pwc-archive/papers-with-abstracts huggingface dataset:
The Growth of Oligarchy in a Yard-Sale Model of Asset Exchange: A Logistic Equation for Wealth Condensation
Date: 2016-08-20 00:00:00

The addition of wealth-attained advantage (WAA) to the Yard-Sale Model (YSM)
of asset exchange has been demonstrated to induce wealth condensation. In a
model of WAA for which the bias is a continuous function of the wealth
difference of the transacting agents, the condensation was shown to arise from
a second-order phase transition to a coexistence regime. In this paper, we
present the first analytic time-dependent results for this model, by showing
that the condensed wealth obeys a logistic equation in time.
Super-resolved 3-D imaging of live cells organelles from bright-field photon transmission micrographs
Date: 2016-08-21 00:00:00

Current biological and medical research is aimed at obtaining a detailed
spatiotemporal map of a live cell's interior to describe and predict cell's
physiological state. We present here an algorithm for complete 3-D modelling of
cellular structures from a z-stack of images obtained using label-free
wide-field bright-field light-transmitted microscopy. The method visualizes 3-D
objects with a volume equivalent to the area of a camera pixel multiplied by
the z-height. The computation is based on finding pixels of unchanged
intensities between two consecutive images of an object spread function. These
pixels represent strongly light-diffracting, light-absorbing, or light-emitting
objects. To accomplish this, variables derived from R\'{e}nyi entropy are used
to suppress camera noise. Using this algorithm, the detection limit of objects
is only limited by the technical specifications of the microscope setup--we
achieve the detection of objects of the size of one camera pixel. This method
allows us to obtain 3-D reconstructions of cells from bright-field microscopy
images that are comparable in quality to those from electron microscopy images.
Optimal Switching under Ambiguity and Its Applications in Finance
Date: 2016-08-22 00:00:00

In this paper, we study optimal switching problems under ambiguity. To
characterize the optimal switching under ambiguity in the finite horizon, we
use multidimensional reflected backward stochastic differential equations
(multidimensional RBSDEs) and show that a value function of the optimal
switching under ambiguity coincides with a solutions to multidimensional RBSDEs
with allowing negative switching costs. Furthermore, we naturally extend the
finite horizon problem to the infinite horizon problem. In some applications,
we show that ambiguity affects an optimal switching strategy with the different
way to a usual switching problem without ambiguity.
PowerWalk: Scalable Personalized PageRank via Random Walks with Vertex-Centric Decomposition
Authors: Liu Qin, Li Zhenguo, Lui John C. S., Cheng Jiefeng
Date: 2016-08-22 00:00:00

Most methods for Personalized PageRank (PPR) precompute and store all
accurate PPR vectors, and at query time, return the ones of interest directly.
However, the storage and computation of all accurate PPR vectors can be
prohibitive for large graphs, especially in caching them in memory for
real-time online querying. In this paper, we propose a distributed framework
that strikes a better balance between offline indexing and online querying. The
offline indexing attains a fingerprint of the PPR vector of each vertex by
performing billions of "short" random walks in parallel across a cluster of
machines. We prove that our indexing method has an exponential convergence,
achieving the same precision with previous methods using a much smaller number
of random walks. At query time, the new PPR vector is composed by a linear
combination of related fingerprints, in a highly efficient vertex-centric
decomposition manner. Interestingly, the resulting PPR vector is much more
accurate than its offline counterpart because it actually uses more random
walks in its estimation. More importantly, we show that such decomposition for
a batch of queries can be very efficiently processed using a shared
decomposition. Our implementation, PowerWalk, takes advantage of advanced
distributed graph engines and it outperforms the state-of-the-art algorithms by
orders of magnitude. Particularly, it responses to tens of thousands of queries
on graphs with billions of edges in just a few seconds.
New economic windows on income and wealth: The k-generalized family of distributions
Date: 2016-08-22 00:00:00

