pwc-archive/papers-with-abstracts huggingface dataset:
Optimal number of spacers in CRISPR arrays
Date: 2017-05-30 00:00:00

We estimate the number of spacers in a CRISPR array of a bacterium which
maximizes its protection against a viral attack. The optimality follows from a
competition between two trends: too few distinct spacers make the bacteria
vulnerable to an attack by a virus with mutated corresponding protospacers,
while an excessive variety of spacers dilutes the number of the CRISPR
complexes armed with the most recent and thus most effective spacers. We first
evaluate the optimal number of spacers in a simple scenario of an infection by
a single viral species and later consider a more general case of multiple viral
species. We find that depending on such parameters as the concentration of
CRISPR-CAS interference complexes and its preference to arm with more recently
acquired spacers, the rate of viral mutation, and the number of viral species,
the predicted optimal array length lies within a range quite reasonable from
the viewpoint of recent experiments.
Twitter Hashtag Recommendation using Matrix Factorization
Authors: Alvari Hamidreza
Date: 2017-05-30 00:00:00
Tasks: Recommendation Systems, valid

Twitter, one of the biggest and most popular microblogging Websites, has
evolved into a powerful communication platform which allows millions of active
users to generate huge volume of microposts and queries on a daily basis. To
accommodate effective categorization and easy search, users are allowed to make
use of hashtags, keywords or phrases prefixed by hash character, to categorize
and summarize their posts. However, valid hashtags are not restricted and thus
are created in a free and heterogeneous style, increasing difficulty of the
task of tweet categorization. In this paper, we propose a low-rank weighted
matrix factorization based method to recommend hashtags to the users solely
based on their hashtag usage history and independent from their tweets'
contents. We confirm using two-sample t-test that users are more likely to
adopt new hashtags similar to the ones they have previously adopted. In
particular, we formulate the problem of hashtag recommendation into an
optimization problem and incorporate hashtag correlation weight matrix into it
to account for the similarity between different hashtags. We finally leverage
widely used matrix factorization from recommender systems to solve the
optimization problem by capturing the latent factors of users and hashtags.
Empirical experiments demonstrate that our method is capable to properly
recommend hashtags.
Uncertainty Quantification of Oscillation Suppression during DBS in a Coupled Finite Element and Network Model
Date: 2017-05-30 00:00:00
Tasks: Uncertainty Quantification

Models of the cortico-basal ganglia network and volume conductor models of
the brain can provide insight into the mechanisms of action of deep brain
stimulation (DBS). In this study, the coupling of a network model, under
parkinsonian conditions, to the extracellular field distribution obtained from
a three dimensional finite element model of a rodent's brain during DBS is
presented. This coupled model is used to investigate the influence of
uncertainty in the electrical properties of brain tissue and encapsulation
tissue, formed around the electrode after implantation, on the suppression of
oscillatory neural activity during DBS. The resulting uncertainty in this
effect of DBS on the network activity is quantified using a computationally
efficient and non-intrusive stochastic approach based on the generalized
Polynomial Chaos. The results suggest that variations in the electrical
properties of brain tissue may have a substantial influence on the level of
suppression of oscillatory activity during DBS. Applying a global sensitivity
analysis on the suppression of the simulated oscillatory activity showed that
the influence of uncertainty in the electrical properties of the encapsulation
tissue had only a minor influence, in agreement with previous experimental and
computational studies investigating the mechanisms of current-controlled DBS in
the literature.
MDA in Capillary for Whole Genome Amplification
Date: 2017-05-30 00:00:00

