pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Inducer and Operator Binding for Cyclic-AMP Receptor Protein Mutants
Date: 2017-12-19 00:00:00

Allosteric transcription factors undergo binding events both at their inducer
binding sites as well as at distinct DNA binding domains, and it is often
difficult to disentangle the structural and functional consequences of these
two classes of interactions. In this work, we compare the ability of two
statistical mechanical models - the Monod-Wyman-Changeux (MWC) and the
Koshland-N\'emethy-Filmer (KNF) models of protein conformational change - to
characterize the multi-step activation mechanism of the broadly acting
cyclic-AMP receptor protein (CRP). We first consider the allosteric transition
resulting from cyclic-AMP binding to CRP, then analyze how CRP binds to its
operator, and finally investigate the ability of CRP to activate gene
expression. In light of these models, we examine data from a beautiful recent
experiment that created a single-chain version of the CRP homodimer, thereby
enabling each subunit to be mutated separately. Using this construct, six
mutants were created using all possible combinations of the wild type subunit,
a D53H mutant subunit, and an S62F mutant subunit. We demonstrate that both the
MWC and KNF models can explain the behavior of all six mutants using a small,
self-consistent set of parameters. In comparing the results, we find that the
MWC model slightly outperforms the KNF model in the quality of its fits, but
more importantly the parameters inferred by the MWC model are more in line with
structural knowledge of CRP. In addition, we discuss how the conceptual
framework developed here for CRP enables us to not merely analyze data
retrospectively, but has the predictive power to determine how combinations of
mutations will interact, how double mutants will behave, and how each construct
would regulate gene expression.
System modeling of receptor-induced apoptosis
Date: 2017-12-19 00:00:00

Receptor-induced apoptosis is a complex signal transduction pathway involving
numerous protein/protein interactions and post-transcriptional modifications.
The response to death receptor stimulation varies significantly from one cell
line to another and even from one cell to another within a given cell line. In
this context, it is often difficult to assess whether the molecular mechanisms
identified so far are sufficient to explain the rich quantitative observations
now available, and to detect possible gaps in our understanding. This is
precisely where computational systems biology approaches may contribute. In
this chapter, we review studies done in this direction, focusing on those that
provided significant insight on the functioning of this complex pathway by
tightly integrating experimental and computational approaches.
SNR Wall for Cooperative Spectrum Sensing Using Generalized Energy Detector
Date: 2017-12-19 00:00:00

Cognitive radio (CR) is a promising scheme to improve the spectrum
utilization. Spectrum sensing (SS) is one of the main tasks of CR. Cooperative
spectrum sensing (CSS) is used in CR to improve detection capability. Due to
its simplicity and low complexity, sensing based on energy detection known as
conventional energy detection (CED) is widely adopted. CED can be generalized
by changing the squaring operation of the amplitude of received samples by an
arbitrary positive power p which is referred to as the generalized energy
detector (GED). The performance of GED degrades when there exists noise
uncertainty (NU). In this paper, we investigate the performance of CSS by
considering the noise NU when all the secondary users (SUs) employ GED. We
derive the signal to noise ratio (SNR) wall for CSS for both hard and soft
decision combining. All the derived expressions are validated using Monte Carlo
(MC) simulations.
A Production Oriented Approach for Vandalism Detection in Wikidata - The Buffaloberry Vandalism Detector at WSDM Cup 2017
Authors: Crescenzi Rafael  Austral University, Fernandez Marcelo  Austral University, Calabria Federico A. Garcia  Austral University, Albani Pablo  Austral University, Tauziet Diego  Austral University, Baravalle Adriana  Austral University, D'Ambrosio Andrés Sebastián  Austral University
Date: 2017-12-19 00:00:00
Tasks: Misinformation

