pwc-archive/papers-with-abstracts huggingface dataset:
Target Type Identification for Entity-Bearing Queries
Authors: Garigliotti Darío, Hasibi Faegheh, Balog Krisztian
Date: 2017-07-27 00:00:00
Tasks: Retrieval, Vocal Bursts Type Prediction

Identifying the target types of entity-bearing queries can help improve
retrieval performance as well as the overall search experience. In this work,
we address the problem of automatically detecting the target types of a query
with respect to a type taxonomy. We propose a supervised learning approach with
a rich variety of features. Using a purpose-built test collection, we show that
our approach outperforms existing methods by a remarkable margin. This is an
extended version of the article published with the same title in the
Proceedings of SIGIR'17.
JCTC: A Large Job posting Corpus for Text Classification
Authors: Xu Haoyu  1 and 2, Gu Chongyang  1 and 3, Zhou Han, Kou Sengpan, Zhang Junjie
Date: 2017-06-12 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

The absence of an appropriate text classification corpus makes the massive
amount of online job information unusable for labor market analysis. This paper
presents JCTC, a large job posting corpus for text classification. In JCTC
construction framework, a formal specification issued by the Chinese central
government is chosen as the classification standard. The unsupervised learning
(WE-cos), supervised learning algorithm (SVM) and human judgements are all used
in the construction process. JCTC has 102581 online job postings distributed in
465 categories. The method proposed here can not only ameliorate the high
demands on people's skill and knowledge, but reduce the subjective influences
as well. Besides, the method is not limited in Chinese. We benchmark five
state-of-the-art deep learning approaches on JCTC providing baseline results
for future studies. JCTC might be the first job posting corpus for text
classification and the largest one in Chinese. With the help of JCTC, related
organizations are able to monitor, analyze and predict the labor market in a
comprehensive, accurate and timely manner.
Mean-variance portfolio selection with nonlinear wealth dynamics and random coefficients
Authors: Shaolin Ji, Hanqing Jin, Xiaomin Shi
Date: 2017-05-16 00:00:00

This paper studies the continuous time mean-variance portfolio selection problem with one kind of non-linear wealth dynamics. To deal the expectation constraint, an auxiliary stochastic control problem is firstly solved by two new generalized stochastic Riccati equations from which a candidate portfolio in feedback form is constructed, and the corresponding wealth process will never cross the vertex of the parabola. In order to verify the optimality of the candidate portfolio, the convex duality (requires the monotonicity of the cost function) is established to give another more direct expression of the terminal wealth level. The variance-optimal martingale measure and the link between the non-linear financial market and the classical linear market are also provided. Finally, we obtain the efficient frontier in closed form. From our results, people are more likely to invest their money in riskless asset compared with the classical linear market.
Can morphological changes of erythrocytes be driven by hemoglobin?
Date: 2017-05-17 00:00:00

At 49 C erythrocytes undergo morphological changes due to an internal force,
but the origin of the force that drives changes is not clear. Here we point out
that our recent experiments on thermally induced force-release in hemoglobin
can provide an explanation for the morphological changes of erythrocytes.
Reverse-engineering biological networks from large data sets
Date: 2017-05-25 00:00:00
Tasks: Drug Discovery

Much of contemporary systems biology owes its success to the abstraction of a
network, the idea that diverse kinds of molecular, cellular, and organismal
species and interactions can be modeled as relational nodes and edges in a
graph of dependencies. Since the advent of high-throughput data acquisition
technologies in fields such as genomics, metabolomics, and neuroscience, the
automated inference and reconstruction of such interaction networks directly
from large sets of activation data, commonly known as reverse-engineering, has
become a routine procedure. Whereas early attempts at network
reverse-engineering focused predominantly on producing maps of system
architectures with minimal predictive modeling, reconstructions now play
instrumental roles in answering questions about the statistics and dynamics of
the underlying systems they represent. Many of these predictions have clinical
relevance, suggesting novel paradigms for drug discovery and disease treatment.
While other reviews focus predominantly on the details and effectiveness of
individual network inference algorithms, here we examine the emerging field as
a whole. We first summarize several key application areas in which inferred
networks have made successful predictions. We then outline the two major
classes of reverse-engineering methodologies, emphasizing that the type of
prediction that one aims to make dictates the algorithms one should employ. We
conclude by discussing whether recent breakthroughs justify the computational
costs of large-scale reverse-engineering sufficiently to admit it as a mainstay
in the quantitative analysis of living systems.
Genetic control of plasticity of oil yield for combined abiotic stresses using a joint approach of crop modeling and genome-wide association
Date: 2017-05-18 00:00:00

