pwc-archive/papers-with-abstracts huggingface dataset:
Information flow between resting state networks
Date: 2015-05-13 00:00:00

The resting brain dynamics self-organizes into a finite number of correlated
patterns known as resting state networks (RSNs). It is well known that
techniques like independent component analysis can separate the brain activity
at rest to provide such RSNs, but the specific pattern of interaction between
RSNs is not yet fully understood. To this aim, we propose here a novel method
to compute the information flow (IF) between different RSNs from resting state
magnetic resonance imaging. After haemodynamic response function blind
deconvolution of all voxel signals, and under the hypothesis that RSNs define
regions of interest, our method first uses principal component analysis to
reduce dimensionality in each RSN to next compute IF (estimated here in terms
of Transfer Entropy) between the different RSNs by systematically increasing k
(the number of principal components used in the calculation). When k = 1, this
method is equivalent to computing IF using the average of all voxel activities
in each RSN. For k greater than one our method calculates the k-multivariate IF
between the different RSNs. We find that the average IF among RSNs is
dimension-dependent, increasing from k =1 (i.e., the average voxels activity)
up to a maximum occurring at k =5 to finally decay to zero for k greater than
10. This suggests that a small number of components (close to 5) is sufficient
to describe the IF pattern between RSNs. Our method - addressing differences in
IF between RSNs for any generic data - can be used for group comparison in
health or disease. To illustrate this, we have calculated the interRSNs IF in a
dataset of Alzheimer's Disease (AD) to find that the most significant
differences between AD and controls occurred for k =2, in addition to AD
showing increased IF w.r.t. controls.
Pricing complexity options
Date: 2016-03-31 00:00:00

We consider options that pay the complexity deficiency of a sequence of up
and down ticks of a stock upon exercise. We study the price of European and
American versions of this option numerically for automatic complexity, and
theoretically for Kolmogorov complexity. We also consider run complexity, which
is a restricted form of automatic complexity.
Damping Properties of the Hair Bundle
Date: 2015-05-14 00:00:00

The viscous liquid surrounding a hair bundle dissipates energy and dampens
oscillations, which poses a fundamental physical challenge to the high
sensitivity and sharp frequency selectivity of hearing. To identify the
mechanical forces at play, we constructed a detailed finite-element model of
the hair bundle. Based on data from the hair bundle of the bullfrog's sacculus,
this model treats the interaction of stereocilia both with the surrounding
liquid and with the liquid in the narrow gaps between the individual
stereocilia. The investigation revealed that grouping stereocilia in a bundle
dramatically reduces the total drag. During hair-bundle deflections, the tip
links potentially induce drag by causing small but very dissipative relative
motions between stereocilia; this effect is offset by the horizontal top
connectors that restrain such relative movements at low frequencies. For higher
frequencies the coupling liquid is sufficient to assure that the hair bundle
moves as a unit with a low total drag. This work reveals the mechanical
characteristics originating from hair-bundle morphology and shows
quantitatively how a hair bundle is adapted for sensitive mechanotransduction.
Extracting grid characteristics from spatially distributed place cell inputs using non-negative PCA
Date: 2015-07-14 00:00:00
Tasks: Dimensionality Reduction

Many recent models study the downstream projection from grid cells to place
cells, while recent data has pointed out the importance of the feedback
projection. We thus asked how grid cells are affected by the nature of the
input from the place cells. We propose a two-layered neural network with
feedforward weights connecting place-like input cells to grid cell outputs.
Place-to-grid weights were learned via a generalized Hebbian rule. The
architecture of this network highly resembles neural networks used to perform
Principal Component Analysis (PCA). Our results indicate that if the components
of the feedforward neural network were non-negative, the output converged to a
hexagonal lattice. Without the non-negativity constraint the output converged
to a square lattice. Consistent with experiments, grid alignment to walls was
~7{\deg} and grid spacing ratio between consecutive modules was ~1.4. Our
results express a possible linkage between place-cell to grid-cell interactions
and PCA, suggesting that grid cells represent a process of constrained
dimensionality reduction that can be viewed also as a process of variance
maximization of the information from place-cells.
A dynamical model of the adaptive immune system: effects of cells promiscuity, antigens and B-B interactions
Date: 2015-05-14 00:00:00

