pwc-archive/papers-with-abstracts huggingface dataset:
Interactive Web Application for Exploring Matrices of Neural Connectivity
Date: 2017-02-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

We present here a browser-based application for visualizing patterns of
connectivity in 3D stacked data matrices with large numbers of pairwise
relations. Visualizing a connectivity matrix, looking for trends and patterns,
and dynamically manipulating these values is a challenge for scientists from
diverse fields, including neuroscience and genomics. In particular,
high-dimensional neural data include those acquired via electroencephalography
(EEG), electrocorticography (ECoG), magnetoencephalography (MEG), and
functional MRI. Neural connectivity data contains multivariate attributes for
each edge between different brain regions, which motivated our lightweight,
open source, easy-to-use visualization tool for the exploration of these
connectivity matrices to highlight connections of interest. Here we present a
client-side, mobile-compatible visualization tool written entirely in
HTML5/JavaScript that allows in-browser manipulation of user-defined files for
exploration of brain connectivity. Visualizations can highlight different
aspects of the data simultaneously across different dimensions. Input files are
in JSON format, and custom Python scripts have been written to parse MATLAB or
Python data files into JSON-loadable format. We demonstrate the analysis of
connectivity data acquired via human ECoG recordings as a domain-specific
implementation of our application. We envision applications for this
interactive tool in fields seeking to visualize pairwise connectivity.
Blood Vessel Tortuosity Selects against Evolution of Agressive Tumor Cells in Confined Tissue Environments: a Modeling Approach
Date: 2017-06-28 00:00:00

Cancer is a disease of cellular regulation, often initiated by genetic
mutation within cells, and leading to a heterogeneous cell population within
tissues. In the competition for nutrients and growth space within the tumors
the phenotype of each cell determines its success. Selection in this process is
imposed by both the microenvironment (neighboring cells, extracellular matrix,
and diffusing substances), and the whole of the organism through for example
the blood supply. In this view, the development of tumor cells is in close
interaction with their increasingly changing environment: the more cells can
change, the more their environment will change. Furthermore, instabilities are
also introduced on the organism level: blood supply can be blocked by increased
tissue pressure or the tortuosity of the tumor-neovascular vessels. This
coupling between cell, microenvironment, and organism results in behavior that
is hard to predict. Here we introduce a cell-based computational model to study
the effect of blood flow obstruction on the micro-evolution of cells within a
cancerous tissue. We demonstrate that stages of tumor development emerge
naturally, without the need for sequential mutation of specific genes.
Secondly, we show that instabilities in blood supply can impact the overall
development of tumors and lead to the extinction of the dominant aggressive
phenotype, showing a clear distinction between the fitness at the cell level
and survival of the population. This provides new insights into potential side
effects of recent tumor vasculature renormalization approaches.
Cusps Enable Line Attractors for Neural Computation
Date: 2017-11-29 00:00:00

Line attractors in neuronal networks have been suggested to be the basis of
many brain functions, such as working memory, oculomotor control, head
movement, locomotion, and sensory processing. In this paper, we make the
connection between line attractors and pulse-gating in feedforward neuronal
networks. In this context, because of their neutral stability along a
one-dimensional manifold, line attractors are associated with a
time-translational invariance that allows graded information to be propagated
from one neuronal population to the next. To understand how pulse-gating
manifests itself in a high-dimensional, non-linear, feedforward
integrate-and-fire network, we use a Fokker-Planck approach to analyze system
dynamics. We make a connection between pulse-gated propagation in the
Fokker-Planck and population-averaged mean-field (firing rate) models, then
identify an approximate line attractor in state space as the essential
structure underlying graded information propagation. An analysis of the line
attractor shows that it consists of three fixed points: a central saddle with
an unstable manifold along the line and stable manifolds orthogonal to the
line, which is surrounded on either side by stable fixed points. Along the
manifold defined by the fixed points, slow dynamics give rise to a ghost. We
show that this line attractor arises at a cusp catastrophe, where a fold
bifurcation develops as a function of synaptic noise; and that the ghost
dynamics near the fold of the cusp underly the robustness of the line
attractor. Understanding the dynamical aspects of this cusp catastrophe allows
us to show how line attractors can persist in biologically realistic neuronal
networks and how the interplay of pulse gating, synaptic coupling and neuronal
stochasticity can be used to enable attracting one-dimensional manifolds and
thus, dynamically control the processing of graded information.
Applications of Discrete Mathematics for Understanding Dynamics of Synapses and Networks in Neuroscience
Date: 2017-02-21 00:00:00

