pwc-archive/papers-with-abstracts huggingface dataset:
Shaping Epigenetic Memory via Genomic Bookmarking
Date: 2017-11-28 00:00:00

Reconciling the stability of epigenetic patterns with the rapid turnover of
histone modifications and their adaptability to external stimuli is an
outstanding challenge. Here, we propose a new biophysical mechanism that can
establish and maintain robust yet plastic epigenetic domains via genomic
bookmarking (GBM). We model chromatin as a recolourable polymer whose segments
bear non-permanent histone marks (or colours) which can be modified by "writer"
proteins. The three-dimensional chromatin organisation is mediated by protein
bridges, or "readers", such as Polycomb Repressive Complexes and Transcription
Factors. The coupling between readers and writers drives spreading of
biochemical marks and sustains the memory of local chromatin states across
replication and mitosis. In contrast, GBM-targeted perturbations destabilise
the epigenetic patterns. Strikingly, we demonstrate that GBM alone can explain
the full distribution of Polycomb marks in a whole Drosophila chromosome. We
finally suggest that our model provides a starting point for an understanding
of the biophysics of cellular differentiation and reprogramming.
Multi-radial LBP Features as a Tool for Rapid Glomerular Detection and Assessment in Whole Slide Histopathology Images
Date: 2017-09-20 00:00:00
Tasks: whole slide images

We demonstrate a simple and effective automated method for the segmentation
of glomeruli from large (~1 gigapixel) histopathological whole-slide images
(WSIs) of thin renal tissue sections and biopsies, using an adaptation of the
well-known local binary patterns (LBP) image feature vector to train a support
vector machine (SVM) model. Our method offers high precision (>90%) and
reasonable recall (>70%) for glomeruli from WSIs, is readily adaptable to
glomeruli from multiple species, including mouse, rat, and human, and is robust
to diverse slide staining methods. Using 5 Intel(R) Core(TM) i7-4790 CPUs with
40 GB RAM, our method typically requires ~15 sec for training and ~2 min to
extract glomeruli reproducibly from a WSI. Deploying a deep convolutional
neural network trained for glomerular recognition in tandem with the SVM
suffices to reduce false positives to below 3%. We also apply our LBP-based
descriptor to successfully detect pathologic changes in a mouse model of
diabetic nephropathy. We envision potential clinical and laboratory
applications for this approach in the study and diagnosis of glomerular
disease, and as a means of greatly accelerating the construction of feature
sets to fuel deep learning studies into tissue structure and pathology.
Variation Evolving for Optimal Control Computation, a Compact Way
Date: 2020-02-16 00:00:00

A compact version of the variation evolving method (VEM) is developed in the
primal variable space for optimal control computation. Following the idea that
originates from the Lyapunov continuous-time dynamics stability theory in the
control field, the optimal solution is analogized to the stable equilibrium
point of a dynamic system and obtained asymptotically through the variation
motion. With the introduction of a virtual dimension, namely the variation
time, the evolution partial differential equation (EPDE), which seeks the
optimal solution with a theoretical guarantee, is developed for the optimal
control problem (OCP) with free terminal states, and the equivalent optimality
conditions with no employment of costates are established in the primal space.
These conditions show that the optimal feedback control law is generally not
analytically available because the optimal control is related to the future
states. Since the derived EPDE is suitable to be computed with the
semi-discrete method in the field of PDE numerical calculation, the optimal
solution may be obtained by solving the resulting finite-dimensional
initial-value problem (IVP).
Deep Active Inference
Date: 2018-10-11 00:00:00
Tasks: Variational Inference

This work combines the free energy principle from cognitive neuroscience and
the ensuing active inference dynamics with recent advances in variational
inference in deep generative models, and evolution strategies to introduce the
"deep active inference" agent. This agent minimises a variational free energy
bound on the average surprise of its sensations, which is motivated by a
homeostatic argument. It does so by optimising the parameters of a generative
latent variable model of its sensory inputs, together with a variational
density approximating the posterior distribution over the latent variables,
given its observations, and by acting on its environment to actively sample
input that is likely under this generative model. The internal dynamics of the
agent are implemented using deep and recurrent neural networks, as used in
machine learning, making the deep active inference agent a scalable and very
flexible class of active inference agent. Using the mountain car problem, we
show how goal directed behaviour can be implemented by defining appropriate
priors on the latent states in the agent's model. Furthermore, we show that the
deep active inference agent can learn a generative model of the environment,
which can be sampled from to understand the agent's beliefs about the
environment and its interaction therewith.
Testing if the market microstructure noise is fully explained by the informational content of some variables from the limit order book
Date: 2019-02-19 00:00:00

