pwc-archive/papers-with-abstracts huggingface dataset:
Efficient simulations of tubulin-driven axonal growth
Date: 2016-04-14 00:00:00

This work concerns efficient and reliable numerical simulations of the
dynamic behaviour of a moving-boundary model for tubulin-driven axonal growth.
The model is nonlinear and consists of a coupled set of a partial differential
equation (PDE) and two ordinary differential equations. The PDE is defined on a
computational domain with a moving boundary, which is part of the solution.
Numerical simulations based on standard explicit time-stepping methods are too
time consuming due to the small time steps required for numerical stability. On
the other hand standard implicit schemes are too complex due to the nonlinear
equations that needs to be solved in each step. Instead, we propose to use the
Peaceman--Rachford splitting scheme combined with temporal and spatial scalings
of the model. Simulations based on this scheme have shown to be efficient,
accurate, and reliable which makes it possible to evaluate the model, e.g.\ its
dependency on biological and physical model parameters. These evaluations show
among other things that the initial axon growth is very fast, that the active
transport is the dominant reason over diffusion for the growth velocity, and
that the polymerization rate in the growth cone does not affect the final axon
length.
On the stochastic evolution of finite populations
Date: 2017-04-13 00:00:00
Tasks: valid

This work is a systematic study of discrete Markov chains that are used to
describe the evolution of a two-types population. Motivated by results valid
for the well-known Moran (M) and Wright-Fisher (WF) processes, we define a
general class of Markov chains models which we term the Kimura class. It
comprises the majority of the models used in population genetics, and we show
that many well-known results valid for M and WF processes are still valid in
this class. In all Kimura processes, a mutant gene will either fixate or become
extinct, and we present a necessary and sufficient condition for such processes
to have the probability of fixation strictly increasing in the initial
frequency of mutants. This condition implies that there are WF processes with
decreasing fixation probability --- in contradistinction to M processes which
always have strictly increasing fixation probability. As a by-product, we show
that an increasing fixation probability defines uniquely an M or WF process
which realises it, and that any fixation probability with no state having
trivial fixation can be realised by at least some WF process. These results are
extended to a subclass of processes that are suitable for describing
time-inhomogeneous dynamics. We also discuss the traditional identification of
frequency dependent fitnesses and pay-offs, extensively used in evolutionary
game theory, the role of weak selection when the population is finite, and the
relations between jumps in evolutionary processes and frequency dependent
fitnesses.
Optimality of spatially inhomogeneous search strategies
Date: 2016-08-26 00:00:00

We consider random search processes alternating stochastically between
diffusion and ballistic motion, in which the distribution function of ballistic
motion directions varies from point to point in space. The specific space
dependence of the directional distribution together with the switching rates
between the two modes of motion establishes a spatially inhomogeneous search
strategy. We show that the mean first passage times for several standard search
problems - narrow escape, reaction partner finding, reaction-escape - can be
minimized with a directional distribution that is reminiscent of the spatial
organization of the cytoskeleton filaments of cells with a centrosome: radial
ballistic transport from center to periphery and back, and ballistic transport
in random directions within a concentric shell of thickness $\Delta_{\rm opt}$
along the domain boundary. The results suggest that living cells realize
efficient search strategies for various intracellular transport problems
economically through a spatial cytoskeleton organization that involves radial
microtubules in the central region and only a narrow actin cortex rather than a
cell body filled with randomly oriented actin filaments.
Context tree selection for functional data
Date: 2018-01-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Model Selection

It has been repeatedly conjectured that the brain retrieves statistical
regularities from stimuli. Here we present a new statistical approach allowing
to address this conjecture. This approach is based on a new class of stochastic
processes driven by chains with memory of variable length. It leads to a new
experimental protocol in which sequences of auditory stimuli generated by a
stochastic chain are presented to volunteers while electroencephalographic
(EEG) data is recorded from their scalp. A new statistical model selection
procedure for functional data is introduced and proved to be consistent.
Applied to samples of EEG data collected using our experimental protocol it
produces results supporting the conjecture that the brain effectively
identifies the structure of the chain generating the sequence of stimuli.
Information processing and Fechner's problem as a choice of arithmetic
Date: 2016-02-25 00:00:00