Mathematical modeling has broad applications in neuroscience whether modeling
the dynamics of a single synapse or an entire network of neurons. In Part I, we
model vesicle replenishment and release at the photoreceptor synapse to better
understand how visual information is processed. In Part II, we explore a simple
model of neural networks with the goal of discovering how network structure
shapes the behavior of the network.
  To fully understand how visual information is processed requires an
understanding of the way signals are transformed at the ribbon synapse of
photoreceptor neurons. These synapses possess a ribbon-like structure capable
of storing around 100 synaptic vesicles, allowing graded responses through the
release of different numbers of vesicles in response to visual input. These
responses depend critically on the ability of the ribbon to replenish itself as
ribbon sites empty upon release. The rate of vesicle replenishment is thus an
important factor in shaping neural coding in the retina. In collaboration with
experimental neuroscientists we developed a mathematical model to describe the
dynamics of vesicle release and replenishment at the ribbon synapse.
  To learn more about how network architecture shapes the dynamics of the
network, we study a specific type of threshold-linear network that is
constructed from a simple directed graph. The network construction guarantees
that differences in dynamics arise solely from differences in the connectivity
of the underlying graph. By design, the activity of these networks is bounded
and there are no stable fixed points. Computational experiments show that most
of these networks yield limit cycles where the neurons fire in sequence. We
devised an algorithm to predict the sequence of firing using the structure of
the underlying graph. Using the algorithm we classify all the networks of this
type on five or fewer nodes.
Visual response properties of MSTd emerge from a sparse population code
Date: 2017-02-22 00:00:00
Tasks: Dimensionality Reduction

Neurons in the dorsal subregion of the medial superior temporal (MSTd) area
respond to large, complex patterns of retinal flow, implying a role in the
analysis of self-motion. Some neurons are selective for the expanding radial
motion that occurs as an observer moves through the environment ("heading"),
and computational models can account for this finding. However, ample evidence
suggests that MSTd neurons may exhibit a continuum of visual response
selectivity to large-field motion stimuli, but the underlying computational
principles by which these response properties are derived remain poorly
understood. Here we describe a computational model of MSTd based on the
hypothesis that neurons in MSTd efficiently encode the continuum of large-field
retinal flow patterns on the basis of inputs received from neurons in MT, with
receptive fields that resemble basis vectors recovered with nonnegative matrix
factorization (NMF). These assumptions are sufficient to quantitatively
simulate neurophysiological response properties of MSTd cells such as radial,
circular, and spiral motion tuning, suggesting that these properties might
simply be a by-product of MSTd neurons performing dimensionality reduction on
their inputs. At the population level, model MSTd accurately predicts heading
using a sparse distributed code, consistent with the idea that biological MSTd
might operate in a sparseness regime well-suited to efficiently encode a number
of self-motion variables. The present work provides an alternative to the
template-model view of MSTd, and offers a biologically plausible account of the
receptive field structure across a wide range of visual response properties in
MSTd.
EOMM: An Engagement Optimized Matchmaking Framework
Authors: Chen Zhengxing, Xue Su, Kolen John, Aghdaie Navid, Zaman Kazi A., Sun Yizhou, El-nasr Magy Seif
Date: 2017-02-22 00:00:00
Tasks: Fairness

