pwc-archive/papers-with-abstracts huggingface dataset:
Unlocking datasets by calibrating populations of models to data density: a study in atrial electrophysiology
Date: 2017-06-21 00:00:00
Tasks: Rhythm

The understanding of complex physical or biological systems nearly always
requires a characterisation of the variability that underpins these processes.
In addition, the data used to calibrate such models may also often exhibit
considerable variability. A recent approach to deal with these issues has been
to calibrate populations of models (POMs), that is multiple copies of a single
mathematical model but with different parameter values. To date this
calibration has been limited to selecting models that produce outputs that fall
within the ranges of the dataset, ignoring any trends that might be present in
the data. We present here a novel and general methodology for calibrating POMs
to the distributions of a set of measured values in a dataset. We demonstrate
the benefits of our technique using a dataset from a cardiac atrial
electrophysiology study based on the differences in atrial action potential
readings between patients exhibiting sinus rhythm (SR) or chronic atrial
fibrillation (cAF) and the Courtemanche--Ramirez--Nattel model for human atrial
action potentials. Our approach accurately captures the variability inherent in
the experimental population, and allows us to identify the differences
underlying stratified data as well as the effects of drug block.
Enriching Existing Test Collections with OXPath
Authors: Schaer Philipp, Neumann Mandy
Date: 2017-06-21 00:00:00
Tasks: Retrieval

Extending TREC-style test collections by incorporating external resources is
a time consuming and challenging task. Making use of freely available web data
requires technical skills to work with APIs or to create a web scraping program
specifically tailored to the task at hand. We present a light-weight
alternative that employs the web data extraction language OXPath to harvest
data to be added to an existing test collection from web resources. We
demonstrate this by creating an extended version of GIRT4 called GIRT4-XT with
additional metadata fields harvested via OXPath from the social sciences portal
Sowiport. This allows the re-use of this collection for other evaluation
purposes like bibliometrics-enhanced retrieval. The demonstrated method can be
applied to a variety of similar scenarios and is not limited to extending
existing collections but can also be used to create completely new ones with
little effort.
Analysis of Bacteriostatic Effect of Chinese Herbal Medicine Against E.coli
Date: 2017-06-15 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

To analyze the bacteriostatic effect of Chinese traditional herbal medicines
on E. coli, total 35 different preparations (decoction, volatile oil and
distillate) of Chinese traditional herbal medicines were tested using plate
culture method. The results showed that 18 preparations of traditional Chinese
herbal medicines have different inhibition effect on E. coli in vitro. The
results also revealed that different process and combination affect the
bacteriostatic effect and different medicines could be used in singles or
combined to treat E.coli disease
Hippocampal Spike-Timing Correlations Lead to Hexagonal Grid Fields
Date: 2017-06-21 00:00:00

Space is represented in the mammalian brain by the activity of hippocampal
place cells as well as in their spike-timing correlations. Here we propose a
theory how this temporal code is transformed to spatial firing rate patterns
via spike-timing-dependent synaptic plasticity. The resulting dynamics of
synaptic weights resembles well-known pattern formation models in which a
lateral inhibition mechanism gives rise to a Turing instability. We identify
parameter regimes in which hexagonal firing patterns develop as they have been
found in medial entorhinal cortex.
Statistical abstraction for multi-scale spatio-temporal systems
Date: 2017-06-22 00:00:00
Tasks: Gaussian Processes, regression

Spatio-temporal systems exhibiting multi-scale behaviour are common in
applications ranging from cyber-physical systems to systems biology, yet they
present formidable challenges for computational modelling and analysis. Here we
consider a prototypic scenario where spatially distributed agents decide their
movement based on external inputs and a fast-equilibrating internal
computation. We propose a generally applicable strategy based on statistically
abstracting the internal system using Gaussian Processes, a powerful class of
non-parametric regression techniques from Bayesian Machine Learning. We show on
a running example of bacterial chemotaxis that this approach leads to accurate
and much faster simulations in a variety of scenarios.
Stop-loss and Leverage in optimal Statistical Arbitrage with an application to Energy market
Date: 2017-06-21 00:00:00