Wikidata is a free and open knowledge base from the Wikimedia Foundation,
that not only acts as a central storage of structured data for other projects
of the organization, but also for a growing array of information systems,
including search engines. Like Wikipedia, Wikidata's content can be created and
edited by anyone; which is the main source of its strength, but also allows for
malicious users to vandalize it, risking the spreading of misinformation
through all the systems that rely on it as a source of structured facts. Our
task at the WSDM Cup 2017 was to come up with a fast and reliable prediction
system that narrows down suspicious edits for human revision. Elaborating on
previous works by Heindorf et al. we were able to outperform all other
contestants, while incorporating new interesting features, unifying the
programming language used to only Python and refactoring the feature extractor
into a simpler and more compact code base.
Large-Scale Vandalism Detection with Linear Classifiers - The Conkerberry Vandalism Detector at WSDM Cup 2017
Authors: Grigorev Alexey  Searchmetrics GmbH
Date: 2017-12-19 00:00:00

Nowadays many artificial intelligence systems rely on knowledge bases for
enriching the information they process. Such Knowledge Bases are usually
difficult to obtain and therefore they are crowdsourced: they are available for
everyone on the internet to suggest edits and add new information.
Unfortunately, they are sometimes targeted by vandals who put inaccurate or
offensive information there. This is especially bad for the systems that use
these Knowledge Bases: for them it is important to use reliable information to
make correct inferences.
  One of such knowledge bases is Wikidata, and to fight vandals the organizers
of WSDM Cup 2017 challenged participants to build a model for detecting
mistrustful edits. In this paper we present the second place solution to the
cup: we show that it is possible to achieve competitive performance with simple
linear classification. With our approach we can achieve AU ROC of 0.938 on the
test data. Additionally, compared to other approaches, ours is significantly
faster. The solution is made available on GitHub.
Ensemble Models for Detecting Wikidata Vandalism with Stacking - Team Honeyberry Vandalism Detector at WSDM Cup 2017
Authors: Yamazaki Tomoya  Yahoo Japan Corporation, Sasaki Mei  Yahoo Japan Corporation, Murakami Naoya  Yahoo Japan Corporation, Makabe Takuya  Yahoo Japan Corporation, Iwasawa Hiroki  Yahoo Japan Corporation
Date: 2017-12-19 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, feature selection

The WSDM Cup 2017 is a binary classification task for classifying Wikidata
revisions into vandalism and non-vandalism. This paper describes our method
using some machine learning techniques such as under-sampling, feature
selection, stacking and ensembles of models. We confirm the validity of each
technique by calculating AUC-ROC of models using such techniques and not using
them. Additionally, we analyze the results and gain useful insights into
improving models for the vandalism detection task. The AUC-ROC of our final
submission after the deadline resulted in 0.94412.
Wikidata Vandalism Detection - The Loganberry Vandalism Detector at WSDM Cup 2017
Authors: Zhu Qi  University of Illinois Urbana-Champaign, Ng Hongwei  University of Illinois Urbana-Champaign, Liu Liyuan  University of Illinois Urbana-Champaign, Ji Ziwei  University of Illinois Urbana-Champaign, Jiang Bingjie  University of Illinois Urbana-Champaign, Shen Jiaming  University of Illinois Urbana-Champaign, Gui Huan  University of Illinois Urbana-Champaign
Date: 2017-12-19 00:00:00

Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation.
As it can be edited by anyone, entries frequently get vandalized, leading to
the possibility that it might spread of falsified information if such posts are
not detected. The WSDM 2017 Wiki Vandalism Detection Challenge requires us to
solve this problem by computing a vandalism score denoting the likelihood that
a revision corresponds to an act of vandalism and performance is measured using
the ROC-AUC obtained on a held-out test set. This paper provides the details of
our submission that obtained an ROC-AUC score of 0.91976 in the final
evaluation.
Real-value and confidence prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning
Date: 2017-12-19 00:00:00
Tasks: Clustering, Prediction, Protein Structure Prediction