Understanding the genetic basis of phenotypic plasticity is crucial for
predicting and managing climate change effects on wild plants and crops. Here,
we combined crop modeling and quantitative genetics to study the genetic
control of oil yield plasticity for multiple abiotic stresses in sunflower.
  First we developed stress indicators to characterize 14 environments for
three abiotic stresses (cold, drought and nitrogen) using the SUNFLO crop model
and phenotypic variations of three commercial varieties. The computed plant
stress indicators better explain yield variation than descriptors at the
climatic or crop levels. In those environments, we observed oil yield of 317
sunflower hybrids and regressed it with three selected stress indicators. The
slopes of cold stress norm reaction were used as plasticity phenotypes in the
following genome-wide association study.
  Among the 65,534 tested SNP, we identified nine QTL controlling oil yield
plasticity to cold stress. Associated SNP are localized in genes previously
shown to be involved in cold stress responses: oligopeptide transporters, LTP,
cystatin, alternative oxidase, or root development. This novel approach opens
new perspectives to identify genomic regions involved in
genotype-by-environment interaction of a complex traits to multiple stresses in
realistic natural or agronomical conditions.
TableQA: Question Answering on Tabular Data
Authors: Vakulenko Svitlana, Savenkov Vadim
Date: 2017-08-30 00:00:00
Tasks: Question Answering

Tabular data is difficult to analyze and to search through, yielding for new
tools and interfaces that would allow even non tech-savvy users to gain
insights from open datasets without resorting to specialized data analysis
tools or even without having to fully understand the dataset structure. The
goal of our demonstration is to showcase answering natural language questions
from tabular data, and to discuss related system configuration and model
training aspects. Our prototype is publicly available and open-sourced (see
https://svakulenko.ai.wu.ac.at/tableqa).
Application of Differential Equations in Projecting Growth Trajectories
Date: 2017-06-24 00:00:00

Mathematical method based on a direct or indirect analysis of growth rates is
described. It is shown how simple assumptions and a relatively easy analysis
can be used to describe mathematically complicated trends and to predict
growth. Only rudimentary knowledge of calculus is required. Projected
trajectories based on such simple initial assumptions are easier to accept and
to understand than alternative complicated projections based on more
complicated assumptions and on more intricate computational procedures.
Examples of the growth of population and of the growth of the Gross Domestic
Product are used to illustrate the application of this method of forecasting.
The mre11 A470 Alleles Influence the. Heritability and Segregation of Telosomes in Saccharomyces cerevisiae
Date: 2018-04-30 00:00:00
Tasks: Vocal Bursts Type Prediction