In this paper we develop a statistical arbitrage trading strategy with two
key elements in hi-frequency trading: stop-loss and leverage. We consider, as
in Bertram (2009), a mean-reverting process for the security price with
proportional transaction costs; we show how to introduce stop-loss and leverage
in an optimal trading strategy.
  We focus on repeated strategies using a self-financing portfolio. For every
given stop-loss level we derive analytically the optimal investment strategy
consisting of optimal leverage and market entry/exit levels.
  First we show that the optimal strategy a' la Bertram depends on the
probabilities to reach entry/exit levels, on expected First-Passage-Times and
on expected First-Exit-Times from an interval. Then, when the underlying
log-price follows an Ornstein-Uhlenbeck process, we deduce analytical
expressions for expected First-Exit-Times and we derive the long-run return of
the strategy as an elementary function of the stop-loss.
  Following industry practice of pairs trading we consider an example of pair
in the energy futures' market, reporting in detail the analysis for a spread on
Heating-Oil and Gas-Oil futures in one year sample of half-an-hour market
prices.
Time as it could Be measured in Artificial Living Systems
Date: 2017-06-20 00:00:00

Being able to measure time, whether directly or indirectly, is a significant
advantage for an organism. It permits it to predict regular events, and prepare
for them on time. Thus, clocks are ubiquitous in biology. In the present paper,
we consider the most minimal abstract pure clocks and investigate their
characteristics with respect to their ability to measure time. Amongst other,
we find fundamentally diametral clock characteristics, such as oscillatory
behaviour for local time measurement or decay-based clocks measuring time
periods in scales global to the problem. We include also cascades of
independent clocks ("clock bags") and composite clocks with controlled
dependency; the latter show various regimes of markedly different dynamics.
Living Labs - An Ethical Challenge for Researchers and Platform Providers
Authors: Schaer Philipp
Date: 2017-06-22 00:00:00
Tasks: Experimental Design, Information Retrieval, Retrieval

The infamous Facebook emotion contagion experiment is one of the most
prominent and best-known online experiments based on the concept of what we
here call "living labs". In these kinds of experiments, real-world applications
such as social web platforms trigger experimental switches inside their system
to present experimental changes to their users - most of the time without the
users being aware of their role as virtual guinea pigs. In the Facebook example
the researches changed the way users' personal timeline was compiled to test
the influence on the users' moods and feelings. The reactions to these
experiments showed the inherent ethical issues such living labs settings bring
up, mainly the study's lack of informed consent procedures, as well as a more
general critique of the flaws in the experimental design.
  In this chapter, we describe additional use cases: The so-called living labs
that focus on experimentation with information systems such as search engines
and wikis and especially on their real-world usage. The living labs paradigm
allows researchers to conduct research in real-world environments or systems.
In the field of information science and especially information retrieval -
which is the scientific discipline that is concerned with the research of
search engines, information systems, and search related algorithms and
techniques - it is still common practice to perform in vitro or offline
evaluations using static test collections. Living labs are widely unknown or
unavailable to academic researchers in these fields. A main benefit of living
labs is their potential to offer new ways and possibilities to experiment with
information systems and especially their users, but on the other hand they
introduce a whole set of ethical issues that we would like to address in this
chapter.
Feeding the human brain model
Date: 2017-06-22 00:00:00
Tasks: model

The goal of the Human Brain Project is to develop during the next decade an
infrastructure necessary for running a simulation of the entire human brain
constrained by current experimental data. One of the key issues is therefore to
integrate and make accessible the experimental data necessary to constrain and
fully specify this model. The required data covers many different spatial
scales, ranging from the molecular scale to the whole brain and these data are
obtained using a variety of techniques whose measurements may not be directly
comparable. Furthermore, these data are incomplete, and will remain so at least
for the coming decade. Here we review new neuroinformatics techniques that need
to be developed and applied to address these issues.
General Compound Hawkes Processes in Limit Order Books
Date: 2017-06-27 00:00:00

