pwc-archive/papers-with-abstracts huggingface dataset:
Biochemical mechanism of resistance in some Brassica genotypes against Lipaphis erysimi (Kaltenbach) (Homoptera: Aphididae)
Date: 2017-03-23 00:00:00

Two years study was carried out during 2006-07 and 2007-08 crop seasons to
study the response of different genotypes of oilseeds Brassica to Lipaphis
erysimi (Kaltenbach) infestation both under field and screen house conditions
and to find out the relationship of various biochemical constituents to aphid
infestation. Among the various genotypes, the population of L. erysimi was
significantly high on Brassica rapa variety brown sarson cv. BSH 1 and B. rapa
var. yellow sarson cv. YST 151 in unprotected set i.e. 53.7 and 52.3 aphids/
plant, respectively. However, it was the lowest on Eruca sativa cv. T 27 (4.7
aphids/plant) followed by B. carinata cv. DLSC 2 (20.9 aphids/ plant) which
suffered the least yield loss i.e. 5.79 and 10.59 per cent, respectively.
Almost similar trend was observed in seedling mortality, which was the maximum
in BSH 1 and YST 151, while no seedling mortality was observed in the case of T
27 during both the years of study. Analysis of various biochemical constituents
revealed that glucosinolates, total phenols and ortho-dihydroxy phenols had
inverse relationship with the aphid infestation. Higher amount of these
biochemical constituents in T 27 and DLSC 2 was responsible for lower aphid
infestation on these genotypes.
Quantification of avoidable yield losses in oilseed Brassica caused by insect pests
Date: 2017-03-23 00:00:00

A six year field study was conducted from 2001 2002 to 2006 2007 at Punjab
Agricultural University, Ludhiana, India to study the losses in seed yield of
different Brassica species (B. juncea, B. napus, B. carinata, B. rapa and Eruca
sativa) by the infestation of insect pests. The experiment was conducted in two
different sets viz. protected/sprayed and unprotected, in a randomized block
design, with three replications. Data on the infestation of insect pests, and
seed yield were recorded at weekly intervals and at harvest, respectively. The
loss in seed yield, due to mustard aphid and cabbage caterpillar, varied from
6.5 to 26.4 per cent. E. sativa suffered the least loss in seed yield and
harboured the minimum population of mustard aphid (2.1 aphids/plant) and
cabbage caterpillar (2.4 larvae/plant). On the other hand, B. carinata was
highly susceptible to the cabbage caterpillar (26.2 larvae/plant) and suffered
the maximum yield loss (26.4%).
Multilinear compressive sensing and an application to convolutional linear networks
Authors: François Malgouyres, Joseph Landsberg
Date: 2017-03-23 00:00:00
Tasks: compressed sensing, Compressive Sensing

We study a deep linear network endowed with a structure. It takes the form of a matrix $X$ obtained by multiplying $K$ matrices (called factors and corresponding to the action of the layers). The action of each layer (i.e. a factor) is obtained by applying a fixed linear operator to a vector of parameters satisfying a constraint. The number of layers is not limited. Assuming that $X$ is given and factors have been estimated, the error between the product of the estimated factors and $X$ (i.e. the reconstruction error) is either the statistical or the empirical risk. In this paper, we provide necessary and sufficient conditions on the network topology under which a stability property holds. The stability property requires that the error on the parameters defining the factors (i.e. the stability of the recovered parameters) scales linearly with the reconstruction error (i.e. the risk). Therefore, under these conditions on the network topology, any successful learning task leads to stably defined features and therefore interpretable layers/network.In order to do so, we first evaluate how the Segre embedding and its inverse distort distances. Then, we show that any deep structured linear network can be cast as a generic multilinear problem (that uses the Segre embedding). This is the {\em tensorial lifting}. Using the tensorial lifting, we provide necessary and sufficient conditions for the identifiability of the factors (up to a scale rearrangement). We finally provide the necessary and sufficient condition called \NSPlong~(because of the analogy with the usual Null Space Property in the compressed sensing framework) which guarantees that the stability property holds. We illustrate the theory with a practical example where the deep structured linear network is a convolutional linear network. As expected, the conditions are rather strong but not empty. A simple test on the network topology can be implemented to test if the condition holds.
Quantifying and suppressing ranking bias in a large citation network
Authors: Vaccario Giacomo, Medo Matus, Wider Nicolas, Mariani Manuel Sebastian
Date: 2017-03-23 00:00:00

