pwc-archive/papers-with-abstracts huggingface dataset:
A distance constrained synaptic plasticity model of C. elegans neuronal network
Date: 2016-11-25 00:00:00

Brain research has been driven by enquiry for principles of brain structure
organization and its control mechanisms. The neuronal wiring map of C. elegans,
the only complete connectome available till date, presents an incredible
opportunity to learn basic governing principles that drive structure and
function of its neuronal architecture. Despite its apparently simple nervous
system, C. elegans is known to possess complex functions. The neuronal
architecture forms an important underlying framework which specifies phenotypic
features associated to sensation, movement, conditioning and memory. In this
study, with the help of graph theoretical models, we investigated the C.
elegans neuronal network to identify network features that are critical for its
control. The 'driver neurons' are associated with important biological
functions such as reproduction, signalling processes and anatomical structural
development. We created 1D and 2D network models of C. elegans neuronal system
to probe the role of features that confer controllability and small world
nature. The simple 1D ring model is critically poised for the number of feed
forward motifs, neuronal clustering and characteristic path-length in response
to synaptic rewiring, indicating optimal rewiring. Using empirically observed
distance constraint in the neuronal network as a guiding principle, we created
a distance constrained synaptic plasticity model that simultaneously explains
small world nature, saturation of feed forward motifs as well as observed
number of driver neurons. The distance constrained model suggests optimum long
distance synaptic connections as a key feature specifying control of the
network.
Analysis of the nonlinear option pricing model under variable transaction costs
Date: 2016-03-12 00:00:00

In this paper we analyze a nonlinear Black--Scholes model for option pricing
under variable transaction costs. The diffusion coefficient of the nonlinear
parabolic equation for the price $V$ is assumed to be a function of the
underlying asset price and the Gamma of the option. We show that the
generalizations of the classical Black--Scholes model can be analyzed by means
of transformation of the fully nonlinear parabolic equation into a quasilinear
parabolic equation for the second derivative of the option price. We show
existence of a classical smooth solution and prove useful bounds on the option
prices. Furthermore, we construct an effective numerical scheme for
approximation of the solution. The solutions are obtained by means of the
efficient numerical discretization scheme of the Gamma equation. Several
computational examples are presented.
Deciphering infant mortality. Part 1: empirical evidence
Date: 2016-03-13 00:00:00

This paper is not (or at least not only) about human infant mortality. In
line with reliability theory, "infant" will refer here to the time interval
following birth during which the mortality (or failure) rate decreases. This
definition provides a systems science perspective in which birth constitutes a
sudden transition which falls within the field of application of the "Transient
Shock" (TS) conjecture put forward in Richmond et al. (2016c). This conjecture
provides predictions about the timing and shape of the death rate peak. (i) It
says that there will be a death rate spike whenever external conditions change
abruptly and drastically. (ii) It predicts that after a steep rising there will
be a much longer hyperbolic relaxation process. These predictions can be tested
by considering living organisms for which birth is a multi-step process. Thus,
for fish there are three states: egg, yolk-sac phase, young adult. The TS
conjecture predicts a mortality spike at the end of the yolk-sac phase, and
this timing is indeed confirmed by observation. Secondly, the hyperbolic nature
of the relaxation process can be tested using high accuracy Swiss statistics
which give postnatal death rates from one hour after birth up to the age of 10
years. It turns out that since the 19th century despite a great overall
reduction in infant mortality, the shape of the age-specific death rate has
remained basically unchanged. This hyperbolic pattern is not specific to
humans. It can also be found in small primates as recorded in the archives of
zoological gardens. Our ultimate objective is to set up a chain of cases which
starts from simple systems and then moves up step by step to more complex
organisms. The cases discussed here can be seen as initial landmarks.
Resolving coiled shapes reveals new reorientation behaviors in C. elegans
Date: 2016-03-13 00:00:00

