output tables to define networks of industrial relations. Here, these
networks are induced from output tables of the Portuguese national accounting
system, being each inter-industry relation defined by the output made by any
two industries for the products that they both produce. The topological
analysis of these networks allows to uncover a particular structure that comes
out during the Portuguese adjustment program. The evolution of the industrial
networks shows an important structural change in 2011-2014, confirming the
usefulness of inducting similarity networks from output tables and the
consequent promising power of the graph formulation for the analysis of
inter-industry relations.
Validation of gait characteristics extracted from raw accelerometry during walking against measures of physical function, mobility, fatigability, and fitness
Date: 2017-11-16 00:00:00

Background. Wearable accelerometry devices allow collection of high-density
activity data in large epidemiological studies both in-the-lab as well as
in-the-wild (free-living). Such data can be used to detect and identify periods
of sustained harmonic walking. This report aims to establish whether the micro-
and macro-features of walking identified in the laboratory and free-living
environments are associated with measures of physical function, mobility,
fatigability, and fitness.
  Methods. Fifty-one older adults (median age 77.5) enrolled in the
Developmental Epidemiologic Cohort Study in Pittsburgh, Pennsylvania were
included in the analyses. The study included an in-the-lab component as well as
7 days of monitoring in-the-wild. Participants were equipped with hip-worn
Actigraph GT3X+ activity monitors, which collect high-density raw accelerometry
data. We applied a walking identification algorithm to the data and defined
features of walking, such as participant-specific walking acceleration and
cadence. The association between these walking features and physical function,
mobility, fatigability, and fitness was quantified using linear regression
analysis.
  Results. Micro-scale features of walking (acceleration and cadence) estimated
from in-the-lab and in-the-wild data were associated with measures of physical
function, mobility, fatigability, and fitness. In-the-lab median walking
acceleration was strongly inversely associated with physical function,
mobility, fatigability and fitness. Additionally, in-the-wild daily walking
time was inversely associated with usual- and fast-paced 400m walking time.
  Conclusions. The proposed accelerometry-derived walking features are
significantly associated with measures of physical function, mobility,
fatigability, and fitness, which provides evidence of convergent validity.
Computationally Discovered Potentiating Role of Glycans on NMDA Receptors
Date: 2016-12-19 00:00:00

N-methyl-D-aspartate receptors (NMDARs) are glycoproteins in the brain
central to learning and memory. The effects of glycosylation on the structure
and dynamics of NMDARs are largely unknown. In this work, we use extensive
molecular dynamics simulations of GluN1 and GluN2B ligand binding domains
(LBDs) of NMDARs to investigate these effects. Our simulations predict that
intra-domain interactions involving the glycan attached to residue GluN1-N440
stabilize closed-clamshell conformations of the GluN1 LBD. The glycan on
GluN2B-N688 shows a similar, though weaker, effect. Based on these results, and
assuming the transferability of the results of LBD simulations to the full
receptor, we predict that glycans at GluN1-N440 might play a potentiator role
in NMDARs. To validate this prediction, we perform electrophysiological
analysis of full-length NMDARs with a glycosylation-preventing GluN1-N440Q
mutation, and demonstrate an increase in the glycine EC50 value. Overall, our
results suggest an intramolecular potentiating role of glycans on NMDA
receptors.
Structure, Function, and Control of the Musculoskeletal Network
Date: 2016-12-13 00:00:00

The human body is a complex organism whose gross mechanical properties are
enabled by an interconnected musculoskeletal network controlled by the nervous
system. The nature of musculoskeletal interconnection facilitates stability,
voluntary movement, and robustness to injury. However, a fundamental
understanding of this network and its control by neural systems has remained
elusive. Here we utilize medical databases and mathematical modeling to reveal
the organizational structure, predicted function, and neural control of the
musculoskeletal system. We construct a whole-body musculoskeletal network in
which single muscles connect to multiple bones via both origin and insertion
points. We demonstrate that a muscle's role in this network predicts
susceptibility of surrounding components to secondary injury. Finally, we
illustrate that sets of muscles cluster into network communities that mimic the
organization of motor cortex control modules. This novel formalism for
describing interactions between the muscular and skeletal systems serves as a
foundation to develop and test therapeutic responses to injury, inspiring
future advances in clinical treatments.
How Predictable are Symptoms in Psychopathological Networks? A Reanalysis of 18 Published Datasets
Date: 2017-06-20 00:00:00