It is widely recognized that citation counts for papers from different fields
cannot be directly compared because different scientific fields adopt different
citation practices. Citation counts are also strongly biased by paper age since
older papers had more time to attract citations. Various procedures aim at
suppressing these biases and give rise to new normalized indicators, such as
the relative citation count. We use a large citation dataset from Microsoft
Academic Graph and a new statistical framework based on the Mahalanobis
distance to show that the rankings by well known indicators, including the
relative citation count and Google's PageRank score, are significantly biased
by paper field and age. We propose a general normalization procedure motivated
by the $z$-score which produces much less biased rankings when applied to
citation count and PageRank score.
Localization of a microtubule organizing center by kinesin motors
Authors: Chikashi Arita, Jonas Bosche, Alexander Lück, Ludger Santen
Date: 2017-03-23 00:00:00

The microtubule (MT) motor Kip3p is very processive kinesin that promotes catastrophes and pausing in particular on cortical contact. These properties explain the role of Kip3p in positioning the mitotic spindle in budding yeast and potentially other processes controlled by kinesin-8 family members. We present a theoretical approach to positioning of a MT network in a cell. In order to explore the underlying mechanism we introduce an idealized system of two MTs connected by a microtubule organizing center (MTOC). The dynamics of Kip3p is modeled by interacting stochastic particles, which allows us to study the effects of motor-induced depolymerization under spatial confinement. We find that localization in the middle of the system is realized in a parameter regime where the motor densities on the MTs are increasing with the distance from the MTOC. Localization at an asymmetric position is also possible by tuning the kinesin input rates at the MT minus ends or attachment rates depending on different compartments of the cell.
BIISQ: Bayesian nonparametric discovery of Isoforms and Individual Specific Quantification
Date: 2017-03-24 00:00:00
Tasks: Diversity, Variational Inference

Most human protein-coding genes can be transcribed into multiple possible
distinct mRNA isoforms. These alternative splicing patterns encourage molecular
diversity and dysregulation of isoform expression plays an important role in
disease etiology. However, isoforms are difficult to characterize from
short-read RNA-seq data because they share identical subsequences and exist in
tissue- and sample-specific frequencies. Here, we develop BIISQ, a Bayesian
nonparametric model to discover Isoforms and Individual Specific Quantification
from RNA-seq data. BIISQ does not require known isoform reference sequences but
instead estimates isoform composition directly with an isoform catalog shared
across samples. We develop a stochastic variational inference approach for
efficient and robust posterior inference and demonstrate superior precision and
recall for short read RNA-seq simulations and simulated short read data from
PacBio long read sequencing when compared to state-of-the-art isoform
reconstruction methods. BIISQ achieves the most significant gains for longer
(in terms of exons) isoforms and isoforms that are lowly expressed (over 500%
more transcripts correctly inferred at low coverage in simulations). Finally,
we estimate isoforms in the GEUVADIS RNA-seq data, identify genetic variants
that regulate transcript ratios, and demonstrate variant enrichment in
functional elements related to mRNA splicing regulation.
Cohort effects in mortality modelling: a Bayesian state-space approach
Date: 2017-03-24 00:00:00
Tasks: Bayesian Inference

