pwc-archive/papers-with-abstracts huggingface dataset:
Modelling the impact of financialization on agricultural commodity markets
Date: 2016-07-26 00:00:00

We propose a stylized model of production and exchange in which long-term
investors set their production decision over a horizon {\tau} , the "time to
produce", and are liquidity constrained, while financial investors trade over a
much shorter horizon {\delta} (<< {\tau} ) and are therefore more duly informed
on the exogenous shocks affecting the production output. The equilibrium
solution proves that: (i) long-term producers modify their production decisions
to anticipate the impact of short-term investors allocations on prices; (ii)
short-term investments return a positive expected profit commensurate to the
informational advantage. While the presence of financial investors improves the
efficiency of risk allocation in the short-term and reduces price volatility,
the model shows that the aggregate effect of commodity market financialization
results in rising the volatility of both farms' default risk and production
output.
Online shopping key features analysis in Mures county
Date: 2015-05-21 00:00:00
Tasks: Descriptive

The aim of this paper is to get an overview of the online buyer profile, and
also some key aspects in the way the online shopping is conducted. In this
project we conducted a quantitative research, consisting of a questionnaire
based survey. For data processing and interpretation we used SPSS statistical
software and Excel. For data analysis, we used the descriptive statistics
indicators, and a series of bi-varied analysis for testing some statistical
assumptions. Viewed at first with skepticism by the Internet users in Romania,
because of the many news about how dangerous the credit card payments are, the
online stores have gained much ground and trust in the recent years. Since the
study was conducted mainly in the online environment, we can not talk about the
representativeness of the sample, only about a trend observed in the studied
population. The study helps us understand the population reactions and
attitudes regarding the online shopping. The study revealed some important
issues regarding the online shopping in Mures county, issues that are described
in detail in the content of this paper.
Test Models for Statistical Inference: Two-Dimensional Reaction Systems Displaying Limit Cycle Bifurcations and Bistability
Date: 2017-05-29 00:00:00
Tasks: Time Series, Time Series Analysis

Theoretical results regarding two-dimensional ordinary-differential equations
(ODEs) with second-degree polynomial right-hand sides are summarized, with an
emphasis on limit cycles, limit cycle bifurcations and multistability. The
results are then used for construction of two reaction systems, which are at
the deterministic level described by two-dimensional third-degree kinetic ODEs.
The first system displays a homoclinic bifurcation, and a coexistence of a
stable critical point and a stable limit cycle in the phase plane. The second
system displays a multiple limit cycle bifurcation, and a coexistence of two
stable limit cycles. The deterministic solutions (obtained by solving the
kinetic ODEs) and stochastic solutions (noisy time-series generating by the
Gillespie algorithm, and the underlying probability distributions obtained by
solving the chemical master equation (CME)) of the constructed systems are
compared, and the observed differences highlighted. The constructed systems are
proposed as test problems for statistical methods, which are designed to detect
and classify properties of given noisy time-series arising from biological
applications.
A W-test collapsing method for rare variant testing with applications to exome sequencing data of hypertensive disorder
Date: 2016-07-26 00:00:00

Advancement in sequencing technology enables the study of association between
complex disorders and rare variants with low minor allele frequencies. One of
the major challenges in rare variant testing is lack of statistical power of
traditional testing methods due to extremely low variances of single nucleotide
polymorphisms. In this paper, we introduce a W-test collapsing method that
evaluates the distributional differences in cases and controls using a combined
log of odds ratio. The proposed method is compared with the Weighted-Sum
Statistic and Sequence Kernel Association Test using simulation data sets and
showed better performances and faster computing speed. In the study of real
next generation sequencing data set of hypertensive disorder, we identified
genes of interesting biological functions that are associated to metabolism
disorder and inflammation, which include the MACROD1, NLRP7, AGK, PAK6 and
APBB1. The W-test collapsing method offers a fast, effective and alternative
way for rare variants association analysis.
Beyond Movie Recommendations: Solving the Continuous Cold Start Problem in E-commerceRecommendations
Authors: Kiseleva Julia, Tuzhilin Alexander, Kamps Jaap, Mueller Melanie J. I., Bernardi Lucas, Davis Chad, Kovacek Ivan, Einarsen Mats Stafseng, Hiemstra Djoerd
Date: 2016-07-26 00:00:00
Tasks: Collaborative Filtering, CPU, Recommendation Systems

