pwc-archive/papers-with-abstracts huggingface dataset:
The stabilizing effect of volatility in financial markets
Date: 2017-08-29 00:00:00

In financial markets, greater volatility is usually considered synonym of
greater risk and instability. However, large market downturns and upturns are
often preceded by long periods where price returns exhibit only small
fluctuations. To investigate this surprising feature, here we propose using the
mean first hitting time, i.e. the average time a stock return takes to undergo
for the first time a large negative or positive variation, as an indicator of
price stability, and relate this to a standard measure of volatility. In an
empirical analysis of daily returns for $1071$ stocks traded in the New York
Stock Exchange, we find that this measure of stability displays nonmonotonic
behavior, with a maximum, as a function of volatility. Also, we show that the
statistical properties of the empirical data can be reproduced by a nonlinear
Heston model. This analysis implies that, contrary to conventional wisdom, not
only high, but also low volatility values can be associated with higher
instability in financial markets.
Design Patterns for Fusion-Based Object Retrieval
Authors: Zhang Shuo, Balog Krisztian
Date: 2017-08-29 00:00:00
Tasks: Object, Retrieval

We address the task of ranking objects (such as people, blogs, or verticals)
that, unlike documents, do not have direct term-based representations. To be
able to match them against keyword queries, evidence needs to be amassed from
documents that are associated with the given object. We present two design
patterns, i.e., general reusable retrieval strategies, which are able to
encompass most existing approaches from the past. One strategy combines
evidence on the term level (early fusion), while the other does it on the
document level (late fusion). We demonstrate the generality of these patterns
by applying them to three different object retrieval tasks: expert finding,
blog distillation, and vertical ranking.
EntiTables: Smart Assistance for Entity-Focused Tables
Authors: Zhang Shuo, Balog Krisztian
Date: 2017-08-29 00:00:00

Tables are among the most powerful and practical tools for organizing and
working with data. Our motivation is to equip spreadsheet programs with smart
assistance capabilities. We concentrate on one particular family of tables,
namely, tables with an entity focus. We introduce and focus on two specific
tasks: populating rows with additional instances (entities) and populating
columns with new headings. We develop generative probabilistic models for both
tasks. For estimating the components of these models, we consider a knowledge
base as well as a large table corpus. Our experimental evaluation simulates the
various stages of the user entering content into an actual table. A detailed
analysis of the results shows that the models' components are complimentary and
that our methods outperform existing approaches from the literature.
The effect of population abundances on the stability of large random ecosystems
Date: 2017-08-29 00:00:00

Random matrix theory successfully connects the structure of interactions of
large ecological communities to their ability to respond to perturbations. One
of the most debated aspects of this approach is the missing role of population
abundances. Despite being one of the most studied patterns in ecology, and one
of the most empirically accessible quantities, population abundances are always
neglected in random matrix approaches and their role in determining stability
is still not understood. Here, we tackle this question by explicitly including
population abundances in a random matrix framework. We obtain an analytical
formula that describes the spectrum of a large community matrix for arbitrary
feasible species abundance distributions. The emerging picture is remarkably
simple: while population abundances affect the rate to return to equilibrium
after a perturbation, the stability of large ecosystems is uniquely determined
by the interaction matrix. We confirm this result by showing that the
likelihood of having a feasible and unstable solution in the Lotka-Volterra
system of equations decreases exponentially with the number of species for
stable interaction matrices.
Evolution of a Fluctuating Population in a Randomly Switching Environment
Date: 2017-10-13 00:00:00

