pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it)
Authors: Lioma Christina, Larsen Birger, Petersen Casper, Simonsen Jakob Grue
Date: 2016-06-27 00:00:00
Tasks: Information Retrieval, Retrieval

What if Information Retrieval (IR) systems did not just retrieve relevant
information that is stored in their indices, but could also "understand" it and
synthesise it into a single document? We present a preliminary study that makes
a first step towards answering this question. Given a query, we train a
Recurrent Neural Network (RNN) on existing relevant information to that query.
We then use the RNN to "deep learn" a single, synthetic, and we assume,
relevant document for that query. We design a crowdsourcing experiment to
assess how relevant the "deep learned" document is, compared to existing
relevant documents. Users are shown a query and four wordclouds (of three
existing relevant documents and our deep learned synthetic document). The
synthetic document is ranked on average most relevant of all.
Neural Autoregressive Collaborative Filtering for Implicit Feedback
Authors: Zheng Yin, Liu Cailiang, Tang Bangsheng, Zhou Hanning
Date: 2016-09-13 00:00:00
Tasks: Collaborative Filtering

This paper proposes implicit CF-NADE, a neural autoregressive model for
collaborative filtering tasks using implicit feedback ( e.g. click, watch,
browse behaviors). We first convert a users implicit feedback into a like
vector and a confidence vector, and then model the probability of the like
vector, weighted by the confidence vector. The training objective of implicit
CF-NADE is to maximize a weighted negative log-likelihood. We test the
performance of implicit CF-NADE on a dataset collected from a popular digital
TV streaming service. More specifically, in the experiments, we describe how to
convert watch counts into implicit relative rating, and feed into implicit
CF-NADE. Then we compare the performance of implicit CF-NADE model with the
popular implicit matrix factorization approach. Experimental results show that
implicit CF-NADE significantly outperforms the baseline.
Enhanced capital-asset pricing model for the reconstruction of bipartite financial networks
Date: 2017-09-11 00:00:00

Reconstructing patterns of interconnections from partial information is one
of the most important issues in the statistical physics of complex networks. A
paramount example is provided by financial networks. In fact, the spreading and
amplification of financial distress in capital markets is strongly affected by
the interconnections among financial institutions. Yet, while the aggregate
balance sheets of institutions are publicly disclosed, information on single
positions is mostly confidential and, as such, unavailable. Standard approaches
to reconstruct the network of financial interconnection produce unrealistically
dense topologies, leading to a biased estimation of systemic risk. Moreover,
reconstruction techniques are generally designed for monopartite networks of
bilateral exposures between financial institutions, thus failing in reproducing
bipartite networks of security holdings (\eg, investment portfolios). Here we
propose a reconstruction method based on constrained entropy maximization,
tailored for bipartite financial networks. Such a procedure enhances the
traditional {\em capital-asset pricing model} (CAPM) and allows to reproduce
the correct topology of the network. We test this ECAPM method on a dataset,
collected by the European Central Bank, of detailed security holdings of
European institutional sectors over a period of six years (2009-2015). Our
approach outperforms the traditional CAPM and the recently proposed MECAPM both
in reproducing the network topology and in estimating systemic risk due to
fire-sales spillovers. In general, ECAPM can be applied to the whole class of
weighted bipartite networks described by the fitness model.
Natural calcium carbonate for biomedical applications
Date: 2016-06-23 00:00:00