We analyse a minimal model for the primary response in the adaptive immune
system comprising three different players: antigens, T and B cells. We assume
B-T interactions to be diluted and sampled locally from heterogeneous degree
distributions, which mimic B cells receptors' promiscuity. We derive dynamical
equations for the order parameters quantifying the B cells activation and study
the nature and stability of the stationary solutions using linear stability
analysis and Monte Carlo simulations.The system's behaviour is studied in
different scaling regimes of the number of B cells, dilution in the
interactions and number of antigens. Our analysis shows that: (i) B cells
activation depends on the number of receptors in such a way that cells with an
insufficient number of triggered receptors cannot be activated; (ii) idiotypic
(i.e. B-B) interactions enhance parallel activation of multiple clones,
improving the system's ability to fight different pathogens in parallel; (iii)
the higher the fraction of antigens within the host the harder is for the
system to sustain parallel signalling to B cells, crucial for the homeostatic
control of cell numbers.
Persistence in Phage-Bacteria Communities with Nested and One-to-One Infection Networks
Date: 2015-05-28 00:00:00

We show that a bacteria and bacteriophage system with either a perfectly
nested or a one-to-one infection network is permanent, a.k.a uniformly
persistent, provided that bacteria that are superior competitors for nutrient
devote the least to defence against infection and the virus that are the most
efficient at infecting host have the smallest host range. By ensuring that the
density-dependent reduction in bacterial growth rates are independent of
bacterial strain, we are able to arrive at the permanence conclusion sought by
Jover et al (J. Theor. Biol. 332:65-77, 2013). The same permanence results hold
for the one-to-one infection network considered by Thingstad (Limnol Oceanogr
45:1320-1328, 2000) but without virus efficiency ordering. Additionally we show
the global stability for the nested infection network, and the global dynamics
for the one-to-one network.
Cellular reprogramming dynamics follow a simple one-dimensional reaction coordinate
Date: 2015-05-14 00:00:00
Tasks: Time Series, Time Series Analysis

Cellular reprogramming, the conversion of one cell type to another, has
fundamentally transformed our conception of cell types. Cellular reprogramming
induces global changes in gene expression involving hundreds of transcription
factors and thousands of genes and understanding how cells globally alter their
gene expression profile during reprogramming is an open problem. Here we
reanalyze time-series data on cellular reprogramming from differentiated cell
types to induced pluripotent stem cells (iPSCs) and show that gene expression
dynamics during reprogramming follow a simple one-dimensional reaction
coordinate. This reaction coordinate is independent of both the time it takes
to reach the iPSC state as well as the details of experimental protocol used.
Using Monte-Carlo simulations, we show that such a reaction coordinate emerges
naturally from epigenetic landscape models of cell identity where cellular
reprogramming is viewed as a "barrier-crossing" between the starting and ending
cell fates. The model also provides gene-level insight into reprogramming
dynamics and resolves a debate in the stem cell field about the different
phases of reprogramming dynamics. Overall, our analysis and model suggest that
gene expression dynamics during reprogramming follow a canonical trajectory
consistent with the idea of an "optimal path" in gene expression space for
reprogramming.
Potential of longevity: hidden in structural complexity
Date: 2018-05-25 00:00:00