In this paper, we study various new Hawkes processes, namely, so-called
general compound and regime-switching general compound Hawkes processes to
model the price processes in the limit order books. We prove Law of Large
Numbers (LLN) and Functional Central Limit Theorems (FCLT) for these processes.
The latter two FCLTs are applied to limit order books where we use these
asymptotic methods to study the link between price volatility and order flow in
our two models by studying the diffusion limits of these price processes. The
volatilities of price changes are expressed in terms of parameters describing
the arrival rates and price changes.
Specializing Joint Representations for the task of Product Recommendation
Authors: Nedelec Thomas, Smirnova Elena, Vasile Flavian
Date: 2017-07-18 00:00:00
Tasks: Collaborative Filtering, Product Recommendation, Retrieval

We propose a unified product embedded representation that is optimized for
the task of retrieval-based product recommendation. To this end, we introduce a
new way to fuse modality-specific product embeddings into a joint product
embedding, in order to leverage both product content information, such as
textual descriptions and images, and product collaborative filtering signal. By
introducing the fusion step at the very end of our architecture, we are able to
train each modality separately, allowing us to keep a modular architecture that
is preferable in real-world recommendation deployments. We analyze our
performance on normal and hard recommendation setups such as cold-start and
cross-category recommendations and achieve good performance on a large product
shopping dataset.
Positional information readout in $Ca^{2+}$ signaling
Authors: Vaibhav H. Wasnik, Peter Lipp, Karsten Kruse
Date: 2017-06-23 00:00:00

Living cells respond to spatial signals. Signal transmission to the cell interior often involves the release of second messengers like $Ca^{2+}$ . They will eventually trigger a physiological response by activating kinases that in turn activate target proteins through phosphorylation. Here, we investigate theoretically how positional information can be accurately read out by protein phosphorylation in spite of rapid second messenger diffusion. We find that accuracy is increased by binding of the kinases to the cell membrane prior to phosphorylation and by increasing the rate of $Ca^{2+}$ loss from the cell interior. These findings could explain some salient features of conventional protein kinases C.
Non-Local Macroeconomic Transactions and Credits-Loans Surface-Like Waves
Date: 2017-05-29 00:00:00

This paper describes surface-like waves of macroeconomic Credits-Loans
transactions on economic space. We use agent's risk ratings as their
coordinates and describe evolution of macro variables by transactions between
agents. Aggregations of agent's variables with risk coordinates x on economic
space define macro variables as function of x. Aggregations of transactions
between agents at point x and y determine functions of two variables (x,y) on
economic space. As example we study Credits transactions provided from agents
at point x to agents at point y and thus amount of Loans received by agents at
point y from agents at point x at moment t during time term dt. We model
evolution of macro transactions by hydrodynamic-like equations. Agents fill
macro domain on economic space that is bounded by minimum risk ratings of most
secure and maximum risk ratings of most risky agents. Economic and financial
shocks can disturb steady borders of macro domain and cause perturbations of
transactions. Such disturbances can generate waves that can propagate along
risk borders alike to surface waves in fluids. As example, we describe simple
model interactions between two transactions by hydrodynamic like equations in a
closed form. We introduce notions of "macro accelerations" and their potentials
that establish steady state distributions of transactions on economic space.
For this model in linear approximation we describe surface-like waves and show
that perturbations induced by surface-like waves can exponentially grow up
inside macro domain and induce macro instabilities in a low risk area.
Description of possible steady state distributions of transactions and
surface-like waves on economic space might be important for macro modeling and
policy-making.
The effect of the behavior of an average consumer on the public debt dynamics
Date: 2017-04-23 00:00:00