Many e-commerce websites use recommender systems or personalized rankers to
personalize search results based on their previous interactions. However, a
large fraction of users has no prior inter-actions, making it impossible to use
collaborative filtering or rely on user history for personalization. Even the
most active users mayvisit only a few times a year and may have volatile needs
or different personas, making their personal history a sparse and noisy signal
at best. This paper investigates how, when we cannot rely on the user history,
the large scale availability of other user interactions still allows us to
build meaningful profiles from the contextual data and whether such contextual
profiles are useful to customize the ranking, exemplified by data from a major
online travel agentBooking.com.Our main findings are threefold: First, we
characterize the Continuous Cold Start Problem(CoCoS) from the viewpoint of
typical e-commerce applications. Second, as explicit situational con-text is
not available in typical real world applications, implicit cues from
transaction logs used at scale can capture essential features of situational
context. Third, contextual user profiles can be created offline, resulting in a
set of smaller models compared to a single huge non-contextual model, making
contextual ranking available with negligible CPU and memory footprint. Finally
we conclude that, in an online A/B test on live users, our contextual ranker
in-creased user engagement substantially over a non-contextual base-line, with
click-through-rate (CTR) increased by 20%. This clearly demonstrates the value
of contextual user profiles in a real world application.
The Ornstein-Uhlenbeck process with migration: evolution with interactions
Date: 2016-07-27 00:00:00

The Ornstein-Uhlenbeck (OU) process plays a major role in the analysis of the
evolution of phenotypic traits along phylogenies. The standard OU process
includes drift and stabilizing selection and assumes that species evolve
independently. However, especially in plants, there is ample evidence of
hybridization and introgression during evolution. In this work we present a
statistical approach with analytical solutions that allows for the inclusion of
adaptation and migration in a common phylogenetic framework. We furthermore
present a detailed simulation study that clearly indicates the adverse effects
of ignoring migration. Similarity between species due to migration could be
misinterpreted as very strong convergent evolution without proper correction
for these additional dependencies. Our model can also be useful for studying
local adaptation among populations within the same species. Finally, we show
that our model can be interpreted in terms of ecological interactions between
species, providing a general framework for the evolution of traits between
"interacting" species or populations.
A mathematical model for growth of solid tumors and combination therapy with an application to colorectal cancer
Date: 2017-05-30 00:00:00

We present a mathematical model, based on ordinary differential equations,
for the evolution of solid tumors and their response to treatment. Specifically
the effects of a cytotoxic agent and a monoclonal antibody are included as
control term in the equations. The variables considered here are: the number of
cancerous cells sensitive to chemotherapy, the number of cancerous cells
resistant to chemotherapy, the degree of angiogenesis and the average intensity
of VEGF. The rules that govern the quantities mentioned above are based on a
geometrical argument: we approximate the tumor mass as a sphere and thus derive
basic formulae for the normoxic cells and for VEGF production. The monoclonal
antibody acts on VEGF and thus has in uence to the global degree of
angiogenesis. Numerical estimates on some of the parameters are performed in
order to match the main landmark in tumor progression and reaction to treatment
in the specific case of colorectal cancer.
Asymmetric volatility connectedness on forex markets
Date: 2016-07-27 00:00:00