In order to understand the phenomenon of longevity in biological world, the
relationship between the potential of longevity and the structural complexity
of an organism is analyzed. I. The potential of longevity is the maximum
lifespan of an organism if it lives in an ideal environment. The longevity of
an organism includes two parts: the time for development (development time) and
the time for structure-maintenance (maintenance time). II. The development time
of an organism depends on its structural complexity. The maintenance time is
related to two factors: the degree of damage-exposure and the potential of
functionality for structure-maintenance. The potential of functionality is
built in structural complexity. Since both of development time and maintenance
time are determined by structural complexity, the potential of longevity of an
organism is hidden in structural complexity. III. The individuals of different
species have different potentials of longevity because they have different
structural complexities. An animal has limited longevity because it has limited
structural complexity. Limited structural complexity and limited longevity are
essential for the survival of a species. IV. Despite having the same potential
of longevity, the individuals of a species can have different lifespans. The
lifespan of an individual is more related to the degree of damage-exposure,
which is determined by the living environment and the living habit of the
individual. In conclusion, the potential of longevity of an organism is hidden
in structural complexity, but the real lifespan of an organism is more related
to the living environment.
Textual Spatial Cosine Similarity
Authors: Crocetti Giancarlo
Date: 2015-05-15 00:00:00

When dealing with document similarity many methods exist today, like cosine
similarity. More complex methods are also available based on the semantic
analysis of textual information, which are computationally expensive and rarely
used in the real time feeding of content as in enterprise-wide search
environments. To address these real-time constraints, we developed a new
measure of document similarity called Textual Spatial Cosine Similarity, which
is able to detect similitude at the semantic level using word placement
information contained in the document. We will see in this paper that two
degenerate cases exist for this model, which coincide with Cosine Similarity on
one side and with a paraphrasing detection model to the other.
From 0D to 1D spatial models using OCMat
Date: 2015-05-15 00:00:00

We show that the standard class of optimal control models in OCMat can be
used to analyze 1D spatial distributed systems. This approach is an
intermediate step on the way to the FEM discretization approach presented in
Grass and Uecker (2015). Therefore, the spatial distributed model is
transformed into a standard model by a finite difference discretization. This
(high dimensional) standard model is then analyzed using OCMAT. As an example
we apply this method to the spatial distributed shallow lake model formulated
in Brock and Xepapadeas (2008). The results are then compared with those of the
FEM discretization in GRass and Uecker (2015)
Algebraic identification of the effective connectivity of constrained geometric network models of neural signaling
Date: 2015-05-15 00:00:00

Cellular neural circuit and networks consisting of interconnected neurons and
glia are ulti- mately responsible for the information processing associated
with information processing in the brain. While there are major efforts aimed
at mapping the structural and (electro)physiological connectivity of brain
networks, such as the White House BRAIN Initiative aimed at the devel- opment
of neurotechnologies capable of high density neural recordings, theoretical and
compu- tational methods for analyzing and making sense of all this data seem to
be further behind. Here, we propose and provide a summary of an approach for
calculating effective connectivity from experimental observations of neuronal
network activity. The proposed method operates on network-level data, makes use
of all relevant prior knowledge, such as dynamical models of individual cells
in the network and the physical structural connectivity of the network, and is
broadly applicable to large classes of biological and non-biological networks.
Location Prediction of Social Images via Generative Model
Authors: Zhang Xiaoming, Li Zhoujun, Wang Senzhang, Yang Yang, Lv Xueqiang
Date: 2015-05-15 00:00:00
Tasks: Language Modeling, Language Modelling, model, Prediction

The vast amount of geo-tagged social images has attracted great attention in
research of predicting location using the plentiful content of images, such as
visual content and textual description. Most of the existing researches use the
text-based or vision-based method to predict location. There still exists a
problem: how to effectively exploit the correlation between different types of
content as well as their geographical distributions for location prediction. In
this paper, we propose to predict image location by learning the latent
relation between geographical location and multiple types of image content. In
particularly, we propose a geographical topic model GTMI (geographical topic
model of social image) to integrate multiple types of image content as well as
the geographical distributions, In GTMI, image topic is modeled on both text
vocabulary and visual feature. Each region has its own distribution over topics
and hence has its own language model and vision pattern. The location of a new
image is estimated based on the joint probability of image content and
similarity measure on topic distribution between images. Experiment results
demonstrate the performance of location prediction based on GTMI.
An objective function for self-limiting neural plasticity rules
Date: 2015-05-15 00:00:00

