pwc-archive/papers-with-abstracts huggingface dataset:
Hypotheses generation using link prediction in a bipartite graph
Authors: Kim Jung-Hun, Segev Aviv
Date: 2017-12-26 00:00:00
Tasks: Collaborative Filtering, Link Prediction

The large volume of scientific publications is likely to have hidden
knowledge that can be used for suggesting new research topics. We propose an
automatic method that is helpful for generating research hypotheses in the
field of physics using the massive number of physics journal publications. We
convert the text data of titles and abstract sections in publications to a
bipartite graph, extracting words of physical matter composed of chemical
elements and extracting related keywords in the paper. The proposed method
predicts the formation of new links between matter and keyword nodes based on
collaborative filtering and matter popularity. The formation of links
represents research hypotheses, as it suggests the new possible relationships
between physical matter and keywords for physical properties or phenomena. The
suggested method has better performance than existing methods for link
prediction in the entire bipartite graph and the subgraph that contains only a
specific keyword, such as `antiferromagnetism' or `superconductivity.'
Measures of hydroxymethylation
Date: 2017-08-17 00:00:00

Hydroxymethylcytosine (5hmC) methylation is known to be a possible epigenetic
mark impacting genome stability. In this paper, we address the existing 5hmC
measure $\Delta \beta$ and discuss its properties both analytically and
empirically on real data. Then we introduce several alternative
hydroxymethylation measures and compare their properties with those of $\Delta
\beta$. All results will be illustrated by means of real data analyses.
Entropy Transfer and Dynamics of Allostery in Proteins
Date: 2017-08-16 00:00:00

Allostery is an intrinsic spatiotemporal property of all proteins, resulting
from long range correlations in the order of several nanometers and time scales
of nanoseconds. Information is carried asymmetrically from one part to another
by entropy transfer. Here, we present a master equation model of allosteric
communication in proteins based on the transfer entropy concept of Schreiber
(PRL, 85, 465, 2000). We show how the model relates the path and velocity of
asymmetric entropy transfer to conformational transitions over the rugged
energy surface of proteins and how this relates to function.
qBitcoin: A Peer-to-Peer Quantum Cash System
Date: 2017-11-19 00:00:00
Tasks: Quantization

A decentralized online quantum cash system, called qBitcoin, is given. We
design the system which has great benefits of quantization in the following
sense. Firstly, quantum teleportation technology is used for coin transaction,
which prevents from the owner of the coin keeping the original coin data even
after sending the coin to another. This was a main problem in a classical
circuit and a blockchain was introduced to solve this issue. In qBitcoin, the
double-spending problem never happens and its security is guaranteed
theoretically by virtue of quantum information theory. Making a block is time
consuming and the system of qBitcoin is based on a quantum chain, instead of
blocks. Therefore a payment can be completed much faster than Bitcoin. Moreover
we employ quantum digital signature so that it naturally inherits properties of
peer-to-peer (P2P) cash system as originally proposed in Bitcoin.
Nonclassical phase diagram for virus bacterial co-evolution mediated by CRISPR
Date: 2017-08-16 00:00:00

CRISPR is a newly discovered prokaryotic immune system. Bacteria and archaea
with this system incorporate genetic material from invading viruses into their
genomes, providing protection against future infection by similar viruses. The
conditions for coexistence of prokaryots and viruses is an interesting problem
in evolutionary biology. In this work, we show an intriguing phase diagram of
the virus extinction probability, which is more complex than that of the
classical predator-prey model. As the CRISPR incorporates genetic material,
viruses are under pressure to evolve to escape the recognition by CRISPR. When
bacteria have a small rate of deleting spacers, a new parameter region in which
bacteria and viruses can coexist arises, and it leads to a more complex
coexistence patten for bacteria and viruses. For example, when the virus
mutation rate is low, the virus extinction probability changes non-montonically
with the bacterial exposure rate. The virus and bacteria co-evolution not only
alters the virus extinction probability, but also changes the bacterial
population structure. Additionally, we show that recombination is a successful
strategy for viruses to escape from CRISPR recognition when viruses have
multiple proto-spacers, providing support for a recombination-mediated escape
mechanism suggested experimentally. Finally, we suggest that the reentrant
phase diagram, in which phages can progress through three phases of extinction
and two phases of abundance at low spacer deletion rates as a function of
exposure rate to bacteria, is an experimentally testable phenomenon.
Automatic Organisation and Quality Analysis of User-Generated Content with Audio Fingerprinting
Date: 2017-08-17 00:00:00