Sea shells are found to be a very rich natural resource for calcium
carbonate. Sea shells are made up of CaCO3 mainly in the aragonite form, which
are columnar or fibrous or microsphere structured crystals. The bioactivity of
nanoparticles of sea shell has been studied in this work. The sea shells
collected were thoroughly washed, dried and pulverized. The powder was sieved
and particles in the range of 45 to 63 microns were collected. The powdered sea
shells were characterized using X-Ray Diffraction and Field Emission Scanning
Electron Microscopy. The XRD data showed that the particles were mainly
microspheres. Traces of calcite and vaterite were also present. Experiments
were conducted to study the aspirin and strontium ranelate drug loading into
the sea shell powder using soak and dry method. Different concentrations of
drug solution was made in ethanol and water. The shell powder was soaked in
drug solutions and was kept soaking for 48 hrs with intermittent
ultrasonication. The mixture was gently dried in a vacuum oven. The in vitro
drug release studies were done using Phosphate Buffered Saline. The FESEM
images displayed a distribution of differently sized and shaped particles. The
sea shells due to its natural porosity and crystallinity are expected to be
useful for drug delivery. About 50% drug entrapment efficiency for aspirin and
39% for strontium ranelate was seen. A burst release of the drug (80 percent)
was observed within two hours for both the drugs studied. Rest of the drug was
released slowly in 19 hrs. Further modification of the sea shell with non toxic
polymers is also planned as a part of this work. Sea shell powder has become a
potential candidate for drug delivery due to all the aforementioned advantages.
H(O)TA: estimation of DNA methylation and hydroxylation levels and efficiencies from time course data
Date: 2016-06-26 00:00:00

Methylation and hydroxylation of cytosines to form 5-methylcytosine (5mC) and
5-droxymethylcytosine (5hmC) belong to the most important epigenetic
modifications and their vital role in the regulation of gene expression has
been widely recognized. Recent experimental techniques allow to infer
methylation and hydroxylation levels at CpG dinucleotides but require a
sophisticated statistical analysis to achieve accurate estimates.
Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation
Authors: Manotumruksa Jarana, Macdonald Craig, Ounis Iadh
Date: 2016-06-24 00:00:00
Tasks: Word Embeddings

Venue recommendation aims to assist users by making personalised suggestions
of venues to visit, building upon data available from location-based social
networks (LBSNs) such as Foursquare. A particular challenge for this task is
context-aware venue recommendation (CAVR), which additionally takes the
surrounding context of the user (e.g. the user's location and the time of day)
into account in order to provide more relevant venue suggestions. To address
the challenges of CAVR, we describe two approaches that exploit word embedding
techniques to infer the vector-space representations of venues, users' existing
preferences, and users' contextual preferences. Our evaluation upon the test
collection of the TREC 2015 Contextual Suggestion track demonstrates that we
can significantly enhance the effectiveness of a state-of-the-art venue
recommendation approach, as well as produce context-aware recommendations that
are at least as effective as the top TREC 2015 systems.
A Neural Network Approach to Efficient Valuation of Large Portfolios of Variable Annuities
Date: 2016-06-24 00:00:00
Tasks: Spatial Interpolation

Managing and hedging the risks associated with Variable Annuity (VA) products
require intraday valuation of key risk metrics for these products. The complex
structure of VA products and computational complexity of their accurate
evaluation have compelled insurance companies to adopt Monte Carlo (MC)
simulations to value their large portfolios of VA products. Because the MC
simulations are computationally demanding, especially for intraday valuations,
insurance companies need more efficient valuation techniques. Recently, a
framework based on traditional spatial interpolation techniques has been
proposed that can significantly decrease the computational complexity of MC
simulation (Gan and Lin, 2015). However, traditional interpolation techniques
require the definition of a distance function that can significantly impact
their accuracy. Moreover, none of the traditional spatial interpolation
techniques provide all of the key properties of accuracy, efficiency, and
granularity (Hejazi et al., 2015). In this paper, we present a neural network
approach for the spatial interpolation framework that affords an efficient way
to find an effective distance function. The proposed approach is accurate,
efficient, and provides an accurate granular view of the input portfolio. Our
numerical experiments illustrate the superiority of the performance of the
proposed neural network approach compared to the traditional spatial
interpolation schemes.
Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval
Authors: Roy Dwaipayan, Ganguly Debasis, Mitra Mandar, Jones Gareth J. F.
Date: 2016-06-25 00:00:00
Tasks: Document Ranking, Information Retrieval, Language Modeling, Language Modelling, Retrieval