Self-organization provides a framework for the study of systems in which
complex patterns emerge from simple rules, without the guidance of external
agents or fine tuning of parameters. Within this framework, one can formulate a
guiding principle for plasticity in the context of unsupervised learning, in
terms of an objective function. In this work we derive Hebbian, self-limiting
synaptic plasticity rules from such an objective function and then apply the
rules to the non-linear bars problem.
Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz
Date: 2015-05-15 00:00:00
Tasks: Diversity, Management, Portfolio Optimization

Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a
backtesting study of the portfolio optimization strategy based on the extreme
risk index (ERI). This method uses multivariate extreme value theory to
minimize the probability of large portfolio losses. With more than 400 stocks
to choose from, our study seems to be the first application of extreme value
techniques in portfolio management on a large scale. The primary aim of our
investigation is the potential of ERI in practice. The performance of this
strategy is benchmarked against the minimum variance portfolio and the equally
weighted portfolio. These fundamental strategies are important benchmarks for
large-scale applications. Our comparison includes annualized portfolio returns,
maximal drawdowns, transaction costs, portfolio concentration, and asset
diversity in the portfolio. In addition to that we study the impact of an
alternative tail index estimator. Our results show that the ERI strategy
significantly outperforms both the minimum-variance portfolio and the equally
weighted portfolio on assets with heavy tails.
Forecasting Financial Extremes: A Network Degree Measure of Super-exponential Growth
Date: 2015-05-15 00:00:00
Tasks: Time Series, Time Series Analysis

Investors in stock market are usually greedy during bull markets and scared
during bear markets. The greed or fear spreads across investors quickly. This
is known as the herding effect, and often leads to a fast movement of stock
prices. During such market regimes, stock prices change at a super-exponential
rate and are normally followed by a trend reversal that corrects the previous
over reaction. In this paper, we construct an indicator to measure the
magnitude of the super-exponential growth of stock prices, by measuring the
degree of the price network, generated from the price time series. Twelve major
international stock indices have been investigated. Error diagram tests show
that this new indicator has strong predictive power for financial extremes,
both peaks and troughs. By varying the parameters used to construct the error
diagram, we show the predictive power is very robust. The new indicator has a
better performance than the LPPL pattern recognition indicator.
Evaluating Link Prediction Methods
Authors: Yang Yang, Lichtenwalter Ryan N., Chawla Nitesh V.
Date: 2015-05-15 00:00:00
Tasks: Link Prediction, Prediction

Link prediction is a popular research area with important applications in a
variety of disciplines, including biology, social science, security, and
medicine. The fundamental requirement of link prediction is the accurate and
effective prediction of new links in networks. While there are many different
methods proposed for link prediction, we argue that the practical performance
potential of these methods is often unknown because of challenges in the
evaluation of link prediction, which impact the reliability and reproducibility
of results. We describe these challenges, provide theoretical proofs and
empirical examples demonstrating how current methods lead to questionable
conclusions, show how the fallacy of these conclusions is illuminated by
methods we propose, and develop recommendations for consistent, standard, and
applicable evaluation metrics. We also recommend the use of precision-recall
threshold curves and associated areas in lieu of receiver operating
characteristic curves due to complications that arise from extreme imbalance in
the link prediction classification problem.
Helicase processivity and not the unwinding velocity exhibits universal increase with force
Date: 2015-05-15 00:00:00