Whole genome amplification (WGA) plays an important role in sample
preparation of low-input templates for high-throughput sequencing. Multiple
displacement amplification (MDA), a popular isothermal WGA method, suffers a
major hurdle of highly uneven amplification. Optimizations have been made in
the past by separating the reagents into numbers of tiny chambers or droplets
in microfluidic devices, which significantly improves the amplification
uniformity of MDA. However, skill barrier still exists for biological
researchers to handle chip fabrication and droplet manipulation. Here, we
present a novel MDA protocol, in-capillary MDA (icMDA), which significantly
simplifies the manipulation and improves the uniformity of amplification by
dispersing reagents in a long quasi-1D capillary tubing. We demonstrated that
icMDA is able to accurately detect SNVs with higher efficiency and sensitivity.
Moreover, this straightforward method employs neither customized instruments
nor complicated operations, making it a ready-to-use approach for most
laboratories.
Auditing Search Engines for Differential Satisfaction Across Demographics
Authors: Mehrotra Rishabh, Anderson Ashton, Diaz Fernando, Sharma Amit, Wallach Hanna, Yilmaz Emine
Date: 2017-05-24 00:00:00
Tasks: Causal Inference

Many online services, such as search engines, social media platforms, and
digital marketplaces, are advertised as being available to any user, regardless
of their age, gender, or other demographic factors. However, there are growing
concerns that these services may systematically underserve some groups of
users. In this paper, we present a framework for internally auditing such
services for differences in user satisfaction across demographic groups, using
search engines as a case study. We first explain the pitfalls of na\"ively
comparing the behavioral metrics that are commonly used to evaluate search
engines. We then propose three methods for measuring latent differences in user
satisfaction from observed differences in evaluation metrics. To develop these
methods, we drew on ideas from the causal inference literature and the
multilevel modeling literature. Our framework is broadly applicable to other
online services, and provides general insight into interpreting their
evaluation metrics.
Modelling the order of driver mutations and metabolic mutations as structures in cancer dynamics
Date: 2017-06-26 00:00:00

Recent works have stressed the important role that random mutations have in
the development of cancer phenotype. We challenge this current view by means of
bioinformatic data analysis and computational modelling approaches. Not all the
mutations are equally important for the development of metastasis. The survival
of cancer cells from the primary tumour site to the secondary seeding sites
depends on the occurrence of very few driver mutations promoting oncogenic cell
behaviours and on the order with which these mutations occur. We introduce a
model in the framework of Cellular Automata to investigate the effects of
metabolic mutations and mutation order on cancer stemness and tumour cell
migration in bone metastasised breast cancers. The metabolism of the cancer
cell is a key factor in its proliferation rate. Bioinformatics analysis on a
cancer mutation database shows that metabolism-modifying alterations constitute
an important class of key cancer mutations. Our approach models three types of
mutations: drivers, the order of which is relevant for the dynamics, metabolic
which support cancer growth and are estimated from existing databases, and
non--driver mutations. Our results provide a quantitative basis of how the
order of driver mutations and the metabolic mutations in different cancer
clones could impact proliferation of therapy-resistant clonal populations and
patient survival. Further mathematical modelling of the order of mutations is
presented in terms of operators. We believe our work is novel because it
quantifies two important factors in cancer spreading models: the order of
driver mutations and the effects of metabolic mutations.
Evolutionary dynamics of incubation periods
Date: 2017-05-30 00:00:00
Tasks: Open-Ended Question Answering

The incubation period of a disease is the time between an initiating
pathologic event and the onset of symptoms. For typhoid fever, polio, measles,
leukemia and many other diseases, the incubation period is highly variable.
Some affected people take much longer than average to show symptoms, leading to
a distribution of incubation periods that is right skewed and often
approximately lognormal. Although this statistical pattern was discovered more
than sixty years ago, it remains an open question to explain its ubiquity. Here
we propose an explanation based on evolutionary dynamics on graphs. For simple
models of a mutant or pathogen invading a network-structured population of
healthy cells, we show that skewed distributions of incubation periods emerge
for a wide range of assumptions about invader fitness, competition dynamics,
and network structure. The skewness stems from stochastic mechanisms associated
with two classic problems in probability theory: the coupon collector and the
random walk. Unlike previous explanations that rely crucially on heterogeneity,
our results hold even for homogeneous populations. Thus, we predict that two
equally healthy individuals subjected to equal doses of equally pathogenic
agents may, by chance alone, show remarkably different time courses of disease.
Dynamics of post-translational modification systems: recent progress and future directions
Date: 2017-10-24 00:00:00

