pwc-archive/papers-with-abstracts huggingface dataset:
Networks of plants: how to measure similarity in vegetable species
Date: 2016-02-18 00:00:00
Tasks: Classification

Despite the common misconception of nearly static organisms, plants do
interact continuously with the environment and with each other. It is fair to
assume that during their evolution they developed particular features to
overcome problems and to exploit possibilities from environment. In this paper
we introduce various quantitative measures based on recent advancements in
complex network theory that allow to measure the effective similarities of
various species. By using this approach on the similarity in fruit-typology
ecological traits we obtain a clear plant classification in a way similar to
traditional taxonomic classification. This result is not trivial, since a
similar analysis done on the basis of diaspore morphological properties do not
provide any clear parameter to classify plants species. Complex network theory
can then be used in order to determine which feature amongst many can be used
to distinguish scope and possibly evolution of plants. Future uses of this
approach range from functional classification to quantitative determination of
plant communities in nature.
Looking for central tendencies in the conformational freedom of proteins using NMR measurements
Date: 2016-02-18 00:00:00

We study the conformational freedom of a protein made by two rigid domains
connected by a flexible linker. The conformational freedom is represented as an
unknown probability distribution on the space of allowed states. A new
algorithm for the calculation of the Maximum Allowable Probability is proposed,
which can be extended to any type of measurements. In this paper we use Pseudo
Contact Shifts and Residual Dipolar Coupling. We reconstruct a single central
tendency in the distribution and discuss in depth the results.
Funding, repo and credit inclusive valuation as modified option pricing
Date: 2017-06-11 00:00:00

We take the holistic approach of computing an OTC claim value that
incorporates credit and funding liquidity risks and their interplays, instead
of forcing individual price adjustments: CVA, DVA, FVA, KVA. The resulting
nonlinear mathematical problem features semilinear PDEs and FBSDEs. We show
that for the benchmark vulnerable claim there is an analytical solution, and we
express it in terms of the Black-Scholes formula with dividends. This allows
for a detailed valuation analysis, stress testing and risk analysis via
sensitivities.
The stability of 3 transmembrane and 4 transmembrane human vitamin K epoxide reductase models
Date: 2016-02-19 00:00:00

3 transmembrane and 4 transmembrane helices models are suggested for the
human vitamin K epoxide reductase (VKOR). In this study, we investigate the
stability of the human 3 transmembrane/4 transmembrane VKOR models employing a
coarse-grained normal mode analysis and molecular dynamics simulation. Based on
the analysis of the mobility of each transmembrane domain, we suggest that the
3 transmembrane human VKOR model is more stable than the 4 transmembrane human
VKOR model.
Mechanism of Threshold Elongation of DNA Macromolecule
Date: 2016-02-19 00:00:00

The mechanism of threshold elongation (overstretching) of DNA macromolecules
under the action of external force is studied within the framework of
phenomenological approach. When considering the task it is taken into account
that double-stranded DNA is a polymorphic macromolecule with a set of
metastable states. Accordingly to proposed mechanism, DNA threshold elongation
can take place as a cooperative structural transition of macromolecule to
metastable form, which is stabilized by the external force. For the description
of DNA overstretching, the model included external (stretching) and internal
(conformation) displacement components is constructed. As assumed, both
components are coupled on the pathway of double helix transformations. It is
shown, that under force action DNA deformation proceeds in two stages. First
one is the restructuring of the double helix, which leads to formation of
conformational bistability in DNA macromolecule. On the second stage, the
conformational transition and the deformation induced by it consistently cover
the macromolecule as a threshold process. The comparison of calculated
characteristics of DNA overstretching process with experimental data shows good
agreement. Analysis of the results obtained, together with the available
literature data allow to conclude, that overstretching transition in the double
helix is dynamical process and can spread in DNA chain. In the same time, in
DNA with A$\cdot$T-rich content due to large dissipation the overstretching
process leads to force induced melting transition, and should have nearly
static character.
Noise Fit, Estimation Error and a Sharpe Information Criterion
Date: 2019-12-11 00:00:00
Tasks: Model Selection