Cohort effects are important factors in determining the evolution of human
mortality for certain countries. Extensions of dynamic mortality models with
cohort features have been proposed in the literature to account for these
factors under the generalised linear modelling framework. In this paper we
approach the problem of mortality modelling with cohort factors incorporated
through a novel formulation under a state-space methodology. In the process we
demonstrate that cohort factors can be formulated naturally under the
state-space framework, despite the fact that cohort factors are indexed
according to year-of-birth rather than year. Bayesian inference for cohort
models in a state-space formulation is then developed based on an efficient
Markov chain Monte Carlo sampler, allowing for the quantification of parameter
uncertainty in cohort models and resulting mortality forecasts that are used
for life expectancy and life table constructions. The effectiveness of our
approach is examined through comprehensive empirical studies involving male and
female populations from various countries. Our results show that cohort
patterns are present for certain countries that we studied and the inclusion of
cohort factors are crucial in capturing these phenomena, thus highlighting the
benefits of introducing cohort models in the state-space framework. Forecasting
of cohort models is also discussed in light of the projection of cohort
factors.
Dynamical compensation and structural identifiability: analysis, implications, and reconciliation
Date: 2017-03-24 00:00:00

The concept of dynamical compensation has been recently introduced to
describe the ability of a biological system to keep its output dynamics
unchanged in the face of varying parameters. Here we show that, according to
its original definition, dynamical compensation is equivalent to lack of
structural identifiability. This is relevant if model parameters need to be
estimated, which is often the case in biological modelling. This realization
prompts us to warn that care should we taken when using an unidentifiable model
to extract biological insight: the estimated values of structurally
unidentifiable parameters are meaningless, and model predictions about
unmeasured state variables can be wrong. Taking this into account, we explore
alternative definitions of dynamical compensation that do not necessarily imply
structural unidentifiability. Accordingly, we show different ways in which a
model can be made identifiable while exhibiting dynamical compensation. Our
analyses enable the use of the new concept of dynamical compensation in the
context of parameter identification, and reconcile it with the desirable
property of structural identifiability.
Advances in Microfluidics and Lab-on-a-Chip Technologies
Date: 2017-03-24 00:00:00
Tasks: Diagnostic, Management

Advances in molecular biology are enabling rapid and efficient analyses for
effective intervention in domains such as biology research, infectious disease
management, food safety, and biodefense. The emergence of microfluidics and
nanotechnologies has enabled both new capabilities and instrument sizes
practical for point-of-care. It has also introduced new functionality, enhanced
sensitivity, and reduced the time and cost involved in conventional molecular
diagnostic techniques. This chapter reviews the application of microfluidics
for molecular diagnostics methods such as nucleic acid amplification,
next-generation sequencing, high resolution melting analysis, cytogenetics,
protein detection and analysis, and cell sorting. We also review microfluidic
sample preparation platforms applied to molecular diagnostics and targeted to
sample-in, answer-out capabilities.
Analyzing Evolving Stories in News Articles
Authors: Barranco Roberto Camacho, Boedihardjo Arnold P., Hossain M. Shahriar
Date: 2017-12-21 00:00:00
Tasks: Articles

There is an overwhelming number of news articles published every day around
the globe. Following the evolution of a news-story is a difficult task given
that there is no such mechanism available to track back in time to study the
diffusion of the relevant events in digital news feeds. The techniques
developed so far to extract meaningful information from a massive corpus rely
on similarity search, which results in a myopic loopback to the same topic
without providing the needed insights to hypothesize the origin of a story that
may be completely different than the news today. In this paper, we present an
algorithm that mines historical data to detect the origin of an event, segments
the timeline into disjoint groups of coherent news articles, and outlines the
most important documents in a timeline with a soft probability to provide a
better understanding of the evolution of a story. Qualitative and quantitative
approaches to evaluate our framework demonstrate that our algorithm discovers
statistically significant and meaningful stories in reasonable time.
Additionally, a relevant case study on a set of news articles demonstrates that
the generated output of the algorithm holds the promise to aid prediction of
future entities in a story.
Towards a probability-free theory of continuous martingales
Date: 2017-03-25 00:00:00

Without probability theory, we define classes of supermartingales,
martingales, and semimartingales in idealized financial markets with continuous
price paths. This allows us to establish probability-free versions of a number
of standard results in martingale theory, including the Dubins-Schwarz theorem,
the Girsanov theorem, and results concerning the It\^o integral. We also
establish the existence of an equity premium and a CAPM relationship in this
probability-free setting.
Sequence dependent aggregation of peptides and fibril formation
Date: 2017-09-20 00:00:00