Post-translational modification (PTM) of proteins plays a key role in signal
transduction, and hence significant effort has gone toward understanding how
PTM networks process information. This involves, on the theory side, analyzing
the dynamical systems arising from such networks. Which networks are, for
instance, bistable? Which networks admit sustained oscillations? Which
parameter values enable such behaviors? In this Perspective, we highlight
recent progress in this area and point out some important future directions.
Along the way, we summarize several techniques for analyzing general networks,
such as eliminating variables to obtain steady-state parametrizations, and
harnessing results on how incorporating intermediates affects dynamics.
Trends in Banking 2017 and onwards
Date: 2017-05-31 00:00:00

The changing nature of the relationship between a retail bank and its
customers is examined, particularly with respect to new financial concepts,
debt and regulation. The traditional image of a bank is portrayed as a physical
building a classical Doric portico. This image conveys concepts of service,
soundness, strength, stability and security ("five-S"). That "five-S" concept
is changing, and the evidence for changes that affect customers directly is
considered. A fundamental legal problem associated with those changes is
highlighted: a bank is no longer solely responsible for the safeguard of
customer monies. A solution to this problem is proposed: banks should be
jointly liable with perpetrators of criminal activity in the event of frauds as
an encouragement to recognise and mitigate fraud.
Net reproduction functions for nonlinear structured population models
Date: 2018-02-26 00:00:00

The goal of this note is to present a general approach to define the net
reproduction function for a large class of nonlinear physiologically structured
population models. In particular, we are going to show that this can be
achieved in a natural way by reformulating a nonlinear problem as a family of
linear ones; each of the linear problems describing the evolution of the
population in a different, but constant environment. The reformulation of a
nonlinear population model as a family of linear ones is a new approach, and
provides an elegant way to study qualitative questions, for example the
existence of positive steady states. To define the net reproduction number for
any fixed (constant) environment, i.e. for the linear models, we use a fairly
recent spectral theoretic result, which characterizes the connection between
the spectral bound of an unbounded operator and the spectral radius of a
corresponding bounded operator. For nonlinear models, varying the environment
naturally leads to a net reproduction function.
LRSE: A Lightweight Efficient Searchable Encryption Scheme using Local and Global Representations
Authors: Zhao Ruihui, Sun Yuanliang, Iwaihara Mizuho
Date: 2017-05-24 00:00:00
Tasks: Cloud Computing, Retrieval

Cloud computing is emerging as a revolutionary computing paradigm, while
security and privacy become major concerns in the cloud scenario. For which
Searchable Encryption (SE) technology is proposed to support efficient
retrieval of encrypted data. However, the absence of lightweight ranked search
with higher search quality in a harsh adversary model is still a typical
shortage in existing SE schemes. In this paper, we propose a novel SE scheme
called LRSE which firstly integrates machine learning methods into the
framework of SE and combines local and global representations of encrypted
cloud data to achieve the above design goals. In LRSE, we employ an improved
secure kNN scheme to guarantee sufficient privacy protection. Our detailed
security analysis shows that LRSE satisfies our formulated privacy
requirements. Extensive experiments performed on benchmark datasets demonstrate
that LRSE indeed achieves state-of-the-art search quality with lowest system
cost.
A Formal Approach to Modeling the Cost of Cognitive Control
Date: 2017-05-31 00:00:00