Helicases, involved in a number of cellular functions, are motors that
translocate along singlestranded nucleic acid and couple the motion to
unwinding double-strands of a duplex nucleic acid. The junction between double
and single strands creates a barrier to the movement of the helicase, which can
be manipulated in vitro by applying mechanical forces directly on the nucleic
acid strands. Single molecule experiments have demonstrated that the unwinding
velocities of some helicases increase dramatically with increase in the
external force, while others show little response. In contrast, the unwinding
processivity always increases when the force increases. The differing responses
of the unwinding velocity and processivity to force has lacked explanation. By
generalizing a previous model of processive unwinding by helicases, we provide
a unified framework for understanding the dependence of velocity and
processivity on force and the nucleic acid sequence. We predict that the
sensitivity of unwinding processivity to external force is a universal feature
that should be observed in all helicases. Our prediction is illustrated using
T7 and NS3 helicases as case studies. Interestingly, the increase in unwinding
processivity with force depends on whether the helicase forces base pair
opening by direct interaction or if such a disruption occurs spontaneously due
to thermal uctuations. Based on the theoretical results, we propose that
proteins like single-strand binding proteins associated with helicases in the
replisome, may have co-evolved with helicases to increase the unwinding
processivity even if the velocity remains unaffected.
Stochastic models of evidence accumulation in changing environments
Date: 2015-09-30 00:00:00
Tasks: Decision Making

Organisms and ecological groups accumulate evidence to make decisions.
Classic experiments and theoretical studies have explored this process when the
correct choice is fixed during each trial. However, we live in a constantly
changing world. What effect does such impermanence have on classical results
about decision making? To address this question we use sequential analysis to
derive a tractable model of evidence accumulation when the correct option
changes in time. Our analysis shows that ideal observers discount prior
evidence at a rate determined by the volatility of the environment, and the
dynamics of evidence accumulation is governed by the information gained over an
average environmental epoch. A plausible neural implementation of an optimal
observer in a changing environment shows that, in contrast to previous models,
neural populations representing alternate choices are coupled through
excitation. Our work builds a bridge between statistical decision making in
volatile environments and stochastic nonlinear dynamics.
RNAiFold 2.0: A web server and software to design custom and Rfam-based RNA molecules
Date: 2015-05-15 00:00:00

Several algorithms for RNA inverse folding have been used to design synthetic
riboswitches, ribozymes and thermoswitches, whose activity has been
experimentally validated. The RNAiFold software is unique among approaches for
inverse folding in that (exhaustive) constraint programming is used instead of
heuristic methods. For that reason, RNAiFold can generate all sequences that
fold into the target structure, or determine that there is no solution.
RNAiFold 2.0 is a complete overhaul of RNAiFold 1.0, rewritten from the now
defunct COMET language to C++. The new code properly extends the capabilities
of its predecessor by providing a user-friendly pipeline to design synthetic
constructs having the functionality of given Rfam families. In addition, the
new software supports amino acid constraints, even for proteins translated in
different reading frames from overlapping coding sequences; moreover, structure
compatibility/incompatibility constraints have been expanded. With these
features, RNAiFold 2.0 allows the user to design single RNA molecules as well
as hybridization complexes of two RNA molecules.
  The web server, source code and linux binaries are publicly accessible at
http://bioinformatics.bc.edu/clotelab/RNAiFold2.0
Fundamental limits on the accuracy of demographic inference based on the sample frequency spectrum
Date: 2015-05-16 00:00:00

The sample frequency spectrum (SFS) of DNA sequences from a collection of
individuals is a summary statistic which is commonly used for parametric
inference in population genetics. Despite the popularity of SFS-based inference
methods, currently little is known about the information-theoretic limit on the
estimation accuracy as a function of sample size. Here, we show that using the
SFS to estimate the size history of a population has a minimax error of at
least $O(1/\log s)$, where $s$ is the number of independent segregating sites
used in the analysis. This rate is exponentially worse than known convergence
rates for many classical estimation problems in statistics. Another surprising
aspect of our theoretical bound is that it does not depend on the dimension of
the SFS, which is related to the number of sampled individuals. This means
that, for a fixed number $s$ of segregating sites considered, using more
individuals does not help to reduce the minimax error bound. Our result
pertains to populations that have experienced a bottleneck, and we argue that
it can be expected to apply to many populations in nature.