Deciphering the links between amino acid sequence and amyloid fibril
formation is key for understanding protein misfolding diseases. Here we use
Monte Carlo simulations to study aggregation of short peptides in a
coarse-grained model with hydrophobic-polar (HP) amino acid sequences and
correlated side chain orientations for hydrophobic contacts. A significant
heterogeneity is observed in the aggregate structures and in the thermodynamics
of aggregation for systems of different HP sequences and different number of
peptides. Fibril-like ordered aggregates are found for several sequences that
contain the common HPH pattern while other sequences may form helix bundles or
disordered aggregates. A wide variation of the aggregation transition
temperatures among sequences, even among those of the same hydrophobic
fraction, indicates that not all sequences undergo aggregation at a presumable
physiological temperature. The transition is found to be the most cooperative
for sequences forming fibril-like structures. For a fibril-prone sequence, it
is shown that fibril formation follows the nucleation and growth mechanism.
Interestingly, a binary mixture of peptides of an aggregation-prone and a
non-aggregation-prone sequence shows association and conversion of the latter
to the fibrillar structure. Our study highlights the role of sequence in
selecting fibril-like aggregates and also the impact of structural template on
fibril formation by peptides of unrelated sequences.
Apache Lucene as Content-Based-Filtering Recommender System: 3 Lessons Learned
Authors: Langer Stefan, Beel Joeran
Date: 2017-03-26 00:00:00
Tasks: Management, Recommendation Systems

For the past few years, we used Apache Lucene as recommendation frame-work in
our scholarly-literature recommender system of the reference-management
software Docear. In this paper, we share three lessons learned from our work
with Lucene. First, recommendations with relevance scores below 0.025 tend to
have significantly lower click-through rates than recommendations with
relevance scores above 0.025. Second, by picking ten recommendations randomly
from Lucene's top50 search results, click-through rate decreased by 15%,
compared to recommending the top10 results. Third, the number of returned
search results tend to predict how high click-through rates will be: when
Lucene returns less than 1,000 search results, click-through rates tend to be
around half as high as if 1,000+ results are returned.
Exact and approximate limit behaviour of the Yule tree's cophenetic index
Date: 2018-04-30 00:00:00

In this work we study the limit distribution of an appropriately normalized
cophenetic index of the pure-birth tree conditioned on $n$ contemporary tips.
We show that this normalized phylogenetic balance index is a submartingale that
converges almost surely and in L2. We link our work with studies on trees
without branch lengths and show that in this case the limit distribution is a
contraction-type distribution, similar to the Quicksort limit distribution. In
the continuous branch case we suggest approximations to the limit distribution.
We propose heuristic methods of simulating from these distributions and it may
be observed that these algorithms result in reasonable tails. Therefore, we
propose a way based on the quantiles of the derived distributions for
hypothesis testing, whether an observed phylogenetic tree is consistent with
the pure-birth process. Simulating a sample by the proposed heuristics is
rapid, while exact simulation (simulating the tree and then calculating the
index) is a time-consuming procedure. We conduct a power study to investigate
how well the cophenetic indices detect deviations from the Yule tree and apply
the methodology to empirical phylogenies.
Existence of recombination-selection equilibria for sexual populations
Date: 2017-03-24 00:00:00

We study a birth and death model for the adapatation of a sexual population
to an environment. The population is structured by a phenotypical trait, and,
possibly, an age variable. Recombination is modeled by Fisher's infinitesimal
operator. We prove the existence of principal eigenelements for the
corresponding eigenproblem. As the infinitesimal operator is 1-homogeneous but
nor linear nor monotone, the general Krein-Rutman theory cannot be applied to
this problem.
Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia
Authors: Beel Joeran, Aizawa Akiko, Breitinger Corinna, Gipp Bela
Date: 2017-04-20 00:00:00
Tasks: Recommendation Systems