Background. Protein dihedral angles provide a detailed description of protein
local conformation. Predicted dihedral angles can be used to narrow down the
conformational space of the whole polypeptide chain significantly, thus aiding
protein tertiary structure prediction. However, direct angle prediction from
sequence alone is challenging.
  Method. In this study, we present a novel method to predict real-valued
angles by combining clustering and deep learning. That is, we first generate
certain clusters of angles (each assigned a label) and then apply a deep
residual neural network to predict the label posterior probability. Finally, we
output real-valued prediction by a mixture of the clusters with their predicted
probabilities. At the same time, we also estimate the bound of the prediction
errors at each residue from the predicted label probabilities.
  Result. In this article, we present a novel method (named RaptorX-Angle) to
predict real-valued angles by combining clustering and deep learning. Tested on
a subset of PDB25 and the targets in the latest two Critical Assessment of
protein Structure Prediction (CASP), our method outperforms the existing
state-of-art method SPIDER2 in terms of Pearson Correlation Coefficient (PCC)
and Mean Absolute Error (MAE). Our result also shows approximately linear
relationship between the real prediction errors and our estimated bounds. That
is, the real prediction error can be well approximated by our estimated bounds.
  Conclusions. Our study provides an alternative and more accurate prediction
of dihedral angles, which may facilitate protein structure prediction and
functional study.
First-Order Asymptotics of Path-Dependent Derivatives in Multiscale Stochastic Volatility Environment
Date: 2017-12-20 00:00:00

In this paper, we extend the first-order asymptotics analysis of Fouque et
al. to general path-dependent financial derivatives using Dupire's functional
Ito calculus. The main conclusion is that the market group parameters
calibrated to vanilla options can be used to price to the same order exotic,
path-dependent derivatives as well. Under general conditions, the first-order
condition is represented by a conditional expectation that could be numerically
evaluated. Moreover, if the path-dependence is not too severe, we are able to
find path-dependent closed-form solutions equivalent to the fist-order
approximation of path-independent options derived in Fouque et al.
Additionally, we exemplify the results with Asian options and options on
quadratic variation.
Improvement of Resting-state EEG Analysis Process with Spectrum Weight-Voting based on LES
Date: 2018-01-17 00:00:00
Tasks: BIG-bench Machine Learning, Classification, EEG, Electroencephalogram (EEG)

EEG is a non-invasive technique for recording brain bioelectric activity,
which has potential applications in various fields such as human-computer
interaction and neuroscience. However, there are many difficulties in analyzing
EEG data, including its complex composition, low amplitude as well as low
signal-to-noise ratio. Some of the existing methods of analysis are based on
feature extraction and machine learning to differentiate the phase of
schizophrenia that samples belong to. However, medical research requires the
use of machine learning not only to give more accurate classification results,
but also to give the results that can be applied to pathological studies. The
main purpose of this study is to obtain the weight values as the representation
of influence of each frequency band on the classification of schizophrenia
phases on the basis of a more effective classification method using the LES
feature extraction, and then the weight values are processed and applied to
improve the accuracy of machine learning classification. We propose a method
called weight-voting to obtain the weights of sub-bands features by using
results of classification for voting to fit the actual categories of EEG data,
and using weights for reclassification. Through this method, we can first
obtain the influence of each band in distinguishing three schizophrenia phases,
and analyze the effect of band features on the risk of schizophrenia
contributing to the study of psychopathology. Our results show that there is a
high correlation between the change of weight of low gamma band and the
difference between HC, CHR and FES. If the features revised according to
weights are used for reclassification, the accuracy of result will be improved
compared with the original classifier, which confirms the role of the band
weight distribution.
Quantum approaches to music cognition
Date: 2019-01-18 00:00:00