When the in-sample Sharpe ratio is obtained by optimizing over a
k-dimensional parameter space, it is a biased estimator for what can be
expected on unseen data (out-of-sample). We derive (1) an unbiased estimator
adjusting for both sources of bias: noise fit and estimation error. We then
show (2) how to use the adjusted Sharpe ratio as model selection criterion
analogously to the Akaike Information Criterion (AIC). Selecting a model with
the highest adjusted Sharpe ratio selects the model with the highest estimated
out-of-sample Sharpe ratio in the same way as selection by AIC does for the
log-likelihood as measure of fit.
Blunt Honesty, Incentives, and Knowledge Exchange
Date: 2016-02-14 00:00:00

We propose a simple mechanism to facilitate the buying and selling of useful,
bluntly honest information. The for-profit, arm's length knowledge exchange
this mechanism enables may dramatically increase the pace of scientific
progress.
Household Income Distribution in the USA
Date: 2016-02-19 00:00:00

In this article we present an alternative model for the distribution of
household incomes in the United States. We provide arguments from two differing
perspectives which both yield the proposed income distribution curve, and then
fit this curve to empirical data on household income distribution obtained from
the United States Census Bureau.
Solar energy production: Short-term forecasting and risk management
Date: 2016-02-19 00:00:00
Tasks: Management, Time Series, Time Series Analysis

Electricity production via solar energy is tackled via short-term forecasts
and risk management. Our main tool is a new setting on time series. It allows
the definition of "confidence bands" where the Gaussian assumption, which is
not satisfied by our concrete data, may be abandoned. Those bands are quite
convenient and easily implementable. Numerous computer simulations are
presented.
Statistical modeling of isoform splicing dynamics from RNA-seq time series data
Date: 2016-02-19 00:00:00
Tasks: Experimental Design, Time Series, Time Series Analysis

Isoform quantification is an important goal of RNA-seq experiments, yet it
remains prob- lematic for genes with low expression or several isoforms. These
difficulties may in principle be ameliorated by exploiting correlated
experimental designs, such as time series or dosage response experiments. Time
series RNA-seq experiments, in particular, are becoming in- creasingly popular,
yet there are no methods that explicitly leverage the experimental design to
improve isoform quantification. Here we present DICEseq, the first isoform
quantification method tailored to correlated RNA-seq experiments. DICEseq
explicitly models the corre- lations between different RNA-seq experiments to
aid the quantification of isoforms across experiments. Numerical experiments on
simulated data sets show that DICEseq yields more accurate results than
state-of-the-art methods, an advantage that can become considerable at low
coverage levels. On real data sets, our results show that DICEseq provides
substan- tially more reproducible and robust quantifications, increasing the
correlation of estimates from replicate data sets by up to 10% on genes with
low or moderate expression levels (bot- tom third of all genes). Furthermore,
DICEseq permits to quantify the trade-off between temporal sampling of RNA and
depth of sequencing, frequently an important choice when planning experiments.
Our results have strong implications for the design of RNA-seq ex- periments,
and offer a novel tool for improved analysis of such data sets. Python code is
freely available at http://diceseq.sf.net.
A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health
Date: 2017-05-26 00:00:00
Tasks: BIG-bench Machine Learning

Drought threatens food and water security around the world, and this threat
is likely to become more severe under climate change. High resolution
predictive information can help farmers, water managers, and others to manage
the effects of drought. We have created an open source tool to produce
short-term forecasts of vegetation health at high spatial resolution, using
data that are global in coverage. The tool automates downloading and processing
Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, and training
gradient-boosted machine models on hundreds of millions of observations to
predict future values of the Enhanced Vegetation Index. We compared the
predictive power of different sets of variables (raw spectral MODIS data and
Level-3 MODIS products) in two regions with distinct agro-ecological systems,
climates, and cloud coverage: Sri Lanka and California. Our tool provides
considerably greater predictive power on held-out datasets than simpler
baseline models.
Control of epidemics via social partnership adjustment
Date: 2016-12-24 00:00:00

