pwc-archive/papers-with-abstracts huggingface dataset:
matLeap: A fast adaptive Matlab-ready tau-leaping implementation suitable for Bayesian inference
Date: 2016-08-25 00:00:00
Tasks: Bayesian Inference

Background: Species abundance distributions in chemical reaction network
models cannot usually be computed analytically. Instead, stochas- tic
simulation algorithms allow sample from the the system configuration. Although
many algorithms have been described, no fast implementation has been provided
for {\tau}-leaping which i) is Matlab-compatible, ii) adap- tively alternates
between SSA, implicit and explicit {\tau}-leaping, and iii) provides summary
statistics necessary for Bayesian inference. Results: We provide a
Matlab-compatible implementation of the adap- tive explicit-implicit
{\tau}-leaping algorithm to address the above-mentioned deficits. matLeap
provides equal or substantially faster results compared to two widely used
simulation packages while maintaining accuracy. Lastly, matLeap yields summary
statistics of the stochastic process unavailable with other methods, which are
indispensable for Bayesian inference. Conclusions: matLeap addresses
shortcomings in existing Matlab-compatible stochastic simulation software,
providing significant speedups and sum- mary statistics that are especially
useful for researchers utilizing particle- filter based methods for Bayesian
inference. Code is available for download at
https://github.com/claassengroup/matLeap. Contact:
justin.feigelman@imsb.biol.ethz.ch
Multi-behavioral Sequential Prediction with Recurrent Log-bilinear Model
Authors: Liu Qiang, Wu Shu, Wang Liang
Date: 2017-01-27 00:00:00
Tasks: Collaborative Filtering, Position, Prediction

With the rapid growth of Internet applications, sequential prediction in
collaborative filtering has become an emerging and crucial task. Given the
behavioral history of a specific user, predicting his or her next choice plays
a key role in improving various online services. Meanwhile, there are more and
more scenarios with multiple types of behaviors, while existing works mainly
study sequences with a single type of behavior. As a widely used approach,
Markov chain based models are based on a strong independence assumption. As two
classical neural network methods for modeling sequences, recurrent neural
networks cannot well model short-term contexts, and the log-bilinear model is
not suitable for long-term contexts. In this paper, we propose a Recurrent
Log-BiLinear (RLBL) model. It can model multiple types of behaviors in
historical sequences with behavior-specific transition matrices. RLBL applies a
recurrent structure for modeling long-term contexts. It models several items in
each hidden layer and employs position-specific transition matrices for
modeling short-term contexts. Moreover, considering continuous time difference
in behavioral history is a key factor for dynamic prediction, we further extend
RLBL and replace position-specific transition matrices with time-specific
transition matrices, and accordingly propose a Time-Aware Recurrent
Log-BiLinear (TA-RLBL) model. Experimental results show that the proposed RLBL
model and TA-RLBL model yield significant improvements over the competitive
compared methods on three datasets, i.e., Movielens-1M dataset, Global
Terrorism Database and Tmall dataset with different numbers of behavior types.
Modelling the effect of curvature on the collective behaviour of cells growing new tissue
Date: 2016-11-30 00:00:00

The growth of several biological tissues is known to be controlled in part by
local geometrical features, such as the curvature of the tissue interface. This
control leads to changes in tissue shape that in turn can affect the tissue's
evolution. Understanding the cellular basis of this control is highly
significant for bioscaffold tissue engineering, the evolution of bone
microarchitecture, wound healing, and tumour growth. While previous models have
proposed geometrical relationships between tissue growth and curvature, the
role of cell density and cell vigor remains poorly understood. We propose a
cell-based mathematical model of tissue growth to investigate the systematic
influence of curvature on the collective crowding or spreading of
tissue-synthesising cells induced by changes in local tissue surface area
during the motion of the interface. Depending on the strength of diffusive
damping, the model exhibits complex growth patterns such as undulating motion,
efficient smoothing of irregularities, and the generation of cusps. We compare
this model with in-vitro experiments of tissue deposition in bioscaffolds of
different geometries. By accounting for the depletion of active cells, the
model is able to capture both smoothing of initial substrate geometry and
tissue deposition slowdown as observed experimentally.
Quantile Dependence between Stock Markets and its Application in Volatility Forecasting
Date: 2016-08-25 00:00:00