Telomeres, the nucleoprotein complexes at the termini of linear chromosomes,
are essential for the processes of end replication, end-protection, and
chromatin segregation. The Mre11 complex is involved in multiple cellular roles
in DNA repair and structure in the regulation and function of telomere size
homeostasis. In this study, we characterize yeast telomere chromatin structure,
phenotypic heritability, and chromatin segregation in both wild-type [MRE11]
and A470 motif alleles. MRE11 strains confer a telomere size of 300 base pairs
of G+T irregular simple sequence repeats. This DNA and a portion of
subtelomeric DNA is embedded in a telosome: an MNase-resistant non-nucleosomal
particle. Chromatin immunoprecipitation shows a three to four-fold lower
occupancy of Mre11A470T proteins than wild-type proteins in telosomes.
Telosomes containing the Mre11A470T protein confer a greater resistance to
MNase digestion than wild-type telosomes. The integration of a wild-type MRE11
allele into an ectopic locus in the genome of a mre11A470T mutant and the
introduction of a mre11A470T allele at an ectopic site in a wild-type strain
lead to unexpectedly differing results. In each case, the replicated sister
chromatids inherit telosomes containing only the protein encoded by the genomic
mre11 locus, even in the presence of protein encoded by the opposing ectopic
allele. We hypothesize that the telosome segregates by a conservative
mechanism. These data support a mechanism for the linkage between sister
chromatid replication and maintenance of either identical mutant or identical
wild-type telosomes after replication of sister chromatids. These data suggest
the presence of an active mechanism for chromatin segregation in yeast.
Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate
Date: 2017-05-16 00:00:00
Tasks: Dimensionality Reduction, Prediction

Prediction of poly(lactic co glycolic acid) (PLGA) micro- and nanoparticles'
dissolution rates plays a significant role in pharmaceutical and medical
industries. The prediction of PLGA dissolution rate is crucial for drug
manufacturing. Therefore, a model that predicts the PLGA dissolution rate could
be beneficial. PLGA dissolution is influenced by numerous factors (features),
and counting the known features leads to a dataset with 300 features. This
large number of features and high redundancy within the dataset makes the
prediction task very difficult and inaccurate. In this study, dimensionality
reduction techniques were applied in order to simplify the task and eliminate
irrelevant and redundant features. A heterogeneous pool of several regression
algorithms were independently tested and evaluated. In addition, several
ensemble methods were tested in order to improve the accuracy of prediction.
The empirical results revealed that the proposed evolutionary weighted ensemble
method offered the lowest margin of error and significantly outperformed the
individual algorithms and the other ensemble techniques.
SPRINT: Ultrafast protein-protein interaction prediction of the entire human interactome
Date: 2017-05-19 00:00:00

Proteins perform their functions usually by interacting with other proteins.
Predicting which proteins interact is a fundamental problem. Experimental
methods are slow, expensive, and have a high rate of error. Many computational
methods have been proposed among which sequence-based ones are very promising.
However, so far no such method is able to predict effectively the entire human
interactome: they require too much time or memory. We present SPRINT (Scoring
PRotein INTeractions), a new sequence-based algorithm and tool for predicting
protein-protein interactions. We comprehensively compare SPRINT with
state-of-the-art programs on seven most reliable human PPI datasets and show
that it is more accurate while running orders of magnitude faster and using
very little memory. SPRINT is the only program that can predict the entire
human interactome. Our goal is to transform the very challenging problem of
predicting the entire human interactome into a routine task. The source code of
SPRINT is freely available from github.com/lucian-ilie/SPRINT/ and the datasets
and predicted PPIs from www.csd.uwo.ca/faculty/ilie/SPRINT/.
Local risk-minimization with multiple assets under illiquidity with applications in energy markets
Date: 2018-06-29 00:00:00

We propose a hedging approach for general contingent claims when liquidity is
a concern and trading is subject to transaction cost. Multiple assets with
different liquidity levels are available for hedging. Our risk criterion
targets a tradeoff between minimizing the risk against fluctuations in the
stock price and incurring low liquidity costs. Following \c{C}etin U., Jarrow
R.A., and Protter P. (2004) we work in an arbitrage-free setting assuming a
supply curve for each asset. In discrete time, following the ideas in Schweizer
M. (1998) and Lamberton D., Pham H., Schweizer M. (1998) we prove the existence
of a locally risk-minimizing strategy under mild conditions on the price
process. Under stochastic and time-dependent liquidity risk we give a
closed-form solution for an optimal strategy in the case of a linear supply
curve model. Finally we show how our hedging method can be applied in energy
markets where futures with different maturities are available for trading. The
futures closest to their delivery period are usually the most liquid but
depending on the contingent claim not necessary optimal in terms of hedging. In
a simulation study we investigate this tradeoff and compare the resulting hedge
strategies with the classical ones.
End-to-End Cross-Modality Retrieval with CCA Projections and Pairwise Ranking Loss
Authors: Dorfer Matthias, Schlüter Jan, Vall Andreu, Korzeniowski Filip, Widmer Gerhard
Date: 2018-04-16 00:00:00
Tasks: Retrieval