Background Network analyses on psychopathological data focus on the network
structure and its derivatives such as node centrality. One conclusion one can
draw from centrality measures is that the node with the highest centrality is
likely to be the node that is determined most by its neighboring nodes.
However, centrality is a relative measure: knowing that a node is highly
central gives no information about the extent to which it is determined by its
neighbors. Here we provide an absolute measure of determination (or
controllability) of a node - its predictability. We introduce predictability,
estimate the predictability of all nodes in 18 prior empirical network papers
on psychopathology, and statistically relate it to centrality.
  Methods We carried out a literature review and collected 25 datasets from 18
published papers in the field (several mood and anxiety disorders, substance
abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art
net- work models to all datasets, and computed the predictability of all nodes.
  Results Predictability was unrelated to sample size, moderately high in most
symptom networks, and differed considerable both within and between datasets.
Predictability was higher in community than clinical samples, highest for mood
and anxiety disorders, and lowest for psychosis.
  Conclusions Predictability is an important additional characterization of
symptom networks because it gives an absolute measure of the controllability of
each node. It allows conclusions about how self-determined a symptom network
is, and may help to inform intervention strategies. Limitations of
predictability along with future directions are discussed.
Robust permanence for ecological equations with internal and external feedbacks
Date: 2016-12-20 00:00:00

Species experience both internal feedbacks with endogenous factors such as
trait evolution and external feedbacks with exogenous factors such as weather.
These feedbacks can play an important role in determining whether populations
persist or communities of species coexist. To provide a general mathematical
framework for studying these effects, we develop a theorem for coexistence for
ecological models accounting for internal and external feedbacks. Specifically,
we use average Lyapunov functions and Morse decompositions to develop
sufficient and necessary conditions for robust permanence, a form of
coexistence robust to large perturbations of the population densities and small
structural perturbations of the models. We illustrate how our results can be
applied to verify permanence in non-autonomous models, structured population
models, including those with frequency-dependent feedbacks, and models of
eco-evolutionary dynamics. In these applications, we discuss how our results
relate to previous results for models with particular types of feedbacks.
The Impact of Negative Interest Rates on Optimal Capital Injections
Date: 2016-12-20 00:00:00

In the present paper, we investigate the optimal capital injection behaviour
of an insurance company if the interest rate is allowed to become negative. The
surplus process of the considered insurance entity is assumed to follow a
Brownian motion with drift. The changes in the interest rate are described via
a Markov-switching process. It turns out that in times with a positive rate, it
is optimal to inject capital only if the company becomes insolvent. However, if
the rate is negative it might be optimal to hold a strictly positive reserve.
We establish an algorithm for finding the value function and the optimal
strategy, which is proved to be of barrier type. Using the iteration argument,
we show that the value function solves the Hamilton--Jacobi--Bellman equation,
corresponding to the problem.
An EEG study of creativity in expert classical musicians
Date: 2016-12-18 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Previous research has shown positive correlations between EEG alpha activity
and performing creative tasks. In this study, expert classical musicians (n=4)
were asked to play their instrument while being monitored with a wireless EEG
headset. Data was collected during two rehearsal types: (a) in their regular,
fixed ensemble;; (b) in an improvised, mixed ensemble with unfamiliar musicians
and less rehearsal time. A positive correlation was found between alpha power
and the improvised setup (p<0.01, d=0.4). A positive correlation was also found
between alpha power and more intense play (p<0.01, d=0.2). There was a negative
correlation between alpha power and arousal due to stress, e.g., frowning after
playing a false note (p<0.01, d=0.6). Finally, the real-time capabilities of
wireless EEG monitoring were explored with a data visualisation during live
performance on stage.
The feasibility of equilibria in large ecosystems: a primary but neglected concept in the complexity-stability debate
Date: 2016-12-20 00:00:00

The consensus that complexity begets stability in ecosystems was challenged
in the seventies, a result recently extended to ecologically-inspired networks.
The approaches assume the existence of a feasible equilibrium, i.e. with
positive abundances. However, this key assumption has not been tested. We
provide analytical results complemented by simulations which show that
equilibrium feasibility vanishes in species rich systems. This result leaves us
in the uncomfortable situation in which the existence of a feasible equilibrium
assumed in local stability criteria is far from granted. We extend our analyses
by changing interaction structure and intensity, and find that feasibility and
stability is warranted irrespective of species richness with weak interactions.
Interestingly, we find that the dynamical behaviour of ecologically inspired
architectures is very different and richer than that of unstructured systems.
Our results suggest that a general understanding of ecosystem dynamics requires
focusing on the interplay between interaction strength and network
architecture.
Video Stream Retrieval of Unseen Queries using Semantic Memory
Authors: Cappallo Spencer, Mensink Thomas, Snoek Cees G. M.
Date: 2016-12-20 00:00:00
Tasks: Retrieval