Only few digital libraries and reference managers offer recommender systems,
although such systems could assist users facing information overload. In this
paper, we introduce Mr. DLib's recommendations-as-a-service, which allows third
parties to easily integrate a recommender system into their products. We
explain the recommender approaches implemented in Mr. DLib (content-based
filtering among others), and present details on 57 million recommendations,
which Mr. DLib delivered to its partner GESIS Sowiport. Finally, we outline our
plans for future development, including integration into JabRef, establishing a
living lab, and providing personalized recommendations.
Towards Effective Research-Paper Recommender Systems and User Modeling based on Mind Maps
Authors: Beel Joeran
Date: 2017-03-27 00:00:00
Tasks: Articles, Management, Recommendation Systems

While user-modeling and recommender systems successfully utilize items like
emails, news, and movies, they widely neglect mind-maps as a source for user
modeling. We consider this a serious shortcoming since we assume user modeling
based on mind maps to be equally effective as user modeling based on other
items. Hence, millions of mind-mapping users could benefit from user-modeling
applications such as recommender systems. The objective of this doctoral thesis
is to develop an effective user-modeling approach based on mind maps. To
achieve this objective, we integrate a recommender system in our mind-mapping
and reference-management software Docear. The recommender system builds user
models based on the mind maps, and recommends research papers based on the user
models. As part of our research, we identify several variables relating to
mind-map-based user modeling, and evaluate the variables' impact on
user-modeling effectiveness with an offline evaluation, a user study, and an
online evaluation based on 430,893 recommendations displayed to 4,700 users. We
find, among others, that the number of analyzed nodes, modification time,
visibility of nodes, relations between nodes, and number of children and
siblings of a node affect the effectiveness of user modeling. When all
variables are combined in a favorable way, this novel approach achieves
click-through rates of 7.20%, which is nearly twice as effective as the best
baseline. In addition, we show that user modeling based on mind maps performs
about as well as user modeling based on other items, namely the research
articles users downloaded or cited. Our findings let us to conclude that user
modeling based on mind maps is a promising research field, and that developers
of mind-mapping applications should integrate recommender systems into their
applications. Such systems could create additional value for millions of
mind-mapping users.
A Numerical Method for Pricing Discrete Double Barrier Option by Legendre Multiwavelet
Date: 2017-03-28 00:00:00
Tasks: CPU

In this Article, a fast numerical numerical algorithm for pricing discrete
double barrier option is presented. According to Black-Scholes model, the price
of option in each monitoring date can be evaluated by a recursive formula upon
the heat equation solution. These recursive solutions are approximated by using
Legendre multiwavelets as orthonormal basis functions and expressed in
operational matrix form. The most important feature of this method is that its
CPU time is nearly invariant when monitoring dates increase. Besides, the rate
of convergence of presented algorithm was obtained. The numerical results
verify the validity and efficiency of the numerical method.
Analysis of Realized Volatility for Nikkei Stock Average on the Tokyo Stock Exchange
Date: 2017-03-28 00:00:00

We calculate realized volatility of the Nikkei Stock Average (Nikkei225)
Index on the Tokyo Stock Exchange and investigate the return dynamics. To avoid
the bias on the realized volatility from the non-trading hours issue we
calculate realized volatility separately in the two trading sessions, i.e.
morning and afternoon, of the Tokyo Stock Exchange and find that the
microstructure noise decreases the realized volatility at small sampling
frequency. Using realized volatility as a proxy of the integrated volatility we
standardize returns in the morning and afternoon sessions and investigate the
normality of the standardized returns by calculating variance, kurtosis and 6th
moment. We find that variance, kurtosis and 6th moment are consistent with
those of the standard normal distribution, which indicates that the return
dynamics of the Nikkei Stock Average are well described by a Gaussian random
process with time-varying volatility.
Non-parametric and semi-parametric asset pricing
Date: 2017-03-28 00:00:00

We find that the CAPM fails to explain the small firm effect even if its
non-parametric form is used which allows time-varying risk and non-linearity in
the pricing function. Furthermore, the linearity of the CAPM can be rejected,
thus the widely used risk and performance measures, the beta and the alpha, are
biased and inconsistent. We deduce semi-parametric measures which are
non-constant under extreme market conditions in a single factor setting; on the
other hand, they are not significantly different from the linear estimates of
the Fama-French three-factor model. If we extend the single factor model with
the Fama-French factors, the simple linear model is able to explain the US
stock returns correctly.