Over the last decades, the distribution of income and wealth has been
deteriorating in many countries, leading to increased inequalities within and
between societies. This tendency has revived the interest in the subject
greatly, yet it still receives very little attention within the realm of
mainstream economic thinking. One reason for this is that the basic paradigm of
"standard economics", the representative-agent General Equilibrium framework,
is badly equipped to cope with distributional issues. Here we argue that when
the economy is treated as a complex system composed of many heterogeneous
interacting agents who give rise to emergent phenomena, to address the main
stylized facts of income/wealth distribution requires leaving the toolbox of
mainstream economics in favour of alternative approaches. The "k-generalized"
family of income/wealth distributions, building on the categories of
complexity, is an example of how advances in the field can be achieved within
new interdisciplinary research contexts.
Modeling the Influence of Local Environmental Factors on Malaria Transmission in Benin and Its Implications for Cohort Study
Date: 2016-08-19 00:00:00
Tasks: regression

Malaria remains endemic in tropical areas, especially in Africa. For the
evaluation of new tools and to further ourunderstanding of host-parasite
interactions, knowing the environmental risk of transmission-even at a very
local scale-isessential. The aim of this study was to assess how malaria
transmission is influenced and can be predicted by local climaticand
environmental factors. As the entomological part of a cohort study of 650
newborn babies in nine villages in the ToriBossito district of Southern Benin
between June 2007 and February 2010, human landing catches were performed to
assessthe density of malaria vectors and transmission intensity. Climatic
factors as well as household characteristics were recordedthroughout the study.
Statistical correlations between Anopheles density and environmental and
climatic factors weretested using a three-level Poisson mixed regression model.
The results showed both temporal variations in vector density(related to season
and rainfall), and spatial variations at the level of both village and house.
These spatial variations could belargely explained by factors associated with
the house's immediate surroundings, namely soil type, vegetation index andthe
proximity of a watercourse. Based on these results, a predictive regression
model was developed using a leave-one-outmethod, to predict the spatiotemporal
variability of malaria transmission in the nine villages. This study points up
theimportance of local environmental factors in malaria transmission and
describes a model to predict the transmission risk ofindividual children, based
on environmental and behavioral characteristics.
Some Models for Epidemics of Vector-Transmitted Diseases
Date: 2016-08-22 00:00:00

Vector-transmitted diseases such as dengue fever and chikungunya have been
spreading rapidly in many parts of the world. The Zika virus has been known
since 1947 and invaded South America in 2013. It can be transmitted not only by
(mosquito) vectors but also directly through sexual contact. Zika has developed
into a serious global health problem because, while most cases are asymptomatic
or very light, babies born to Zika - infected mothers may develop microcephaly
and other very serious birth defects.
  We formulate and analyze two epidemic models for vector-transmitted diseases,
one appropriate for dengue and chikungunya fever outbreaks and one that
includes direct transmission appropriate for Zika virus outbreaks. This is
especially important because the Zika virus is the first example of a disease
that can be spread both indirectly through a vector and directly (through
sexual contact). In both cases, we obtain expressions for the basic
reproduction number and show how to use the initial exponential growth rate to
estimate the basic reproduction number. However, for the model that includes
direct transmission some additional data would be needed to identify the
fraction of cases transmitted directly. Data for the 2015 Zika virus outbreak
in Barranquilla, Colombia has been used to fit parameters to the model
developed here and to estimate the basic reproduction number.
Infusing Collaborative Recommenders with Distributed Representations
Authors: Zanotti Greg, Horvath Miller, Barbosa Lucas Nunes, Immedisetty Venkata Trinadh Kumar Gupta, Gemmell Jonathan
Date: 2016-08-22 00:00:00
Tasks: Recommendation Systems

