pwc-archive/papers-with-abstracts huggingface dataset:
A Search/Crawl Framework for Automatically Acquiring Scientific Documents
Authors: Gollapalli Sujatha Das, Patel Krutarth, Caragea Cornelia
Date: 2016-04-18 00:00:00

Despite the advancements in search engine features, ranking methods,
technologies, and the availability of programmable APIs, current-day
open-access digital libraries still rely on crawl-based approaches for
acquiring their underlying document collections. In this paper, we propose a
novel search-driven framework for acquiring documents for scientific portals.
Within our framework, publicly-available research paper titles and author names
are used as queries to a Web search engine. Next, research papers and sources
of research papers are identified from the search results using accurate
classification modules. Our experiments highlight not only the performance of
our individual classifiers but also the effectiveness of our overall
Search/Crawl framework. Indeed, we were able to obtain approximately 0.665
million research documents through our fully-automated framework using about
0.076 million queries. These prolific results position Web search as an
effective alternative to crawl methods for acquiring both the actual documents
and seed URLs for future crawls.
Why one-size-fits-all vaso-modulatory interventions fail to control glioma invasion: in silico insights
Date: 2016-04-18 00:00:00
Tasks: All

There is an ongoing debate on the therapeutic potential of vaso-modulatory
interventions against glioma invasion. Prominent vasculature-targeting
therapies involve functional tumour-associated blood vessel deterioration and
normalisation. The former aims at tumour infarction and nutrient deprivation
medi- ated by vascular targeting agents that induce occlusion/collapse of
tumour blood vessels. In contrast, the therapeutic intention of normalising the
abnormal structure and function of tumour vascular net- works, e.g. via
alleviating stress-induced vaso-occlusion, is to improve chemo-, immuno- and
radiation therapy efficacy. Although both strategies have shown therapeutic
potential, it remains unclear why they often fail to control glioma invasion
into the surrounding healthy brain tissue. To shed light on this issue, we
propose a mathematical model of glioma invasion focusing on the interplay
between the mi- gration/proliferation dichotomy (Go-or-Grow) of glioma cells
and modulations of the functional tumour vasculature. Vaso-modulatory
interventions are modelled by varying the degree of vaso-occlusion. We
discovered the existence of a critical cell proliferation/diffusion ratio that
separates glioma invasion re- sponses to vaso-modulatory interventions into two
distinct regimes. While for tumours, belonging to one regime, vascular
modulations reduce the tumour front speed and increase the infiltration width,
for those in the other regime the invasion speed increases and infiltration
width decreases. We show how these in silico findings can be used to guide
individualised approaches of vaso-modulatory treatment strategies and thereby
improve success rates.
High order finite difference schemes on non-uniform meshes for the time-fractional Black-Scholes equation
Date: 2016-04-18 00:00:00

We construct a three-point compact finite difference scheme on a non-uniform
mesh for the time-fractional Black-Scholes equation. We show that for special
graded meshes used in finance, the Tavella-Randall and the quadratic meshes the
numerical solution has a fourth-order accuracy in space. Numerical experiments
are discussed.
Understanding the Systems Biology of Pathogen Virulence Using Semantic Methodologies
Date: 2016-04-18 00:00:00

Systems biology approaches to the integrative study of cells, organs and
organisms offer the best means of understanding in a holistic manner the
diversity of molecular assays that can be now be implemented in a high
throughput manner. Such assays can sample the genome, epigenome, proteome,
metabolome and microbiome contemporaneously, allowing us for the first time to
perform a complete analysis of physiological activity. The central problem
remains empowering the scientific community to actually implement such an
integration, across seemingly diverse data types and measurements. One
promising solution is to apply semantic techniques on a self-consistent and
implicitly correct ontological representation of these data types. In this
paper we describe how we have applied one such solution, based around the
InterMine data warehouse platform which uses as its basis the Sequence
Ontology, to facilitate a systems biology analysis of virulence in the
apicomplexan pathogen $Toxoplasma~gondii$, a common parasite that infects up to
half the worlds population, with acute pathogenic risks for immuno-compromised
individuals or pregnant mothers. Our solution, which we named `toxoMine', has
provided both a platform for our collaborators to perform such integrative
analyses and also opportunities for such cyberinfrastructure to be further
developed, particularly to take advantage of possible semantic similarities of
value to knowledge discovery in the Omics enterprise. We discuss these
opportunities in the context of further enhancing the capabilities of this
powerful integrative platform.
Species tree inference from gene splits by Unrooted STAR methods
Date: 2016-04-18 00:00:00