Matchmaking connects multiple players to participate in online
player-versus-player games. Current matchmaking systems depend on a single core
strategy: create fair games at all times. These systems pair similarly skilled
players on the assumption that a fair game is best player experience. We will
demonstrate, however, that this intuitive assumption sometimes fails and that
matchmaking based on fairness is not optimal for engagement.
  In this paper, we propose an Engagement Optimized Matchmaking (EOMM)
framework that maximizes overall player engagement. We prove that equal-skill
based matchmaking is a special case of EOMM on a highly simplified assumption
that rarely holds in reality. Our simulation on real data from a popular game
made by Electronic Arts, Inc. (EA) supports our theoretical results, showing
significant improvement in enhancing player engagement compared to existing
matchmaking methods.
Structural Change in (Economic) Time Series
Date: 2017-02-17 00:00:00
Tasks: Time Series, Time Series Analysis

Methods for detecting structural changes, or change points, in time series
data are widely used in many fields of science and engineering. This chapter
sketches some basic methods for the analysis of structural changes in time
series data. The exposition is confined to retrospective methods for univariate
time series. Several recent methods for dating structural changes are compared
using a time series of oil prices spanning more than 60 years. The methods
broadly agree for the first part of the series up to the mid-1980s, for which
changes are associated with major historical events, but provide somewhat
different solutions thereafter, reflecting a gradual increase in oil prices
that is not well described by a step function. As a further illustration, 1990s
data on the volatility of the Hang Seng stock market index are reanalyzed.
Evolution of the Ubiquitin-Activating Enzyme Uba1 (E1)
Date: 2017-02-22 00:00:00

Ubiquitin tags diseased proteins and initiates an enzyme conjugation cascade,
which has three stages. The first-stage enzyme Uba1 (E1) has evolved only
modestly from slime mold to humans, and is > 14 times larger than Ub. Here we
use critical point thermodynamic scaling theory to connect Uba1 (E1) evolution
from yeast and slime mold to fruit flies and humans to subtle changes in its
amino acid sequences.
The Diffusion of Humans and Cultures in the Course of the Spread of Farming
Date: 2017-02-22 00:00:00

The most profound change in the relationship between humans and their
environment was the introduction of agriculture and pastoralism. [....] For an
understanding of the expansion process, it appears appropriate to apply a
diffusive model. Broadly, these numerical modeling approaches can be catego-
rized in correlative, continuous and discrete. Common to all approaches is the
comparison to collections of radiocarbon data that show the apparent wave of
advance of the transition to farming. However, these data sets differ in entry
density and data quality. Often they disregard local and regional specifics and
research gaps, or dating uncertainties. Thus, most of these data bases may only
be used on a very general, broad scale. One of the pitfalls of using
irregularly spaced or irregularly documented radiocarbon data becomes evident
from the map generated by Fort (this volume, Chapter 16): while the general
east-west and south-north trends become evident, some areas appear as having
undergone anomalously early transitions to farming. This may be due to faulty
entries into the data base or regional problems with radiocarbon dating, if not
unnoticed or undocumented laboratory mistakes.
The Quest for the FFA led to the Expertise Account of its Specialization
Date: 2017-02-22 00:00:00

This article is written in response to a Progressions article by Kanwisher in
the Journal of Neuroscience, The Quest for the FFA and Where It Led (Kanwisher,
2017). I reflect on the extensive research program dedicated to the study of
how and why perceptual expertise explains the many ways that faces are special,
a research program which both predates and follows the Kanwisher (1997)
landmark article where the fusiform face area (FFA) is named. The expertise
accounts suggests that the FFA is an area recruited by expertise individuating
objects that are perceptually similar because they share a configuration of
parts. While Kanwisher (2017) discussed the expertise account only very briefly
and only to dismiss it, there is strong and replicable evidence that responses
in the FFA are highly sensitive to experience with non-face objects. I point
out that Kanwisher was well positioned to present these findings in their
historical context as she participated in the design of the first fMRI study on
car and bird expertise, as well as the first replication of this finding.
Perhaps most relevant to readers interested in the neural bases of face
processing, it is important to distinguish studies that describe the phenomenon
of face-selectivity from those that test an explanation for this phenomenon. In
the Progressions article, arguments for a face-dedicated processing module that
is not the result of our experience with faces are provided without attention
to a great deal of expertise research which is directly inconsistent with that
claim. The claim also lacks more direct support, as face-selective responses in
the visual system are not found in infants and children and face-selective
activity in FFA does not appear to be heritable.
Robust Hedging of Options on a Leveraged Exchange Traded Fund
Date: 2017-02-23 00:00:00