The increase of the quantity of user-generated content experienced in social
media has boosted the importance of analysing and organising the content by its
quality. Here, we propose a method that uses audio fingerprinting to organise
and infer the quality of user-generated audio content. The proposed method
detects the overlapping segments between different audio clips to organise and
cluster the data according to events, and to infer the audio quality of the
samples. A test setup with concert recordings manually crawled from YouTube is
used to validate the presented method. The results show that the proposed
method achieves better results than previous methods.
Automatic Organisation, Segmentation, and Filtering of User-Generated Audio Content
Authors: Mordido Gonçalo, Magalhães João, Cavaco Sofia
Date: 2017-08-17 00:00:00

Using solely the information retrieved by audio fingerprinting techniques, we
propose methods to treat a possibly large dataset of user-generated audio
content, that (1) enable the grouping of several audio files that contain a
common audio excerpt (i.e., are relative to the same event), and (2) give
information about how those files are correlated in terms of time and quality
inside each event. Furthermore, we use supervised learning to detect incorrect
matches that may arise from the audio fingerprinting algorithm itself, whilst
ensuring our model learns with previous predictions. All the presented methods
were further validated by user-generated recordings of several different
concerts manually crawled from YouTube.
An indifference approach to the cost of capital constraints: KVA and beyond
Date: 2017-08-17 00:00:00

The strengthening of capital requirements has induced banks and traders to
consider charging a so called capital valuation adjustment (KVA) to the clients
in OTC transactions. This roughly corresponds to charge the clients ex-ante the
profit requirement that is asked to the trading desk. In the following we try
to delineate a possible way to assess the impact of capital constraints in the
valuation of a deal. We resort to an optimisation stemming from an indifference
pricing approach, and we study both the linear problem from the point of view
of the whole bank and the non-linear problem given by the viewpoint of
shareholders. We also consider the case where one optimises the median rather
than the mean statistics of the profit and loss distribution.
Genetic-metabolic networks can be modeled as toric varieties
Date: 2017-11-10 00:00:00

The mathematical modelling of genetic-metabolic networks is of out most
importance in the field of systems biology. Different formalisms and a huge
variety of classical mathematical tools have been used to describe and analyse
such networks. Michael A. Savageau defined a formalism to model
genetic-metabolic networks called S-Systems, [12], [13], [15]. There is a limit
in the number of nodes that can be analysed when these systems are solved using
classical numerical methods such as non-linear dynamic analysis and linear
optimization algorithms. We propose to use toric algebraic geometry to solve
S-systems. In this work we prove that S-systems are toric varieties and that as
a consequence Hilbert basis can be used to solve them. This is achieved by
applying two theorems, proved here, the theorem about Embedding of S-Systems in
toric varieties and the theorem about Toric Resolution on S-Systems. In
addition, we define the realization of minimal phenotypic polytopes, phenotypic
toric ideals and phenotypic toric varieties as a generalization of the
phenotypic polytopes described by M. Savageau, [8], [9], [10]. Also we studied
a synthetic oscillatory network of two genes under diverse environmental
control modes, we reproduce S. Savageau results and additionally we obtain an
invariant sustained oscillatory region. The implications of the results here
presented is that, in principle, they will facilitate solving large scale
genetic-metabolic networks by means of toric algebraic geometry tools as well
as facilitate elucidating their dynamics.
EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets
Authors: Hasanain Maram, Suwaileh Reem, Elsayed Tamer, Kutlu Mucahid, Almerekhi Hind
Date: 2017-08-21 00:00:00
Tasks: Event Detection, Information Retrieval, Retrieval