In this paper, we build tests for the presence of residual noise in a model
where the market microstructure noise is a known parametric function of some
variables from the limit order book. The tests compare two distinct
quasi-maximum likelihood estimators of volatility, where the related model
includes a residual noise in the market microstructure noise or not. The limit
theory is investigated in a general nonparametric framework. In the presence of
residual noise, we examine the central limit theory of the related
quasi-maximum likelihood estimation approach.
A variational approach to moment-closure approximations for the kinetics of biomolecular reaction networks
Date: 2017-11-20 00:00:00

Approximate solutions of the chemical master equation and the chemical
Fokker-Planck equation are an important tool in the analysis of biomolecular
reaction networks. Previous studies have highlighted a number of problems with
the moment-closure approach used to obtain such approximations, calling it an
ad-hoc method. In this article, we give a new variational derivation of
moment-closure equations which provides us with an intuitive understanding of
their properties and failure modes and allows us to correct some of these
problems. We use mixtures of product-Poisson distributions to obtain a flexible
parametric family which solves the commonly observed problem of divergences at
low system sizes. We also extend the recently introduced entropic matching
approach to arbitrary ansatz distributions and Markov processes, demonstrating
that it is a special case of variational moment closure. This provides us with
a particularly principled approximation method. Finally, we extend the above
approaches to cover the approximation of multi-time joint distributions,
resulting in a viable alternative to process-level approximations which are
often intractable.
Environmental fitness heterogeneity in the Moran process
Date: 2018-12-18 00:00:00

Many mathematical models of evolution assume that all individuals experience
the same environment. Here, we study the Moran process in heterogeneous
environments. The population is of finite size with two competing types, which
are exposed to a fixed number of environmental conditions. Reproductive rate is
determined by both the type and the environment. We first calculate the
condition for selection to favor the mutant relative to the resident wild type.
In large populations, the mutant is favored if and only if the mutant's spatial
average reproductive rate exceeds that of the resident. But environmental
heterogeneity elucidates an interesting asymmetry between the mutant and the
resident. Specifically, mutant heterogeneity suppresses its fixation
probability; if this heterogeneity is strong enough, it can even completely
offset the effects of selection (including in large populations). In contrast,
resident heterogeneity has no effect on a mutant's fixation probability in
large populations and can amplify it in small populations.
Improving average ranking precision in user searches for biomedical research datasets
Authors: Teodoro Douglas, Mottin Luc, Gobeill Julien, Gaudinat Arnaud, Vachon Thérèse, Ruch Patrick
Date: 2017-09-10 00:00:00
Tasks: Management, Retrieval, Word Embeddings

Availability of research datasets is keystone for health and life science
study reproducibility and scientific progress. Due to the heterogeneity and
complexity of these data, a main challenge to be overcome by research data
management systems is to provide users with the best answers for their search
queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we
investigate a novel ranking pipeline to improve the search of datasets used in
biomedical experiments. Our system comprises a query expansion model based on
word embeddings, a similarity measure algorithm that takes into consideration
the relevance of the query terms, and a dataset categorisation method that
boosts the rank of datasets matching query constraints. The system was
evaluated using a corpus with 800k datasets and 21 annotated user queries. Our
system provides competitive results when compared to the other challenge
participants. In the official run, it achieved the highest infAP among the
participants, being +22.3% higher than the median infAP of the participant's
best submissions. Overall, it is ranked at top 2 if an aggregated metric using
the best official measures per participant is considered. The query expansion
method showed positive impact on the system's performance increasing our
baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively.
Our similarity measure algorithm seems to be robust, in particular compared to
Divergence From Randomness framework, having smaller performance variations
under different training conditions. Finally, the result categorization did not
have significant impact on the system's performance. We believe that our
solution could be used to enhance biomedical dataset management systems. In
particular, the use of data driven query expansion methods could be an
alternative to the complexity of biomedical terminologies.
On portfolios generated by optimal transport
Date: 2017-09-25 00:00:00

First introduced by Fernholz in stochastic portfolio theory, functionally
generated portfolio allows its investment performance to be attributed to
directly observable and easily interpretable market quantities. In previous
works we showed that Fernholz's multiplicatively generated portfolio has deep
connections with optimal transport and the information geometry of
exponentially concave functions. Recently, Karatzas and Ruf introduced a new
additive portfolio generation whose relation with optimal transport was studied
by Vervuurt. We show that additively generated portfolio can be interpreted in
terms of the well-known dually flat information geometry of Bregman divergence.
Moreover, we characterize, in a sense to be made precise, all possible forms of
functional portfolio constructions that contain additive and multiplicative
generations as special cases. Each construction involves a divergence
functional on the unit simplex measuring the market volatility captured, and
admits a pathwise decomposition for the portfolio value. We illustrate with an
empirical example.
Plant survival and keystone pollinator species in stochastic coextinction models: role of intrinsic dependence on animal-pollination
Date: 2017-09-11 00:00:00