We exploit the reduced space of C. elegans postures to develop a novel
tracking algorithm which captures both simple shapes and also self-occluding
coils, an important, yet unexplored, component of worm behavior. We apply our
algorithm to show that visually complex, coiled sequences are a superposition
of two simpler patterns: the body wave dynamics and a head-curvature pulse. We
demonstrate the precise coiled dynamics of an escape response and uncover new
behaviors in spontaneous, large amplitude coils; deep reorientations occur
through classical Omega-shaped postures and also through larger, new postural
excitations which we label here as delta-turns. We find that omega and delta
turns occur independently, the serpentine analog of a random left-right step,
suggesting a distinct triggering mechanism. We also show that omega and delta
turns display approximately equal rates and adapt to food-free conditions on a
similar timescale, a simple strategy to avoid navigational bias.
Contagion and Stability in Financial Networks
Date: 2016-03-13 00:00:00

This paper investigates two mechanisms of financial contagion that are,
firstly, the correlated exposure of banks to the same source of risk, and
secondly the direct exposure of banks in the interbank market. It will consider
a random network of banks which are connected through the inter-bank market and
will discuss the desirable level of banks exposure to the same sources of risk,
that is investment in similar portfolios, for different levels of network
connectivity when peering through the lens of the systemic cost incurred to the
economy from the banks simultaneous failure. It demonstrates that for all
levels of network connectivity, certain levels of diversifying individual banks
diversifications are not optimum under any condition. So, given an acceptable
level of systemic cost, the regulator could let banks decrease their capital
buffers by moving away from the non-optimum area.
Model of the Songbird Nucleus HVC as a Network of Central Pattern Generators
Date: 2016-07-18 00:00:00

We propose a functional architecture of the adult songbird nucleus HVC in
which the core element is a "functional syllable unit" (FSU). In this model,
HVC is organized into FSUs, each of which provides the basis for the production
of one syllable in vocalization. Within each FSU, the inhibitory neuron
population takes one of two operational states: (A) simultaneous firing wherein
all inhibitory neurons fire simultaneously, and (B) competitive firing of the
inhibitory neurons. Switching between these basic modes of activity is
accomplished via changes in the synaptic strengths among the inhibitory
neurons. The inhibitory neurons connect to excitatory projection neurons such
that during state (A) the activity of projection neurons is suppressed, while
during state (B) patterns of sequential firing of projection neurons can occur.
The latter state is stabilized by feedback from the projection to the
inhibitory neurons. Song composition for specific species is distinguished by
the manner in which different FSUs are functionally connected to each other.
  Ours is a computational model built with biophysically based neurons. We
illustrate that many observations of HVC activity are explained by the dynamics
of the proposed population of FSUs, and we identify aspects of the model that
are currently testable experimentally. In addition, and standing apart from the
core features of an FSU, we propose that the transition between modes may be
governed by the biophysical mechanism of neuromodulation.
Analysis of Blink Rate Variability during reading and memory testing
Date: 2016-03-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

In this paper we investigated how statistical properties of the blink rate
variability changes during two mental tasks: reading a passage and memory
testing. To construct time series of inter-blink intervals (blink rate
variability) we detected exact blink time in EEG recordings using our blink
detection algorithm. We found that among 13 subjects, all subjects blinked less
during reading session. Moreover, standard deviation of the blink rate
variability is higher during reading. Thus, we conclude that the variability of
inter-blink intervals decreases during tasks that require concentration and
intense mental activity.
Birdsong dialect patterns explained using magnetic domains
Date: 2016-03-12 00:00:00