Fechner's law and its modern generalizations can be regarded as
manifestations of alternative forms of arithmetic, coexisting at stimulus and
sensation levels. The world of sensations may be thus described by a
generalization of the standard mathematical calculus.
A Quantitative Analysis of Localized Robustness of MYCN in Neuroblastoma
Date: 2015-12-13 00:00:00
Tasks: Prognosis

The amplification of the gene MYCN (V-myc myelocytomatosis viral-valeted
oncogene, neuroblastoma derived) has been a well-documented indicator for poor
prognosis in neuroblastoma, a childhood cancer. Unfortunately, there has been
limited success in understanding MYCN functionality in the landscape of
neuroblastoma and more importantly, given that MYCN has been deemed
undruggable, the need to potentially illuminate key opportunities that
indirectly target MYCN is of great interest. To this end, this work employs an
emerging quantitative technique from network science, namely network curvature,
to quantify the biological robustness of MYCN and its surrounding neighborhood.
In particular, when amplified in Stage IV cancer, MYCN exhibits higher
curvature (more robust) than those samples with under expressed MYCN levels.
When examining the surrounding neighborhood, the above argument still holds for
network curvature, but is lost when only analyzing differential expression - a
common technique amongst oncologists and computational/molecular biologists.
This finding points to the problem (and possible solution) of drug targeting in
the context of complexity and indirect cell signaling affects that have often
been obfuscated through traditional techniques.
On the Structure and Efficient Computation of IsoRank Node Similarities
Date: 2016-02-24 00:00:00

The alignment of protein-protein interaction (PPI) networks has many
applications, such as the detection of conserved biological network motifs, the
prediction of protein interactions, and the reconstruction of phylogenetic
trees [1, 2, 3]. IsoRank is one of the first global network alignment
algorithms [4, 5, 6], where the goal is to match all (or most) of the nodes of
two PPI networks. The IsoRank algorithm first computes a pairwise node
similarity metric, and then generates a matching between the two node sets
based on this metric. The metric is a convex combination of a structural
similarity score (with weight $ \alpha $) and an extraneous amino-acid sequence
similarity score for two proteins (with weight $ 1 - \alpha $). In this short
paper, we make two contributions. First, we show that when IsoRank similarity
depends only on network structure ($\alpha = 1$), the similarity of two nodes
is only a function of their degrees. In other words, IsoRank similarity is
invariant to any network rewiring that does not affect the node degrees. This
result suggests a reason for the poor performance of IsoRank in structure-only
($ \alpha = 1 $) alignment. Second, using ideas from [7, 8], we develop an
approximation algorithm that outperforms IsoRank (including recent versions
with better scaling, e.g., [9]) by several orders of magnitude in time and
memory complexity, despite only a negligible loss in precision.
Should employers pay their employees better? An asset pricing approach
Date: 2016-10-14 00:00:00

We uncover a new anomaly in asset pricing that is linked to the remuneration:
the more a company spends on salaries and benefits per employee, the better its
stock performs, on average. Moreover, the companies adopting similar
remuneration policies share a common risk, which is comparable to that of the
value premium. For this purpose,we set up an original methodology that uses
firm financial characteristics to build factors that are less correlated than
in the standard asset pricing methodology. We quantify the importance of these
factors from an asset pricing perspective by introducing the factor correlation
level as a directly accessible proxy of eigenvalues of the correlation matrix.
A rational explanation of the remuneration anomaly involves the positive
correlation between pay and employee performance.
On the existence of shadow prices for optimal investment with random endowment
Date: 2017-02-23 00:00:00