A major difficulty in applying word vector embeddings in IR is in devising an
effective and efficient strategy for obtaining representations of compound
units of text, such as whole documents, (in comparison to the atomic words),
for the purpose of indexing and scoring documents. Instead of striving for a
suitable method for obtaining a single vector representation of a large
document of text, we rather aim for developing a similarity metric that makes
use of the similarities between the individual embedded word vectors in a
document and a query. More specifically, we represent a document and a query as
sets of word vectors, and use a standard notion of similarity measure between
these sets, computed as a function of the similarities between each constituent
word pair from these sets. We then make use of this similarity measure in
combination with standard IR based similarities for document ranking. The
results of our initial experimental investigations shows that our proposed
method improves MAP by up to $5.77\%$, in comparison to standard text-based
language model similarity, on the TREC ad-hoc dataset.
Optimal Down Regulation of mRNA Translation
Date: 2016-06-28 00:00:00
Tasks: Translation

Down regulation of mRNA translation is an important problem in various
bio-medical domains ranging from developing effective medicines for tumors and
for viral diseases to developing attenuated virus strains that can be used for
vaccination. Here, we study the problem of down regulation of mRNA translation
using a mathematical model called the ribosome flow model (RFM). In the RFM,
the mRNA molecule is modeled as a chain of $n$ sites. The flow of ribosomes
between consecutive sites is regulated by $n+1$ transition rates. Given a set
of feasible transition rates, that models the outcome of all possible
mutations, we consider the problem of maximally down regulating the translation
rate by altering the rates within this set of feasible rates. Under certain
conditions on the feasible set, we show that an optimal solution can be
determined efficiently. We also rigorously analyze two special cases of the
down regulation optimization problem. Our results suggest that one must focus
on the position along the mRNA molecule where the transition rate has the
strongest effect on the protein production rate. However, this rate is not
necessarily the slowest transition rate along the mRNA molecule. We discuss
some of the biological implications of these results.
Risk and Ambiguity in Information Seeking: Eye Gaze Patterns Reveal Contextual Behaviour in Dealing with Uncertainty
Authors: Wittek Peter, Liu Ying-Hsang, Darányi Sándor, Gedeon Tom, Lim Ik Soo
Date: 2016-06-27 00:00:00

Information foraging connects optimal foraging theory in ecology with how
humans search for information. The theory suggests that, following an
information scent, the information seeker must optimize the tradeoff between
exploration by repeated steps in the search space vs. exploitation, using the
resources encountered. We conjecture that this tradeoff characterizes how a
user deals with uncertainty and its two aspects, risk and ambiguity in economic
theory. Risk is related to the perceived quality of the actually visited patch
of information, and can be reduced by exploiting and understanding the patch to
a better extent. Ambiguity, on the other hand, is the opportunity cost of
having higher quality patches elsewhere in the search space. The aforementioned
tradeoff depends on many attributes, including traits of the user: at the two
extreme ends of the spectrum, analytic and wholistic searchers employ entirely
different strategies. The former type focuses on exploitation first,
interspersed with bouts of exploration, whereas the latter type prefers to
explore the search space first and consume later. Based on an eye-tracking
study of experts' interactions with novel search interfaces in the biomedical
domain, we demonstrate that perceived risk shifts the balance between
exploration and exploitation in either type of users, tilting it against vs. in
favour of ambiguity minimization. Since the pattern of behaviour in information
foraging is quintessentially sequential, risk and ambiguity minimization cannot
happen simultaneously, leading to a fundamental limit on how good such a
tradeoff can be. This in turn connects information seeking with the emergent
field of quantum decision theory.
On Keiding's Equation and its relation to differential equations about prevalence and incidence in chronic disease epidemiology
Date: 2016-06-27 00:00:00
Tasks: Epidemiology