We show how bad and good volatility propagate through forex markets, i.e., we
provide evidence for asymmetric volatility connectedness on forex markets.
Using high-frequency, intra-day data of the most actively traded currencies
over 2007 - 2015 we document the dominating asymmetries in spillovers that are
due to bad rather than good volatility. We also show that negative spillovers
are chiefly tied to the dragging sovereign debt crisis in Europe while positive
spillovers are correlated with the subprime crisis, different monetary policies
among key world central banks, and developments on commodities markets. It
seems that a combination of monetary and real-economy events is behind the net
positive asymmetries in volatility spillovers, while fiscal factors are linked
with net negative spillovers.
Evidence accumulation and change rate inference in dynamic environments
Date: 2017-01-11 00:00:00

In a constantly changing world, animals must account for environmental
volatility when making decisions. To appropriately discount older, irrelevant
information, they need to learn the rate at which the environment changes. We
develop an ideal observer model capable of inferring the present state of the
environment along with its rate of change. Key to this computation is an update
of the posterior probability of all possible changepoint counts. This
computation can be challenging, as the number of possibilities grows rapidly
with time. However, we show how the computations can be simplified in the
continuum limit by a moment closure approximation. The resulting
low-dimensional system can be used to infer the environmental state and change
rate with accuracy comparable to the ideal observer. The approximate
computations can be performed by a neural network model via a rate-correlation
based plasticity rule. We thus show how optimal observers accumulate evidence
in changing environments, and map this computation to reduced models which
perform inference using plausible neural mechanisms.
Cytomegalovirus Antigenic Mimicry of Human Alloreactive Peptides: A Potential Trigger for Graft versus Host Disease
Date: 2016-07-28 00:00:00

The association between human cytomegalovirus (hCMV) reactivation and the
development of graft-versus-host-disease (GVHD) has been observed in stem cell
transplantation (SCT). Seventy seven SCT donor-recipient pairs (DRP) (HLA
matched unrelated donor (MUD), n=50; matched related donor (MRD), n=27)
underwent whole exome sequencing to identify single nucleotide polymorphisms
(SNPs) generating alloreactive peptide libraries for each DRP (9-mer
peptide-HLA complexes); Human CMV CROSS (Cross-Reactive Open Source Sequence)
Database was compiled from NCBI; HLA class I binding affinity for each DRPs HLA
was calculated by NetMHCpan 2.8 and hCMV- derived 9-mers algorithmically
compared to the alloreactive peptide-HLA complex libraries. Short consecutive
(6 or greater) amino acid (AA) sequence homology matching hCMV to recipient
peptides was considered for HLA-bound-peptide (IC50<500 nM) cross reactivity.
Of the 70,686 hCMV 9-mers contained within the hCMV CROSS database, 29,658.8
+/- 9038.5 were found to match MRD DRP alloreactive peptides and 52,910.2 +/-
16121.8 matched MUD DRP peptides (Student's T-test, p<0.001). In silico
analysis revealed multiple high affinity, immunogenic CMV-Human peptide matches
(IC50<500 nM) expressed in GVHD-affected tissue-specific manner (proteins
expressed at 10 RPKM or greater). hCMV+GVHD was found in 18 patients, 13
developing hCMV viremia before GVHD onset with a subset analysis of 7 instances
of hCMV viremia prior to acute GVHD onset (n=3), chronic GVHD (n=2) and acute +
chronic GVHD (n=2) indicating cross reactive peptide expression within affected
organs. We propose that based on our analysis and preliminary clinical
correlations that hCMV immune cross-reactivity may cause antigenic mimicry of
human alloreactive peptides triggering GVHD.
Stochastic timing in gene expression for simple regulatory strategies
Date: 2017-02-23 00:00:00
Tasks: Translation