The $\text{NJ}_{st}$ method was proposed by Liu and Yu to infer a species
tree topology from unrooted topological gene trees. While its statistical
consistency under the multispecies coalescent model was established only for a
4-taxon tree, simulations demonstrated its good performance on gene trees
inferred from sequences for many taxa. Here we prove the statistical
consistency of the method for an arbitrarily large species tree. Our approach
connects $\text{NJ}_{st}$ to a generalization of the STAR method of Liu, Pearl
and Edwards, and a previous theoretical analysis of it. We further show
$\text{NJ}_{st}$ utilizes only the distribution of splits in the gene trees,
and not their individual topologies. Finally, we discuss how multiple samples
per taxon per gene should be handled for statistical consistency.
Gap Risk KVA and Repo Pricing: An Economic Capital Approach in the Black-Scholes-Merton Framework
Date: 2016-10-29 00:00:00

Although not a formal pricing consideration, gap risk or hedging errors are
the norm of derivatives businesses. Starting with the gap risk during a margin
period of risk of a repurchase agreement (repo), this article extends the
Black-Scholes-Merton option pricing framework by introducing a reserve capital
approach to the hedging error's irreducible variability. An extended partial
differential equation is derived with two new terms for expected gap loss and
economic capital charge, leading to the gap risk economic value adjustment and
capital valuation adjustment (KVA) respectively. Practical repo pricing
formulae is obtained showing that the break-even repo rate decomposes into cost
of fund and economic capital charge in KVA. At zero haircut, a one-year term
repo on main equities could command a capital charge as large as 50 basis
points for a 'BBB' rated borrower.
Ensemble Enabled Weighted PageRank
Authors: Luo Dongsheng, Gong Chen, Hu Renjun, Duan Liang, Ma Shuai
Date: 2016-04-19 00:00:00
Tasks: Articles

This paper describes our solution for WSDM Cup 2016. Ranking the query
independent importance of scholarly articles is a critical and challenging
task, due to the heterogeneity and dynamism of entities involved. Our approach
is called Ensemble enabled Weighted PageRank (EWPR). To do this, we first
propose Time-Weighted PageRank that extends PageRank by introducing a time
decaying factor. We then develop an ensemble method to assemble the authorities
of the heterogeneous entities involved in scholarly articles. We finally
propose to use external data sources to further improve the ranking accuracy.
Our experimental study shows that our EWPR is a good choice for ranking
scholarly articles.
Count-Min Tree Sketch: Approximate counting for NLP
Authors: Pitel Guillaume, Fouquier Geoffroy, Marchand Emmanuel, Mouhamadsultane Abdul
Date: 2016-06-15 00:00:00

The Count-Min Sketch is a widely adopted structure for approximate event
counting in large scale processing. In a previous work we improved the original
version of the Count-Min-Sketch (CMS) with conservative update using
approximate counters instead of linear counters. These structures are
computationaly efficient and improve the average relative error (ARE) of a CMS
at constant memory footprint. These improvements are well suited for NLP tasks,
in which one is interested by the low-frequency items. However, if Log counters
allow to improve ARE, they produce a residual error due to the approximation.
In this paper, we propose the Count-Min Tree Sketch (Copyright 2016 eXenSa. All
rights reserved) variant with pyramidal counters, which are focused toward
taking advantage of the Zipfian distribution of text data.
Optimal investment and consumption with downside risk constraint in jump-diffusion models
Date: 2016-03-21 00:00:00

This paper extends the results of the article [C. Kl\"{u}ppelberg and S. M.
Pergamenchtchikov. Optimal consumption and investment with bounded downside
risk for power utility functions. In Optimality and Risk: {\it Modern Trends in
Mathematical Finance. The Kabanov Festschrift}, pages 133-169, 2009] to a
jump-diffusion setting. We show that under the assumption that only positive
jumps in the asset prices are allowed, the explicit optimal strategy can be
found in the subset of admissible strategies satisfying the same risk
constraint as in the pure diffusion setting. When negative jumps probably
happen, the regulator should be more conservative. In that case, we suggest to
impose on the investor's portfolio a stricter constraint which depends on the
probability of having negative jumps in the assets during the whole considered
horizon.
Regime switching vine copula models for global equity and volatility indices
Date: 2016-04-19 00:00:00

For nearly every major stock market there exist equity and implied volatility
indices. These play important roles within finance: be it as a benchmark, a
measure of general uncertainty or a way of investing or hedging. It is well
known in the academic literature, that correlations and higher moments between
different indices tend to vary in time. However, to the best of our knowledge,
no one has yet considered a global setup including both, equity and implied
volatility indices of various continents, and allowing for a changing
dependence structure. We aim to close this gap by applying Markov-switching
$R$-vine models to investigate the existence of different, global dependence
regimes. In particular, we identify times of "normal" and "abnormal" states
within a data set consisting of North-American, European and Asian indices. Our
results confirm the existence of joint points in time at which global regime
switching takes place.
Risk Aversion and Catastrophic Risks: the Pill Experiment
Date: 2015-08-25 00:00:00