A leveraged exchange traded fund (LETF) is an exchange traded fund that uses
financial derivatives to amplify the price changes of a basket of goods. In
this paper, we consider the robust hedging of European options on a LETF,
finding model-free bounds on the price of these options.
  To obtain an upper bound, we establish a new optimal solution to the
Skorokhod embedding problem (SEP) using methods introduced in
Beiglb\"ock-Cox-Huesmann. This stopping time can be represented as the hitting
time of some region by a Brownian motion, but unlike other solutions of e.g.
Root, this region is not unique. Much of this paper is dedicated to
characterising the choice of the embedding region that gives the required
optimality property. Notably, this appears to be the first solution to the SEP
where the solution is not uniquely characterised by its geometric structure,
and an additional condition is needed on the stopping region to guarantee that
it is the optimiser. An important part of determining the optimal region is
identifying the correct form of the dual solution, which has a financial
interpretation as a model-independent superhedging strategy.
The knowledge paradox: why knowing more is knowing less
Date: 2017-02-20 00:00:00

To provide an explanation of the evolution of scientific knowledge, I start
from the assumption that knowledge is based on concepts, and propose that each
concept about reality is affected by vagueness. This entails a paradox, which I
term Knowledge Paradox (KP): i.e. we need concepts to acquire knowledge about
the real world but each concept is a step away from reality. The KP provides a
unifying context for the sorites and the liar paradoxes. Any concept is viewed
as a sorites, i.e. it is impossible to set a boundary between what is, and what
is not, the entity to which the concept refers. Hence, any statement about
reality can be reduced to a liar, wherefrom the KP follows in its most general
form: -If I know, then I do not know-. The KP is self-referential but not
contradictory, as it can be referred to two levels of knowledge: -if I
know(epistemic), then I do not know(ontic)-, where the ontic level is made
unachievable by concept vagueness. Such an interpretation of scientific
knowledge provides an understanding of its dynamics. Concept proliferation
within theories produces periods of knowledge decay that are episodically
reversed by the formulation of new theories based on a smaller number of
synthetic concepts.
Time-Series Adaptive Estimation of Vaccination Uptake Using Web Search Queries
Authors: Hansen Niels Dalum, Mølbak Kåre, Cox Ingemar J., Lioma Christina
Date: 2017-02-23 00:00:00
Tasks: Time Series, Time Series Analysis

Estimating vaccination uptake is an integral part of ensuring public health.
It was recently shown that vaccination uptake can be estimated automatically
from web data, instead of slowly collected clinical records or population
surveys. All prior work in this area assumes that features of vaccination
uptake collected from the web are temporally regular. We present the first ever
method to remove this assumption from vaccination uptake estimation: our method
dynamically adapts to temporal fluctuations in time series web data used to
estimate vaccination uptake. We show our method to outperform the state of the
art compared to competitive baselines that use not only web data but also
curated clinical data. This performance improvement is more pronounced for
vaccines whose uptake has been irregular due to negative media attention (HPV-1
and HPV-2), problems in vaccine supply (DiTeKiPol), and targeted at children of
12 years old (whose vaccination is more irregular compared to younger
children).
Surfing on protein waves: proteophoresis as a mechanism for bacterial genome partitioning
Date: 2017-07-19 00:00:00