This paper introduces a formal method to model the level of demand on control
when executing cognitive processes. The cost of cognitive control is parsed
into an intensity cost which encapsulates how much additional input information
is required so as to get the specified response, and an interaction cost which
encapsulates the level of interference between individual processes in a
network. We develop a formal relationship between the probability of successful
execution of desired processes and the control signals (additive control
biases). This relationship is also used to specify optimal control policies to
achieve a desired probability of activation for processes. We observe that
there are boundary cases when finding such control policies which leads us to
introduce the interaction cost. We show that the interaction cost is influenced
by the relative strengths of individual processes, as well as the
directionality of the underlying competition between processes.
Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction
Date: 2017-05-31 00:00:00
Tasks: Prediction, Transcription Factor Binding Site Prediction

We present a simple and efficient method for prediction of transcription
factor binding sites from DNA sequence. Our method computes a random
approximation of a convolutional kernel feature map from DNA sequence and then
learns a linear model from the approximated feature map. Our method outperforms
state-of-the-art deep learning methods on five out of six test datasets from
the ENCODE consortium, while training in less than one eighth the time.
Item-Item Music Recommendations With Side Information
Authors: Demir Özgür, Yakushev Alexey Rodriguez, Keddo Rany, Kallio Ursula
Date: 2017-12-18 00:00:00
Tasks: Collaborative Filtering, Diversity

Online music services have tens of millions of tracks. The content itself is
broad and covers various musical genres as well as non-musical audio content
such as radio plays and podcasts. The sheer scale and diversity of content
makes it difficult for a user to find relevant tracks. Relevant recommendations
are therefore crucial for a good user experience. Here we present a method to
compute track-track similarities using collaborative filtering signals with
side information. On a data set from music streaming service SoundCloud, the
method here outperforms the widely adopted implicit matrix factorization
technique. The implementation of our method is open sourced and can be applied
to related item-item recommendation tasks with side information.
A Complete Year of User Retrieval Sessions in a Social Sciences Academic Search Engine
Authors: Mayr Philipp, Kacem Ameni
Date: 2017-09-23 00:00:00
Tasks: Retrieval

In this paper, we present an open data set extracted from the transaction log
of the social sciences academic search engine sowiport. The data set includes a
filtered set of 484,449 retrieval sessions which have been carried out by
sowiport users in the period from April 2014 to April 2015. We propose a
description of interactions performed by the academic search engine users that
can be used in different applications such as result ranking improvement, user
modeling, query reformulation analysis, search pattern recognition.
Asymptotic scaling properties of the posterior mean and variance in the Gaussian scale mixture model
Date: 2017-11-28 00:00:00

The Gaussian scale mixture model (GSM) is a simple yet powerful probabilistic
generative model of natural image patches. In line with the well-established
idea that sensory processing is adapted to the statistics of the natural
environment, the GSM has also been considered a model of the early visual
system, as a reasonable "first-order" approximation of the internal model that
the primary visual cortex (V1) implements. According to this view, neural
activities in V1 represent the posterior distribution under the GSM given a
particular visual stimulus. Indeed, (approximate) inference under the GSM has
successfully accounted for various nonlinearities in the mean (trial-average)
responses of V1 neurons, as well as the dependence of (across-trial) response
variability with stimulus contrast found in V1 recordings. However, previous
work almost exclusively relied on numerical simulations to obtain these
results. Thus, for a deeper insight into the realm of possible behaviours the
GSM can (and cannot) exhibit and predict, here we present analytical
derivations for the limiting behaviour of the mean and (co)variance of the GSM
posterior at very low and very high contrast levels. These results should guide
future work exploring neural circuit dynamics appropriate for implementing
inference under the GSM.
Widespread bursts of diversification in microbial phylogenies
Date: 2017-10-24 00:00:00
Tasks: Diversity