Quantum cognition emerged as an important discipline of mathematical
psychology during the last two decades. Using abstract analogies between mental
phenomena and the formal framework of physical quantum theory, quantum
cognition demonstrated its ability to resolve several puzzles from cognitive
psychology. Until now, quantum cognition essentially exploited ideas from
projective (Hilbert space) geometry, such as quantum probability or quantum
similarity. However, many powerful tools provided by physical quantum theory,
e.g., symmetry groups have not been utilized in the field of quantum cognition
research sofar. Inspired by seminal work by Guerino Mazzola on the symmetries
of tonal music, our study aims at elucidating and reconciling static and
dynamic tonal attraction phenomena in music psychology within the quantum
cognition framework. Based on the fundamental principles of octave equivalence,
fifth similarity and transposition symmetry of tonal music that are reflected
by the structure of the circle of fifths, we develop different wave function
descriptions over this underlying tonal space. We present quantum models for
static and dynamic tonal attraction and compare them with traditional
computational models in musicology. Our approach replicates and also improves
predictions based on symbolic models of music perception.
Cointegration in functional autoregressive processes
Date: 2018-10-29 00:00:00
Tasks: valid, Vocal Bursts Type Prediction

This paper defines the class of $\mathcal{H}$-valued autoregressive (AR)
processes with a unit root of finite type, where $\mathcal{H}$ is an infinite
dimensional separable Hilbert space, and derives a generalization of the
Granger-Johansen Representation Theorem valid for any integration order
$d=1,2,\dots$. An existence theorem shows that the solution of an AR with a
unit root of finite type is necessarily integrated of some finite integer $d$
and displays a common trends representation with a finite number of common
stochastic trends of the type of (cumulated) bilateral random walks and an
infinite dimensional cointegrating space. A characterization theorem clarifies
the connections between the structure of the AR operators and $(i)$ the order
of integration, $(ii)$ the structure of the attractor space and the
cointegrating space, $(iii)$ the expression of the cointegrating relations, and
$(iv)$ the Triangular representation of the process. Except for the fact that
the number of cointegrating relations that are integrated of order 0 is
infinite, the representation of $\mathcal{H}$-valued ARs with a unit root of
finite type coincides with that of usual finite dimensional VARs, which
corresponds to the special case $\mathcal{H}=\mathbb{R}^p$.
Beyond Powers of Two: Hexagonal Modulation and Non-Binary Coding for Wireless Communication Systems
Date: 2017-12-20 00:00:00

Adaptive modulation and coding (AMC) is widely employed in modern wireless
communication systems to improve the transmission efficiency by adjusting the
transmission rate according to the channel conditions. Thus, AMC can provide
very efficient use of channel resources especially over fading channels.
Quadrature Amplitude Modulation (QAM) is an ef- ficient and widely employed
digital modulation technique. It typically employs a rectangular signal
constellation. Therefore the decision regions of the constellation are square
partitions of the two-dimensional signal space. However, it is well known that
hexagons rather than squares provide the most compact regular tiling in two
dimensions. A compact tiling means a dense packing of the constellation points
and thus more energy efficient data transmission. Hexagonal modulation can be
difficult to implement because it does not fit well with the usual power-
of-two symbol sizes employed with binary data. To overcome this problem,
non-binary coding is combined with hexagonal modulation in this paper to
provide a system which is compatible with binary data. The feasibility and
efficiency are evaluated using a software-defined radio (SDR) based prototype.
Extensive simulation results are presented which show that this approach can
provide improved energy efficiency and spectrum utilization in wireless
communication systems.
Trading Strategies with Position Limits
Date: 2017-12-19 00:00:00
Tasks: Position

Whether you trade futures for yourself or a hedge fund, your strategy is
counted. Long and short position limits make the number of unique strategies
finite. Formulas of the numbers of strategies, transactions, do nothing actions
are derived. A discrete distribution of actions, corresponding probability
mass, cumulative distribution and characteristic functions, moments, extreme
values are presented. Strategies time slice distributions are determined.
Vector properties of trading strategies are studied. Algebraic not associative,
commutative, initial magmas with invertible elements control trading positions
and strategies. Maximum profit strategies, MPS, and optimal trading elements
can define trading patterns. Dynkin introduced the term interpreted in English
as "Markov time" in 1963. Neftci applied it for the formalization of Technical
Analysis in 1991.
A comprehensive statistical study of metabolic and protein-protein interaction network properties
Date: 2018-12-13 00:00:00
Tasks: Descriptive