Environment plays a fundamental role in the competition for resources, and
hence in the evolution of populations. Here, we study a well-mixed, finite
population consisting of two strains competing for the limited resources
provided by an environment that randomly switches between states of abundance
and scarcity. Assuming that one strain grows slightly faster than the other, we
consider two scenarios--one of pure resource competition, and one in which one
strain provides a public good--and investigate how environmental randomness
(external noise) coupled to demographic (internal) noise determines the
population's fixation properties and size distribution. By analytical means and
simulations, we show that these coupled sources of noise can significantly
enhance the fixation probability of the slower-growing species. We also show
that the population size distribution can be unimodal, bimodal or multimodal
and undergoes noise-induced transitions between these regimes when the rate of
switching matches the population's growth rate.
Targeted and Imaging-guided In Vivo Photodynamic Therapy of Tumors Using Dual-functional, Aggregation-induced Emission Nanoparticles
Date: 2017-08-23 00:00:00

Dual-functional nanoparticles, with the property of aggregation-induced
emission and the capability of reactive oxygen species, were used to achieve
passive/active targeting of tumor. Good contrast in in vivo imaging and obvious
therapeutic efficiency were realized with a low dose of AIE nanoparticles as
well as a low power density of light, resulting in negligible side effects.
Balance of excitation and inhibition determines 1/f power spectrum in neuronal networks
Date: 2017-08-29 00:00:00

The $1/f$-like decay observed in the power spectrum of electro-physiological
signals, along with scale-free statistics of the so-called neuronal avalanches,
constitute evidences of criticality in neuronal systems. Recent in vitro
studies have shown that avalanche dynamics at criticality corresponds to some
specific balance of excitation and inhibition, thus suggesting that this is a
basic feature of the critical state of neuronal networks. In particular, a lack
of inhibition significantly alters the temporal structure of the spontaneous
avalanche activity and leads to an anomalous abundance of large avalanches.
Here we study the relationship between network inhibition and the scaling
exponent $\beta$ of the power spectral density (PSD) of avalanche activity in a
neuronal network model inspired in Self-Organized Criticality (SOC). We find
that this scaling exponent depends on the percentage of inhibitory synapses and
tends to the value $\beta = 1$ for a percentage of about 30%. More
specifically, $\beta$ is close to $2$, namely brownian noise, for purely
excitatory networks and decreases towards values in the interval $[1,1.4]$ as
the percentage of inhibitory synapses ranges between 20 and 30%, in agreement
with experimental findings. These results indicate that the level of inhibition
affects the frequency spectrum of resting brain activity and suggest the
analysis of the PSD scaling behavior as a possible tool to study pathological
conditions.
Quality Enhancement by Weighted Rank Aggregation of Crowd Opinion
Authors: Chatterjee Sujoy, Mukhopadhyay Anirban, Bhattacharyya Malay
Date: 2017-08-31 00:00:00

Expertise of annotators has a major role in crowdsourcing based opinion
aggregation models. In such frameworks, accuracy and biasness of annotators are
occasionally taken as important features and based on them priority of the
annotators are assigned. But instead of relying on a single feature, multiple
features can be considered and separate rankings can be produced to judge the
annotators properly. Finally, the aggregation of those rankings with perfect
weightage can be done with an aim to produce better ground truth prediction.
Here, we propose a novel weighted rank aggregation method and its efficacy with
respect to other existing approaches is shown on artificial dataset. The
effectiveness of weighted rank aggregation to enhance quality prediction is
also shown by applying it on an Amazon Mechanical Turk (AMT) dataset.
Lightweight Efficient Multi-keyword Ranked Search over Encrypted Cloud Data using Dual Word Embeddings
Authors: Zhao Ruihui, Iwaihara Mizuho
Date: 2017-05-22 00:00:00
Tasks: Cloud Computing, Management, Retrieval, Word Embeddings