Until recently, much of the microbial world was hidden from view. A global
research effort has changed this, unveiling and quantifying microbial diversity
across enormous range of critically-important contexts, from the human
microbiome, to plant-soil interactions, to marine life. Yet what has remained
largely hidden is the interplay of ecological and evolutionary processes that
led to the diversity we observe in the present day. We introduce a theoretical
framework to quantify the effect of ecological innovations in microbial
evolutionary history, using a new, coarse-grained approach that is robust to
the incompleteness and ambiguities in microbial community data. Applying this
methodology, we identify a balance of gradual, ongoing diversification and
rapid bursts across a vast range of microbial habitats. Moreover, we find
universal quantitative similarities in the tempo of diversification,
independent of habitat type.
Exciton transport in the PE545 complex: insight from atomistic QM/MM-based quantum master equations and elastic network models
Date: 2017-05-29 00:00:00

In this paper we work out a parameterization of the environment noise within
the Haken-Strobl-Reinenker (HSR) model for the PE545 light-harvesting complex
based on atomic-level quantum mechanics/molecular mechanics (QM/MM)
simulations. We use this approach to investigate the role of different auto-
and cross-correlations in the HSR noise tensor, confirming that site-energy
autocorrelations (pure dephasing) terms dominate the noise-induced exciton
mobility enhancement, followed by site energy-coupling cross-correlations for
specific triplets of pigments. Interestingly, several cross-correlations of the
latter kind, together with coupling-coupling cross-correlations, display clear
low-frequency signatures in their spectral densities in the region 30-70
inverse centimeters. These slow components lie at the limits of validity of the
HSR approach, requiring that environmental fluctuations be faster than typical
exciton transfer time scales. We show that a simple coarse-grained
elastic-network-model (ENM) analysis of the PE545 protein naturally spotlights
collective normal modes in this frequency range, that represent specific
concerted motions of the subnetwork of cysteines that are covalenty linked to
the pigments. This analysis strongly suggests that protein scaffolds in
light-harvesting complexes are able to express specific collective,
low-frequency normal modes providing a fold-rooted blueprint of exciton
transport pathways. We speculate that ENM-based mixed quantum classical
methods, such as Ehrenfest dynamics, might be promising tools to disentangle
the fundamental designing principles of these dynamical processes in natural
and artificial light-harvesting structures.
RBA like problem with thermo-kinetics is non convex
Date: 2017-05-23 00:00:00

The aim of this short note is to show that the class of problem involving
kinetic or thermo-kinetic constraints in addition to the usual stoechiometric
one is non convex.
Hedging in fractional Black-Scholes model with transaction costs
Date: 2017-07-26 00:00:00

We consider conditional-mean hedging in a fractional Black-Scholes pricing
model in the presence of proportional transaction costs. We develop an explicit
formula for the conditional-mean hedging portfolio in terms of the recently
discovered explicit conditional law of the fractional Brownian motion.
Econophysics of Macro-Finance: Local Multi-fluid Models and Surface-like Waves of Financial Variables
Date: 2017-05-17 00:00:00

This paper models macro financial variables alike to financial fluids with
local interactions and describes surface-like waves of Investment and Profits.
We regard macro-finance as ensemble of economic agents and use their risk
ratings as coordinates on economic space. Aggregations of agent's financial
variables with risk coordinates x on economic space define macro financial
variables as function of x. We describe evolution and interactions between
macro financial variables alike to financial fluids by hydrodynamic-like
equations. Minimum and maximum risk grades define most secure and most risky
agents respectively. That determines borders of macro-finance domain that is
filled by economic agents. Perturbations of agent's risk coordinates near risk
borders of macro domain cause disturbances of macro financial variables like
Investment and Profits. Such disturbances can generate waves that propagate
along risk borders. These waves may exponentially amplify perturbations inside
of macro domain and impact financial sustainability. We study simple model
Investment and Profits and describe linear approximation of steady state
distributions of Investment and Profits on macro-finance domain that fulfill
dreams of Investors: "more risks-more Profits". We describe Investment and
Profits waves on risk border of economic space alike to surface waves in
fluids. We present simple examples that specify waves as possible origin of
time fluctuations of macro financial variables. Description of possible steady
state distributions of macro financial variables and financial risk waves on
economic space could help for better policy-making and managing sustainable
macro-finance.