In this paper, we consider a num\'eraire-based utility maximization problem
under constant proportional transaction costs and random endowment. Assuming
that the agent cannot short sell assets and is endowed with a strictly positive
contingent claim, a primal optimizer of this utility maximization problem
exists. Moreover, we observe that the original market with transaction costs
can be replaced by a frictionless shadow market that yields the same
optimality. On the other hand, we present an example to show that in some case
when these constraints are relaxed, the existence of shadow prices is still
warranted.
Network biomarkers of schizophrenia by graph theoretical investigations of Brain Functional Networks
Date: 2016-08-27 00:00:00
Tasks: Functional Connectivity

Brain Functional Networks (BFNs), graph theoretical models of brain activity
data, provide a systems perspective of complex functional connectivity within
the brain. Neurological disorders are known to have basis in abnormal
functional activities, that could be captured in terms of network markers.
Schizophrenia is a pathological condition characterized with altered brain
functional state. We created weighted and binary BFN models of schizophrenia
patients as well as healthy subjects starting from fMRI data in an effort to
search for network biomarkers of the disease. We investigated 45 topological
features of BFNs and their higher order combinations (2 to 8). We find that
network features embodying modularity, betweenness, assortativity and edge
density emerge as key markers of schizophrenia. Also, features derived from
weighted BFNs were observed to be more effective in disease classification as
compared to those from binary BFNs. These topological markers may provide
insights into mechanisms of functional activity underlying disease phenotype
and could further be used for designing algorithms for clinical diagnosis of
schizophrenia as well as its early detection.
The deterministic Kermack-McKendrick model bounds the general stochastic epidemic
Date: 2017-02-10 00:00:00

We prove that, for Poisson transmission and recovery processes, the classic
Susceptible $\to$ Infected $\to$ Recovered (SIR) epidemic model of Kermack and
McKendrick provides, for any given time $t>0$, a strict lower bound on the
expected number of suscpetibles and a strict upper bound on the expected number
of recoveries in the general stochastic SIR epidemic. The proof is based on the
recent message passing representation of SIR epidemics applied to a complete
graph.
Random Forest DBSCAN for USPTO Inventor Name Disambiguation
Authors: Kim Kunho, Khabsa Madian, Giles C. Lee
Date: 2017-09-14 00:00:00
Tasks: Blocking, Clustering, Feature Importance

Name disambiguation and the subsequent name conflation are essential for the
correct processing of person name queries in a digital library or other
database. It distinguishes each unique person from all other records in the
database. We study inventor name disambiguation for a patent database using
methods and features from earlier work on author name disambiguation and
propose a feature set appropriate for a patent database. A random forest was
selected for the pairwise linking classifier since they outperform Naive Bayes,
Logistic Regression, Support Vector Machines (SVM), Conditional Inference Tree,
and Decision Trees. Blocking size, very important for scaling, was selected
based on experiments that determined feature importance and accuracy. The
DBSCAN algorithm is used for clustering records, using a distance function
derived from random forest classifier. For additional scalability clustering
was parallelized. Tests on the USPTO patent database show that our method
successfully disambiguated 12 million inventor mentions within 6.5 hours.
Evaluation on datasets from USPTO PatentsView inventor name disambiguation
competition shows our algorithm outperforms all algorithms in the competition.
Sparse Kalman Filtering Approaches to Covariance Estimation from High Frequency Data in the Presence of Jumps
Date: 2016-04-14 00:00:00

Estimation of the covariance matrix of asset returns from high frequency data
is complicated by asynchronous returns, market mi- crostructure noise and
jumps. One technique for addressing both asynchronous returns and market
microstructure is the Kalman-EM (KEM) algorithm. However the KEM approach
assumes log-normal prices and does not address jumps in the return process
which can corrupt estimation of the covariance matrix.
  In this paper we extend the KEM algorithm to price models that include jumps.
We propose two sparse Kalman filtering approaches to this problem. In the first
approach we develop a Kalman Expectation Conditional Maximization (KECM)
algorithm to determine the un- known covariance as well as detecting the jumps.
For this algorithm we consider Laplace and the spike and slab jump models, both
of which promote sparse estimates of the jumps. In the second method we take a
Bayesian approach and use Gibbs sampling to sample from the posterior
distribution of the covariance matrix under the spike and slab jump model.
Numerical results using simulated data show that each of these approaches
provide for improved covariance estima- tion relative to the KEM method in a
variety of settings where jumps occur.
Site-specific recombinatorics: in situ cellular barcoding with the Cre Lox system
Date: 2016-02-28 00:00:00
Tasks: Diversity