Timing is essential for many cellular processes, from cellular responses to
external stimuli to the cell cycle and circadian clocks. Many of these
processes are based on gene expression. For example, an activated gene may be
required to reach in a precise time a threshold level of expression that
triggers a specific downstream process. However, gene expression is subject to
stochastic fluctuations, naturally inducing an uncertainty in this
threshold-crossing time with potential consequences on biological functions and
phenotypes. Here, we consider such "timing fluctuations", and we ask how they
can be controlled. Our analytical estimates and simulations show that, for an
induced gene, timing variability is minimal if the threshold level of
expression is approximately half of the steady-state level. Timing fuctuations
can be reduced by increasing the transcription rate, while they are insensitive
to the translation rate. In presence of self-regulatory strategies, we show
that self-repression reduces timing noise for threshold levels that have to be
reached quickly, while selfactivation is optimal at long times. These results
lay a framework for understanding stochasticity of endogenous systems such as
the cell cycle, as well as for the design of synthetic trigger circuits.
Extracting Food Substitutes From Food Diary via Distributional Similarity
Authors: Achananuparp Palakorn, Weber Ingmar
Date: 2016-07-29 00:00:00
Tasks: Food recommendation

In this paper, we explore the problem of identifying substitute relationship
between food pairs from real-world food consumption data as the first step
towards the healthier food recommendation. Our method is inspired by the
distributional hypothesis in linguistics. Specifically, we assume that foods
that are consumed in similar contexts are more likely to be similar dietarily.
For example, a turkey sandwich can be considered a suitable substitute for a
chicken sandwich if both tend to be consumed with french fries and salad. To
evaluate our method, we constructed a real-world food consumption dataset from
MyFitnessPal's public food diary entries and obtained ground-truth human
judgements of food substitutes from a crowdsourcing service. The experiment
results suggest the effectiveness of the method in identifying suitable
substitutes.
Caveat on the Boltzmann distribution function use in biology
Date: 2017-03-30 00:00:00

Sigmoid semilogarithmic functions with shape of Boltzmann equations, have
become extremely popular to describe diverse biological situations. Part of the
popularity is due to the easy avail- ability of software which fits Boltzmann
functions to data, without much knowledge of the fitting procedure or the
statistical properties of the parameters derived from the procedure. The
purpose of this paper is to explore the plasticity of the Boltzmann function to
fit data, some aspects of the optimization procedure to fit the function to
data and how to use this plastic function to differentiate the effect of
treatment on data and to attest the statistical significance of treatment
effect on the data.
Training and spontaneous reinforcement of neuronal assemblies by spike timing
Date: 2016-07-30 00:00:00

The synaptic connectivity of cortex is plastic, with experience shaping the
ongoing interactions between neurons. Theoretical studies of spike
timing-dependent plasticity (STDP) have focused on either just pairs of neurons
or large-scale simulations where analytic insight is lacking. A simple account
for how fast spike time correlations affect both micro- and macroscopic network
structure remains lacking. We develop a low-dimensional mean field theory
showing how STDP gives rise to strongly coupled assemblies of neurons with
shared stimulus preferences, with the connectivity actively reinforced by spike
train correlations during spontaneous dynamics. Furthermore, the stimulus
coding by cell assemblies is actively maintained by these internally generated
spiking correlations, suggesting a new role for noise correlations in neural
coding. Assembly formation has often been associated with firing rate-based
plasticity schemes; our theory provides an alternative and complementary
framework, where temporal correlations and STDP form and actively maintain
learned structure in cortical networks.
Direct quantification of brown algae-derived fucoidans in human plasma by a fluorescent probe assay
Date: 2016-07-30 00:00:00

Fucoidan is a generic term for a class of fucose rich, structurally diverse
sulfated polysaccharides that are found in brown algae and other marine
organisms. Depending on the species from which the fucoidan is extracted, a
wide variety of biological activities including antitumor, antiinflammatory,
immune-modulating, antiviral, antibacterial and pro- and anticoagulant
activities has been described. Fucoidans have the advantage of low toxicity and
oral bioavailibiity and are viable drug candidates, preclinical and pilot
clinical trials show promising results. The availability of robust assays, in
particular for analysing the blood levels of fucoidan, is a fundamental
requirement for pharmacokinetic analysis in drug development projects. This
contribution describes the application of a commercially availbale,
protein-free fluorescent probe assay (Heparin Red) for the direct
quantification of several fucoidans (from Fucus vesiculosus, Macrocystis
pyrifera, and Undaria pinnatifida) in human plasma. By only minor adapation of
the established protocol for heparin detection, a concentration range 0,5 to 20
microgram per mL fucoidan can be addressed. A preliminary analysis of matrix
effects suggests acceptable interindividual variability and no interference by
endogeneous chondroitin sulfate. This study identifies the Heparin Red assay as
a simple, time-saving mix-and-read method for the quantification of fucoidans
in human plasma.
A Graph Framework for Multimodal Medical Information Processing
Authors: Drakopoulos Georgios, Megalooikonomou Vasileios
Date: 2017-02-22 00:00:00
Tasks: Graph Ranking