Epidemic control is of great importance for human society. Adjusting
interacting partners is an effective individualized control strategy.
Intuitively, it is done either by shortening the interaction time between
susceptible and infected individuals or by increasing the opportunities for
contact between susceptible individuals. Here, we provide a comparative study
on these two control strategies by establishing an epidemic model with
non-uniform stochastic interactions. It seems that the two strategies should be
similar, since shortening the interaction time between susceptible and infected
individuals somehow increases the chances for contact between susceptible
individuals. However, analytical results indicate that the effectiveness of the
former strategy sensitively depends on the infectious intensity and the
combinations of different interaction rates, whereas the latter one is quite
robust and efficient. Simulations are shown in comparison with our analytical
predictions. Our work may shed light on the strategic choice of disease
control.
Web Item Reviewing Made Easy By Leveraging Available User Feedback
Authors: Nazi Azade, Das Mahashweta, Das Gautam
Date: 2016-02-20 00:00:00

The widespread use of online review sites over the past decade has motivated
businesses of all types to possess an expansive arsenal of user feedback to
mark their reputation. Though a significant proportion of purchasing decisions
are driven by average rating, detailed reviews are critical for activities like
buying expensive digital SLR camera. Since writing a detailed review for an
item is usually time-consuming, the number of reviews available in the Web is
far from many. Given a user and an item our goal is to identify the top-$k$
meaningful phrases/tags to help her review the item easily. We propose
general-constrained optimization framework based on three measures - relevance
(how well the result set of tags describes an item), coverage (how well the
result set of tags covers the different aspects of an item), and polarity (how
well sentiment is attached to the result set of tags). By adopting different
definitions of coverage, we identify two concrete problem instances that enable
a wide range of real-world scenarios. We develop practical algorithms with
theoretical bounds to solve these problems efficiently. We conduct experiments
on synthetic and real data crawled from the web to validate the effectiveness
of our solutions.
A model-based approach to assess the effectiveness of pest biocontrol by natural enemies
Date: 2016-02-21 00:00:00
Tasks: Management

Main goal: The aim of this note is to propose a modeling approach for
assessing the effectiveness of pest biocontrol by natural enemies in
diversified agricultural landscapes including several pesticide-based
management strategies. Our approach combines a stochastic landscape model with
a spatially-explicit model of population dynamics. It enables us to analyze the
effect of the landscape composition (proportion of semi-natural habitat,
non-treated crops, slightly treated crops and conventionally treated crops) on
the effectiveness of pest biocontrol. Effectiveness is measured through
environmental and agronomical descriptors, measuring respectively the impact of
the pesticides on the environment and the average agronomic productivity of the
whole landscape taking into account losses caused by pests.
  Conclusions: The effectiveness of the pesticide, the intensity of the
treatment and the pest intrinsic growth rate are found to be the main drivers
of landscape productivity. The loss in productivity due to a reduced use of
pesticide can be partly compensated by biocontrol. However, the model suggests
that it is not possible to maintain a constant level of productivity while
reducing the use of pesticides, even with highly efficient natural enemies.
Fragmentation of the semi-natural habitats and increased crop rotation tend to
slightly enhance the effectiveness of biocontrol but have a marginal effect
compared to the predation rate by natural enemies.
  This note was written in the framework of the ANR project PEERLESS
"Predictive Ecological Engineering for Landscape Ecosystem Services and
Sustainability"(ANR-12-AGRO-0006).
Credit risk and companies' inter-organizational networks: Assessing impact of suppliers and buyers on CDS spreads
Date: 2016-02-21 00:00:00