The songs and calls of many bird species, like human speech, form distinct
regional dialects. We suggest that the process of dialect formation is
analogous to the physical process of magnetic domain formation. We take the
coastal breeding grounds of the Puget Sound white crowned sparrow as an
example. Previous field studies suggest that birds of this species learn
multiple songs early in life, and when establishing a territory for the first
time, retain one of these dialects in order to match the majority of their
neighbours. We introduce a simple lattice model of the process, showing that
this matching behaviour can produce single dialect domains provided the death
rate of adult birds is sufficiently low. We relate death rate to thermodynamic
temperature in magnetic materials, and calculate the critical death rate by
analogy with the Ising model. Using parameters consistent with the known
behavior of these birds we show that coastal dialect domain shapes may be
explained by viewing them as low temperature "stripe states".
Bifurcation of Transition Paths Induced by Coupled Bistable Systems
Date: 2016-05-20 00:00:00

We discuss the transition paths in a coupled bistable system consisting of
interacting multiple identical bistable motifs. We propose a simple model of
coupled bistable gene circuits as an example, and show that its transition
paths are bifurcating. We then derive a criterion to predict the bifurcation of
transition paths in a generalized coupled bistable system. We confirm the
validity of the theory for the example system by numerical simulation. We also
demonstrate in the example system that, if the steady states of individual gene
circuits are not changed by the coupling, the bifurcation pattern is not
dependent on the number of gene circuits. We further show that the transition
rate exponentially decreases with the number of gene circuits when the
transition path does not bifurcate, while a bifurcation facilitates the
transition by lowering the quasi-potential energy barrier.
An approach towards debiasing user ratings
Authors: Mishra Abhinav
Date: 2016-03-14 00:00:00
Tasks: Recommendation Systems

With increasing importance of e-commerce, many websites have emerged where
users can express their opinions about products, such as movies, books, songs,
etc. Such interactions can be modeled as bipartite graphs where the weight of
the directed edge from a user to a product denotes a rating that the user
imparts to the product. These graphs are used for recommendation systems and
discovering most reliable (trusted) products. For these applications, it is
important to capture the bias of a user when she is rating a product. Users
have inherent bias---many users always impart high ratings while many others
always rate poorly. It is necessary to know the bias of a reviewer while
reading the review of a product. It is equally important to compensate for this
bias while assigning a ranking for an object. In this paper, we propose an
algorithm to capture the bias of a user and then subdue it to compute the true
rating a product deserves. Experiments show the efficiency and effectiveness of
our system in capturing the bias of users and then computing the true ratings
of a product.
Human judgment vs. theoretical models for the management of ecological resources
Date: 2016-03-15 00:00:00
Tasks: Decision Making, Management, Model Optimization

Despite major advances in quantitative approaches to natural resource
management, there has been resistance to using these tools in the actual
practice of managing ecological populations. Given a managed system and a set
of assumptions, translated into a model, optimization methods can be used to
solve for the most cost effective management actions. However, when the
underlying assumptions are not met, such methods can potentially lead to poor
decisions. Managers who develop decisions based on past experience and
judgment, without the aid of mathematical models, can potentially learn about
the system and develop flexible management strategies. However, these
strategies are often based on subjective criteria and equally invalid and often
unstated assumptions. Given the drawbacks of both methods, it is unclear
whether simple quantitative models improve environmental decision making over
expert opinion. In this paper, we explore how well students, using their
experience and judgment, manage simulated fishery populations in an online
computer game and compare their management outcomes to the performance of
model-based decisions. We consider harvest decisions generated using four
different quantitative models: 1. the model used to produce the simulated
population dynamics observed in the game, with all underlying parameter values
known [a control], 2. the same model, but with unknown parameter values that
must be estimated during the game from observed data, 3. models that are
structurally different from those used to simulate the population dynamics and
4. a model that ignores age structure. Humans on average performed much worse
than the models in cases 1 - 3. When the models ignored age structure, they
generated poorly performing management decisions, but still outperformed
students using experience and judgment 66 percent of the time.
Learning Optimal Social Dependency for Recommendation
Authors: Liu Yong, Zhao Peilin, Liu Xin, Wu Min, Li Xiao-Li
Date: 2016-03-15 00:00:00
Tasks: Recommendation Systems