Cloud computing is emerging as a revolutionary computing paradigm which
pro-vides a flexible and economic strategy for data management and resource
sharing. Security and privacy become major concerns in the cloud scenario, for
which Searchable Encryption (SE) technology is proposed to support efficient
retrieval of encrypted data. However, the absence of lightweight ranked search
is still a typical shortage in existing SE schemes. In this paper, we propose a
Lightweight Efficient Multi-keyword Ranked Search over Encrypted Cloud Data
using Dual Word Embeddings (LRSE) scheme that supports top-k retrieval in the
known background model. For the first time, we formulate the privacy issue and
design goals for lightweight ranked search in SE. We employ word embedding
trained on the whole English Wikipedia using word2vec to replace the general
dictionary, afterwards we make use of Dual Embedding Space Model (DESM) to
substitute traditional Vector Space Model (VSM), based on which we achieve the
goal of lightweight ranked search with higher precision and solve the
challenging prob-lems caused by updating the traditional dictionary in existing
SE schemes. In LRSE, we employ an improved secure kNN scheme to guarantee
sufficient pri-vacy protection. Our security analysis shows that LRSE satisfies
our formulated privacy requirements and extensive experiments performed on
real-world datasets demonstrate that LRSE indeed accords with our proposed
design goals.
Extending Yagil exchange ratio determination model to the case of stochastic dividends
Date: 2017-08-31 00:00:00

This article extends, in a stochastic environment, the Yagil (1987) model
which establishes, in a deterministic dividend discount model, a range for the
exchange ratio in a stock-for-stock merger agreement. Here, we generalize
Yagil's work letting both pre- and post-merger dividends grow randomly over
time. If Yagil focuses only on changes in stock prices before and after the
merger, our stochastic environment allows to keep in account both shares'
expected values and variance, letting us to identify a more complex bargaining
region whose shape depends on mean and standard deviation of the dividends'
growth rate.
Multilayer Aggregation with Statistical Validation: Application to Investor Networks
Date: 2018-05-29 00:00:00

Multilayer networks are attracting growing attention in many fields,
including finance. In this paper, we develop a new tractable procedure for
multilayer aggregation based on statistical validation, which we apply to
investor networks. Moreover, we propose two other improvements to their
analysis: transaction bootstrapping and investor categorization. The
aggregation procedure can be used to integrate security-wise and time-wise
information about investor trading networks, but it is not limited to finance.
In fact, it can be used for different applications, such as gene,
transportation, and social networks, were they inferred or observable.
Additionally, in the investor network inference, we use transaction
bootstrapping for better statistical validation. Investor categorization allows
for constant size networks and having more observations for each node, which is
important in the inference especially for less liquid securities. Furthermore,
we observe that the window size used for averaging has a substantial effect on
the number of inferred relationships. We apply this procedure by analyzing a
unique data set of Finnish shareholders during the period 2004-2009. We find
that households in the capital have high centrality in investor networks,
which, under the theory of information channels in investor networks suggests
that they are well-informed investors.
The signatures of conscious access and phenomenology are consistent with large-scale brain communication at criticality
Date: 2017-08-31 00:00:00

Conscious awareness refers to the association of information processing in
the brain that is accompanied by subjective, reportable experiences. Current
models of conscious access propose that sufficiently strong sensory stimuli
ignite a global network of regions allowing further processing. The immense
number of possible experiences indicates that brain activity associated with
conscious awareness must be highly differentiated. However, information must
also be integrated to account for the unitary nature of consciousness. We
present a conceptual computational model that identifies conscious access with
self-sustained percolation in an anatomical network. We show that if activity
propagates at the critical threshold, the amount of integrated information
(Phi) is maximal after conscious access, as well as other related markers. We
also identify a posterior hotspot of regions with high levels of information
sharing during conscious access. Finally, competitive activity spreading
qualitatively describes the results of paradigms such as backward masking and
binocular rivalry.
Dynamic Analysis of a Predator and Prey Model with Some Computational Simulations
Date: 2017-08-31 00:00:00