This article focuses on the work of O. Chanel and G. Chichilnisky (2013) on
the flaws of expected utility theory while assessing the value of life.
Expected utility is a fundamental tool in decision theory. However, it does not
fit with the experimental results when it comes to catastrophic outcomes
---see, for example, Chichilnisky (2009) for more details. In the experiments
conducted by Olivier Chanel in 1998 and 2009, several subjects are ask to
imagine they are presented 1 billion identical pills. They are paid \$220,000
to take and swallow one, knowing that one out of 1 billion is deadly. The
objective of this article is to show that risk aversion phenomenon cannot
explain the experimental results found. This is an additional reason why a new
kind of utility function is necessary: the axioms proposed by Graciela
Chichilnisky will be briefly presented, and it will be shown that it better
fits with experiments than any risk aversion utility function.
Document Retrieval using Predication Similarity
Authors: Gunaratna Kalpa
Date: 2016-04-19 00:00:00
Tasks: Information Retrieval, Retrieval

Document retrieval has been an important research problem over many years in
the information retrieval community. State-of-the-art techniques utilize
various methods in matching documents to a given document including keywords,
phrases, and annotations. In this paper, we propose a new approach for document
retrieval that utilizes predications (subject-predicate-object triples)
extracted from the documents. We represent documents as sets of predications.
We measure the similarity between predications to compute the similarity
between documents. Our approach utilizes the hierarchical information available
in ontologies in computing concept-concept similarity, making the approach
flexible. Predication-based document similarity is more precise and forms the
basis for a semantically aware document retrieval system. We show that the
approach is competitive with an existing state-of-the-art related document
retrieval technique in the biomedical domain.
Multidimensional matching
Date: 2016-04-19 00:00:00

We present a general analysis of multidimensional matching problems with
transferable utility, paying particular attention to the case in which the
dimensions of heterogeneity on the two sides of the market are unequal. A
particular emphasis is put on problems where agents on one side of the market
are multidimensional and agents on the other side are uni-dimensional, we
describe a general approach to solve such problems. Lastly, we analyze several
examples, including an hedonic model with differentiated products, a marriage
market model where wives are differentiated in income and fertility, and a
competitive variation of the Rochet-Chon\'e problem. In the latter example, we
show that the bunching phenomena, observed by Rochet and Chon\'e in the
monopoly context, do not occur in the competitive context
Random selection of factors preserves the correlation structure in a linear factor model to a high degree
Date: 2018-12-22 00:00:00
Tasks: Time Series, Time Series Analysis

In a very high-dimensional vector space, two randomly-chosen vectors are
almost orthogonal with high probability. Starting from this observation, we
develop a statistical factor model, the random factor model, in which factors
are chosen at random based on the random projection method. Randomness of
factors has the consequence that covariance matrix is well preserved in a
linear factor representation. It also enables derivation of probabilistic
bounds for the accuracy of the random factor representation of time-series,
their cross-correlations and covariances. As an application, we analyze
reproduction of time-series and their cross-correlation coefficients in the
well-diversified Russell 3,000 equity index.
Human Sexual Dimorphism of the Relative Cerebral Area Volumes in the Data of the Human Connectome Project
Date: 2016-04-20 00:00:00
Tasks: Diffusion  MRI

The average human brain volume of the males is larger than that of the
females. Several MRI voxel-based morphometry studies show that the gray
matter/white matter ratio is larger in females. Here we have analyzed the
recent public release of the Human Connectome Project, and by using the
diffusion MRI data of 511 subjects (209 men and 302 women), we have found that
the relative volumes of numerous subcortical areas and the gray matter of most
cortical areas are significantly larger in women than in men. Additionally, we
have discovered differences of the strengths of the sexual correlations between
the same structures in different hemispheres.
Towards a fully predictive model of flight paths in pigeons navigating in the familiar area: prediction across differing individuals
Date: 2016-04-20 00:00:00

This paper will detail the basis of our previously developed predictive model
for pigeon flight paths based on observations of the specific individual being
predicted. We will then describe how this model can be adapted to predict the
flight of a new, unobserved bird, based on observations of other individuals
from the same release site. We will test the accuracy of these predictions
relative to naive models with no previous flight information and those trained
on the focal bird's own previous flights, and discuss the implications of these
results for the nature of navigational cue use in the familiar area. Finally we
will discuss how visual cues may be explicitly encoded in the model in future
work.
Optimal timing of cross-sectional network samples in longitudinal network studies
Date: 2016-04-20 00:00:00