This paper examines quantile dependence between international stock markets
and evaluates its use for improving volatility forecasting. First, we analyze
quantile dependence and directional predictability between the US stock market
and stock markets in the UK, Germany, France and Japan. We use the
cross-quantilogram, which is a correlation statistic of quantile hit processes.
The detailed dependence between stock markets depends on specific quantile
ranges and this dependence is generally asymmetric; the negative spillover
effect is stronger than the positive spillover effect and there exists strong
directional predictability from the US market to the UK, Germany, France and
Japan markets. Second, we consider a simple quantile-augmented volatility model
that accommodates the quantile dependence and directional predictability
between the US market and these other markets. The quantile-augmented
volatility model provides superior in-sample and out-of-sample volatility
forecasts.
Unit-linked life insurance policies: optimal hedging in partially observable market models
Date: 2016-12-24 00:00:00

In this paper we investigate the hedging problem of a unit-linked life
insurance contract via the local risk-minimization approach, when the insurer
has a restricted information on the market. In particular, we consider an
endowment insurance contract, that is a combination of a term insurance policy
and a pure endowment, whose final value depends on the trend of a stock market
where the premia the policyholder pays are invested. We assume that the stock
price process dynamics depends on an exogenous unobservable stochastic factor
that also influences the mortality rate of the policyholder. To allow for
mutual dependence between the financial and the insurance markets, we use the
progressive enlargement of filtration approach. We characterize the optimal
hedging strategy in terms of the integrand in the Galtchouk-Kunita-Watanabe
decomposition of the insurance claim with respect to the minimal martingale
measure and the available information flow. We provide an explicit formula by
means of predictable projection of the corresponding hedging strategy under
full information with respect to the natural filtration of the risky asset
price and the minimal martingale measure. Finally, we discuss applications in a
Markovian setting via filtering.
Toward a closed-loop subcutaneous delivery of L-DOPA
Date: 2016-08-24 00:00:00

L-DOPA has been the gold standard treatment for Parkinson's disease since 50
years. Being the direct biochemical precursor of dopamine, L-DOPA is
effectively converted in the brain, but two major phenomena reduce its
therapeutic action: i) competition with amino acids in the gut wall and in the
blood brain barrier and ii) its fast kinetics (absorption, distribution,
metabolism, and elimination). Continuous administration of L-DOPA, such as
jejunal pumps, have addressed the issue of fast absorption. Considering a
subcutaneous delivery of L-DOPA allows to bypass the gastrointestinal tract and
avoid competition with dietary amino acids. Remains the competition at the
blood barrier between amino acids and L-DOPA, which we address by proposing a
closed-loop controlled, continuous subcutaneous delivery pump. In the
proof-of-concept format, the delivery strategy evaluated on comprehensive model
of L-DOPA kinetics, holds the promise of improving the treatment of late-stage
Parkinson's disease patients.
A Neuro-Fuzzy Model of Time-Varying Decision Boundaries
Date: 2016-08-25 00:00:00
Tasks: Decision Making

In a recent study, we reported the results of a new decision making paradigm
in which the participants were asked to balance between their speed and
accuracy to maximize the total reward they achieve during the experiment. The
results of computational modeling provided strong evidence suggesting that the
participants used time-varying decision boundaries. Previous theoretical
studies of the optimal speed-accuracy trade-off suggested that the participants
may learn to use these time-varying boundaries to maximize their average reward
rate. The results in our experiment, however, showed that the participants used
such boundaries even at the beginning of the experiment and without any prior
experience in the task. In this paper, we hypothesize that these boundaries are
the results of using some heuristic rules to make decisions in the task. To
formulate decision making by these heuristic rules as a computational
framework, we use the fuzzy logic theory. Based on this theory, we propose a
new computational framework for decision making in evidence accumulation tasks.
In this framework, there is no explicit decision boundary. Instead, the
subject's desire to stop accumulating evidence and responding at each moment
within a trial and for a given value of the accumulated evidence, is determined
by a set of fuzzy "IF-TEHN rules". We then use the back-propagation method to
derive an algorithm for fitting the fuzzy model to each participant's data. We
then investigate how the difference in the participants' performance in the
experiment is reflected in the difference in the parameters of the fitted model
Invariant death
Date: 2016-08-25 00:00:00