Coextinction models are useful to understand community robustness to species
loss and resilience to disturbances. We simulated pollinator extinctions in
pollination networks by using a hybrid model that combined a recently developed
stochastic coextinction model (SCM) for plant extinctions and a topological
model (TCM) for animal extinctions. Our model accounted for variation in
interaction strengths and included empirical estimates of plant dependence on
pollinators to set seeds. The stochastic nature of such model allowed us
determining plant survival to single (and multiple) extinction events, and
identifying which pollinators (keystone species) were more likely to trigger
secondary extinctions. Consistently across three different pollinator removal
sequences, plant robustness was lower than in a pure TCM, and plant survival
was more determined by dependence on the mutualism than by interaction
strength. As expected, highly connected and dependent plants were the most
sensitive to pollinator loss and collapsed faster in extinction cascades. We
predict that the relationship between dependence and plant connectivity is
crucial to determine network robustness to interaction loss. Finally, we showed
that honeybees and several beetles were keystone species in our communities.
This information is of great value to foresee consequences of pollinator losses
facing current global change and to identify target species for effective
conservation.
SweetRS: Dataset for a recommender systems of sweets
Authors: Kidziński Łukasz
Date: 2017-09-10 00:00:00
Tasks: Benchmarking, Matrix Completion, Recommendation Systems

Benchmarking recommender system and matrix completion algorithms could be
greatly simplified if the entire matrix was known. We built a \url{sweetrs.org}
platform with $77$ candies and sweets to rank. Over $2000$ users submitted over
$44000$ grades resulting in a matrix with $28\%$ coverage. In this report, we
give the full description of the environment and we benchmark the
\textsc{Soft-Impute} algorithm on the dataset.
A Modified Levy Jump-Diffusion Model Based on Market Sentiment Memory for Online Jump Prediction
Date: 2017-09-11 00:00:00

In this paper, we propose a modified Levy jump diffusion model with market
sentiment memory for stock prices, where the market sentiment comes from data
mining implementation using Tweets on Twitter. We take the market sentiment
process, which has memory, as the signal of Levy jumps in the stock price. An
online learning and optimization algorithm with the Unscented Kalman filter
(UKF) is then proposed to learn the memory and to predict possible price jumps.
Experiments show that the algorithm provides a relatively good performance in
identifying asset return trends.
A Broad Learning Approach for Context-Aware Mobile Application Recommendation
Authors: Liang Tingting, He Lifang, Lu Chun-Ta, Chen Liang, Yu Philip S., Wu Jian
Date: 2017-09-11 00:00:00
Tasks: Feature Importance

With the rapid development of mobile apps, the availability of a large number
of mobile apps in application stores brings challenge to locate appropriate
apps for users. Providing accurate mobile app recommendation for users becomes
an imperative task. Conventional approaches mainly focus on learning users'
preferences and app features to predict the user-app ratings. However, most of
them did not consider the interactions among the context information of apps.
To address this issue, we propose a broad learning approach for
\textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor
\textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to
effectively integrate user's preference, app category information and
multi-view features to facilitate the performance of app rating prediction. The
multidimensional structure is employed to capture the hidden relationships
between multiple app categories with multi-view features. We develop an
efficient factorization method which applies Tucker decomposition to learn the
full-order interactions within multiple categories and features. Furthermore,
we employ a group $\ell_{1}-$norm regularization to learn the group-wise
feature importance of each view with respect to each app category. Experiments
on two real-world mobile app datasets demonstrate the effectiveness of the
proposed method.
Evolution of Cooperation in Public Goods Games with Stochastic Opting-Out
Date: 2017-09-12 00:00:00

This paper investigates the evolution of strategic play where players drawn
from a finite well-mixed population are offered the opportunity to play in a
public goods game. All players accept the offer. However, due to the
possibility of unforeseen circumstances, each player has a fixed probability of
being unable to participate in the game, unlike similar models which assume
voluntary participation. We first study how prescribed stochastic opting-out
affects cooperation in finite populations. Moreover, in the model, cooperation
is favored by natural selection over both neutral drift and defection if return
on investment exceeds a threshold value defined solely by the population size,
game size, and a player's probability of opting-out. Ultimately, increasing the
probability that each player is unable to fulfill her promise of participating
in the public goods game facilitates natural selection of cooperators. We also
use adaptive dynamics to study the coevolution of cooperation and opting-out
behavior. However, given rare mutations minutely different from the original
population, an analysis based on adaptive dynamics suggests that the over time
the population will tend towards complete defection and non-participation, and
subsequently, from there, participating cooperators will stand a chance to
emerge by neutral drift. Nevertheless, increasing the probability of
non-participation decreases the rate at which the population tends towards
defection when participating. Our work sheds light on understanding how
stochastic opting-out emerges in the first place and its role in the evolution
of cooperation.
Dithymoquinone as a novel inhibitor for 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) to prevent renal failure
Date: 2017-07-23 00:00:00
Tasks: Molecular Docking