We study the relation between the age-specific prevalence, incidence and
mortality in an illness-death model consisting of the three states Healthy,
Ill, Dead. The dependency on three different time scales (age, calendar time,
disease duration) is considered. It is shown that Keiding's equation published
in 1991 is a generalisation of the solution of Brunet and Struchiner's partial
differential equation from 1999. In a special case, we propose a particularly
simple estimate of the incidence from prevalence data.
Information integration from distributed threshold-based interactions
Date: 2016-06-27 00:00:00
Tasks: TAG

We consider a collection of distributed units that interact with one another
through the sending of messages. Each message carries a positive ($+1$) or
negative ($-1$) tag and causes the receiving unit to send out messages as a
function of the tags it has received and a threshold. This simple model
abstracts some of the essential characteristics of several systems used in the
field of artificial intelligence, and also of biological systems epitomized by
the brain. We study the integration of information inside a temporal window as
the model's dynamics unfolds. We quantify information integration by the total
correlation, relative to the window's duration ($w$), of a set of random
variables valued as a function of message arrival. Total correlation refers to
the rise of information gain above and beyond that which the units already
achieve individually, being therefore related to consciousness studies in some
models. We report on extensive computational experiments that explore the
interrelations of the model's parameters (two probabilities and the threshold),
highlighting relevant scenarios of message traffic and how they impact the
behavior of total correlation as a function of $w$. We find that total
correlation can occur at significant fractions of the maximum possible value
and provide semi-analytical results on the message-traffic characteristics
associated with values of $w$ for which it peaks. We then reinterpret the
model's parameters in terms of the current best estimates of some quantities
pertaining to cortical structure and dynamics. We find the resulting
possibilities for best values of $w$ to be well aligned with the time frames
within which percepts are thought to be processed and eventually rendered
conscious.
A Preliminary Mathematical Model for the Dynamic Transmission of Dengue, Chikungunya and Zika
Date: 2016-06-27 00:00:00

Aedes aegypti is a known vector of Dengue, Chikungunya and Zika and the goal
of this study is to propose the first mathematical model to describe the
dynamic transmission of these three diseases. We present two preliminary models
that consist of the SEIR model for the human populations and an SEI model for
the vector to describe (a) the single transmission dynamics of dengue,
Chikungunya or Zika, and (b) any possible coinfection between two diseases in
the same population. In order to do that, we obtain an analytical solution of
the system of 17 and 30 coupled differential equations for each model
respectively, and later obtain the eigenvalues by analyzing the Jacobian matrix
in order to begin the development of a surveillance system to prevent the
spread of these three diseases.
A parallelizable sampling method for parameter inference of large biochemical reaction models
Date: 2016-06-27 00:00:00
Tasks: Density Estimation

The development of mechanistic models of biological systems is a central part
of Systems Biology. One major task in developing these models is the inference
of the correct model parameters. Due to the size of most realistic models and
their possibly complex dynamical behaviour one must usually rely on sample
based methods. In this paper we present a novel algorithm that reliably
estimates model parameters for deterministic as well as stochastic models from
trajectory data. Our algorithm samples iteratively independent particles from
the level sets of the likelihood and recovers the posterior from these level
sets. The presented approach is easily parallelizable and, by utilizing density
estimation through Dirichlet Process Gaussian Mixture Models, can deal with
high dimensional parameter spaces. We illustrate that our algorithm is
applicable to large, realistic deterministic and stochastic models and succeeds
in inferring the correct posterior from a given number of observed
trajectories. This algorithm presents a novel, computationally feasible
approach to identify parameters of large biochemical reaction models based on
sample path data.
Ligand-receptor binding kinetics in surface plasmon resonance cells: A Monte Carlo analysis
Date: 2016-09-14 00:00:00