Cross-modality retrieval encompasses retrieval tasks where the fetched items
are of a different type than the search query, e.g., retrieving pictures
relevant to a given text query. The state-of-the-art approach to cross-modality
retrieval relies on learning a joint embedding space of the two modalities,
where items from either modality are retrieved using nearest-neighbor search.
In this work, we introduce a neural network layer based on Canonical
Correlation Analysis (CCA) that learns better embedding spaces by analytically
computing projections that maximize correlation. In contrast to previous
approaches, the CCA Layer (CCAL) allows us to combine existing objectives for
embedding space learning, such as pairwise ranking losses, with the optimal
projections of CCA. We show the effectiveness of our approach for
cross-modality retrieval on three different scenarios (text-to-image,
audio-sheet-music and zero-shot retrieval), surpassing both Deep CCA and a
multi-view network using freely learned projections optimized by a pairwise
ranking loss, especially when little training data is available (the code for
all three methods is released at: https://github.com/CPJKU/cca_layer).
Identifiability of phylogenetic parameters from k-mer data under the coalescent
Date: 2017-05-19 00:00:00

Distances between sequences based on their $k$-mer frequency counts can be
used to reconstruct phylogenies without first computing a sequence alignment.
Past work has shown that effective use of k-mer methods depends on 1)
model-based corrections to distances based on $k$-mers and 2) breaking long
sequences into blocks to obtain repeated trials from the sequence-generating
process. Good performance of such methods is based on having many high-quality
blocks with many homologous sites, which can be problematic to guarantee a
priori.
  Nature provides natural blocks of sequences into homologous regions---namely,
the genes. However, directly using past work in this setting is problematic
because of possible discordance between different gene trees and the underlying
species tree. Using the multispecies coalescent model as a basis, we derive
model-based moment formulas that involve the divergence times and the
coalescent parameters. From this setting, we prove identifiability results for
the tree and branch length parameters under the Jukes-Cantor model of sequence
mutations.
Faceted classification: management and use
Authors: Slavic Aida
Date: 2017-05-19 00:00:00
Tasks: Classification, General Classification, Information Retrieval, Management, Retrieval

The paper discusses issues related to the use of faceted classifications in
an online environment. The author argues that knowledge organization systems
can be fully utilized in information retrieval only if they are exposed and
made available for machine processing. The experience with classification
automation to date may be used to speed up and ease the conversion of existing
faceted schemes or the creation of management tools for new systems. The author
suggests that it is possible to agree on a set of functional requirements for
supporting faceted classifications online that are equally relevant for the
maintenance of classifications, the creation of classification indexing tools,
or the management of classifications in an authority file. It is suggested that
a set of requirements for analytico-synthetic classifications may be put
forward to improve standards for the use and exchange of knowledge organization
systems.
Speeding up Memory-based Collaborative Filtering with Landmarks
Authors: Lima Gustavo R., Mello Carlos E., Zimbrao Geraldo
Date: 2017-05-19 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recommender systems play an important role in many scenarios where users are
overwhelmed with too many choices to make. In this context, Collaborative
Filtering (CF) arises by providing a simple and widely used approach for
personalized recommendation. Memory-based CF algorithms mostly rely on
similarities between pairs of users or items, which are posteriorly employed in
classifiers like k-Nearest Neighbor (kNN) to generalize for unknown ratings. A
major issue regarding this approach is to build the similarity matrix.
Depending on the dimensionality of the rating matrix, the similarity
computations may become computationally intractable. To overcome this issue, we
propose to represent users by their distances to preselected users, namely
landmarks. This procedure allows to drastically reduce the computational cost
associated with the similarity matrix. We evaluated our proposal on two
distinct distinguishing databases, and the results showed our method has
consistently and considerably outperformed eight CF algorithms (including both
memory-based and model-based) in terms of computational performance.
Classification revisited: a web of knowledge
Authors: Slavic Aida
Date: 2017-05-19 00:00:00
Tasks: Classification, General Classification