This article introduces a new language-independent approach for creating a
large-scale high-quality test collection of tweets that supports multiple
information retrieval (IR) tasks without running a shared-task campaign. The
adopted approach (demonstrated over Arabic tweets) designs the collection
around significant (i.e., popular) events, which enables the development of
topics that represent frequent information needs of Twitter users for which
rich content exists. That inherently facilitates the support of multiple tasks
that generally revolve around events, namely event detection, ad-hoc search,
timeline generation, and real-time summarization. The key highlights of the
approach include diversifying the judgment pool via interactive search and
multiple manually-crafted queries per topic, collecting high-quality
annotations via crowd-workers for relevancy and in-house annotators for
novelty, filtering out low-agreement topics and inaccessible tweets, and
providing multiple subsets of the collection for better availability. Applying
our methodology on Arabic tweets resulted in EveTAR , the first
freely-available tweet test collection for multiple IR tasks. EveTAR includes a
crawl of 355M Arabic tweets and covers 50 significant events for which about
62K tweets were judged with substantial average inter-annotator agreement
(Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating
existing algorithms in the respective tasks. Results indicate that the new
collection can support reliable ranking of IR systems that is comparable to
similar TREC collections, while providing strong baseline results for future
studies over Arabic tweets.
On the structure of RNA branching polytopes
Date: 2017-08-18 00:00:00

The prevalent method for RNA secondary structure prediction for a single
sequence is free energy minimization based on the nearest neighbor
thermodynamic model (NNTM). One of the least well-developed parts of the model
is the energy function assigned to the multibranch loops. Parametric analysis
can be performed to elucidate the dependance of the prediction on the branching
parameters used in the NNTM. Since the objective function is linear, this boils
down to analyzing the normal fans of the branching polytopes. Here we show that
because of the way the multibranch loops are scored under the NNTM, certain
branching patterns are possible for all sequences. We do this by characterizing
the dominant parts of the parameter space obtained by looking at the relevant
section of the normal fan; therefore, we conclude that the structures that are
normally found in nature are obtained for a relatively small set of parameters.
Portfolio Optimization with Entropic Value-at-Risk
Date: 2017-08-18 00:00:00
Tasks: Computational Efficiency, Portfolio Optimization

The entropic value-at-risk (EVaR) is a new coherent risk measure, which is an
upper bound for both the value-at-risk (VaR) and conditional value-at-risk
(CVaR). As important properties, the EVaR is strongly monotone over its domain
and strictly monotone over a broad sub-domain including all continuous
distributions, while well-known monotone risk measures, such as VaR and CVaR
lack these properties. A key feature for a risk measure, besides its financial
properties, is its applicability in large-scale sample-based portfolio
optimization. If the negative return of an investment portfolio is a
differentiable convex function, the portfolio optimization with the EVaR
results in a differentiable convex program whose number of variables and
constraints is independent of the sample size, which is not the case for the
VaR and CVaR. This enables us to design an efficient algorithm using
differentiable convex optimization. Our extensive numerical study shows the
high efficiency of the algorithm in large scales, compared to the existing
convex optimization software packages. The computational efficiency of the EVaR
portfolio optimization approach is also compared with that of CVaR-based
portfolio optimization. This comparison shows that the EVaR approach generally
performs similarly, and it outperforms as the sample size increases. Moreover,
the comparison of the portfolios obtained for a real case by the EVaR and CVaR
approaches shows that the EVaR approach can find portfolios with better
expectations and VaR values at high confidence levels.
Integrative analysis reveals disrupted pathways regulated by microRNAs in cancer
Date: 2017-08-18 00:00:00
Tasks: Dimensionality Reduction