Companies do not operate in a vacuum. As companies move towards an
increasingly specialized production function and their reach is becoming truly
global, their aptitude in managing and shaping their inter-organizational
network is a determining factor in measuring their health. Current models of
company financial health often lack variables explaining the
inter-organizational network, and as such, assume that (1) all networks are the
same and (2) the performance of partners do not impact companies. This paper
aims to be a first step in the direction of removing these assumptions.
Specifically, the impact is illustrated by examining the effects of customer
and supplier concentrations and partners' credit risk on credit-default swap
(CDS) spreads while controlling for credit risk and size. We rely upon
supply-chain data from Bloomberg that provides insight into companies'
relationships. The empirical results show that a well diversified customer
network lowers CDS spread, while having stable partners with low default
probabilities increase spreads. The latter result suggests that successful
companies do not focus on building a stable eco-system around themselves, but
instead focus on their own profit maximization at the cost of the financial
health of their suppliers' and customers'. At a more general level, the results
indicate the importance of considering the inter-organizational networks, and
highlight the value of including network variables in credit risk models.
Systematic Target Function Annotation of Human Transcription Factors
Date: 2016-02-21 00:00:00
Tasks: Experimental Design, Specificity

Transcription factors (TFs), the key players in transcriptional regulation,
have attracted great experimental attention, yet the functions of most human
TFs remain poorly understood. Recent capabilities in genome-wide protein
binding profiling have stimulated systematic studies of the hierarchical
organization of human gene regulatory network and DNA-binding specificity of
TFs, shedding light on combinatorial gene regulation. We show here that these
data also enable a systematic annotation of the biological functions and
functional diversity of TFs. We compiled a human gene regulatory network for
384 TFs covering the 146,096 TF-target gene relationships, extracted from over
850 ChIP-seq experiments as well as the literature. By integrating this network
of TF-TF and TF-target gene relationships with 3,715 functional concepts from
six sources of gene function annotations, we obtained over 9,000 confident
functional annotations for 279 TFs. We observe extensive connectivity between
transcription factors and Mendelian diseases, GWAS phenotypes, and
pharmacogenetic pathways. Further, we show that transcription factors link
apparently unrelated functions, even when the two functions do not share common
genes. Finally, we analyze the pleiotropic functions of TFs and suggest that
increased number of upstream regulators contributes to the functional
pleiotropy of TFs. Our computational approach is complementary to focused
experimental studies on TF functions, and the resulting knowledge can guide
experimental design for discovering the unknown roles of TFs in human disease
and drug response.
Clustering of neural codewords revealed by a first-order phase transition
Date: 2016-02-22 00:00:00
Tasks: Clustering

A network of neurons in the central nervous system collectively represents
information by its spiking activity states. Typically observed states, i.e.,
codewords, occupy only a limited portion of the state space due to constraints
imposed by network interactions. Geometrical organization of codewords in the
state space, critical for neural information processing, is poorly understood
due to its high dimensionality. Here, we explore the organization of neural
codewords using retinal data by computing the entropy of codewords as a
function of Hamming distance from a particular reference codeword.
Specifically, we report that the retinal codewords in the state space are
divided into multiple distinct clusters separated by entropy-gaps, and that
this structure is shared with well-known associative memory networks in a
recallable phase. Our analysis also elucidates a special nature of the
all-silent state. The all-silent state is surrounded by the densest cluster of
codewords and located within a reachable distance from most codewords. This
codeword-space structure quantitatively predicts typical deviation of a
state-trajectory from its initial state. Altogether, our findings reveal a
non-trivial heterogeneous structure of the codeword-space that shapes
information representation in a biological network.
Non-concave optimal investment and no-arbitrage: a measure theoretical approach
Date: 2016-08-26 00:00:00

We consider non-concave and non-smooth random utility functions with do- main
of definition equal to the non-negative half-line. We use a dynamic pro-
gramming framework together with measurable selection arguments to establish
both the no-arbitrage condition characterization and the existence of an
optimal portfolio in a (generically incomplete) discrete-time financial market
model with finite time horizon. In contrast to the existing literature, we
propose to consider a probability space which is not necessarily complete.
Lagged and instantaneous dynamical influences related to brain structural connectivity
Date: 2016-02-22 00:00:00
Tasks: Functional Connectivity, Time Series Analysis