The vision of the Semantic Web (SW) is gradually unfolding and taking shape
through a web of linked data, a part of which is built by capturing semantics
stored in existing knowledge organization systems (KOS), subject metadata and
resource metadata. The content of vast bibliographic collections is currently
categorized by some widely used bibliographic classification and we may soon
see them being mined for information and linked in a meaningful way across the
Web. Bibliographic classifications are designed for knowledge mediation which
offers both a rich terminology and different ways in which concepts can be
categorized and related to each other in the universe of knowledge. From
1990-2010 they have been used in various resource discovery services on the Web
and continue to be used to support information integration in a number of
international digital library projects. In this chapter we will revisit some of
the ways in which universal classifications, as language independent concept
schemes, can assist humans and computers in structuring and presenting
information and formulating queries. Most importantly, we highlight issues
important to understanding bibliographic classifications, both in terms of
their unused potential and technical limitations.
Wealth dynamics in a sentiment-driven market
Date: 2017-05-19 00:00:00

We study dynamics of a simulated world with stock and money, driven by the
externally given processes which we refer to as sentiments. The considered
sentiments influence the buy/sell stock trading attitude, the perceived price
uncertainty, and the trading intensity of all or a part of the market
participants. We study how the wealth of market participants evolves in time in
such an environment. We discuss the opposite perspective in which the
parameters of the sentiment processes can be inferred a posteriori from the
observed market behavior.
Compressing Over-the-Counter Markets
Date: 2019-06-14 00:00:00

Over-the-counter markets are at the center of the postcrisis global reform of
the financial system. We show how the size and structure of such markets can
undergo rapid and extensive changes when participants engage in portfolio
compression, a post-trade netting technology. Tightly-knit and concentrated
trading structures, as featured by many large over-the-counter markets, are
especially susceptible to reductions of notional and reconfigurations of
network structure resulting from compression activities. Using
transaction-level data on credit-default-swaps markets, we estimate reduction
levels consistent with the historical development observed in these markets
since the Global Financial Crisis. Finally, we study the effect of a mandate to
centrally clear over-the-counter markets. When participants engage in both
central clearing and portfolio compression, we find large netting failures if
clearinghouses proliferate. Allowing for compression across clearinghouses
by-and-large offsets this adverse effect.
A Novel Simplified Model for Blood Coagulation: A piecewise dynamical model for thrombin with robust predictive capabilities
Date: 2017-05-19 00:00:00
Tasks: model

Realistic description of patient-specific mechanical properties of clotting
dynamics presents a major challenge. Available patient-specific data falls
short of robustly characterizing myriads of complex dynamic interactions that
happen during clotting. We propose a simplified switching model for a key part
of the coagulation cascade that describes dynamics of just four variables. The
model correctly predicts prolonged activity of thrombin, an important enzyme in
the clotting process, in certain plasma factor compositions. The activity
sustains beyond the time which is conventionally considered to be the end of
clotting. This observation along with the simplified model is hypothesized as a
necessary step towards effectively studying patient-specific properties of
clotting dynamics in realistic geometries.
Personalized Ranking for Context-Aware Venue Suggestion
Authors: Aliannejadi Mohammad, Mele Ida, Crestani Fabio
Date: 2017-05-20 00:00:00

Making personalized and context-aware suggestions of venues to the users is
very crucial in venue recommendation. These suggestions are often based on
matching the venues' features with the users' preferences, which can be
collected from previously visited locations. In this paper we present a novel
user-modeling approach which relies on a set of scoring functions for making
personalized suggestions of venues based on venues content and reviews as well
as users context. Our experiments, conducted on the dataset of the TREC
Contextual Suggestion Track, prove that our methodology outperforms
state-of-the-art approaches by a significant margin.