Cellular barcoding is a significant, recently developed, biotechnology tool
that enables the familial identification of progeny of individual cells in
vivo. Most existing approaches rely on ex vivo viral transduction of cells with
barcodes, followed by adoptive transfer into an animal, which works well for
some systems, but precludes barcoding cells in their native environment, such
as those inside solid tissues. With a view to overcoming this limitation, we
propose a new design for a genetic barcoding construct based on the Cre Lox
system that induces randomly created stable barcodes in cells in situ by
exploiting inherent sequence distance constraints during site-specific
recombination. Leveraging this previously unused feature, we identify the
cassette with maximal code diversity. This proves to be orders of magnitude
higher than what is attainable with previously considered Cre Lox barcoding
approaches and is well suited for its intended applications as it exceeds the
number of lymphocytes or hematopoietic progenitor cells in mice. Moreover, it
can be built using established technology.
Deep Cross-Modal Hashing
Authors: Jiang Qing-Yuan, Li Wu-Jun
Date: 2016-02-15 00:00:00
Proceeding: CVPR 2017 7
Tasks: Cross-Modal Retrieval, Retrieval

Due to its low storage cost and fast query speed, cross-modal hashing (CMH)
has been widely used for similarity search in multimedia retrieval
applications. However, almost all existing CMH methods are based on
hand-crafted features which might not be optimally compatible with the
hash-code learning procedure. As a result, existing CMH methods with
handcrafted features may not achieve satisfactory performance. In this paper,
we propose a novel cross-modal hashing method, called deep crossmodal hashing
(DCMH), by integrating feature learning and hash-code learning into the same
framework. DCMH is an end-to-end learning framework with deep neural networks,
one for each modality, to perform feature learning from scratch. Experiments on
two real datasets with text-image modalities show that DCMH can outperform
other baselines to achieve the state-of-the-art performance in cross-modal
retrieval applications.
Controllability analysis and control synthesis for the ribosome flow model
Date: 2017-05-17 00:00:00
Tasks: Protein Folding, Translation

The ribosomal density along the coding region of the mRNA molecule affect
various fundamental intracellular phenomena including: protein production
rates, organismal fitness, ribosomal drop off, and co-translational protein
folding. Thus, regulating translation in order to obtain a desired ribosomal
profile along the mRNA molecule is an important biological problem. We study
this problem using a model for mRNA translation, called the ribosome flow model
(RFM). In the RFM, the mRNA molecule is modeled as chain of n sites. The n
state-variables describe the ribosomal density profile along the mRNA molecule,
whereas the transition rates from each site to the next are controlled by n+1
positive constants. To study the problem of controlling the density profile, we
consider some or all of the transition rates as time-varying controls. We
consider the following problem: given an initial and a desired ribosomal
density profile, determine the time-varying values of the transition rates that
steer the RFM to this density profile, if they exist. Specifically, we consider
two control problems. In the first, all transition rates can be regulated and
the goal is to steer the ribosomal density profile and the protein production
rate from a given initial value to a desired value. In the second, a single
transition rate is controlled and the goal is to steer the production rate to a
desired value. In the first case, we show that the system is controllable, i.e.
the control is powerful enough to steer the RFM to any desired value, and we
provide closed-form expressions for constant control functions (or transition
rates) asymptotically steering the RFM to the desired value. For the second
problem, we show that the production rate can be steered to any desired value
in a feasible region determined by the other, constant transition rates. We
discuss some of the biological implications of these results.
Pollinator foraging flexibility and coexistence of competing plants
Date: 2016-08-10 00:00:00