Efficient bacterial chromosome segregation typically requires the coordinated
action of a three-components, fueled by adenosine triphosphate machinery called
the partition complex. We present a phenomenological model accounting for the
dynamic activity of this system. The model is obtained by coupling simple
linear reaction-diffusion equations with a proteophoresis, or "volumetric"
chemophoresis, force field. This minimal description, in the sense of Occam's
Razor principle, captures most known experimental observations: dynamic
oscillations of complex components, complex separation and subsequent
symmetrical positioning. The predictions of our model are in phenomenological
agreement with and provide substantial insights into recent experiments. From a
non-linear physics view point, this system explores the active separation of
matter at micrometric scales with a dynamical instability between static
positioning and travelling wave regimes.
Ambiguity invokes Creativity : looking through Quantum physics
Date: 2017-02-23 00:00:00
Tasks: Sentence

Creativity, defined as the tendency to generate or recognize new ideas or
alternatives and to make connections between seemingly unrelated phenomena, is
too vast a horizon to be summed up in such a simple sentence. The extreme
abstractness of creativity makes it harder to quantify in its entirety. Yet, a
lot of efforts have been made both by psychologists and neurobiologists to
identify its signature. A general conformity is expressed in the Free
association theory, i.e. the more freely a persons conceptual nodes are
connected, the more divergent thinker (also, creative) he or she is. Also,
tolerance of ambiguity is found to be related to divergent thinking. In this
study, we approach the problem of creativity from a theoretical physics
standpoint. Theoretically, for the initial conceptual state, the next jump to
any other node is equally probable and non-deterministic. Repeated intervention
of external stimulus (analogous to a measurement) is responsible for such
jumps. And to study such a non-deterministic system with continuous
measurements, Quantum theory has been proven the most successful, time and
again. We suggest that this collection of nodes form a system which is likely
to be governed by quantum physics and specify the transformations which could
help explain the conceptual jump between states. Our argument, from the point
of view of physics is that the initial evolution of the creative process is
identical, person or field independent. To answer the next obvious question
about individual creativity, we hypothesize that the quantum system, under
continuous measurements (in the form of external stimuli) evolves with chaotic
dynamics, hence separating a painter from a musician. Possible experimental
methodology of these effects has also been suggested using ambiguous figures.
Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media
Authors: Khandpur Rupinder Paul, Ji Taoran, Jan Steve, Wang Gang, Lu Chang-Tien, Ramakrishnan Naren
Date: 2017-02-24 00:00:00
Tasks: Cyber Attack Detection

Social media is often viewed as a sensor into various societal events such as
disease outbreaks, protests, and elections. We describe the use of social media
as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our
approach detects a broad range of cyber-attacks (e.g., distributed denial of
service (DDOS) attacks, data breaches, and account hijacking) in an
unsupervised manner using just a limited fixed set of seed event triggers. A
new query expansion strategy based on convolutional kernels and dependency
parses helps model reporting structure and aids in identifying key event
characteristics. Through a large-scale analysis over Twitter, we demonstrate
that our approach consistently identifies and encodes events, outperforming
existing methods.
A Unified Approach for Drawdown (Drawup) of Time-Homogeneous Markov Processes
Date: 2017-02-24 00:00:00
Tasks: Management

Drawdown (resp. drawup) of a stochastic process, also referred as the
reflected process at its supremum (resp. infimum), has wide applications in
many areas including financial risk management, actuarial mathematics and
statistics. In this paper, for general time-homogeneous Markov processes, we
study the joint law of the first passage time of the drawdown (resp. drawup)
process, its overshoot, and the maximum of the underlying process at this first
passage time. By using short-time pathwise analysis, under some mild regularity
conditions, the joint law of the three drawdown quantities is shown to be the
unique solution to an integral equation which is expressed in terms of
fundamental two-sided exit quantities of the underlying process. Explicit forms
for this joint law are found when the Markov process has only one-sided jumps
or is a L\'{e}vy process (possibly with two-sided jumps). The proposed
methodology provides a unified approach to study various drawdown quantities
for the general class of time-homogeneous Markov processes.