MicroRNAs (miRNAs) are small endogenous regulatory molecules that modulate
gene expression post-transcriptionally. Although differential expression of
miRNAs have been implicated in many diseases (including cancers), the
underlying mechanisms of action remain unclear. Because each miRNA can target
multiple genes, miRNAs may potentially have functional implications for the
overall behavior of entire pathways. Here we investigate the functional
consequences of miRNA dysregulation through an integrative analysis of miRNA
and mRNA expression data using a novel approach that incorporates pathway
information a priori. By searching for miRNA-pathway associations that differ
between healthy and tumor tissue, we identify specific relationships at the
systems-level which are disrupted in cancer. Our approach is motivated by the
hypothesis that if a miRNA and pathway are associated, then the expression of
the miRNA and the collective behavior of the genes in a pathway will be
correlated. As such, we first obtain an expression-based summary of pathway
activity using Isomap, a dimension reduction method which can articulate
nonlinear structure in high-dimensional data. We then search for miRNAs that
exhibit differential correlations with the pathway summary between phenotypes
as a means of finding aberrant miRNA-pathway coregulation in tumors. We apply
our method to cancer data using gene and miRNA expression datasets from The
Cancer Genome Atlas (TCGA) and compare ${\sim}10^5$ miRNA-pathway relationships
between healthy and tumor samples from four tissues (breast, prostate, lung,
and liver). Many of the flagged pairs we identify have a biological basis for
disruption in cancer.
Emergent L\'evy behavior in single-cell stochastic gene expression
Date: 2017-10-24 00:00:00

Single-cell gene expression is inherently stochastic; its emergent behavior
can be defined in terms of the chemical master equation describing the
evolution of the mRNA and protein copy numbers as the latter tends to infinity.
We establish two types of "macroscopic limits": the Kurtz limit is consistent
with the classical chemical kinetics, while the L\'{e}vy limit provides a
theoretical foundation for an empirical equation proposed in [Phys. Rev. Lett.
97:168302, 2006]. Furthermore, we clarify the biochemical implications and
ranges of applicability for various macroscopic limits and calculate a
comprehensive analytic expression for the protein concentration distribution in
autoregulatory gene networks. The relationship between our work and modern
population genetics is discussed.
Modelling Word Burstiness in Natural Language: A Generalised Polya Process for Document Language Models in Information Retrieval
Authors: Cummins Ronan
Date: 2017-08-20 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Retrieval

We introduce a generalised multivariate Polya process for document language
modelling. The framework outlined here generalises a number of statistical
language models used in information retrieval for modelling document
generation. In particular, we show that the choice of replacement matrix M
ultimately defines the type of random process and therefore defines a
particular type of document language model. We show that a particular variant
of the general model is useful for modelling term-specific burstiness.
Furthermore, via experimentation we show that this variant significantly
improves retrieval effectiveness over a strong baseline on a number of small
test collections.
Opto-magnetic imaging of neural network activity in brain slices at high resolution using color centers in diamond
Date: 2017-08-21 00:00:00

We suggest a novel approach for wide-field imaging of the neural network
dynamics of brain slices that uses highly sensitivity magnetometry based on
nitrogen-vacancy (NV) centers in diamond. In-vitro recordings in brain slices
is a proven method for the characterization of electrical neural activity and
has strongly contributed to our understanding of the mechanisms that govern
neural information processing. However, traditional recordings can only acquire
signals from a few positions simultaneously, which severely limits their
ability to characterize the dynamics of the underlying neural networks. We
suggest to radically extend the scope of this method using the wide-field
imaging of the neural magnetic fields across the slice by means of NV
magnetometry. Employing comprehensive computational simulations and theoretical
analyses, we characterize the spatiotemporal characteristics of the neural
magnetic fields and derive the required key performance parameters of an
imaging setup based on NV magnetometry. In particular, we determine how the
technical parameters determine the achievable spatial resolution for an optimal
reconstruction of the neural currents from the measured field distributions.
Finally, we compare the imaging of neural slice activity with that of a single
planar pyramidal cell. Our results suggest that imaging of neural slice
activity will be possible with the upcoming generation of NV magnetic field
sensors, while imaging of the activity of a single planar cell remains more
challenging.
Effect of Local Population Uncertainty on Cooperation in Bacteria
Date: 2017-08-21 00:00:00