Mathematical modelling and numerical simulations of interaction populations
are crucial topics in systems biology. The interactions of ecological models
may occur among individuals of the same species or individuals of different
species. Describing the dynamics of such models occasionally requires some
techniques of model analysis. Choosing appropriate techniques of model analysis
is often a difficult task. We define a prey (mouse) and predator (cat) model.
The system is modelled by a pair of non-linear ordinary differential equations
using mass action law, under constant rates. A proper scaling is suggested to
minimize the number of parameters. More interestingly, we propose a homotopy
technique with n expanding parame- ters for finding some analytical approximate
solutions. Furthermore, using the local sensitivity method is another important
step forward in this study because it helps to identify critical model
parameters. Numerical simulations are provided using Matlab for different
parameters and initial conditions.
Truncation selection and diffusion on lattices
Date: 2018-06-27 00:00:00

Evolutionary games on graphs have been extensively studied. A variety of
graph structures, graph dynamics, and behaviours of replicators have been
explored. These models have primarily been studied in the framework of
facilitation of cooperation, and much previous research has shed light on this
field of study. However, there has been little attention devoted to truncation
selection as most models employ proportional selection (such as in the
replicator equation) or `imitate the best.' Here we systematically explore
truncation selection on periodic square lattices, where replicators below a
fitness threshold are culled and the reproduction probabilities are equal for
all survivors. We employ two variations of this method: independent truncation,
where the threshold is fixed; and dependent truncation, which is a
generalization of `imitate the best.' Further, we explore the effects of
diffusion in our networks in the following orders of operation:
contest-diffusion-offspring (CDO), and diffusion-contest-offspring (DCO). CDO
and DCO frequently facilitate and diminish cooperation, respectively. For
independent truncation, we find three regimes determined by the fitness
threshold: cooperation decreases as we raise the threshold; polymorphisms and
extinction can occur; and the entire population goes extinct. Further, we show
how an intermediate sucker's payoff maximizes cooperation in the DCO
independent truncation model. We find that dependent truncation affects games
differently; lower levels reduce cooperation for the Hawk Dove game and
increase it for the Stag Hunt, and higher levels produce the opposite effects.
We compare these truncation methods to proportional selection, and show that
they can facilitate cooperation. We conclude that truncation selection can
impact the prevalence of cooperation in complex ways, and therefore merit
further study.
Whole Genome Phylogenetic Tree Reconstruction Using Colored de Bruijn Graphs
Date: 2017-09-23 00:00:00

We present kleuren, a novel assembly-free method to reconstruct phylogenetic
trees using the Colored de Bruijn Graph. kleuren works by constructing the
Colored de Bruijn Graph and then traversing it, finding bubble structures in
the graph that provide phylogenetic signal. The bubbles are then aligned and
concatenated to form a supermatrix, from which a phylogenetic tree is inferred.
We introduce the algorithms that kleuren uses to accomplish this task, and show
its performance on reconstructing the phylogenetic tree of 12 Drosophila
species. kleuren reconstructed the established phylogenetic tree accurately,
and is a viable tool for phylogenetic tree reconstruction using whole genome
sequences. Software package available at: https://github.com/Colelyman/kleuren
Econophysics of Business Cycles: Aggregate Economic Fluctuations, Mean Risks and Mean Square Risks
Date: 2017-08-31 00:00:00

This paper presents hydrodynamic-like model of business cycles aggregate
fluctuations of economic and financial variables. We model macroeconomics as
ensemble of economic agents on economic space and agent's risk ratings play
role of their coordinates. Sum of economic variables of agents with coordinate
x define macroeconomic variables as functions of time and coordinates x. We
describe evolution and interactions between macro variables on economic space
by hydrodynamic-like equations. Integral of macro variables over economic space
defines aggregate economic or financial variables as functions of time t only.
Hydrodynamic-like equations define fluctuations of aggregate variables. Motion
of agents from low risk to high risk area and back define the origin for
repeated fluctuations of aggregate variables. Economic or financial variables
on economic space may define statistical moments like mean risk, mean square
risk and higher. Fluctuations of statistical moments describe phases of
financial and economic cycles. As example we present a simple model relations
between Assets and Revenue-on-Assets and derive hydrodynamic-like equations
that describe evolution and interaction between these variables.
Hydrodynamic-like equations permit derive systems of ordinary differential
equations that describe fluctuations of aggregate Assets, Assets mean risks and
Assets mean square risks. Our approach allows describe business cycle aggregate
fluctuations induced by interactions between any number of economic or
financial variables.
Towards physical principles of biological evolution
Authors: Mikhail I. Katsnelson, Yuri I. Wolf, Eugene V. Koonin
Date: 2017-09-01 00:00:00