Recommender systems assist users in navigating complex information spaces and
focus their attention on the content most relevant to their needs. Often these
systems rely on user activity or descriptions of the content. Social annotation
systems, in which users collaboratively assign tags to items, provide another
means to capture information about users and items. Each of these data sources
provides unique benefits, capturing different relationships.
  In this paper, we propose leveraging multiple sources of data: ratings data
as users report their affinity toward an item, tagging data as users assign
annotations to items, and item data collected from an online database. Taken
together, these datasets provide the opportunity to learn rich distributed
representations by exploiting recent advances in neural network architectures.
We first produce representations that subjectively capture interesting
relationships among the data. We then empirically evaluate the utility of the
representations to predict a user's rating on an item and show that it
outperforms more traditional representations. Finally, we demonstrate that
traditional representations can be combined with representations trained
through a neural network to achieve even better results.
Anchoring historical sequences using a new source of astro-chronological tie-points
Date: 2016-08-17 00:00:00

The discovery of past spikes in atmospheric radiocarbon activity, caused by
major solar energetic particle events, has opened up new possibilities for
high-precision chronometry. The two spikes, or Miyake Events, have now been
widely identified in tree-rings that grew in the years 775 and 994 CE.
Furthermore, all other plant material that grew in these years would also have
incorporated the anomalously high concentrations of radiocarbon. Crucially,
some plant-based artefacts, such as papyrus documents, timber beams and linen
garments, can also be allocated to specific positions within long, currently
unfixed, historical sequences. Thus, Miyake Events represent a new source of
tie-points that could provide the means for anchoring early chronologies to the
absolute timescale. Here, we explore this possibility, outlining the most
expeditious approaches, the current challenges and obstacles, and how they
might best be overcome.
Age-dependent Branching Processes and Applications to the Luria-Delbr\"uck Experiment
Date: 2016-08-22 00:00:00

Microbial populations adapt to their environment by acquiring advantageous
mutations, but in the early twentieth century, questions about how these
organisms acquire mutations arose. The experiment of Salvador Luria and Max
Delbr\"uck that won them a Nobel Prize in 1969 confirmed that mutations don't
occur out of necessity, but instead can occur many generations before there is
a selective advantage, and thus organisms follow Darwinian evolution instead of
Lamarckian. Since then, new areas of research involving microbial evolution
have spawned as a result of their experiment. Determining the mutation rate of
a cell is one such area. Probability distributions that determine the number of
mutants in a large population have been derived by D. E. Lea, C. A. Coulson,
and J. B. S. Haldane. However, not much work has been done when time of cell
division is dependent on the cell age, and even less so when cell division is
asymmetric, which is the case in most microbial populations. Using probability
generating function methods, we rigorously construct a probability distribution
for the cell population size given a life-span distribution for both mother and
daughter cells, and then determine its asymptotic growth rate. We use this to
construct a probability distribution for the number of mutants in a large cell
population, which can be used with likelihood methods to estimate the cell
mutation rate.
L\'evy-Vasicek Models and the Long-Bond Return Process
Date: 2016-09-13 00:00:00

The classical derivation of the well-known Vasicek model for interest rates
is reformulated in terms of the associated pricing kernel. An advantage of the
pricing kernel method is that it allows one to generalize the construction to
the L\'evy-Vasicek case, avoiding issues of market incompleteness. In the
L\'evy-Vasicek model the short rate is taken in the real-world measure to be a
mean-reverting process with a general one-dimensional L\'evy driver admitting
exponential moments. Expressions are obtained for the L\'evy-Vasicek bond
prices and interest rates, along with a formula for the return on a unit
investment in the long bond, defined by $L_t = \lim_{T \rightarrow \infty}
P_{tT} / P_{0T}$, where $P_{tT}$ is the price at time $t$ of a $T$-maturity
discount bond. We show that the pricing kernel of a L\'evy-Vasicek model is
uniformly integrable if and only if the long rate of interest is strictly
positive.
RELARM: A rating model based on relative PCA attributes and k-means clustering
Date: 2016-08-23 00:00:00
Tasks: Attribute, Clustering, Object

Following widely used in visual recognition concept of relative attributes,
the article establishes definition of the relative PCA attributes for a class
of objects defined by vectors of their parameters. A new rating model (RELARM)
is built using relative PCA attribute ranking functions for rating object
description and k-means clustering algorithm. Rating assignment of each rating
object to a rating category is derived as a result of cluster centers
projection on the specially selected rating vector. Empirical study has shown a
high level of approximation to the existing S & P, Moody's and Fitch ratings.
Evidence of natural hybridization and introgression between Medicago ciliaris and Medicago intertexta
Date: 2016-08-23 00:00:00