Understanding the mathematical properties of graphs underling biological
systems could give hints on the evolutionary mechanisms behind these
structures. In this article we perform a complete statistical analysis over
thousands of graphs representing metabolic and protein-protein interaction
(PPI) networks. First, we investigate the quality of fits obtained for the
nodes degree distributions to power-law functions. This analysis suggests that
a power-law distribution poorly describes the data except for the far right
tail in the case of PPI networks. Next we obtain descriptive statistics for the
main graph parameters and try to identify the properties that deviate from the
expected values had the networks been built by randomly linking nodes with the
same degree distribution. This survey identifies the properties of biological
networks which are not solely the result of their degree distribution, but
emerge from yet unidentified mechanisms other than those that drive these
distributions. The findings suggest that, while PPI networks have properties
that differ from their expected values in their randomized versions with great
statistical significance, the differences for metabolic networks have a smaller
statistical significance, though it is possible to identify some drift.
Inferring User Interests in Microblogging Social Networks: A Survey
Authors: Piao Guangyuan, Breslin John G.
Date: 2018-08-14 00:00:00
Tasks: Survey

With the growing popularity of microblogging services such as Twitter in
recent years, an increasing number of users are using these services in their
daily lives. The huge volume of information generated by users raises new
opportunities in various applications and areas. Inferring user interests plays
a significant role in providing personalized recommendations on microblogging
services, and also on third-party applications providing social logins via
these services, especially in cold-start situations. In this survey, we review
user modeling strategies with respect to inferring user interests from previous
studies. To this end, we focus on four dimensions of inferring user interest
profiles: (1) data collection, (2) representation of user interest profiles,
(3) construction and enhancement of user interest profiles, and (4) the
evaluation of the constructed profiles. Through this survey, we aim to provide
an overview of state-of-the-art user modeling strategies for inferring user
interest profiles on microblogging social networks with respect to the four
dimensions. For each dimension, we review and summarize previous studies based
on specified criteria. Finally, we discuss some challenges and opportunities
for future work in this research domain.
PERS: A Personalized and Explainable POI Recommender System
Authors: Baral Ramesh, Li Tao
Date: 2017-12-20 00:00:00
Tasks: Explainable Recommendation, Recommendation Systems

The Location-Based Social Networks (LBSN) (e.g., Facebook) have many factors
(for instance, ratings, check-in time, etc.) that play a crucial role for the
Point-of-Interest (POI) recommendations. Unlike ratings, the reviews can help
users to elaborate their opinion and share the extent of consumption experience
in terms of the relevant factors of interest (aspects). Though some of the
existing recommendation systems have been using the user reviews, most of them
are less transparent and non-interpretable. These reasons have induced
considerable attention towards explainable and interpretable recommendation. To
the best of our knowledge, this is the first paper to exploit the user reviews
to incorporate the sentiment and opinions on different aspects for the
personalized and explainable POI recommendation. In this paper, we propose a
model termed as PERS (Personalized Explainable POI Recommender System) which
models the review-aspect category correlation by exploiting deep neural
network, formulates the user-aspect category bipartite relation as a bipartite
graph, and models the explainable recommendation using bipartite core-based and
ranking-based methods. The major contributions of this paper are: (i) it models
users and locations based on the aspects posted by user via reviews, (ii) it
exploits a deep neural network to model the review-aspect category correlation,
(iii) it provisions the incorporation of multiple contexts (e.g., categorical,
spatial, etc.) in the POI recommendation model, (iv) it formulates the
preference of users' on aspect category as a bipartite relation, represents it
as a location-aspect category bipartite graph, and models the explainable
recommendation with the notion of ordered dense subgraph extraction using
bipartite core-based and ranking-based approaches, and (v) it evaluates the
generated recommendation with three real-world datasets.