Bacteria populations rely on mechanisms such as quorum sensing to coordinate
complex tasks that cannot be achieved by a single bacterium. Quorum sensing is
used to measure the local bacteria population density, and it controls
cooperation by ensuring that a bacterium only commits the resources for
cooperation when it expects its neighbors to reciprocate. This paper proposes a
simple model for sharing a resource in a bacterial environment, where knowledge
of the population influences each bacterium's behavior. Game theory is used to
model the behavioral dynamics, where the net payoff (i.e., utility) for each
bacterium is a function of its current behavior and that of the other bacteria.
The game is first evaluated with perfect knowledge of the population. Then, the
unreliability of diffusion introduces uncertainty in the local population
estimate and changes the perceived payoffs. The results demonstrate the
sensitivity to the system parameters and how population uncertainty can
overcome a lack of explicit coordination.
Explainable Recommendation: Theory and Applications
Authors: Zhang Yongfeng
Date: 2017-08-21 00:00:00
Tasks: Explainable Recommendation, Recommendation Systems, Sentiment Analysis, Time Series Analysis

Although personalized recommendation has been investigated for decades, the
wide adoption of Latent Factor Models (LFM) has made the explainability of
recommendations a critical issue to both the research community and practical
application of recommender systems. For example, in many practical systems the
algorithm just provides a personalized item recommendation list to the users,
without persuasive personalized explanation about why such an item is
recommended while another is not. Unexplainable recommendations introduce
negative effects to the trustworthiness of recommender systems, and thus affect
the effectiveness of recommendation engines. In this work, we investigate
explainable recommendation in aspects of data explainability, model
explainability, and result explainability, and the main contributions are as
follows:
  1. Data Explainability: We propose Localized Matrix Factorization (LMF)
framework based Bordered Block Diagonal Form (BBDF) matrices, and further
applied this technique for parallelized matrix factorization.
  2. Model Explainability: We propose Explicit Factor Models (EFM) based on
phrase-level sentiment analysis, as well as dynamic user preference modeling
based on time series analysis. In this work, we extract product features and
user opinions towards different features from large-scale user textual reviews
based on phrase-level sentiment analysis techniques, and introduce the EFM
approach for explainable model learning and recommendation.
  3. Economic Explainability: We propose the Total Surplus Maximization (TSM)
framework for personalized recommendation, as well as the model specification
in different types of online applications. Based on basic economic concepts, we
provide the definitions of utility, cost, and surplus in the application
scenario of Web services, and propose the general framework of web total
surplus calculation and maximization.
Three faces of node importance in network epidemiology: Exact results for small graphs
Date: 2017-10-14 00:00:00
Tasks: Epidemiology

We investigate three aspects of the importance of nodes with respect to
Susceptible-Infectious-Removed (SIR) disease dynamics: influence maximization
(the expected outbreak size given a set of seed nodes), the effect of
vaccination (how much deleting nodes would reduce the expected outbreak size)
and sentinel surveillance (how early an outbreak could be detected with sensors
at a set of nodes). We calculate the exact expressions of these quantities, as
functions of the SIR parameters, for all connected graphs of three to seven
nodes. We obtain the smallest graphs where the optimal node sets are not
overlapping. We find that: node separation is more important than centrality
for more than one active node, that vaccination and influence maximization are
the most different aspects of importance, and that the three aspects are more
similar when the infection rate is low.