The present study, investigated some reproductive and fertility parameters in
some wild populations, originating from the North Tunisia (4 populations of
Medicago ciliaris and 3 populations of Medicago intertexta). Previous finding
revealed that these species are genetically distinct and easily recognized by
the number of flowers per inflorescence and pod dimensions. However,
biometrical traits and isozyme patterns intermediacy between these two species
had detected the existence of a potential spontaneous interspecific hybrid
originating from Sedjnane locality in Tunisia. Indeed, the present work has
shown significant decrease of pollen fertility and seed production for this
population when compared to the others(pollen viability 75%, pollen
germinability 8% and pod production=9%). These results suggested a possible
natural interspecific hybrid and confirming introgressive hybridization
possibility between M. intertexta and M. ciliaris.
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes
Date: 2017-05-25 00:00:00
Tasks: Gaussian Processes, Time Series, Time Series Analysis

Multiple biological processes are driven by oscillatory gene expression at
different time scales. Pulsatile dynamics are thought to be widespread, and
single-cell live imaging of gene expression has lead to a surge of dynamic,
possibly oscillatory, data for different gene networks. However, the regulation
of gene expression at the level of an individual cell involves reactions
between finite numbers of molecules, and this can result in inherent randomness
in expression dynamics, which blurs the boundaries between aperiodic
fluctuations and noisy oscillators. Thus, there is an acute need for an
objective statistical method for classifying whether an experimentally derived
noisy time series is periodic. Here we present a new data analysis method that
combines mechanistic stochastic modelling with the powerful methods of
non-parametric regression with Gaussian processes. Our method can distinguish
oscillatory gene expression from random fluctuations of non-oscillatory
expression in single-cell time series, despite peak-to-peak variability in
period and amplitude of single-cell oscillations. We show that our method
outperforms the Lomb-Scargle periodogram in successfully classifying cells as
oscillatory or non-oscillatory in data simulated from a simple genetic
oscillator model and in experimental data. Analysis of bioluminescent live cell
imaging shows a significantly greater number of oscillatory cells when
luciferase is driven by a {\it Hes1} promoter (10/19), which has previously
been reported to oscillate, than the constitutive MoMuLV 5' LTR (MMLV) promoter
(0/25). The method can be applied to data from any gene network to both
quantify the proportion of oscillating cells within a population and to measure
the period and quality of oscillations. It is publicly available as a MATLAB
package.
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories
Date: 2016-10-27 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Time Series, Time Series Analysis

A central challenge in neuroscience is understanding how neural system
implements computation through its dynamics. We propose a nonlinear time series
model aimed at characterizing interpretable dynamics from neural trajectories.
Our model assumes low-dimensional continuous dynamics in a finite volume. It
incorporates a prior assumption about globally contractional dynamics to avoid
overly enthusiastic extrapolation outside of the support of observed
trajectories. We show that our model can recover qualitative features of the
phase portrait such as attractors, slow points, and bifurcations, while also
producing reliable long-term future predictions in a variety of dynamical
models and in real neural data.
Grand Challenges for Global Brain Sciences
Date: 2016-10-27 00:00:00
Tasks: Anatomy

The next grand challenges for society and science are in the brain sciences.
A collection of 60+ scientists from around the world, together with 10+
observers from national, private, and foundations, spent two days together
discussing the top challenges that we could solve as a global community in the
next decade. We eventually settled on three challenges, spanning anatomy,
physiology, and medicine. Addressing all three challenges requires novel
computational infrastructure. The group proposed the advent of The
International Brain Station (TIBS), to address these challenges, and launch
brain sciences to the next level of understanding.
Probing Three-dimensional Collective Cancer Invasion with DIGME
Date: 2016-08-24 00:00:00