In nematodes, environmental or physiological perturbations alter death's
scaling of time. In human cancer, genetic perturbations alter death's curvature
of time. Those changes in scale and curvature follow the constraining contours
of death's invariant geometry. I show that the constraints arise from a
fundamental extension to the theories of randomness, invariance and scale. A
generalized Gompertz law follows. The constraints imposed by the invariant
Gompertz geometry explain the tendency of perturbations to stretch or bend
death's scaling of time. Variability in death rate arises from a combination of
constraining universal laws and particular biological processes.
Average activity of excitatory and inhibitory neural populations
Date: 2016-08-26 00:00:00

We develop an extension of the Ott-Antonsen method that allows obtaining the
mean activity (spiking rate) of a population of excitable units. By means of
the Ott-Antonsen method, equations for the dynamics of the order parameters of
coupled excitatory and inhibitory populations of excitable units are obtained,
and their mean activities are computed. Two different excitable systems are
studied: Adler units and theta neurons. The resulting bifurcation diagrams are
compared to those obtained from studying the phenomenological Wilson-Cowan
model in some regions of the parameter space. Compatible behaviors, as well as
higher dimensional chaotic solutions, are observed. We study numerical
simulations to further validate the equations.
Stem Cell Therapy for Alzheimer's Disease
Date: 2016-08-26 00:00:00

The loss of neuronal cells in the central nervous system may happen in
numerous neurodegenerative illnesses. Alzheimer's Disease (AD) is an intricate,
irreversible, dynamic neurodegenerative sickness. It is the main source of
age-related dementia, influencing roughly 5.3 million individuals in the United
States alone. Promotion is a typical feeble ailment in individuals more than 65
years, bringing on disability described by decrease in memory, failure to learn
and do every day exercises, intellectual weakness and influences the personal
satisfaction of patients. Pathologic qualities of AD are an irregular
development of specific proteins called Beta-amyloid "plaques" and Tau
"Tangles" in the mind. Notwithstanding, current treatments against AD are just
to calm manifestations and palliative yet are not the cure and a few promising
medications competitors have fizzled in late clinical trials. There is
consequently a critical need to enhance our comprehension for pathogenesis of
this sickness, making new and creative prescient models with powerful
treatments. As of late, stem cell treatment has been appeared to have a
potential way to deal with different illnesses, including neurodegenerative
disorders. In light of the far reaching nature of AD pathology, stem cell
substitution procedures have been seen as an extraordinarily difficult and
impossible treatment approach. Stem Cell may likewise offer an effective new
way to deal with model and concentrate AD. Patient derived induced Pluripotent
Stem Cells (iPSCs), for instance, may propel our comprehension of disease
mechanism. In this review we will examine the capability of stem cells to help
in these testing tries.
Foreign Exchange Market Performance: Evidence from Bivariate Time Series Approach
Date: 2016-08-27 00:00:00
Tasks: Time Series, Time Series Analysis

There are many studies dealing with the analysis of similarity among
currencies in foreign exchange market by using network analysis approach. In
those studies, each currency is represented by a univariate time series of
exchange rate return. This is the standard practice to analyze the underlying
information in the foreign exchange market. In this paper, Escoufier's RV
coefficient is applied to measure the similarity among currencies where each of
them is represented by bivariate time series. Based on that coefficient, we
analyze the topological structure of the currencies. An example of FOREX
analysis will be presented and discussed to illustrate the advantages of RV
coefficient.
Financial Market Dynamics: Superdiffusive or not?
Date: 2017-09-14 00:00:00