Biological systems reach organizational complexity that far exceeds the complexity of any known inanimate objects. Biological entities undoubtedly obey the laws of quantum physics and statistical mechanics. However, is modern physics sufficient to adequately describe, model and explain the evolution of biological complexity? Detailed parallels have been drawn between statistical thermodynamics and the population-genetic theory of biological evolution. Based on these parallels, we outline new perspectives on biological innovation and major transitions in evolution, and introduce a biological equivalent of thermodynamic potential that reflects the innovation propensity of an evolving population. Deep analogies have been suggested to also exist between the properties of biological entities and processes, and those of frustrated states in physics, such as glasses. We extend such analogies by examining frustration-type phenomena, such as conflicts between different levels of selection, in biological evolution. We further address evolution in multidimensional fitness landscapes from the point of view of percolation theory and suggest that percolation at level above the critical threshold dictates the tree-like evolution of complex organisms. Taken together, these multiple connections between fundamental processes in physics and biology imply that construction of a meaningful physical theory of biological evolution might not be a futile effort.
How images determine our visual search strategy
Date: 2017-08-31 00:00:00

When searching for a target within an image our brain can adopt different
strategies, but which one does it choose? This question can be answered by
tracking the motion of the eye while it executes the task. Following many
individuals performing various search tasks we distinguish between two
competing strategies. Motivated by these findings, we introduce a model that
captures the interplay of the search strategies and allows us to create
artificial eye-tracking trajectories, which could be compared to the
experimental ones. Identifying the model parameters allows us to quantify the
strategy employed in terms of ensemble averages, characterizing each
experimental cohort. In this way we can discern with high sensitivity the
relation between the visual landscape and the average strategy, disclosing how
small variations in the image induce changes in the strategy.
Payoff Information and Learning in Signaling Games
Date: 2020-01-14 00:00:00
Tasks: Active Learning

We add the assumption that players know their opponents' payoff functions and
rationality to a model of non-equilibrium learning in signaling games. Agents
are born into player roles and play against random opponents every period.
Inexperienced agents are uncertain about the prevailing distribution of
opponents' play, but believe that opponents never choose conditionally
dominated strategies. Agents engage in active learning and update beliefs based
on personal observations. Payoff information can refine or expand learning
predictions, since patient young senders' experimentation incentives depend on
which receiver responses they deem plausible. We show that with payoff
knowledge, the limiting set of long-run learning outcomes is bounded above by
rationality-compatible equilibria (RCE), and bounded below by uniform RCE. RCE
refine the Intuitive Criterion (Cho and Kreps, 1987) and include all divine
equilibria (Banks and Sobel, 1987). Uniform RCE sometimes but not always
exists, and implies universally divine equilibrium.
Modeling Sheep pox Disease from the 1994-1998 Epidemic in Evros Prefecture, Greece
Date: 2017-08-28 00:00:00
Tasks: Model Selection, regression

Sheep pox is a highly transmissible disease which can cause serious loss of
livestock and can therefore have major economic impact. We present data from
sheep pox epidemics which occurred between 1994 and 1998. The data include
weekly records of infected farms as well as a number of covariates. We
implement Bayesian stochastic regression models which, in addition to various
explanatory variables like seasonal and environmental/meteorological factors,
also contain serial correlation structure based on variants of the
Ornstein-Uhlenbeck process. We take a predictive view in model selection by
utilizing deviance-based measures. The results indicate that seasonality and
the number of infected farms are important predictors for sheep pox incidence.