Multicellular migration and pattern formation play important roles in
developmental biology, cancer metastasis and wound healing. To understand the
collective cell dynamics in three dimensional extracellular matrix (ECM), we
have developed a simple and mechanical-based strategy, Diskoid In Geometrically
Micropatterned ECM (DIGME). DIGME allows easy engineering of the shape of 3-D
tissue organoid, the mesoscale ECM heterogeneity, and the fiber alignment of
collagen-based ECM all at the same time. We have employed DIGME to study the
collective cancer invasion and find that DIGME provides a powerful tool to
probe three dimensional dynamics of tumor organoid in patterned
microenvironment.
Fractal approach towards power-law coherency to measure cross-correlations between time series
Date: 2017-02-24 00:00:00
Tasks: Time Series, Time Series Analysis

We focus on power-law coherency as an alternative approach towards studying
power-law cross-correlations between simultaneously recorded time series. To be
able to study empirical data, we introduce three estimators of the power-law
coherency parameter $H_{\rho}$ based on popular techniques usually utilized for
studying power-law cross-correlations -- detrended cross-correlation analysis
(DCCA), detrending moving-average cross-correlation analysis (DMCA) and height
cross-correlation analysis (HXA). In the finite sample properties study, we
focus on the bias, variance and mean squared error of the estimators. We find
that the DMCA-based method is the safest choice among the three. The HXA method
is reasonable for long time series with at least $10^4$ observations, which can
be easily attainable in some disciplines but problematic in others. The
DCCA-based method does not provide favorable properties which even deteriorate
with an increasing time series length. The paper opens a new venue towards
studying cross-correlations between time series.
Sedano: A News Stream Processor for Business
Authors: Scaiella Ugo, Berardi Giacomo, Mega Giuliano, Santoro Roberto
Date: 2016-08-24 00:00:00
Tasks: Articles, Entity Linking

We present Sedano, a system for processing and indexing a continuous stream
of business-related news. Sedano defines pipelines whose stages analyze and
enrich news items (e.g., newspaper articles and press releases). News data
coming from several content sources are stored, processed and then indexed in
order to be consumed by Atoka, our business intelligence product. Atoka users
can retrieve news about specific companies, filtering according to various
facets. Sedano features both an entity-linking phase, which finds mentions of
companies in news, and a classification phase, which classifies news according
to a set of business events. Its flexible architecture allows Sedano to be
deployed on commodity machines while being scalable and fault-tolerant.
Full Computational Reproducibility in Biological Science: Methods, Software and a Case Study in Protein Biology
Date: 2016-08-24 00:00:00

Independent computational reproducibility of scientific results is rapidly
becoming of pivotal importance in scientific progress as computation itself
plays a more and more central role in so many branches of science.
Historically, reproducibility has followed the familiar Popperian [38] model
whereby theory cannot be verified by scientific testing, it can only be
falsified. Ultimately, this implies that if an experiment cannot be reproduced
independently to some satisfactory level of precision, its value is essentially
unquantifiable; put brutally, it is impossible to determine its scientific
value. The burgeoning presence of software in most scientific work adds a new
and particularly opaque layer of complexity [29]. In spite of much recent
interest in many scientific areas, emphasis remains more on procedures,
strictures and discussion [12, 14, 16, 29, 30, 37, 41], reflecting the
inexperience of most scientific journals when it comes to software, rather than
the details of how computational reproducibility is actually achieved, for
which there appear to be relatively few guiding examples [6, 10, 17]. After
considering basic principles, here we show how full computational
reproducibility can be achieved in practice at every stage using a case study
of a multi-gigabyte protein study on the open SwissProt protein database, from
data download all the way to individual figure by figure reproduction as an
exemplar for general scientific computation.
Hypothesis: Homologous recombination depends on parallel search
Date: 2016-08-15 00:00:00

It is not known how a cell manages to find a specific DNA sequence
sufficiently fast to repair a broken chromosome through homologous
recombination. I propose that the solution is based on a parallelized search
implemented by freely diffusing molecules programmed with sequences
corresponding to those flanking the break site.