An important issue within the present economic crisis is understanding the
dynamics of the public debt of a given country, and how the behavior of average
consumers and tax payers in that country affects it. Starting from a model of
the average consumer behavior introduced earlier by the authors, we propose a
simple model to quantitatively address this issue. The model is then studied
and analytically solved under some reasonable simplifying assumptions. In this
way we obtain a condition under which the public debt steadily decreases.
Intergenerational mobility measures in a bivariate normal model
Date: 2017-06-23 00:00:00
Tasks: model

We model the joint log-income distribution of parents and children and derive
analytic expressions for canonical relative and absolute intergenerational
mobility measures. We find that both types of mobility measures can be
expressed as a function of the other.
An Investigation of the Structural Characteristics of the Indian IT Sector and the Capital Goods Sector: An Application of the R Programming in Time Series Decomposition and Forecasting
Date: 2017-05-14 00:00:00
Tasks: Time Series, Time Series Analysis

Time series analysis and forecasting of stock market prices has been a very
active area of research over the last two decades. Availability of extremely
fast and parallel architecture of computing and sophisticated algorithms has
made it possible to extract, store, process and analyze high volume stock
market time series data very efficiently. In this paper, we have used time
series data of the two sectors of the Indian economy: Information Technology
and Capital Goods for the period January 2009 till April 2016 and have studied
the relationships of these two time series with the time series of DJIA index,
NIFTY index and the US Dollar to Indian Rupee exchange rate. We establish by
graphical and statistical tests that while the IT sector of India has a strong
association with DJIA index and the Dollar to Rupee exchange rate, the Indian
CG sector exhibits a strong association with the NIFTY index. We contend that
these observations corroborate our hypotheses that the Indian IT sector is
strongly coupled with the world economy whereas the CG sector of India reflects
internal economic growth of India. We also present several models of regression
between the time series which exhibit strong association among them. The
effectiveness of these models have been demonstrated by very low values of
their forecasting errors.
A Vision for Health Informatics: Introducing the SKED Framework.An Extensible Architecture for Scientific Knowledge Extraction from Data
Date: 2017-06-24 00:00:00
Tasks: Data Integration

The goals of the Triple Aim of health care and the goals of P4 medicine
outline objectives that require a significant health informatics component.
However, the goals do not provide specifications about how all of the new
individual patient data will be combined in meaningful ways and with data from
other sources, like epidemiological data, to promote the health of individuals
and society. We seem to have more data than ever before but few resources and
means to use it efficiently. We need a general, extensible solution that
integrates and homogenizes data of disparate origin, incompatible formats, and
multiple spatial and temporal scales. To address this problem, we introduce the
Scientific Knowledge Extraction from Data (SKED) architecture, as a
technology-agnostic framework to minimize the overhead of data integration,
permit reuse of analytical pipelines, and guarantee reproducible quantitative
results. The SKED architecture consists of a Resource Allocation Service to
locate resources, and the definition of data primitives to simplify and
harmonize data. SKED allows automated knowledge discovery and provides a
platform for the realization of the major goals of modern health care.
Sparsity Enables Estimation of both Subcortical and Cortical Activity from MEG and EEG
Date: 2017-06-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Subcortical structures play a critical role in brain function. However,
options for assessing electrophysiological activity in these structures are
limited. Electromagnetic fields generated by neuronal activity in subcortical
structures can be recorded non-invasively using magnetoencephalography (MEG)
and electroencephalography (EEG). However, these subcortical signals are much
weaker than those due to cortical activity. In addition, we show here that it
is difficult to resolve subcortical sources, because distributed cortical
activity can explain the MEG and EEG patterns due to deep sources. We then
demonstrate that if the cortical activity can be assumed to be spatially
sparse, both cortical and subcortical sources can be resolved with M/EEG.
Building on this insight, we develop a novel hierarchical sparse inverse
solution for M/EEG. We assess the performance of this algorithm on realistic
simulations and auditory evoked response data and show that thalamic and
brainstem sources can be correctly estimated in the presence of cortical
activity. Our analysis and method suggest new opportunities and offer practical
tools for characterizing electrophysiological activity in the subcortical
structures of the human brain.
Introducing Data Primitives: Data Formats for the SKED Framework
Date: 2017-06-25 00:00:00
Tasks: Time Series, Time Series Analysis