Contemporary neuroimaging methods can shed light on the basis of human neural
and cognitive specializations, with important implications for neuroscience and
medicine. Different MRI acquisitions provide different brain networks at the
macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural
connectivity (SC) coincident with the bundles of parallel fibers between brain
areas, functional MRI (fMRI) accounts for the variations in the
blood-oxygenation-level-dependent T2* signal, providing functional connectivity
(FC).Understanding the precise relation between FC and SC, that is, between
brain dynamics and structure, is still a challenge for neuroscience. To
investigate this problem, we acquired data at rest and built the corresponding
SC (with matrix elements corresponding to the fiber number between brain areas)
to be compared with FC connectivity matrices obtained by 3 different methods:
directed dependencies by an exploratory version of structural equation modeling
(eSEM), linear correlations (C) and partial correlations (PC). We also
considered the possibility of using lagged correlations in time series; so, we
compared a lagged version of eSEM and Granger causality (GC). Our results were
two-fold: firstly, eSEM performance in correlating with SC was comparable to
those obtained from C and PC, but eSEM (not C nor PC) provides information
about directionality of the functional interactions. Second, interactions on a
time scale much smaller than the sampling time, captured by instantaneous
connectivity methods, are much more related to SC than slow directed influences
captured by the lagged analysis. Indeed the performance in correlating with SC
was much worse for GC and for the lagged version of eSEM. We expect these
results to supply further insights to the interplay between SC and functional
patterns, an important issue in the study of brain physiology and function.
Autocrine signaling and quorum sensing: Extreme ends of a common spectrum
Date: 2016-02-18 00:00:00

"Secrete-and-sense cells" can communicate by secreting a signaling molecule
while also producing a receptor that detects the molecule. The cell can
potentially "talk" to itself ("self-communication") or talk to neighboring
cells with the same receptor ("neighbor-communication"). The predominant forms
of secrete-and-sense cells are self-communicating "autocrine cells" that are
largely found in animals, and neighbor-communicating "quorum sensing cells"
that are mostly associated with bacteria. While assumed to function independent
of one another, recent studies have discovered quorum sensing organs and
autocrine signaling microbes. Moreover, similar types of genetic circuits
control many autocrine and quorum sensing cells. We outline these recent
findings and explain how autocrine and quorum sensing are two sides of a
many-sided "dice" created by the versatile secrete-and-sense cell.
Family-joining: A fast distance-based method for constructing generally labeled trees
Date: 2016-06-13 00:00:00

The widely used model for evolutionary relationships is a bifurcating tree
with all taxa/observations placed at the leaves. This is not appropriate if the
taxa have been densely sampled across evolutionary time and may be in a direct
ancestral relationship, or if there is not enough information to fully resolve
all the branching points in the evolutionary tree. In this paper, we present a
fast distance-based agglomeration method called family-joining (FJ) for
constructing so-called generally labeled trees in which taxa may be placed at
internal vertices and the tree may contain polytomies. FJ constructs such trees
on the basis of pairwise distances and a distance threshold. We tested three
methods for threshold selection, FJ-AIC, FJ-BIC and FJ-CV, which minimize
Akaike information criterion, Bayesian information criterion, and
cross-validation error, respectively. When compared with related methods on
simulated data, FJ-BIC was among the best at reconstructing the correct tree
across a wide range of simulation scenarios. FJ-BIC was applied to HIV
sequences sampled from individuals involved in a known transmission chain. The
FJ-BIC tree was found to be compatible with almost all transmission events. On
average, internal branches in the FJ-BIC tree have higher bootstrap support
than branches in the leaf-labeled bifurcating tree constructed using RAxML.
$36\%$ and $25\%$ of the internal branches in the FJ-BIC tree and RAxML tree,
respectively, have bootstrap support greater than $70\%$. To the best of our
knowledge the method presented here is the first attempt at modeling
evolutionary relationships using generally labeled trees.