We use the optimal foraging theory to study coexistence between two plant
species and a generalist pollinator. We compare conditions for plant
coexistence for non-adaptive vs. adaptive pollinators that adjust their
foraging strategy to maximize fitness. When pollinators have fixed preferences,
we show that plant coexistence typically requires both weak competition between
plants for resources (e.g., space or nutrients) and pollinator preferences that
are not too biased in favour of either plant. We also show how plant
coexistence is promoted by indirect facilitation via the pollinator. When
pollinators are adaptive foragers, pollinator's diet maximizes pollinator's
fitness measured as the per capita population growth rate. Simulations show
that this has two conflicting consequences for plant coexistence. On the one
hand, when competition between pollinators is weak, adaptation favours
pollinator specialization on the more profitable plant which increases
asymmetries in plant competition and makes their coexistence less likely. On
the other hand, when competition between pollinators is strong, adaptation
promotes generalism, which facilitates plant coexistence. In addition, adaptive
foraging allows pollinators to survive sudden loss of the preferred plant host,
thus preventing further collapse of the entire community.
  Keywords: mutualism, competition, optimal foraging, evolutionarily stable
strategy, coexistence, adaptation rate
Scalability and Total Recall with Fast CoveringLSH
Authors: Pham Ninh, Pagh Rasmus
Date: 2016-08-19 00:00:00

Locality-sensitive hashing (LSH) has emerged as the dominant algorithmic
technique for similarity search with strong performance guarantees in
high-dimensional spaces. A drawback of traditional LSH schemes is that they may
have \emph{false negatives}, i.e., the recall is less than 100\%. This limits
the applicability of LSH in settings requiring precise performance guarantees.
Building on the recent theoretical "CoveringLSH" construction that eliminates
false negatives, we propose a fast and practical covering LSH scheme for
Hamming space called \emph{Fast CoveringLSH (fcLSH)}. Inheriting the design
benefits of CoveringLSH our method avoids false negatives and always reports
all near neighbors. Compared to CoveringLSH we achieve an asymptotic
improvement to the hash function computation time from $\mathcal{O}(dL)$ to
$\mathcal{O}(d + L\log{L})$, where $d$ is the dimensionality of data and $L$ is
the number of hash tables. Our experiments on synthetic and real-world data
sets demonstrate that \emph{fcLSH} is comparable (and often superior) to
traditional hashing-based approaches for search radius up to 20 in
high-dimensional Hamming space.
Unravelling the trading invariance hypothesis
Date: 2016-09-21 00:00:00

We confirm and substantially extend the recent empirical result of Andersen
et al. \cite{Andersen2015}, where it is shown that the amount of risk $W$
exchanged in the E-mini S\&P futures market (i.e. price times volume times
volatility) scales like the 3/2 power of the number of trades $N$. We show that
this 3/2-law holds very precisely across 12 futures contracts and 300 single US
stocks, and across a wide range of time scales. However, we find that the
"trading invariant" $I=W/N^{3/2}$ proposed by Kyle and Obizhaeva is in fact
quite different for different contracts, in particular between futures and
single stocks. Our analysis suggests $I/{\cal C}$ as a more natural candidate,
where $\cal C$ is the average spread cost of a trade, defined as the average of
the trade size times the bid-ask spread. We also establish two more complex
scaling laws for the volatility $\sigma$ and the traded volume $V$ as a
function of $N$, that reveal the existence of a characteristic number of trades
$N_0$ above which the expected behaviour $\sigma \sim \sqrt{N}$ and $V \sim N$
hold, but below which strong deviations appear, induced by the size of
the~tick.
Efficient Breeding by Genomic Mating
Date: 2016-06-27 00:00:00

In this article, we propose an approach to breeding which focuses on mating
instead of truncation selection, our method uses genome-wide marker information
in a similar fashion to genomic selection so we refer it to as genomic mating.
Using concepts of estimated breeding values, risk (usefulness) and inbreeding,
an efficient mating approach is formulated for improvement of breeding values
in the long run. We have used a genetic algorithm to find solutions to this
optimization problem. Results from our simulations point to the efficiency of
genomic mating for breeding complex traits compared to truncation selection.