Surface plasmon resonance (SPR) chips are widely used to measure association
and dissociation rates for the binding kinetics between two species of
chemicals, e.g., cell receptors and ligands. It is commonly assumed that
ligands are spatially well mixed in the SPR region, and hence a mean-field rate
equation description is appropriate. This approximation however ignores the
spatial fluctuations as well as temporal correlations induced by multiple local
rebinding events, which become prominent for slow diffusion rates and high
binding affinities. We report detailed Monte Carlo simulations of ligand
binding kinetics in an SPR cell subject to laminar flow. We extract the binding
and dissociation rates by means of the techniques frequently employed in
experimental analysis that are motivated by the mean-field approximation. We
find major discrepancies in a wide parameter regime between the thus extracted
rates and the known input simulation values. These results underscore the
crucial quantitative importance of spatio-temporal correlations in binary
reaction kinetics in SPR cell geometries, and demonstrate the failure of a
mean-field analysis of SPR cells in the regime of high Damk\"ohler number Da >
0.1, where the spatio-temporal correlations due to diffusive transport and
ligand-receptor rebinding events dominate the dynamics of SPR systems.
Integrated Information and Metastability in Systems of Coupled Oscillators
Date: 2016-06-27 00:00:00

It has been shown that sets of oscillators in a modular network can exhibit a
rich variety of metastable chimera states, in which synchronisation and
desynchronisation coexist. Independently, under the guise of integrated
information theory, researchers have attempted to quantify the extent to which
a complex dynamical system presents a balance of integrated and segregated
activity. In this paper we bring these two areas of research together by
showing that the system of oscillators in question exhibits a critical peak of
integrated information that coincides with peaks in other measures such as
metastability and coalition entropy.
Among-site variability in the stochastic dynamics of East African coral reefs
Date: 2016-06-27 00:00:00
Tasks: State Space Models, Time Series, Time Series Analysis

Coral reefs are dynamic systems whose composition is highly influenced by
unpredictable biotic and abiotic factors. Understanding the spatial scale at
which long-term predictions of reef composition can be made will be crucial for
guiding conservation efforts. Using a 22-year time series of benthic
composition data from 20 reefs on the Kenyan and Tanzanian coast, we studied
the long-term behaviour of Bayesian vector autoregressive state-space models
for reef dynamics, incorporating among-site variability. We estimate that if
there were no among-site variability, the total long-term variability would be
approximately one third of its current value. Thus among-site variability
contributes more to long-term variability in reef composition than does
temporal variability. Individual sites are more predictable than previously
thought, and predictions based on current snapshots are informative about
long-term properties. Our approach allowed us to identify a subset of possible
climate refugia sites with high conservation value, where the long-term
probability of coral cover <= 0.1 was very low. Analytical results show that
this probability is most strongly influenced by among-site variability and by
interactions among benthic components within sites. These findings suggest that
conservation initiatives might be successful at the site scale as well as the
regional scale.
The right time to learn: mechanisms and optimization of spaced learning
Date: 2016-06-27 00:00:00

For many types of learning, spaced training that involves repeated long
inter-trial intervals (ITIs) leads to more robust memory formation than does
massed training that involves short or no intervals. Several cognitive theories
have been proposed to explain this superiority, but only recently has data
begun to delineate the underlying cellular and molecular mechanisms of spaced
training. We review these theories and data here. Computational models of the
implicated signaling cascades have predicted that spaced training with
irregular ITIs can enhance learning. This strategy of using models to predict
optimal spaced training protocols, combined with pharmacotherapy, suggests
novel ways to rescue impaired synaptic plasticity and learning.
Characterization of Methicillin-resistant Staphylococcus aureus Isolates from Fitness Centers in Memphis Metropolitan Area, USA
Date: 2016-06-27 00:00:00
Tasks: Diversity

Indoor skin-contact surfaces of public fitness centers may serve as
reservoirs of potential human transmission of methicillin-resistant
Staphylococcus aureus (MRSA). We found a high prevalence of multi-drug
resistant (MDR)-MRSA of CC59 lineage harboring a variety of extracellular toxin
genes from surface swab samples collected from inanimate surfaces of fitness
centers in Memphis metropolitan area, USA. Our findings underscore the role of
inanimate surfaces as potential sources of transmission of MDR-MRSA strains
with considerable genetic diversity.