3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) is a major
endogenous ligand found in the human serum albumin (HSA) of renal failure
patients. It gets accumulated in the HSA and its concentration in sera of
patients may reflect the chronicity of renal failure [1-4]. It is considered
uremic toxin due to its damaging effect on the renal cells. The high
concentrations of CMPF inhibit the binding of other ligands to HSA. Removal of
CMPF is difficult through conventional hemodialysis due to its strong binding
affinity. We hypothesized that the competitive inhibition may be helpful in
removal of CMPF binding to HSA. A compound with higher HSA binding affinity
than CMPF could be useful to prevent CMPF from binding so that CMPF could be
excreted by the body through the urine. We studied an active compound
dihydrothymoquinone/ dithymoquinone (DTQ) found in black cumin seed (Nigella
sativa), which has higher binding affinity for HSA. Molecular docking
simulations were performed to find the binding affinity of CMPF and DTQ with
HSA. DTQ was found to have higher binding affinity possessing more interactions
with the binding residues than the CMPF. We studied the binding pocket
flexibility of CMPF and DTQ to analyze the binding abilities of both the
compounds. We have also predicted the ADME properties for DTQ which shows
higher lipophilicity, higher gastrointestinal (GI) absorption, and blood-brain
barrier (BBB) permeability. We discovered that DTQ has potential to act as an
inhibitor of CMPF and can be considered as a candidate for the formation of the
therapeutic drug against CMPF.
Application of electromagnetic centroids to colocalization of fluorescing objects in tissue sections
Date: 2019-08-14 00:00:00

Light microscopy as well as image acquisition and processing suffer from
physical and technical prejudices which preclude a correct interpretation of
biological observations which can be reflected in, e.g., medical and
pharmacological praxis. Using the examples of a diffracting microbead and
fluorescently labelled tissue, this article clarifies some ignored aspects of
image build-up in the light microscope and introduce algorithms for maximal
extraction of information from the 3D microscopic experiments. We provided a
correct set-up of the microscope and we sought a voxel (3D pixel) called an
electromagnetic centroid which localizes the information about the object. In
diffraction imaging and light emission, this voxel shows a minimal intensity
change in two consecutive optical cuts. This approach further enabled us to
identify z-stack of a DAPI-stained tissue section where at least one object of
a relevant fluorescent marker was in focus. The spatial corrections (overlaps)
of the DAPI-labelled region with in-focus autofluorescent regions then enabled
us to co-localize these three regions in the optimal way when considering
physical laws and information theory. We demonstrate that superresolution down
to the Nobelish level can be obtained from commonplace widefield bright-field
and fluorescence microscopy and bring new perspectives on co-localization in
fluorescent microscopy.
A neuronal network model of interictal and recurrent ictal activity
Date: 2017-09-11 00:00:00

We propose a neuronal network model which undergoes a saddle-node bifurcation
on an invariant circle as the mechanism of the transition from the interictal
to the ictal (seizure) state. In the vicinity of this transition, the model
captures important dynamical features of both interictal and ictal states. We
study the nature of interictal spikes and early warnings of the transition
predicted by this model. We further demonstrate that recurrent seizures emerge
due to the interaction between two networks.
Random walks and market efficiency in Chinese and Indian equity markets
Date: 2017-09-12 00:00:00

Hypothesis of Market Efficiency is an important concept for the investors
across the globe holding diversified portfolios. With the world economy getting
more integrated day by day, more people are investing in global emerging
markets. This means that it is pertinent to understand the efficiency of these
markets. This paper tests for market efficiency by studying the impact of
global financial crisis of 2008 and the recent Chinese crisis of 2015 on stock
market efficiency in emerging stock markets of China and India. The data for
last 20 years was collected from both Bombay Stock Exchange (BSE200) and the
Shanghai Stock Exchange Composite Index and divided into four sub-periods, i.e.
before financial crisis period (period-I), during recession (period-II), after
recession and before Chinese Crisis (periodIII) and from the start of Chinese
crisis till date (period- IV). Daily returns for the SSE and BSE were examined
and tested for randomness using a combination of auto correlation tests, runs
tests and unit root tests (Augmented Dickey-Fuller) for the entire sample
period and the four sub-periods. The evidence from all these tests supports
that both the Indian and Chinese stock markets do not exhibit weak form of
market efficiency. They do not follow random walk overall and in the first
three periods (1996 till the 2015) implying that recession did not impact the
markets to a great extent, although the efficiency in percentage terms seems to
be increasing after the global financial crisis of 2008.