Social recommender systems exploit users' social relationships to improve the
recommendation accuracy. Intuitively, a user tends to trust different subsets
of her social friends, regarding with different scenarios. Therefore, the main
challenge of social recommendation is to exploit the optimal social dependency
between users for a specific recommendation task. In this paper, we propose a
novel recommendation method, named probabilistic relational matrix
factorization (PRMF), which aims to learn the optimal social dependency between
users to improve the recommendation accuracy, with or without users' social
relationships. Specifically, in PRMF, the latent features of users are assumed
to follow a matrix variate normal (MVN) distribution. The positive and negative
dependency between users are modeled by the row precision matrix of the MVN
distribution. Moreover, we have also proposed an efficient alternating
algorithm to solve the optimization problem of PRMF. The experimental results
on real datasets demonstrate that the proposed PRMF method outperforms
state-of-the-art social recommendation approaches, in terms of root mean square
error (RMSE) and mean absolute error (MAE).
Rising temperatures increased recruitment of brown tiger prawn (Penaeus esculentus) in Moreton Bay (Australia)
Date: 2016-03-15 00:00:00

Abiotic factors are fundamental drivers of the dynamics of wild marine fish
populations. Identifying and quantifying their influence on species targeted by
the fishing industry is difficult and very important for managing fisheries in
a changing climate. Using multiple regression, we investigated the influence of
both temperature and rainfall on the variability of recruitment of a tropical
species, the brown tiger prawn (Penaeus esculentus), in Moreton Bay which is
located near the southern limit of its distribution on the east coast of
Australia. A step-wise selection between environmental variables identified
that variations in recruitment from 1990 to 2014 were best explained by a
combination of temperature and spawning stock biomass. Temperature explains 35%
of recruitment variability and spawning stock biomass 33%. This analysis
suggests that increasing temperatures have increased recruitment of brown tiger
prawn in Moreton Bay.
Dynamic Analysis of Bet-Hedging Strategies as a Protection Mechanism against Environmental Fluctuations
Date: 2016-03-15 00:00:00

In order to increase their robustness against environmental fluctuations,
many biological populations have developed bet-hedging mechanisms in which the
population `bets' against the presence of prolonged favorable environmental
conditions by having a few individual behaving as if they sensed a threatening
or stressful environment. As a result, the population (as a whole) increases
its chances of surviving environmental fluctuations in the long term, while
sacrificing short-term performance. In this paper, we propose a theoretical
framework, based on Markov jump linear systems, to model and evaluate the
performance of bet-hedging strategies in the presence of stochastic
fluctuations. We illustrate our results using numerical simulations.
Recurrent Network Models Of Sequence Generation And Memory
Date: 2016-03-14 00:00:00
Tasks: Decision Making

Sequential activation of neurons is a common feature of network activity
during a variety of behaviors, including working memory and decision making.
Previous network models for sequences and memory emphasized specialized
architectures in which a principled mechanism is pre-wired into their
connectivity. Here we demonstrate that, starting from random connectivity and
modifying a small fraction of connections, a largely disordered recur- rent
network can produce sequences and implement working memory efficiently. We use
this process, called Partial In-Network Training (PINning), to model and match
cellular resolution imaging data from the posterior parietal cortex during a
virtual memory- guided two-alternative forced-choice task. Analysis of the
connectivity reveals that sequences propagate by the cooperation between
recurrent synaptic interactions and external inputs, rather than through
feedforward or asymmetric connections. Together our results suggest that neural
sequences may emerge through learning from largely unstructured network
architectures.
Stochastic resonance controlled upregulation of internal noise after hearing loss as a putative correlate of tinnitus-related neuronal hyperactivity
Date: 2016-03-15 00:00:00