When choosing the timing of cross-sectional network snapshots in longitudinal
social network studies, the effect on the precision of parameter estimates
generally plays a minor role. Often the timing is opportunistic or determined
by a variety of considerations such as organizational constraints, funding, and
availability of study participants. Theory to guide the timing of network
snapshots is also missing. We use a statistical framework to relate the timing
to the precision of the parameter estimates, specifically, the sum of the
relative widths of their confidence intervals. We illustrate this
computationally using the STERGM suite of the statnet package to estimate the
parameters of the network dynamics. Analytically, we derive simple
approximations for the optimal timing when the parameters correspond to the
rates for different network events. We find that the optimal time depends the
most on the network processes with short expected durations and few expected
events such as the dissolution of ties. We apply our approximations to a simple
example of a dynamic network with formation and dissolution of iid ties.
Incorporating Semantic Knowledge into Latent Matching Model in Search
Authors: Wang Shuxin, Jiang Xin, Li Hang, Xu Jun, Wang Bin
Date: 2016-04-21 00:00:00

The relevance between a query and a document in search can be represented as
matching degree between the two objects. Latent space models have been proven
to be effective for the task, which are often trained with click-through data.
One technical challenge with the approach is that it is hard to train a model
for tail queries and tail documents for which there are not enough clicks. In
this paper, we propose to address the challenge by learning a latent matching
model, using not only click-through data but also semantic knowledge. The
semantic knowledge can be categories of queries and documents as well as
synonyms of words, manually or automatically created. Specifically, we
incorporate semantic knowledge into the objective function by including
regularization terms. We develop two methods to solve the learning task on the
basis of coordinate descent and gradient descent respectively, which can be
employed in different settings. Experimental results on two datasets from an
app search engine demonstrate that our model can make effective use of semantic
knowledge, and thus can significantly enhance the accuracies of latent matching
models, particularly for tail queries.
Different evolutionary paths to complexity for small and large populations of digital organisms
Date: 2016-04-21 00:00:00

A major aim of evolutionary biology is to explain the respective roles of
adaptive versus non-adaptive changes in the evolution of complexity. While
selection is certainly responsible for the spread and maintenance of complex
phenotypes, this does not automatically imply that strong selection enhances
the chance for the emergence of novel traits, that is, the origination of
complexity. Population size is one parameter that alters the relative
importance of adaptive and non-adaptive processes: as population size
decreases, selection weakens and genetic drift grows in importance. Because of
this relationship, many theories invoke a role for population size in the
evolution of complexity. Such theories are difficult to test empirically
because of the time required for the evolution of complexity in biological
populations. Here, we used digital experimental evolution to test whether large
or small asexual populations tend to evolve greater complexity. We find that
both small and large---but not intermediate-sized---populations are favored to
evolve larger genomes, which provides the opportunity for subsequent increases
in phenotypic complexity. However, small and large populations followed
different evolutionary paths towards these novel traits. Small populations
evolved larger genomes by fixing slightly deleterious insertions, while large
populations fixed rare beneficial insertions that increased genome size. These
results demonstrate that genetic drift can lead to the evolution of complexity
in small populations and that purifying selection is not powerful enough to
prevent the evolution of complexity in large populations.
Current and Cretaceous-Cenozoic diversification of Angiosperms
Date: 2016-04-21 00:00:00

Cretaceous-Cenozoic history of angiosperms led to the a certain character of
the distribution of taxa of different levels (the number of species and genera
in families, species/genera ratio in families, the number of species in the
genera). In most cases, these distributions are satisfactorily described by a
power law (Pareto distribution). In logarithmic coordinates power function is a
straight line. Empirical curves repeat this line is good enough, but on the
right side of the graph (at low volumes taxa), there is a marked deviation of
theoretical from the empirical curves. This suggests that the small volumes of
taxa should be greater for full compliance with the theoretical curves.
Modeling ratios among genera and species in families showed that only in the
case of dynamic extinction factor observed satisfactory agreement between
observed and calculated the number of species in a wide range of iterations.
This suggested that there was a differential extinction of species during the
evolution of angiosperms. This implies that the rate of extinction had to be
minimal in genera with a large number of species. On the contrary, extinction
rates may be increased by orders of magnitude with a decrease in the number of
species. As a result, large genera became getting bigger and small genera
become less. The species frequencies of distribution in the genera varied
according to a power law. The initial divergence of taxa volume, which led to
their further division into large and small, could be caused by the emergence
and expansion of herbs with their functional and adaptive capabilities.
Optimal trading with online parameters revisions
Date: 2016-04-21 00:00:00

The aim of this paper is to explain how parameters adjustments can be
integrated in the design or the control of automates of trading. Typically, we
are interested by the online estimation of the market impacts generated by
robots or single orders, and how they/the controller should react in an optimal
way to the informations generated by the observation of the realized impacts.
This can be formulated as an optimal impulse control problem with unknown
parameters, on which a prior is given. We explain how a mix of the classical
Bayesian updating rule and of optimal control techniques allows one to derive
the dynamic programming equation satisfied by the corresponding value function,
from which the optimal policy can be inferred. We provide an example of
convergent finite difference scheme and consider typical examples of
applications.