The behavior of stock market returns over a period of 1-60 days has been
investigated for S&P 500 and Nasdaq within the framework of nonextensive
Tsallis statistics. Even for such long terms, the distributions of the returns
are non-Gaussian. They have fat tails indicating that the stock returns do not
follow a random walk model. In this work, a good fit to a Tsallis q-Gaussian
distribution is obtained for the distributions of all the returns using the
method of Maximum Likelihood Estimate. For all the regions of data considered,
the values of the scaling parameter q, estimated from one day returns, lie in
the range 1.4 to 1.65. The estimated inverse mean square deviations (beta) show
a power law behavior in time with exponent values between -0.91 and -1.1
indicating normal to mildly subdiffusive behavior. Quite often, the dynamics of
market return distributions is modelled by a Fokker-Plank (FP) equation either
with a linear drift and a nonlinear diffusion term or with just a nonlinear
diffusion term. Both of these cases support a q-Gaussian distribution as a
solution. The distributions obtained from current estimated parameters are
compared with the solutions of the FP equations. For negligible drift term, the
inverse mean square deviations (betaFP) from the FP model follow a power law
with exponent values between -1.25 and -1.48 indicating superdiffusion. When
the drift term is non-negligible, the corresponding betaFP do not follow a
power law and become stationary after certain characteristic times that depend
on the values of the drift parameter and q. Neither of these behaviors is
supported by the results of the empirical fit.
Causality and Correlations between BSE and NYSE indexes: A Janus Faced Relationship
Date: 2016-08-28 00:00:00
Tasks: Time Series Analysis

We study the multi-scale temporal correlations and causality connections
between the New York Stock Exchange (NYSE) and Bombay Stock Exchange (BSE)
monthly average closing price indexes for a period of 300 months, encompassing
the time period of the liberalisation of the Indian economy and its gradual
global exposure. In multi-scale analysis; clearly identifiable 1, 2 and 3 year
non-stationary periodic modulations in NYSE and BSE have been observed, with
NYSE commensurating changes in BSE at 3 years scale. Interestingly, at one year
time scale, the two exchanges are phase locked only during the turbulent times,
while at the scale of three year, in-phase nature is observed for a much longer
time frame. The two year time period, having characteristics of both one and
three year variations, acts as the transition regime. The normalised NYSE's
stock value is found to Granger cause those of BSE, with a time lag of 9
months. Surprisingly, observed Granger causality of high frequency variations
reveals BSE behaviour getting reflected in the NYSE index fluctuations, after a
smaller time lag. This Janus faced relationship, shows that smaller stock
exchanges may provide a natural setting for simulating market fluctuations of
much bigger exchanges. This possibly arises due to the fact that high frequency
fluctuations form an universal part of the financial time series, and are
expected to exhibit similar characteristics in open market economies.
Rethinking Financial Contagion
Date: 2016-08-28 00:00:00

How, and to what extent, does an interconnected financial system endogenously
amplify external shocks? This paper attempts to reconcile some apparently
different views emerged after the 2008 crisis regarding the nature and the
relevance of contagion in financial networks. We develop a common framework
encompassing several network contagion models and show that, regardless of the
shock distribution and the network topology, precise ordering relationships on
the level of aggregate systemic losses hold among models. We argue that the
extent of contagion crucially depends on the amount of information that each
model assumes to be available to market players. Under no uncertainty about the
network structure and values of external assets, the well-known Eisenberg and
Noe (2001) model applies, which delivers the lowest level of contagion. This is
due to a property of loss conservation: aggregate losses after contagion are
equal to the losses incurred by those institutions initially hit by a shock.
This property implies that many contagion analyses rule out by construction any
loss amplification, treating de facto an interconnected system as a single
aggregate entity, where losses are simply mutualised. Under higher levels of
uncertainty, as captured for instance by the DebtRank model, losses become
non-conservative and get compounded through the network. This has important
policy implications: by reducing the levels of uncertainty in times of distress
(e.g. by obtaining specific data on the network) policymakers would be able to
move towards more conservative scenarios. Empirically, we compare the magnitude
of contagion across models on a sample of the largest European banks during the
years 2006-2016. In particular, we analyse contagion effects as a function of
the size of the shock and the type of external assets shocked.
Short-Time Expansions for Call Options on Leveraged ETFs Under Exponential L\'evy models With Local Volatility
Date: 2017-06-21 00:00:00