Retrieval of live, user-broadcast video streams is an under-addressed and
increasingly relevant challenge. The on-line nature of the problem requires
temporal evaluation and the unforeseeable scope of potential queries motivates
an approach which can accommodate arbitrary search queries. To account for the
breadth of possible queries, we adopt a no-example approach to query retrieval,
which uses a query's semantic relatedness to pre-trained concept classifiers.
To adapt to shifting video content, we propose memory pooling and memory
welling methods that favor recent information over long past content. We
identify two stream retrieval tasks, instantaneous retrieval at any particular
time and continuous retrieval over a prolonged duration, and propose means for
evaluating them. Three large scale video datasets are adapted to the challenge
of stream retrieval. We report results for our search methods on the new stream
retrieval tasks, as well as demonstrate their efficacy in a traditional,
non-streaming video task.
Information, Impact, Ignorance, Illegality, Investing, and Inequality
Date: 2018-02-13 00:00:00

We note a simple mechanism that may at least partially resolve several
outstanding economic puzzles, including why the cyclically adjusted price to
earnings ratio of the S&P 500 index has been oddly high for the past two
decades, why gains to capital have outpaced gains to wages, and the persistence
of the equity premium.
A Hidden Markov Movement Model for rapidly identifying behavioral states from animal tracks
Date: 2016-12-20 00:00:00
Tasks: Management

1. Electronic telemetry is frequently used to document animal movement
through time. Methods that can identify underlying behaviors driving specific
movement patterns can help us understand how and why animals use available
space, thereby aiding conservation and management efforts. For aquatic animal
tracking data with significant measurement error, a Bayesian state-space model
called the first-Difference Correlated Random Walk with Switching (DCRWS) has
often been used for this purpose. However, for aquatic animals, highly accurate
tracking data of animal movement are now becoming more common.
  2. We developed a new Hidden Markov Model (HMM) for identifying behavioral
states from animal tracks with negligible error, which we called the Hidden
Markov Movement Model (HMMM). We implemented as the basis for the HMMM the
process equation of the DCRWS, but we used the method of maximum likelihood and
the R package TMB for rapid model fitting.
  3. We compared the HMMM to a modified version of the DCRWS for highly
accurate tracks, the DCRWSnome, and to a common HMM for animal tracks fitted
with the R package moveHMM. We show that the HMMM is both accurate and suitable
for multiple species by fitting it to real tracks from a grey seal, lake trout,
and blue shark, as well as to simulated data.
  4. The HMMM is a fast and reliable tool for making meaningful inference from
animal movement data that is ideally suited for ecologists who want to use the
popular DCRWS implementation for highly accurate tracking data. It additionally
provides a groundwork for development of more complex modelling of animal
movement with TMB. To facilitate its uptake, we make it available through the R
package swim.
Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks
Date: 2016-12-21 00:00:00
Tasks: Object, Object Categorization

Deep convolutional neural networks (CNNs) trained for object classification
have a number of striking similarities with the primate ventral visual stream.
In particular, activity in early, intermediate, and late layers is closely
related to activity in V1, V4, and the inferotemporal cortex (IT). This study
further compares activity in late layers of object-classification CNNs to
activity patterns reported in the IT electrophysiology literature. There are a
number of close similarities, including the distributions of population
response sparseness across stimuli, and the distribution of size tuning
bandwidth. Statisics of scale invariance, responses to clutter and occlusion,
and orientation tuning are less similar. Statistics of object selectivity are
quite different. These results agree with recent studies that highlight strong
parallels between object-categorization CNNs and the ventral stream, and also
highlight differences that could perhaps be reduced in future CNNs.
Pricing Derivatives in Hermite Markets
Date: 2016-12-24 00:00:00

We introduce Hermite fractional financial markets, where market uncertainties
are described by multidimensional Hermite motions. Hermite markets include as
particular cases financial markets driven by multivariate fractional Brownian
motion and multivariate Rosenblatt motion. Conditions for no-arbitrage and
market completeness for Hermite markets are derived. Perpetual derivatives,
bonds forwards, and futures are priced. The corresponding partial and
partial-differential equations are derived.
Analytic solution to variance optimization with no short-selling
Date: 2017-01-21 00:00:00