Subjective tinnitus (ST) is generally assumed to be a consequence of hearing
loss (HL). In animal studies acoustic trauma can lead to behavioral signs of
ST, in human studies ST patients without increased hearing thresholds were
found to suffer from so called hidden HL. Additionally, ST is correlated with
pathologically increased spontaneous firing rates and neuronal hyperactivity
(NH) along the auditory pathway. Homeostatic plasticity (HP) has been proposed
as a compensation mechanism leading to the development of NH, arguing that
after HL initially decreased mean firing rates of neurons are subsequently
restored by increased spontaneous rates. However all HP models fundamentally
lack explanatory power since the function of keeping mean firing rate constant
remains elusive as does the benefit this might have in terms of information
processing. Furthermore the neural circuitry being able to perform the
comparison of preferred with actual mean firing rate remains unclear. Here we
propose an entirely new interpretation of ST related development of NH in terms
of information theory. We suggest that stochastic resonance (SR) plays a key
role in short- and long-term plasticity within the auditory system and is the
ultimate cause of NH and ST. SR has been found ubiquitous in neuroscience and
refers to the phenomenon that sub-threshold, unperceivable signals can be
transmitted by adding noise to sensor input. We argue that after HL, SR serves
to lift signals above the increased hearing threshold, hence subsequently
decreasing thresholds again. The increased amount of internal noise is the
correlate of the NH, which finally leads to the development of ST, due to
neuronal plasticity along the auditory pathway. We demonstrate the plausibility
of our hypothesis by using a computational model and provide exemplarily
findings of human and animal studies that are consistent with our model.
Population balances in case of crossing characteristic curves: Application to T-cells immune response
Date: 2016-03-19 00:00:00

The progression of a cell population where each individual is characterized
by the value of an internal variable varying with time (e.g. size, weight, and
protein concentration) is typically modeled by a Population Balance Equation, a
first order linear hyperbolic partial differential equation. The
characteristics described by internal variables usually vary monotonically with
the passage of time. A particular difficulty appears when the characteristic
curves exhibit different slopes from each other and therefore cross each other
at certain times. In particular such crossing phenomenon occurs during T-cells
immune response when the concentrations of protein expressions depend upon each
other and also when some global protein (e.g. Interleukin signals) is also
involved which is shared by all T-cells. At these crossing points, the linear
advection equation is not possible by using the classical way of hyperbolic
conservation laws. Therefore, a new Transport Method is introduced in this
article which allowed us to find the population density function for such
processes. The newly developed Transport method (TM) is shown to work in the
case of crossing and to provide a smooth solution at the crossing points in
contrast to the classical PDF techniques.
Malaria elimination campaigns in the Lake Kariba region of Zambia: a spatial dynamical model
Date: 2016-03-15 00:00:00
Tasks: Management

Background As more regions approach malaria elimination, understanding how
different interventions interact to reduce transmission becomes critical. The
Lake Kariba area of Southern Province, Zambia, is part of a multi-country
elimination effort and presents a particular challenge as it is an
interconnected region of variable transmission intensities.
  Methods In 2012-13, six rounds of mass-screen-and-treat drug campaigns were
carried out in the Lake Kariba region. A spatial dynamical model of malaria
transmission in the Lake Kariba area, with transmission and climate modeled at
the village scale, was calibrated to the 2012-13 prevalence survey data, with
case management rates, insecticide-treated net usage, and drug campaign
coverage informed by surveillance. The model was used to simulate the effect of
various interventions implemented in 2014-22 on reducing regional transmission,
achieving elimination by 2022, and maintaining elimination through 2028.
  Findings The model captured the spatio-temporal trends of decline and rebound
in malaria prevalence in 2012-13 at the village scale. Simulations predicted
that elimination required repeated mass drug administrations coupled with
simultaneous increase in net usage. Drug campaigns targeted only at high-burden
areas were as successful as campaigns covering the entire region.
  Interpretation Elimination in the Lake Kariba region is possible through
coordinating mass drug campaigns with high-coverage vector control. Targeting
regional hotspots is a viable alternative to global campaigns when human
migration within an interconnected area is responsible for maintaining
transmission in low-burden areas.