Multimodal medical information processing is currently the epicenter of
intense interdisciplinary research, as proper data fusion may lead to more
accurate diagnoses. Moreover, multimodality may disambiguate cases of
co-morbidity. This paper presents a framework for retrieving, analyzing, and
storing medical information as a multilayer graph, an abstract format suitable
for data fusion and further processing. At the same time, this paper addresses
the need for reliable medical information through co-author graph ranking. A
use case pertaining to frailty based on Python and Neo4j serves as an
illustration of the proposed framework.
Attention Span For Personalisation
Authors: Hurtado Joan Figuerola
Date: 2016-07-30 00:00:00
Tasks: Recommendation Systems

A click on an item is arguably the most widely used feature in recommender
systems. However, a click is one out of 174 events a browser can trigger. This
paper presents a framework to effectively collect and store data from event
streams. A set of mining methods is provided to extract user engagement
features such as: attention span, scrolling depth and visible impressions. In
this work, we present an experiment where recommendations based on attention
span drove 340% higher click-through-rate than clicks.
Self-organization in a distributed coordination game through heuristic rules
Date: 2016-07-31 00:00:00
Tasks: Attribute, Reinforcement Learning (RL)

In this paper we consider a distributed coordination game played by a large
number of agents with finite information sets, which characterizes emergence of
a single dominant attribute out of a large number of competitors. Formally, $N$
agents play a coordination game repeatedly which has exactly $N$ Nash
equilibria and all of the equilibria are equally preferred by the agents. The
problem is to select one equilibrium out of $N$ possible equilibria in the
least number of attempts. We propose a number of heuristic rules based on
reinforcement learning to solve the coordination problem. We see that the
agents self-organize into clusters with varying intensities depending on the
heuristic rule applied although all clusters but one are transitory in most
cases. Finally, we characterize a trade-off in terms of the time requirement to
achieve a degree of stability in strategies and the efficiency of such a
solution.
Application of Malliavin calculus to exact and approximate option pricing under stochastic volatility
Date: 2016-07-31 00:00:00

The article is devoted to models of financial markets with stochastic
volatility, which is defined by a functional of Ornstein-Uhlenbeck process or
Cox-Ingersoll-Ross process. We study the question of exact price of European
option. The form of the density function of the random variable, which
expresses the average of the volatility over time to maturity is established
using Malliavin calculus.The result allows calculate the price of the option
with respect to minimum martingale measure when the Wiener process driving the
evolution of asset price and the Wiener process, which defines volatility, are
uncorrelated.
Exploring Deep Space: Learning Personalized Ranking in a Semantic Space
Authors: Vuurens Jeroen B. P., Larson Martha, de Vries Arjen P.
Date: 2016-08-22 00:00:00
Tasks: Recommendation Systems

Recommender systems leverage both content and user interactions to generate
recommendations that fit users' preferences. The recent surge of interest in
deep learning presents new opportunities for exploiting these two sources of
information. To recommend items we propose to first learn a user-independent
high-dimensional semantic space in which items are positioned according to
their substitutability, and then learn a user-specific transformation function
to transform this space into a ranking according to the user's past
preferences. An advantage of the proposed architecture is that it can be used
to effectively recommend items using either content that describes the items or
user-item ratings. We show that this approach significantly outperforms
state-of-the-art recommender systems on the MovieLens 1M dataset.