Background: The past few years have seen a tremendous increase in the size
and complexity of datasets. Scientific and clinical studies must to incorporate
datasets that cross multiple spatial and temporal scales to describe a
particular phenomenon. The storage and accessibility of these heterogeneous
datasets in a way that is useful to researchers and yet extensible to new data
types is a major challenge.
  Methods: In order to overcome these obstacles, we propose the use of data
primitives as a common currency between analytical methods. The four data
primitives we have identified are time series, text, annotated graph and
triangulated mesh, with associated metadata. Using only data primitives to
store data and as algorithm input, output, and intermediate results, promotes
interoperability, scalability, and reproducibility in scientific studies.
  Results: Data primitives were used in a multi-omic, multi-scale systems
biology study of malaria infection in non-human primates to perform many types
of integrative analysis quickly and efficiently.
  Conclusions: Using data primitives as a common currency for both data storage
and for cross talk between analytical methods enables the analysis of complex
multi-omic, multi-scale datasets in a reproducible modular fashion.
A preference elicitation interface for collecting dense recommender datasets with rich user information
Authors: Analytis Pantelis P., Schnabel Tobias, Herzog Stefan, Barkoczi Daniel, Joachims Thorsten
Date: 2017-06-27 00:00:00
Tasks: counterfactual, Diversity, Recommendation Systems

We present an interface that can be leveraged to quickly and effortlessly
elicit people's preferences for visual stimuli, such as photographs, visual art
and screensavers, along with rich side-information about its users. We plan to
employ the new interface to collect dense recommender datasets that will
complement existing sparse industry-scale datasets. The new interface and the
collected datasets are intended to foster integration of research in
recommender systems with research in social and behavioral sciences. For
instance, we will use the datasets to assess the diversity of human preferences
in different domains of visual experience. Further, using the datasets we will
be able to measure crucial psychological effects, such as preference
consistency, scale acuity and anchoring biases. Last, we the datasets will
facilitate evaluation in counterfactual learning experiments.
Parsing spatiotemporal dynamical stability in ECoG during seizure onset, propagation, and termination
Date: 2017-06-26 00:00:00

Understanding brain dynamics in epilepsy is critical for establishing
rigorous control objectives that enable new therapeutic methods to mitigate
seizure occurrence. In multichannel electrocorticography (ECoG) recordings
acquired in 21 subjects during a total of 94 seizures, we apply dynamical
systems stability analysis to assess the balance versus imbalance of seizure
dynamics across different timescales and brain regions. Specifically, we
consider a sliding time window multivariate autoregressive linear approximation
of the data captured by the ECoG channels, where eigendecomposition of the
estimated matrix of coefficients describes the contribution of different
regions to the spatiotemporal process (eigenvectors) associated with a
particular timescale (eigenvalues). Interestingly, we observe a pattern of
eigenvalue evolution and slowly changing (or approximately time-invariant)
eigenvectors across both seizures and subjects. The seizure-onset is marked by
an increase in high frequency spatial information to which a few regions
contribute for a long period. By contrast, the seizure termination is
characterized by a sudden, small time period change in dynamics to which many
regions contribute. As the seizure terminates, the relatively stable ictal
dynamics rapidly transition into the post-ictal regime, marked by relatively
fast-damping oscillations. Our methodology offers a subject-specific
characterization of the spatiotemporal behavior of the seizure, providing new
insights into the dynamic patterns and functional interactions between brain
regions that occur over different timescales. More generally, our approach
informs the development of engineering objectives that can be used to deploy
new control strategies to prevent seizure evolution or to hasten seizure
termination.