A large portfolio of independent returns is optimized under the variance risk
measure with a ban on short positions. The no-short selling constraint acts as
an asymmetric $\ell_1$ regularizer, setting some of the portfolio weights to
zero and keeping the out of sample estimator for the variance bounded, avoiding
the divergence present in the non-regularized case. However, the
susceptibility, i.e. the sensitivity of the optimal portfolio weights to
changes in the returns, diverges at a critical value $r=2$. This means that a
ban on short positions does not prevent the phase transition in the
optimization problem, it merely shifts the critical point from its
non-regularized value of $r=1$ to $2$. At $r=2$ the out of sample estimator for
the portfolio variance stays finite and the estimated in-sample variance
vanishes. We have performed numerical simulations to support the analytic
results and found perfect agreement for $N/T<2$. Numerical experiments on
finite size samples of symmetrically distributed returns show that above this
critical point the probability of finding solutions with zero in-sample
variance increases rapidly with increasing $N$, becoming one in the large $N$
limit. However, these are not legitimate solutions of the optimization problem,
as they are infinitely sensitive to any change in the input parameters, in
particular they will wildly fluctuate from sample to sample. We also calculate
the distribution of the optimal weights over the random samples and show that
the regularizer preferentially removes the assets with large variances, in
accord with one's natural expectation.
The Global Dynamical Complexity of the Human Brain Network
Date: 2016-12-21 00:00:00

How much information do large brain networks integrate as a whole over the
sum of their parts? Can the dynamical complexity of such networks be globally
quantified in an information-theoretic way and be meaningfully coupled to brain
function? Recently, measures of dynamical complexity such as integrated
information have been proposed. However, problems related to the normalization
and Bell number of partitions associated to these measures make these
approaches computationally infeasible for large-scale brain networks. Our goal
in this work is to address this problem. Our formulation of network integrated
information is based on the Kullback-Leibler divergence between the
multivariate distribution on the set of network states versus the corresponding
factorized distribution over its parts. We find that implementing the maximum
information partition optimizes computations. These methods are well-suited for
large networks with linear stochastic dynamics. We compute the integrated
information for both, the system's attractor states, as well as non-stationary
dynamical states of the network. We then apply this formalism to brain networks
to compute the integrated information for the human brain's connectome.
Compared to a randomly re-wired network, we find that the specific topology of
the brain generates greater information complexity.
Conditional loss probabilities for systems of economic agents sharing light-tailed claims with analysis of portfolio diversification benefits
Date: 2016-12-21 00:00:00

We analyze systems of agents sharing light-tailed risky claims issued by
different financial objects. Assuming exponentially distributed claims, we
obtain that both agents' and system's losses follow generalized exponential
mixture distributions. We show that this leads to qualitatively different
results on individual and system risks compared to heavy-tailed claims
previously studied in the literature. By deducing conditional loss
distributions we investigate the impact of stress situations on agents' and
system's losses. Moreover, we present a criterion for agents to decide whether
holding few objects or portfolio diversification minimizes their risks in
system crisis situations.
Leverage and Uncertainty
Date: 2016-12-21 00:00:00
Tasks: Management

Risk and uncertainty will always be a matter of experience, luck, skills, and
modelling. Leverage is another concept, which is critical for the investor
decisions and results. Adaptive skills and quantitative probabilistic methods
need to be used in successful management of risk, uncertainty and leverage. The
author explores how uncertainty beyond risk determines consistent leverage in a
simple model of the world with fat tails due to significant, not fully
quantifiable and not too rare events. Among particular technical results, for
the single asset fractional Kelly criterion is derived in the presence of the
fat tails associated with subjective uncertainty. For the multi-asset
portfolio, Kelly criterion provides an insightful perspective on Risk Parity
strategies, which can be extended for the assets with fat tails.
Cost-efficient vaccination protocols for network epidemiology
Date: 2017-05-20 00:00:00
Tasks: Epidemiology

We investigate methods to vaccinate contact networks -- i.e. removing nodes
in such a way that disease spreading is hindered as much as possible -- with
respect to their cost-efficiency. Any real implementation of such protocols
would come with costs related both to the vaccination itself, and gathering of
information about the network. Disregarding this, we argue, would lead to
erroneous evaluation of vaccination protocols. We use the
susceptible-infected-recovered model -- the generic model for diseases making
patients immune upon recovery -- as our disease-spreading scenario, and analyze
outbreaks on both empirical and model networks. For different relative costs,
different protocols dominate. For high vaccination costs and low costs of
gathering information, the so-called acquaintance vaccination is the most cost
efficient. For other parameter values, protocols designed for query-efficient
identification of the network's largest degrees are most efficient.