In this article, we consider the small-time asymptotics of options on a
\emph{Leveraged Exchange-Traded Fund} (LETF) when the underlying Exchange
Traded Fund (ETF) exhibits both local volatility and jumps of either finite or
infinite activity. Our main results are closed-form expressions for the leading
order terms of off-the-money European call and put LETF option prices, near
expiration, with explicit error bounds. We show that the price of an
out-of-the-money European call on a LETF with positive (negative) leverage is
asymptotically equivalent, in short-time, to the price of an out-of-the-money
European call (put) on the underlying ETF, but with modified spot and strike
prices. Similar relationships hold for other off-the-money European options. In
particular, our results suggest a method to hedge off-the-money LETF options
near expiration using options on the underlying ETF. Finally, a second order
expansion for the corresponding implied volatility is also derived and
illustrated numerically.
Topical Generalization for Presentation of User Profiles
Authors: Olieman Alex, Kamps Jaap, Satyukov Gleb, de Valk Emil
Date: 2016-11-20 00:00:00
Tasks: Profile Generation

Fine-grained user profile generation approaches have made it increasingly
feasible to display on a profile page in which topics a user has expertise or
interest. Earlier work on topical user profiling has been directed at enhancing
search and personalization functionality, but making such profiles useful for
human consumption presents new challenges. With this work, we have taken a
first step toward a semantic layout mode for topical user profiles. We have
developed a topical generalization approach which finds coherent groups of
topics and adds labels to them, based on their association with broader topics
in the Wikipedia category graph. A nested layout mode, employing topical
generalization, is compared with a simpler flat layout mode in our user study.
The results indicate that users favor the nested structure over flat profiles,
but tend to overlook the specific topics on the lower level. We propose a third
layout mode to address this issue.
Population Genetics with Fluctuating Population Sizes
Date: 2016-08-29 00:00:00

Standard neutral population genetics theory with a strictly fixed population
size has important limitations. An alternative model that allows independently
fluctuating population sizes and reproduces the standard neutral evolution is
reviewed. We then study a situation such that the competing species are neutral
at the equilibrium population size but population size fluctuations
nevertheless favor fixation of one species over the other. In this case, a
separation of timescales emerges naturally and allows adiabatic elimination of
a fast population size variable to deduce the fluctuations-induced selection
dynamics near the equilibrium population size. The results highlight the
incompleteness of the standard population genetics with a strictly fixed
population size.
Bibliometrics and Information Retrieval: Creating Knowledge through Research Synergies
Authors: Bar-Ilan Judit, Koopman Rob, Wang Shenghui, Scharnhorst Andrea, John Marcus, Mayr Philipp, Wolfram Dietmar
Date: 2016-08-29 00:00:00
Tasks: Information Retrieval, Retrieval

This panel brings together experts in bibliometrics and information retrieval
to discuss how each of these two important areas of information science can
help to inform the research of the other. There is a growing body of literature
that capitalizes on the synergies created by combining methodological
approaches of each to solve research problems and practical issues related to
how information is created, stored, organized, retrieved and used. The session
will begin with an overview of the common threads that exist between IR and
metrics, followed by a summary of findings from the BIR workshops and examples
of research projects that combine aspects of each area to benefit IR or metrics
research areas, including search results ranking, semantic indexing and
visualization. The panel will conclude with an engaging discussion with the
audience to identify future areas of research and collaboration.